Quantitative Trading Analysis with R
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Quantitative Trading Analysis with R

Learn quantitative trading analysis from basic to expert level through a practical course with R statistical software.
Bestselling
4.0 (47 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
625 students enrolled
Created by Diego Fernandez
Last updated 5/2017
English
Current price: $10 Original price: $50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 7.5 hours on-demand video
  • 7 Articles
  • 21 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Download index replicating fund data to perform quantitative trading analysis operations by installing related packages and running script 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 main trading categories through indicators such as simple moving averages SMA, moving averages convergence-divergence MACD, Bollinger Bands®, relative strength index RSI and statistical arbitrage through z-score.
  • Evaluate simulated strategy historical risk adjusted performance through trading statistics, returns and risk management metrics.
  • Calculate main trading statistics such as net trading profit and loss, maximum drawdown, profit to maximum drawdown and equity curve.
  • Measure principal strategy performance metrics such as annualized returns, standard deviation and Sharpe ratio.
  • Estimate key risk management metrics such as maximum adverse excursion MAE, maximum favorable excursion MFE and Kelly ratio.
  • Maximize historical risk adjusted performance by optimizing strategy parameters through an exhaustive grid search of set combinations.
  • Minimize optimization over-fitting through walk forward analysis implemented as step-forward cross-validation by dividing data into rolling training and testing samples.
View Curriculum
Requirements
  • R statistical software is required. Downloading instructions included.
  • RStudio Integrated Development Environment (IDE) is recommended. Downloading instructions included.
  • R script files provided by instructor.
  • Prior basic R statistical software knowledge is useful but not required.
Description

Learn quantitative trading analysis through a practical course with R statistical software using index replicating fund historical data 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 take decisions as DIY 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

  • Download index replicating fund data to perform quantitative trading analysis operations by installing related packages and running script on RStudio IDE.
  • Implement trading strategies by defining indicators, identifying signals they generate and outlining rules that accompany them.
  • Explore strategies based on simple moving averages SMA, moving averages convergence-divergence MACD, Bollinger Bands®, relative strength index RSI and statistical arbitrage through z-score.
  • Calculate main trading statistics such as net profit and loss to maximum drawdown ratio and equity curve.
  • Measure principal performance metrics such as annualized returns, standard deviation and Sharpe ratio.
  • Estimate key risk management metrics such as maximum adverse excursion MAE, maximum favorable excursion MFE and Kelly ratio.
  • Maximize historical risk adjusted performance by optimizing strategy parameters.
  • Minimize historically optimized strategy over-fitting through walk forward analysis.

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 DIY 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 index replicating fund historical data for back-testing to achieve greater effectiveness. 

Content and Overview

This practical course contains 53 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 download index replicating fund data to perform quantitative trading analysis operations by installing related packages and running script 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. You’ll do this while exploring main strategy categories of trend-following and mean-reversion with indicators such as simple moving averages SMA, moving averages convergence-divergence MACD, Bollinger Bands®, relative strength index RSI and statistical arbitrage through z-score.

After that, you’ll do strategy reporting by evaluating simulated strategy risk adjusted performance with historical data while exploring main areas of trading statistics, performance and risk management metrics. For this, you’ll calculate main trading statistics such as net profit and loss, maximum drawdown, profit to maximum drawdown ratio and equity curve. You’ll also measure principal performance metrics such as annualized returns standard deviation and Sharpe ratio. And you’ll estimate key risk management metrics such as maximum adverse excursion MAE, maximum favorable excursion MFE and Kelly ratio.

Later, you’ll optimize strategy parameters by maximizing historical risk adjusted performance measured by net trading profit and loss, maximum drawdown and profit to maximum drawdown metrics. You’ll implement this through an exhaustive grid search of parameter set combinations.

Finally, you’ll do strategy walk forward analysis to avoid historical parameters optimization over-fitting or data snooping by implementing a step-forward cross-validation. You’ll apply this through a rolling walk forward analysis which divides data into a training sample for parameters set optimization that is then validated on a test sample. Next, you’ll repeat this process one step-forward at a time until the end of time series.

Who is the target audience?
  • Students at any knowledge level who want to learn about quantitative trading analysis using R statistical software.
  • Finance professionals or academic researchers who wish to deepen their knowledge in quantitative finance.
  • DIY investors also at any knowledge level who desire to learn about quantitative trading analysis and put it in practice.
  • This course is NOT about “get rich quick” trading strategies or magic formulas.
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Curriculum For This Course
53 Lectures
07:17:23
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Course Overview
6 Lectures 27:14

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

Preview 04:23

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (course overview, strategy implementation, strategy reporting, strategy parameters optimization, walk forward analysis and bibliography).

Preview 02:42

In this lecture you will learn quantitative trading analysis definition, R statistical software and RStudio Integrated Development Environment (IDE) downloading websites.

Quantitative Trading Analysis
03:46

In this lecture you will learn quantitative trading analysis data downloading into RStudio Integrated Development Environment (IDE), data sources, R script code files originally in .TXT format that need to be converted in .R format with quantitative trading analysis computation instructions, R packages installation (quantmod, zoo, xts, TTR, PerformanceAnalytics, FinancialInstrument, blotter and quantstrat) and related code (Sys.setenv(), getSymbols(), currency(), stock() functions).

Quantitative Trading Analysis Data
16:17

Before starting course please download .TXT R script files as additional resources.

Course Script Files
00:03

You can download .PDF section slides file as additional resources.

Course Overview Slides
00:02
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Strategy Implementation
16 Lectures 01:59:47

You can download .PDF section slides file as additional resources.

Strategy Implementation Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to strategy implementation (strategy indicators, strategy signals, strategy rules and strategy application).

Strategy Implementation Overview
08:08

In this lecture you will learn strategy indicators definitions.

Strategy Indicators
02:12

In this lecture you will learn trend-following strategy 1 (double simple moving averages SMA) indicators definition and main calculations (rm.strat(), strategy(), add.indicator() functions).

Preview 10:07

In this lecture you will learn trend-following strategy 2 (moving averages convergence-divergence MACD) indicators definition and main calculations (rm.strat(), strategy(), add.indicator() functions).

Trend-Following Strategy 2 Indicators
10:17

In this lecture you will learn mean-reversion strategy 1 (Bollinger Bands®) indicators definition and main calculations (rm.strat(), strategy(), add.indicator() functions).

Mean-Reversion Strategy 1 Indicators
06:56

In this lecture you will learn mean-reversion strategy 2 (relative strength index RSI) indicators definition and main calculations (rm.strat(), strategy(), add.indicator() functions).

Mean-Reversion Strategy 2 Indicators
08:32

In this lecture you will learn mean-reversion strategy 3 (statistical arbitrage through z-score) indicators definition and main calculations (rm.strat(), strategy(), add.indicator() functions).

Mean-Reversion Strategy 3 Indicators
19:57

In this lecture you will learn strategy signals definitions.

Strategy Signals
00:54

In this lecture you will learn trend-following strategy 1 (double simple moving averages SMA) signals definition and main calculations (add.signal() function).

Trend-Following Strategy 1 Signals
05:48

In this lecture you will learn trend-following strategy 2 (moving averages convergence-divergence MACD) signals definition and main calculations (add.signal() function).

Trend-Following Strategy 2 Signals
06:00

In this lecture you will learn mean-reversion strategy 1 (Bollinger Bands®) signals definition and main calculations (add.signal() function).

Mean-Reversion Strategy 1 Signals
05:31

In this lecture you will learn mean-reversion strategy 2 (relative strength index RSI) signals definition and main calculations (add.signal() function).

Mean-Reversion Strategy 2 Signals
05:43

In this lecture you will learn mean-reversion strategy 3 (statistical arbitrage through z-score) signals definition and main calculations (add.signals() function).

