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Convert ReinforcedScalp to parametrized strategy

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This commit is contained in:
Matthias
2021-09-05 16:02:14 +02:00
parent b01325133c
commit 2d4cd26262
2 changed files with 70 additions and 188 deletions
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user_data/hyperopts/ReinforcedSmoothScalp_hyperopt.py
-153
View File
@@ -1,153 +0,0 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from typing import Any, Callable, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class ReinforcedSmoothScalp(IHyperOpt):
"""
Default hyperopt provided by the Freqtrade bot.
You can override it with your own Hyperopt
"""
@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 = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
# if 'rsi-enabled' in params and params['rsi-enabled']:
# conditions.append(dataframe['rsi'] < params['rsi-value'])
if 'fastk-enabled' in params and params['fastk-enabled']:
conditions.append(dataframe['fastk'] < params['fastk-value'])
# TRIGGERS
# if 'trigger' in params:
# if params['trigger'] == 'bb_lower':
# conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
# if params['trigger'] == 'macd_cross_signal':
# conditions.append(qtpylib.crossed_above(
# dataframe['macd'], dataframe['macdsignal']
# ))
# if params['trigger'] == 'sar_reversal':
# conditions.append(qtpylib.crossed_above(
# dataframe['close'], dataframe['sar']
# ))
# Check that volume is not 0
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(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(15, 45, name='fastk-value'),
Integer(20, 50, name='adx-value'),
# Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='fastk-enabled'),
# Categorical([True, False], name='rsi-enabled'),
# Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@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:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-fastk-enabled' in params and params['sell-fastk-enabled']:
conditions.append(dataframe['fastk'] > params['sell-fastk-value'])
if 'sell-cci-enabled' in params and params['sell-cci-enabled']:
conditions.append(dataframe['cci'] > params['sell-cci-value'])
# TRIGGERS
# if 'sell-trigger' in params:
# if params['sell-trigger'] == 'sell-bb_upper':
# conditions.append(dataframe['close'] > dataframe['bb_upperband'])
# if params['sell-trigger'] == 'sell-macd_cross_signal':
# conditions.append(qtpylib.crossed_above(
# dataframe['macdsignal'], dataframe['macd']
# ))
# if params['sell-trigger'] == 'sell-sar_reversal':
# conditions.append(qtpylib.crossed_above(
# dataframe['sar'], dataframe['close']
# ))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-fastk-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(100, 200, name='sell-cci-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-cci-enabled'),
Categorical([True, False], name='sell-fastk-enabled'),
# Categorical(['sell-bb_upper',
# 'sell-macd_cross_signal',
# 'sell-sar_reversal'], name='sell-trigger')
]
user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py
+70 -35
View File
@@ -1,6 +1,8 @@
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from functools import reduce
from freqtrade.strategy import IStrategy
from freqtrade.strategy import timeframe_to_minutes
from freqtrade.strategy import BooleanParameter, IntParameter
from pandas import DataFrame
from technical.util import resample_to_interval, resampled_merge
import numpy # noqa
@@ -33,6 +35,27 @@ class ReinforcedSmoothScalp(IStrategy):
# resample factor to establish our general trend. Basically don't buy if a trend is not given
resample_factor = 5
buy_adx = IntParameter(20, 50, default=32, space='buy')
buy_fastd = IntParameter(15, 45, default=30, space='buy')
buy_fastk = IntParameter(15, 45, default=26, space='buy')
buy_mfi = IntParameter(10, 25, default=22, space='buy')
buy_adx_enabled = BooleanParameter(default=True, space='buy')
buy_fastd_enabled = BooleanParameter(default=True, space='buy')
buy_fastk_enabled = BooleanParameter(default=False, space='buy')
buy_mfi_enabled = BooleanParameter(default=True, space='buy')
sell_adx = IntParameter(50, 100, default=53, space='sell')
sell_cci = IntParameter(100, 200, default=183, space='sell')
sell_fastd = IntParameter(50, 100, default=79, space='sell')
sell_fastk = IntParameter(50, 100, default=70, space='sell')
sell_mfi = IntParameter(75, 100, default=92, space='sell')
sell_adx_enabled = BooleanParameter(default=False, space='sell')
sell_cci_enabled = BooleanParameter(default=True, space='sell')
sell_fastd_enabled = BooleanParameter(default=True, space='sell')
sell_fastk_enabled = BooleanParameter(default=True, space='sell')
sell_mfi_enabled = BooleanParameter(default=False, space='sell')
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tf_res = timeframe_to_minutes(self.timeframe) * 5
df_res = resample_to_interval(dataframe, tf_res)
@@ -60,43 +83,55 @@ class ReinforcedSmoothScalp(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(dataframe['open'] < dataframe['ema_low']) &
(dataframe['adx'] > 30) &
(dataframe['mfi'] < 30) &
(
(dataframe['fastk'] < 30) &
(dataframe['fastd'] < 30) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
) &
(dataframe['resample_sma'] < dataframe['close'])
)
# |
# # try to get some sure things independent of resample
# ((dataframe['rsi'] - dataframe['mfi']) < 10) &
# (dataframe['mfi'] < 30) &
# (dataframe['cci'] < -200)
),
'buy'] = 1
conditions = []
if self.buy_mfi_enabled.value:
conditions.append(dataframe['mfi'] < self.buy_mfi.value)
if self.buy_fastd_enabled.value:
conditions.append(dataframe['fastd'] < self.buy_fastd.value)
if self.buy_fastk_enabled.value:
conditions.append(dataframe['fastk'] < self.buy_fastk.value)
if self.buy_adx_enabled.value:
conditions.append(dataframe['adx'] > self.buy_adx.value)
# Some static conditions which always apply
conditions.append(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
conditions.append(dataframe['resample_sma'] < dataframe['close'])
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
(
(dataframe['open'] >= dataframe['ema_high'])
) |
(
(qtpylib.crossed_above(dataframe['fastk'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
conditions = []
# Some static conditions which always apply
conditions.append(dataframe['open'] > dataframe['ema_high'])
if self.sell_mfi_enabled.value:
conditions.append(dataframe['mfi'] > self.sell_mfi.value)
if self.sell_fastd_enabled.value:
conditions.append(dataframe['fastd'] > self.sell_fastd.value)
if self.sell_fastk_enabled.value:
conditions.append(dataframe['fastk'] > self.sell_fastk.value)
if self.sell_adx_enabled.value:
conditions.append(dataframe['adx'] < self.sell_adx.value)
if self.sell_cci_enabled.value:
conditions.append(dataframe['cci'] > self.sell_cci.value)
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
)
) & (dataframe['cci'] > 100)
)
,
'sell'] = 1
return dataframe