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Applied Machine Learning with R (Trading Use Case) – 2020

Finance & Accounting

Rating4.1
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Applied Machine Learning with R (Trading Use Case) – 2020, available at $59.99, has an average rating of 4.05, with 40 lectures, 1 quizzes, based on 80 reviews, and has 407 subscribers.

You will learn about Understand how to develop a quantitative trading strategy how to use machine learning for trading in R Learn about different type of machine learning algorithms (Naive Bayes, support vector machines and random Forest) for developing profitable trading strategies Learn to write simple and powerful codes in r for quantitative finance Understand the difference between trading actors in the market and learn about manual and systematic trading strategies How to predict the price direction of any asset class using custom written scripts and algorithms in R Use different hyperparameters to improve predictive power of classification based machine learning models Learn how to analyse PnL and performance metrics of trading strategies This course is ideal for individuals who are Investment professionals interested to learn to apply classification-based machine learning techniques to investing and trading strategies or Amateur traders and semi-professional quants looking for original innovative trading and quantitative finance ideas or Data scientist and enthusiasts interested in different machine learning use cases or Experienced and beginner R users interested in quantitative analysis/trading using R or anyone looking for how machine learning can be applied into investing It is particularly useful for Investment professionals interested to learn to apply classification-based machine learning techniques to investing and trading strategies or Amateur traders and semi-professional quants looking for original innovative trading and quantitative finance ideas or Data scientist and enthusiasts interested in different machine learning use cases or Experienced and beginner R users interested in quantitative analysis/trading using R or anyone looking for how machine learning can be applied into investing.

Enroll now: Applied Machine Learning with R (Trading Use Case) – 2020

Summary

Title: Applied Machine Learning with R (Trading Use Case) – 2020

Price: $59.99

Average Rating: 4.05

Number of Lectures: 40

Number of Quizzes: 1

Number of Published Lectures: 40

Number of Published Quizzes: 1

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: $124.99

Quality Status: approved

Status: Live

What You Will Learn

  • Understand how to develop a quantitative trading strategy
  • how to use machine learning for trading in R
  • Learn about different type of machine learning algorithms (Naive Bayes, support vector machines and random Forest) for developing profitable trading strategies
  • Learn to write simple and powerful codes in r for quantitative finance
  • Understand the difference between trading actors in the market and learn about manual and systematic trading strategies
  • How to predict the price direction of any asset class using custom written scripts and algorithms in R
  • Use different hyperparameters to improve predictive power of classification based machine learning models
  • Learn how to analyse PnL and performance metrics of trading strategies

Who Should Attend

  • Investment professionals interested to learn to apply classification-based machine learning techniques to investing and trading strategies
  • Amateur traders and semi-professional quants looking for original innovative trading and quantitative finance ideas
  • Data scientist and enthusiasts interested in different machine learning use cases
  • Experienced and beginner R users interested in quantitative analysis/trading using R
  • anyone looking for how machine learning can be applied into investing

Target Audiences

  • Investment professionals interested to learn to apply classification-based machine learning techniques to investing and trading strategies
  • Amateur traders and semi-professional quants looking for original innovative trading and quantitative finance ideas
  • Data scientist and enthusiasts interested in different machine learning use cases
  • Experienced and beginner R users interested in quantitative analysis/trading using R
  • anyone looking for how machine learning can be applied into investing

The course is designed to fully immerse you into the complete quantitative trading/finance workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you from zero to hero using R. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes.

Course elements:

  • Learn about trading and the quantitative trading workflow. Develop a solid understand of what is required to do quantitative trading analysis and the advantages and disadvantages.

  • Learn how to write simple and complex codes in r with some r refresher lecture. Learn how to use the quantmod package to access/load free market data from yahoo finance and other sources.

  • Learn how to download futures data from NinjaTrader. Load the data in R and do data preparation and visualization.

  • Explore various trading ideas/hypothesis on the web, and learn how to generate original trading ideas.

  • Learn and understand what machine learning is and get a good grip of the type of machine learning algorithms available to solve different type of problems ( namely classification and regression problems).

  • Code along while learning about feature engineering, write algorithms for training and testing support vector machine, naïve bayes and random forest models and use these to predict the next price direction of crude oil futures. Realize that these strategies can be used for other trading instruments/products.

  • Compare the model performance and do portfolio selection by only selecting the non correlated models.

Disclaimer

This course is for educational purpose and does not constitute trading or investment advice. All content, teaching material and codes are presented with sharing and learning purpose and with no guarantee of exactness or completeness.

No past performance is indicative of future performance and the trading strategies presented here are based on hypothetical and historical backtesting. Trading futures, forex and options involves the risk of loss. Please consider carefully if trading is appropriate to your financial situation. Only risk capital you can afford to lose, and the risk of loss being substantial, you should consider carefully the inherent risks.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course curriculum

Chapter 2: Quantitative trading

Lecture 1: Introduction to trading

Lecture 2: Trading products/instruments

Lecture 3: Discretionary and Systematic trading

Lecture 4: Introduction to Quantitative trading

Chapter 3: Hypothesis/idea

Lecture 1: Where to look for quantitative trading ideas

Lecture 2: Our course trading idea/hypothesis

Lecture 3: Technical indicators included in our hypothesis

Chapter 4: R Refresher

Lecture 1: R and RStudio installation setup

Lecture 2: Basic R syntaxes and codes – (part 1)

Lecture 3: Basic R syntaxes and codes – (part 2)

Chapter 5: Data preparation and visualisation

Lecture 1: Data sources

Lecture 2: Yahoo finance market data

Lecture 3: NinjaTrader – How to download daily price data

Lecture 4: Challenges and biases in free datasets

Lecture 5: Quantmod package to retrieve and plot market data

Lecture 6: Data preparation and visualisation part 1

Lecture 7: Data preparation and visualisation part 2

Chapter 6: Feature Engineering

Lecture 1: What we will do (feature engineering)

Lecture 2: What else can we do?

Lecture 3: Feature engineering – part 1 (R code)

Lecture 4: Feature engineering – part 2 (R code)

Chapter 7: Introduction to Machine Learning

Lecture 1: Intro to Machine Learning

Lecture 2: Regression – Intro to Linear Regression

Lecture 3: Classification – Machine learning for classification problems

Chapter 8: Naive-Bayes – Implementation in our trading strategy

Lecture 1: Introduction to Naive Bayes (NV)

Lecture 2: Where to find the master code file?

Lecture 3: NB – Training and testing – Part 1

Lecture 4: NB – Training and testing – Part 2

Chapter 9: Support Vector Machine – Implementation in our trading strategy

Lecture 1: Introduction to Support Vector Machines (SVM)

Lecture 2: SVM – Training and testing

Chapter 10: Random Forest – Implementation in our trading strategy

Lecture 1: Introduction to Random Forest

Lecture 2: Random Forest – training and testing – part 1

Lecture 3: Random Forest – training and testing – part 2

Chapter 11: Models performance comparison and scoring

Lecture 1: Model performance comparison – part 1

Lecture 2: Model Performance comparison – part 2

Lecture 3: Backtest analysis (multiple training and testing) part 1

Lecture 4: Backtest analysis (multiple training and testing) part 2

Lecture 5: Portfolio selection and way forward

Instructors

Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 7 votes
  • 3 stars: 15 votes
  • 4 stars: 27 votes
  • 5 stars: 28 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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