{"product_id":"hands-on-ai-trading-with-python-quantconnect-and-aws-isbn-9781394268436","title":"Hands-On AI Trading with Python, QuantConnect, and AWS","description":"\u003cp\u003e\u003cb\u003eMaster the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHands-On AI Trading with Python, QuantConnect, and AWS\u003c\/i\u003e explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt.\u003c\/p\u003e \u003cp\u003eUnlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks.\u003c\/p\u003e \u003cp\u003eThe book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExamples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.\u003c\/li\u003e \u003cli\u003eUse principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.\u003c\/li\u003e \u003cli\u003ePredict market volatility regimes and allocate funds accordingly.\u003c\/li\u003e \u003cli\u003ePredict daily returns of tech stocks using classifiers.\u003c\/li\u003e \u003cli\u003eForecast Forex pairs' future prices using Support Vector Machines and wavelets.\u003c\/li\u003e \u003cli\u003ePredict trading day momentum or reversion risk using TensorFlow and temporal CNNs.\u003c\/li\u003e \u003cli\u003eApply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.\u003c\/li\u003e \u003cli\u003ePerform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.\u003c\/li\u003e \u003cli\u003eBetter Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.\u003c\/li\u003e \u003cli\u003eAI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with \u003ci\u003eHands-On AI Trading with Python, QuantConnect, and AWS\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003eBiographies xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface: QuantConnect xv\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction xxiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Foundations of Capital Markets and Quantitative Trading 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Foundations of Capital Markets 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMarket Mechanics 3\u003c\/p\u003e \u003cp\u003eMarket Participants 4\u003c\/p\u003e \u003cp\u003eTrading Is the “Play” 4\u003c\/p\u003e \u003cp\u003eThe Stage and Basic Rules of Trading—The Limit Order Book 4\u003c\/p\u003e \u003cp\u003eActors—Liquidity Trader, Market Maker, and\u003c\/p\u003e \u003cp\u003eInformed Trader 5\u003c\/p\u003e \u003cp\u003eLiquidity Trader 5\u003c\/p\u003e \u003cp\u003eMarket Maker 5\u003c\/p\u003e \u003cp\u003eInformed Trader 6\u003c\/p\u003e \u003cp\u003eAI Actors Wanted! 7\u003c\/p\u003e \u003cp\u003eData and Data Feeds 7\u003c\/p\u003e \u003cp\u003eCustom and Alternative Data 9\u003c\/p\u003e \u003cp\u003eBrokerages and Transaction Costs 10\u003c\/p\u003e \u003cp\u003eTransaction Costs 11\u003c\/p\u003e \u003cp\u003eSecurity Identifiers 13\u003c\/p\u003e \u003cp\u003eAssets and Derivatives 15\u003c\/p\u003e \u003cp\u003eUS Equities 15\u003c\/p\u003e \u003cp\u003eUS Equity Options 19\u003c\/p\u003e \u003cp\u003eIndex Options 21\u003c\/p\u003e \u003cp\u003eUS Futures 21\u003c\/p\u003e \u003cp\u003eCryptocurrency 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Foundations of Quantitative Trading 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eResearch Process 25\u003c\/p\u003e \u003cp\u003eResearch 25\u003c\/p\u003e \u003cp\u003eBacktesting 26\u003c\/p\u003e \u003cp\u003eParameter Optimization 26\u003c\/p\u003e \u003cp\u003ePaper and Live Trading 26\u003c\/p\u003e \u003cp\u003eTesting and Debugging Tools 26\u003c\/p\u003e \u003cp\u003eDebuggers 27\u003c\/p\u003e \u003cp\u003eLogging 27\u003c\/p\u003e \u003cp\u003eCharting 27\u003c\/p\u003e \u003cp\u003eObject Store 28\u003c\/p\u003e \u003cp\u003eCoding Process 28\u003c\/p\u003e \u003cp\u003eTime and Look-ahead Bias 29\u003c\/p\u003e \u003cp\u003eLook-ahead Bias 29\u003c\/p\u003e \u003cp\u003eMarket Hours and Scheduling 30\u003c\/p\u003e \u003cp\u003eStrategy Styles 30\u003c\/p\u003e \u003cp\u003eTrading Signals 31\u003c\/p\u003e \u003cp\u003eAllocating Capital 31\u003c\/p\u003e \u003cp\u003eRegimes and Portfolios of Strategies 