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

Learn the complete quantitative finance workflow and use machine learning algorithms in R to develop trading strategies

Created by: The Trading Whisperer
Data scientist, trader and chairman of an investment club

Created by: Data Science Sketch

What Will I 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

Requirements

• basic knowledge of trading and finance
• beginner R user
• Awareness of machine learning

Target audience

• 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