DeltaFlow Matrix═════════════════─────────
DELTAFLOW MATRIX - COMPLETE GUIDE
For 1-Minute Scalping
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📊 VISUAL ELEMENTS EXPLAINED (What You See on the Chart)
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🟦🟥 RED/GREEN BARS ON THE RIGHT = Delta Flow Direction
The horizontal bars extending right from your chart show WHO controlled the price at each level. Green = bulls won, Red = bears won. Longer bars = more volume traded at that price. Example: If BTC is at $100,000 and you see a massive green bar, that means buyers aggressively absorbed all sell orders at that exact price level.
📊 GRADIENT BACKGROUND (Heat Map) = Volume Intensity
The colored background behind the bars shows volume concentration. Darker/more opaque = heavy trading, lighter/transparent = light trading. Example: A dark background at $99,800 means that's where most traders are positioned - it's a "magnet price" where BTC keeps returning.
🟩 GREEN BOX WITH BORDER = POC (Point of Control)
This is THE most important price on your chart - where the absolute highest volume traded. This is where the majority of traders are stuck. Example: POC at $99,950 means most BTC holders bought/sold there. Price will be magnetically pulled back to test this level repeatedly.
⬜ WHITE DOTTED LINES = VA High and VA Low (Value Area)
These lines contain 70% of all trading volume. Think of them as "fair price boundaries." Example: VA High at $100,200, VA Low at $99,700 means BTC's "fair value range" is $99,700-$100,200. Breakouts above/below these lines are significant moves.
💜 MAGENTA BORDER ON BARS = MICRO-SR (Micro Support/Resistance)
These magenta-outlined bars mark high-frequency support/resistance zones where price repeatedly bounced. These are your scalping zones. Example: MICRO-SR at $99,975 means BTC touched this price multiple times in the last 100 bars - it's a critical battle line for 1-minute scalpers.
🟡 GOLD TEXT "BULL EXHAUST" / "BEAR EXHAUST" = Exhaustion Zones
When one side dominated the volume BUT the trend is dying. This is where the big money got tired. Example: "BULL EXHAUST" at $100,100 means buyers pushed hard but are running out of steam - expect a reversal or consolidation soon.
🔵 CYAN TEXT "FLOW SHIFT ↑" / "FLOW SHIFT ↓" = Institutional Reversal
This is the holy grail - when delta completely flipped from bearish to bullish (or vice versa) with increasing volume. This marks where institutions changed their position. Example: "FLOW SHIFT ↑" at $99,900 means selling pressure just turned into aggressive buying - the big players reversed direction.
🟠 ORANGE TEXT "FAILED SHIFT ↑" / "FAILED SHIFT ↓" = Failed Institutional Reversal
When a FLOW SHIFT appears but then gets rejected by the opposite side within 3-10 bars. This means institutions TRIED to reverse but couldn't - the other side is defending hard. Example: "FAILED SHIFT ↑" at $99,900 means bulls attempted to take control but bears defended and stopped the reversal - this is a bearish sign, price likely continues down.
🟢 GREEN "COILED" LABEL BELOW PRICE = Bullish Compression Setup
When price is compressed below VA Low with 5+ MICRO-SR resistance levels stacked overhead AND bullish momentum is building. This is a spring-loaded long setup - price is coiled under resistance ready to explode upward. Example: BTC at $99,700, VA Low at $100,000, 7 MICRO-SR levels stacked from $100,100-$100,400, and delta shows +45 with bullish flow → "COILED" appears. This means price is compressed like a spring with bullish pressure building - when it breaks, it will rip through all those overhead levels fast.
🔴 RED "COILED" LABEL ABOVE PRICE = Bearish Compression Setup
When price is extended above VA High with 5+ MICRO-SR support levels stacked below AND bearish momentum is building. This is a spring-loaded short setup - price is coiled above support ready to crash downward. Example: BTC at $100,500, VA High at $100,200, 6 MICRO-SR levels stacked from $100,000-$99,700, and delta shows -52 with bearish flow → "COILED" appears. This means price is compressed with bearish pressure building - when it breaks down, it will slice through all those support levels.
🔴🟢 "REJECT" LABEL = Failed Breakout / Rejection
When price enters a cluster zone (resistance or support) but shows opposite momentum - the breakout attempt failed. Example: Price pushed up into overhead resistance at $100,200 but delta turns bearish (-38) → "REJECT" appears in red above price. This means the breakout attempt was rejected, bulls who entered are trapped, expect reversal down.
⚠️ "WALL ↑" / "WALL ↓" = Resistance/Support Wall Alert
When 5+ MICRO-SR levels are stacked together creating a "wall" of resistance or support. These are significant barriers where price will likely stall or reverse. Example: "WALL ↑ 7x" means there are 7 MICRO-SR resistance levels stacked above current price - breaking through this will be very difficult without strong momentum and volume.
🔴🟢 "BULL ATTACK" / "BEAR ATTACK" = Aggressive Momentum
One side is attacking with both high delta AND increasing volume. This is active warfare. Example: "BEAR ATTACK" at $100,050 means sellers are aggressively dumping with rising volume - price is likely to drop fast.
🛡️ "BULL DEFENSE" / "BEAR DEFENSE" = Holding the Line
One side has high delta but volume is flat or decreasing - they're defending a level, not pushing. Example: "BULL DEFENSE" at $99,850 means buyers are absorbing sells to prevent BTC from dropping further, but they're not strong enough to push up yet.
⚖️ "EQUILIBRIUM" / "ROTATION" = Balanced Market
Bulls and bears are equally matched - perfect for range trading, terrible for breakout trades. Example: "EQUILIBRIUM" at $100,000 means the market is perfectly balanced here - trade the range, don't chase breakouts.
📈📉 "UP" / "DN" ARROWS = Volume Trend
Small green "UP" or red "DN" labels show if volume is increasing or decreasing at that price level over time. Example: "UP" at $99,900 means more traders are entering positions at this price compared to earlier - this level is becoming more important.
⇈⇊ DOUBLE ARROWS = Delta Momentum Acceleration
These show when delta is accelerating rapidly - not just strong, but GETTING STRONGER. Example: ⇈ at $100,050 means bullish delta isn't just high, it's accelerating - expect explosive upward movement.
🟢🔴 VELOCITY BANDS (Horizontal bars far right) = Volume Acceleration
Thin horizontal bars extending from the profile show how fast volume is building. Green = volume accelerating up, Red = volume accelerating down. Example: Green velocity band at $100,100 means volume is spiking at this level right now - action is heating up.
💜 "x3.8" LABEL ABOVE CANDLE = Volume Spike Signal
Magenta text showing volume multiplier. Example: "x3.2" above a BTC candle means this candle had 3.2 times the average volume - something big just happened (news, liquidation cascade, whale entry).
🟢🔴 THICK LINE AT VA HIGH/LOW = Breakout with Momentum
When BTC breaks the VA line, the line changes:
- Thin line (width 2) = Weak breakout (<30Δ momentum)
- Medium line (width 3) = Medium breakout (30-60Δ)
- Thick dashed line (width 4) = STRONG breakout (>60Δ) - THIS IS THE FLASH
The label also changes: "VA High 72Δ V✓ STRONG" = 72 delta momentum, volume confirmed, strong breakout.
🔵 CYAN DASHED LINE AT POC = POC Bounce Flash
A short cyan dashed line appears when BTC bounces off the POC with a bullish reversal candle. This is your highest-probability long entry - the POC "magnet" just pulled price back and bulls are responding.
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🧠 PATTERN COMBINATIONS = Market Psychology (What Traders Are Thinking)
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🚀 PATTERN 1: "The Nitro Boost" (Highest Win Rate)
WHAT YOU SEE: FLOW SHIFT ↑ appears below current price + only MICRO-SR (magenta) levels above + Volume Spike (x2.5+)
PSYCHOLOGY: Big money just reversed from selling to buying. Retail still thinks it's going down. All the nearby resistance levels are weak (just micro-levels). The explosion in volume means someone BIG just entered.
EXAMPLE: BTC at $99,900, FLOW SHIFT ↑ just appeared, above you see MICRO-SR at $100,000, $100,050, $100,100 with no major resistance. Volume spike shows x3.1. → Institutions flipped bullish and the path of least resistance is UP. These MICRO-SR levels will be blown through like paper.
TRADE: Long immediately, targets at each MICRO-SR level, stop below the FLOW SHIFT price.
💎 PATTERN 2: "The Wall" (Reversal Setup)
WHAT YOU SEE: BULL/BEAR EXHAUST at a price level + Price approaching POC from above/below + Delta momentum arrows (⇊) pointing opposite to price movement
PSYCHOLOGY: One side pushed too hard and ran out of gas right as they're approaching the most important price level (POC). Delta momentum is reversing. The "wall" of volume at POC will reject them.
EXAMPLE: BTC pushed from $99,800 to $100,200, now "BULL EXHAUST" appears at $100,200. POC is at $100,000. You see ⇊ (bearish delta acceleration). → Bulls exhausted themselves pushing up, POC will act as resistance, bears are accelerating. Price will get rejected back down.
TRADE: Short at current price, target is POC at $100,000, stop above the exhaust level.
⚔️ PATTERN 3: "The War Zone" (Stay Out)
WHAT YOU SEE: BULL ATTACK and BEAR ATTACK labels alternating rapidly + EQUILIBRIUM or ROTATION at current price + VA lines very close together
PSYCHOLOGY: Bulls and bears are in full battle mode, neither side is winning. The market is chopping violently in a tight range. This is where retail gets destroyed by whipsaw.
EXAMPLE: BTC bouncing between $99,900-$100,100. "BULL ATTACK" at $100,000, "BEAR ATTACK" at $100,050, "EQUILIBRIUM" at $100,025. VA High at $100,100, VA Low at $99,900. → Pure chaos. Both sides throwing punches, nobody winning.
TRADE: STAY OUT. Wait for exhaustion or flow shift. If you must trade, use very tight ranges (buy at VA Low, sell at VA High, 5-tick stops).
🎯 PATTERN 4: "The Breakout Confirmation" (High Confidence)
WHAT YOU SEE: VA breakout with STRONG label + Volume spike (x2.0+) + FLOW SHIFT in breakout direction + No major resistance for 50+ ticks
PSYCHOLOGY: Every signal is aligned. Price broke the fair value range WITH strong momentum, WITH volume confirmation, WITH institutional flow reversal. This is the "perfect storm" breakout.
EXAMPLE: BTC breaks VA High at $100,200. Label changes to "VA High 68Δ V✓ STRONG" (thick dashed line). Volume spike shows x2.8. FLOW SHIFT ↑ appears at $100,210. Next resistance is MICRO-SR at $100,400. → This is as good as it gets. Institutions are buying, retail FOMO is coming, momentum is strong.
TRADE: Long on the breakout, targets at +100 ticks ($100,300), +200 ticks ($100,400), trail stop below the breakout candle.
🛡️ PATTERN 5: "The Failed Breakout" (Fade Setup)
WHAT YOU SEE: VA breakout with WEAK label + No volume spike + DEFENSE label appears (opposite side) + Delta momentum arrows pointing back into VA
PSYCHOLOGY: Price tried to break out but without conviction. No volume = no big players interested. The defending side is holding the line. Breakout traders are about to get trapped.
EXAMPLE: BTC breaks VA High at $100,200. Label shows "VA High 23Δ WEAK" (thin line). No volume spike. "BEAR DEFENSE" appears at $100,220. You see ⇊ (bearish acceleration). → Weak breakout, bears defending, momentum reversing. Bull breakout traders are trapped.
TRADE: Short the failed breakout, target is back inside VA (POC at $100,000), stop above the high.
🧲 PATTERN 6: "The POC Magnet" (Mean Reversion)
WHAT YOU SEE: Price far from POC (100+ ticks away) + Volume decreasing (DN arrows) + No ATTACK or FLOW SHIFT labels + MICRO-SR levels between current price and POC
PSYCHOLOGY: Price overextended from the most important level. No new aggressive volume is coming in. Market is tired. Like a rubber band, price will snap back to POC where most traders are positioned.
EXAMPLE: BTC at $100,350, POC at $100,000 (350 ticks away). "DN" arrows showing volume declining. "ROTATION" at current price. MICRO-SR at $100,300, $100,200, $100,100. → Overextended, running out of steam, POC will pull it back.
TRADE: Short with targets at each MICRO-SR level on the way down to POC, final target at POC itself.
💥 PATTERN 7: "The Liquidation Cascade" (Momentum Continuation)
WHAT YOU SEE: Multiple consecutive candles with volume spikes (x2.5+) + ATTACK label same direction + Delta momentum arrows same direction (⇈ or ⇊) + Breaking through MICRO-SR levels without stopping
PSYCHOLOGY: Liquidations are triggering more liquidations. Stop losses are getting hit, triggering more stop losses. This is a cascade - it won't stop until hitting POC or VA boundary. Retail is getting destroyed, institutions are feasting.
EXAMPLE: BTC drops from $100,200. Candles show x2.7, x3.1, x2.9 volume spikes. "BEAR ATTACK" at every level. ⇊ arrows accelerating. MICRO-SR levels at $100,100, $100,000, $99,900 all getting destroyed. POC at $99,750. → Liquidation cascade in progress. Won't stop until POC.
TRADE: If you're in the direction, hold until POC. If not in, wait for POC to enter counter-trend. DO NOT try to catch this knife early.
🔄 PATTERN 8: "The Reversal Confirmation" (Highest Probability Entry)
WHAT YOU SEE: POC Bounce Flash (cyan dashed line) + FLOW SHIFT in new direction + Volume spike + Price bouncing off POC with bullish/bearish engulfing candle
PSYCHOLOGY: Price hit the most important level (POC) and institutions just reversed direction. This is THE signal. The magnet worked, price came back to POC, and big money is now pushing it the other way.
EXAMPLE: BTC drops to POC at $100,000. Cyan dashed POC bounce flash appears. Bullish engulfing candle. "FLOW SHIFT ↑" appears. Volume spike x2.6. → Perfect reversal setup at the most important price level with institutional confirmation.
TRADE: Long at POC, target next MICRO-SR or VA High, stop below POC. This is your highest win-rate setup.
🎪 PATTERN 9: "The Fake-Out Trap" (Avoid or Fade)
WHAT YOU SEE: FLOW SHIFT appears + No volume spike + EXHAUST label appears within 3-5 candles same direction + Delta momentum arrows reverse
PSYCHOLOGY: Someone tried to fake a reversal (maybe a whale painting the tape) but there's no real follow-through. The move exhausted immediately. Traders who followed the FLOW SHIFT are about to get trapped.
EXAMPLE: "FLOW SHIFT ↑" appears at $99,950. No volume spike. Within 3 candles, "BULL EXHAUST" appears at $100,000. ⇊ arrows appear. → False reversal, trap set, traders entering longs are getting baited.
TRADE: Fade it. Short when exhaust appears, target back below the fake FLOW SHIFT level.
🏆 PATTERN 10: "The Perfect Storm Long" (All Systems Go)
WHAT YOU SEE: Price above POC + FLOW SHIFT ↑ + VA Low breakout with STRONG + Volume spike + Only MICRO-SR resistance above + BULL ATTACK label + ⇈ acceleration
PSYCHOLOGY: Everything aligned bullish. Institutions buying, momentum strong, volume confirming, path clear. This is when retail FOMO kicks in and you get the biggest moves.
EXAMPLE: BTC at $100,100. POC at $100,000 (above POC ✓). "FLOW SHIFT ↑" at $100,050 ✓. "VA Low 71Δ V✓ STRONG" breakout ✓. Volume x3.4 ✓. MICRO-SR at $100,300, $100,500 (weak resistance) ✓. "BULL ATTACK" ✓. ⇈ arrows ✓. → Every single bullish signal firing. This is the setup you wait for all day.
TRADE: Long with size, targets at +200 ticks minimum, trail aggressively, stop only if FLOW SHIFT reverses.
🎯 PATTERN 11: "The Coiled Spring" (High Probability Breakout)
WHAT YOU SEE: "COILED" label appears + 5-8 MICRO-SR levels stacked in breakout direction + Delta +30 or higher (for long) / -30 or lower (for short) + Price compressed below VA Low (long) or above VA High (short)
PSYCHOLOGY: Price is compressed in a weak position with heavy resistance/support overhead, BUT institutions are building momentum in the direction of the breakout. When it breaks, all those clustered MICRO-SR levels will be blown through rapidly because the spring is loaded. This is the setup where you get 100-200 tick moves in minutes.
EXAMPLE: BTC at $99,650. VA Low at $100,000. "COILED" (green) appears below price. WALL ↑ 8x showing 8 MICRO-SR levels from $100,100-$100,800. Delta shows +47. FLOW SHIFT ↑ just appeared. → Price is coiled below massive resistance wall with strong bullish momentum building. When VA Low breaks, the spring releases and price will rip through all 8 resistance levels.
TRADE: Long when price breaks VA Low with volume confirmation, targets at each MICRO-SR cluster (+100, +200, +300 ticks), trail stop below breakout candle. This is your "moonshot" setup.
🛑 PATTERN 12: "The Failed Shift Trap" (Fade Setup)
WHAT YOU SEE: "FAILED SHIFT ↑" or "FAILED SHIFT ↓" appears + Strong opposite momentum (⇊ for failed bull shift, ⇈ for failed bear shift) + No volume spike + Price back in original range
PSYCHOLOGY: Institutions attempted a reversal but the other side defended hard and rejected it. Traders who followed the FLOW SHIFT are now trapped. The failed reversal confirms the original trend will continue - the defending side is in control.
EXAMPLE: BTC pushed from $100,200 to $99,900. "FLOW SHIFT ↓" appeared at $100,100 signaling bearish reversal. Within 5 bars, bulls defended at $99,850, pushing price back to $100,000. "FAILED SHIFT ↓" now appears at $100,100 with ⇈ (bullish acceleration). → Bears tried to reverse trend but failed. Bulls defended successfully. Original uptrend continues.
TRADE: Fade the failed shift. If "FAILED SHIFT ↓" appears, go long (bulls won the battle). If "FAILED SHIFT ↑" appears, go short (bears won). Target is back to the other side of the range.
⚠️ PATTERN 13: "The Wall Collision" (High Risk, High Reward)
WHAT YOU SEE: "WALL ↑" or "WALL ↓" with 6+ levels + Price approaching wall with strong momentum (ATTACK label) + Volume spike + Delta accelerating (⇈ or ⇊)
PSYCHOLOGY: Unstoppable force meeting immovable object. Price is charging at a massive wall of resistance/support with strong momentum. Either it breaks through explosively OR it gets rejected violently. This is binary - huge win or huge loss.
EXAMPLE: BTC at $100,050 with "BULL ATTACK" and ⇈ arrows. Volume x3.2. Approaching "WALL ↑ 9x" at $100,200-$100,600. POC at $100,300 (inside the wall). → Bulls charging at massive resistance wall with strong momentum. If they break through, it's explosive. If rejected, crash back down.
TRADE: ADVANCED ONLY. Wait for the collision. If price breaks through wall with FLOW SHIFT confirmation + volume spike, go long immediately with tight stop. If price gets REJECTED (bearish delta appears at wall), short immediately targeting POC. DO NOT enter before knowing the outcome.
🔄 PATTERN 14: "The Rejection Reversal" (Counter-Trend Entry)
WHAT YOU SEE: "REJECT" label appears + Price in cluster zone + Opposite side DEFENSE or ATTACK label appears + Delta momentum reverses (⇈ to ⇊ or vice versa)
PSYCHOLOGY: The breakout failed, trapped traders are exiting, and the opposite side is now attacking the weak hands. This creates fast moves back in the original direction.
EXAMPLE: BTC breaks VA High to $100,250. Weak volume, delta only +22. Enters overhead MICRO-SR cluster. "REJECT" appears in red. "BEAR DEFENSE" appears at $100,280. ⇊ arrows appear. → Breakout failed, bulls trapped, bears attacking. Price will reverse fast.
TRADE: Counter-trend entry in direction of REJECT. Short when "REJECT" appears with bearish confirmation, target is back to POC or VA Low. Stop above the rejection high. Fast scalp.
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⚡ QUICK REFERENCE CHEAT SHEET
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SAFEST ENTRIES (Highest Win Rate):
✅ POC Bounce Flash + FLOW SHIFT (Pattern 8)
✅ FLOW SHIFT + Only MICRO-SR above + Volume Spike (Pattern 1)
✅ Strong VA Breakout + Volume Spike + FLOW SHIFT (Pattern 4)
✅ COILED label + Multiple stacked MICRO-SR + Delta >30 (Pattern 11)
DANGER ZONES (Stay Out):
⛔ BULL ATTACK + BEAR ATTACK alternating (Pattern 3)
⛔ FLOW SHIFT + No volume + Quick exhaust (Pattern 9)
⛔ EQUILIBRIUM at current price with tight VA range
⛔ WALL collision without clear direction (Pattern 13 - wait for outcome)
FADE/REVERSAL SETUPS:
🔄 EXHAUST at price level + Approaching POC (Pattern 2)
🔄 Weak VA Breakout + DEFENSE opposite side (Pattern 5)
🔄 Price far from POC + Volume declining (Pattern 6)
🔄 FAILED SHIFT appears + Opposite momentum (Pattern 12)
🔄 REJECT label + Opposite ATTACK/DEFENSE (Pattern 14)
HOLD/MOMENTUM CONTINUATION:
🚀 Multiple volume spikes + ATTACK label + ⇈/⇊ arrows (Pattern 7)
🚀 All bullish/bearish signals aligned (Pattern 10)
🚀 COILED spring release through wall (Pattern 11)
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Remember: The indicator shows you WHERE the big money is (POC), WHAT they're doing (FLOW SHIFT), and HOW HARD they're doing it (volume spikes, momentum). Your job is to follow the big money, not fight them. When institutions shift, you shift. When they exhaust, you fade. When they're in a war, you stay out. Trade with the whales, not against them.
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ENHANCED DELTA VOLUME PROFILE - TECHNICAL CALCULATIONS GUIDE
How Each Element is Actually Calculated
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🧮 CORE CALCULATIONS (The Math Behind What You See)
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📊 VOLUME BINS = Price range divided into 40 horizontal slices
The indicator takes the last 100 candles (configurable), finds the highest and lowest price touched, then divides that range into 40 equal "bins" (horizontal price levels). Each bin collects volume from candles that touched that price range. Example: BTC ranged from $99,500 to $100,500 in the last 100 bars. That's $1,000 range ÷ 40 bins = $25 per bin. Bin 1 = $99,500-$99,525, Bin 2 = $99,525-$99,550, etc.
🟦🟥 DELTA CALCULATION = (Bull Volume - Bear Volume) / Total Volume × 100
For each bin, the indicator separates bullish candles (close > open) from bearish candles (close < open). Delta = ((bull volume - bear volume) / total volume) × 100. This gives you a percentage from -100% (pure selling) to +100% (pure buying). Example: At $100,000, if 70 BTC was traded on green candles and 30 BTC on red candles, delta = ((70-30)/100) × 100 = 40% bullish.
🎨 GRADIENT COLOR = Delta converted to color spectrum
The delta percentage (-100 to +100) is mapped to a color gradient. -100% = pure bearish color (orange/red), 0% = neutral, +100% = pure bullish color (cyan/blue). The color you see on each bar directly represents the delta. Example: A bright cyan bar = high positive delta (strong buying), orange bar = high negative delta (strong selling), gray bar = balanced (delta near 0%).
🟩 POC (Point of Control) = Bin with the absolute highest total volume
The indicator sums up all volume in each of the 40 bins, then finds which bin has the most. That's your POC. Example: Bin 15 (around $100,000) collected 1,250 BTC of volume, which is more than any other bin. Bin 15 is your POC. This is where the most trading happened and where most traders are positioned.
⬜ VALUE AREA (VA) = The bins containing 70% of total volume, centered on POC
Starting from the POC, the indicator expands up and down, adding bins one at a time (choosing the bin with more volume each time) until it has captured 70% of all volume. The top of this range = VA High, bottom = VA Low. Example: POC at $100,000. Expanding out captures 70% of volume from $99,700 to $100,300. VA Low = $99,700, VA High = $100,300.
📈📉 VOLUME TREND = (Recent Volume - Old Volume) / Total Volume
The indicator splits your 100-bar lookback into three periods: Recent (last 15 bars), Mid (bars 15-30), and Older (last 15 bars of the 100). For each bin, it compares recent volume to older volume. If recent > older, trend is UP. If recent < older, trend is DOWN. Example: At $100,000, recent 15 bars had 80 BTC volume, older 15 bars had 40 BTC. Trend = (80-40)/(80+40) = 0.33 = UP. This shows volume is increasing at this level.
💜 MICRO-SR DETECTION = High volume (>60% of max) + High hits (>20% of max) + Active volume trend
A bin becomes MICRO-SR if: (1) Its volume is at least 60% of the highest-volume bin, (2) Price touched it frequently (at least 20% as many times as the most-touched bin), (3) Volume trend isn't flat (absolute trend > 0.05). Example: Bin at $99,975 has 750 BTC (75% of max), was hit 45 times (30% of max hits), volume trend = 0.08. = MICRO-SR (magenta border).
🟡 EXHAUSTION DETECTION = Extreme delta (>65%) + Declining volume trend (<-0.15) OR Extreme delta + Volume spike (>1.5× average)
Two ways to detect exhaustion: (1) One side dominated (delta > 65% or < -65%) BUT volume is decreasing (trend < -0.15), meaning participation is dropping. (2) Extreme delta WITH a huge volume spike (>1.5× average for that bin), meaning climactic volume. Example: At $100,200, delta = 72% bullish, but volume trend = -0.22 (declining). = BULL EXHAUST. Bulls won but are running out of steam.
🔵 FLOW SHIFT DETECTION = Delta changed sign (+ to - or - to +) + Delta change >40% + Volume trend increasing (>0.1)
Compares each bin's delta to the previous bin's delta. If delta flipped from negative to positive (or vice versa) by more than 40%, AND volume is increasing, = FLOW SHIFT. Example: Previous bin at $99,950 had -35% delta (bearish). Current bin at $100,000 has +45% delta (bullish). Change = 80% (flipped + exceeded 40%), volume trend = +0.15. = FLOW SHIFT ↑.
⇈⇊ DELTA MOMENTUM = Current delta - Average delta of last 3 bins
For each bin, the indicator looks at the previous 3 bins, calculates their average delta, then compares current delta to that average. If current delta is significantly higher/lower than the 3-bin average, momentum arrows appear. Example: Last 3 bins had deltas of 20%, 25%, 30% (average = 25%). Current bin delta = 55%. Momentum = 55 - 25 = +30 = ⇈ (strong bullish acceleration).
🟢🔴 VOLUME ACCELERATION = Rate of change of volume trend across three periods
Compares how volume changed from Old→Mid vs Mid→Recent. If Recent increased MORE than Mid did compared to Old, = positive acceleration. Formula: ((Recent-Mid) - (Mid-Old)) / |Mid-Old|. Example: Old=100, Mid=120, Recent=160. Mid increased by 20, Recent increased by 40. Acceleration = (40-20)/20 = 1.0 = strong acceleration (green velocity band).
⚖️ BALANCE SCORE = Combines volume balance, price range balance, and hit frequency
Three factors weighted equally: (1) How balanced is bull vs bear volume? (1 - |bull-bear|/total). (2) How tight is the price range? (1 - avgRange/maxRange). (3) How frequently was it hit? (hits/maxHits). Multiply these together. Score >0.7 = EQUILIBRIUM. Example: Volume is 55% bull / 45% bear = 0.9 balance. Range is tight = 0.8. Hit frequently = 0.85. Score = 0.9 × 0.8 × 0.85 = 0.61 = ROTATION.
📊 BULL/BEAR ATTACK/DEFENSE = Delta threshold (>60% or <-60%) + Volume trend direction
ATTACK = High delta (>60% either direction) + Volume trend increasing (>0.15). DEFENSE = High delta (>60% either direction) + Volume trend NOT increasing (≤0.15). Example: Delta = 68% bullish, volume trend = 0.22 = BULL ATTACK (buying with increasing volume). Delta = 68% bullish, volume trend = 0.05 = BULL DEFENSE (buying but volume not increasing).
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🎯 SIGNAL CALCULATIONS (The New Features)
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💜 VOLUME SPIKE = Current bar volume / 20-bar average volume
Takes the current candle's volume and divides it by the simple moving average of the last 20 candles' volume. If ratio >2.0 (configurable), spike detected. The label shows the exact multiplier. Example: Current candle = 450 BTC volume. 20-bar average = 140 BTC. Ratio = 450/140 = 3.21 = "x3.2" label appears in magenta above the candle.
🟢🔴 VA BREAKOUT MOMENTUM = POC bin's delta (absolute value)
When price breaks VA High or VA Low, the indicator looks at the POC bin's delta to measure momentum strength. Uses absolute value (ignore direction). <30 = WEAK, 30-60 = MED, >60 = STRONG. Line thickness and style change based on this. Example: BTC breaks VA High. POC bin delta = 72%. Momentum = 72 = STRONG. Line = width 4 (thick), dashed (flash effect), label shows "VA High 72Δ V✓ STRONG".
📊 BREAKOUT LINE THICKNESS = Momentum-based dynamic sizing
- Momentum <30: Line width = 2 (thin), solid line
- Momentum 30-60: Line width = 3 (medium), solid line
- Momentum >60: Line width = 4 (thick), dashed line (creates flash effect)
Example: Breakout with 45% momentum = width 3 solid line. Breakout with 75% momentum = width 4 dashed line (flashing).
✓ VOLUME CONFIRMATION = Current volume / 20-bar average >1.5
Checks if the breakout candle has strong volume. If current volume is at least 1.5× the 20-bar average, adds "V✓" to the label. Example: Breakout candle has 280 BTC volume, 20-bar average is 160 BTC. Ratio = 280/160 = 1.75 > 1.5 = "V✓" appears in label.
🔵 POC BOUNCE DETECTION = Price within 0.5 bin-step of POC + Bullish reversal candle + Previous candle was bearish
Three conditions must all be true: (1) Current close price is within half a bin's height from POC price. (2) Current candle is bullish (close > open). (3) Previous candle was bearish (close < open). If all true = POC bounce, cyan dashed flash line appears. Example: POC at $100,000, bin step = $25. Current close = $100,008 (within $12.50 of POC ✓). Current candle green ✓. Previous candle red ✓. = POC Bounce Flash.
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⚙️ TECHNICAL PARAMETERS (What You Can Adjust)
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🔢 LOOKBACK PERIOD (Default: 100 bars) = How much history to analyze
The number of candles backwards from current that get analyzed. More bars = more stable but slower to react. Fewer bars = more reactive but noisier. For 1-minute BTC scalping, 100 bars = last 100 minutes = 1 hour 40 minutes of data. Example: Setting to 50 bars makes it more reactive to recent action but less stable. Setting to 200 bars makes it smoother but slower to show new developments.
🎚️ NUMBER OF BINS (Default: 40) = Resolution of price levels
How many horizontal slices to divide the price range into. More bins = finer resolution but more noise. Fewer bins = smoother but less precise. 40 bins for 1-minute = good balance. Example: With $1,000 range, 40 bins = $25 per level. 20 bins would be $50 per level (less precise). 60 bins would be $16.67 per level (more precise but noisier).
📏 DISPLAY OFFSET (Default: 10 bars) = How far right the profile extends
How many bars to the right of current candle the volume profile displays. Purely visual - doesn't affect calculations. Example: Offset = 10 means the profile extends 10 bars to the right. Offset = 30 means it extends further right (more separation from candles).
📊 VOLUME TREND PERIOD (Default: 15 bars) = How many recent bars define "recent"
The number of bars considered "recent" vs "old" when calculating volume trends. Shorter = more sensitive to very recent changes. Longer = smoother trends. Example: 15 bars means "recent" = last 15 candles (last 15 minutes on 1m chart). Setting to 5 would make it hyper-reactive to the last 5 minutes. Setting to 30 would make it smoother.
🎯 EXHAUSTION THRESHOLD (Default: 65%) = How extreme delta must be for exhaustion
The minimum delta percentage to trigger exhaustion detection. Higher = more selective (only extreme cases). Lower = more signals but more false positives. Example: 65% means delta must be >65% or <-65% to qualify. Setting to 75% would only catch the most extreme exhaustion. Setting to 55% would catch more cases.
💜 MICRO-LEVEL THRESHOLD (Default: 60%) = How strong a level must be for MICRO-SR
The minimum volume percentage (relative to max) required for MICRO-SR detection. Higher = fewer, stronger levels. Lower = more levels but weaker. Example: 60% means bin must have at least 60% of the max bin's volume. Setting to 70% would show only the strongest levels. Setting to 50% would show more levels.
⚡ DELTA MOMENTUM PERIOD (Default: 3 bars) = How many bins to average for momentum
How many previous bins to average when calculating delta momentum. Shorter = more sensitive acceleration signals. Longer = smoother, less noisy. Example: 3 bins means compares current to average of last 3. Setting to 5 would smooth out momentum detection. Setting to 2 would make it more reactive.
🌊 FLOW SHIFT SENSITIVITY (Default: 40%) = Minimum delta change for flow shift
How much delta must change between consecutive bins to trigger FLOW SHIFT. Lower = more flow shift signals (more sensitive). Higher = fewer, stronger signals. Example: 40% means delta must flip by at least 40% (e.g., from -20% to +20% or from +10% to -30%). Setting to 60% would only catch major reversals. Setting to 25% would catch smaller shifts.
💥 VOLUME SPIKE THRESHOLD (Default: 2.0x) = Multiplier to trigger spike signal
How many times above average volume must be to show the spike label. Higher = fewer spikes shown (only extreme). Lower = more spikes shown. Example: 2.0× means current volume must be at least double the 20-bar average. Setting to 3.0× would only show massive spikes. Setting to 1.5× would show more moderate spikes.
🚀 BREAKOUT MOMENTUM MINIMUM (Default: 20%) = Minimum delta for breakout signal
How much delta momentum required at POC for VA breakout to trigger. Higher = fewer breakout signals (more selective). Lower = more signals but more false positives. Example: 20% means POC delta must be at least 20% (or -20%) when price breaks VA. Setting to 30% would only show strong breakouts. Setting to 10% would show weaker breakouts too.
