Research Pipeline & Deployment: Strategy Lifecycle Guide › Summary

This chapter covered the complete lifecycle of a quantitative trading strategy, from initial hypothesis through production deployment and ongoing evaluation.

The main components of a reproducible research pipeline include disciplined hypothesis formulation, careful data quality control, systematic feature engineering, and structured iteration with experiment tracking.

Version control with Git manages code changes, while experiment tracking tools like MLflow capture the full context of each research run, which allows reproducibility and systematic comparison across experiments.

Paper trading bridges backtesting and live trading, revealing issues with data feeds, execution timing, and system reliability that historical simulations cannot detect. Strategies should pass paper trading validation criteria before receiving real capital.

Production deployment requires scheduling, monitoring, alerting, and automated risk controls. Circuit breakers halt trading when predefined limits are breached, and gives time for human assessment.

Continuous evaluation using statistical tests helps detect performance degradation, though these tests have limitations given the non-stationarity of financial markets. Strategy lifecycle management provides a framework for transitioning strategies through research, production, and eventual retirement.

The next chapter on position sizing and leverage management builds on these deployment concepts by examining how to determine appropriate capital allocation across strategies and instruments.