Building Trading Strategies with a Shared Backtest-to-Live Workflow

Summary

The document describes Lumibot as a Python framework for creating rule-based strategies, AI-assisted trading systems, and hybrid approaches. Conventional Python logic can handle indicators, schedules, position sizing, and risk controls, while AI agents can be added to examine market information such as price data, company filings, macroeconomic releases, news, and account state before a decision.

A central idea is to keep one strategy lifecycle across historical backtests, paper trading, and live execution through brokers, allowing behavior to be inspected before connecting a real account. The framework also describes time-series analysis through DuckDB, external tool connections, replayable agent runs, and read-only permissions for research agents. These are capability descriptions rather than a documented strategy or empirical evaluation: the text reports no backtest results, live performance, or comparison with other systems. It therefore explains an implementation workflow, while leaving the quality of any strategy and the reliability of its AI decisions to be established separately.

Key ideas

• The framework supports rule-based, AI-assisted, and hybrid trading strategies in Python.
• Rules, indicators, schedules, position sizing, and risk controls can be coded as ordinary strategy logic.
• AI agents can inspect market and account information before a strategy decision.
• A shared lifecycle supports historical backtesting, paper trading, and live broker execution.
• The document describes platform capabilities but gives no empirical strategy results or AI reliability evidence.

Tags

# main




    Lumibot is a Python framework for building deterministic strategies, AI trading teams, and hybrid trading systems that backtest, paper trade, and run live through real brokers.

    Use normal Python for rules, indicators, schedules, position sizing, and risk controls. Add AI agents when you want a strategy to research market data, SEC filings, macro data, news, technical indicators, and account state before making a decision.

    The same strategy lifecycle works from historical backtests to paper trading and live trading, so you can inspect behavior before connecting a real account.

    Lumibot also supports AI trading agents inside the strategy lifecycle, including DuckDB-powered time-series analysis, external MCP tools, replayable agent runs, and trading permissions that keep research agents read-only.

Shown in full with attribution under the source's licence. Licence: GPL-3.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.