Mutanabby_AI | ONEUSDT_MR1
ONEUSDT Mean-Reversion Strategy | 74.68% Win Rate | 417% Net Profit
This is a long-only mean-reversion strategy designed specifically for ONEUSDT on the 1-hour timeframe. The core logic identifies oversold conditions following sharp declines and enters positions when selling pressure exhausts, capturing the subsequent recovery bounce.
Backtested Period: June 2019 – December 2025 (~6 years)
Performance Summary
| Metric | Value |
|--------|-------|
| Net Profit | +417.68% |
| Win Rate | 74.68% |
| Profit Factor | 4.019 |
| Total Trades | 237 |
| Sharpe Ratio | 0.364 |
| Sortino Ratio | 1.917 |
| Max Drawdown | 51.08% |
| Avg Win | +3.14% |
| Avg Loss | -2.30% |
| Buy & Hold Return | -80.44% |
Strategy Logic :
Entry Conditions (Long Only):
The strategy seeks confluence of three conditions that identify exhausted selling:
1. Prior Move Filter:*The price change from 5 bars ago to 3 bars ago must be ≥ -7% (ensures we're not entering during freefall)
2. Current Move Filter: The price change over the last 2 bars must be ≤ 0% (confirms momentum is stalling or reversing)
3. Three-Bar Decline: The price change from 5 bars ago to 3 bars ago must be ≤ -5% (confirms a significant recent drop occurred)
When all three conditions align, the strategy identifies a potential reversal point where sellers are exhausted.
Exit Conditions:
- Primary Exit: Close above the previous bar's high while the open of the previous bar is at or below the close from 9 bars ago (profit-taking on strength)
- Trailing Stop: 11x ATR trailing stop that locks in profits as price rises
Risk Management
- Position Sizing:Fixed position based on account equity divided by entry price
- Trailing Stop:11× ATR (14-period) provides wide enough room for crypto volatility while protecting gains
- Pyramiding:Up to 4 orders allowed (can scale into winning positions)
- **Commission:** 0.1% per trade (realistic exchange fees included)
Important Disclaimers
⚠️ This is NOT financial advice.
- Past performance does not guarantee future results
- Backtest results may contain look-ahead bias or curve-fitting
- Real trading involves slippage, liquidity issues, and execution delays
- This strategy is optimized for ONEUSDT specifically — results may differ on other pairs
- Always test before risking real capital
Recommended Usage
- Timeframe:*1H (as designed)
- Pair: ONEUSDT (Binance)
- Account Size: Ensure sufficient capital to survive max drawdown
Source Code
Feedback Welcome
I'm sharing this strategy freely for educational purposes. Please:
- Drop a comment with your backtesting results any you analysis
- Share any modifications that improve performance
- Let me know if you spot any issues in the logic
Happy trading
As a quant trader, do you think this strategy will survive in live trading?
Yes or No? And why?
I want to hear from you guys
Statistics
EMA + ATR Semi-Auto strategy -Kohei Matsumura-EMAとATRを自動調節するストラテジー
This is an EMA- and ATR-based trading strategy that adapts its parameters according to recent market behavior and performance characteristics.
The strategy dynamically adjusts trend sensitivity and risk management settings to maintain robustness across varying market conditions, while operating strictly on confirmed price data.
GIX Analizor strategiiGIX Analyzer – Intelligent Time Filters + X Strategy
This script combines the X Strategy with an advanced system for filtering trades based on time intervals. The strategy allows:
Filtering by preset trading hours (active sessions )
Filtering by a fully customizable time interval (hour + minute, Romania time )
Filtering by calendar range (Start Date → End Date)
Simultaneous activation of both time-filter modes for maximum control
Trading only within valid time ranges, while keeping all logic unchanged
This indicator provides high flexibility for testing and optimizing trading entries based on hours, minutes, and calendar periods—while preserving the simplicity and efficiency of any strategy
Market Dynamics - Backtest Engine [NeuraAlgo]Market Dynamics – Backtest Engine
Market Dynamics – Backtest Engine is an advanced research-grade trading framework engineered by NeuraAlgo.
🔹 Core Engine – Dynamic Trend Model
The strategy leverages the NeuraAlgo – Market Dynamics indicator as its foundation, providing intelligent insights to guide trading decisions. It is designed to automatically identify the optimal settings for the NeuraAlgo – Market Dynamics indicator, helping traders fine-tune their strategy for maximum efficiency, accuracy, and profitability. This engine dynamically adapts to market conditions, ensuring your strategy stays optimized in real-time.
🔹 Optimization Engine
A built-in optimization module allows automatic testing of:
Winrate-focused configurations
Profit-focused configurations
Sensitivity ranges
Step sizes
Main Entry, Main Filter, Feature Filter, and Risk Manager categories
This enables rapid identification of optimal parameters similar to a lightweight AI optimizer.
This Backtesting + Auto Optimization Engine includes an integrated optimizer that automatically tests sensitivity ranges:
Maximize Winrate
Maximize Profits
Optimize Main Entries, Risk Manager, or Feature Filters
Users can set:
start sensitivity
step size
parameter category
The engine autonomously computes which parameter delivers the strongest performance.
🔹 How To Use
1. Identify the Parameters
First, you need to know which indicator parameters can be optimized. For the NeuraAlgo – Market Dynamics indicator, these might include:
Trend sensitivity
Smoothing periods
Threshold values for bullish/bearish signals
These parameters are the inputs your engine will test.
2. Define a Range
For each parameter, define a range of values to test. Example:
Sensitivity: 2 → 10
Trend period: 14 → 50
Threshold: 0.1 → 1.0
The more granular the range, the more precise the optimization—but it will also take longer.
3. Run Backtest Optimization
Attach the strategy to a chart.
Select optimization mode in your engine (or set the range for each parameter).
Start the backtest: the engine will simulate trades for every combination of parameter values.
The system will automatically record key metrics for each run:
Net profit
Win rate
Profit factor
Max drawdown
4. Analyze the Results
After the backtest, your engine will display a results table or chart showing performance for each parameter combination. Look for:
Highest net profit
Highest win rate
Or a combination depending on your strategy goals
Some engines will highlight the “best” parameter set automatically.
5. Apply Optimal Settings
Once identified:
Select the best-performing parameter values.
Apply them to your live strategy or paper trade.
Optionally, forward test to confirm they work on unseen market data.
Congratulations! The setup is now optimized.
🔹 Conclusion
The backtest optimization process helps you find the best parameter values for the NeuraAlgo – Market Dynamics indicator by systematically testing different settings and measuring their performance. By analyzing metrics like net profit, win rate, and drawdown, you can select optimized parameters that are more likely to perform consistently in real trading. Proper optimization ensures your strategy is data-driven, adaptable, and reduces guesswork, giving you a stronger edge in the market.
5-Min Range Breakout (09:30 NY on MNQ)This is a 5 - min orb strat that a youtuber mentioned and i had a manual look for a while and thought it was actually pretty good but my results are bad. Feel free to look yourself with this code.
Basically this strat is using the 5min orb then go down to 1min timeframe and wait for a breakout with FVG confirmation. So candle after breaking candle is our entry only if FVG is formed.
However i do notice if you dump this code onto 5min timefraem and above you start consistently making money but it is a very small amount for me so you all can have it. Good starter strat on 5min or 10min timeframe
EMA + Sessions + RSI Strategy v1.0A professional trading strategy that combines multiple technical indicators for high-probability entries. This system uses EMA crossovers, RSI zone filtering, and trend confirmation to identify optimal trading opportunities while managing risk with advanced position management tools.
Key Features:
✅ Dual Entry Signals (EMA21 + EMA100 crossover conditions)
✅ Trend Filter EMA750 (trade only with the major trend)
✅ Complete Risk Management (SL 1%, TP 3% default)
✅ Trailing Stop & Breakeven (maximize profits, protect capital)
✅ Compact Statistics Table (real-time performance metrics)
✅ RSI & Session Filters (avoid low-probability setups)
✅ Optional Pyramiding (scale into winning positions)
Perfect for swing trading and trend-following on any timeframe. Fully customizable to match your trading style.
Simple Grid Trading v1.0 [PUCHON]Simple Grid Trading v1.0
Overview
This is a Long-Only Grid Trading Strategy developed in Pine Script v6 for TradingView. It is designed to profit from market volatility by placing a series of Buy Limit orders at predefined price levels. As the price drops, the strategy accumulates positions. As the price rises, it sells these positions at a profit.
Features
Grid Types : Supports both Arithmetic (equal price spacing) and Geometric (equal percentage spacing) grids.
Flexible Order Management : Uses strategy.order for precise control and prevents duplicate orders at the same level.
Performance Dashboard : A real-time table displaying key metrics like Capital, Cashflow, and Drawdown.
Advanced Metrics : Includes Max Drawdown (MaxDD) , Avg Monthly Return , and CAGR calculations.
Customizable : Fully adjustable price range, grid lines, and lot size.
Dashboard Metrics
The dashboard (default: Bottom Right) provides a quick snapshot of the strategy's performance:
Initial Capital : The starting capital defined in the strategy settings.
Lot Size : The fixed quantity of assets purchased per grid level.
Avg. Profit per Grid : The average realized profit for each closed trade.
Cashflow : The total realized net profit (closed trades only).
MaxDD : Maximum Drawdown . The largest percentage drop in equity (realized + unrealized) from a peak.
Avg Monthly Return : The average percentage return generated per month.
CAGR : Compound Annual Growth Rate . The mean annual growth rate of the investment over the specified time period.
Strategy Settings (Inputs)
Grid Settings
Upper Price : The highest price level for the grid.
Lower Price : The lowest price level for the grid.
Number of Grid Lines : The total number of levels (lines) in the grid.
Grid Type :
Arithmetic: Distance between lines is fixed in price terms (e.g., $10, $20, $30).
Geometric: Distance between lines is fixed in percentage terms (e.g., 1%, 2%, 3%).
Lot Size : The fixed amount of the asset to buy at each level.
Dashboard Settings
Show Dashboard : Toggle to hide/show the performance table.
Position : Choose where the dashboard appears on the chart (e.g., Bottom Right, Top Left).
How It Works
Initialization : On the first bar, the script calculates the price levels based on your Upper/Lower price and Grid Type.
Entry Logic :
The strategy places Buy Limit orders at every grid level below the current price.
It checks if a position already exists at a specific level to avoid "stacking" multiple orders on the same line.
Exit Logic :
For every Buy order, a corresponding Sell Limit (Take Profit) order is placed at the next higher grid level.
MaxDD Calculation :
The script continuously tracks the highest equity peak.
It calculates the drawdown on every bar (including intra-bar movements) to ensure accuracy.
Displayed as a percentage (e.g., 5.25%).
Disclaimer
This script is for educational and backtesting purposes only. Grid trading involves significant risk, especially in strong trending markets where the price may move outside your grid range. Always use proper risk management.
Alt Trading: FuturesOne
The FuturesOne Indicator + Strategy will be continuously enhanced to ensure our users receive the most effective and profit-focused trading system at the best possible value. Version 0 (V0) of the FuturesOne Strategy is built on a refined Opening Range Breakout (ORB) framework, augmented with a quantitative regime-detection and filtering layer. This design allows users to tailor their approach: they may opt for consistent daily ORB opportunities or select a mode that applies quantitative filters to surface fewer, but higher-probability, trade setups.
STRATEGY 1 │ Red Dragon │ Model 1 │ Pro │ [Titans_Invest]The Red Dragon Model 1 is a fully automated trading strategy designed to operate BTC/USDT.P on the 4-hour chart with precision, stability, and consistency. It was built to deliver reliable behavior even during strong market movements, maintaining operational discipline and avoiding abrupt variations that could interfere with the trader’s decision-making.
Its core is based on a professionally engineered logical structure that combines trend filters, confirmation criteria, and balanced risk management. Every component was designed to work in an integrated way, eliminating noise, avoiding unnecessary trades, and protecting capital in critical moments. There are no secret mechanisms or hidden logic: everything is built to be objective, clean, and efficient.
