Advanced Triple Strategy ScalperHere are the three scalping strategies presented in the video "3 Scalping Strategies That Work Every Day (Backtested & Proven)" by Asia Forex Mentor – Ezekiel Chew:
### Scalper’s Trend Filter (Triple EMA)
This strategy uses three EMAs (25, 50, 100) on the 5-minute chart to filter high-probability trades aligned with momentum .
- Only trade when all three EMAs are angled in the same direction and clearly separated (no crossing or tangling) .
- Enter when price pulls back toward the 25 or 50 EMA and then bounces back toward the 25 EMA, but do not enter if price closes below the 100 EMA .
- Set stop-loss just below the 50 EMA or swing low and aim for a risk-to-reward ratio of 1:1.5 .
### Flip Zone Trap (Reversal Catching)
This method identifies precise reversal moments where market structure shifts from weakness to strength .
- Use the 15-min chart to locate key support or resistance zones where price previously reacted .
- Wait for price to stop making lower lows and begin making higher highs (or vice versa for shorts); confirm with a trendline break AND follow-through (higher lows & highs within 5-7 candles) .
- Use confirmation candles (bullish engulfing, pin bar rejection) at the zone before entry .
### Liquidity Shift Trigger (Smart Money Trap)
This system leverages institutional stop hunts and liquidity sweeps at key zones for sniper entries .
- Start with a 15-min chart to identify structure breaks and points of interest (order blocks, flip zones, demand zones) .
- Drop to 1-min chart and wait for price to enter the refined zone and sweep liquidity (sharp wick/spike below/above key level) .
- Once liquidity is swept, wait for a clean structure shift (break of most recent internal high or low) within 5–6 candles—if confirmed, refine entry to the candle that caused the break and enter when price returns to that candle with a strong reaction .
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### Practical Application
- These strategies are systematic, rule-based, and designed to cut out fake moves, avoid early stop-outs, and align entries with momentum and institutional activity .
- Perfect for short timeframes and volatile pairs like XAUUSD, especially if paired with additional confirmation from other technical analysis tools .
All three strategies emphasize filtering noise, waiting for momentum/trend confirmation, and avoiding impulsive entries—key principles for consistent scalping success
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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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Enhanced Multi-Indicator StrategyEnhanced Multi-Indicator Strategy v7 is a trend-following confirmation tool that combines several classic indicators into one clear “voting system.”
Instead of relying on a single signal, this script counts how many indicators agree on a bullish or bearish bias and only fires when the majority lines up and the trend is strong.
It’s designed to help you:
Filter out low-quality signals
Avoid trading against the dominant trend
Get clean, one-time BUY/SELL markers instead of noisy spam signals
How it Works
The indicator evaluates up to 10 components on each bar:
Trend & Structure
Moving Average (MA 50)
EMA Fast (20) vs EMA Slow (50)
Momentum
RSI (14)
MACD (12/26/9)
KDJ
Volatility & Price Location
Bollinger Bands (20, 2)
VWAP
Volume
Volume vs Volume MA (20)
Trend Strength & Direction
ADX + DMI (trend strength and up/down direction)
Optional
Ichimoku Cloud (price vs Senkou A/B)
Each indicator votes bullish or bearish.
The script then:
Counts how many are bullish → bullish_count
Counts how many are bearish → bearish_count
Requires at least Min Indicators for Entry (user-defined) to agree in one direction
Requires ADX above a threshold (default 20) to confirm trend strength
(Optionally) forces entries to follow the DMI trend:
Longs only in uptrends
Shorts only in downtrends
Only when these conditions are met does the script consider a valid long zone or short zone.
Signals: 1x Fire + Cooldown
To keep the chart clean and prevent over-trading:
A BUY arrow is plotted only on the first bar when price enters a new bullish zone.
A SELL arrow is plotted only on the first bar when price enters a new bearish zone.
A cooldown (in bars) can be configured separately for BUY and SELL so the script will not fire again in the same direction too frequently.
Background colors (optional):
Green background = bullish zone (majority indicators bullish + strong trend)
Red background = bearish zone (majority indicators bearish + strong trend)
This makes it easy to see when the “environment” is favorable for longs or shorts, while the arrows highlight the first opportunity in each zone.
Inputs & Tuning
Key inputs:
Min Indicators for Entry – how many indicators must agree (e.g. 5–7 for stricter filtering)
Strict Trend Filter – if enabled, entries must follow DMI trend (recommended for trend-following)
Cooldown BUY / SELL – minimum number of bars before a new signal in the same direction
Toggles to enable/disable each component (MA, EMA, RSI, MACD, KDJ, BB, Volume, VWAP, ADX, Ichimoku)
General ideas:
Increase Min Indicators for Entry and/or cooldown to reduce the number of signals and focus on stronger trends.
Lower values will make the indicator more active, suitable for shorter-term trading or scalping.
Important Note
This is an indicator, not a full trading system:
It does not manage entries/exits, position sizing, or risk by itself.
Always combine it with your own risk management, stop-loss / take-profit rules, and higher-timeframe context.
Use it as a confirmation / regime tool:
Trade only in the direction of the active zone,
Take BUY signals during bullish regimes,
Take SELL signals during bearish regimes.
Multi EMA + Indicators + Mini-Dashboard + Reversals v6📘 Multi EMA + Indicators + Mini-Dashboard + Reversals v6
🧩 Overview
This indicator is a multi-EMA setup that combines trend, momentum, and reversal analysis in a single visual framework.
It integrates four exponential moving averages (EMAs), key oscillators (RSI, MACD, Stochastic, CCI), volatility filtering (ATR), and a dynamic mini-dashboard that summarizes all signals in real time.
Its purpose is to help traders visually confirm trend alignment, filter valid entries, and identify possible trend continuation or reversal points.
It can display buy/sell arrows, detect reversal candles, and issue alerts when trading conditions are met.
⚙️ Core Components
1. Moving Averages (EMA Setup)
EMA1 (fast) and EMA2 (medium) define the short-term trend and trigger bias.
When the price is above both EMAs → bullish bias.
When below → bearish bias.
EMA3 and EMA4 act as trend filters. Their slopes (up or down) confirm overall momentum and help validate signals.
Each EMA has customizable lengths, sources, and colors for up/down trends.
This “EMA stack” is the foundation of the setup — a structured trend-following framework that adapts to market speed and volatility.
2. Momentum and Confirmation Filters
Each indicator can be individually enabled or disabled for flexibility.
RSI: confirms direction (above/below 50).
MACD: detects momentum crossover (MACD > Signal for bullish confirmation).
Stochastic: identifies trend continuation (K > D for longs, K < D for shorts).
CCI: adds trend bias above/below a threshold.
ATR Filter: filters out small, low-volatility candles to reduce noise.
You can activate only the filters that fit your trading plan — for instance, trend traders often use RSI and MACD, while scalpers may rely on Stochastic and ATR.
3. Reversal Detection
The indicator includes an optional Reversal Section that independently detects potential turning points.
It combines multiple configurable criteria:
Candlestick patterns (Bullish Hammer, Shooting Star).
Large Candle filter — detects unusually large bars (relative to close).
Price-to-EMA distance — identifies overextended moves that might revert.
RSI Divergence — detects potential momentum shifts.
RSI Overbought/Oversold zones (70/30 by default).
Doji Candles — sign of indecision.
A bullish or bearish reversal signal appears when enough selected criteria are met.
All sub-modules can be toggled on/off individually, giving you full control over sensitivity.
4. Signal Logic
Buy and sell signals are triggered when EMA alignment and the chosen confirmations agree:
Buy Signal
→ Price above EMA1 & EMA2
→ Confirmations (RSI/MACD/Stoch/CCI/ATR) pass
→ Trend filters (EMA3/EMA4) point upward
Sell Signal
→ Price below EMA1 & EMA2
→ Confirmations align bearishly
→ Trend filters (EMA3/EMA4) slope downward
Reversal signals can appear independently, even against the current EMA trend, depending on your settings.
5. Visual Dashboard
A mini-dashboard appears near the chart showing:
Current trade bias (LONG / SHORT / NEUTRAL)
EMA3 and EMA4 trend directions (↑ / ↓)
Quick visual bars (🟩 / 🟥) for each filter: RSI, MACD, Stoch, ATR, CCI, EMA filters
Reversal criteria status (Doji, RSI divergence, candle size, etc.)
This panel gives you a compact overview of all indicator states at a glance.
The color of the panel changes dynamically — green for bullish, red for bearish, gray for neutral.
6. Alerts
Built-in alerts allow automation or notifications:
Buy Alert
Sell Alert
Reversal Buy
Reversal Sell
You can connect these alerts to TradingView notifications or external bots for semi-automated execution.
💡 How to Use
✅ Trend-Following Setup
Focus on trades in the direction of EMA1 & EMA2.
Confirm with EMA3 & EMA4 trending in the same direction.
Use RSI/MACD/Stoch filters to ensure momentum supports the trade.
Avoid entries when ATR filter indicates low volatility.
🔄 Reversal Setup
Enable the Reversal section for potential tops/bottoms.
Look for reversal buy signals near support zones or after strong downtrends.
Use RSI divergence or Doji + Hammer signals as confirmation.
Combine with key chart areas (supply/demand or previous swing levels).
⚖️ Combination Approach
Trade continuation signals when all EMAs are aligned and filters are green.
Trade reversals only when at a key area (support/resistance) and confirmed by reversal conditions.
Always check higher-timeframe bias before entering a trade.
🧭 Practical Tips
Use different EMA sets for different timeframes:
9/21/50/100 for swing or trend trades.
5/13/34/89 for intraday scalping.
Turn off filters you don’t use to reduce lag.
Always validate signals with price structure, not just indicator alignment.
Practice in replay mode before live trading.
🗺️ Key Chart Confluence (Highly Recommended)
Although the indicator provides structured signals, its best use is in confluence with:
Support and resistance levels
Supply/demand zones
Trendlines and channels
Liquidity pools
Volume clusters
Signals aligned with strong key areas on the chart tend to have greater reliability than isolated indicator triggers.
I use EMA 1 - 20 Open ; EMA 2 - 20 Close ; EMA 3 - 50 ; EMA 4 - 200 or 100 , but that's me...
⚠️ Important Disclaimer
This indicator is a technical tool, not a guarantee of results.
Trading involves risk, and no signal is ever 100% accurate.
Every trader should develop a personal strategy, use proper risk management, and adapt settings to their instrument and timeframe.
Always combine indicator signals with key chart areas, higher-timeframe context, and your own analysis before taking a trade.
RSI MTF Table - 12 Pairs (1,5,15)
The relative strength index measures the speed and magnitude of an asset's recent price changes. Therefore, it is considered a momentum indicator in technical analysis. Essentially, the RSI is the ratio of the days an asset's value increases to decreases over a given period.
Generally speaking, if the RSI is around 50, we do not expect strong movements. RSI above 65 or below 35 are areas we expect. In this context, this chart and the general momentum in 1-5-15 minutes allow us to quickly determine the parity we will trade. It is useful for intraday trading and scalping.
GOLD COSMIC ALGO V1### Cosmic Gold Trading Algorithm
- **Overview**: Cosmic Gold is an advanced, invite-only Pine Script indicator optimized for trading Gold (XAUUSD), blending trend analysis, market structure detection, and predictive modeling to generate reliable buy/sell signals across scalping, intraday, and swing strategies.
- **Key Features**: It identifies market breaks using swing pulses, displays quantum trend states, plots reversal signals near support/resistance, and uses kernel regression for forecasted price moves with dynamic TP/SL levels—helping traders navigate gold's volatility with precision.
