Investing Algorithm Framework

<h1 align="center"> Investing Algorithm Framework </h1> <p align="center"> <i align="center">The full quant workflow in one framework: build strategies, vector & event-driven backtest at scale, compare in a single dashboard, and deploy the winner 🚀</i> </p> <h4 align="center"> <a href="https://github.com/coding-kitties/investing-algorithm-framework/actions/workflows/test.yml?query=branch%3Amain"> <img src="https://img.shields.io/github/actions/workflow/status/coding-kitties/investing-algorithm-framework/test.yml?branch=main&label=linux&style=flat-square&logo=linux&logoColor=white" alt="linux main" style="height: 20px;"> </a> <a href="https://github.com/coding-kitties/investing-algorithm-framework/actions/workflows/test.yml?query=branch%3Amain"> <img src="https://img.shields.io/github/actions/workflow/status/coding-kitties/investing-algorithm-framework/test.yml?branch=main&label=macos&style=flat-square&logo=apple&logoColor=white" alt="macos main" style="height: 20px;"> </a> <a href="https://github.com/coding-kitties/investing-algorithm-framework/actions/workflows/test.yml?query=branch%3Amain"> <img src="https://img.shields.io/github/actions/workflow/status/coding-kitties/investing-algorithm-framework/test.yml?branch=main&label=windows&style=flat-square&logo=windows&logoColor=white" alt="windows main" style="height: 20px;"> </a> <a href="https://pypi.org/project/investing-algorithm-framework/"> <img src="https://img.shields.io/pypi/v/investing-algorithm-framework.svg?style=flat-square" alt="pypi" style="height: 20px;"> </a> <a href="https://pepy.tech/project/investing-algorithm-framework"> <img src="https://pepy.tech/badge/investing-algorithm-framework/month?style=flat-square" alt="downloads" style="height: 20px;"> </a> <a href="https://opensource.org/licenses/Apache-2.0"> <img src="https://img.shields.io/badge/apache%202.0-blue.svg?style=flat-square&label=license" alt="license" style="height: 20px;"> </a> <br> <a href="https://discord.gg/jQsnnYZgzR"> <img src="https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord" alt="discord" style="height: 20px;"> </a> <a href="https://www.reddit.com/r/InvestingBots/"> <img src="https://img.shields.io/badge/reddit-FF4500.svg?style=flat-square&logo=reddit&logoColor=white" alt="reddit" style="height: 20px;"> </a> <a href="https://github.com/coding-kitties/investing-algorithm-framework/stargazers"> <img src="https://img.shields.io/github/stars/coding-kitties/investing-algorithm-framework?style=flat-square" alt="stars" style="height: 20px;"> </a> </h4> <p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="static/features/hero-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="static/features/hero-light.svg"> <img src="static/features/hero-dark.svg" alt="Investing Algorithm Framework — features overview" style="max-width: 100%;"> </picture> </p> <p align="center"> <a href="https://discord.gg/jQsnnYZgzR"> <img src="https://img.shields.io/badge/Join%20our%20Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white" alt="Join our Discord"> </a> </p> <p align="center"> _{Proudly sponsored by} <br> <a href="https://www.finterion.com/" target="_blank"><picture><source media="(prefers-color-scheme: dark)" srcset="static/sponsors/finterion-dark.png"><source media="(prefers-color-scheme: light)" srcset="static/sponsors/finterion-light.png"><img src="static/sponsors/finterion-light.png" alt="Finterion" width="180"></picture></a> </p>

Introduction

v9.0.0 alpha is out! The pre-release of v9.0 is now available on PyPI as an alpha pre-release. Since pip doesn't install pre-releases by default, pin the version explicitly or pass --pre:

pip install investing-algorithm-framework==9.0.0a1

You can find the blog post here: v9.0 Release.

Investing Algorithm Framework is a Python framework that covers the entire quant workflow: define a strategy once, vector-backtest thousands of parameter variants to find promising signals, narrow down with a storage layer that ranks 10k+ results in milliseconds, validate the winners in a realistic event-driven simulation, compare everything in a single interactive HTML dashboard, and deploy the best performer live, all with the same TradingStrategy class, no code rewrites between stages.

