kernc/backtesting.py
Backtesting.py
backtesting.py is a Python trading backtesting framework used to simulate trading strategies against historical price data to evaluate performance and risk. It includes a technical trade simulator, a quantitative performance analyzer, and a financial strategy optimizer.
The framework features a parallel strategy simulator that distributes execution across multiple processor cores to reduce computation time. It also provides tools for strategy parameter optimization, allowing the identification of performant settings through the use of heatmaps and metrics.
The system covers trade execution management, including the modeling of commissions, spreads, and order behaviors such as hedging. Risk management is handled through trade exit rules like stop-loss, take-profit, and trailing stop-loss mechanisms. Performance is quantified using success metrics, risk ratios, and growth rates, which can be rendered as equity curves and price action charts.
Features
- Algorithmic Trading Simulators - Implements a full platform for evaluating automated investment strategies against historical market data.
- Virtual Trade Execution - Provides a simulation environment to execute trades against virtual portfolios for strategy testing.
- Strategy Success Metrics - Generates quantitative metrics to measure the effectiveness and risk of a trading system over time.
- Automated Exit Strategies - Implements automated exit strategies using stop-loss, take-profit, and trailing stop-loss mechanisms.
- Quantitative Performance Metrics - Calculates quantitative risk metrics and growth rates to determine strategy stability and effectiveness.
- Trading Simulation Engines - Provides a simulation engine that models broker commissions, spreads, and risk-based exit rules.
- Trading Strategy Backtesters - Provides a comprehensive framework for evaluating financial trading strategies against historical market data.
- Commission Models - Implements models for commissions and spreads to accurately simulate real-world transaction costs.
- Financial Performance Metrics - Calculates detailed success statistics including growth rates, risk ratios, and win rates to quantify strategy effectiveness.
- Trade Performance Analyzers - Includes a quantitative analyzer for calculating financial success statistics and generating equity curves.
- Strategy Parameter Optimization - Provides mathematical methods for finding optimal configurations of quantitative trading parameters via heatmaps.
- Trading Risk Management - Implements risk controls including stop-loss and take-profit rules within the simulation environment.
- Trading Strategy Optimizers - Includes tools for running multiple strategy iterations in parallel to identify optimal parameters.
- Plotting and Visualization - Produces visual plots and equity curves to evaluate the success and stability of a trading system.
- Trade Visualization Tools - Provides interactive charts of price action and equity curves to manually inspect trade entries and exits.
- Parallel Simulation Engines - Ships a multi-core execution engine that runs trading simulations across several datasets to reduce computation time.
- Multi-Dataset Backtesting - Allows running a single strategy across several different datasets to validate results through repeated simulation.
- Performance Visualization - Provides graphical tools for rendering equity curves and drawdown charts to analyze strategy performance.
- Backtesting Workload Distribution - Distributes strategy simulation and indicator tasks across multiple CPU cores to reduce computation time.
- Trading Frameworks - Framework for inferring strategy viability on historical data.