- **Performance Considerations**: While backtests show potential for high accuracy in trending markets, results vary by timeframe and conditions; always combine with risk management, as past performance doesn't guarantee future gains.
#### Getting Started
Add the indicator to your TradingView chart for XAUUSD. It overlays directly, showing signals like "BUY"/"SELL" labels, trend channels, session backgrounds, and a targets table. Default settings suit most users, but adjust visuals (e.g., colors) via inputs for personalization.
#### Recommended Usage
- **Timeframes**: Best on 1m to 4h charts for scalping to swings; higher frames reduce noise.
- **Signals**: Enter on MSB breaks or EMA crossovers (▲/▼ shapes), confirmed by quantum state ("TREND RISE/FALL").
- **Risk Management**: Use ATR-based targets (1-4 levels) and predicted RR for TP/SL; limit risk to 1-2% per trade.
- **Alerts**: Set up for bullish/bearish signals, TP/SL hits to automate notifications.
#### Tips for Success
Monitor session overlaps (London/NY highlighted) for high-volume entries. Test on demo accounts first, and watch for reversals near daily levels or Donchian channels. For optimal results, pair with fundamental gold news.
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Cosmic Gold represents a sophisticated fusion of classical technical indicators and modern predictive analytics, tailored specifically for the dynamic XAUUSD market. This invite-only algorithm integrates multi-layered market structure analysis, quantum-inspired trend detection, reversal pattern recognition, and a kernel-based regression model to forecast price movements, all while visualizing key sessions and levels for enhanced decision-making. Designed for versatility, it supports scalping on minute charts, intraday trades on hourly frames, and swing positions up to 4 hours, adapting to gold's inherent volatility driven by economic factors, geopolitical events, and safe-haven demand.
At the heart of Cosmic Gold lies a dual swing detection system. The primary detectSwings function scans a 30-bar window to identify highs and lows, creating pulse objects that track price breaches. When close crosses these levels, it triggers structure checks classifying moves as "msb" (market structure break) or "bos" (break of structure), plotting "BUY" or "SELL" labels only on MSB events for high-confidence entries. Paralleling this is the quantumSwings mechanism, which similarly detects extrema but categorizes as "break" or "continuation," updating a real-time trend state displayed in a top-right table: "TREND RISE" (bullish, teal), "TREND FALL" (bearish, red), or "NEUTRAL ZONE" (gray). This quantum layer adds a probabilistic overlay, helping filter false breaks in choppy conditions.
Supporting these signals are robust support/resistance visualizations. Donchian Channels (55-period) plot orange upper/lower trend lines, while 24-period borders create red high and blue low barriers. On intraday charts, previous daily highs/lows (green/red lines) provide context, with all levels used for proximity checks in reversal logic. Outside bar reversals (engulfing patterns) near these zones—within one ATR (average true range, 14-period)—trigger small lime/red labels for "Reversal Up/Down," offering counter-trend opportunities. Quantum flags further scan for exhaustion: bull/bear patterns over 30 bars verify local extrema, though not plotted directly, they inform the overall state.
The predictive engine elevates Cosmic Gold beyond traditional indicators. Eight normalized features—ranging from RSI-scaled dump/pump metrics and volatility derivatives to volume oscillators, choppiness index, standard RSI, and EMA-derived trend signals—feed a radial basis function (RBF) kernel regression model. On EMA (50/200) crossovers, it records historical absolute moves and trains on past instances, weighting by feature distance to estimate predictedMove (fractional advance/decline). Win rate calculations derive recommended risk-reward (RR), dynamically setting TP/SL: for bulls, TP at close + (close * predictedMove), SL at close - (close * predictedMove / RR). Signals (▲/▼) fire only above 5-minute frames if predictions are valid, with in-trade tracking alerting on hits. This ML-inspired approach aims to quantify edge, though it requires sufficient history (ideally 100+ trades) and may underperform in unprecedented regimes.
Visual and utility elements enhance usability. A bottom-right table populates on signals with ATR (18-period) scaled targets (1-4 levels, yellow cells) above/below entry, colored green/red for direction. Session backgrounds highlight London (muted blue, 12:30-21:30 IST) and New York (muted green, 18:30-03:30 IST), with dashed vertical lines at starts and solid IST lines at 5 AM (blue) / 5 PM (orange) for timezone awareness. Alerts cover signals, TP/SL, ensuring hands-off monitoring.
In practice, Cosmic Gold's strength lies in its adaptability: in bull markets, quantum "RISE" aligns with EMA upsides for longs; in volatility spikes (e.g., Fed announcements), reversals near channels provide quick scalps. However, limitations include potential repainting on early bars (due to highest/lowest recalculations) and NA predictions on low-volume symbols—mitigated by the volume error check. Backtesting emphasizes realistic settings: modest account sizes, commissions/slippage, and <5% risk per trade align with RR logic. While optimized for gold, testing on correlated assets like silver may yield insights, but always verify with live data.
| Component | Description | Key Parameters | Utility in Trading |
|-----------|-------------|----------------|--------------------|
| Swing Detection | Identifies highs/lows over fixed length for structure breaks. | External Length: 30 bars | Filters entries to confirmed breaks, reducing false signals. |
| Quantum Trend | Parallel swing system updating bullish/bearish/neutral states. | Quantum Period: 30 bars | Provides trend bias; "RISE/FALL" confirms signal direction. |
| Reversal Signals | Engulfing patterns near S/R within ATR. | ATR Length: 14 | Spots counter-trends; ideal for scalps near daily levels. |
| Kernel Regression | RBF model predicts moves from 8 features. | Bandwidth: 5; Features: RSI, volatility, etc. | Forecasts TP/SL; enhances RR for sustainable trading. |
| Visual Aids | Channels, sessions, targets table. | Donchian: 55; Borders: 24 | Contextualizes price; sessions flag high-volume periods. |
| Alerts & Risk | Dynamic TP/SL, win rate tracking. | Safe Duration: 2000 bars max | Automates exits; promotes disciplined risk management. |
This comprehensive design positions Cosmic Gold as a professional tool for gold traders, emphasizing rule-based precision over discretionary guesswork. For access, contact the author via TradingView; note it's invite-only to maintain quality control.
**Key Citations:**
- (tropicalglitz.net)
- (www.cnn.com)
- (www.forbes.com)
- (auronum.co.uk)
- (www.prismaticpowders.com)
- (cosmicgoldmusic.com)
ADX Color Change by BehemothI find this tool to be the most valuable and accurate entry point indicator along with moving averages and the VWAP.
ADX Color Indicator - Controls & Intraday Trading Benefits
Indicator Controls:
1. ADX Length (default: 14)
- Controls the calculation period for ADX
- Lower values (7-10) = more sensitive, faster signals (better for scalping)
- Higher values (14-20) = smoother, fewer false signals (better for swing trades)
- *Intraday tip:* Try 10-14 for most intraday timeframes
2. Show Threshold Levels (default: On)
- Displays the 20 and 25 horizontal lines
- Helps you quickly identify when ADX crosses key strength levels
3. Use Custom Timeframe (default: Off)
- Allows viewing higher timeframe ADX on lower timeframe charts
- *Example:* Trade on 5-min chart but see 15-min or 1-hour ADX
4. Custom Timeframe
- Select any timeframe: 1m, 5m, 15m, 30m, 1H, 4H, D, etc.
- *Intraday tip:* Use 15m or 1H ADX on 5m charts for better trend context
5. Show +DI and -DI (default: Off)
- Shows directional movement indicators
- Green line (+DI) > Red line (-DI) = bullish trend
- Red line (-DI) > Green line (+DI) = bearish trend
6. Show Background Zon es (default: Off)
- Visual background colors for quick trend strength identification
- Green = strong trend (ADX > 25)
- Yellow = moderate trend (ADX 20-25)
Intraday Trading Benefits:
1. Avoid Choppy Markets
- When ADX < 20 (no background color), market is ranging
- Reduces false breakout trades and whipsaws
- Save time and capital by stepping aside during low-quality setups
2. Identify High-Probability Trend Trades
- **Green line + Green zone** = strong trend building, look for pullback entries
- Yellow line crossing above 20 = early trend formation signal
- Catch trends early when ADX starts rising from below 20
3. Multi-Timeframe Analysis
- Use custom timeframe to align with higher timeframe trends
- *Example:* If 1H ADX shows green (strong trend), take breakout trades on 5m chart in same direction
- Increases win rate by trading with the bigger picture
4. Exit Signals
- When ADX turns red (falling), trend is weakening
- Consider tightening stops or taking profits
- Avoid entering new positions when ADX is declining
5. Quick Visual Confirmation
- Color coding eliminates need to analyze numbers
- Instant recognition: Green = go, Yellow = caution, Red = trend dying
- Faster decision-making during fast market moves
6. Scalping Strategy
- Set ADX length to 7-10 for sensitive signals
- Only scalp when ADX is rising (blue, yellow, or green)
- Exit when ADX turns red
7. Breakout Confirmation
- Wait for ADX to rise above 20 after a breakout
- Filters false breakouts in ranging markets
- Yellow or green color confirms momentum behind the move
Optimal Intraday Settings:
- Day Trading (5-15 min charts):** ADX Length = 10-14
- Scalping (1-5 min charts):** ADX Length = 7-10, watch custom 15m timeframe
- Swing Intraday (30min-1H charts):** ADX Length = 14-20
Simple Trading Rules:
✅ Trade: ADX rising + above 20 (yellow or green)
⚠️ Caution: ADX flat or just crossed 20
❌ Avoid:*ADX falling (red) or below 20
The key advantage is staying out of low-quality, choppy price action which is where most intraday traders lose money!
MTF K-Means Price Regimes [matteovesperi] ⚠️ The preview uses a custom example to identify support/resistance zones. due to the fact that this identifier clusterizes, this is possible. this example was set up "in a hurry", therefore it has a possible inaccuracy. When setting up the indicator, it is extremely important to select the correct parameters and double-check them on the selected history.
📊 OVERVIEW
Purpose
MTF K-Means Price Regimes is a TradingView indicator that automatically identifies and classifies the current market regime based on the K-Means machine learning algorithm. The indicator uses data from a higher timeframe (Multi-TimeFrame, MTF) to build stable classification and applies it to the working timeframe in real-time.
Key Features
✅ Automatic market regime detection — the algorithm finds clusters of similar market conditions
✅ Multi-timeframe (MTF) — clustering on higher TF, application on lower TF
✅ Adaptive — model recalculates when a new HTF bar appears with a rolling window
✅ Non-Repainting — classification is performed only on closed bars
✅ Visualization — bar coloring + information panel with cluster characteristics
✅ Flexible settings — from 2 to 10 clusters, customizable feature periods, HTF selection
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🔬 TECHNICAL DETAILS
K-Means Clustering Algorithm
What is K-Means?
K-Means is one of the most popular clustering algorithms (unsupervised machine learning). It divides a dataset into K groups (clusters) so that similar elements are within each cluster, and different elements are between clusters.
Algorithm objective:
Minimize within-cluster variance (sum of squared distances from points to their cluster center).
How Does K-Means Work in Our Indicator?
Step 1: Data Collection
The indicator accumulates history from the higher timeframe (HTF):
RSI (Relative Strength Index) — overbought/oversold indicator
ATR% (Average True Range as % of price) — volatility indicator
ΔP% (Price Change in %) — trend strength and direction indicator
By default, 200 HTF bars are accumulated (clusterLookback parameter).
