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

Jiri Pik , Ernest P. Chan , Jared Broad , Philip Sun , Vivek Singh 지음

Wiley 2025년 01월 22일 출간 국내도서 : 2025년 01월 29일 출간

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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 M

목차

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

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작가정보

저자(글) Jiri Pik

저자(글) Ernest P. Chan

저자(글) Jared Broad

저자(글) Philip Sun

저자(글) Vivek Singh