Since one of my strategies has been performing well for the past 6 months, I would release the basic template of it. Before I give the code, I have to give warning first.
DON’T USE THIS STRATEGY IN THIS BASIC FORM!!!! THIS CODE IS MEANT TO BE USED AS BASE OF YOUR DEVELOPMENT.
And one more thing. There won’t be any paid version of this code. You need to put in the work yourself.
This is a very simple strategy based of MA offset. If you don’t know what it is, you can search online for topics about it. I won’t explain this strategy. You would need to replace rsi_1 and others with real RSI conditions. I’m putting them there as placeholders. As said above, never use the code below directly for trading. You will lose money. Please develop the code below. Have fun.
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy import IStrategy, informative
from freqtrade.strategy import (merge_informative_pair,
DecimalParameter, IntParameter, BooleanParameter, CategoricalParameter, stoploss_from_open,
stoploss_from_absolute)
from pandas import DataFrame, Series
from typing import Dict, List, Optional, Tuple, Union
from functools import reduce
from freqtrade.persistence import Trade
from datetime import datetime, timedelta, timezone
from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_minutes
import talib.abstract as ta
import math
import pandas_ta as pta
import logging
from logging import FATAL
logger = logging.getLogger(__name__)
def tv_wma(df, length = 9) -> DataFrame:
"""
Source: Tradingview "Moving Average Weighted"
Pinescript Author: Unknown
Args :
dataframe : Pandas Dataframe
length : WMA length
field : Field to use for the calculation
Returns :
dataframe : Pandas DataFrame with new columns 'tv_wma'
"""
norm = 0
sum = 0
for i in range(1, length - 1):
weight = (length - i) * length
norm = norm + weight
sum = sum + df.shift(i) * weight
tv_wma = (sum / norm) if norm > 0 else 0
return tv_wma
def tv_hma(dataframe, length = 9, field = 'close') -> DataFrame:
"""
Source: Tradingview "Hull Moving Average"
Pinescript Author: Unknown
Args :
dataframe : Pandas Dataframe
length : HMA length
field : Field to use for the calculation
Returns :
dataframe : Pandas DataFrame with new columns 'tv_hma'
"""
h = 2 * tv_wma(dataframe[field], math.floor(length / 2)) - tv_wma(dataframe[field], length)
tv_hma = tv_wma(h, math.floor(math.sqrt(length)))
# dataframe.drop("h", inplace=True, axis=1)
return tv_hma
class Kasuari_btc_template (IStrategy):
def version(self) -> str:
return "Kasuari-v1-btc"
INTERFACE_VERSION = 3
lev = 10
roi = 0.0015
# ROI table:
minimal_roi = {
"0": (roi * lev)
}
optimize_buy_hma1 = False
buy_length_hma1 = IntParameter(1, 50, default=6, optimize=optimize_buy_hma1)
buy_offset_hma1 = IntParameter(16, 20, default=20, optimize=optimize_buy_hma1)
optimize_buy_hma2 = False
buy_length_hma2 = IntParameter(1, 50, default=6, optimize=optimize_buy_hma2)
buy_offset_hma2 = IntParameter(16, 20, default=20, optimize=optimize_buy_hma2)
optimize_buy_hma3 = False
buy_length_hma3 = IntParameter(1, 50, default=6, optimize=optimize_buy_hma3)
buy_offset_hma3 = IntParameter(20, 24, default=20, optimize=optimize_buy_hma3)