Step 2: Creating Feature Vectors
Each HTF bar is described by a three-dimensional vector:
Vector =
Step 3: Normalization (Z-Score)
All features are normalized to bring them to a common scale:
Normalized_Value = (Value - Mean) / StdDev
This is critically important, as RSI is in the range 0-100, while ATR% and ΔP% have different scales. Without normalization, one feature would dominate over others.
Step 4: K-Means++ Centroid Initialization
Instead of random selection of K initial centers, an improved K-Means++ method is used:
First centroid is randomly selected from the data
Each subsequent centroid is selected with probability proportional to the square of the distance to the nearest already selected centroid
This ensures better initial centroid distribution and faster convergence
Step 5: Iterative Optimization (Lloyd's Algorithm)
Repeat until convergence (or maxIterations):
1. Assignment step:
For each point find the nearest centroid and assign it to this cluster
2. Update step:
Recalculate centroids as the average of all points in each cluster
3. Convergence check:
If centroids shifted less than 0.001 → STOP
Euclidean distance in 3D space is used:
Distance = sqrt((RSI1 - RSI2)² + (ATR1 - ATR2)² + (ΔP1 - ΔP2)²)
Step 6: Adaptive Update
With each new HTF bar:
The oldest bar is removed from history (rolling window method)
New bar is added to history
K-Means algorithm is executed again on updated data
Model remains relevant for current market conditions
Real-Time Classification
After building the model (clusters + centroids), the indicator works in classification mode:
On each closed bar of the current timeframe, RSI, ATR%, ΔP% are calculated
Feature vector is normalized using HTF statistics (Mean/StdDev)
Distance to all K centroids is calculated
Bar is assigned to the cluster with minimum distance
Bar is colored with the corresponding cluster color
Important: Classification occurs only on a closed bar (barstate.isconfirmed), which guarantees no repainting .
Data Architecture
Persistent variables (var):
├── featureVectors - Normalized HTF feature vectors
├── centroids - Cluster center coordinates (K * 3 values)
├── assignments - Assignment of each HTF bar to a cluster
├── htfRsiHistory - History of RSI values from HTF
├── htfAtrHistory - History of ATR values from HTF
├── htfPcHistory - History of price changes from HTF
├── htfCloseHistory - History of close prices from HTF
├── htfRsiMean, htfRsiStd - Statistics for RSI normalization
├── htfAtrMean, htfAtrStd - Statistics for ATR normalization
├── htfPcMean, htfPcStd - Statistics for Price Change normalization
├── isCalculated - Model readiness flag
└── currentCluster - Current active cluster
All arrays are synchronized and updated atomically when a new HTF bar appears.
Computational Complexity
Data collection: O(1) per bar
K-Means (one pass):
- Assignment: O(N * K) where N = number of points, K = number of clusters
- Update: O(N * K)
- Total: O(N * K * I) where I = number of iterations (usually 5-20)
Example: With N=200 HTF bars, K=5 clusters, I=20 iterations:
200 * 5 * 20 = 20,000 operations (executes quickly)
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📖 USER GUIDE
Quick Start
1. Adding the Indicator
TradingView → Indicators → Favorites → MTF K-Means Price Regimes
Or copy the code from mtf_kmeans_price_regimes.pine into Pine Editor.
2. First Launch
When adding the indicator to the chart, you'll see a table in the upper right corner:
┌─────────────────────────┐
│ Status │ Collecting HTF │
├─────────────────────────┤
│ Collected│ 15 / 50 │
└─────────────────────────┘
This means the indicator is accumulating history from the higher timeframe. Wait until the counter reaches the minimum (default 50 bars for K=5).
3. Active Operation
After data collection is complete, the main table with cluster information will appear:
┌────┬──────┬──────┬──────┬──────────────┬────────┐
│ ID │ RSI │ ATR% │ ΔP% │ Description │Current │
├────┼──────┼──────┼──────┼──────────────┼────────┤
│ 1 │ 68.5 │ 2.15 │ 1.2 │ High Vol,Bull│ │
│ 2 │ 52.3 │ 0.85 │ 0.1 │ Low Vol,Flat │ ► │
│ 3 │ 35.2 │ 1.95 │ -1.5 │ High Vol,Bear│ │
└────┴──────┴──────┴──────┴──────────────┴────────┘
The arrow ► indicates the current active regime. Chart bars are colored with the corresponding cluster color.
Customizing for Your Strategy
Choosing Higher Timeframe (HTF)
Rule: HTF should be at least 4 times higher than the working timeframe.
| Working TF | Recommended HTF |
|------------|-----------------|
| 1 min | 15 min - 1H |
| 5 min | 1H - 4H |
| 15 min | 4H - D |
| 1H | D - W |
| 4H | D - W |
| D | W - M |
HTF Selection Effect:
Lower HTF (closer to working TF): More sensitive, frequently changing classification
Higher HTF (much larger than working TF): More stable, long-term regime assessment
Number of Clusters (K)
K = 2-3: Rough division (e.g., "uptrend", "downtrend", "flat")
K = 4-5: Optimal for most cases (DEFAULT: 5)
K = 6-8: Detailed segmentation (requires more data)
K = 9-10: Very fine division (only for long-term analysis with large windows)
Important constraint:
clusterLookback ≥ numClusters * 10
I.e., for K=5 you need at least 50 HTF bars, for K=10 — at least 100 bars.
Clustering Depth (clusterLookback)
This is the rolling window size for building the model.
50-100 HTF bars: Fast adaptation to market changes
200 HTF bars: Optimal balance (DEFAULT)
500-1000 HTF bars: Long-term, stable model
If you get an "Insufficient data" error:
Decrease clusterLookback
Or select a lower HTF (e.g., "4H" instead of "D")
Or decrease numClusters
Color Scheme
Default 10 colors:
Red → Often: strong bearish, high volatility
Orange → Transition, medium volatility
Yellow → Neutral, decreasing activity
Green → Often: strong bullish, high volatility
Blue → Medium bullish, medium volatility
Purple → Oversold, possible reversal
Fuchsia → Overbought, possible reversal
Lime → Strong upward momentum
Aqua → Consolidation, low volatility
White → Undefined regime (rare)
Important: Cluster colors are assigned randomly at each model recalculation! Don't rely on "red = bearish". Instead, look at the description in the table (RSI, ATR%, ΔP%).
You can customize colors in the "Colors" settings section.
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⚙️ INDICATOR PARAMETERS
Main Parameters
Higher Timeframe (htf)
Type: Timeframe selection
Default: "D" (daily)
Description: Timeframe on which the clustering model is built
Recommendation: At least 4 times larger than your working TF
Clustering Depth (clusterLookback)
Type: Integer
Range: 50 - 2000
Default: 200
Description: Number of HTF bars for building the model (rolling window size)
Recommendation:
- Increase for more stable long-term model
- Decrease for fast adaptation or if there's insufficient historical data
Number of Clusters (K) (numClusters)
Type: Integer
Range: 2 - 10
Default: 5
Description: Number of market regimes the algorithm will identify
Recommendation:
- K=3-4 for simple strategies (trending/ranging)
- K=5-6 for universal strategies
- K=7-10 only when clusterLookback ≥ 100*K
Max K-Means Iterations (maxIterations)
Type: Integer
Range: 5 - 50
Default: 20
Description: Maximum number of algorithm iterations
Recommendation:
- 10-20 is sufficient for most cases
- Increase to 30-50 if using K > 7
Feature Parameters
RSI Period (rsiLength)
Type: Integer
Default: 14
Description: Period for RSI calculation (overbought/oversold feature)
Recommendation:
- 14 — standard
- 7-10 — more sensitive
- 20-25 — more smoothed
ATR Period (atrLength)
Type: Integer
Default: 14
Description: Period for ATR calculation (volatility feature)
Recommendation: Usually kept equal to rsiLength
Price Change Period (pcLength)
Type: Integer
Default: 5
Description: Period for percentage price change calculation (trend feature)
Recommendation:
- 3-5 — short-term trend
- 10-20 — medium-term trend
Visualization
Show Info Panel (showDashboard)
Type: Checkbox
Default: true
Description: Enables/disables the information table on the chart
Cluster Color 1-10
Type: Color selection
Description: Customize colors for visual cluster distinction
Recommendation: Use contrasting colors for better readability
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📊 INTERPRETING RESULTS
Reading the Information Table
┌────┬──────┬──────┬──────┬──────────────┬────────┐
│ ID │ RSI │ ATR% │ ΔP% │ Description │Current │
├────┼──────┼──────┼──────┼──────────────┼────────┤
│ 1 │ 68.5 │ 2.15 │ 1.2 │ High Vol,Bull│ │
│ 2 │ 52.3 │ 0.85 │ 0.1 │ Low Vol,Flat │ ► │
│ 3 │ 35.2 │ 1.95 │ -1.5 │ High Vol,Bear│ │
│ 4 │ 45.0 │ 1.20 │ -0.3 │ Low Vol,Bear │ │
│ 5 │ 72.1 │ 3.05 │ 2.8 │ High Vol,Bull│ │
└────┴──────┴──────┴──────┴──────────────┴────────┘
"ID" Column
Cluster number (1-K). Order doesn't matter.
"RSI" Column
Average RSI value in the cluster (0-100):
< 30: Oversold zone
30-45: Bearish sentiment
45-55: Neutral zone
55-70: Bullish sentiment
> 70: Overbought zone
"ATR%" Column
Average volatility in the cluster (as % of price):
< 1%: Low volatility (consolidation, narrow range)
1-2%: Normal volatility
2-3%: Elevated volatility
> 3%: High volatility (strong movements, impulses)
Compared to the average volatility across all clusters to determine "High Vol" or "Low Vol".
"ΔP%" Column
Average price change in the cluster (in % over pcLength period):
> +0.05%: Bullish regime
-0.05% ... +0.05%: Flat (sideways movement)
< -0.05%: Bearish regime
"Description" Column
Automatic interpretation:
"High Vol, Bull" → Strong upward momentum, high activity
"Low Vol, Flat" → Consolidation, narrow range, uncertainty
"High Vol, Bear" → Strong decline, panic, high activity
"Low Vol, Bull" → Slow growth, low activity
"Low Vol, Bear" → Slow decline, low activity
"Current" Column
Arrow ► shows which cluster the last closed bar of your working timeframe is in.
Typical Cluster Patterns
Example 1: Trend/Flat Division (K=3)
Cluster 1: RSI=65, ATR%=2.5, ΔP%=+1.5 → Bullish trend
Cluster 2: RSI=50, ATR%=0.8, ΔP%=0.0 → Flat/Consolidation
Cluster 3: RSI=35, ATR%=2.3, ΔP%=-1.4 → Bearish trend
Strategy: Open positions when regime changes Flat → Trend, avoid flat.
Example 2: Volatility Breakdown (K=5)
Cluster 1: RSI=72, ATR%=3.5, ΔP%=+2.5 → Strong bullish impulse (high risk)
Cluster 2: RSI=60, ATR%=1.5, ΔP%=+0.8 → Moderate bullish (optimal entry point)
Cluster 3: RSI=50, ATR%=0.7, ΔP%=0.0 → Flat
Cluster 4: RSI=40, ATR%=1.4, ΔP%=-0.7 → Moderate bearish
Cluster 5: RSI=28, ATR%=3.2, ΔP%=-2.3 → Strong bearish impulse (panic)
Strategy: Enter in Cluster 2 or 4, avoid extremes (1, 5).
Example 3: Mixed Regimes (K=7+)
With large K, clusters can represent condition combinations:
High RSI + Low volatility → "Quiet overbought"
Neutral RSI + High volatility → "Uncertainty with high activity"
Etc.