Even though it is based on professional quantitative engineering, Red Dragon Model 1 remains extremely simple to operate. All logic is clearly displayed and fully accessible within TradingView itself, making it easy to understand for both beginners and experienced traders. The structure is organized so that any user can quickly view entry conditions, exit criteria, additional filters, adjustable parameters, and the full mechanics behind the strategy’s behavior.
In addition, the architecture was built to minimize unnecessary complexity. Parameters are straightforward, intuitive, and operate in a balanced way without requiring deep adjustments or advanced knowledge. Traders have full freedom to analyze the strategy, understand the logic, and make personal adaptations if desired—always with total transparency inside TradingView.
The strategy was also designed to deliver consistent operational behavior over the long term. Its confirmation criteria reduce impulsive trades; its filters isolate noise; and its overall logic prioritizes high-quality entries in structured market movements. The goal is to provide a stable, clear, and repeatable flow—essential characteristics for any medium-term quantitative approach.
Combining clarity, professional structure, and ease of use, Red Dragon Model 1 offers a solid foundation both for users who want a ready-to-use automated strategy and for those looking to study quantitative models in greater depth.
This entire project was built with extreme dedication, backed by more than 14,000 hours of hands-on experience in Pine Script, continuously refining patterns, techniques, and structures until reaching its current level of maturity. Every line of code reflects this long process of improvement, resulting in a strategy that unites professional engineering, transparency, accessibility, and reliable execution.
🔶 MAIN FEATURES
• Fully automated and robust: Operates without manual intervention, ideal for traders seeking consistency and stability. It delivers reliable performance even in volatile markets thanks to the solid quantitative engineering behind the system.
• Multiple layers of confirmation: Combines 10 key technical indicators with 15 adaptive filters to avoid false signals. It only triggers entries when all trend, market strength, and contextual criteria align.
• Configurable and adaptable filters: Each of the 15 filters can be enabled, disabled, or adjusted by the user, allowing the creation of personalized statistical models for different assets and timeframes. This flexibility gives full freedom to optimize the strategy according to individual preferences.
• Clear and accessible logic: All entry and exit conditions are explicitly shown within the TradingView parameters. The strategy has no hidden components—any user can quickly analyze and understand each part of the system.
• Integrated exclusive tools: Includes complete backtest tables (desktop and mobile versions) with annualized statistics, along with real-time entry conditions displayed directly on the chart. These tools help monitor the strategy across devices and track performance and risk metrics.
• No repaint: All signals are static and do not change after being plotted. This ensures the trader can trust every entry shown without worrying about indicators rewriting past values.
🔷 ENTRY CONDITIONS & RISK MANAGEMENT
Red Dragon Model 1 triggers buy (long) or sell (short) signals only when all configured conditions are satisfied. For example:
• Volume:
• The system only trades when current volume exceeds the volume moving average multiplied by a user-defined factor, indicating meaningful market participation.
• RSI:
• Confirms bullish bias when RSI crosses above its moving average, and bearish bias when crossing below.
• ADX:
• Enters long when +DI is above –DI with ADX above a defined threshold, indicating directional strength to the upside (and the opposite conditions for shorts).
• Other indicators (MACD, SAR, Ichimoku, Support/Resistance, etc.)
Each one must confirm the expected direction before a final signal is allowed.
When all bullish criteria are met simultaneously, the system enters Long; when all criteria indicate a bearish environment, the system enters Short.
In addition, the strategy uses fixed Take Profit and Stop Loss targets for risk control:
Currently: TP around 1.5% and SL around 2.0% per trade, ensuring consistent and transparent risk management on every position.
⚙️ INDICATORS
__________________________________________________________
1) 🔊 Volume: Avoids trading on flat charts.
2) 🍟 MACD: Tracks momentum through moving averages.
3) 🧲 RSI: Indicates overbought or oversold conditions.
4) 🅰️ ADX: Measures trend strength and potential entry points.
5) 🥊 SAR: Identifies changes in price direction.
6) ☁️ Cloud: Accurately detects changes in market trends.
7) 🌡️ R/F: Improves trend visualization and helps avoid pitfalls.
8) 📐 S/R: Fixed support and resistance levels.
9)╭╯MA: Moving Averages.
10) 🔮 LR: Forecasting using Linear Regression.
__________________________________________________________
🟢 ENTRY CONDITIONS 🔴
__________________________________________________________
IF all conditions are 🟢 = 📈 Long
IF all conditions are 🔴 = 📉 Short
__________________________________________________________
🚨 CURRENT TRIGGER SIGNAL 🚨
__________________________________________________________
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult)
🧲 RSI
🟢 LONG = (RSI) > (RSI_MA)
🔴 SHORT = (RSI) < (RSI_MA)
🟢 ALL ENTRY CONDITIONS AVAILABLE 🔴
__________________________________________________________
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult)
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult) and (close) > (open)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult) and (close) < (open)
🍟 MACD
🟢 LONG = (MACD) > (Signal Smoothing)
🔴 SHORT = (MACD) < (Signal Smoothing)
🧲 RSI
🟢 LONG = (RSI) < (Upper)
🔴 SHORT = (RSI) > (Lower)
🧲 RSI
🟢 LONG = (RSI) > (RSI_MA)
🔴 SHORT = (RSI) < (RSI_MA)
🅰️ ADX
🟢 LONG = (+DI) > (-DI) and (ADX) > (Treshold)
🔴 SHORT = (+DI) < (-DI) and (ADX) > (Treshold)
🥊 SAR
🟢 LONG = (close) > (SAR)
🔴 SHORT = (close) < (SAR)
☁️ Cloud
🟢 LONG = (Cloud A) > (Cloud B)
🔴 SHORT = (Cloud A) < (Cloud B)
☁️ Cloud
🟢 LONG = (Kama) > (Kama )
🔴 SHORT = (Kama) < (Kama )
🌡️ R/F
🟢 LONG = (high) > (UP Range) and (upward) > (0)
🔴 SHORT = (low) < (DOWN Range) and (downward) > (0)
🌡️ R/F
🟢 LONG = (high) > (UP Range)
🔴 SHORT = (low) < (DOWN Range)
📐 S/R
🟢 LONG = (close) > (Resistance)
🔴 SHORT = (close) < (Support)
╭╯MA2️⃣
🟢 LONG = (Cyan Bar MA2️⃣)
🔴 SHORT = (Red Bar MA2️⃣)
╭╯MA2️⃣
🟢 LONG = (close) > (MA2️⃣)
🔴 SHORT = (close) < (MA2️⃣)
╭╯MA2️⃣
🟢 LONG = (Positive MA2️⃣)
🔴 SHORT = (Negative MA2️⃣)
__________________________________________________________
🎯 TP / SL 🛑
__________________________________________________________
🎯 TP: 1.5 ％
🛑 SL: 2.0 ％
__________________________________________________________
🪄 UNIQUE FEATURES OF THIS STRATEGY
____________________________________
1) 𝄜 Table Backtest for Mobile.
2) 𝄜 Table Backtest for Computer.
3) 𝄜 Table Backtest for Computer & Annual Performance.
4) 𝄜 Live Entry Conditions.
1) 𝄜 Table Backtest for Mobile.
2) 𝄜 Table Backtest for Computer.
3) 𝄜 Table Backtest for Computer & Annual Performance.
4) 𝄜 Live Entry Conditions.
_____________________________
𝄜 BACKTEST / PERFORMANCE 𝄜
_____________________________
• Net Profit: +634.47%, Maximum Drawdown: -18.44%.
🪙 PAIR / TIMEFRAME ⏳
🪙 PAIR: BINANCE:BTCUSDT.P
⏳ TIME: 4 hours (240m)
✅ ON ☑️ OFF
✅ LONG
✅ SHORT
🎯 TP / SL 🛑
🎯 TP: 1.5 (％)
🛑 SL: 2.0 (％)
⚙️ CAPITAL MANAGEMENT
💸 Initial Capital: 10000 $ (TradingView)
💲 Order Size: 10 ％ (Of Equity)
🚀 Leverage: 10 x (Exchange)
💩 Commission: 0.03 ％ (Exchange)
📆 BACKTEST
🗓️ Start: Setember 24, 2019
🗓️ End: November 21, 2025
🗓️ Days: 2250
🗓️ Yers: 6.17
🗓️ Bars: 13502
📊 PERFORMANCE
💲 Net Profit: + 63446.89 $
🟢 Net Profit: + 634.47 ％
💲 DrawDown Maximum: - 10727.48 $
🔴 DrawDown Maximum: - 18.44 ％
🟢 Total Closed Trades: 1042
🟡 Percent Profitable: 63.92 ％
🟡 Profit Factor: 1.247
💲 Avg Trade: + 60.89 $
⏱️ Avg # Bars in Trades
🕯️ Avg # Bars: 4
⏳ Avg # Hrs: 15
✔️ Trades Winning: 666
❌ Trades Losing: 376
✔️ Maximum Consecutive Wins: 11
❌ Maximum Consecutive Losses: 7
📺 Live Performance : br.tradingview.com
• Use this strategy on the recommended pair and timeframe above to replicate the tested results.
• Feel free to experiment and explore other settings, assets, and timeframes.
Pressure Pivots - MPI (Strategy)⇋ PRESSURE PIVOTS — MARKET PRESSURE INDEX STRATEGY
A comprehensive reversal trading system that combines order flow pressure analysis, multi-factor confluence detection, and adaptive machine learning to identify high-probability turning points in liquid markets.
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CORE INNOVATION: MARKET PRESSURE INDEX (MPI)
Traditional indicators measure price movement. The Market Pressure Index measures the force behind the movement.
How MPI Works:
Every bar tells two stories through volume distribution:
• Buy Pressure: Volume × (Close - Low) / (High - Low)
• Sell Pressure: Volume × (High - Close) / (High - Low)
• Net Pressure: Buy Pressure - Sell Pressure
This raw pressure is then normalized against baseline activity to create the bounded MPI (-1.0 to +1.0):
• Smooth Pressure: EMA(Net Pressure, period)
• Baseline Activity: SMA(|Net Pressure|, period × 2)
• MPI: (Smooth Pressure / Baseline) × Sensitivity
What MPI Reveals:
MPI > +0.7: Extreme buy pressure → Exhaustion potential
MPI = +0.2 to +0.7: Healthy bullish momentum
MPI = -0.2 to +0.2: Neutral/balanced pressure
MPI = -0.7 to -0.2: Healthy bearish momentum
MPI < -0.7: Extreme sell pressure → Exhaustion potential
Why It Works:
Two bars can both move 10 points, but if one closes at the high on high volume (aggressive buying) and the other closes mid-range on average volume (weak buying), only MPI distinguishes between sustainable momentum and exhaustion. This volume-weighted pressure analysis reveals conviction behind price moves—the key to timing reversals.
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SEVEN-FACTOR CONFLUENCE SYSTEM
MPI extremes alone aren't enough. The system requires multiple independent confirmations through weighted scoring:
1. DIVERGENCE (Weight: 3.0) — Premium Signal Type: DIV
Price makes new high but MPI makes lower high (or inverse for bullish)
• Detection: Tracks pivots with 5-bar lookback, compares price vs MPI at pivot points
• Signal: Purple triangles, highest weight (pressure weakening while price extends)
2. LIQUIDITY SWEEP (Weight: 2.5) — Premium Signal Type: LIQ
Price breaks swing high/low within 0.3 ATR then reverses
• Detection: Break within tolerance + close back through level
• Signal: Orange triangles, second-highest weight (stop hunt reversal)
3. ORDER FLOW IMBALANCE (Weight: 2.0) — Premium Signal Type: OF
Aggressive buying/selling 50% above normal
• Detection: EMA(aggressive volume) vs SMA(imbalance) threshold
• Signal: Aqua triangles, institutional positioning
4. VELOCITY EXHAUSTION (Weight: 1.5)
Parabolic move (2+ ATRs in 3 bars) + extreme MPI
• Detection: |3-bar price change / ATR| > threshold + MPI > ±0.5
• Indicates: Momentum deceleration, blow-off top/bottom
5. WICK REJECTION (Weight: 1.5)
Single bar: wick > 60% of range, or sequence: 2 bars with 40% + 30% wicks
• Detection: Shooting stars (bearish) or hammers (bullish)
• Indicates: Intrabar rejection, battle won by opposing side
6. VOLUME SPIKE (Weight: 1.0)
Volume > 20-bar average × multiplier (default: 2.0x)
• Detection: Participation surge confirmation
• Lowest weight: Can be manipulated, better as confirmation
7. POSITION FACTOR (Weight: 1.0)
At 10-bar highest (bearish) or lowest (bullish)
• Detection: Structural positioning for reversal
• Base requirement: Must be at extreme to score
Scoring Logic:
Premium Signals (DIV/LIQ/OF): Must score ≥6.0 (default premiumThreshold)
Standard Signals (STD): Must score ≥4.0 (default standardThreshold)
Example Scoring:
Divergence (3.0) + Liquidity Sweep (2.5) + Volume (1.0) = 6.5 → FIRES (DIV signal)
Recent High (1.0) + Wick (1.5) + Volume (1.0) + Velocity (1.5) = 5.0 → FIRES (STD signal)
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ADAPTIVE LEARNING ENGINE
Unlike static strategies, this system learns from every trade and optimizes itself.
