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vectorbt

by Oleg Polakow · Library · also known as VectorBT, vectorbtpro

Python library for vectorized backtesting and portfolio analysis built on pandas, NumPy and Numba, with a commercial PRO edition

Apache-2.0 WITH Commons-Clause licenseFreePaidLocalSelf-hostable
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Link verified Nov 2, 2026

Overview

vectorbt is a Python library by Oleg Polakow for backtesting trading strategies as array operations rather than event loops. Signals, orders and portfolio values are represented as pandas and NumPy arrays and compiled with Numba, which makes it practical to test thousands of parameter combinations in one pass and to inspect results with interactive Plotly charts. It covers indicator construction, signal generation, portfolio simulation from signals or orders, and performance statistics such as drawdowns and trade records. The open-source edition is distributed under Apache-2.0 with a Commons Clause that forbids selling the software, and a separately licensed vectorbt PRO continues development with additional features. The vectorized model trades realism for speed: path-dependent logic and complex order handling are harder to express than in event-driven engines.

Capabilities

  • Vectorized simulation of many parameter sets at once
  • Portfolio construction from signals, orders or holdings
  • Built-in indicators and an indicator factory
  • Trade, drawdown and return statistics
  • Interactive Plotly visualizations
  • Numba-accelerated computation on pandas data

Typical workflows

  1. Sweep moving-average windows across a universe and compare equity curves
  2. Simulate a signal-based portfolio and export trade records to pandas

Strengths

  • Fast parameter sweeps on a single machine
  • Stays within the pandas and NumPy ecosystem

Limitations

  • Vectorized model makes path-dependent strategies awkward
  • Open-source license includes a Commons Clause restricting resale
  • Newer features land in the paid PRO edition

Prerequisites

  • Python with pandas, NumPy and Numba
  • Price data from an external source

Who it is for

  • Quantitative researchers
  • Python-savvy retail traders

Alternatives & comparison

Zipline Reloaded and QuantConnect's LEAN are event-driven engines that step through time; vectorbt computes whole strategies as array operations, which is faster for sweeps but less faithful for order-level logic.

  • pandas

    The standard Python DataFrame library for labeled tabular data, time series and I/O to CSV, Excel, SQL, Parquet and more

    Open sourceOpen source
  • QuantConnect

    Algorithmic trading platform built on the open-source LEAN engine, with cloud backtesting, research notebooks and brokerage-connected live trading

    MixedFreemium
  • Zipline Reloaded

    Community-maintained fork of Quantopian's Zipline, an event-driven Python backtester with data bundles and the Pipeline API

    Open sourceOpen source
Compare vectorbt with its alternatives →

Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

  • License
    LICENSE.md — Apache 2.0 with Commons Clause

    https://github.com/polakowo/vectorbt/blob/master/LICENSE.md

    Supports: license_spdx, source_model

    checked Nov 1, 2026
  • Official site
    vectorbt documentation

    https://vectorbt.dev/

    Supports: name, summary, capabilities

    checked Nov 1, 2026
  • Official site
    vectorbt PRO

    https://vectorbt.pro/

    Supports: pricing_model

    checked Nov 1, 2026
  • Repository
    polakowo/vectorbt

    https://github.com/polakowo/vectorbt

    Supports: product_status, languages

    checked Nov 1, 2026
Confidence
High
Reviewed
Nov 1, 2026
Links
Verified · Nov 2, 2026

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