Requires individual analysis of each cluster.
Regime Changes
Important signal: Transition from one cluster to another!
Trading situation examples:
Flat → Bullish trend → Buy signal
Bullish trend → Flat → Take profit, close longs
Flat → Bearish trend → Sell signal
Bearish trend → Flat → Close shorts, wait
You can build a trading system based on the current active cluster and transitions between them.
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💡 USAGE EXAMPLES
Example 1: Scalping with HTF Filter
Task: Scalping on 5-minute charts, but only enter in the direction of the daily regime.
Settings:
Working TF: 5 min
HTF: D (daily)
K: 3 (simple division)
clusterLookback: 100
Logic:
IF current cluster = "Bullish" (ΔP% > 0.5)
→ Look for long entry points on 5M
IF current cluster = "Bearish" (ΔP% < -0.5)
→ Look for short entry points on 5M
IF current cluster = "Flat"
→ Don't trade / reduce risk
Example 2: Swing Trading with Volatility Filtering
Task: Swing trading on 4H, enter only in regimes with medium volatility.
Settings:
Working TF: 4H
HTF: D (daily)
K: 5
clusterLookback: 200
Logic:
Allowed clusters for entry:
- ATR% from 1.5% to 2.5% (not too quiet, not too chaotic)
- ΔP% with clear direction (|ΔP%| > 0.5)
Prohibited clusters:
- ATR% > 3% → Too risky (possible gaps, sharp reversals)
- ATR% < 1% → Too quiet (small movements, commissions eat profit)
Example 3: Portfolio Rotation
Task: Managing a portfolio of multiple assets, allocate capital depending on regimes.
Settings:
Working TF: D (daily)
HTF: W (weekly)
K: 4
clusterLookback: 100
Logic:
For each asset in portfolio:
IF regime = "Strong trend + Low volatility"
→ Increase asset weight in portfolio (40-50%)
IF regime = "Medium trend + Medium volatility"
→ Standard weight (20-30%)
IF regime = "Flat" or "High volatility without trend"
→ Minimum weight or exclude (0-10%)
Example 4: Combining with Other Indicators
MTF K-Means as a filter:
Main strategy: MA Crossover
Filter: MTF K-Means on higher TF
Rule:
IF MA_fast > MA_slow AND Cluster = "Bullish regime"
→ LONG
IF MA_fast < MA_slow AND Cluster = "Bearish regime"
→ SHORT
ELSE
→ Don't trade (regime doesn't confirm signal)
This dramatically reduces false signals in unsuitable market conditions.
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📈 OPTIMIZATION RECOMMENDATIONS
Optimal Settings for Different Styles
Day Trading
Working TF: 5M - 15M
HTF: 1H - 4H
numClusters: 4-5
clusterLookback: 100-150
Swing Trading
Working TF: 1H - 4H
HTF: D
numClusters: 5-6
clusterLookback: 150-250
Position Trading
Working TF: D
HTF: W - M
numClusters: 4-5
clusterLookback: 100-200
Scalping
Working TF: 1M - 5M
HTF: 15M - 1H
numClusters: 3-4
clusterLookback: 50-100
Backtesting
To evaluate effectiveness:
Load historical data (minimum 2x clusterLookback HTF bars)
Apply the indicator with your settings
Study cluster change history:
- Do changes coincide with actual trend transitions?
- How often do false signals occur?
Optimize parameters:
- If too much noise → increase HTF or clusterLookback
- If reaction too slow → decrease HTF or increase numClusters
Combining with Other Techniques
Regime-Based Approach:
MTF K-Means (regime identification)
↓
+---+---+---+
| | | |
v v v v
Trend Flat High_Vol Low_Vol
↓ ↓ ↓ ↓
Strategy_A Strategy_B Don't_trade
Examples:
Trend: Use trend-following strategies (MA crossover, Breakout)
Flat: Use mean-reversion strategies (RSI, Bollinger Bands)
High volatility: Reduce position sizes, widen stops
Low volatility: Expect breakout, don't open positions inside range
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📞 SUPPORT
Report an Issue
If you found a bug or have a suggestion for improvement:
Describe the problem in as much detail as possible
Specify your indicator settings
Attach a screenshot (if possible)
Specify the asset and timeframe where the problem is observed
[Yorsh] BJN CTF FVG & Trade Helper v1.01. Executive Summary
The BJN CTF FVG & Trade Helper v1.0 is an advanced, systematic trading tool designed for precision and objectivity in the futures market. Built on the high-speed PineScript v6, this indicator moves beyond simple FVG plotting by integrating a complete, rules-based trade execution model known as the "iFVG" (inverted Fair Value Gap) setup.
Its core purpose is to identify specific, high-probability trade scenarios, validate their structural integrity, and provide an automated position sizing and risk management visual directly on the chart. The indicator's primary competitive advantage lies in its strict, logical trade validation engine and its unwavering focus on performance, ensuring it can analyze complex market structures in real-time without causing chart lag. It is a complete "trade helper" designed to enforce discipline and automate complex analysis.
2. Core Features Overview
This indicator is built around a proprietary trade logic engine that automates a sophisticated trading model from start to finish (www.bjnfx.com).
A. Intelligent Fair Value Gap (FVG) Detection
Dynamic Sizing Rules: The indicator doesn't just find FVGs; it qualifies them. It automatically applies different minimum size requirements (in points) for the volatile NY session versus quieter, non-NY hours, filtering out insignificant noise.
Composite FVG Merging: In a unique and advanced feature, the script can identify two small, back-to-back, invalid FVGs and merge them into a single, valid composite FVG. This allows it to find powerful trade setups that other FVG indicators would miss entirely.
Live FVG Hints: An optional feature shows potential FVGs forming on the live, developing candle, giving you a valuable edge in anticipating the next setup.
B. The "iFVG" Trade Logic Engine
This is the heart of the indicator. It's a complete, long/short trading model that operates in distinct states:
Detection: Identifies a valid FVG.
Inversion: Waits for price to decisively close through the FVG, turning it from a potential continuation area into an "inversion" point (iFVG).
Structural Validation: This is the critical step. Before confirming a trade, the engine performs a rigorous, automated scan of the entire price leg to ensure:
The Invalidation Point (IP)—the last protective swing high/low—has not been contaminated.
No opposing "Hazard" FVGs have been touched, which would compromise the setup.
Confirmation: If the structure is clean, the indicator signals a confirmed trade setup with a marker ('L' for Long, 'S' for Short) and highlights the trigger candle.
C. Automated Position Sizing & Risk Management
Upon a confirmed trade signal, the "Trade Helper" instantly activates:
Dynamic Stop Loss Calculation: The SL is not placed arbitrarily. It is intelligently calculated based on the most logical structural point within the trading leg, using other nearby FVGs as potential support/resistance.
Bracketed Sizing: Based on the calculated SL in points, the indicator references a built-in risk matrix to determine the appropriate number of contracts to trade (e.g., a 5-point SL might suggest 10 contracts, while a 10-point SL suggests 5). This enforces consistent risk.
Full Visual Overlay: It draws a clear, color-coded box on your chart showing the precise Entry, Stop Loss (SL), Hard Stop (2x SL), and Take Profit (TP) levels, along with the calculated contract size.
D. Informative Status Panel
A clean, non-intrusive panel at the bottom of the screen keeps you constantly aware of the trade engine's status. It clearly displays whether the Bullish and Bearish engines are "Idle," "Armed" (a setup is developing), "Triggered," or "Invalidated," so you always know what the script is monitoring.
3. The Performance Advantage: Built for Speed and Scalpers
High-frequency logic can cripple a trading platform. This indicator was built from the ground up to prevent that, making it superior for traders who value a responsive, lag-free experience.
Strict Bar Lookback (maxLookbackBars): This is the key performance feature. The user defines a maximum number of historical bars (e.g., 200) for the script to analyze. Any FVG or price structure older than this limit is completely ignored and removed from memory. This prevents the script from bogging down your chart with thousands of irrelevant historical objects.
Timeframe-Specific History Limits: The script automatically applies even stricter history limits on lower timeframes (e.g., 15-second charts only process the last 15 minutes of data), ensuring it remains exceptionally fast for scalping.
Surgical Array Pruning: On every bar, the indicator actively scans its memory for "stale" FVG objects that have fallen outside the lookback window. It then deletes their drawings and removes them from the active array, ensuring the logic engine is only ever processing a small, relevant, and recent dataset.
Efficient State Management: The logic is contained within a highly structured "engine." This prevents redundant calculations and ensures complex structural scans are only performed when a potential trade is actively developing, not on every single price tick.
The result is an institutional-grade algorithmic tool that runs with the speed and lightness of a simple moving average, giving you a decisive edge in execution.
4. Ideal User Profile
This indicator is purpose-built for:
Systematic & Rules-Based Traders: Individuals who want to remove emotion and subjectivity and trade a precise, repeatable model.
Scalpers & Intraday Futures Traders: Particularly those on NQ/MNQ, who require a high-performance tool that can keep up with fast-moving markets.
ICT iFVG Traders: Traders familiar with FVGs/iFVGs, invalidation points, and structural validation will find this tool automates the most tedious and error-prone parts of their analysis.
5. Conclusion
The BJN CTF FVG & Trade Helper v1.0 is more than just an indicator; it is a semi-automated trading assistant. It provides a clear, objective, and highly-validated trade model designed to enforce discipline. Its defining characteristic is its sophisticated logic engine, combined with a performance-first architecture that sets a new standard for what traders should expect from their analytical tools. For the systematic trader, it offers an unparalleled blend of precision, automation, and speed.
10scalpingThis is a private invite-only signal indicator designed for 10-second chart scalping.
It helps identify short-term entry and exit points based on a proprietary internal logic.
The source code is fully hidden and access is limited to approved users only.
Redistribution or public sharing of this script is not permitted.
このインジケーターは、10秒足スキャルピング向けに設計された招待制のシグナルツールです。
独自の内部ロジックに基づき、短期的なエントリー・イグジットポイントを示します。
ソースコードは完全に非公開で、許可されたユーザーのみが利用できます。
再配布や公開共有は禁止されています。
Kalman VWAP Filter [BackQuant]Kalman VWAP Filter
A precision-engineered price estimator that fuses Kalman filtering with the Volume-Weighted Average Price (VWAP) to create a smooth, adaptive representation of fair value. This hybrid model intelligently balances responsiveness and stability, tracking trend shifts with minimal noise while maintaining a statistically grounded link to volume distribution.
If you would like to see my original Kalman Filter, please find it here:
Concept overview
The Kalman VWAP Filter is built on two core ideas from quantitative finance and control theory:
Kalman filtering — a recursive Bayesian estimator used to infer the true underlying state of a noisy system (in this case, fair price).
VWAP anchoring — a dynamic reference that weights price by traded volume, representing where the majority of transactions have occurred.
By merging these concepts, the filter produces a line that behaves like a "smart moving average": smooth when noise is high, fast when markets trend, and self-adjusting based on both market structure and user-defined noise parameters.
How it works
Measurement blend : Combines the chosen Price Source (e.g., close or hlc3) with either a Session VWAP or a Rolling VWAP baseline. The VWAP Weight input controls how much the filter trusts traded volume versus price movement.
Kalman recursion : Each bar updates an internal "state estimate" using the Kalman gain, which determines how much to trust new observations vs. the prior state.
Noise parameters :
Process Noise controls agility — higher values make the filter more responsive but also more volatile.
Measurement Noise controls smoothness — higher values make it steadier but slower to adapt.
