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Udemy - Machine Trading Analysis With Python
TORRENT SUMMARY
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Learn machine trading analysis from basic to expert level through a practical course with Python programming language.
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.
Requirements
Use Winrar to Extract. And use a shorter path when extracting, such as C: drive
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