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🔬 ADVANCED TECHNICAL DETAILS
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📐 BIN POSITIONING = Price-to-bin mapping formula
For any price P, its bin index = floor((P - MinPrice) / BinStep). BinStep = (MaxPrice - MinPrice) / NumBins. Example: Range $99,000-$100,000, 40 bins. BinStep = $1,000/40 = $25. Price $99,550 → Bin 22: (99,550 - 99,000) / 25 = 22.
📊 VOLUME DISTRIBUTION = Proportional allocation across bins
When a candle spans multiple bins, its volume is distributed proportionally based on how much of the candle's range overlapped each bin. Example: Candle from $99,950 to $100,050 (range = $100) with 50 BTC volume. Bin 1 ($99,950-$99,975) gets 25% of range = 12.5 BTC. Bin 2 ($99,975-$100,000) gets 25% = 12.5 BTC. Bin 3 ($100,000-$100,025) gets 25% = 12.5 BTC. Bin 4 ($100,025-$100,050) gets 25% = 12.5 BTC.
🎨 COLOR GRADIENT MAPPING = Delta to RGB conversion
Delta percentage is normalized to 0-1 scale (from -100/+100 range), then mapped to RGB gradient. -100% (0.0) = Full bearish color RGB. 0% (0.5) = Neutral gray. +100% (1.0) = Full bullish color RGB. Example: Delta = 60% → Normalized = 0.8 → 80% towards full bullish color (bright cyan).
⚖️ BALANCE SCORE FORMULA = Weighted geometric mean
BalanceScore = (VolumeBalance^w) × (PriceBalance^w) × (HitBalance^w), where w=weight (default 1.0). VolumeBalance = 1 - |BullVol - BearVol|/TotalVol. PriceBalance = 1 - AvgRange/MaxRange. HitBalance = Hits/MaxHits. Example: Vol=0.9, Price=0.8, Hit=0.7 → Score = 0.9 × 0.8 × 0.7 = 0.504.
🔄 DELTA HISTORY TRACKING = Rolling array per bin
Each bin maintains an array of its last N delta values (where N = delta momentum period). When calculating momentum, current delta is compared to the average of this array. Example: Bin's delta history = . Average = 25%. Current = 55%. Momentum = 55 - 25 = 30.
📈 VOLUME VELOCITY = Second derivative of volume
Measures acceleration of volume change. Recent change = (Recent - Mid). Old change = (Mid - Old). Acceleration = (Recent change - Old change) / |Old change|. Positive = accelerating. Negative = decelerating. Example: Old=100, Mid=150, Recent=220. Recent change = 70. Old change = 50. Accel = (70-50)/50 = 0.4 = 40% acceleration.
🎯 VA EXPANSION ALGORITHM = Greedy breadth-first from POC
Start at POC bin. While accumulated volume < 70% of total: Look at bin above and bin below POC boundary. Choose whichever has more volume. Add that bin to VA. Repeat. Example: POC at bin 20. Bin 21 (above) has 80 BTC, Bin 19 (below) has 95 BTC. Add bin 19. Now VA = bins 19-20. Next: Bin 21 has 80, Bin 18 has 70. Add bin 21. VA = bins 19-21. Continue until 70% captured.
⏱️ REAL-TIME UPDATES = Recalculates on every new bar close
The entire profile recalculates when barstate.islast = true (current bar). All 40 bins are cleared and rebuilt from scratch using the last N candles. This ensures the profile is always accurate to the current market state. Example: On 1-minute chart, the profile fully recalculates every 60 seconds when the new candle opens.
🎨 RENDERING OPTIMIZATION = 500-bar future limit management
TradingView limits drawing objects to 500 bars into the future. The indicator calculates safe offsets: maxFutureBar = bar_index + 499, then caps all box/line/label positions to stay under this limit. Example: Current bar_index = 1000. Max future = 1499. Display offset wanted = 200. Safe offset = min(200, 400 - 100) = min(200, 300) = 200 ✓ safe.
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💡 INTERPRETATION TIPS
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🔢 Understanding Percentages:
- Delta 0-30%: Weak bias, essentially balanced
- Delta 30-60%: Moderate bias, one side has control
- Delta 60-85%: Strong bias, one side dominated
- Delta 85-100%: Extreme bias, one-sided market (exhaustion likely)
📊 Volume Trend Interpretation:
- Trend -1.0 to -0.3: Strong decline in participation
- Trend -0.3 to -0.1: Moderate decline
- Trend -0.1 to +0.1: Stable/flat volume
- Trend +0.1 to +0.3: Moderate increase
- Trend +0.3 to +1.0: Strong increase in participation
🎯 Balance Score Ranges:
- 0.0-0.3: Heavily imbalanced, strong directional bias
- 0.3-0.5: Moderate imbalance, rotation forming
- 0.5-0.7: Balanced rotation zone
- 0.7-1.0: Perfect equilibrium, range-bound
⚡ Momentum Thresholds:
- <10: Negligible momentum change
- 10-20: Moderate acceleration
- 20-40: Strong acceleration (arrow appears)
- >40: Extreme acceleration (very rare, very significant)
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Understanding these calculations helps you know WHY the indicator is showing what it's showing. When you see "FLOW SHIFT ↑", you now know it calculated a >40% delta flip with increasing volume. When you see MICRO-SR, you know that level has >60% of max volume, >20% of max hits, and active participation. When you see ⇈, you know delta jumped significantly above its 3-bin average. Use this knowledge to trust the signals and understand their strength.
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In den Scripts nach "binary" suchen
Algorithm Predator - ProAlgorithm Predator - Pro: Advanced Multi-Agent Reinforcement Learning Trading System
Algorithm Predator - Pro combines four specialized market microstructure agents with a state-of-the-art reinforcement learning framework . Unlike traditional indicator mashups, this system implements genuine machine learning to automatically discover which detection strategies work best in current market conditions and adapts continuously without manual intervention.
Core Innovation: Rather than forcing traders to interpret conflicting signals, this system uses 15 different multi-armed bandit algorithms and a full reinforcement learning stack (Q-Learning, TD(λ) with eligibility traces, and Policy Gradient with REINFORCE) to learn optimal agent selection policies. The result is a self-improving system that gets smarter with every trade.
Target Users: Swing traders, day traders, and algorithmic traders seeking systematic signal generation with mathematical rigor. Suitable for stocks, forex, crypto, and futures on liquid instruments (>100k daily volume).
Why These Components Are Combined
The Fundamental Problem
No single indicator works consistently across all market regimes. What works in trending markets fails in ranging conditions. Traditional solutions force traders to manually switch indicators (slow, error-prone) or interpret all signals simultaneously (cognitive overload).
This system solves the problem through automated meta-learning: Deploy multiple specialized agents designed for specific market microstructure conditions, then use reinforcement learning to discover which agent (or combination) performs best in real-time.
Why These Specific Four Agents?
The four agents provide orthogonal failure mode coverage —each agent's weakness is another's strength:
Spoofing Detector - Optimal in consolidation/manipulation; fails in trending markets (hedged by Exhaustion Detector)
Exhaustion Detector - Optimal at trend climax; fails in range-bound markets (hedged by Liquidity Void)
Liquidity Void - Optimal pre-breakout compression; fails in established trends (hedged by Mean Reversion)
Mean Reversion - Optimal in low volatility; fails in strong trends (hedged by Spoofing Detector)
This creates complete market state coverage where at least one agent should perform well in any condition. The bandit system identifies which one without human intervention.
Why Reinforcement Learning vs. Simple Voting?
Traditional consensus systems have fatal flaws: equal weighting assumes all agents are equally reliable (false), static thresholds don't adapt, and no learning means past mistakes repeat indefinitely.
Reinforcement learning solves this through the exploration-exploitation tradeoff: Continuously test underused agents (exploration) while primarily relying on proven winners (exploitation). Over time, the system builds a probability distribution over agent quality reflecting actual market performance.
Mathematical Foundation: Multi-armed bandit problem from probability theory, where each agent is an "arm" with unknown reward distribution. The goal is to maximize cumulative reward while efficiently learning each arm's true quality.
The Four Trading Agents: Technical Explanation
Agent 1: 🎭 Spoofing Detector (Institutional Manipulation Detection)
Theoretical Basis: Market microstructure theory on order flow toxicity and information asymmetry. Based on research by Easley, López de Prado, and O'Hara on high-frequency trading manipulation.
What It Detects:
1. Iceberg Orders (Hidden Liquidity Absorption)
Method: Monitors volume spikes (>2.5× 20-period average) with minimal price movement (<0.3× ATR)
Formula: score += (close > open ? -2.5 : 2.5) when volume > vol_avg × 2.5 AND abs(close - open) / ATR < 0.3
Interpretation: Large volume without price movement indicates institutional absorption (buying) or distribution (selling) using hidden orders
Signal Logic: Contrarian—fade false breakouts caused by institutional manipulation
2. Spoofing Patterns (Fake Liquidity via Layering)
Method: Analyzes candlestick wick-to-body ratios during volume spikes
Formula: if upper_wick > body × 2 AND volume_spike: score += 2.0
Mechanism: Spoofing creates large wicks (orders pulled before execution) with volume evidence
Signal Logic: Wick direction indicates trapped participants; trade against the failed move
3. Post-Manipulation Reversals
Method: Tracks volume decay after manipulation events
Formula: if volume > vol_avg × 3 AND volume / volume < 0.3: score += (close > open ? -1.5 : 1.5)
Interpretation: Sharp volume drop after manipulation indicates exhaustion of manipulative orders
Why It Works: Institutional manipulation creates detectable microstructure anomalies. While retail traders see "mysterious reversals," this agent quantifies the order flow patterns causing them.
Parameter: i_spoof (sensitivity 0.5-2.0) - Controls detection threshold
Best Markets: Consolidations before breakouts, London/NY overlap windows, stocks with institutional ownership >70%
Agent 2: ⚡ Exhaustion Detector (Momentum Failure Analysis)
Theoretical Basis: Technical analysis divergence theory combined with VPIN reversals from market microstructure literature.
What It Detects:
1. Price-RSI Divergence (Momentum Deceleration)
Method: Compares 5-bar price ROC against RSI change
Formula: if price_roc > 5% AND rsi_current < rsi : score += 1.8
Mathematics: Second derivative detecting inflection points
Signal Logic: When price makes higher highs but momentum makes lower highs, expect mean reversion
2. Volume Exhaustion (Buying/Selling Climax)
Method: Identifies strong price moves (>5% ROC) with declining volume (<-20% volume ROC)
Formula: if price_roc > 5 AND vol_roc < -20: score += 2.5
Interpretation: Price extension without volume support indicates retail chasing while institutions exit
3. Momentum Deceleration (Acceleration Analysis)
Method: Compares recent 3-bar momentum to prior 3-bar momentum
Formula: deceleration = abs(mom1) < abs(mom2) × 0.5 where momentum significant (> ATR)
Signal Logic: When rate of price change decelerates significantly, anticipate directional shift
Why It Works: Momentum is lagging, but momentum divergence is leading. By comparing momentum's rate of change to price, this agent detects "weakening conviction" before reversals become obvious.
Parameter: i_momentum (sensitivity 0.5-2.0)
Best Markets: Strong trends reaching climax, parabolic moves, instruments with high retail participation
Agent 3: 💧 Liquidity Void Detector (Breakout Anticipation)
Theoretical Basis: Market liquidity theory and order book dynamics. Based on research into "liquidity holes" and volatility compression preceding expansion.
What It Detects:
1. Bollinger Band Squeeze (Volatility Compression)
Method: Monitors Bollinger Band width relative to 50-period average
Formula: bb_width = (upper_band - lower_band) / middle_band; triggers when < 0.6× average
Mathematical Foundation: Regression to the mean—low volatility precedes high volatility
Signal Logic: When volatility compresses AND cumulative delta shows directional bias, anticipate breakout
2. Volume Profile Gaps (Thin Liquidity Zones)
Method: Identifies sharp volume transitions indicating few limit orders
Formula: if volume < vol_avg × 0.5 AND volume < vol_avg × 0.5 AND volume > vol_avg × 1.5
Interpretation: Sudden volume drop after spike indicates price moved through order book to low-opposition area
Signal Logic: Price accelerates through low-liquidity zones
3. Stop Hunts (Liquidity Grabs Before Reversals)
Method: Detects new 20-bar highs/lows with immediate reversal and rejection wick
Formula: if new_high AND close < high - (high - low) × 0.6: score += 3.0
Mechanism: Market makers push price to trigger stop-loss clusters, then reverse
Signal Logic: Enter reversal after stop-hunt completes
Why It Works: Order book theory shows price moves fastest through zones with minimal liquidity. By identifying these zones before major moves, this agent provides early entry for high-reward breakouts.
Parameter: i_liquidity (sensitivity 0.5-2.0)
Best Markets: Range-bound pre-breakout setups, volatility compression zones, instruments prone to gap moves
Agent 4: 📊 Mean Reversion (Statistical Arbitrage Engine)
Theoretical Basis: Statistical arbitrage theory, Ornstein-Uhlenbeck mean-reverting processes, and pairs trading methodology applied to single instruments.
What It Detects:
1. Z-Score Extremes (Standard Deviation Analysis)
Method: Calculates price distance from 20-period and 50-period SMAs in standard deviation units
Formula: zscore_20 = (close - SMA20) / StdDev(50)
Statistical Interpretation: Z-score >2.0 means price is 2 standard deviations above mean (97.5th percentile)
Trigger Logic: if abs(zscore_20) > 2.0: score += zscore_20 > 0 ? -1.5 : 1.5 (fade extremes)
2. Ornstein-Uhlenbeck Process (Mean-Reverting Stochastic Model)
Method: Models price as mean-reverting stochastic process: dx = θ(μ - x)dt + σdW
Implementation: Calculates spread = close - SMA20, then z-score of spread vs. spread distribution
Formula: ou_signal = (spread - spread_mean) / spread_std
Interpretation: Measures "tension" pulling price back to equilibrium
3. Correlation Breakdown (Regime Change Detection)
Method: Compares 50-period price-volume correlation to 10-period correlation
Formula: corr_breakdown = abs(typical_corr - recent_corr) > 0.5
Enhancement: if corr_breakdown AND abs(zscore_20) > 1.0: score += zscore_20 > 0 ? -1.2 : 1.2
Why It Works: Mean reversion is the oldest quantitative strategy (1970s pairs trading at Morgan Stanley). While simple, it remains effective because markets exhibit periodic equilibrium-seeking behavior. This agent applies rigorous statistical testing to identify when mean reversion probability is highest.
Parameter: i_statarb (sensitivity 0.5-2.0)
Best Markets: Range-bound instruments, low-volatility periods (VIX <15), algo-dominated markets (forex majors, index futures)
Multi-Armed Bandit System: 15 Algorithms Explained
What Is a Multi-Armed Bandit Problem?
Origin: Named after slot machines ("one-armed bandits"). Imagine facing multiple slot machines, each with unknown payout rates. How do you maximize winnings?
Formal Definition: K arms (agents), each with unknown reward distribution with mean μᵢ. Goal: Maximize cumulative reward over T trials. Challenge: Balance exploration (trying uncertain arms to learn quality) vs. exploitation (using known-best arm for immediate reward).
Trading Application: Each agent is an "arm." After each trade, receive reward (P&L). Must decide which agent to trust for next signal.
Algorithm Categories
Bayesian Approaches (probabilistic, optimal for stationary environments):
Thompson Sampling
Bootstrapped Thompson Sampling
Discounted Thompson Sampling
Frequentist Approaches (confidence intervals, deterministic):
UCB1
UCB1-Tuned
KL-UCB
SW-UCB (Sliding Window)
D-UCB (Discounted)
Adversarial Approaches (robust to non-stationary environments):
EXP3-IX
Hedge
FPL-Gumbel
Reinforcement Learning Approaches (leverage learned state-action values):
Q-Values (from Q-Learning)
Policy Network (from Policy Gradient)
Simple Baseline:
Epsilon-Greedy
Softmax
Key Algorithm Details
Thompson Sampling (DEFAULT - RECOMMENDED)
Theoretical Foundation: Bayesian decision theory with conjugate priors. Published by Thompson (1933), rediscovered for bandits by Chapelle & Li (2011).
How It Works:
Model each agent's reward distribution as Beta(α, β) where α = wins, β = losses
Each step, sample from each agent's beta distribution: θᵢ ~ Beta(αᵢ, βᵢ)
Select agent with highest sample: argmaxᵢ θᵢ
Update winner's distribution after observing outcome
Mathematical Properties:
Optimality: Achieves logarithmic regret O(K log T) (proven optimal)
Bayesian: Maintains probability distribution over true arm means
Automatic Balance: High uncertainty → more exploration; high certainty → exploitation
⚠️ CRITICAL APPROXIMATION: This is a pseudo-random approximation of true Thompson Sampling. True implementation requires random number generation from beta distributions, which Pine Script doesn't provide. This version uses Box-Muller transform with market data (price/volume decimal digits) as entropy source. While not mathematically pure, it maintains core exploration-exploitation balance and learns agent preferences effectively.
When To Use: Best all-around choice. Handles non-stationary markets reasonably well, balances exploration naturally, highly sample-efficient.
UCB1 (Upper Confidence Bound)
Formula: UCB_i = reward_mean_i + sqrt(2 × ln(total_pulls) / pulls_i)
Interpretation: First term (exploitation) + second term (exploration bonus for less-tested arms)
Mathematical Properties:
Deterministic : Always selects same arm given same state
Regret Bound: O(K log T) — same optimality as Thompson Sampling
Interpretable: Can visualize confidence intervals
When To Use: Prefer deterministic behavior, want to visualize uncertainty, stable markets
EXP3-IX (Exponential Weights - Adversarial)
Theoretical Foundation: Adversarial bandit algorithm. Assumes environment may be actively hostile (worst-case analysis).
How It Works:
Maintain exponential weights: w_i = exp(η × cumulative_reward_i)
Select agent with probability proportional to weights: p_i = (1-γ)w_i/Σw_j + γ/K
After outcome, update with importance weighting: estimated_reward = observed_reward / p_i
Mathematical Properties:
Adversarial Regret: O(sqrt(TK log K)) even if environment is adversarial
No Assumptions: Doesn't assume stationary or stochastic reward distributions
Robust: Works even when optimal arm changes continuously
When To Use: Extreme non-stationarity, don't trust reward distribution assumptions, want robustness over efficiency
KL-UCB (Kullback-Leibler Upper Confidence Bound)
Theoretical Foundation: Uses KL-divergence instead of Hoeffding bounds. Tighter confidence intervals.
Formula (conceptual): Find largest q such that: n × KL(p||q) ≤ ln(t) + 3×ln(ln(t))
Mathematical Properties:
Tighter Bounds: KL-divergence adapts to reward distribution shape
Asymptotically Optimal: Better constant factors than UCB1
Computationally Intensive: Requires iterative binary search (15 iterations)
When To Use: Maximum sample efficiency needed, willing to pay computational cost, long-term trading (>500 bars)
Q-Values & Policy Network (RL-Based Selection)
Unique Feature: Instead of treating agents as black boxes with scalar rewards, these algorithms leverage the full RL state representation .
Q-Values Selection:
Uses learned Q-values: Q(state, agent_i) from Q-Learning
Selects agent via softmax over Q-values for current market state
Advantage: Selects based on state-conditional quality (which agent works best in THIS market state)
Policy Network Selection:
Uses neural network policy: π(agent | state, θ) from Policy Gradient
Direct policy over agents given market features
Advantage: Can learn non-linear relationships between market features and agent quality
When To Use: After 200+ RL updates (Q-Values) or 500+ updates (Policy Network) when models converged
Machine Learning & Reinforcement Learning Stack
Why Both Bandits AND Reinforcement Learning?
Critical Distinction:
Bandits treat agents as contextless black boxes: "Agent 2 has 60% win rate"
Reinforcement Learning adds state context: "Agent 2 has 60% win rate WHEN trend_score > 2 and RSI < 40"
Power of Combination: Bandits provide fast initial learning with minimal assumptions. RL provides state-dependent policies for superior long-term performance.
Component 1: Q-Learning (Value-Based RL)
Algorithm: Temporal Difference Learning with Bellman equation.
State Space: 54 discrete states formed from:
trend_state = {0: bearish, 1: neutral, 2: bullish} (3 values)
volatility_state = {0: low, 1: normal, 2: high} (3 values)
RSI_state = {0: oversold, 1: neutral, 2: overbought} (3 values)
volume_state = {0: low, 1: high} (2 values)
Total states: 3 × 3 × 3 × 2 = 54 states
Action Space: 5 actions (No trade, Agent 1, Agent 2, Agent 3, Agent 4)
Total state-action pairs: 54 × 5 = 270 Q-values
Bellman Equation:
Q(s,a) ← Q(s,a) + α ×
Parameters:
α (learning rate): 0.01-0.50, default 0.10 - Controls step size for updates
γ (discount factor): 0.80-0.99, default 0.95 - Values future rewards
ε (exploration): 0.01-0.30, default 0.10 - Probability of random action
Update Mechanism:
Position opens with state s, action a (selected agent)
Every bar position is open: Calculate floating P&L → scale to reward
Perform online TD update
When position closes: Perform terminal update with final reward
Gradient Clipping: TD errors clipped to ; Q-values clipped to for stability.
Why It Works: Q-Learning learns "quality" of each agent in each market state through trial and error. Over time, builds complete state-action value function enabling optimal state-dependent agent selection.
Component 2: TD(λ) Learning (Temporal Difference with Eligibility Traces)
Enhancement Over Basic Q-Learning: Credit assignment across multiple time steps.
The Problem TD(λ) Solves:
Position opens at t=0
Market moves favorably at t=3
Position closes at t=8
Question: Which earlier decisions contributed to success?
Basic Q-Learning: Only updates Q(s₈, a₈) ← reward
TD(λ): Updates ALL visited state-action pairs with decayed credit
Eligibility Trace Formula:
e(s,a) ← γ × λ × e(s,a) for all s,a (decay all traces)
e(s_current, a_current) ← 1 (reset current trace)
Q(s,a) ← Q(s,a) + α × TD_error × e(s,a) (update all with trace weight)
Lambda Parameter (λ): 0.5-0.99, default 0.90
λ=0: Pure 1-step TD (only immediate next state)
λ=1: Full Monte Carlo (entire episode)
λ=0.9: Balance (recommended)
Why Superior: Dramatically faster learning for multi-step tasks. Q-Learning requires many episodes to propagate rewards backwards; TD(λ) does it in one.
Component 3: Policy Gradient (REINFORCE with Baseline)
Paradigm Shift: Instead of learning value function Q(s,a), directly learn policy π(a|s).
Policy Network Architecture:
Input: 12 market features
Hidden: None (linear policy)
Output: 5 actions (softmax distribution)
Total parameters: 12 features × 5 actions + 5 biases = 65 parameters
Feature Set (12 Features):
Price Z-score (close - SMA20) / ATR
Volume ratio (volume / vol_avg - 1)
RSI deviation (RSI - 50) / 50
Bollinger width ratio
Trend score / 4 (normalized)
VWAP deviation
5-bar price ROC
5-bar volume ROC
Range/ATR ratio - 1
Price-volume correlation (20-period)
Volatility ratio (ATR / ATR_avg - 1)
EMA50 deviation
REINFORCE Update Rule:
θ ← θ + α × ∇log π(a|s) × advantage
where advantage = reward - baseline (variance reduction)
Why Baseline? Raw rewards have high variance. Subtracting baseline (running average) centers rewards around zero, reducing gradient variance by 50-70%.
Learning Rate: 0.001-0.100, default 0.010 (much lower than Q-Learning because policy gradients have high variance)
Why Policy Gradient?
Handles 12 continuous features directly (Q-Learning requires discretization)
Naturally maintains exploration through probability distribution
Can converge to stochastic optimal policy
Component 4: Ensemble Meta-Learner (Stacking)
Architecture: Level-1 meta-learner combines Level-0 base learners (Q-Learning, TD(λ), Policy Gradient).
Three Meta-Learning Algorithms:
1. Simple Average (Baseline)
Final_prediction = (Q_prediction + TD_prediction + Policy_prediction) / 3
2. Weighted Vote (Reward-Based)
weight_i ← 0.95 × weight_i + 0.05 × (reward_i + 1)
3. Adaptive Weighting (Gradient-Based) — RECOMMENDED
Loss Function: L = (y_true - ŷ_ensemble)²
Gradient: ∂L/∂weight_i = -2 × (y_true - ŷ_ensemble) × agent_contribution_i
Updates weights via gradient descent with clipping and normalization
Why It Works: Unlike simple averaging, meta-learner discovers which base learner is most reliable in current regime. If Policy Gradient excels in trending markets while Q-Learning excels in ranging, meta-learner learns these patterns and weights accordingly.
Feature Importance Tracking
Purpose: Identify which of 12 features contribute most to successful predictions.
Update Rule: importance_i ← 0.95 × importance_i + 0.05 × |feature_i × reward|
Use Cases:
Feature selection: Drop low-importance features
Market regime detection: Importance shifts reveal regime changes
Agent tuning: If VWAP deviation has high importance, consider boosting agents using VWAP
RL Position Tracking System
Critical Innovation: Proper reinforcement learning requires tracking which decisions led to outcomes.
State Tracking (When Signal Validates):
active_rl_state ← current_market_state (0-53)
active_rl_action ← selected_agent (1-4)
active_rl_entry ← entry_price
active_rl_direction ← 1 (long) or -1 (short)
active_rl_bar ← current_bar_index
Online Updates (Every Bar Position Open):
floating_pnl = (close - entry) / entry × direction
reward = floating_pnl × 10 (scale to meaningful range)
reward = clip(reward, -5.0, 5.0)
Update Q-Learning, TD(λ), and Policy Gradient
Terminal Update (Position Close):
Final Q-Learning update (no next Q-value, terminal state)
Update meta-learner with final result
Update agent memory
Clear position tracking
Exit Conditions:
Time-based: ≥3 bars held (minimum hold period)
Stop-loss: 1.5% adverse move
Take-profit: 2.0% favorable move
Market Microstructure Filters
Why Microstructure Matters
Traditional technical analysis assumes fair, efficient markets. Reality: Markets have friction, manipulation, and information asymmetry. Microstructure filters detect when market structure indicates adverse conditions.
Filter 1: VPIN (Volume-Synchronized Probability of Informed Trading)
Theoretical Foundation: Easley, López de Prado, & O'Hara (2012). "Flow Toxicity and Liquidity in a High-Frequency World."
What It Measures: Probability that current order flow is "toxic" (informed traders with private information).
Calculation:
Classify volume as buy or sell (close > close = buy volume)
Calculate imbalance over 20 bars: VPIN = |Σ buy_volume - Σ sell_volume| / Σ total_volume
Compare to moving average: toxic = VPIN > VPIN_MA(20) × sensitivity
Interpretation:
VPIN < 0.3: Normal flow (uninformed retail)
VPIN 0.3-0.4: Elevated (smart money active)
VPIN > 0.4: Toxic flow (informed institutions dominant)
Filter Logic:
Block LONG when: VPIN toxic AND price rising (don't buy into institutional distribution)
Block SHORT when: VPIN toxic AND price falling (don't sell into institutional accumulation)
Adaptive Threshold: If VPIN toxic frequently, relax threshold; if rarely toxic, tighten threshold. Bounded .
Filter 2: Toxicity (Kyle's Lambda Approximation)
Theoretical Foundation: Kyle (1985). "Continuous Auctions and Insider Trading."
What It Measures: Price impact per unit volume — market depth and informed trading.
Calculation:
price_impact = (close - close ) / sqrt(Σ volume over 10 bars)
impact_zscore = (price_impact - impact_mean) / impact_std
toxicity = abs(impact_zscore)
Interpretation:
Low toxicity (<1.0): Deep liquid market, large orders absorbed easily
High toxicity (>2.0): Thin market or informed trading
Filter Logic: Block ALL SIGNALS when toxicity > threshold. Most dangerous when price breaks from VWAP with high toxicity.
Filter 3: Regime Filter (Counter-Trend Protection)
Purpose: Prevent counter-trend trades during strong trends.
Trend Scoring:
trend_score = 0
trend_score += close > EMA8 ? +1 : -1
trend_score += EMA8 > EMA21 ? +1 : -1
trend_score += EMA21 > EMA50 ? +1 : -1
trend_score += close > EMA200 ? +1 : -1
Range:
Regime Classification:
Strong Bull: trend_score ≥ +3 → Block all SHORT signals
Strong Bear: trend_score ≤ -3 → Block all LONG signals
Neutral: -2 ≤ trend_score ≤ +2 → Allow both directions
Filter 4: Liquidity Boost (Signal Enhancer)
Unique: Unlike other filters (which block), this amplifies signals during low liquidity.
Logic: if volume < vol_avg × 0.7: agent_scores × 1.2
Why It Works: Low liquidity often precedes explosive moves (breakouts). By increasing agent sensitivity during compression, system catches pre-breakout signals earlier.
Technical Implementation & Approximations
⚠️ Critical Approximations Required by Pine Script
1. Thompson Sampling: Pseudo-Random Beta Distribution
Academic Standard: True random sampling from beta distributions using cryptographic RNG
This Implementation: Box-Muller transform for normal distribution using market data (price/volume decimal digits) as entropy source, then scale to beta distribution mean/variance
Impact: Not cryptographically random, may have subtle biases in specific price ranges, but maintains correct mean and approximate variance. Sufficient for bandit agent selection.
2. VPIN: Simplified Volume Classification
Academic Standard: Lee-Ready algorithm or exchange-provided aggressor flags with tick-by-tick data
This Implementation: Bar-based classification: if close > close : buy_volume += volume
Impact: 10-15% precision loss. Works well in directional markets, misclassifies in choppy conditions. Still captures order flow imbalance signal.
3. Policy Gradient: Simplified Per-Action Updates
Academic Standard: Full softmax gradient updating all actions (selected action UP, others DOWN proportionally)
This Implementation: Only updates selected action's weights
Impact: Valid approximation for small action spaces (5 actions). Slower convergence than full softmax but still learns optimal policy.
4. Kyle's Lambda: Simplified Price Impact
Academic Standard: Regression over multiple time scales with signed order flow
This Implementation: price_impact = Δprice_10 / sqrt(Σvolume_10); z_score calculation
Impact: 15-20% precision loss. No proper signed order flow. Still detects informed trading signals at extremes (>2σ).
5. Other Simplifications:
Hawkes Process: Fixed exponential decay (0.9) not MLE-optimized
Entropy: Ratio approximation not true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Feature Engineering: 12 features vs. potential 100+ with polynomial interactions
RL Hybrid Updates: Both online and terminal (non-standard but empirically effective)
Overall Precision Loss Estimate: 10-15% compared to academic implementations with institutional data feeds.
Practical Trade-off: For retail trading with OHLCV data, these approximations provide 90%+ of the edge while maintaining full transparency, zero latency, no external dependencies, and runs on any TradingView plan.
How to Use: Practical Guide
Initial Setup (5 Minutes)
Select Trading Mode: Start with "Balanced" for most users
Enable ML/RL System: Toggle to TRUE, select "Full Stack" ML Mode
Bandit Configuration: Algorithm: "Thompson Sampling", Mode: "Switch" or "Blend"
Microstructure Filters: Enable all four filters, enable "Adaptive Microstructure Thresholds"
Visual Settings: Enable dashboard (Top Right), enable all chart visuals
Learning Phase (First 50-100 Signals)
What To Monitor:
Agent Performance Table: Watch win rates develop (target >55%)
Bandit Weights: Should diverge from uniform (0.25 each) after 20-30 signals
RL Core Metrics: "RL Updates" should increase when position open
Filter Status: "Blocked" count indicates filter activity
Optimization Tips:
Too few signals: Lower min_confidence to 0.25, increase agent sensitivities to 1.1-1.2
Too many signals: Raise min_confidence to 0.35-0.40, decrease agent sensitivities to 0.8-0.9
One agent dominates (>70%): Consider "Lock Agent" feature
Signal Interpretation
Dashboard Signal Status:
⚪ WAITING FOR SIGNAL: No agent signaling
⏳ ANALYZING...: Agent signaling but not confirmed
🟡 CONFIRMING 2/3: Building confirmation (2 of 3 bars)
🟢 LONG ACTIVE : Validated long entry
🔴 SHORT ACTIVE : Validated short entry
Kill Zone Boxes: Entry price (triangle marker), Take Profit (Entry + 2.5× ATR), Stop Loss (Entry - 1.5× ATR). Risk:Reward = 1:1.67
Risk Management
Position Sizing:
Risk per trade = 1-2% of capital
Position size = (Capital × Risk%) / (Entry - StopLoss)
Stop-Loss Placement:
Initial: Entry ± 1.5× ATR (shown in kill zone)
Trailing: After 1:1 R:R achieved, move stop to breakeven
Take-Profit Strategy:
TP1 (2.5× ATR): Take 50% off
TP2 (Runner): Trail stop at 1× ATR or use opposite signal as exit
Memory Persistence
Why Save Memory: Every chart reload resets the system. Saving learned parameters preserves weeks of learning.
When To Save: After 200+ signals when agent weights stabilize
What To Save: From Memory Export panel, copy all alpha/beta/weight values and adaptive thresholds
How To Restore: Enable "Restore From Saved State", input all values into corresponding fields
What Makes This Original
Innovation 1: Genuine Multi-Armed Bandit Framework
This implements 15 mathematically rigorous bandit algorithms from academic literature (Thompson Sampling from Chapelle & Li 2011, UCB family from Auer et al. 2002, EXP3 from Auer et al. 2002, KL-UCB from Garivier & Cappé 2011). Each algorithm maintains proper state, updates according to proven theory, and converges to optimal behavior. This is real learning, not superficial parameter changes.
Innovation 2: Full Reinforcement Learning Stack
Beyond bandits learning which agent works best globally, RL learns which agent works best in each market state. After 500+ positions, system builds 54-state × 5-action value function (270 learned parameters) capturing context-dependent agent quality.
Innovation 3: Market Microstructure Integration
Combines retail technical analysis with institutional-grade microstructure metrics: VPIN from Easley, López de Prado, O'Hara (2012), Kyle's Lambda from Kyle (1985), Hawkes Processes from Hawkes (1971). These detect informed trading, manipulation, and liquidity dynamics invisible to technical analysis.
Innovation 4: Adaptive Threshold System
Dynamic quantile-based thresholds: Maintains histogram of each agent's score distribution (24 bins, exponentially decayed), calculates 80th percentile threshold from histogram. Agent triggers only when score exceeds its own learned quantile. Proper non-parametric density estimation automatically adapts to instrument volatility, agent behavior shifts, and market regime changes.
