Backtesting Trading Strategies in Python with LumiBot

Choose a backtesting source from the strategy’s asset class and required bar interval:

LumiBot backtesting data choices

Need	Start with	Typical granularity	Setup
Daily stocks and ETFs	Yahoo	Daily	No data-provider credentials
Intraday stocks and options	ThetaData	Minute, hour, and daily	Data Downloader and provider access
Stocks, options, forex, or crypto	Polygon.io	Intraday and daily	Polygon.io API key
Futures and market-data schemas	Databento	Tick through daily, by dataset	Databento API key and dataset access
Your own stock or futures data	Pandas	Whatever the supplied file contains	Local data prepared in LumiBot’s format
Interactive Brokers history	IBKR REST	Provider-supported intervals	Client Portal and Data Downloader access
Options with your own Alpaca account	Alpaca	Minute and daily	Alpaca API key (free accounts include option history from about February 2024)
Prediction contracts	Polymarket	Market price history	Polymarket market identifiers

Use Yahoo for the simplest free daily-stock example. Use ThetaData when a stock or option strategy needs intraday history, and use Pandas when you already own the data and can prepare it in LumiBot’s input format.

Managed Backtesting on BotSpot

Backtesting is better on BotSpot when you want to move faster than a local setup. BotSpot already has the workflow around Lumibot: hosted data setup, parallel backtest workers, generated artifacts, charts, logs, and the path from a passing backtest into paper or live trading.

• Backtesting data included. Use supported hosted stock, futures, options, macro, filings, and other data sources without sourcing every vendor, API key, downloader, and local file yourself. Some data is included; premium datasets can be much cheaper than buying direct subscriptions.

• Parallel experiments. Launch multiple strategy variants on BotSpot servers and compare results instead of waiting for one local run at a time.

• Better artifacts. Inspect charts, trades, logs, files, decisions, and audit history from one place instead of stitching together local output folders.

• Lumibot-tuned iteration. BotSpot’s AI workflows and MCP tools understand Lumibot strategy structure, so Codex, Claude Code, Cursor, and other agents can run backtests and inspect results instead of only editing Python.

• Ready for deployment. A strategy that survives backtesting can move into paper or live trading with supported broker connections, monitoring, alerts, and kill-switch controls already available.

Agentic Backtesting

Lumibot also supports agentic backtesting. A strategy can create one or more AI agents, run them from normal lifecycle methods, analyze point-in-time data with DuckDB, and replay identical agent runs from cache on the next backtest instead of paying for another model call.

This matters if you want:

• an AI trading agent that makes decisions inside on_trading_iteration()

• an LLM trading bot that can also be tested historically

• external MCP tools attached to a strategy

• backtest/live parity for agent-driven strategies

See Build AI Trading Agents in Python with LumiBot for the full agent runtime guide and usage examples.

Files Generated from Backtesting

When you run a backtest, several important files are generated, each prefixed by the strategy name and the date. These files provide detailed insights into the performance and behavior of the strategy.

Contents:

• How to Backtest a Python Trading Strategy with LumiBot
  • Installing LumiBot
  • Choosing a Data Source
  • Running a Backtest with Polygon.io
  • Optional: Using Environment Variables for Backtest Configuration
• Files Generated from Backtesting
  • Tearsheet HTML
  • Trades Files
  • Indicators Files
  • Conclusion
• Backtesting Function
  • run_backtest()
• Backtesting Performance (Speed + Parity)
  • Warm vs cold runs
  • Quick diagnosis checklist
  • Execution model and realistic parallelism
  • Profiling (YAPPI)
  • Environment variables
  • ThetaData options: common performance pitfalls
• Free Daily Stock Backtesting with Yahoo and LumiBot
  • Option 1: Provide Start and End in Code
  • Using Environment Variables (Optional)
  • Option 2: Rely on Environment Variables (No Start/End in Code)
• Pandas (CSV or other data)
  • In Summary
  • Options Expiration Settlement Policy
  • Optional: Environment Variables
• Polygon.io Backtesting
  • Here’s the full code (with explicit dates):
• Databento Backtesting with LumiBot
  • Overview
  • Setting Up DataBento
  • Basic Usage
  • Supported Assets
  • Time Frames
  • Advanced Configuration
  • Data Quality Features
  • Caching
  • Best Practices
  • Example: Multi-Asset Strategy
  • Error Handling
  • Performance Optimization
  • Troubleshooting
  • Cost Considerations
• ThetaData Options and Stock Backtesting with LumiBot
  • Session close coverage (index/stock minute bars)
  • Options pricing and mark-to-market (important)
  • Optional: Environment Variables
• Interactive Brokers (REST) Backtesting
  • Status
  • Quick Start
  • Supported Data
  • Daily Stocks/Indexes: Warmup + Corporate Actions
  • How History Is Downloaded (stocks and indexes)
  • Futures Exchange Routing (auto + override)
  • Expired Futures Contracts (conids)
  • Caching
  • Multi-provider routing (Theta + IBKR)
  • Crypto futures and perpetuals
  • Market Data Subscriptions (IBKR)
  • Authentication / Session Behavior
  • Configuration Notes
• Alpaca Backtesting
  • Setup
  • Selecting Alpaca from the environment
  • Options example
  • How option data works
  • Known limits
• LumiBot Backtest Tear Sheets and Performance Reports
  • Machine-readable tearsheet metrics
  • Cashflow-adjusted returns
  • Custom metrics workflow
  • Release-order note
• Trades Files
  • Cash Events
  • Option Lifecycle Statuses
• Indicators Files
• Logs CSV

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