Repolode
Submit
coding-kitties

coding-kitties/investing-algorithm-framework

Python framework for quantitative trading that covers strategy definition, vector and event-driven backtesting, result dashboards, and paper or live trading.

View on GitHubWebsite
Stars
2,231
Forks
318
Open issues
90
Last commit
3 days ago

Repolode describes open-source projects and does not give investment advice. Trading and investing can lose money: assess the risk yourself and follow the laws where you live before using this project.

What is it?

Investing Algorithm Framework is a Python framework for the full quantitative trading workflow. A strategy is defined once as a TradingStrategy subclass and can then be explored with fast Polars-based vector backtests, validated bar by bar in an event-driven simulation, and run in paper or live trading without rewriting the logic.

Backtest results are stored as versioned .obtf bundles and indexed in SQLite, so large sets of runs can be ranked and filtered. An interactive HTML dashboard compares strategies with more than 80 performance metrics, and a Monte Carlo permutation test estimates whether observed results could arise by chance. Declarative risk rules cover position sizing, exposure limits, stop losses, take profits and cooldowns, and execution cost models cover commissions and slippage.

Live trading connects to exchanges through CCXT, and custom order executors can be implemented for other brokers. The project also ships a CLI, an MCP server for AI tools, and scaffolding for self-hosted, AWS Lambda and Azure Functions deployments.

Key features

  • 01Define a strategy once and run it in vector backtests, event-driven backtests, paper trading and live trading.
  • 02Polars-powered vector engine for sweeping large numbers of signal variants.
  • 03Versioned .obtf result bundles with a SQLite index for ranking and filtering backtests.
  • 04Over 80 performance metrics, including Sharpe, Sortino, Calmar and VaR/CVaR.
  • 05Declarative risk rules for sizing, exposure, stop losses, take profits and cooldowns.
  • 06Monte Carlo permutation testing and walk-forward or out-of-sample study windows.

Use cases

  • Researching and comparing trading strategy variants on historical crypto or market data.
  • Validating a backtested strategy with event-driven simulation before paper trading.
  • Running an automated trading bot against exchanges supported through CCXT.
  • Deploying a strategy as a serverless function on AWS Lambda or Azure Functions.

Quick start

  1. 1

    Install the v9.0.0 alpha prerelease from PyPI

    pip install investing-algorithm-framework==9.0.0a22
  2. 2

    Define a TradingStrategy subclass and run backtests with the app as shown in the README

Things to know

  • The current release is a v9.0.0 alpha, so APIs may change.

Topics

#algorithmic-trading#backtesting#quantitative-finance#cryptocurrency#trading-bot#python#backtesting-trading-strategies#quantitative#quantitative-analysis#quantitative-trading

FAQ

What is investing-algorithm-framework?
Python framework for quantitative trading that covers strategy definition, vector and event-driven backtesting, result dashboards, and paper or live trading. Investing Algorithm Framework is a Python framework for the full quantitative trading workflow. A strategy is defined once as a TradingStrategy subclass and can then be explored with fast Polars-based vector backtests, validated bar by bar in an event-driven simulation, and run in paper or live trading without rewriting the logic.
Is investing-algorithm-framework free to use? What license does it have?
investing-algorithm-framework is open source under the Apache-2.0 license. Check the license terms before using it in commercial products.
Is investing-algorithm-framework still maintained?
Actively maintained. The last commit to investing-algorithm-framework was on Nov 4, 2026.
What can I use investing-algorithm-framework for?
Researching and comparing trading strategy variants on historical crypto or market data. Validating a backtested strategy with event-driven simulation before paper trading. Running an automated trading bot against exchanges supported through CCXT. Deploying a strategy as a serverless function on AWS Lambda or Azure Functions.
How do I install investing-algorithm-framework?
Install the v9.0.0 alpha prerelease from PyPI: pip install investing-algorithm-framework==9.0.0a22 Define a TradingStrategy subclass and run backtests with the app as shown in the README
Where can I get investing-algorithm-framework?
The source code and installation instructions are on GitHub: https://github.com/coding-kitties/investing-algorithm-framework

Summary generated by AI from the project's README. GitHub stats refresh daily.

Related projects