polakowo/vectorbt
The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one. observed · 2026-08-28
Health v2 · maintenance only
91/100
- Activity 95
- Release rhythm 80
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 59
- age_days: 3214
- days_rel: 59
- days_push: 31
- n_releases_24m: 4
Adoption not part of the score
8847 stars · 1135 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
library · maturity active
data-visualization machine-learning analytics data-science charts fintech data-science time-series analytics python cross-platform backtesting trading-strategies quantitative-finance numba rust-engine portfolio-optimization parameter-optimization jupyter plotly technical-analysis cryptocurrency docker
9 sources
- readme: https://github.com/polakowo/vectorbt · fetched 2026-08-28 · 2c277878e921
- homepage: https://vectorbt.dev · fetched 2026-08-29 · 607ddbd0a6be
- site_page: https://vectorbt.dev/getting-started/features · fetched 2026-08-29 · af9d889a7e0d
- site_page: https://vectorbt.dev/getting-started/installation · fetched 2026-08-29 · 4835f1a51027
- site_page: https://vectorbt.dev/getting-started/usage · fetched 2026-08-29 · 8642fedf23b7
- site_page: https://vectorbt.dev/getting-started/resources · fetched 2026-08-29 · ae16d046ec18
- site_page: https://vectorbt.dev/getting-started/contributing · fetched 2026-08-29 · f915e5591ba7
- site_page: https://vectorbt.dev/api/utils/docs · fetched 2026-08-29 · 747d7f7120a5
- registry_pypi: https://pypi.org/pypi/vectorbt/json · fetched 2026-08-29 · 23c9a5856995
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| polakowo/vectorbt | main | 91 |
For agents
markdown · JSON · MCP: product_card(name="polakowo/vectorbt")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem