Strategy
Martingale Grid BotMartingale Grid Bot (MGB)
The strategy is designed to test grid trading with a possible increase in the size of each subsequent order based on the martingale principle. The strategy itself does not contain any trade decision logic and is fully driven by external signals coming from indicators used on the chart. A new grid of orders is created when an entry signal is received, provided that there is no active grid.
❗ Warning
Trading with leverage and martingale involves increased risk. This strategy is a rather rough tool and is intended for testing purposes only. The author is not responsible for any possible discrepancies between the strategy results and real trading.
Settings
Direction - Trading direction. Possible values: Long and Short.
Entry Signal Source - Source of the entry signal that initiates the creation of a new order grid. If one of the price sources (open, high, low, close, etc.) is selected, a new grid will be created automatically after the previous grid is fully closed.
Start Time - Date and time when the strategy starts operating. Marked on the chart with a vertical dashed line.
No Repainting Mode - A mode intended to reduce discrepancies between historical and real-time strategy behavior (repainting).
Enabled — a new grid is created only after the bar is closed. The first order can be filled no earlier than on the next bar.
Disabled — in real-time, a new grid can be created immediately upon receiving a signal or after the previous grid is closed by take profit or stop loss.
❗ Attention
For correct real-time operation, recalculation on every tick must be enabled in the strategy settings.
GRID
Grid Depth % - The depth of the order grid, specified as a percentage of the closing price at the moment the grid is created.
Orders Count - The number of orders in the grid. The first order is placed at the current closing price at the time of grid creation.
Martingale Multiplier - Position size multiplier. Each subsequent order in the grid will be increased by this factor. The size of the first order is defined in the strategy settings.
Leverage - Leverage multiplier for margin trading. Used to check available funds when creating grid orders. It is recommended to use it together with the margin parameters in the strategy settings.
Take Profit % - Calculated on each strategy update based on the average entry price. If none of the grid orders have been filled yet, the take-profit level for the first order is displayed on the chart.
Stop Loss % - Calculated from the price of the first grid order and does not change during the strategy operation. Orders whose price exceeds the stop-loss level will be automatically canceled.
TABLE
Show Table - Controls the display of the table with all orders of the current grid. If there is no active grid, no order data is displayed. Text and background colors are determined automatically based on the chart background color.
Order Size - Determines how the grid order size is displayed: in contracts or in currency.
Table Size - Font size in the table.
Timezone - Used to correctly display the order fill time relative to the chart time. The order fill time (status: filled) can be seen by hovering over the corresponding status cell in the table.
VISUAL
Long Entry - Color of the dotted lines representing grid orders when trading long. Also defines the color of the vertical line indicating the strategy start time.
Short Entry - Color of the dotted lines representing grid orders when trading short. Also defines the color of the vertical line indicating the strategy start time.
Take Profit - Color of the solid line representing the take-profit level.
Stop Loss - Color of the solid line representing the stop-loss level.
Strategy
SPY Quant ML + Session Filter Strategy [CocoChoco]S&P 500 Quant: Machine Learning & Mean Reversion (Session-Filtered)
Overview
This is a professional-grade quantitative strategy designed specifically for the S&P 500. It combines classical statistical mean reversion (Z-Score) with a modern Machine Learning filter and rigorous institutional-grade risk management.
The strategy is optimized for traders who prioritize high win rates and capital preservation, specifically avoiding the "gap risk" associated with holding positions overnight.
Core Methodology
1. Statistical Entry (The Z-Score Engine)
The strategy identifies "oversold" conditions in a bullish context. It calculates the Z-Score of the price relative to its 20-period Mean (SMA). By default, it looks for a -1.2 Standard Deviation extension, signaling a high-probability "dip" ripe for a snap-back to the mean.
2. Trend & ML Filters
To avoid "catching a falling knife," the strategy uses two layers of confirmation:
Trend Filter: Only takes Long positions when the price is above the 200-period SMA, ensuring we only buy dips in a confirmed uptrend.
ML Correlation Filter: A Machine Learning-inspired module that analyzes the correlation between RSI and Volatility (ATR). It only permits entries when market internal dynamics suggest a reversal is technically "healthy."
3. Institutional Risk Management
This script is built for "safety-first" automation:
Hard Stop Loss: Fixed at 1.5% to protect against sudden market shocks.
Active Trailing: A dual-trigger trailing stop. It activates once the price touches the 20 SMA (The Mean) OR once a trade reaches a 0.50% profit threshold. This ensures near-winners are protected and large runners are captured.
Intraday Circuit Breaker: Includes a Max Daily Drawdown (2%) limit. If hit, the script automatically closes losing positions and halts trading for the day, while allowing winning positions to continue.
Key Features
Session-Specific: Tailored for the US Trading Session (UTC/NY times).
Zero Overnight Risk: Automatically flattens all positions before the market close (16:00 NY Time).
Holiday Intelligence: Hard-coded logic for US Market Holidays and Early Closes (2026–2028), ensuring the bot doesn't get stuck in illiquid holiday markets.
Hourly Entry Cap: Limits entries to one per hour to prevent over-concentration during a single price leg.
How to Use
Timeframe: I suggest you use it on the 5-minute or 1-hour timeframe for optimal results.
Instrument: Designed for the S&P 500, but highly effective on SPY, IVV, and ES (Futures).
Pyramiding: Designed to handle up to 3 concurrent positions, allowing the strategy to scale into a move as the Z-Score deepens.
Automation Ready
This script is fully compatible with webhook-based automation tools. All signals (Entry, SL, Trail, Market Close, and Daily Limit) are clearly labeled in the Alert comments for seamless execution. I haven't tasted it though. This is not financial advice. Please perform your own tests and manage your risk.
Disclaimer
Past performance does not guarantee future results. This script is a tool for quantitative analysis and should be used as part of a broader diversified trading plan. Strategy
ATR-Based Z-Score (with Signal Line)The ATR-Based Z-Score is an advanced, volatility-normalized oscillator designed to identify extreme price deviations more reliably than the standard Z-Score.
By replacing the traditional Standard Deviation with the Average True Range (ATR) in the denominator, this indicator eliminates the "volatility paradox" where rapid price spikes cause standard oscillators to prematurely return to zero, even as the price continues to crash.
Why this version is superior
In a classic Z-Score calculation:
Z = (Price - SMA) / (Standard Deviation)
A sudden impulsive price drop causes the Standard Deviation to explode. Because you are dividing by a rapidly increasing number, the Z-Score often "rises" while the price is still falling.
The ATR-Based Solution:
Z = (Price - SMA) / ATR
By using a long-period ATR as the denominator, the volatility measure remains stable and "clean." This ensures that the indicator’s troughs align much more accurately with actual price bottoms, staying in the oversold territory until the momentum truly shifts.
Key Features
Volatility Cleaning: The ATR-normalization prevents the indicator from "flattening out" during impulsive price movements.
Integrated Signal Line: A customizable Moving Average of the Z-Score values helps filter noise and confirms entry/exit points.
Independent Periods: You can set the Price MA (responsiveness) and the ATR (volatility baseline) separately to fine-tune the indicator to different timeframes.
How to Trade with it
1. Mean Reversion (Buy the Dip / Sell the Rip)
Long: Wait for the Z-Score to drop below a significant level (e.g., -10.0). Enter when the Z-Score crosses back above its Signal Line.
Short: Wait for the Z-Score to rise above +10.0 and enter when it crosses below the Signal Line.
2. Breakout Trading
A strong push of the Z-Score beyond the +/- 7.0 levels can indicate a powerful trend breakout.
In this case, the Signal Line crossover serves as an effective Exit Signal, telling you that the initial momentum of the breakout is fading.
Summary
✅ This indicator is designed for traders who find standard oscillators too "nervous" during volatile periods. By decoupling price deviation from immediate variance spikes, the ATR-Based Z-Score provides a rock-solid foundation for identifying true market extremes and high-probability reversal points. Indicator
Strategy
DkS Market Structure Breakout Strategy Crypto & ForexDkS Market Structure Breakout Strategy Crypto & Forex
🔍 Overview
DkSPro – Universal Market Analysis is a structure-based trading strategy designed for Crypto and Forex markets, focused on trend alignment, breakout confirmation, and volume validation.
This strategy is built to filter low-quality trades, avoid ranging conditions, and reduce false breakouts by requiring multiple layers of confirmation before any trade is executed.
It is intended for scalping and intraday trading, prioritizing consistency and risk control over trade frequency.
🧠 Strategy Logic (How It Works)
DkSPro follows a sequential decision process, not a single-indicator signal:
Trend Bias (EMA Structure)
A fast and slow EMA define the directional bias.
Long trades are only allowed during bullish EMA alignment.
Short trades are only allowed during bearish EMA alignment.
This prevents counter-trend and ranging-market entries.
Market Structure & Breakout Validation
The strategy identifies recent swing highs and lows.
Trades are triggered only after a confirmed breakout of structure, not during consolidation.
This avoids early entries and false momentum moves.
Volume Confirmation
Volume must exceed its moving average by a defined multiplier.
This ensures participation and filters out low-liquidity breakouts.
Volume thresholds adapt depending on the selected trading mode.
Momentum Confirmation (RSI)
RSI is used strictly as a momentum filter, not as a standalone signal.
It confirms that price movement aligns with the breakout direction.
Risk Management (Mandatory)
Every position includes a predefined Stop Loss and Take Profit.
Position sizing is based on a fixed percentage of equity, keeping risk per trade within sustainable limits.
All conditions must align simultaneously; otherwise, no trade is executed.
⚙️ Trading Modes
SAFE Mode
Stronger volume and RSI thresholds
Fewer trades, higher selectivity
Designed for risk control and consistency
AGGRESSIVE Mode
Slightly relaxed filters
Higher trade frequency during strong momentum
Intended for experienced users only
📊 Markets & Assets
This strategy has been actively used and tested on:
🟢 Crypto (Binance / Binance.US)
SOL-USDT
XRP-USDT
Other high-liquidity pairs (BTC, ETH)
Crypto mode benefits from stronger volume confirmation to adapt to higher volatility.
🔵 Forex
Major pairs such as EURUSD, GBPUSD, USDJPY
Optimized for liquid markets with lower relative volume
The same structural logic applies to both markets, with volume behavior naturally adapting to each asset class.
⏱ Recommended Timeframes
Crypto: 5m – 15m
Forex: 15m – 1H
Lower timeframes (1m) are not recommended due to noise and unreliable volume behavior.
🧪 Backtesting & Settings Transparency
Default strategy properties are intentionally conservative to reflect realistic conditions:
Initial capital: $20,000
Position size: 2% of equity
Commission: 0.08%
Slippage: 1 tick
Fixed Stop Loss and Take Profit on every trade
Backtests should be performed on sufficient historical data (ideally 6–12 months) to ensure a statistically meaningful sample size (100+ trades).
