profitable-expert-advisor/backtesting/MT5

MetaTrader5 Python Backtesting Framework

A comprehensive Python backtesting framework for algorithmic trading strategies using MetaTrader5 historical data.

Features

• Easy Strategy Development: Inherit from BaseStrategy and implement your trading logic
• MT5 Integration: Uses MetaTrader5 Python library for historical data and indicators
• Multiple Indicators: Built-in support for RSI, EMA, SMA, ATR, MACD, and more
• Risk Management: Built-in position sizing, stop loss, take profit, and drawdown protection
• Performance Analysis: Comprehensive metrics and visualization tools
• Example Strategies: Ready-to-use example strategies (RSI Scalping, EMA Crossover, RSI Reversal)

Installation

1. Install MetaTrader5: Make sure you have MetaTrader5 installed on your system.

2. Install Python dependencies:

pip install -r requirements.txt

3. Initialize MT5 Connection: The framework will automatically connect to MT5 when you run a backtest. Make sure MT5 is installed and you have a demo or live account configured.

Quick Start

Running a Backtest

Use the command-line interface to run a backtest:

python run_backtest.py --strategy RSIReversalStrategy --symbol XAUUSD --start 2023-01-01 --end 2024-01-01

Creating Your Own Strategy

1. Create a new Python file or add to example_strategies.py:

from base_strategy import BaseStrategy
import MetaTrader5 as mt5

class MyStrategy(BaseStrategy):
    def __init__(self, symbol, timeframe, initial_balance=10000.0):
        super().__init__(symbol, timeframe, initial_balance)
        # Initialize your strategy parameters
        self.my_param = 42
    
    def get_required_indicators(self):
        return {
            'rsi': {'period': 14, 'applied_price': mt5.PRICE_CLOSE},
            'ema': {'period': 50, 'applied_price': mt5.PRICE_CLOSE}
        }
    
    def on_bar(self, bar_data):
        # Your trading logic here
        rsi = bar_data.get('rsi')
        ema = bar_data.get('ema')
        current_price = bar_data['close']
        
        # Example: Buy when RSI < 30 and price > EMA
        if rsi < 30 and current_price > ema:
            if self.position is None:
                self.open_position('BUY', 0.1, current_price)
    
    def get_parameters(self):
        return {'my_param': self.my_param}

2. Run your strategy:

from datetime import datetime
from backtest_engine import BacktestEngine
from performance_analyzer import PerformanceAnalyzer

# Create strategy
strategy = MyStrategy('XAUUSD', mt5.TIMEFRAME_H1, initial_balance=10000.0)

# Run backtest
engine = BacktestEngine(
    strategy,
    start_date=datetime(2023, 1, 1),
    end_date=datetime(2024, 1, 1)
)

results = engine.run()

# Analyze results
analyzer = PerformanceAnalyzer(results)
analyzer.generate_report('my_backtest_results')

Available Strategies

RSIScalpingStrategy

RSI-based scalping strategy that enters on RSI crossovers.

Parameters:

• rsi_period: RSI period (default: 14)
• rsi_overbought: Overbought level (default: 70)
• rsi_oversold: Oversold level (default: 30)
• rsi_target_buy: Exit target for long positions (default: 80)
• rsi_target_sell: Exit target for short positions (default: 20)

EMAStrategy

Simple EMA crossover strategy.

Parameters:

• ema_period: EMA period (default: 50)

RSIReversalStrategy

RSI reversal strategy similar to your MQL5 implementations.

Parameters:

• rsi_period: RSI period (default: 14)
• rsi_overbought: Overbought level (default: 70)
• rsi_oversold: Oversold level (default: 30)
• rsi_exit: Neutral exit level (default: 50)

Command Line Options

python run_backtest.py --help

Required Arguments:

• --strategy: Strategy name (RSIScalpingStrategy, EMAStrategy, RSIReversalStrategy)
• --start: Start date (YYYY-MM-DD)
• --end: End date (YYYY-MM-DD)

Optional Arguments:

• --symbol: Trading symbol (default: XAUUSD)
• --timeframe: Timeframe M1, M5, M15, M30, H1, H4, D1 (default: H1)
• --balance: Initial balance (default: 10000)
• --output: Output directory (default: backtest_results)
• --rsi-period: RSI period (default: 14)
• --rsi-overbought: RSI overbought level (default: 70)
• --rsi-oversold: RSI oversold level (default: 30)
• --ema-period: EMA period (default: 50)
• --lot-size: Lot size (default: 0.1)
• --stop-loss: Stop loss in pips (default: 50)
• --take-profit: Take profit in pips (default: 100)

