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BB_RPB_3c_dca.py
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BB_RPB_3c_dca.py
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# --- Do not remove these libs ---
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas_ta as pta
import math
import logging
logger = logging.getLogger(__name__)
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from functools import reduce
from technical.indicators import RMI, zema
# --------------------------------
def ha_typical_price(bars):
res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.
return Series(index=bars.index, data=res)
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
# VWAP bands
def VWAPB(dataframe, window_size=20, num_of_std=1):
df = dataframe.copy()
df['vwap'] = qtpylib.rolling_vwap(df,window=window_size)
rolling_std = df['vwap'].rolling(window=window_size).std()
df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std)
df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std)
return df['vwap_low'], df['vwap'], df['vwap_high']
# Williams %R
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
"""Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
of its recent trading range.
The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
"""
highest_high = dataframe["high"].rolling(center=False, window=period).max()
lowest_low = dataframe["low"].rolling(center=False, window=period).min()
WR = Series(
(highest_high - dataframe["close"]) / (highest_high - lowest_low),
name=f"{period} Williams %R",
)
return WR * -100
class BB_RPB_3c(IStrategy):
'''
Trying best to prevent 5th SO to gain the best potential profit.
'''
##########################################################################
# Hyperopt result area
# buy space
buy_params = {
##
"buy_adx": 20,
"buy_fastd": 20,
"buy_fastk": 22,
"buy_ema_cofi": 0.98,
"buy_ewo_high": 4.179,
##
"buy_clucha_bbdelta_close": 0.01965,
"buy_clucha_bbdelta_tail": 0.95089,
"buy_clucha_closedelta_close": 0.00556,
"buy_clucha_rocr_1h": 0.54904,
##
"buy_gumbo_ema": 1.121,
"buy_gumbo_ewo_low": -9.442,
"buy_gumbo_cti": -0.374,
"buy_gumbo_r14": -51.971,
##
"buy_vwap_closedelta": 19.108,
"buy_vwap_cti": -0.022,
"buy_vwap_width": 0.392,
##
"buy_lambo2_ema": 0.983,
"buy_lambo2_rsi14": 44,
"buy_lambo2_rsi4": 44,
##
"buy_V_bb_width": 0.067,
"buy_V_cti": -0.672,
"buy_V_mfi": 38.796,
"buy_V_r14": -53.601,
}
# sell space
sell_params = {
"pHSL": -1, # disable
"pPF_1": 0.014,
"pPF_2": 0.072,
"pSL_1": 0.011,
"pSL_2": 0.063,
"base_nb_candles_sell": 23,
"high_offset": 1.051,
"high_offset_2": 1.02,
}
# ROI
minimal_roi = {
"0": 0.10,
}
# Optimal timeframe for the strategy
timeframe = '5m'
inf_1h = '1h'
# Disabled
stoploss = -1
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# Custom stoploss
use_custom_stoploss = True
use_sell_signal = True
startup_candle_count: int = 400
############################################################################
## Buy params
is_optimize_cofi = False
buy_ema_cofi = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_cofi)
buy_fastk = IntParameter(20, 45, default=20, optimize = is_optimize_cofi)
buy_fastd = IntParameter(20, 45, default=20, optimize = is_optimize_cofi)
buy_adx = IntParameter(20, 45, default=30, optimize = is_optimize_cofi)
buy_ewo_high = DecimalParameter(-12, 12, default=3.553, optimize = is_optimize_cofi)
is_optimize_clucha = False
buy_clucha_bbdelta_close = DecimalParameter(0.0005, 0.042, default=0.034, decimals=5, optimize = is_optimize_clucha)
buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.1, default=0.95, decimals=5, optimize = is_optimize_clucha)
buy_clucha_closedelta_close = DecimalParameter(0.0005, 0.025, default=0.019, decimals=5, optimize = is_optimize_clucha)
buy_clucha_rocr_1h = DecimalParameter(0.001, 1.0, default=0.131, decimals=5, optimize = is_optimize_clucha)
is_optimize_gumbo = False
buy_gumbo_ema = DecimalParameter(0.9, 1.2, default=0.97 , optimize = is_optimize_gumbo)
buy_gumbo_ewo_low = DecimalParameter(-12.0, 5, default=-5.585, optimize = is_optimize_gumbo)
is_optimize_gumbo_protection = False
buy_gumbo_cti = DecimalParameter(-0.9, -0.0, default=-0.5 , optimize = is_optimize_gumbo_protection)
buy_gumbo_r14 = DecimalParameter(-100, -44, default=-60 , optimize = is_optimize_gumbo_protection)
is_optimize_vwap = False
buy_vwap_width = DecimalParameter(0.05, 10.0, default=0.80 , optimize = is_optimize_vwap)
buy_vwap_closedelta = DecimalParameter(10.0, 30.0, default=15.0, optimize = is_optimize_vwap)
buy_vwap_cti = DecimalParameter(-0.9, -0.0, default=-0.6 , optimize = is_optimize_vwap)
is_optimize_lambo2 = False
buy_lambo2_ema = DecimalParameter(0.85, 1.15, default=0.942 , optimize = is_optimize_lambo2)
buy_lambo2_rsi4 = IntParameter(15, 45, default=45, optimize = is_optimize_lambo2)
buy_lambo2_rsi14 = IntParameter(15, 45, default=45, optimize = is_optimize_lambo2)
is_optimize_V = False
buy_V_bb_width = DecimalParameter(0.04, 0.1, default=0.01 , optimize = is_optimize_V)
buy_V_cti = DecimalParameter(-0.95, -0.5, default=-0.6 , optimize = is_optimize_V)