📈 Originality & Usefulness
DkSPro is not a simple indicator mashup.
Each component serves a specific role in a layered confirmation system:
EMAs define direction
Structure defines timing
Volume validates participation
RSI confirms momentum
Risk management controls exposure
Removing any layer significantly reduces signal quality. The strategy is designed as a complete decision framework, not a signal generator.
⚠️ Important Notes
This script is an analysis and execution tool, not financial advice.
Market conditions change, and no strategy performs well in all environments.
Users are encouraged to backtest, forward test, and adjust position sizing according to their own risk tolerance.
🧩 Version Notice
This publication represents a consolidated and refined version of an internal experimental script.
No parallel or duplicate versions are intended.
All future improvements will be released exclusively using PulseWire’s Update feature.
🇪🇸 Descripción en Español (Resumen)
DkSPro es una estrategia basada en estructura de mercado, diseñada para Crypto y Forex, que combina tendencia, ruptura de estructura, volumen y control de riesgo.
Solo opera cuando todas las condiciones se alinean, evitando rangos, falsas rupturas y sobreoperar.
Ha sido utilizada en Binance con pares como SOL-USDT y XRP-USDT, así como en Forex, siempre con gestión de riesgo fija y condiciones realistas. Strategy
Indicator
VIOP Scalping - OriginalVIOP Scalping – Original is a rule-based scalping strategy ported from an original C# logic set. It aims to trade only when trend direction, momentum, and trend strength align, then manages the position using fixed take-profit/stop-loss percentages with an optional trailing mechanism to protect gains during favorable moves.
This strategy is provided for educational and backtesting purposes only. It is not financial advice and does not guarantee profitability. Always test thoroughly before any live usage.
Core Concept
Follow the dominant WMA trend, confirm momentum with EMA separation, filter conditions with RSI + ADX, then exit using fixed TP/SL with trailing behavior after a defined profit threshold.
How the Strategy Works
Trend Direction is defined by WMA: above WMA = long bias, below WMA = short bias.
Momentum is measured via Fast EMA vs Slow EMA and the EMA difference.
Trend Strength is confirmed using ADX (must exceed a threshold).
RSI filters trades to avoid entering when momentum is likely overextended or weak.
A no-trade session blocks entries during a predefined time window (default 09:30–10:05).
Exit logic uses fixed percent TP/SL, with an optional trailing mechanism that activates after a profit threshold.
Inputs and Settings
Trend and Indicator Settings
Main Trend WMA: Determines directional bias (price above = long, price below = short).
Fast EMA / Slow EMA: Used to measure momentum and directional separation.
RSI Period: Filters entries based on RSI range constraints.
ADX Period: Measures trend strength (must exceed threshold to allow entries).
Threshold Settings
EMA Difference Threshold: Minimum EMA separation required to validate momentum.
ADX Threshold: Minimum ADX required to confirm trend strength.
RSI Long Ceiling: RSI must remain below this value for long entries.
RSI Short Floor: RSI must remain above this value for short entries.
Risk Management Settings
Take Profit %: Default TP distance in percent.
Strong Trend Take Profit %: Higher TP used when a “strong trend” condition is detected.
Stop Loss %: Fixed SL distance in percent.
Trailing Activation %: Profit threshold at which trailing starts.
Trailing Distance %: Trailing offset distance used once trailing is active.
Time Filter
No-Trade Hours: Default session is 09:30–10:05. During this window, the strategy does not open new trades.
Entry Logic
No-Trade Time Filter
If the current bar falls inside the no-trade session, entries are blocked.
Long Entry Conditions
Price is above the WMA trend line.
EMA difference is positive and greater than the EMA Difference Threshold.
EMA momentum is increasing (current EMA diff > previous EMA diff).
RSI is within the defined range (RSI > 48 and RSI < RSI Long Ceiling).
Close is higher than the previous close.
ADX is above the ADX Threshold.
Short Entry Conditions
Price is below the WMA trend line.
EMA difference is negative and lower than -EMA Difference Threshold.
Bearish momentum is increasing (current EMA diff < previous EMA diff).
RSI is within the defined range (RSI < 52 and RSI > RSI Short Floor).
Close is lower than the previous close.
ADX is above the ADX Threshold.
Strong Trend Logic (Dynamic TP Selection)
If price is far from the WMA (absolute distance > 20 points) AND EMA separation is strong (absolute EMA diff > 1.5 points), the strategy treats the environment as a strong trend.
In strong trend mode, the strategy uses “Strong Trend Take Profit %” instead of the default “Take Profit %”.
Exit Management (TP/SL + Trailing)
The strategy uses fixed percentage-based TP and SL levels.
Trailing logic is enabled via strategy.exit and activates only after price moves in profit by the defined Trailing Activation %.
Once activated, trailing follows price using the defined Trailing Distance % offset.
This is designed to secure partial gains during extended moves while still allowing room for continuation.
What You See on the Chart
WMA Trend Line (Main Trend Filter).
Fast EMA and Slow EMA (Momentum Confirmation).
Strategy entry/exit markers generated by PulseWire.
Recommended Use
Scalping systems that rely on trend-following and momentum confirmation.
Markets where ADX filtering helps avoid choppy conditions.
Traders who want a simple, parameter-driven TP/SL system with trailing after confirmation.
Important Notes
The no-trade session depends on your chart/session settings. Ensure your symbol/session configuration matches your intended market hours.
Percent-based exits scale with price; results will vary across instruments and volatility regimes.
Always validate behavior using bar replay, forward testing, and realistic commission/slippage assumptions.
Strategy
Strategy
Prism Band Dynamics [JOAT]Prism Band Dynamics - Bollinger-Style Bands with Force Detection
Introduction and Purpose
Prism Band Dynamics is an open-source overlay indicator that creates dynamic Bollinger-style bands with an innovative "force detection" system. The core problem this indicator solves is that standard Bollinger Bands show volatility but don't indicate directional momentum. When all three band components (upper, lower, basis) move in the same direction, it indicates strong directional force that standard bands don't highlight.
This indicator addresses that by detecting when all band components align directionally, providing a clear signal of market force.
Why Force Detection Matters
Standard Bollinger Bands expand and contract based on volatility, but they don't tell you about directional momentum. Force detection adds this dimension:
1. Bullish Force - Upper band, lower band, AND basis all moving up together. This indicates strong upward momentum where even the lower support level is rising.
2. Bearish Force - Upper band, lower band, AND basis all moving down together. This indicates strong downward momentum where even the upper resistance level is falling.
3. Neutral - Mixed movement indicates consolidation or uncertainty.
How Force Detection Works
bool upperUp = upper > upper
bool lowerUp = lower > lower
bool basisUp = basis > basis
int forceFull = if upperUp and lowerUp and basisUp
1 // Bullish force
else if upperDn and lowerDn and basisDn
-1 // Bearish force
else
0 // Neutral
Additional Features
Squeeze Detection - Identifies when band width contracts below threshold, often preceding large moves
Gradient Fills - Color intensity reflects force strength
Direction Change Arrows - Visual markers when force direction shifts
Dashboard Information
Force - Current force status (BULLISH/BEARISH/NEUTRAL)
Position - Price location within bands (Upper/Mid/Lower Zone)
Band Width - Current width percentage with expansion/contraction label
Volatility - Squeeze status (SQUEEZE/NORMAL)
Force Count - Bars since last force change
How to Use This Indicator
For Trend Following:
1. Enter long when force turns BULLISH
2. Enter short when force turns BEARISH
3. Exit or reduce when force turns NEUTRAL
For Squeeze Breakouts:
1. Watch for SQUEEZE status in dashboard
2. Prepare for breakout in either direction
3. Enter when force confirms direction after squeeze
For Mean Reversion:
1. Only trade mean-reversion when force is NEUTRAL
2. Avoid fading moves when force is active
3. Use band touches as entry points during neutral force
Input Parameters
Length (20) - Period for basis and standard deviation
Multiplier (2.0) - Standard deviation multiplier for bands
MA Type (SMA) - Basis calculation method
Squeeze Threshold (0.5) - Band width percentage for squeeze detection
Timeframe Recommendations
4H-Daily: Cleanest force signals
1H: Good balance of signals and reliability
15m: More signals but more noise
Limitations
Force detection can lag during rapid reversals
Squeeze breakouts can fail (false breakouts)
Works best in markets with clear trending/ranging phases
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Force detection does not guarantee trend continuation. Always use proper risk management.
- Made with passion by officialjackofalltrades
Indicator
Indicator
Indicator
Multi-indicator Signal Builder [Skyrexio]Overview
Multi-Indicator Signal Builder is a versatile, all-in-one script designed to streamline your trading workflow by combining multiple popular technical indicators under a single roof.
It features a single-entry, single-exit logic, intrabar stop-loss/take-profit handling, an optional time filter, a visually accessible condition table, and a built-in statistics label.
Traders can choose any combination of 12+ indicators (RSI, Ultimate Oscillator, Bollinger %B, Moving Averages, ADX, Stochastic, MACD, PSAR, MFI, CCI, Heikin Ashi, and a “TV Screener” placeholder) to form entry or exit conditions.
This script aims to simplify strategy creation and analysis , making it a powerful toolkit for technical traders.
Indicators Overview
RSI (Relative Strength Index)
Measures recent price changes to evaluate overbought or oversold conditions on a 0–100 scale.
Ultimate Oscillator (UO)
Uses weighted averages of three different timeframes, aiming to confirm price momentum while avoiding false divergences.
Bollinger %B
Expresses price relative to Bollinger Bands, indicating whether price is near the upper band (overbought) or lower band (oversold).
Moving Average (MA)
Smooths price data over a specified period. The script supports both SMA and EMA to help identify trend direction and potential crossovers.
ADX (Average Directional Index)
Gauges the strength of a trend (0–100). Higher ADX signals stronger momentum, while lower ADX indicates a weaker trend.
Stochastic
Compares a closing price to a price range over a given period to identify momentum shifts and potential reversals.
MACD (Moving Average Convergence/Divergence)
Tracks the difference between two EMAs plus a signal line, commonly used to spot momentum flips through crossovers.
PSAR (Parabolic SAR)
Plots a trailing stop-and-reverse dot that moves with the trend. Often used to signal potential reversals when price crosses PSAR.
MFI (Money Flow Index)
Similar to RSI but incorporates volume data. A reading above 80 can suggest overbought conditions, while below 20 may indicate oversold.
CCI (Commodity Channel Index)
Identifies cyclical trends or overbought/oversold levels by comparing current price to an average price over a set timeframe.
Heikin Ashi
A type of candlestick charting that filters out market noise. The script uses a streak-based approach (multiple consecutive bullish or bearish bars) to gauge mini-trends.
