Algorithmic Trading with Machine Learning

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prerequisites
intermediate Python • beginner scikit-learn • basics of LightGBM
skills learned
engineer financial features • build and test fundamental and advanced ML models to predict asset returns with scikit-learn and LightGBM • develop a trading strategy by defining rules that translate model predictions into trades
Stefan Jansen
4 weeks · 7-10 hours per week · INTERMEDIATE
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catalog / Data Science / Machine Learning
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Look inside
Machine learning and the growing availability of diverse financial data has created powerful and exciting new approaches to quantitative investment. In this liveProject, you’ll step into the role of a data scientist for a hedge fund to deliver a machine learning model that can inform a profitable trading strategy.

You’ll go hands-on to build an end-to-end strategy workflow that includes sourcing market data, engineering predictive features, and designing and comparing various ML models. Throughout the liveProject you will work with libraries and tools from the industry-standard Python data ecosystem. You’ll tackle challenges such as training a regularized linear regression model, tuning a gradient boosting ML model, and evaluating the performance of your strategy—all essential skills for success in this highly lucrative area of machine learning.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Stefan Jansen
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets. Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author of ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.

prerequisites

The liveProject is for intermediate Python programmers who know the basics of data science. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of pandas
  • Basics of scikit-learn
  • Basics of LightGBM
  • Basics of Jupyter Notebook
TECHNIQUES
  • Basics of financial metrics and technical indicators
  • Basics of ML

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