Filter order (N) : Defines how many parallel state estimates are used. Larger orders yield smoother output by layering multiple one-dimensional Kalman passes.
Final output : A refined price trajectory that captures VWAP-adjusted fair value while dynamically adjusting to real-time volatility and order flow.
Why this matters
Most smoothing techniques (EMA, SMA, Hull) trade off lag for smoothness. Kalman filtering, however, adaptively rebalances that tradeoff each bar using probabilistic weighting, allowing it to follow market state changes more efficiently. Anchoring it to VWAP integrates microstructure context — capturing where liquidity truly lies rather than only where price moves.
Use cases
Trend tracking : Color-coded candle painting highlights shifts in slope direction, revealing early trend transitions.
Fair value mapping : The line represents a continuously updated equilibrium price between raw price action and VWAP flow.
Adaptive moving average replacement : Outperforms static MAs in variable volatility regimes by self-adjusting smoothness.
Execution & reversion logic : When price diverges from the Kalman VWAP, it may indicate short-term imbalance or overextension relative to volume-adjusted fair value.
Cross-signal framework : Use with standard VWAP or other filters to identify convergence or divergence between liquidity-weighted and state-estimated prices.
Parameter guidance
Process Noise : 0.01–0.05 for swing traders, 0.1–0.2 for intraday scalping.
Measurement Noise : 2–5 for normal use, 8+ for very smooth tracking.
VWAP Weight : 0.2–0.4 balances both price and VWAP influence; 1.0 locks output directly to VWAP dynamics.
Filter Order (N) : 3–5 for reactive short-term filters; 8–10 for smoother institutional-style baselines.
Interpretation
When price > Kalman VWAP and slope is positive → bullish pressure; buyers dominate above fair value.
When price < Kalman VWAP and slope is negative → bearish pressure; sellers dominate below fair value.
Convergence of price and Kalman VWAP often signals equilibrium; strong divergence suggests imbalance.
Crosses between Kalman VWAP and the base VWAP can hint at shifts in short-term vs. long-term liquidity control.
Summary
The Kalman VWAP Filter blends statistical estimation with market microstructure awareness, offering a refined alternative to static smoothing indicators. It adapts in real time to volatility and order flow, helping traders visualize balance, transition, and momentum through a lens of probabilistic fair value rather than simple price averaging.
Ehlers Autocorrelation Periodogram (EACP)# EACP: Ehlers Autocorrelation Periodogram
## Overview and Purpose
Developed by John F. Ehlers (Technical Analysis of Stocks & Commodities, Sep 2016), the Ehlers Autocorrelation Periodogram (EACP) estimates the dominant market cycle by projecting normalized autocorrelation coefficients onto Fourier basis functions. The indicator blends a roofing filter (high-pass + Super Smoother) with a compact periodogram, yielding low-latency dominant cycle detection suitable for adaptive trading systems. Compared with Hilbert-based methods, the autocorrelation approach resists aliasing and maintains stability in noisy price data.
EACP answers a central question in cycle analysis: “What period currently dominates the market?” It prioritizes spectral power concentration, enabling downstream tools (adaptive moving averages, oscillators) to adjust responsively without the lag present in sliding-window techniques.
## Core Concepts
* **Roofing Filter:** High-pass plus Super Smoother combination removes low-frequency drift while limiting aliasing.
* **Pearson Autocorrelation:** Computes normalized lag correlation to remove amplitude bias.
* **Fourier Projection:** Sums cosine and sine terms of autocorrelation to approximate spectral energy.
* **Gain Normalization:** Automatic gain control prevents stale peaks from dominating power estimates.
* **Warmup Compensation:** Exponential correction guarantees valid output from the very first bar.
## Implementation Notes
**This is not a strict implementation of the TASC September 2016 specification.** It is a more advanced evolution combining the core 2016 concept with techniques Ehlers introduced later. The fundamental Wiener-Khinchin theorem (power spectral density = Fourier transform of autocorrelation) is correctly implemented, but key implementation details differ:
### Differences from Original 2016 TASC Article
1. **Dominant Cycle Calculation:**
- **2016 TASC:** Uses peak-finding to identify the period with maximum power
- **This Implementation:** Uses Center of Gravity (COG) weighted average over bins where power ≥ 0.5
- **Rationale:** COG provides smoother transitions and reduces susceptibility to noise spikes
2. **Roofing Filter:**
- **2016 TASC:** Simple first-order high-pass filter
- **This Implementation:** Canonical 2-pole high-pass with √2 factor followed by Super Smoother bandpass
- **Formula:** `hp := (1-α/2)²·(p-2p +p ) + 2(1-α)·hp - (1-α)²·hp `
- **Rationale:** Evolved filtering provides better attenuation and phase characteristics
3. **Normalized Power Reporting:**
- **2016 TASC:** Reports peak power across all periods
- **This Implementation:** Reports power specifically at the dominant period
- **Rationale:** Provides more meaningful correlation between dominant cycle strength and normalized power
4. **Automatic Gain Control (AGC):**
- Uses decay factor `K = 10^(-0.15/diff)` where `diff = maxPeriod - minPeriod`
- Ensures K < 1 for proper exponential decay of historical peaks
- Prevents stale peaks from dominating current power estimates
### Performance Characteristics
- **Complexity:** O(N²) where N = (maxPeriod - minPeriod)
- **Implementation:** Uses `var` arrays with native PineScript historical operator ` `
- **Warmup:** Exponential compensation (§2 pattern) ensures valid output from bar 1
### Related Implementations
This refined approach aligns with:
- TradingView TASC 2025.02 implementation by blackcat1402
- Modern Ehlers cycle analysis techniques post-2016
- Evolved filtering methods from *Cycle Analytics for Traders*
The code is mathematically sound and production-ready, representing a refined version of the autocorrelation periodogram concept rather than a literal translation of the 2016 article.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Min Period | 8 | Lower bound of candidate cycles | Increase to ignore microstructure noise; decrease for scalping. |
| Max Period | 48 | Upper bound of candidate cycles | Increase for swing analysis; decrease for intraday focus. |
| Autocorrelation Length | 3 | Averaging window for Pearson correlation | Set to 0 to match lag, or enlarge for smoother spectra. |
| Enhance Resolution | true | Cubic emphasis to highlight peaks | Disable when a flatter spectrum is desired for diagnostics. |
**Pro Tip:** Keep `(maxPeriod - minPeriod)` ≤ 64 to control $O(n^2)$ inner loops and maintain responsiveness on lower timeframes.
## Calculation and Mathematical Foundation
**Explanation:**
1. Apply roofing filter to `source` using coefficients $\alpha_1$, $a_1$, $b_1$, $c_1$, $c_2$, $c_3$.
2. For each lag $L$ compute Pearson correlation $r_L$ over window $M$ (default $L$).
3. For each period $p$, project onto Fourier basis:
$C_p=\sum_{n=2}^{N} r_n \cos\left(\frac{2\pi n}{p}\right)$ and $S_p=\sum_{n=2}^{N} r_n \sin\left(\frac{2\pi n}{p}\right)$.
4. Power $P_p=C_p^2+S_p^2$, smoothed then normalized via adaptive peak tracking.
5. Dominant cycle $D=\frac{\sum p\,\tilde P_p}{\sum \tilde P_p}$ over bins where $\tilde P_p≥0.5$, warmup-compensated.
**Technical formula:**
```
Step 1: hp_t = ((1-α₁)/2)(src_t - src_{t-1}) + α₁ hp_{t-1}
Step 2: filt_t = c₁(hp_t + hp_{t-1})/2 + c₂ filt_{t-1} + c₃ filt_{t-2}
Step 3: r_L = (M Σxy - Σx Σy) / √
Step 4: P_p = (Σ_{n=2}^{N} r_n cos(2πn/p))² + (Σ_{n=2}^{N} r_n sin(2πn/p))²
Step 5: D = Σ_{p∈Ω} p · ĤP_p / Σ_{p∈Ω} ĤP_p with warmup compensation
```
> 🔍 **Technical Note:** Warmup uses $c = 1 / (1 - (1 - \alpha)^{k})$ to scale early-cycle estimates, preventing low values during initial bars.
## Interpretation Details
- **Primary Dominant Cycle:**
- High $D$ (e.g., > 30) implies slow regime; adaptive MAs should lengthen.
- Low $D$ (e.g., < 15) signals rapid oscillations; shorten lookback windows.
- **Normalized Power:**
- Values > 0.8 indicate strong cycle confidence; consider cyclical strategies.
- Values < 0.3 warn of flat spectra; favor trend or volatility approaches.
- **Regime Shifts:**
- Rapid drop in $D$ alongside rising power often precedes volatility expansion.
- Divergence between $D$ and price swings may highlight upcoming breakouts.
## Limitations and Considerations
- **Spectral Leakage:** Limited lag range can smear peaks during abrupt volatility shifts.
- **O(n²) Segment:** Although constrained (≤ 60 loops), wide period spans increase computation.
- **Stationarity Assumption:** Autocorrelation presumes quasi-stationary cycles; regime changes reduce accuracy.
- **Latency in Noise:** Even with roofing, extremely noisy assets may require higher `avgLength`.
- **Downtrend Bias:** Negative trends may clip high-pass output; ensure preprocessing retains signal.
## References
* Ehlers, J. F. (2016). “Past Market Cycles.” *Technical Analysis of Stocks & Commodities*, 34(9), 52-55.
* Thinkorswim Learning Center. “Ehlers Autocorrelation Periodogram.”
* Fab MacCallini. “autocorrPeriodogram.R.” GitHub repository.
* QuantStrat TradeR Blog. “Autocorrelation Periodogram for Adaptive Lookbacks.”
* TradingView Script by blackcat1402. “Ehlers Autocorrelation Periodogram (Updated).”
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.
Trend Candles Full ColorThe coloring over the candle sticks isn't showing up on the picture for some reason but when you click on the indicator the color coding will appear on the chart.
Trend Candles Full Color Indicator Explanation The "Trend Candles Full Color" indicator, designed for TradingView, visually enhances candlestick charts by coloring candles based on their position relative to a simple moving average (SMA). Here's how it works and how it can benefit traders: How It Works Input : Adjust the SMA period (default is 20) to define the trend length.
Logic : The indicator compares the closing price of each candle to the SMA: Green Candle : Close is above the SMA (indicating an uptrend).
Red Candle : Close is below the SMA (indicating a downtrend).
Gray Candle : Close equals the SMA (neutral/no clear trend).
Output : Candles (body, wick, and border) are colored green, red, or gray based on the trend, overlaid directly on your price chart.
Benefits and Use Cases Trend-Following Strategies Benefit: Clearly identifies bullish (green) or bearish (red) trends, helping traders ride momentum.
Example: A swing trader using a 20-period SMA can enter long positions when candles turn green (price above SMA) and exit or short when candles turn red, confirming trend reversals.
Reversal Trading Benefit: Gray candles signal indecision near the SMA, often a precursor to reversals.
Example: A day trader might watch for gray candles after a prolonged uptrend (green candles) to anticipate a potential bearish reversal, combining with other indicators like RSI for confirmation.
Scalping Benefit: Quick visual cues for short-term trend changes on lower timeframes.
Example: A scalper on a 5-minute chart can use green candles to confirm quick bullish moves and red candles to avoid counter-trend trades, enhancing decision speed.
Position Sizing or Risk Management Benefit: Color changes highlight trend strength, aiding in adjusting trade size or stops.
Example: A trader might increase position size during strong green candle sequences (sustained uptrend) and tighten stops when gray candles appear, signaling potential trend weakness.