TV Screener
A placeholder condition designed to integrate external buy/sell logic (like a PulseWire “Buy” or “Sell” rating). Users can override or reference external signals if desired.
Unique Features
Multi-Indicator Entry and Exit
You can selectively enable any subset of 12+ classic indicators, each with customizable parameters and conditions. A position opens only if all enabled entry conditions are met, and it closes only when all enabled exit conditions are satisfied, helping reduce false triggers.
Single-Entry / Single-Exit with Intrabar SL/TP
The script supports a single position at a time. Once a position is open, it monitors intrabar to see if the price hits your stop-loss or take-profit levels before the bar closes, making results more realistic for fast-moving markets.
Time Window Filter
Users may specify a start/end date range during which trades are allowed, making it convenient to focus on specific market cycles for backtesting or live trading.
Condition Table and Statistics
A table at the bottom of the chart lists all active entry/exit indicators. Upon each closed trade, an integrated statistics label displays net profit, total trades, win/loss count, average and median PnL, etc.
Seamless Alerts and Automation
• Configure alerts in PulseWire using “Any alert() function call.”
• The script sends JSON alert messages you can route to your own webhook.
• The indicator can be integrated with Skyrexio alert bots to automate execution on major cryptocurrency exchanges.
Optional MA/PSAR Plots
For added visual clarity, optionally plot the chosen moving averages or PSAR on the chart to confirm signals without stacking multiple indicators.
Methodology
Multi-Indicator Entry Logic
When multiple entry indicators are enabled (e.g., RSI + Stochastic + MACD), the script requires all signals to align before generating an entry. Each indicator can be set for crossovers, crossunders, thresholds (above/below), etc. This “AND” logic aims to filter out low-confidence triggers.
Single-Entry Intrabar SL/TP
• One Position At a Time: Once an entry signal triggers, a trade opens at the bar’s close.
• Intrabar Checks: Stop-loss and take-profit levels (if enabled) are monitored on every tick. If either is reached, the position closes immediately, without waiting for the bar to end.
Exit Logic
All Conditions Must Agree: If the trade is still open (SL/TP not triggered), then all enabled exit indicators must confirm a closure before the script exits on the bar’s close.
Time Filter
Optional Trading Window: You can activate a date/time range to constrain entries and exits strictly to that interval.
Justification of Methodology
Indicator Confluence: Combining multiple tools (RSI, MACD, etc.) can reduce noise and false signals.
Intrabar SL/TP: Capturing real-time spikes or dips provides a more precise reflection of typical live trading scenarios.
Single-Entry Model: Straightforward for both manual and automated tracking (especially important in bridging to bots).
Custom Date Range: Helps refine backtesting for specific market conditions or to avoid known irregular data periods.
How to Use
Add the Script to Your Chart
• In PulseWire, open Indicators , search for “Multi-indicator Signal Builder” .
• Click to add it to your chart.
Configure Inputs
• Time Filter: Set a start and end date for trades.
• Alerts Messages: Input any JSON or text payload needed by your external service or bot.
• Entry Conditions: Enable and configure any indicators (e.g., RSI, MACD) for a confluence-based entry.
• Close Conditions: Enable exit indicators, along with optional SL (negative %) and TP (positive %) levels.
Set Up Alerts
• In PulseWire, select “Create Alert” → Condition = “Any alert() function call” → choose this script.
• Entry Alert: Triggers on the script’s entry signal.
• Close Alert: Triggers on the script’s close signal (or if SL/TP is hit).
• Skyrexio Alert Bots: You can route these alerts via webhook to Skyrexio alert bots to automate order execution on major crypto exchanges (or any other supported broker).
Visual Reference
• A condition table at the bottom summarizes active signals.
• Statistics Label updates automatically as trades are closed, showing PnL stats and distribution metrics.
Backtesting Guidelines
Symbol/Timeframe: Works on multiple assets and timeframes; always do thorough testing.
Realistic Costs: Adjust commissions and potential slippage to match typical exchange conditions.
Risk Management: If using the built-in stop-loss/take-profit, set percentages that reflect your personal risk tolerance.
Longer Test Horizons: Verify performance across diverse market cycles to gauge reliability.
Example of statistic calculation
Test Period: 2023-01-01 to 2025-12-31
Initial Capital: $1,000
Commission: 0.1%, Slippage ~5 ticks
Trade Count: 680 (varies by strategy conditions)
Win rate: 75.44% (varies by strategy conditions)
Net Profit: +90.14% (varies by strategy conditions)
Disclaimer
This indicator is provided strictly for informational and educational purposes.
It does not constitute financial or trading advice.
Past performance never guarantees future results.
Always test thoroughly in demo environments before using real capital.
Enjoy exploring the Multi-Indicator Signal Builder! Experiment with different indicator combinations and adjust parameters to align with your trading preferences, whether you trade manually or link your alerts to external automation services. Happy trading and stay safe! Indicator
Quantum Reversal Detector [JOAT]
Quantum Reversal Detector - Multi-Factor Reversal Probability Analysis
Introduction and Purpose
Quantum Reversal Detector is an open-source overlay indicator that combines multiple reversal detection methods into a unified probability-based framework. The core problem this indicator addresses is the unreliability of single-factor reversal signals. A price touching support means nothing without momentum confirmation; an RSI oversold reading means nothing without price structure context.
This indicator solves that by requiring multiple independent factors to align before generating reversal signals, then expressing the result as a probability score rather than a binary signal.
Why These Components Work Together
The indicator combines five analytical approaches, each addressing a different aspect of reversal detection:
1. RSI Extremes - Identifies momentum exhaustion (overbought/oversold)
2. MACD Crossovers - Confirms momentum direction change
3. Support/Resistance Proximity - Ensures price is at a significant level
4. Multi-Depth Momentum - Analyzes momentum across multiple timeframes
5. Statistical Probability - Quantifies reversal likelihood using Bayesian updating
These components are not randomly combined. Each filter catches reversals that others miss:
RSI catches momentum exhaustion but misses structural reversals
MACD catches momentum shifts but lags price action
S/R proximity catches structural levels but ignores momentum
Multi-depth momentum catches divergences across timeframes
Probability scoring combines all factors into actionable confidence levels
How the Detection System Works
Step 1: Pattern Detection
The indicator first identifies potential reversal conditions:
// Check if price is at support/resistance
float lowestLow = ta.lowest(low, period)
float highestHigh = ta.highest(high, period)
bool atSupport = low <= lowestLow * 1.002
bool atResistance = high >= highestHigh * 0.998
// Check RSI conditions
float rsi = ta.rsi(close, 14)
bool oversold = rsi < 30
bool overbought = rsi > 70
// Check MACD crossover
float macd = ta.ema(close, 12) - ta.ema(close, 26)
float signal = ta.ema(macd, 9)
bool macdBullish = ta.crossover(macd, signal)
bool macdBearish = ta.crossunder(macd, signal)
// Combine for reversal detection
if atSupport and oversold and macdBullish
bullishReversal := true
Step 2: Multi-Depth Momentum Analysis
The indicator calculates momentum across multiple periods to detect divergences:
calculateQuantumMomentum(series float price, simple int period, simple int depth) =>
float totalMomentum = 0.0
for i = 0 to depth - 1
int currentPeriod = period * (i + 1)
float momentum = ta.roc(price, currentPeriod)
totalMomentum += momentum
totalMomentum / depth
This creates a composite momentum reading that smooths out noise while preserving genuine momentum shifts.
Step 3: Bayesian Probability Calculation
The indicator uses Bayesian updating to calculate reversal probability:
bayesianProbability(series float priorProb, series float likelihood, series float evidence) =>
float posterior = evidence > 0 ? (likelihood * priorProb) / evidence : priorProb
math.min(math.max(posterior, 0.0), 1.0)
The prior probability starts at 50% and updates based on:
RSI extreme readings increase likelihood
MACD crossovers increase likelihood
S/R proximity increases likelihood
Momentum divergence increases likelihood
Step 4: Confidence Intervals
Using Monte Carlo simulation concepts, the indicator estimates price distribution:
monteCarloSimulation(series float price, series float volatility, simple int iterations) =>
float sumPrice = 0.0
float sumSqDiff = 0.0
for i = 0 to iterations - 1
float randomFactor = (i % 10 - 5) / 10.0
float simulatedPrice = price + volatility * randomFactor
sumPrice += simulatedPrice
float avgPrice = sumPrice / iterations
// Calculate standard deviation for confidence intervals
This provides 95% and 99% confidence bands around the current price.
Signal Classification
Signals are classified by confirmation level:
Confirmed Reversal : Pattern detected for N consecutive bars (default 3)
High Probability : Confirmed + Bayesian probability > 70%
Ultra High Probability : High probability + PDF above average
Dashboard Information
The dashboard displays:
Bayesian Probability - Updated reversal probability (0-100%)
Quantum Momentum - Multi-depth momentum average
RSI - Current RSI value with overbought/oversold status
Volatility - Current ATR as percentage of price
Reversal Signal - BULLISH, BEARISH, or NONE
Divergence - Momentum divergence detection
MACD - Current MACD histogram value
S/R Zone - AT SUPPORT, AT RESISTANCE, or NEUTRAL
95% Confidence - Price range with 95% probability
Bull/Bear Targets - ATR-based reversal targets
Visual Elements
Quantum Bands - ATR-based upper and lower channels
Probability Field - Circle layers showing probability distribution
Confidence Bands - 95% and 99% confidence interval circles
Reversal Labels - REV markers at confirmed reversals
High Probability Markers - Star diamonds at high probability setups
Reversal Zones - Boxes around confirmed reversal areas
Divergence Markers - Triangles at momentum divergences
How to Use This Indicator
For Reversal Trading:
1. Wait for Bayesian Probability to exceed 70%
2. Confirm price is at S/R zone (dashboard shows AT SUPPORT or AT RESISTANCE)
3. Check that RSI is in extreme territory (oversold for longs, overbought for shorts)
4. Enter when REV label appears with high probability marker
For Risk Management:
1. Use the 95% confidence band as a stop-loss reference
2. Use Bull/Bear Targets for take-profit levels
3. Higher probability readings warrant larger position sizes
For Filtering False Signals:
1. Increase Confirmation Bars to require more consecutive signals
2. Only trade when probability exceeds 70%
3. Require divergence confirmation for highest conviction
Input Parameters
Reversal Period (21) - Lookback for S/R and momentum calculations
Quantum Depth (5) - Number of momentum layers for multi-depth analysis
Confirmation Bars (3) - Consecutive bars required for confirmation
Detection Sensitivity (1.2) - Band width and target multiplier
Bayesian Probability (true) - Enable probability calculation
Monte Carlo Simulation (true) - Enable confidence interval calculation
Normal Distribution (true) - Enable PDF calculation
Confidence Intervals (true) - Enable confidence bands
Timeframe Recommendations
1H-4H: Best for swing trading reversals
Daily: Fewer but more significant reversal signals
15m-30m: More signals, requires higher probability threshold
Limitations
Statistical concepts are simplified implementations for Pine Script
Monte Carlo uses deterministic pseudo-random factors, not true randomness
Bayesian probability uses simplified prior/likelihood model
Reversal detection does not guarantee actual reversals will occur
Confirmation bars add lag to signal generation
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each component works.
