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Udacity

Momentum-Based Trading

via Udacity

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Overview

In this course, learners will explore how to design, backtest, and optimize a working momentum-based ML trading strategy.

Syllabus

  • What is Momentum-Based Trading
    • Normal distribution and geometric Brownian motion are key to momentum trading. Shapiro-Wilk’s and Student’s t-tests are useful statistical tools for developing momentum-based trading strategies.
  • Identifying and Extracting Momentum Features
    • Explore geometric Brownian motion for stock price modeling, calibration, forecasting, and confidence intervals, followed by deriving and coding the Black-Scholes formula for European option pricing.
  • Constructing a Momentum Trading Model
    • Building a momentum-based trading program using MySQL/SQLite, Python, and geometric Brownian motion for price forecasting, confidence intervals, and Monte-Carlo simulation for scenario analysis.
  • Backtesting and Optimization Techniques
    • The lesson covers back-testing momentum strategies, evaluating with Sharpe ratio and maximum drawdown, and quantitative risk management using Value-at-Risk (VaR) and Expected Shortfall (ES) in Python.
  • Project: Build a Momentum-Based Algorithmic Trading Program
    • Build a momentum-based strategy to trade the S&P 500 index. You can later expand and customize you project to suit your needs. You will use the Python packages numpy, scipy and sqlite3, among others.

Taught by

Xiaolei Xie

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