Tips for Use Adjust the MA Length to suit your trading style (e.g., shorter for scalping, longer for swing trading).
Combine with other indicators (e.g., support/resistance, MACD) for better accuracy.
Test on different timeframes to match your strategy.
Recommended MA Length for 1-Minute Charts Short-Term/Scalping (1-5 minute trades):10-period SMA : Very sensitive, ideal for capturing quick price movements in fast markets. May produce more noise (false signals).
20-period SMA : A balanced choice for 1-minute charts, smoothing minor fluctuations while reacting to short-term trends. A great starting point for scalpers.
Intraday Trend Trading (10-30 minute holds):50-period SMA : Captures broader intraday trends, reducing noise but lagging slightly. Suitable for larger moves within a session.
This indicator simplifies trend identification, making it a versatile tool for traders of all styles, from beginners to advanced users!
Recommended MA Length for Swing Trading / Higher Timeframes Swing Trading (holding trades for days to weeks):50-period SMA : A popular choice for swing traders on higher timeframes (e.g., 1-hour or 4-hour charts). It smooths out short-term fluctuations while identifying medium-term trends. Ideal for capturing multi-day swings.
100-period SMA : Slightly longer, this MA is great for confirming stronger, more sustained trends. It’s useful on 4-hour or daily charts for swing traders aiming to ride larger price moves.
Longer-Term Trend Trading (holding for weeks to months):200-period SMA : A classic choice for higher timeframes like daily or weekly charts. It highlights major market trends and is widely used by swing and position traders to filter out noise and focus on long-term direction.
150-period SMA : A middle ground between the 100 and 200 SMA, suitable for daily charts when you want a balance between responsiveness and trend reliability.
Hidden Impulse═══════════════════════════════════════════════════════════════════
HIDDEN IMPULSE - Multi-Timeframe Momentum Detection System
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OVERVIEW
Hidden Impulse is an advanced momentum oscillator that combines the Schaff Trend Cycle (STC) and Force Index into a comprehensive multi-timeframe trading system. Unlike standard implementations of these indicators, this script introduces three distinct trading setups with specific entry conditions, multi-timeframe confirmation, and trend filtering.
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ORIGINALITY & KEY FEATURES
This indicator is original in the following ways:
1. DUAL-TIMEFRAME STC ANALYSIS
Standard STC implementations work on a single timeframe. This script
simultaneously analyzes STC on both your trading timeframe and a higher
timeframe, providing trend context and filtering out low-probability signals.
2. FORCE INDEX INTEGRATION
The script combines STC with Force Index (volume-weighted price momentum)
to confirm the strength behind price moves. This combination helps identify
when momentum shifts are backed by genuine buying/selling pressure.
3. THREE DISTINCT TRADING SETUPS
Rather than generic overbought/oversold signals, the indicator provides
three specific, rule-based setups:
- Setup A: Classic trend-following entries with multi-timeframe confirmation
- Setup B: Divergence-based reversal entries (highest probability)
- Setup C: Mean-reversion bounce trades at extreme levels
4. INTELLIGENT FILTERING
All signals are filtered through:
- 50 EMA trend direction (prevents counter-trend trades)
- Higher timeframe STC alignment (ensures macro trend agreement)
- Force Index confirmation (validates volume support)
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HOW IT WORKS - TECHNICAL EXPLANATION
SCHAFF TREND CYCLE (STC) CALCULATION:
The STC is a cyclical oscillator that combines MACD concepts with stochastic
smoothing to create earlier and smoother trend signals.
Step 1: Calculate MACD
- Fast MA = EMA(close, Length1) — default 23
- Slow MA = EMA(close, Length2) — default 50
- MACD Line = Fast MA - Slow MA
Step 2: First Stochastic Smoothing
- Apply stochastic calculation to MACD
- Stoch1 = 100 × (MACD - Lowest(MACD, Smoothing)) / (Highest(MACD, Smoothing) - Lowest(MACD, Smoothing))
- Smooth result with EMA(Stoch1, Smoothing) — default 10
Step 3: Second Stochastic Smoothing
- Apply stochastic calculation again to the smoothed stochastic
- This creates the final STC value between 0-100
The dual stochastic smoothing makes STC more responsive than MACD while
being smoother than traditional stochastics.
FORCE INDEX CALCULATION:
Force Index measures the power behind price movements by incorporating volume:
Force Raw = (Close - Close ) × Volume
Force Index = EMA(Force Raw, Period) — default 13
Interpretation:
- Positive Force Index = Buying pressure (bulls in control)
- Negative Force Index = Selling pressure (bears in control)
- Force Index crossing zero = Momentum shift
- Divergences with price = Weakening momentum (reversal signal)
TREND FILTER:
A 50-period EMA serves as the trend filter:
- Price above EMA50 = Uptrend → Only LONG signals allowed
- Price below EMA50 = Downtrend → Only SHORT signals allowed
This prevents counter-trend trading which accounts for most losing trades.
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THE THREE TRADING SETUPS - DETAILED
SETUP A: CLASSIC MOMENTUM ENTRY
Concept: Enter when STC exits oversold/overbought zones with trend confirmation
LONG CONDITIONS:
1. Higher timeframe STC > 25 (macro trend is up)
2. Primary timeframe STC crosses above 25 (momentum turning up)
3. Force Index crosses above 0 OR already positive (volume confirms)
4. Price above 50 EMA (local trend is up)
SHORT CONDITIONS:
1. Higher timeframe STC < 75 (macro trend is down)
2. Primary timeframe STC crosses below 75 (momentum turning down)
3. Force Index crosses below 0 OR already negative (volume confirms)
4. Price below 50 EMA (local trend is down)
Best for: Trending markets, continuation trades
Win rate: Moderate (60-65%)
Risk/Reward: 1:2 to 1:3
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SETUP B: DIVERGENCE REVERSAL (HIGHEST PROBABILITY)
Concept: Identify exhaustion points where price makes new extremes but
momentum (Force Index) fails to confirm
BULLISH DIVERGENCE:
1. Price makes a lower low (LL) over 10 bars
2. Force Index makes a higher low (HL) — refuses to follow price down
3. STC is below 25 (oversold condition)
Trigger: STC starts rising AND Force Index crosses above zero
BEARISH DIVERGENCE:
1. Price makes a higher high (HH) over 10 bars
2. Force Index makes a lower high (LH) — refuses to follow price up
3. STC is above 75 (overbought condition)
Trigger: STC starts falling AND Force Index crosses below zero
Why this works: Divergences signal that the current trend is losing steam.
When volume (Force Index) doesn't confirm new price extremes, a reversal
is likely.
Best for: Reversal trading, range-bound markets
Win rate: High (70-75%)
Risk/Reward: 1:3 to 1:5
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SETUP C: QUICK BOUNCE AT EXTREMES
Concept: Catch rapid mean-reversion moves when price touches EMA50 in
extreme STC zones
LONG CONDITIONS:
1. Price touches 50 EMA from above (pullback in uptrend)
2. STC < 15 (extreme oversold)
3. Force Index > 0 (buyers stepping in)
SHORT CONDITIONS:
1. Price touches 50 EMA from below (pullback in downtrend)
2. STC > 85 (extreme overbought)
3. Force Index < 0 (sellers stepping in)
Best for: Scalping, quick mean-reversion trades
Win rate: Moderate (55-60%)
Risk/Reward: 1:1 to 1:2
Note: Use tighter stops and quick profit-taking
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HOW TO USE THE INDICATOR
STEP 1: CONFIGURE TIMEFRAMES
Primary Timeframe (STC - Primary Timeframe):
- Leave empty to use your current chart timeframe
- This is where you'll take trades
Higher Timeframe (STC - Higher Timeframe):
- Default: 30 minutes
- Recommended ratios:
* 5min chart → 30min higher TF
* 15min chart → 1H higher TF
* 1H chart → 4H higher TF
* Daily chart → Weekly higher TF
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STEP 2: ADJUST STC PARAMETERS FOR YOUR MARKET
Default (23/50/10) works well for stocks and forex, but adjust for:
CRYPTO (volatile):
- Length 1: 15
- Length 2: 35
- Smoothing: 8
(Faster response for rapid price movements)
STOCKS (standard):
- Length 1: 23
- Length 2: 50
- Smoothing: 10
(Balanced settings)
FOREX MAJORS (slower):
- Length 1: 30
- Length 2: 60
- Smoothing: 12
(Filters out noise in 24/7 markets)
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STEP 3: ENABLE YOUR PREFERRED SETUPS
Toggle setups based on your trading style:
Conservative Trader:
✓ Setup B (Divergence) — highest win rate
✗ Setup A (Classic) — only in strong trends
✗ Setup C (Bounce) — too aggressive
Trend Trader:
✓ Setup A (Classic) — primary signals
✓ Setup B (Divergence) — for entries on pullbacks
✗ Setup C (Bounce) — not suitable for trending
Scalper:
✓ Setup C (Bounce) — quick in-and-out
✓ Setup B (Divergence) — high probability scalps
✗ Setup A (Classic) — too slow
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STEP 4: READ THE SIGNALS
ON THE CHART:
Labels appear when conditions are met:
Green labels:
- "LONG A" — Setup A long entry
- "LONG B DIV" — Setup B divergence long (best signal)
- "LONG C" — Setup C bounce long
Red labels:
- "SHORT A" — Setup A short entry
- "SHORT B DIV" — Setup B divergence short (best signal)
- "SHORT C" — Setup C bounce short
IN THE INDICATOR PANEL (bottom):
- Blue line = Primary timeframe STC
- Orange dots = Higher timeframe STC (optional)
- Green/Red bars = Force Index histogram
- Dashed lines at 25/75 = Entry/Exit zones
- Background shading = Oversold (green) / Overbought (red)
INFO TABLE (top-right corner):
Shows real-time status:
- STC values for both timeframes
- Force Index direction
- Price position vs EMA
- Current trend direction
- Active signal type
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TRADING STRATEGY & RISK MANAGEMENT
ENTRY RULES:
Priority ranking (best to worst):
1st: Setup B (Divergence) — wait for these
2nd: Setup A (Classic) — in confirmed trends only
3rd: Setup C (Bounce) — scalping only
Confirmation checklist before entry:
☑ Signal label appears on chart
☑ TREND in info table matches signal direction
☑ Higher timeframe STC aligned (check orange dots or table)
☑ Force Index confirming (check histogram color)
───────────────────────────────────────────────────────────────────
STOP LOSS PLACEMENT:
Setup A (Classic):
- LONG: Below recent swing low
- SHORT: Above recent swing high
- Typical: 1-2 ATR distance
Setup B (Divergence):
- LONG: Below the divergence low
- SHORT: Above the divergence high
- Typical: 0.5-1.5 ATR distance
Setup C (Bounce):
- LONG: 5-10 pips below EMA50
- SHORT: 5-10 pips above EMA50
- Typical: 0.3-0.8 ATR distance
───────────────────────────────────────────────────────────────────
TAKE PROFIT TARGETS:
Conservative approach:
- Exit when STC reaches opposite level
- LONG: Exit when STC > 75
- SHORT: Exit when STC < 25
Aggressive approach:
- Hold until opposite signal appears
- Trail stop as STC moves in your favor
Partial profits:
- Take 50% at 1:2 risk/reward
- Let remaining 50% run to target
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WHAT TO AVOID:
❌ Trading Setup A in sideways/choppy markets
→ Wait for clear trend or use Setup B only
❌ Ignoring higher timeframe STC
→ Always check orange dots align with your direction
❌ Taking signals against the major trend
→ If weekly trend is down, be cautious with longs
❌ Overtrading Setup C
→ Maximum 2-3 bounce trades per session
❌ Trading during low volume periods
→ Force Index becomes unreliable
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ALERTS CONFIGURATION
The indicator includes 8 alert types:
Individual setup alerts:
- "Setup A - LONG" / "Setup A - SHORT"
- "Setup B - DIV LONG" / "Setup B - DIV SHORT" ⭐ recommended
- "Setup C - BOUNCE LONG" / "Setup C - BOUNCE SHORT"
Combined alerts:
- "ANY LONG" — fires on any long signal
- "ANY SHORT" — fires on any short signal
Recommended alert setup:
- Create "Setup B - DIV LONG" and "Setup B - DIV SHORT" alerts
- These are the highest probability signals
- Set "Once Per Bar Close" to avoid false alerts
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VISUALIZATION SETTINGS
Show Labels on Chart:
Toggle on/off the signal labels (green/red)
Disable for cleaner chart once you're familiar with the indicator
Show Higher TF STC:
Toggle the orange dots showing higher timeframe STC
Useful for visual confirmation of multi-timeframe alignment
Info Panel:
Cannot be disabled — always shows current status
Positioned top-right to avoid chart interference
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EXAMPLE TRADE WALKTHROUGH
