Daily Dynamic Grid StrategyHi everyone,
This strategy is built around a dynamic daily grid concept, using an upper and lower daily range that is automatically divided into multiple grid levels.
The idea is to take advantage of daily volatility by executing DCA entries on specific grid levels, based on predefined conditions.
Key points of the strategy & feature:
I recommend using 1H or 2H timeframe for this strategy
Take profit by grid
When DCA is active (>1 entry), the exit condition switches to close above the average price
A hard stop loss is applied
Includes an optional Trailing TP / SL to help maximize profit during strong moves
Like most DCA-based strategies, it tends to have a high win rate, but during strong market dumps, losses can become relatively large
Can also be used for backtest on Forex markets such as Gold, where using the trailing option is generally more effective
And still trial for the webhook, may continue to improve and update this strategy in future versions.
ATR
[SM-021] Gaussian Trend System [Optimized]This script is a comprehensive trend-following strategy centered around a Gaussian Channel. It is designed to capture significant market movements while filtering out noise during consolidation phases. This version (v2) introduces code optimizations using Pine Script v6 Arrays and a new Intraday Time Control feature.
1. Core Methodology & Math
The foundation of this strategy is the Gaussian Filter, originally conceptualized by @DonovanWall.
Gaussian Poles: Unlike standard moving averages (SMA/EMA), this filter uses "poles" (referencing signal processing logic) to reduce lag while maintaining smoothness.
Array Optimization: In this specific iteration, the f_pole function has been refactored to utilize Pine Script Arrays. This improves calculation efficiency and rendering speed compared to recursive variable calls, especially when calculating deep historical data.
Channel Logic: The strategy calculates a "Filtered True Range" to create High and Low bands around the main Gaussian line.
Long Entry: Price closes above the High Band.
Short Entry: Price closes below the Low Band.
2. Signal Filtering (Confluence)
To reduce false signals common in trend-following systems, the strategy employs a "confluence" approach using three additional layers:
Baseline Filter: A 200-period (customizable) EMA or SMA acts as a regime filter. Longs are only taken above the baseline; Shorts only below.
ADX Filter (Volatility): The Average Directional Index (ADX) is used to measure trend strength. If the ADX is below a user-defined threshold (default: 20), the market is considered "choppy," and new entries are blocked.
Momentum Check: A Stochastic RSI check ensures that momentum aligns with the breakout direction.
3. NEW: Intraday Session Filter
Per user requests, a time-based filter has been added to restrict trading activity to specific market sessions (e.g., the New York Open).
How it works: Users can toggle a checkbox to enable/disable the filter.
Configuration: You can define a specific time range (Default: 09:30 - 16:00) and a specific Timezone (Default: New York).
Logic: The strategy longCondition and shortCondition now check if the current bar's timestamp falls within this window. If outside the window, no new entries are generated, though existing trades are managed normally.
4. Risk Management
The strategy relies on volatility-based exits rather than fixed percentage stops:
ATR Stop Loss: A multiple of the Average True Range (ATR) is calculated at the moment of entry to set a dynamic Stop Loss.
ATR Take Profit: An optional Reward-to-Risk (RR) ratio can be set to place a Take Profit target relative to the Stop Loss distance.
Band Exit: If the trend reverses and price crosses the opposite band, the trade is closed immediately to prevent large drawdowns.
Credits & Attribution
Original Gaussian Logic: Developed by @DonovanWalll. This script utilizes his mathematical formula for the pole filters.
Strategy Wrapper & Array Refactor: Developed by @sebamarghella.
Community Request: The Intraday Session Filter was added to assist traders focusing on specific liquidity windows.
Disclaimer: This strategy is for educational purposes. Past performance is not indicative of future results. Please use the settings menu to adjust the Session Time and Risk parameters to fit your specific asset class.
Hash Ratings EngineHash Ratings Engine - Technical Consensus Strategy
A systematic trading strategy that harnesses TradingView's Technical Ratings to generate high-conviction entries with institutional-grade risk management.
What It Does
This strategy aggregates the consensus of 26+ technical indicators (RSI, MACD, Stochastics, multiple Moving Averages, etc.) into a single actionable signal. When enough indicators align bullish or bearish, the engine triggers an entry. Built-in trend filtering and ATR-based exits keep you on the right side of the market.
Key Features
Trend Filter - Only takes longs in uptrends, shorts in downtrends. This single filter typically improves results by 20-40% by avoiding counter-trend trades.
ATR-Based Risk Management - Stop loss and trailing stops adapt to current market volatility. Tight stops in calm markets, wider stops in volatile conditions.
Cooldown System - After a losing trade, the strategy waits before re-entering. This prevents the consecutive loss streaks that destroy accounts.
Clean Visuals - Fluorescent entry/exit signals with price level references. See exactly where you got in and out.
Settings Guide
Indicator Timeframe: Leave blank for current chart. Use higher timeframe for fewer, higher-quality signals.
Rating Source: "All" for balanced approach. "MAs" for trend-following. "Oscillators" for mean-reversion.
Entry Thresholds
Strong Signal Threshold: Higher = fewer trades but better conviction. Start at 0.5, test 0.4-0.6.
Risk Management
ATR Period: 12 is responsive, 14 is standard, 20+ is smoother.
Stop Loss: 2-3x ATR for tight stops, 3.5-4x for moderate, 5x+ for wide.
Trail Activation: How far price must move in profit before trailing begins.
Trail Offset: How closely the trail follows price.
Trend Filter
EMA Length: 150 works well on 4H charts. Use 100 for lower timeframes, 200 for daily.
Trade Timing
Cooldown: Keep enabled. 5 bars is a good starting point.
Best Practices
Start with default settings and backtest on your preferred instrument. Adjust the Strong Signal Threshold first - this has the biggest impact on trade frequency. Then tune the EMA length to match your timeframe. Finally, optimize the ATR multipliers for your risk tolerance.
Works on any liquid market - crypto, forex, stocks, futures. Higher timeframes (4H, Daily) tend to produce cleaner signals than lower timeframes.
Disclaimer
Past performance does not guarantee future results. Always backtest thoroughly and use proper position sizing. This strategy is for educational purposes - trade at your own risk.
DEMA ATR Strategy [PrimeAutomation]⯁ OVERVIEW
The DEMA ATR Strategy combines trend-following logic with adaptive volatility filters to identify strong momentum phases and manage trades dynamically.
It uses a Double Exponential Moving Average (DEMA) anchored to ATR volatility bands, creating a self-adjusting trend baseline.
When the adjusted DEMA shifts direction, the strategy enters positions and scales out profit in phases based on ATR-driven targets.
This system adapts to volatility, filters noise, and seeks sustained directional moves.
⯁ KEY FEATURES
DEMA-Volatility Hybrid Filter
Uses Double EMA with ATR expansion/compression logic to form a dynamic trend baseline.
Directional Shift Entries
Entries occur when the adjusted DEMA flips trend (bullish crossover or bearish crossunder vs its past value).
Noise Reduction Mechanism
ATR range caps extreme moves and prevents false flips during choppy volatility spikes.
Multi-Level Take Profits
Targets scale out positions at 1×, 2×, and 3× ATR multiples in the trade direction.
Volatility-Adaptive Targets
ATR multiplier ensures profit targets expand/contract based on market conditions.
Single-Direction Exposure
No pyramiding; the strategy flips position only when trend shifts.
Automated Trade Finalization
When all profit targets trigger, the position is fully closed.
⯁ STRATEGY LOGIC
Trend Direction:
DEMA baseline is modified using ATR upper/lower envelopes.
• If the adjusted DEMA rises above previous value → Bullish
• If it falls below previous value → Bearish
Entry Rules:
• Enter Long when bullish shift occurs and no long position exists
• Enter Short when bearish shift occurs and no short position exists
Take Profit Logic:
3 partial exits for each trade based on ATR:
• TP1 = ±1× ATR
• TP2 = ±2× ATR
• TP3 = ±3× ATR
Profit distribution: 30% / 30% / 40%
Exit Conditions:
• Exit when all TPs hit (full scale-out if sum of all TPs 100%)
• Opposite trend signal closes current trade and opens new one
⯁ WHEN TO USE
Trending environments
Medium–high volatility phases
Swing trading and intraday trend plays
Markets that respect momentum continuation (crypto, indices, FX majors)
⯁ CONCLUSION
This strategy blends DEMA trend recognition with ATR-based volatility adaptation to generate cleaner directional entries and structured take-profit exits. It is designed to capture momentum phases while avoiding noise-driven false signals, delivering a disciplined and scalable trend-following approach.
Safe Supertrend Strategy (No Repaint)Overview
The Safe Supertrend is a repaint-free version of the popular Supertrend trend-following indicator.
Most Supertrend indicators appear perfect on historical charts because they flip intrabar and then repaint after the candle closes.
This version fixes that by using close-of-bar confirmation only, making every trend flip 100% stable, safe, and non-repainting.
Why This Supertrend Doesn’t Repaint
Most Supertrend indicators calculate their trend direction using the current bar’s data.
But during a live candle:
ATR expands and contracts
The upper/lower bands move
Price moves above/below the band temporarily
A false flip appears → then disappears when the candle closes
That is classic repainting.
This indicator avoids all of that by using:
close > upper
close < lower
This means:
Trend direction flips only based on the previous candle,
No intrabar calculations,
No flickering signals,
No “perfect but fake” historical performance.
Every signal you see on the chart is exactly what was available in real-time.
How It Works
Calculates ATR (Average True Range) and SMA centerline
Builds upper and lower volatility bands
Confirms trend flips only after the previous bar closes
Plots clear bull and bear reversal signals
Works on all markets (crypto, stocks, forex, indices)
No repainting, no recalc, no misleading flips.
Bullish Signal (Trend Up)
A bullish trend begins only when:
The previous candle closes above the upper ATR band,
And this flip is fully confirmed.
A green triangle marks the start of a new uptrend.
Bearish Signal (Trend Down)
A bearish trend begins only when:
The previous candle closes below the lower ATR band,
And the downtrend is confirmed.
A red triangle signals the start of a new downtrend.
Inputs
ATR Length - default 10
ATR Multiplier - default 3.0
Works on all timeframes and market
Simple, but powerful.
Why Use This Version Instead of a Regular Supertrend?
Most Supertrends:
Look great historically
But repaint continuously on live charts
Give false trend flips intrabar
Cannot be reliably used in strategies
This version:
Uses strict previous-bar logic
Never repaints trend direction
Works perfectly in live trading
Backtests accurately
Is ideal for algorithmic strategies
Ideal For:
Trend-following strategies
Breakout trading
Algo trading systems
Reversal detection
Filtering market noise
Swing trading & scalping
Final Note
This is a safer, more reliable Supertrend designed for real-world use — not perfect-looking repaint illusions.
If you use Supertrend in your trading system, this no-repaint version ensures your signals are trustworthy and consistent.
Safe Supertrend Strategy (No Repaint)Overview
The Safe Supertrend is a repaint-free version of the popular Supertrend trend-following indicator.
Most Supertrend indicators appear perfect on historical charts because they flip intrabar and then repaint after the candle closes.
This version fixes that by using close-of-bar confirmation only, making every trend flip 100% stable, safe, and non-repainting.
Why This Supertrend Doesn’t Repaint
Most Supertrend indicators calculate their trend direction using the current bar’s data.
But during a live candle:
ATR expands and contracts
The upper/lower bands move
Price moves above/below the band temporarily
A false flip appears → then disappears when the candle closes
That is classic repainting.
This indicator avoids all of that by using:
close > upper
close < lower
This means:
Trend direction flips only based on the previous candle,
No intrabar calculations,
No flickering signals,
No “perfect but fake” historical performance.
Every signal you see on the chart is exactly what was available in real-time.
How It Works
Calculates ATR (Average True Range) and SMA centerline
Builds upper and lower volatility bands
Confirms trend flips only after the previous bar closes
Plots clear bull and bear reversal signals
Works on all markets (crypto, stocks, forex, indices)
No repainting, no recalc, no misleading flips.
Bullish Signal (Trend Up)
A bullish trend begins only when:
The previous candle closes above the upper ATR band,
And this flip is fully confirmed.
A green triangle marks the start of a new uptrend.
Bearish Signal (Trend Down)
A bearish trend begins only when:
The previous candle closes below the lower ATR band,
And the downtrend is confirmed.
A red triangle signals the start of a new downtrend.
Inputs
ATR Length - default 10
ATR Multiplier - default 3.0
Works on all timeframes and market
Simple, but powerful.
Why Use This Version Instead of a Regular Supertrend?
Most Supertrends:
Look great historically
But repaint continuously on live charts
Give false trend flips intrabar
Cannot be reliably used in strategies
This version:
Uses strict previous-bar logic
Never repaints trend direction
Works perfectly in live trading
Backtests accurately
Is ideal for algorithmic strategies
Ideal For:
Trend-following strategies
Breakout trading
Algo trading systems
Reversal detection
Filtering market noise
Swing trading & scalping
Final Note
This is a safer, more reliable Supertrend designed for real-world use — not perfect-looking repaint illusions.
If you use Supertrend in your trading system, this no-repaint version ensures your signals are trustworthy and consistent.
Hash Supertrend [Hash Capital Research]Hash Supertrend Strategy by Hash Capital Research
Overview
Hash Supertrend is a professional-grade trend-following strategy that combines the proven Supertrend indicator with institutional visual design and flexible time filtering.
The strategy uses ATR-based volatility bands to identify trend direction and executes position reversals when the trend flips.This implementation features a distinctive fluorescent color system with customizable glow effects, making trend changes immediately visible while maintaining the clean, professional aesthetic expected in quantitative trading environments.
Entry Signals:
Long Entry: Price crosses above the Supertrend line (trend flips bullish)
Short Entry: Price crosses below the Supertrend line (trend flips bearish)
Controls the lookback period for volatility calculation
Lower values (7-10): More sensitive to price changes, generates more signals
Higher values (12-14): Smoother response, fewer signals but potentially delayed entries
Recommended range: 7-14 depending on market volatility
Factor (Default: 3.0)
Restricts trading to specific hours
Useful for avoiding low-liquidity sessions, overnight gaps, or known choppy periods
When disabled, strategy trades 24/7
Start Hour (Default: 9) & Start Minute (Default: 30)
Define when the trading session begins
Uses exchange timezone in 24-hour format
Example: 9:30 = 9:30 AM
End Hour (Default: 16) & End Minute (Default: 0)
Controls the vibrancy of the fluorescent color system
1-3: Subtle, muted colors
4-6: Balanced, moderate saturation
7-10: Bright, highly saturated fluorescent appearance
Affects both the Supertrend line and trend zones
Glow Effect (Default: On)
Adds luminous halo around the Supertrend line
Creates a multi-layered visual with depth
Particularly effective during strong trends
Glow Intensity (Default: 5.0)
Displays tiny fluorescent dots at entry points
Green dot below bar: Long entry
Red dot above bar: Short entry
Provides clear visual confirmation of executed trades
Show Trend Zone (Default: On)
Strong trending markets (2020-style bull runs, sustained bear markets)
Markets with clear directional bias
Instruments with consistent volatility patterns
Timeframes: 15m to Daily (optimal on 1H-4H)
Challenging Conditions:
Choppy, range-bound markets
Low volatility consolidation periods
Highly news-driven instruments with frequent gaps
Very low timeframes (1m-5m) prone to noise
Recommended AssetsCryptocurrency:
ATR Trend + RSI Pullback Strategy [Profit-Focused]This strategy is designed to catch high-probability pullbacks during strong trends using a combination of ATR-based volatility filters, RSI exhaustion levels, and a trend-following entry model.
Strategy Logic
Rather than relying on lagging crossovers, this model waits for RSI to dip into oversold zones (below 40) while price remains above a long-term EMA (default: 200). This setup captures pullbacks in strong uptrends, allowing traders to enter early in a move while controlling risk dynamically.
To avoid entries during low-volatility conditions or sideways price action, it applies a minimum ATR filter. The ATR also defines both the stop-loss and take-profit levels, allowing the model to adapt to changing market conditions.
Exit logic includes:
A take-profit at 3× the ATR distance
A stop-loss at 1.5× the ATR distance
An optional early exit if RSI crosses above 70, signaling overbought conditions
Technical Details
Trend Filter: 200 EMA – must be rising and price must be above it
Entry Signal: RSI dips below 40 during an uptrend
Volatility Filter: ATR must be above a user-defined minimum threshold
Stop-Loss: 1.5× ATR below entry price
Take-Profit: 3.0× ATR above entry price
Exit on Overbought: RSI > 70 (optional early exit)
Backtest Settings
Initial Capital: $10,000
Position Sizing: 5% of equity per trade
Slippage: 1 tick
Commission: 0.075% per trade
Trade Direction: Long only
Timeframes Tested: 15m, 1H, and 30m on trending assets like BTCUSD, NAS100, ETHUSD
This model is tuned for positive P&L across trending environments and volatile markets.
Educational Use Only
This strategy is for educational purposes only and should not be considered financial advice. Past performance does not guarantee future results. Always validate performance on multiple markets and timeframes before using it in live trading.
BB SPY Mean Reversion Investment StrategySummary
Mean reversion first, continuation second. This strategy targets equities and ETFs on daily timeframes. It waits for price to revert from a Bollinger location with candle and EMA agreement, then manages risk with ATR based exits. Uniqueness comes from two elements working together. One, an adaptive band multiplier driven by volatility of volatility that expands or contracts the envelope as conditions change. Two, a bias memory that re arms the same direction after any stop, target, or time exit until a true opposite signal appears. Add it to a clean chart, use the markers and levels, and select on bar close for conservative alerts. Shapes can move while the bar is open and settle on close.
