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NotAnotherSMAOffSetStrategy_V2.py
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NotAnotherSMAOffSetStrategy_V2.py
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# --- Do not remove these libs ---
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
# @Rallipanos
#########################################################################################################################
## Do not run backtesting over servicebreaks. It gives bad result. Test timeranges that does not have servicebreaks!!!!
#########################################################################################################################
# Buy hyperspace params:
buy_params = {
"base_nb_candles_buy": 14,
"ewo_high": 2.327,
"ewo_high_2": -2.327,
"ewo_low": -20.988,
"low_offset": 0.975,
"low_offset_2": 0.955,
"rsi_buy": 69
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell": 24,
"high_offset": 0.998,
"high_offset_2": 1
}
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['low'] * 100
return emadif
class NotAnotherSMAOffSetStrategy_V2(IStrategy):
INTERFACE_VERSION = 2
# ROI table:
minimal_roi = {
"0": 0.215,
"40": 0.032,
"87": 0.016,
"201": 0
}
# Stoploss:
stoploss = -0.35
# 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)
low_offset_2 = DecimalParameter(
0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=True)
high_offset = DecimalParameter(
0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
high_offset_2 = DecimalParameter(
0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)
# Protection
fast_ewo = 50
slow_ewo = 200
ewo_low = DecimalParameter(-20.0, -8.0,
default=buy_params['ewo_low'], space='buy', optimize=True)
ewo_high = DecimalParameter(
2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)
ewo_high_2 = DecimalParameter(
-6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=True)
rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.005
trailing_stop_positive_offset = 0.025
trailing_only_offset_is_reached = True
# Sell signal
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.01
ignore_roi_if_buy_signal = False
## Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
# Optimal timeframe for the strategy
timeframe = '5m'
inf_1h = '1h'
process_only_new_candles = True
startup_candle_count = 200
plot_config = {
'main_plot': {
'ma_buy': {'color': 'orange'},
'ma_sell': {'color': 'orange'},
},
}
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
if (last_candle is not None):
if (sell_reason in ['sell_signal']):
if (last_candle['hma_50'] > last_candle['ema_100']) and (last_candle['rsi'] < 45): #*1.2
return False
if (last_candle is not None):
if (sell_reason in ['sell_signal']):
if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951): #*1.2
return False
return True
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Calculate all ma_buy values
for val in self.base_nb_candles_buy.range:
dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
# Calculate all ma_sell values
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
# Elliot
dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
dataframe['vol_7_max'] = dataframe['volume'].rolling(window=20).max()
dataframe['vol_14_max'] = dataframe['volume'].rolling(window=14).max()
dataframe['vol_7_min'] = dataframe['volume'].rolling(window=20).min()
dataframe['vol_14_min'] = dataframe['volume'].rolling(window=14).min()
dataframe['roll_7'] = 100*((dataframe['volume']-dataframe['vol_7_max'])/(dataframe['vol_7_max']-dataframe['vol_7_min']))
dataframe['vol_base']=ta.SMA(dataframe['roll_7'], timeperiod=5)
dataframe['vol_ma_26'] = ta.SMA(dataframe['volume'], timeperiod=26)
dataframe['vol_ma_200'] = ta.SMA(dataframe['volume'], timeperiod=100)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['vol_base']>-96)&
(dataframe['vol_base']<-77)&
(dataframe['rsi_fast'] <35)&
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
(dataframe['EWO'] > self.ewo_high.value) &
(dataframe['rsi'] < self.rsi_buy.value) &
(dataframe['volume'] > 0)&
(dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
),
['buy', 'buy_tag']] = (1, 'ewo1')
dataframe.loc[
(
(dataframe['vol_base']>-96)&
(dataframe['vol_base']> -20)&
(dataframe['rsi_fast'] <35)&
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
(dataframe['EWO'] > self.ewo_high.value) &
(dataframe['rsi'] < self.rsi_buy.value) &
(dataframe['volume'] > 0)&
(dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
),
['buy', 'buy_tag']] = (1, 'ewo3')
dataframe.loc[
( (dataframe['vol_base']>-96)&
(dataframe['vol_base']<-77)&
(dataframe['rsi_fast'] <35)&
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) &
(dataframe['EWO'] > self.ewo_high_2.value) &
(dataframe['rsi'] < self.rsi_buy.value) &
(dataframe['volume'] > 0)&
(dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))&
(dataframe['rsi']<25)
),
['buy', 'buy_tag']] = (1, 'ewo2')
dataframe.loc[
(
(dataframe['vol_base']>-96)&
(dataframe['vol_base']<-77)&
(dataframe['rsi_fast'] < 35)&
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
(dataframe['EWO'] < self.ewo_low.value) &
(dataframe['volume'] > 0)&
(dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
),
['buy', 'buy_tag']] = (1, 'ewolow')
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
( (dataframe['close']>dataframe['sma_9'])&
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
(dataframe['rsi']>50)&
(dataframe['volume'] > 0)&
(dataframe['rsi_fast']>dataframe['rsi_slow'])
)
|
(
(dataframe['close']<dataframe['hma_50'])&
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
(dataframe['volume'] > 0)&
(dataframe['rsi_fast']>dataframe['rsi_slow'])
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
]=1
return dataframe