SETUP B DIVERGENCE LONG EXAMPLE:
1. Market Context:
- Price in downtrend, below 50 EMA
- Multiple lower lows forming
- STC below 25 (oversold)
2. Divergence Formation:
- Price makes new low at $45.20
- Force Index refuses to make new low (higher low forms)
- This indicates selling pressure weakening
3. Signal Trigger:
- STC starts turning up
- Force Index crosses above zero
- Label appears: "LONG B DIV"
4. Trade Execution:
- Entry: $45.50 (current price at signal)
- Stop Loss: $44.80 (below divergence low)
- Target 1: $47.90 (STC reaches 75) — risk/reward 1:3.4
- Target 2: Opposite signal or trail stop
5. Trade Management:
- Price rallies to $47.20
- STC reaches 68 (approaching target zone)
- Take 50% profit, move stop to breakeven
- Exit remaining at $48.10 when STC crosses 75
Result: 3.7R gain
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ADVANCED TIPS
1. MULTI-TIMEFRAME CONFLUENCE
For highest probability trades, wait for:
- Primary TF signal
- Higher TF STC aligned (>25 for longs, <75 for shorts)
- Even higher TF trend in same direction (manual check)
2. VOLUME CONFIRMATION
Watch the Force Index histogram:
- Increasing bar size = Strengthening momentum
- Decreasing bar size = Weakening momentum
- Use this to gauge signal strength
3. AVOID THESE MARKET CONDITIONS
- Major news events (Force Index becomes erratic)
- Market open first 30 minutes (volatility spikes)
- Low liquidity instruments (Force Index unreliable)
- Extreme trending days (wait for pullbacks)
4. COMBINE WITH SUPPORT/RESISTANCE
Best signals occur near:
- Key horizontal levels
- Fibonacci retracements
- Previous day's high/low
- Psychological round numbers
5. SESSION AWARENESS
- Asia session: Use lower timeframes, Setup C works well
- London session: Setup A and B both effective
- New York session: All setups work, highest volume
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INDICATOR WINDOWS LAYOUT
MAIN CHART:
- Price action
- 50 EMA (green/red)
- Signal labels
- Info panel
INDICATOR WINDOW:
- STC oscillator (blue line, 0-100 scale)
- Higher TF STC (orange dots, optional)
- Force Index histogram (green/red bars)
- Reference levels (25, 50, 75)
- Background zones (green oversold, red overbought)
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PERFORMANCE OPTIMIZATION
For best results:
Backtesting:
- Test on your specific instrument and timeframe
- Adjust STC parameters if win rate < 55%
- Record which setup works best for your market
Position Sizing:
- Risk 1-2% per trade
- Setup B can use 2% risk (higher win rate)
- Setup C should use 1% risk (lower win rate)
Trade Frequency:
- Setup B: 2-5 signals per week (be patient)
- Setup A: 5-10 signals per week
- Setup C: 10+ signals per week (scalping)
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CREDITS & REFERENCES
This indicator builds upon established technical analysis concepts:
Schaff Trend Cycle:
- Developed by Doug Schaff (1996)
- Original concept published in Technical Analysis of Stocks & Commodities
- Implementation based on standard STC formula
Force Index:
- Developed by Dr. Alexander Elder
- Described in "Trading for a Living" (1993)
- Classic volume-momentum indicator
The multi-timeframe integration, three-setup system, and specific
entry conditions are original contributions of this indicator.
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DISCLAIMER
This indicator is a technical analysis tool and does not guarantee profits.
Past performance is not indicative of future results. Always:
- Use proper risk management
- Test on demo account first
- Combine with fundamental analysis
- Never risk more than you can afford to lose
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SUPPORT & QUESTIONS
If you find this indicator helpful, please:
- Leave a like and comment
- Share your feedback and results
- Report any bugs or issues
For questions about usage or optimization for specific markets,
feel free to comment below.
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Dynamic Market Structure (MTF) - Dow TheoryDynamic Market Structure (MTF)
OVERVIEW
This advanced indicator provides a comprehensive and fully customizable solution for analyzing market structure based on classic Dow Theory principles. It automates the identification of key structural points, including Higher Highs (HH), Higher Lows (HL), Lower Lows (LL), and Lower Highs (LH).
Going beyond simple pivot detection, this tool visualizes the flow of the trend by plotting dynamic Breaks of Structure (BOS) and potential reversals with Changes of Character (CHoCH). It is designed to be a flexible and powerful tool for traders who use price action and trend analysis as a core part of their strategy.
CORE CONCEPTS
The indicator is built on the foundational principles of Dow Theory:
Uptrend: A series of Higher Highs and Higher Lows.
Downtrend: A series of Lower Lows and Lower Highs.
Break of Structure (BOS): Occurs when price action continues the current trend by creating a new HH in an uptrend or a new LL in a downtrend.
Change of Character (CHoCH): Occurs when the established trend sequence is broken, signaling a potential reversal. For example, when a Lower Low forms after a series of Higher Highs.
CALCULATION METHODOLOGY
This section explains the indicator's underlying logic:
Pivot Detection: The indicator's core logic is based on TradingView's built-in ta.pivothigh() and ta.pivotlow() functions. The sensitivity of this detection is fully controlled by the user via the Pivot Lookback Left and Pivot Lookback Right settings.
Structure Calculation (BOS/CHoCH): The script identifies market structure by analyzing the sequence of these confirmed pivots.
A bullish BOS is plotted when a new ta.pivothigh is confirmed at a price higher than the previous confirmed ta.pivothigh.
A bearish CHoCH is plotted when a new ta.pivotlow is confirmed at a price lower than the previous confirmed ta.pivotlow , breaking the established sequence of higher lows.
The logic is mirrored for bearish BOS and bullish CHoCH.
Invalidation Levels: This feature identifies the last confirmed pivot before a structure break (e.g., the last ta.pivotlow before a bullish BOS) and plots a dotted line from it to the breakout bar. This level is considered the structural invalidation point for that move.
MTF Confirmation: This unique feature provides confluence by analyzing a second, lower timeframe. When a pivot (e.g., a Higher Low) is confirmed on the main chart, the script requests pivot data from the user-selected lower timeframe. If a corresponding trend reversal is detected on that lower timeframe (e.g., a break of its own minor downtrend), the pivot is labeled "Firm" (FHL); otherwise, it is labeled "Soft" (SHL).
KEY FEATURES
This indicator is packed with advanced features designed to provide a deeper level of market insight:
Dynamic Structure Lines: BOS and CHoCH levels are plotted with clean, dashed lines that dynamically start at the old pivot and terminate precisely at the breakout bar, keeping the chart clean and precise.
Invalidation Levels: For every structure break, the indicator can plot a dotted "Invalidation" line (INV). This marks the critical support or resistance pivot that, if broken, would negate the previous move, providing a clear reference for risk management.
Multi-Timeframe (MTF) Confirmation: Add a layer of confluence to your analysis by confirming pivots on a lower timeframe. The indicator can label Higher Lows and Lower Highs as either "Firm" (FHL/FLH) if confirmed by a reversal on a lower timeframe, or "Soft" (SHL/SLH) if not.
Flexible Pivot Detection: Fully adjustable Pivot Lookback settings for the left and right sides allow you to tune the indicator's sensitivity to match any timeframe or trading style, from long-term investing to short-term scalping.
Full Customization: Take complete control of the indicator's appearance. A dedicated style menu allows you to customize the colors for all bullish, bearish, and reversal elements, including the transparency of the trend-based candle coloring.
HOW TO USE
Trend Identification: Use the sequence of HH/HL and LL/LH, along with the trend-colored candles, to quickly assess the current market direction on any timeframe.
Entry Signals: A confirmed BOS can signal a potential entry in the direction of the trend. A CHoCH can signal a potential reversal, offering an opportunity to enter a new trend early.
Risk Management: Use the automatically plotted "Invalidation" (INV) lines as a logical reference point for placing stop losses. A break of this level indicates that the structure you were trading has failed.
Confluence: Use the "Firm" pivot signals from the MTF analysis to identify high-probability swing points that are supported by price action on multiple timeframes.
SETTINGS BREAKDOWN
Pivot Lookback Left/Right: Controls the sensitivity of pivot detection. Higher numbers find more significant (but fewer) pivots.
MTF Confirmation: Enable/disable the "Firm" vs. "Soft" pivot analysis and select your preferred lower timeframe for confirmation.
Style Settings: Customize all colors and the transparency of the candle coloring to match your chart's theme.
Show Invalidation Levels: Toggle the visibility of the dotted invalidation lines.
This indicator is a powerful tool for visualizing and trading with the trend. Experiment with the settings to find a configuration that best fits your personal trading strategy.
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer
Version: PineScriptv6
📌Description
The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not.
At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape.
The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions.
🚀Points of Innovation
Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions
Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples
Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data
Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback
Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel
Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds
🔧Core Components
RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions
Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations
Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations
Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell
Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small
Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels
🔥Key Features
56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility
Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness
Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles
Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading
Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency
Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells
Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences
Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count
🎨Visualization
Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency
Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance
Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation
Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve
Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns
Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers
📖Usage Guidelines
RSI Period
Default: 14
Range: 1 to unlimited
Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications.
MACD Fast Length
Default: 12
Range: 1 to unlimited
Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification.
MACD Slow Length
Default: 26
Range: 1 to unlimited
Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix.
MACD Signal Length
Default: 9
Range: 1 to unlimited
Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts.
Volume MA Period
Default: 20
Range: 1 to unlimited
Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts.
Statistics Lookback Period
Default: 200
Range: 50 to 500
Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics.
Forward Performance Bars
Default: 5
Range: 1 to 20
Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation.
Color Intensity Sensitivity
Default: 2.0
Range: 0.5 to 5.0, step 0.5
Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets.
Minimum Occurrences for Coloring
Default: 3
Range: 1 to 10
Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis.
Table Position
Default: top_right
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement.
Show Current State Panel
Default: true
Options: true, false
Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed.
Info Panel Position
Default: bottom_left
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout.
Win Rate Smoothing Strength
Default: 5
Range: 1 to 20
Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates.
