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Merge pull request #3214 from hroff-1902/hyperopt-best-asterisk

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Better handling and description of asterisk in Hyperopt output
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Matthias 2020-04-25 15:28:02 +02:00 committed by GitHub
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2 changed files with 5 additions and 5 deletions
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6
docs/hyperopt.md
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@ -6,9 +6,7 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
search will burn all your CPU cores, make your laptop sound like a fighter jet search will burn all your CPU cores, make your laptop sound like a fighter jet
and still take a long time. and still take a long time.
In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a 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).
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).
Hyperopt requires historic data to be available, just as backtesting does. Hyperopt requires historic data to be available, just as backtesting does.
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. 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.
@ -311,7 +309,7 @@ You can also enable position stacking in the configuration file by explicitly se
### Reproducible results ### Reproducible results
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. 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.
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. 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.

2
freqtrade/optimize/hyperopt.py
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@ -306,6 +306,7 @@ class Hyperopt:
trials['is_profit'] = False trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* ' trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best' trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str) trials['Trades'] = trials['Trades'].astype(str)
@ -396,6 +397,7 @@ class Hyperopt:
trials['is_profit'] = False trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*' trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best' trials.loc[trials['is_best'], 'Best'] = 'Best'
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Epoch'] = trials['Epoch'].astype(str) trials['Epoch'] = trials['Epoch'].astype(str)
trials['Trades'] = trials['Trades'].astype(str) trials['Trades'] = trials['Trades'].astype(str)