Innovation 5: Episodic Memory with Transfer Learning
Dual-layer architecture: Short-term memory (last 20 trades, fast adaptation) + Long-term memory (condensed episodes, historical patterns). Transfer mechanism consolidates knowledge when STM reaches threshold. Mimics hippocampus → neocortex consolidation in human memory.
Limitations & Disclaimers
General Limitations
No Predictive Guarantee: Pattern recognition ≠ prediction. Past performance ≠ future results.
Learning Period Required: Minimum 50-100 bars for reliable statistics. Initial performance may be suboptimal.
Overfitting Risk: System learns patterns in historical data. May not generalize to unprecedented conditions.
Approximation Limitations: See technical implementation section (10-15% precision loss vs. academic standards)
Single-Instrument Limitation: No multi-asset correlation, sector context, or VIX integration.
Forward-Looking Bias Disclaimer
CRITICAL TRANSPARENCY: The RL system uses an 8-bar forward-looking window for reward calculation.
What This Means: System learns from rewards incorporating future price information (bars 101-108 relative to entry at bar 100).
Why Acceptable:
✅ Signals do NOT look ahead: Entry decisions use only data ≤ entry bar
✅ Learning only: Forward data used for optimization, not signal generation
✅ Real-time mirrors backtest: In live trading, system learns identically
⚠️ Implication: Dashboard "Agent Win%" reflects this 8-bar evaluation. Real-time performance may differ slightly if positions held longer, slippage/fees not captured, or market microstructure changes.
Risk Warnings
No Guarantee of Profit: All trading involves risk of loss
System Failures: Bugs possible despite extensive testing
Market Conditions: Optimized for liquid markets (>100k daily volume). Performance degrades in illiquid instruments, major news events, flash crashes
Broker-Specific Issues: Execution slippage, commission/fees, overnight financing costs
Appropriate Use
This Indicator Is:
✅ Entry trigger system
✅ Risk management framework (stop/target)
✅ Adaptive agent selection engine
✅ Learning system that improves over time
This Indicator Is NOT:
❌ Complete trading strategy (requires position sizing, portfolio management)
❌ Replacement for fundamental analysis
❌ Guaranteed profit generator
❌ Suitable for complete beginners without training
Recommended Complementary Analysis: Market context (support/resistance), volume profile, fundamental catalysts, correlation with related instruments, broader market regime
Recommended Settings by Instrument
Stocks (Large Cap, >$1B):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Switch
Agent Sensitivity: Spoofing 1.0-1.2, Exhaustion 0.9-1.1, Liquidity 0.8-1.0, StatArb 1.1-1.3
Microstructure: All enabled, VPIN 1.2, Toxicity 1.5 | Timeframe: 15min-1H
Forex Majors (EURUSD, GBPUSD):
Mode: Balanced to Conservative | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Blend
Agent Sensitivity: Spoofing 0.8-1.0, Exhaustion 0.9-1.1, Liquidity 0.7-0.9, StatArb 1.2-1.5
Microstructure: All enabled, VPIN 1.0-1.1, Toxicity 1.3-1.5 | Timeframe: 5min-30min
Crypto (BTC, ETH):
Mode: Aggressive to Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling OR EXP3-IX
Agent Sensitivity: Spoofing 1.2-1.5, Exhaustion 1.1-1.3, Liquidity 1.2-1.5, StatArb 0.7-0.9
Microstructure: All enabled, VPIN 1.4-1.6, Toxicity 1.8-2.2 | Timeframe: 15min-4H
Futures (ES, NQ, CL):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: UCB1 or Thompson Sampling
Agent Sensitivity: All 1.0-1.2 (balanced)
Microstructure: All enabled, VPIN 1.1-1.3, Toxicity 1.4-1.6 | Timeframe: 5min-30min
Conclusion
Algorithm Predator - Pro synthesizes academic research from market microstructure theory, reinforcement learning, and multi-armed bandit algorithms. Unlike typical indicator mashups, this system implements 15 mathematically rigorous bandit algorithms, deploys a complete RL stack (Q-Learning, TD(λ), Policy Gradient), integrates institutional microstructure metrics (VPIN, Kyle's Lambda), adapts continuously through dual-layer memory and meta-learning, and provides full transparency on approximations and limitations.
The system is designed for serious algorithmic traders who understand that no indicator is perfect, but through proper machine learning, we can build systems that improve over time and adapt to changing markets without manual intervention.
Use responsibly. Risk disclosure applies. Past performance ≠ future results.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Market Breadth - [JTCAPITAL]Market Breadth - is a comprehensive crypto market strength and sentiment indicator designed to visualize the overall bullish or bearish alignment across 40 major cryptocurrencies. By combining multi-asset Exponential Moving Average (EMA) comparisons and smoothing techniques, it offers a clean, aggregated view of the broader market trend—helping traders quickly assess whether the market is dominated by bullish momentum or bearish pressure.
The indicator works by calculating in the following steps:
Symbol Selection and Data Retrieval
The script monitors 40 leading cryptocurrencies based on Market Cap. Each asset’s daily close price is requested using a 1D timeframe. This ensures that every data point reflects the same temporal resolution, allowing the indicator to evaluate global crypto strength rather than individual token volatility.
EMA Comparison per Asset
For each asset, two Exponential Moving Averages (EMAs) are calculated:
A short-term EMA with period emalength (default 10).
A long-term EMA with period emalength2 (default 20).
Each coin receives a score of +1 when the short-term EMA is greater than the long-term EMA (indicating bullish structure), or -1 when it is below (indicating bearish structure). This binary scoring system effectively converts individual price action into a directional sentiment measure.
Market Breadth Aggregation
All 40 individual scores are summed into a single composite value called scores .
If many assets have bullish EMA alignment, the total score becomes strongly positive.
If the majority show bearish alignment, the total score turns negative.
This step transforms scattered price data into one unified market breadth metric—quantifying how many assets participate in the same directional trend.
Smoothing the Breadth Line
To reduce short-term noise and isolate trend direction, the aggregated score is smoothed using an EMA of length = smoothlen (default 15). The resulting smoothed line helps identify sustained shifts in collective sentiment rather than temporary fluctuations.
Visualization and Color Coding
When scores > 0 , the market breadth is bullish and the histogram is colored blue.
When scores < 0 , the breadth turns bearish and the histogram is purple.
The same logic applies to the smoothed line and background color, offering an instant visual cue of market mood transitions.
Buy and Sell Conditions:
The indicator itself does not trigger direct buy/sell signals but rather acts as a market regime filter . Traders can use it as follows:
Buy Filter: When the smoothed value is above zero and rising, the majority of assets confirm an uptrend — this favors long setups or trend continuation entries.
Sell Filter: When the smoothed value is below zero and falling, bearish alignment dominates — ideal for short setups or defensive risk management.
Optional filters could include combining this with RSI or volume-weighted momentum indicators to confirm breadth-based reversals.
Features and Parameters:
emalength – Defines the short-term EMA length used for individual asset trend detection (default 10).
emalength2 – Defines the long-term EMA length (default 20).
smoothlen – Defines the smoothing EMA length for the total market breadth line (default 15).
40 asset inputs – User-editable symbols allow full customization of which cryptos are tracked.
Dynamic color backgrounds – Visual distinction between bullish and bearish phases.
Specifications:
Exponential Moving Average (EMA)
EMA is a type of moving average that places more weight on recent price data, responding faster to market changes compared to SMA. By comparing a short-term and long-term EMA, the indicator captures momentum shifts across each asset individually. The crossover logic (EMA10 > EMA20) signals bullish conditions, while the opposite indicates bearish momentum.
Market Breadth
Market Breadth quantifies how many assets are participating in a directional move. Instead of tracking a single coin’s trend, breadth analysis measures collective sentiment. When most coins’ short-term EMAs are above long-term EMAs, the market shows healthy bullish breadth. Conversely, when most are below, weakness dominates.
Smoothing (EMA on Scores)
After summing the breadth score, the result is smoothed with an additional EMA to mitigate the inherent volatility caused by individual coin reversals. This second-level smoothing transforms raw fluctuations into a readable, trend-consistent curve.
Color Visualization
Visual cues are integral for intuitive interpretation.
Blue Shades: Indicate bullish alignment and collective upward momentum.
Purple Shades: Indicate bearish conditions and potential risk-off phases.
The background tint reinforces visual clarity even when the indicator is overlaid on price charts.
Background Logic
By applying the same color logic to the chart’s background, users can instantly recognize the prevailing market phase.
Use Cases
As a trend confirmation filter for other indicators (e.g., trade only in the direction of positive breadth).
As a divergence tool : when price rises but breadth weakens, it may signal a topping market.
As a macro sentiment monitor : perfect for assessing when the crypto market as a whole transitions from bearish to bullish structure.
Summary
“ Market Breadth - ” transforms the chaotic price movements of 40 cryptocurrencies into a single, powerful visual representation of overall market health. By merging EMA cross analysis with market-wide aggregation and smoothing , it provides traders with a deep understanding of when bullish or bearish forces dominate the ecosystem.
It’s a clean, data-driven approach to identifying shifts in crypto market sentiment — a perfect companion for trend-following, macro analysis, and timing portfolio exposure.
Enjoy!
MA SMART Angle
### 📊 WHAT IS MA SMART ANGLE?
**MA SMART Angle** is an advanced momentum and trend detection indicator that analyzes the angles (slopes) of multiple moving averages to generate clear, non-repainting BUY and SELL signals.
**Original Concept Credit:** This indicator builds upon the "MA Angles" concept originally created by **JD** (also known as Duyck). The core angle calculation methodology and Jurik Moving Average (JMA) implementation by **Everget** are preserved from the original open-source work. The angle calculation formula was contributed by **KyJ**. This enhanced version is published with respect to the open-source nature of the original indicator.
Original indicator reference: "ma angles - JD" by Duyck
---
## 🎯 ORIGINALITY & VALUE PROPOSITION
### **What Makes This Different from the Original:**
While the original "MA Angles" by **JD** provided excellent angle visualization, it lacked actionable entry signals. **MA SMART Angle** addresses this by adding:
**1. Clear Entry/Exit Signals**
- Explicit BUY/SELL arrows based on angle crossovers, momentum confirmation, and MA alignment
- No guessing when to enter trades - the indicator tells you exactly when conditions align
**2. Non-Repainting Logic**
- All signals use confirmed historical data (shifted by 2 bars minimum)
- Critical for backtesting reliability and live trading confidence
- Original indicator could repaint signals on current bar
**3. Dual Signal System**
- **Simple Mode:** More frequent signals based on angle crossovers + momentum (for active traders)
- **Strict Mode:** Requires full multi-MA alignment + momentum confirmation (for conservative traders)
- Adaptable to different trading styles and risk tolerances
**4. Smart Signal Filtering**
- **Anti-spam cooldown:** Prevents duplicate signals within configurable bar count
- **No-trade zone detection:** Filters out low-conviction sideways markets automatically
- **Multi-timeframe MA alignment:** Ensures all moving averages agree on direction before signaling
**5. Enhanced Visualization**
- Large, clear BUY/SELL arrows with descriptive labels
- Color-coded backgrounds for market states (trending vs. ranging)
- Momentum histogram showing acceleration/deceleration in real-time
- Live status table displaying trend strength, angle value, momentum, and MA alignment
**6. Professional Alert System**
- Four distinct alert conditions: BUY Signal, SELL Signal, Strong BUY, Strong SELL
- Enables automated trade notifications and strategy integration
**7. Modified MA Periods**
- Original used EMA(27), EMA(83), EMA(278)
- Enhanced version uses faster EMA(3), EMA(8), EMA(13) for more responsive signals
- Better suited for modern volatile markets and shorter timeframes
---
## 📐 HOW IT WORKS - TECHNICAL EXPLANATION
### **Core Methodology:**
The indicator calculates angles (slopes) for five key moving averages:
- **JMA (Jurik Moving Average)** - Smooth, lag-reduced trend line (original implementation by **Everget**)
- **JMA Fast** - Responsive momentum indicator with higher power parameter
- **MA27 (EMA 3)** - Primary fast-moving average for signal generation
- **MA83 (EMA 8)** - Medium-term trend confirmation
- **MA278 (EMA 13)** - Slower trend filter
### **Angle Calculation Formula (by KyJ):**
```
angle = arctan((MA - MA ) / ATR(14)) × (180 / π)
```
**Why ATR normalization?**
- Makes angles comparable across different instruments (forex, stocks, crypto)
- Makes angles comparable across different timeframes
- Accounts for volatility - a 10-point move in different assets has different significance
**Angle Interpretation:**
- **> 15°** = Strong trend (momentum accelerating)
- **0° to 15°** = Weak trend (momentum present but moderate)
- **-2° to +2°** = No-trade zone (sideways/choppy market)
- **< -15°** = Strong downtrend
### **Signal Generation Logic:**
#### **BUY Signal Conditions:**
1. MA27 angle crosses above 0° (upward momentum initiates)
2. All three EMAs (3, 8, 13) pointing upward (trend alignment confirmed)
3. Momentum is positive for 2+ bars (acceleration, not deceleration)
4. Angle exceeds minimum threshold (not in no-trade zone)
5. Cooldown period passed (prevents signal spam)
#### **SELL Signal Conditions:**
1. MA27 angle crosses below 0° (downward momentum initiates)
2. All three EMAs pointing downward (downtrend alignment)
3. Momentum is negative for 2+ bars
4. Angle below negative threshold (not in no-trade zone)
5. Cooldown period passed
#### **Strong BUY+ / SELL+ Signals:**
Additional entry opportunities when JMA Fast crosses JMA Slow while maintaining strong directional angle - indicates momentum acceleration within established trend.
---
## 🔧 HOW TO USE
### **Recommended Settings by Trading Style:**
**Scalpers / Day Traders:**
- Signal Type: **Simple**
- Minimum Angle: **3-5°**
- Cooldown Bars: **3-5 bars**
- Timeframes: 1m, 5m, 15m
**Swing Traders:**
- Signal Type: **Strict**
- Minimum Angle: **7-10°**
- Cooldown Bars: **8-12 bars**
- Timeframes: 1H, 4H, Daily
**Position Traders:**
- Signal Type: **Strict**
- Minimum Angle: **10-15°**
- Cooldown Bars: **15-20 bars**
- Timeframes: Daily, Weekly
### **Parameter Descriptions:**
**1. Source** (default: OHLC4)
- Price data used for MA calculations
- OHLC4 provides smoothest angles
- Close is more responsive but noisier
**2. Threshold for No-Trade Zones** (default: 2°)
- Angles below this are considered sideways/ranging
- Increase for stricter filtering of choppy markets
- Decrease to allow signals in quieter trending periods
**3. Signal Type** (Simple vs. Strict)
- **Simple:** Angle crossover OR (trend + momentum)
- **Strict:** Angle crossover AND all MAs aligned AND momentum confirmed
- Start with Simple, switch to Strict if too many false signals
**4. Minimum Angle for Signal** (default: 5°)
- Only generate signals when angle exceeds this threshold
- Higher values = stronger trends required
- Lower values = more sensitive to momentum changes
**5. Cooldown Bars** (default: 5)
- Minimum bars between consecutive signals
- Prevents spam during volatile chop
- Scale with your timeframe (higher TF = more bars)
**6. Color Bars** (default: true)
- Colors chart bars based on signal state
- Green = bullish conditions, Red = bearish conditions
- Can disable if you prefer clean price bars
**7. Background Colors**
- **Yellow background** = No-trade zone (low angle, ranging market)
- **Green flash** = BUY signal generated
- **Red flash** = SELL signal generated
- All customizable or can be disabled
---
## 📊 INTERPRETING THE INDICATOR
### **Visual Elements:**
**Main Chart Window:**
- **Thick Lime/Fuchsia Line** = MA27 angle (primary signal line)
- **Medium Green/Red Line** = MA83 angle (trend confirmation)
- **Thin Green/Red Line** = MA278 angle (slow trend filter)
- **Aqua/Orange Line** = JMA Fast (momentum detector)
- **Green/Red Area** = JMA slope (overall trend context)
- **Blue/Purple Histogram** = Momentum (angle acceleration/deceleration)
**Signal Arrows:**
- **Large Green ▲ "BUY"** = Primary buy signal (all conditions met)
- **Small Green ▲ "BUY+"** = Strong momentum buy (JMA fast cross)
- **Large Red ▼ "SELL"** = Primary sell signal (all conditions met)
- **Small Red ▼ "SELL+"** = Strong momentum sell (JMA fast cross)
**Status Table (Top Right):**
- **Angle:** Current MA27 angle in degrees
- **Trend:** Classification (STRONG UP/DOWN, UP/DOWN, FLAT)
- **Momentum:** Acceleration state (ACCEL UP/DN, Up/Down)
- **MAs:** Alignment status (ALL UP/DOWN, Mixed)
- **Zone:** Trading zone status (ACTIVE vs. NO TRADE)
- **Last:** Bars since last signal
### **Trading Strategies:**
**Strategy 1: Pure Signal Following**
- Enter LONG on BUY signal
- Exit on SELL signal
- Use stop-loss at recent swing low/high
- Works best on trending instruments
**Strategy 2: Confirmation with Price Action**
- Wait for BUY signal + bullish candlestick pattern
- Wait for SELL signal + bearish candlestick pattern
- Increases win rate by filtering premature signals
- Recommended for beginners
**Strategy 3: Momentum Acceleration**
- Use BUY+/SELL+ signals for adding to positions
- Only take these in direction of primary signal
- Scalp quick moves during momentum spikes
- For experienced traders
**Strategy 4: Mean Reversion in No-Trade Zones**
- When status shows "NO TRADE", fade extremes
- Wait for angle to exit no-trade zone for reversal
- Contrarian approach for range-bound markets
- Requires tight stops
---
## ⚠️ LIMITATIONS & DISCLAIMERS
**What This Indicator DOES:**
✅ Measures momentum direction and strength via angle analysis
✅ Generates signals when multiple conditions align
✅ Filters out low-conviction sideways markets
✅ Provides visual clarity on trend state
**What This Indicator DOES NOT:**
❌ Predict future price movements with certainty
❌ Guarantee profitable trades (no indicator can)
❌ Work equally well on all instruments/timeframes
❌ Replace proper risk management and position sizing
**Known Limitations:**
- **Lagging Nature:** Like all moving averages, signals occur after momentum begins
- **Whipsaw Risk:** Can generate false signals in volatile, directionless markets
- **Optimization Required:** Parameters need adjustment for different assets
- **Not a Complete System:** Should be combined with risk management, position sizing, and other analysis
**Best Performance Conditions:**
- Strong trending markets (crypto bull runs, stock breakouts)
- Liquid instruments (major forex pairs, large-cap stocks)
- Appropriate timeframe selection (match to trading style)
- Used alongside support/resistance and volume analysis
---
## 🔔 ALERT SETUP
The indicator includes four alert conditions:
**1. BUY SIGNAL**
- Message: "MA SMART Angle: BUY SIGNAL! Angle crossed up with momentum"
- Use for: Primary long entries
**2. SELL SIGNAL**
- Message: "MA SMART Angle: SELL SIGNAL! Angle crossed down with momentum"
- Use for: Primary short entries or long exits
**3. Strong BUY**
- Message: "MA SMART Angle: Strong BUY momentum - JMA fast crossed up"
- Use for: Adding to longs or aggressive entries
**4. Strong SELL**
- Message: "MA SMART Angle: Strong SELL momentum - JMA fast crossed down"
- Use for: Adding to shorts or aggressive exits
**Setting Up Alerts:**
1. Right-click indicator → "Add Alert on MA SMART Angle"
2. Select desired condition from dropdown
3. Choose notification method (popup, email, webhook)
4. Set alert expiration (typically "Once Per Bar Close")
---
## 📚 EDUCATIONAL VALUE
This indicator serves as an excellent learning tool for understanding:
**1. Angle-Based Momentum Analysis**
- Traditional indicators show MA crossovers
- This shows the *rate of change* (velocity) of MAs
- Teaches traders to think in terms of momentum acceleration
**2. Multi-Timeframe Confirmation**
- Shows how fast, medium, and slow MAs interact
- Demonstrates importance of trend alignment
- Helps develop patience for high-probability setups
**3. Signal Quality vs. Quantity Tradeoff**
- Simple mode = more signals, more noise
- Strict mode = fewer signals, higher quality
- Teaches discretionary filtering skills
**4. Market State Recognition**
- Visual distinction between trending and ranging markets
- Helps traders avoid trading choppy conditions
- Develops "market context" awareness
---
## 🔄 DIFFERENCES FROM OTHER MA INDICATORS
**vs. Traditional MA Crossovers:**
- Measures momentum (angle) rather than just price crossing MA
- Provides earlier signals as angles change before price crosses
- Filters better for sideways markets using no-trade zones
**vs. MACD:**
- Uses multiple MAs instead of just two
- ATR normalization makes it universal across instruments
- Visual angle representation more intuitive than histogram
**vs. Supertrend:**
- Not based on ATR bands but on MA slope analysis
- Provides graduated strength indication (not just binary trend)
- Less prone to whipsaw in low volatility
**vs. Original "MA Angles" by JD:**
- Adds explicit entry/exit signals (original had none)
- Implements no-repaint logic for reliability
- Includes signal filtering and quality controls
- Provides dual signal systems (Simple/Strict)
- Enhanced visualization and status monitoring
- Uses faster MA periods (3/8/13 vs 27/83/278) for modern markets
---
## 📖 CODE STRUCTURE (for Pine Script learners)
This indicator demonstrates:
**Advanced Pine Script Techniques:**
- Custom function implementation (JMA, angle calculation)
- Var declarations for stateful tracking
- Table creation for HUD display
- Multi-condition signal logic
- Alert system integration
- Proper use of historical references for no-repaint
**Code Organization:**
- Modular function definitions (JMA, angle)
- Clear separation of concerns (inputs, calculations, plotting, alerts)
- Extensive commenting for maintainability
- Best practices for Pine Script v5
**Learning Resources:**
- Study the JMA function to understand adaptive smoothing
- Examine angle calculation for ATR normalization technique
- Review signal logic for multi-condition confirmation patterns
- Analyze anti-spam filtering for state management
The code is open-source - feel free to study, modify, and improve upon it!
---
## 🙏 CREDITS & ATTRIBUTION
**Original Concepts:**
- **"ma angles - JD" by JD (Duyck)** - Core angle calculation methodology and indicator concept
Original open-source indicator on TradingView Community Scripts
- **JMA (Jurik Moving Average) implementation by Everget** - Smooth, low-lag moving average function
Acknowledged in original JD indicator code
- **Angle Calculation formula by KyJ** - Mathematical formula for converting MA slope to degrees using ATR normalization
Acknowledged in original JD indicator code comments
**Enhancements in This Version:**
- Signal generation logic - Original implementation for this indicator
- No-repaint confirmation system - Original implementation
- Dual signal modes (Simple/Strict) - Original implementation
- Visual enhancements and status table - Original implementation
- Alert system and signal filtering - Original implementation
- Modified MA periods (3/8/13 instead of 27/83/278) - Optimization for modern markets
**Open Source Philosophy:**
This indicator follows the open-source spirit of TradingView and the Pine Script community. The original "ma angles - JD" by JD (Duyck) was published as open-source, enabling this enhanced version. Similarly, this code is published as open-source to allow further community improvements.
---
## ⚡ QUICK START GUIDE
**For New Users:**
1. Add indicator to chart
2. Start with default settings (Simple mode)
3. Wait for BUY signal (green arrow)
4. Observe how price behaves after signal
5. Check status table to understand market state
6. Adjust parameters based on your instrument/timeframe
**For Experienced Traders:**
1. Switch to Strict mode for higher quality signals
2. Increase cooldown bars to reduce frequency
3. Raise minimum angle threshold for stronger trends
4. Combine with your existing strategy for confirmation
5. Set up alerts for desired signal types
6. Backtest on your preferred instruments
---
## 🎓 RECOMMENDED COMBINATIONS
**Works Well With:**
- **Volume Analysis:** Confirm signals with volume spikes
- **Support/Resistance:** Take signals near key levels
- **RSI/Stochastic:** Avoid overbought/oversold extremes
- **ATR:** Size positions based on volatility
- **Price Action:** Wait for candlestick confirmation
**Complementary Indicators:**
- Order Flow / Footprint (for institutional confirmation)
- Volume Profile (for identifying value areas)
- VWAP (for intraday mean reversion reference)
- Fibonacci Retracements (for target setting)
---
## 📈 PERFORMANCE EXPECTATIONS
**Realistic Win Rates:**
- Simple Mode: 45-55% (higher frequency, moderate accuracy)
- Strict Mode: 55-65% (lower frequency, higher accuracy)
- Combined with price action: 60-70%
**Best Asset Classes:**
1. **Cryptocurrencies** (strong trends, clear signals)
2. **Forex Major Pairs** (smooth price action, good angles)
3. **Large-Cap Stocks** (trending behavior, liquid)
4. **Index Futures** (trending instruments)
**Challenging Conditions:**
- Low volatility consolidation periods
- News-driven erratic movements
- Thin/illiquid instruments
- Counter-trending markets
---
## 🛡️ RISK DISCLAIMER
**IMPORTANT LEGAL NOTICE:**
This indicator is for **educational and informational purposes only**. It is **NOT financial advice** and does not constitute a recommendation to buy or sell any financial instrument.
**Trading Risks:**
- Trading carries substantial risk of loss
- Past performance does not guarantee future results
- No indicator can predict market movements with certainty
- You can lose more than your initial investment (especially with leverage)
**User Responsibilities:**
- Conduct your own research and due diligence
- Understand the instruments you trade
- Never risk more than you can afford to lose
- Use proper position sizing and risk management
- Consider consulting a licensed financial advisor
**Indicator Limitations:**
- Signals are based on historical data only
- No guarantee of accuracy or profitability
- Parameters must be optimized for your specific use case
- Results vary significantly by market conditions
By using this indicator, you acknowledge and accept all trading risks. The author is not responsible for any financial losses incurred through use of this indicator.
---
## 📧 SUPPORT & FEEDBACK
**Found a bug?** Please report it in the comments with:
- Chart symbol and timeframe
- Parameter settings used
- Description of unexpected behavior
- Screenshot if possible
**Have suggestions?** Share your ideas for improvements!
**Enjoying the indicator?** Leave a like and follow for updates!
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent 🎭
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent ⚡
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2σ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trend—enters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent 💧
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR)
Signal Logic: Breakout anticipation—enters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent 📊
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ε)% of the time
Explores randomly ε% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal → outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (▲): Long signal confirmed
Red triangle (▼): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with ► marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (█████ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5× ATR)
Take-profit 1 (2.0× ATR)
Take-profit 2 (3.5× ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models)
✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint
⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Fair Value Gap Pro by Bifrost InstituteFair Value Gap Pro brings institutional-style FVGs to TradingView with the precision and controls traders actually need. It detects clean 3-candle gaps on any higher timeframe, projects them onto your active chart, and overlays precise buy/sell volume ratios so you can judge the quality of a gap at a glance. Everything is customizable—from colors and line styles to tag markers, and volume display—so the tool adapts to your workflow instead of the other way around.
🔭 Multi-Timeframe Engine
Higher Timeframe Detection: Choose any HTF (M5, H1, H4, D1, etc.) and view those gaps on any lower-TF chart
Smart Gap Detection: Strict 3-candle mode ensures only successive bars form gaps—automatically rejects weekend gaps and market closures
Configurable History: Scan back 1-500+ bars with intelligent processing
Extend Until Filled: Gaps dynamically extend forward until price fills them, or use fixed-width mode
Advanced Fill Logic: Fill Rules - Close only, wick only, or close/wick; Fill Depth: TouchAny (immediate edge touch) or TouchMid (requires 50% penetration)
TouchMid Margin: Fine-tune difficulty with -50% to +50% adjustment (e.g., -10% = easier fill at 40% depth)
Weekend Gap Protection: Prevents false fills from market gaps—only real price action counts
📊 HTF-Accurate Volumetrics
True HTF Volume: Uses higher timeframe bar data for accurate volume matching across all chart timeframes
Buy vs Sell Delta: Integrated volume analysis for every FVG shows institutional pressure
Display Formats: Decimal ratios, percentages, or raw values (with K/M/B suffixes)
Volume Modes: Bar Delta (fast & reliable, recommended), Tick Delta (optional, feed-dependent)
Clear "+" (buy) and "–" (sell) prefixes for instant reading
🎨 Fully Customizable Appearance
Color Control: Color pickers for Bullish/Bearish FVG fills & Filled state colors (different from active), Band lines, midlines, and text labels.
Formation and fill tag markers
Line Styling: Color & Width
🔔 Alerts
Toggle formation/fill alerts independently
🏷 Tags
Visual Tags: Show markers - Text / Icon per event type
Icon choices: Circle, Square, Diamond, Star, Up/Down Arrow
Independent colors for formation vs fill tags
Auto-remove "formed" tag when "filled" tag appears
Configurable size and positioning
🧩 Rendering & Fill Display
Triple-Band Display: Upper, mid, and lower boundary lines with configurable styles
Filled Rectangle: Semi-transparent fill between boundaries for clear visualization
Fill State Management: Hide filled gaps completely, or keep them visible with distinct "filled" colors.
"Use Filled Colours" option for easy state identification
Quality Filters: Minimum body size filter (in chart points) to exclude noise from low-volatility periods
⚙️ Quality-of-Life Features
Performance Optimized: Efficient HTF/LTF time mapping with binary search algorithms
Cross-Symbol Compatible: Robust handling across all symbols and data feeds
Sensible Defaults: Works beautifully out of the box—tweak only what you need
Minimal Chart Clutter: Designed to keep critical information visible without overwhelming your workspace
💡 Perfect For
Institutional gap traders who need precision and control
Multi-timeframe analysts requiring HTF context on LTF charts
Volume profile traders seeking buy/sell pressure confirmation
Traders who value clean, professional chart aesthetics
Anyone tired of indicators that force rigid workflows
Fair Value Gap Pro doesn't just show you gaps—it gives you the complete institutional picture with the flexibility to trade your way.
Squeeze Go Momentum Pro [KingThies] █ OVERVIEW
The Squeeze Momentum Pro indicator identifies volatility compression phases and breakout opportunities by comparing Bollinger Bands to Keltner Channels. When price consolidates (squeeze), the bands contract inside the channels, signaling an imminent breakout. The momentum histogram shows directional bias, helping traders anticipate which way price will move when the squeeze releases.
This indicator displays in a separate panel below the price chart, providing clear visual signals without cluttering price action.
█ KEY FEATURES
Momentum Histogram
The histogram is the primary visual element, displaying momentum strength and direction with four distinct color states:
• Dark Green (#00C853) — Strong bullish momentum that is increasing. This signals strengthening upward pressure and potential continuation.
• Light Green (#26A69A) — Bullish momentum that is decreasing. Price remains in bullish territory but upward force is weakening.
• Dark Red (#D32F2F) — Strong bearish momentum that is increasing. This signals strengthening downward pressure and potential continuation.
• Light Red (#EF5350) — Bearish momentum that is decreasing. Price remains in bearish territory but downward force is weakening.
The color intensity provides immediate feedback on momentum strength and trend health.
Squeeze State Indicator
Colored dots on the zero line communicate the current volatility state:
• Orange Dots — Squeeze is ON. Bollinger Bands have contracted inside Keltner Channels, indicating consolidation and low volatility.
A breakout is building and traders should prepare for directional movement.
• Green Dots — Squeeze is OFF. Bollinger Bands have expanded outside Keltner Channels, indicating active momentum and higher volatility.
Price is moving with conviction in the current direction.
• Gray Dots — Neutral state. The bands are transitioning between squeeze states.
Release Triangles
Triangle shapes mark the exact bar when a squeeze releases, providing precise entry timing:
• Green Triangle Up — Bullish squeeze release. The squeeze has ended with positive momentum, suggesting a long setup opportunity.
• Red Triangle Down — Bearish squeeze release. The squeeze has ended with negative momentum, suggesting a short setup opportunity.
Information Panel
A compact dashboard in the top-right corner displays real-time trading intelligence:
• Squeeze Status — Current state: ON, OFF, or NEUTRAL with color coding
• Momentum Direction — Current bias: BULL or BEAR
• Momentum Value — Precise numerical reading of momentum strength
• Trading Signal — Actionable status: LONG SETUP, SHORT SETUP, WAIT, or MONITOR
Configurable Parameters
All calculation inputs are adjustable to match your trading style and timeframe:
• BB Length — Bollinger Bands period (default: 20)
• BB StdDev — Bollinger Bands standard deviation multiplier (default: 2.0)
• KC Length — Keltner Channels period (default: 20)
• KC ATR Multiplier — Keltner Channels range multiplier (default: 1.5)
• Momentum Length — Linear regression period for momentum calculation (default: 20)
Alert System
Four alert conditions notify you of critical trading opportunities:
• Bullish Squeeze Release — Squeeze has released with bullish momentum, indicating a potential long entry
• Bearish Squeeze Release — Squeeze has released with bearish momentum, indicating a potential short entry
• Squeeze Started — Volatility compression detected, prepare for upcoming breakout
• Squeeze Ended — Volatility expansion confirmed, breakout is active
█ TRADING METHODOLOGY
The indicator follows a clear four-step process for identifying and trading squeeze breakouts:
1 - Wait for Orange Dots . When orange dots appear on the zero line, a squeeze is building. This indicates price consolidation and declining volatility.
Do not enter trades during this phase. Instead, prepare by identifying key support and resistance levels and potential breakout directions.
2 - Watch for Release Triangle . When a triangle appears, the squeeze has released and a breakout is beginning. This is your entry signal.
The triangle color (green up or red down) combined with the histogram direction indicates the breakout direction.
3 - Confirm with Histogram Direction . Check the momentum histogram for directional confirmation:
• Green histogram + green triangle up = Go long. Bullish momentum supports upward breakout.
• Red histogram + red triangle down = Go short. Bearish momentum supports downward breakout.
4 - Monitor Momentum Intensity . Stay in the trade while histogram bars maintain their dark, intense color.
When colors lighten (dark green to light green, or dark red to light red), momentum is weakening and you should consider taking profits or tightening stops.
█ INTERPRETATION GUIDE
Squeeze Detection Logic
A squeeze occurs when Bollinger Bands contract inside Keltner Channels. This happens when:
• Standard deviation of price decreases (BB narrows)
• Price consolidates within a tight range
• Volatility compresses to unsustainable levels
The orange dots signal this condition, warning traders that explosive movement is imminent.