Mean-Reversion Strategy 3 Signals
06:25

In this lecture you will learn strategy rules definition and main calculations (add.rule() function).

Strategy Rules
12:52

In this lecture you will learn strategy application definition and main calculations (initPortf(), initAcct(), initOrders(), applyStrategy(), updatePortf(), updateAcct(), updateEndEq() functions).

Strategy Application
10:23
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Strategy Reporting
16 Lectures 02:30:44

You can download .PDF section slides file as additional resources.

Strategy Reporting Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to strategy reporting (trading statistics, performance metrics and risk management). 

Strategy Reporting Overview
06:55

In this lecture you will learn trading statistics definition.

Trading Statistics
05:36

In this lecture you will learn trend-following strategy 1 (double simple moving averages SMA) trading statistics definition and main calculations (tradeStats(), perTradeStats(), getOrderBook(), chart.theme(), chart.Posn(), getAccount() functions).

Trend-Following Strategy 1 Trading Statistics
14:34

In this lecture you will learn trend-following strategy 2 (moving averages convergence-divergence MACD) trading statistics definition and main calculations (tradeStats(), perTradeStats(), getOrderBook(), chart.theme(), chart.Posn(), getAccount() functions).

Trend-Following Strategy 2 Trading Statistics
12:51

In this lecture you will learn mean-reversion strategy 1 (Bollinger Bands®) trading statistics definition and main calculations (tradeStats(), perTradeStats(), getOrderBook(), chart.theme(), chart.Posn(), getAccount() functions).

Mean-Reversion Strategy 1 Trading Statistics
13:12

In this lecture you will learn mean-reversion strategy 2 (relative strength index RSI) trading statistics definition and main calculations (tradeStats(), perTradeStats(), getOrderBook(), chart.theme(), chart.Posn(), getAccount() functions).

Mean-Reversion Strategy 2 Trading Statistics
10:53

In this lecture you will learn mean-reversion strategy 3 (statistical arbitrage through z-score) trading statistics definition and main calculations (tradeStats(), perTradeStats(), getOrderBook(), chart.theme(), chart.Posn(), getAccount() functions).

Mean-Reversion Strategy 3 Trading Statistics
15:05

In this lecture you will learn performance metrics definition and main calculations (Return.calculate(),  charts.PerformanceSummary(),  table.AnnualizedReturns() functions).

Performance Metrics
15:35

In this lecture you will learn risk management metrics definition.

Risk Management Metrics
04:07

In this lecture you will learn trend-following strategy 1 (double simple moving averages SMA) risk management definition and main calculations (chart.ME(), KellyRatio() functions).

Trend-Following Strategy 1 Risk Management
09:27

In this lecture you will learn trend-following strategy 2 (moving averages convergence-divergence MACD) risk management definition and main calculations (chart.ME(), KellyRatio() functions).

Trend-Following Strategy 2 Risk Management
06:51

In this lecture you will learn mean-reversion strategy 1 (Bollinger Bands®) risk management definition and main calculations (chart.ME(), KellyRatio() functions).

Mean-Reversion Strategy 1 Risk Management
08:53

In this lecture you will learn mean-reversion strategy 2 (relative strength index RSI) risk management definition and main calculations (chart.ME(), KellyRatio() functions).

Mean-Reversion Strategy 2 Risk Management
07:07

In this lecture you will learn mean-reversion strategy 3 (statistical arbitrage through z-score) risk management definition and main calculations (chart.ME(), KellyRatio() functions).

Mean-Reversion Strategy 3 Risk Management
07:54

In this lecture you will learn stop-loss and trailing-stop definition and main calculations (rm.strat(), add.rule(), tradeStats(), perTradeStats(), chart.ME()).

Stop-Loss and Trailing-Stop
11:42
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Strategy Parameters Optimization
7 Lectures 01:10:53

You can download .PDF section slides file as additional resources.

