Classification-based machine learning for trading in R (Udemy.com)
Created by: The Trading Whisperer
Produced in 2021
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Overall Score : 96 / 100
Course Description
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. 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, naAve 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.
- 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
Instructor Details
- 4.8 Rating
12 Reviews
The Trading Whisperer
Data scientist and passionate about the financial market. I have been trading for the last 3 years with very good results in the last 2 years. I have shifted mainly from a discretionary to a more quantitative trader. I used my skills in machine learning to generate reliable and profitable trading strategies.