This indicator does not constitute financial advice. Reversal detection is probabilistic, not predictive. The probability scores represent statistical likelihood based on historical patterns, not guaranteed outcomes. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses.
- Made with passion by officialjackofalltrades
Indicator
ICT Entry V1 [TS_Indie]📌 Description – ICT Entry V1
This trading system is based on price action, combined with FVG, iFVG, and liquidity, and it uses the mechanism from the indicator “Smallest Swing ” to validate swings that become liquidity.
⚙️ Core Logic & Working Mechanism
I won’t explain FVG in detail, as most traders are already familiar with it.
Let’s focus on the mechanism of iFVG instead.
The concept of iFVG is based on a supply-to-demand flip and a demand-to-supply flip within an FVG zone.
For an iFVG to be confirmed, the candle close must break through the FVG.
A wick alone does not count as a valid iFVG confirmation.
The confirmation of market structure swings uses a pivot length mechanism combined with price action.
It validates a swing by detecting a structure break formed by candles making new highs or new lows.
📈 Buy Setup
1.Liquidity sweep on the demand side, with price closing above the liquidity level.
2.A demand zone is formed as FVG and iFVG, where iFVG is located above FVG.
3.The gap between the upper box of FVG and the lower box of iFVG must be within the defined Min and Max range.
4.Market Structure must be in a Bullish trend.
5.Place a Pending Order at the upper box of FVG and set Stop Loss at the lower box of FVG (Entry and Stop Loss can be adjusted using Entry Zone and ATR-based Stop Loss).
📉 Sell Setup
1.Liquidity sweep on the supply side, with price closing below the liquidity level.
2.A supply zone is formed as FVG and iFVG, where iFVG is located below FVG.
3.The gap between the lower box of FVG and the upper box of iFVG must be within the defined Min and Max range.
4.Market Structure must be in a Bearish trend.
5.Place a Pending Order at the lower box of FVG and set Stop Loss at the upper box of FVG (Entry and Stop Loss can be adjusted using Entry Zone and ATR-based Stop Loss).
⚙️ Liquidity Sweep Conditions
➯ When a liquidity sweep occurs on the demand side, the system will start looking for Buy Setup conditions.
➯ When a liquidity sweep occurs on the supply side, the system will immediately switch to looking for Sell Setup conditions.
➯ The system will always prioritize the most recent liquidity sweep and search for setups based on that direction.
➯ The liquidity sweep condition will be invalidated when price closes back below (for demand sweep) or above (for supply sweep) the most recently swept liquidity level.
⭐ Pending Order Cancellation Conditions
A Pending Order will be canceled under the following conditions:
1.A new Price Action signal appears on either the Buy or Sell side.
2.When Time Session is enabled, the Pending Order is canceled once price exits the selected session.
🕹 Order Management Rule
When there is an active open position, the indicator restricts the creation of new Pending Orders to prevent overlapping positions.
⚠️ Disclaimer
This indicator is designed for educational and research purposes only. It does not guarantee profits and should not be considered financial advice. Trading in financial markets involves significant risk, including the potential loss of capital.
🥂 Community Sharing
If you find parameter settings that work well or produce strong statistical results, feel free to share them with the community so we can improve and develop this indicator together. Strategy
Adaptive Bull Ratio Strategy█ Overview: Why This Strategy
Most option strategies fall into two traps:
They are too rigid: A "Call Ratio Spread" works great in slow markets but gets destroyed if the market rallies hard.
They are too simple: A simple "Buy Call" suffers from time decay (Theta) if the market chops sideways.
The Adaptive Bull Ratio Strategy solves both . It is a living strategy that "shifts gears" based on price action.
It is called "Adaptive" because it morphs its structure three times during a trade. It starts conservative to harvest Time Decay, but if the market explodes upwards, it "uncaps" itself to ride the trend aggressively.
█ The Entry Philosophy: Why Supertrend?
The default setting uses the Supertrend indicator as the trigger. This is intentional:
Volatility Awareness: Supertrend adapts to market noise using ATR. In high volatility, bands widen to prevent false entries.
Trend Confirmation: Since Phase 1 involves selling options, entering "too early" against a falling market is dangerous. Supertrend forces patience, waiting for a confirmed reversal (Close > Trend Line), ensuring the momentum is actually in your favor before you commit capital.
The "Drift" Benefit: This strategy excels in markets that "drift" upwards. Supertrend identifies these trends while filtering out short-term chop.
Flexibility with External Sources:
While Supertrend is the default, the strategy is designed to be flexible. You can enable the 'Enable External Source' option in the settings to plug in any custom indicator (e.g., Moving Averages, Parabolic SAR, or a proprietary trendline).
The Golden Rule for External Sources: The script interprets a Bullish Signal whenever your External Source line is below the Close price (Ext Source < Close).
Compatibility: As long as your custom indicator behaves like a support line in an uptrend (plotting below the candles), it will work seamlessly with this strategy's logic.
█ The "Long Only" Rationale: Avoiding the Volatility Trap
Why not trade this on the short side (Puts) during crashes?
The Volatility Trap (Vega Risk): In Bull markets, Implied Volatility (IV) usually drops, helping your sold options decay faster. In Bear markets, IV explodes (panic). Selling OTM Puts during a crash is dangerous as their value skyrockets, neutralizing gains.
Velocity Risk: Bear markets crash fast ("Elevator Down"). Prices can blow through adjustment levels faster than the strategy can safely roll down, causing slippage.
Structural Skew: OTM Puts are inherently more expensive. Buying expensive ITM Puts and selling expensive OTM Puts shifts the breakeven further away, making V-shape recoveries painful.
█ How It Works & Stands Out
This strategy actively transforms risk profiles based on market movement:
Phase 1: The "Safe" Start (Entry)
Setup: Initiates a Call Ratio Spread (Buy 2 ITM, Sell 4 OTM) + Protective Puts.
Logic: Profits from sideways drift or slow rallies via Time Decay (Theta). The sold options finance the trade.
Phase 2: The "Shift" (Adjustment Level 1)
Trigger: Market moves above Leg 2 (3 OTM Call).
Action: Rolls Up the position. Exits initial legs, enters new higher legs, and adds a Short Put to finance the roll.
Impact: Aggressive. You bet the trend is strong enough to support the added downside risk of the short put.
Phase 3: The "Uncap" (Adjustment Level 2)
Trigger: Market moves above Leg 3 (4 OTM Call).
Action: Exits all Sold Calls.
Impact: Uncaps profit potential. The trade becomes a Net Long position (Long Calls + Short Puts), allowing you to ride a massive rally without a ceiling.
Phase 4: The "Lock-In" (Optional Trail Adjustment)
Trigger: The market goes parabolic (price rises X levels above Leg 3, configurable in settings).
Action (If Enabled):
Call Adj: Exits the Phase 3 calls and buys fresh 1-OTM calls (Rolling Up to lock profits).
Put Adj: Exits all Put legs (Removing downside risk completely).
Impact: Maximum Safety. This phase is about "banking" the windfall from a massive rally and leaving a smaller, risk-free runner to capture any final extension.
█ How to Start: A Quick Setup Guide
Step 1: Map Expiry Dates
Manually input your trading expiry dates in Settings -> Expiry Management.
Format: YYYY-MM-DD (e.g., 2025-12-25). Strict adherence required for DhanHQ.
Step 2: Configure Symbol & Size
Exchange/Symbol: Enter NSE and NIFTY (or your ticker).
Lot Multiplier: Default is 1. Set to 2 to double all quantities (e.g., Buy 2 becomes Buy 4).
Step 3: Understand Visuals
Entry Window (Light Blue): Strategy is scanning for new trades.
Non-Entry Window (Dark Blue): Trading blocked (Day before Expiry & Expiry Day). Only management allowed.
Green Box: Valid Late Entry Zone.
Red Dashed Line: Invalidation Level (if price touches this, no late entry).
Fuchsia Line: Trigger level for Special Trail Adjustments (Phase 4).
IMPORTANT: Broker & Technology Heads-Up:
The alerts generated by this script ({"secret": "...", "alertType": "multi_leg_order"...}) are specifically formatted for the DhanHQ webhook structure.
Dhan Users: Plug-and-play.
Other Brokers: You need middleware (NextLevelBot, Quantiply) to parse the JSON.
█ Risk Disclaimer & Advice
Trading options involves substantial risk.
The Whipsaw Risk: In Phase 2, you are Long Calls and Short Puts. A sharp reversal causes losses on both sides.
Margin: Selling options requires significant margin. Keep a 15-20% cash buffer to handle adjustments instantly.
Testing: This strategy is optimized for NIFTY Weekly Options. Effectiveness on BankNifty or Stocks is untested and may require parameter tuning.
Advice:
Backtest: Use PulseWire Replay.
Paper Trade: Run for at least one expiry cycle before live deployment.
Consult: Seek professional financial advice before trading.
Practical Tips for Smooth Execution
For a new trader deploying this system, these operational tips are vital:
Capital Buffer: Do not trade at your limit. Always keep 10-15% free cash in your broker account. Adjustments (specifically Phase 2, where you sell an extra Put) require additional margin instantly. If margin is short, the order fails, and your hedge breaks.
Liquidity Awareness : The script trades "Far Deep OTM" options (Leg 4) to reduce margin. On indices like Nifty/BankNifty, this is fine. On individual stocks, these deep strikes might be illiquid. Check the option chain volume before deploying on stocks.
Trust the Process (but Verify) : While the algo drives, you are the pilot.
Check your API connection every morning.
Ensure the "Entry Window" background color on the chart matches your real-world date.
Verify that your broker executed all legs of a multi-leg order (partial fills are rare but possible).
The "Human" Stop: If major news breaks (e.g., unexpected election results, war announcements), volatility can expand faster than any algo can react. It is acceptable—and smart—to pause the strategy during known "Black Swan" events or earnings releases.
█ Timeframe Selection: The 30-Minute Standard
Critical Requirement: This indicator must be applied to a 30-minute chart.
Why?
Noise Filtering: The Supertrend logic is tuned to capture multi-day trends. Lower timeframes (5m, 15m) are full of "noise"—random fluctuations that look like trend changes but aren't.
Execution Logic (The Hybrid Engine): The script has a built-in "Dual Timeframe" architecture.
