machine-learning-for-trading: an end-to-end research-to-production pipeline for building and deploying ML-driven trading strategies

machine-learning-for-trading: an end-to-end research-to-production pipeline for building and deploying ML-driven trading strategies

What it solves

It provides a comprehensive, end-to-end framework for building, testing, and deploying machine learning-driven trading strategies. The project addresses the gap between theoretical ML models and the operational reality of live markets by enforcing a disciplined research-to-production workflow that accounts for transaction costs, risk management, and the "evidence boundary" to prevent overfitting and data leakage.

How it works

The project is organized around a structured pipeline that moves from data infrastructure to live execution:

  1. Data Layer: Uses Polars for fast manipulation and integrates 19+ data providers via the ml4t-data library.
  2. Feature Engineering: Implements a variety of signal types (momentum, volatility, sentiment via FinBERT) and labels (triple-barrier) using ml4t-engineer.
  3. Modeling: Employs a wide toolkit including gradient boosting (XGBoost, LightGBM), deep time-series architectures (PatchTST, Mamba), and causal ML for isolating treatment effects.
  4. Validation: Uses walk-forward cross-validation and rigorous statistical tests (Deflated Sharpe Ratio, White's Reality Check) to separate tuning from evaluation.
  5. Implementation: Converts signals into portfolios using mean-variance optimization or Hierarchical Risk Parity, incorporating cost models and risk overlays.
  6. Advanced AI: Integrates RAG for SEC filings, knowledge graphs, and multi-agent systems for financial research.
  7. Production: Deploys via broker integrations (Interactive Brokers, Alpaca) with MLOps governance like drift detection and circuit breakers.

Who it’s for

Quantitative traders, financial researchers, and ML engineers who want to move beyond simple backtesting to build production-ready, auditable trading systems across various asset classes (ETFs, Crypto, FX, Equities).

Highlights

  • End-to-End Workflow: Covers the entire lifecycle from raw data sourcing to live execution and monitoring.
  • Rigorous Validation: Explicitly handles multiple-testing and overfitting problems common in financial backtesting.
  • Diverse Model Toolkit: Includes everything from linear baselines to state-of-the-art deep learning and causal ML.
  • Agentic Workflows: Features autonomous research agents and multi-agent forecasting systems using LangGraph and Claude SDK.
  • Production Libraries: Ships with six specialized Python packages (ml4t-data, ml4t-engineer, ml4t-models, ml4t-diagnostic, ml4t-backtest, ml4t-live).

Sources