Unlock the full potential of quantitative trading with Prescriptions for Quant Traders Using R—a practical, hands-on guide for turning data into trading decisions.
This book is written for quantitative traders, financial analysts, and data scientists who want more than theory. Organized into ten structured parts, it delivers step-by-step “prescriptions” using R—script-driven tasks that solve real-world trading problems. Each prescription is designed to be immediately actionable, so readers can move from concept to implementation without guesswork.
What makes this book different is its clarity, utility, and its link to online video walk-throughs of R scripts. Modeled in the spirit of a Scientific American article, the text flows seamlessly without interruptions, yet a comprehensive bibliography ensures depth and rigor. Every chapter blends explanation with executable code, enabling traders to:
- Design systematic trading strategies in R — and understand why they work.
- Apply robust statistical and econometric methods to financial data for better predictions.
- Evaluate risk and performance metrics to refine strategies with confidence.
- Automate workflows so insights move faster from model to market.
Whether you are a retail trader aiming to compete with professionals, a financial analyst seeking sharper models, or a data scientist expanding into trading, this book bridges the gap between theory and practice.
Readers will find special value in the prescription-based approach. Just as a doctor prescribes remedies, these scripts deliver targeted solutions to trading challenges—from portfolio optimization and volatility forecasting to Bayesian inference and machine learning integration.
In a marketplace where retail participation continues to grow, Prescriptions for Quant Traders Using R equips you with the knowledge, tools, and confidence to approach trading systematically. The result: deeper insights, stronger discipline, and strategies that stand the test of real-world markets.
Whether you’re coding your first model or refining a sophisticated strategy, this book provides the prescriptions you need to succeed.
Table of Contents:
Preface List of Figures List of Tables Listings 1 Accessing Financial Data 2 Managing Financial Data 3 Charting 4 Technical Analysis 5 Backtesting 6 Charles Schwab (Trader) API 7 Approaches to Monitoring Trades 8 Approaches to Risk Management 9 Approaches to Staying Current with News and Economic Data 10 Approaches to Assessing Market Performance 11 Approaches to Research and Continuing Education 12 Adaptation 13 YouTube Channel for Quant Trading Strategies Using R Index
About the Author :
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). Jason’s 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. Jason’s 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.
Dr. Oskars Linares is Founder (2015), Research Director and Quant Strategist, OIS Market Research Group, Michigan, USA—a research and investment group specializ- ing in generating premium using equity, index, and futures options. Oskars is a member of the International Institute of Forecasters. He developed a Minimal-Model (MinMod) to inform the OIS Market Research Group’s equity, index, and futures trading. He also developed an SDE ARIMA- variant forecaster to assist decision-making selecting option strike prices using empirical probability distributions with Bayesian updating. Oskars began his mathematical modeling career under the gentle guidance of Dr. Loren Zech (Senior Scientist, Laboratory of Mathematical Biology, National Cancer Institute, National Institutes of Health, Bethesda, MD) using S-PLUS, and began migrating to R in 1995 while at the University of Michigan, Ann Arbor. Working with Dr. Ray Boston at UPENN, Oskars applied Bayesian multilevel models for repeated measurement data in their research. Oskars has published over 80 peer-reviewed scientific research papers in prestigious scientific journals, several book chapters, and is co-author of the first editions of Investigating Biological Systems Using Mod- eling (Academic Press, 1999) and Plain English for Doctors and Other Medical Scientists (Oxford University Press, 2017). He received the Great Seal of the United States Award (1993) for his advancements in mathematical-medicine research on aging. Oskars now lives in R¯ıga, Latvija.
