137 lines
4.7 KiB
Django/Jinja
137 lines
4.7 KiB
Django/Jinja
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# --- Do not remove these libs ---
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from functools import reduce
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from typing import Any, Callable, Dict, List
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from skopt.space import Categorical, Dimension, Integer, Real # noqa
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta # noqa
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class {{ hyperopt }}(IHyperOpt):
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"""
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This is a Hyperopt template to get you started.
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More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
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You should:
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- Add any lib you need to build your hyperopt.
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You must keep:
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- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
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The methods roi_space, generate_roi_table and stoploss_space are not required
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and are provided by default.
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However, you may override them if you need 'roi' and 'stoploss' spaces that
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differ from the defaults offered by Freqtrade.
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Sample implementation of these methods will be copied to `user_data/hyperopts` when
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creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
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or is available online under the following URL:
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https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
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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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"""
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Buy strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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{{ buy_guards | indent(12) }}
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# TRIGGERS
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if 'trigger' in params:
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if params['trigger'] == 'bb_lower':
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conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
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if params['trigger'] == 'macd_cross_signal':
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conditions.append(qtpylib.crossed_above(
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dataframe['macd'], dataframe['macdsignal']
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))
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if params['trigger'] == 'sar_reversal':
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conditions.append(qtpylib.crossed_above(
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dataframe['close'], dataframe['sar']
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))
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# Check that the candle had volume
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conditions.append(dataframe['volume'] > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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@staticmethod
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def indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching buy strategy parameters.
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"""
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return [
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{{ buy_space | indent(12) }}
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]
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@staticmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the sell strategy parameters to be used by Hyperopt.
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"""
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def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Sell strategy Hyperopt will build and use.
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"""
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conditions = []
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# GUARDS AND TRENDS
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{{ sell_guards | indent(12) }}
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# TRIGGERS
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if 'sell-trigger' in params:
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if params['sell-trigger'] == 'sell-bb_upper':
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conditions.append(dataframe['close'] > dataframe['bb_upperband'])
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if params['sell-trigger'] == 'sell-macd_cross_signal':
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conditions.append(qtpylib.crossed_above(
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dataframe['macdsignal'], dataframe['macd']
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))
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if params['sell-trigger'] == 'sell-sar_reversal':
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conditions.append(qtpylib.crossed_above(
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dataframe['sar'], dataframe['close']
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))
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# Check that the candle had volume
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conditions.append(dataframe['volume'] > 0)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell'] = 1
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return dataframe
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return populate_sell_trend
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@staticmethod
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def sell_indicator_space() -> List[Dimension]:
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"""
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Define your Hyperopt space for searching sell strategy parameters.
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"""
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return [
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{{ sell_space | indent(12) }}
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]
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