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NASOSv4.py
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NASOSv4.py
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# for live trailing_stop = False and use_custom_stoploss = True
# for backtest trailing_stop = True and use_custom_stoploss = False
# --- Do not remove these libs ---
# --- Do not remove these libs ---
from logging import FATAL
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
# @pluxury
# Buy hyperspace params:
buy_params = {
"base_nb_candles_buy": 8,
"ewo_high": 2.403,
"ewo_high_2": -5.585,
"ewo_low": -14.378,
"lookback_candles": 3,
"low_offset": 0.984,
"low_offset_2": 0.942,
"profit_threshold": 1.008,
"rsi_buy": 72
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell": 16,
"high_offset": 1.084,
"high_offset_2": 1.401,
"pHSL": -0.15,
"pPF_1": 0.016,
"pPF_2": 0.024,
"pSL_1": 0.014,
"pSL_2": 0.022
}
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 NASOSv4(IStrategy):
INTERFACE_VERSION = 2
# ROI table:
minimal_roi = {
# "0": 0.283,
# "40": 0.086,
# "99": 0.036,
"0": 10
}
# Stoploss:
stoploss = -0.15
# SMAOffset
base_nb_candles_buy = IntParameter(
2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
base_nb_candles_sell = IntParameter(
2, 25, 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
lookback_candles = IntParameter(
1, 24, default=buy_params['lookback_candles'], space='buy', optimize=True)
profit_threshold = DecimalParameter(1.0, 1.03,
default=buy_params['profit_threshold'], space='buy', optimize=True)
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(50, 100, default=buy_params['rsi_buy'], space='buy', optimize=True)
# trailing stoploss hyperopt parameters
# hard stoploss profit
pHSL = DecimalParameter(-0.200, -0.040, default=-0.15, decimals=3,
space='sell', optimize=True, load=True)
# profit threshold 1, trigger point, SL_1 is used
pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3,
space='sell', optimize=True, load=True)
pSL_1 = DecimalParameter(0.008, 0.020, default=0.014, decimals=3,
space='sell', optimize=True, load=True)
# profit threshold 2, SL_2 is used
pPF_2 = DecimalParameter(0.040, 0.100, default=0.024, decimals=3,
space='sell', optimize=True, load=True)
pSL_2 = DecimalParameter(0.020, 0.070, default=0.022, decimals=3,
space='sell', optimize=True, load=True)
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.001
trailing_stop_positive_offset = 0.016
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': 'ioc'
}
# Optimal timeframe for the strategy
timeframe = '5m'
inf_1h = '1h'
process_only_new_candles = True
startup_candle_count = 200
use_custom_stoploss = False
plot_config = {
'main_plot': {
'ma_buy': {'color': 'orange'},
'ma_sell': {'color': 'orange'},
},
}
slippage_protection = {
'retries': 3,
'max_slippage': -0.02
}
# Custom Trailing Stoploss by Perkmeister
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# # hard stoploss profit
HSL = self.pHSL.value
PF_1 = self.pPF_1.value
SL_1 = self.pSL_1.value
PF_2 = self.pPF_2.value
SL_2 = self.pSL_2.value
# For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
# between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
# rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
if (current_profit > PF_2):
sl_profit = SL_2 + (current_profit - PF_2)
elif (current_profit > PF_1):
sl_profit = SL_1 + ((current_profit - PF_1)*(SL_2 - SL_1)/(PF_2 - PF_1))
else:
sl_profit = HSL
# if current_profit < 0.001 and current_time - timedelta(minutes=600) > trade.open_date_utc:
# return -0.005
return stoploss_from_open(sl_profit, current_profit)
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']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951): # *1.2
return False
# slippage
try:
state = self.slippage_protection['__pair_retries']
except KeyError:
state = self.slippage_protection['__pair_retries'] = {}
candle = dataframe.iloc[-1].squeeze()
slippage = (rate / candle['close']) - 1
if slippage < self.slippage_protection['max_slippage']:
pair_retries = state.get(pair, 0)
if pair_retries < self.slippage_protection['retries']:
state[pair] = pair_retries + 1
return False
state[pair] = 0
return True
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
return informative_pairs
def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
# EMA
# informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
# informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
# # RSI
# informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
# bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
# informative_1h['bb_lowerband'] = bollinger['lower']
# informative_1h['bb_middleband'] = bollinger['mid']
# informative_1h['bb_upperband'] = bollinger['upper']
return informative_1h
def normal_tf_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)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
informative_1h = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(
dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dont_buy_conditions = []
dont_buy_conditions.append(
(
# don't buy if there isn't 3% profit to be made
(dataframe['close_1h'].rolling(self.lookback_candles.value).max()
< (dataframe['close'] * self.profit_threshold.value))
)
)
dataframe.loc[
(
(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['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['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')
if dont_buy_conditions:
for condition in dont_buy_conditions:
dataframe.loc[condition, 'buy'] = 0
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