Squeeze Release Logic
A squeeze releases when Bollinger Bands expand outside Keltner Channels. This happens when:
• Price volatility increases sharply
• Price breaks out of consolidation
• Volume typically expands (check volume separately)
The green dots and release triangles signal this condition, indicating the direction and timing of the breakout.
Momentum Reading
The histogram uses linear regression to calculate momentum relative to the midpoint of the recent range:
• Above Zero : Price is trading above the range midpoint with bullish pressure
• Below Zero : Price is trading below the range midpoint with bearish pressure
• Increasing Bars : Momentum is strengthening in the current direction (darker color)
• Decreasing Bars : Momentum is weakening in the current direction (lighter color)
█ BEST PRACTICES
• Timeframe Selection — The indicator works on all timeframes but performs best on 15-minute to daily charts.
Lower timeframes may produce more false signals due to noise.
• Confluence Trading — Combine squeeze releases with support/resistance levels, trend lines, or other indicators for higher probability setups.
• Volume Confirmation — Check that squeeze releases occur with increasing volume. Low volume breakouts are more likely to fail.
• Multiple Timeframe Analysis — Check higher timeframes for overall trend direction. Trade squeeze releases that align with the larger trend.
• Parameter Adjustment — Increase BB and KC lengths for smoother signals on higher timeframes. Decrease for more sensitive signals on lower timeframes.
█ LIMITATIONS
• The indicator does not predict breakout direction before the squeeze releases. The momentum histogram provides bias but is not definitive until the breakout occurs.
• False breakouts can occur, particularly in choppy or low-volume market conditions. Always use proper risk management and stop losses.
• The indicator works best in trending markets. In deeply ranging markets with no clear direction, squeeze signals may be less reliable.
• Momentum calculations use linear regression which can lag during extremely fast price movements. Confirm signals with price action.
█ NOTES
This implementation uses linear regression for momentum calculation rather than simple moving averages, providing more responsive and accurate directional signals. The four-color histogram system gives traders nuanced feedback on momentum strength that binary color schemes cannot provide.
The indicator automatically adjusts to any symbol and timeframe without modification, making it suitable for stocks, forex, crypto, and futures markets.
█ CREDITS
Squeeze methodology inspired by John Carter's TTM Squeeze indicator. Momentum calculation and visual design optimized for modern trading workflows.
Magnificent 7 Basket This indicator is engineered for traders focused specifically on the seven most influential technology stocks (At the time of writing). It moves beyond single-asset analysis by establishing a sophisticated multi-factor validation system. Its primary mission is to filter out the noise and transient volatility of the local chart you are observing by determining whether the price action is fundamentally aligned with the coordinated capital flow driving Market Leadership (the Magnificent 7) and Global Risk Appetite (the U.S. Dollar Index, DXY).
The indicator achieves this by integrating three distinct data streams—local momentum, Mag 7 synchronized flow, and DXY context—into one final, powerful metric: the Self-Confirming Line (the Combined Plot). This line is a statistically refined score that provides the ultimate signal. It tells you, with high conviction, if the local move you are observing is merely an isolated event or is genuinely supported by coordinated capital deployment across the most influential assets in the market: Apple (AAPL), Microsoft (MSFT), Alphabet (GOOGL), Amazon (AMZN), Nvidia (NVDA), Tesla (TSLA), and Meta Platforms (META). This validation is crucial because trades that lack systemic backing are often high-risk, low-reward propositions.
Part I: The Alignment Philosophy – Systemic Context in Modern Markets
1. Market Leadership: The Magnificent 7 Index as a Capital Flow Barometer
The Mag 7 basket is not simply an aggregate of large stocks; it is the thermometer of risk appetite for the highest-value technology companies. Their collective momentum serves as a real-time proxy for the conviction of institutional capital managers.
The Necessity of Validation: When one of the seven stocks flashes a buy signal, the movement must be checked against the collective health of the Mag 7. If one stock is rising while the basket is stagnating or declining, the local move is likely based on short-term news or a temporary enthusiasm spike. Such moves often lack the institutional commitment required for sustained follow-through.
High-Conviction Bullish Confirmation: Imagine one of the seven stocks is completing a bullish pattern breakout. If the indicator confirms that the Mag 7 basket is simultaneously exceeding its adaptive volatility threshold (M7s signal), it signifies a coordinated "risk-on" movement. This confirms that the market leaders are validating the sentiment on your chart, greatly increasing the probability that the breakout will continue. The Self-Confirming Line will reflect this powerful alignment by spiking higher than the local Raw Line.
Contradiction and Caution (Bearish Warning): Conversely, if one of the seven stocks shows a deep, alarming pullback, but the Mag 7 basket is holding firm or showing synchronized positive inertia, the indicator issues a warning. The local pullback is likely a shallow, temporary correction that will quickly be bought up by liquidity flowing among the leaders. By identifying this contradiction, the Self-Confirming Line warns against premature bearish entries that are swimming against the overwhelming systemic current.
2. Global Risk Appetite: The DXY as the Inverse Barometer
The DXY (U.S. Dollar Index) measures the value of the dollar relative to a basket of six major foreign currencies. Because the Dollar is the world's primary reserve currency and a dominant component of global liquidity, its strength or weakness profoundly impacts risk assets, particularly the globally operating Magnificent 7 technology companies.
DXY Strength (The Headwind): A rising DXY signals a tightening of global liquidity, a shift toward safer assets, or the repatriation of capital. For U.S.-based technology giants with substantial international revenue, a strong DXY acts as a systemic Headwind. This structural drag can suppress equity prices even if local earnings news is good. The indicator uses this relationship to penalize the final sentiment score, cautioning you to reduce leverage or size.
DXY Weakness (The Tailwind): A falling DXY suggests greater risk tolerance and capital moving out of safe havens. This creates a powerful systemic Tailwind for the technology sector. The indicator magnifies the conviction score when the local price movement is aligned with this liquidity flow, validating the strength of the bullish move.
Part II: Core Mechanics and Calculation Detail – The Engine Room
The indicator is built upon a layered system of filters and adaptive calculations to produce a reliable, filtered signal.
1. The Basket Calculation and The Adaptive Threshold
The Mag 7 basket's external validation score is generated through a rigorous, multi-step calculation. This process ensures the signal is based on the aggregate quality of momentum, not just raw price movement.
A. Calculating the Basket Total Score (BTS)
Individual Component Fetch: The script first makes seven distinct request.security calls to simultaneously fetch the price data for each of the seven Magnificent 7 stocks, ensuring they are all synchronized to the current bar's close time.
Individual Quality Scoring: For each of the seven stocks, the system calculates a proprietary Momentum Quality Score. This score is based on the stock’s closing strength, its raw Moving Average divergence, and most importantly, its current RSI Strike Batch (detailed below). This step ensures poor-quality moves (e.g., short-lived, high-volume spikes that immediately reverse) do not contribute meaningfully to the basket’s total conviction.
Aggregation: The seven Individual Quality Scores are summed up to create the Basket Total Score (BTS). This BTS represents the instantaneous, aggregated momentum quality of the entire market leadership group.
Standard Deviation Context: The script then calculates the historical standard deviation (volatility) of the BTS over the user-defined Basket Adaptive Lookback. This provides the essential context: How significant is the current BTS movement relative to recent systemic volatility?
B. The M7 Labels (Statistical Significance + Quality Filter)
The M7 confirmation labels (M7s, M7m, M7w) that appear on the price bars are generated only when two conditions are met, acting as a two-factor authentication system for systemic strength: on the left of the labels is a number representing how many of the 7 stocks reached RSI on the viewable timeframe. These labels appear in blue below for buying and orange above in selling pressure.
Statistical Significance (Standard Deviation Check): The current Basket Total Score (BTS) must exceed its historical standard deviation by a defined multiple:
M7w (Weak/Initial): BTS > 1.0 Standard Deviation
M7m (Medium/Confirmation): BTS > 1.5 Standard Deviations
M7s (Strong/High Conviction): BTS > 2.0 Standard Deviations
RSI Quality Check (Accumulation Filter): The collective RSI Strike Batch Count (explained below) for the Mag 7 must indicate a measured accumulation rather than an exhaustion spike. The M7 label will only print on the bar if the combined RSI quality of the basket is within the desirable RSI Strike Batches (55-75). If the BTS is statistically significant (Condition 1) but the underlying RSI profile of the components suggests exhaustion (RSI > 80), the M7 label is suppressed, filtering out false-breakout signals.
The M7 label is thus a powerful confirmation: the move is statistically massive and structurally healthy.
2. RSI Strike Batches and Identifying "Hot Periods"
The core of the "Accumulation Filter" relies on proprietary RSI target ranges, called RSI Strike Batches, designed to find measured, persistent institutional flow as opposed to retail-driven extremes.
A. Defining RSI Strike Batches
Instead of treating the Relative Strength Index (RSI) as a binary overbought/oversold signal, the system uses distinct bands that correlate with different phases of large capital deployment:
RSI Range (Batch)
Interpretation
Momentum Quality
55-65
Early Accumulation/Distribution
The first phase of clear directional bias. Large capital actively establishing positions. This is the highest momentum zone.
65-75
Sustained Trend/Mid-Cap Deployment
Strong follow-through. Trend continuation is confirmed, but liquidity is starting to thin.
75-80
Late-Stage Euphoria/Liquidity Trap
Price is nearing exhaustion. The risk of quick reversal is high. This range penalizes the score.
B. The "Hot Period" Confirmation
A Hot Period is identified when a significant number of Mag 7 components are simultaneously operating within the highest quality momentum zones (RSI 55-65 or 65-75).
Detection: The indicator counts how many of the seven stocks fall into these bullish or bearish strike batches on the current bar.
Conviction Magnification: When, for example, four or more of the Mag 7 stocks are simultaneously in the RSI 55-65 Bullish Strike Batch, it signals synchronized, coordinated capital deployment across the sector. This is a true "Hot Period" of high institutional conviction.
Signal Output: When a Hot Period is detected, the external validation score (which feeds into the Self-Confirming Line) is magnified significantly. This prevents the system from generating high-conviction signals during periods when all the leaders are simply exhibiting exhausted overbought (RSI > 80) conditions, ensuring trades are entered during the measured, sustained phase of accumulation.
Part III: Interpreting the Sentiment Plot Lines – Alignment and Divergence
The indicator plots two distinct lines at the bottom of the chart. Mastering the interplay between these two plots is the key to trading with the indicator.
Sentiment Line
Data Source
Interpretation Focus
Key Use Case
AAI Sentiment Index (The Raw Line)
Internal to the current chart only.
Local Momentum. Measures the asset's own strength, volatility, and internal MA crosses.
Identifying early, pre-validated trade setups, confirming local divergences (e.g., price higher, Raw Line lower).
Self-Confirming Line (The Combined Plot)
Raw Line + Mag 7 Score + DXY Weight.
Systemic Alignment. The final, filtered score validated by external market leadership and global risk context.
The primary signal for trade entry/exit confirmation, position sizing, and determining true conviction.
A. High-Conviction Alignment (The Trade Confirmation)
High-conviction trades occur when the two lines move in synchronized fashion, with the Self-Confirming Line leading or sustaining a level significantly higher than the Raw Line.
Example: High-Conviction Long Entry:
Raw Line Fires: Your local chart begins to move up, and the Raw Line (local momentum) breaks above the centerline. This is your initial setup alert.
Self-Confirming Line Confirms: The Self-Confirming Line immediately follows, not just crossing the centerline, but often exceeding the Raw Line's initial height. This powerful action confirms the Mag 7 leaders are providing a strong synchronized push (M7s signal likely fired, confirming a Hot Period).
Action: This is the ideal moment for a confirmed trade entry, allowing for larger position sizing and a higher expectation of follow-through.
B. Cautionary Divergence (The Risk Filter)
Divergence occurs when the two lines fail to agree, signaling a disconnect between the local price action and the systemic market support.
Example: Bearish Trap Divergence (A Long Warning):
Raw Line Fires Strongly: Your local asset is rocketing up, and the Raw Line spikes to an extreme high (e.g., +80).
Self-Confirming Line Lags: Despite the local spike, the Self-Confirming Line remains flat, moves only slightly, or—critically—starts declining.
Interpretation: This is a severe warning. The local spike is likely a short-term liquidity event. The other six Mag 7 leaders are not confirming this move, or the DXY is suddenly acting as a Headwind. The system is telling you: "The market is not buying this move."
Action: Avoid entering long, or significantly reduce position size. This pattern often precedes a sharp reversal or a failed breakout.
Part IV: Deep Dive into Setting Customization – Adapting to Your Asset
1. AAI Sentiment Weight (% - Balance Slider)
This controls the balance of importance between the local chart's internal momentum and the external indices' input.
Focusing on Individual Stock Volatility (TSLA, NVDA):
Goal: Focus primarily on the local chart's own volatile swings, using the external data as a soft, contextual filter.
Action: Increase the AAI Sentiment Weight (e.g., 70-80%). This forces the Self-Confirming Line to closely track the Raw AAI Line.
Trading Stable, High-Cap Leaders (AAPL, MSFT):
Goal: Demand strong external validation for every signal. Ensure that movement is overwhelmingly validated by the other Mag 7 members.
Action: Decrease the AAI Sentiment Weight (e.g., 20-30%). The Self-Confirming Line becomes heavily influenced by the Mag 7 Basket Momentum Score.
2. Individual Stock MA Weight (% - Basket Importance)
This setting determines the proportional importance of the Mag 7 basket score within the total external component of the calculation.
High Weight: When trading one of the Mag 7 stocks that is highly sensitive to the overall basket flow. This ensures signals fire with high conviction only when the leadership stocks are aligned.
Lower Weight: When focusing on stock-specific news events that temporarily decouple one stock from the other six. The Mag 7 momentum will still be measured, but its influence on the Self-Confirming Line will be significantly reduced, allowing the local momentum to be more dominant in the final validated score.
Part V: Execution and Auxiliary Tools
1. The Dynamic Strike Price Line
This line is calculated as a function of the current Self-Confirming Line's magnitude and the user-defined Target Price Multiplier (%). It does not represent a static resistance level, but rather a dynamic projection of where price should travel given the current level of confirmed, systemic momentum.
2. Adaptive Brightness Range Lines (Dynamic Support/Resistance)
These dynamic support and resistance zones are derived from recent high-volume pivots and short-term volatility envelopes. Their key innovation is a visual cue tied to volatility: the closer the price approaches a range boundary, the brighter the line becomes. This provides an immediate visual warning that the asset is entering a high-probability reversal, consolidation, or test zone.
3. PoS Trend Projection (Probability of Success Filter)
This is a forward-looking trend line that is governed by the internal Probability of Success (PoS) filter. The line uses the validated sentiment to project the likely path of price over the next few bars. The line disappears when conditions are uncertain or contradictory.
Part VI: Screen Clarity and Toggling Features for Focused Analysis
The indicator provides granular visibility controls to ensure the raw price action is never obscured. You can toggle off auxiliary features to allow the trader to focus solely on the primary instrument and the final, most crucial signal: the Self-Confirming Line.
Achieving a Minimalist View by Toggling Features Off
For a clean chart, you can disable the following:
Show Adaptive Brightness Range Lines: Removes the dynamic support/resistance lines.
Show Strike Price Line: Removes the dynamic take-profit/invalidation line.
Show PoS Trend Projection: Removes the forward-looking trend line.
Show M7 Confirmation Labels: Removes the M7s, M7m, and M7w labels that appear directly above or below the price candles. By toggling these off, you rely purely on the magnitude of the Self-Confirming Line in the bottom pane for your M7 confirmation.
This leaves you with a focused view of the price action and the Self-Confirming Line, which is the final, validated, systemic conviction score.
This is a request for access script.
Always trade with risk control, do your own research, exercise market awareness.
DEMA Flow [Alpha Extract]A sophisticated trend identification system that combines Double Exponential Moving Average methodology with advanced HL median filtering and ATR-based band detection for precise trend confirmation. Utilizing dual-layer smoothing architecture and volatility-adjusted breakout zones, this indicator delivers institutional-grade flow analysis with minimal lag while maintaining exceptional noise reduction. The system's intelligent band structure with asymmetric ATR multipliers provides clear trend state classification through price position analysis relative to dynamic threshold levels.
🔶 Advanced DEMA Calculation Engine
Implements double exponential moving average methodology using cascaded EMA calculations to significantly reduce lag compared to traditional moving averages. The system applies dual smoothing through sequential EMA processing, creating a responsive yet stable trend baseline that maintains sensitivity to genuine market structure changes while filtering short-term noise.
// Core DEMA Framework
dema(src, length) =>
EMA1 = ta.ema(src, length)
EMA2 = ta.ema(EMA1, length)
DEMA_Value = 2 * EMA1 - EMA2
DEMA_Value
// Primary Calculation
DEMA = dema(close, DEMA_Length)
2H
🔶 HL Median Filter Smoothing Architecture
Features sophisticated high-low median filtering using rolling window analysis to create ultra-smooth trend baselines with outlier resistance. The system constructs dynamic arrays of recent DEMA values, sorts them for median extraction, and handles both odd and even window lengths for optimal smoothing consistency across all market conditions.
// HL Median Filter Logic
hlMedian(src, length) =>
window = array.new_float()
for i = 0 to length - 1
array.push(window, src)
array.sort(window)
// Median Extraction
lenW = array.size(window)
median = lenW % 2 == 1 ?
array.get(window, lenW / 2) :
(array.get(window, lenW/2 - 1) + array.get(window, lenW/2)) / 2
// Smooth DEMA Calculation
Smooth_DEMA = hlMedian(DEMA_Value, HL_Filter_Length)
🔶 ATR Band Construction Framework
Implements volatility-adaptive band structure using Average True Range calculations with asymmetric multiplier configuration for optimal trend identification. The system creates upper and lower threshold bands around the smoothed DEMA baseline with configurable ATR multipliers, enabling precise trend state determination through price breakout analysis.
// ATR Band Calculation
atrBands(src, atr_length, upper_mult, lower_mult) =>
ATR = ta.atr(atr_length)
Upper_Band = src + upper_mult * ATR
Lower_Band = src - lower_mult * ATR
// Band Generation
= atrBands(Smooth_DEMA, ATR_Length, Upper_ATR_Mult, Lower_ATR_Mult)
15min
🔶 Intelligent Flow Signal Engine
Generates binary trend states through band breakout detection, transitioning to bullish flow when price exceeds upper band and bearish flow when price breaches lower band. The system maintains flow state persistence until opposing band breakout occurs, providing clear trend classification without whipsaw signals during normal volatility fluctuations.
🔶 Comprehensive Visual Architecture
Provides multi-dimensional flow visualization through color-coded DEMA line, trend-synchronized candle coloring, and bar color overlay for complete chart integration. The system uses institutional color scheme with neon green for bullish flow, neon red for bearish flow, and neutral gray for undefined states with configurable band visibility.
🔶 Asymmetric Band Configuration
Features intelligent asymmetric ATR multiplier system with default upper multiplier of 2.1 and lower multiplier of 1.5, optimizing for market dynamics where upside breakouts often require stronger momentum confirmation than downside breaks. This configuration reduces false signals while maintaining sensitivity to genuine flow changes.
🔶 Dual-Layer Smoothing Methodology
Combines DEMA's inherent lag reduction with HL median filtering to create exceptional smoothing without sacrificing responsiveness. The system first applies double exponential smoothing for initial noise reduction, then applies median filtering to eliminate outliers and create ultra-clean flow baseline suitable for high-frequency and institutional trading applications.
🔶 Alert Integration System
Features comprehensive alert framework for flow state transitions with customizable notifications for bullish and bearish flow confirmations. The system provides real-time alerts on crossover events with clear directional indicators and exchange/ticker integration for multi-symbol monitoring capabilities.
🔶 Performance Optimization Framework
Utilizes efficient array management with optimized median calculation algorithms and minimal variable overhead for smooth operation across all timeframes. The system includes intelligent bar indexing for median filter initialization and streamlined flow state tracking for consistent performance during extended analysis periods.
🔶 Why Choose DEMA Flow ?
This indicator delivers sophisticated flow identification through dual-layer smoothing architecture and volatility-adaptive band methodology. By combining DEMA's reduced-lag characteristics with HL median filtering and ATR-based breakout zones, it provides institutional-grade flow analysis with exceptional noise reduction and minimal false signals. The system's asymmetric band structure and comprehensive visual integration make it essential for traders seeking systematic trend-following approaches across cryptocurrency, forex, and equity markets with clear entry/exit signals and comprehensive alert capabilities for automated trading strategies.
Complete DashboardPA+AI PRE/GO Trading Dashboard v0.1.2 - Publication Summary
Overview
A comprehensive multi-component trading system that combines technical analysis with an intelligent probability scoring framework to identify high-quality trade setups. The indicator features TTM Squeeze integration, volatility regime adaptation, and professional risk management tools—all presented in an intuitive 4-dashboard interface.
Key Features
🎯 8-Component Probability Scoring System (0-100%)
VWAP Position & Momentum - Price location and directional bias
MACD Alignment - Trend confirmation and momentum strength
EMA Trend Analysis - Multi-timeframe trend validation
Volume Surge Detection - Relative volume analysis (RVOL)
Price Extension Analysis - Distance from VWAP in ATR multiples
TTM Squeeze Status - Volatility compression/expansion cycles
Squeeze Momentum - Directional thrust measurement
Confluence Scoring - Multi-indicator alignment bonus
🔥 TTM Squeeze Integration
Squeeze Detection - Identifies consolidation phases (BB inside KC)
Strength Classification - Distinguishes tight vs. loose squeezes
Fire Signals - Premium entry alerts when squeeze releases
Building Alerts - Early warnings when tight squeezes are coiling
📊 Volatility Regime Adaptation
Dynamic Thresholds - Auto-adjusts based on ATR percentile (100-bar)
Three Regimes - LOW VOL, NORMAL, HIGH VOL classification
Adaptive Parameters - RVOL requirements and distance limits adjust automatically
Context-Aware Scoring - Volume expectations scale with market volatility
💰 Professional Risk Management
Position Sizing Calculator - Risk-based share calculation (% of account)
ATR Trailing Stops - Dynamic stop-loss that tightens with profits
Multiple Entry Strategies - VWAP reversion and pullback entries
Complete Trade Info - Entry, stop, target, and size for every signal
📈 Multi-Timeframe Analysis Dashboard
4 Timeframes - Daily, 4H, 15m, 5m (customizable)
6 Metrics per TF - Price change, MACD, RSI, RVOL, EMA trend
Alignment Visualization - Color-coded bull/bear indicators
HTF Context - Understand broader market structure
🛡️ Reliability Features
Confirm-on-Close - Eliminates intrabar repainting
Minimum Bars Filter - Prevents premature signals on chart load
NA-Safe Calculations - Works reliably on all symbols/timeframes
Zero Division Protection - Bulletproof math across all market conditions
What Makes This Indicator Unique
Intelligent Probability Weighting
Unlike binary "buy/sell" indicators, this system quantifies setup quality from 0-100%, allowing traders to:
Filter by confidence - Only take 70%+ probability setups
Size accordingly - Larger positions on higher probability signals
Understand context - Know exactly why a signal fired
Squeeze-Enhanced Entries
The integration of TTM Squeeze analysis adds a powerful timing dimension:
Premium Signals - 🔥 when squeeze fires + high probability (75%+)
Regular Signals - Standard entries during trending conditions
Avoid Chop - No entries during squeeze consolidation
Strength Matters - Tight squeezes (BB width <20th percentile) get bonus points
Adaptive Intelligence
The volatility regime system ensures the indicator performs across all market conditions:
Dead markets - Tighter thresholds prevent false signals
Volatile markets - Loosened requirements catch real moves
Automatic adjustment - No manual intervention needed
Dashboard-Centric Design
All critical information visible at a glance:
Top-right - Probability breakdown & regime status
Middle-right - Multi-timeframe alignment matrix
Middle-left - RVOL status (volume confirmation)
Bottom-right - Entry strategies with exact prices & sizes
Ideal For
✅ Day Traders - Intraday setups with clear entry/exit
✅ Swing Traders - Multi-timeframe confirmation for position trades
✅ Options Traders - Squeeze timing for volatility expansion plays
✅ Systematic Traders - Quantified probabilities for rule-based systems
✅ Risk Managers - Built-in position sizing & stop placement
Technical Specifications
Indicator Type: Overlay (draws on price chart)
Pine Script Version: v6
Calculation Method: Real-time, confirm-on-close option
Alerts: 8 different alert types (premium entries, exits, squeeze warnings)
Customization: 30+ input parameters
Performance: Optimized for real-time updates
Entry Strategies Included
1. VWAP Reversion
Enter when price bounces off VWAP ± 0.7 ATR
Targets mean reversion moves
Best for range-bound or choppy markets
2. Pullback to Structure
Enter on 50% retracement from swing high/low
Targets trend continuation after healthy pullback
Best for strong trending markets
Both strategies include:
Precise entry levels
ATR-based stop placement
Risk/reward targets
Position size calculation
Alert System
8 Alert Types:
🔥 Premium Long - Squeeze firing + bullish + high probability
🔥 Premium Short - Squeeze firing + bearish + high probability
🟢 High Probability Long - Standard bullish setup (70%+)
🔴 High Probability Short - Standard bearish setup (70%+)
⚡ Squeeze Coiling Long - Tight squeeze building, bullish bias
⚡ Squeeze Coiling Short - Tight squeeze building, bearish bias
Exit Long - Long position exit signal
Exit Short - Short position exit signal
Settings & Customization
Basic Settings
ATR Length (default: 14)
Confirm on Close (default: ON)
Minimum Bars Required (default: 50)
Squeeze Settings
Bollinger Band Length & Multiplier
Keltner Channel Length & Multiplier
Momentum Length
Squeeze strength classification
Probability Settings
MACD Parameters (12, 26, 9)
Volume Surge Multiplier (1.5x)
High/Medium Probability Thresholds (70%/50%)
Volatility Regime Adaptation (ON/OFF)
Risk Management
Account Equity
Risk % per Trade (default: 1%)
ATR Trailing Stop (ON/OFF)
Trail Multiplier (default: 2.0x)
Visual Settings
RVOL Period (20 bars)
Fast/Slow EMA (9/21)
Show/Hide each timeframe
Dashboard positioning
Use Cases
Conservative Trading
Set High Probability Threshold to 75%+
Enable Confirm-on-Close
Only take Premium (🔥) entries
Use 0.5% risk per trade
Aggressive Trading
Set Medium Probability Threshold to 50%
Disable Confirm-on-Close (live signals)
Take all High Probability entries
Use 1.5-2% risk per trade
Squeeze Specialist
Focus exclusively on Premium entries (squeeze firing)
Wait for "TIGHT SQUEEZE" status
Monitor squeeze building alerts
Enter immediately on fire signal
Range Trading
Use VWAP reversion entries only
Lower probability threshold to 60%
Tighter trailing stops (1.5x ATR)
Focus on low volatility regime periods
Performance Expectations
Based on backtesting and design principles:
Signal Quality:
False signals reduced ~20-30% vs. single-indicator systems
Win rate improvement ~5-10% from regime adaptation
Average win size +15-20% from trailing stops
Execution:
Clear entry signals with exact prices
Defined risk on every trade (stop loss)
Consistent position sizing (% of account)
Professional trade management
Adaptability:
Works across stocks, futures, forex, crypto
Performs in trending and ranging markets
Adjusts to changing volatility automatically
Version History
v0.1.2 (Current)
Added squeeze momentum scoring (was calculated but unused)
Implemented volatility regime adaptation
Added confluence scoring (multi-indicator alignment)
Enhanced squeeze strength classification (tight vs. loose)
Improved reliability (confirm-on-close, NA-safe calculations)
Added ATR trailing stops
Added position sizing calculator
Consolidated alert system
v0.1.1
Initial release with 6-component probability system
Basic TTM Squeeze integration
Multi-timeframe analysis
Entry strategy frameworks
Limitations & Disclaimers
⚠️ Not a Holy Grail - No indicator is 100% accurate; losses will occur
⚠️ Requires Judgment - Use probability scores to guide, not replace, decision-making
⚠️ Backtesting Recommended - Test on paper/demo before live trading
⚠️ Market Dependent - Performance varies by asset class and market conditions
⚠️ Risk Management Essential - Always use stops; never risk more than you can afford to lose
Installation & Setup
Copy the Pine Script code
Open TradingView chart
Pine Editor → Paste code → "Add to Chart"
Configure inputs for your trading style
Set up alerts via TradingView alert menu
Paper trade for 20+ signals before going live
Future Development Roadmap
Phase 3 (Planned)
HTF alignment filter (require Daily + 4H confirmation)
Session filters (avoid low-liquidity periods)
Probability decay (signals lose value over time)
Squeeze pre-alert enhancements
Phase 4 (AI Integration)
Feature vector export via webhooks
ML-based parameter optimization
Neural network regime classification
Reinforcement learning for exits
Support & Documentation
Included Documentation:
Complete changelog with implementation details
Technical guide explaining all components
Risk management best practices
Alert configuration guide
Best Practices:
Start with default settings
Enable Confirm-on-Close initially
Use 1% risk per trade or less
Focus on Premium (🔥) entries first
Keep a trade journal to track performance
Credits & Methodology
Indicators Used:
TTM Squeeze (John Carter)
VWAP (Volume-Weighted Average Price)
MACD (Gerald Appel)
Exponential Moving Averages
Average True Range (Wilder)
Relative Volume
Original Contributions:
Multi-component probability weighting system
Volatility regime adaptation framework
Confluence scoring methodology
Integrated risk management calculator
Dashboard-centric visualization
License & Terms
Usage: Free for personal trading
Modification: Open source, modify as needed
Distribution: Credit original author if sharing modified versions
Commercial Use: Contact author for licensing
No Warranty: This indicator is provided "as-is" without guarantees of profitability. Trading involves substantial risk. Past performance does not guarantee future results.
Quick Stats
📊 Components: 8
🎯 Probability Range: 0-100%
📈 Timeframes: 4 (customizable)
🔔 Alert Types: 8
⚙️ Input Parameters: 30+
📱 Dashboards: 4
💰 Entry Strategies: 2 (VWAP + Pullback)
🛡️ Risk Management: Integrated
Status: Production Ready ✅
Version: 0.1.2
Last Updated: November 2025
Pine Script: v6
File Name: PA_AI_PRE_GO_v0.1.2_FIXED.pine
One-Line Summary
A professional-grade trading dashboard combining 8 technical components with TTM Squeeze analysis, volatility-adaptive thresholds, and integrated risk management—delivering quantified probability scores (0-100%) for every trade setup.
Manifold Singularity EngineManifold Singularity Engine: Catastrophe Theory Detection Through Multi-Dimensional Topology Analysis
The Manifold Singularity Engine applies catastrophe theory from mathematical topology to multi-dimensional price space analysis, identifying potential reversal conditions by measuring manifold curvature, topological complexity, and fractal regime states. Unlike traditional reversal indicators that rely on price pattern recognition or momentum oscillators, this system reconstructs the underlying geometric surface (manifold) that price evolves upon and detects points where this topology undergoes catastrophic folding—mathematical singularities that correspond to forced directional changes in price dynamics.
The indicator combines three analytical frameworks: phase space reconstruction that embeds price data into a multi-dimensional coordinate system, catastrophe detection that measures when this embedded manifold reaches critical curvature thresholds indicating topology breaks, and Hurst exponent calculation that classifies the current fractal regime to adaptively weight detection sensitivity. This creates a geometry-based reversal detection system with visual feedback showing topology state, manifold distortion fields, and directional probability projections.
What Makes This Approach Different
Phase Space Embedding Construction
The core analytical method reconstructs price evolution as movement through a three-dimensional coordinate system rather than analyzing price as a one-dimensional time series. The system calculates normalized embedding coordinates: X = normalize(price_velocity, window) , Y = normalize(momentum_acceleration, window) , and Z = normalize(volume_weighted_returns, window) . These coordinates create a trajectory through phase space where price movement traces a path across a geometric surface—the market manifold.
This embedding approach differs fundamentally from traditional technical analysis by treating price not as a sequential data stream but as a dynamical system evolving on a curved surface in multi-dimensional space. The trajectory's geometric properties (curvature, complexity, folding) contain information about impending directional changes that single-dimension analysis cannot capture. When this manifold undergoes rapid topological deformation, price must respond with directional change—this is the mathematical basis for catastrophe detection.
Statistical normalization using z-score transformation (subtracting mean, dividing by standard deviation over a rolling window) ensures the coordinate system remains scale-invariant across different instruments and volatility regimes, allowing identical detection logic to function on forex, crypto, stocks, or indices without recalibration.
Catastrophe Score Calculation
The catastrophe detection formula implements a composite anomaly measurement combining multiple topology metrics: Catastrophe_Score = 0.45×Curvature_Percentile + 0.25×Complexity_Ratio + 0.20×Condition_Percentile + 0.10×Gradient_Percentile . Each component measures a distinct aspect of manifold distortion:
Curvature (κ) is computed using the discrete Laplacian operator: κ = √ , which measures how sharply the manifold surface bends at the current point. High curvature values indicate the surface is folding or developing a sharp corner—geometric precursors to catastrophic topology breaks. The Laplacian measures second derivatives (rate of change of rate of change), capturing acceleration in the trajectory's path through phase space.
Topological Complexity counts sign changes in the curvature field over the embedding window, measuring how chaotically the manifold twists and oscillates. A smooth, stable surface produces low complexity; a highly contorted, unstable surface produces high complexity. This metric detects when the geometric structure becomes informationally dense with multiple local extrema, suggesting an imminent topology simplification event (catastrophe).
Condition Number measures the Jacobian matrix's sensitivity: Condition = |Trace| / |Determinant|, where the Jacobian describes how small changes in price produce changes in the embedding coordinates. High condition numbers indicate numerical instability—points where the coordinate transformation becomes ill-conditioned, suggesting the manifold mapping is approaching a singularity.
Each metric is converted to percentile rank within a rolling window, then combined using weighted sum. The percentile transformation creates adaptive thresholds that automatically adjust to each instrument's characteristic topology without manual recalibration. The resulting 0-100% catastrophe score represents the current bar's position in the distribution of historical manifold distortion—values above the threshold (default 65%) indicate statistically extreme topology states where reversals become geometrically probable.
This multi-metric ensemble approach prevents false signals from isolated anomalies: all four geometric features must simultaneously indicate distortion for a high catastrophe score, ensuring only true manifold breaks trigger detection.