Performance Tracking:
Every trade records:
• Entry Score: Confluence level at entry
• Signal Type: DIV / LIQ / OF / STD
• Win/Loss: Boolean outcome
• R-Multiple: (Exit - Entry) / (Entry - Stop)
• MAE: Maximum Adverse Excursion (worst drawdown)
• MFE: Maximum Favorable Excursion (best profit reached)
Three Adaptive Parameters:
1. Signal Threshold Adaptation
If Win Rate < Target (45%): RAISE threshold → fewer signals, better quality
If Win Rate > Target + 10% AND good R: LOWER threshold → more signals, profitable
2. Stop Distance Adaptation
If Avg MAE > 0.85 AND WR < 50%: WIDEN stops → reduce premature exits
If Avg MAE < 0.4 AND WR > 55%: TIGHTEN stops → reduce risk
3. Target Distance Adaptation
If Avg MFE > Target × 1.5: EXTEND targets → capture more of runners
If Avg MFE < Target × 0.7: SHORTEN targets → take profits faster
Signal Type Filtering:
The system tracks performance by type (DIV/LIQ/OF/STD):
• If Type WR < 40% AND Avg R < 0.8: Type DISABLED
• If Type WR ≥ 40% OR Avg R ≥ 0.8: Type RE-ENABLED
Example: If OF signals consistently lose while DIV signals win, system automatically stops taking OF signals and focuses on DIV.
Warmup Period:
First 30 trades (default) gather baseline data with relaxed thresholds. After warmup, full adaptation activates.
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COMPLETE POSITION MANAGEMENT
Dynamic Position Sizing:
Base Contracts = (Equity × Risk%) / (Stop Distance × Point Value)
Then multiplied by:
• Score Bonus: Up to +50% for highest-scoring signals
• Signal Type Bonus: DIV signals +50%, LIQ signals +30%
• Streak Multiplier: After 3 losses: 50% reduction, After 3 wins: 25% increase
Example: High-scoring DIV signal on winning streak = 3-4× larger position than weak STD signal on losing streak
Entry Modes:
Single Entry: Full size at once, exit at TP2 (or partial at TP1)
Tiered Entry: 40% at TP1 (2R), 60% at TP2 (4R adaptive)
Stop Management (3 Modes):
Structural: Beyond recent 20-bar swing high/low + buffer
ATR: Fixed ATR multiplier (default: 2.0 ATR, then adapts)
Hybrid: Attempt structural, fallback to ATR if invalid
Plus:
• Breakeven: Move stop to entry ± 1 tick when 1R reached
• Trailing: Activate when 1.5R reached, trail 0.8R behind price
• Max Loss Override: Cap dollar risk regardless of calculation
Target Management:
Fixed Mode: TP1 = 2R, TP2 = 4R
Adaptive Mode: TP1 = 2R fixed, TP2 adapts based on MFE analysis
Partial Exits: Default 50% at TP1, remainder at TP2 or trailing stop
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COMPREHENSIVE RISK CONTROLS
Daily Limits:
• Max Daily Loss: $2,000 default → HALT trading
• Max Daily Trades: 15 default → prevent overtrading
• Max Concurrent: 2 positions → limit correlation risk
Session Controls:
• Trading Hours: Specify start/end times + timezone
• Weekend Block: Optional (avoid crypto weekend volatility)
Prop Firm Protection (Live Trading Only):
• Daily Loss Limit: Stricter of general or prop limit ($1,000 default)
• Trailing Drawdown: Tracks high water mark, HALTS if breach ($2,500 default)
• Reset on Reload: Optional high water mark reset
Liquidity Filter (Optional):
• Time-Based: Avoid first/last X minutes of session
• Volume-Based: Require minimum volume ratio (0.5× average default)
Market Regime Filter (Optional):
• ADX-Based: Only trade when ADX > threshold (trending)
• Block: Consolidation (ADX < 20) or Transitional regimes
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REAL-TIME DASHBOARD
MPI Gauge Section:
Shows current pressure: 🟢 STRONG BUY (+0.5 to +1.0), 🟩 BUY PRESSURE (+0.2 to +0.5), ⚪ NEUTRAL (-0.2 to +0.2), 🟥 SELL PRESSURE (-0.5 to -0.2), 🔴 STRONG SELL (-1.0 to -0.5)
Signal Status Section:
• Active Signals: "🔴 DIV SELL" (purple background), "🟢 LIQ BUY" (orange), "🔵 OF SELL" (aqua), "🟢 STD BUY" (green)
• Warnings: "⚠️ BEAR WARNING" / "⚠️ BULL WARNING" (yellow) — setup forming, not full signal
• Scanning: "⏳ SCANNING..." (gray) — no signal active
• Confidence Bar: Visual score display "██████░░░░" showing confluence strength
Divergence Indicator:
"🟣 BEARISH DIVERGENCE" or "🟡 BULLISH DIVERGENCE" when detected
Performance Statistics:
• Overall Win Rate: Wins/Total with visual bar (lime ≥70%, yellow 50-70%, red <50%)
• Directional: Bearish vs Bullish win rates separately
• By Signal Type: DIV / LIQ / OF / STD individual performance tracking
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KEY PARAMETERS EXPLAINED
🎯 Pressure Engine:
• MPI Period (5-50, default: 14): Smoothing period — lower for scalping, higher for position trading
• MPI Sensitivity (0.5-5.0, default: 1.5): Amplification — lower compresses range, higher more extremes
🔍 Detection:
• Wick Threshold (0.3-0.9, default: 0.6): Minimum wick-to-range ratio for rejection
• Volume Spike (1.2-3.0x, default: 2.0): Multiplier above average for spike
• Aggressive Ratio (0.5-0.9, default: 0.65): Close position in range for aggressive orders
• Velocity Threshold (1.0-5.0 ATR, default: 2.0): ATR-normalized move for exhaustion
• MPI Extreme (0.5-0.95, default: 0.7): Level considered overbought/oversold
⚖️ Weights:
• Divergence: 3.0 (highest — pressure weakening)
• Liquidity: 2.5 (second — stop hunts)
• Order Flow: 2.0 (institutional positioning)
• Velocity: 1.5 (momentum exhaustion)
• Wick: 1.5 (rejection patterns)
• Volume: 1.0 (lowest — can be manipulated)
🎚️ Thresholds:
• Premium (4.0-15.0, default: 6.0): Score for DIV/LIQ/OF signals
• Standard (2.0-8.0, default: 4.0): Score for STD signals
• Warning Confluence (1-4, default: 2): Factors for yellow diamond warnings
🧬 Adaptive:
• Enable (true/false, default: true): Master learning switch
• Warmup Trades (5-100, default: 30): Data collection before adaptation
• Lookback (20-200, default: 50): Recent trades for performance calculation
• Adapt Speed (0.05-0.50, default: 0.15): Parameter adjustment rate
• Target Win Rate (30-70%, default: 45%): Optimization goal
• Target R-Multiple (0.5-5.0, default: 1.5): Risk/reward goal
💼 Position:
• Base Risk (0.1-10.0%, default: 1.5%): Equity risked per trade
• Max Contracts (1-100, default: 10): Hard position limit
• DIV Bonus (1.0-3.0x, default: 1.5): Size multiplier for divergence signals
• LIQ Bonus (1.0-3.0x, default: 1.3): Size multiplier for liquidity signals
🛡️ Stops:
• Mode (Structural/ATR/Hybrid, default: ATR): Stop placement method
• ATR Multiplier (0.5-5.0, default: 2.0): Stop distance in ATRs (adapts)
• Breakeven at (0.3-3.0R, default: 1.0R): When to move stop to entry
• Trail Trigger (0.5-5.0R, default: 1.5R): When to activate trailing
• Trail Offset (0.3-3.0R, default: 0.8R): Distance behind price
🎯 Targets:
• Mode (Fixed/Adaptive, default: Fixed): Target placement method
• TP1 (0.5-10.0R, default: 2.0R): First target for partial exit
• TP2 (1.0-15.0R, default: 4.0R): Final target (adapts in adaptive mode)
• Partial % (0-100%, default: 50%): Position percentage to exit at TP1
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PROFESSIONAL USAGE PROTOCOL
Phase 1: Paper Trading (Weeks 1-4)
• Setup: Default settings, all adaptive features ON, 0.5% base risk
• Goal: 30+ trades for warmup, observe MPI behavior and signal frequency
• Adjust: MPI sensitivity if stuck near neutral or always at extremes
• Threshold: Raise/lower if too many/few signals
Phase 2: Micro Live (Weeks 5-8)
• Requirements: WR >43%, at least one type >55%, Avg R >0.8
• Setup: 10-25% intended size, 0.5-1.0% risk, 1 position max
• Focus: Execution quality, match dashboard performance
• Journal: Screenshot every signal, track outcomes
Phase 3: Full Scale (Month 3+)
• Requirements: WR >45% over 50+ trades, Avg R >1.2, drawdown <15%
• Progression: Months 3-4 (1.0-1.5% risk), 5-6 (1.5-2.0%), 7+ (1.5-2.5%)
• Maintenance: Weekly dashboard review, monthly deep analysis
• Warnings: Reduce size if WR drops >10%, consecutive losses >7, or drawdown >20%
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DEVELOPMENT INSIGHTS
The Pressure Insight: Emerged from analyzing intrabar volume distribution. Within every candlestick, volume accumulates at different price levels. MPI deconstructs this to reveal conviction behind moves.
The Confluence Challenge: Early versions using MPI extremes alone achieved only 42% win rate. The seven-factor confluence system emerged from testing which combinations produced reliable reversals. Divergence + liquidity sweep became the strongest setup (68% win rate in isolation).
The Adaptive Breakthrough: Per-signal-type performance tracking revealed DIV signals winning at 71% while OF signals languished at 38%. Adaptive filtering disabled weak types automatically, recovering win rate from 39% to 54% during the 2022 volatility spike.
The Position Sizing Revelation: Dynamic sizing based on signal quality and recent performance increased Sharpe ratio from 1.2 to 1.9 while decreasing max drawdown from 18% to 12% over 500 trades. Bigger positions on better signals = geometric edge amplification.
The Risk Control Lesson: Testing with $50K accounts revealed catastrophic failure modes: daily loss cascades, overtrading commission bleed, weekend gap blowouts. Multi-layer controls (daily limits, concurrent caps, prop firm protection) became essential.
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LIMITATIONS & ASSUMPTIONS
What This Is NOT:
• NOT a Holy Grail: Typical performance 52-58% WR, 1.3-1.8 avg R, probabilistic edge
• NOT Predictive: Identifies high-probability conditions, doesn't forecast prices
• NOT Market-Agnostic: Best on liquid auction-driven markets (futures, forex, major crypto)
• NOT Hands-Off: Requires oversight for news events, gaps, system anomalies
• NOT Immune to Regime Changes: Adaptive engine helps but cannot predict black swans
Critical Assumptions:
1. Volume reflects intent (valid for regulated markets, violated by wash trading)
2. Pressure extremes mean-revert (true in ranging/exhaustion, fails in paradigm shifts)
3. Stop hunts exist (valid in liquid markets, less in thin/random walk periods)
4. Past patterns persist (valid in stable regimes, fails when structure fundamentally changes)
Works Best On: Major futures (ES, NQ, CL), liquid forex pairs (EUR/USD, GBP/USD), large-cap stocks, BTC
Performs Poorly On: Low-volume stocks, illiquid crypto pairs, news-driven headline events
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RISK DISCLOSURE
Trading futures, forex, and leveraged instruments involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. This strategy is provided for educational purposes only and should not be construed as financial advice.
