adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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

Browse Source 
Better handling and description of asterisk in Hyperopt output
This commit is contained in:
Matthias 2020-04-25 15:28:02 +02:00 committed by GitHub
commit 1761f5af1a
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 5 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
docs/hyperopt.md
View File

@ -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.

4
freqtrade/optimize/hyperopt.py
View File

@ -304,8 +304,9 @@ class Hyperopt:
trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best'] 'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
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)