Decision Layer (30m): Uses the chart timeframe to decide when to be Bullish or Bearish.
Execution Layer (5m): Internally fetches 5-minute data to manage the how (Adjustments, Late Entries, and precise invalidation).
The Risk of Lower Timeframes: If you run the main chart on 5-minutes, you destroy this hierarchy. You will get too many signals, pay too much brokerage, and the internal logic may behave erratically.
Recommendation: Always keep your PulseWire chart interval at 30m. Do not switch to lower timeframes expecting "faster" signals; you will likely just get "false" signals.
█ Testing Scope, Feedback
⚠️ Important Note on Asset Classes:
This strategy logic and the associated strike step calculations have been rigorously tested ONLY on NIFTY Index Options with Weekly Expiry.
BankNifty / Sensex / FinNifty: The volatility characteristics (ATR) and strike intervals of these instruments differ significantly from NIFTY. The effectiveness of this strategy on these other scripts has not been verified and may require different parameter tuning (e.g., strike_step or ATR Length).
Stocks: Individual stock options often lack the liquidity required for the "Deep OTM" legs, leading to potential execution failures.
We encourage traders to backtest this logic on other indices and share their findings! If you find a robust parameter set for BankNifty or observe unique behaviors on other scripts, please let us know in the comments below so we can improve the algorithm for everyone. Your feedback is appriciated. Indicator
Golden Vector Trend Orchestrator (GVTO)Golden Vector Trend Orchestrator (GVTO) is a composite trend-following strategy specifically engineered for XAUUSD (Gold) and volatile assets on H4 (4-Hour) and Daily timeframes.
This script aims to solve a common problem in trend trading: "Whipsaws in Sideways Markets." Instead of relying on a single indicator, GVTO employs a Multi-Factor Confluence System that filters out low-probability trades by requiring alignment across Trend Structure, Momentum, and Volatility.
🛠 Methodology & Logic
The strategy executes trades only when four distinct technical conditions overlap (Confluence). If any single condition is not met, the trade is filtered out to preserve capital.
1. Market Structure Filter (200 EMA)
Indicator: Exponential Moving Average (Length 200).
Logic: The 200 EMA acts as the baseline for the long-term trend regime.
Bullish Regime: Price must close above the 200 EMA.
Bearish Regime: Price must close below the 200 EMA.
Purpose: Prevents counter-trend trading against the macro direction.
2. Signal Trigger & Trailing Stop (Supertrend)
Indicator: Supertrend (ATR Length 14, Factor 3.5).
Logic: Uses Average True Range (ATR) to detect trend reversals while accounting for volatility.
Purpose: Provides the specific entry signal and acts as a dynamic trailing stop-loss to let profits run while cutting losses when the trend invalidates.
3. Volatility Gatekeeper (ADX Filter)
Indicator: Average Directional Index (Length 14).
Threshold: > 25.
Logic: A high ADX value indicates a strong trend presence, regardless of direction.
Purpose: This is the most critical filter. It prevents the strategy from entering trades during "choppy" or ranging markets (consolidation zones) where trend-following systems typically fail.
4. Momentum Confirmation (DMI)
Indicator: Directional Movement Index (DI+ and DI-).
Logic: Checks if the buying pressure (DI+) is physically stronger than selling pressure (DI-), or vice versa.
Purpose: Ensures that the price movement is backed by genuine momentum, not just a momentary price spike.
📋 How to Use This Strategy
🟢 LONG (BUY) Setup
A Buy signal is generated only when ALL of the following occur simultaneously:
Price Action: Price closes ABOVE the 200 EMA (Orange Line).
Trigger: Supertrend flips to GREEN (Bullish).
Strength: ADX is greater than 25 (Strong Trend).
Momentum: DI+ (Plus Directional Indicator) is greater than DI- (Minus).
🔴 SHORT (SELL) Setup
A Sell signal is generated only when ALL of the following occur simultaneously:
Price Action: Price closes BELOW the 200 EMA (Orange Line).
Trigger: Supertrend flips to RED (Bearish).
Strength: ADX is greater than 25 (Strong Trend).
Momentum: DI- (Minus Directional Indicator) is greater than DI+ (Plus).
🛡 Exit Strategy
Stop Loss / Take Profit: The strategy utilizes the Supertrend Line as a dynamic Trailing Stop.
Exit Long: When Supertrend turns Red.
Exit Short: When Supertrend turns Green.
Note: Traders can also use the real-time P/L Dashboard included in the script to manually secure profits based on their personal Risk:Reward ratio.
📊 Included Features
Real-Time P/L Dashboard: A table in the top-right corner displays the current trend status, ADX strength, and the Unrealized Profit/Loss % of the current active position.
Smart Labeling: Buy/Sell labels are coded to appear only on the initial entry trigger. They do not repaint and do not spam the chart if the trend continues (no pyramiding visualization).
Visual Aids: Background color changes (Green/Red) to visually represent the active trend based on the Supertrend status.
⚠️ Risk Warning & Best Practices
Asset Class: Optimized for XAUUSD (Gold) due to its high volatility nature. It also works well on Crypto (BTC, ETH) and Major Forex Pairs.
Timeframe: Highly recommended for H4 (4 Hours) or D1 (Daily). Using this on lower timeframes (M5, M15) may result in false signals due to market noise.
News Events: Automated strategies cannot predict economic news (CPI, NFP). Exercise caution or pause trading during high-impact economic releases. Strategy
CryptoFlux Dynamo [JOAT]CryptoFlux Dynamo: Velocity Scalping Strategy
WHAT THIS STRATEGY IS
CryptoFlux Dynamo is an open-source Pine Script v6 strategy designed for momentum-based scalping on cryptocurrency perpetual futures. It combines multiple technical analysis methods into a unified system that adapts its behavior based on current market volatility conditions.
This script is published open-source so you can read, understand, and modify the complete logic. The description below explains everything the strategy does so that traders who cannot read Pine Script can fully understand how it works before using it.
HOW THIS STRATEGY IS ORIGINAL AND WHY THE INDICATORS ARE COMBINED
This strategy uses well-known indicators (MACD, EMA, RSI, MFI, Bollinger Bands, Keltner Channels, ATR). The originality is not in the individual indicators themselves, but in the specific way they are integrated into a regime-adaptive system. Here is the detailed justification for why these components are combined and how they work together:
The Problem Being Solved:
Standard indicator-based strategies use fixed thresholds. For example, a typical MACD strategy might enter when the histogram crosses above zero. However, in cryptocurrency markets, volatility changes dramatically throughout the day and week. A MACD crossover during a low-volatility consolidation period has very different implications than the same crossover during a high-volatility trending period. Using the same entry thresholds and stop distances in both conditions leads to either:
Too many false signals during consolidation (if thresholds are loose)
Missing valid opportunities during expansion (if thresholds are tight)
Stops that are too tight during volatility spikes (causing premature exits)
Stops that are too wide during compression (giving back profits)
The Solution Approach:
This strategy first classifies the current volatility regime using normalized ATR (ATR as a percentage of price), then dynamically adjusts ALL other parameters based on that classification. This creates a context-aware system rather than a static threshold comparison.
How Each Component Contributes to the System:
ATR-Based Regime Classification (The Foundation)
The strategy calculates ATR over 21 periods, smooths it with a 13-period EMA to reduce noise from wicks, then divides by price to get a normalized percentage. This ATR% is classified into three regimes:
- Compression (ATR% < 0.8%): Market is consolidating, breakouts are more likely but false signals are common
- Expansion (ATR% 0.8% - 1.6%): Normal trending conditions
- Velocity (ATR% > 1.6%): High volatility, larger moves but also larger adverse excursions
This regime classification then controls stop distances, profit targets, trailing stop offsets, and signal strength requirements. The regime acts as a "meta-parameter" that tunes the entire system.
EMA Ribbon (8/21/34) - Trend Structure Detection
The three EMAs establish trend direction and structure. When EMA 8 > EMA 21 > EMA 34, the trend structure is bullish. The slope of the middle EMA (21) is calculated over 8 bars and converted to degrees using arctangent. This slope measurement quantifies trend strength, not just direction.
Why these specific periods? The 8/21/34 sequence follows Fibonacci-like spacing and provides good separation on 5-minute cryptocurrency charts. The fast EMA (8) responds to immediate price action, the mid EMA (21) represents the short-term trend, and the slow EMA (34) acts as a trend filter.
The EMA ribbon works with the regime classification: during compression regimes, the strategy requires stronger ribbon alignment before entry because false breakouts are more common.
MACD (8/21/5) - Momentum Measurement
The MACD uses faster parameters (8/21/5) than the standard (12/26/9) because cryptocurrency markets move faster than traditional markets. The histogram is smoothed with a 5-period EMA to reduce noise.
The key innovation is the adaptive histogram baseline. Instead of using a fixed threshold, the strategy calculates a rolling baseline from the smoothed absolute histogram value, then multiplies by a sensitivity factor (1.15). This means the threshold for "significant momentum" automatically adjusts based on recent momentum levels.
The MACD works with the regime classification: during velocity regimes, the histogram baseline is effectively higher because recent momentum has been stronger, preventing entries on relatively weak momentum.
RSI (21 period) and MFI (21 period) - Independent Momentum Confirmation
RSI measures momentum using price changes only. MFI (Money Flow Index) measures momentum using price AND volume. By requiring both to confirm, the strategy filters out price moves that lack volume support.
The 21-period length is longer than typical (14) to reduce noise on 5-minute charts. The trigger threshold (55 for longs, 45 for shorts) is slightly offset from 50 to require momentum in the trade direction, not just neutral readings.
These indicators work together: a signal requires RSI > 55 AND MFI > 55 for longs. This dual confirmation reduces false signals from price manipulation or low-volume moves.
Bollinger Bands (1.5 mult) and Keltner Channels (1.8 mult) - Squeeze Detection
When Bollinger Bands contract inside Keltner Channels, volatility is compressing and a breakout is likely. This is the "squeeze" condition. When the bands expand back outside the channels, the squeeze "releases."
The strategy uses a 1.5 multiplier for Bollinger Bands (tighter than standard 2.0) and 1.8 for Keltner Channels. These values were chosen to identify meaningful squeezes on 5-minute cryptocurrency charts without triggering too frequently.
The squeeze detection works with the regime classification: squeeze releases during compression regimes receive additional signal strength points because breakouts from consolidation are more significant.
Volume Impulse Detection - Institutional Participation Filter
The strategy calculates a volume baseline (34-period SMA) and standard deviation. A "volume impulse" is detected when current volume exceeds the baseline by 1.15x OR when the volume z-score exceeds 0.5.
This filter ensures entries occur when there is meaningful market participation, not during low-volume periods where price moves are less reliable.
Volume impulse is required for all entries and adds points to the composite signal strength score.