buy_V_r14 = DecimalParameter(-100, 0, default=-60 , optimize = is_optimize_V)
buy_V_mfi = DecimalParameter(10, 40, default=30 , optimize = is_optimize_V)
## Sell params
base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=False)
high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=False)
## Trailing params
# hard stoploss profit
pHSL = DecimalParameter(-0.350, -0.040, default=-0.08, decimals=3, space='sell', load=True, optimize=False)
# profit threshold 1, trigger point, SL_1 is used
pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True, optimize=False)
pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True, optimize=False)
# profit threshold 2, SL_2 is used
pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True, optimize=False)
pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True, optimize=False)
############################################################################
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
return informative_pairs
## Custom Trailing stoploss ( credit to Perkmeister for this custom stoploss to help the strategy ride a green candle )
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
# Only for hyperopt invalid return
if (sl_profit >= current_profit):
return -0.99
return stoploss_from_open(sl_profit, current_profit)
############################################################################
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Bollinger bands (hyperopt hard to implement)
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])
## BB 1
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
dataframe['bb_middleband1'] = bollinger1['mid']
dataframe['bb_upperband1'] = bollinger1['upper']
# BinH
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
# SMA
dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
# CTI
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
# EMA
dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
# 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['rsi_6'] = ta.RSI(dataframe, timeperiod=6)
dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8)
dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
# Elliot
dataframe['EWO'] = EWO(dataframe, 50, 200)
# Cofi
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
# Heiken Ashi
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
## BB 40
bollinger2_40 = qtpylib.bollinger_bands(ha_typical_price(dataframe), window=40, stds=2)
dataframe['bb_lowerband2_40'] = bollinger2_40['lower']
dataframe['bb_middleband2_40'] = bollinger2_40['mid']
dataframe['bb_upperband2_40'] = bollinger2_40['upper']
# ClucHA
dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs()
dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs()
dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()
dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)
# Williams %R
dataframe['r_14'] = williams_r(dataframe, period=14)
# T3 Average
dataframe['T3'] = T3(dataframe)
# VWAP
vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1)
dataframe['vwap_upperband'] = vwap_high
dataframe['vwap_middleband'] = vwap
dataframe['vwap_lowerband'] = vwap_low
dataframe['vwap_width'] = ( (dataframe['vwap_upperband'] - dataframe['vwap_lowerband']) / dataframe['vwap_middleband'] ) * 100
# Avg
dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
dataframe['cci'] = ta.CCI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
# Calculate all ma_sell values
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
############################################################################
# 1h tf
inf_tf = '1h'
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
# Heikin Ashi
inf_heikinashi = qtpylib.heikinashi(informative)
informative['ha_close'] = inf_heikinashi['close']
informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168)
# Bollinger bands
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
informative['bb_lowerband2'] = bollinger2['lower']
informative['bb_middleband2'] = bollinger2['mid']
informative['bb_upperband2'] = bollinger2['upper']
informative['bb_width'] = ((informative['bb_upperband2'] - informative['bb_lowerband2']) / informative['bb_middleband2'])
# T3 Average
informative['T3'] = T3(informative)
# RSI
informative['rsi'] = ta.RSI(informative, timeperiod=14)
informative['rsi_42'] = ta.RSI(informative, timeperiod=42)
# EMA
informative['ema_200'] = ta.EMA(informative, timeperiod=200)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
############################################################################
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'buy_tag'] = ''
is_cofi = (
(dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
(dataframe['fastk'] < self.buy_fastk.value) &
(dataframe['fastd'] < self.buy_fastd.value) &
(dataframe['adx'] > self.buy_adx.value) &
(dataframe['EWO'] > self.buy_ewo_high.value)
)
is_clucHA = (
(dataframe['rocr_1h'] > self.buy_clucha_rocr_1h.value ) &
(
(dataframe['bb_lowerband2_40'].shift() > 0) &
(dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close.value) &
(dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close.value) &
(dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail.value) &
(dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) &
(dataframe['ha_close'] < dataframe['ha_close'].shift())
)
)
is_gumbo = ( # Modified from gumbo1, creadit goes to original author @raph92
(dataframe['EWO'] < self.buy_gumbo_ewo_low.value) &
(dataframe['bb_middleband2_1h'] >= dataframe['T3_1h']) &
(dataframe['T3'] <= dataframe['ema_8'] * self.buy_gumbo_ema.value) &
(dataframe['cti'] < self.buy_gumbo_cti.value) &
(dataframe['r_14'] < self.buy_gumbo_r14.value)
)
is_vwap = (
(dataframe['close'] < dataframe['vwap_lowerband']) &
(dataframe['vwap_width'] > self.buy_vwap_width.value) &
(dataframe['closedelta'] > dataframe['close'] * self.buy_vwap_closedelta.value / 1000 ) &
(dataframe['cti'] < self.buy_vwap_cti.value)
)
is_lambo_2 = (
(dataframe['close'] < dataframe['ema_14'] * self.buy_lambo2_ema.value) &
(dataframe['rsi_fast'] < self.buy_lambo2_rsi4.value) &
(dataframe['rsi'] < self.buy_lambo2_rsi14.value)
)
is_V = (
(dataframe['bb_width'] > self.buy_V_bb_width.value) &
(dataframe['cti'] < self.buy_V_cti.value) &
(dataframe['r_14'] < self.buy_V_r14.value) &
(dataframe['mfi'] < self.buy_V_mfi.value)
)
is_additional_check = (
(dataframe['rsi_84'] < 60) &
(dataframe['rsi_112'] < 60) &
(dataframe['rsi_42_1h'] < 56) &
(dataframe['volume'] > 0)
)
# condition append
conditions.append(is_cofi) # ~3.21 90.8%
dataframe.loc[is_cofi, 'buy_tag'] += 'cofi '
conditions.append(is_clucHA) # ~68.2%
dataframe.loc[is_clucHA, 'buy_tag'] += 'cluc '
conditions.append(is_gumbo) # ~2.63 / 90.6% / 41.49% F (263 %)
dataframe.loc[is_gumbo, 'buy_tag'] += 'gumbo '
conditions.append(is_vwap) # ~67.3%
dataframe.loc[is_vwap, 'buy_tag'] += 'vwap '
conditions.append(is_lambo_2) # ~67.7%
dataframe.loc[is_lambo_2, 'buy_tag'] += 'lambo2 '
conditions.append(is_V) # ~67.9%
dataframe.loc[is_V, 'buy_tag'] += 'V '
if conditions:
dataframe.loc[
is_additional_check
&
reduce(lambda x, y: x | y, conditions)
, 'buy' ] = 1
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['sma_9'] > (dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1)*0.005 )) &
(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'
]=0 #disable
return dataframe
def T3(dataframe, length=5):
"""
T3 Average by HPotter on Tradingview
https://www.tradingview.com/script/qzoC9H1I-T3-Average/
"""
df = dataframe.copy()
df['xe1'] = ta.EMA(df['close'], timeperiod=length)
df['xe2'] = ta.EMA(df['xe1'], timeperiod=length)
df['xe3'] = ta.EMA(df['xe2'], timeperiod=length)
df['xe4'] = ta.EMA(df['xe3'], timeperiod=length)
df['xe5'] = ta.EMA(df['xe4'], timeperiod=length)
df['xe6'] = ta.EMA(df['xe5'], timeperiod=length)
b = 0.7
c1 = -b * b * b
c2 = 3 * b * b + 3 * b * b * b
c3 = -6 * b * b - 3 * b - 3 * b * b * b
c4 = 1 + 3 * b + b * b * b + 3 * b * b
df['T3Average'] = c1 * df['xe6'] + c2 * df['xe5'] + c3 * df['xe4'] + c4 * df['xe3']
return df['T3Average']
class BB_RPB_3c_dca (BB_RPB_3c):
position_adjustment_enable = True
initial_safety_order_trigger = -0.0193
max_safety_orders = 5
safety_order_step_scale = 1.52 #SS
safety_order_volume_scale = 1.68 #OS
max_dca_multiplier = (1 + max_safety_orders)
if (max_safety_orders > 0):
if (safety_order_volume_scale > 1):
max_dca_multiplier = (2 + (safety_order_volume_scale * (math.pow(safety_order_volume_scale,(max_safety_orders - 1)) - 1) / (safety_order_volume_scale - 1)))
elif (safety_order_volume_scale < 1):
max_dca_multiplier = (2 + (safety_order_volume_scale * (1 - math.pow(safety_order_volume_scale,(max_safety_orders - 1))) / (1 - safety_order_volume_scale)))
# Let unlimited stakes leave funds open for DCA orders
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
**kwargs) -> float:
if self.config['stake_amount'] == 'unlimited':
return proposed_stake / self.max_dca_multiplier
# Use default stake amount.
return proposed_stake
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: float,
max_stake: float, **kwargs):
if current_profit > self.initial_safety_order_trigger:
return None
count_of_buys = trade.nr_of_successful_buys()
if 1 <= count_of_buys <= self.max_safety_orders:
safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys)
if (self.safety_order_step_scale > 1):
safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1))
elif (self.safety_order_step_scale < 1):
safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale,(count_of_buys - 1))) / (1 - self.safety_order_step_scale))
if current_profit <= (-1 * abs(safety_order_trigger)):
try:
stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None)
# This calculates base order size
stake_amount = stake_amount / self.max_dca_multiplier
# This then calculates current safety order size
stake_amount = stake_amount * math.pow(self.safety_order_volume_scale,(count_of_buys - 1))
amount = stake_amount / current_rate
logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}")
return stake_amount
except Exception as exception:
logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}')
return None
return None