Strategy Parameters Optimization Slides
00:02

In this lecture you will learn strategy parameters optimization definition.

Strategy Parameters Optimization
02:22

In this lecture you will learn trend-following strategy 1 (double simple moving averages SMA) parameters optimization definition and main calculations (add.distribution() and apply.paramset() functions).

Trend-Following Strategy 1 Parameters Optimization
15:50

In this lecture you will learn trend-following strategy 2 (moving averages convergence-divergence MACD) parameters optimization definition and main calculations (add.distribution() and apply.paramset() functions).

Trend-Following Strategy 2 Parameters Optimization
12:56

In this lecture you will learn mean-reversion strategy 1 (Bollinger Bands®) parameters optimization definition and main calculations (add.distribution() and apply.paramset() functions).

Mean-Reversion Strategy 1 Parameters Optimization
11:50

In this lecture you will learn mean-reversion strategy 2 (relative strength index RSI) parameters optimization definition and main calculations (add.distribution() and apply.paramset() functions).

Mean-Reversion Strategy 2 Parameters Optimization
14:05

In this lecture you will learn mean-reversion strategy 3 (statistical arbitrage through z-score) parameters optimization definition and main calculations (add.distribution() and apply.paramset() functions).

Mean-Reversion Strategy 3 Parameters Optimization
13:48
+
Walk Forward Analysis
7 Lectures 01:08:39

You can download .PDF section slides file as additional resources.

Walk Forward Analysis Slides
00:02

In this lecture you will learn walk forward analysis definition.

Walk Forward Analysis
03:38

In this lecture you will learn trend-following strategy 1 (double simple moving averages SMA) walk forward analysis definition and main calculations (add.distribution() and walk.forward() functions).

Trend-Following Strategy 1 Walk Forward Analysis
13:06

In this lecture you will learn trend-following strategy 2 (moving averages convergence-divergence MACD) walk forward analysis definition and main calculations (add.distribution() and walk.forward() functions).

Trend-Following Strategy 2 Walk Forward Analysis
12:20

In this lecture you will learn mean-reversion strategy 1 (Bollinger Bands®) walk forward analysis definition and main calculations (add.distribution() and walk.forward()  functions).

Mean-Reversion Strategy 1 Walk Forward Analysis
11:19

In this lecture you will learn mean-reversion strategy 2 (relative strength index RSI) walk forward analysis definition and main calculations (add.distribution() and walk.forward() functions).

Mean-Reversion Strategy 2 Walk Forward Analysis
11:33

In this lecture you will learn mean-reversion strategy 3 (statistical arbitrage through z-score) walk forward analysis definition and main calculations (add.distribution() and walk.forward() functions).

Mean-Reversion Strategy 3 Walk Forward Analysis
16:41
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Bibliography
1 Lecture 00:02

You can download .PDF section slides file as additional resources.

Course Bibliography
00:02
About the Instructor
Diego Fernandez
3.8 Average rating
429 Reviews
2,966 Students
21 Courses
Exfinsis

Diego Fernandez is author of high-quality online courses and ebooks at Exfinsis for anyone who wants to become an expert in financial data analysis.

His main areas of expertise are financial analysis and data science. Within financial analysis he has focused on computational finance, quantitative finance and trading strategies analysis. Within data science he has concentrated on machine learning, applied statistics and econometrics. For all of this he has become proficient in Microsoft Excel®, R statistical software® and Python programming language® analysis tools. 

He has important online business development experience at fast-growing startups and blue-chip companies in several European countries. He has always exceeded expected professional objectives by starting with a comprehensive analysis of business environment and then efficiently executing formulated strategy.

He also achieved outstanding performance in his undergraduate and postgraduate degrees at world-class academic institutions. This outperformance allowed him to become teacher assistant for specialized subjects and constant student leader within study groups. 

His motivation is a lifelong passion for financial data analysis which he intends to transmit in all of the courses.