c9ae507bb7
- Introduced a comprehensive framework for incremental trading strategies, including modules for strategy execution, backtesting, and data processing. - Added key components such as `IncTrader`, `IncBacktester`, and various trading strategies (e.g., `MetaTrendStrategy`, `BBRSStrategy`, `RandomStrategy`) to facilitate real-time trading and backtesting. - Implemented a robust backtesting framework with configuration management, parallel execution, and result analysis capabilities. - Developed an incremental indicators framework to support real-time data processing with constant memory usage. - Enhanced documentation to provide clear usage examples and architecture overview, ensuring maintainability and ease of understanding for future development. - Ensured compatibility with existing strategies and maintained a focus on performance and scalability throughout the implementation.
480 lines
19 KiB
Python
480 lines
19 KiB
Python
"""
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Backtester Utilities
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This module provides utility functions for data loading, system resource management,
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and result saving for the incremental backtesting framework.
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"""
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import os
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import json
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import pandas as pd
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import numpy as np
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import psutil
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from typing import Dict, List, Any, Optional
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import logging
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from datetime import datetime
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logger = logging.getLogger(__name__)
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class DataLoader:
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"""
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Data loading utilities for backtesting.
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This class handles loading and preprocessing of market data from various formats
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including CSV and JSON files.
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"""
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def __init__(self, data_dir: str = "data"):
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"""
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Initialize data loader.
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Args:
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data_dir: Directory containing data files
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"""
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self.data_dir = data_dir
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os.makedirs(self.data_dir, exist_ok=True)
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def load_data(self, file_path: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""
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Load data with optimized dtypes and filtering, supporting CSV and JSON input.
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Args:
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file_path: Path to the data file (relative to data_dir)
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start_date: Start date for filtering (YYYY-MM-DD format)
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end_date: End date for filtering (YYYY-MM-DD format)
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Returns:
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pd.DataFrame: Loaded OHLCV data with DatetimeIndex
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"""
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full_path = os.path.join(self.data_dir, file_path)
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if not os.path.exists(full_path):
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raise FileNotFoundError(f"Data file not found: {full_path}")
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# Determine file type
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_, ext = os.path.splitext(file_path)
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ext = ext.lower()
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try:
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if ext == ".json":
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return self._load_json_data(full_path, start_date, end_date)
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else:
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return self._load_csv_data(full_path, start_date, end_date)
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except Exception as e:
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logger.error(f"Error loading data from {file_path}: {e}")
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# Return an empty DataFrame with a DatetimeIndex
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return pd.DataFrame(index=pd.to_datetime([]))
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def _load_json_data(self, file_path: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""Load data from JSON file."""
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with open(file_path, 'r') as f:
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raw = json.load(f)
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data = pd.DataFrame(raw["Data"])
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# Convert columns to lowercase
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data.columns = data.columns.str.lower()
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# Convert timestamp to datetime
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data["timestamp"] = pd.to_datetime(data["timestamp"], unit="s")
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# Filter by date range
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data = data[(data["timestamp"] >= start_date) & (data["timestamp"] <= end_date)]
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logger.info(f"JSON data loaded: {len(data)} rows for {start_date} to {end_date}")
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return data.set_index("timestamp")
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def _load_csv_data(self, file_path: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""Load data from CSV file."""
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# Define optimized dtypes
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dtypes = {
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'Open': 'float32',
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'High': 'float32',
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'Low': 'float32',
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'Close': 'float32',
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'Volume': 'float32'
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}
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# Read data with original capitalized column names
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data = pd.read_csv(file_path, dtype=dtypes)
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# Handle timestamp column
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if 'Timestamp' in data.columns:
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data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='s')
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# Filter by date range
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data = data[(data['Timestamp'] >= start_date) & (data['Timestamp'] <= end_date)]
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# Convert column names to lowercase
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data.columns = data.columns.str.lower()
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logger.info(f"CSV data loaded: {len(data)} rows for {start_date} to {end_date}")
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return data.set_index('timestamp')
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else:
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# Attempt to use the first column if 'Timestamp' is not present
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data.rename(columns={data.columns[0]: 'timestamp'}, inplace=True)
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data['timestamp'] = pd.to_datetime(data['timestamp'], unit='s')
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data = data[(data['timestamp'] >= start_date) & (data['timestamp'] <= end_date)]
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data.columns = data.columns.str.lower()
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logger.info(f"CSV data loaded (first column as timestamp): {len(data)} rows for {start_date} to {end_date}")
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return data.set_index('timestamp')
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def validate_data(self, data: pd.DataFrame) -> bool:
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"""
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Validate loaded data for required columns and basic integrity.
