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Econometric Algorithmic Trading using Machine Learning With Python: From Stationarity to Execution Building Tradable Signals with Econometrics and ML by Richman, Grant

Econometric Algorithmic Trading using Machine Learning With Python: From Stationarity to Execution Building Tradable Signals with Econometrics and ML

by Grant Richman
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Book cover type: Paperback
  • ISBN13: 9798264522604
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 394
  • Original Price: GBP 29.58
  • Language: English
  • Edition: N/A
  • Item Weight: 527 grams
  • BISAC Subject(s): Investments & Securities / Analysis & Trading Strategies

Build institutional-grade trading signals with econometrics and machine learning-end to end in Python. This is a dense, model-first handbook for quants who want repeatable alpha, robust risk, and friction-aware execution without the fluff. Every chapter moves from rigorous theory to end-of-chapter multiple-choice questions, and finishes with full, runnable Python code demonstrations you can adapt to your pipeline today.

Inside, you will learn to

  • Stabilize noisy financial series, differentiate fractionally, and decide when to model levels vs. spreads.
  • Forecast returns and volatility with ARIMA/ARFIMA, HAR/MIDAS, and advanced GARCH variants under fat tails and leverage.
  • Model multi-asset risk with DCC/BEKK and factor structures for scalable portfolio construction.
  • Extract stationary spreads and design error-correction trading rules with VECM and threshold dynamics.
  • Track time-varying betas with Kalman filters; decode regimes with Markov-switching; manage breaks with structural change tests.
  • Quantify microstructure effects, estimate efficient prices, and model order flow and jumps via Hawkes/ACD.
  • Build high-dimensional alpha models using lasso/elastic net, boosting (XGBoost/LightGBM/CatBoost), kernels, GPs, and deep nets.
  • Capture nonlinear dependence and tail risk with copulas and EVT; forecast quantiles and expected shortfall for risk-aware sizing.
  • Identify causal effects with D-i-D, IV, RDD, and double ML; target policy to tradable subpopulations.
  • Allocate across signals with online learning and bandits; trade under realistic impact with Almgren-Chriss and propagator models.
  • Deploy RL for execution and market making, with proper off-policy evaluation and conservative objectives.
  • Evaluate your edge correctly with Diebold-Mariano, MCS, Reality Check, SPA, and deflated Sharpe to avoid data snooping.

Who it's for

  • Quant researchers, portfolio managers, and traders upgrading from ad hoc heuristics to statistically defensible, production-ready models.
  • Data scientists entering quantitative finance who need a rigorous bridge from ML theory to tradable implementation.
  • Graduate students and practitioners seeking a compact, code-complete reference for model-driven trading.

How each chapter works

  • Theory: assumptions, identification, estimation, diagnostics, and forecasting.
  • Checkpoint: multiple-choice questions to test comprehension and common pitfalls.
  • Practice: full Python code demonstrations-from data prep and estimation to validation, backtesting, and interpretation.
Turn research into PnL with a book that rewards rigor. Build, test, and trade with confidence.

Note: Educational content only. Markets carry risk; no strategy guarantees profits.

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