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Advanced Trading Analysis with Python

Last Update: March, 2019

1. Course Objective

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

2. Skills Learned

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

  • Implement trend-following trading strategies (simple moving averages, moving averages convergence/divergence MACD) and mean-reversion trading strategies (Bollinger bands®, relative strength index RSI, statistical arbitrage z-score).
  • Maximize historical risk adjusted performance (strategy parameters optimization).
  • Evaluate simulated strategy optimization trials historical risk adjusted performance (annualized return, standard deviation and Sharpe ratio).
  • Reduce strategy parameters optimization back-testing over-fitting or data snooping (multiple hypothesis testing adjustment, individual time series bootstrap hypothesis testing multiple comparison adjustment).
  • Estimate strategy optimization trials population mean statistical inference test multiple probability values and adjust them for multiple hypothesis testing (family-wise error rate/Bonferroni procedure, false discovery rate/Benjamini-Hochberg procedure).
  • Simulate individual strategy optimization trial bootstrap population mean probability distribution (random fixed block re-sampling with replacement), approximate bootstrap population mean statistical inference test percentile probability value and correct it for multiple comparison (family-wise error rate adjustment).

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 advanced quantitative finance.
  • Experienced investor who desires to research advanced quantitative trading strategies.
  • This course is NOT about “get rich quick” trading systems or magic formulas.
My online courses are closed for enrollment.