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Args:
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data: DataFrame to validate
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Returns:
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bool: True if data is valid
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"""
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if data.empty:
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logger.error("Data is empty")
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return False
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required_columns = ['open', 'high', 'low', 'close', 'volume']
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missing_columns = [col for col in required_columns if col not in data.columns]
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if missing_columns:
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logger.error(f"Missing required columns: {missing_columns}")
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return False
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# Check for NaN values
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if data[required_columns].isnull().any().any():
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logger.warning("Data contains NaN values")
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# Check for negative prices
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price_columns = ['open', 'high', 'low', 'close']
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if (data[price_columns] <= 0).any().any():
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logger.warning("Data contains non-positive prices")
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# Check OHLC consistency
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if not ((data['low'] <= data['open']) &
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(data['low'] <= data['close']) &
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(data['high'] >= data['open']) &
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(data['high'] >= data['close'])).all():
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logger.warning("Data contains OHLC inconsistencies")
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return True
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class SystemUtils:
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"""
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System resource management utilities.
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This class provides methods for determining optimal system resource usage
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for parallel processing and performance optimization.
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"""
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def __init__(self):
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"""Initialize system utilities."""
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pass
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def get_optimal_workers(self) -> int:
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"""
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Determine optimal number of worker processes based on system resources.
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Returns:
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int: Optimal number of worker processes
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"""
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cpu_count = os.cpu_count() or 4
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memory_gb = psutil.virtual_memory().total / (1024**3)
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# Heuristic: Use 75% of cores, but cap based on available memory
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# Assume each worker needs ~2GB for large datasets
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workers_by_memory = max(1, int(memory_gb / 2))
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workers_by_cpu = max(1, int(cpu_count * 0.75))
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optimal_workers = min(workers_by_cpu, workers_by_memory)
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logger.info(f"System resources: {cpu_count} CPUs, {memory_gb:.1f}GB RAM")
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logger.info(f"Using {optimal_workers} workers for processing")
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return optimal_workers
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def get_system_info(self) -> Dict[str, Any]:
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"""
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Get comprehensive system information.
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Returns:
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Dict containing system information
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"""
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memory = psutil.virtual_memory()
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return {
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"cpu_count": os.cpu_count(),
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"memory_total_gb": memory.total / (1024**3),
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"memory_available_gb": memory.available / (1024**3),
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"memory_percent": memory.percent,
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"optimal_workers": self.get_optimal_workers()
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}
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class ResultsSaver:
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"""
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Results saving utilities for backtesting.
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This class handles saving backtest results in various formats including
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CSV, JSON, and comprehensive reports.
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"""
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def __init__(self, results_dir: str = "results"):
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"""
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Initialize results saver.
