Fix #3065
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@ -6,9 +6,7 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
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search will burn all your CPU cores, make your laptop sound like a fighter jet
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search will burn all your CPU cores, make your laptop sound like a fighter jet
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and still take a long time.
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and still take a long time.
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In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a
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In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
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ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace
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that minimizes the value of the [loss function](#loss-functions).
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Hyperopt requires historic data to be available, just as backtesting does.
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Hyperopt requires historic data to be available, just as backtesting does.
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To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
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To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
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@ -311,7 +309,7 @@ You can also enable position stacking in the configuration file by explicitly se
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### Reproducible results
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### Reproducible results
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The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with a leading asterisk sign at the Hyperopt output.
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The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
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The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
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The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
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