This is a continuation of the series on forecasting techniques. Locally weighted linear regression is a non-parametric algorithm, that is, the model does not learn a fixed set of parameters as is done in ordinary linear regression. Rather parameters Θ (theta) are computed individually for each query point x. While computing Θ, a higher “preference” is given to the points in the training set lying in the vicinity of x than the points lying far away from x. For a detailed discussion see geeksforgeeks.org/ml-locally-weighted-linear-regression/ and for the formula see fawda123.github.io/swmp_workshop_2016/training_modules/module2_wrtds/wrtds.pdf.
Here you can see a shortcut application of this technique to time series with results unexpectedly favorable for price data labelling.
Good at detecting pullbacks. Can be incorporated into a trading system as a signal generator. Alerting is included.
In true TradingView spirit, the author of this script has published it open-source, so traders can understand and verify it. Cheers to the author! You may use it for free, but reuse of this code in publications is governed by House rules. Per aggiungerlo al grafico, mettilo tra i preferiti.
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