Cycle Oscillator - Trend Alignment Filter
The strategy calculates a 55-period EMA as a cycle basis, then measures price deviation from this basis as a percentage. When price is more than 0.15% above the cycle basis, the cycle is bullish. When more than 0.15% below, the cycle is bearish.
This filter prevents counter-trend entries. Long signals require bullish cycle alignment; short signals require bearish cycle alignment.
BTC Dominance Filter (Optional) - Market Regime Filter
The strategy can optionally use BTC.D (Bitcoin Dominance) as a market regime filter. When BTC dominance is rising (slope > 0.12), the market is in "risk-off" mode and long entries on altcoins are filtered. When dominance is falling (slope < -0.12), short entries are filtered.
This filter is optional because the BTC.D data feed may lag during low-liquidity periods.
How The Components Work Together (The Mashup Justification):
The strategy uses a composite scoring system where each signal pathway contributes points:
Trend Break pathway (30 points): Requires EMA ribbon alignment + positive slope + price breaks above recent structure high
Momentum Surge pathway (30 points): Requires MACD histogram > adaptive baseline + MACD line > signal + RSI > 55 + MFI > 55 + volume impulse
Squeeze Release pathway (25 points): Requires BB inside KC (squeeze) then release + momentum bias + histogram confirmation
Micro Pullback pathway (15 points): Requires shallow retracement to fast EMA within established trend + histogram confirmation + volume impulse
Additional modifiers:
+5 points if volume impulse is present, -5 if absent
+5 points in velocity regime, -2 in compression regime
+5 points if cycle is aligned, -5 if counter-trend
A trade only executes when the composite score reaches the minimum threshold (default 55) AND all filters agree (session, cycle bias, BTC dominance if enabled).
This scoring system is the core innovation: instead of requiring ALL conditions to be true (which would generate very few signals) or ANY condition to be true (which would generate too many false signals), the strategy requires ENOUGH conditions to be true, with different conditions contributing different weights based on their reliability.
HOW THE STRATEGY CALCULATES ENTRIES AND EXITS
Entry Logic:
1. Calculate current volatility regime from ATR%
2. Calculate all indicator values (MACD, EMA, RSI, MFI, squeeze, volume)
3. Evaluate each signal pathway and sum points
4. Check all filters (session, cycle, dominance, kill switch)
5. If composite score >= 55 AND all filters pass, generate entry signal
6. Calculate position size based on risk per trade and regime-adjusted stop distance
7. Execute entry with regime name as comment
Position Sizing Formula:
RiskCapital = Equity * (0.65 / 100)
StopDistance = ATR * StopMultiplier(regime)
RawQuantity = RiskCapital / StopDistance
MaxQuantity = Equity * (12 / 100) / Price
Quantity = min(RawQuantity, MaxQuantity)
Quantity = round(Quantity / 0.001) * 0.001
This ensures each trade risks approximately 0.65% of equity regardless of volatility, while capping total exposure at 12% of equity.
Stop Loss Calculation:
Stop distance is ATR multiplied by a regime-specific multiplier:
Compression regime: 1.05x ATR (tighter stops because moves are smaller)
Expansion regime: 1.55x ATR (standard stops)
Velocity regime: 2.1x ATR (wider stops to avoid premature exits during volatility)
Take Profit Calculation:
Target distance is ATR multiplied by regime-specific multiplier and base risk/reward:
Compression regime: 1.6x ATR * 1.8 base R:R * 0.9 regime bonus = approximately 2.6x ATR
Expansion regime: 2.05x ATR * 1.8 base R:R * 1.0 regime bonus = approximately 3.7x ATR
Velocity regime: 2.8x ATR * 1.8 base R:R * 1.15 regime bonus = approximately 5.8x ATR
Trailing Stop Logic:
When adaptive trailing is enabled, the strategy calculates a trailing offset based on ATR and regime:
Compression regime: 1.1x base offset (looser trailing to avoid noise)
Expansion regime: 1.0x base offset (standard)
Velocity regime: 0.8x base offset (tighter trailing to lock in profits during fast moves)
The trailing stop only activates when it would be tighter than the initial stop.
Momentum Fail-Safe Exits:
The strategy closes positions early if momentum reverses:
Long positions close if MACD histogram turns negative OR EMA ribbon structure breaks (fast EMA crosses below mid EMA)
Short positions close if MACD histogram turns positive OR EMA ribbon structure breaks
This prevents holding through momentum reversals even if stop loss hasn't been hit.
Kill Switch:
If maximum drawdown exceeds 6.5%, the strategy disables new entries until manually reset. This prevents continued trading during adverse conditions.
HOW TO USE THIS STRATEGY
Step 1: Apply to Chart
Use a 5-minute chart of a high-liquidity cryptocurrency perpetual (BTC/USDT, ETH/USDT recommended)
Ensure at least 200 bars of history are loaded for indicator stabilization
Use standard candlestick charts only (not Heikin Ashi, Renko, or other non-standard types)
Step 2: Understand the Visual Elements
EMA Ribbon: Three lines (8/21/34 periods) showing trend structure. Bullish when stacked upward, bearish when stacked downward.
Background Color: Shows current volatility regime
- Indigo/dark blue = Compression (low volatility)
- Purple = Expansion (normal volatility)
- Magenta/pink = Velocity (high volatility)
Bar Colors: Reflect signal strength divergence. Brighter colors indicate stronger directional bias.
Triangle Markers: Entry signals. Up triangles below bars = long entry. Down triangles above bars = short entry.
Dashboard (top-right): Real-time display of regime, ATR%, signal strengths, position status, stops, targets, and risk metrics.
Step 3: Interpret the Dashboard
Regime: Current volatility classification (Compression/Expansion/Velocity)
ATR%: Normalized volatility as percentage of price
Long/Short Strength: Current composite signal scores (0-100)
Cycle Osc: Price deviation from 55-period EMA as percentage
Dominance: BTC.D slope and filter status
Position: Current position direction or "Flat"
Stop/Target: Current stop loss and take profit levels
Kill Switch: Status of drawdown protection
Volume Z: Current volume z-score
Impulse: Whether volume impulse condition is met
Step 4: Adjust Parameters for Your Needs
For more conservative trading: Increase "Minimum Composite Signal Strength" to 65 or higher
For more aggressive trading: Decrease to 50 (but expect more false signals)
For higher timeframes (15m+): Increase "Structure Break Window" to 12-15, increase "RSI Momentum Trigger" to 58
For lower liquidity pairs: Increase "Volume Impulse Multiplier" to 1.3, increase slippage in strategy properties
To disable short selling: Uncheck "Enable Short Structure"
To disable BTC dominance filter: Uncheck "BTC Dominance Confirmation"
STRATEGY PROPERTIES (BACKTEST SETTINGS)
These are the exact settings used in the strategy's Properties dialog box. You must use these same settings when evaluating the backtest results shown in the publication:
Initial Capital: $100,000
Justification: This amount is higher than typical retail accounts. I chose this value to demonstrate percentage-based returns that scale proportionally. The strategy uses percentage-based position sizing (0.65% risk per trade), so a $10,000 account would see the same percentage returns with 10x smaller position sizes. The absolute dollar amounts in the backtest should be interpreted as percentages of capital.
Commission: 0.04% (commission_value = 0.04)
Justification: This reflects typical perpetual futures exchange fees. Major exchanges charge between 0.02% (maker) and 0.075% (taker). The 0.04% value is a reasonable middle estimate. If your exchange charges different fees, adjust this value accordingly. Higher fees will reduce net profitability.
Slippage: 1 tick
Justification: This is conservative for liquid pairs like BTC/USDT on major exchanges during normal conditions. For less liquid altcoins or during high volatility, actual slippage may be higher. If you trade less liquid pairs, increase this value to 2-3 ticks for more realistic results.
Pyramiding: 1
Justification: No position stacking. The strategy holds only one position at a time. This simplifies risk management and prevents overexposure.
calc_on_every_tick: true
Justification: The strategy evaluates on every price update, not just bar close. This is necessary for scalping timeframes where waiting for bar close would miss opportunities. Note that this setting means backtest results may differ slightly from bar-close-only evaluation.
calc_on_order_fills: true
Justification: The strategy recalculates immediately after order fills for faster response to position changes.
RISK PER TRADE JUSTIFICATION
The default risk per trade is 0.65% of equity. This is well within the PulseWire guideline that "risking more than 5-10% on a trade is not typically considered viable."
With the 12% maximum exposure cap, even if the strategy takes multiple consecutive losses, the total risk remains manageable. The kill switch at 6.5% drawdown provides additional protection by halting new entries during adverse conditions.
The position sizing formula ensures that stop distance (which varies by regime) is accounted for, so actual risk per trade remains approximately 0.65% regardless of volatility conditions.
SAMPLE SIZE CONSIDERATIONS
For statistically meaningful backtest results, you should select a dataset that generates at least 100 trades. On 5-minute BTC/USDT charts, this typically requires:
2-3 months of data during normal market conditions
1-2 months during high-volatility periods
3-4 months during low-volatility consolidation periods
The strategy's selectivity (requiring 55+ composite score plus all filters) means it generates fewer signals than less filtered approaches. If your backtest shows fewer than 100 trades, extend the date range or reduce the minimum signal strength threshold.
Fewer than 100 trades produces statistically unreliable results. Win rate, profit factor, and other metrics can vary significantly with small sample sizes.
STRATEGY DESIGN COMPROMISES AND LIMITATIONS
Every strategy involves trade-offs. Here are the compromises made in this design and the limitations you should understand:
Selectivity vs. Opportunity Trade-off
The 55-point minimum threshold filters many potential trades. This reduces false signals but also misses valid setups that don't meet all criteria. Lowering the threshold increases trade frequency but decreases win rate. There is no "correct" threshold; it depends on your preference for fewer higher-quality signals vs. more signals with lower individual quality.
Regime Classification Lag
The ATR-based regime detection uses historical data (21 periods + 13-period smoothing). It cannot predict sudden volatility spikes. During flash crashes or black swan events, the strategy may be classified in the wrong regime for several bars before the classification updates. This is an inherent limitation of any lagging indicator.
Indicator Parameter Sensitivity
The default parameters (MACD 8/21/5, EMA 8/21/34, RSI 21, etc.) are tuned for BTC/ETH perpetuals on 5-minute charts during 2024 market conditions. Different assets, timeframes, or market regimes may require different parameters. There is no guarantee that parameters optimized on historical data will perform similarly in the future.
BTC Dominance Filter Limitations
The CRYPTOCAP:BTC.D data feed may lag during low-liquidity periods or weekends. The dominance slope calculation uses a 5-bar SMA, adding additional delay. If you notice the filter behaving unexpectedly, consider disabling it.
