Trading Strategy Development with ML

Trading Strategy Development with Machine Learning is an in-depth workshop focusing on the challenges and nuances of working with financial data and applying machine learning to generate trading strategies. We will walk you through the complete lifecycle of trading strategies creation and improvement using machine learning, and ending with automated execution, with unique insights and commentaries from research and practice.

The course makes extensive use of Python packages such as Pandas, Scikit-learn and LightGBM. It is co-taught by Dr. Ernest Chan and Dr. Hamlet Medina.

12 - 13 May 2022


2 Days


London, UK – Tower Hotel, London E1


Ernest Chan

Course Fee:

£1990 + VAT


Part 1

Overview: Challenges of Financial Data Science and Machine Learning

  • Data cleansing: Why even simple daily data cannot be trusted
  • Features engineering: Claims that this step is easy for deep learning are false
  • Features selection: What even experts can get wrong here
  • Machine learning: shallow + deep learning work best together
  • Avoiding data snooping and selection bias: using CPCV
  • Metalabelling: improving your proprietary strategy without telling anyone
  • Backtesting: beyond machine learning
  • Automated execution: choosing a platform

Data Cleansing and Features Engineering

  • Checking and adjusting price and volume data in stocks and futures
  • Survivorship bias and how to find it
  • Stationarity and fractional differentiation
  • Sanity checks for news sentiment data
  • Sanity checks for earnings data
  • What is a security master and how to create one where none existed?
  • Aggregating and encoding categorical data into features

Part 2

Machine Learning

  • Better Start Simple: An example of simple features and shallow machine learning using
  • logistic regression with L1 and L2 regularizations
  • Deeper learning: Random forests and gradient boosted trees with Scikit-Learn and LightGBM
  • Features selection using Mean Decrease Accuracy and SHAP
  • Cross validation and hyperparameters optimization
  • Metrics for measuring machine learning outcomes
  • Metalabelling: what common base models to use?

Part 3


  • Machine learning suggests, but does not determine, trading strategy
  • Various ways of using the output of ML for trading
  • Reduce data snooping bias: using CPCV

Automated Execution

  • Using QuantConnect to automate strategies