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.
The book aims to demonstrate how algorithmic trading can empower retail traders to compete more effectively in markets long dominated by institutional giants. It is aimed at anyone who wants to learn, or use R, for AI/Machine Learning and algorithmic trading.
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 BiasVariance 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 IBs 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
Jason Guevara is a financial analyst and accountant. He maintains a YouTube channel (https://www.youtube.com/@quantroom) dedicated to developing practical R scripts to assist active traders and R quants. Jason also does contract work for OIS Market Research Group as an R financial systems architect, coder, and developer. Jason provides a unique blend of financial expertise and coding experience to the quant finance field. Jason holds a Bachelor of Science degree in Finance and a minor in Economics from California State University (CSU)Northridge (2014). Jasons passion for markets began during the Great Recession. The rise of algorithmic trading at that time ignited his passion which to date continues to fuel his productivity. Jason uses his R programming skills to craft algorithmic trading scripts for personal exploration, research, and applications. He has been programming in R since 2012. Jasons dedicated YouTube channel is the premier guide for traders looking to master R in finance. By sharing his expertise online, he equips traders with the confidence to navigate the complex field of algorithmic trading.
Riards Bulavs is a graduate with a Bakalaurs finanss [ B.Sc. in Finance] from The University of Latvia (2025). Riards joined the OIS Market Research Group in July 2025 as a Research Associate Analyzing Financial Market Data, Implementing Financial Models, and trading equity, index, and futures instruments using quantitative methods and machine trading. Prior to joining the OIS Market Research Group, Riards was a student at the Emerio & Lourdes Linares Research and Education Center, where he learned to use the Interactive Brokers (IBKR) TraderWorkstation (TWS) for trading equity, index, and futures instruments. Riards successfully mastered TWS and the TWS API. He also mastered minimal-model (MinMod) trading tactics and option strike price selection using the Greeks and stochastic differential equation (SDE) derived empirical probability distributions. Born in Jrmala, a Latvian resort city on the Gulf of Rga, Riards provides a unique blend of financial knowledge and quant coding experience in C++, Python, and R. Riards specializes in crafting algorithmic trading strategies for exploration, research, and applications.
Dr. Oskars Linares is the Founder (2015), Research Director, and Quant Strategist at the OIS Market Research Group, an investment collective specializing in premium generation across equity, index, and futures options. A member of the Econometric Society and International Institute of Forecasters, Dr. Linares developed the proprietary Minimal-Model (MinMod) to guide OIS trading operations. His technical toolkit includes an SDE ARIMA variant forecaster, which leverages empirical probability distributions and Bayesian updating to optimize strike price selection. Oskars began his mathematical modeling career at the University of Michigan, Ann Arbor under the dual mastery of Professors Jeffrey B. Halter and John A. Jacquez. Training under Jacqueza pioneer in compartmental analysis Dr. Linares focused on nonlinear differential equations modeling and began a 30-year tenure with R (migrating from S-PLUS in 1995). His quest for mathematical rigor led him to the National Institutes of Health (NIH) in Bethesda, where he worked with Dr. Loren A. Zech at the Laboratory of Mathematical Biology. There, he utilized SAAM (Simulation, Analysis, And Modeling) and S-PLUS to navigate biomathematical complexity. This journey culminated at UPENN with Dr. Raymond C. Boston, applying Bayesian multilevel models to repeated measurement data to manage stochastic instability. With over 100 peer-reviewed scientific abstracts presented at Society meetings and papers to his name, Dr. Linares has ensured his models have remained statistically robust. Oskars has also published several book chapters, and is co-author of the first editions of Investigating Biological Systems Using Modeling (Academic Press, 1999), Plain English for Doctors and Other Medical Scientists (Oxford University Press, 2017), Diagnosing and Treating Medicus Incomprehensibilis (Oxford University Press, 2018), Prescriptions for Quant Traders Using R: Videos and Scripts (CRC Press, 2026). He received the Great Seal of the United States Award (1993) for his advancements in mathematical-medicine research on aging. Oskars now lives in Rga, Latvija.