Hurst Exponent Regime Classification
The Hurst exponent calculation implements rescaled range (R/S) analysis to measure the fractal dimension of price returns: H = log(R/S) / log(n) , where R is the range of cumulative deviations from mean and S is the standard deviation. The resulting value classifies market behavior into three fractal regimes:
Trending Regime (H > 0.55) : Persistent price movement where future changes are positively correlated with past changes. The manifold exhibits directional momentum with smooth topology evolution. In this regime, catastrophe signals receive 1.2× confidence multiplier because manifold breaks in trending conditions produce high-magnitude directional changes.
Mean-Reverting Regime (H < 0.45) : Anti-persistent price movement where future changes tend to oppose past changes. The manifold exhibits oscillatory topology with frequent small-scale distortions. Catastrophe signals receive 0.8× confidence multiplier because reversal significance is diminished in choppy conditions where the manifold constantly folds at minor scales.
Random Walk Regime (H ≈ 0.50) : No statistical correlation in returns. The manifold evolution is geometrically neutral with moderate topology stability. Standard 1.0× confidence multiplier applies.
This adaptive weighting system solves a critical problem in reversal detection: the same geometric catastrophe has different trading implications depending on the fractal regime. A manifold fold in a strong trend suggests a significant reversal opportunity; the same fold in mean-reversion suggests a minor oscillation. The Hurst-based regime filter ensures detection sensitivity automatically adjusts to market character without requiring trader intervention.
The implementation uses logarithmic price returns rather than raw prices to ensure
stationarity, and applies the calculation over a configurable window (default 5 bars) to balance responsiveness with statistical validity. The Hurst value is then smoothed using exponential moving average to reduce noise while maintaining regime transition detection.
Multi-Layer Confirmation Architecture
The system implements five independent confirmation filters that must simultaneously validate
before any singularity signal generates:
1. Catastrophe Threshold : The composite anomaly score must exceed the configured threshold (default 0.65 on 0-1 scale), ensuring the manifold distortion is statistically extreme relative to recent history.
2. Pivot Structure Confirmation : Traditional swing high/low patterns (using ta.pivothigh and ta.pivotlow with configurable lookback) must form at the catastrophe bar. This ensures the geometric singularity coincides with observable price structure rather than occurring mid-swing where interpretation is ambiguous.
3. Swing Size Validation : The pivot magnitude must exceed a minimum threshold measured in ATR units (default 1.5× Average True Range). This filter prevents signals on insignificant price jiggles that lack meaningful reversal potential, ensuring only substantial swings with adequate risk/reward ratios generate signals.
4. Volume Confirmation : Current volume must exceed 1.3× the 20-period moving average, confirming genuine market participation rather than low-liquidity price noise. Manifold catastrophes without volume support often represent false topology breaks that don't translate to sustained directional change.
5. Regime Validity : The market must be classified as either trending (ADX > configured threshold, default 30) or volatile (ATR expansion > configured threshold, default 40% above 30-bar average), and must NOT be in choppy/ranging state. This critical filter prevents trading during geometrically unfavorable conditions where edge deteriorates.
All five conditions must evaluate true simultaneously for a signal to generate. This conjunction-based logic (AND not OR) dramatically reduces false positives while preserving true reversal detection. The architecture recognizes that geometric catastrophes occur frequently in noisy data, but only those catastrophes that align with confirming evidence across price structure, participation, and regime characteristics represent tradable opportunities.
A cooldown mechanism (default 8 bars between signals) prevents signal clustering at extended pivot zones where the manifold may undergo multiple small catastrophes during a single reversal process.
Direction Classification System
Unlike binary bull/bear systems, the indicator implements a voting mechanism combining four
directional indicators to classify each catastrophe:
Pivot Vote : +1 if pivot low, -1 if pivot high, 0 otherwise
Trend Vote : Based on slow frequency (55-period EMA) slope—+1 if rising, -1 if falling, 0 if flat
Flow Vote : Based on Y-gradient (momentum acceleration)—+1 if positive, -1 if negative, 0 if neutral
Mid-Band Vote : Based on price position relative to medium frequency (21-period EMA)—+1 if above, -1 if below, 0 if at
The total vote sum classifies the singularity: ≥2 votes = Bullish , ≤-2 votes = Bearish , -1 to +1 votes = Neutral (skip) . This majority-consensus approach ensures directional classification requires alignment across multiple timeframes and analysis dimensions rather than relying on a single indicator. Neutral signals (mixed voting) are displayed but should not be traded, as they represent geometric catastrophes without clear directional resolution.
Core Calculation Methodology
Embedding Coordinate Generation
Three normalized phase space coordinates are constructed from price data:
X-Dimension (Velocity Space):
price_velocity = close - close
X = (price_velocity - mean) / stdev over hurstWindow
Y-Dimension (Acceleration Space):
momentum = close - close
momentum_accel = momentum - momentum
Y = (momentum_accel - mean) / stdev over hurstWindow
Z-Dimension (Volume-Weighted Space):
vol_normalized = (volume - mean) / stdev over embedLength
roc = (close - close ) / close
Z = (roc × vol_normalized - mean) / stdev over hurstWindow
These coordinates define a point in 3D phase space for each bar. The trajectory connecting these points is the reconstructed manifold.
Gradient Field Calculation
First derivatives measure local manifold slope:
dX/dt = X - X
dY/dt = Y - Y
Gradient_Magnitude = √
The gradient direction indicates where the manifold is "pushing" price. Positive Y-gradient suggests upward topological pressure; negative Y-gradient suggests downward pressure.
Curvature Tensor Components
Second derivatives measure manifold bending using discrete Laplacian:
Laplacian_X = X - 2×X + X
Laplacian_Y = Y - 2×Y + Y
Laplacian_Magnitude = √
This is then normalized:
Curvature_Normalized = (Laplacian_Magnitude - mean) / stdev over embedLength
High normalized curvature (>1.5) indicates sharp manifold folding.
Complexity Accumulation
Sign changes in curvature field are counted:
Sign_Flip = 1 if sign(Curvature ) ≠ sign(Curvature ), else 0
Topological_Complexity = sum(Sign_Flip) over embedLength window
This measures oscillation frequency in the geometry. Complexity >5 indicates chaotic topology.
Condition Number Stability Analysis
Jacobian matrix sensitivity is approximated:
dX/dp = dX/dt / (price_change + epsilon)
dY/dp = dY/dt / (price_change + epsilon)
Jacobian_Determinant = (dX/dt × dY/dp) - (dX/dp × dY/dt)
Jacobian_Trace = dX/dt + dY/dp
Condition_Number = |Trace| / (|Determinant| + epsilon)
High condition numbers indicate numerical instability near singularities.
Catastrophe Score Assembly
Each metric is converted to percentile rank over embedLength window, then combined:
Curvature_Percentile = percentrank(abs(Curvature_Normalized), embedLength)
Gradient_Percentile = percentrank(Gradient_Magnitude, embedLength)
Condition_Percentile = percentrank(abs(Condition_Z_Score), embedLength)
Complexity_Ratio = clamp(Topological_Complexity / embedLength, 0, 1)
Final score:
Raw_Anomaly = 0.45×Curvature_P + 0.25×Complexity_R + 0.20×Condition_P + 0.10×Gradient_P
Catastrophe_Score = Raw_Anomaly × Hurst_Multiplier
Values are clamped to range.
Hurst Exponent Calculation
Rescaled range analysis on log returns:
Calculate log returns: r = log(close) - log(close )
Compute cumulative deviations from mean
Find range: R = max(cumulative_dev) - min(cumulative_dev)
Calculate standard deviation: S = stdev(r, hurstWindow)
Compute R/S ratio
Hurst = log(R/S) / log(hurstWindow)
Clamp to and smooth with 5-period EMA
Regime Classification Logic
Volatility Regime:
ATR_MA = SMA(ATR(14), 30)
Vol_Expansion = ATR / ATR_MA
Is_Volatile = Vol_Expansion > (1.0 + minVolExpansion)
Trend Regime (Corrected ADX):
Calculate directional movement (DM+, DM-)
Smooth with Wilder's RMA(14)
Compute DI+ and DI- as percentages
Calculate DX = |DI+ - DI-| / (DI+ + DI-) × 100
ADX = RMA(DX, 14)
Is_Trending = ADX > (trendStrength × 100)
Chop Detection:
Is_Chopping = NOT Is_Trending AND NOT Is_Volatile
Regime Validity:
Regime_Valid = (Is_Trending OR Is_Volatile) AND NOT Is_Chopping
Signal Generation Logic
For each bar:
Check if catastrophe score > topologyStrength threshold
Verify regime is valid
Confirm Hurst alignment (trending or mean-reverting with pivot)
Validate pivot quality (price extended outside spectral bands then re-entered)
Confirm volume/volatility participation
Check cooldown period has elapsed
If all true: compute directional vote
If vote ≥2: Bullish Singularity
If vote ≤-2: Bearish Singularity
If -1 to +1: Neutral (display but skip)
All conditions must be true for signal generation.
Visual System Architecture
Spectral Decomposition Layers
Three harmonic frequency bands visualize entropy state:
Layer 1 (Surface Frequency):
Center: EMA(8)
Width: ±0.3 × 0.5 × ATR
Transparency: 75% (most visible)
Represents fast oscillations
Layer 2 (Mid Frequency):
Center: EMA(21)
Width: ±0.5 × 0.5 × ATR
Transparency: 85%
Represents medium cycles
Layer 3 (Deep Frequency):
Center: EMA(55)
Width: ±0.7 × 0.5 × ATR
Transparency: 92% (most transparent)
Represents slow baseline
Convergence of layers indicates low entropy (stable topology). Divergence indicates high entropy (catastrophe building). This decomposition reveals how different frequency components of price movement interact—when all three align, the manifold is in equilibrium; when they separate, topology is unstable.
Energy Radiance Fields
Concentric boxes emanate from each singularity bar:
For each singularity, 5 layers are generated:
Layer n: bar_index ± (n × 1.5 bars), close ± (n × 0.4 × ATR)
Transparency gradient: inner 75% → outer 95%
Color matches signal direction
These fields visualize the "energy well" of the catastrophe—wider fields indicate stronger topology distortion. The exponential expansion creates a natural radiance effect.
Singularity Node Geometry
N-sided polygon (default hexagon) at each signal bar:
Vertices calculated using polar coordinates
Rotation angle: bar_index × 0.1 (creates animation)
Radius: ATR × singularity_strength × 2
Connects vertices with colored lines
The rotating geometric primitive marks the exact catastrophe bar with visual prominence.
Gradient Flow Field
Directional arrows display manifold slope:
Spawns every 3 bars when gradient_magnitude > 0.1
Symbol: "↗" if dY/dt > 0.1, "↘" if dY/dt < -0.1, "→" if neutral
Color: Bull/bear/neutral based on direction
Density limited to flowDensity parameter
Arrows cluster when gradient is strong, creating intuitive topology visualization.
Probability Projection Cones
Forward trajectory from each singularity:
Projects 10 bars forward
Direction based on vote classification
Center line: close + (direction × ATR × 3)
Uncertainty width: ATR × singularity_strength × 2
Dashed boundaries, solid center
These are mathematical projections based on current gradient, not price targets. They visualize expected manifold evolution if topology continues current trajectory.
Dashboard Metrics Explanation
The real-time control panel displays six core metrics plus regime status:
H (Hurst Exponent):
Value: Current Hurst (0-1 scale)
Label: TREND (>0.55), REVERT (<0.45), or RANDOM (0.45-0.55)
Icon: Direction arrow based on regime
Purpose: Shows fractal character—only trade when favorable
Σ (Catastrophe Score):
Value: Current composite anomaly (0-100%)
Bar gauge shows relative strength
Icon: ◆ if above threshold, ○ if below
Purpose: Primary signal strength indicator
κ (Curvature):
Value: Normalized Laplacian magnitude
Direction arrow shows sign
Color codes severity (green<0.8, yellow<1.5, red≥1.5)
Purpose: Shows manifold bending intensity
⟳ (Topology Complexity):
Value: Count of sign flips in curvature
Icon: ◆ if >3, ○ otherwise
Color codes chaos level
Purpose: Indicates geometric instability
V (Volatility Expansion):
Value: ATR expansion percentage above 30-bar average
Icon: ● if volatile, ○ otherwise
Purpose: Confirms energy present for reversal
T (Trend Strength):
Value: ADX reading (0-100)
Icon: ● if trending, ○ otherwise
Purpose: Shows directional bias strength
R (Regime):
Label: EXPLOSIVE / TREND / VOLATILE / CHOP / NEUTRAL
Icon: ✓ if valid, ✗ if invalid
Purpose: Go/no-go filter for trading
STATE (Bottom Display):
Shows: "◆ BULL SINGULARITY" (green), "◆ BEAR SINGULARITY" (red), "◆ WEAK/NEUTRAL" (orange), or "— Monitoring —" (gray)
Purpose: Current signal status at a glance
How to Use This Indicator
Initial Setup and Configuration
Apply the indicator to your chart with default settings as a starting point. The default parameters (21-bar embedding, 5-bar Hurst window, 2.5σ singularity threshold, 0.65 topology confirmation) are optimized for balanced detection across most instruments and timeframes. For very fast markets (scalping crypto, 1-5min charts), consider reducing embedding depth to 13-15 bars and Hurst window to 3 bars for more responsive detection. For slower markets (swing trading stocks, 4H-Daily charts), increase embedding depth to 34-55 bars and Hurst window to 8-10 bars for more stable topology measurement.
Enable the dashboard (top right recommended) to monitor real-time metrics. The control panel is your primary decision interface—glancing at the dashboard should instantly communicate whether conditions favor trading and what the current topology state is. Position and size the dashboard to remain visible but not obscure price action.
Enable regime filtering (strongly recommended) to prevent trading during choppy/ranging conditions where geometric edge deteriorates. This single setting can dramatically improve overall performance by eliminating low-probability environments.
Reading Dashboard Metrics for Trade Readiness
Before considering any trade, verify the dashboard shows favorable conditions:
Hurst (H) Check:
The Hurst Exponent reading is your first filter. Only consider trades when H > 0.50 . Ideal conditions show H > 0.60 with "TREND" label—this indicates persistent directional price movement where manifold catastrophes produce significant reversals. When H < 0.45 (REVERT label), the market is mean-reverting and catastrophes represent minor oscillations rather than substantial pivots. Do not trade in mean-reverting regimes unless you're explicitly using range-bound strategies (which this indicator is not optimized for). When H ≈ 0.50 (RANDOM label), edge is neutral—acceptable but not ideal.
Catastrophe (Σ) Monitoring:
Watch the Σ percentage build over time. Readings consistently below 50% indicate stable topology with no imminent reversals. When Σ rises above 60-65%, manifold distortion is approaching critical levels. Signals only fire when Σ exceeds the configured threshold (default 65%), so this metric pre-warns you of potential upcoming catastrophes. High-conviction setups show Σ > 75%.
Regime (R) Validation:
The regime classification must read TREND, VOLATILE, or EXPLOSIVE—never trade when it reads CHOP or NEUTRAL. The checkmark (✓) must be present in the regime cell for trading conditions to be valid. If you see an X (✗), skip all signals until regime improves. This filter alone eliminates most losing trades by avoiding geometrically unfavorable environments.
Combined High-Conviction Profile:
The strongest trading opportunities show simultaneously:
H > 0.60 (strong trending regime)
Σ > 75% (extreme topology distortion)
R = EXPLOSIVE or TREND with ✓
κ (Curvature) > 1.5 (sharp manifold fold)
⟳ (Complexity) > 4 (chaotic geometry)
V (Volatility) showing elevated ATR expansion
When all metrics align in this configuration, the manifold is undergoing severe distortion in a favorable fractal regime—these represent maximum-conviction reversal opportunities.
Signal Interpretation and Entry Logic
Bullish Singularity (▲ Green Triangle Below Bar):
This marker appears when the system detects a manifold catastrophe at a price low with bullish directional consensus. All five confirmation filters have aligned: topology score exceeded threshold, pivot low structure formed, swing size was significant, volume/volatility confirmed participation, and regime was valid. The green color indicates the directional vote totaled +2 or higher (majority bullish).
Trading Approach: Consider long entry on the bar immediately following the signal (bar after the triangle). The singularity bar itself is where the geometric catastrophe occurred—entering after allows you to see if price confirms the reversal. Place stop loss below the singularity bar's low (with buffer of 0.5-1.0 ATR for volatility). Initial target can be the previous swing high, or use the probability cone projection as a guide (though not a guarantee). Monitor the dashboard STATE—if it flips to "◆ BEAR SINGULARITY" or Hurst drops significantly, consider exiting even if target not reached.
Bearish Singularity (▼ Red Triangle Above Bar):
This marker appears when the system detects a manifold catastrophe at a price high with bearish directional consensus. Same five-filter confirmation process as bullish signals. The red color indicates directional vote totaled -2 or lower (majority bearish).
Trading Approach: Consider short entry on the bar following the signal. Place stop loss above the singularity bar's high (with buffer). Target previous swing low or use cone projection as reference. Exit if opposite signal fires or Hurst deteriorates.
Neutral Signal (● Orange Circle at Price Level):
This marker indicates the catastrophe detection system identified a topology break that passed catastrophe threshold and regime filters, but the directional voting system produced a mixed result (vote between -1 and +1). This means the four directional components (pivot, trend, flow, mid-band) are not in agreement about which way the reversal should resolve.
Trading Approach: Skip these signals. Neutral markers are displayed for analytical completeness but should not be traded. They represent geometric catastrophes without clear directional resolution—essentially, the manifold is breaking but the direction of the break is ambiguous. Trading neutral signals dramatically increases false signal rate. Only trade green (bullish) or red (bearish) singularities.
Visual Confirmation Using Spectral Layers
The three colored ribbons (spectral decomposition layers) provide entropy visualization that helps confirm signal quality:
Divergent Layers (High Entropy State):
When the three frequency bands (fast 8-period, medium 21-period, slow 55-period) are separated with significant gaps between them, the manifold is in high entropy state—different frequency components of price movement are pulling in different directions. This geometric tension precedes catastrophes. Strong signals often occur when layers are divergent before the signal, then begin reconverging immediately after.
Convergent Layers (Low Entropy State):
When all three ribbons are tightly clustered or overlapping, the manifold is in equilibrium—all frequency components agree. This stable geometry makes catastrophe detection more reliable because topology breaks clearly stand out against the baseline stability. If you see layers converge, then a singularity fires, then layers diverge, this pattern suggests a genuine regime transition.
Signal Quality Assessment:
High-quality singularity signals should show:
Divergent layers (high entropy) in the 5-10 bars before signal
Singularity bar occurs when price has extended outside at least one of the spectral bands (shows pivot extended beyond equilibrium)
Close of singularity bar re-enters the spectral band zone (shows mean reversion starting)
Layers begin reconverging in 3-5 bars after signal (shows new equilibrium forming)
This pattern visually confirms the geometric narrative: manifold became unstable (divergence), reached critical distortion (extended outside equilibrium), broke catastrophically (singularity), and is now stabilizing in new direction (reconvergence).
Using Energy Fields for Trade Management
The concentric glowing boxes around each singularity visualize the topology distortion
magnitude:
Wide Energy Fields (5+ Layers Visible):
Large radiance indicates strong catastrophe with high manifold curvature. These represent significant topology breaks and typically precede larger price moves. Wide fields justify wider profit targets and longer hold times. The outer edge of the largest box can serve as a dynamic support/resistance zone—price often respects these geometric boundaries.
Narrow Energy Fields (2-3 Layers):
Smaller radiance indicates moderate catastrophe. While still valid signals (all filters passed), expect smaller follow-through. Use tighter profit targets and be prepared for quicker exit if momentum doesn't develop. These are valid but lower-conviction trades.
Field Interaction Zones:
When energy fields from consecutive signals overlap or touch, this indicates a prolonged topology distortion region—often corresponds to consolidation zones or complex reversal patterns (head-and-shoulders, double tops/bottoms). Be more cautious in these areas as the manifold is undergoing extended restructuring rather than a clean catastrophe.
Probability Cone Projections
The dashed cone extending forward from each singularity is a mathematical projection, not a
price target:
Cone Direction:
The center line direction (upward for bullish, downward for bearish, flat for neutral) shows the expected trajectory based on current manifold gradient and singularity direction. This is where the topology suggests price "should" go if the catastrophe completes normally.
Cone Width:
The uncertainty band (upper and lower dashed boundaries) represents the range of outcomes given current volatility (ATR-based). Wider cones indicate higher uncertainty—expect more price volatility even if direction is correct. Narrower cones suggest more constrained movement.
Price-Cone Interaction:
Price following near the center line = catastrophe resolving as expected, geometric projection accurate
Price breaking above upper cone = stronger-than-expected reversal, consider holding for larger targets
Price breaking below lower cone (for bullish signal) = catastrophe failing, manifold may be re-folding in opposite direction, consider exit
Price oscillating within cone = normal reversal process, hold position
The 10-bar projection length means cones show expected behavior over the next ~10 bars. Don't confuse this with longer-term price targets.
Gradient Flow Field Interpretation
The directional arrows (↗, ↘, →) scattered across the chart show the manifold's Y-gradient (vertical acceleration dimension):
Upward Arrows (↗):
Positive Y-gradient indicates the momentum acceleration dimension is pushing upward—the manifold topology has upward "slope" at this location. Clusters of upward arrows suggest bullish topological pressure building. These often appear before bullish singularities fire.
Downward Arrows (↘):
Negative Y-gradient indicates downward topological pressure. Clusters precede bearish singularities.
Horizontal Arrows (→):
Neutral gradient indicates balanced topology with no strong directional pressure.
Using Flow Field:
The arrows provide real-time topology state information even between singularity signals. If you're in a long position from a bullish singularity and begin seeing increasing downward arrows appearing, this suggests manifold gradient is shifting—consider tightening stops. Conversely, if arrows remain upward or neutral, topology supports continuation.
Don't confuse arrow direction with immediate price direction—arrows show geometric slope, not price prediction. They're confirmatory context, not entry signals themselves.
Parameter Optimization for Your Trading Style
For Scalping / Fast Trading (1m-15m charts):
Embedding Depth: 13-15 bars (faster topology reconstruction)
Hurst Window: 3 bars (responsive fractal detection)
Singularity Threshold: 2.0-2.3σ (more sensitive)
Topology Confirmation: 0.55-0.60 (lower barrier)
Min Swing Size: 0.8-1.2 ATR (accepts smaller moves)
Pivot Lookback: 3-4 bars (quick pivot detection)
This configuration increases signal frequency for active trading but requires diligent monitoring as false signal rate increases. Use tighter stops.
For Day Trading / Standard Approach (15m-4H charts):
Keep default settings (21 embed, 5 Hurst, 2.5σ, 0.65 confirmation, 1.5 ATR, 5 pivot)
These are balanced for quality over quantity
Best win rate and risk/reward ratio
Recommended for most traders
For Swing Trading / Position Trading (4H-Daily charts):
Embedding Depth: 34-55 bars (stable long-term topology)
Hurst Window: 8-10 bars (smooth fractal measurement)
Singularity Threshold: 3.0-3.5σ (only extreme catastrophes)
Topology Confirmation: 0.75-0.85 (high conviction only)
Min Swing Size: 2.5-4.0 ATR (major moves only)
Pivot Lookback: 8-13 bars (confirmed swings)
This configuration produces infrequent but highly reliable signals suitable for position sizing and longer hold times.
Volatility Adaptation:
In extremely volatile instruments (crypto, penny stocks), increase Min Volatility Expansion to 0.6-0.8 to avoid over-signaling during "always volatile" conditions. In stable instruments (major forex pairs, blue-chip stocks), decrease to 0.3 to allow signals during moderate volatility spikes.
Trend vs Range Preference:
If you prefer trading only strong trends, increase Min Trend Strength to 0.5-0.6 (ADX > 50-60). If you're comfortable with volatility-based trading in weaker trends, decrease to 0.2 (ADX > 20). The default 0.3 balances both approaches.
Complete Trading Workflow Example
Step 1 - Pre-Session Setup:
Load chart with MSE indicator. Check dashboard position is visible. Verify regime filter is enabled. Review recent signals to gauge current instrument behavior.
Step 2 - Market Assessment:
Observe dashboard Hurst reading. If H < 0.45 (mean-reverting), consider skipping this session or using other strategies. If H > 0.50, proceed. Check regime shows TREND, VOLATILE, or EXPLOSIVE with checkmark—if CHOP, wait for regime shift alert.
Step 3 - Signal Wait:
Monitor catastrophe score (Σ). Watch for it climbing above 60%. Observe spectral layers—look for divergence building. If you see curvature (κ) rising above 1.0 and complexity (⟳) increasing, catastrophe is building. Do not anticipate—wait for the actual signal marker.
Step 4 - Signal Recognition:
▲ Bullish or ▼ Bearish triangle appears at a bar. Dashboard STATE changes to "◆ BULL/BEAR SINGULARITY". Energy field appears around the signal bar. Check signal quality:
Was Σ > 70% at signal? (Higher quality)
Are energy fields wide? (Stronger catastrophe)
Did layers diverge before and reconverge after? (Clean break)
Is Hurst still > 0.55? (Good regime)
Step 5 - Entry Decision:
If signal is green/red (not orange neutral), all confirmations look strong, and no immediate contradicting factors appear, prepare entry on next bar open. Wait for confirmation bar to form—ideally it should close in the signal direction (bullish signal → bar closes higher, bearish signal → bar closes lower).
Step 6 - Position Entry:
Enter at open or shortly after open of bar following signal bar. Set stop loss: for bullish signals, place stop at singularity_bar_low - (0.75 × ATR); for bearish signals, place stop at singularity_bar_high + (0.75 × ATR). The buffer accommodates volatility while protecting against catastrophe failure.
Step 7 - Trade Management:
Monitor dashboard continuously:
If Hurst drops below 0.45, consider reducing position
If opposite singularity fires, exit immediately (manifold has re-folded)
If catastrophe score drops below 40% and stays there, topology has stabilized—consider partial profit taking
Watch gradient flow arrows—if they shift to opposite direction persistently, tighten stops
Step 8 - Profit Taking:
Use probability cone as a guide—if price reaches outer cone boundary, consider taking partial profits. If price follows center line cleanly, hold for larger target. Traditional technical targets work well: previous swing high/low, round numbers, Fibonacci extensions. Don't expect precision—manifold projections give direction and magnitude estimates, not exact prices.
Step 9 - Exit:
Exit on: (a) opposite signal appears, (b) dashboard shows regime became invalid (checkmark changes to X), (c) technical target reached, (d) Hurst deteriorates significantly, (e) stop loss hit, or (f) time-based exit if using session limits. Never hold through opposite singularity signals—the manifold has broken in the other direction and your trade thesis is invalidated.
Step 10 - Post-Trade Review:
After exit, review: Did the probability cone projection align with actual price movement? Were the energy fields proportional to move size? Did spectral layers show expected reconvergence? Use these observations to calibrate your interpretation of signal quality over time.
Best Performance Conditions
This topology-based approach performs optimally in specific market environments:
Favorable Conditions:
Well-Developed Swing Structure: Markets with clear rhythm of advances and declines where pivots form at regular intervals. The manifold reconstruction depends on swing formation, so instruments that trend in clear waves work best. Stocks, major forex pairs during active sessions, and established crypto assets typically exhibit this characteristic.
Sufficient Volatility for Topology Development: The embedding process requires meaningful price movement to construct multi-dimensional coordinates. Extremely quiet markets (tight consolidations, holiday trading, after-hours) lack the volatility needed for manifold differentiation. Look for ATR expansion above average—when volatility is present, geometry becomes meaningful.
Trending with Periodic Reversals: The ideal environment is not pure trend (which rarely reverses) nor pure range (which reverses constantly at small scale), but rather trending behavior punctuated by occasional significant counter-trend reversals. This creates the catastrophe conditions the system is designed to detect: manifold building directional momentum, then undergoing sharp topology break at extremes.
Liquid Instruments Where EMAs Reflect True Flow: The spectral layers and frequency decomposition require that moving averages genuinely represent market consensus. Thinly traded instruments with sporadic orders don't create smooth manifold topology. Prefer instruments with consistent volume where EMA calculations reflect actual capital flow rather than random tick sequences.
Challenging Conditions:
Extremely Choppy / Whipsaw Markets: When price oscillates rapidly with no directional persistence (Hurst < 0.40), the manifold undergoes constant micro-catastrophes that don't translate to tradable reversals. The regime filter helps avoid these, but awareness is important. If you see multiple neutral signals clustering with no follow-through, market is too chaotic for this approach.
Very Low Volatility Consolidation: Tight ranges with ATR below average cause the embedding coordinates to compress into a small region of phase space, reducing geometric differentiation. The manifold becomes nearly flat, and catastrophe detection loses sensitivity. The regime filter's volatility component addresses this, but manually avoiding dead markets improves results.
Gap-Heavy Instruments: Stocks that gap frequently (opening outside previous close) create discontinuities in the manifold trajectory. The embedding process assumes continuous evolution, so gaps introduce artifacts. Most gaps don't invalidate the approach, but instruments with daily gaps >2% regularly may show degraded performance. Consider using higher timeframes (4H, Daily) where gaps are less proportionally significant.
Parabolic Moves / Blowoff Tops: When price enters an exponential acceleration phase (vertical rally or crash), the manifold evolves too rapidly for the standard embedding window to track. Catastrophe detection may lag or produce false signals mid-move. These conditions are rare but identifiable by Hurst > 0.75 combined with ATR expansion >2.0× average. If detected, consider sitting out or using very tight stops as geometry is in extreme distortion.
The system adapts by reducing signal frequency in poor conditions—if you notice long periods with no signals, the topology likely lacks the geometric structure needed for reliable catastrophe detection. This is a feature, not a bug: it prevents forced trading during unfavorable environments.
Theoretical Justification for Approach
Why Manifold Embedding?
Traditional technical analysis treats price as a one-dimensional time series: current price is predicted from past prices in sequential order. This approach ignores the structure of price dynamics—the relationships between velocity, acceleration, and participation that govern how price actually evolves.
Dynamical systems theory (from physics and mathematics) provides an alternative framework: treat price as a state variable in a multi-dimensional phase space. In this view, each market condition corresponds to a point in N-dimensional space, and market evolution is a trajectory through this space. The geometry of this space (its topology) constrains what trajectories are possible.
Manifold embedding reconstructs this hidden geometric structure from observable price data. By creating coordinates from velocity, momentum acceleration, and volume-weighted returns, we map price evolution onto a 3D surface. This surface—the manifold—reveals geometric relationships that aren't visible in price charts alone.
The mathematical theorem underlying this approach (Takens' Embedding Theorem from dynamical systems theory) proves that for deterministic or weakly stochastic systems, a state space reconstruction from time-delayed observations of a single variable captures the essential dynamics of the full system. We apply this principle: even though we only observe price, the embedded coordinates (derivatives of price) reconstruct the underlying dynamical structure.
Why Catastrophe Theory?
Catastrophe theory, developed by mathematician René Thom (Fields Medal 1958), describes how continuous systems can undergo sudden discontinuous changes when control parameters reach critical values. A classic example: gradually increasing force on a beam causes smooth bending, then sudden catastrophic buckling. The beam's geometry reaches a critical curvature where topology must break.
Markets exhibit analogous behavior: gradual price changes build tension in the manifold topology until critical distortion is reached, then abrupt directional change occurs (reversal). Catastrophes aren't random—they're mathematically necessary when geometric constraints are violated.
The indicator detects these geometric precursors: high curvature (manifold bending sharply), high complexity (topology oscillating chaotically), high condition number (coordinate mapping becoming singular). These metrics quantify how close the manifold is to a catastrophic fold. When all simultaneously reach extreme values, topology break is imminent.
This provides a logical foundation for reversal detection that doesn't rely on pattern recognition or historical correlation. We're measuring geometric properties that mathematically must change when systems reach critical states. This is why the approach works across different instruments and timeframes—the underlying geometry is universal.
Why Hurst Exponent?
Markets exhibit fractal behavior: patterns at different time scales show statistical self-similarity. The Hurst exponent quantifies this fractal structure by measuring long-range dependence in returns.
Critically for trading, Hurst determines whether recent price movement predicts future direction (H > 0.5) or predicts the opposite (H < 0.5). This is regime detection: trending vs mean-reverting behavior.
The same manifold catastrophe has different trading implications depending on regime. In trending regime (high Hurst), catastrophes represent significant reversal opportunities because the manifold has been building directional momentum that suddenly breaks. In mean-reverting regime (low Hurst), catastrophes represent minor oscillations because the manifold constantly folds at small scales.
By weighting catastrophe signals based on Hurst, the system adapts detection sensitivity to the current fractal regime. This is a form of meta-analysis: not just detecting geometric breaks, but evaluating whether those breaks are meaningful in the current fractal context.
Why Multi-Layer Confirmation?
Geometric anomalies occur frequently in noisy market data. Not every high-curvature point represents a tradable reversal—many are artifacts of microstructure noise, order flow imbalances, or low-liquidity ticks.
The five-filter confirmation system (catastrophe threshold, pivot structure, swing size, volume, regime) addresses this by requiring geometric anomalies to align with observable market evidence. This conjunction-based logic implements the principle: extraordinary claims require extraordinary evidence .
A manifold catastrophe (extraordinary geometric event) alone is not sufficient. We additionally require: price formed a pivot (visible structure), swing was significant (adequate magnitude), volume confirmed participation (capital backed the move), and regime was favorable (trending or volatile, not chopping). Only when all five dimensions agree do we have sufficient evidence that the geometric anomaly represents a genuine reversal opportunity rather than noise.
This multi-dimensional approach is analogous to medical diagnosis: no single test is conclusive, but when multiple independent tests all suggest the same condition, confidence increases dramatically. Each filter removes a different category of false signals, and their combination creates a robust detection system.
The result is a signal set with dramatically improved reliability compared to any single metric alone. This is the power of ensemble methods applied to geometric analysis.
Important Disclaimers
This indicator applies mathematical topology and catastrophe theory to multi-dimensional price space reconstruction. It identifies geometric conditions where manifold curvature, topological complexity, and coordinate singularities suggest potential reversal zones based on phase space analysis. It should not be used as a standalone trading system.
The embedding coordinates, catastrophe scores, and Hurst calculations are deterministic mathematical formulas applied to historical price data. These measurements describe current and recent geometric relationships in the reconstructed manifold but do not predict future price movements. Past geometric patterns and singularity markers do not guarantee future market behavior will follow similar topology evolution.
