Buch, Englisch, 352 Seiten, Format (B × H): 156 mm x 234 mm
Buch, Englisch, 352 Seiten, Format (B × H): 156 mm x 234 mm
ISBN: 978-1-041-26468-2
Verlag: Taylor & Francis Ltd
Algorithmic Trading via AI/Machine Learning with R aims to demonstrate how algorithmic trading can empower retail traders to compete more effectively in markets long dominated by institutional giants. By translating advanced techniques into practical, systematic strategies, the book shows how automation, disciplined risk management, and data-driven decision making can help individuals filter out market noise, avoid manipulation, and exploit opportunities that once belonged exclusively to large firms.
The book’s purpose is to give you a framework where R is not just a statistical environment, but a trading laboratory and execution engine. Every chapter includes reproducible examples you can extend into your own practice and research pipeline. By the end, you will not merely understand algorithmic trading—you will have built, tested, and connected live strategies to market data. At its core, it demonstrates how R—a language renowned for statistical computing—can be transformed into a complete research and execution platform for trading.
This book is aimed at anyone who wants to learn, or use R, for AI/Machine Learning and algorithmic trading. It is also for individuals doing or interested in doing securities research and financial systems development and for retail traders who may wish to use R to gain an algorithmic trading edge.
Key Features:
- Follows a clearly defined, pedagogical structure that builds from foundational R tools to full automation and integration with APIs.
- Argues that while retail traders cannot match Wall Street’s scale, they can use algorithms to level the playing field—building consistency, resilience, and an edge in a market designed to favor the powerful.
- All the book’s scripts can be accessed on the book’s GitHub branch.
- The QuantRoom YouTube channel (@quantroom) provides video tutorials and scripts that complement the book’s content showcasing real-time problem-solving.
- Delivers a more engaging and accessible way to master algorithmic trading using R and the Schwab Trader API.
- The Appendix expands the book’s scope beyond R by presenting a side-by-side comparison between the C++ TWS API and the IBrokers R interface, illustrating how high-level R commands map directly to their low-level C++ counterparts.
Zielgruppe
Professional Practice & Development, Professional Reference, and Professional Training
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
- Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Stochastik
Weitere Infos & Material
Preface List of Figures List of Tables Listings 1 Key AI/ML R Packages 1.1 Introduction 1.2 Algorithmic Trading and AI/ML Packages 1.2.1 General ML Frameworks 1.2.2 Deep Learning 1.2.3 Bayesian Methods 1.2.4 Explainability 1.2.5 Algorithmic Trading Packages 1.2.6 Strategy & Backtesting 1.2.7 Risk & Performance 1.2.8 Execution & Integration 1.2.9 Conclusion 1.3 A Modern Comparative Analysis of Python vs R for Algorithmic Trading 1.3.1 Introduction 1.3.2 Python Ecosystem and Libraries 1.3.3 R Ecosystem and Libraries (Modern Workflow) 1.3.4 Conclusion and Recommendation 2 Market Data Acquisition 2.1 Introduction 2.2 Core Market Data Packages 2.2.1 quantmod 2.2.2 tidyquant 2.2.3 IBrokers 2.2.4 Charles Schwab (Trader) API 2.2.5 Rblpapi 2.2.6 alphavantager 2.2.7 Quandl 2.2.8 crypto2, cryptowatchR 2.2.9 xts and zoo 2.2.10 data.table 2.2.11 Conclusion 2.3 Data Storage Solutions 2.3.1 Introduction 2.3.2 SQLite and PostgreSQL 2.3.3 Parquet, Feather, and FST 2.3.4 Cloud Storage and Data Lakes 2.3.5 Hybrid Approach 2.3.6 MongoDB 2.3.7 DuckDB 2.3.8 Conclusion and Recommendations 2.4 Data Wrangling Packages for Algorithmic Trading 2.4.1 Core Time-Series Structures 2.4.2 High-Performance