Quant Trading using Machine Learning



This course takes a completely practical approach to applying Machine Learning techniques to Quant Trading. Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory. Completely Practical: This course has just enough theory to get you started with both Quant Trading and Machine Learning. The focus is on practically applying ML techniques to develop sophisticated Quant Trading models. From setting up your own historical price database in MySQL to writing hundreds of lines of Python code, the focus is on doing from the get go. Machine Learning Techniques: We'll cover a variety of machine learning techniques, from K-Nearest Neighbors and Decision Trees to pretty advanced techniques like Random Forests and Gradient Boosted Classifiers. But, in practice Machine Learning is not just about the algorithms. Feature Engineering, Parameter Tuning, Avoiding overfitting; these are all a part and parcel of developing Machine Learning applications and we do it all in this course. Quant Trading: Quant Trading is a perfect example of an area where the use of Machine Learning leads to a step change in the quality of the models used. Traditional models often depend on Excel and building sophisticated models requires a huge amount of manual effort and domain knowledge. Machine Learning libraries available today allow you to build highly sophisticated models that give you much better performance with much less effort.


What's Covered: Quant Trading : Financial Markets, Stocks, Indices, Futures, Return, Risk, Sharpe Ratio, Momentum Investing, Mean Reversion, Developing trading strategies using Excel, Backtesting Machine Learning: Decision Trees, Ensemble Learning, Random Forests, Gradient Boosted Classifiers, Nearest Neighbors, Feature engineering, Overfitting, Parameter Tuning MySQL: Set up a historical price database in MySQL using Python. Python Libraries : Pandas, Scikit-Learn, XGBoost, Hyperopt. What is the target audience? Yep! Quant traders who have not used Machine learning techniques before to develop trading strategies Yep! Analytics professionals, modelers, big data professionals who want to get hands-on experience with Machine Learning Yep! Anyone who is interested in Machine Learning and wants to learn through a practical, project-based approach.

Goal of Course

Develop Quant Trading models using advanced Machine Learning techniques. Compare and evaluate strategies using Sharpe Ratios. Use techniques like Random Forests and K-Nearest Neighbors to develop Quant Trading models. Use Gradient Boosted trees and tune them for high performance. Use techniques like Feature engineering, parameter tuning and avoiding overfitting. Build an end-to-end application from data collection and preparation to model selection.


Developing Trading Strategies in Excel

Setting up your Development Environment

Decision Trees, Ensemble Learning and Random Forests

A Trading Strategy as Machine Learning Classification

Feature Engineering

Engineering a Complex Feature - A Categorical Variable with Past Trends

Building a Machine Learning Classifier in Python

Nearest Neighbors Classifier

Gradient Boosted Trees

Introduction to Quant Trading