The adaptive engine learns from historical data—there is no guarantee that past relationships will persist. Market conditions change, volatility regimes shift, and black swan events occur. No strategy can eliminate the risk of loss.
Users must validate performance on their specific instruments and timeframes before risking capital. The developer makes no warranties regarding profitability or suitability. Users assume all responsibility for trading decisions and outcomes.
"The market doesn't care about your indicators. It only cares about pressure—who's willing to pay more, who's desperate to sell. Find the exhaustion. Trade the reversal. Let the system learn the rest."
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
STRATEGY 1 │ Red Dragon │ Model 1 │ [Titans_Invest]The Red Dragon Model 1 is a fully automated trading strategy designed to operate BTC/USDT.P on the 4-hour chart with precision, stability, and consistency. It was built to deliver reliable behavior even during strong market movements, maintaining operational discipline and avoiding abrupt variations that could interfere with the trader’s decision-making.
Its core is based on a professionally engineered logical structure that combines trend filters, confirmation criteria, and balanced risk management. Every component was designed to work in an integrated way, eliminating noise, avoiding unnecessary trades, and protecting capital in critical moments. There are no secret mechanisms or hidden logic: everything is built to be objective, clean, and efficient.
Even though it is based on professional quantitative engineering, Red Dragon Model 1 remains extremely simple to operate. All logic is clearly displayed and fully accessible within TradingView itself, making it easy to understand for both beginners and experienced traders. The structure is organized so that any user can quickly view entry conditions, exit criteria, additional filters, adjustable parameters, and the full mechanics behind the strategy’s behavior.
In addition, the architecture was built to minimize unnecessary complexity. Parameters are straightforward, intuitive, and operate in a balanced way without requiring deep adjustments or advanced knowledge. Traders have full freedom to analyze the strategy, understand the logic, and make personal adaptations if desired—always with total transparency inside TradingView.
The strategy was also designed to deliver consistent operational behavior over the long term. Its confirmation criteria reduce impulsive trades; its filters isolate noise; and its overall logic prioritizes high-quality entries in structured market movements. The goal is to provide a stable, clear, and repeatable flow—essential characteristics for any medium-term quantitative approach.
Combining clarity, professional structure, and ease of use, Red Dragon Model 1 offers a solid foundation both for users who want a ready-to-use automated strategy and for those looking to study quantitative models in greater depth.
This entire project was built with extreme dedication, backed by more than 14,000 hours of hands-on experience in Pine Script, continuously refining patterns, techniques, and structures until reaching its current level of maturity. Every line of code reflects this long process of improvement, resulting in a strategy that unites professional engineering, transparency, accessibility, and reliable execution.
🔶 MAIN FEATURES
• Fully automated and robust: Operates without manual intervention, ideal for traders seeking consistency and stability. It delivers reliable performance even in volatile markets thanks to the solid quantitative engineering behind the system.
• Multiple layers of confirmation: Combines 10 key technical indicators with 15 adaptive filters to avoid false signals. It only triggers entries when all trend, market strength, and contextual criteria align.
• Configurable and adaptable filters: Each of the 15 filters can be enabled, disabled, or adjusted by the user, allowing the creation of personalized statistical models for different assets and timeframes. This flexibility gives full freedom to optimize the strategy according to individual preferences.
• Clear and accessible logic: All entry and exit conditions are explicitly shown within the TradingView parameters. The strategy has no hidden components—any user can quickly analyze and understand each part of the system.
• Integrated exclusive tools: Includes complete backtest tables (desktop and mobile versions) with annualized statistics, along with real-time entry conditions displayed directly on the chart. These tools help monitor the strategy across devices and track performance and risk metrics.
• No repaint: All signals are static and do not change after being plotted. This ensures the trader can trust every entry shown without worrying about indicators rewriting past values.
🔷 ENTRY CONDITIONS & RISK MANAGEMENT
Red Dragon Model 1 triggers buy (long) or sell (short) signals only when all configured conditions are satisfied. For example:
• Volume:
• The system only trades when current volume exceeds the volume moving average multiplied by a user-defined factor, indicating meaningful market participation.
• RSI:
• Confirms bullish bias when RSI crosses above its moving average, and bearish bias when crossing below.
• ADX:
• Enters long when +DI is above –DI with ADX above a defined threshold, indicating directional strength to the upside (and the opposite conditions for shorts).
• Other indicators (MACD, SAR, Ichimoku, Support/Resistance, etc.)
Each one must confirm the expected direction before a final signal is allowed.
When all bullish criteria are met simultaneously, the system enters Long; when all criteria indicate a bearish environment, the system enters Short.
In addition, the strategy uses fixed Take Profit and Stop Loss targets for risk control:
Currently: TP around 1.5% and SL around 2.0% per trade, ensuring consistent and transparent risk management on every position.
⚙️ INDICATORS
__________________________________________________________
1) 🔊 Volume: Avoids trading on flat charts.
2) 🍟 MACD: Tracks momentum through moving averages.
3) 🧲 RSI: Indicates overbought or oversold conditions.
4) 🅰️ ADX: Measures trend strength and potential entry points.
5) 🥊 SAR: Identifies changes in price direction.
6) ☁️ Cloud: Accurately detects changes in market trends.
7) 🌡️ R/F: Improves trend visualization and helps avoid pitfalls.
8) 📐 S/R: Fixed support and resistance levels.
9)╭╯MA: Moving Averages.
10) 🔮 LR: Forecasting using Linear Regression.
__________________________________________________________
🟢 ENTRY CONDITIONS 🔴
__________________________________________________________
IF all conditions are 🟢 = 📈 Long
IF all conditions are 🔴 = 📉 Short
__________________________________________________________
🚨 CURRENT TRIGGER SIGNAL 🚨
__________________________________________________________
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult)
🧲 RSI
🟢 LONG = (RSI) > (RSI_MA)
🔴 SHORT = (RSI) < (RSI_MA)
🟢 ALL ENTRY CONDITIONS AVAILABLE 🔴
__________________________________________________________
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult)
🔊 Volume
🟢 LONG = (volume) > (MA_volume) * (Volume Mult) and (close) > (open)
🔴 SHORT = (volume) > (MA_volume) * (Volume Mult) and (close) < (open)
🍟 MACD
🟢 LONG = (MACD) > (Signal Smoothing)
🔴 SHORT = (MACD) < (Signal Smoothing)
🧲 RSI
🟢 LONG = (RSI) < (Upper)
🔴 SHORT = (RSI) > (Lower)
🧲 RSI
🟢 LONG = (RSI) > (RSI_MA)
🔴 SHORT = (RSI) < (RSI_MA)
🅰️ ADX
🟢 LONG = (+DI) > (-DI) and (ADX) > (Treshold)
🔴 SHORT = (+DI) < (-DI) and (ADX) > (Treshold)
🥊 SAR
🟢 LONG = (close) > (SAR)
🔴 SHORT = (close) < (SAR)
☁️ Cloud
🟢 LONG = (Cloud A) > (Cloud B)
🔴 SHORT = (Cloud A) < (Cloud B)
☁️ Cloud
🟢 LONG = (Kama) > (Kama )
🔴 SHORT = (Kama) < (Kama )
🌡️ R/F
🟢 LONG = (high) > (UP Range) and (upward) > (0)
🔴 SHORT = (low) < (DOWN Range) and (downward) > (0)
🌡️ R/F
🟢 LONG = (high) > (UP Range)
🔴 SHORT = (low) < (DOWN Range)
📐 S/R
🟢 LONG = (close) > (Resistance)
🔴 SHORT = (close) < (Support)
╭╯MA2️⃣
🟢 LONG = (Cyan Bar MA2️⃣)
🔴 SHORT = (Red Bar MA2️⃣)
╭╯MA2️⃣
🟢 LONG = (close) > (MA2️⃣)
🔴 SHORT = (close) < (MA2️⃣)
╭╯MA2️⃣
🟢 LONG = (Positive MA2️⃣)
🔴 SHORT = (Negative MA2️⃣)
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🎯 TP / SL 🛑
__________________________________________________________
🎯 TP: 1.5 ％
🛑 SL: 2.0 ％
__________________________________________________________
🪄 UNIQUE FEATURES OF THIS STRATEGY
____________________________________
1) 𝄜 Table Backtest for Mobile.
2) 𝄜 Table Backtest for Computer.
3) 𝄜 Table Backtest for Computer & Annual Performance.
4) 𝄜 Live Entry Conditions.
1) 𝄜 Table Backtest for Mobile.
2) 𝄜 Table Backtest for Computer.
3) 𝄜 Table Backtest for Computer & Annual Performance.
4) 𝄜 Live Entry Conditions.
_____________________________
𝄜 BACKTEST / PERFORMANCE 𝄜
_____________________________
• Net Profit: +634.47%, Maximum Drawdown: -18.44%.
🪙 PAIR / TIMEFRAME ⏳
🪙 PAIR: BINANCE:BTCUSDT.P
⏳ TIME: 4 hours (240m)
✅ ON ☑️ OFF
✅ LONG
✅ SHORT
🎯 TP / SL 🛑
🎯 TP: 1.5 (％)
🛑 SL: 2.0 (％)
⚙️ CAPITAL MANAGEMENT
💸 Initial Capital: 10000 $ (TradingView)
💲 Order Size: 10 ％ (Of Equity)
🚀 Leverage: 10 x (Exchange)
💩 Commission: 0.03 ％ (Exchange)
📆 BACKTEST
🗓️ Start: Setember 24, 2019
🗓️ End: November 21, 2025
🗓️ Days: 2250
🗓️ Yers: 6.17
🗓️ Bars: 13502
📊 PERFORMANCE
💲 Net Profit: + 63446.89 $
🟢 Net Profit: + 634.47 ％
💲 DrawDown Maximum: - 10727.48 $
🔴 DrawDown Maximum: - 18.44 ％
🟢 Total Closed Trades: 1042
🟡 Percent Profitable: 63.92 ％
🟡 Profit Factor: 1.247
💲 Avg Trade: + 60.89 $
⏱️ Avg # Bars in Trades
🕯️ Avg # Bars: 4
⏳ Avg # Hrs: 15
✔️ Trades Winning: 666
❌ Trades Losing: 376
✔️ Maximum Consecutive Wins: 11
❌ Maximum Consecutive Losses: 7
📺 Live Performance : br.tradingview.com
• Use this strategy on the recommended pair and timeframe above to replicate the tested results.
• Feel free to experiment and explore other settings, assets, and timeframes.
MTF Scalper - alemicihanMulti-Timeframe Scalper Strategy: Aligning the Big Picture for Quick Gains
This article presents a robust futures trading strategy designed for high-frequency scalping in the crypto market. It’s built on the principle of minimizing risk by ensuring that short-term entries are always aligned with the dominant, higher-timeframe trend.
The Core Concept: Alignment is Key
A Balanced Trend Follower approach, now refined for rapid scalping, uses a Multi-Timeframe (MTF) confirmation system to filter out market noise and increase the probability of a successful trade.
The strategy operates on a Low Timeframe (LTF) chart (e.g., 3m, 5m, or 15m) but only executes trades if the direction is validated by three Higher Timeframes (HTF).
ComponentPurposeFunctionHTF (D, 4h, 1h) EMA => Trend Confirmation =>Checks if the current price is above/below all three Exponential Moving Averages (EMA 20). This provides a strong directional bias.
LTF (5m) Stochastic RSI => Momentum Entry => Generates the actual buy/sell signal by spotting a swift crossover, indicating fresh momentum in the direction of the confirmed HTF trend.
How The Signal Is Generated
Trend Alignment: The system first confirms the trend. If the price is trading above the Daily, 4-Hour, and 1-Hour EMAs, the market is deemed to be in a Strong LONG Trend. Only LONG signals are permitted.
