add hyperopts for BinHV45 strategy

user_data/hyperopts/BinHV45HyperOpt.py

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+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class BinHV45HyperOpt(IHyperOpt):
+    """
+    This is a Hyperopt template to get you started.
+
+    More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
+
+    You should:
+    - Add any lib you need to build your hyperopt.
+
+    You must keep:
+    - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
+
+    The methods roi_space, generate_roi_table and stoploss_space are not required
+    and are provided by default.
+    However, you may override them if you need 'roi' and 'stoploss' spaces that
+    differ from the defaults offered by Freqtrade.
+    Sample implementation of these methods will be copied to `user_data/hyperopts` when
+    creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
+    or is available online under the following URL:
+    https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
+    """
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            conditions.append(dataframe['lower'].shift().gt(0))
+            conditions.append(dataframe['bbdelta'].gt(
+                dataframe['close'] * params['bbdelta'] / 1000))
+            conditions.append(dataframe['closedelta'].gt(
+                dataframe['close'] * params['closedelta'] / 1000))
+            conditions.append(dataframe['tail'].lt(dataframe['bbdelta'] * params['tail'] / 1000))
+            conditions.append(dataframe['close'].lt(dataframe['lower'].shift()))
+            conditions.append(dataframe['close'].le(dataframe['close'].shift()))
+
+            # Check that the candle had volume
+            conditions.append(dataframe['volume'] > 0)
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+        return [
+            Integer(1, 15, name='bbdelta'),
+            Integer(15, 20, name='closedelta'),
+            Integer(20, 30, name='tail'),
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            no sell signal
+            """
+            dataframe['sell'] = 0
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        return []