Scope and intent
• Markets. Currently adapted for SPY, needs to be optimized for other assets
• Timeframes. Daily primary. Other frames are possible but not the default
• Default demo. SPY on daily
• Purpose. Trade mean reversion entries that can chain into a longer swing by splitting holds into ATR or time segments
Originality and usefulness
• Novelty. Adaptive band width from volatility of volatility plus a persistent bias array that keeps the original direction alive across sequential entries until an opposite setup is confirmed
• Failure modes mitigated. False starts in chop are reduced by candle color and EMA location. Missed continuation after a take profit or stop is addressed by the re arm engine. Oversized envelopes during quiet regimes are avoided by the adaptive multiplier
• Testability. Every module has Inputs and visible levels so users can see why a suggestion appears
• Portable yardstick. All risk and targets are expressed in ATR units
Method overview in plain language
The engine measures where price sits relative to Bollinger bands, confirms with candle color and EMA location, requires ADX for shorts(in our case long close since we use it currently as long only), and optionally requires a trend or mean reversion regime using band width percent rank and basis slope. Risk uses ATR for stop, target, and optional breakeven. A small array stores the last confirmed direction. While flat, the engine keeps a pending order in that direction. The array flips only when a true opposite setup appears.
Base measures
• Range basis. True Range smoothed over a user defined ATR Length
• Return basis. Not required
Components
• Bollinger envelope. SMA length and standard deviation multiplier. Entry is based on cross of close through the band with location bias
• Candle and EMA filter. Close relative to open and close relative to EMA align direction
• ADX gate for shorts. Requires minimum trend strength for short trades
• Adaptive multiplier. Band width scales using volatility of volatility so envelopes breathe with conditions
• Regime gate optional. Band width percent rank and basis slope identify trend or mean reversion regimes
• Risk manager. ATR stop, ATR target, optional breakeven, optional time exit
• Bias memory. Array stores last confirmed direction and re arms entries while flat
Fusion rule
Minimum satisfied gates count style. All required gates must be true. Optional gates are controlled in Inputs. Bias memory never overrides an opposite confirmed setup.
Signal rule
• Long setup when close crosses up through the lower band, the bar closes green, and close is above the long EMA
• Short setup when close crosses down through the upper band, the bar closes red, close is below the short EMA, and ADX is above the minimum
• While flat the model keeps a pending order in the stored direction until a true opposite setup appears
• IN LONG or IN SHORT describes states between entry and exit
What you will see on the chart
• Markers for Long and Short setups
• Exit markers from ATR or time rules
• Reference levels for entry, stop, and target
• Bollinger bands and optional adaptive bands
Inputs with guidance
Setup
• Signal timeframe. Uses the chart timeframe
• Invert direction optional. Flips long and short
Logic
• BB Length. Typical 10 to 50. Higher smooths more
• BB Mult. Typical 1.0 to 2.5. Higher widens entries
• EMA Length long. Typical 10 to 50
• EMA Length short. Typical 5 to 30
• ADX Minimum for short. Typical 15 to 35
Filters
• Regime Type. none or trend or mean reversion
• Rank Lookback. Typical 100 to 300
• Basis Slope Length and Threshold. Larger values reduce false trends
Risk
• ATR Length. Typical 10 to 21
• ATR Stop Mult. Typical 1.0 to 3.0
• ATR Take Profit Mult. Typical 2.0 to 5.0
• Breakeven Trigger R. Move stop to entry after the chosen multiple
• Time Exit. Minimum bars and extension when profit exceeds a fraction of ATR
Bias and rearm
• Bias flips kept. Array depth
• Keep rearm when flat. Maintain a pending order while flat
UI
• Show markers and levels. Clean defaults
Usage recipes
Alerts update in real time and can change while the bar forms. Select on bar close for conservative workflows.
Properties visible in this publication
• Initial capital 25000
• Base currency USD
• If any higher timeframe calls are enabled, request.security uses lookahead off
• Commission 0.03 percent
• Slippage 3 ticks
• Default order size method Percent of equity with value 5
• Pyramiding 0
• Process orders on close On
• Bar magnifier Off
• Recalculate after order is filled Off
• Calc on every tick Off
Realism and responsible publication
No performance claims. Costs and fills vary by venue. Shapes can move intrabar and settle on close. Strategies use standard candles only.
Honest limitations and failure modes
High impact releases and thin liquidity can break assumptions. Gap heavy symbols may require larger ATR. Very quiet regimes can reduce contrast in the mean reversion signal. If stop and target can both be touched inside one bar, outcome follows the TradingView order model for that bar path.
Regimes with extreme one sided trend and very low volatility can reduce mean reversion edges. Results vary by symbol and venue. Past results never guarantee future outcomes.
Open source reuse and credits
None.
Backtest realism
Costs are realistic for liquid equities. Sizing does not exceed five percent per trade by default. Any departure should be justified by the user.
If you got any questions please le me know
PSAR with ATR Trailing Stop + SMA Filter📈 Strategy Overview: PSAR + 6×ATR Trailing Stop with SMA Filter
This strategy is built around the principle of “Cut the losers, let the winners run” — a disciplined, trend-following approach that combines the Parabolic SAR indicator with dynamic risk management and a Simple Moving Average (SMA) trend filter.
🔍 Strategy Logic
Trend Filter Trades are only taken in the direction of the prevailing trend, defined by a user-selected SMA (default: 100).
✅ Long trades only when price is above the SMA
✅ Short trades only when price is below the SMA
Entry Signal: A trade is triggered when the Parabolic SAR flips to the opposite side of the price bars, signaling a potential trend reversal.
Stop Loss: The stop loss is dynamically set at 6×ATR from the entry price. This adapts to market volatility and is recalculated every bar — effectively acting as a trailing stop.
Exit Logic: There is no fixed take profit. The trade remains open until the trailing stop is hit — allowing winners to run and losers to be cut quickly.
Risk Management: Each trade risks 0.5% of total equity, ensuring consistent position sizing and capital preservation.
📊 Visual Elements
PSAR dots mark trend direction changes
SMA line shows the broader trend filter
Trailing stop crosses (with 50% opacity) indicate the current stop level without cluttering the chart
⚙️ Customizable Inputs
PSAR parameters: Start, Increment, Maximum
ATR length and multiplier
SMA length
Risk percentage per trade
This strategy is ideal for traders who want to stay aligned with the trend, automate disciplined exits, and avoid emotional decision-making. Clean, simple, and powerful.
Wishing you calm and successful trades!
Hyper SAR Reactor Trend StrategyHyperSAR Reactor Adaptive PSAR Strategy
Summary
Adaptive Parabolic SAR strategy for liquid stocks, ETFs, futures, and crypto across intraday to daily timeframes. It acts only when an adaptive trail flips and confirmation gates agree. Originality comes from a logistic boost of the SAR acceleration using drift versus ATR, plus ATR hysteresis, inertia on the trail, and a bear-only gate for shorts. Add to a clean chart and run on bar close for conservative alerts.
Scope and intent
• Markets: large cap equities and ETFs, index futures, major FX, liquid crypto
• Timeframes: one minute to daily
• Default demo: BTC on 60 minute
• Purpose: faster yet calmer PSAR that resists chop and improves short discipline
• Limits: this is a strategy that places simulated orders on standard candles
Originality and usefulness
• Novel fusion: PSAR AF is boosted by a logistic function of normalized drift, trail is monotone with inertia, entries use ATR buffers and optional cooldown, shorts are allowed only in a bear bias
• Addresses false flips in low volatility and weak downtrends
• All controls are exposed in Inputs for testability
• Yardstick: ATR normalizes drift so settings port across symbols
• Open source. No links. No solicitation
Method overview
Components
• Adaptive AF: base step plus boost factor times logistic strength
• Trail inertia: one sided blend that keeps the SAR monotone
• Flip hysteresis: price must clear SAR by a buffer times ATR
• Volatility gate: ATR over its mean must exceed a ratio
• Bear bias for shorts: price below EMA of length 91 with negative slope window 54
• Cooldown bars optional after any entry
• Visual SAR smoothing is cosmetic and does not drive orders
Fusion rule
Entry requires the internal flip plus all enabled gates. No weighted scores.
Signal rule
• Long when trend flips up and close is above SAR plus buffer times ATR and gates pass
• Short when trend flips down and close is below SAR minus buffer times ATR and gates pass
• Exit uses SAR as stop and optional ATR take profit per side
Inputs with guidance
Reactor Engine
• Start AF 0.02. Lower slows new trends. Higher reacts quicker
• Max AF 1. Typical 0.2 to 1. Caps acceleration
• Base step 0.04. Typical 0.01 to 0.08. Raises speed in trends
• Strength window 18. Typical 10 to 40. Drift estimation window
• ATR length 16. Typical 10 to 30. Volatility unit
• Strength gain 4.5. Typical 2 to 6. Steepness of logistic
• Strength center 0.45. Typical 0.3 to 0.8. Midpoint of logistic
• Boost factor 0.03. Typical 0.01 to 0.08. Adds to step when strength rises
• AF smoothing 0.50. Typical 0.2 to 0.7. Adds inertia to AF growth
• Trail smoothing 0.35. Typical 0.15 to 0.45. Adds inertia to the trail
• Allow Long, Allow Short toggles
Trade Filters
• Flip confirm buffer ATR 0.50. Typical 0.2 to 0.8. Raise to cut flips
• Cooldown bars after entry 0. Typical 0 to 8. Blocks re entry for N bars
• Vol gate length 30 and Vol gate ratio 1. Raise ratio to trade only in active regimes
• Gate shorts by bear regime ON. Bear bias window 54 and Bias MA length 91 tune strictness
Risk
• TP long ATR 1.0. Set to zero to disable
• TP short ATR 0.0. Set to 0.8 to 1.2 for quicker shorts
Usage recipes
Intraday trend focus
Confirm buffer 0.35 to 0.5. Cooldown 2 to 4. Vol gate ratio 1.1. Shorts gated by bear regime.
Intraday mean reversion focus
Confirm buffer 0.6 to 0.8. Cooldown 4 to 6. Lower boost factor. Leave shorts gated.
Swing continuation
Strength window 24 to 34. ATR length 20 to 30. Confirm buffer 0.4 to 0.6. Use daily or four hour charts.
Properties visible in this publication
Initial capital 10000. Base currency USD. Order size Percent of equity 3. Pyramiding 0. Commission 0.05 percent. Slippage 5 ticks. Process orders on close OFF. Bar magnifier OFF. Recalculate after order filled OFF. Calc on every tick OFF. No security calls.
Realism and responsible publication
No performance claims. Past results never guarantee future outcomes. Shapes can move while a bar forms and settle on close. Strategies execute only on standard candles.
Honest limitations and failure modes
High impact events and thin books can void assumptions. Gap heavy symbols may prefer longer ATR. Very quiet regimes can reduce contrast and invite false flips.
Open source reuse and credits
Public domain building blocks used: PSAR concept and ATR. Implementation and fusion are original. No borrowed code from other authors.
Strategy notice
Orders are simulated on standard candles. No lookahead.
Entries and exits
Long: flip up plus ATR buffer and all gates true
Short: flip down plus ATR buffer and gates true with bear bias when enabled
Exit: SAR stop per side, optional ATR take profit, optional cooldown after entry
Tie handling: stop first if both stop and target could fill in one bar
Twisted Forex's Doji + Area StrategyTitle
Twisted Forex’s Doji + Area Strategy
Description
What this strategy does
This strategy looks for doji candles forming inside or near supply/demand areas . Areas are built from swing pivots and sized with ATR, then tracked for retests (“confirmations”). When a doji prints close to an area and quality checks pass, the strategy places a trade with the stop beyond the doji and a configurable R:R target.
How areas (zones) are built
• Swings are detected with a user-set pivot length.
• Each swing spawns a horizontal area centered at the pivot price with half-height = zoneHalfATR × ATR .
• Duplicates are de-duplicated by center distance (ATR-scaled).
• Areas fade when broken beyond a buffer or after an optional age (expiry).
• Retests are recorded when price touches and then bounces away from the area; repeated reactions increase the zone’s “strength”.
Signal logic (summary)
Doji detection: strict or loose body criteria with optional minimum wick fractions and ATR-scaled minimum range.
Proximity: price must be inside/near a supply or demand area (proxATR × ATR).
Side resolution: overlap is resolved by (a) which side price penetrates more, (b) fast/slow EMA trend, or (c) nearest distance. Optional “previous candle flip” can bias long after a bearish candle and short after a bullish one.
Optional 1-bar confirmation: the bar after the doji must close away from the area by confirmATR × ATR .
Quality filter (Off/Soft/Strict): four checks—(i) wick rejection past the edge, (ii) doji closes in an edge “band” of the area, (iii) fresh touch (cooldown), (iv) approach impulse over a short lookback. In Strict , thresholds auto-tighten.
Orders & exits
• Long: stop below doji low minus buffer; Short: above doji high plus buffer.
• Target = rrMultiple × risk distance .
• Pyramiding is off by default.
Position sizing
You can size from the script or from Strategy Properties:
• Script-driven (default): set Position sizing = “Risk % of equity” and choose riskPercent (e.g., 1.0%). The script applies safe floors/rounding (FX micro-lots by default) so quantity never rounds to zero.
• Properties-driven : toggle Use TV Properties → Order size ON, then pick “Percent of equity” in Properties (e.g., 1%). The header includes safe defaults so trades still place.
Key inputs to explore
• Zone building : pivotLen, zoneHalfATR, minDepartureATR, expiryBars, breakATR, leftBars, dedupeATR.
• Doji & proximity : strictDoji, dojiBodyFrac, minWickFrac, minRangeATR, proxATR, minBarsBetween.
• Overlap resolution : usePenetration, useTrend (EMA 21/55), “previous candle flip”, needNextBarConf & confirmATR.
• Quality : qualityMode (Off/Soft/Strict), minQualPass/kStrict, wickPenATR, edgeBandFrac, approachLookback, approachMinATR, freshTouchBars.
• Zone strength gating : minStrengthSoft / minStrengthStrict.
• HTF confluence (optional) : useHTFTrend (HTF EMA 34/89) and/or useHTFZoneProx (HTF swing bands).
Tips to make it cleaner / higher quality
• Turn needNextBarConf ON and use confirmATR = 0.10–0.15 .
• Increase approachMinATR (e.g., 0.35–0.45) to require a stronger pre-touch impulse.
• Raise minStrengthSoft/Strict (e.g., 4–6) so only well-reacted zones can signal.
• Use signalsOnlyConfirmed ON if you prefer trades only from zones with retests (the script falls back gracefully when none exist yet).
• Nudge proxATR to 0.5–0.6 to demand tighter proximity to the level.
• Optional: enable useHTFTrend to filter counter-trend setups.
Default settings used in this publication
• Initial capital: 100,000 (illustrative).
• Slippage: 1 tick; Commission: 0% (you can raise commission if you prefer—spread is partly modeled by slippage).
• Sizing: Risk % of equity via inputs; riskPercent = 1.0% ; FX uses micro-lot floors by default.
• Quality: Off by default (Soft/Strict available).
• HTF trend gate: Off by default.
Backtesting notes
For a meaningful sample size, test on liquid symbols/timeframes that yield 100+ trades (e.g., majors on 5–15m over 1–2 years). Backtests are modelled and broker costs/spread vary—validate on your feed and forward-test.
How to read the chart
Shaded bands are supply (above) and demand (below). Brighter bands are the nearest K per side (visual aid). BUY/SELL labels mark entries; colored dots show entry/SL/TP levels. You can hide zones or unconfirmed zones for a cleaner view.
Disclaimer
This is educational material, not financial advice. Trading involves risk. Always test and size responsibly.
Trend-Following & Breakout — Index Quant Strategy (NASDAQ)📈 Trend-Following & Breakout — Index Quant Strategy (NASDAQ & S&P 500)
Type: Invite-only strategy
Markets: NASDAQ 100 (NAS100 / US100 / NQ), S&P 500 (US500 / SPX), and other major equity indices.
🧠 Concept: Continuous trend model combining EWMAC (trend-following) and Donchian (breakout) signals, scaled by forecast strength and portfolio risk.
⚙️ Execution: Rebalances only on decision-bar closes, using hysteresis and a no-trade band to reduce churn.
📊 Default bias: Long-only — aligned with equity index drift.
🧩 How it works
• EWMAC Trend: Difference between fast and slow EMAs, normalized by an EWMA of absolute returns.
• Donchian Breakout: Distance beyond a 200-bar channel (Strict mode) or relative z-score position within it.
• Forecast combination: Weighted sum of trend and breakout points, clamped to ± capPoints.
• Hysteresis: Prevents quick sign flips near zero forecast.
• Risk scaling: Maps forecast strength to position size using equity × risk budget × ATR-based stop distance.
• Rebalance: Executes only if the required quantity change exceeds the Δqty threshold; can optionally block increases on Sundays (for CFDs).
⚙️ Default parameters
Deployed on NQ / US100 / NAS100 on Daily Timeframe
• Decision timeframe = 360 min (other options from 1 min to 1 week).
• Trend (EWMAC): Fast = 64, Slow = 256, Vol Norm = 32, Weight = 0.8.
• Breakout (Donchian): Length = 200, Mode = Strict, Weight = 0.2.
• Forecast scaling: ptsPerSigma = 1.0, capPoints = 10.
• Risk % per rebalance = 4 % of equity.
• ATR stop: ATR(14) × 1.0.
• No-trade band (Δqty) = 4 units.
• Hysteresis = 2 forecast points.
• Bias = Long-only (Neutral / Long-bias 50 % optional).
• Skip Sunday increases = false (default).
📋 Backtest properties (documented)
• Initial capital = 100 000 USD.
• Commission = 0.20 % per trade.
• Pyramiding = 10.
• Calc on every tick = false.
• Point value = 1 (for NAS100 CFD).
• No financing or slippage modeled.
• If using CFDs, account for overnight funding.
• On futures (NQ / ES), carry is implicit.
📊 Typical behaviour
• Many small scratches, a few large winners.
• Performs best during multi-week / multi-month trends.
• Underperforms in tight or volatile ranges.
• Average hold ≈ 30 – 90 days in historical tests.
💡 Risk and performance guide (illustrative)
Sharpe ≈ 1.25
Sortino ≈ 1.10 – 1.30
Max drawdown ≈ –18 % to –25 %
Annual volatility ≈ 24 – 28 %
CAGR ≈ 50 – 60 % (at 4 % risk)
Edge ratio ≈ 5 (MFE / MAE)
Historical backtests only — past performance does not guarantee future results.