optimize_buy_hma4 = False
buy_length_hma4 = IntParameter(1, 50, default=6, optimize=optimize_buy_hma4)
buy_offset_hma4 = IntParameter(20, 24, default=20, optimize=optimize_buy_hma4)
optimize_buy_dema1 = False
buy_length_dema1 = IntParameter(1, 50, default=6, optimize=optimize_buy_dema1)
buy_offset_dema1 = IntParameter(16, 20, default=20, optimize=optimize_buy_dema1)
optimize_buy_dema2 = False
buy_length_dema2 = IntParameter(1, 50, default=6, optimize=optimize_buy_dema2)
buy_offset_dema2 = IntParameter(16, 20, default=20, optimize=optimize_buy_dema2)
optimize_buy_dema3 = False
buy_length_dema3 = IntParameter(1, 50, default=6, optimize=optimize_buy_dema3)
buy_offset_dema3 = IntParameter(20, 24, default=20, optimize=optimize_buy_dema3)
optimize_buy_dema4 = False
buy_length_dema4 = IntParameter(1, 50, default=6, optimize=optimize_buy_dema4)
buy_offset_dema4 = IntParameter(20, 24, default=20, optimize=optimize_buy_dema4)
optimize_buy_tema1 = False
buy_length_tema1 = IntParameter(1, 40, default=6, optimize=optimize_buy_tema1)
buy_offset_tema1 = IntParameter(16, 20, default=20, optimize=optimize_buy_tema1)
optimize_buy_tema2 = False
buy_length_tema2 = IntParameter(1, 40, default=6, optimize=optimize_buy_tema2)
buy_offset_tema2 = IntParameter(16, 20, default=20, optimize=optimize_buy_tema2)
optimize_buy_tema3 = False
buy_length_tema3 = IntParameter(1, 40, default=6, optimize=optimize_buy_tema3)
buy_offset_tema3 = IntParameter(20, 24, default=20, optimize=optimize_buy_tema3)
optimize_buy_tema4 = False
buy_length_tema4 = IntParameter(1, 40, default=6, optimize=optimize_buy_tema4)
buy_offset_tema4 = IntParameter(20, 24, default=20, optimize=optimize_buy_tema4)
optimize_buy_rsi1 = False
buy_rsi1 = IntParameter(1, 10, default=6, optimize=optimize_buy_rsi1)
buy_rolling_rsi1 = IntParameter(2, 30, default=20, optimize=optimize_buy_rsi1)
buy_diff_rsi1 = IntParameter(2, 10, default=6, optimize=optimize_buy_rsi1)
optimize_buy_rsi2 = False
buy_rsi2 = IntParameter(10, 19, default=6, optimize=optimize_buy_rsi2)
buy_rolling_rsi2 = IntParameter(2, 30, default=20, optimize=optimize_buy_rsi2)
buy_diff_rsi2 = IntParameter(2, 10, default=6, optimize=optimize_buy_rsi2)
optimize_buy_rsi3 = False
buy_rsi3 = IntParameter(1, 10, default=6, optimize=optimize_buy_rsi3)
buy_rolling_rsi3 = IntParameter(2, 30, default=10, optimize=optimize_buy_rsi3)
buy_diff_rsi3 = IntParameter(2, 10, default=6, optimize=optimize_buy_rsi3)
optimize_buy_rsi4 = False
buy_rsi4 = IntParameter(10, 19, default=6, optimize=optimize_buy_rsi4)
buy_rolling_rsi4 = IntParameter(2, 30, default=20, optimize=optimize_buy_rsi4)
buy_diff_rsi4 = IntParameter(1, 10, default=6, optimize=optimize_buy_rsi4)
optimize_buy_mfi_1 = False
buy_mfi_1 = IntParameter(1, 10, default=6, optimize=optimize_buy_mfi_1)
buy_rolling_mfi_1 = IntParameter(1, 30, default=20, optimize=optimize_buy_mfi_1)
buy_diff_mfi_1 = IntParameter(1, 10, default=6, optimize=optimize_buy_mfi_1)
optimize_buy_mfi_2 = False
buy_mfi_2 = IntParameter(10, 19, default=6, optimize=optimize_buy_mfi_2)
buy_rolling_mfi_2 = IntParameter(1, 30, default=20, optimize=optimize_buy_mfi_2)
buy_diff_mfi_2 = IntParameter(1, 10, default=6, optimize=optimize_buy_mfi_2)
