Advanced Trading Analysis with Python

Learn advanced trading analysis through a practical course with Python programming language using S&P 500® Index ETF prices for back-testing. It explores main concepts from proficient to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.

Become an Advanced Trading Analysis Expert in this Practical Course with Python

• Read or download S&P 500® Index ETF prices data and perform advanced trading analysis operations by installing related packages and running code on Python PyCharm IDE.

• Implement trading strategies through trend-following indicators such as simple moving averages, moving averages convergence-divergence and mean-reversion indicators such as Bollinger bands®, relative strength index, statistical arbitrage through z-score.

• Maximize historical risk adjusted performance by optimizing strategy parameters through an exhaustive grid search of all indicators parameters combinations.

• Evaluate simulated strategy optimization trials historical risk adjusted performance through annualized return, annualized standard deviation and annualized Sharpe ratio metrics.

• Minimize strategy parameters optimization overfitting or data snooping through multiple hypothesis testing adjustment.

• Approximate population mean statistical inference two tails tests multiple probability values.

• Adjust population mean multiple probability values through family-wise error rate or Bonferroni procedure and false discovery rate or Benjamini-Hochberg procedure.

• Reduce strategy parameters optimization overfitting or data snooping through individual time series bootstrap hypothesis testing multiple comparison adjustment.

• Simulate population mean probability distribution through random fixed block re-sampling with replacement.

• Estimate bootstrap population mean statistical inference percentile confidence interval and two tails test percentile probability value.

• Correct individual bootstrap population mean probability value multiple comparison through family-wise error rate adjustment.

Become an Advanced Trading Analysis Expert and Put Your Knowledge in Practice

Learning advanced trading analysis is indispensable for finance careers in areas such as advanced quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in advanced quantitative finance. And it is necessary for experienced investors advanced quantitative trading research and development.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for back-testing to achieve greater effectiveness.

Content and Overview

This practical course contains 43 lectures and 7 hours of content. It’s designed for advanced trading analysis knowledge levels and a basic understanding of Python programming language is useful but not required.

At first, you’ll learn how to read or download S&P 500® Index ETF prices historical data to perform advanced trading analysis operations by installing related packages and running code on Python PyCharm IDE.

Then, you’ll implement trading strategy based on its category. Next, you’ll explore main strategy categories such as trend-following and mean-reversion. For trend-following strategy category, you’ll use indicators such as simple moving averages and moving averages convergence-divergence. For mean-reversion strategy category, you’ll use indicators such as Bollinger bands®, relative strength index and statistical arbitrage through z-score. After that, you’ll optimize strategy parameters by maximizing historical risk adjusted performance through an exhaustive grid search of all indicators parameters combinations. Later, you’ll explore main strategy parameters optimization objectives such as final portfolio equity metric. Then, you’ll do strategy reporting by evaluating optimization trials simulated risk adjusted performance using historical data. Next, you’ll explore main strategy reporting areas such as performance metrics. For performance metrics, you’ll use annualized return, annualized standard deviation and annualized Sharpe ratio.

After that, you’ll do multiple hypothesis testing adjustment to reduce historical parameters optimization over-fitting or data snooping. Later, you’ll define multiple hypothesis testing statistical inference. Then, you’ll define probability value estimation. For probability value estimation, you’ll do multiple population mean two tails tests. Next, you’ll define multiple probability values estimation adjustment. For multiple probability values estimation adjustment, you’ll do family-wise error rate or Bonferroni procedure and false discovery rate or Benjamini-Hochberg procedure multiple probability values estimations adjustments.

Later, you’ll do individual time series bootstrap hypothesis testing multiple comparison adjustment to reduce historical parameters optimization over-fitting or data snooping. Then, you’ll do individual time series bootstrap for population mean probability distribution simulation through random fixed block re-samples with replacement. Next, you’ll define bootstrap parameters estimation statistical inference. After that, you’ll define point estimation. For point estimation, you’ll do population mean point estimation. Later, you’ll define bootstrap confidence intervals estimation. For bootstrap confidence intervals estimation, you’ll do bootstrap population mean percentile confidence interval estimation. Then, you’ll define bootstrap hypothesis testing. Next, you’ll define bootstrap probability value estimation. For bootstrap probability value estimation, you’ll do bootstrap population mean percentile two tails test. Finally, you’ll define individual bootstrap probability value estimation multiple comparison adjustment. For individual bootstrap probability value estimation multiple comparison adjustment, you’ll do family-wise error rate individual probability value estimation multiple comparison adjustment.