{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/20"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"},{"label":"Financial","url":"https://skillfed.io/packages/category/office-business-financial"},{"label":"Investment","url":"https://skillfed.io/packages/category/office-business-financial-investment"}],"enrichment":{"capability":"Lumibot is a Python framework for building, backtesting, and running algorithmic trading strategies and AI agents across stocks, options, crypto, futures, and forex using a single codebase that works identically in backtest and live trading.","skillfed_tags":["trading-bot","backtesting","multi-asset"],"use_cases":["Backtest a multi-asset strategy (stocks, options, crypto, futures) against historical data to validate logic before risking capital.","Paper-trade a strategy through Alpaca or another broker to test execution and slippage in real market conditions without live money.","Deploy a deterministic trading bot that runs on a schedule, checking technical indicators and submitting orders through a live broker connection.","Build an AI trading team where multiple agents debate market evidence and a trader agent decides whether to execute, all within the same Lumibot loop.","Transition a backtest-validated strategy to live trading by changing only the broker configuration, keeping strategy code identical."],"what_it_does":"Lumibot is a framework that lets you write a single Python strategy class and run it in three modes: backtest against historical data, paper trade through a broker's simulation, or execute live orders\u2014without rewriting your logic. It abstracts away broker APIs (Alpaca, Interactive Brokers, Tradier, Schwab, and others) and data sources (Yahoo Finance, Polygon, Alpha Vantage, Quandl) so you focus on strategy rules, position sizing, and risk controls.\n\nThe framework also includes a built-in AI agent runtime where you can define multiple agents with different roles\u2014researcher, bull, bear, trader\u2014that reason through market data, filings, indicators, and macro context before submitting orders. You can mix deterministic Python logic with AI reasoning, or use either approach alone. Backtests produce inspectable artifacts (orders, fills, equity curves), and the same strategy code transitions directly to paper or live trading once you swap the broker configuration.","worth_installing":"Yes, if you are building algorithmic trading strategies or AI trading agents. Lumibot is actively maintained, permissively licensed, and solves the core problem of code reuse across backtest and live trading. The 50 runtime dependencies are substantial but reflect the breadth of brokers and data sources it supports; install friction is low. No known vulnerabilities. Start with backtest and paper trading to validate your logic before going live."},"id":"lumibot","links":{"html":"https://skillfed.io/packages/lumibot","md":"https://skillfed.io/packages/lumibot.md","pypi":"https://pypi.org/project/lumibot/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":null,"license_treatment":"permissive","name":"lumibot","python_support":"supports_current","summary":"Python framework for algorithmic trading: backtesting and live deployment for stocks, options, crypto, futures, and forex. Same code for backtest and live trading."},"popularity":{"monthly_downloads":106381,"position":12654,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"4.5.83"}