Hands-On AI Trading with Python, Quantconnect, and AWS - Ernest P Chan

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Auteur(s) : Ernest P Chan - Jared Broad - Jiri Pik - Philip Sun - Vivek Singh

Editeur : Wiley

Langue : Anglais

Parution : 01/01/2025

Format : Moyen, de 350g à 1kg

Nombre de pages : 416.0

ISBN : 9781394268436

Résumé :

Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance

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

Unlike 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.

The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used:

• Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.
• Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.
• Predict market volatility regimes and allocate funds accordingly.
• Predict daily returns of tech stocks using classifiers.
• Forecast Forex pairs' future prices using Support Vector Machines and wavelets.
• Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.
• Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.
• Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.
• Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.
• AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation.

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

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Biographie:

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...

Sommaire:

JIRI PIK: 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.

ERNEST P. CHAN: 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 Quantitative Trading and Machine Trading.

JARED BROAD: Founder and CEO of QuantConnect(TM), Jared Broad has empowered over 300,000 algorithmic traders worldwide with a platform that simplifies strategy design, backtesting, and live deployment.

PHILIP SUN: 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.

VIVEK SINGH: 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....