investing-algorithm-framework

Introduction

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.

• 📊 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 — Assess the statistical robustness of your strategies by running them across randomized market scenarios to see how often your results could occur by chance