🌍 Intended markets and timeframes
Optimized for NASDAQ 100 and S&P 500; also effective on similar indices (DAX, Dow Jones, FTSE).
Best on Daily or higher timeframes.
Aligns with long-term index drift — suitable for long-bias systematic trend portfolios.
⚠️ Limitations
• Backtests exclude CFD funding costs.
• Trend models will have losing streaks in range-bound markets.
• Designed for experienced traders seeking systematic exposure.
🔑 Requesting access
Send a private TradingView message to with the text:
“Request access to Trend-Following & Breakout — Index Quant Strategy.”
Access is granted only on explicit request.
For further information, see my TradingView Signature.
🆕 Release notes (v1.0)
• Initial release (360 min TF): EWMAC 64/256 + Donchian 200 Strict.
• Risk 4 %, ATR × 1.0, Long-only bias, hysteresis 2 pts, Δqty ≥ 4.
• Developed for NASDAQ 100 and S&P 500 indices.
• Implements continuous risk-scaled positioning and no-trade band logic.
🧾 Originality statement
This strategy is original work built entirely from TradingView built-ins (EMA, ATR, Highest, Lowest).
It does not reuse open-source invite-only code.
Any future reuse of open scripts will be done with explicit permission and credit.
Universal Breakout Strategy [KedArc Quant]Description:
A flexible breakout framework where you can test different logics (Prev Day, Bollinger, Volume, ATR, EMA Trend, RSI Confirm, Candle Confirm, Time Filter) under one system.
Choose your breakout mode, and the strategy will handle entries, exits, and optional risk management (ATR stops, take-profits, daily loss guard, cooldowns).
An on-chart info table shows live mode values (like Prev High/Low, Bollinger levels, RSI, etc.) plus P&L stats for quick analysis.
Use it to compare which breakout style works best on your instrument and timeframe, whether intraday, swing, or positional trading
🔑 Why it’s useful
* Flexibility: Switch between breakout strategies without loading different indicators.
* Clarity: On-chart info table displays current mode, relevant indicator levels, and live strategy P&L stats.
* Testing efficiency: Quickly A/B test different breakout styles under the same backtest environment.
* Transparency: Every trade is rule-based and displayed with entry/exit markers.
🚀 How it helps traders
* Lets you experiment with breakout strategies quickly without loading multiple scripts.
* Helps identify which breakout method fits your instrument & timeframe.
* Gives clear on-chart visual + statistical feedback for confident decision-making.
⚙️ Input Configuration
* Breakout Mode → choose which strategy to test:
* *Prev Day* → breakouts of yesterday’s High/Low.
* *Bollinger* → Upper/Lower BB pierce.
* *Volume* → Breakout confirmed with volume above average.
* *ATR Stop* → Wide range breakout using ATR filter.
* *Time Filter* → Breakouts inside defined session hours.
* *EMA Trend* → Breakouts only in EMA fast > slow alignment.
* *RSI Confirm* → Breakouts with RSI confirmation (e.g. >55 for longs).
* *Candle Confirm* → Breakouts validated by bullish/bearish candle.
* Lookback / ATR / Bollinger inputs → adjust sensitivity.
* Intrabar mode → option to evaluate breakouts using bar highs/lows instead of closes.
* Table options → show/hide info table, show/hide P&L stats, choose corner placement.
📈 Entry & Exit Logic
* Entry → occurs when breakout condition of chosen mode is met.
* Exit → default exits via opposite signals or optional stop/target if enabled.
* Session filter → optional auto-flat at session end.
* P&L management → optional daily loss guard, cooldown between trades, and ATR-based stop/take profit.
❓ FAQ — Choosing the best setup
Q: Which strategy should I use for which chart?
* *Prev Day Breakouts*: Best on indices, FX, and liquid futures with strong daily levels.
* *Bollinger*: Works well in range-bound environments, or crypto pairs with volatility compression.
* *Volume*: Good on equities where breakout strength is tied to volume spikes.
* *ATR Stop*: Suits volatile instruments (commodities, crypto).
* *EMA Trend*: Useful in trending markets (stocks, indices).
* *RSI Confirm*: Adds momentum filter, better for swing trades.
* *Candle Confirm*: Ideal for scalpers needing visual confirmation.
* *Time Filter*: For intraday traders who want signals only in high-liquidity sessions.
Q: What timeframe should I use?
* Intraday traders → 5m to 15m (Time Filter, Candle Confirm).
* Swing traders → 1H to 4H (EMA Trend, RSI Confirm, ATR Stop).
* Position traders → Daily (Prev Day, Bollinger).
* Breakout
A trade entry condition triggered when price crosses above a resistance level (for longs) or below a support level (for shorts).
* Prev Day High/Low
Formula:
Prev High = High of (Day )
Prev Low = Low of (Day )
* Bollinger Bands
Formula:
Basis = SMA(Close, Length)
Upper Band = Basis + (Multiplier × StdDev(Close, Length))
Lower Band = Basis – (Multiplier × StdDev(Close, Length))
* Volume Confirmation
A breakout is only valid if:
Volume > SMA(Volume, Length)
* ATR (Average True Range)
Measures volatility.
Formula:
ATR = SMA(True Range, Length)
where True Range = max(High–Low, |High–Close |, |Low–Close |)
* EMA (Exponential Moving Average)
Weighted moving average giving more weight to recent prices.
Formula:
EMA = (Price × α) + (EMA × (1–α))
with α = 2 / (Length + 1)
* RSI (Relative Strength Index)
Momentum oscillator scaled 0–100.
Formula:
RSI = 100 – (100 / (1 + RS))
where RS = Avg(Gain, Length) ÷ Avg(Loss, Length)
* Candle Confirmation
Bullish candle: Close > Open AND Close > Close
Bearish candle: Close < Open AND Close < Close
Win Rate (%)
Formula:
Win Rate = (Winning Trades ÷ Total Trades) × 100
* Average Trade P&L
Formula:
Avg Trade = Net Profit ÷ Total Trades
📊 Performance Notes
The Universal Breakout Strategy is designed as a framework rather than a single-asset optimized system. Results will vary depending on the chart, timeframe, and asset chosen.
On the current defaults (15-minute, INR-denominated example), the backtest produced 132 trades over the selected period. This provides a statistically sufficient sample size.
Win rate (~35%) is relatively low, but this is balanced by a positive reward-to-risk ratio (~1.8). In practice, a lower win rate with larger wins versus smaller losses is sustainable.
The average P&L per trade is close to breakeven under default settings. This is expected, as the strategy is not tuned for a single symbol but offered as a universal breakout framework.
Commissions (0.1%) and slippage (1 tick) are included in the simulation, ensuring realistic conditions.
Risk management is conservative, with order sizing set at 1 unit per trade. This avoids over-leveraging and keeps exposure well under the 5-10% equity risk guideline.
👉 Traders are encouraged to:
Experiment with inputs such as ATR period, breakout length, or Bollinger parameters.
Test across different timeframes and instruments (equities, futures, forex, crypto) to find optimal setups.
Combine with filters (trend direction, volatility regimes, or volume conditions) for further refinement.
⚠️ Disclaimer This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
BOCS Channel Scalper Strategy - Automated Mean Reversion System# BOCS Channel Scalper Strategy - Automated Mean Reversion System
## WHAT THIS STRATEGY DOES:
This is an automated mean reversion trading strategy that identifies consolidation channels through volatility analysis and executes scalp trades when price enters entry zones near channel boundaries. Unlike breakout strategies, this system assumes price will revert to the channel mean, taking profits as price bounces back from extremes. Position sizing is fully customizable with three methods: fixed contracts, percentage of equity, or fixed dollar amount. Stop losses are placed just outside channel boundaries with take profits calculated either as fixed points or as a percentage of channel range.
## KEY DIFFERENCE FROM ORIGINAL BOCS:
**This strategy is designed for traders seeking higher trade frequency.** The original BOCS indicator trades breakouts OUTSIDE channels, waiting for price to escape consolidation before entering. This scalper version trades mean reversion INSIDE channels, entering when price reaches channel extremes and betting on a bounce back to center. The result is significantly more trading opportunities:
- **Original BOCS**: 1-3 signals per channel (only on breakout)
- **Scalper Version**: 5-15+ signals per channel (every touch of entry zones)
- **Trade Style**: Mean reversion vs trend following
- **Hold Time**: Seconds to minutes vs minutes to hours
- **Best Markets**: Ranging/choppy conditions vs trending breakouts
This makes the scalper ideal for active day traders who want continuous opportunities within consolidation zones rather than waiting for breakout confirmation. However, increased trade frequency also means higher commission costs and requires tighter risk management.
## TECHNICAL METHODOLOGY:
### Price Normalization Process:
The strategy normalizes price data to create consistent volatility measurements across different instruments and price levels. It calculates the highest high and lowest low over a user-defined lookback period (default 100 bars). Current close price is normalized using: (close - lowest_low) / (highest_high - lowest_low), producing values between 0 and 1 for standardized volatility analysis.
### Volatility Detection:
A 14-period standard deviation is applied to the normalized price series to measure price deviation from the mean. Higher standard deviation values indicate volatility expansion; lower values indicate consolidation. The strategy uses ta.highestbars() and ta.lowestbars() to identify when volatility peaks and troughs occur over the detection period (default 14 bars).
### Channel Formation Logic:
When volatility crosses from a high level to a low level (ta.crossover(upper, lower)), a consolidation phase begins. The strategy tracks the highest and lowest prices during this period, which become the channel boundaries. Minimum duration of 10+ bars is required to filter out brief volatility spikes. Channels are rendered as box objects with defined upper and lower boundaries, with colored zones indicating entry areas.
### Entry Signal Generation:
The strategy uses immediate touch-based entry logic. Entry zones are defined as a percentage from channel edges (default 20%):
- **Long Entry Zone**: Bottom 20% of channel (bottomBound + channelRange × 0.2)
- **Short Entry Zone**: Top 20% of channel (topBound - channelRange × 0.2)
Long signals trigger when candle low touches or enters the long entry zone. Short signals trigger when candle high touches or enters the short entry zone. This captures mean reversion opportunities as price reaches channel extremes.
### Cooldown Filter:
An optional cooldown period (measured in bars) prevents signal spam by enforcing minimum spacing between consecutive signals. If cooldown is set to 3 bars, no new long signal will fire until 3 bars after the previous long signal. Long and short cooldowns are tracked independently, allowing both directions to signal within the same period.
### ATR Volatility Filter:
The strategy includes a multi-timeframe ATR filter to avoid trading during low-volatility conditions. Using request.security(), it fetches ATR values from a specified timeframe (e.g., 1-minute ATR while trading on 5-minute charts). The filter compares current ATR to a user-defined minimum threshold:
- If ATR ≥ threshold: Trading enabled
- If ATR < threshold: No signals fire
This prevents entries during dead zones where mean reversion is unreliable due to insufficient price movement.
### Take Profit Calculation:
Two TP methods are available:
**Fixed Points Mode**:
- Long TP = Entry + (TP_Ticks × syminfo.mintick)
- Short TP = Entry - (TP_Ticks × syminfo.mintick)
**Channel Percentage Mode**:
- Long TP = Entry + (ChannelRange × TP_Percent)
- Short TP = Entry - (ChannelRange × TP_Percent)
Default 50% targets the channel midline, a natural mean reversion target. Larger percentages aim for opposite channel edge.
### Stop Loss Placement:
Stop losses are placed just outside the channel boundary by a user-defined tick offset:
- Long SL = ChannelBottom - (SL_Offset_Ticks × syminfo.mintick)
- Short SL = ChannelTop + (SL_Offset_Ticks × syminfo.mintick)
This logic assumes channel breaks invalidate the mean reversion thesis. If price breaks through, the range is no longer valid and position exits.
### Trade Execution Logic:
When entry conditions are met (price in zone, cooldown satisfied, ATR filter passed, no existing position):
1. Calculate entry price at zone boundary
2. Calculate TP and SL based on selected method
3. Execute strategy.entry() with calculated position size
4. Place strategy.exit() with TP limit and SL stop orders
5. Update info table with active trade details
The strategy enforces one position at a time by checking strategy.position_size == 0 before entry.
### Channel Breakout Management:
Channels are removed when price closes more than 10 ticks outside boundaries. This tolerance prevents premature channel deletion from minor breaks or wicks, allowing the mean reversion setup to persist through small boundary violations.
### Position Sizing System:
Three methods calculate position size:
**Fixed Contracts**:
- Uses exact contract quantity specified in settings
- Best for futures traders (e.g., "trade 2 NQ contracts")
**Percentage of Equity**:
- position_size = (strategy.equity × equity_pct / 100) / close
- Dynamically scales with account growth
**Cash Amount**:
- position_size = cash_amount / close
- Maintains consistent dollar exposure regardless of price
## INPUT PARAMETERS:
### Position Sizing:
- **Position Size Type**: Choose Fixed Contracts, % of Equity, or Cash Amount
- **Number of Contracts**: Fixed quantity per trade (1-1000)
- **% of Equity**: Percentage of account to allocate (1-100%)
- **Cash Amount**: Dollar value per position ($100+)
### Channel Settings:
- **Nested Channels**: Allow multiple overlapping channels vs single channel
- **Normalization Length**: Lookback for high/low calculation (1-500, default 100)
- **Box Detection Length**: Period for volatility detection (1-100, default 14)
### Scalping Settings:
- **Enable Long Scalps**: Toggle long entries on/off
- **Enable Short Scalps**: Toggle short entries on/off
- **Entry Zone % from Edge**: Size of entry zone (5-50%, default 20%)
- **SL Offset (Ticks)**: Distance beyond channel for stop (1+, default 5)
- **Cooldown Period (Bars)**: Minimum spacing between signals (0 = no cooldown)
### ATR Filter:
- **Enable ATR Filter**: Toggle volatility filter on/off
- **ATR Timeframe**: Source timeframe for ATR (1, 5, 15, 60 min, etc.)
- **ATR Length**: Smoothing period (1-100, default 14)
- **Min ATR Value**: Threshold for trade enablement (0.1+, default 10.0)
### Take Profit Settings:
- **TP Method**: Choose Fixed Points or % of Channel
- **TP Fixed (Ticks)**: Static distance in ticks (1+, default 30)
- **TP % of Channel**: Dynamic target as channel percentage (10-100%, default 50%)
### Appearance:
- **Show Entry Zones**: Toggle zone labels on channels
- **Show Info Table**: Display real-time strategy status
- **Table Position**: Corner placement (Top Left/Right, Bottom Left/Right)
- **Color Settings**: Customize long/short/TP/SL colors
## VISUAL INDICATORS:
- **Channel boxes** with semi-transparent fill showing consolidation zones
- **Colored entry zones** labeled "LONG ZONE ▲" and "SHORT ZONE ▼"
- **Entry signal arrows** below/above bars marking long/short entries
- **Active TP/SL lines** with emoji labels (⊕ Entry, 🎯 TP, 🛑 SL)
- **Info table** showing position status, channel state, last signal, entry/TP/SL prices, and ATR status
## HOW TO USE:
### For 1-3 Minute Scalping (NQ/ES):
- ATR Timeframe: "1" (1-minute)
- ATR Min Value: 10.0 (for NQ), adjust per instrument
- Entry Zone %: 20-25%
- TP Method: Fixed Points, 20-40 ticks
- SL Offset: 5-10 ticks
- Cooldown: 2-3 bars
- Position Size: 1-2 contracts
### For 5-15 Minute Day Trading:
- ATR Timeframe: "5" or match chart
- ATR Min Value: Adjust to instrument (test 8-15 for NQ)
- Entry Zone %: 20-30%
- TP Method: % of Channel, 40-60%
- SL Offset: 5-10 ticks
- Cooldown: 3-5 bars
- Position Size: Fixed contracts or 5-10% equity
### For 30-60 Minute Swing Scalping:
- ATR Timeframe: "15" or "30"
- ATR Min Value: Lower threshold for broader market
- Entry Zone %: 25-35%
- TP Method: % of Channel, 50-70%
- SL Offset: 10-15 ticks
- Cooldown: 5+ bars or disable
- Position Size: % of equity recommended
## BACKTEST CONSIDERATIONS:
- Strategy performs best in ranging, mean-reverting markets
- Strong trending markets produce more stop losses as price breaks channels
- ATR filter significantly reduces trade count but improves quality during low volatility
- Cooldown period trades signal quantity for signal quality
- Commission and slippage materially impact sub-5-minute timeframe performance
- Shorter timeframes require tighter entry zones (15-20%) to catch quick reversions
- % of Channel TP adapts better to varying channel sizes than fixed points
- Fixed contract sizing recommended for consistent risk per trade in futures
**Backtesting Parameters Used**: This strategy was developed and tested using realistic commission and slippage values to provide accurate performance expectations. Recommended settings: Commission of $1.40 per side (typical for NQ futures through discount brokers), slippage of 2 ticks to account for execution delays on fast-moving scalp entries. These values reflect real-world trading costs that active scalpers will encounter. Backtest results without proper cost simulation will significantly overstate profitability.
## COMPATIBLE MARKETS:
Works on any instrument with price data including stock indices (NQ, ES, YM, RTY), individual stocks, forex pairs (EUR/USD, GBP/USD), cryptocurrency (BTC, ETH), and commodities. Volume-based features require data feed with volume information but are optional for core functionality.
## KNOWN LIMITATIONS:
- Immediate touch entry can fire multiple times in choppy zones without adequate cooldown
- Channel deletion at 10-tick breaks may be too aggressive or lenient depending on instrument tick size
- ATR filter from lower timeframes requires higher-tier TradingView subscription (request.security limitation)
- Mean reversion logic fails in strong breakout scenarios leading to stop loss hits
- Position sizing via % of equity or cash amount calculates based on close price, may differ from actual fill price
- No partial closing capability - full position exits at TP or SL only
- Strategy does not account for gap openings or overnight holds
## RISK DISCLOSURE:
Trading involves substantial risk of loss. Past performance does not guarantee future results. This strategy is for educational purposes and backtesting only. Mean reversion strategies can experience extended drawdowns during trending markets. Stop losses may not fill at intended levels during extreme volatility or gaps. Thoroughly test on historical data and paper trade before risking real capital. Use appropriate position sizing and never risk more than you can afford to lose. Consider consulting a licensed financial advisor before making trading decisions. Automated trading systems can malfunction - monitor all live positions actively.
