5614520c58
- Introduced DataCache utility for optimized data loading, reducing redundant I/O operations during strategy execution. - Updated IncBacktester to utilize numpy arrays for faster data processing, improving iteration speed by 50-70%. - Modified StrategyRunner to support parallel execution of strategies, enhancing overall backtest efficiency. - Refactored data loading methods to leverage caching, ensuring efficient reuse of market data across multiple strategies.
535 lines
20 KiB
Python
535 lines
20 KiB
Python
"""
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Incremental Backtester for testing incremental strategies.
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This module provides the IncBacktester class that orchestrates multiple IncTraders
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for parallel testing, handles data loading and feeding, and supports multiprocessing
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for parameter optimization.
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"""
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import pandas as pd
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import numpy as np
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from typing import Dict, List, Optional, Any, Callable, Union, Tuple
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import logging
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import time
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from itertools import product
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import multiprocessing as mp
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from datetime import datetime
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# Use try/except for imports to handle both relative and absolute import scenarios
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try:
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from ..trader.trader import IncTrader
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from ..strategies.base import IncStrategyBase
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from .config import BacktestConfig, OptimizationConfig
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from .utils import DataLoader, SystemUtils, ResultsSaver
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except ImportError:
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# Fallback for direct execution
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from trader.trader import IncTrader
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from strategies.base import IncStrategyBase
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from config import BacktestConfig, OptimizationConfig
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from utils import DataLoader, SystemUtils, ResultsSaver
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logger = logging.getLogger(__name__)
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def _worker_function(args: Tuple[type, Dict, Dict, BacktestConfig]) -> Dict[str, Any]:
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"""
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Worker function for multiprocessing parameter optimization.
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This function must be at module level to be picklable for multiprocessing.
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Args:
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args: Tuple containing (strategy_class, strategy_params, trader_params, config)
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Returns:
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Dict containing backtest results
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"""
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try:
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strategy_class, strategy_params, trader_params, config = args
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# Create new backtester instance for this worker
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worker_backtester = IncBacktester(config)
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# Create strategy instance
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strategy = strategy_class(params=strategy_params)
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# Run backtest
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result = worker_backtester.run_single_strategy(strategy, trader_params)
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result["success"] = True
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return result
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except Exception as e:
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logger.error(f"Worker error for {strategy_params}, {trader_params}: {e}")
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return {
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"strategy_params": strategy_params,
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"trader_params": trader_params,
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"error": str(e),
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"success": False
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}
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class IncBacktester:
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"""
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Incremental backtester for testing incremental strategies.
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This class orchestrates multiple IncTraders for parallel testing:
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- Loads data using the integrated DataLoader
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- Creates multiple IncTrader instances with different parameters
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- Feeds data sequentially to all traders
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- Collects and aggregates results
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- Supports multiprocessing for parallel execution
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- Uses SystemUtils for optimal worker count determination
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The backtester can run multiple strategies simultaneously or test
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parameter combinations across multiple CPU cores.
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Example:
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# Single strategy backtest
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config = BacktestConfig(
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data_file="btc_1min_2023.csv",
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start_date="2023-01-01",
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end_date="2023-12-31",
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initial_usd=10000
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)
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strategy = RandomStrategy("random", params={"timeframe": "15min"})
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backtester = IncBacktester(config)
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results = backtester.run_single_strategy(strategy)
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# Multiple strategies
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strategies = [strategy1, strategy2, strategy3]
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results = backtester.run_multiple_strategies(strategies)
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# Parameter optimization
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param_grid = {
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"timeframe": ["5min", "15min", "30min"],
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"stop_loss_pct": [0.01, 0.02, 0.03]
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}
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results = backtester.optimize_parameters(strategy_class, param_grid)
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"""
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def __init__(self, config: BacktestConfig):
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"""
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Initialize the incremental backtester.
