{"product_id":"econometric-algorithmic-trading-using-machine-learning-with-python-from-stationarity-to-execution-building-tradable-signals-with-econometrics-and-ml-9798264522604","title":"Econometric Algorithmic Trading using Machine Learning With Python: From Stationarity to Execution Building Tradable Signals with Econometrics and ML","description":"\u003cp\u003e • Author(s): Grant Richman\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Investments \u0026amp; Securities - Analysis \u0026amp; Trading Strategies\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\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","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46332950839447,"sku":"9798264522604","price":3350.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798264522604.webp?v=1768668214","url":"https:\/\/atlanticbooks.com\/products\/econometric-algorithmic-trading-using-machine-learning-with-python-from-stationarity-to-execution-building-tradable-signals-with-econometrics-and-ml-9798264522604","provider":"Atlantic Books","version":"1.0","type":"link"}