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Make BinHV45 use hyperoptable parameters (removes hyperopt file)

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This commit is contained in:
Matthias
2021-08-03 20:18:03 +02:00
parent ea14ec9f9b
commit f2de8f7833
2 changed files with 23 additions and 106 deletions
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user_data/hyperopts/BinHV45HyperOpt.py
-97
View File
@@ -1,97 +0,0 @@
# 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):
"""
Hyperopt file for optimizing BinHV45Strategy.
Uses ranges to find best parameter combination for bbdelta, closedelta and tail
of the buy strategy.
Sell strategy is ignored, because it's ignored in BinHV45Strategy as well.
This strategy therefor works without explicit sell signal therefor hyperopting
for 'roi' is recommend as well
Also, this is just ONE way to optimize this strategy - others might also include
disabling certain conditions completely. This file is just a starting point, feel free
to improve and PR.
"""
@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 []
user_data/strategies/berlinguyinca/BinHV45.py
+23 -9
View File
@@ -1,7 +1,6 @@
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from freqtrade.strategy import IStrategy
from freqtrade.strategy import IntParameter
from pandas import DataFrame
import numpy as np
# --------------------------------
@@ -19,6 +18,8 @@ def bollinger_bands(stock_price, window_size, num_of_std):
class BinHV45(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.0125
}
@@ -26,10 +27,23 @@ class BinHV45(IStrategy):
stoploss = -0.05
timeframe = '1m'
buy_bbdelta = IntParameter(low=1, high=15, default=30, space='buy', optimize=True)
buy_closedelta = IntParameter(low=15, high=20, default=30, space='buy', optimize=True)
buy_tail = IntParameter(low=20, high=30, default=30, space='buy', optimize=True)
# Hyperopt parameters
buy_params = {
"buy_bbdelta": 7,
"buy_closedelta": 17,
"buy_tail": 25,
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
dataframe['mid'] = np.nan_to_num(mid)
dataframe['lower'] = np.nan_to_num(lower)
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
dataframe['upper'] = bollinger['upper']
dataframe['mid'] = bollinger['mid']
dataframe['lower'] = bollinger['lower']
dataframe['bbdelta'] = (dataframe['mid'] - dataframe['lower']).abs()
dataframe['pricedelta'] = (dataframe['open'] - dataframe['close']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
@@ -40,9 +54,9 @@ class BinHV45(IStrategy):
dataframe.loc[
(
dataframe['lower'].shift().gt(0) &
dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
dataframe['bbdelta'].gt(dataframe['close'] * self.buy_bbdelta.value / 1000) &
dataframe['closedelta'].gt(dataframe['close'] * self.buy_closedelta.value / 1000) &
dataframe['tail'].lt(dataframe['bbdelta'] * self.buy_tail.value / 1000) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift())
),