Momentum Trigger: Once the trend is confirmed, a Long Signal is generated only when the Stochastic K-Line crosses above the D-Line, indicating a momentum shift (a pullback ending) towards the main trend direction.
Short Signal: The inverse logic applies to the Short Trend confirmation and entry signal.
Mandatory Risk Management: ATR-Based Exit
Given the high leverage nature of futures and scalping, static Stop-Loss (SL) and Take-Profit (TP) levels are inefficient. This strategy uses the Average True Range (ATR) indicator to dynamically set profit and loss targets based on current market volatility.
Stop Loss (SL): Set dynamically at 1.5 x ATR below (for long) or above (for short) the entry price. This gives the trade enough room to breathe without risking excessive capital.
Take Profit (TP): Set dynamically at 3.0 x ATR, establishing a robust Risk-to-Reward Ratio of 1:2.
Final Thoughts on Testing
This sophisticated approach combines the reliability of MTF analysis with the speed of momentum indicators. However, data analysis is key. Backtesting these parameters (EMA, ATR Multipliers, RSI/Stochastic lengths) on your chosen asset (like BTC/USDT or ETH/USDT) and timeframe is crucial to achieving optimal performance.
HPAS mean reversion strategy testerTakes Krown HPAS values hardcoded and simulates longs and short with configurable standard deviation multiplier TP/SL. Best used on lower timeframes
Pivot Fib 4H — EAStrategy uses the pivot standard to open position, it has well define entry and exit point with SL, it also has a proper money management plan, maximum 4 trades a day, each trade risk 0.5% of the account, I have it EA version of it also.
Reversal Point Dynamics - Machine Learning⇋ Reversal Point Dynamics - Machine Learning
RPD Machine Learning: Self-Adaptive Multi-Armed Bandit Trading System
RPD Machine Learning is an advanced algorithmic trading system that implements genuine machine learning through contextual multi-armed bandits, reinforcement learning, and online adaptation. Unlike traditional indicators that use fixed rules, RPD learns from every trade outcome , automatically discovers which strategies work in current market conditions, and continuously adapts without manual intervention .
Core Innovation: The system deploys six distinct trading policies (ranging from aggressive trend-following to conservative range-bound strategies) and uses LinUCB contextual bandit algorithms with Random Fourier Features to learn which policy performs best in each market regime. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation , automatically shifting between policies as market conditions evolve.
Target Users: Quantitative traders, algorithmic trading developers, systematic traders, and data-driven investors who want a system that adapts over time . Suitable for stocks, futures, forex, and cryptocurrency on any liquid instrument with >100k daily volume.
The Problem This System Solves
Traditional Technical Analysis Limitations
Most trading systems suffer from three fundamental challenges :
Fixed Parameters: Static settings (like "buy when RSI < 30") work well in backtests but may struggle when markets change character. What worked in low-volatility environments may not work in high-volatility regimes.
Strategy Degradation: Manual optimization (curve-fitting) produces systems that perform well on historical data but may underperform in live trading. The system never adapts to new market conditions.
Cognitive Overload: Running multiple strategies simultaneously forces traders to manually decide which one to trust. This leads to hesitation, late entries, and inconsistent execution.
How RPD Machine Learning Addresses These Challenges
Automated Strategy Selection: Instead of requiring you to choose between trend-following and mean-reversion strategies, RPD runs all six policies simultaneously and uses machine learning to automatically select the best one for current conditions. The decision happens algorithmically, removing human hesitation.
Continuous Learning: After every trade, the system updates its understanding of which policies are working. If the market shifts from trending to ranging, RPD automatically detects this through changing performance patterns and adjusts selection accordingly.
Context-Aware Decisions: Unlike simple voting systems that treat all conditions equally, RPD analyzes market context (ADX regime, entropy levels, volatility state, volume patterns, time of day, historical performance) and learns which combinations of context features correlate with policy success.
Machine Learning Architecture: What Makes This "Real" ML
Component 1: Contextual Multi-Armed Bandits (LinUCB)
What Is a Multi-Armed Bandit Problem?
Imagine facing six slot machines, each with unknown payout rates. The exploration-exploitation dilemma asks: Should you keep pulling the machine that's worked well (exploitation) or try others that might be better (exploration)? RPD solves this for trading policies.
Academic Foundation:
RPD implements Linear Upper Confidence Bound (LinUCB) from the research paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" (Li et al., 2010, WWW Conference). This algorithm is used in content recommendation and ad placement systems.
How It Works:
Each policy (AggressiveTrend, ConservativeRange, VolatilityBreakout, etc.) is treated as an "arm." The system maintains:
Reward History: Tracks wins/losses for each policy
Contextual Features: Current market state (8-10 features including ADX, entropy, volatility, volume)
Uncertainty Estimates: Confidence in each policy's performance
UCB Formula: predicted_reward + α × uncertainty
The system selects the policy with highest UCB score , balancing proven performance (predicted_reward) with potential for discovery (uncertainty bonus). Initially, all policies have high uncertainty, so the system explores broadly. After 50-100 trades, uncertainty decreases, and the system focuses on known-performing policies.
Why This Matters:
Traditional systems pick strategies based on historical backtests or user preference. RPD learns from actual outcomes in your specific market, on your timeframe, with your execution characteristics.
Component 2: Random Fourier Features (RFF)
The Non-Linearity Challenge:
Market relationships are often non-linear. High ADX may indicate favorable conditions when volatility is normal, but unfavorable when volatility spikes. Simple linear models struggle to capture these interactions.
Academic Foundation:
RPD implements Random Fourier Features from "Random Features for Large-Scale Kernel Machines" (Rahimi & Recht, 2007, NIPS). This technique approximates kernel methods (like Support Vector Machines) while maintaining computational efficiency for real-time trading.
How It Works:
The system transforms base features (ADX, entropy, volatility, etc.) into a higher-dimensional space using random projections and cosine transformations:
Input: 8 base features
Projection: Through random Gaussian weights
Transformation: cos(W×features + b)
Output: 16 RFF dimensions
This allows the bandit to learn non-linear relationships between market context and policy success. For example: "AggressiveTrend performs well when ADX >25 AND entropy <0.6 AND hour >9" becomes naturally encoded in the RFF space.
Why This Matters:
Without RFF, the system could only learn "this policy has X% historical performance." With RFF, it learns "this policy performs differently in these specific contexts" - enabling more nuanced selection.
Component 3: Reinforcement Learning Stack
Beyond bandits, RPD implements a complete RL framework :
Q-Learning: Value-based RL that learns state-action values. Maps 54 discrete market states (trend×volatility×RSI×volume combinations) to 5 actions (4 policies + no-trade). Updates via Bellman equation after each trade. Converges toward optimal policy after 100-200 trades.
TD(λ) with Eligibility Traces: Extension of Q-Learning that propagates credit backwards through time . When a trade produces an outcome, TD(λ) updates not just the final state-action but all states visited during the trade, weighted by eligibility decay (λ=0.90). This accelerates learning from multi-bar trades.
Policy Gradient (REINFORCE): Learns a stochastic policy directly from 12 continuous market features without discretization. Uses gradient ascent to increase probability of actions that led to positive outcomes. Includes baseline (average reward) for variance reduction.
Meta-Learning: The system learns how to learn by adapting its own learning rates based on feature stability and correlation with outcomes. If a feature (like volume ratio) consistently correlates with success, its learning rate increases. If unstable, rate decreases.
Why This Matters:
Q-Learning provides fast discrete decisions. Policy Gradient handles continuous features. TD(λ) accelerates learning. Meta-learning optimizes the optimization. Together, they create a robust, multi-approach learning system that adapts more quickly than any single algorithm.
Component 4: Policy Momentum Tracking (v2 Feature)
The Recency Challenge:
Standard bandits treat all historical data equally. If a policy performed well historically but struggles in current conditions due to regime shift, the system may be slow to adapt because historical success outweighs recent underperformance.
RPD's Solution:
Each policy maintains a ring buffer of the last 10 outcomes. The system calculates:
Momentum: recent_win_rate - global_win_rate (range: -1 to +1)
Confidence: consistency of recent results (1 - variance)
Policies with positive momentum (recent outperformance) get an exploration bonus. Policies with negative momentum and high confidence (consistent recent underperformance) receive a selection penalty.
Effect: When markets shift, the system detects the shift more quickly through momentum tracking, enabling faster adaptation than standard bandits.
Signal Generation: The Core Algorithm
Multi-Timeframe Fractal Detection
RPD identifies reversal points using three complementary methods :
1. Quantum State Analysis:
Divides price range into discrete states (default: 6 levels)
Peak signals require price in top states (≥ state 5)
Valley signals require price in bottom states (≤ state 1)
Prevents mid-range signals that may struggle in strong trends
2. Fractal Geometry:
Identifies swing highs/lows using configurable fractal strength
Confirms local extremum with neighboring bars
Validates reversal only if price crosses prior extreme
3. Multi-Timeframe Confirmation:
Analyzes higher timeframe (4× default) for alignment
MTF confirmation adds probability bonus
Designed to reduce false signals while preserving valid setups
Probability Scoring System
Each signal receives a dynamic probability score (40-99%) based on:
Base Components:
Trend Strength: EMA(velocity) / ATR × 30 points
Entropy Quality: (1 - entropy) × 10 points
Starting baseline: 40 points
Enhancement Bonuses:
Divergence Detection: +20 points (price/momentum divergence)
RSI Extremes: +8 points (RSI >65 for peaks, <40 for valleys)
Volume Confirmation: +5 points (volume >1.2× average)
Adaptive Momentum: +10 points (strong directional velocity)
MTF Alignment: +12 points (higher timeframe confirms)
Range Factor: (high-low)/ATR × 3 - 1.5 points (volatility adjustment)
Regime Bonus: +8 points (trending ADX >25 with directional agreement)
Penalties:
High Entropy: -5 points (entropy >0.85, chaotic price action)
Consolidation Regime: -10 points (ADX <20, no directional conviction)
Final Score: Clamped to 40-99% range, classified as ELITE (>85%), STRONG (75-85%), GOOD (65-75%), or FAIR (<65%)
Entropy-Based Quality Filter
What Is Entropy?
Entropy measures randomness in price changes . Low entropy indicates orderly, directional moves. High entropy indicates chaotic, unpredictable conditions.
Calculation:
Count up/down price changes over adaptive period
Calculate probability: p = ups / total_changes
Shannon entropy: -p×log(p) - (1-p)×log(1-p)
Normalized to 0-1 range
Application:
Entropy <0.5: Highly ordered (ELITE signals possible)
Entropy 0.5-0.75: Mixed (GOOD signals)
Entropy >0.85: Chaotic (signals blocked or heavily penalized)
Why This Matters:
Prevents trading during choppy, news-driven conditions where technical patterns may be less reliable. Automatically raises quality bar when market is unpredictable.
Regime Detection & Market Microstructure - ADX-Based Regime Classification
RPD uses Wilder's Average Directional Index to classify markets:
Bull Trend: ADX >25, +DI > -DI (directional conviction bullish)
Bear Trend: ADX >25, +DI < -DI (directional conviction bearish)
Consolidation: ADX <20 (no directional conviction)
Transitional: ADX 20-25 (forming direction, ambiguous)
Filter Logic:
Blocks all signals during Transitional regime (avoids trading during uncertain conditions)
Blocks Consolidation signals unless ADX ≥ Min Trend Strength
Adds probability bonus during strong trends (ADX >30)
Effect: Designed to reduce signal frequency while focusing on higher-quality setups.
Divergence Detection
Bearish Divergence:
Price makes higher high
Velocity (price momentum) makes lower high
Indicates weakening upward pressure → SHORT signal quality boost
Bullish Divergence:
Price makes lower low
Velocity makes higher low
Indicates weakening downward pressure → LONG signal quality boost
Bonus: Adds probability points and additional acceleration factor. Divergence signals have historically shown higher success rates in testing.
