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Produktbild: Hands-On AI Trading with Python, QuantConnect, and AWS

Hands-On AI Trading with Python, QuantConnect, and AWS

52,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

18.02.2025

Verlag

Wiley

Seitenzahl

416

Maße (L/B/H)

25,6/19/3,2 cm

Gewicht

1044 g

Sprache

Englisch

ISBN

978-1-394-26843-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

18.02.2025

Verlag

Wiley

Seitenzahl

416

Maße (L/B/H)

25,6/19/3,2 cm

Gewicht

1044 g

Sprache

Englisch

ISBN

978-1-394-26843-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Hands-On AI Trading with Python, QuantConnect, and AWS

  • Biographies xiii

    Preface: QuantConnect xv

    Introduction xxiii

    Part I Foundations of Capital Markets and Quantitative Trading 1

    Chapter 1 Foundations of Capital Markets 3

    Market Mechanics 3

    Market Participants 4

    Trading Is the "Play" 4

    The Stage and Basic Rules of Trading-The Limit Order Book 4

    Actors-Liquidity Trader, Market Maker, and

    Informed Trader 5

    Liquidity Trader 5

    Market Maker 5

    Informed Trader 6

    AI Actors Wanted! 7

    Data and Data Feeds 7

    Custom and Alternative Data 9

    Brokerages and Transaction Costs 10

    Transaction Costs 11

    Security Identifiers 13

    Assets and Derivatives 15

    US Equities 15

    US Equity Options 19

    Index Options 21

    US Futures 21

    Cryptocurrency 23

    Chapter 2 Foundations of Quantitative Trading 25

    Research Process 25

    Research 25

    Backtesting 26

    Parameter Optimization 26

    Paper and Live Trading 26

    Testing and Debugging Tools 26

    Debuggers 27

    Logging 27

    Charting 27

    Object Store 28

    Coding Process 28

    Time and Look-ahead Bias 29

    Look-ahead Bias 29

    Market Hours and Scheduling 30

    Strategy Styles 30

    Trading Signals 31

    Allocating Capital 31

    Regimes and Portfolios of Strategies 32

    Parameter Sensitivity Testing and Optimization 33

    1. Remove 33

    2. Replace 34

    3. Reduce 34

    Parameter Sensitivity Testing 34

    Margin Modeling 35

    Equities 35

    Equity Options 36

    Futures 37

    Diversification and Asset Selection 37

    Fundamental Asset Selection 38

    ETF Constituents Asset Selection 39

    Dollar-Volume Asset Selection 40

    Universe Settings 40

    Indicators and Other Data Transformations 41

    Automatic Indicators 41

    Manual Indicators 41

    Indicator Warm Up 42

    Storing Objects 42

    Indicator Events 42

    Sourcing Ideas 42

    Hypothesis-driven Testing 43

    Data Driven Investing 44

    Quantpedia 44

    QuantConnect Research and Strategy Explorer 45

    Part II Foundations of AI and ML in Algorithmic Trading 47

    Step-by-step Guide for AI-based Algorithmic Trading 48

    Chapter 3 Step 1: Problem Definition 49

    Chapter 4 Step 2: Dataset Preparation 53

    Data Collection 53

    Exploratory Data Analysis 53

    Data Preprocessing 54

    Handling Missing Data 55

    Handling Outliers 58

    Feature Engineering 61

    Normalization and Standardization of Features 62

    Transforming Time Series Features to Stationary 64

    Identification of Cointegrated Time Series with Engle-Granger Test 70

    Feature Selection 76

    Correlation Analysis 76

    Feature Importance Analysis 77

    Auto-identification of Features 78

    Dimensionality Reduction/Principal Component Analysis 80

    Splitting of Dataset into Training, Testing, and Possibly Validation Sets 83

    How to Split Your Data 83

    Chapter 5 Step 3: Model Choice, Training, and Application 87

    Regression 88

    Linear Regression 89

    Polynomial Regression 91

    LASSO Regression 93

    Ridge Regression 96

    Markov Switching Dynamic Regression 99

    Decision Tree Regression 103

    Support Vector Machines Regression with

    Wavelet Forecasting 105