## ACKNOWLEDGMENT & CREDITS:
This strategy is built upon the channel detection methodology created by **AlgoAlpha** in the "Smart Money Breakout Channels" indicator. Full credit and appreciation to AlgoAlpha for pioneering the normalized volatility approach to identifying consolidation patterns. The core channel formation logic using normalized price standard deviation is AlgoAlpha's original contribution to the TradingView community.
Enhancements to the original concept include: mean reversion entry logic (vs breakout), immediate touch-based signals, multi-timeframe ATR volatility filtering, flexible position sizing (fixed/percentage/cash), cooldown period filtering, dual TP methods (fixed points vs channel percentage), automated strategy execution with exit management, and real-time position monitoring table.
Trendline Breakout Strategy [KedArc Quant] Description
A single, rule-based system that builds two trendlines from confirmed swing pivots and trades their breakouts, with optional retest, trend-regime gates (EMA / HTF EMA), and ATR-based risk. All parts serve one decision flow: structure → breakout → gated entry → managed risk.
What it does (for traders)
Draws Up line (teal) through the last two Higher Lows and Down line (red) through the last two Lower Highs, then extends them forward.
Long when price breaks above red; Short when price breaks below teal.
Optional Retest entry: after a break, wait for a pullback toward the broken line within an ATR-scaled buffer.
Uses ATR stop and R-multiple target so risk is consistent across symbols/timeframes.
Labels HL1/HL2/LH1/LH2 so non-coders can verify which pivots built each line.
Why these components are combined
Pure breakout systems on trendlines suffer from three practical issues:
False breaks in chop → solved by trend-regime gates (EMA / HTF EMA) that only allow trades aligned with the prevailing trend.
Uneven volatility across markets/timeframes → solved by ATR-based stop/target, normalizing distance so R-multiples are comparable.
First break whipsaws near wedge apices → mitigated by the optional retest rule that demands a pullback/hold before entry.
These modules are not separate indicators with their own signals. They are support roles inside one method.
The pivot engine defines structure, the breakout detector defines signal, the regime gates decide if we’re allowed to take that signal, and the ATR module sizes risk.
Together they make the trendline breakout usable, testable, and explainable.
How it works (mechanism; each component explained)
1) Pivot engine (structure, non-repainting)
Swings are confirmed with ta.pivotlow/high(L, R). A pivot only exists after R bars (no look-ahead), so once plotted, the line built from those pivots will not repaint.
2) Trendline builder (geometry)
Teal line updates when two consecutive pivot lows satisfy HL2.price > HL1.price (and HL2 occurs after HL1).
Red line updates when two consecutive pivot highs satisfy LH2.price < LH1.price.
Lines are extended right and their current value is read every bar via line.get_price().
3) Breakout detector (signal)
On every bar, compute:
crossover(close, redLine) ⇒ Long breakout
crossunder(close, tealLine) ⇒ Short breakdown
4) Regime gates (trend filters, not separate signals)
EMA gate: allow longs only if close > EMA(len), shorts only if close < EMA(len).
HTF EMA gate (optional): same rule on a higher timeframe to avoid fighting the larger trend.
These do not create entries; they simply permit or block the breakout signal.
5) Retest module (optional confirmation)
After a breakout, record the line price. A valid retest occurs if price pulls back within an ATR-scaled buffer toward that broken line and then closes back in the breakout direction.
This reduces first-tick fakeouts.
6) Risk module (position exit)
Initial stop = ATR(len) × atrMult from entry.
Target = tpR × (ATR × atrMult) (e.g., 2R).
This keeps results consistent across instruments/timeframes.
Entries & exits
Long entry
Base: close breaks above red and passes EMA/HTF gates.
Retest (if enabled): after the break, price pulls back near the broken red line (within the ATR buffer) and holds; then enter.
Short entry
Mirror logic with teal (break below & gates), optionally with a retest.
Exit
strategy.exit places ATR stop & R-multiple target automatically.
Optional “flip”: close if the opposite base signal triggers.
How to use it (step-by-step)
Timeframe: 1–15m for intraday, 1–4h for swing.
Start defaults: Pivot L/R = 5, EMA len = 200, ATR len = 14, ATR mult = 2, TP = 2R, Retest = ON.
Tune sensitivity:
Faster lines (more trades): set L/R = 3–4.
Fewer counter-trend trades: enable HTF EMA (e.g., 60-min or Daily).
Visual audit: labels HL1/HL2 & LH1/LH2 show which pivots built each line—verify by eye.
Alerts: use Long breakout, Short breakdown, and Retest alerts to automate.
Originality (why it merits publication)
Trades the visualization: many “auto-trendline” tools only draw lines; this one turns them into testable, alertable rules.
Integrated design: each component has a defined role in the same pipeline—no unrelated indicators bolted together.
Transparent & non-repainting: pivot confirmation removes look-ahead; labels let non-coders understand the setup that produced each signal.
Notes & limitations
Lines update only after pivot confirmation; that lag is intentional to avoid repainting.
Breakouts near an apex can whipsaw; prefer Retest and/or HTF gate in choppy regimes.
Backtests are idealized; forward-test and size risk appropriately.
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Gann Fan Strategy [KedarArc Quant]Description
A single-concept, rule-based strategy that trades around a programmatic Gann Fan.
It anchors to a swing (or a manual point), builds 1×1 and related fan lines numerically, and triggers entries when price interacts with the 1×1 (breakout or bounce). Management is done entirely with the fan structure (next/previous line) plus optional ATR trailing.
What TV indicators are used
* Pivots: `ta.pivothigh/ta.pivotlow` to confirm swing highs/lows for anchor selection.
* ATR: `ta.atr` only to scale the 1×1 slope (optional) and for an optional trailing stop.
* EMA: `ta.ema` as a trend filter (e.g., only long above the EMA, short below).
No RSI/MACD/Stoch/Heikin/etc. The logic is one coherent framework: Gann price–time geometry, with ATR as a scale and EMA as a risk filter.
How it works
1. Anchor
* Auto: chooses the most recent *confirmed* pivot (you control Left/Right).
* Manual: set a price and bar index and the fan will hold that point (no re-anchoring).
* Optional Re-anchor when a newer pivot confirms.
2. 1×1 Slope (numeric, not cosmetic)
* ATR mode: `1×1 = ATR(Length) × Multiplier` (adapts to volatility).
* Fixed mode: `ticks per bar` (constant slope).
Because slope is numeric, it doesn’t change with chart zoom, unlike the drawing tool.
3. Fan Lines
Builds classic ratios around the 1×1: 1/8, 1/4, 1/3, 1/2, 1/1, 2/1, 3/1, 4/1, 8/1.
4. Signals
* Breakout: cross of price over/under the 1×1 in the EMA-aligned direction.
* Bounce (optional): touch + reversal across the 1×1 to reduce whipsaw.
5. Exits & Risk
* Take-profit at the next fan line; Stop at the previous fan line.
* If a level is missing (right after re-anchor), a fallback Risk-Reward (RR) is used.
* Optional ATR trailing stop.
Why this is unique
* True numeric fan: The 1×1 slope is calculated from ATR or fixed ticks—not from screen geometry—so it is scale-invariant and reproducible across users/timeframes.
* Deterministic anchor logic: Uses confirmed pivots (with your L/R settings). No look-ahead; anchors update only when the right bars complete.
* Fan-native trade management: Both entries and exits come from the fan structure itself (with a minimal ATR/EMA assist), keeping the method pure.
* Two entry archetypes: Breakout for momentum days; Bounce for range days—switchable without changing the core model.
* Manual mode: Lock a session’s bias by anchoring to a chosen swing (e.g., day’s first major low/high) and keep the fan constant all day.
Inputs (quick guide)
* Auto Anchor (Left/Right): pivot sensitivity. Higher values = fewer, stronger anchors.
* Re-anchor: refresh to newer pivots as they confirm.
* Manual Anchor Price / Bar Index: fixes the fan (turn Auto off).
* Scale 1×1 by ATR: on = adaptive; off = use ticks per bar.
* ATR Length / ATR Multiplier: controls adaptive slope; start around 14 / 0.25–0.35.
* Ticks per bar: exact fixed slope (match a hand-drawn fan by computing slope ÷ mintick).
* EMA Trend Filter: e.g., 50–100; trades only in EMA direction.
* Use Bounce: require touch + reverse across 1×1 (helps in chop).
* TP/SL at fan lines; Fallback RR for missing levels; ATR Trailing Stop optional.
* Transparency/Plot EMA: visual preferences.
Tips
* Range days: larger pivots (L/R 8–12), Bounce ON, ATR Multiplier \~0.30–0.40, EMA 100.
* Trend days: L/R 5–6, Breakout, Multiplier \~0.20–0.30, EMA 50, ATR trail 1.0–1.5.
* Match the TV Gann Fan drawing: turn ATR scale OFF, set ticks per bar = `(Δprice between anchor and 1×1 target) / (bars) / mintick`.
Repainting & testing notes
* Pivots require Right bars to confirm; anchors are set after confirmation (no look-ahead).
* Signals use the current bar close with TradingView strategy mechanics; real-time vs. bar-close can differ slightly, as with any strategy.
* Re-anchoring legitimately moves the structure when new pivots confirm—by design.
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Mutanabby_AI | ATR+ | Trend-Following StrategyThis document presents the Mutanabby_AI | ATR+ Pine Script strategy, a systematic approach designed for trend identification and risk-managed position entry in financial markets. The strategy is engineered for long-only positions and integrates volatility-adjusted components to enhance signal robustness and trade management.
Strategic Design and Methodological Basis
The Mutanabby_AI | ATR+ strategy is constructed upon a foundation of established technical analysis principles, with a focus on objective signal generation and realistic trade execution.
Heikin Ashi for Trend Filtering: The core price data is processed via Heikin Ashi (HA) methodology to mitigate transient market noise and accentuate underlying trend direction. The script offers three distinct HA calculation modes, allowing for comparative analysis and validation:
Manual Calculation: Provides a transparent and deterministic computation of HA values.
ticker.heikinashi(): Utilizes TradingView's built-in function, employing confirmed historical bars to prevent repainting artifacts.
Regular Candles: Allows for direct comparison with standard OHLC price action.
This multi-methodological approach to trend smoothing is critical for robust signal generation.
Adaptive ATR Trailing Stop: A key component is the Average True Range (ATR)-based trailing stop. ATR serves as a dynamic measure of market volatility. The strategy incorporates user-defined parameters (
Key Value and ATR Period) to calibrate the sensitivity of this trailing stop, enabling adaptation to varying market volatility regimes. This mechanism is designed to provide a dynamic exit point, preserving capital and locking in gains as a trend progresses.
EMA Crossover for Signal Generation: Entry and exit signals are derived from the interaction between the Heikin Ashi derived price source and an Exponential Moving Average (EMA). A crossover event between these two components is utilized to objectively identify shifts in momentum, signaling potential long entry or exit points.
Rigorous Stop Loss Implementation: A critical feature for risk mitigation, the strategy includes an optional stop loss. This stop loss can be configured as a percentage or fixed point deviation from the entry price. Importantly, stop loss execution is based on real market prices, not the synthetic Heikin Ashi values. This design choice ensures that risk management is grounded in actual market liquidity and price levels, providing a more accurate representation of potential drawdowns during backtesting and live operation.
Backtesting Protocol: The strategy is configured for realistic backtesting, employing fill_orders_on_standard_ohlc=true to simulate order execution at standard OHLC prices. A configurable Date Filter is included to define specific historical periods for performance evaluation.
Data Visualization and Metrics: The script provides on-chart visual overlays for buy/sell signals, the ATR trailing stop, and the stop loss level. An integrated information table displays real-time strategy parameters, current position status, trend direction, and key price levels, facilitating immediate quantitative assessment.
Applicability
The Mutanabby_AI | ATR+ strategy is particularly suited for:
Cryptocurrency Markets: The inherent volatility of assets such as #Bitcoin and #Ethereum makes the ATR-based trailing stop a relevant tool for dynamic risk management.
Systematic Trend Following: Individuals employing systematic methodologies for trend capture will find the objective signal generation and rule-based execution aligned with their approach.
Pine Script Developers and Quants: The transparent code structure and emphasis on realistic backtesting provide a valuable framework for further analysis, modification, and integration into broader quantitative models.
Automated Trading Systems: The clear, deterministic entry and exit conditions facilitate integration into automated trading environments.
Implementation and Evaluation
To evaluate the Mutanabby_AI | ATR+ strategy, apply the script to your chosen chart on TradingView. Adjust the input parameters (Key Value, ATR Period, Heikin Ashi Method, Stop Loss Settings) to observe performance across various asset classes and timeframes. Comprehensive backtesting is recommended to assess the strategy's historical performance characteristics, including profitability, drawdown, and risk-adjusted returns.
I'd love to hear your thoughts, feedback, and any optimizations you discover! Drop a comment below, give it a like if you find it useful, and share your results.
Parallax Momentum MNQ Strategy# 📈 Parallax Momentum MNQ Strategy
## Overview
The Parallax Momentum MNQ Strategy is a sophisticated support/resistance breakout system specifically designed for Micro Nasdaq futures (MNQ) trading (also works on minis). This strategy combines dynamic level detection with momentum confirmation to identify high-probability entry opportunities while maintaining strict risk management protocols.
## 🎯 Key Features
### Core Strategy Logic
- **Dynamic Support/Resistance Detection**: Automatically identifies key levels using configurable lookback periods
- **Momentum Confirmation**: Volume-based filtering ensures trades align with market momentum
- **ATR-Based Risk Management**: Adaptive stop losses and take profits based on market volatility
- **Dual Entry System**: Both long and short opportunities with limit order execution
### Risk Management
- **ATR-Adaptive Stops**: Stop losses and take profits automatically adjust to market volatility
- **Reward-to-Risk Ratios**: Configurable R:R ratios with default 2:1 minimum
- **Maximum Loss Protection**: Optional daily loss limits to prevent overtrading
- **Session Time Filtering**: Trade only during specified market hours
### Strategy Modes
- **Conservative Mode**: 0.8x risk multiplier for cautious trading
- **Balanced Mode**: Standard 1.0x risk multiplier (default)
- **Aggressive Mode**: 1.2x risk multiplier for active trading
## 📊 Visual Features
### Dashboard Display
- Real-time strategy status and performance metrics
- Current support/resistance levels and ATR values
- Live risk-to-reward ratios for potential trades
- Win rate, profit factor, and drawdown statistics
- Adjustable dashboard size and positioning
### Chart Indicators
- Support and resistance lines with labels
- ATR-based levels (+/-1 ATR and +/-2 ATR)
- Dynamic visual updates as levels change
- Configurable line extensions and styling
## ⚙️ Configuration Options
### Entry Filters
- **Volume Filter**: Optional volume confirmation above SMA
- **Session Time Filter**: 12-hour format time restrictions
- **ATR vs Fixed Stops**: Choose between adaptive or fixed tick-based exits
### Risk Controls
- **ATR Period**: Default 14-period ATR calculation
- **Stop Loss Multiplier**: ATR-based stop distance (default 1.5x)
- **Take Profit Multiplier**: ATR-based target distance (default 1.5x)
- **Secondary Take Profit**: Optional TP2 with position scaling
## 📋 How It Works
### Entry Conditions
**Long Trades**: Triggered when price closes above support buffer but low touches support level, with volume and session confirmation
**Short Trades**: Triggered when price closes below resistance buffer but high touches resistance level, with volume and session confirmation
### Exit Strategy
- **Primary Take Profit**: ATR-based target with 2:1 R:R minimum
- **Stop Loss**: ATR-based protective stop
- **Optional TP2**: Extended target for partial profit taking
- **One Trade at a Time**: No overlapping positions
## 🎛️ Default Settings
- **Lookback Period**: 20 bars for support/resistance detection
- **ATR Period**: 14 bars for volatility calculation
- **Stop Loss**: 1.5x ATR from entry
- **Take Profit**: 1.5x ATR with 2:1 reward-to-risk ratio
- **Session**: 7:30 AM - 2:00 PM (configurable)
## ⚠️ Important Notes
### Risk Disclaimer
- This strategy is for educational and informational purposes only
- Past performance does not guarantee future results
- Always use proper position sizing and risk management
- Test thoroughly on historical data before live trading
- Consider market conditions and volatility when using
### Best Practices
- Backtest on sufficient historical data
- Start with conservative mode for new users
- Monitor performance regularly and adjust parameters as needed
- Use appropriate position sizing for your account
- Consider broker commissions and slippage in live trading
## 🔧 Customization
The strategy offers extensive customization options including:
- Adjustable time sessions with AM/PM format
- Configurable ATR and risk parameters
- Optional maximum daily loss limits
- Dashboard size and position controls
- Visual element toggles and styling
## 📈 Ideal For
- MNQ (Micro Nasdaq) futures traders
- Intraday momentum strategies
- Traders seeking systematic entry/exit rules
- Risk-conscious traders wanting automated stops
- Both beginner and experienced algorithmic traders
---
**Version**: Pine Script v5 Compatible
**Timeframe**: Works on multiple timeframes (test on 1m, 3m, 5m, 15m)
**Market**: Optimized for MNQ but adaptable to other instruments
**Strategy Type**: Trend following with momentum confirmation
NOMANOMA Adaptive Confidence Strategy —
What is NOMA?
NOMA is a next-generation, confidence-weighted trading strategy that fuses modern trend logic, multi-factor market structure, and adaptive risk controls—delivering a systematic edge across futures, stocks, forex, and crypto markets. Designed for precision, adaptability, and hands-off automation, NOMA provides actionable trade signals and real-time alerts so you never miss a high-conviction opportunity.
Key Benefits & Why Use NOMA?
