polakowo/vectorbt
Vectorbt
VectorBT is a vectorized trading strategy backtesting framework that simulates thousands of strategy configurations in a single pass over historical price data. It operates as a parameter optimization engine, a portfolio performance analyzer, a technical indicator calculator, and a financial data fetcher, all built around a DataFrame-centric data model that uses NumPy broadcasting for signal alignment and compiled code acceleration for performance.
The framework distinguishes itself through its ability to run large-scale parameter sweeps by constructing every combination of strategy parameters as a single array dimension, enabling one-pass evaluation of the full grid. It includes a walk-forward validation framework for testing strategy robustness across changing market conditions, and generates interactive visualizations using Plotly for exploring backtest results and indicators. The project also provides external data source abstraction for fetching market data from providers like Yahoo Finance.
Beyond its core backtesting and optimization capabilities, VectorBT supports computing custom technical indicators, generating crossover trading signals, and analyzing portfolio performance with trade-level metrics and drawdown statistics. It can schedule recurring analyses and send notifications through Telegram, and offers a one-line backtesting interface for quick strategy evaluation.
Features
- Trading Strategy Backtesters - Defines entry and exit signals and computes comprehensive portfolio performance metrics for one or more assets.
- Vectorized Backtesters - Simulates thousands of strategy configurations in a single vectorized pass over historical data.
- Technical Indicator Calculators - Computes moving averages, crossovers, and other built-in indicators with external library support.
- Crossover Signal Generators - Generates crossover trading signals from moving average crossovers for backtesting.
- Historical Price Data Fetchers - Fetches historical price data from external providers for backtesting and analysis.
- Vectorized Parameter Sweeps - Executes thousands of strategy parameter combinations in parallel using vectorized operations to reduce grid search time.
- Vectorized Backtesting Engines - Runs thousands of strategy configurations simultaneously by representing each instance as a vector in a multi-dimensional array.
- Financial Market Visualizers - Creates interactive plots, heatmaps, and animations for exploring price data, indicators, and backtest outcomes.
- Dataframe Engines - Represents all financial time series, signals, and portfolio states as pandas DataFrames.
- Financial Charting - Creates interactive financial charts and dashboards using Plotly for exploring backtest results.
- JIT Compilation Accelerators - Offloads performance-critical loops to Numba and Cython to eliminate Python interpreter overhead.
- Strategy Parameter Grid Sweeps - Constructs every combination of strategy parameters as a single array dimension for one-pass evaluation.
- Vectorized Array Operations - Packs thousands of strategy configurations into multi-dimensional arrays and applies operations simultaneously.
- Portfolio Performance Metrics - Computes trade-level metrics, drawdowns, Sharpe ratio, and other portfolio performance statistics.
- Strategy Parameter Optimization - Sweeps across many combinations of strategy parameters to find optimal configurations.
- Large-Scale Parameter Sweeps - Tests thousands of window sizes or random signal configurations across assets and timeframes at once.
- Technical Indicator Engines - Calculates moving averages, crossovers, and other built-in technical indicators for trading signals.
- Trading Signal Triggers - Calculates technical indicators like moving averages and detects crossovers to produce entry and exit signals.
- Vectorized Backtesting Engines - Packs thousands of strategy configurations into NumPy arrays and accelerates the hot path with Numba and Rust to execute all simulations.
- NumPy Broadcasting Alignments - Aligns entry and exit signals across multiple assets using NumPy broadcasting without explicit iteration.
- Walk-Forward Optimizers - Runs walk-forward analysis to validate strategy stability across different market conditions.
- Walk-Forward Validation - Splits historical data into sequential training and testing windows to evaluate strategy robustness.
- Trading Strategy Robustness Validation - Runs walk-forward optimization and generates labels for machine learning to confirm strategy stability.
- Performance Visualization - Generates interactive charts and dashboards using Plotly and Jupyter Widgets to explore trading results.
- Market Data Access APIs - Downloads and preprocesses market data from Yahoo Finance and other sources for backtesting.
- Historical Data Downloads - Downloads historical market data from external providers like Yahoo Finance for backtesting.
- Interactive Plotting Frameworks - Generates browser-ready charts and dashboards using Plotly for exploring backtest results.
- Sliding Window Averaging - Calculates a moving average with a configurable window size over a price series.
- Performance Charting - Generates Plotly-based charts, heatmaps, and dashboards for exploring backtest results.
- Financial Data - Wraps Yahoo Finance and other providers behind a uniform interface returning normalized DataFrames.
- Strategy Performance Reports - Generates interactive plots and heatmaps of strategy performance, and animates technical indicators across multiple symbols.
- Financial Performance Dashboards - Displays rich, interactive charts and dashboards in Jupyter notebooks or a browser using Plotly.
- Data Fetching - Downloads price data from external sources like Yahoo Finance and prepares it for backtesting.
- Trading and Backtesting - Toolkit for backtesting and algorithmic research.
- Trading Frameworks - Vectorized backtesting accelerated by Numba and Pandas.
