Improve documentation regarding loading methods from hyperopt
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@ -23,17 +23,23 @@ Configuring hyperopt is similar to writing your own strategy, and many tasks wil
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Depending on the space you want to optimize, only some of the below are required:
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* fill `populate_indicators` - probably a copy from your strategy
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* fill `buy_strategy_generator` - for buy signal optimization
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* fill `indicator_space` - for buy signal optimzation
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* fill `sell_strategy_generator` - for sell signal optimization
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* fill `sell_indicator_space` - for sell signal optimzation
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Optional, but recommended:
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!!! Note
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`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
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Optional - can also be loaded from a strategy:
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* copy `populate_indicators` from your strategy - otherwise default-strategy will be used
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* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
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* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
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!!! Note
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Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
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Rarely you may also need to override:
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* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
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@ -156,7 +162,7 @@ that minimizes the value of the [loss function](#loss-functions).
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The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
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When you want to test an indicator that isn't used by the bot currently, remember to
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add it to the `populate_indicators()` method in `hyperopt.py`.
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add it to the `populate_indicators()` method in your custom hyperopt file.
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## Loss-functions
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@ -270,6 +276,14 @@ For example, to use one month of data, pass the following parameter to the hyper
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freqtrade hyperopt --timerange 20180401-20180501
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```
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### Running Hyperopt using methods from a strategy
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Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
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```bash
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freqtrade --strategy SampleStrategy hyperopt --customhyperopt SampleHyperopt
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```
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### Running Hyperopt with Smaller Search Space
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Use the `--spaces` argument to limit the search space used by hyperopt.
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@ -341,8 +355,7 @@ So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that t
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(dataframe['rsi'] < 29.0)
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```
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Translating your whole hyperopt result as the new buy-signal
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would then look like:
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Translating your whole hyperopt result as the new buy-signal would then look like:
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```python
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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@ -37,6 +37,9 @@ class AdvancedSampleHyperOpts(IHyperOpt):
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"""
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@staticmethod
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
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"""
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dataframe['adx'] = ta.ADX(dataframe)
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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@ -229,8 +232,9 @@ class AdvancedSampleHyperOpts(IHyperOpt):
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators. Should be a copy of from strategy
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must align to populate_indicators in this file
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Based on TA indicators.
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Can be a copy of from the strategy, or will be loaded from the strategy.
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must align to populate_indicators used (either from this File, or from the strategy)
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Only used when --spaces does not include buy
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"""
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dataframe.loc[
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@ -246,8 +250,9 @@ class AdvancedSampleHyperOpts(IHyperOpt):
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators. Should be a copy of from strategy
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must align to populate_indicators in this file
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Based on TA indicators.
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Can be a copy of from the strategy, or will be loaded from the strategy.
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must align to populate_indicators used (either from this File, or from the strategy)
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Only used when --spaces does not include sell
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"""
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dataframe.loc[
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