Quantitative Trading Analysis with Python

Last Update: September, 2018

1. Course Objective

Learn Quantitative Trading Analysis main topics using Python programming language® in this practical course for all knowledge levels. Feel free to take a look at Course Curriculum.

2. Skills Learned

At the end of this course you will know how to:

  • Implement trading strategies (technical indicators, trading signals and rules).
  • Explore trend-following strategy category (simple moving averages, moving averages convergence/divergence MACD) and mean-reversion strategy category (Bollinger bands®, relative strength index RSI, statistical arbitrage z-score).
  • Evaluate simulated strategy historical risk adjusted performance (trading statistics, performance metrics).
  • Calculate main trading statistics (net trading profit and loss, maximum drawdown, equity curve).
  • Measure principal strategy performance metrics (annualized return, standard deviation, and Sharpe ratio).
  • Maximize historical risk adjusted performance (strategy parameters optimization).
  • Reduce strategy parameters optimization back-testing over-fitting or data snooping (training/testing subsets delimiting).

3. Typical Student

This course is ideal for you as:

  • Undergraduate or postgraduate who wants to learn about the subject.
  • Finance professional or academic researcher who wishes to deepen your knowledge in quantitative finance.
  • Experienced investor who desires to research quantitative trading strategies.
  • This course is NOT about “get rich quick” trading systems or magic formulas.