Timeframe
5m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 2.5%
Interface Version
2
Startup Candles
200
Indicators
1
freqtrade/freqtrade-strategies
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, DecimalParameter, IntParameter, CategoricalParameter
# @Rallipanos
def ewo(dataframe, ema_length=5, ema2_length=35):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df['close'] * 100
return emadif
class NotAnotherSMAOffsetStrategyLite(IStrategy):
INTERFACE_VERSION = 2
# Buy hyperspace params:
buy_params = {
'base_nb_candles_buy': 14,
'low_offset': 0.975,
}
# Sell hyperspace params:
sell_params = {
'base_nb_candles_sell': 24,
'high_offset': 0.991,
}
minimal_roi = {'0': 0.025}
stoploss = -0.1
# use_custom_stoploss = True
# SMAOffset
base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
# Protection
fast_ewo = 50
slow_ewo = 200
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.01
ignore_roi_if_buy_signal = False
order_time_in_force = {'buy': 'gtc', 'sell': 'ioc'}
timeframe = '5m'
process_only_new_candles = True
startup_candle_count = 200
plot_config = {
'main_plot': {
'ma_buy': {'color': 'orange'},
'ma_sell': {'color': 'orange'},
},
}
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
if current_profit < -0.05 and current_time - timedelta(minutes=720) > trade.open_date_utc:
return -0.01
return 1
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
for length in set(list(self.base_nb_candles_buy.range) + list(self.base_nb_candles_sell.range)):
dataframe[f'ema_{length}'] = ta.EMA(dataframe, timeperiod=length)
dataframe['ewo'] = ewo(dataframe, self.fast_ewo, self.slow_ewo)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(
(dataframe['close'] < (dataframe[f'ema_{self.base_nb_candles_buy.value}'] * self.low_offset.value))
&
(dataframe['ewo'] > 0)
&
(dataframe['volume'] > 0)
), 'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(
(dataframe['close'] > (dataframe[f'ema_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
&
(dataframe['volume'] > 0)
), 'sell'] = 1
return dataframe