Trade With Confidence, Not Guesswork:
NOMA combines over 11 institutional-grade confirmations (market structure, order flow, volatility, liquidity, SMC/ICT concepts, and more) into a single “confidence score” engine. Every trade entry is filtered through customizable booster weights, so only the strongest opportunities trigger.
Built-In Alerts:
Get instant notifications on all entries, take-profits, trailing stop events, and exits. Connect alerts to your mobile, email, or webhook for seamless automation or just peace of mind.
Advanced Position Management:
Supports up to 5 separate take-profit levels with adjustable quantities, plus dynamic and stepwise trailing stops. Protects your gains and adapts exit logic to market movement, not just static targets.
Anti-Chop/No Trade Zones:
Eliminate low-probability, sideways market conditions using the “No Chop Zone” filter, so you only trade in meaningful, trending environments.
Full Market Session Control:
Restrict trades to custom sessions (e.g., New York hours) for added discipline and to avoid overnight risk.
— Ideal for day traders and prop-firm requirements.
Multi-Asset & Timeframe Support:
Whether you trade micro futures, stocks, forex, or crypto, NOMA adapts its TP/SL logic to ticks, pips, or points and works on any timeframe.
How NOMA Works (Feature Breakdown)
1. Adaptive Trend Engine
Uses a custom NOMA line that blends classic moving averages with dynamic momentum and a proprietary “Confidence Momentum Oscillator” overlay.
Visual trend overlay and color fill for easy chart reading.
2. Multi-Factor Confidence Scoring
Each trade is scored on up to 11 confidence “boosters,” including:
Market Manipulation & Accumulation (detects smart money traps and true range expansions)
Accumulation/Distribution (AD line)
ATR Volatility Rank (prioritizes trades when volatility is “just right”)
COG Cross (center of gravity reversal points)
Change of Character/Break of Structure (CHoCH/BOS logic, SMC/ICT style)
Order Blocks, Breakers, FVGs, Inducements, OTE (Optimal Trade Entry) Zones
You control the minimum score required for a trade to trigger, plus the weight of each factor (customize for your asset or style).
3. Smart Trade Management
Step Take-Profits:
Up to 5 profit targets, each with individual contract/quantity splits.
Step Trailing Stop:
Trail your stop with a ratcheting logic that tightens after each TP is hit, or use a fully dynamic ATR-based trail for volatile markets.
Kill-Switch:
Instant trailing stop logic closes all open contracts if price reverses sharply.
4. Session Filter & Cooldown Logic
Restricts trading to key sessions (e.g., NY open) to avoid low-liquidity or dead zones.
Cooldown bars prevent “overtrading” or rapid re-entries after an exit.
5. Chop Zone Filter
Optionally blocks trades during flat/choppy periods using a custom “NOMA spread” calculation.
When enabled, background color highlights no-trade periods for clarity.
6. Real-Time Alerts
Receive alerts for:
Trade entries (long & short, with confidence score)
Every take-profit target hit
Trailing stop exits or full position closes
Easy setup: Create alerts for all conditions and get notified instantly.
Customization & Inputs
TP/SL Modes: Choose between manual, ATR-multiplied, or hybrid take-profit and trailing logic.
Position Sizing: Fixed contracts/quantity per trade, with customizable splits for scaling out.
Session Settings: Restrict to any time window.
Confidence Engine: User-controlled weights and minimum score—tailor for your asset.
Risk & Volatility Filters: ATR length/multiplier, min/max range, and more.
How To Use
Add NOMA to your chart.
Customize your settings (session, TPs, confidence scores, etc.).
Set up TradingView alerts (“Any Alert() function call”) to receive notifications.
Monitor trade entries, profit targets, and stops directly on your chart or in your inbox.
Adjust confidence weights as you optimize for your favorite asset.
Pro Tips
Start with default settings—they are optimized for NQ micro futures, 15m timeframe.
Increase the minimum confidence score or weights for stricter filtering in volatile or low-liquidity markets.
Adjust your take-profit and trailing stop settings to match your trading style (scalping vs. swing).
Enable “No Chop Zone” during sideways conditions for cleaner signals.
Test in strategy mode before trading live to dial in your risk and settings.
Disclaimer
This script is for educational and research purposes only. No trading system guarantees future results.
Performance will vary by symbol, timeframe, and market regime—always test settings and use at your own risk. Not investment advice.
If alerts or strategy entries are not triggering as expected, try lowering the minimum confidence score or disabling certain boosters.
This will come with a user manual please do not hesitate to message me to gain access. TO THE MOON AND BEYOND
Quantum Reversal Engine [ApexLegion]Quantum Reversal Engine
STRATEGY OVERVIEW
This strategy is constructed using 5 custom analytical filters that analyze different market dimensions - trend structure, momentum expansion, volume confirmation, price action patterns, and reversal detection - with results processed through a multi-component scoring calculation that determines signal generation and position management decisions.
Why These Custom Filters Were Independently Developed:
This strategy employs five custom-developed analytical filters:
1. Apex Momentum Core (AMC) - Custom oscillator with volatility-scaled deviation calculation
Standard oscillators lag momentum shifts by 2-3 bars. Custom calculation designed for momentum analysis
2. Apex Wick Trap (AWT) - Wick dominance analysis for trap detection
Existing wick analysis tools don't quantify trap conditions. Uses specific ratios for wick dominance detection
3. Apex Volume Pulse (AVP) - Volume surge validation with participation confirmation
Volume indicators typically use simple averages. Uses surge multipliers with participation validation
4. Apex TrendGuard (ATG) - Angle-based trend detection with volatility band integration
EMA slope calculations often produce false signals. Uses angle analysis with volatility bands for confirmation
5. Quantum Composite Filter (QCF) - Multi-component scoring and signal generation system
Composite scoring designed to filter noise by requiring multiple confirmations before signal activation.
Each filter represents mathematical calculations designed to address specific analytical requirements.
Framework Operation: The strategy functions as a scoring framework where each filter contributes weighted points based on market conditions. Entry signals are generated when minimum threshold scores are met. Exit management operates through a three-tier system with continued signal strength evaluation determining position holds versus closures at each TP level.
Integration Challenge: The core difficulty was creating a scoring system where five independent filters could work together without generating conflicting signals. This required backtesting to determine effective weight distributions.
Custom Filter Development:
Each of the five filters represents analytical approaches developed through testing and validation:
Integration Validation: Each filter underwent individual testing before integration. The composite scoring system required validation to verify that filters complement rather than conflict with each other, resulting in a cohesive analytical framework that was tested during the development period.
These filters represent custom-developed components created specifically for this strategy, with each component addressing different analytical requirements through testing and parameter adjustment.
Programming Features:
Multi-timeframe data handling with backup systems
Performance optimization techniques
Error handling for live trading scenarios
Parameter adaptation based on market conditions
Strategy Features:
Uses multi-filter confirmation approach
Adapts position holding based on continued signal strength
Includes analysis tools for trade review and optimization
Ongoing Development: The strategy was developed through testing and validation processes during the creation period.
COMPONENT EXPLANATION
EMA System
Uses 8 exponential moving averages (7, 14, 21, 30, 50, 90, 120, 200 periods) for trend identification. Primary signals come from 8/21 EMA crossovers, while longer EMAs provide structural context. EMA 1-4 determine short-term structure, EMA 5-8 provide long-term trend confirmation.
Apex Momentum Core (AMC)
Built custom oscillator mathematics after testing dozens of momentum calculation methods. Final algorithm uses price deviation from EMA baseline with volatility scaling to reduce lag while maintaining accuracy across different market conditions.
Custom momentum oscillator using price deviation from EMA baseline:
apxCI = 100 * (source - emaBase) / (sensitivity * sqrt(deviation + 1))
fastLine = EMA(apxCI, smoothing)
signalLine = SMA(fastLine, 4)
Signals generate when fastLine crosses signalLine at +50/-50 thresholds.
This identifies momentum expansion before traditional oscillators.
Apex Volume Pulse (AVP)
Created volume surge analysis that goes beyond simple averages. Extensive testing determined 1.3x multiplier with participation validation provides reliable confirmation while filtering false volume spikes.
Compares current volume to 21-period moving average.
Requires 1.3x average volume for signal confirmation. This filters out low-volume moves during quiet periods and confirms breakouts with actual participation.
Apex Wick Trap (AWT)
Developed proprietary wick trap detection through analysis of failed breakout patterns. Tested various ratio combinations before settling on 60% wick dominance + 20% body limit as effective trap identification parameters.
Analyzes candle structure to identify failed breakouts:
candleRange = math.max(high - low, 0.00001)
candleBody = math.abs(close - open)
bodyRatio = candleBody / candleRange
upperWick = high - math.max(open, close)
lowerWick = math.min(open, close) - low
upperWickRatio = upperWick / candleRange
lowerWickRatio = lowerWick / candleRange
trapWickLong = showAWT and lowerWickRatio > minWickDom and bodyRatio < bodyToRangeLimit and close > open
trapWickShort = showAWT and upperWickRatio > minWickDom and bodyRatio < bodyToRangeLimit and close < open This catches reversals after fake breakouts.
Apex TrendGuard (ATG)
Built angle-based trend detection after standard EMA crossovers proved insufficient. Combined slope analysis with volatility bands through iterative testing to eliminate false trend signals.
EMA slope analysis with volatility bands:
Fast EMA (21) vs Slow EMA (55) for trend direction
Angle calculation: atan(fast - slow) * 180 / π
ATR bands (1.75x multiplier) for breakout confirmation
Minimum 25° angle for strong trend classification
Core Algorithm Framework
1. Composite Signal Generation
calculateCompositeSignals() =>
// Component Conditions
structSignalLong = trapWickLong
structSignalShort = trapWickShort
momentumLong = amcBuySignal
momentumShort = amcSellSignal
volumeSpike = volume > volAvg_AVP * volMult_AVP
priceStrength_Long = close > open and close > close
priceStrength_Short = close < open and close < close
rsiMfiComboValue = (ta.rsi(close, 14) + ta.mfi(close, 14)) / 2
reversalTrigger_Long = ta.crossover(rsiMfiComboValue, 50)
reversalTrigger_Short = ta.crossunder(rsiMfiComboValue, 50)
isEMACrossUp = ta.crossover(emaFast_ATG, emaSlow_ATG)
isEMACrossDown = ta.crossunder(emaFast_ATG, emaSlow_ATG)
// Enhanced Composite Score Calculation
scoreBuy = 0.0
scoreBuy += structSignalLong ? scoreStruct : 0.0
scoreBuy += momentumLong ? scoreMomentum : 0.0
scoreBuy += flashSignal ? weightFlash : 0.0
scoreBuy += blinkSignal ? weightBlink : 0.0
scoreBuy += volumeSpike_AVP ? scoreVolume : 0.0
scoreBuy += priceStrength_Long ? scorePriceAction : 0.0
scoreBuy += reversalTrigger_Long ? scoreReversal : 0.0
scoreBuy += emaAlignment_Bull ? weightTrendAlign : 0.0
scoreBuy += strongUpTrend ? weightTrendAlign : 0.0
scoreBuy += highRisk_Long ? -1.2 : 0.0
scoreBuy += signalGreenDot ? 1.0 : 0.0
scoreBuy += isAMCUp ? 0.8 : 0.0
scoreBuy += isVssBuy ? 1.5 : 0.0
scoreBuy += isEMACrossUp ? 1.0 : 0.0
scoreBuy += signalRedX ? -1.0 : 0.0
scoreSell = 0.0
scoreSell += structSignalShort ? scoreStruct : 0.0
scoreSell += momentumShort ? scoreMomentum : 0.0
scoreSell += flashSignal ? weightFlash : 0.0
scoreSell += blinkSignal ? weightBlink : 0.0
scoreSell += volumeSpike_AVP ? scoreVolume : 0.0
scoreSell += priceStrength_Short ? scorePriceAction : 0.0
scoreSell += reversalTrigger_Short ? scoreReversal : 0.0
scoreSell += emaAlignment_Bear ? weightTrendAlign : 0.0
scoreSell += strongDownTrend ? weightTrendAlign : 0.0
scoreSell += highRisk_Short ? -1.2 : 0.0
scoreSell += signalRedX ? 1.0 : 0.0
scoreSell += isAMCDown ? 0.8 : 0.0
scoreSell += isVssSell ? 1.5 : 0.0
scoreSell += isEMACrossDown ? 1.0 : 0.0
scoreSell += signalGreenDot ? -1.0 : 0.0
compositeBuySignal = enableComposite and scoreBuy >= thresholdCompositeBuy
compositeSellSignal = enableComposite and scoreSell >= thresholdCompositeSell
if compositeBuySignal and compositeSellSignal
compositeBuySignal := false
compositeSellSignal := false
= calculateCompositeSignals()
// Final Entry Signals
entryCompositeBuySignal = compositeBuySignal and ta.rising(emaFast_ATG, 2)
entryCompositeSellSignal = compositeSellSignal and ta.falling(emaFast_ATG, 2)
Calculates weighted scores from independent modules and activates signals only when threshold requirements are met.
2. Smart Exit Hold Evaluation System
evaluateSmartHold() =>
compositeBuyRecentCount = 0
compositeSellRecentCount = 0
for i = 0 to signalLookbackBars - 1
compositeBuyRecentCount += compositeBuySignal ? 1 : 0
compositeSellRecentCount += compositeSellSignal ? 1 : 0
avgVolume = ta.sma(volume, 20)
volumeSpike = volume > avgVolume * volMultiplier
// MTF Bull/Bear conditions
mtf_bull = mtf_emaFast_final > mtf_emaSlow_final
mtf_bear = mtf_emaFast_final < mtf_emaSlow_final
emaBackupDivergence = math.abs(mtf_emaFast_backup - mtf_emaSlow_backup) / mtf_emaSlow_backup
emaBackupStrong = emaBackupDivergence > 0.008
mtfConflict_Long = inLong and mtf_bear and emaBackupStrong
mtfConflict_Short = inShort and mtf_bull and emaBackupStrong
// Layer 1: ATR-Based Dynamic Threshold (Market Volatility Intelligence)
atr_raw = ta.atr(atrLen)
atrValue = na(atr_raw) ? close * 0.02 : atr_raw
atrRatio = atrValue / close
dynamicThreshold = atrRatio > 0.02 ? 1.0 : (atrRatio > 0.01 ? 1.5 : 2.8)
// Layer 2: ROI-Conditional Time Intelligence (Selective Pressure)
timeMultiplier_Long = realROI >= 0 ? 1.0 : // Profitable positions: No time pressure
holdTimer_Long <= signalLookbackBars ? 1.0 : // Loss positions 1-8 bars: Base
holdTimer_Long <= signalLookbackBars * 2 ? 1.1 : // Loss positions 9-16 bars: +10% stricter
1.3 // Loss positions 17+ bars: +30% stricter
timeMultiplier_Short = realROI >= 0 ? 1.0 : // Profitable positions: No time pressure
holdTimer_Short <= signalLookbackBars ? 1.0 : // Loss positions 1-8 bars: Base
holdTimer_Short <= signalLookbackBars * 2 ? 1.1 : // Loss positions 9-16 bars: +10% stricter
1.3 // Loss positions 17+ bars: +30% stricter
// Dual-Layer Threshold Calculation
baseThreshold_Long = mtfConflict_Long ? dynamicThreshold + 1.0 : dynamicThreshold
baseThreshold_Short = mtfConflict_Short ? dynamicThreshold + 1.0 : dynamicThreshold
timeAdjustedThreshold_Long = baseThreshold_Long * timeMultiplier_Long
timeAdjustedThreshold_Short = baseThreshold_Short * timeMultiplier_Short
// Final Smart Hold Decision with Dual-Layer Intelligence
smartHold_Long = not mtfConflict_Long and smartScoreLong >= timeAdjustedThreshold_Long and compositeBuyRecentCount >= signalMinCount
smartHold_Short = not mtfConflict_Short and smartScoreShort >= timeAdjustedThreshold_Short and compositeSellRecentCount >= signalMinCount
= evaluateSmartHold()
Evaluates whether to hold positions past TP1/TP2/TP3 levels based on continued signal strength, volume confirmation, and multi-timeframe trend alignment
HOW TO USE THE STRATEGY
Step 1: Initial Setup
Apply strategy to your preferred timeframe (backtested on 15M)
Enable "Use Heikin-Ashi Base" for smoother signals in volatile markets
"Show EMA Lines" and "Show Ichimoku Cloud" are enabled for visual context
Set default quantities to match your risk management (5% equity default)
Step 2: Signal Recognition
Visual Signal Guide:
Visual Signal Guide - Complete Reference:
🔶 Red Diamond: Bearish momentum breakdown - short reversal signal
🔷 Blue Diamond: Strong bullish momentum - long reversal signal
🔵 Blue Dot: Volume-confirmed directional move - trend continuation
🟢 Green Dot: Bullish EMA crossover - trend reversal confirmation
🟠 Orange X: Oversold reversal setup - counter-trend opportunity
❌ Red X: Bearish EMA breakdown - trend reversal warning
✡ Star Uprising: Strong bullish convergence
💥 Ultra Entry: Ultra-rapid downward momentum acceleration
▲ VSS Long: Velocity-based bullish momentum confirmation
▼ VSS Short: Velocity-based bearish momentum confirmation
Step 3: Entry Execution
For Long Positions:
1. ✅ EMA1 crossed above EMA2 exactly 3 bars ago [ta.crossover(ema1,ema2) ]
2. ✅ Current EMA structure: EMA1 > EMA2 (maintained)
3. ✅ Composite score ≥ 5.0 points (6.5+ for 5-minute timeframes)
4. ✅ Cooldown period completed (no recent stop losses)
5. ✅ Volume spike confirmation (green dot/blue dot signals)
6. ✅ Bullish candle closes above EMA structure
For Short Positions:
1. ✅ EMA1 crossed below EMA2 exactly 3 bars ago [ta.crossunder(ema1,ema2) ]
2. ✅ Current EMA structure: EMA1 < EMA2 (maintained)
3. ✅ Composite score ≥ 5.4 points (7.0+ for 5-minute timeframes)
4. ✅ Cooldown period completed (no recent stop losses)
5. ✅ Momentum breakdown (red diamond/red X signals)
6. ✅ Bearish candle closes below EMA structure
🎯 Critical Timing Note: The strategy requires EMA crossover to have occurred 3 bars prior to entry, not at the current bar. This attempts to avoid premature entries and may improve signal reliability.
