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ERMD_Strategy_4h_lever.py
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ERMD_Strategy_4h_lever.py
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# --- Do not remove these libs ---
from datetime import datetime
from functools import reduce
from typing import Optional, Union, List
from freqtrade.persistence import Trade
from freqtrade.persistence import Trade
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, \
stoploss_from_open
import pandas as pd
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import warnings
import numpy as np
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
DEBUG = False
Debug = False
Debug_Amount = False
Debug_Stoploss = False
class ERMDStrategy4hlever(IStrategy):
INTERFACE_VERSION: int = 3
can_short = False # enable short direction
timeframe = "4h"
startup_candle_count = 200 # how many candles we need to skip to start receiving robust signals
class HyperOpt:
def trailing_space() -> List[Dimension]:
# All parameters here are mandatory, you can only modify their type or the range.
return [
# Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.
Categorical([True, False], name='trailing_stop'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
stoploss = -0.09 # maximum stop loss distance
exit_profit_only = False
trailing_stop = False
trailing_stop_positive = 0.309
trailing_stop_positive_offset = 0.364
trailing_only_offset_is_reached = False
# take profit
tp1_dist = 0.2
tp1_size = 0.3
tp2_dist = 0.4
tp2_size = 0.3
tp3_dist = 0.6
tp3_size = 0.4
minimal_roi = {
"0": 0.896,
"1294": 0.295,
"3324": 0.104,
"4645": 0
}
buy_cust_stoploss_ = DecimalParameter(0.01, 0.09, decimals=3, default=0.052, optimize=True)
# Parameters: signal
buy_ema_enabled = BooleanParameter(default=False, optimize=True)
buy_ema1_length = IntParameter(3, 100, default=30, optimize=True)
buy_ema2_length = IntParameter(3, 100, default=21, optimize=True)
buy_ema3_length = IntParameter(3, 100, default=57, optimize=True)
buy_rsi_enabled = BooleanParameter(default=False, optimize=True)
buy_rsi_length = IntParameter(3, 100, default=49, optimize=True)
buy_rsi_threshold = IntParameter(50, 100, default=57, optimize=True)
buy_macd_enabled = BooleanParameter(default=False, optimize=True)
buy_macd_fast = IntParameter(3, 50, default=4, optimize=True)
buy_macd_slow = IntParameter(20, 200, default=104, optimize=True)
buy_macd_smooth = IntParameter(1, 30, default=13, optimize=True)
buy_candlestick_enabled = BooleanParameter(default=True, optimize=True)
sell_ema_enabled = BooleanParameter(default=True, optimize=True)
sell_ema1_length = IntParameter(3, 100, default=48, optimize=True)
sell_ema2_length = IntParameter(3, 100, default=65, optimize=True)
sell_ema3_length = IntParameter(3, 100, default=64, optimize=True)
sell_rsi_enabled = BooleanParameter(default=True, optimize=True)
sell_rsi_length = IntParameter(3, 100, default=9, optimize=True)
sell_rsi_threshold = IntParameter(0, 50, default=30, optimize=True)
sell_macd_enabled = BooleanParameter(default=False, optimize=True)
sell_macd_fast = IntParameter(3, 50, default=26, optimize=True)
sell_macd_slow = IntParameter(20, 200, default=192, optimize=True)
sell_macd_smooth = IntParameter(1, 30, default=24, optimize=True)
sell_candlestick_enabled = BooleanParameter(default=False, optimize=True)
sell_willy_exit = BooleanParameter(default=False, space='sell', optimize=True)
sell_high_line = IntParameter(-25, 0, default=-12, space="sell", optimize=True)
sell_low_line = IntParameter(-100, -75, default=-96, space="sell", optimize=True)
sell_ema = IntParameter(7, 22, default=18, space='sell', optimize=True)
sell_willy = IntParameter(15, 30, default=17, space='sell', optimize=True)
buy_sl_mult = DecimalParameter(0.5, 5, default=4, decimals=1, space='buy', optimize=True)
# signals
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" adds indicators to dataframe """