Hierarchical Policy System - The Six Trading Policies
1. AggressiveTrend (Policy 0):
Probability Threshold: 60% (trades more frequently)
Entropy Threshold: 0.70 (tolerates moderate chaos)
Stop Multiplier: 2.5× ATR (wider stops for trends)
Target Multiplier: 5.0R (larger targets)
Entry Mode: Pyramid (scales into winners)
Best For: Strong trending markets, breakouts, momentum continuation
2. ConservativeRange (Policy 1):
Probability Threshold: 75% (more selective)
Entropy Threshold: 0.60 (requires order)
Stop Multiplier: 1.8× ATR (tighter stops)
Target Multiplier: 3.0R (modest targets)
Entry Mode: Single (one-shot entries)
Best For: Range-bound markets, low volatility, mean reversion
3. VolatilityBreakout (Policy 2):
Probability Threshold: 65% (moderate)
Entropy Threshold: 0.80 (accepts high entropy)
Stop Multiplier: 3.0× ATR (wider stops)
Target Multiplier: 6.0R (larger targets)
Entry Mode: Tiered (splits entry)
Best For: Compression breakouts, post-consolidation moves, gap opens
4. EntropyScalp (Policy 3):
Probability Threshold: 80% (very selective)
Entropy Threshold: 0.40 (requires extreme order)
Stop Multiplier: 1.5× ATR (tightest stops)
Target Multiplier: 2.5R (quick targets)
Entry Mode: Single
Best For: Low-volatility grinding moves, tight ranges, highly predictable patterns
5. DivergenceHunter (Policy 4):
Probability Threshold: 70% (quality-focused)
Entropy Threshold: 0.65 (balanced)
Stop Multiplier: 2.2× ATR (moderate stops)
Target Multiplier: 4.5R (balanced targets)
Entry Mode: Tiered
Best For: Divergence-confirmed reversals, exhaustion moves, trend climax
6. AdaptiveBlend (Policy 5):
Probability Threshold: 68% (balanced)
Entropy Threshold: 0.75 (balanced)
Stop Multiplier: 2.0× ATR (standard)
Target Multiplier: 4.0R (standard)
Entry Mode: Single
Best For: Mixed conditions, general trading, fallback when no clear regime
Policy Clustering (Advanced/Extreme Modes)
Policies are grouped into three clusters based on regime affinity:
Cluster 1 (Trending): AggressiveTrend, DivergenceHunter
High regime affinity (0.8): Performs well when ADX >25
Moderate vol affinity (0.6): Works in various volatility
Cluster 2 (Ranging): ConservativeRange, AdaptiveBlend
Low regime affinity (0.3): Better suited for ADX <20
Low vol affinity (0.4): Optimized for calm markets
Cluster 3 (Breakout): VolatilityBreakout
Moderate regime affinity (0.6): Works in multiple regimes
High vol affinity (0.9): Requires high volatility for optimal characteristics
Hierarchical Selection Process:
Calculate cluster scores based on current regime and volatility
Select best-matching cluster
Run UCB selection within chosen cluster
Apply momentum boost/penalty
This two-stage process reduces learning time - instead of choosing among 6 policies from scratch, system first narrows to 1-2 policies per cluster, then optimizes within cluster.
Risk Management & Position Sizing
Dynamic Kelly Criterion Sizing (Optional)
Traditional Fixed Sizing Challenge:
Using the same position size for all signal probabilities may be suboptimal. Higher-probability signals could justify larger positions, lower-probability signals smaller positions.
Kelly Formula:
f = (p × b - q) / b
Where:
p = win probability (from signal score)
q = loss probability (1 - p)
b = win/loss ratio (average_win / average_loss)
f = fraction of capital to risk
RPD Implementation:
Uses Fractional Kelly (1/4 Kelly default) for safety. Full Kelly is theoretically optimal but can recommend large position sizes. Fractional Kelly reduces volatility while maintaining adaptive sizing benefits.
Enhancements:
Probability Bonus: Normalize(prob, 65, 95) × 0.5 multiplier
Divergence Bonus: Additional sizing on divergence signals
Regime Bonus: Additional sizing during strong trends (ADX >30)
Momentum Adjustment: Hot policies receive sizing boost, cold policies receive reduction
Safety Rails:
Minimum: 1 contract (floor)
Maximum: User-defined cap (default 10 contracts)
Portfolio Heat: Max total risk across all positions (default 4% equity)
Multi-Mode Stop Loss System
ATR Mode (Default):
Stop = entry ± (ATR × base_mult × policy_mult)
Consistent risk sizing
Ignores market structure
Best for: Futures, forex, algorithmic trading
Structural Mode:
Finds swing low (long) or high (short) over last 20 bars
Identifies fractal pivots within lookback
Places stop below/above structure + buffer (0.1× ATR)
Best for: Stocks, instruments that respect structure
Hybrid Mode (Intelligent):
Attempts structural stop first
Falls back to ATR if:
Structural level is invalid (beyond entry)
Structural stop >2× ATR away (too wide)
Best for: Mixed instruments, adaptability
Dynamic Adjustments:
Breakeven: Move stop to entry + 1 tick after 1.0R profit
Trailing: Trail stop 0.8R behind price after 1.5R profit
Timeout: Force close after 30 bars (optional)
Tiered Entry System
Challenge: Equal sizing on all signals may not optimize capital allocation relative to signal quality.
Solution:
Tier 1 (40% of size): Enters immediately on all signals
Tier 2 (60% of size): Enters only if probability ≥ Tier 2 trigger (default 75%)
Example:
Calculated optimal size: 10 contracts
Signal probability: 72%
Tier 2 trigger: 75%
Result: Enter 4 contracts only (Tier 1)
Same signal at 80% probability
Result: Enter 10 contracts (4 Tier 1 + 6 Tier 2)
Effect: Automatically scales size to signal quality, optimizing capital allocation.
Performance Optimization & Learning Curve
Warmup Phase (First 50 Trades)
Purpose: Ensure all policies get tested before system focuses on preferred strategies.
Modifications During Warmup:
Probability thresholds reduced 20% (65% becomes 52%)
Entropy thresholds increased 20% (more permissive)
Exploration rate stays high (30%)
Confidence width (α) doubled (more exploration)
Why This Matters:
Without warmup, system might commit to early-performing policy without testing alternatives. Warmup forces thorough exploration before focusing on best-performing strategies.
Curriculum Learning
Phase 1 (Trades 1-50): Exploration
Warmup active
All policies tested
High exploration (30%)
Learning fundamental patterns
Phase 2 (Trades 50-100): Refinement
Warmup ended, thresholds normalize
Exploration decaying (30% → 15%)
Policy preferences emerging
Meta-learning optimizing
Phase 3 (Trades 100-200): Specialization
Exploration low (15% → 8%)
Clear policy preferences established
Momentum tracking fully active
System focusing on learned patterns
Phase 4 (Trades 200+): Maturity
Exploration minimal (8% → 5%)
Regime-policy relationships learned
Auto-adaptation to market shifts
Stable performance expected
Convergence Indicators
System is learning well when:
Policy switch rate decreasing over time (initially ~50%, should drop to <20%)
Exploration rate decaying smoothly (30% → 5%)
One or two policies emerge with >50% selection frequency
Performance metrics stabilizing over time
Consistent behavior in similar market conditions
System may need adjustment when:
Policy switch rate >40% after 100 trades (excessive exploration)
Exploration rate not decaying (parameter issue)
All policies showing similar selection (not differentiating)
Performance declining despite relaxed thresholds (underlying signal issue)
Highly erratic behavior after learning phase
Advanced Features
Attention Mechanism (Extreme Mode)
Challenge: Not all features are equally important. Trading hour might matter more than price-volume correlation, but standard approaches treat them equally.
Solution:
Each RFF dimension has an importance weight . After each trade:
Calculate correlation: sign(feature - 0.5) × sign(reward)
Update importance: importance += correlation × 0.01
Clamp to range
Effect: Important features get amplified in RFF transformation, less important features get suppressed. System learns which features correlate with successful outcomes.
Temporal Context (Extreme Mode)
Challenge: Current market state alone may be incomplete. Historical context (was volatility rising or falling?) provides additional information.
Solution:
Includes 3-period historical context with exponential decay (0.85):
Current features (weight 1.0)
1 bar ago (weight 0.85)
2 bars ago (weight 0.72)
Effect: Captures momentum and acceleration of market features. System learns patterns like "rising volatility with falling entropy" that may precede significant moves.
Transfer Learning via Episodic Memory
Short-Term Memory (STM):
Last 20 trades
Fast adaptation to immediate regime
High learning rate
Long-Term Memory (LTM):
Condensed historical patterns
Preserved knowledge from past regimes
Low learning rate
Transfer Mechanism:
When STM fills (20 trades), patterns consolidated into LTM . When similar regime recurs later, LTM provides faster adaptation than starting from scratch.
Practical Implementation Guide - Recommended Settings by Instrument
Futures (ES, NQ, CL):
Adaptive Period: 20-25
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.5%
Stop Mode: ATR or Hybrid
Timeframe: 5-15 min
Forex Majors (EURUSD, GBPUSD):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.0-1.5%
Stop Mode: ATR
Timeframe: 5-30 min
Cryptocurrency (BTC, ETH):
Adaptive Period: 20-25
ML Mode: Extreme (handles non-stationarity)
RFF Dimensions: 32 (captures complexity)
Policies: 6
Base Risk: 1.0% (volatility consideration)
Stop Mode: Hybrid
Timeframe: 15 min - 4 hr
Stocks (Large Cap):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 5-6
Base Risk: 1.5-2.0%
Stop Mode: Structural or Hybrid
Timeframe: 15 min - Daily
Scaling Strategy
Phase 1 (Testing - First 50 Trades):
Max Contracts: 1-2
Goal: Validate system on your instrument
Monitor: Performance stabilization, learning progress
Phase 2 (Validation - Trades 50-100):
Max Contracts: 2-3
Goal: Confirm learning convergence
Monitor: Policy stability, exploration decay
Phase 3 (Scaling - Trades 100-200):
Max Contracts: 3-5
Enable: Kelly sizing (1/4 Kelly)
Goal: Optimize capital efficiency
Monitor: Risk-adjusted returns
Phase 4 (Full Deployment - Trades 200+):
Max Contracts: 5-10
Enable: Full momentum tracking
Goal: Sustained consistent performance
Monitor: Ongoing adaptation quality
Limitations & Disclaimers
Statistical Limitations
Learning Sample Size: System requires minimum 50-100 trades for basic convergence, 200+ trades for robust learning. Early performance (first 50 trades) may not reflect mature system behavior.
Non-Stationarity Risk: Markets change over time. A system trained on one market regime may need time to adapt when conditions shift (typically 30-50 trades for adjustment).
Overfitting Possibility: With 16-32 RFF dimensions and 6 policies, system has substantial parameter space. Small sample sizes (<200 trades) increase overfitting risk. Mitigated by regularization (λ) and fractional Kelly sizing.
Technical Limitations
Computational Complexity: Extreme mode with 32 RFF dimensions, 6 policies, and full RL stack requires significant computation. May perform slowly on lower-end systems or with many other indicators loaded.
Pine Script Constraints:
No true matrix inversion (uses diagonal approximation for LinUCB)
No cryptographic RNG (uses market data as entropy)
No proper random number generation for RFF (uses deterministic pseudo-random)
These approximations reduce mathematical precision compared to academic implementations but remain functional for trading applications.
Data Requirements: Needs clean OHLCV data. Missing bars, gaps, or low liquidity (<100k daily volume) can degrade signal quality.
Forward-Looking Bias Disclaimer
Reward Calculation Uses Future Data: The RL system evaluates trades using an 8-bar forward-looking window. This means when a position enters at bar 100, the reward calculation considers price movement through bar 108.
Why This is Disclosed:
Entry signals do NOT look ahead - decisions use only data up to entry bar
Forward data used for learning only, not signal generation
In live trading, system learns identically as bars unfold in real-time
Simulates natural learning process (outcomes are only known after trades complete)
Implication: Backtested metrics reflect this 8-bar evaluation window. Live performance may vary if:
- Positions held longer than 8 bars
- Slippage/commissions differ from backtest settings
- Market microstructure changes (wider spreads, different execution quality)
Risk Warnings
No Guarantee of Profit: All trading involves substantial risk of loss. Machine learning systems can fail if market structure fundamentally changes or during unprecedented events.