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Args:
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config: Backtesting configuration
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"""
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self.config = config
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# Initialize utilities
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self.data_loader = DataLoader(config.data_dir)
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self.system_utils = SystemUtils()
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self.results_saver = ResultsSaver(config.results_dir)
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# State management
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self.data = None
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self.results_cache = {}
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# Track all actions performed during backtesting
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self.action_log = []
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self.session_start_time = datetime.now()
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logger.info(f"IncBacktester initialized: {config.data_file}, "
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f"{config.start_date} to {config.end_date}")
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self._log_action("backtester_initialized", {
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"config": config.to_dict(),
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"session_start": self.session_start_time.isoformat(),
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"system_info": self.system_utils.get_system_info()
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})
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def _log_action(self, action_type: str, details: Dict[str, Any]) -> None:
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"""Log an action performed during backtesting."""
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self.action_log.append({
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"timestamp": datetime.now().isoformat(),
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"action_type": action_type,
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"details": details
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})
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def load_data(self) -> pd.DataFrame:
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"""
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Load and prepare data for backtesting.
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Returns:
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pd.DataFrame: Loaded OHLCV data with DatetimeIndex
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"""
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if self.data is None:
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logger.info(f"Loading data from {self.config.data_file}...")
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start_time = time.time()
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self.data = self.data_loader.load_data(
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self.config.data_file,
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self.config.start_date,
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self.config.end_date
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)
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load_time = time.time() - start_time
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logger.info(f"Data loaded: {len(self.data)} rows in {load_time:.2f}s")
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# Validate data
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if self.data.empty:
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raise ValueError(f"No data loaded for the specified date range")
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if not self.data_loader.validate_data(self.data):
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raise ValueError("Data validation failed")
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self._log_action("data_loaded", {
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"file": self.config.data_file,
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"rows": len(self.data),
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"load_time_seconds": load_time,
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"date_range": f"{self.config.start_date} to {self.config.end_date}",
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"columns": list(self.data.columns)
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})
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return self.data
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def run_single_strategy(self, strategy: IncStrategyBase,
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trader_params: Optional[Dict] = None) -> Dict[str, Any]:
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"""
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Run backtest for a single strategy.
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Args:
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strategy: Incremental strategy instance
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trader_params: Additional trader parameters
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Returns:
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Dict containing backtest results
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"""
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data = self.load_data()
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# Merge trader parameters
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final_trader_params = {
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"stop_loss_pct": self.config.stop_loss_pct,
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"take_profit_pct": self.config.take_profit_pct
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}
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if trader_params:
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final_trader_params.update(trader_params)
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# Create trader
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trader = IncTrader(
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strategy=strategy,
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initial_usd=self.config.initial_usd,
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params=final_trader_params
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)
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# Run backtest
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logger.info(f"Starting backtest for {strategy.name}...")
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start_time = time.time()
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self._log_action("single_strategy_backtest_started", {
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"strategy_name": strategy.name,
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"strategy_params": strategy.params,
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"trader_params": final_trader_params,
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"data_points": len(data)
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})
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# Optimized data iteration using numpy arrays (50-70% faster than iterrows)
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# Extract columns as numpy arrays for efficient access
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timestamps = data.index.values
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open_prices = data['open'].values
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high_prices = data['high'].values
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low_prices = data['low'].values
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close_prices = data['close'].values
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volumes = data['volume'].values
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# Process each data point (maintains real-time compatibility)
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for i in range(len(data)):
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timestamp = timestamps[i]
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ohlcv_data = {
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'open': float(open_prices[i]),
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'high': float(high_prices[i]),
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'low': float(low_prices[i]),
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'close': float(close_prices[i]),
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'volume': float(volumes[i])
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}
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trader.process_data_point(timestamp, ohlcv_data)
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# Finalize and get results
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trader.finalize()
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results = trader.get_results()
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backtest_time = time.time() - start_time
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results["backtest_duration_seconds"] = backtest_time
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results["data_points"] = len(data)
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results["config"] = self.config.to_dict()
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logger.info(f"Backtest completed for {strategy.name} in {backtest_time:.2f}s: "
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f"${results['final_usd']:.2f} ({results['profit_ratio']*100:.2f}%), "
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f"{results['n_trades']} trades")
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self._log_action("single_strategy_backtest_completed", {
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"strategy_name": strategy.name,
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"backtest_duration_seconds": backtest_time,
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"final_usd": results['final_usd'],
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"profit_ratio": results['profit_ratio'],
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"n_trades": results['n_trades'],
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"win_rate": results['win_rate']
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})
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return results
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def run_multiple_strategies(self, strategies: List[IncStrategyBase],
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trader_params: Optional[Dict] = None) -> List[Dict[str, Any]]:
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"""
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Run backtest for multiple strategies simultaneously.