for val in self.buy_sl_mult.range:
dataframe[f'sl_l{val}'] = self.cust_stoploss(dataframe, length=20, mult=val)['Sl_low']
for val in self.buy_sl_mult.range:
dataframe[f'sl_h{val}'] = self.cust_stoploss(dataframe, length=20, mult=val)['Sl_high']
# BUY
# EMAs
for val in self.buy_ema1_length.range:
dataframe[f"buy_ema1_{val}"] = ta.EMA(dataframe, val)
for val in self.buy_ema2_length.range:
dataframe[f"buy_ema2_{val}"] = ta.EMA(dataframe, val)
for val in self.buy_ema3_length.range:
dataframe[f"buy_ema3_{val}"] = ta.EMA(dataframe, val)
# RSI
for val in self.buy_rsi_length.range:
dataframe[f"buy_rsi_{val}"] = ta.RSI(dataframe, val)
# MACD
for fast_val in self.buy_macd_fast.range:
for slow_val in self.buy_macd_slow.range:
for smooth_val in self.buy_macd_smooth.range:
if smooth_val < fast_val < slow_val:
macd = ta.MACD(dataframe, fast_val, slow_val, smooth_val)
dataframe[f'buy_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
dataframe[f'buy_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']
else:
macd = ta.MACD(dataframe, 12, 26, 9)
dataframe[f'buy_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
dataframe[f'buy_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']
# SELL
# EMAs
for val in self.sell_ema1_length.range:
dataframe[f"sell_ema1_{val}"] = ta.EMA(dataframe, val)
for val in self.sell_ema2_length.range:
dataframe[f"sell_ema2_{val}"] = ta.EMA(dataframe, val)
for val in self.sell_ema3_length.range:
dataframe[f"sell_ema3_{val}"] = ta.EMA(dataframe, val)
# RSI
for val in self.sell_rsi_length.range:
dataframe[f"sell_rsi_{val}"] = ta.RSI(dataframe, val)
# MACD
for fast_val in self.sell_macd_fast.range:
for slow_val in self.sell_macd_slow.range:
for smooth_val in self.sell_macd_smooth.range:
if smooth_val < fast_val < slow_val:
macd = ta.MACD(dataframe, fast_val, slow_val, smooth_val)
dataframe[f'sell_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
dataframe[f'sell_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']
else:
macd = ta.MACD(dataframe, 12, 26, 9)
dataframe[f'sell_macd_{fast_val}_{slow_val}_{smooth_val}'] = macd['macd']
dataframe[f'sell_macds_{fast_val}_{slow_val}_{smooth_val}'] = macd['macdsignal']
for val in self.sell_willy.range:
dataframe[f'sell_willy{val}'] = self.willy_ema(dataframe, low_line=-80,
up_line=-20, willyLen=val, emaLen=13)[
'Willy']
for val in self.sell_ema.range:
dataframe[f'sell_ema_will{val}'] = \
self.willy_ema(dataframe, low_line=-80, up_line=-20, willyLen=21, emaLen=val)[
'Ema_will']
# DOJI candlestick pattern
doji = ta.CDLDOJI(dataframe)
doji_dir = (dataframe["high"] - dataframe["close"]) / (dataframe["close"] - dataframe["low"])
dataframe.loc[((doji == 100) & (doji_dir > 1), "doji")] = dataframe["high"]
dataframe.loc[((doji == 100) & (doji_dir < 1), "doji")] = dataframe["low"]
# MARUBOZU candlestick pattern
body = (dataframe["close"] - dataframe["open"]).abs()
wick_top = dataframe["high"] - dataframe[["open", "close"]].max(axis=1)
wick_bottom = dataframe[["open", "close"]].min(axis=1) - dataframe["low"]
marubozu = (wick_top / body < 0.25) & (wick_bottom / body < 0.25)
dataframe.loc[(marubozu & (body > 0), 'marubozu')] = dataframe["high"]
dataframe.loc[(marubozu & (body < 0), 'marubozu')] = dataframe["low"]
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" generate signals """
# BUY signal
# only trade if there is volume
conditions = [(dataframe['volume'] > 0)]
# Close > EMA1 > EMA2 > EMA3
if self.buy_ema_enabled.value:
conditions.append((dataframe["close"] > dataframe[f"buy_ema1_{self.buy_ema1_length.value}"]) &
(dataframe[f"buy_ema1_{self.buy_ema1_length.value}"] > dataframe[
f"buy_ema2_{self.buy_ema2_length.value}"]) &
(dataframe[f"buy_ema2_{self.buy_ema2_length.value}"] > dataframe[
f"buy_ema3_{self.buy_ema3_length.value}"]))
# RSI > RSI_Threshold_Long
if self.buy_rsi_enabled:
conditions.append(dataframe[f"buy_rsi_{self.buy_rsi_length.value}"] > self.buy_rsi_threshold.value)
# MACD cross MACDs above or MACD > 0
if self.buy_macd_enabled.value:
macd = dataframe[
f'buy_macd_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}']
macds = dataframe[
f'buy_macds_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}']
conditions.append(qtpylib.crossed_above(macd, macds) | (macd > 0))
# Candlestick patterns: any DOJI and green MARUBOZU
if self.buy_candlestick_enabled.value:
doji = dataframe['doji'] == dataframe["high"]
marubozu = dataframe['marubozu'] == dataframe["high"]
conditions.append(doji | marubozu)
# combine conditions and return
buy_conditions = reduce(lambda x, y: x & y, conditions)
dataframe.loc[buy_conditions, 'enter_long'] = 1
# SELL signal
# only trade if there is volume
conditions = [(dataframe['volume'] > 0)]
# Close < EMA1 < EMA2 < EMA3
if self.sell_ema_enabled.value:
conditions.append((dataframe["close"] < dataframe[f"sell_ema1_{self.sell_ema1_length.value}"]) &
(dataframe[f"sell_ema1_{self.sell_ema1_length.value}"] < dataframe[
f"sell_ema2_{self.sell_ema2_length.value}"]) &
(dataframe[f"sell_ema2_{self.sell_ema2_length.value}"] < dataframe[
f"sell_ema3_{self.sell_ema3_length.value}"]))
# RSI < RSI_Threshold_Long
if self.sell_rsi_enabled:
conditions.append(dataframe[f"sell_rsi_{self.sell_rsi_length.value}"] < self.sell_rsi_threshold.value)
# MACD cross MACDs below or MACD < 0
if self.sell_macd_enabled.value:
macd = dataframe[
f'sell_macd_{self.sell_macd_fast.value}_{self.sell_macd_slow.value}_{self.sell_macd_smooth.value}']
macds = dataframe[
f'sell_macds_{self.sell_macd_fast.value}_{self.sell_macd_slow.value}_{self.sell_macd_smooth.value}']
conditions.append(qtpylib.crossed_below(macd, macds) | (macd < 0))
# Candlestick patterns, any DOJI and red MARUBOZU
if self.sell_candlestick_enabled.value:
doji = dataframe['doji'] == dataframe['low']
marubozu = dataframe['marubozu'] == dataframe['low']
conditions.append(doji | marubozu)
# combine conditions and return
sell_conditions = reduce(lambda x, y: x & y, conditions)
dataframe.loc[sell_conditions, 'enter_short'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.sell_willy_exit.value:
dataframe.loc[(dataframe[f'sell_ema_will{self.sell_ema.value}'] > self.sell_high_line.value),
'exit_long'] = 1
if self.sell_willy_exit.value:
dataframe.loc[(dataframe[f'sell_ema_will{self.sell_ema.value}'] < self.sell_low_line.value),
'exit_short'] = 1
return dataframe
stop_info = {}
def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
""" exit on EMA3 cross, or exit at entry candle low (long) or high (short) """
# print("cust stoploss", current_profit, self.buy_cust_stoploss_.value * -1)
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
close = current_rate
# exit on EMA20 cross
if trade.is_short:
ema3 = last_candle[f"sell_ema3_{self.sell_ema3_length.value}"]
if close > ema3:
if DEBUG:
print(
f"{pair} EMA20 exit short at {current_time}, price={current_rate}, level={ema3}, profit={current_profit}")
return "ema20_cross_exit"
else:
ema3 = last_candle[f"buy_ema3_{self.buy_ema3_length.value}"]
if close < ema3:
if DEBUG:
print(
f"{pair} EMA20 exit long at {current_time}, price={current_rate}, level={ema3}, profit={current_profit}")
return "ema20_cross_exit"
# exit on entry candle low/high
if trade.pair in self.stop_info:
if trade.is_short:
if close > self.stop_info[trade.pair]["level"]:
if DEBUG:
print(f"{pair} entry candle high break exit short at {current_time}, price={current_rate}, "
f"level={self.stop_info[trade.pair]['level']}, profit={current_profit}")
return "candle-based stop_loss"
else:
if close < self.stop_info[trade.pair]['level']:
if DEBUG:
print(f"{pair} entry candle low break exit long at {current_time}, price={current_rate}, "
f"level={self.stop_info[trade.pair]['level']}, profit={current_profit}")
return "candle-based stop_loss"
# exit on 3rd take profit, if it sums to 100%
if self.tp1_size + self.tp2_size + self.tp3_size == 1:
if trade.is_short:
ema1 = last_candle[f"sell_ema1_{self.sell_ema1_length.value}"]
if close < ema1 * (1 - self.tp3_dist):
if DEBUG:
print(f"{pair} 3rd take profit filled => exit short at {current_time}, price={current_rate}, "
f"level={ema1 * (1 - self.tp3_dist)}, profit={current_profit}")
return "take profit 3"
else:
ema1 = last_candle[f"buy_ema1_{self.buy_ema1_length.value}"]
if close > ema1 * (1 + self.tp3_dist):
if DEBUG:
print(f"{pair} 3rd take profit filled => exit long at {current_time}, price={current_rate}, "
f"level={ema1 * (1 + self.tp3_dist)}, profit={current_profit}")
return "take profit 3"
if current_profit < self.buy_cust_stoploss_.value * -1:
if Debug_Stoploss:
print("cust stoploss", current_profit, self.buy_cust_stoploss_.value * -1)
return "custon stoploss exit"
return False
# use_custom_stoploss = True
# def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime',
# current_rate: float, current_profit: float, **kwargs) -> float:
# dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
# candle = dataframe.iloc[-1].squeeze()
#
# stoploss_pricel = candle[f'sl_l{self.buy_sl_mult.value}']
# stoploss_prices = candle[f'sl_h{self.buy_sl_mult.value}']
# middle_of_cand = (candle['high'] + candle['low'] + candle['close'] + candle['open']) / 4
# if Debug_Stoploss:
# print('trade ID =>', trade.id, 'Trade is short? =>', trade.is_short)
# print('sl hihg =>', candle[f'sl_h{self.buy_sl_mult.value}'], 'sl low =>',
# candle[f'sl_l{self.buy_sl_mult.value}'])
# print('candle middle =>', middle_of_cand)
# if not trade.is_short:
# if Debug_Stoploss:
# print(' ')
# print('Trade is long', 'Stoploss ================================>',
# (middle_of_cand - stoploss_pricel) / candle['close'] * -1)
# print(' ')
# return (middle_of_cand - stoploss_pricel) / candle['close'] * -1
#
# if trade.is_short:
# if Debug_Stoploss:
# print(' ')
# print('Trade is short', 'Stoploss ==============================>',
# (middle_of_cand - stoploss_prices) / candle['close'])
# print(' ')
# return (middle_of_cand - stoploss_prices) / candle['close']
#
# return 100
# Position Size
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
stake_em = self.wallets.get_total_stake_amount() / 3
# if Debug_Amount:
# print(stake_em, (self.buy_cust_stoploss_.value * 100))
# print("Stake amount", stake_em / self.buy_cust_stoploss_.value)
# return stake_em
# dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
# candle = dataframe.iloc[-1].squeeze()
# stoploss_pricel = candle[f'sl_l{self.buy_sl_mult.value}']
# stoploss_prices = candle[f'sl_h{self.buy_sl_mult.value}']
# middle_of_cand = (candle['high'] + candle['low'] + candle['close'] + candle['open']) / 4
# if side == "long":
# stoploss_ = (middle_of_cand - stoploss_pricel) / candle['close'] * 100
# amount_ = self.wallets.get_total_stake_amount() / 100
# if Debug_Amount:
# print('stoploss from amount = >', stoploss_)
# print('full wallet =>>>', self.wallets.get_total_stake_amount())
# print('amount stoploss', amount_)
# print('Amount final =>>', amount_ / stoploss_ * 100)
# return (amount_ / stoploss_) * 100
#
# else:
# stoploss_ = (middle_of_cand - stoploss_prices) / candle['close'] * -1 * 100
# amount_ = self.wallets.get_total_stake_amount() / 100
# if Debug_Amount:
# print('stoploos from amount =>', stoploss_)
# print('full wallet =>>>', self.wallets.get_total_stake_amount())
# print('amount stoploss', amount_)
# print('Amount final=>>', (amount_ / stoploss_) * 100)
# return (amount_ / stoploss_) * 100
# return proposed_stake
# Take Profit
position_adjustment_enable = True
max_entry_position_adjustment = -1
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> Optional[float]:
""" custom take profits """
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
if trade.is_short:
if trade.nr_of_successful_exits == 0:
