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Introduction

Welcome to the Investing Algorithm Framework documentation! This framework provides a comprehensive platform for creating, testing, and deploying algorithmic trading strategies.

What is the Investing Algorithm Framework?​

The Investing Algorithm Framework is a Python-based library designed to help developers and traders build sophisticated algorithmic trading systems. It provides:

  • Strategy Development: Tools for creating and implementing trading strategies
  • Backtesting: Comprehensive backtesting capabilities with both event-based and vector-based approaches
  • Data Management: Integration with multiple data sources and providers
  • Order Management: Advanced order execution and portfolio management
  • Performance Analysis: Detailed analytics and metrics for strategy evaluation
  • Deployment: Production-ready deployment capabilities

Key Features​

  • 📊 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
  • 🚀 Deployment — Once the best strategy is identified through backtesting and comparison, deploy it to production locally or in the cloud (AWS Lambda / Azure Functions) to start live trading
  • ⚔️ Multi-Strategy Comparison — Rank, filter & compare strategies in a single interactive report
  • 🪟 Multi-Window Robustness — Test across different time periods with window coverage analysis
  • 📈 Equity & Drawdown Charts — Overlay equity curves, rolling Sharpe, drawdown & return distributions
  • 🗓️ Monthly Heatmaps & Yearly Returns — Calendar heatmap per strategy with return/growth toggles
  • 🎯 Return Scenario Projections — Good, average, bad & very bad year projections from backtest data
  • 📉 Benchmark Comparison — Beat-rate analysis vs Buy & Hold, DCA, risk-free & custom benchmarks
  • 📄 One-Click HTML Report — Self-contained file, no server, dark & light theme, shareable
  • 📦 Custom .iafbt Backtest Bundle Format — An explicit, versioned, compressed, language-portable container (zstd + msgpack with magic-byte header) plus a separate parquet index for fast filtering without loading. ~21× smaller and ~27× fewer files than standard filebased directory layouts, with parallel I/O for fast load/save of large amounts of backtests.
  • 🌐 Load External Data — Fetch CSV, JSON, or Parquet from any URL with caching and auto-refresh
  • 📝 Record Custom Variables — Track any indicator or metric during backtests with context.record()
  • 🚀 Build → Backtest → Deploy — Local dev, cloud deploy (AWS / Azure), or monetize on Finterion

Getting Started​

Ready to start building your first trading algorithm? Head over to our Installation Guide to get up and running in minutes!

Community & Support​

Let's dive in and start building your algorithmic trading system!