stable/tests/strategy/strats/hyperoptable_strategy.py

115 lines
4.0 KiB
Python

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from pandas import DataFrame
from strategy_test_v3 import StrategyTestV3
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import BooleanParameter, DecimalParameter, IntParameter, RealParameter
class HyperoptableStrategy(StrategyTestV3):
"""
Default Strategy provided by freqtrade bot.
Please do not modify this strategy, it's intended for internal use only.
Please look at the SampleStrategy in the user_data/strategy directory
or strategy repository https://github.com/freqtrade/freqtrade-strategies
for samples and inspiration.
"""
buy_params = {
'buy_rsi': 35,
# Intentionally not specified, so "default" is tested
# 'buy_plusdi': 0.4
}
sell_params = {
'sell_rsi': 74,
'sell_minusdi': 0.4
}
buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
load=False)
protection_enabled = BooleanParameter(default=True)
protection_cooldown_lookback = IntParameter([0, 50], default=30)
@property
def protections(self):
prot = []
if self.protection_enabled.value:
prot.append({
"method": "CooldownPeriod",
"stop_duration_candles": self.protection_cooldown_lookback.value
})
return prot
bot_loop_started = False
def bot_loop_start(self):
self.bot_loop_started = True
def bot_start(self, **kwargs) -> None:
"""
Parameters can also be defined here ...
"""
self.buy_rsi = IntParameter([0, 50], default=30, space='buy')
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['rsi'] < self.buy_rsi.value) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > self.buy_plusdi.value)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > self.buy_plusdi.value)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > self.sell_minusdi.value)
),
'sell'] = 1
return dataframe