Example Commands

# RSI Scalping on Gold, 1-hour timeframe
python run_backtest.py --strategy RSIScalpingStrategy --symbol XAUUSD --timeframe H1 --start 2023-01-01 --end 2024-01-01

# EMA Strategy on EUR/USD, 4-hour timeframe
python run_backtest.py --strategy EMAStrategy --symbol EURUSD --timeframe H4 --start 2023-01-01 --end 2024-01-01 --ema-period 100

# RSI Reversal with custom parameters
python run_backtest.py --strategy RSIReversalStrategy --symbol XAUUSD --start 2023-01-01 --end 2024-01-01 --rsi-period 28 --rsi-overbought 64 --rsi-oversold 13

Output

The backtest generates:

1. Console Summary: Performance metrics printed to console
2. Equity Curve Chart: Visual representation of account balance over time
3. Drawdown Chart: Drawdown visualization
4. Monthly Returns Chart: Monthly performance breakdown
5. Trades CSV: Detailed trade log in CSV format

All files are saved in the specified output directory (default: backtest_results/).

Performance Metrics

The framework calculates:

• Total Return: Percentage return on initial balance
• Win Rate: Percentage of winning trades
• Profit Factor: Total profit / Total loss
• Average Win/Loss: Average profit per winning/losing trade
• Maximum Drawdown: Largest peak-to-trough decline
• Total Trades: Number of completed trades

BaseStrategy API

Methods to Override

• on_bar(bar_data): Called on each new bar with market data and indicators
• get_parameters(): Return strategy parameters for logging
• get_required_indicators(): Specify which indicators are needed

Available Methods

• open_position(order_type, volume, price, sl=None, tp=None, comment=""): Open a position
• close_position(close_price): Close current position
• check_stop_loss_take_profit(current_price): Check SL/TP (called automatically)
• get_performance_metrics(): Get performance statistics

Bar Data Structure

The bar_data dictionary passed to on_bar() contains:

{
    'time': datetime,           # Bar timestamp
    'open': float,              # Opening price
    'high': float,              # High price
    'low': float,               # Low price
    'close': float,             # Closing price
    'tick_volume': int,         # Tick volume
    'spread': int,              # Spread in points
    'rsi': float,              # RSI value (if requested)
    'ema': float,              # EMA value (if requested)
    'indicators': {             # All requested indicators
        'rsi': float,
        'ema': float,
        ...
    }
}

Supported Indicators

• RSI: Relative Strength Index
• EMA: Exponential Moving Average
• SMA: Simple Moving Average
• ATR: Average True Range
• MACD: Moving Average Convergence Divergence

To add more indicators, modify BacktestEngine.setup_indicators().

Risk Management

The framework includes built-in risk management:

• Position Sizing: Configurable min/max lot sizes
• Stop Loss/Take Profit: Automatic SL/TP checking
• Spread Filtering: Skip trades when spread is too high
• Drawdown Protection: Track and limit maximum drawdown
• Margin Management: Prevent over-leveraging

Tips

1. Test on Demo First: Always test strategies on demo accounts before live trading
2. Start Small: Begin with small position sizes and gradually increase
3. Multiple Timeframes: Test strategies on different timeframes
4. Parameter Optimization: Use the framework to optimize strategy parameters
5. Compare Strategies: Run multiple strategies and compare results

Troubleshooting

MT5 Connection Issues

• Ensure MetaTrader5 is installed and running
• Check that you have a demo or live account configured
• Verify symbol names match MT5 format (e.g., 'XAUUSD' not 'GOLD')

No Data Available

• Check date range - ensure data exists for the specified period
• Verify symbol name is correct
• Check that MT5 has historical data for the symbol/timeframe

Indicator Errors

• Ensure indicator parameters are valid
• Check that enough bars are available for indicator calculation
• Verify indicator handle creation succeeded

Contributing

Feel free to extend this framework with:

• Additional indicators
• More sophisticated risk management
• Optimization tools
• Walk-forward analysis
• Monte Carlo simulation

License

This framework is provided for educational and research purposes.

Disclaimer

Trading involves substantial risk of loss. This framework is provided for educational purposes only. Always test thoroughly on a demo account before using with real money. Past performance does not guarantee future results.