What's Next?

What's Next

You have implemented the core toolkit of quantitative trading strategies. Here are natural next steps:

Backtesting Frameworks — Apply your knowledge to Backtrader or Zipline to run backtests on real historical data with more realistic market simulation.
Portfolio Theory — Explore mean-variance optimization, the efficient frontier, and factor models (Fama-French).
Options Pricing — Learn Black-Scholes, the Greeks, and delta hedging — the mathematics of derivatives.
Time Series Analysis — ARIMA, GARCH, cointegration tests (Engle-Granger, Johansen), and the Kalman filter for dynamic hedge ratios.
Machine Learning for Trading — Feature engineering from technical indicators, cross-validation for financial data, and regime detection.

Quantitative Trading by Ernest Chan — Practical guide to building systematic strategies; excellent treatment of backtesting pitfalls.
Advances in Financial Machine Learning by Marcos López de Prado — State-of-the-art techniques for applying ML to financial data.
Algorithmic Trading and DMA by Barry Johnson — Deep dive into market microstructure, execution, and transaction cost analysis.
Active Portfolio Management by Grinold & Kahn — The foundational text on quantitative active management and the fundamental law of active management.
Options, Futures, and Other Derivatives by John Hull — The standard reference for derivatives pricing and risk management.