Quantitative Trading Analysis with Python, Learn quantitative trading analysis from basic to expert level through practical course with Python programming language
- Created by Diego Fernandez
- 7.5 hours on-demand video
- 7 articles
- 18 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
What you'll learn
- 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.
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