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.