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bt - flexible backtesting for Python

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README.md

Original

bt — Flexible Backtesting for Python

Build Status PyPI Version PyPI License

Build, test, and compare investment strategies from reusable Python components. bt combines strategy logic with historical price data, tracks portfolio positions and transactions, and provides performance statistics and charts through ffn.

  • Compose strategy logic: combine algorithms for scheduling, security selection, weighting, and rebalancing.
  • Build portfolios of strategies: nest strategies and securities in a common tree.
  • Model trading costs: configure commissions and transaction cost models.
  • Compare results: inspect returns, weights, transactions, drawdowns, and other statistics.

Install

pip install bt

See the installation guide for additional details.

A first backtest

This example uses synthetic prices, so it runs without downloading market data:

import numpy as np
import pandas as pd

import bt

prices = pd.DataFrame(
    {
        "asset_a": np.linspace(100, 120, 252),
        "asset_b": np.linspace(100, 110, 252),
    },
    index=pd.bdate_range("2020-01-01", periods=252),
)

strategy = bt.Strategy(
    "equal_weight",
    [
        bt.algos.RunMonthly(),
        bt.algos.SelectAll(),
        bt.algos.WeighEqually(),
        bt.algos.Rebalance(),
    ],
)
result = bt.run(bt.Backtest(strategy, prices))
result.display()

The strategy selects both assets, gives each equal weight, and rebalances monthly. Replace the synthetic prices with your own data to explore a strategy. Backtest results depend on data quality and modeling assumptions; they do not predict future performance.

Explore the documentation

The published documentation is at https://pmorissette.github.io/bt/.

Contribute

See the development guide for environment setup, tests, documentation builds, and Copier template updates. Report bugs and propose improvements through GitHub issues.

bt is released under the MIT license.

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