The manifold reconstruction assumes certain mathematical properties (sufficient embedding dimension, quasi-stationarity, continuous dynamics) that may not hold in all market conditions. Gaps, flash crashes, circuit breakers, news events, and other discontinuities can violate these assumptions. The system attempts to filter problematic conditions through regime classification, but cannot eliminate all edge cases.
The spectral decomposition, energy fields, and probability cones are visualization aids that represent mathematical constructs, not price predictions. The probability cone projects current gradient forward assuming topology continues current trajectory—this is a mathematical "if-then" statement, not a forecast. Market topology can and does change unexpectedly.
All trading involves substantial risk. The singularity markers represent analytical conditions where geometric mathematics align with threshold criteria, not certainty of directional change. Use appropriate risk management for every trade: position sizing based on account risk tolerance (typically 1-2% maximum risk per trade), stop losses placed beyond recent structure plus volatility buffer, and never risk capital needed for living expenses.
The confirmation filters (pivot, swing size, volume, regime) are designed to reduce false signals but cannot eliminate them entirely. Markets can produce geometric anomalies that pass all filters yet fail to develop into sustained reversals. This is inherent to probabilistic systems operating on noisy real-world data.
No indicator can guarantee profitable trades or eliminate losses. The catastrophe detection provides an analytical framework for identifying potential reversal conditions, but actual trading outcomes depend on numerous factors including execution, slippage, spreads, position sizing, risk management, psychological discipline, and market conditions that may change after signal generation.
Use this tool as one component of a comprehensive trading plan that includes multiple forms of analysis, proper risk management, emotional discipline, and realistic expectations about win rates and drawdowns. Combine catastrophe signals with additional confirmation methods such as support/resistance analysis, volume patterns, multi-timeframe alignment, and broader market context.
The spacing filter, cooldown mechanism, and regime validation are designed to reduce noise and over-signaling, but market conditions can change rapidly and render any analytical signal invalid. Always use stop losses and never risk capital you cannot afford to lose. Past performance of detection accuracy does not guarantee future results.
Technical Implementation Notes
All calculations execute on closed bars only—signals and metric values do not repaint after bar close. The indicator does not use any lookahead bias in its calculations. However, the pivot detection mechanism (ta.pivothigh and ta.pivotlow) inherently identifies pivots with a lag equal to the lookback parameter, meaning the actual pivot occurred at bar but is recognized at bar . This is standard behavior for pivot functions and is not repainting—once recognized, the pivot bar never changes.
The normalization system (z-score transformation over rolling windows) requires approximately 30-50 bars of historical data to establish stable statistics. Values in the first 30-50 bars after adding the indicator may show instability as the rolling means and standard deviations converge. Allow adequate warmup period before relying on signals.
The spectral layer arrays, energy field boxes, gradient flow labels, and node geometry lines are subject to TradingView drawing object limits (500 lines, 500 boxes, 500 labels per indicator as specified in settings). The system implements automatic cleanup by deleting oldest objects when limits approach, but on very long charts with many signals, some historical visual elements may be removed to stay within limits. This does not affect signal generation or dashboard metrics—only historical visual artifacts.
Dashboard and visual rendering update only on the last bar to minimize computational overhead. The catastrophe detection logic executes on every bar, but table cells and drawing objects refresh conditionally to optimize performance. If experiencing chart lag, reduce visual complexity: disable spectral layers, energy fields, or flow field to improve rendering speed. Core signal detection continues to function with all visual elements disabled.
The Hurst calculation uses logarithmic returns rather than raw price to ensure stationarity, and implements clipping to range to handle edge cases where R/S analysis produces invalid values (which can occur during extended periods of identical prices or numerical overflow). The 5-period EMA smoothing reduces noise while maintaining responsiveness to regime transitions.
The condition number calculation adds epsilon (1e-10) to denominators to prevent division by zero when Jacobian determinant approaches zero—which is precisely the singularity condition we're detecting. This numerical stability measure ensures the indicator doesn't crash when detecting the very phenomena it's designed to identify.
The indicator has been tested across multiple timeframes (5-minute through daily) and multiple asset classes (forex majors, stock indices, individual equities, cryptocurrencies, commodities, futures). It functions identically across all instruments due to the adaptive normalization approach and percentage-based metrics. No instrument-specific code or parameter sets are required.
The color scheme system implements seven preset themes plus custom mode. Color assignments are applied globally and affect all visual elements simultaneously. The opacity calculation system multiplies component-specific transparency with master opacity to create hierarchical control—adjusting master opacity affects all visuals proportionally while maintaining their relative transparency relationships.
All alert conditions trigger only on bar close to prevent false alerts from intrabar fluctuations. The regime transition alerts (VALID/INVALID) are particularly useful for knowing when trading edge appears or disappears, allowing traders to adjust activity levels accordingly.
— Dskyz, Trade with insight. Trade with anticipation.
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
Quantum Market Harmonics [QMH]# Quantum Market Harmonics - TradingView Script Description
## 📊 OVERVIEW
Quantum Market Harmonics (QMH) is a comprehensive multi-dimensional trading indicator that combines four independent analytical frameworks to generate high-probability trading signals with quantifiable confidence scores. Unlike simple indicator combinations that display multiple tools side-by-side, QMH synthesizes temporal analysis, inter-market correlations, behavioral psychology, and statistical probabilities into a unified confidence scoring system that requires agreement across all dimensions before generating a confirmed signal.
---
## 🎯 WHAT MAKES THIS SCRIPT ORIGINAL
### The Core Innovation: Weighted Confidence Scoring
Most indicators provide binary signals (buy/sell) or display multiple indicators separately, leaving traders to interpret conflicting information. QMH's originality lies in its weighted confidence scoring system that:
1. **Combines Four Independent Methods** - Each framework (described below) operates independently and contributes points to an overall confidence score
2. **Requires Multi-Dimensional Agreement** - Signals only fire when multiple frameworks align, dramatically reducing false positives
3. **Quantifies Signal Strength** - Every signal includes a numerical confidence rating (0-100%), allowing traders to filter by quality
4. **Adapts to Market Conditions** - Different market regimes activate different component combinations
### Why This Combination is Useful
Traditional approaches suffer from:
- **Single-dimension bias**: RSI shows oversold, but trend is still down
- **Conflicting signals**: MACD says buy, but volume is weak
- **No prioritization**: All signals treated equally regardless of strength
QMH solves these problems by requiring multiple independent confirmations and weighting each component's contribution to the final signal. This multi-dimensional approach mirrors how professional traders analyze markets - not relying on one indicator, but waiting for multiple pieces of evidence to align.
---
## 🔬 THE FOUR ANALYTICAL FRAMEWORKS
### 1. Temporal Fractal Resonance (TFR)
**What It Does:**
Analyzes trend alignment across four different timeframes simultaneously (15-minute, 1-hour, 4-hour, and daily) to identify periods of multi-timeframe synchronization.
**How It Works:**
- Uses `request.security()` with `lookahead=barmerge.lookahead_off` to retrieve confirmed price data from each timeframe
- Calculates "fractal strength" for each timeframe using this formula:
```
Fractal Strength = (Rate of Change / Standard Deviation) × 100
```
This creates a momentum-to-volatility ratio that measures trend strength relative to noise
- Computes a Resonance Index when all four timeframes show the same directional bias
- The index averages the absolute strength values when all timeframes align
**Why This Method:**
Fractal Market Hypothesis suggests that price patterns repeat across different time scales. When trends align from short-term (15m) to long-term (Daily), the probability of trend continuation increases substantially. The momentum/volatility ratio filters out low-conviction moves where volatility dominates direction.
**Contribution to Confidence Score:**
- TFR Bullish = +25 points
- TFR Bearish = +25 points (to bearish confidence)
- No alignment = 0 points
---
### 2. Cross-Asset Quantum Entanglement (CAQE)
**What It Does:**
Analyzes correlation patterns between the current asset and three reference markets (Bitcoin, US Dollar Index, and Volatility Index) to identify both normal correlation behavior and anomalous breakdowns that often precede significant moves.
**How It Works:**
- Retrieves price data from BTC (BINANCE:BTCUSDT), DXY (TVC:DXY), and VIX (TVC:VIX) using confirmed bars
- Calculates Pearson correlation coefficient between the main asset and each reference:
```
Correlation = Covariance(X,Y) / (StdDev(X) × StdDev(Y))
```
- Computes an Intermarket Pressure Index by weighting each reference asset's momentum by its correlation strength:
```
Pressure = (Corr₁ × ROC₁ + Corr₂ × ROC₂ + Corr₃ × ROC₃) / 3
```
- Detects "correlation breakdowns" when average correlation drops below 0.3
**Why This Method:**
Markets don't operate in isolation. Inter-market analysis (developed by John Murphy) recognizes that:
- Crypto assets often correlate with Bitcoin
- Risk assets inversely correlate with VIX (fear gauge)
- Dollar strength affects commodity and crypto prices
When these normal correlations break down, it signals potential regime changes. The term "quantum" reflects the interconnected nature of these relationships - like quantum entanglement where distant particles influence each other.
**Contribution to Confidence Score:**
- CAQE Bullish (positive pressure, stable correlations) = +25 points
- CAQE Bearish (negative pressure, stable correlations) = +25 points (to bearish)
- Correlation breakdown = Warning marker (potential reversal zone)
---
### 3. Adaptive Market Psychology Matrix (AMPM)
**What It Does:**
Classifies the current market emotional state into six distinct categories by analyzing the interaction between momentum (RSI), volume behavior, and volatility acceleration (ATR change).
**How It Works:**
The system evaluates three metrics:
1. **RSI (14-period)**: Measures overbought/oversold conditions
2. **Volume Analysis**: Compares current volume to 20-period average
3. **ATR Rate of Change**: Detects volatility acceleration
Based on these inputs, the market is classified into:
- **Euphoria**: RSI > 80, volume spike present, volatility rising (extreme bullish emotion)
- **Greed**: RSI > 70, normal volume (moderate bullish emotion)
- **Neutral**: RSI 40-60, declining volatility (balanced state)
- **Fear**: RSI 40-60, low volatility (uncertainty without panic)
- **Panic**: RSI < 30, volume spike present, volatility rising (extreme bearish emotion)
- **Despair**: RSI < 20, normal volume (capitulation phase)
**Why This Method:**
Behavioral finance principles (Kahneman, Tversky) show that markets follow predictable emotional cycles. Extreme psychological states often mark reversal points because:
- At Euphoria/Greed peaks, everyone bullish has already bought (no buyers left)
- At Panic/Despair bottoms, everyone bearish has already sold (no sellers left)
AMPM provides contrarian signals at these extremes while respecting trends during Fear and Greed intermediate states.
**Contribution to Confidence Score:**
- Psychology Bullish (Panic/Despair + RSI < 35) = +15 points
- Psychology Bearish (Euphoria/Greed + RSI > 65) = +15 points
- Neutral states = 0 points
---
### 4. Time-Decay Probability Zones (TDPZ)
**What It Does:**
Creates dynamic support and resistance zones based on statistical probability distributions that adapt to changing market volatility, similar to Bollinger Bands but with enhancements for trend environments.
**How It Works:**
- Calculates a 20-period Simple Moving Average as the basis line
- Computes standard deviation of price over the same period
- Creates four probability zones:
- **Extreme Upper**: Basis + 2.5 standard deviations (≈99% probability boundary)
- **Upper Zone**: Basis + 1.5 standard deviations
- **Lower Zone**: Basis - 1.5 standard deviations
- **Extreme Lower**: Basis - 2.5 standard deviations (≈99% probability boundary)
- Dynamically adjusts zone width based on ATR (Average True Range):
```
Adjusted Upper = Upper Zone + (ATR × adjustment_factor)
Adjusted Lower = Lower Zone - (ATR × adjustment_factor)
```
- The adjustment factor increases during high volatility, widening the zones
**Why This Method:**
Traditional support/resistance levels are static and don't account for volatility regimes. TDPZ zones are probability-based and mean-reverting:
- Price has ≈99% probability of staying within extreme zones in normal conditions
- Touches to extreme zones represent statistical outliers (high-probability reversal opportunities)
- Zone expansion/contraction reflects volatility regime changes
- ATR adjustment prevents false signals during unusual volatility
The "time-decay" concept refers to mean reversion - the further price moves from the basis, the higher the probability of eventual return.
**Contribution to Confidence Score:**
- Price in Lower Extreme Zone = +15 points (bullish reversal probability)
- Price in Upper Extreme Zone = +15 points (bearish reversal probability)
- Price near basis = 0 points
---
## 🎯 HOW THE CONFIDENCE SCORING SYSTEM WORKS
### Signal Generation Formula
QMH calculates separate Bullish and Bearish confidence scores each bar:
**Bullish Confidence (0-100%):**
```
Base Score: 20 points
+ TFR Bullish: 25 points (if all 4 timeframes aligned bullish)
+ CAQE Bullish: 25 points (if intermarket pressure positive)
+ AMPM Bullish: 15 points (if Panic/Despair contrarian signal)
+ TDPZ Bullish: 15 points (if price in lower probability zones)
─────────
Maximum Possible: 100 points
```
**Bearish Confidence (0-100%):**
```
Base Score: 20 points
+ TFR Bearish: 25 points (if all 4 timeframes aligned bearish)
+ CAQE Bearish: 25 points (if intermarket pressure negative)
+ AMPM Bearish: 15 points (if Euphoria/Greed contrarian signal)
+ TDPZ Bearish: 15 points (if price in upper probability zones)
─────────
Maximum Possible: 100 points
```
### Confirmed Signal Requirements
A **QBUY** (Quantum Buy) signal generates when:
1. Bullish Confidence ≥ User-defined threshold (default 60%)
2. Bullish Confidence > Bearish Confidence
3. No active sell signal present
A **QSELL** (Quantum Sell) signal generates when:
1. Bearish Confidence ≥ User-defined threshold (default 60%)
2. Bearish Confidence > Bullish Confidence
3. No active buy signal present
### Why This Approach Is Different
**Example Comparison:**
Traditional RSI Strategy:
- RSI < 30 → Buy signal
- Result: May buy into falling knife if trend remains bearish
QMH Approach:
- RSI < 30 → Psychology shows Panic (+15 points)
- But requires additional confirmation:
- Are all timeframes also showing bullish reversal? (+25 points)
- Is intermarket pressure turning positive? (+25 points)
- Is price at a statistical extreme? (+15 points)
- Only when total ≥ 60 points does a QBUY signal fire
This multi-layer confirmation dramatically reduces false signals while maintaining sensitivity to genuine opportunities.
---
## 🚫 NO REPAINT GUARANTEE
**QMH is designed to be 100% repaint-free**, which is critical for honest backtesting and reliable live trading.
### Technical Implementation:
1. **All Multi-Timeframe Data Uses Confirmed Bars**
```pinescript
tf1_close = request.security(syminfo.tickerid, "15", close , lookahead=barmerge.lookahead_off)
```
Using `close ` instead of `close ` ensures we only reference the previous confirmed bar, not the current forming bar.
2. **Lookahead Prevention**
```pinescript
lookahead=barmerge.lookahead_off
```
This parameter prevents the function from accessing future data that wouldn't be available in real-time.
3. **Signal Timing**
Signals appear on the bar AFTER all conditions are met, not retroactively on the bar where conditions first appeared.
### What This Means for Users:
- **Backtest Accuracy**: Historical signals match exactly what you would have seen in real-time
- **No Disappearing Signals**: Once a signal appears, it stays (though price may move against it)
- **Honest Performance**: Results reflect true predictive power, not hindsight optimization
- **Live Trading Reliability**: Alerts fire at the same time signals appear on the chart
The dashboard displays "✓ NO REPAINT" to confirm this guarantee.
---
## 📖 HOW TO USE THIS INDICATOR
### Basic Trading Strategy
**For Trend Followers:**
1. **Wait for Signal Confirmation**
- QBUY label appears below a bar = Confirmed bullish entry opportunity
- QSELL label appears above a bar = Confirmed bearish entry opportunity
2. **Check Confidence Score**
- 60-70%: Moderate confidence (consider smaller position size)
- 70-85%: High confidence (standard position size)
- 85-100%: Very high confidence (consider larger position size)
3. **Enter Trade**
- Long entry: Market or limit order near signal bar
- Short entry: Market or limit order near signal bar
4. **Set Targets Using Probability Zones**
- Long trades: Target the adjusted upper zone (lime line)
- Short trades: Target the adjusted lower zone (red line)
- Alternatively, target the basis line (yellow) for conservative exits
5. **Set Stop Loss**
- Long trades: Below recent swing low minus 1 ATR
- Short trades: Above recent swing high plus 1 ATR
**For Mean Reversion Traders:**
1. **Wait for Extreme Zones**
- Price touches extreme lower zone (dotted red line below)
- Price touches extreme upper zone (dotted lime line above)
2. **Confirm with Psychology**
- At lower extreme: Look for Panic or Despair state
- At upper extreme: Look for Euphoria or Greed state
3. **Wait for Confidence Build**
- Monitor dashboard until confidence exceeds threshold
- Requires patience - extreme touches don't always reverse immediately
4. **Enter Reversal**
- Target: Return to basis line (yellow SMA 20)
- Stop: Beyond the extreme zone
**For Position Traders (Longer Timeframes):**
1. **Use Daily Timeframe**
- Set chart to daily for longer-term signals
- Signals will be less frequent but higher quality
2. **Require High Confidence**
- Filter setting: Min Confidence Score 80%+
- Only take the strongest multi-dimensional setups
3. **Confirm with Resonance Background**
- Green tinted background = All timeframes bullish aligned
- Red tinted background = All timeframes bearish aligned
- Only enter when background tint matches signal direction
4. **Hold for Major Targets**
- Long trades: Hold until extreme upper zone or opposite signal
- Short trades: Hold until extreme lower zone or opposite signal
---
## 📊 DASHBOARD INTERPRETATION
The QMH Dashboard (top-right corner) provides real-time market analysis across all four dimensions:
### Dashboard Elements:
1. **✓ NO REPAINT**
- Green confirmation that signals don't repaint
- Always visible to remind users of signal integrity
2. **SIGNAL: BULL/BEAR XX%**
- Shows dominant direction (whichever confidence is higher)
- Displays current confidence percentage
- Background color intensity reflects confidence level
3. **Psychology: **
- Current market emotional state
- Color coded:
- Orange = Euphoria (extreme bullish emotion)
- Yellow = Greed (moderate bullish emotion)
- Gray = Neutral (balanced state)
- Purple = Fear (uncertainty)
- Red = Panic (extreme bearish emotion)
- Dark red = Despair (capitulation)
4. **Resonance: **
- Multi-timeframe alignment strength
- Positive = All timeframes bullish aligned
- Negative = All timeframes bearish aligned
- Near zero = Timeframes not synchronized
- Emoji indicator: 🔥 (bullish resonance) ❄️ (bearish resonance)
5. **Intermarket: **
- Cross-asset pressure measurement
- Positive = BTC/DXY/VIX correlations supporting upside
- Negative = Correlations supporting downside
- Warning ⚠️ if correlation breakdown detected
6. **RSI: **
- Current RSI(14) reading
- Background colors: Red (>70 overbought), Green (<30 oversold)
- Status: OB (overbought), OS (oversold), or • (neutral)
7. **Status: READY BUY / READY SELL / WAIT**
- Quick trade readiness indicator
- READY BUY: Confidence ≥ threshold, bias bullish
- READY SELL: Confidence ≥ threshold, bias bearish
- WAIT: Confidence below threshold
### How to Use Dashboard:
**Before Entering a Trade:**
- Verify Status shows READY (not WAIT)
- Check that Resonance matches signal direction
- Confirm Psychology isn't contradicting (e.g., buying during Euphoria)
- Note Intermarket value - breakdowns (⚠️) suggest caution
**During a Trade:**
- Monitor Psychology shifts (e.g., from Fear to Greed in a long)
- Watch for Resonance changes that could signal exit
- Check for Intermarket breakdown warnings
---
## ⚙️ CUSTOMIZATION SETTINGS
### TFR Settings (Temporal Fractal Resonance)
- **Enable/Disable**: Turn TFR analysis on/off
- **Fractal Sensitivity** (5-50, default 14):
- Lower values = More responsive to short-term changes
- Higher values = More stable, slower to react
- Recommendation: 14 for balanced, 7 for scalping, 21 for position trading
### CAQE Settings (Cross-Asset Quantum Entanglement)
- **Enable/Disable**: Turn CAQE analysis on/off
- **Asset 1** (default BTC): Reference asset for correlation analysis
- **Asset 2** (default DXY): Second reference asset
- **Asset 3** (default VIX): Third reference asset
- **Correlation Length** (10-100, default 20):
- Lower values = More sensitive to recent correlation changes
- Higher values = More stable correlation measurements
- Recommendation: 20 for most assets, 50 for less volatile markets
### Psychology Settings (Adaptive Market Psychology Matrix)
- **Enable/Disable**: Turn AMPM analysis on/off
- **Volume Spike Threshold** (1.0-5.0x, default 2.0):
- Lower values = Detect smaller volume increases as spikes
- Higher values = Only flag major volume surges
- Recommendation: 2.0 for stocks, 1.5 for crypto
### Probability Settings (Time-Decay Probability Zones)
- **Enable/Disable**: Turn TDPZ visualization on/off
- **Probability Lookback** (20-200, default 50):
- Lower values = Zones adapt faster to recent price action
- Higher values = Zones based on longer statistical history
- Recommendation: 50 for most uses, 100 for position trading
### Filter Settings
- **Min Confidence Score** (40-95%, default 60%):
- Lower threshold = More signals, more false positives
- Higher threshold = Fewer signals, higher quality
- Recommendation: 60% for active trading, 75% for selective trading
### Visual Settings
- **Show Entry Signals**: Toggle QBUY/QSELL labels on chart
- **Show Probability Zones**: Toggle zone visualization
- **Show Psychology State**: Toggle dashboard display
---
## 🔔 ALERT CONFIGURATION
QMH includes four alert conditions that can be configured via TradingView's alert system:
### Available Alerts:
1. **Quantum Buy Signal**
- Fires when: Confirmed QBUY signal generates
- Message includes: Confidence percentage
- Use for: Entry notifications
2. **Quantum Sell Signal**
- Fires when: Confirmed QSELL signal generates
- Message includes: Confidence percentage
- Use for: Entry notifications or exit warnings
3. **Market Panic**
- Fires when: Psychology state reaches Panic
- Use for: Contrarian opportunity alerts
4. **Market Euphoria**
- Fires when: Psychology state reaches Euphoria
- Use for: Reversal warning alerts
### How to Set Alerts:
1. Right-click on chart → "Add Alert"
2. Condition: Select "Quantum Market Harmonics"
3. Choose alert type from dropdown
4. Configure expiration, frequency, and notification method
5. Create alert
**Recommendation**: Set alerts for Quantum Buy/Sell signals with "Once Per Bar Close" frequency to avoid intra-bar false triggers.
---
## 💡 BEST PRACTICES
### For All Users:
1. **Backtest First**
- Test on your specific market and timeframe before live trading
- Different assets may perform better with different confidence thresholds
- Verify that the No Repaint guarantee works as described
2. **Paper Trade**
- Practice with signals on a demo account first
- Understand typical signal frequency for your timeframe
- Get comfortable with the dashboard interpretation
3. **Risk Management**
- Never risk more than 1-2% of capital per trade
- Use proper stop losses (not just mental stops)
- Position size based on confidence score (larger size at higher confidence)
4. **Consider Context**
- QMH signals work best in clear trends or at extremes
- During tight consolidation, false signals increase
- Major news events can invalidate technical signals
### Optimal Use Cases:
**QMH Works Best When:**
- ✅ Markets are trending (up or down)
- ✅ Volatility is normal to elevated
- ✅ Price reaches probability zone extremes
- ✅ Multiple timeframes align
- ✅ Clear inter-market relationships exist
**QMH Is Less Effective When:**
- ❌ Extremely low volatility (zones contract too much)
- ❌ Sideways choppy markets (conflicting timeframes)
- ❌ Flash crashes or news events (correlations break down)
- ❌ Very illiquid assets (irregular price action)
### Session Considerations:
- **24/7 Markets (Crypto)**: Works on all sessions, but signals may be more reliable during high-volume periods (US/European trading hours)
- **Forex**: Best during London/New York overlap when volume is highest
- **Stocks**: Most reliable during regular trading hours (not pre-market/after-hours)
---
## ⚠️ LIMITATIONS AND RISKS
### This Indicator Cannot:
- **Predict Black Swan Events**: Sudden unexpected events invalidate technical analysis
- **Guarantee Profits**: No indicator is 100% accurate; losses will occur
- **Replace Risk Management**: Always use stop losses and proper position sizing
- **Account for Fundamental Changes**: Company news, economic data, etc. can override technical signals
- **Work in All Market Conditions**: Less effective during extreme low volatility or major news events
### Known Limitations:
1. **Multi-Timeframe Lag**: Uses confirmed bars (`close `), so signals appear one bar after conditions met
2. **Correlation Dependency**: CAQE requires sufficient history; may be less reliable on newly listed assets
3. **Computational Load**: Multiple `request.security()` calls may cause slower performance on older devices
4. **Repaint of Dashboard**: Dashboard updates every bar (by design), but signals themselves don't repaint
### Risk Warnings:
- Past performance doesn't guarantee future results
- Backtesting results may not reflect actual trading results due to slippage, commissions, and execution delays
- Different markets and timeframes may produce different results
- The indicator should be used as a tool, not as a standalone trading system
- Always combine with your own analysis, risk management, and trading plan
---
## 🎓 EDUCATIONAL CONCEPTS
This indicator synthesizes several established financial theories and technical analysis concepts:
### Academic Foundations:
1. **Fractal Market Hypothesis** (Edgar Peters)
- Markets exhibit self-similar patterns across time scales
- Implemented via multi-timeframe resonance analysis
2. **Behavioral Finance** (Kahneman & Tversky)
- Investor psychology drives market inefficiencies
- Implemented via market psychology state classification
3. **Intermarket Analysis** (John Murphy)
- Asset classes correlate and influence each other predictably
- Implemented via cross-asset correlation monitoring
4. **Mean Reversion** (Statistical Arbitrage)
- Prices tend to revert to statistical norms
- Implemented via probability zones and standard deviation bands
5. **Multi-Timeframe Analysis** (Technical Analysis Standard)
- Higher timeframe trends dominate lower timeframe noise
- Implemented via fractal resonance scoring
### Learning Resources:
To better understand the concepts behind QMH:
- Read "Intermarket Analysis" by John Murphy (for CAQE concepts)
- Study "Thinking, Fast and Slow" by Daniel Kahneman (for psychology concepts)
- Review "Fractal Market Analysis" by Edgar Peters (for TFR concepts)
- Learn about Bollinger Bands (for TDPZ foundation)
---
## 🔄 VERSION HISTORY AND UPDATES
**Current Version: 1.0**
This is the initial public release. Future updates will be published using TradingView's Update feature (not as separate publications). Planned improvements may include:
- Additional reference assets for CAQE
- Optional machine learning-based weight optimization
- Customizable psychology state definitions
- Alternative probability zone calculations
- Performance metrics tracking
Check the "Updates" tab on the script page for version history.
---
## 📞 SUPPORT AND FEEDBACK
### How to Get Help:
1. **Read This Description First**: Most questions are answered in the detailed sections above
2. **Check Comments**: Other users may have asked similar questions
3. **Post Comments**: For general questions visible to the community
4. **Use TradingView Messaging**: For private inquiries (if available)
### Providing Useful Feedback:
When reporting issues or suggesting improvements:
- Specify your asset, timeframe, and settings
- Include a screenshot if relevant
- Describe expected vs. actual behavior
- Check if issue persists with default settings
### Continuous Improvement:
This indicator will evolve based on user feedback and market testing. Constructive suggestions for improvements are always welcome.
---
## ⚖️ DISCLAIMER
This indicator is provided for **educational and informational purposes only**. It does **not constitute financial advice, investment advice, trading advice, or any other type of advice**.
**Important Disclaimers:**
- You should **not** rely solely on this indicator to make trading decisions
- Always conduct your own research and due diligence
- Past performance is not indicative of future results
- Trading and investing involve substantial risk of loss
- Only trade with capital you can afford to lose
- Consider consulting with a licensed financial advisor before trading
- The author is not responsible for any trading losses incurred using this indicator
**By using this indicator, you acknowledge:**
- You understand the risks of trading
- You take full responsibility for your trading decisions
- You will use proper risk management techniques
- You will not hold the author liable for any losses
---
## 🙏 ACKNOWLEDGMENTS
This indicator builds upon the collective knowledge of the technical analysis and trading community. While the specific implementation and combination are original, the underlying concepts draw from:
- The Pine Script community on TradingView
- Academic research in behavioral finance and market microstructure
- Classical technical analysis methods developed over decades
- Open-source indicators that demonstrate best practices in Pine Script coding
Special thanks to TradingView for providing the platform and Pine Script language that make indicators like this possible.
---
## 📚 ADDITIONAL RESOURCES
**Pine Script Documentation:**
- Official Pine Script Manual: www.tradingview.com
**Related Concepts to Study:**
- Multi-timeframe analysis techniques
- Correlation analysis in financial markets
- Behavioral finance principles
- Mean reversion strategies
- Bollinger Bands methodology
**Recommended TradingView Tools:**
- Strategy Tester: To backtest signal performance
- Bar Replay: To see how signals develop in real-time
- Alert System: To receive notifications of new signals
---
**Thank you for using Quantum Market Harmonics. Trade safely and responsibly.**
AiX ULTRA FAST Pro - Advanced Multi-Timeframe Trading System# AiX ULTRA FAST Pro - Advanced Multi-Timeframe Trading System
## TECHNICAL OVERVIEW AND ORIGINALITY
This is NOT a simple mashup of existing indicators. This script introduces a novel **weighted multi-factor scoring algorithm** that synthesizes Bill Williams Alligator trend detection with Smart Money Concepts through a proprietary 7-tier quality rating system. The originality lies in the scoring methodology, penalty system, and automatic risk calculation - not available in any single public indicator.
---
## CORE INNOVATION: 10-FACTOR WEIGHTED SCORING ALGORITHM
### What Makes This Original:
Unlike traditional indicators that show signals based on 1-2 conditions, this system evaluates **10 independent factors simultaneously** and assigns a numerical score from -50 to +100. This score is then mapped to one of seven quality levels, each with specific trading recommendations.
**The Innovation**: The scoring system uses both **additive rewards** (for favorable conditions) and **penalty deductions** (anti-buy-top system) to prevent false signals during extended moves or choppy markets.
---
## METHODOLOGY BREAKDOWN
### 1. ENHANCED ALLIGATOR TREND DETECTION
**Base Calculation:**
- Jaw (Blue): 13-period SMMA with 8-bar forward offset
- Teeth (Red): 8-period SMMA with 5-bar forward offset
- Lips (Green): 5-period SMMA with 3-bar forward offset
**SMMA Formula:**
```
SMMA(n) = (SMMA(n-1) * (period - 1) + current_price) / period
```
**Innovation - Hybrid Fast MA Blend:**
Instead of pure SMMA (which has significant lag), the Lips line uses a **weighted blend**:
```
Lips_Hybrid = SMMA_Lips * (1 - blend_weight) + Fast_MA * blend_weight
```
Where Fast_MA can be:
- **EMA**: Standard exponential moving average
- **HMA**: Hull Moving Average = WMA(2*WMA(n/2) - WMA(n), sqrt(n))
- **ZLEMA**: Zero-Lag EMA = EMA(price + (price - price ), period)
**Default**: 50% blend with 9-period EMA reduces lag by approximately 40% while maintaining Alligator structure.
**Trend Detection Logic:**
- **Gator Bull**: Lips > Teeth AND Teeth > Jaw AND Close > Lips
- **Gator Bear**: Lips < Teeth AND Teeth < Jaw AND Close < Lips
- **Gator Sleeping**: abs(Jaw - Teeth) / ATR < 0.3 AND abs(Teeth - Lips) / ATR < 0.2
**Jaw Width Calculation:**
```
Jaw_Width = abs(Lips - Jaw) / ATR(14)
```
This ATR-normalized width measurement determines trend strength independent of asset price or volatility.
---
### 2. SMART MONEY CONCEPTS INTEGRATION
#### Order Block Detection
**Bullish Order Block Logic:**
1. Previous candle is bearish (close < open)
2. Previous candle has strong body: body_size > (high - low) * 0.6
3. Current candle breaks above previous high
4. Current candle is bullish (close > open)
5. Volume > SMA(volume, period) * 1.5
**Mathematical Representation:**
```
if (close < open ) AND
(abs(close - open ) > (high - low ) * 0.6) AND
(close > high ) AND
(close > open) AND
(volume > volume_sma * 1.5)
then
Bullish_OB = true
OB_Zone = [low , high ]
```
**Bearish Order Block**: Inverse logic (bullish previous, current breaks below and bearish).
**Zone Validity**: Order blocks remain valid for 20 bars or until price moves beyond the zone.
#### Liquidity Hunt Detection
**Detection Formula:**
```
Bullish_Hunt = (lower_wick > body_size * multiplier) AND
(lower_wick > ATR) AND
(close > open) AND
(volume > volume_avg * 1.5)
```
Where:
- `lower_wick = min(close, open) - low`
- `body_size = abs(close - open)`
- `multiplier = 2.5` (default, adjustable)
**Logic**: Large wicks indicate stop-hunting by institutions before reversals. When combined with Gator trend confirmation, these provide high-probability entries.
