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"""
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Base Strategy Class for MetaTrader5 Backtesting
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This module provides a base class that all trading strategies should inherit from.
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Implement your trading logic by overriding the on_bar() method.
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"""
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from abc import ABC, abstractmethod
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from datetime import datetime
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from typing import Optional, Dict, Any
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import MetaTrader5 as mt5
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class BaseStrategy(ABC):
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"""
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Base class for all trading strategies.
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Inherit from this class and implement:
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- on_bar(): Your trading logic for each bar
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- get_parameters(): Return strategy parameters
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"""
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def __init__(self, symbol: str, timeframe: int, initial_balance: float = 10000.0):
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"""
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Initialize the strategy.
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Args:
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symbol: Trading symbol (e.g., 'XAUUSD', 'EURUSD')
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timeframe: MT5 timeframe constant (e.g., mt5.TIMEFRAME_H1)
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initial_balance: Starting account balance
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"""
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self.symbol = symbol
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self.timeframe = timeframe
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self.initial_balance = initial_balance
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self.current_balance = initial_balance
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self.equity = initial_balance
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# Position tracking
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self.position = None # {'type': 'BUY'/'SELL', 'volume': float, 'open_price': float, 'open_time': datetime}
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self.trades = []
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self.closed_trades = []
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# Performance metrics
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self.max_drawdown = 0.0
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self.peak_equity = initial_balance
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self.total_profit = 0.0
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self.total_loss = 0.0
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self.winning_trades = 0
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self.losing_trades = 0
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# Risk management
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self.max_lot_size = 0.1
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self.min_lot_size = 0.01
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self.max_spread = 1000 # in points
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self.max_drawdown_percent = 0.2 # 20% max drawdown
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@abstractmethod
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def on_bar(self, bar_data: Dict[str, Any]) -> None:
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"""
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Called on each new bar. Implement your trading logic here.
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Args:
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bar_data: Dictionary containing:
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- 'time': datetime of the bar
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- 'open': float opening price
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- 'high': float high price
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- 'low': float low price
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- 'close': float closing price
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- 'tick_volume': int tick volume
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- 'spread': int spread in points
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- 'rsi': Optional[float] RSI value if requested
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- 'ema': Optional[float] EMA value if requested
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- 'indicators': Dict with any other requested indicators
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"""
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pass
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@abstractmethod
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def get_parameters(self) -> Dict[str, Any]:
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"""
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Return strategy parameters for logging/reporting.
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Returns:
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Dictionary of parameter names and values
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"""
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pass
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def get_required_indicators(self) -> Dict[str, Dict[str, Any]]:
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"""
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Specify which indicators are needed by the strategy.
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Returns:
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Dictionary mapping indicator names to their parameters.
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Example: {
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'rsi': {'period': 14, 'applied_price': mt5.PRICE_CLOSE},
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'ema': {'period': 50, 'applied_price': mt5.PRICE_CLOSE}
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}
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"""
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return {}
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def open_position(self, order_type: str, volume: float, price: float,
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sl: Optional[float] = None, tp: Optional[float] = None,
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comment: str = "") -> bool:
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"""
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Open a trading position.
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Args:
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order_type: 'BUY' or 'SELL'
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volume: Lot size
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price: Entry price
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sl: Stop loss price (optional)
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tp: Take profit price (optional)
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comment: Trade comment
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Returns:
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True if position opened successfully
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"""
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if self.position is not None:
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return False # Position already open
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# Validate volume
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volume = max(self.min_lot_size, min(volume, self.max_lot_size))
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# Calculate margin requirement (simplified)
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contract_size = 100000 # Standard lot size
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margin_required = volume * contract_size * price * 0.01 # 1% margin (adjust as needed)
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if margin_required > self.equity * 0.9: # Don't use more than 90% of equity
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return False
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self.position = {
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'type': order_type,
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'volume': volume,
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'open_price': price,
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'open_time': datetime.now(),
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'sl': sl,
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'tp': tp,
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'comment': comment
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}
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return True
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def close_position(self, close_price: float) -> Optional[Dict[str, Any]]:
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"""
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Close the current position.
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Args:
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close_price: Price at which to close
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Returns:
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Trade result dictionary or None if no position
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"""
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if self.position is None:
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return None
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# Calculate profit/loss
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if self.position['type'] == 'BUY':
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pips = (close_price - self.position['open_price']) * 10000 # For 5-digit brokers
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profit = pips * self.position['volume'] * 10 # Simplified P&L calculation
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else: # SELL
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pips = (self.position['open_price'] - close_price) * 10000
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profit = pips * self.position['volume'] * 10
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trade_result = {
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'type': self.position['type'],
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'volume': self.position['volume'],
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'open_price': self.position['open_price'],
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'close_price': close_price,
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'open_time': self.position['open_time'],
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'close_time': datetime.now(),
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'profit': profit,
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'pips': pips,
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'comment': self.position.get('comment', '')
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}
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# Update balance and metrics
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self.current_balance += profit
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self.equity = self.current_balance
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if profit > 0:
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self.winning_trades += 1
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self.total_profit += profit
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else:
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self.losing_trades += 1
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self.total_loss += abs(profit)
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# Update drawdown
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if self.equity > self.peak_equity:
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self.peak_equity = self.equity
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drawdown = (self.peak_equity - self.equity) / self.peak_equity
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if drawdown > self.max_drawdown:
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self.max_drawdown = drawdown
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self.closed_trades.append(trade_result)
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self.position = None
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return trade_result
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def check_stop_loss_take_profit(self, current_price: float) -> bool:
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"""
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Check if stop loss or take profit should be triggered.
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Args:
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current_price: Current market price
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Returns:
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True if position was closed
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"""
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if self.position is None:
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return False
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should_close = False
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if self.position['type'] == 'BUY':
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if self.position.get('sl') and current_price <= self.position['sl']:
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should_close = True
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if self.position.get('tp') and current_price >= self.position['tp']:
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should_close = True
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else: # SELL
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if self.position.get('sl') and current_price >= self.position['sl']:
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should_close = True
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if self.position.get('tp') and current_price <= self.position['tp']:
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should_close = True
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if should_close:
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self.close_position(current_price)
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return True
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return False
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def get_performance_metrics(self) -> Dict[str, Any]:
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"""
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Calculate and return performance metrics.
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Returns:
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Dictionary with performance statistics
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"""
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total_trades = len(self.closed_trades)
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win_rate = (self.winning_trades / total_trades * 100) if total_trades > 0 else 0
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avg_win = (self.total_profit / self.winning_trades) if self.winning_trades > 0 else 0
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avg_loss = (self.total_loss / self.losing_trades) if self.losing_trades > 0 else 0
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profit_factor = (self.total_profit / self.total_loss) if self.total_loss > 0 else 0
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total_return = ((self.equity - self.initial_balance) / self.initial_balance) * 100
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return {
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'initial_balance': self.initial_balance,
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'final_balance': self.equity,
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'total_return_pct': total_return,
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'total_trades': total_trades,
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'winning_trades': self.winning_trades,
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'losing_trades': self.losing_trades,
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'win_rate_pct': win_rate,
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'total_profit': self.total_profit,
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'total_loss': self.total_loss,
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'profit_factor': profit_factor,
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'avg_win': avg_win,
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'avg_loss': avg_loss,
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'max_drawdown_pct': self.max_drawdown * 100,
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'parameters': self.get_parameters()
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}
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