32\u003c\/p\u003e \u003cp\u003eParameter Sensitivity Testing and Optimization 33\u003c\/p\u003e \u003cp\u003e1. Remove 33\u003c\/p\u003e \u003cp\u003e2. Replace 34\u003c\/p\u003e \u003cp\u003e3. Reduce 34\u003c\/p\u003e \u003cp\u003eParameter Sensitivity Testing 34\u003c\/p\u003e \u003cp\u003eMargin Modeling 35\u003c\/p\u003e \u003cp\u003eEquities 35\u003c\/p\u003e \u003cp\u003eEquity Options 36\u003c\/p\u003e \u003cp\u003eFutures 37\u003c\/p\u003e \u003cp\u003eDiversification and Asset Selection 37\u003c\/p\u003e \u003cp\u003eFundamental Asset Selection 38\u003c\/p\u003e \u003cp\u003eETF Constituents Asset Selection 39\u003c\/p\u003e \u003cp\u003eDollar-Volume Asset Selection 40\u003c\/p\u003e \u003cp\u003eUniverse Settings 40\u003c\/p\u003e \u003cp\u003eIndicators and Other Data Transformations 41\u003c\/p\u003e \u003cp\u003eAutomatic Indicators 41\u003c\/p\u003e \u003cp\u003eManual Indicators 41\u003c\/p\u003e \u003cp\u003eIndicator Warm Up 42\u003c\/p\u003e \u003cp\u003eStoring Objects 42\u003c\/p\u003e \u003cp\u003eIndicator Events 42\u003c\/p\u003e \u003cp\u003eSourcing Ideas 42\u003c\/p\u003e \u003cp\u003eHypothesis-driven Testing 43\u003c\/p\u003e \u003cp\u003eData Driven Investing 44\u003c\/p\u003e \u003cp\u003eQuantpedia 44\u003c\/p\u003e \u003cp\u003eQuantConnect Research and Strategy Explorer 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Foundations of AI and ML in Algorithmic Trading 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStep-by-step Guide for AI-based Algorithmic Trading 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Step 1: Problem Definition 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Step 2: Dataset Preparation 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Collection 53\u003c\/p\u003e \u003cp\u003eExploratory Data Analysis 53\u003c\/p\u003e \u003cp\u003eData Preprocessing 54\u003c\/p\u003e \u003cp\u003eHandling Missing Data 55\u003c\/p\u003e \u003cp\u003eHandling Outliers 58\u003c\/p\u003e \u003cp\u003eFeature Engineering 61\u003c\/p\u003e \u003cp\u003eNormalization and Standardization of Features 62\u003c\/p\u003e \u003cp\u003eTransforming Time Series Features to Stationary 64\u003c\/p\u003e \u003cp\u003eIdentification of Cointegrated Time Series with Engle-Granger Test 70\u003c\/p\u003e \u003cp\u003eFeature Selection 76\u003c\/p\u003e \u003cp\u003eCorrelation Analysis 76\u003c\/p\u003e \u003cp\u003eFeature Importance Analysis 77\u003c\/p\u003e \u003cp\u003eAuto-identification of Features 78\u003c\/p\u003e \u003cp\u003eDimensionality Reduction\/Principal Component Analysis 80\u003c\/p\u003e \u003cp\u003eSplitting of Dataset into Training, Testing, and Possibly Validation Sets 83\u003c\/p\u003e \u003cp\u003eHow to Split Your Data 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Step 3: Model Choice, Training, and Application 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRegression 88\u003c\/p\u003e \u003cp\u003eLinear Regression 89\u003c\/p\u003e \u003cp\u003ePolynomial Regression 91\u003c\/p\u003e \u003cp\u003eLASSO Regression 93\u003c\/p\u003e \u003cp\u003eRidge Regression 96\u003c\/p\u003e \u003cp\u003eMarkov Switching Dynamic Regression 99\u003c\/p\u003e \u003cp\u003eDecision Tree Regression 103\u003c\/p\u003e \u003cp\u003eSupport Vector Machines Regression with\u003c\/p\u003e \u003cp\u003eWavelet Forecasting 105\u003c\/p\u003e \u003cp\u003eClassification 110\u003c\/p\u003e \u003cp\u003eMulticlass Random Forest Model 110\u003c\/p\u003e \u003cp\u003eLogistic Regression 114\u003c\/p\u003e \u003cp\u003eHidden Markov Models 117\u003c\/p\u003e \u003cp\u003eGaussian Naive Bayes 119\u003c\/p\u003e \u003cp\u003eConvolutional Neural Networks 122\u003c\/p\u003e \u003cp\u003eRanking 127\u003c\/p\u003e \u003cp\u003eLGBRanker Ranking 127\u003c\/p\u003e \u003cp\u003eClustering 130\u003c\/p\u003e \u003cp\u003eOPTICS Clustering 130\u003c\/p\u003e \u003cp\u003eLanguage Models 132\u003c\/p\u003e \u003cp\u003eOpenAI Language Model 132\u003c\/p\u003e \u003cp\u003eAmazon Chronos Model 135\u003c\/p\u003e \u003cp\u003eFinBERT Model 