Backtest vs. Live Execution Differences
PulseWire backtesting does not replicate actual broker execution. Key differences:
Backtests assume perfect fills at calculated prices; real execution involves order book depth, latency, and partial fills
The calc_on_every_tick setting improves backtest realism but still cannot capture sub-bar price action or order book dynamics
Commission and slippage settings are estimates; actual costs vary by exchange, time of day, and market conditions
Funding rates on perpetual futures are not modeled in backtests and can significantly impact profitability over time
Exchange-specific limitations (position limits, liquidation mechanics, order types) are not modeled
Market Condition Dependencies
This strategy is designed for trending and breakout conditions. During extended sideways consolidation with no clear direction, the strategy may generate few signals or experience whipsaws. No strategy performs well in all market conditions.
Cryptocurrency-Specific Risks
Cryptocurrency markets operate 24/7 without session boundaries. This means:
No natural "overnight" risk reduction
Volatility can spike at any time
Liquidity varies significantly by time of day
Exchange outages or issues can occur at any time
WHAT THIS STRATEGY DOES NOT DO
To be straightforward about limitations:
This strategy does not guarantee profits. Past backtest performance does not indicate future results.
This strategy does not predict the future. It reacts to current conditions based on historical patterns.
This strategy does not account for funding rates, which can significantly impact perpetual futures profitability.
This strategy does not model exchange-specific execution issues (partial fills, requotes, outages).
This strategy does not adapt to fundamental news events or black swan scenarios.
This strategy is not optimized for all market conditions. It may underperform during extended consolidation.
IMPORTANT RISK WARNINGS
Past performance does not guarantee future results. The backtest results shown reflect specific historical market conditions and parameter settings. Markets change constantly, and strategies that performed well historically may underperform or lose money in the future. A single backtest run does not constitute proof of future profitability.
Trading involves substantial risk of loss. Cryptocurrency derivatives are highly volatile instruments. You can lose your entire investment. Only trade with capital you can afford to lose completely.
This is not financial advice. This strategy is provided for educational and informational purposes only. It does not constitute investment advice, trading recommendations, or any form of financial guidance. The author is not a licensed financial advisor.
You are responsible for your own decisions. Before using this strategy with real capital:
Thoroughly understand the code and logic by reading the open-source implementation
Forward test with paper trading or very small positions for an extended period
Verify that commission, slippage, and execution assumptions match your actual trading environment
Understand that live results will differ from backtest results
Consider consulting with a qualified financial advisor
No guarantees or warranties. This strategy is provided "as is" without any guarantees of profitability, accuracy, or suitability for any purpose. The author is not responsible for any losses incurred from using this strategy.
OPEN-SOURCE CODE STRUCTURE
The strategy code is organized into these sections for readability:
Configuration Architecture: Input parameters organized into logical groups (Core Controls, Optimization Constants, Regime Intelligence, Signal Pathways, Risk Architecture, Visualization)
Helper Functions: calcQty() for position sizing, clamp01() and normalize() for value normalization, calcMFI() for Money Flow Index calculation
Core Indicator Engine: EMA ribbon, ATR and regime classification, MACD with adaptive baseline, RSI, MFI, volume analytics, cycle oscillator, BTC dominance filter, squeeze detection
Signal Pathway Logic: Trend break, momentum surge, squeeze release, micro pullback pathways with composite scoring
Entry/Exit Orchestration: Signal filtering, position sizing, entry execution, stop/target calculation, trailing stop logic, momentum fail-safe exits
Visualization Layer: EMA plots, regime background, bar coloring, signal labels, dashboard table
You can read and modify any part of the code. Understanding the logic before deployment is strongly recommended.
- Made with passion by officialjackofalltrades
Strategy
Max Pain Options [QuantLabs] v5 (Balanced)Institutional Grade Options Analysis: Max Pain, Gamma & Pin Risk
For years, PulseWire users have been flying blind without access to Options Chain data. QuantLabs: Max Pain & Gamma Exposure changes that. This is not just a support/resistance indicator—it is a sophisticated, algorithmic model that reverse-engineers the incentives of Market Makers using synthetic Black-Scholes logic.
This tool visualizes the "invisible hand" of the market: the hedging requirements of large dealers who are forced to buy or sell to keep their books neutral.
CORE FEATURES:
🔴 Max Pain Gravity Model The bright red line represents the "Max Pain" strike—the price level where the maximum amount of Options Open Interest (Calls + Puts) expires worthless.
Theory: As OpEx (Expiration) approaches, Market Makers maximize profits by pinning the price to this level.
Strategy: Use this as a mean-reversion target. If price is far away, look for a snap-back to the red line.
🟣 Gamma Exposure Profiles (The Purple Lines) These neon histograms show you the estimated "Gamma Walls."
Long Gamma: Dealers trade against the trend (stabilizing price).
Short Gamma: Dealers trade with the trend (accelerating volatility).
Visual: The larger the purple bar, the harder it will be for price to break through that level.
📦 Algorithmic "Pin Risk" Zones The dashed red box highlights the "Kill Zone." When price enters this area near expiration, volatility often dies as dealers pin the asset to kill retail premiums.
Warning: Do not expect breakouts while inside the Pin Zone.
📊 Institutional HUD A clean, non-intrusive dashboard provides real-time Greeks and risk analysis:
Pin Risk: High/Medium/Low probability of a pinned close.
Exp Mode: Detects if the market is in "Short Gamma" (Squeeze territory) or "Long Gamma" (Chop territory).
HOW IT WORKS (The Math): Since live options data is not available via Pine Script, this engine uses a proprietary Synthetic OI Distribution Model. It inputs Volume, Volatility (IV), and Time-to-Expiry into a modified Black-Scholes equation to probability-map where the heavy open interest likely sits.
SETTINGS & CUSTOMIZATION:
Responsiveness: Tuned for the "Goldilocks Zone" (Spread: 12, Decay: 22) to catch local liquidity walls without over-fitting.
Visuals: Designed for Dark Mode. High-contrast Neon aesthetics for maximum readability. Indicator
Liquidity Maxing [JOAT]Liquidity Maxing - Institutional Liquidity Matrix
Introduction
Liquidity Maxing is an open-source strategy for PulseWire built around institutional market structure concepts. It identifies structural shifts, evaluates trades through multi-factor confluence, and implements layered risk controls.
The strategy is designed for swing trading on 4-hour timeframes, focusing on how institutional order flow manifests in price action through structure breaks, inducements, and liquidity sweeps.
Core Functionality
Liquidity Maxing performs three primary functions:
Tracks market structure to identify when control shifts between buyers and sellers
Scores potential trades using an eight-factor confluence system
Manages position sizing and risk exposure dynamically based on volatility and user-defined limits
The goal is selective trading when multiple conditions align, rather than frequent entries.
Market Structure Engine
The structure engine tracks three key events:
Break of Structure (BOS): Price pushes beyond a prior pivot in the direction of trend
Change of Character (CHoCH): Control flips from bullish to bearish or vice versa
Inducement Sweeps (IDM): Market briefly runs stops against trend before moving in the real direction
The structure module continuously updates strong highs and lows, labeling structural shifts visually. IDM markers are optional and disabled by default to maintain chart clarity.
The trade engine requires valid structure alignment before considering entries. No structure, no trade.
Eight-Factor Confluence System
Instead of relying on a single indicator, Liquidity Maxing uses an eight-factor scoring system:
Structure alignment with current trend
RSI within healthy bands (different ranges for up and down trends)
MACD momentum agreement with direction
Volume above adaptive baseline
Price relative to main trend EMA
Session and weekend filter (configurable)
Volatility expansion/contraction via ATR shifts
Higher-timeframe EMA confirmation
Each factor contributes one point to the confluence score. The default minimum confluence threshold is 6 out of 8, but you can adjust this from 1-8 based on your preference for trade frequency versus selectivity.
Only when structure and confluence agree does the strategy proceed to risk evaluation.
Dynamic Risk Management
Risk controls are implemented in multiple layers:
ATR-based stops and targets with configurable risk-to-reward ratio (default 2:1)
Volatility-adjusted position sizing to maintain consistent risk per trade as ranges expand or compress
Daily and weekly risk budgets that halt new entries once thresholds are reached
Correlation cooldown to prevent clustered trades in the same direction
Global circuit breaker with maximum drawdown limit and emergency kill switch
If any guardrail is breached, the strategy will not open new positions. The dashboard clearly displays risk state for transparency.
Market Presets
The strategy includes configuration presets optimized for different market types:
Crypto (BTC/ETH): RSI bands 70/30, volume multiplier 1.2, enhanced ATR scaling
Forex Majors: RSI bands 75/25, volume multiplier 1.5
Indices (SPY/QQQ): RSI bands 70/30, volume multiplier 1.3
Custom: Default values for user customization
For crypto assets, the strategy automatically applies ATR volatility scaling to account for higher volatility characteristics.
Monitoring and Dashboards
The strategy includes optional monitoring layers:
Risk Operations Dashboard (top-right):
Trend state
Confluence score
ATR value
Current position size percentage
Global drawdown
Daily and weekly risk consumption
Correlation guard state
Alert mode status
Performance Console (top-left):
Net profit
Current equity
Win rate percentage
Average trade value
Sharpe-style ratio (rolling 50-bar window)
Profit factor
Open trade count
Optional risk tint on chart background provides visual indication of "safe to trade" versus "halted" state.
All visualization elements can be toggled on/off from the inputs for clean chart viewing or full telemetry during parameter tuning.
Alerts and Automation
The strategy supports alert integration with two formats:
Standard alerts: Human-readable messages for long, short, and risk-halt conditions
Webhook format: JSON-formatted payloads ready for external execution systems (optional)
Alert messages are predictable and unambiguous, suitable for manual review or automated forwarding to execution engines.
Built-in Validation Suite
The strategy includes an optional validation layer that can be enabled from inputs. It checks:
Internal consistency of structure and confluence metrics
Sanity and ordering of risk parameters
Position sizing compliance with user-defined floors and caps
This validation is optional and not required for trading, but provides transparency into system operation during development or troubleshooting.