Maximum Drawdown: With 1.5% base risk and 4% max total risk, expect potential drawdowns. Historical drawdowns do not predict future drawdowns. Extreme market conditions can exceed expectations.
Black Swan Events: System has not been tested under: flash crashes, trading halts, circuit breakers, major geopolitical shocks, or other extreme events. Such events can exceed stop losses and cause significant losses.
Leverage Risk: Futures and forex involve leverage. Adverse moves combined with leverage can result in losses exceeding initial investment. Use appropriate position sizing for your risk tolerance.
System Failures: Code bugs, broker API failures, internet outages, or exchange issues can prevent proper execution. Always monitor automated systems and maintain appropriate safeguards.
Appropriate Use
This System Is:
✅ A machine learning framework for adaptive strategy selection
✅ A signal generation system with probabilistic scoring
✅ A risk management system with dynamic sizing
✅ A learning system designed to adapt over time
This System Is NOT:
❌ A price prediction system (does not forecast exact prices)
❌ A guarantee of profits (can and will experience losses)
❌ A replacement for due diligence (requires monitoring and understanding)
❌ Suitable for complete beginners (requires understanding of ML concepts, risk management, and trading fundamentals)
Recommended Use:
Paper trade for 100 signals before risking capital
Start with minimal position sizing (1-2 contracts) regardless of calculated size
Monitor learning progress via dashboard
Scale gradually over several months only after consistent results
Combine with fundamental analysis and broader market context
Set account-level risk limits (e.g., maximum drawdown threshold)
Never risk more than you can afford to lose
What Makes This System Different
RPD implements academically-derived machine learning algorithms rather than simple mathematical calculations or optimization:
✅ LinUCB Contextual Bandits - Algorithm from WWW 2010 conference (Li et al.)
✅ Random Fourier Features - Kernel approximation from NIPS 2007 (Rahimi & Recht)
✅ Q-Learning, TD(λ), REINFORCE - Standard RL algorithms from Sutton & Barto textbook
✅ Meta-Learning - Learning rate adaptation based on feature correlation
✅ Online Learning - Real-time updates from streaming data
✅ Hierarchical Policies - Two-stage selection with clustering
✅ Momentum Tracking - Recent performance analysis for faster adaptation
✅ Attention Mechanism - Feature importance weighting
✅ Transfer Learning - Episodic memory consolidation
Key Differentiators:
Actually learns from trade outcomes (not just parameter optimization)
Updates model parameters in real-time (true online learning)
Adapts to changing market regimes (not static rules)
Improves over time through reinforcement learning
Implements published ML algorithms with proper citations
Conclusion
RPD Machine Learning represents a different approach from traditional technical analysis to adaptive, self-learning systems . Instead of manually optimizing parameters (which can overfit to historical data), RPD learns behavior patterns from actual trading outcomes in your specific market.
The combination of contextual bandits, reinforcement learning, random fourier features, hierarchical policy selection, and momentum tracking creates a multi-algorithm learning system designed to handle non-stationary markets better than static approaches.
After the initial learning phase (50-100 trades), the system achieves autonomous adaptation - automatically discovering which strategies work in current conditions and shifting allocation without human intervention. This represents an approach where systems adapt over time rather than remaining static.
Use responsibly. Paper trade extensively. Scale gradually. Understand that past performance does not guarantee future results and all trading involves risk of loss.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Any Strategy BacktestA simple script for backtesting your strategies with TP and SL settings. For this to work, your indicators must have sources for long and short conditions.
Adaptive ATR Guardian PRO+ (Locked Lines)🎯 核心交易功能 / Core Trading Features
1. 智能参数配置系统 / Intelligent Parameter Configuration
多风格选择：稳健/激进/保守三种交易风格
Multi-style Selection: Conservative/Aggressive/Moderate trading styles
多时间周期：M5/M15/H1三种时间框架
Multi-timeframe: M5/M15/H1 timeframes
自适应参数：根据风格自动调整所有技术参数
Adaptive Parameters: Automatically adjusts all technical parameters based on style
2. 高级信号生成系统 / Advanced Signal Generation
双均线策略：快慢EMA交叉信号
Dual MA Strategy: Fast/Slow EMA crossover signals
趋势过滤：100周期EMA作为趋势方向过滤
Trend Filter: 100-period EMA for trend direction filtering
ADX强度确认：ADX > 最小值才确认趋势有效
ADX Strength Confirmation: ADX > minimum value for valid trend
交易时段控制：可设置交易开始和结束时间
Trading Session Control: Configurable start and end times
3. 智能风险管理 / Intelligent Risk Management
动态止损：基于ATR的智能止损计算
Dynamic Stop Loss: ATR-based intelligent stop loss calculation
分批止盈：TP1平仓50%，TP2平仓剩余50%
Partial Take Profit: TP1 closes 50%, TP2 closes remaining 50%
追踪止损：TP2部分启用追踪止损功能
Trailing Stop: TP2 portion uses trailing stop functionality
品种自适应：BTC和黄金品种特殊参数调整
Symbol Adaptation: Special parameter adjustments for BTC and Gold
4. 专业订单管理 / Professional Order Management
自动平仓：新信号自动平掉反向仓位
Auto Close: New signals automatically close opposite positions
仓位管理：基于账户权益的百分比仓位
Position Management: Percentage-based position sizing
佣金计算：包含交易佣金成本
Commission Calculation: Includes trading commission costs
📊 高级可视化功能 / Advanced Visualization Features
1. 实时交易线系统 / Real-time Trading Lines System
入场线：蓝色虚线，显示入场价格
Entry Line: Blue dashed line showing entry price
止损线：红色实线，显示止损价格
Stop Loss Line: Red solid line showing stop loss price
TP1线：青色实线，显示第一目标位
TP1 Line: Teal solid line showing first target
TP2线：青色实线，显示第二目标位
TP2 Line: Teal solid line showing second target
2. 智能标签管理 / Intelligent Label Management
动态字号：根据时间周期自动调整标签大小
Dynamic Font Size: Auto-adjusts label size based on timeframe
位置优化：标签固定在入场K线右侧3根位置
Position Optimization: Labels fixed 3 bars right of entry candle
实时更新：线条和标签随图表滚动延伸
Real-time Updates: Lines and labels extend with chart scrolling
3. 专业信息面板 / Professional Information Panel
策略状态：交易风格、时间周期、持仓方向
Strategy Status: Trading style, timeframe, position direction
指标数据：ADX强度、ATR波动率数值
Indicator Data: ADX strength, ATR volatility values
交易信息：入场价格、止损价格、止盈价格
Trade Information: Entry price, stop loss, take profit prices
实时更新：每根K线更新最新数据
Real-time Updates: Updates data on every candle
4. 模式状态标签 / Mode Status Label
顶部状态栏：显示周期、风格、ADX、ATR、持仓状态
Top Status Bar: Shows timeframe, style, ADX, ATR, position status
颜色编码：蓝色主题，专业视觉效果
Color Coding: Blue theme, professional visual appearance
⚙️ 技术特色功能 / Technical Special Features
1. 自适应波动率调整 / Adaptive Volatility Adjustment
ATR基准：基于14周期ATR计算
ATR Baseline: Based on 14-period ATR calculation
波动率调整：ATR相对于50周期均线的调整系数
Volatility Adjustment: ATR adjustment coefficient relative to 50-period MA
动态止盈：止盈距离根据波动率动态调整
Dynamic Take Profit: TP distances dynamically adjusted based on volatility
2. 多品种优化 / Multi-Symbol Optimization
BTC特殊处理：更大的止损倍数和TP2倍数
BTC Special Handling: Larger stop loss and TP2 multipliers
黄金特殊处理：适中的参数调整
Gold Special Handling: Moderate parameter adjustments
通用品种：标准参数适用于其他品种
General Symbols: Standard parameters for other symbols
3. 时间智能控制 / Intelligent Time Control
交易时段：可配置的交易时间窗口
Trading Sessions: Configurable trading time windows
时段逻辑：支持跨午夜的时间段设置
Session Logic: Supports cross-midnight time periods
时间过滤：只在交易时段内产生信号
Time Filtering: Only generates signals during trading hours
4. 内存管理优化 / Memory Management Optimization
自动清理：平仓时自动删除所有线条和标签
Auto Cleanup: Automatically deletes all lines and labels on position close
资源回收：避免图表元素堆积
Resource Recycling: Prevents chart element accumulation
性能优化：高效的实时更新机制
Performance Optimization: Efficient real-time update mechanism
🛡️ 风险控制功能 / Risk Control Features
1. 多层过滤系统 / Multi-layer Filtering System
趋势方向过滤 / Trend direction filtering
ADX强度过滤 / ADX strength filtering
交易时间过滤 / Trading time filtering
品种特性过滤 / Symbol characteristic filtering
2. 动态参数系统 / Dynamic Parameter System
快慢均线周期自适应 / Fast/slow MA period adaptation
止损倍数动态调整 / Stop loss multiplier dynamic adjustment
止盈倍数风格化配置 / Take profit multiplier style-based configuration
追踪止损灵敏度设置 / Trailing stop sensitivity settings
3. 资金管理 / Money Management
固定百分比仓位 / Fixed percentage position sizing
佣金成本计入 / Commission costs included
无金字塔加仓 / No pyramiding (no adding to positions)
自动反向平仓 / Automatic opposite position closing
📈 用户体验功能 / User Experience Features
1. 可视化定制 / Visualization Customization
交易线显示/隐藏开关 / Trading lines show/hide toggle
信息面板显示控制 / Information panel display control
线条延伸长度可调 / Line extension length adjustable
颜色方案统一管理 / Color scheme unified management
2. 实时监控 / Real-time Monitoring
持仓状态实时显示 / Real-time position status display
关键价格水平标记 / Key price level markings
指标数值动态更新 / Indicator values dynamic updates
交易统计信息 / Trading statistics information
3. 专业布局 / Professional Layout
右上角信息面板 / Top-right information panel
顶部状态标签 / Top status label
图表交易线条 / Chart trading lines
整洁的视觉层次 / Clean visual hierarchy
Vandan V2Vandan V2 is an automated trading strategy for NQ1! (E-mini Nasdaq-100) based on short-term mean reversion with dynamic risk control. It combines volatility filters and overbought/oversold signals to capture local market imbalances.
Backtested from 2015 to 2025, it achieved a +730% total return, Profit Factor of 1.40, max drawdown of only 1.61%, and over 106,000 trades. Designed for systematic scalping or intraday arbitrage with a limit of 3 simultaneous contracts.
CEO Synapse v1.0CEO Synapse — Uyarlanabilir Rejim Stratejisi
This script is invite-only.
What Does This Strategy Do?
Markets are complex systems requiring various expertise. The "CEO Synapse" strategy adopts a "digital dashboard" approach based on the reality that a single viewpoint is insufficient. The strategy combines multiple analytical engines, each developed by me, analyzing different aspects of the market (structure, momentum, rhythm). It detects trend and momentum deviations in markets. A trading decision is made only when there is consensus among these expert engines. The "Synapse Engine" uses adaptive filtering and consensus logic for position management based on market regime (trend/range).
It eliminates the problem of traditional indicators generating misleading signals alone and failing to adapt to volatility and regime changes. Its dynamic threshold mechanism, adaptive periods, and special noise filters reduce unnecessary trades.
Original Methodology and Proprietary Logic: This algorithm does not rely on or copy any open source strategy code. The system uses commonly accepted indicators' mathematical principles such as ADX, EMA, SMA, ATR, True Range, etc., as data sources. The author's methodology combines dynamic period EMA, multi-filter consensus, adaptive threshold, and regime-based execution.
Though our strategy creates an original decision-making mechanism, it leverages foundational building blocks of technical analysis. The traditional indicators we use and their purposes are:
ADX (Average Directional Index): This indicator measures a trend’s strength, not its direction. Our strategy uses ADX as a filter to open positions only under sufficiently strong and distinct trend market conditions. This largely prevents misleading signals in weak or sideways markets.
Moving Averages (EMA and SMA): They form the backbone to determine the main trend direction. By smoothing price data, they reduce noise and reveal the market's general trend. But our strategy processes their outputs not as traditional crossover signals, but as input to an advanced consensus logic with dynamically adjusted periods based on market rhythm combined with other filters.