✅Best Use Cases
Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment
Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters
Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates
Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns
Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells
Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence
⚠️Limitations
Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period
Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells
Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data
Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets
💡What Makes This Unique
Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis
Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools
Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork
Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues
Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements
🔬How It Works
1. State Classification and Encoding
Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80)
Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close
These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations
The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix
2. Historical Data Accumulation
As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period
When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window
This continuous process builds a comprehensive database of past market conditions and their subsequent price movements
3. Forward Return Calculation and Statistics Update
On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes
For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return
This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification
Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells
The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples
💡Note:
The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights.
DAMMU Swing Trading PRODammu Scalping Pro – Short Notes
1️⃣ Purpose:
Scalping and swing trading tool for 15-min and 1-min charts.
Designed for trend continuation, pullbacks, and reversals.
Works well with Heikin Ashi candles (optional).
2️⃣ Core Components:
EMAs:
Fast: EMA5-12
Medium: EMA12-36 Ribbon
Long: EMA75/89 (1-min), EMA180/200 (15-min), EMA540/633
Price Action Channel (PAC): EMA-based High, Low, Close channel.
Fractals: Regular & filtered (BW) fractals for swing recognition.
Higher Highs / Lower Highs / Higher Lows / Lower Lows (HH, LH, HL, LL).
Pivot Points: Optional display with labels.
3️⃣ Bar Coloring:
Blue: Close above PAC
Red: Close below PAC
Gray: Close inside PAC
4️⃣ Alerts:
Swing Buy/Sell arrows based on PAC breakout and EMA200 filter.
Optional “Big Arrows” mode for visibility.
Alert messages: "SWING_UP" and "SWING_DN"
5️⃣ Workflow / Usage Tips:
Set chart to 15-min (for trend) + 1-min (for entry).
Optionally enable Heikin Ashi candles.
Trade long only above EMA200, short only below EMA200.
Watch for pullbacks into EMA channels or ribbons.
Confirm trend resumption via PAC breakout & bar color change.
Use fractals and pivot points to draw trendlines and locate support/resistance.
6️⃣ Optional Filters:
Filter PAC signals with 200 EMA.
Filter fractals for “Pristine/Ideal” patterns (BW filter).
7️⃣ Visuals:
EMA ribbons, PAC fill, HH/LL squares, fractal triangles.
Pivot labels & candle numbering for patterns.
8️⃣ Notes:
No extra indicators needed except optionally SweetSpot Gold2 for major S/R levels.
Suitable for scalping pullbacks with trend confirmation.
If you want, I can make an even shorter “one-screen cheat sheet” with colors, alerts, and EMAs, perfect for real-time chart reference.
Do you want me to do that?
DAMMU Swing Trading PRODammu Scalping Pro – Short Notes
1️⃣ Purpose:
Scalping and swing trading tool for 15-min and 1-min charts.
Designed for trend continuation, pullbacks, and reversals.
Works well with Heikin Ashi candles (optional).
2️⃣ Core Components:
EMAs:
Fast: EMA5-12
Medium: EMA12-36 Ribbon
Long: EMA75/89 (1-min), EMA180/200 (15-min), EMA540/633
Price Action Channel (PAC): EMA-based High, Low, Close channel.
Fractals: Regular & filtered (BW) fractals for swing recognition.
Higher Highs / Lower Highs / Higher Lows / Lower Lows (HH, LH, HL, LL).
Pivot Points: Optional display with labels.
3️⃣ Bar Coloring:
Blue: Close above PAC
Red: Close below PAC
Gray: Close inside PAC
4️⃣ Alerts:
Swing Buy/Sell arrows based on PAC breakout and EMA200 filter.
Optional “Big Arrows” mode for visibility.
Alert messages: "SWING_UP" and "SWING_DN"
5️⃣ Workflow / Usage Tips:
Set chart to 15-min (for trend) + 1-min (for entry).
Optionally enable Heikin Ashi candles.
Trade long only above EMA200, short only below EMA200.
Watch for pullbacks into EMA channels or ribbons.
Confirm trend resumption via PAC breakout & bar color change.
Use fractals and pivot points to draw trendlines and locate support/resistance.
6️⃣ Optional Filters:
Filter PAC signals with 200 EMA.
Filter fractals for “Pristine/Ideal” patterns (BW filter).
7️⃣ Visuals:
EMA ribbons, PAC fill, HH/LL squares, fractal triangles.
Pivot labels & candle numbering for patterns.
8️⃣ Notes:
No extra indicators needed except optionally SweetSpot Gold2 for major S/R levels.
Suitable for scalping pullbacks with trend confirmation.
If you want, I can make an even shorter “one-screen cheat sheet” with colors, alerts, and EMAs, perfect for real-time charT
Copter 2.0💡 The indicator is designed for trading on any timeframe and includes a comprehensive system for determining entry and exit points based on technical analysis, price and volume.
📊 In the new version of Copter 2.0, the take profit and stop loss functions have been added
Let's analyze its key components:
✔️ Trend levels and extremes:
- The indicator determines local highs and lows for a certain period.
- the breakdown of these levels serves as a signal to open positions.
- the High-Low price dynamics analysis method is used to find key entry points.
✔️ Volumes:
-The indicator uses a configurable volume threshold to filter out candles with low volume and display only those with significant volume.
- the algorithm analyzes market data and sets an entry signal (opening a trade) and exit (profit taking/closing a position)
📍 Therefore, whether you are a beginner or an experienced trader, the indicator can help you stay ahead of the game and make more informed trading decisions.
📍 As a result, the trader can be sure that the signal is based on data analysis.
A long or short position can be stopped with either a profit or a small loss without prejudice to the potential profit.
✔️ Signal filtering:
- volume and volatile indicators are used to confirm the trend
- if a volume or volatility filter does not confirm the breakdown, the input signal is ignored
- analysis of moving averages of volumes and ATR is used
✔️ The use of the RSI in overbought and oversold analysis:
- the RSI indicator analyzes the strength of the current trend
- if the RSI exceeds 70, exit from a long position is possible
- if the RSI falls below 30, exit from a short position is possible
✔️ The use of EMA 20 and EMA 200
is additional moving average data that determines the current trend and the trend on higher timeframes.
- the main idea is that when they cross, we can see a change in trend movement and determine the general mood at the moment, based on which signals appear to open/close a deal.
- also, the indicator analyzes the past movement, thus determining the future direction
- based on the opening and closing of the past days, weeks, months.
✔️ Stop loss and risk management
- when entering a trade, a dynamic stop loss is set based on the percentage price change
- exit the position is carried out when a stop loss or a signal from the RSI is reached.
- it helps to minimize losses and protect profits
The market is unstable, and it is impossible to know what awaits it in the future.
The only way to manage risk is to limit the loss by setting a stop loss at 1% - 2% of the entry point.
It is recommended to set the profit in the ratio 1:1, 1:2,1:3, with partial fixation of 40%, 30%, 30% or wait for the indicator signal (TP)
We recommend fixing positions in parts. There will be a signal in the opposite direction when the volume is released.
To match the risk of the transaction, we recommend that you do not enter with high leverage.
Trade only with the amount that you are willing to lose.
With increased volatility in the market and flat, the indicator can give many signals.
After a strong fall or growth, we recommend not to open positions, because the probability of a flat is high.
✔️ Visualization of entry and exit points
- Entry points (long and short) are graphically displayed. green - long, orange - short
- stop loss levels are marked for clarity of risk management
✔️Recommendations for working with the indicator!
Entry/exit is performed on the next candle after the candle with the signal (buy/sell)
All timeframes and any trading pairs are used (when selecting settings for each one)
The indicator combines several methods of technical analysis:
- work with support and resistance levels
- filtering of signals based on volumes and volatility
- Overbought and oversold analysis using the RSI
- automatic risk management through stop loss
This approach makes the indicator a useful tool for short-term trading and active scalping.
❗️ NO REPAINT ! ❗️
RSI + Stochastic для M1 скальпингаRSI + Stochastic for M1 Scalping
This indicator combines Relative Strength Index (RSI) and Stochastic Oscillator into a single tool designed specifically for short-term scalping on the 1-minute chart. While both oscillators are widely used, they often produce many signals on their own. This script focuses on signal confirmation through synchronization, which reduces false entries and helps scalpers react faster in fast-moving markets.
How it works
RSI (7-period by default) tracks short-term momentum and highlights overbought (>70) or oversold (<30) conditions.
Stochastic Oscillator (%K = 5, %D = 3, smoothing = 3) adds sensitivity to micro-swings, providing context for intraday momentum.
The indicator generates a visual background highlight only when both oscillators confirm the same condition:
Green zone → RSI and Stochastic are both oversold, suggesting potential exhaustion of downward pressure.
Red zone → RSI and Stochastic are both overbought, indicating potential exhaustion of upward pressure.
Why this mashup is different
Rather than simply plotting RSI and Stochastic together, this tool emphasizes confluence-based filtering:
Signals appear only at extreme conditions across both oscillators, which helps reduce market noise common on M1 charts.
Background coloring makes it easier to spot high-probability setups visually, without needing to interpret multiple plots separately.
The parameter defaults are optimized for scalping strategies, but users can adjust them to fit their style.
How to use
Best suited for M1 and M5 timeframes where overbought/oversold conditions appear frequently.
Can be used to time entries and exits around support/resistance or trend continuation zones.
Works well as a confirmation filter alongside price action or volume-based indicators.
⚠️ Disclaimer: This indicator does not guarantee profitable trades. Always test on demo accounts and combine with risk management before applying to live markets.
Multi-Timeframe Trend Table - EMA Based Trend Analysis📊 Stay Aligned with Higher Timeframe Trends While Scalping
This powerful indicator displays real-time trend direction for 1-hour and 4-hour timeframes in a clean, easy-to-read table format. Perfect for traders who want to align their short-term trades with higher timeframe momentum.
🎯 Key Features
Multi-Timeframe Analysis: Monitor 1H and 4H trends while trading on any timeframe (3min, 5min, 15min, etc.)
EMA-Based Logic: Uses proven EMA 50 and EMA 100 crossover methodology
Visual Clarity: Color-coded table with green (uptrend) and red (downtrend) indicators
Customizable Display: Toggle EMA values and adjust table position
Real-Time Updates: Automatically refreshes with each bar close
Lightweight: Minimal resource usage with efficient data requests
📈 How It Works
The indicator determines trend direction using a simple but effective rule:
UPTREND: Price is above both EMA 50 AND EMA 100
DOWNTREND: Price is below either EMA 50 OR EMA 100
🔧 Settings
Show EMA Values: Display actual EMA 50/100 values in the table
Table Position: Choose from 4 corner positions (Top Right, Top Left, Bottom Right, Bottom Left)
Plot Current EMAs: Optional display of EMA lines on your current chart
💡 Trading Applications
✅ Trend Confirmation: Ensure your trades align with higher timeframe direction
✅ Risk Management: Avoid counter-trend trades in strong directional markets
✅ Entry Timing: Use lower timeframe for entries while respecting higher timeframe bias
✅ Scalping Enhancement: Perfect for 1-5 minute scalping with higher timeframe context
🎨 Visual Design
Clean, professional table design
Intuitive color coding (Green = Up, Red = Down)
Compact size that doesn't obstruct your chart
Clear typography for quick reading
📋 Perfect For
Day traders and scalpers
Swing traders seeking trend confirmation
Multi-timeframe analysis enthusiasts
Traders who want simple, effective trend identification
🚀 Easy Setup
Add to any chart (works on all timeframes)
Customize table position and settings
Start trading with higher timeframe awareness
Watch the table update automatically
No complex configurations needed - just add and trade!
This indicator is designed for educational and informational purposes. Always combine with proper risk management and your own analysis.
Commodity Channel Index (CCI)An indicator with increased convenience and customization options. Effective for scalping.
