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Lumibot

Lumibot is a Python framework and command-line tool for building algorithmic trading strategies, backtesting them on historical data, and connecting configured strategies to supported brokers. Strategies can use Python rules, AI agents, or both, and the framework covers multiple asset classes including crypto.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Lumibot — OpenViST project profile in Trading & Crypto Agents.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Lumibot when you want a Python-based workflow that brings strategy development and historical backtesting together with a documented path to broker-connected trading.

Who it is for

  • Python developers building automated trading strategies
  • Quantitative researchers comparing strategies through historical backtests
  • Developers connecting strategy logic to supported brokers

Documented uses

  • Create editable Python strategy classes or start from documented strategy templates.
  • Run historical backtests and review resulting trades and tearsheets.
  • Develop AI-agent trading workflows alongside conventional Python rules.
  • Configure a separate paper or live broker runner for a strategy.
Before you choose

Requirements & limits

  • Python 3.10 or later is required for the documented Yahoo-data backtest example.
  • The Yahoo historical-data example requires internet access and does not require a broker account.
  • Broker-connected runs require a configured broker; the documented Alpaca paper example uses API credentials.
  • Backtest and broker-run entry points are distinct; changing a backtesting setting alone does not launch a broker runner.
  • The public repository lists the GPL-3.0 license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run a documented Yahoo-data backtest

Backtest a basic Python strategy that buys and holds 10 AAPL shares.

Before you start

  • Python 3.10 or later
  • Lumibot installed with pip
  • Internet access for Yahoo historical prices

Steps

  1. Install Lumibot with the command below.

    Documented command
    pip install lumibot

  2. Save the documented MyStrategy example as my_strategy.py; it submits a 10-share AAPL buy order on the first iteration.

  3. Run the file with the command below to call the strategy’s Yahoo historical-data backtest.

    Documented command
    python my_strategy.py

Expected outcome

A historical simulation of the example strategy over the specified date range.

Guide limitations

  • This example is a traditional Python strategy and makes no AI calls.
  • The documented backtest uses Yahoo historical prices and internet access; a backtest does not configure a broker run.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
10 October 2026
Original website title
Lumiwealth/lumibot · Official homepage title · retained in its source language
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