Lumiwealth/lumibot

What it solves

Lumibot provides a unified Python framework for building, backtesting, and deploying algorithmic trading strategies. It bridges the gap between simple AI demos and production-ready trading systems by allowing users to combine deterministic Python logic (risk controls, indicators) with agentic AI reasoning to make informed trading decisions.

How it works

Lumibot operates as a strategy loop where users can implement either fixed rules or AI-driven agents. It includes a built-in AI agent runtime that allows agents to access financial tools—such as market data, SEC filings, FRED macro data, and technical indicators—to reason through evidence and submit orders. Users can design "AI Trading Teams" where multiple agents (e.g., a Researcher, a Bull, and a Bear) debate a trade before a final Trader agent executes it. The same code can be used for historical backtesting, paper trading, and live trading across various brokers.

Who it’s for

It is designed for algorithmic traders, quantitative analysts, and developers who want to build AI-powered trading bots that are inspectable, backtestable, and integrated with real broker APIs.

Highlights

• Hybrid Intelligence: Combine deterministic Python guardrails with LLM-based reasoning.
• AI Trading Teams: Support for multi-agent workflows, including specialist desks and debate-style decision making.
• Detailed Backtesting: Replay historical data to inspect agent decisions, orders, and performance artifacts.
• Broad Broker Support: Integration with brokers like Alpaca, IBKR, Tradier, Schwab, and others.
• Integrated Tooling: Built-in tools for agents to query DuckDB, read news, and access fundamental data.