Algorithmic Trading with Machine Learning you own this product

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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liveProject liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $49.99
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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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

you will learn

In this liveProject, you’ll learn skills for designing and implementing an end-to-end machine learning model specializing in asset return prediction. These skills are in high demand from companies across the banking and investment industries.

  • Source financial market data containing historic price information
  • Engineer financial features based on commonly used technical indicators deemed to contain some predictive information about future returns
  • Build, tune and test an ML model to predict asset returns for a given price horizon
  • Evaluate the signal quality of the resulting model predictions using Alphalens
  • Develop a trading strategy by defining rules that translate model predictions into trades
  • Backtest your trading strategy using Zipline
  • Evaluate your backtest result with common performance and risk measures using pyfolio

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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