    Classification 110

    Multiclass Random Forest Model 110

    Logistic Regression 114

    Hidden Markov Models 117

    Gaussian Naive Bayes 119

    Convolutional Neural Networks 122

    Ranking 127

    LGBRanker Ranking 127

    Clustering 130

    OPTICS Clustering 130

    Language Models 132

    OpenAI Language Model 132

    Amazon Chronos Model 135

    FinBERT Model 137

    Part III Advanced Applications of AI in Trading and Risk Management 141

    Getting Started with Source Code 141

    Chapter 6 Applied Machine Learning 143

    Example 1-ML Trend Scanning with MLFinlab 143

    Example 2-Factor Preprocessing Techniques for Regime Detection 148

    Example 3-Reversion vs. Trending: Strategy Selection by Classification 154

    Example 4-Alpha by Hidden Markov Models 158

    Example 5-FX SVM Wavelet Forecasting 170

    Example 6-Dividend Harvesting Selection of

    High-Yield Assets 176

    Example 7-Effect of Positive-Negative Splits 181

    Example 8-Stop Loss Based on Historical Volatility and Drawdown Recovery 185

    Example 9-ML Trading Pairs Selection 197

    Example 10-Stock Selection through Clustering

    Fundamental Data 207

    Example 11-Inverse Volatility Rank and Allocate to Future Contracts 214

    Example 12-Trading Costs Optimization 221

    Example 13-PCA Statistical Arbitrage Mean Reversion 228

    Example 14-Temporal CNN Prediction 233

    Example 15-Gaussian Classifier for Direction Prediction 242

    Example 16-LLM Summarization of Tiingo News Articles 250

    Example 17-Head Shoulders Pattern Matching with CNN 256

    Example 18-Amazon Chronos Model 265

    Example 19-FinBERT Model 272

    Chapter 7 Better Hedging with Reinforcement Learning 281

    Introduction 281

    A New AI Trading Assistant 281

    Continuous Hedging Is Not Required 282

    Machine Learning Comes to the Rescue 283

    A Simplified but Effective Reinforcement

    Learning Approach 284

    Overview of the Reinforcement Learning 285

    Identification 285

    Simulation 286

    Ref inement Training on Actual Market Data 287

    Testing and Implementation 287

    Implementation on QuantConnect 288

    Primary Research Notebook 289

    The Policy Network 290

    Model Functions 292

    Fine-tuning with Market Data 296

    Results 300

    Conclusion 303

    Chapter 8 AI for Risk Management and Optimization 305

    What Is Corrective AI and Conditional

    Parameter Optimization? 305

    Feature Engineering 308

    Applying Corrective AI to Daily Seasonal Forex Trading 312

    What Is Conditional Parameter Optimization? 318

    Applying Conditional Parameter Optimization to an ETF Strategy 319

    Unconditional vs. Conditional Parameter Optimizations 320

    Performance Comparisons 322

    Conditional Portfolio Optimization 322

    Regime Changes Obliterate Traditional Portfolio Optimization Methods 322

    Learning to Optimize 324

    Ranking Is Easier Than Predicting 325

    The Fama-French Lineage 327

    Comparison with Conventional Optimization Methods 327

    Model Tactical Asset Allocation Portfolio 331

    CPO Software-as-a-Service 333

    Conclusion 340

    Definitions of Spread_EMA & Spread_VAR 340

    Chapter 9 Application of Large Language Models and Generative AI in Trading 341

    Role of Generative AI in Creating Alpha 341

    Selecting an LLM for Building a Generative AI Application 342

    Prompt Engineering 344

    Prompt Engineering in Practice 345

    Addressing Model "Hallucination" 346

    Question Answering Using a Retrieval Augmented Application in SageMaker Canvas 347

    RAG Application Costs and Optimization Techniques 350

    Testing Our Infrastructure 351

    Summarization 356

    Useful AI Platforms and Services 359

    ChatGPT 359

    Gemini 359

    Bedrock 359

    SageMaker 359

    Q Business 360

    References 361

    Subject Index 363

    Code Index 379