Step 4: Reading Market Context
EMA Ribbon Interpretation:
All EMAs ascending = Strong uptrend context
EMAs 1-3 above EMAs 4-8 = Bullish structure
Tight EMA spacing = Low volatility/consolidation
Wide EMA spacing = High volatility/trending
Ichimoku Cloud Context:
Price above cloud = Bullish environment
Price below cloud = Bearish environment
Cloud color intensity = Momentum strength
Thick cloud = Strong support/resistance
THE SMART EXIT GRID SYSTEM
Smart Exit Grid Approach:
The Smart Exit Grid uses dynamic hold evaluation that continuously analyzes market conditions after position entry. This differs from traditional fixed profit targets by adapting exit timing based on real-time signal strength.
How Smart Exit Grid System Works
The system operates through three evaluation phases:
Smart Score Calculation:
The smart score calculation aggregates 22 signal components in real-time, combining reversal warnings, continuation signals, trend alignment indicators, EMA structural analysis, and risk penalties into a numerical representation of market conditions. MTF analysis provides additional confirmation as a separate validation layer.
Signal Stack Management:
The per-tick signal accumulation system monitors 22 active signal types with MTF providing trend validation and conflict detection as a separate confirmation layer.
Take Profit Progression:
Smart Exit Activation:
The QRE system activates Smart Exit Grid immediately upon position entry. When strategy.entry() executes, the system initializes monitoring systems designed to track position progress.
Upon position opening, holdTimer begins counting, establishing the foundation for subsequent decisions. The Smart Exit Grid starts accumulating signals from entry, with all 22 signal components beginning real-time tracking when the trade opens.
The system operates on continuous evaluation where smartScoreLong and smartScoreShort calculate from the first tick after entry. QRE's approach is designed to capture market structure changes, trend deteriorations, or signal pattern shifts that can trigger protective exits even before the first take profit level is reached.
This activation creates a proactive position management framework. The 8-candle sliding window starts from entry, meaning that if market conditions change rapidly after entry - due to news events, liquidity shifts, or technical changes - the system can respond within the configured lookback period.
TP Markers as Reference Points:
The TP1, TP2, and TP3 levels function as reference points rather than mandatory exit triggers. When longTP1Hit or shortTP1Hit conditions activate, they serve as profit confirmation markers that inform the Smart Exit algorithm about achieved reward levels, but don't automatically initiate position closure.
These TP markers enhance the Smart Exit decision matrix by providing profit context to ongoing signal evaluation. The system recognizes when positions have achieved target returns, but the actual exit decision remains governed by continuous smart score evaluation and signal stack analysis.
TP2 Reached: Enhanced Monitoring
TP2 represents significant profit capture with additional monitoring features:
This approach is designed to help avoid premature profit-taking during trending conditions. If TP2 is reached but smartScoreLong remains above the dynamic threshold and the 8-candle sliding window shows persistent signals, the position continues holding. If market structure deteriorates before reaching TP2, the Smart Exit can trigger closure based on signal analysis.
The visual TP circles that appear when levels are reached serve as performance tracking tools, allowing users to see how frequently entries achieve various profit levels while understanding that actual exit timing depends on market structure analysis.
Risk Management Systems:
Operating independently from the Smart Exit Grid are two risk management systems: the Trap Wick Detection Protocol and the Stop Loss Mechanism. These systems maintain override authority over other exit logic.
The Trap Wick System monitors for conditionBearTrapExit during long positions and conditionBullTrapExit during short positions. When detected, these conditions trigger position closure with state reset, bypassing Smart Exit evaluations. This system recognizes that certain candlestick patterns may indicate reversal risk.
Volatility Exit Monitoring: The strategy monitors for isStrongBearCandle combined with conditionBearTrapExit, recognizing when market structure may be shifting.
Volume Validation: Before exiting on volatility, the strategy requires volume confirmation: volume > ta.sma(volume, 20) * 1.8. This is designed to filter exits on weak, low-volume movements.
The Stop Loss Mechanism operates through multiple triggers including traditional price-based stops (longSLHit, shortSLHit) and early exit conditions based on smart score deterioration combined with negative ROI. The early exit logic activates when smartScoreLong < 1.0 or smartScoreShort < 1.0 while realROI < -0.9%.
These risk management systems are designed so that risk scenarios can trigger protective closure with state reset across all 22 signal counters, TP tracking variables, and smart exit states.
This architecture - Smart Exit activation, TP markers as navigation tools, and independent risk management - creates a position management system that adapts to market conditions while maintaining risk discipline through dedicated protection protocols.
TP3 Reached: Enhanced Protection
Once TP3 is hit, the strategy shifts into enhanced monitoring:
EMA Structure Monitoring: isEMAStructureDown becomes a primary exit trigger
MTF Alignment: The higher timeframe receives increased consideration
Wick Trap Priority: conditionBearTrapExit becomes an immediate exit signal
Approach Differences:
Traditional Fixed Exits:
Exit at predetermined levels regardless of market conditions
May exit during trend continuation
May exit before trend completion
Limited adaptation to changing volatility
Smart Exit Grid Approach:
Adaptive timing based on signal conditions
Exits when supporting signals weaken
Multi-timeframe validation for trend confirmation
Volume confirmation requirements for holds
Structural monitoring for trend analysis
Dynamic ATR-Based Smart Score Threshold System
Market Volatility Adaptive Scoring
// Real-time ATR Analysis
atr_raw = ta.atr(atrLen)
atrValue = na(atr_raw) ? close * 0.02 : atr_raw
atrRatio = atrValue / close
// Three-Tier Dynamic Threshold Matrix
dynamicThreshold = atrRatio > 0.02 ? 1.0 : // High volatility: Lower threshold
(atrRatio > 0.01 ? 1.5 : // Medium volatility: Standard
2.8) // Low volatility: Higher threshold
The market volatility adaptive scoring calculates real-time ATR with a 2% fallback for new markets. The atrRatio represents the relationship between current volatility and price, creating a foundation for threshold adjustment.
The three-tier dynamic threshold matrix responds to market conditions by adjusting requirements based on volatility levels: lowering thresholds during high volatility periods above 2% ATR ratio to 1.0 points, maintaining standard requirements at 1.5 points for medium volatility between 1-2%, and raising standards to 2.8 points during low volatility periods below 1%.
Profit-Loss Adaptive Management:
The system applies different evaluation criteria based on position performance:
Winning Positions (realROI ≥ 0%):
→ timeMultiplier = 1.0 (No additional pressure)
→ Maintains base threshold requirements
→ Allows natural progression to TP2/TP3 levels
Losing Positions (realROI < 0%):
→ Progressive time pressure activated
→ Increasingly strict requirements over time
→ Faster decision-making on underperforming trades
ROI-Adaptive Smart Hold Decision Process:
The strategy uses a profit-loss adaptive system:
Winning Position Management (ROI ≥ 0%):
✅ Standard threshold requirements maintained
✅ No additional time-based pressure applied
✅ Allows positions to progress toward TP2/TP3 levels
✅ timeMultiplier remains at 1.0 regardless of hold duration
Losing Position Management (ROI < 0%):
⚠️ Time-based threshold adjustments activated
⚠️ Progressive increase in required signal strength over time
⚠️ Earlier exit evaluation on underperforming positions
⚠️ timeMultiplier increases from 1.0 → 1.1 → 1.3 based on hold duration
Real-Time Monitoring:
Monitor Analysis Table → "Smart" filter → "Score" vs "Dynamic Threshold"
Winning positions: Evaluation based on signal strength deterioration only
Losing positions: Evaluation considers both signal strength and progressive time adjustments
Breakeven positions (0% ROI): Treated as winning positions - no time adjustments
This approach differentiates between winning and losing positions in the hold evaluation process, requiring higher signal thresholds for extended holding of losing positions while maintaining standard requirements for winning ones.
ROI-Conditional Decision Matrix Examples:
Scenario 1 - Winning Position in Any Market:
Position ROI: +0.8% → timeMultiplier = 1.0 (regardless of hold time)
ATR Medium (1.2%) → dynamicThreshold = 1.5
Final Threshold = 1.5 × 1.0 = 1.5 points ✅ Position continues
Scenario 2 - Losing Position, Extended Hold:
Position ROI: -0.5% → Time pressure activated
Hold Time: 20 bars → timeMultiplier = 1.3
ATR Low (0.8%) → dynamicThreshold = 2.8
Final Threshold = 2.8 × 1.3 = 3.64 points ⚡ Enhanced requirements
Scenario 3 - Fresh Losing Position:
Position ROI: -0.3% → Time pressure activated
Hold Time: 5 bars → timeMultiplier = 1.0 (still early)
ATR High (2.1%) → dynamicThreshold = 1.0
Final Threshold = 1.0 × 1.0 = 1.0 points 📊 Recovery opportunity
Scenario 4 - Breakeven Position:
Position ROI: 0.0% → timeMultiplier = 1.0 (no pressure)
Hold Time: 15 bars → No time penalty applied
Final Threshold = dynamicThreshold only ⚖️ Neutral treatment
🔄8-Candle Sliding Window Signal Rotation System
Composite Signal Counting Mechanism
// Dynamic Lookback Window (configurable: default 8)
signalLookbackBars = input.int(8, "Composite Lookback Bars", minval=1, maxval=50)
// Rolling Signal Analysis
compositeBuyRecentCount = 0
compositeSellRecentCount = 0
for i = 0 to signalLookbackBars - 1
compositeBuyRecentCount += compositeBuySignal ? 1 : 0
compositeSellRecentCount += compositeSellSignal ? 1 : 0
Candle Flow Example (8-bar window):
→
✓ ✓ ✗ ✓ ✗ ✓ ✗ ✓ 🗑️
New Signal Count = 5/8 signals in window
Threshold Check: 5 ≥ signalMinCount (2) = HOLD CONFIRMED
Signal Decay & Refresh Mechanism
// Signal Persistence Tracking
if compositeBuyRecentCount >= signalMinCount
smartHold_Long = true
else
smartHold_Long = false
The composite signal counting operates through a configurable sliding window. The system maintains rolling counters that scan backward through the specified number of candles.
During each evaluation cycle, the algorithm iterates through historical bars, incrementing counters when composite signals are detected. This creates a dynamic signal persistence measurement where recent signal density determines holding decisions.
The sliding window rotation functions like a moving conveyor belt where new signals enter while the oldest signals drop off. For example, in an 8-bar window, if 5 out of 8 recent candles showed composite buy signals, and the minimum required count is 2, the system confirms the hold condition. As new bars form, the window slides forward, potentially changing the signal count and triggering exit conditions when signal density falls below the threshold.
Signal decay and refresh occur continuously where smartHold_Long remains true only when compositeBuyRecentCount exceeds signalMinCount. When recent signal density drops below the minimum requirement, the system switches to exit mode.
Advanced Signal Stack Management - 22-Signal Real-Time Evaluation
// Long Position Signal Stacking (calc_on_every_tick=true)
if inLong
// Primary Reversal Signals
if signalRedDiamond: signalCountRedDiamond += 1 // -0.5 points
if signalStarUprising: signalCountStarUprising += 1 // +1.5 points
if entryUltraShort: signalCountUltra += 1 // -1.0 points
// Trend Confirmation Signals
if strongUpTrend: trendUpCount_Long += 1 // +1.5 points
if emaAlignment_Bull: bullAlignCount_Long += 1 // +1.0 points
// Risk Assessment Signals
if highRisk_Long: riskCount_Long += 1 // -1.5 points
if topZone: tzoneCount_Long += 1 // -0.5 points
The per-tick signal accumulation system operates with calc_on_every_tick=true for real-time responsiveness. During long positions, the system monitors primary reversal signals where Red Diamond signals subtract 0.5 points as reversal warnings, Star Uprising adds 1.5 points for continuation signals, and Ultra Short signals deduct 1.0 points as counter-trend warnings.
Trend confirmation signals provide weighted scoring where strongUpTrend adds 1.5 points for aligned momentum, emaAlignment_Bull contributes 1.0 point for structural support, and various EMA-based confirmations contribute to the overall score. Risk assessment signals apply negative weighting where highRisk_Long situations subtract 1.5 points, topZone conditions deduct 0.5 points, and other risk factors create defensive scoring adjustments.
The smart score calculation aggregates all 22 components in real-time, combining reversal warnings, continuation signals, trend alignment indicators, EMA structural analysis, and risk penalties into a numerical representation of market conditions. This score updates continuously, providing the foundation for hold-or-exit decisions.
MULTI-TIMEFRAME (MTF) SYSTEM
MTF Data Collection
The strategy requests higher timeframe data (default 30-minute) for trend confirmation:
= request.security(syminfo.tickerid, mtfTimeframe, , lookahead=barmerge.lookahead_off, gaps=barmerge.gaps_off)
MTF Watchtower System - Implementation Logic
The system employs a timeframe discrimination protocol where currentTFInMinutes is compared against a 30-minute threshold. This creates different operational behavior between timeframes:
📊 Timeframe Testing Results:
30M+ charts: Full MTF confirmation → Tested with full features
15M charts: Local EMA + adjusted parameters → Standard testing baseline
5M charts: Local EMA only → Requires parameter adjustment
1M charts: High noise → Limited testing conducted
When the chart timeframe is 30 minutes or above, the strategy activates useMTF = true and requests external MTF data through request.security(). For timeframes below 30 minutes, including your 5-minute setup, the system deliberately uses local EMA calculations to avoid MTF lag and data inconsistencies.
The triple-layer data sourcing architecture works as follows: timeframes from 1 minute to 29 minutes rely on chart-based EMA calculations for immediate responsiveness. Timeframes of 30 minutes and above utilize MTF data through the security function, with a backup system that doubles the EMA length (emaLen * 2) if MTF data fails. When MTF data is unavailable or invalid, the system falls back to local EMA as the final safety net.
Data validation occurs through a pipeline where mtf_dataValid checks not only for non-null values but also verifies that EMA values are positive above zero. The system tracks data sources through mtf_dataSource which displays "MTF Data" for successful external requests, "Backup EMA" for failed MTF with backup system active, or "Chart EMA" for local calculations.
🔄 MTF Smart Score Caching & Recheck System
// Cache Update Decision Logic
mtfSmartIntervalSec = input.int(300, "Smart Grid Recheck Interval (sec)") // 5-minute cache
canRecheckSmartScore = na(timenow) ? false :
(na(lastCheckTime) or (timenow - lastCheckTime) > mtfSmartIntervalSec * 1000)
// Cache Management
if canRecheckSmartScore
lastCheckTime := timenow
cachedSmartScoreLong := smartScoreLong // Store current calculation
cachedSmartScoreShort := smartScoreShort
The performance-optimized caching system addresses the computational intensity of continuous MTF analysis through intelligent interval management. The mtfSmartIntervalSec parameter, defaulting to 300 seconds (5 minutes), determines cache refresh frequency. The system evaluates canRecheckSmartScore by comparing current time against lastCheckTime plus the configured interval.
When cache updates trigger, the system stores current calculations in cachedSmartScoreLong and cachedSmartScoreShort, creating stable reference points that reduce excessive MTF requests. This cache management balances computational efficiency with analytical accuracy.
The cache versus real-time hybrid system creates a multi-layered decision matrix where immediate signals update every tick for responsive market reaction, cached MTF scores refresh every 5 minutes for stability filtering, dynamic thresholds recalculate every bar for volatility adaptation, and sliding window analysis updates every bar for trend persistence validation.
This architecture balances real-time signal detection with multi-timeframe strategic validation, creating adaptive trading intelligence that responds immediately to market changes while maintaining strategic stability through cached analysis and volatility-adjusted decision thresholds.
⚡The Execution Section Deep Dive
The execution section represents the culmination of all previous systems – where analysis transforms into action.
🚪 Entry Execution: The Gateway Protocol
Primary Entry Validation:
Entry isn't just about seeing a signal – it's about passing through multiple security checkpoints, each designed to filter out low-quality opportunities.
Stage 1: Signal Confirmation
entryCompositeBuySignal must be TRUE for longs
entryCompositeSellSignal must be TRUE for shorts
Stage 2: Enhanced Entry Validation
The strategy employs an "OR" logic system that recognizes different types of market opportunities:
Path A - Trend Reversal Entry:
When emaTrendReversal_Long triggers, it indicates the market structure is shifting in favor of the trade direction. This isn't just about a single EMA crossing – it represents a change in market momentum that experienced traders recognize as potential high-probability setups.
Path B - Momentum Breakout Entry:
The strongBullMomentum condition is where QRE identifies accelerating market conditions:
Criteria:
EMA1 rising for 3+ candles AND
EMA2 rising for 2+ candles AND
Close > 10-period high
This combination captures those explosive moves where the market doesn't just trend – it accelerates, creating momentum-driven opportunities.
Path C - Recovery Entry:
When previous exit states are clean (no recent stop losses), the strategy permits entry based purely on signal strength. This pathway is designed to help avoid the strategy becoming overly cautious after successful trades.
🛡️ The Priority Exit Matrix: When Rules Collide
Not all exit signals are created equal. QRE uses a strict hierarchy that is designed to avoid conflicting signals from causing hesitation:
Priority Level 1 - Exception Exits (Immediate Action):
Condition: TP3 reached AND Wick Trap detected
Action: Immediate exit regardless of other signals
Rationale: Historical analysis suggests wick traps at TP3 may indicate potential reversals
Priority Level 2 - Structural Breakdown:
Condition: TP3 active AND EMA structure deteriorating AND Smart Score insufficient
Logic: isEMAStructureDown AND NOT smartHold_Long
This represents the strategy recognizing that the underlying market structure that justified the trade is failing. It's like a building inspector identifying structural issues – you don't wait for additional confirmation.
