# polakowo/vectorbt

The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.

Repository: https://github.com/polakowo/vectorbt
Canonical: https://ross.abutalabs.com/products/vectorbt
Homepage: https://vectorbt.dev
Language: Python
License: NOASSERTION
License Family: other
Topics: backtesting, trading, algorithmic-trading, trading-strategies, cryptocurrency, data-visualization, time-series, data-science, finance, machine-learning, portfolio-optimization, quantitative-finance, quantitative-analysis, algorithmic-traiding
Last push: 2026-08-02T09:14:10+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 95, release rhythm 80, longevity 100
- inputs: {"age_days": 3214, "days_push": 31, "days_rel": 59, "gap_med": 59, "n_releases_24m": 4}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8847, forks 1135 (observed 2026-08-28T04:10:25.990650+00:00)

## What it is
VectorBT is a Python library for backtesting and analyzing trading strategies at scale, operating on pandas and NumPy objects accelerated by Numba and an optional Rust engine. It enables testing thousands of strategy parameter combinations in seconds, with interactive Plotly-based visualization in Jupyter.

## Use cases
- backtest trading strategies in python
- run parameter grid search over thousands of strategy configurations
- test moving average crossover strategies on crypto data
- optimize trading strategy parameters at scale
- analyze portfolio performance down to individual trades
- engineer time-series features for ML models
- visualize backtest results with interactive charts

## When to choose
- you need to backtest thousands of strategy variants quickly without loops
- you want full local control over data and execution instead of web-based backtesters
- you work in Jupyter and want interactive performance dashboards
- you need fast vectorized pandas/NumPy operations for financial time series

## When to avoid
- you need live trading execution or broker integration rather than backtesting
- you require advanced features like leverage, limit orders, or parallelization that are only in the paid PRO edition
- you need a permissively licensed library - it uses a Fair Code license, not OSI-approved open source
- you want a simple event-driven backtester for a single strategy rather than vectorized mass testing

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, machine-learning, analytics, data-science, charts
- domain: fintech, data-science, time-series, analytics
- platform: python, cross-platform
- tags: backtesting, trading-strategies, quantitative-finance, numba, rust-engine, portfolio-optimization, parameter-optimization, jupyter, plotly, technical-analysis, cryptocurrency, docker

## Member repositories
- polakowo/vectorbt (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:25.990650+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:24:47.925217+00:00, confidence not recorded.
  - readme: https://github.com/polakowo/vectorbt (fetched 2026-08-28T04:10:25.990650+00:00, sha 2c277878e921)
  - homepage: https://vectorbt.dev (fetched 2026-08-29T08:25:15.553795+00:00, sha 607ddbd0a6be)
  - site_page: https://vectorbt.dev/getting-started/features (fetched 2026-08-29T08:25:15.563623+00:00, sha af9d889a7e0d)
  - site_page: https://vectorbt.dev/getting-started/installation (fetched 2026-08-29T08:25:15.566203+00:00, sha 4835f1a51027)
  - site_page: https://vectorbt.dev/getting-started/usage (fetched 2026-08-29T08:25:15.567977+00:00, sha 8642fedf23b7)
  - site_page: https://vectorbt.dev/getting-started/resources (fetched 2026-08-29T08:25:15.569756+00:00, sha ae16d046ec18)
  - site_page: https://vectorbt.dev/getting-started/contributing (fetched 2026-08-29T08:25:15.572265+00:00, sha f915e5591ba7)
  - site_page: https://vectorbt.dev/api/utils/docs (fetched 2026-08-29T08:25:15.574411+00:00, sha 747d7f7120a5)
  - registry_pypi: https://pypi.org/pypi/vectorbt/json (fetched 2026-08-29T08:25:15.576177+00:00, sha 23c9a5856995)
- Data as of 2026-08-30T08:39:29.467469+00:00.