Quantitative Trading Strategies in R
A step-by-step approach to building solid quantitative trading strategies using R
Quantitative and algorithmic trading now accounts for over one-third of all trading across financial markets in the world. This course is created with the objective of teaching retail traders and professional quants traders how to build and execute their own quantitative trading strategies. The primary focus of this course is on understanding the process of designing a successful trading strategy and learning to use R for statistical modeling and analysis of financial data, building a trading strategy, and then backtesting and risk management of the trading strategy.
You will learn about how to set up a strategy using the R quantstrat package. The course provides complete working and setup of the strategy using quantstrat, including identifying and setting up indicators, creating signals based on these indicators, outlining the trading rules, and backtesting and risk management of the strategy.
What you will learn?
Understand the fundamentals of quantitative trading strategies and how traders build strategies in the real world.
Explore various types of quantitative trading strategies such as momentum strategies, mean-reversion strategies, and market-making strategies.
Steps to build and backtest a successful quantitative trading strategy with a focus on risk management
Download financial data from multiple sources and analyze it using the quantmod library
Exploratory data analysis including statistics and charting using the quantmod and ggplot2 library
Learn how to build and backtest a trading strategy using the quantstrat package
Evaluate a strategy using trading statistics, performance metrics, and risk management metrics
Calculate main trading statistics such as net trading profit and loss, gross profit, gross loss, profit ratio, maximum drawdown, maximum drawdown, and equity curve.
Measure important performance metrics such as cumulative returns, annualized returns, annualized Sharpe ratio, and Calmar ratio.
Estimate key risk management metrics such as annualized standard deviation, maximum drawdown, and value at risk.
Evaluate the strategy based on these statistics and charts and then optimize your strategy based on insights.
Detailed concepts and explanations about each topic
Step-by-step instructions along with complete R code to build and execute the trading strategies
Three end-to-end case studies that take you through building a trading strategy and then backtest it and measure its performance
Complete downloadable R code for all the three case studies