{"product_id":"econometric-algorithmic-trading-using-machine-grant-richman-9798264522604","title":"Econometric Algorithmic Trading using Machine Learning With Python: From Stationarity to Execution Building Tradable Signals with Econometrics and ML","description":"\u003cp\u003eBuild 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.\u003c\/p\u003e\u003cp\u003eInside, you will learn to\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eStabilize noisy financial series, differentiate fractionally, and decide when to model levels vs. spreads.\u003c\/li\u003e\n\u003cli\u003eForecast returns and volatility with ARIMA\/ARFIMA, HAR\/MIDAS, and advanced GARCH variants under fat tails and leverage.\u003c\/li\u003e\n\u003cli\u003eModel multi-asset risk with DCC\/BEKK and factor structures for scalable portfolio construction.\u003c\/li\u003e\n\u003cli\u003eExtract stationary spreads and design error-correction trading rules with VECM and threshold dynamics.\u003c\/li\u003e\n\u003cli\u003eTrack time-varying betas with Kalman filters; decode regimes with Markov-switching; manage breaks with structural change tests.\u003c\/li\u003e\n\u003cli\u003eQuantify microstructure effects, estimate efficient prices, and model order flow and jumps via Hawkes\/ACD.\u003c\/li\u003e\n\u003cli\u003eBuild high-dimensional alpha models using lasso\/elastic net, boosting (XGBoost\/LightGBM\/CatBoost), kernels, GPs, and deep nets.\u003c\/li\u003e\n\u003cli\u003eCapture nonlinear dependence and tail risk with copulas and EVT; forecast quantiles and expected shortfall for risk-aware sizing.\u003c\/li\u003e\n\u003cli\u003eIdentify causal effects with D-i-D, IV, RDD, and double ML; target policy to tradable subpopulations.\u003c\/li\u003e\n\u003cli\u003eAllocate across signals with online learning and bandits; trade under realistic impact with Almgren-Chriss and propagator models.\u003c\/li\u003e\n\u003cli\u003eDeploy RL for execution and market making, with proper off-policy evaluation and conservative objectives.\u003c\/li\u003e\n\u003cli\u003eEvaluate your edge correctly with Diebold-Mariano, MCS, Reality Check, SPA, and deflated Sharpe to avoid data snooping.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWho it's for\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eQuant researchers, portfolio managers, and traders upgrading from ad hoc heuristics to statistically defensible, production-ready models.\u003c\/li\u003e\n\u003cli\u003eData scientists entering quantitative finance who need a rigorous bridge from ML theory to tradable implementation.\u003c\/li\u003e\n\u003cli\u003eGraduate students and practitioners seeking a compact, code-complete reference for model-driven trading.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eHow each chapter works\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eTheory: assumptions, identification, estimation, diagnostics, and forecasting.\u003c\/li\u003e\n\u003cli\u003eCheckpoint: multiple-choice questions to test comprehension and common pitfalls.\u003c\/li\u003e\n\u003cli\u003ePractice: full Python code demonstrations-from data prep and estimation to validation, backtesting, and interpretation.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eTurn research into PnL with a book that rewards rigor. Build, test, and trade with confidence.\u003c\/b\u003e\u003cp\u003eNote: Educational content only. Markets carry risk; no strategy guarantees profits.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Grant Richman\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798264522604\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/09\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 394\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.16lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.81d","brand":"Grant Richman","offers":[{"title":"Paperback","offer_id":48588359762175,"sku":"9798264522604","price":39.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/econometric-algorithmic-trading-using-machine-grant-richman-9798264522604","provider":"WR Book House","version":"1.0","type":"link"}