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Update averageStrategy to use Strategy parameters

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This commit is contained in:
Matthias
2021-09-05 19:25:35 +02:00
parent 2d4cd26262
commit 5f46653b2d
2 changed files with 18 additions and 138 deletions
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user_data/hyperopts/AverageHyperopt.py
-134
View File
@@ -1,134 +0,0 @@
import talib.abstract as ta
from pandas import DataFrame
from typing import Dict, Any, Callable, List
from functools import reduce
from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
shortRangeBegin = 10
shortRangeEnd = 20
mediumRangeBegin = 100
mediumRangeEnd = 120
class AverageHyperopt(IHyperOpt):
"""
Hyperopt file for optimizing AverageStrategy.
Uses ranges of EMA periods to find the best parameter combination.
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
for short in range(shortRangeBegin, shortRangeEnd):
dataframe[f'maShort({short})'] = ta.EMA(dataframe, timeperiod=short)
for medium in range(mediumRangeBegin, mediumRangeEnd):
dataframe[f'maMedium({medium})'] = ta.EMA(dataframe, timeperiod=medium)
return dataframe
@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 = []
# TRIGGERS
if 'trigger' in params:
trigger = [int(item) for item in params['trigger'].split('-')]
conditions.append(qtpylib.crossed_above(
dataframe[f"maShort({trigger[0]})"],
dataframe[f"maMedium({trigger[1]})"])
)
# 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 strategy parameters
"""
buyTriggerList = []
for short in range(shortRangeBegin, shortRangeEnd):
for medium in range(mediumRangeBegin, mediumRangeEnd):
"""
The output will be '{short}-{long}' so we can split it on the trigger
this will prevent an error on scikit-optimize not accepting tuples as
first argument to Categorical
"""
buyTriggerList.append(
'{}-{}'.format(short, medium)
)
return [
Categorical(buyTriggerList, 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
"""
# print(params)
conditions = []
# TRIGGERS
if 'sell-trigger' in params:
trigger = [int(item) for item in params['sell-trigger'].split('-')]
conditions.append(qtpylib.crossed_above(
dataframe[f"maMedium({trigger[1]})"],
dataframe[f"maShort({trigger[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
"""
sellTriggerList = []
for short in range(shortRangeBegin, shortRangeEnd):
for medium in range(mediumRangeBegin, mediumRangeEnd):
"""
The output will be '{short}-{long}' so we can split it on the trigger
this will prevent an error on scikit-optimize not accepting tuples as
first argument to Categorical
"""
sellTriggerList.append(
'{}-{}'.format(short, medium)
)
return [
Categorical(sellTriggerList, name='sell-trigger')
]
user_data/strategies/berlinguyinca/AverageStrategy.py
+18 -4
View File
@@ -1,5 +1,7 @@
# --- Do not remove these libs ---
from functools import reduce
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from pandas import DataFrame
# --------------------------------
@@ -29,10 +31,14 @@ class AverageStrategy(IStrategy):
# Optimal timeframe for the strategy
timeframe = '4h'
buy_range_short = IntParameter(5, 20, default=8)
buy_range_long = IntParameter(20, 120, default=21)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)
# Combine all ranges ... to avoid duplicate calculation
for val in list(set(list(self.buy_range_short.range) + list(self.buy_range_long.range))):
dataframe[f'ema{val}'] = ta.EMA(dataframe, timeperiod=val)
return dataframe
@@ -44,7 +50,11 @@ class AverageStrategy(IStrategy):
"""
dataframe.loc[
(
qtpylib.crossed_above(dataframe['maShort'], dataframe['maMedium'])
qtpylib.crossed_above(
dataframe[f'ema{self.buy_range_short.value}'],
dataframe[f'ema{self.buy_range_long.value}']
) &
(dataframe['volume'] > 0)
),
'buy'] = 1
@@ -58,7 +68,11 @@ class AverageStrategy(IStrategy):
"""
dataframe.loc[
(
qtpylib.crossed_above(dataframe['maMedium'], dataframe['maShort'])
qtpylib.crossed_above(
dataframe[f'ema{self.buy_range_long.value}'],
dataframe[f'ema{self.buy_range_short.value}']
) &
(dataframe['volume'] > 0)
),
'sell'] = 1
return dataframe