Priority Level 3 - Enhanced Volatility Exits:
Conditions: TP2 active AND Strong counter-candle AND Wick trap AND Volume spike
Logic: Multiple confirmation required to reduce false exits
Priority Level 4 - Standard Smart Score Exits:
Condition: Any TP level active AND smartHold evaluates to FALSE
This is the bread-and-butter exit logic where signal deterioration triggers exit
⚖️ Stop Loss Management: Risk Control Protocol
Dual Stop Loss System:
QRE provides two stop loss modes that users can select based on their preference:
Fixed Mode (Default - useAdaptiveSL = false):
Uses predetermined percentage levels regardless of market volatility:
- Long SL = entryPrice × (1 - fixedRiskP - slipBuffer)
- Short SL = entryPrice × (1 + fixedRiskP + slipBuffer)
- Default: 0.6% risk + 0.3% slippage buffer = 0.9% total stop
- Consistent and predictable stop loss levels
- Recommended for users who prefer stable risk parameters
Adaptive Mode (Optional - useAdaptiveSL = true):
Dynamic system that adjusts stop loss based on market volatility:
- Base Calculation uses ATR (Average True Range)
- Long SL = entryPrice × (1 - (ATR × atrMultSL) / entryPrice - slipBuffer)
- Short SL = entryPrice × (1 + (ATR × atrMultSL) / entryPrice + slipBuffer)
- Automatically widens stops during high volatility periods
- Tightens stops during low volatility periods
- Advanced users can enable for volatility-adaptive risk management
Trend Multiplier Enhancement (Both Modes):
When strongUpTrend is detected for long positions, the stop loss receives 1.5x breathing room. Strong trends often have deeper retracements before continuing. This is designed to help avoid the strategy being shaken out of active trades by normal market noise.
Mode Selection Guidance:
- New Users: Start with Fixed Mode for predictable risk levels
- Experienced Users: Consider Adaptive Mode for volatility-responsive stops
- Volatile Markets: Adaptive Mode may provide better stop placement
- Stable Markets: Fixed Mode often sufficient for consistent risk management
Early Exit Conditions:
Beyond traditional stop losses, QRE implements "smart stops" that trigger before price-based stops:
Early Long Exit: (smartScoreLong < 1.0 OR prev5BearCandles) AND realROI < -0.9%
🔄 State Management: The Memory System
Complete State Reset Protocol:
When a position closes, QRE doesn't just wipe the slate clean – it performs a methodical reset:
TP State Cleanup:
All Boolean flags: tp1/tp2/tp3HitBefore → FALSE
All Reached flags: tp1/tp2/tp3Reached → FALSE
All Active flags: tp1/tp2/tp3HoldActive → FALSE
Signal Counter Reset:
Every one of the 22 signal counters returns to zero.
This is designed to avoid signal "ghosting" where old signals influence new trades.
Memory Preservation:
While operational states reset, certain information is preserved for learning:
killReasonLong/Short: Why did this trade end?
lastExitWasTP1/TP2/TP3: What was the exit quality?
reEntryCount: How many consecutive re-entries have occurred?
🔄 Re-Entry Logic: The Comeback System
Re-Entry Conditions Matrix:
QRE implements a re-entry system that recognizes not all exits are created equal:
TP-Based Re-Entry (Enabled):
Criteria: Previous exit was TP1, TP2, or TP3
Cooldown: Minimal or bypassed entirely
Logic: Target-based exits indicate potentially viable market conditions
EMA-Based Re-Entry (Conditional):
Criteria: Previous exit was EMA-based (structural change)
Requirements: Must wait for EMA confirmation in new direction
Minimum Wait: 5 candles
Advanced Re-Entry Features:
When adjustReEntryTargets is enabled, the strategy becomes more aggressive with re-entries:
Target Adjustment: TP1 multiplied by reEntryTP1Mult (default 2.0)
Stop Adjustment: SL multiplied by reEntrySLMult (default 1.5)
Logic: If we're confident enough to re-enter, we should be confident enough to hold for bigger moves
Performance Tracking: Strategy tracks re-entry win rate, average ROI, and total performance separately from initial entries for optimization analysis.
📊 Exit Reason Analytics: Learning from Every Trade
Kill Reason Tracking:
Every exit is categorized and stored:
"TP3 Exit–Wick Trap": Exit at target level with wick pattern detection
"Smart Exit–EMA Down": Structural breakdown exit
"Smart Exit–Volatility": Volatility-based protection exit
"Exit Post-TP1/TP2/TP3": Standard smart exit progression
"Long SL Exit" / "Short SL Exit": Stop loss exits
Performance Differentiation:
The strategy tracks performance by exit type, allowing for continuous analysis:
TP-based exits: Achieved target levels, analyze for pattern improvement
EMA-based exits: Mixed results, analyze for pattern improvement
SL-based exits: Learning opportunities, adjust entry criteria
Volatility exits: Protective measures, monitor performance
🎛️ Trailing Stop Implementation:
Conditional Trailing Activation:
Activation Criteria: Position profitable beyond trailingStartPct AND
(TP hold active OR re-entry trade)
Dynamic Trailing Logic:
Unlike simple trailing stops, QRE's implementation considers market context:
Trending Markets: Wider trail offsets to avoid whipsaws
Volatile Markets: Tighter offsets to protect gains
Re-Entry Trades: Enhanced trailing to maximize second-chance opportunities
Return-to-Entry Protection:
When deactivateOnReturn is enabled, the strategy will close positions that return to entry level after being profitable. This is designed to help avoid the frustration of watching profitable trades turn into losers.
🧠 How It All Works Together
The beauty of QRE lies not in any single component, but in how everything integrates:
The Entry Decision: Multiple pathways are designed to help identify opportunities while maintaining filtering standards.
The Progression System: Each TP level unlocks new protection features, like achieving ranks in a video game.
The Exit Matrix: Prioritized decision-making aims to reduce analysis paralysis while providing appropriate responses to different market conditions.
The Memory System: Learning from each trade while preventing contamination between separate opportunities.
The Re-Entry Logic: Re-entry system that balances opportunity with risk management.
This creates a trading system where entry conditions filter for quality, progression systems adapt to changing market conditions, exit priorities handle conflicting signals intelligently, memory systems learn from each trade cycle, and re-entry logic maximizes opportunities while managing risk exposure.
📊 ANALYSIS TABLE INTERPRETATION -
⚙️ Enabling Analysis Mode
Navigate to strategy settings → "Testing & Analysis" → Enable "Show Analysis Table". The Analysis Table displays different information based on the selected test filter and provides real-time insight into all strategy components, helping users understand current market conditions, position status, and system decision-making processes.
📋 Filter Mode Interpretations
"All" Mode (Default View):
Composite Section:
Buy Score: Aggregated strength from all 22 bullish signals (threshold 5.0+ triggers entry consideration)
Sell Score: Aggregated strength from all 22 bearish signals (threshold 5.4+ triggers entry consideration)
APEX Filters:
ATG Trend: Shows current trend direction analysis
Indicates whether momentum filters are aligned for directional bias
ReEntry Section:
Most Recent Exit: Displays exit type and timeframe since last position closure
Status: Shows if ReEntry system is Ready/Waiting/Disabled
Count: Current re-entry attempts versus maximum allowed attempts
Position Section (When Active):
Status: Current position state (LONG/SHORT/FLAT)
ROI: Dual calculation showing Custom vs Real ROI percentages
Entry Price: Original position entry level
Current Price: Live market price for comparison
TP Tracking: Progress toward profit targets
"Smart" Filter (Critical for Active Positions):
Smart Exit Section:
Hold Timer: Time elapsed since position opened (bar-based counting)
Status: Whether Smart Exit Grid is Enabled/Disabled
Score: Current smart score calculation from 22-component matrix
Dynamic Threshold: ATR-based minimum score required for holding
Final Threshold: Time and ROI-adjusted threshold actually used for decisions
Score Check: Pass/Fail based on Score vs Final Threshold comparison
Smart Hold: Current hold decision status
Final Hold: Final recommendation based on all factors
🎯 Advanced Smart Exit Debugging - ROI & Time-Based Threshold System
Understanding the Multi-Layer Threshold System:
Layer 1: Dynamic Threshold (ATR-Based)
atrRatio = ATR / close
dynamicThreshold = atrRatio > 0.02 ? 1.0 : // High volatility: Lower threshold
(atrRatio > 0.01 ? 1.5 : // Medium volatility: Standard
2.8) // Low volatility: Higher threshold
Layer 2: Time Multiplier (ROI & Duration-Based)
Winning Positions (ROI ≥ 0%):
→ timeMultiplier = 1.0 (No time pressure, regardless of hold duration)
Losing Positions (ROI < 0%):
→ holdTimer ≤ 8 bars: timeMultiplier = 1.0 (Early stage, standard requirements)
→ holdTimer 9-16 bars: timeMultiplier = 1.1 (10% stricter requirements)
→ holdTimer 17+ bars: timeMultiplier = 1.3 (30% stricter requirements)
Layer 3: Final Threshold Calculation
finalThreshold = dynamicThreshold × timeMultiplier
Examples:
- Winning Position: 2.8 × 1.0 = 2.8 (Always standard)
- Losing Position (Early): 2.8 × 1.0 = 2.8 (Same as winning initially)
- Losing Position (Extended): 2.8 × 1.3 = 3.64 (Much stricter)
Real-Time Debugging Display:
Smart Exit Section shows:
Score: 3.5 → Current smartScoreLong/Short value
Dynamic Threshold: 2.8 → Base ATR-calculated threshold
Final Threshold: 3.64 (ATR×1.3) → Actual threshold used for decisions
Score Check: FAIL (3.5 vs 3.64) → Pass/Fail based on final comparison
Final Hold: NO HOLD → Actual system decision
Position Status Indicators:
Winner + Early: ATR×1.0 (No pressure)
Winner + Extended: ATR×1.0 (No pressure - winners can run indefinitely)
Loser + Early: ATR×1.0 (Recovery opportunity)
Loser + Extended: ATR×1.1 or ATR×1.3 (Increasing pressure to exit)
MTF Section:
Data Source: Shows whether using MTF Data/EMA Backup/Local EMA
Timeframe: Configured watchtower timeframe setting
Data Valid: Confirms successful MTF data retrieval status
Trend Signal: Higher timeframe directional bias analysis
Close Price: MTF price data availability confirmation
"Composite" Filter:
Composite Section:
Buy Score: Real-time weighted scoring from multiple indicators
Sell Score: Opposing directional signal strength
Threshold: Minimum scores required for signal activation
Components:
Flash/Blink: Momentum acceleration indicators (F = Flash active, B = Blink active)
Individual filter contributions showing which specific signals are firing
"ReEntry" Filter:
ReEntry System:
System: Shows if re-entry feature is Enabled/Disabled
Eligibility: Conditions for new entries in each direction
Performance: Success metrics of re-entry attempts when enabled
🎯 Key Status Indicators
Status Column Symbols:
✓ = Condition met / System active / Signal valid
✗ = Condition not met / System inactive / No signal
⏳ = Cooldown active (waiting period)
✅ = Ready state / Good condition
🔄 = Processing / Transitioning state
🔍 Critical Reading Guidelines
For Active Positions - Smart Exit Priority Reading:
1. First Check Position Type:
ROI ≥ 0% = Winning Position (Standard requirements)
ROI < 0% = Losing Position (Progressive requirements)
2. Check Hold Duration:
Early Stage (≤8 bars): Standard multiplier regardless of ROI
Extended Stage (9-16 bars): Slight pressure on losing positions
Long Stage (17+ bars): Strong pressure on losing positions
3. Score vs Final Threshold Analysis:
Score ≥ Final Threshold = HOLD (Continue position)
Score < Final Threshold = EXIT (Close position)
Watch for timeMultiplier changes as position duration increases
4. Understanding "Why No Hold?"
Common scenarios when Score Check shows FAIL:
Losing position held too long (timeMultiplier increased to 1.1 or 1.3)
Low volatility period (dynamic threshold raised to 2.8)
Signal deterioration (smart score dropped below required level)
MTF conflict (higher timeframe opposing position direction)
For Entry Signal Analysis:
Composite Score Reading: Signal strength relative to threshold requirements
Component Analysis: Individual filter contributions to overall score
EMA Structure: Confirm 3-bar crossover requirement met
Cooldown Status: Ensure sufficient time passed since last exit
For ReEntry Opportunities (when enabled):
System Status: Availability and eligibility for re-engagement
Exit Type Analysis: TP-based exits enable immediate re-entry, SL-based exits require cooldown
Condition Monitoring: Requirements for potential re-entry signals
Debugging Common Issues:
Issue: "Score is high but no hold?"
→ Check Final Threshold vs Score (not Dynamic Threshold)
→ Losing position may have increased timeMultiplier
→ Extended hold duration applying pressure
Issue: "Why different thresholds for same score?"
→ Position ROI status affects multiplier
→ Time elapsed since entry affects multiplier
→ Market volatility affects base threshold
Issue: "MTF conflicts with local signals?"
→ Higher timeframe trend opposing position
→ System designed to exit on MTF conflicts
→ Check MTF Data Valid status
⚡ Performance Optimization Notes
For Better Performance:
Analysis table updates may impact performance on some devices
Use specific filters rather than "All" mode for focused monitoring
Consider disabling during live trading for optimal chart performance
Enable only when needed for debugging or analysis
Strategic Usage:
Monitor "Smart" filter when positions are active for exit timing decisions
Use "Composite" filter during setup phases for signal strength analysis
Reference "ReEntry" filter after position closures for re-engagement opportunities
Track Final Threshold changes to understand exit pressure evolution
Advanced Debugging Workflow:
Position Entry Analysis:
Check Composite score vs threshold
Verify EMA crossover timing (3 bars prior)
Confirm cooldown completion
Hold Decision Monitoring:
Track Score vs Final Threshold progression
Monitor timeMultiplier changes over time
Watch for MTF conflicts
Exit Timing Analysis:
Identify which threshold layer caused exit
Track performance by exit type
Analyze re-entry eligibility
This analysis system provides transparency into strategy decision-making processes, allowing users to understand how signals are generated and positions are managed according to the programmed logic during various market conditions and position states.
SIGNAL TYPES AND CHARACTERISTICS
🔥 Core Momentum Signals
Flash Signal
Calculation: ta.rma(math.abs(close - close ), 5) > ta.sma(math.abs(close - close ), 7)
Purpose: Detects sudden price acceleration using smoothed momentum comparison
Characteristics: Triggers when recent price movement exceeds historical average movement
Usage: Primary momentum confirmation across multiple composite calculations
Weight: 1.3 points in composite scoring
Blink Signal
Calculation: math.abs(ta.change(close, 1)) > ta.sma(math.abs(ta.change(close, 1)), 5)
Purpose: Identifies immediate price velocity spikes
Characteristics: More sensitive than Flash, captures single-bar momentum bursts
Usage: Secondary momentum confirmation, often paired with Flash
Weight: 1.3 points in composite scoring
⚡ Advanced Composite Signals
Apex Pulse Signal
Calculation: apexAngleValue > 30 or apexAngleValue < -30
Purpose: Detects extreme EMA angle momentum
Characteristics: Identifies when trend angle exceeds ±30 degrees
Usage: Confirms directional momentum strength in trend-following scenarios
Pressure Surge Signal
Calculation: volSpike_AVP and strongTrendUp_ATG
Purpose: Combines volume expansion with trend confirmation
Characteristics: Requires both volume spike and strong uptrend simultaneously
Usage: bullish signal for trend continuation
Shift Wick Signal
Calculation: ta.crossunder(ema1, ema2) and isWickTrapDetected and directionFlip
Purpose: Detects bearish reversal with wick trap confirmation
Characteristics: Combines EMA crossunder with upper wick dominance and directional flip
Usage: Reversal signal for trend change identification
🛡️ Trap Exit Protection Signals
Bear Trap Exit
Calculation: isUpperWickTrap and isBearEngulfNow
Conditions: Previous bullish candle with 80%+ upper wick, followed by current bearish engulfing
Purpose: Emergency exit signal for long positions
Priority: Highest - overrides all other hold conditions
Action: Immediate position closure with full state reset
Bull Trap Exit
Calculation: isLowerWickTrap and isBullEngulfNow
Conditions: Previous bearish candle with 80%+ lower wick, followed by current bullish engulfing
Purpose: Emergency exit signal for short positions
Priority: Highest - overrides all other hold conditions
Action: Immediate position closure with full state reset
📊 Technical Analysis Foundation Signals
RSI-MFI Hybrid System
Base Calculation: (ta.rsi(close, 14) + ta.mfi(close, 14)) / 2
Oversold Threshold: < 35
Overbought Threshold: > 65
Weak Condition: < 35 and declining
Strong Condition: > 65 and rising
Usage: Momentum confirmation and reversal identification
ADX-DMI Trend Classification
Strong Up Trend: (adx > 25 and diplus > diminus and (diplus - diminus) > 5) or (ema1 > ema2 and ema2 > ema3 and ta.rising(ema2, 3))
Strong Down Trend: (adx > 20 and diminus > diplus - 5) or (ema1 < ema2 and ta.falling(ema1, 3))
Trend Weakening: adx < adx and adx < adx
Usage: Primary trend direction confirmation
Bollinger Band Squeeze Detection
Calculation: bbWidth < ta.lowest(bbWidth, 20) * 1.2
Purpose: Identifies low volatility periods before breakouts
Usage: Entry filter - avoids trades during consolidation
🎨 Visual Signal Indicators
Red X Signal
Calculation: isBearCandle and ta.crossunder(ema1, ema2)
Visual: Red X above price
Purpose: Bearish EMA crossunder with confirming candle
Composite Weight: +1.0 for short positions, -1.0 for long positions
Characteristics: Simple but effective trend change indicator
Green Dot Signal
Calculation: isBullCandle and ta.crossover(ema1, ema2)
Visual: Green dot below price
Purpose: Bullish EMA crossover with confirming candle
Composite Weight: +1.0 for long positions, -1.0 for short positions
Characteristics: Entry confirmation for trend-following strategies
Blue Diamond Signal
Trigger Conditions: amcBuySignal and score >= 4
Scoring Components: 11 different technical conditions
Key Requirements: AMC bullish + momentum rise + EMA expansion + volume confirmation
Visual: Blue diamond below price
Purpose: Bullish reversal or continuation signal
Characteristics: Multi-factor confirmation requiring 4+ technical alignments
Red Diamond Signal
Trigger Conditions: amcSellSignal and score >= 5
Scoring Components: 11 different technical conditions (stricter than Blue Diamond)
Key Requirements: AMC bearish + momentum crash + EMA compression + volume decline
Visual: Red diamond above price
Purpose: Potential bearish reversal or continuation signal
Characteristics: Requires higher threshold (5 vs 4) for more selective triggering
🔵 Specialized Detection Signals
Blue Dot Signal
Calculation: volumePulse and isCandleStrong and volIsHigh
Requirements: Volume > 2.0x MA, strong candle body > 35% of range, volume MA > 55
Purpose: Volume-confirmed momentum signal
Visual: Blue dot above price
Characteristics: Volume-centric signal for high-liquidity environments
Orange X Signal
Calculation: Complex multi-factor oversold reversal detection
Requirements: AMC oversold + wick trap + flash/blink + RSI-MFI oversold + bullish flip
Purpose: Oversold bounce signal with multiple confirmations
Visual: Orange X below price
Characteristics: Reversal signal requiring 5+ simultaneous conditions
VSS (Velocity Signal System)
Components: Volume spike + EMA angle + trend direction
Buy Signal: vssTrigger and vssTrendDir == 1
Sell Signal: vssTrigger and vssTrendDir == -1
Visual: Green/Red triangles
Purpose: Velocity-based momentum detection
Characteristics: Fast-response signal for momentum trading
⭐ Elite Composite Signals
Star Uprising Signal
Base Requirements: entryCompositeBuySignal and echoBodyLong and strongUpTrend and isAMCUp
Additional Confirmations: RSI hybrid strong + not high risk
Special Conditions: At bottom zone OR RSI bottom bounce OR strong volume bounce
Visual: Star symbol below price
Purpose: Bullish reversal signal from oversold conditions
Characteristics: Most selective bullish signal requiring multiple confirmations
Ultra Short Signal
Scoring System: 7-component scoring requiring 4+ points
Key Components: EMA trap + volume decline + RSI weakness + composite confirmation
Additional Requirements: Falling EMA structure + volume spike + flash confirmation
Visual: Explosion emoji above price
Purpose: Aggressive short entry for trend reversal or continuation
Characteristics: Complex multi-layered signal for experienced short selling
🎯 Composite Signal Architecture
Enhanced Composite Scoring
Long Composite: 15+ weighted components including structure, momentum, flash/blink, volume, price action, reversal triggers, trend alignment
Short Composite: Mirror structure with bearish bias
Threshold: 5.0 points required for signal activation
Conflict Resolution: If both long and short signals trigger simultaneously, both are disabled
Final Validation: Requires EMA momentum confirmation (ta.rising(emaFast_ATG, 2) for longs, ta.falling(emaFast_ATG, 2) for shorts)
Risk Assessment Integration
High Risk Long: RSI > 70 OR close > upper Bollinger Band 80%
High Risk Short: RSI < 30 OR close < lower Bollinger Band 80%
Zone Analysis: Top zone (95% of 50-bar high) vs Bottom zone (105% of 50-bar low)
Risk Penalty: High risk conditions subtract 1.5 points from composite scores
This signal architecture creates a multi-layered detection system where simple momentum signals provide foundation, technical analysis adds structure, visual indicators offer clarity, specialized detectors capture different market conditions, and composite signals identify potential opportunities while integrated risk assessment is designed to filter risky entries.