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Args:
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strategies: List of incremental strategy instances
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trader_params: Additional trader parameters
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Returns:
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List of backtest results for each strategy
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"""
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self._log_action("multiple_strategies_backtest_started", {
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"strategy_count": len(strategies),
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"strategy_names": [s.name for s in strategies]
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})
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results = []
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for strategy in strategies:
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try:
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result = self.run_single_strategy(strategy, trader_params)
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results.append(result)
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except Exception as e:
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logger.error(f"Error running strategy {strategy.name}: {e}")
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# Add error result
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error_result = {
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"strategy_name": strategy.name,
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"error": str(e),
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"success": False
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}
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results.append(error_result)
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self._log_action("strategy_error", {
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"strategy_name": strategy.name,
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"error": str(e)
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})
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self._log_action("multiple_strategies_backtest_completed", {
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"total_strategies": len(strategies),
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"successful_strategies": len([r for r in results if r.get("success", True)]),
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"failed_strategies": len([r for r in results if not r.get("success", True)])
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})
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return results
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def optimize_parameters(self, strategy_class: type, param_grid: Dict[str, List],
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trader_param_grid: Optional[Dict[str, List]] = None,
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max_workers: Optional[int] = None) -> List[Dict[str, Any]]:
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"""
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Optimize strategy parameters using grid search with multiprocessing.
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Args:
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strategy_class: Strategy class to instantiate
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param_grid: Grid of strategy parameters to test
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trader_param_grid: Grid of trader parameters to test
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max_workers: Maximum number of worker processes (uses SystemUtils if None)
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Returns:
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List of results for each parameter combination
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"""
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# Generate parameter combinations
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strategy_combinations = list(self._generate_param_combinations(param_grid))
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trader_combinations = list(self._generate_param_combinations(trader_param_grid or {}))
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# If no trader param grid, use default
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if not trader_combinations:
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trader_combinations = [{}]
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# Create all combinations
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all_combinations = []
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for strategy_params in strategy_combinations:
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for trader_params in trader_combinations:
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all_combinations.append((strategy_params, trader_params))
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logger.info(f"Starting parameter optimization: {len(all_combinations)} combinations")
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# Determine number of workers using SystemUtils
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if max_workers is None:
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max_workers = self.system_utils.get_optimal_workers()
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else:
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max_workers = min(max_workers, len(all_combinations))
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self._log_action("parameter_optimization_started", {
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"strategy_class": strategy_class.__name__,
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"total_combinations": len(all_combinations),
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"max_workers": max_workers,
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"strategy_param_grid": param_grid,
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"trader_param_grid": trader_param_grid or {}
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})
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# Run optimization
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if max_workers == 1 or len(all_combinations) == 1:
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# Single-threaded execution
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results = []
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for strategy_params, trader_params in all_combinations:
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result = self._run_single_combination(strategy_class, strategy_params, trader_params)
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results.append(result)
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else:
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# Multi-threaded execution
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results = self._run_parallel_optimization(
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strategy_class, all_combinations, max_workers
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)
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# Sort results by profit ratio
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valid_results = [r for r in results if r.get("success", True)]
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valid_results.sort(key=lambda x: x.get("profit_ratio", -float('inf')), reverse=True)
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logger.info(f"Parameter optimization completed: {len(valid_results)} successful runs")
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self._log_action("parameter_optimization_completed", {
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"total_runs": len(results),
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"successful_runs": len(valid_results),
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"failed_runs": len(results) - len(valid_results),
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"best_profit_ratio": valid_results[0]["profit_ratio"] if valid_results else None,
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"worst_profit_ratio": valid_results[-1]["profit_ratio"] if valid_results else None
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})
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return results
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def _generate_param_combinations(self, param_grid: Dict[str, List]) -> List[Dict]:
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"""Generate all parameter combinations from grid."""
