Quantitative Trading Analysis with R

Full Course Content Last Update 08/2018

Learn quantitative trading analysis through a practical course with R statistical software using S&P 500® Index ETF prices for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.

Become a Quantitative Trading Analysis Expert in this Practical Course with R

• Read or download S&P 500® Index ETF prices data and perform quantitative trading analysis operations by installing related packages and running script code on RStudio IDE.

• Implement trading strategies based on their category and frequency by defining indicators, identifying signals they generate and outlining rules that accompany them.

• Explore strategy categories through trend-following indicators such as simple moving averages, moving averages convergence-divergence and mean-reversion indicators such as Bollinger bands®, relative strength index, statistical arbitrage through z-score.

• Evaluate simulated strategy historical risk adjusted performance through 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, profit to maximum drawdown and equity curve.

• Measure principal strategy performance metrics such as annualized returns, annualized standard deviation and annualized Sharpe ratio.

• Estimate key risk management metrics such as maximum adverse excursion and maximum favorable excursion.

• Maximize historical risk adjusted performance by optimizing strategy parameters through an exhaustive grid search of all indicators parameters combinations.

• Minimize optimization over-fitting or data snooping through walk forward analysis implemented as time-series or step-forward cross-validation by sequentially resampling asset prices data into rolling fixed length training subsets for in-sample strategy parameters optimizations and testing subsets for out-of-sample optimized strategy parameters validations.

Become a Quantitative Trading Analysis Expert and Put Your Knowledge in Practice

Learning quantitative trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors quantitative trading research and development.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data back-testing to achieve greater effectiveness.

Content and Overview

This practical course contains 59 lectures and 7 hours of content. It’s designed for all quantitative trading analysis knowledge levels and a basic understanding of R statistical software is useful but not required.

At first, you’ll learn how to read or download S&P 500® Index ETF prices historical data to perform quantitative trading analysis operations by installing related packages and running script code on RStudio IDE.

Then, you’ll implement trading strategy by defining indicators based on its category and frequency, identifying trading signals these generate, outlining trading rules that accompany them and applying all of the above. Next, you’ll explore main strategy categories such as trend-following and mean-reversion. For trend-following strategy category, you’ll use indicators such as simple moving averages and moving averages convergence-divergence. For mean-reversion strategy category, you’ll use indicators such as Bollinger bands®, relative strength index and statistical arbitrage through z-score.

After that, you’ll do strategy reporting by evaluating simulated strategy risk adjusted performance using historical data. Next, you’ll explore main strategy reporting areas such as trading statistics, performance metrics and risk management metrics. For trading statistics, you’ll use net trading profit and loss, gross profit, gross loss, profit factor, maximum drawdown, profit to maximum drawdown and equity curve. For performance metrics, you’ll use annualized return, annualized standard deviation and annualized Sharpe ratio. For risk management metrics, you’ll use maximum adverse excursion and maximum favorable excursion charts.

Later, you’ll optimize strategy parameters by maximizing historical risk adjusted performance through an exhaustive grid search of all indicators parameters combinations. Next, you’ll explore main strategy parameters optimization objectives such as net trading profit and loss, maximum drawdown and profit to maximum drawdown metrics.

Then, you’ll do strategy walk forward analysis to reduce historical parameters optimization over-fitting or data snooping through time-series or step-forward cross-validation. Next, you’ll implement asset prices time series data sequential resampling into fixed length training and testing without replacement subsets. For training data subsets, you’ll do sequential in-sample strategy parameters optimization.  For testing data subsets, you’ll do sequential out-of-sample validation of previously optimized strategy parameters. Finally, you’ll repeat this process one step-forward up to the end of asset prices time series data.