optimize_buy_mfi_3 = False
buy_mfi_3 = IntParameter(1, 10, default=6, optimize=optimize_buy_mfi_3)
buy_rolling_mfi_3 = IntParameter(1, 19, default=10, optimize=optimize_buy_mfi_3)
buy_diff_mfi_3 = IntParameter(1, 10, default=6, optimize=optimize_buy_mfi_3)
optimize_buy_mfi_4 = False
buy_mfi_4 = IntParameter(10, 19, default=6, optimize=optimize_buy_mfi_4)
buy_rolling_mfi_4 = IntParameter(12, 30, default=20, optimize=optimize_buy_mfi_4)
buy_diff_mfi_4 = IntParameter(1, 10, default=6, optimize=optimize_buy_mfi_4)
optimize_sell_ema1 = False
sell_length_ema1 = IntParameter(1, 50, default=6, optimize=optimize_sell_ema1)
sell_offset_ema1 = IntParameter(20, 24, default=20, optimize=optimize_sell_ema1)
optimize_sell_ema2 = False
sell_length_ema2 = IntParameter(1, 50, default=6, optimize=optimize_sell_ema2)
sell_offset_ema2 = IntParameter(16, 20, default=20, optimize=optimize_sell_ema2)
optimize_sell_ema3 = False
sell_length_ema3 = IntParameter(1, 50, default=6, optimize=optimize_sell_ema3)
sell_offset_ema3 = IntParameter(16, 20, default=20, optimize=optimize_sell_ema3)
optimize_sell_ema4 = False
sell_length_ema4 = IntParameter(1, 50, default=6, optimize=optimize_sell_ema4)
sell_offset_ema4 = IntParameter(20, 24, default=20, optimize=optimize_sell_ema4)
sell_clear_old_trade = IntParameter(11, 25, default=20, optimize=False)
sell_clear_old_trade_profit = IntParameter(0, 5, default=0, optimize=False)
# Stoploss:
stoploss = -0.99
# Trailing stop:
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.03
trailing_only_offset_is_reached = True
# Sell signal
use_exit_signal = True
exit_profit_only = False
exit_profit_offset = 0.01
ignore_roi_if_entry_signal = False
timeframe = '15m'
can_short = True
timeframe_minutes = timeframe_to_minutes(timeframe)
process_only_new_candles = True
startup_candle_count = 999
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=15)
dataframe['rsi_45'] = ta.RSI(dataframe['close'], timeperiod=45)
dataframe['mfi'] = ta.MFI(dataframe, timeperiod=15)
dataframe['mfi_45'] = ta.MFI(dataframe, timeperiod=45)
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastk'] = stoch_fast['fastk']
dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0)
if not self.optimize_buy_hma1:
dataframe['hma_offset_buy1'] = tv_hma(dataframe, int(5 * self.buy_length_hma1.value)) * 0.05 * self.buy_offset_hma1.value
if not self.optimize_buy_hma2:
dataframe['hma_offset_buy2'] = tv_hma(dataframe, int(5 * self.buy_length_hma2.value)) * 0.05 * self.buy_offset_hma2.value
if not self.optimize_buy_hma3:
dataframe['hma_offset_buy3'] = tv_hma(dataframe, int(5 * self.buy_length_hma3.value)) * 0.05 * self.buy_offset_hma3.value
if not self.optimize_buy_hma4:
dataframe['hma_offset_buy4'] = tv_hma(dataframe, int(5 * self.buy_length_hma4.value)) * 0.05 * self.buy_offset_hma4.value
if not self.optimize_buy_dema1:
dataframe['dema_offset_buy1'] = ta.DEMA(dataframe, int(5 * self.buy_length_dema1.value)) * 0.05 * self.buy_offset_dema1.value
if not self.optimize_buy_dema2:
dataframe['dema_offset_buy2'] = ta.DEMA(dataframe, int(5 * self.buy_length_dema2.value)) * 0.05 * self.buy_offset_dema2.value
if not self.optimize_buy_dema3:
dataframe['dema_offset_buy3'] = ta.DEMA(dataframe, int(5 * self.buy_length_dema3.value)) * 0.05 * self.buy_offset_dema3.value
if not self.optimize_buy_dema4:
dataframe['dema_offset_buy4'] = ta.DEMA(dataframe, int(5 * self.buy_length_dema4.value)) * 0.05 * self.buy_offset_dema4.value
if not self.optimize_buy_tema1:
dataframe['tema_offset_buy1'] = ta.TEMA(dataframe, int(5 * self.buy_length_tema1.value)) * 0.05 * self.buy_offset_tema1.value
if not self.optimize_buy_tema2:
dataframe['tema_offset_buy2'] = ta.TEMA(dataframe, int(5 * self.buy_length_tema2.value)) * 0.05 * self.buy_offset_tema2.value
if not self.optimize_buy_tema3:
dataframe['tema_offset_buy3'] = ta.TEMA(dataframe, int(5 * self.buy_length_tema3.value)) * 0.05 * self.buy_offset_tema3.value
if not self.optimize_buy_tema4:
dataframe['tema_offset_buy4'] = ta.TEMA(dataframe, int(5 * self.buy_length_tema4.value)) * 0.05 * self.buy_offset_tema4.value
if not self.optimize_sell_ema1:
column_name = f"ema_{int(5 * self.sell_length_ema1.value)}"
if column_name not in dataframe.columns:
dataframe[column_name] = ta.EMA(dataframe, int(5 * self.sell_length_ema1.value))
if not self.optimize_sell_ema2:
column_name = f"ema_{int(5 * self.sell_length_ema2.value)}"
if column_name not in dataframe.columns:
dataframe[column_name] = ta.EMA(dataframe, int(5 * self.sell_length_ema2.value))
if not self.optimize_sell_ema3:
column_name = f"ema_{int(5 * self.sell_length_ema3.value)}"
if column_name not in dataframe.columns:
dataframe[column_name] = ta.EMA(dataframe, int(5 * self.sell_length_ema3.value))
if not self.optimize_sell_ema4:
column_name = f"ema_{int(5 * self.sell_length_ema4.value)}"
if column_name not in dataframe.columns:
dataframe[column_name] = ta.EMA(dataframe, int(5 * self.sell_length_ema4.value))
vol_20_max = dataframe['volume'].rolling(window=20).max()
vol_20_min = dataframe['volume'].rolling(window=20).min()
roll_20 = ((vol_20_max - dataframe['volume']) / (vol_20_max - vol_20_min))
dataframe['vol_base'] = roll_20.rolling(5).mean()
dataframe['vol_20'] = roll_20
vol_40_max = dataframe['volume'].rolling(window=40).max()
vol_40_min = dataframe['volume'].rolling(window=40).min()
roll_40 = ((vol_40_max - dataframe['volume']) / (vol_40_max - vol_40_min))
dataframe['vol_40_base'] = roll_40.rolling(5).mean()
dataframe['vol_40'] = roll_40
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions_short = []
if self.optimize_buy_hma1:
dataframe['hma_offset_buy1'] = tv_hma(dataframe, int(5 * self.buy_length_hma1.value)) * 0.05 * self.buy_offset_hma1.value
if self.optimize_buy_hma2:
dataframe['hma_offset_buy2'] = tv_hma(dataframe, int(5 * self.buy_length_hma2.value)) * 0.05 * self.buy_offset_hma2.value
if self.optimize_buy_hma3:
dataframe['hma_offset_buy3'] = tv_hma(dataframe, int(5 * self.buy_length_hma3.value)) * 0.05 * self.buy_offset_hma3.value
if self.optimize_buy_hma4:
dataframe['hma_offset_buy4'] = tv_hma(dataframe, int(5 * self.buy_length_hma4.value)) * 0.05 * self.buy_offset_hma4.value
if self.optimize_buy_dema1:
dataframe['dema_offset_buy1'] = ta.DEMA(dataframe, int(5 * self.buy_length_dema1.value)) * 0.05 * self.buy_offset_dema1.value
if self.optimize_buy_dema2:
dataframe['dema_offset_buy2'] = ta.DEMA(dataframe, int(5 * self.buy_length_dema2.value)) * 0.05 * self.buy_offset_dema2.value
if self.optimize_buy_dema3:
dataframe['dema_offset_buy3'] = ta.DEMA(dataframe, int(5 * self.buy_length_dema3.value)) * 0.05 * self.buy_offset_dema3.value
if self.optimize_buy_dema4:
dataframe['dema_offset_buy4'] = ta.DEMA(dataframe, int(5 * self.buy_length_dema4.value)) * 0.05 * self.buy_offset_dema4.value
if self.optimize_buy_tema1:
dataframe['tema_offset_buy1'] = ta.TEMA(dataframe, int(5 * self.buy_length_tema1.value)) * 0.05 * self.buy_offset_tema1.value
if self.optimize_buy_tema2:
dataframe['tema_offset_buy2'] = ta.TEMA(dataframe, int(5 * self.buy_length_tema2.value)) * 0.05 * self.buy_offset_tema2.value
if self.optimize_buy_tema3:
dataframe['tema_offset_buy3'] = ta.TEMA(dataframe, int(5 * self.buy_length_tema3.value)) * 0.05 * self.buy_offset_tema3.value
if self.optimize_buy_tema4:
dataframe['tema_offset_buy4'] = ta.TEMA(dataframe, int(5 * self.buy_length_tema4.value)) * 0.05 * self.buy_offset_tema4.value
dataframe['enter_tag'] = ''
dataframe['enter_long'] = 0
add_check = (
dataframe['live_data_ok']
)
no_hma_1 = []
buy_offset_hma1 = (
(dataframe['close'] < dataframe['hma_offset_buy1'])
&
rsi_1
)
dataframe.loc[buy_offset_hma1, 'enter_tag'] += 'l_h_1 '
if no_hma_1:
conditions.append(buy_offset_hma1 & (reduce(lambda x, y: x | y, no_hma_1) == False))
else:
conditions.append(buy_offset_hma1)
no_hma_2 = []
buy_offset_hma2 = (
(dataframe['close'] < dataframe['hma_offset_buy2'])
&
rsi_12
&
rsi_24
)
dataframe.loc[buy_offset_hma2, 'enter_tag'] += 'l_h_2 '
if no_hma_2:
conditions.append(buy_offset_hma2 & (reduce(lambda x, y: x | y, no_hma_2) == False))
else:
conditions.append(buy_offset_hma2)
no_hma_3 = []
buy_offset_hma3 = (
(dataframe['close'] > dataframe['hma_offset_buy3'])
&
rsi_13
&
rsi_34
)
dataframe.loc[buy_offset_hma3, 'enter_tag'] += 's_h_3 '
if no_hma_3:
conditions_short.append(buy_offset_hma3 & (reduce(lambda x, y: x | y, no_hma_3) == False))
else:
conditions_short.append(buy_offset_hma3)
no_hma_4 = []
buy_offset_hma4 = (
(dataframe['close'] > dataframe['hma_offset_buy4'])
&
rsi_4
)
dataframe.loc[buy_offset_hma4, 'enter_tag'] += 's_h_4 '
if no_hma_4:
conditions_short.append(buy_offset_hma4 & (reduce(lambda x, y: x | y, no_hma_4) == False))
else:
conditions_short.append(buy_offset_hma4)
no_dema_1 = []
buy_offset_dema1 = (
((dataframe['close'] < dataframe['dema_offset_buy1']).rolling(2).min() > 0)
&
rsi_1
)
dataframe.loc[buy_offset_dema1, 'enter_tag'] += 'l_d_1 '
if no_dema_1:
conditions.append(buy_offset_dema1 & (reduce(lambda x, y: x | y, no_dema_1) == False))
else:
conditions.append(buy_offset_dema1)
no_dema_2 = []
buy_offset_dema2 = (
((dataframe['close'] < dataframe['dema_offset_buy2']).rolling(3).min() > 0)
&
rsi_12
&
rsi_24
)
dataframe.loc[buy_offset_dema2, 'enter_tag'] += 'l_dema3_12 '
if no_dema_2:
conditions.append(buy_offset_dema2 & (reduce(lambda x, y: x | y, no_dema_2) == False))
else:
conditions.append(buy_offset_dema2)
no_dema_3 = []
buy_offset_dema3 = (
(dataframe['close'] > dataframe['dema_offset_buy3'])
&
rsi_13
&
rsi_34
)
