Machine Learning for Trading

Who this course is for?

• Finance professionals
• Investment management professionals
• Traders

Prerequisite

• Basic competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL will be helpful. You should have a background in statistics and foundational knowledge of financial markets.

Course Outline

• Module 1: Introduction to Trading, Machine Learning and GCP
  • Fundamentals of trading, including the concept of trend, returns, stop-loss and volatility
  • Differences between supervised/unsupervised and regression/classification machine learning models
  • Profit sources and structure of basic quantitative trading strategies
  • Model generalization
  • Differences between regression and forecasting
  • Steps needed to create development and implementation backtesters
  • Google Cloud Platform to build basic machine learning models in Jupyter Notebooks
• Module 2: Using Machine Learning in Trading and Finance
  • Design basic quantitative trading strategies
  •  Keras and Tensorflow
  • Backtesting
  • Pair trading strategy prediction models
  • Momentum-based trading model
• Module 3: Trading Strategies as a Reinforcement Learning Problem
  • What is reinforcement learning (RL) and how trading is an RL problem
  • Build trading strategies using RL
  • The benefits of using RL vs. other learning methods
  • Difference between actor-based policies and value-based policies
  • Incorporating RL into a momentum trading strategy