mirror of
https://github.com/B-Wear/QuantumEdge.git
synced 2026-08-08 21:07:46 +00:00
B-Wear
+266
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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import logging
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from typing import Dict, List, Optional
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import ccxt
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from concurrent.futures import ThreadPoolExecutor
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import json
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import os
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from .trading_strategy import TradingStrategy
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from .config import load_config
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logger = logging.getLogger(__name__)
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class Backtester:
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def __init__(self, config_path: str):
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# Load configuration
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self.config = load_config(config_path)
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# Initialize exchange
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self.exchange = self._initialize_exchange()
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# Initialize trading strategy
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self.strategy = TradingStrategy(self.config)
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# Initialize results storage
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self.results = {
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'trades': [],
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'equity_curve': [],
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'performance_metrics': {}
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}
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def _initialize_exchange(self) -> ccxt.Exchange:
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"""Initialize the cryptocurrency exchange"""
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try:
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exchange_class = getattr(ccxt, self.config['exchange']['name'])
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exchange = exchange_class({
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'apiKey': self.config['exchange']['api_key'],
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'secret': self.config['exchange']['api_secret'],
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'enableRateLimit': True
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})
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# Test connection
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exchange.load_markets()
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logger.info(f"Successfully connected to {self.config['exchange']['name']}")
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return exchange
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except Exception as e:
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logger.error(f"Error initializing exchange: {str(e)}")
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raise
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def fetch_historical_data(self,
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symbol: str,
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timeframe: str,
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start_date: datetime,
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end_date: datetime) -> pd.DataFrame:
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"""Fetch historical market data"""
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try:
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# Convert dates to timestamps
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start_timestamp = int(start_date.timestamp() * 1000)
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end_timestamp = int(end_date.timestamp() * 1000)
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# Fetch OHLCV data
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ohlcv = self.exchange.fetch_ohlcv(
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symbol,
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timeframe=timeframe,
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since=start_timestamp,
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limit=1000 # Maximum limit per request
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)
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# Convert to DataFrame
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df = pd.DataFrame(
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ohlcv,
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columns=['timestamp', 'open', 'high', 'low', 'close', 'volume']
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)
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# Convert timestamp to datetime
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
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df.set_index('timestamp', inplace=True)
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return df
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except Exception as e:
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logger.error(f"Error fetching historical data for {symbol}: {str(e)}")
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return None
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def run_backtest(self,
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symbol: str,
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timeframe: str,
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start_date: datetime,
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end_date: datetime,
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initial_capital: float = 50.0) -> Dict:
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"""Run backtest for a given period"""
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try:
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logger.info(f"Starting backtest for {symbol} from {start_date} to {end_date}")
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# Fetch historical data
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df = self.fetch_historical_data(symbol, timeframe, start_date, end_date)
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if df is None:
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raise ValueError("Failed to fetch historical data")
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# Initialize variables
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current_capital = initial_capital
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position = 0
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entry_price = 0
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trades = []
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equity_curve = []
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# Iterate through data
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for i in range(len(df)):
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current_data = df.iloc[:i+1]
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current_price = current_data['close'].iloc[-1]
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# Generate trading signal
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signal = self.strategy.analyze_market(symbol, current_data)
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if signal:
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# Execute trade if conditions are met
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if signal.action == 'buy' and position <= 0:
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position = 1
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entry_price = current_price
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trades.append({
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'timestamp': current_data.index[-1],
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'action': 'buy',
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'price': current_price,
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'position_size': signal.position_size
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})
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elif signal.action == 'sell' and position >= 0:
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position = -1
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entry_price = current_price
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trades.append({
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'timestamp': current_data.index[-1],
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'action': 'sell',
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'price': current_price,
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'position_size': signal.position_size
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})
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# Update position PnL
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if position != 0:
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pnl = position * (current_price - entry_price) * signal.position_size
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current_capital += pnl
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# Record equity
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equity_curve.append({
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'timestamp': current_data.index[-1],
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'equity': current_capital
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})
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# Calculate performance metrics
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self.results['trades'] = trades
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self.results['equity_curve'] = equity_curve
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self.results['performance_metrics'] = self._calculate_performance_metrics(trades)
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return self.results
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except Exception as e:
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logger.error(f"Error running backtest: {str(e)}")
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return None
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def _calculate_performance_metrics(self, trades: List[Dict]) -> Dict:
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"""Calculate performance metrics from trades"""
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if not trades:
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return {}
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# Convert trades to DataFrame
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df = pd.DataFrame(trades)
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# Calculate basic metrics
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total_trades = len(df)
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winning_trades = len(df[df['action'] == 'buy'])
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losing_trades = len(df[df['action'] == 'sell'])
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win_rate = winning_trades / total_trades if total_trades > 0 else 0
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# Calculate returns
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returns = df['price'].pct_change()
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total_return = (1 + returns).prod() - 1
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annual_return = (1 + total_return) ** (252 / len(df)) - 1
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# Calculate risk metrics
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volatility = returns.std() * np.sqrt(252)
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sharpe_ratio = annual_return / volatility if volatility > 0 else 0
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# Calculate drawdown
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cumulative_returns = (1 + returns).cumprod()
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rolling_max = cumulative_returns.expanding().max()
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drawdowns = (cumulative_returns - rolling_max) / rolling_max
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max_drawdown = drawdowns.min()
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return {
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'total_trades': total_trades,
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'winning_trades': winning_trades,
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'losing_trades': losing_trades,
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'win_rate': win_rate,
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'total_return': total_return,
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'annual_return': annual_return,
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'volatility': volatility,
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'sharpe_ratio': sharpe_ratio,
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'max_drawdown': max_drawdown
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}
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def save_results(self, filepath: str):
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"""Save backtest results to file"""
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try:
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with open(filepath, 'w') as f:
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json.dump(self.results, f, default=str)
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logger.info(f"Backtest results saved to {filepath}")
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return True
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except Exception as e:
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logger.error(f"Error saving results: {str(e)}")
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return False
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def load_results(self, filepath: str):
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"""Load backtest results from file"""
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try:
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with open(filepath, 'r') as f:
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self.results = json.load(f)
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logger.info(f"Backtest results loaded from {filepath}")
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return True
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except Exception as e:
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logger.error(f"Error loading results: {str(e)}")
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return False
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def main():
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# Load configuration
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config_path = os.path.join(os.path.dirname(__file__), '..', 'config', 'config.json')
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# Create backtester
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backtester = Backtester(config_path)
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# Define backtest parameters
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symbol = "BTC/USDT"
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timeframe = "1h"
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start_date = datetime.now() - timedelta(days=30)
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end_date = datetime.now()
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initial_capital = 50.0
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# Run backtest
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results = backtester.run_backtest(
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symbol=symbol,
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timeframe=timeframe,
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start_date=start_date,
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end_date=end_date,
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initial_capital=initial_capital
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)
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if results:
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# Print performance metrics
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metrics = results['performance_metrics']
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print("\nBacktest Results:")
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print(f"Total Trades: {metrics['total_trades']}")
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print(f"Win Rate: {metrics['win_rate']:.2%}")
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print(f"Total Return: {metrics['total_return']:.2%}")
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print(f"Annual Return: {metrics['annual_return']:.2%}")
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print(f"Sharpe Ratio: {metrics['sharpe_ratio']:.2f}")
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print(f"Max Drawdown: {metrics['max_drawdown']:.2%}")
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# Save results
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backtester.save_results('backtest_results.json')
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else:
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print("Backtest failed to complete")
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if __name__ == "__main__":
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main()
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