Strategy Parameters
Market Presets:
Configuration Preset: Choose between Crypto (BTC/ETH), Forex Majors, Indices (SPY/QQQ), or Custom
Market Structure Architecture:
Pivot Length: Default 5 bars
Filter by Inducement (IDM): Default enabled
Visualize Structure: Default enabled
Structure Lookback: Default 50 bars
Risk & Capital Preservation:
Risk:Reward Ratio: Default 2.0
ATR Period: Default 14
ATR Multiplier (Stop): Default 2.0
Max Drawdown Circuit Breaker: Default 10%
Risk per Trade (% Equity): Default 1.5%
Daily Risk Limit: Default 6%
Weekly Risk Limit: Default 12%
Min Position Size (% Equity): Default 0.25%
Max Position Size (% Equity): Default 5%
Correlation Cooldown (bars): Default 3
Emergency Kill Switch: Default disabled
Signal Confluence:
RSI Length: Default 14
Trend EMA: Default 200
HTF Confirmation TF: Default Daily
Allow Weekend Trading: Default enabled
Minimum Confluence Score (0-8): Default 6
Backtesting Considerations
When backtesting this strategy, consider the following:
Commission: Default 0.05% (adjustable in strategy settings)
Initial Capital: Default $100,000 (adjustable)
Position Sizing: Uses percentage of equity (default 2% per trade)
Timeframe: Optimized for 4-hour charts, though can be tested on other timeframes
Results will vary significantly based on:
Market conditions and volatility regimes
Parameter settings, especially confluence threshold
Risk limit configuration
Symbol characteristics (crypto vs forex vs equities)
Past performance does not guarantee future results. Win rate, profit factor, and other metrics should be evaluated in context of drawdown periods, trade frequency, and market conditions.
How to Use This Strategy
This is a framework that requires understanding and parameter tuning, not a one-size-fits-all solution.
Recommended workflow:
Start on 4-hour timeframe with default parameters and appropriate market preset
Run backtests and study performance console metrics: focus on drawdown behavior, win rate, profit factor, and trade frequency
Adjust confluence threshold to match your risk appetite—higher thresholds mean fewer but more selective trades
Set realistic daily and weekly risk budgets appropriate for your account size and risk tolerance
Consider ATR multiplier adjustments based on market volatility characteristics
Only connect alerts or automation after thorough testing and parameter validation
Treat this as a risk framework with an integrated entry engine, not merely an entry signal generator. The risk controls are as important as the trade signals.
Strategy Limitations
Designed for swing trading timeframes; may not perform optimally on very short timeframes
Requires sufficient market structure to identify pivots; may struggle in choppy or low-volatility environments
Crypto markets require different parameter tuning than traditional markets
Risk limits may prevent entries during favorable setups if daily/weekly budgets are exhausted
Correlation cooldown may delay entries that would otherwise be valid
Backtesting results depend on data quality and may not reflect live trading with slippage
Design Philosophy
Many indicators tell you when price crossed a moving average or RSI left oversold. This strategy addresses questions institutional traders ask:
Who is in control of the market right now?
Is this move structurally significant or just noise?
Do I want to add more risk given what I've already done today/week?
If I'm wrong, exactly how painful can this be?
The strategy provides disciplined, repeatable answers to these questions through systematic structure analysis, confluence filtering, and multi-layer risk management.
Technical Implementation
The strategy uses Pine Script v6 with:
Custom types for structure, confluence, and risk state management
Functional programming approach for reusable calculations
State management through persistent variables
Optional visual elements that can be toggled independently
The code is open-source and can be modified to suit individual needs. All important logic is visible in the source code.
Disclaimer
This script is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation. Trading involves substantial risk of loss and is not suitable for all investors. Past performance, whether real or indicated by historical tests of strategies, is not indicative of future results.
No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between backtested results and actual results subsequently achieved by any particular trading strategy.
The user should be aware of the risks involved in trading and should trade only with risk capital. The authors and publishers of this script are not responsible for any losses or damages, including without limitation, any loss of profit, which may arise directly or indirectly from use of or reliance on this script.
This strategy uses technical analysis methods and indicators that are not guaranteed to be accurate or profitable. Market conditions change, and strategies that worked in the past may not work in the future. Users should thoroughly test any strategy in a paper trading environment before risking real capital.
Commission and slippage settings in backtests may not accurately reflect live trading conditions. Real trading results will vary based on execution quality, market liquidity, and other factors not captured in backtesting.
The user assumes full responsibility for all trading decisions made using this script. Always consult with a qualified financial advisor before making investment decisions.
Enjoy - officialjackofalltrades Strategy
Strategy
ML Adaptive SuperTrend Strategy [trade_crush]# ML Adaptive SuperTrend Strategy - User Guide
## Introduction
The **ML Adaptive SuperTrend Strategy** is a sophisticated trading tool that combines traditional trend-following logic with **Machine Learning (K-Means Clustering)** to dynamically adapt to market volatility. Unlike standard SuperTrend indicators that use a fixed ATR, this strategy analyzes historical volatility to categorize the current market into distinct clusters, providing more precise entries and exits.
>
> **Special Thanks:** This strategy is based on the innovative work of **AlgoAlpha**. You can explore their extensive library of high-quality indicators and strategies on PulseWire: (www.pulsewire.com).
---
## Machine Learning Engine (K-Means)
The core of this strategy is its ability to "learn" from recent market behavior.
- **K-Means Clustering**: The script takes the last $N$ bars of ATR data and runs an iterative clustering algorithm to find three "centroids" representing **High**, **Medium**, and **Low** volatility.
- **Adaptive ATR**: Based on the current volatility, the strategy selects the nearest centroid to use as the ATR value for the SuperTrend calculation. This ensures the trailing stop tightens during low volatility and widens during high volatility to avoid "noise".
---
## Key Features
### 1. Non-Repainting Signals
- **Confirm Signals**: When enabled, signals are only triggered after a bar closes. This ensures that the arrows and entries you see on the chart are permanent and reliable for backtesting.
### 2. Intelligent Risk Management
- **Multiple SL/TP Types**: Choose between **Percentage** based stops or **ATR** based stops for both Stop Loss and Take Profit.
- **Trailing Stop Loss (TSL)**:
- Supports both Percentage and ATR modes.
- **Activation Offset**: Only activates the trailing mechanism after the price has moved a certain percentage in your favor, protecting early-stage trades.
### 3. Risk-Based Position Sizing
- **Dynamic Quantity**: If enabled, the strategy automatically calculates the trade size based on your **Risk % Per Trade** and the distance to your **Stop Loss**. This ensures you never lose more than your defined risk on a single trade.
---
## User Input Guide
### SuperTrend & ML Settings
- **ATR Length**: The window used to calculate market volatility.
- **SuperTrend Factor**: The multiplier that determines the distance of the trailing stop from the price.
- **Use ML Adaptive ATR**: Toggle between the ML-enhanced logic and standard ATR.
- **Training Data Length**: How many historical bars the ML engine analyzes to find clusters.
### Risk Management
- **Stop Loss Type**: Set to Percentage, ATR, or None.
- **TS Activation Offset**: The profit buffer required before the trailing stop starts following the price.
- **Use Risk-Based Sizing**: Toggle this to let the script manage your position size automatically.
---
## How to Trade with This Strategy
1. **Monitor the Dashboard**: Check the top-right table to see which volatility cluster the market is currently in.
2. **Observe the Fills**: The adaptive fills (green/red) visualize the "breathing room" the strategy is giving the price.
3. **Execution**: The strategy enters on "ML Bullish" (Triangle Up) and "ML Bearish" (Triangle Down) signals.
4. **Exits**: The script will automatically exit based on your SL, TP, or Trailing Stop settings.
---
## Credits
Original Concept: **AlgoAlpha**
Strategy Conversion & Enhancements: **Antigravity AI**
Strategy
Antigravity OCC Strategy (MA 5 + Delayed TSL)# OCC Strategy Optimized (MA 5 + Delayed TSL) - User Guide
## Introduction
The **OCC Strategy Optimized** is an enhanced version of the classic **Open Close Cross (OCC)** strategy. This strategy is designed for high-precision trend following, utilizing the crossover logic of Open and Close moving averages to identify market shifts. This optimized version incorporates advanced risk management, multi-timeframe analysis, and a variety of moving average types to provide a robust trading solution for modern markets.
>
> **Special Thanks:** This strategy is based on the original work of **JustUncleL**, a renowned Pine Script developer. You can find their work and profile on PulseWire here: (in.pulsewire.com).
---
## Key Features
### 1. Optimized Core Logic
- **MA Period (Default: 5):** The strategy is tuned with a shorter MA length to reduce lag and capture trends earlier.
- **Crossing Logic:** Signals are generated when the Moving Average of the **Close** crosses the Moving Average of the **Open**.
### 2. Multi-Timeframe (MTF) Analysis
- **Alternate Resolution:** Use a higher timeframe (Resolution Multiplier) to filter out noise. By default, it uses $3 \times$ your current chart timeframe to confirm the trend.
- **Non-Repainting:** Includes an optional delay offset to ensure signals are confirmed and do not disappear (repaint) after the bar closes.
### 3. Advanced Risk Management
This script features a hierarchical exit system to protect your capital and lock in profits:
- **Fixed Stop Loss (Initial):** Protects against sudden market reversals immediately after entry.
- **Delayed Trailing Stop Loss (TSL):**
- **Activation Delay:** The TSL only activates after the trade reaches a specific profit threshold (e.g., 1%). This prevents being stopped out too early in the trade's development.
- **Ratchet Trail:** Once activated, the stop loss "ratchets" up/down, never moving backward, ensuring you lock in profits as the trend continues.
- **Take Profit (TP):** A fixed percentage target to exit the trade at a pre-defined profit level.
### 4. Versatility
- **12 MA Types:** Choose from SMA, EMA, DEMA, TEMA, WMA, VWMA, SMMA, HullMA, LSMA, ALMA, SSMA, and TMA.
- **Trade Direction:** Toggle between Long-only, Short-only, or Both.
- **Visuals:** Optional bar coloring to visualize the trend directly on the candlesticks.
---
## User Input Guide
### Core Settings
- **Use Alternate Resolution?:** Enable this to use the MTF logic.
- **Multiplier for Alternate Resolution:** How many charts higher the "filter" timeframe should be.
- **MA Type:** Select your preferred moving average smoothing method.
- **MA Period:** The length of the Open/Close averages.
- **Delay Open/Close MA:** Use `1` or higher to force non-repainting behavior.
### Risk Management Settings
- **Use Trailing Stop Loss?:** Enables the TSL system.
- **Trailing Stop %:** The distance the stop follows behind the price.
- **TSL Activation % (Delay):** The profit % required before the TSL starts moving.
- **Initial Fixed Stop Loss %:** Your hard stop if the trade immediately goes against you.
- **Take Profit %:** Your ultimate profit target for the trade.
---
## How to Trade with This Strategy
1. **Identify the Trend:** Look for the Moving Average lines (Close vs Open) to cross.
2. **Wait for Confirmation:** If using MTF, ensure the higher timeframe also shows a trend change.
3. **Manage the Trade:** Let the TSL work. Once the trade hits the activation threshold, the TSL will take over, protecting your runner.
4. **Position Sizing:** Adjust the `Properties` tab in the script settings to match your desired capital allocation (Default is 10% of equity).
---
## Credits
Original Strategy by: **JustUncleL**
Optimized and Enhanced by: **Antigravity AI**
Strategy