if current_rate < (1 - self.tp1_dist) * dataframe[f"sell_ema1_{self.sell_ema1_length.value}"].iloc[
-1].squeeze():
return -trade.amount_requested * self.tp1_size * current_rate
if trade.nr_of_successful_exits == 1:
if current_rate < (1 - self.tp2_dist) * dataframe[f"sell_ema1_{self.sell_ema1_length.value}"].iloc[
-1].squeeze():
return -trade.amount_requested * self.tp2_size * current_rate
if trade.nr_of_successful_exits == 2:
if current_rate < (1 - self.tp3_dist) * dataframe[f"sell_ema1_{self.sell_ema1_length.value}"].iloc[
-1].squeeze():
if self.tp1_size + self.tp2_size + self.tp3_size != 1:
return -trade.amount_requested * self.tp3_size * current_rate
else:
if trade.nr_of_successful_exits == 0:
if current_rate > (1 + self.tp1_dist) * dataframe[f"buy_ema1_{self.buy_ema1_length.value}"].iloc[
-1].squeeze():
return -trade.amount_requested * self.tp1_size * current_rate
if trade.nr_of_successful_exits == 1:
if current_rate > (1 + self.tp2_dist) * dataframe[f"buy_ema1_{self.buy_ema1_length.value}"].iloc[
-1].squeeze():
return -trade.amount_requested * self.tp2_size * current_rate
if trade.nr_of_successful_exits == 2:
if current_rate > (1 + self.tp3_dist) * dataframe[f"buy_ema1_{self.buy_ema1_length.value}"].iloc[
-1].squeeze():
if self.tp1_size + self.tp2_size + self.tp3_size != 1:
return -trade.amount_requested * self.tp3_size * current_rate
return None
# set up plotting
@property
def plot_config(self):
plot_config = {
'main_plot': {
f"buy_ema1_{self.buy_ema1_length.value}": {},
f"buy_ema2_{self.buy_ema2_length.value}": {},
f"buy_ema3_{self.buy_ema3_length.value}": {},
f"doji": {"type": "scatter"},
f"marubozu": {"type": "scatter"}
},
'subplots': {
"MACD": {
f'buy_macd_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}': {},
f'buy_macds_{self.buy_macd_fast.value}_{self.buy_macd_slow.value}_{self.buy_macd_smooth.value}': {},
},
"RSI": {
f'buy_rsi_{self.buy_rsi_length.value}': {}
}
}}
return plot_config
def willy_ema(self, dataframe: DataFrame, low_line=-80.0, up_line=-20.0, willyLen=21, emaLen=13):
def max_help(open, high, low, close):
return max(open, high, low, close)
def min_help(open, high, low, close):
return min(open, high, low, close)
df = dataframe.copy()
df['highest'] = np.vectorize(max_help)(df['open'], df['high'], df['low'], df['close'])
df['lowest'] = np.vectorize(min_help)(df['open'], df['high'], df['low'], df['close'])
df['upper'] = df['highest'].rolling(willyLen).max()
df['lower'] = df['lowest'].rolling(willyLen).min()
df['willy'] = 100 * (df['close'] - df['upper']) / (df['upper'] - df['lower'])
df['ema_will'] = ta.EMA(df['willy'], emaLen)
# df['ema_willy'] =
df.drop(['highest', 'lowest', 'upper', 'lower'], inplace=True, axis=1)
df['low_line'] = low_line
df['up_line'] = up_line
return DataFrame(index=df.index, data={
'Willy': df['willy'],
'Ema_will': df['ema_will'],
'Low_line': df['low_line'],
'Up_line': df['up_line']
})
def cust_stoploss(self, dataframe: DataFrame, mult, length):
df = dataframe.copy()
df['sma'] = qtpylib.sma(df['close'], length)
df['tr'] = qtpylib.atr(df, length)
df['sl_high'] = qtpylib.sma(df['tr'], length)
def help1(x, mul, high):
return x * mul + high
df['sl_high'] = np.vectorize(help1)(df['sl_high'], mult, df['high'])
df['sl_low'] = qtpylib.sma(df['tr'], length)
def help1(x, mul, low):
return low - x * mul
df['sl_low'] = np.vectorize(help1)(df['sl_low'], mult, df['low'])
df.drop(['sma', 'tr'], axis=1, inplace=True)
return DataFrame(index=df.index, data={
'Sl_high': df['sl_high'],
'Sl_low': df['sl_low'],
})
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
side: str, **kwargs) -> bool:
hal_per = self.wallets.get_total_stake_amount() / 200
usdt_amount = amount * rate
if usdt_amount > hal_per:
if Debug_Amount:
print(f"Amount of trade USDT{usdt_amount} -- (minimal{hal_per})")
return True
if Debug_Amount:
print(f"Trade was rejected ============================>>>>> actual === {usdt_amount} - minimal === {hal_per}")
return False