Add documentation for hyperopt.strategy availability
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@ -6,7 +6,7 @@ class.
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## Derived hyperopt classes
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Custom hyperop classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
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Custom hyperopt classes can be derived in the same way [it can be done for strategies](strategy-customization.md#derived-strategies).
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Applying to hyperoptimization, as an example, you may override how dimensions are defined in your optimization hyperspace:
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@ -32,6 +32,51 @@ or
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$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
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```
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## Sharing methods with your strategy
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Hyperopt classes provide access to the Strategy via the `strategy` class attribute.
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This can be a great way to reduce code duplication if used correctly, but will also complicate usage for inexperienced users.
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``` python
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from pandas import DataFrame
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from freqtrade.strategy.interface import IStrategy
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class MyAwesomeStrategy(IStrategy):
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buy_params = {
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'rsi-value': 30,
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'adx-value': 35,
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}
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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return self.buy_strategy_generator(self.buy_params, dataframe, metadata)
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@staticmethod
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def buy_strategy_generator(params, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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qtpylib.crossed_above(dataframe['rsi'], params['rsi-value']) &
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dataframe['adx'] > params['adx-value']) &
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dataframe['volume'] > 0
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)
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, 'buy'] = 1
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return dataframe
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class MyAwesomeHyperOpt(IHyperOpt):
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...
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@staticmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the buy strategy parameters to be used by Hyperopt.
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"""
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Call strategy's buy strategy generator
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return self.StrategyClass.buy_strategy_generator(params, dataframe, metadata)
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return populate_buy_trend
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```
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## Creating and using a custom loss function
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To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
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@ -283,7 +283,7 @@ So let's write the buy strategy using these values:
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"""
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Define the buy strategy parameters to be used by Hyperopt.
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"""
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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if 'adx-enabled' in params and params['adx-enabled']:
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