Wrangling 2.4.3 Tidy Financial Wrangling 2.4.4 Date and Text Utilities 2.5 Conclusion 3 Trading Models & Strategy Design 3.1 Trend Following 3.1.1 Moving Averages and Crossovers 3.1.2 Commentary 3.2 Mean Reversion 3.2.1 Bollinger Bands and Thresholds 3.2.2 Commentary 3.3 Statistical Arbitrage (Pairs Trading) 3.3.1 Pairs Trading 3.3.2 Commentary 3.4 Cyclical 3.4.1 Fast Fourier Transform (FFT) 3.4.2 Spectral Leakage Reduction 3.4.3 Commentary 3.5 Cluster 3.5.1 Frequency Distribution Histogram 3.5.2 Commentary 3.6 Chart Patterns 3.6.1 Double Top/Bottom 3.6.2 Commentary 3.7 Seasonality 3.7.1 Market Inefficiencies 3.7.2 Commentary 3.8 Gaps Up/Down 3.8.1 Price Gaps 3.8.2 Commentary 3.9 Time Series 3.9.1 ARIMA Models 3.9.2 Commentary 3.10 Price Shocks 3.10.1 Relative Strength Index (RSI) 3.10.2 Commentary 3.11 Volatility Breakout 3.11.1 Volatility Breakout Signals 3.11.2 Commentary 3.12 Machine Learning-Based 3.12.1 Decision Tree Classifier 3.12.2 Commentary 4 Performance Testing 4.1 Backtesting with Historical Data I 4.1.1 Introduction 4.1.2 Backtesting in R 4.1.3 Performance Backtest 4.1.4 Limitations of Backtesting 4.2 Backtesting with Historical Data II 4.2.1 Overview 4.2.2 Trading Logic 4.2.3 Modeling Assumptions 4.2.4 Performance Interpretation 4.2.5 Strategy Results 4.2.6 Benchmark Results 4.2.7 Key Definitions 4.2.8 Conclusion 4.3 Forward Testing: Assessing Algorithm Performance in Real-Time 4.3.1 Introduction 4.3.2 Real-Time Data: Acquisition, Processing, and Storage 4.3.3 Forward Testing: Methods and Best Practices 4.3.4 Evaluating Forward-Test Outcomes 4.4 Evaluating Performance: Metrics and Methods 4.5 Managing Risk: Strategies for Control and Mitigation 5 AI/ML for Finance 5.1 Supervised Learning 5.1.1 Logistic Regression Results 5.1.2 Random Forest Interpretation 5.1.3 Support Vector Regression Interpretation 5.1.4 Bias–Variance Tradeoff: A Key to Model Performance 5.1.5 Cross-Validation Techniques for Model Evaluation 5.1.6 Balancing Complexity and Simplicity: Overfitting and Under-fitting in Financial Models 5.1.7 Lasso Interpretation 5.1.8 Optimizing Model Performance through Hyperparameter Tuning 5.1.9 Ensemble Learning for Financial Prediction 5.2 Unsupervised Learning (Clustering) 5.2.1 K-Means 5.2.2 Hierarchical Clustering 5.3 Deep Learning (Neural Networks) 5.3.1 Feedforward Neural Network 5.3.2 Implementing Deep Learning with TensorFlow and Keras 6 Case Studies in AI/ML-Enhanced Trading Strategies 6.1 Introduction 6.2 Case Study 1: Momentum 6.3 Case Study 2: Mean Reversion 6.4 Case Study 3: Sentiment Analysis 6.5 Case Study 4: Portfolio Optimization 6.6 Case Study 5: Market-Making 6.7 Case Study 6: Stock Grouping 6.8 Case Study 7: Predicting Stock Trends 6.9 Case Study 8: PCA Application 6.10 Case Study 9: Unsupervised Portfolio Analysis 6.11 Case Study 10: Deep Learning Models 7 Getting Started with the Interactive Brokers TWS API 7.1 Introduction to R and RStudio 7.2 Installing R and RStudio 7.3 Configuring IB’s Trader Workstation 7.4 Introduction to IBrokers Package (Core Methods) 7.4.1 twsConnect 7.4.2 isConnected 7.4.3 twsConnectionTime 7.4.4 reqAccountUpdates 7.4.5 reqCurrentTime 7.4.6 reqIds 7.4.7 twsContract 7.4.8 reqHistoricalData 7.4.9 reqMktData 7.4.10 reqMktDepth 7.4.11 reqRealTimeBars 7.4.12 placeOrder 7.4.13 cancelOrder 8 Algorithmic Trading: Automation and Monitoring 8.1 The Landscape of Algorithmic Trading 8.1.1 From Strategies to Systems 8.1.2 Building for Resilience 8.1.3 Tools, Education, and the Roadmap Ahead 8.1.4 The Reality of Success in Algorithmic Trading 8.2 Designing and Implementing a Trading Strategy 9 QuantRoom Videos & Scripts 9.1 Introduction 9.2 Interactive Brokers Videos & Scripts 9.3 Charles Schwab (Trader) API Videos & Scripts Appendix A Comparison of C++ TWS API and R IBrokers Package Appendix B The R C++ Application Programming Interface (API) Index
