158 lines
2.9 KiB
Markdown
158 lines
2.9 KiB
Markdown
# MT5 XAUUSD LSTM PPO Trading Bot
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A MetaTrader 5 reinforcement learning trading bot for XAUUSD (Gold).
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The bot combines an LSTM neural network for sequence learning with Proximal Policy Optimization (PPO) to learn trading decisions directly from historical market data.
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Unlike traditional bots that rely on fixed rules, the PPO agent learns when to Buy, Sell or Hold from thousands of market examples.
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The program should be in a folder or the desktop where the 5m csv file with OHLC data from https://www.kaggle.com/datasets/novandraanugrah/xauusd-gold-price-historical-data-2004-2024 is, in training it will then make a LSTM-PPO-saves folder where the training is saved (used for making decisions, also in testing).
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---
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## Features
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### Reinforcement Learning
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- LSTM policy network
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- PPO training
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- Continuous online training
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- Live inference on MT5
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- Automatic checkpoint saving/loading
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### Technical Indicators
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- EMA 7
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- EMA 21
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- EMA Difference (Momentum)
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- ADX
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- +DI
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- -DI
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- Stochastic
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- VWAP
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- VWAP Bands
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- VWAP Slope
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- Volume Moving Average
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### Smart Money Concepts
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- Bullish Order Blocks
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- Bearish Order Blocks
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- Bullish Fair Value Gaps
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- Bearish Fair Value Gaps
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- Bullish Rejection Blocks
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- Bearish Rejection Blocks
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- Equal Highs
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- Equal Lows
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- Market Breaks
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- Indecision Candles
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### PPO State Features
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Current state contains:
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- OHLC
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- EMA trend
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- Momentum
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- ADX trend strength
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- DI Direction
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- Stochastic
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- VWAP
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- VWAP Bands
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- VWAP Position
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- VWAP Slope
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- Volume MA
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- Order Blocks
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- Fair Value Gaps
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- Rejection Blocks
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- Equal Highs/Lows
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- Buy Score
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- Sell Score
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---
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## Current Strategy
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Current reward structure:
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- Take Profit: 20 pips
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- Stop Loss: 40 pips
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- Risk/Reward: 1 : 0.5
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The current focus is high-probability momentum trades rather than large swing trades.
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---
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## Training
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The PPO agent trains continuously over historical MT5 data.
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Example metrics during training:
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- Win rate: 70–85%
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- Profit Factor: 1.5–3+
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- Weekly performance: typically 10–30R during training (varies by market conditions)
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These figures are training statistics only and are not guarantees of future performance.
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---
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## Requirements
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- Python 3.11+
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- MetaTrader 5
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- MetaTrader5
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- pandas
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- numpy
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- torch
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Install dependencies:
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```bash
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pip install MetaTrader5 pandas numpy torch
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```
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---
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## Running
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Train:
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```bash
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python mt5-xau-lstm-ppo-bot.py --train
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```
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Live trading:
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```bash
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python mt5-xau-lstm-ppo-bot.py --test
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```
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Train and trade simultaneously:
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```bash
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python mt5-xau-lstm-ppo-bot.py --train --test
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```
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---
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## Project Goals
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Current work focuses on:
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- Improving PPO policy learning
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- Better feature engineering
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- Dynamic trade management
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- VWAP and volume analysis
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- Smart Money Concept detection
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- MFE/MAE prediction research
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- Higher timeframe context
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---
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## Disclaimer
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This project is for educational and research purposes only.
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Trading leveraged products involves substantial risk. Always test thoroughly on historical data and demo accounts before risking real capital.
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