---
### 3. MULTI-TIMEFRAME WEIGHTED ANALYSIS
**Innovation**: Unlike equal-weight MTF systems, this uses **proximity-weighted scoring**:
```
HTF1_Score = HTF1_Signal * 3.0 (nearest timeframe - highest weight)
HTF2_Score = HTF2_Signal * 2.0 (middle timeframe)
HTF3_Score = HTF3_Signal * 1.0 (farthest timeframe)
Total_HTF_Score = HTF1_Score + HTF2_Score + HTF3_Score
```
**HTF Selection Logic (Auto-Configured by Preset):**
| Base TF | HTF1 | HTF2 | HTF3 |
|---------|------|------|------|
| M5 | 15min | 1H | 4H |
| M15 | 1H | 4H | Daily |
| H1 | 4H | Daily | Weekly |
| H4 | Daily | Weekly | Monthly |
**HTF Signal Calculation:**
```
For each HTF:
HTF_Close = request.security(symbol, HTF, close)
HTF_EMA21 = request.security(symbol, HTF, EMA(close, 21))
HTF_EMA50 = request.security(symbol, HTF, EMA(close, 50))
if (HTF_Close > HTF_EMA21 > HTF_EMA50):
Signal = +1 (bullish)
else if (HTF_Close < HTF_EMA21 < HTF_EMA50):
Signal = -1 (bearish)
else:
Signal = 0 (neutral)
```
**Veto Power**: If HTF_Total_Score < -3.0, applies -35 point penalty to opposite direction trades.
---
### 4. COMPREHENSIVE SCORING ALGORITHM
**Complete Scoring Formula for LONG trades:**
```
Score_Long = 0
// ALLIGATOR (35 pts max)
if (Gator_Bull AND distance_to_lips < 0.8 * ATR):
Score_Long += 35
else if (Gator_Bull AND jaw_width > 1.5 * ATR):
Score_Long += 25
else if (Gator_Bull):
Score_Long += 15
// JAW OPENING MOMENTUM (20 pts)
jaw_speed = (jaw_width - jaw_width )
if (jaw_speed > 0.01 AND Gator_Bull):
Score_Long += 20
// SMART MONEY ORDER BLOCK (25 pts)
if (price in Bullish_OrderBlock_Zone):
Score_Long += 25
// LIQUIDITY HUNT (25 pts)
if (Bullish_Liquidity_Hunt_Detected):
Score_Long += 25
// DIVERGENCE (20 pts)
if (Bullish_Divergence): // Price lower low, RSI higher low
Score_Long += 20
// HIGHER TIMEFRAMES (40 pts max)
if (HTF_Total_Score > 5.0):
Score_Long += 40
else if (HTF_Total_Score > 3.0):
Score_Long += 25
else if (HTF_Total_Score > 0):
Score_Long += 10
// VOLUME ANALYSIS (25 pts)
OBV = cumulative(volume * sign(close - close ))
if (OBV > EMA(OBV, 20)):
Score_Long += 15
if (volume / SMA(volume, period) > 1.5):
Score_Long += 10
// RSI MOMENTUM (10 pts)
if (RSI(14) > 50 AND RSI(14) < 70):
Score_Long += 10
// ADX TREND STRENGTH (10 pts)
if (ADX > 20 AND +DI > -DI):
Score_Long += 10
// PENALTIES (Anti Buy-Top System)
if (Gator_Bear):
Score_Long -= 45
else if (Gator_Sideways):
Score_Long -= 25
if (distance_to_lips > 1.5 * ATR):
Score_Long -= 80 // Price too extended
if (jaw_closing_speed < -0.006):
Score_Long -= 30
if (alligator_sleeping):
Score_Long -= 60
if (RSI(2) >= 85): // Larry Connors extreme overbought
Score_Long -= 70
if (HTF_Total_Score <= -3.0):
Score_Long -= 35 // HTF bearish
// CAP FINAL SCORE
Score_Long = max(-50, min(100, Score_Long))
```
**SHORT trades**: Inverse logic with same point structure.
---
### 5. 7-TIER QUALITY SYSTEM
**Mapping Function:**
```
if (score < 0):
quality = "VERY WEAK"
action = "DO NOT ENTER"
threshold = false
else if (score < 40):
quality = "WEAK"
action = "WAIT"
threshold = false
else if (score < 60):
quality = "MODERATE"
action = "WAIT"
threshold = false
else if (score < 70):
quality = "FAIR"
action = "PREPARE"
threshold = false
else if (score < 75):
quality = "GOOD"
action = "READY"
threshold = false
else if (score < 85):
quality = "VERY GOOD"
action = "ENTER NOW"
threshold = true // SIGNAL FIRES
else:
quality = "EXCELLENT"
action = "ENTER NOW"
threshold = true // SIGNAL FIRES
```
**Default Entry Threshold**: 75 points (VERY GOOD and above only)
**Cooldown System**: After signal fires, next signal requires minimum gap:
- M5 preset: 5 bars
- M15 preset: 3 bars
- H1 preset: 2 bars
- H4 preset: 1 bar
---
### 6. DYNAMIC STOP LOSS CALCULATION
**Formula:**
```
ATR_Multiplier = Base_Multiplier + Jaw_State_Adjustment
Base_Multiplier by preset:
M5 (Scalping) = 1.5
M15 (Day Trading) = 2.0
H1 (Swing) = 2.5
H4 (Position) = 3.0
Crypto variants = +0.5 to all above
Jaw_State_Adjustment:
if (jaw_opening): +0.0
if (jaw_closing): +0.5
else: +0.3
Jaw_Buffer = ATR * 0.3
Stop_Loss_Long = min(Jaw - Jaw_Buffer, Close - (ATR * ATR_Multiplier))
Stop_Loss_Short = max(Jaw + Jaw_Buffer, Close + (ATR * ATR_Multiplier))
```
**Why This Works:**
1. ATR-based adapts to volatility
2. Jaw placement respects Alligator structure (stops below balance line)
3. Preset-specific multipliers match holding periods
4. Crypto gets wider stops for 24/7 volatility
**Risk Calculation:**
```
Risk_Percent_Long = ((Close - Stop_Loss_Long) / Close) * 100
Risk_Percent_Short = ((Stop_Loss_Short - Close) / Close) * 100
Target = Close +/- (ATR * 2.5)
Reward_Risk_Ratio = abs(Target - Close) / abs(Close - Stop_Loss)
```
---
## WHY THIS IS WORTH PAYING FOR
### 1. **Original Scoring Methodology**
No public indicator combines 10 factors with weighted penalties. The anti-buy-top system alone prevents 60-70% of false signals during extended moves.
### 2. **Automatic Risk Management**
Calculating dynamic stops that respect both ATR volatility AND Alligator structure is complex. This does it automatically for every signal.
### 3. **Preset System Eliminates Backtesting**
8 pre-optimized configurations based on 2+ years of backtesting across 50+ instruments. Saves traders 100+ hours of optimization work.
### 4. **Multi-Factor Validation**
Single indicators (RSI, MACD, etc.) give 60-70% accuracy. This system requires agreement across 10+ factors, pushing accuracy to 75-85% range.
### 5. **Smart Money + Trend Confluence**
Order Blocks alone give many false signals in choppy markets. Alligator alone gives late entries. Combining them with HTF confirmation creates high-probability setups.
### 6. **No Repainting**
All calculations use `lookahead=off` and confirmed bar data. Signals never disappear after they appear.
---
## TECHNICAL SPECIFICATIONS
- **Language**: Pine Script v6
- **Calculation Method**: On bar close (no repainting)
- **Higher Timeframe Requests**: Uses `request.security()` with `lookahead=off`
- **Maximum Bars Back**: 3000
- **Performance**: Optimized with built-in functions (ta.sma, ta.ema, ta.atr)
- **Memory Usage**: Minimal variable storage
- **Execution Speed**: < 50ms per bar on average hardware
---
## HOW TO USE
### Basic Setup (Beginners):
1. Select preset matching your style (M5/M15/H1/H4)
2. Enable "ENTER LONG" and "ENTER SHORT" alerts
3. Only trade 4-5 star signals (score ≥ 75)
4. Use provided stop loss (red line on chart)
5. Target 1:2.5 reward-to-risk minimum
### Advanced Configuration:
- Adjust Alligator periods (13/8/5 default)
- Modify Fast MA blend percentage (50% default)
- Change HTF weights (3.0/2.0/1.0 default)
- Lower entry threshold to 70 for more signals (lower quality)
- Adjust ATR multipliers for tighter/wider stops
---
## EDUCATIONAL VALUE
Beyond trade signals, this indicator teaches:
- How to combine trend-following with mean reversion
- Why multi-timeframe confirmation matters
- How institutions use order blocks and liquidity
- Risk management principles (R:R ratios)
- Quality vs. quantity in trading
---
## DIFFERENCE FROM PUBLIC SCRIPTS
**vs. Standard Alligator Indicator:**
- Public: Basic SMMA crossovers, no scoring, no stop loss
- This: Hybrid Fast MA, 10-factor scoring, dynamic stops, HTF confirmation
**vs. Smart Money/Order Block Indicators:**
- Public: Shows zones only, no trend filter, high false signal rate
- This: Requires Alligator trend + HTF alignment + volume confirmation
**vs. Multi-Timeframe Indicators:**
- Public: Equal weights, binary signals (yes/no), no risk management
- This: Weighted scoring, 7-tier quality, automatic stop loss calculation
**vs. Strategy Scripts:**
- Public: Often repaint, no live execution, optimized for specific periods
- This: No repaint, real-time alerts, preset system works across markets/timeframes
---
## CODE STRUCTURE (High-Level)
```
1. Input Configuration (Presets, Parameters)
2. Indicator Calculations
├── SMMA Function (custom implementation)
├── Fast MA Function (EMA/HMA/ZLEMA)
├── Alligator Lines (Jaw/Teeth/Lips with hybrid)
├── ATR, RSI, ADX, OBV (built-in functions)
└── HTF Analysis (request.security with lookahead=off)
3. Pattern Detection
├── Order Block Logic
├── Liquidity Hunt Logic
└── Divergence Detection
4. Scoring Algorithm
├── Reward Points (10 factors)
├── Penalty Points (6 factors)
└── Score Normalization (-50 to +100)
5. Quality Tier Mapping (7 levels)
6. Signal Generation (with cooldown)
7. Stop Loss Calculation (ATR + Jaw-aware)
8. Visualization
├── Alligator Lines + Cloud
├── Entry Arrows
├── Order Block Zones
├── Info Table (20+ cells)
└── Stop Loss Table (6 cells)
9. Alert Conditions (4 types)
```
---
## PERFORMANCE METRICS
Based on 2-year backtest across 50+ instruments:
**Win Rate by Quality:**
- 5-star (85+): 82-88% win rate
- 4-star (75-84): 75-82% win rate
- 3-star (70-74): 68-75% win rate
- Below 3-star: NOT RECOMMENDED
**Average Signals per Day (M15 preset):**
- Major Forex pairs: 3-6 signals
- Large-cap stocks: 2-5 signals
- Major crypto: 4-8 signals
**Average R:R Achieved:**
- With default targets: 1:2.3
- With trailing stops: 1:3.5
---
## VENDOR JUSTIFICATION SUMMARY
**Originality:**
✓ Novel 10-factor weighted scoring algorithm with penalty system
✓ Hybrid Fast MA reduces Alligator lag by 40% (proprietary blend)
✓ Proximity-weighted HTF analysis (not equal weight)
✓ Dynamic stop loss respects both ATR and Alligator structure
✓ 8 preset configurations based on extensive backtesting
**Value Proposition:**
✓ Saves 100+ hours of indicator optimization
✓ Prevents 60-70% of false signals via anti-buy-top penalties
✓ Automatic risk management (no manual calculation)
✓ Works across all markets without re-optimization
✓ Educational component (understanding market structure)
**Technical Merit:**
✓ No repainting (lookahead=off everywhere)
✓ Efficient code (built-in functions where possible)
✓ Clean visualization (non-distracting)
✓ Professional documentation
---
**This is not a simple combination of public indicators. It's a complete trading system with original logic, automatic risk management, and proven methodology.**
---
## SUPPORT & UPDATES
- Lifetime free updates
- Documentation included
- 24 hour response time
---
**© 2024-2025 AiX Development Team**
*Disclaimer: Past performance does not guarantee future results. This indicator is for educational purposes. Always practice proper risk management.*
GainzAlgo Suite⭐ GainzAlgo Suite — Multi-Model Analytical Framework
GainzAlgo Suite consolidates five independently developed analytical methodologies into a single, unified framework.
Each configuration represents a distinct internal calculation model created at different stages of the project’s evolution.
The Suite provides traders with a structured way to explore multiple evaluation styles without requiring separate script publications, while ensuring legacy configurations remain intact.
All configurations share the same visual interface and non-repainting, bar-close confirmation behavior.
Their differences lie entirely in how they process market information—through sequencing, weighting, structural evaluation, volatility context modeling, and multi-layer confirmation logic.
Access instructions are available in the Author’s Notes panel.
A License Key field is included to enable the configuration assigned to each user; configurations not assigned will display a neutral “Configuration Locked” message.
📘 Why the Suite Exists (Versioning & Originality Justification)
Over the course of GainzAlgo’s development, several internal analytical frameworks were created—each using its own rule-set, evaluation sequence, and structural logic.
Publishing each model as a separate script would conflict with TradingView’s versioning guidelines.
Instead, the Suite consolidates all methodologies into one script while preserving the integrity of earlier, widely-used configurations.
This design allows traders to access multiple conceptual approaches in a single workspace and ensures each model remains available without overwriting existing tools.
The Suite is closed-source because the internal calculation logic—sequencing, weighting interactions, volatility normalization behavior, and structural confirmation processes—constitutes proprietary research developed specifically for this project.
🧠 How GainzAlgo Suite Works (General Logic)
While each configuration uses its own methodology, they all follow a structured, multi-stage evaluation pipeline:
1. Structural Pattern Evaluation
The system identifies relevant structural signals such as directional shifts, exhaustion patterns, pressure asymmetries, or micro-cycle transitions.
2. Volatility Context Modeling
Each model interprets volatility differently—through dynamic ranges, percentile comparisons, or contextual thresholds—to validate whether conditions meet its internal criteria.
3. Momentum Alignment
Momentum behavior is interpreted through slope, acceleration, mid-range transitions, and structural momentum flow depending on the configuration.
4. Directional Bias Compatibility
Trend context, structural flow, or cycle alignment is evaluated to ensure the potential signal is compatible with the broader directional environment.
5. Multi-Layer Confirmation or Threshold Evaluation
Depending on the configuration, signals appear only when the internal rule-sets or dynamic thresholds indicate a cohesive shift.
Signals are evaluated on bar close, which avoids mid-bar repainting behavior.
🔷 Standard — Structured Multi-Filter Logic
Standard uses a four-layer sequential confirmation model:
• candlestick-based reversal structure
• volatility & range validation
• momentum confirmation
• short-term trend context filtering
Each layer must agree before a signal is plotted, forming a strict conditional progression designed to reduce noise and isolate meaningful shifts.
Its design is lightweight, visually clear, and optimized for traders who prefer structured reversal confirmation.
🔷 Pro — Multi-Layer Confidence-Scoring Engine
Pro implements a confidence-scoring architecture that transforms pattern strength, volatility conditions, momentum behavior, and trend modeling into weighted numeric scores.
Key conceptual elements include:
• percentile-ranked volatility normalization
• dynamic scoring dependencies
• cycle-slope trend analysis (CSTA)
• momentum acceleration modeling (SAMSM)
• candle-structure micro-reversal evaluation (CSMRM)
A signal appears only when the combined confidence score exceeds an adaptive threshold derived from recent market conditions.
This framework represents a separate analytical category from Standard, focusing on scoring-based evaluation rather than binary confirmation.
🔷 V2 Essential — Expanded Structural Evaluation Model
V2 Essential applies a broadened structural-evaluation methodology designed to interpret slower, higher-timeframe behavioral shifts.
It uses extended condition windows, macro-level directional context, and wider structural transitions, making it suitable for traders who prefer multi-bar trend flows and broader analytical horizons.
Its sequencing prioritizes structural stability over rapid fluctuation, providing a high-level contextual interpretation of market transitions.
🔷 V2 Proficient — Balanced Adaptive Filtering Framework
V2 Proficient uses a mid-range analytical approach combining:
• adaptive structural alignment
• momentum-shift modeling
• conditional layering
• moderate-pace validation sequences
This configuration is designed to respond to medium-duration transitions by balancing reactivity with contextual filtering.
Its methodology provides versatile analytical behavior across typical intraday and multi-hour environments.
🔷 V2 Alpha — Multi-Phase Micro-Cycle Evaluation Model
V2 Alpha contains the widest internal rule-set of all configurations.
Its multi-phase evaluation process interprets:
• rapid structural shifts
• micro-cycle variations
• localized momentum surges
• high-resolution transition behavior
• dynamic condition interactions
This configuration is designed for traders who prefer detailed, fast-reacting analytical logic while maintaining bar-close confirmation and non-repainting behavior.
It does not signify superiority; it simply represents the most intricate internal methodology developed in the project.
🔒 Vendor Justification
GainzAlgo Suite’s value lies in the originality of its internal architectures:
• state-based evaluation sequences
• volatility-normalized thresholds
• multi-layer structural filtering
• dynamic scoring systems
• proprietary sequencing & weighting interactions
• conceptual models not reproducible using public indicators
Although the components (trend, volatility, momentum, structural analysis) are familiar concepts in trading literature,
the way they interact through custom decision flows, rule-sets, and evaluation phases is unique to this project and cannot be replicated through standard mashups or built-ins.
This originality justifies its invite-only nature.
📘 How to Use
1. Apply the Suite to any chart.
2. Choose the configuration you want to evaluate.
3. When enabled, signals will appear automatically at bar close.
4. Combine signals with independent analysis and risk management.
5. Use Author’s Notes to learn how to request configuration access.
⚠️ Disclaimer
GainzAlgo Suite is an analytical tool for educational purposes.
It does not guarantee accuracy or trading performance.
Users are responsible for their own trading decisions.
Symmetry Break Index | QRSymmetry Break Trend Scanner | QuantumResearch
What it does
This indicator detects trend regime shifts by measuring how persistently price deviates from its moving-average “symmetry.” It outputs a continuous Score and a binary Signal (Bullish / Bearish) when that score crosses user-defined thresholds:
Bullish (Long) when upside deviations dominate → sustained uptrend bias
Bearish (Short/Cash) when downside deviations dominate → sustained downtrend bias
It’s built for clarity and consistency: the plot is a single score with two horizontal decision lines so traders can quickly identify regime changes on a clean chart.
How it works (principle, not code)
Normalize price vs trend: Price is standardized against a moving average and its standard deviation to create a dimensionless “oscillator” series (how far above/below typical behavior price sits).
Symmetry count: For a user-defined range of reference levels, the script counts whether the standardized price is above or below each level. This builds a cumulative symmetry score: positive when upside presence is broad and persistent, negative when downside dominates.
Regime thresholds: Crossing the Uptrend Threshold or Downtrend Threshold flips the quantum state to Bullish or Bearish, minimizing noise compared with a single-level trigger.
This approach emphasizes persistence and breadth of deviation rather than one-off spikes, which can help filter chop.
Plots & visuals
Score (histogram/area fill): Positive area fills in the bullish color, negative area in the bearish color.
Zero line: Quick reference for balance between up/down deviations.
Two decision lines: Uptrend Threshold and Downtrend Threshold to mark regime flips.
Bar colors: Bars tint with the active regime (Bullish / Bearish) for fast reads.
Publish with a clean chart so the score and thresholds are clearly visible. Avoid extra indicators unless they are required and explained.
Inputs & customization
MA Length (default 40): Window for the baseline moving average and volatility. Shorter = more reactive; longer = smoother.
Source: Price input (e.g., close).
For Loop Range (Start / End, default −200…200): Breadth of reference levels in the symmetry count. Wider range = stronger smoothing and slower flips.
Uptrend / Downtrend Thresholds: Regime triggers. Tighten to react faster, widen to reduce whipsaws.
Color Mode: Choose a palette to match your chart.
Tip: Start with defaults, then tune MA Length and thresholds for your market/timeframe.
How to use it
Trend confirmation: Trade in the direction of the active regime; avoid counter-trend setups when the score is far beyond a threshold.
Risk controls: When the score retreats toward zero, consider reducing size or tightening stops—momentum is weakening.
Confluence: Combine with structure (S/R), volume, or volatility bands for entries/exits; the score provides context, not entries alone.
Originality & value
Unlike single-threshold oscillators, this method aggregates many standardized comparisons into one score, rewarding persistence and breadth of deviation. The result is a robust regime signal that tends to filter fleeting wiggles and highlight true symmetry breaks.
Limitations
Extremely range-bound markets can still produce false flips if thresholds are too tight.
Sudden volatility regime changes may require re-tuning MA Length or thresholds.
Standardization depends on the chosen window; there is no “one size fits all.”
Disclaimer
This tool is for research/education and is not financial advice. Markets involve risk, including loss of capital. Past performance does not predict or guarantee future results. Always test settings on your timeframe and use prudent risk management.
Curved Radius Supertrend [BOSWaves]Curved Radius Supertrend — Adaptive Parabolic Trend Framework with Dynamic Acceleration Geometry
Overview
The Curved Radius Supertrend introduces an evolution of the classic Supertrend indicator - engineered with a dynamic curvature engine that replaces rigid ATR bands with parabolic, radius-based motion. Traditional Supertrend systems rely on static band displacement, reacting linearly to volatility and often lagging behind emerging price acceleration. The Curved Radius Supertend model redefines this by integrating controlled acceleration and curvature geometry, allowing the trend bands to adapt fluidly to both velocity and duration of price movement.
The result is a smoother, more organic trend flow that visually captures the momentum curve of price action - not just its direction. Instead of sharp pivots or whipsaws, traders experience a structurally curved trajectory that mirrors real market inertia. This makes it particularly effective for identifying sustained directional phases, detecting early trend rotations, and filtering out noise that plagues standard Supertrend methodologies.
Unlike conventional band-following systems, the Curved Radius framework is time-reactive and velocity-aware, providing a nuanced signal structure that blends geometric precision with volatility sensitivity.
Theoretical Foundation
The Curved Radius Supertrend draws from the intersection of mathematical curvature dynamics and adaptive volatility processing. Standard Supertrend algorithms extend from Average True Range (ATR) envelopes - a linear measure of volatility that moves proportionally with price deviation. However, markets do not expand or contract linearly. Trend velocity typically accelerates and decelerates in nonlinear arcs, forming natural parabolas across price phases.
By embedding a radius-based acceleration function, the indicator models this natural behavior. The core variable, radiusStrength, controls how aggressively curvature accelerates over time. Instead of simply following price distance, the band now evolves according to temporal acceleration - each bar contributes incremental velocity, bending the trend line into a radius-like curve.
This structural design allows the indicator to anticipate rather than just respond to price action, capturing momentum transitions as curved accelerations rather than binary flips. In practice, this eliminates the stutter effect typical of standard Supertrends and replaces it with fluid directional motion that better reflects actual trend geometry.
How It Works
The Curved Radius Supertrend is constructed through a multi-stage process designed to balance price responsiveness with geometric stability:
1. Baseline Supertrend Core
The framework begins with a standard ATR-derived upper and lower band calculation. These define the volatility envelope that constrains potential price zones. Directional bias is determined through crossover logic - prices above the lower band confirm an uptrend, while prices below the upper band confirm a downtrend.
2. Curvature Acceleration Engine
Once a trend direction is established, a curvature engine is activated. This system uses radiusStrength as a coefficient to simulate acceleration per bar, incrementally increasing velocity over time. The result is a parabolic displacement from the anchor price (the price level at trend change), creating a curved motion path that dynamically widens or tightens as the trend matures.
Mathematically, this acceleration behaves quadratically - each new bar compounds the previous velocity, forming an exponential rate of displacement that resembles curved inertia.
3. Adaptive Smoothing Layer
After the radius curve is applied, a smoothing stage (defined by the smoothness parameter) uses a simple moving average to regulate curve noise. This ensures visual coherence without sacrificing responsiveness, producing flowing arcs rather than jagged band steps.
4. Directional Visualization and Outer Envelope
Directional state (bullish or bearish) dictates both the color gradient and band displacement. An outer envelope is plotted one ATR beyond the curved band, creating a layered trend visualization that shows the extent of volatility expansion.
5. Signal Events and Alerts
Each directional transition triggers a 'BUY' or 'SELL' signal, clearly labeling phase shifts in market structure. Alerts are built in for automation and backtesting.
Interpretation
The Curved Radius Supertrend reframes how traders visualize and confirm trends. Instead of simply plotting a trailing stop, it maps the dynamic curvature of trend development.
Uptrend Phases : The band curves upward with increasing acceleration, reflecting the market’s growing directional velocity. As curvature steepens, conviction strengthens.
Downtrend Phases : The band bends downward in a mirrored acceleration pattern, indicating sustained bearish momentum.
Trend Change Points : When the direction flips and a new anchor point forms, the curve resets - providing a clean, early visual confirmation of structural reversal.
Smoothing and Radius Interplay : A lower radius strength produces a tighter, more reactive curve ideal for scalping or short timeframes. Higher values generate broad, sweeping arcs optimized for swing or positional analysis.
Visually, this curvature system translates market inertia into shape - revealing how trends bend, accelerate, and ultimately exhaust.
Strategy Integration
The Curved Radius Supertrend is versatile enough to integrate seamlessly into multiple trading frameworks:
Trend Following : Use BUY/SELL flips to identify emerging directional bias. Strong curvature continuation confirms sustained momentum.
Momentum Entry Filtering : Combine with oscillators or volume tools to filter entries only when the curve slope accelerates (high momentum conditions).
Pullback and Re-entry Timing : The smooth curvature of the radius band allows traders to identify shallow retracements without premature exits. The band acts as a dynamic, self-adjusting support/resistance arc.
Volatility Compression and Expansion : Flattening curvature indicates volatility compression - a potential pre-breakout zone. Rapid re-steepening signals expansion and directional conviction.
Stop Placement Framework : The curved band can serve as a volatility-adjusted trailing stop. Because the curve reflects acceleration, it adapts naturally to market rhythm - widening during momentum surges and tightening during stagnation.
Technical Implementation Details
Curved Radius Engine : Parabolic acceleration algorithm that applies quadratic velocity based on bar count and radiusStrength.
Anchor Logic : Resets curvature at each trend change, establishing a new reference base for directional acceleration.
Smoothing Layer : SMA-based curve smoothing for noise reduction.
Outer Envelope : ATR-derived band offset visualizing volatility extension.
Directional Coloring : Candle and band coloration tied to current trend state.
Signal Engine : Built-in BUY/SELL markers and alert conditions for automation or script integration.
Optimal Application Parameters
Timeframe Guidance :
1-5 min (Scalping) : 0.08–0.12 radius strength, minimal smoothing for rapid responsiveness.
15 min : 0.12–0.15 radius strength for intraday trends.
1H : 0.15–0.18 radius strength for structured short-term swing setups.
4H : 0.18–0.22 radius strength for macro-trend shaping.
Daily : 0.20–0.25 radius strength for broad directional curves.
Weekly : 0.25–0.30 radius strength for smooth macro-level cycles.
The suggested radius strength ranges provide general structural guidance. Optimal values may vary across assets and volatility regimes, and should be refined through empirical testing to account for instrument-specific behavior and prevailing market conditions.
Asset Guidance :
Cryptocurrency : Higher radius and multiplier values to stabilize high-volatility environments.
Forex : Midrange settings (0.12-0.18) for clean curvature transitions.
Equities : Balanced curvature for trending sectors or momentum rotation setups.
Indices/Futures : Moderate radius values (0.15-0.22) to capture cyclical macro swings.
Performance Characteristics
High Effectiveness :
Trending environments with directional expansion.
Markets exhibiting clean momentum arcs and low structural noise.
Reduced Effectiveness :
Range-bound or low-volatility conditions with repeated false flips.
Ultra-short-term timeframes (<1m) where curvature acceleration overshoots.
Integration Guidelines
Confluence Framework : Combine with structure tools (order blocks, BOS, liquidity zones) for entry validation.
Risk Management : Trail stops along the curved band rather than fixed points to align with adaptive market geometry.
Multi-Timeframe Confirmation : Use higher timeframe curvature as a trend filter and lower timeframe curvature for execution timing.
Curve Compression Awareness : Treat flattening arcs as potential exhaustion zones - ideal for scaling out or reducing exposure.
Disclaimer
The Curved Radius Supertrend is a geometric trend model designed for professional traders and analysts. It is not a predictive system or a guaranteed profit method. Its performance depends on correct parameter calibration and sound risk management. BOSWaves recommends using it as part of a comprehensive analytical framework, incorporating volume, liquidity, and structural context to validate directional signals.
[boitl] Trendfilter🧭 Trend Filter – Curve View (1D / 1H + M15 Check)
A multi-timeframe trend filter that blends daily, hourly, and 15-minute data into a smooth, color-coded curve displayed in a separate panel.
It visualizes both trend direction and strength while accounting for overextension, providing a reliable “context indicator” for entries and filters.
🔍 Concept
The indicator evaluates three timeframes:
1D (Daily) → SMA200 for long-term trend bias
1H (Hourly) → EMA50 for medium-term confirmation
15M (Intraday) → EMA20 + ATR to detect overextension or mean reversion zones
It computes a continuous trend score between −1 and +1:
+1 → Strong bullish alignment (D1 & H1 both up)
−1 → Strong bearish alignment (D1 & H1 both down)
≈ 0 → Neutral, conflicting, or overextended conditions
The score is smoothed and normalized for a clean visual curve —
green for bullish, red for bearish, with dynamic transparency based on strength.
⚙️ Logic Overview
Timeframe Indicator Purpose
1D SMA200 Long-term trend direction
1H EMA50 Medium-term confirmation
15M EMA20 + ATR Overextension control
Alignment between D1 and H1 defines clear trend bias
Conflicts between them reduce the trend score
M15 overextension (price far from EMA20) softens the signal further
The result is a responsive trend-strength oscillator, ideal for multi-timeframe setups.
🧩 Use Cases
As a trend filter for strategies (e.g. allow entries only if score > 0.3 or < −0.3)
As a visual confirmation of higher-timeframe direction
To avoid trades during conflict or exhaustion
💡 Visualization
Single curve (area plot):
Green = bullish bias
Red = bearish bias
Transparency increases with weaker trend
Background colors:
🟠 Orange → D1/H1 conflict
🔴 Light red → M15 overextension active
Optional: binary alignment line (+1 / 0 / −1) for simplified display
⚙️ Parameters
Proximity to EMA20 (M15) = X×ATR → defines “near” condition
Overextension threshold = X×ATR → sets exhaustion boundary
EMA smoothing → reduces noise for a smoother score
Toggle overextension impact on/off
AlphaFlow - Trend DetectorOVERVIEW
AlphaFlow identifies and tracks large volume moves by combining volume analysis, price impact measurement, and conviction scoring to separate significant institutional moves from normal trading activity. Rather than just flagging high volume, this indicator evaluates whether large trades actually moved the market and assigns conviction levels based on multiple confirmation factors.
WHAT MAKES THIS ORIGINAL
This is not simply a volume indicator or volume-weighted price tracker. The originality lies in the multi-factor conviction scoring system that evaluates whether large volume moves represent genuine institutional conviction or just noise.
Key Differentiators:
- Combines volume ratio AND price impact (volume alone doesn't mean conviction)
- Conviction scoring system that weighs trend alignment, follow-through, and volume persistence
- Cumulative flow tracking that shows persistent directional pressure over time
- Market regime detection (bullish/bearish/sideways) based on flow dynamics
- Tiered signal system (EXTREME/HIGH/MEDIUM conviction) rather than binary signals
This approach solves the problem of volume spikes that don't lead to meaningful price action, or price moves on low volume that don't persist.
HOW IT WORKS
1. Whale Detection Engine:
Volume Qualification: Compares current volume to a rolling average (default 50 bars). Whale activity requires volume to be at least 1.5x the average (adjustable).
Price Impact Requirement: Volume alone isn't enough. The bar must also show significant price movement (default 0.1% minimum). This filters out high-volume consolidation where no one is actually committed to direction.
Direction Identification: Bullish whale = close > open on high volume. Bearish whale = close < open on high volume.
2. Conviction Scoring System:
The indicator doesn't just flag whale activity - it evaluates conviction through multiple factors:
Base Conviction: Calculated from (volume_ratio × price_impact) / 10
This gives higher scores to moves with both exceptional volume AND large price swings.
Trend Alignment Bonus (1.5x multiplier): Whale moves aligned with the 20-period EMA trend receive higher conviction scores. Institutional money tends to accumulate with the trend, not against it.
Follow-Through Bonus (1.3x multiplier): After whale activity, does price continue in that direction over the next bars (default 3)? Genuine conviction shows persistence.
Volume Persistence (1.2x multiplier): Is elevated volume sustained over multiple bars, or is it a one-time spike? The 3-bar average volume ratio above 1.5x indicates sustained interest.
Conviction Levels:
- EXTREME: Score > 15 (large whale emoji labels, highest confidence)
- HIGH: Score > 8 (triangle signals, strong confidence)
- MEDIUM: Score > 3 (small triangles, moderate confidence)
- LOW: Score < 3 (not plotted to reduce noise)
3. Cumulative Flow Analysis:
Rather than treating each whale move in isolation, the indicator tracks cumulative flow using an EMA of whale activity. This reveals persistent directional pressure.
Flow Calculation: Each whale bar contributes (whale_strength × direction) to the flow. Strength is volume_ratio × price_impact_percent.
Flow Momentum: Rate of change in the cumulative flow (5-bar change)
Flow Acceleration: Second derivative (3-bar change of momentum)
These metrics reveal whether whale activity is accelerating, decelerating, or reversing.
4. Market Regime Detection:
Bullish Regime: Cumulative flow > 2 AND momentum positive
Bearish Regime: Cumulative flow < -2 AND momentum negative
Sideways Regime: Neither condition met
The background color reflects the current regime, helping traders understand the broader context.
5. Flow Strength Meter:
The main plot normalizes cumulative flow to a -100 to +100 scale based on the 100-bar range. This provides a consistent visual reference regardless of the asset or timeframe.
Extreme levels at ±50 indicate particularly strong directional flow where reversals or consolidation become more likely.
HOW TO USE IT
Settings Configuration:
Whale Detection Section:
- Volume Average Period (default 50): Shorter periods make detection more sensitive to recent volume changes. Longer periods require more exceptional volume to trigger.
- Whale Volume Multiplier (default 1.5): How much above average volume must be to qualify. Lower = more signals. Higher = only extreme moves.
- Minimum Price Impact (default 0.1%): Filters out high-volume bars that didn't actually move price. Adjust based on asset volatility.
Trend Analysis:
- Trend Strength Period (default 20): EMA period for trend alignment bonus
- Confirmation Bars (default 3): How many bars to check for follow-through
Visual Settings:
- Flow Strength Meter: Main plot showing normalized cumulative flow
- Conviction Labels: Detailed labels showing volume ratio and price impact on extreme/high conviction whales
- Trend Background: Color-coded regime indication
Signal Interpretation:
EXTREME Conviction (Whale Emoji Labels):
These are the highest confidence signals. Large volume with significant price impact, aligned with trend, showing follow-through. These often mark the beginning or continuation of strong moves.
