3.7  56 reviews on Udemy

Quantitative Trading Analysis with Python

Learn quantitative trading analysis from basic to expert level through practical course with Python programming language
Course from Udemy
 754 students enrolled
 en
Read or download S&P 500® Index ETF prices data and perform quantitative trading analysis operations by installing related packages and running code on Python PyCharm IDE.
Implement trading strategies based on their category and frequency by defining indicators, identifying signals they generate and outlining rules that accompany them.
Explore strategy categories 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.
Evaluate simulated strategy historical risk adjusted performance through trading statistics and performance metrics.
Calculate main trading statistics such as net trading profit and loss, maximum drawdown and equity curve.
Measure principal strategy performance metrics such as annualized returns, annualized standard deviation and annualized Sharpe ratio.
Maximize historical performance by optimizing strategy parameters through an exhaustive grid search of all indicators parameters combinations.
Reduce optimization over-fitting or data snooping through asset prices data delimiting into training subset for in-sample strategy parameters optimization and testing subset for out-of-sample optimized strategy parameters validation.

Full Course Content Last Update 09/2018

Learn quantitative 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 basic 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 a Quantitative Trading Analysis Expert in this Practical Course with Python

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

  • Implement trading strategies based on their category and frequency by defining indicators, identifying signals they generate and outlining rules that accompany them.

  • Explore strategy categories 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.

  • Evaluate simulated strategy historical risk adjusted performance through trading statistics and performance metrics.

  • Calculate main trading statistics such as net trading profit and loss, maximum drawdown and equity curve.

  • Measure principal strategy performance metrics such as annualized returns, annualized standard deviation and annualized Sharpe ratio.

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

  • Reduce optimization over-fitting or data snooping through asset prices data delimiting into training subset for in-sample strategy parameters optimization and testing subset for out-of-sample optimized strategy parameters validation.

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 experienced 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 S&P 500® Index ETF prices historical data back-testing to achieve greater effectiveness.

Content and Overview

This practical course contains 50 lectures and 7 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 read or download S&P 500® Index ETF prices historical data to perform quantitative trading analysis operations by installing related packages and running code on Python PyCharm 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. 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 do strategy reporting by evaluating simulated strategy risk adjusted performance using historical data. Next, you’ll explore main strategy reporting areas such as trading statistics and performance metrics. For trading statistics, you’ll use net trading profit and loss, maximum drawdown and equity curve. For performance metrics, you’ll use annualized return, annualized standard deviation and annualized Sharpe ratio.

Later, you’ll optimize strategy parameters by maximizing historical performance through an exhaustive grid search of all indicators parameters combinations. Next, you’ll explore main strategy parameters optimization objective such as final portfolio equity metric.

Finally, you’ll reduce optimization over-fitting or data snooping through asset prices data delimiting into training subset for in-sample strategy parameters optimization and testing subset for out-of-sample optimized strategy parameters validation.

Quantitative Trading Analysis with Python
$ 24.99
per course
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