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Advanced Applications of AI in Trading and Risk Management 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Started with Source Code 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Applied Machine Learning 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExample 1—ML Trend Scanning with MLFinlab 143\u003c\/p\u003e \u003cp\u003eExample 2—Factor Preprocessing Techniques for Regime Detection 148\u003c\/p\u003e \u003cp\u003eExample 3—Reversion vs. Trending: Strategy Selection by Classification 154\u003c\/p\u003e \u003cp\u003eExample 4—Alpha by Hidden Markov Models 158\u003c\/p\u003e \u003cp\u003eExample 5—FX SVM Wavelet Forecasting 170\u003c\/p\u003e \u003cp\u003eExample 6—Dividend Harvesting Selection of\u003c\/p\u003e \u003cp\u003eHigh-Yield Assets 176\u003c\/p\u003e \u003cp\u003eExample 7—Effect of Positive-Negative Splits 181\u003c\/p\u003e \u003cp\u003eExample 8—Stop Loss Based on Historical Volatility and Drawdown Recovery 185\u003c\/p\u003e \u003cp\u003eExample 9—ML Trading Pairs Selection 197\u003c\/p\u003e \u003cp\u003eExample 10—Stock Selection through Clustering\u003c\/p\u003e \u003cp\u003eFundamental Data 207\u003c\/p\u003e \u003cp\u003eExample 11—Inverse Volatility Rank and Allocate to Future Contracts 214\u003c\/p\u003e \u003cp\u003eExample 12—Trading Costs Optimization 221\u003c\/p\u003e \u003cp\u003eExample 13—PCA Statistical Arbitrage Mean Reversion 228\u003c\/p\u003e \u003cp\u003eExample 14—Temporal CNN Prediction 233\u003c\/p\u003e \u003cp\u003eExample 15—Gaussian Classifier for Direction Prediction 242\u003c\/p\u003e \u003cp\u003eExample 16—LLM Summarization of Tiingo News Articles 250\u003c\/p\u003e \u003cp\u003eExample 17—Head Shoulders Pattern Matching with CNN 256\u003c\/p\u003e \u003cp\u003eExample 18—Amazon Chronos Model 265\u003c\/p\u003e \u003cp\u003eExample 19—FinBERT Model 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Better Hedging with Reinforcement Learning 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 281\u003c\/p\u003e \u003cp\u003eA New AI Trading Assistant 281\u003c\/p\u003e \u003cp\u003eContinuous Hedging Is Not Required 282\u003c\/p\u003e \u003cp\u003eMachine Learning Comes to the Rescue 283\u003c\/p\u003e \u003cp\u003eA Simplified but Effective Reinforcement\u003c\/p\u003e \u003cp\u003eLearning Approach 284\u003c\/p\u003e \u003cp\u003eOverview of the Reinforcement Learning 285\u003c\/p\u003e \u003cp\u003eIdentification 285\u003c\/p\u003e \u003cp\u003eSimulation 286\u003c\/p\u003e \u003cp\u003eRef inement Training on Actual Market Data 287\u003c\/p\u003e \u003cp\u003eTesting and Implementation 287\u003c\/p\u003e \u003cp\u003eImplementation on QuantConnect 288\u003c\/p\u003e \u003cp\u003ePrimary Research Notebook 289\u003c\/p\u003e \u003cp\u003eThe Policy Network 290\u003c\/p\u003e \u003cp\u003eModel Functions 292\u003c\/p\u003e \u003cp\u003eFine-tuning with Market Data 296\u003c\/p\u003e \u003cp\u003eResults 300\u003c\/p\u003e \u003cp\u003eConclusion 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 AI for Risk Management and Optimization 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Corrective AI and Conditional\u003c\/p\u003e \u003cp\u003eParameter Optimization? 305\u003c\/p\u003e \u003cp\u003eFeature Engineering 308\u003c\/p\u003e \u003cp\u003eApplying Corrective AI to Daily Seasonal Forex Trading 312\u003c\/p\u003e \u003cp\u003eWhat Is Conditional Parameter Optimization? 318\u003c\/p\u003e \u003cp\u003eApplying Conditional Parameter Optimization to an ETF Strategy 319\u003c\/p\u003e \u003cp\u003eUnconditional vs. Conditional Parameter Optimizations 320\u003c\/p\u003e \u003cp\u003ePerformance Comparisons 322\u003c\/p\u003e \u003cp\u003eConditional Portfolio Optimization 322\u003c\/p\u003e \u003cp\u003eRegime Changes Obliterate Traditional Portfolio Optimization Methods 322\u003c\/p\u003e \u003cp\u003eLearning to Optimize 324\u003c\/p\u003e \u003cp\u003eRanking Is Easier Than Predicting 325\u003c\/p\u003e \u003cp\u003eThe Fama-French Lineage 327\u003c\/p\u003e \u003cp\u003eComparison