dataframe.loc[buy_offset_dema3, 'enter_tag'] += 's_d_3 '
if no_dema_3:
conditions_short.append(buy_offset_dema3 & (reduce(lambda x, y: x | y, no_dema_3) == False))
else:
conditions_short.append(buy_offset_dema3)
no_dema_4 = []
buy_offset_dema4 = (
((dataframe['close'] > dataframe['dema_offset_buy4']).rolling(3).min() > 0)
&
rsi_4
)
dataframe.loc[buy_offset_dema4, 'enter_tag'] += 's_dema3_34 '
if no_dema_4:
conditions_short.append(buy_offset_dema4 & (reduce(lambda x, y: x | y, no_dema_4) == False))
else:
conditions_short.append(buy_offset_dema4)
no_tema_1 = []
buy_offset_tema1 = (
(dataframe['close'] < dataframe['tema_offset_buy1'])
&
rsi_1
)
dataframe.loc[buy_offset_tema1, 'enter_tag'] += 'l_t_1 '
if no_tema_1:
conditions.append(buy_offset_tema1 & (reduce(lambda x, y: x | y, no_tema_1) == False))
else:
conditions.append(buy_offset_tema1)
no_tema_2 = []
buy_offset_tema2 = (
((dataframe['close'] < dataframe['tema_offset_buy2']).rolling(2).min() > 0)
&
rsi_12
&
rsi_24
)
dataframe.loc[buy_offset_tema2, 'enter_tag'] += 'l_t_2 '
if no_tema_2:
conditions.append(buy_offset_tema2 & (reduce(lambda x, y: x | y, no_tema_2) == False))
else:
conditions.append(buy_offset_tema2)
no_tema_3 = []
buy_offset_tema3 = (
(dataframe['close'] > dataframe['tema_offset_buy3'])
&
rsi_13
&
rsi_34
)
dataframe.loc[buy_offset_tema3, 'enter_tag'] += 's_t_3 '
if no_tema_3:
conditions_short.append(buy_offset_tema3 & (reduce(lambda x, y: x | y, no_tema_3) == False))
else:
conditions_short.append(buy_offset_tema3)
no_tema_4 = []
buy_offset_tema4 = (
(dataframe['close'] > dataframe['tema_offset_buy4'])
&
rsi_4
)
dataframe.loc[buy_offset_tema4, 'enter_tag'] += 's_t_4 '
if no_tema_4:
conditions_short.append(buy_offset_tema4 & (reduce(lambda x, y: x | y, no_tema_4) == False))
else:
conditions_short.append(buy_offset_tema4)
no_rsi_1 = []
buy_offset_rsi1 = (
((dataframe['rsi'] < (5 * self.buy_rsi1.value)).rolling(int(self.buy_rolling_rsi1.value)).min() > 0)
&
((dataframe['rsi'].shift() - dataframe['rsi']) > (3 * self.buy_diff_rsi1.value))
)
dataframe.loc[buy_offset_rsi1, 'enter_tag'] += "l_r_1 "
if no_rsi_1:
conditions.append(buy_offset_rsi1 & (reduce(lambda x, y: x | y, no_rsi_1) == False))
else:
conditions.append(buy_offset_rsi1)
no_rsi_2 = []
buy_offset_rsi2 = (
((dataframe['rsi'] > (5 * self.buy_rsi2.value)).rolling(int(self.buy_rolling_rsi2.value)).min() > 0)
&
((dataframe['rsi'] - dataframe['rsi'].shift()) > (3 * self.buy_diff_rsi2.value))
)
dataframe.loc[buy_offset_rsi2, 'enter_tag'] += "s_r_2 "
if no_rsi_2:
conditions_short.append(buy_offset_rsi2 & (reduce(lambda x, y: x | y, no_rsi_2) == False))
else:
conditions_short.append(buy_offset_rsi2)
no_rsi_3 = []
buy_offset_rsi3 = (
((dataframe['rsi'] < (5 * self.buy_rsi3.value)).rolling(int(self.buy_rolling_rsi3.value)).min() > 0)
&
((dataframe['rsi'].shift() - dataframe['rsi']) > (3 * self.buy_diff_rsi3.value))
)
dataframe.loc[buy_offset_rsi3, 'enter_tag'] += "l_r_3 "
if no_rsi_3:
conditions.append(buy_offset_rsi3 & (reduce(lambda x, y: x | y, no_rsi_3) == False))
else:
conditions.append(buy_offset_rsi3)
no_rsi_4 = []
buy_offset_rsi4 = (
((dataframe['rsi'] > (5 * self.buy_rsi4.value)).rolling(int(self.buy_rolling_rsi4.value)).min() > 0)