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Args:
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results_dir: Directory for saving results
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"""
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self.results_dir = results_dir
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os.makedirs(self.results_dir, exist_ok=True)
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def save_results_csv(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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try:
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# Convert results to DataFrame for easy saving
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df_data = []
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for result in results:
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if result.get("success", True):
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row = {
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"strategy_name": result.get("strategy_name", ""),
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"profit_ratio": result.get("profit_ratio", 0),
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"final_usd": result.get("final_usd", 0),
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"n_trades": result.get("n_trades", 0),
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"win_rate": result.get("win_rate", 0),
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"max_drawdown": result.get("max_drawdown", 0),
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"avg_trade": result.get("avg_trade", 0),
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"total_fees_usd": result.get("total_fees_usd", 0),
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"backtest_duration_seconds": result.get("backtest_duration_seconds", 0),
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"data_points_processed": result.get("data_points_processed", 0)
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}
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# Add strategy parameters
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strategy_params = result.get("strategy_params", {})
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for key, value in strategy_params.items():
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row[f"strategy_{key}"] = value
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# Add trader parameters
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trader_params = result.get("trader_params", {})
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for key, value in trader_params.items():
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row[f"trader_{key}"] = value
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df_data.append(row)
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# Save to CSV
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df = pd.DataFrame(df_data)
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full_path = os.path.join(self.results_dir, filename)
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df.to_csv(full_path, index=False)
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logger.info(f"Results saved to {full_path}: {len(df_data)} rows")
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except Exception as e:
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logger.error(f"Error saving results to {filename}: {e}")
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raise
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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,
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action_log: Optional[List[Dict[str, Any]]] = None,
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session_start_time: Optional[datetime] = None) -> None:
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"""
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Save comprehensive backtest results including summary, individual results, and logs.
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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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action_log: Optional action log
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session_start_time: Optional session start time
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"""
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try:
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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session_start = session_start_time or datetime.now()
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# 1. Save summary report
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if summary is None:
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summary = self._calculate_summary_statistics(results)
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summary_data = {
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"session_info": {
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"timestamp": timestamp,
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"session_start": session_start.isoformat(),
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"session_duration_seconds": (datetime.now() - session_start).total_seconds()
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},
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"summary_statistics": summary,
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"action_log_summary": {
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"total_actions": len(action_log) if action_log else 0,
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"action_types": list(set(action["action_type"] for action in action_log)) if action_log else []
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}
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}
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summary_filename = f"{base_filename}_summary_{timestamp}.json"
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self._save_json(summary_data, summary_filename)
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# 2. Save detailed results CSV
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self.save_results_csv(results, f"{base_filename}_detailed_{timestamp}.csv")
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# 3. Save individual strategy results
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valid_results = [r for r in results if r.get("success", True)]
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for i, result in enumerate(valid_results):
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strategy_filename = f"{base_filename}_strategy_{i+1}_{result['strategy_name']}_{timestamp}.json"
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strategy_data = self._format_strategy_result(result)
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self._save_json(strategy_data, strategy_filename)
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# 4. Save action log if provided
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if action_log:
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action_log_filename = f"{base_filename}_actions_{timestamp}.json"
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action_log_data = {
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"session_info": {
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"timestamp": timestamp,
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"session_start": session_start.isoformat(),
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"total_actions": len(action_log)
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},
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"actions": action_log
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}
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self._save_json(action_log_data, action_log_filename)
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# 5. Create master index file
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index_filename = f"{base_filename}_index_{timestamp}.json"
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index_data = self._create_index_file(base_filename, timestamp, valid_results, summary)
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self._save_json(index_data, index_filename)
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# Print summary
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print(f"\n📊 Comprehensive results saved:")
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print(f" 📋 Summary: {self.results_dir}/{summary_filename}")
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print(f" 📈 Detailed CSV: {self.results_dir}/{base_filename}_detailed_{timestamp}.csv")
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if action_log:
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print(f" 📝 Action Log: {self.results_dir}/{action_log_filename}")
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print(f" 📁 Individual Strategies: {len(valid_results)} files")
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print(f" 🗂️ Master Index: {self.results_dir}/{index_filename}")
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except Exception as e:
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logger.error(f"Error saving comprehensive results: {e}")
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raise
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def _save_json(self, data: Dict[str, Any], filename: str) -> None:
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"""Save data to JSON file."""
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full_path = os.path.join(self.results_dir, filename)
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with open(full_path, 'w') as f:
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json.dump(data, f, indent=2, default=str)
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logger.info(f"JSON saved: {full_path}")
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def _calculate_summary_statistics(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""Calculate summary statistics from results."""
