Machine Trading Analysis with Python, Learn machine trading analysis from basic to expert level through a practical course with Python programming language.
- Created by Diego Fernandez
- 6 hours on-demand video
- 8 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 machine trading analysis operations by installing related packages and running code on Python IDE.
- Define target and predictor algorithm features for supervised regression machine learning task.
- Select relevant predictor features subset through univariate filter methods, deterministic wrapper methods and embedded methods.
- Implement false discovery rate, family-wise error rate for univariate methods, recursive feature elimination for deterministic wrapper methods and least absolute shrinkage and selection operator for embedded methods.
- Extract predictor features transformations through principal component analysis.
- Train algorithm for mapping optimal relationship between target and predictor features through ensemble methods, maximum margin methods and multi-layer perceptron methods.
- Apply gradient boosting machine regression for ensemble methods, radial basis function support vector machine regression for maximum margin methods and artificial neural network regression for multi-layer perceptron methods.
- Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent metrics.
- Assess mean absolute error, mean squared error and root mean squared error for scale-dependent metrics.
- Calculate machine trading strategies for algorithms with highest forecasting accuracy.
- Generate buy or sell trading signals based on target feature prediction crossing centerline cross-over threshold.
- Produce long-only trading positions associated to trading signals.
- Evaluate machine trading strategies performance against buy and hold benchmark using annualized return, annualized standard deviation, annualized Sharpe ratio metrics and cumulative returns chart.
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