Most quant frameworks stop at "here's your backtest result." You get a number, maybe a chart, and then you're on your own figuring out which strategy variant is actually better, whether the result is robust across time windows, and how to go from research to production. This framework closes that gap.

Want to see this in practice? Check out the examples/tutorial/: a series of runnable notebooks that walk you through every stage: defining a strategy, visualizing its signals, sweeping parameters across rolling windows, detecting overfitting with Monte Carlo permutation tests, filtering and ranking with the storage layer, and deploying the winner.

<details open> <summary> <strong>What's New in v9.0</strong> </summary> <br>

Full details: v9.0 release notes · CHANGELOG · OBTF spec

• New Open Backtest Format (.OBTF): OBTF packs studies, universes, windows, vector/event runs, summaries, metrics, trades, orders, positions, snapshots, execution assumptions and Monte Carlo tests into a single versioned .obtf file per algorithm (zstd + MessagePack + Parquet under the hood), so your results are portable, future-proof, and never scattered across folders again.
• Dual engine native: vector and event engines now run as first-class citizens of every backtest, so you can sweep thousands of signal ideas and validate the winners under realistic execution in the same bundle, with zero risk of one engine's save wiping out the other's results. Now a .obtf bundle is a complete record of your vector and event backtests of a single strategy.
• Short and Long Signals support: a couple of new methods (generate_short_signals / generate_cover_signals) are all it takes to unlock full short-selling: SHORT/COVER order routing, correct P&L and collateral handling, and fill-based trade creation across vector, event, and live trading.
• Enhanced Study definitions: reusable Study, Universe and BacktestWindow building blocks give you rolling, anchored, holdout and walk-forward k-fold validation, cross-sectional pipelines, signal cooldowns, and Monte Carlo–backed ranking, so you can trust your edge before you trade it.
• *Custom commision nd slippage models: pluggable slippage and commission models (percentage, fixed, bps, volume-aware) snapshot every study's cost assumptions via ExecutionConfig and attribute fees down to the order and trade level, so your numbers hold up in the real world.
• State of the art backtest storage and indexings: a swappable BacktestStore, SQLite indexing across engines, studies, universes and lineage, content-addressed OHLCV deduplication, and a full iaf CLI for migrating, indexing, ranking and pruning results, so a growing research pipeline never becomes a mess.
• See more, faster: an expanded metrics suite (CAGR, Sharpe, Sortino, Calmar, VaR/CVaR, drawdown/recovery, benchmark comparisons) paired with per-engine, per-study HTML reports and pooled or per-universe summaries, so you spot the winning strategy at a glance.
• Portfolio sync operations: recurring or one-off per-market deposit schedules, environment-based credential resolution make it easier to manage live portfolios.

⚠️ v9.0 is an alpha release with breaking API and persisted-data changes from v8. Legacy readers and selected compatibility properties remain available to assist migration, but new output is written in the v9 OBTF model — validate strategy behavior, execution assumptions and stored backtests before adopting in production.

</details> <details open> <summary> <strong>Features</strong> </summary> <br>

• 🔁 Long & Short signals support for Live trading & Backtesting: Build strategies as a pipeline of entry/exit signals, position sizing, and order generation, each independently overridable. Long-only by default; opt into shorts by overriding two methods. The same strategy class runs unchanged in vector backtests, event-driven backtests, and live.
• 🗂️ Open Backtest Format storage — One {algorithm_id}.ofbt per algorithm holds every study (in-sample sweep, time-OOS, universe-OOS, walk-forward, stress test) as a first-class slot with its own universe, windows, engine runs and summary. See Open-Backtest-Format for the reference spec.
• 📊 30+ Metrics: CAGR, Sharpe, Sortino, Calmar, VaR, CVaR, Max DD, Recovery & more
• 🧮 Cross-Sectional Pipelines: Rank, filter and score entire universes of symbols every iteration with a tidy factor table
• ⚡ Vector Backtesting for Signal Analysis: Quickly test your strategy logic on historical data to see how signals would have behaved before committing to full event-driven backtests
• 🏃 Event-Driven Backtesting: Once promising strategies are identified via vector backtests, run full event-driven backtests to simulate realistic execution and portfolio management
• 🔀 **[Permutation Testing / Monte Carlo Simulations](https