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valid_results = [r for r in results if r.get("success", True)]
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if not valid_results:
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return {
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"total_runs": len(results),
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"successful_runs": 0,
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"failed_runs": len(results),
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"error": "No valid results to summarize"
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}
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# Extract metrics
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profit_ratios = [r["profit_ratio"] for r in valid_results]
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final_balances = [r["final_usd"] for r in valid_results]
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n_trades_list = [r["n_trades"] for r in valid_results]
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win_rates = [r["win_rate"] for r in valid_results]
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max_drawdowns = [r["max_drawdown"] for r in valid_results]
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return {
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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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"profit_ratio": {
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"mean": np.mean(profit_ratios),
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"std": np.std(profit_ratios),
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"min": np.min(profit_ratios),
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"max": np.max(profit_ratios),
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"median": np.median(profit_ratios)
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},
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"final_usd": {
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"mean": np.mean(final_balances),
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"std": np.std(final_balances),
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"min": np.min(final_balances),
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"max": np.max(final_balances),
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"median": np.median(final_balances)
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},
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"n_trades": {
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"mean": np.mean(n_trades_list),
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"std": np.std(n_trades_list),
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"min": np.min(n_trades_list),
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"max": np.max(n_trades_list),
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"median": np.median(n_trades_list)
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},
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"win_rate": {
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"mean": np.mean(win_rates),
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"std": np.std(win_rates),
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"min": np.min(win_rates),
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"max": np.max(win_rates),
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"median": np.median(win_rates)
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},
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"max_drawdown": {
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"mean": np.mean(max_drawdowns),
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"std": np.std(max_drawdowns),
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"min": np.min(max_drawdowns),
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"max": np.max(max_drawdowns),
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"median": np.median(max_drawdowns)
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},
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"best_run": max(valid_results, key=lambda x: x["profit_ratio"]),
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"worst_run": min(valid_results, key=lambda x: x["profit_ratio"])
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}
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def _format_strategy_result(self, result: Dict[str, Any]) -> Dict[str, Any]:
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"""Format individual strategy result for saving."""
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return {
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"strategy_info": {
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"name": result['strategy_name'],
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"params": result.get('strategy_params', {}),
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"trader_params": result.get('trader_params', {})
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},
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"performance": {
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"initial_usd": result['initial_usd'],
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"final_usd": result['final_usd'],
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"profit_ratio": result['profit_ratio'],
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"n_trades": result['n_trades'],
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"win_rate": result['win_rate'],
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"max_drawdown": result['max_drawdown'],
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"avg_trade": result['avg_trade'],
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"total_fees_usd": result['total_fees_usd']
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},
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"execution": {
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"backtest_duration_seconds": result.get('backtest_duration_seconds', 0),
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"data_points_processed": result.get('data_points_processed', 0),
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"warmup_complete": result.get('warmup_complete', False)
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},
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"trades": result.get('trades', [])
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}
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def _create_index_file(self, base_filename: str, timestamp: str,
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valid_results: List[Dict[str, Any]],
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summary: Dict[str, Any]) -> Dict[str, Any]:
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"""Create master index file."""
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return {
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"session_info": {
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"timestamp": timestamp,
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"base_filename": base_filename,
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"total_strategies": len(valid_results)
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},
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"files": {
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"summary": f"{base_filename}_summary_{timestamp}.json",
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"detailed_csv": f"{base_filename}_detailed_{timestamp}.csv",
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"individual_strategies": [
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f"{base_filename}_strategy_{i+1}_{result['strategy_name']}_{timestamp}.json"
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for i, result in enumerate(valid_results)
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]
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},
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"quick_stats": {
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"best_profit": summary.get("profit_ratio", {}).get("max", 0) if summary.get("profit_ratio") else 0,
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"worst_profit": summary.get("profit_ratio", {}).get("min", 0) if summary.get("profit_ratio") else 0,
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"avg_profit": summary.get("profit_ratio", {}).get("mean", 0) if summary.get("profit_ratio") else 0,
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"total_successful_runs": summary.get("successful_runs", 0),
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"total_failed_runs": summary.get("failed_runs", 0)
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}
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} |