ATR (Average True Range): This indicator does not produce direct buy-sell signals but measures current market volatility. Especially in "Sideways Market" regime, take profit and stop loss levels are dynamically set based on ATR instead of fixed values, enabling risk management to adapt to market conditions.
Bollinger Band Logic (using Standard Deviation): Though the strategy does not plot Bollinger Bands directly, it uses Standard Deviation, the underlying mathematical concept, to detect excessive price deviations and volatility spikes, producing critical signals for the AMF PG core engine.
"Synapse Engine" consists of two layers: Decision Center (Dynamic Threshold) which automatically adjusts risk appetite based on performance and regime; and Filter Committee (Consensus Score) which weights separate filters to produce a single score. This combination is not reproducible and commercially valuable. Closed source is mandatory.
No classic open source code used. Only publicly available indicators are used. Parameters, order, and usage are fully customized.
Generated Signals: Trend/range entry/exit (long/short), adaptive trailing stop position management, additional risk control signals with Shock Absorber and Quantum Filter.
Purpose: Detect trend breaks and momentum deviations. Components: Volatility filters, adaptive signal weighting, EMA/SMA. Methodology: Combines price and volume change rates via dynamic weighting functions.
What Problem Does CEO Synapse Solve?
CEO Synapse addresses three main issues caused by traditional technical analysis and single indicator usage:
Problem: Misleading Signals and Market Noise
Traditional indicators (MACD, RSI, etc.) generate many "false" buy-sell signals, especially in sideways and choppy markets, causing traders to constantly enter and exit positions (whipsaw) and incur losses.
CEO Synapse Solution: The strategy never relies on a single signal. The Consensus-Based Decision Mechanism ensures no position is opened unless different analytical engines (structural, momentum, rhythm) agree. This "board of directors" approach filters market noise, processing only high-probability signals.
Problem: Static Analysis and Changing Market Conditions
Markets constantly change character; sometimes strong trend, sometimes narrow range. Most strategies try to function with fixed parameters across all conditions, leading to failure.
CEO Synapse Solution: The strategy has Adaptive Regime Switching. It actively analyzes whether the market is in "Trend Mode" or "Sideways Market Mode" and automatically adjusts entry/exit rules and risk management (take profit/stop loss) to the current regime, allowing chameleon-like adaptation to conditions.
Problem: Fixed Parameters and Declining Performance
Many traders believe they find the "best" settings and never change them for months or years. But as market volatility and cycles change, fixed settings lose effectiveness.
CEO Synapse Solution: The strategy operates on Full Adaptation principle.
Market Rhythm Adaptation: Dynamically adjusts analysis speed (e.g., EMA periods) according to market’s natural cycles.
Performance Adaptation: Continuously optimizes risk appetite (signal threshold) based on recent strategy performance, becoming bolder with gains and more cautious with losses.
In summary, CEO Synapse simplifies decision-making, eliminates market noise, and smartly adapts to changing market conditions, protecting the user from common mistakes.
Why "Invite-Only"?
Offering CEO Synapse as "Invite-Only" is a strategic decision to protect the strategy's commercial value and intellectual property and to provide users with the highest quality experience. Key reasons:
Protection of Proprietary IP:
CEO Synapse is the result of hundreds of hours of research, development, and testing. Its consensus logic, adaptive threshold mechanism, and engine integration are unique and patented. Open sourcing it would instantly destroy this trade secret and competitive edge.
Maintaining Performance Integrity and Effectiveness:
Uncontrolled distribution could lead to misuse or signal theft and sale by malicious actors. The invite-only model preserves the strategy’s integrity and ensures access only for serious investors.
Quality User Experience and Support:
Controlled distribution allows better user experience. High-quality documentation explaining features and best practices can be provided, and future updates and support services can be managed better for a limited user base.
Business Model:
CEO Synapse is positioned as a premium analysis tool. Invite-only access reflects its value and compensates the developer for ongoing maintenance, support, and future improvements.
Usage: Available on all timeframes.
Based entirely on my own adaptive filtering methodology.
Proprietary logic: The algorithm’s unique, non-reproducible logic and methodology. Example: Multi-filter consensus + adaptive threshold + regime-based execution.
Why Is This a Premium Tool?
"CEO Synapse"’s value stems from being a proprietary, integrated system beyond free standard indicators:
Advanced Noise Filtering: Not just reduces noise but adjusts filter sensitivity to current market character. Inspired by public mathematical concepts (cycle analysis, statistical filtering) but uniquely combined with proprietary weighting mechanisms and adaptive consensus logic forming the strategy's commercial value. Core indicators (EMA, ATR, ADX, DMI, etc.) are uniquely processed inside this proprietary system.
Full Adaptation: Instead of fixed parameters, the strategy continuously adapts to the market's natural rhythm, volatility, and past performance.
Consensus-Based Decision Making: Relies on collective intelligence of multiple analytical engines, not a single failure point.
These features substantially increase the ability to extract meaningful, actionable insights from raw market data, making it premium. It improves signal accuracy, reduces risk, and adapts to regime shifts. The dynamic threshold mechanism continuously adjusts risk appetite based on recent performance (profitability) and market regime.
By using this script, you agree not to redistribute, sell, or reverse engineer the source code.
This strategy is for educational purposes only. Past performance does not guarantee future results. Always apply proper risk management and protect your capital.
Risk Management: Maximum Drawdown Protection
The strategy includes a built-in capital protection mechanism. Users can specify the percentage drop from peak capital they tolerate. If the capital hits this drawdown limit, protection activates, closing all open positions and blocking new trades, acting as an emergency brake to guard capital against unexpected market conditions.
Automation Ready: Customizable Webhook Alerts
Fully Compatible Automation (JSON): The strategy outputs fully configurable JSON-formatted alert messages for buy, sell, and close actions. This allows connecting CEO Synapse signals to automation platforms like 3Commas and PineConnector for fully automated trading. Dynamic values like position size ({{strategy.order.contracts}}) are automatically included in alerts.
Strategy Backtest Information
Please remember past performance is not indicative of future results. The published chart and report are based on the BTCUSD pair in a 3-hour timeframe with the following settings:
Test Period: January 1, 2018 – November 3, 2025
Default Position Size: 15% of capital
Pyramiding: Off
Commission: 0.0008
Slippage: 2 ticks
Test Approach: The published test contains 201 trades and is statistically significant. Performing your own tests on different assets and timeframes is strongly recommended. Default settings are a template and should be adjusted per your analysis.
High Accuracy Engulfing Strategy [PIPNEXUS]Title: EMA Engulfing Setup
Description:
This indicator focuses on identifying strong engulfing patterns that form around the EMA line, helping traders catch high-probability moves in line with market direction.
Concept Overview:
The idea is simple — when both the engulfing candle and the candle being engulfed have their bodies touching the EMA line, it often represents a key point of rejection or continuation. These areas can produce clean entries with strong momentum.
How to Use:
1. Wait for a valid engulfing formation near the EMA line.
Both the engulfing and the engulfed candles should have their bodies touching the EMA.
2. Enter in the direction of the engulfing candle once the pattern is confirmed.
3. For pinpoint entries, observe the market during session changes (especially in the first 3–5 minutes after a session opens).
4. For longer and more stable trades, look for the same pattern on 15-minute or 1-hour charts.
5. Always align your trades with the prevailing market structure and avoid counter-trend setups.
Note:
This indicator is designed for technical and educational use. It does not generate buy or sell signals automatically, nor does it guarantee performance. Use it alongside your own market analysis and proper risk management.
Vandan V2Vandan V2 is an automated trend-following strategy for NASDAQ E-mini Futures (NQ1!).
It uses multi-timeframe momentum and volatility filters to identify high-probability entries.
Includes dynamic risk management and trailing logic optimized for intraday trading.
Basic DCA Strategy by Wongsakon KhaisaengThe Core Principle and Philosophy Behind the Basic DCA Strategy
1. Introduction
The Basic DCA Strategy (Dollar-Cost Averaging) represents one of the most fundamental and enduring investment methodologies in the realm of systematic accumulation. The philosophy underpinning DCA is rooted not in speculation or prediction, but in disciplined participation. It assumes that the consistent act of investing a fixed amount of capital over time—regardless of short-term price volatility—can yield superior long-term outcomes through the natural smoothing effect of cost averaging.
This strategy, expressed through the Pine Script code above, formalizes the DCA concept into a fully systematic trading framework, enabling quantitative backtesting and objective evaluation of long-term accumulation efficiency.
2. Mechanism of Operation
At its technical core, the strategy executes a fixed-value buy order at every predefined interval within a specific accumulation period.
Each DCA event invests a constant “Investment Amount (USD)” irrespective of price fluctuations. When prices decline, this constant investment buys a larger quantity of the asset; when prices rise, it purchases fewer units. Over time, this behavior lowers the average cost basis of the accumulated position, effectively neutralizing short-term timing risks.
Mathematically, this is represented as:
Units Purchased = Investment Amount / Closing Price
Cost Basis = Total Invested USD / Total Units Acquired
Portfolio Value = Total Units Acquired × Current Price
The algorithm tracks cumulative investment, acquired units, and commissions dynamically, continuously recalculating key portfolio metrics such as total profit/loss (PnL), CAGR (Compound Annual Growth Rate), and maximum drawdown (peak-to-trough equity decline).
Furthermore, the script juxtaposes DCA results with a Buy & Hold benchmark, where the entire initial capital is invested at once. This comparison highlights the behavioral resilience and volatility resistance of the DCA method relative to market-timing strategies.
3. The Essence of DCA Philosophy
At its philosophical core, DCA is not a trading system, but a behavioral framework for rational capital deployment under uncertainty. It embodies the principle that time in the market often outweighs timing the market.
The DCA approach rejects the illusion of precision forecasting and embraces probabilistic humility—the recognition that even the most skilled investors cannot consistently predict short-term market fluctuations. Instead, it focuses on controlling what is controllable: the frequency, consistency, and size of investment actions.
This mindset reflects a broader principle of risk dispersion through temporal diversification. Rather than concentrating entry risk into a single price point (as in lump-sum investing), DCA spreads exposure across multiple time intervals, thereby converting volatility into opportunity.
In essence, volatility—often perceived as risk—is reframed as a mechanism for mean reversion advantage. The strategy thrives precisely because markets oscillate; each fluctuation provides a chance to accumulate at varied price levels, improving the weighted-average entry over time.
4. Long-Term Rationality Over Short-Term Emotion
DCA’s endurance stems from its ability to neutralize emotional biases inherent in human decision-making. Investors tend to overreact to market euphoria or panic—buying high out of greed and selling low out of fear. By automating purchases through predefined intervals, the DCA model enforces mechanical discipline, detaching decision-making from sentiment.
This transforms investing from an emotional endeavor into a systematic, algorithmic routine governed by rules rather than reactions. In doing so, DCA serves not only as a financial model but also as a psychological safeguard—aligning investor behavior with long-term compounding logic rather than short-term speculation.
5. Comparative Insight: DCA vs. Buy & Hold
While both DCA and Buy & Hold share a long-term investment horizon, they diverge in their treatment of entry timing. The Buy & Hold model assumes full deployment of capital at the beginning, maximizing exposure to growth but also to volatility. Conversely, DCA smooths the entry curve, trading off short-term returns for long-term stability and improved average entry price.
In environments characterized by volatility and cyclical corrections, DCA tends to outperform in terms of risk-adjusted returns, lower drawdowns, and improved investor adherence—since it reduces the psychological pain of entering at local peaks.
6. Conclusion
The Basic DCA Strategy exemplifies the synthesis of mathematical rigor and behavioral discipline. Its algorithmic construction in Pine Script transforms a classical investment philosophy into a quantifiable, testable, and transparent framework.
By automating fixed-amount purchases across time, the system operationalizes the central axiom of DCA: consistency over conviction. It is not concerned with predicting future prices but with ensuring persistent participation—trusting that the market’s upward bias and the power of compounding will reward patience more than precision.
Ultimately, DCA embodies the timeless principle that successful investing is less about forecasting markets, and more about designing behavior that can endure them.
