Quantitative Trading Analysis with Python

Course details

Learn quantitative trading analysis through a practical course with Python programming language using index replicating fund historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or take decisions as DIY investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.

Become a Quantitative Trading Analysis Expert in this Practical Course with Python

• Download index replicating fund data to perform quantitative trading analysis operations by installing related packages and running code on Python IDE.
• Implement trading strategies by defining indicators, identifying signals they generate and outlining rules that accompany them.
• Explore strategies based on simple moving averages SMA, moving averages convergence-divergence MACD, Bollinger Bands®, relative strength index RSI and statistical arbitrage through z-score.
• Evaluate simulated strategy historical risk adjusted performance through trading statistics and performance metrics.
• Calculate main trading statistics such as number of transactions and trades, net trading profit and loss P&L, maximum drawdown and portfolio equity.
• Measure principal performance metrics such as annualized returns, standard deviation and Sharpe ratio.
• Maximize historical performance by optimizing strategy parameters.

Become a Quantitative Trading Analysis Expert and Put Your Knowledge in Practice

Learning quantitative trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for DIY investors' 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 index replicating fund historical data for back-testing to achieve greater effectiveness. 

Content and Overview

This practical course contains 49 lectures and 6 hours of content. It's designed for all quantitative trading analysis knowledge levels and a basic understanding of Python programming language is useful but not required.

At first, you'll learn how to download index replicating fund data to perform quantitative trading analysis operations by installing related packages and running code on Python IDE. 

Then, you'll implement trading strategy by defining indicators based on its category and frequency, identifying trading signals these generate and outlining trading rules that accompany them. You'll do this while exploring main strategy categories of trend-following and mean-reversion with indicators such as simple moving averages SMA, moving averages convergence-divergence MACD, Bollinger Bands®, relative strength index RSI and statistical arbitrage through z-score.

Later, you'll do strategy reporting by evaluating simulated strategy risk adjusted performance with historical data while exploring main areas of trading statistics and performance metrics. For this, you'll calculate main trading statistics such as number of transactions and trades, net trading profit and loss P&L, maximum drawdown and portfolio equity. And you'll also measure principal performance metrics such as annualized returns standard deviation and Sharpe ratio. 

Finally, you'll optimize strategy parameters by maximizing historical performance measured by portfolio equity metric. You'll implement this through a constrained grid search of parameter set combinations