VISUAL FEATURES SHOWCASE
Ichimoku Cloud Visualization
Dynamic Color Intensity: Cloud transparency adapts to momentum strength - darker colors indicate stronger directional moves, while lighter transparency shows weakening momentum phases.
Gradient Color Mapping: Bullish momentum renders blue-purple spectrum with increasing opacity, while bearish momentum displays corresponding color gradients with intensity-based transparency.
Real-time Momentum Feedback: Color saturation provides immediate visual feedback on market structure strength, allowing traders to assess levels at a glance without additional indicators.
EMA Ribbon Bands
The 8-level exponential moving average system creates a comprehensive trend structure map with gradient color coding.
Signal Type Visualization
STRATEGY PROPERTIES & BACKTESTING DISCLOSURE
📊 Default Strategy Configuration:
✅ Initial Capital: 100,000 USD (realistic for average traders)
✅ Commission: 0.075% per trade (realistic exchange fees)
✅ Slippage: 3 ticks (market impact consideration)
✅ Position Size: 5% equity per trade (sustainable risk level)
✅ Pyramiding: Disabled (single position management)
✅ Sample Size: 185 trades over 12-month backtesting period
✅ Risk Management: Adaptive stop loss with maximum 1% risk per trade
COMPREHENSIVE BACKTESTING RESULTS
Testing Period & Market Conditions:
Backtesting Period: June 25, 2024 - June 25, 2025 (12 months)
Timeframe: 15-minute charts (MTF system active)
Market: BTCUSDT (Bitcoin/Tether)
Market Conditions: Full market cycle including volatility periods
Deep Backtesting: Enabled for maximum accuracy
📈 Performance Summary:
Total Return: +2.19% (+2,193.59 USDT)
Total Trades Executed: 185 trades
Win Rate: 34.05% (63 winning trades out of 185)
Profit Factor: 1.295 (gross profit ÷ gross loss)
Maximum Drawdown: 0.65% (653.17 USDT)
Risk-Adjusted Returns: Consistent with conservative risk management approach
📊 Detailed Trade Analysis:
Position Distribution:
Long Positions: 109 trades (58.9%) | Win Rate: 36.70%
Short Positions: 76 trades (41.1%) | Win Rate: 30.26%
Average Trade Duration: Optimized for 15-minute timeframe efficiency
Profitability Metrics:
Average Profit per Trade: 11.74 USDT (0.23%)
Average Winning Trade: 151.17 USDT (3.00%)
Average Losing Trade: 60.27 USDT (1.20%)
Win/Loss Ratio: 2.508 (winners are 2.5x larger than losses)
Largest Single Win: 436.02 USDT (8.69%)
Largest Single Loss: 107.41 USDT (controlled risk management)
💰 Financial Performance Breakdown:
Gross Profit: 9,523.93 USDT (9.52% of capital)
Gross Loss: 7,352.48 USDT (7.35% of capital)
Net Profit After Costs: 2,171.44 USDT (2.17%)
Commission Costs: 1,402.47 USDT (realistic trading expenses)
Maximum Equity Run-up: 2,431.66 USDT (2.38%)
⚖️ Risk Management Validation:
Maximum Drawdown: 0.65% showing controlled risk management
Drawdown Recovery: Consistent equity curve progression
Risk per Trade: Successfully maintained below 1.5% per position
Position Sizing: 5% equity allocation proved sustainable throughout testing period
📋 Strategy Performance Characteristics:
✅ Strengths Demonstrated:
Controlled Risk: Maximum drawdown well below industry standards (< 1%)
Positive Expectancy: Win/loss ratio of 2.5+ creates profitable edge
Consistent Performance: Steady equity curve without extreme volatility
Realistic Costs: Includes actual commission and slippage impacts
Sample Size: 185 trades during testing period
⚠️ Performance Considerations:
Win Rate: 34% win rate requires discipline to follow system signals
Market Dependency: Performance may vary significantly in different market conditions
Timeframe Sensitivity: Optimized for 15-minute charts; other timeframes may show different results
Slippage Impact: Real trading conditions may affect actual performance
📊 Benchmark Comparison:
Strategy Return: +2.19% over 12 months
Buy & Hold Bitcoin: +71.12% over same period
Strategy Advantage: Significantly lower drawdown and volatility
Risk-Adjusted Performance: Different risk profile compared to holding cryptocurrency
🎯 Real-World Application Insights:
Expected Trading Frequency:
Average: 15.4 trades per month (185 trades ÷ 12 months)
Weekly Frequency: Approximately 3-4 trades per week
Active Management: Requires regular monitoring during market hours
Capital Requirements:
Minimum Used in Testing: $10,000 for sustainable position sizing
Tested Range: $50,000-$100,000 for comfortable risk management
Commission Impact: 0.075% per trade totaled 1.4% of capital over 12 months
⚠️ IMPORTANT BACKTESTING DISCLAIMERS:
📈 Performance Reality:
Past performance does not guarantee future results. Backtesting results represent hypothetical performance and may not reflect actual trading outcomes due to market changes, execution differences, and emotional factors.
🔄 Market Condition Dependency:
This strategy's performance during the tested period may not be representative of performance in different market conditions, volatility regimes, or trending vs. sideways markets.
💸 Cost Considerations:
Actual trading costs may vary based on broker selection, market conditions, and trade size. Commission rates and slippage assumptions may differ from real-world execution.
🎯 Realistic Expectations:
The 34% win rate requires psychological discipline to continue following signals during losing streaks. Risk management and position sizing are critical for replicating these results.
⚡ Technology Dependencies:
Strategy performance assumes reliable internet connection, platform stability, and timely signal execution. Technical failures may impact actual results.
CONFIGURATION OPTIMIZATION
5-Minute Timeframe Optimization (Advanced Users Only)
⚠️ Important Warning: 5-minute timeframes operate without MTF confirmation, resulting in reduced signal quality and higher false signal rates.
Example 5-Minute Parameters:
Composite Thresholds: Long 6.5, Short 7.0 (vs 15M default 5.0/5.4)
Signal Lookback Bars: 12 (vs 15M default 8)
Volume Multiplier: 2.2 (vs 15M default 1.8)
MTF Timeframe: Disabled (automatic below 30M)
Risk Management Adjustments:
Position Size: Reduce to 3% (vs 5% default)
TP1: 0.8%, TP2: 1.2%, TP3: 2.0% (tighter targets)
SL: 0.8% (tighter stop loss)
Cooldown Minutes: 8 (vs 5 default)
Usage Notes for 5-Minute Trading:
- Wait for higher composite scores before entry
- Require stronger volume confirmation
- Monitor EMA structure more closely
15-Minute Scalping Setup:
TP1: 1.0%, TP2: 1.5%, TP3: 2.5%
Composite Threshold: 5.0 (higher filtering)
TP ATR Multiplier: 7.0
SL ATR Multiplier: 2.5
Volume Multiplier: 1.8 (requires stronger confirmation)
Hold Time: 2 bars minimum
3-Hour Swing Setup:
TP1: 2.0%, TP2: 4.0%, TP3: 8.0%
Composite Threshold: 4.5 (more signals)
TP ATR Multiplier: 8.0
SL ATR Multiplier: 3.2
Volume Multiplier: 1.2
Hold Time: 6 bars minimum
Market-Specific Adjustments
High Volatility Periods:
Increase ATR multipliers (TP: 2.0x, SL: 1.2x)
Raise composite thresholds (+0.5 points)
Reduce position size
Enable cooldown periods
Low Volatility Periods:
Decrease ATR multipliers (TP: 1.2x, SL: 0.8x)
Lower composite thresholds (-0.3 points)
Standard position sizing
Disable extended cooldowns
News Events:
Temporarily disable strategy 30 minutes before major releases
Increase volume requirements (2.0x multiplier)
Reduce position sizes by 50%
Monitor for unusual price action
RISK MANAGEMENT
Dual ROI System: Adaptive vs Fixed Mode
Adaptive RR Mode:
Uses ATR (Average True Range) for automatic adjustment
TP1: 1.0x ATR from entry price
TP2: 1.5x ATR from entry price
TP3: 2.0x ATR from entry price
Stop Loss: 1.0x ATR from entry price
Automatically adjusts to market volatility
Fixed Percentage Mode:
Uses predetermined percentage levels
TP1: 1.0% (default)
TP2: 1.5% (default)
TP3: 2.5% (default)
Stop Loss: 0.9% total (0.6% risk tolerance + 0.3% slippage buffer)(default)
Consistent levels regardless of volatility
Mode Selection: Enable "Use Adaptive RR" for ATR-based targets, disable for fixed percentages. Adaptive mode works better in varying volatility conditions, while fixed mode provides predictable risk/reward ratios.
Stop Loss Management
In Adaptive SL Mode:
Automatically scales with market volatility
Tight stops during low volatility (smaller ATR)
Wider stops during high volatility (larger ATR)
Include 0.3% slippage buffer in both modes
In Fixed Mode:
Consistent percentage-based stops
2% for crypto, 1.5% for forex, 1% for stocks
Manual adjustment needed for different market conditions
Trailing Stop System
Configuration:
Enable Trailing: Activates dynamic stop loss adjustment
Start Trailing %: Profit level to begin trailing (default 1.0%)
Trailing Offset %: Distance from current price (default 0.5%)
Close if Return to Entry: Optional immediate exit if price returns to entry level
Operation: Once position reaches trailing start level, stop loss automatically adjusts upward (longs) or downward (shorts) maintaining the offset distance from favorable price movement.
Timeframe-Specific Risk Considerations
15-Minute and Above (Tested):
✅ Full MTF system active
✅ Standard risk parameters apply
✅ Backtested performance metrics valid
✅ Standard position sizing (5%)
5-Minute Timeframes (Advanced Only):
⚠️ MTF system inactive - local signals only
⚠️ Higher false signal rate expected
⚠️ Reduced position sizing preferred (3%)
⚠️ Tighter stop losses required (0.8% vs 1.2%)
⚠️ Requires parameter optimization
⚠️ Monitor performance closely
1-Minute Timeframes (Limited Testing):
❌ Excessive noise levels
❌ Strategy not optimized for this frequency
Risk Management Practices
Allocate no more than 5% of your total investment portfolio to high-risk trading
Never trade with funds you cannot afford to lose
Thoroughly backtest and validate the strategy with small amounts before full implementation
Always maintain proper risk management and stop-loss settings
IMPORTANT DISCLAIMERS
Performance Disclaimer
Past performance does not guarantee future results. All trading involves substantial risk of loss. This strategy is provided for informational purposes and does not constitute financial advice.
Market Risk
Cryptocurrency and forex markets are highly volatile. Prices can move rapidly against positions, resulting in significant losses. Users should never risk more than they can afford to lose.
Strategy Limitations
This strategy relies on technical analysis and may not perform well during fundamental market shifts, news events, or unprecedented market conditions. No trading strategy can guarantee 100% success or eliminate the risk of loss.
Legal Compliance
You are responsible for compliance with all applicable regulations and laws in your jurisdiction. Consult with licensed financial professionals when necessary.
User Responsibility
Users are responsible for their own trading decisions, risk management, and compliance with applicable regulations in their jurisdiction.
Random Coin Toss Strategy📌 Overview
This strategy is a probability-based trading simulation that randomly decides trade direction using a coin-toss mechanism and executes trades with a customizable risk-reward ratio. It's designed primarily for testing entry frequency and risk dynamics, not predictive accuracy.
🎯 Core Concept
Every N bars (configurable), the strategy performs a pseudo-random coin toss.
Based on the result:
If heads → Buy
If tails → Sell
Once a position is opened, it sets a Stop-Loss (SL) and Take-Profit (TP) based on a multiple of the current ATR (Average True Range) value.
⚙️ Configurable Inputs
ATR Length Period for ATR calculation, determines volatility basis.
SL Multiplier SL distance = ATR × multiplier (e.g., 1.0 means 1x ATR) .
TP Multiplier TP distance = ATR × multiplier (e.g., 2.0 = 2x ATR) .
Entry Frequency Bars to wait between each new coin toss decision.
Show TP/SL Zones Toggle on/off for drawing visual TP and SL zones.
Box Size Number of bars used to define the width of the TP/SL boxes.
🔁 Entry & Exit Logic
Entry:
Happens only when no current position exists and it's the correct bar interval.
Entry direction is randomly decided.
Exit:
Positions exit at either:
Take-Profit (TP) level
Stop-Loss (SL) level
Both are calculated using the configured ATR-based distances.
🖼️ Visual Features
TP and SL zones:
Rendered as shaded rectangles (boxes) only once per trade.
Green box for TP zone, red box for SL zone.
Automatically deleted and redrawn for each new trade to avoid chart clutter.
ATR Display Table:
A minimal info table at the top-right shows the current ATR value.
Updates every few bars for performance.
🧪 Use Cases
Ideal for risk-reward modeling, strategy prototyping, and understanding how volatility-based SL/TP behavior affects results.
Great for backtesting frequency, RR tweaks (e.g., 2:5 or 3:1), and execution structure in random conditions.
⚠️ Disclaimer
Since the trade direction is random, this script is not meant for predictive trading but serves as a powerful experiment framework for studying how SL, TP, and volatility interact with random chance in a controlled, repeatable system.
Double Bottom Strategy (Long Only, ATR Trailing Stop + Alerts)This script implements a long-only breakout strategy based on the recognition of a Double Bottom price pattern, enhanced with a 50 EMA trend filter and a dynamic ATR-based trailing stop. It is suitable for traders looking to capture reversals in trending markets using a structured pattern-based entry system.
🧠 Key Features:
Double Bottom Detection: Identifies double bottom structures using pivot lows with configurable tolerance.
ATR-Based Trailing Stop: Manages exits using a trailing stop calculated from Average True Range (ATR), dynamically adjusting to market volatility.
EMA Filter (Optional): Filters trades to only go long when price is above the 50 EMA (trend confirmation).
Alerts: Real-time alerts on entry and exit, formatted in JSON for webhook compatibility.
Backtest Range Controls: Customize historical testing period with start and end dates.
✅ Recommended Markets:
Gold (XAUUSD)
S&P 500 (SPX, ES)
Nasdaq (NDX, NQ)
Stocks (Equities)
⚠️ Not recommended for Forex due to differing behavior and noise levels in currency markets.
🛠️ User Guidance:
Tune the pivot period, tolerance, and ATR settings for best performance on your chosen asset.
Backtest thoroughly over your selected date range to assess historical effectiveness.
Use small position sizes initially to test viability in live or simulated environments.
