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.
49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
"""
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Incremental Backtesting Framework
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This module provides comprehensive backtesting capabilities for incremental trading strategies.
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It includes configuration management, data loading, parallel execution, and result analysis.
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Components:
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- IncBacktester: Main backtesting engine
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- BacktestConfig: Configuration management for backtests
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- OptimizationConfig: Configuration for parameter optimization
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- DataLoader: Data loading and validation utilities
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- SystemUtils: System resource management
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- ResultsSaver: Result saving and reporting utilities
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Example:
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from IncrementalTrader.backtester import IncBacktester, BacktestConfig
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from IncrementalTrader.strategies import MetaTrendStrategy
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# Configure 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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# Run single strategy
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strategy = MetaTrendStrategy("metatrend")
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backtester = IncBacktester(config)
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results = backtester.run_single_strategy(strategy)
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# Parameter optimization
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param_grid = {"timeframe": ["5min", "15min", "30min"]}
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results = backtester.optimize_parameters(MetaTrendStrategy, param_grid)
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"""
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from .backtester import IncBacktester
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from .config import BacktestConfig, OptimizationConfig
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from .utils import DataLoader, DataCache, SystemUtils, ResultsSaver
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__all__ = [
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"IncBacktester",
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"BacktestConfig",
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"OptimizationConfig",
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"DataLoader",
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"DataCache",
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"SystemUtils",
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"ResultsSaver",
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] |