with Conventional Optimization Methods 327\u003c\/p\u003e \u003cp\u003eModel Tactical Asset Allocation Portfolio 331\u003c\/p\u003e \u003cp\u003eCPO Software-as-a-Service 333\u003c\/p\u003e \u003cp\u003eConclusion 340\u003c\/p\u003e \u003cp\u003eDefinitions of Spread_EMA \u0026amp; Spread_VAR 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Application of Large Language Models and Generative AI in Trading 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRole of Generative AI in Creating Alpha 341\u003c\/p\u003e \u003cp\u003eSelecting an LLM for Building a Generative AI Application 342\u003c\/p\u003e \u003cp\u003ePrompt Engineering 344\u003c\/p\u003e \u003cp\u003ePrompt Engineering in Practice 345\u003c\/p\u003e \u003cp\u003eAddressing Model “Hallucination” 346\u003c\/p\u003e \u003cp\u003eQuestion Answering Using a Retrieval Augmented Application in SageMaker Canvas 347\u003c\/p\u003e \u003cp\u003eRAG Application Costs and Optimization Techniques 350\u003c\/p\u003e \u003cp\u003eTesting Our Infrastructure 351\u003c\/p\u003e \u003cp\u003eSummarization 356\u003c\/p\u003e \u003cp\u003eUseful AI Platforms and Services 359\u003c\/p\u003e \u003cp\u003eChatGPT 359\u003c\/p\u003e \u003cp\u003eGemini 359\u003c\/p\u003e \u003cp\u003eBedrock 359\u003c\/p\u003e \u003cp\u003eSageMaker 359\u003c\/p\u003e \u003cp\u003eQ Business 360\u003c\/p\u003e \u003cp\u003eReferences 361\u003c\/p\u003e \u003cp\u003eSubject Index 363\u003c\/p\u003e \u003cp\u003eCode Index 379\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJIRI PIK:\u003c\/b\u003e Founder and CEO of RocketEdge.com. A software architect and cloud computing expert, Jiri Pik specializes in designing high-performance trading systems. He has decades of experience in financial technologies and has worked with some of the world’s leading financial institutions, including Goldman Sachs and JPMorgan Chase. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eERNEST P. CHAN:\u003c\/b\u003e A pioneer in applying machine learning to quantitative trading, Ernest P. Chan founded Predictnow.ai and QTS Capital Management. He is author of books such as \u003ci\u003eQuantitative Trading\u003c\/i\u003e and \u003ci\u003eMachine Trading\u003c\/i\u003e. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJARED BROAD:\u003c\/b\u003e Founder and CEO of QuantConnect\u003ci\u003e™\u003c\/i\u003e, Jared Broad has empowered over 300,000 algorithmic traders worldwide with a platform that simplifies strategy design, backtesting, and live deployment. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePHILIP SUN:\u003c\/b\u003e CEO and Co-founder of Adaptive Investment Solutions, LLC, and a seasoned quantitative fund manager, Philip Sun and his team focus on building state-of-the-art AI-driven risk management platform for wealth advisors and institutional investors. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eVIVEK SINGH:\u003c\/b\u003e A product leader at Amazon Web Services (AWS), Vivek Singh spearheads the development of large language models (LLMs) and Generative AI applications, bringing cutting-edge AI technologies to the trading domain.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eRevolutionize Your Trading with Artificial Intelligence\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eHands-On AI Trading with Python™, QuantConnect™, and AWS™\u003c\/i\u003e is a comprehensive guide that bridges the gap between cutting-edge artificial intelligence and the dynamic world of quantitative trading. The authors, Jiri Pik, Ernest P. Chan, Jared Broad, Philip Sun, and Vivek Singh, deliver a practical, data-driven roadmap to modern algorithmic trading, featuring over \u003cb\u003e20 fully implemented real-world examples \u003c\/b\u003eto ignite your creativity and serve as a launchpad for your ideas. \u003c\/p\u003e\u003cp\u003eThis book demystifies the complexities of algorithmic trading by leveraging QuantConnect™ to backtest, optimize, and deploy trading strategies. Unlike conventional resources, this book provides fully implemented Python™ examples, empowering you to focus on innovation over infrastructure. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat’s Inside?\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe book is packed with practical ways to set up data and use AI models in your trading, including Support Vector Machines for price trend forecasting, Convolutional Neural Networks (CNNs) for pattern recognition in stock prices, Markov Chains for dynamic asset allocation, Gaussian Naive Bayes for risk classification, and Reinforcement Learning for optimal trading strategies. \u003c\/p\u003e\u003cp\u003eTechnologies are illustrated with real-world examples, including mean-reversion pairs trading strategies, momentum-based equity trading strategies, volatility-based options strategies, dynamic hedging, portfolio optimization, and asset class selection using Principal Component Analysis (PCA). \u003c\/p\u003e\u003cp\u003eAccompanied by a GitHub repository with source code and strategy results, readers can rapidly test, refine, and experiment with strategies. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eWho Should Read This Book?\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eWhether you’re a seasoned hedge fund professional, an asset manager, or a graduate student in finance, \u003ci\u003eHands-On AI Trading with Python™, QuantConnect™, and AWS™\u003c\/i\u003e equips you with actionable tools to integrate AI into your trading workflows. This book is essential for anyone aiming to excel in today’s competitive financial markets. \u003c\/p\u003e\u003cp\u003eTake control of your trading future today— get your copy and leverage AI to transform your strategies.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePraise for \u003csmall\u003eHANDS-ON AI TRADING\u003c\/small\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e“A must-have for algorithmic traders and students, this book emphasizes designing trading strategies with QuantConnect™. Featuring Python™ examples and advanced AI\/ML models, it offers a clear and accessible presentation ideal for anyone in quantitative finance.”\u003cbr\u003e \u003cb\u003e—PETTER N. KOLM,\u003c\/b\u003e Professor, Courant Institute of Mathematical Sciences, New York University; Awarded “Quant of the Year” in 2021 \u003c\/p\u003e\u003cp\u003e“This concise guide provides a gentle introduction with hands-on examples and expert insights into dissecting and evaluating trades from seasoned traders. The code will make otherwise complex or confusing examples clear. It is an excellent springboard for developing your own strategies.”\u003cbr\u003e \u003cb\u003e—MICHAEL ROBBINS,\u003c\/b\u003e Author of \u003ci\u003eQuantitative Asset Management\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e“This is the book I wish I had when starting out, it would have saved me years! It offers rare insights and practical tutorials, allowing the next generation of quants to stand on the shoulders of giants.”\u003cbr\u003e \u003cb\u003e—JACQUES JOUBERT,\u003c\/b\u003e Quant Researcher and Developer, Co-Founder and CEO of Hudson and Thames Quantitative Research \u003c\/p\u003e\u003cp\u003e“The book ties both theory and industry together while providing code, output, and a platform to implement AI models in a trading environment. Cookbook style makes it a great book for those new to machine learning and AI in quantitative finance.”\u003cbr\u003e \u003cb\u003e—DIMITRI BIANCO,\u003c\/b\u003e Head of Quant Risk and Research, Agora Data, Inc. \u003c\/p\u003e\u003cp\u003e“As a novice trader myself, I have been looking for ways to apply AI in real-world trading scenarios. This book does an excellent job in explaining trading concepts and mapping these to AI concepts to build trading strategies. A must-read if you want to use AI for building wealth.”\u003cbr\u003e \u003cb\u003e—RAJNEESH SINGH,\u003c\/b\u003e Director, Amazon SageMaker \u003c\/p\u003e\u003cp\u003e“This book is an excellent resource for learning machine learning and AI for quantitative trading. 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