Vittus Mikiassen
5.3 KiB
xLSTM PPO Reinforcement Learning Trading Bot for MetaTrader 5 (XAUUSD)
An AI-powered trading bot that combines Extended LSTM (xLSTM) and Proximal Policy Optimization (PPO) to learn profitable trading strategies on XAUUSD.
Unlike traditional indicator-based Expert Advisors, this project learns directly from historical market data using reinforcement learning while incorporating Smart Money Concepts (SMC), technical indicators, market structure, and adaptive risk management.
Features
Reinforcement Learning
- xLSTM policy network
- Proximal Policy Optimization (PPO)
- Sequence-based learning
- Reinforcement learning trading environment
- Reward shaping
- GPU/CPU training support
Smart Money Concepts (SMC)
The bot automatically detects:
- Order Blocks (OB)
- Mitigated Order Blocks
- Breaker Blocks
- Mitigation Blocks (MB)
- Rejection Blocks (RB)
- Fair Value Gaps (FVG)
- Inverse Fair Value Gaps (IFVG)
- FVG Retracements
- Equal Highs (EQH)
- Equal Lows (EQL)
- Market Structure Shifts (MSS)
- Swing Highs
- Swing Lows
- Previous Day High (PDH)
- Previous Day Low (PDL)
- Asia High
- Asia Low
- Trend Lines
- Liquidity Sweeps
- BoS
- Trends
- CHoCHs
Technical Indicators
Included features include:
- EMA 7
- EMA 21
- EMA Difference
- EMA Slopes
- ADX
- +DI
- -DI
- Stochastic Oscillator
- VWAP
- VWAP Upper Band
- VWAP Lower Band
- Volume Moving Average
- Session Detection
- Indecision Candles
Adaptive Trade Management
The bot automatically manages:
- Adaptive Stop Loss
- Adaptive Take Profit
- Partial Profit Taking
- Break-even Movement
- Dynamic Position Sizing
- Risk-based Lot Calculation
Machine Learning Features
The model learns from more than 50 engineered features including:
- Smart Money Concepts
- Trend
- Momentum
- Liquidity
- Session information
- Price distances
- Volume
- Volatility
- Historical sequences
Trading Environment
The custom Gymnasium environment supports:
- Buy
- Sell
- Hold
Rewards consider:
- Profit
- Drawdown
- Risk
- Trade quality
- Spread
- Commission
Project Structure
.
├── bot.py # Main application
├── download/ # Historical CSV data
├── models/ # Saved models
├── logs/ # Training logs
├── scaler.pkl # Feature scaler
└── README.md
Installation
Clone the repository
git clone https://github.com/pressure679/LSTM-PPO-Reinforcement-Learning-Trading-Bot-for-MetaTrader-5-XAUUSD-
cd LSTM-PPO-Reinforcement-Learning-Trading-Bot-for-MetaTrader-5-XAUUSD-
Install dependencies
pip install -r requirements.txt
Requirements
- Python 3.11+
- MetaTrader 5
Python packages:
torch
stable-baselines3
sb3-contrib
gymnasium
MetaTrader5
numpy
pandas
scikit-learn
ta
xlstm
Historical Data
Place historical data inside the download/ folder.
Example:
download/
└── xauusd-m5-bid-2023-01-01-2026-06-01.csv
The bot automatically calculates all technical indicators and Smart Money Concept features before training.
Training
Train the reinforcement learning model:
python bot.py --train
Training automatically:
- Loads historical data
- Calculates technical indicators
- Builds SMC features
- Normalizes inputs
- Creates the trading environment
- Trains the PPO agent
- Saves the trained model
Live Trading
Run the live trading bot:
python bot.py --test
The bot will:
- Connect to MetaTrader 5
- Read live candles
- Generate feature vectors
- Predict actions
- Execute trades
- Manage open positions automatically
Feature List
Current feature set includes:
k
k_smooth
adx
+di
-di
EMA721_DIFF
EMA7_Slope
EMA21_Slope
indecision
bullish_ob
bearish_ob
bullish_fvg
bearish_fvg
bullish_ifvg
bearish_ifvg
eqh
eql
bullish_mb
bearish_mb
bullish_rb
bearish_rb
bullish_bb
bearish_bb
bullish_mss
bearish_mss
bullish_ob_mitigation
bearish_ob_mitigation
bullish_fvg_retracement
bearish_fvg_retracement
bullish
bearish
above_vwap
below_vwap
pdh
pdl
asia_high
asia_low
pdh_distance
pdl_distance
asia_high_distance
asia_low_distance
vwap
vwap_upper
vwap_lower
bullish_ifvg_retracement
bearish_ifvg_retracement
swing_high
swing_low
support_trend_line
resistance_trend_line
seq_len
session
Risk Management
Supports:
- Fixed percentage risk
- Dynamic lot sizing
- Adaptive stop losses
- Adaptive take profits
- Partial exits
- Break-even protection
- Maximum open positions
Training Statistics
The bot reports:
- Win Rate
- Profit Factor
- Sharpe Ratio
- Sortino Ratio
- Weekly Return
- Maximum Drawdown
- Mean Win
- Mean Loss
- Average R
- Trade Count
Roadmap
- Multi-symbol training
- Multi-timeframe observations
- Prioritized Experience Replay
- Hyperparameter optimization
- Walk-forward validation
- ONNX export
- Transformer/xLSTM hybrid policy
- Trade visualization dashboard
- Pattern similarity search
- Explainable AI trade analysis
Disclaimer
This project is intended for educational and research purposes only.
Trading financial markets involves substantial risk. Past performance does not guarantee future results.
Always test on a demo account before trading with real funds.
License
This project is licensed under the MIT License.