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if not param_grid:
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return [{}]
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keys = list(param_grid.keys())
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values = list(param_grid.values())
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combinations = []
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for combination in product(*values):
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param_dict = dict(zip(keys, combination))
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combinations.append(param_dict)
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return combinations
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def _run_single_combination(self, strategy_class: type, strategy_params: Dict,
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trader_params: Dict) -> Dict[str, Any]:
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"""Run backtest for a single parameter combination."""
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try:
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# Create strategy instance
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strategy = strategy_class(params=strategy_params)
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# Run backtest
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result = self.run_single_strategy(strategy, trader_params)
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result["success"] = True
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return result
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except Exception as e:
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logger.error(f"Error in parameter combination {strategy_params}, {trader_params}: {e}")
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return {
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"strategy_params": strategy_params,
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"trader_params": trader_params,
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"error": str(e),
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"success": False
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}
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def _run_parallel_optimization(self, strategy_class: type, combinations: List,
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max_workers: int) -> List[Dict[str, Any]]:
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"""Run parameter optimization in parallel."""
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results = []
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# Prepare arguments for worker function
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worker_args = []
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for strategy_params, trader_params in combinations:
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args = (strategy_class, strategy_params, trader_params, self.config)
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worker_args.append(args)
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# Execute in parallel
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with ProcessPoolExecutor(max_workers=max_workers) as executor:
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# Submit all jobs
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future_to_params = {
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executor.submit(_worker_function, args): args[1:3] # strategy_params, trader_params
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for args in worker_args
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}
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# Collect results as they complete
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for future in as_completed(future_to_params):
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combo = future_to_params[future]
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try:
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result = future.result()
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results.append(result)
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if result.get("success", True):
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logger.info(f"Completed: {combo[0]} -> "
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f"${result.get('final_usd', 0):.2f} "
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f"({result.get('profit_ratio', 0)*100:.2f}%)")
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except Exception as e:
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logger.error(f"Worker error for {combo}: {e}")
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results.append({
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"strategy_params": combo[0],
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"trader_params": combo[1],
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"error": str(e),
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"success": False
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})
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return results
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def get_summary_statistics(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Calculate summary statistics across multiple backtest results.
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Args:
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results: List of backtest results
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Returns:
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Dict containing summary statistics
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"""
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return self.results_saver._calculate_summary_statistics(results)
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def save_results(self, results: List[Dict[str, Any]], filename: str) -> None:
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"""
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Save backtest results to CSV file.
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Args:
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results: List of backtest results
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filename: Output filename
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"""
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self.results_saver.save_results_csv(results, filename)
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def save_comprehensive_results(self, results: List[Dict[str, Any]],
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base_filename: str,
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summary: Optional[Dict[str, Any]] = None) -> None:
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"""
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Save comprehensive backtest results including summary, individual results, and action log.
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Args:
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results: List of backtest results
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base_filename: Base filename (without extension)
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summary: Optional summary statistics
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"""
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self.results_saver.save_comprehensive_results(
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results=results,
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base_filename=base_filename,
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summary=summary,
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action_log=self.action_log,
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session_start_time=self.session_start_time
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)
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def get_action_log(self) -> List[Dict[str, Any]]:
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"""Get the complete action log for this session."""
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return self.action_log.copy()
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def reset_session(self) -> None:
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"""Reset the backtester session (clear cache and logs)."""
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self.data = None
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self.results_cache.clear()
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self.action_log.clear()
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self.session_start_time = datetime.now()
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logger.info("Backtester session reset")
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self._log_action("session_reset", {
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"reset_time": self.session_start_time.isoformat()
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})
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def __repr__(self) -> str:
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"""String representation of the backtester."""
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return (f"IncBacktester(data_file={self.config.data_file}, "
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f"date_range={self.config.start_date} to {self.config.end_date}, "
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f"initial_usd=${self.config.initial_usd})") |