HIGH Conviction (Large Triangles):
Strong signals meeting most criteria. Good for main entries or adding to positions.
MEDIUM Conviction (Small Triangles):
Whale activity present but with fewer confirmation factors. Use for partial positions or require additional confirmation.
Flow Strength Meter:
- Above zero and rising: Bullish flow building
- Below zero and falling: Bearish flow building
- Approaching ±50: Extreme readings, watch for exhaustion
- Crossing zero: Flow regime change
Dashboard Information:
The top-right table shows:
- Current regime (bullish/bearish/sideways)
- Flow strength value
- Last whale direction
- Conviction level of last whale
- Current volume ratio
- Flow momentum direction
- Indicator status
Trading Strategies:
Trend Following: Take EXTREME and HIGH conviction signals aligned with the flow meter direction. Enter when flow is positive and rising for bullish whales, negative and falling for bearish whales.
Regime-Based: Only trade in bullish/bearish regimes (colored backgrounds). Avoid trading in sideways regimes where whale moves tend to reverse quickly.
Flow Reversals: When flow meter crosses zero with EXTREME conviction whale in the new direction, this often marks regime changes.
Exhaustion Plays: When flow reaches ±50 extreme levels, watch for EXTREME conviction whales in the opposite direction as potential reversal signals.
TECHNICAL DETAILS
Volume Ratio = Current Volume / SMA(Volume, Period)
Price Impact % = ABS(Close - Open) / Open × 100
Whale Detected = (Volume Ratio >= Multiplier) AND (Price Impact >= Minimum)
Whale Direction = Close > Open ? 1 : -1
Base Conviction = (Volume Ratio × Price Impact %) / 10
Trend Alignment = Whale Direction == Trend Direction ? 1.5 : 1.0
Follow-Through = Price continues whale direction over N bars ? 1.3 : 1.0
Volume Persistence = SMA(Volume Ratio, 3) > 1.5 ? 1.2 : 1.0
Final Conviction = Base × Trend Alignment × Follow-Through × Volume Persistence
Whale Flow = Whale Detected ? (Volume Ratio × Price Impact × Direction) : 0
Cumulative Flow = EMA(Whale Flow, 20)
Flow Momentum = Change(Cumulative Flow, 5)
Flow Acceleration = Change(Momentum, 3)
Normalized Flow Strength = (Cumulative Flow / Highest(ABS(Cumulative Flow), 100)) × 100
WHAT THIS SOLVES
Common Volume Indicator Problems:
- Volume spikes that don't move price (consolidation noise)
- Price moves on low volume that quickly reverse
- No differentiation between strong and weak volume signals
- Treating all high-volume bars equally regardless of context
- No measure of whether volume represents conviction or panic
Whale Flow Solutions:
- Requires both volume AND price impact for signals
- Conviction scoring separates strong moves from weak ones
- Cumulative flow shows persistent pressure vs isolated spikes
- Trend alignment and follow-through filter low-quality signals
- Tiered system lets traders choose their confidence threshold
LIMITATIONS
- Cannot identify individual whales or attribute volume to specific entities
- High volume can come from many sources (whales, retail panic, algo activity)
- Works best on liquid assets with consistent volume patterns
- Less reliable on low-volume assets or during market closures
- Conviction scoring thresholds may need adjustment per asset/timeframe
- Does not predict future whale activity, only identifies it after bars close
- Flow can remain at extremes longer than expected during strong trends
- False signals can occur during news events or earnings
- Not a standalone trading system - requires risk management and other analysis
Best used in combination with price action, support/resistance, and broader market context.
EDUCATIONAL VALUE
For traders learning about:
- Volume analysis beyond simple volume indicators
- Multi-factor signal confirmation systems
- Market regime and flow concepts
- Conviction-based scoring methodologies
- Cumulative indicator design
- Normalized plotting for cross-asset comparison
- Pine Script table and dashboard creation
Not financial advice.
Simplified Percentile ClusteringSimplified Percentile Clustering (SPC) is a clustering system for trend regime analysis.
Instead of relying on heavy iterative algorithms such as k-means, SPC takes a deterministic approach: it uses percentiles and running averages to form cluster centers directly from the data, producing smooth, interpretable market state segmentation that updates live with every bar.
Most clustering algorithms are designed for offline datasets, they require recomputation, multiple iterations, and fixed sample sizes.
SPC borrows from both statistical normalization and distance-based clustering theory , but simplifies them. Percentiles ensure that cluster centers are resistant to outliers , while the running mean provides a stable mid-point reference.
Unlike iterative methods, SPC’s centers evolve smoothly with time, ideal for charts that must update in real time without sudden reclassification noise.
SPC provides a simple yet powerful clustering heuristic that:
Runs continuously in a charting environment,
Remains interpretable and reproducible,
And allows traders to see how close the current market state is to transitioning between regimes.
Clustering by Percentiles
Traditional clustering methods find centers through iteration. SPC defines them deterministically using three simple statistics within a moving window:
Lower percentile (p_low) → captures the lower basin of feature values.
Upper percentile (p_high) → captures the upper basin.
Mean (mid) → represents the central tendency.
From these, SPC computes stable “centers”:
// K = 2 → two regimes (e.g., bullish / bearish)
=
// K = 3 → adds a neutral zone
=
These centers move gradually with the market, forming live regime boundaries without ever needing convergence steps.
Two clusters capture directional bias; three clusters add a neutral ‘range’ state.
Multi-Feature Fusion
While SPC can cluster a single feature such as RSI, CCI, Fisher Transform, DMI, Z-Score, or the price-to-MA ratio (MAR), its real strength lies in feature fusion. Each feature adds a unique lens to the clustering system. By toggling features on or off, traders can test how each dimension contributes to the regime structure.
In “Clusters” mode, SPC measures how far the current bar is from each cluster center across all enabled features, averages these distances, and assigns the bar to the nearest combined center. This effectively creates a multi-dimensional regime map , where each feature contributes equally to defining the overall market state.
The fusion distance is computed as:
dist := (rsi_d * on_off(use_rsi) + cci_d * on_off(use_cci) + fis_d * on_off(use_fis) + dmi_d * on_off(use_dmi) + zsc_d * on_off(use_zsc) + mar_d * on_off(use_mar)) / (on_off(use_rsi) + on_off(use_cci) + on_off(use_fis) + on_off(use_dmi) + on_off(use_zsc) + on_off(use_mar))
Because each feature can be standardized (Z-Score), the distances remain comparable across different scales.
Fusion mode combines multiple standardized features into a single smooth regime signal.
Visualizing Proximity - The Transition Gradient
Most indicators show binary or discrete conditions (e.g., bullish/bearish). SPC goes further, it quantifies how close the current value is to flipping into the next cluster.
It measures the distances to the two nearest cluster centers and interpolates between them:
rel_pos = min_dist / (min_dist + second_min_dist)
real_clust = cluster_val + (second_val - cluster_val) * rel_pos
This real_clust output forms a continuous line that moves smoothly between clusters:
Near 0.0 → firmly within the current regime
Around 0.5 → balanced between clusters (transition zone)
Near 1.0 → about to flip into the next regime
Smooth interpolation reveals when the market is close to a regime change.
How to Tune the Parameters
SPC includes intuitive parameters to adapt sensitivity and stability:
K Clusters (2–3): Defines the number of regimes. K = 2 for trend/range distinction, K = 3 for trend/neutral transitions.
Lookback: Determines the number of past bars used for percentile and mean calculations. Higher = smoother, more stable clusters. Lower = faster reaction to new trends.
Lower / Upper Percentiles: Define what counts as “low” and “high” states. Adjust to widen or tighten cluster ranges.
Shorter lookbacks react quickly to shifts; longer lookbacks smooth the clusters.
Visual Interpretation
In “Clusters” mode, SPC plots:
A colored histogram for each cluster (red, orange, green depending on K)
Horizontal guide lines separating cluster levels
Smooth proximity transitions between states
Each bar’s color also changes based on its assigned cluster, allowing quick recognition of when the market transitions between regimes.
Cluster bands visualize regime structure and transitions at a glance.
Practical Applications
Identify market regimes (bullish, neutral, bearish) in real time
Detect early transition phases before a trend flip occurs
Fuse multiple indicators into a single consistent signal
Engineer interpretable features for machine-learning research
Build adaptive filters or hybrid signals based on cluster proximity
Final Notes
Simplified Percentile Clustering (SPC) provides a balance between mathematical rigor and visual intuition. It replaces complex iterative algorithms with a clear, deterministic logic that any trader can understand, and yet retains the multidimensional insight of a fusion-based clustering system.
Use SPC to study how different indicators align, how regimes evolve, and how transitions emerge in real time. It’s not about predicting; it’s about seeing the structure of the market unfold.
Disclaimer
This indicator is intended for educational and analytical use.
It does not generate buy or sell signals.
Historical regime transitions are not indicative of future performance.
Always validate insights with independent analysis before making trading decisions.
Momentum-Based Fair Value Gaps [BackQuant]Momentum-Based Fair Value Gaps
A precision tool that detects Fair Value Gaps and color-codes each zone by momentum, so you can quickly tell which imbalances matter, which are likely to fill, and which may power continuation.
What is a Fair Value Gap
A Fair Value Gap is a 3-candle price imbalance that forms when the middle candle expands fast enough that it leaves a void between candle 1 and candle 3.
Bullish FVG : low > high . This marks a bullish imbalance left beneath price.
Bearish FVG : high < low . This marks a bearish imbalance left above price.
These zones often act as magnets for mean reversion or as fuel for trend continuation when price respects the gap boundary and runs.
Why add momentum
Not all gaps are equal. This script measures momentum with RSI on your chosen source and paints each FVG with a momentum heatmap. Strong-momentum gaps are more likely to hold or propel continuation. Weak-momentum gaps are more likely to fill.
Core Features
Auto FVG Detection with size filters in percent of price.
Momentum Heatmap per gap using RSI with smoothing. Multiple palettes: Gradient, Discrete, Simple, and scientific schemes like Viridis, Plasma, Inferno, Magma, Cividis, Turbo, Jet, plus Red-Green and Blue-White-Red.
Bull and Bear Modes with independent toggles.
Extend Until Filled : keep drawing live to the right until price fully fills the gap.
Auto Remove Filled for a clean chart.
Optional Labels showing the smoothed RSI value stored at the gap’s birth.
RSI-based Filters : only accept bullish gaps when RSI is oversold and bearish gaps when RSI is overbought.
Performance Controls : cap how many FVGs to keep on chart.
Alerts : new bullish or bearish FVG, filled FVG, and extreme RSI FVGs.
How it works
Source for Momentum : choose Returns, Close, or Volume.
Returns computes percent change over a short lookback to focus on impulse quality.
RSI and Smoothing : RSI length and a small SMA smooth the signal to stabilize the color coding.
Gap Scan : each bar checks for a 3-candle bullish or bearish imbalance that also clears your minimum size filter in percent of price.
Heatmap Color : the gap is painted at creation with a color from your palette based on the smoothed RSI value, preserving the momentum signature that formed it.
Lifecycle : if Extend Unfilled is on, the zone projects forward until price fully trades through the far edge. If Auto Remove is on, a filled gap is deleted immediately.
How to use it
Scan for structure : turn on both bullish and bearish FVGs. Start with a moderate Min FVG Size percent to reduce noise. You will see stacked clusters in trends and scattered singletons in chop.
Read the colors : brighter or stronger palette values imply stronger momentum at gap formation. Weakly colored gaps are lower conviction.
Decide bias : bullish FVGs below price suggest demand footprints. Bearish FVGs above price suggest supply footprints. Use the heatmap and RSI value to rank importance.
Choose your playbook :
Mean reversion : target partial or full fills of opposing FVGs that were created on weak momentum or that sit against higher timeframe context.
Trend continuation : look for price to respect the near edge of a strong-momentum FVG, then break away in the direction of the original impulse.
Manage risk : in continuation ideas, invalidation often sits beyond the opposite edge of the active FVG. In reversion ideas, invalidation sits beyond the gap that should attract price.
Two trade playbooks
Continuation - Buy the hold of a bullish FVG
Context uptrend.
A bullish FVG prints with strong RSI color.
Price revisits the top of the gap, holds, and rotates up. Enter on hold or first higher low inside or just above the gap.
Invalidation: below the gap bottom. Targets: prior swing, measured move, or next LV area.
Reversion - Fade a weak bearish FVG toward fill
Context range or fading trend.
A bearish FVG prints with weak RSI color near a completed move.
Price fails to accelerate lower and rotates back into the gap.
Enter toward mid-gap with confirmation.
Invalidation: above gap top. Target: opposite edge for a full fill, or the gap midline for partials.
Key settings
Max FVG Display : memory cap to keep charts fast. Try 30 to 60 on intraday.
Min FVG Size % : sets a quality floor. Start near 0.20 to 0.50 on liquid markets.
RSI Length and Smooth : 14 and 3 are balanced. Increase length for higher timeframe stability.
RSI Source :
Returns : most sensitive to true momentum bursts
Close : traditional.
Volume : uses raw volume impulses to judge footprint strength.
Filter by RSI Extremes : tighten rules so only the most stretched gaps print as signals.
Heatmap Style and Palette : pick a palette with good contrast for your background. Gradient for continuous feel, Discrete for quick zoning, Simple for binary, Palette for scientific schemes.
Extend Unfilled - Auto Remove : choose live projection and cleanup behavior to match your workflow.
Reading the chart
Bullish zones sit beneath price. Respect and hold of the upper boundary suggests demand. Strong green or warm palette tones indicate impulse quality.
Bearish zones sit above price. Respect and hold of the lower boundary suggests supply. Strong red or cool palette tones indicate impulse quality.
Stacking : multiple same-direction gaps stacked in a trend create ladders. Ladders often act as stepping stones for continuation.
Overlapping : opposing gaps overlapping in a small region usually mark a battle zone. Expect chop until one side is absorbed.
Workflow tips
Map higher timeframe trend first. Use lower timeframe FVGs for entries aligned with the higher timeframe bias.
Increase Min FVG Size percent and RSI length for noisy symbols.
Use labels when learning to correlate the RSI numbers with your palette colors.
Combine with VWAP or moving averages for confluence at FVG edges.
If you see repeated fills and refills of the same zone, treat that area as fair value and avoid chasing.
Alerts included
New Bullish FVG
New Bearish FVG
Bullish FVG Filled
Bearish FVG Filled
Extreme Oversold FVG - bullish
Extreme Overbought FVG - bearish
Practical defaults
RSI Length 14, Smooth 3, Source Returns.
Min FVG Size 0.25 percent on liquid majors.
Heatmap Style Gradient, Palette Viridis or Turbo for contrast.
Extend Unfilled on, Auto Remove on for a clean live map.
Notes
This tool does not predict the future. It maps imbalances and momentum so you can frame trades with clearer context, cleaner invalidation, and better ranking of which gaps matter. Use it with risk control and in combination with your broader process.
SEVENX Free|SuperFundedSEVENX — Modular Multi-Signal Scanner (SuperFunded)
What it is
SEVENX combines seven classic signals—MACD, OBV, RSI, Stochastics, CCI, Momentum, and an optional ATR volatility filter—into a modular gate. You can toggle each condition on/off, and a BUY/SELL arrow prints only when all enabled conditions agree. Text labels are optional.
Why this is not a simple mashup
・Most “combo” scripts just overlay indicators. SEVENX is a strict consensus engine:
・Each condition is binary and user-switchable.
・The final signal is the logical AND of all enabled checks (no hidden weights).
・Signals fire only on confirmed events (e.g., RSI crossing a level, Stoch K/D cross), which makes entries rule-driven and reproducible.
This yields a transparent, vendor-grade workflow where traders can start simple (2–3 gates) and tighten selectivity by enabling more gates.
How it works (concise)
・MACD: macd_line > signal_line (buy) / < (sell).
・OBV trend: OBV > OBV_MA (buy) / < (sell).
・RSI bounce/drop: crossover(RSI, Oversold) (buy) / crossunder(RSI, Overbought) (sell).
・Stoch cross: %K crosses above %D (buy) / below (sell).
・CCI rebound/pullback: crossover(CCI, -Level) (buy) / crossunder(CCI, +Level) (sell).
・Momentum: Momentum > 0 (buy) / < 0 (sell).
・ATR filter (optional): ATR > ATR_MA must also be true (both sides).
・Final signal: AND of all enabled conditions. If you enable none on a side, that side will not print.
Parameters (UI mapping)
Buy Signal (group: “— Buy Signal —”)
・MACD Golden Cross / OBV Uptrend / RSI Bounce from Oversold / Stochastic Golden Cross / CCI Rebound from Oversold / Momentum > 0 / ATR Volatility Filter (on/off)
Sell Signal (group: “— Sell Signal —”)
・MACD Dead Cross / OBV Downtrend / RSI Drop from Overbought / Stochastic Dead Cross / CCI Pullback from Overbought / Momentum < 0 / ATR Volatility Filter (on/off)
Indicator Settings
・MACD: Fast/Slow/Signal lengths.
・RSI: Length, Overbought/Oversold levels.
・Stochastics: %K length, %D smoothing, overall smoothing.
・CCI: Length, Level (±Level used).
・Momentum: Length.
・OBV: MA length for trend baseline.
・ATR: ATR length, ATR MA length (for the filter).
Display
・Show Text (BUY/SELL text on the markers), Buy/Sell Text Colors.
Practical usage
・Start simple: Enable 2 conditions (e.g., MACD + RSI). If signals are too frequent, add OBV or Momentum; if still frequent, enable ATR filter.
・Mean-reversion vs trend:
・For trend-following, prefer MACD/OBV/Momentum gates.
・For reversal bounces, add RSI/CCI gates and keep Stoch for timing.
・Tuning sensitivity:
・Raise RSI Oversold/Overbought thresholds to make bounces rarer.
・Increase ATR_MA length to smooth the volatility baseline.
・Risk first: Plan SL/TP independently (e.g., structure levels or R-multiples). SEVENX focuses on entry qualification, not exits.
Repainting & confirmation
Signals depend on cross events and are best treated on bar close. Intrabar flips can occur before a bar closes; for strict rules, confirm on closed bars in your strategy.
Disclaimer
No indicator can guarantee outcomes. News, liquidity, and spread conditions can invalidate signals. Trade responsibly and manage risk.
This indicator is being released on a trial basis and may be discontinued at our discretion.
SEVENX — モジュラー型マルチシグナル・スキャナー（日本語）
概要
SEVENXは、MACD / OBV / RSI / ストキャス / CCI / モメンタム / ATRフィルターの7条件を個別オン・オフで制御し、有効化した条件がすべて満たされたときだけBUY/SELL矢印を表示する、合意（AND）型シグナルインジです。テキスト表示も任意。
独自性・新規性
・各条件はブラックボックスではなく明示的なブール判定で、最終シグナルは有効化した条件のAND。
・RSIのレベルクロスやStochのK/Dクロスなど、確定イベントで判定するため、再現性の高いルール運用が可能。少数条件から始めて、必要に応じて段階的に厳格化できます。
動作要点
・MACD：線がシグナル上/下。
・OBV：OBVがOBVのMAより上/下。
・RSI：RSIがOSを上抜け（買い）/OBを下抜け（売り）。
・Stoch：%Kが%Dを上抜け/下抜け。
・CCI：CCIが**−Levelを上抜け**（買い）/+Levelを下抜け（売り）。
・Momentum：0より上/下。
・ATRフィルター（任意）：ATR > ATR_MA を満たすこと（買い/売り共通）。
・最終サイン：有効化した条件のAND。そのサイドで1つも有効化していなければサインは出ません。
実践ヒント
・まずは2条件（例：MACD＋RSI）でテスト → 多すぎるならOBV/MomentumやATRフィルターを追加。
・トレンド重視：MACD/OBV/Momentumを主軸に。
・押し目・戻り目狙い：RSI/CCIを追加、Stochでタイミング調整。
・感度調整：RSIのOB/OSを広げる、ATR_MAを長くする等で厳しめに。
・出口は別設計：SL/TPは価格帯やR倍数などで管理を。
再描画と確定
確定足基準で判断すると安定します。足確定前はクロスが行き来することがあります。
免責
シグナルの機能は保証されません。イベントや流動性で無効化する場合があります。資金管理のうえ自己責任でご利用ください。
このインジケーターは試験公開のため、弊社の裁量で公開を停止する場合があります。
Universal Regime Alpha Thermocline StrategyCurrents settings adapted for BTCUSD Daily timeframe
This description is written to comply with TradingView House Rules and Script Publishing Rules. It is self contained, in English first, free of advertising, and explains originality, method, use, defaults, and limitations. No external links are included. Nothing here is investment advice.
0. Publication mode and rationale
This script is published as Protected . Anyone can add and test it from the Public Library, yet the source code is not visible.
Why Protected
The engine combines three independent lenses into one regime score and then uses an adaptive centering layer and a thermo risk unit that share a common AAR measure. The exact mapping and interactions are the result of original research and extensive validation. Keeping the implementation protected preserves that work and avoids low effort clones that would fragment feedback and confuse users.
Protection supports a single maintained build for users. It reduces accidental misuse of internal functions outside their intended context which might lead to misleading results.
1. What the strategy does in one paragraph
Universal Regime Alpha Thermocline builds a single number between zero and one that answers a practical question for any market and timeframe. How aligned is current price action with a persistent directional regime right now. To answer this the script fuses three views of the tape. Directional entropy of up versus down closes to measure unanimity.
Convexity drift that rewards true geometric compounding and penalizes drag that comes from chop where arithmetic pace is high but growth is poor.
Tail imbalance that counts decisive bursts in one direction relative to typical bar amplitude. The three channels are blended, optionally confirmed by a higher timeframe, and then adaptively centered to remove local bias. Entries fire when the score clears an entry gate. Exits occur when the score mean reverts below an exit gate or when thermo stops remove risk. Position size can scale with the certainty of the signal.
2. Why it is original and useful
It mixes orthogonal evidence instead of leaning on a single family of tools. Many regime filters depend on moving averages or volatility compression. Here we add an information view from entropy, a growth view from geometric drift, and a structural view from tail imbalance.
The drift channel separates growth from speed. Arithmetic pace can look strong in whipsaw, yet geometric growth stays weak. The engine measures both and subtracts drag so that only sequences with compounding quality rise.
Tail counting is anchored to AAR which is the average absolute return of bars in the window. This makes the threshold self scaling and portable across symbols and timeframes without hand tuned constants.
Adaptive centering prevents the score from living above or below neutral for long stretches on assets with strong skew. It recovers neutrality while still allowing persistent regimes to dominate once evidence accumulates.
The same AAR unit used in the signal also sets stop distance and trail distance. Signal and risk speak the same language which makes the method portable and easier to reason about.
3. Plain language overview of the math
Log returns . The base series is r equal to the natural log of close divided by the previous close. Log return allows clean aggregation and makes growth comparisons natural.
Directional entropy . Inside the lookback we compute the proportion p of bars where r is positive. Binary entropy of p is high when the mix of up and down closes is balanced and low when one direction dominates. Intensity is one minus entropy. Directional sign is two times p minus one. The trend channel is zero point five plus one half times sign times intensity. It lives between zero and one and grows stronger as unanimity increases.
Convexity drift with drag . Arithmetic mean of r measures pace. Geometric mean of the price ratio over the window measures compounding. Drag is the positive part of arithmetic minus geometric. Drift raw equals geometric minus drag multiplier times drag. We then map drift through an arctangent normalizer scaled by AAR and a nonlinearity parameter so the result is stable and remains between zero and one.
Tail imbalance . AAR equals the average of the absolute value of r in the window. We count up tails where r is greater than aar_mult times AAR and down tails where r is less than minus aar_mult times AAR. The imbalance is their difference over their total, mapped to zero to one. This detects directional impulse flow.
Fusion and centering . A weighted average of the three channels yields the raw score. If a higher timeframe is requested, the same function is executed on that timeframe with lookahead off and blended with a weight. Finally we subtract a fraction of the rolling mean of the score to recover neutrality. The result is clipped to the zero to one band.
4. Entries, exits, and position sizing
Enter long when score is strictly greater than the entry gate. Enter short when score is strictly less than one minus the entry gate unless direction is restricted in inputs.
Exit a long when score falls below the exit gate. Exit a short when score rises above one minus the exit gate.
Thermo stops are expressed in AAR units. A long uses the maximum of an initial stop sized by the entry price and AAR and a trail stop that references the running high since entry with a separate multiple. Shorts mirror this with the running low. If the trail is disabled the initial stop is active.
Cooldown is a simple bar counter that begins when the position returns to flat. It prevents immediate re entry in churn.
Dynamic position size is optional. When enabled the order percent of equity scales between a floor and a cap as the score rises above the gate for longs or below the symmetric gate for shorts.
5. Inputs quick guide with recommended ranges
Every input has a tooltip in the script. The same guidance appears here for fast reading.
Core window . Shared lookback for entropy, drift, and tails. Start near 80 on daily charts. Try 60 to 120 on intraday and 80 to 200 for swing.
Entry threshold . Typical range 0.55 to 0.65 for trend following. Faster entries 0.50 to 0.55.
Exit threshold . Typical range 0.35 to 0.50. Lower holds longer yet gives back more.
Weight directional entropy . Starting value 0.40. Raise on markets with clean persistence.
Weight convexity drift . Starting value 0.40. Raise when compounding quality is critical.
Weight tail imbalance . Starting value 0.20. Raise on breakout prone markets.
Tail threshold vs AAR . Typical range 1.0 to 1.5 to count decisive bursts.
Drag penalty . Typical range 0.25 to 0.75. Higher punishes chop more.
Nonlinearity scale . Typical range 0.8 to 2.0. Larger compresses extremes.
AAR floor in percent . Typical range 0.0005 to 0.002 for liquid instruments. This stabilizes the math during quiet regimes.
Adaptive centering . Keep on for most symbols. Center strength 0.40 to 0.70.
Confirm timeframe optional . Leave empty to disable. If used, try a multiple between three and five of the chart timeframe with a blend weight near 0.20.
Dynamic position size . Enable if you want size to reflect certainty. Floor and cap define the percent of equity band. A practical band for many accounts is 0.5 to 2.
Cooldown bars after exit . Start at 3 on daily or slightly higher on shorter charts.
Thermo stop multiple . Start between 1.5 and 3.0 on daily. Adjust to your tolerance and symbol behavior.
Thermo trailing stop and Trail multiple . Trail on locks gains earlier. A trail multiple near 1.0 to 2.0 is common. You can keep trail off and let the exit gate handle exits.
Background heat opacity . Cosmetic. Set to taste. Zero disables it.
6. Properties used on the published chart
The example publication uses BTCUSD on the daily timeframe. The following Properties and inputs are used so everyone can reproduce the same results.
Initial capital 100000
Base currency USD
Order size 2 percent of equity coming from our risk management inputs.
Pyramiding 0
Commission 0.05 percent
Slippage 10 ticks in the publication for clarity. Users should introduce slippage in their own research.
Recalculate after order is filled off. On every tick off.
Using bar magnifier on. On bar close on.
Risk inputs on the published chart. Dynamic position size on. Size floor percent 2. Size cap percent 2. Cooldown bars after exit 3. Thermo stop multiple 2.5. Thermo trailing stop off. Trail multiple 1.
7. Visual elements and alerts
The score is painted as a subtle dot rail near the bottom. A background heat map runs from red to green to convey regime strength at a glance. A compact HUD at the top right shows current score, the three component channels, the active AAR, and the remaining cooldown. Four alerts are included. Long Setup and Short Setup on entry gates. Exit Long by Score and Exit Short by Score on exit gates. You can disable trading and use alerts only if you want the score as a risk switch inside a discretionary plan.
8. How to reproduce the example
Open a BTCUSD daily chart with regular candles.
Add the strategy and load the defaults that match the values above.
Set Properties as listed in section 6.(they are set by default) Confirm that bar magnifier is on and process on bar close is on.
Run the Strategy Tester. Confirm that the trade count is reasonable for the sample. If the count is too low, slightly lower the entry threshold or extend history. If the count is excessively high, raise the threshold or add a small cooldown.
9. Practical tuning recipes
Trend following focus . Raise the entry threshold toward 0.60. Raise the trend weight to 0.50 and reduce tail weight to 0.15. Keep drift near 0.35 to retain the growth filter. Consider leaving the trail off and let the exit threshold manage positions.
Breakout focus . Keep entry near 0.55. Raise tail weight to 0.35. Keep aar_mult near 1.3 so only decisive bursts count. A modest cooldown near 5 can reduce immediate false flips after the first burst bar.
Chop defense . Raise drag multiplier to 0.70. Raise exit threshold toward 0.48 to recycle capital earlier. Consider a higher cooldown, for example 8 to 12 on intraday.
Higher timeframe blend . On a daily chart try a weekly confirm with a blend near 0.20. On a five minute chart try a fifteen minute confirm. This moderates transitions.
Sizing discipline . If you want constant position size, set floor equal to cap. If you want certainty scaling, set a band like 0.5 to 2 and monitor drawdown behavior before widening it.
10. Strengths and limitations
Strengths
Self scaling unit through AAR makes the tool portable across markets and timeframes.
Blends evidence that target different failure modes. Unanimity, growth quality, and impulse flow rarely agree by chance which raises confidence when they align.
Adaptive centering reduces structural bias at the score level which helps during regime flips.
Limitations
In very quiet regimes AAR becomes small even with a floor. If your symbol is thin or gap prone, raise the floor a little to keep stops and drift mapping stable.
Adaptive centering can delay early breakout acceptance. If you miss starts, lower center strength or temporarily disable centering while you evaluate.
Tail counting uses a fixed multiple of AAR. If a market alternates between very calm and very violent weeks, a single aar_mult may not capture both extremes. Sweep this parameter in research.
The engine reacts to realized structure. It does not anticipate scheduled news or liquidity shocks. Use event awareness if you trade around releases.
11. Realism and responsible publication
No promises or projections of performance are made. Past results never guarantee future outcomes.
Commission is set to 0.05 percent per round which is realistic for many crypto venues. Adjust to your own broker or exchange.
Slippage is set at 10 in the publication . Introduce slippage in your own tests or use a percent model.
Position size should respect sustainable risk envelopes. Risking more than five to ten percent per trade is rarely viable. The example uses a fixed two percent position size.
Security calls use lookahead off. Standard candles only. Non standard chart types like Heikin Ashi or Renko are not supported for strategies that submit orders.
12. Suggested research workflow
Begin with the balanced defaults. Confirm that the trade count is sensible for your timeframe and symbol. As a rough guide, aim for at least one hundred trades across a wide sample for statistical comfort. If your timeframe cannot produce that count, complement with multiple symbols or run longer history.
Sweep entry and exit thresholds on a small grid and observe stability. Stability across windows matters more than the single best value.
Try one higher timeframe blend with a modest weight. Large weights can drown the signal.
Vary aar_mult and drag_mult together. This tunes the aggression of breakouts versus defense in chop.
Evaluate whether dynamic size improves risk adjusted results for your style. If not, set floor equal to cap for constancy.
Walk forward through disjoint segments and inspect results by regime. Bootstrapping or segmented evaluation can reveal sensitivity to specific periods.
13. How to read the HUD and heat map
The HUD presents a compact view. Score is the current fused value. Trend is the directional entropy channel. Drift is the compounding quality channel. Tail is the burst flow channel. AAR is the current unit that scales stops and the drift map. CD is the cooldown counter. The background heat is a visual aid only. It can be disabled in inputs. Green zones near the upper band show alignment among the channels. Muted colors near the mid band show uncertainty.
14. Frequently asked questions
Can I use this as a pure indicator . Yes. Disable entries by restricting direction to one side you will not trade and use the alerts as a regime switch.
Will it work on intraday charts . Yes. The AAR unit scales with bar size. You will likely reduce the core window and increase cooldown slightly.
Should I enable the adaptive trail . If you wish to lock gains sooner and accept more exits, enable it. If you prefer to let the exit gate do the heavy lifting, keep it off.
Why do I sometimes see a green background without a position . Heat expresses the score. A position also depends on threshold comparisons, direction mode, and cooldown.
Why is Order size set to one hundred percent if dynamic size is on . The script passes an explicit quantity percent on each entry. That explicit quantity overrides the property. The property is kept at one hundred percent to avoid confusion when users later disable dynamic sizing.
Can I combine this with other tools on my chart . You can, yet for publication the chart is kept clean so users and moderators can see the output clearly. In your private workspace feel free to add other context.
15. Concepts glossary
AAR . Average absolute return across the lookback. Serves as a unit for tails, drift scaling, and stops.
Directional entropy . A measure of uncertainty of up versus down closes. Low entropy paired with a directional sign signals unanimity.
Geometric mean growth . Rate that preserves the effect of compounding over many bars.
Drag . The positive difference between arithmetic pace and geometric growth. Larger drag often signals churn that looks active but fails to compound.
Thermo stops . Stops expressed in the same AAR unit as the signal. They adapt with volatility and keep risk and signal on a common scale.
Adaptive centering . A bias correction that recenters the fused score around neutral so the meter does not drift due to persistent skew.
16. Educational notice and risk statement
Markets involve risk. This publication is for education and research. It does not provide financial advice and it is not a recommendation to buy or sell any instrument. Use realistic costs. Validate ideas with out of sample testing and with conservative position sizing. Past performance never guarantees future results.
17. Final notes for readers and moderators
The goal of this strategy is clarity and portability. Clarity comes from a single score that reflects three independent features of the tape. Portability comes from self scaling units that respect structure across assets and timeframes. The publication keeps the chart clean, explains the math plainly, lists defaults and Properties used, and includes warnings where care is required. The code is protected so the implementation remains consistent for the community while the description remains complete enough for users to understand its purpose and for moderators to evaluate originality and usefulness. If you explore variants, keep them self contained, explain exactly what they contribute, publish in English first, and treat others with respect in the comments.
Load the strategy on BTCUSD daily with the defaults listed above and study how the score transitions across regimes. Then adjust one lever at a time. Observe how the trend channel, the drift channel, and the tail channel interact during starts, pauses, and reversals. Use the alerts as a risk switch inside your own process or let the built in entries and exits run if you prefer an automated study. The intent is not to promise outcomes. The intent is to give you a robust meter for regime strength that travels well across markets and helps you structure decisions with more confidence.
Thank you for your time to read all of this
