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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.…mehr
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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Autorenporträt
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.
Inhaltsangabe
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
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
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