&
((dataframe['rsi'] - dataframe['rsi'].shift()) > (3 * self.buy_diff_rsi4.value))
)
dataframe.loc[buy_offset_rsi4, 'enter_tag'] += "s_r_4 "
if no_rsi_4:
conditions_short.append(buy_offset_rsi4 & (reduce(lambda x, y: x | y, no_rsi_4) == False))
else:
conditions_short.append(buy_offset_rsi4)
no_mfi_1 = []
buy_offset_mfi_1 = (
((dataframe['mfi'] < (5 * self.buy_mfi_1.value)).rolling(int(self.buy_rolling_mfi_1.value)).min() > 0)
&
((dataframe['mfi'].shift() - dataframe['mfi']) > (3 * self.buy_diff_mfi_1.value))
)
dataframe.loc[buy_offset_mfi_1, 'enter_tag'] += "l_m_1 "
if no_mfi_1:
conditions.append(buy_offset_mfi_1 & (reduce(lambda x, y: x | y, no_mfi_1) == False))
else:
conditions.append(buy_offset_mfi_1)
no_mfi_2 = []
buy_offset_mfi_2 = (
((dataframe['mfi'] > (5 * self.buy_mfi_2.value)).rolling(int(self.buy_rolling_mfi_2.value)).min() > 0)
&
((dataframe['mfi'] - dataframe['mfi'].shift()) > (3 * self.buy_diff_mfi_2.value))
)
dataframe.loc[buy_offset_mfi_2, 'enter_tag'] += "s_m_2 "
if no_mfi_2:
conditions_short.append(buy_offset_mfi_2 & (reduce(lambda x, y: x | y, no_mfi_2) == False))
else:
conditions_short.append(buy_offset_mfi_2)
no_mfi_3 = []
buy_offset_mfi_3 = (
((dataframe['mfi'] < (5 * self.buy_mfi_3.value)).rolling(int(self.buy_rolling_mfi_3.value)).min() > 0)
&
((dataframe['mfi'].shift() - dataframe['mfi']) > (3 * self.buy_diff_mfi_3.value))
)
dataframe.loc[buy_offset_mfi_3, 'enter_tag'] += "l_m_3 "
if no_mfi_3:
conditions.append(buy_offset_mfi_3 & (reduce(lambda x, y: x | y, no_mfi_3) == False))
else:
conditions.append(buy_offset_mfi_3)
no_mfi_4 = []
buy_offset_mfi_4 = (
((dataframe['mfi'] > (5 * self.buy_mfi_4.value)).rolling(int(self.buy_rolling_mfi_4.value)).min() > 0)
&
((dataframe['mfi'] - dataframe['mfi'].shift()) > (3 * self.buy_diff_mfi_4.value))
)
dataframe.loc[buy_offset_mfi_4, 'enter_tag'] += "s_m_4 "
if no_mfi_4:
conditions_short.append(buy_offset_mfi_4 & (reduce(lambda x, y: x | y, no_mfi_4) == False))
else:
conditions_short.append(buy_offset_mfi_4)
if conditions:
no_long = []
dataframe.loc[
reduce(lambda x, y: x | y, conditions)
&
add_check,
'enter_long'
]= 1
if no_long:
dataframe.loc[
reduce(lambda x, y: x | y, no_long),
'enter_long'
]= 0
if conditions_short:
no_short = []
dataframe.loc[
reduce(lambda x, y: x | y, conditions_short)
&
add_check,
'enter_short',
]= 1
if no_short:
dataframe.loc[
reduce(lambda x, y: x | y, no_short),
'enter_short',
]= 0
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['exit_tag'] = ''
conditions = []
conditions_short = []
add_check = (
(dataframe['volume'] > 0)
)
if self.optimize_sell_ema1:
column_name = f"ema_{int(5 * self.sell_length_ema1.value)}"
if column_name not in dataframe.columns:
dataframe[column_name] = ta.EMA(dataframe, int(5 * self.sell_length_ema1.value))
if self.optimize_sell_ema2:
column_name = f"ema_{int(5 * self.sell_length_ema2.value)}"
if column_name not in dataframe.columns:
dataframe[column_name] = ta.EMA(dataframe, int(5 * self.sell_length_ema2.value))
if self.optimize_sell_ema3:
column_name = f"ema_{int(5 * self.sell_length_ema3.value)}"
if column_name not in dataframe.columns:
dataframe[column_name] = ta.EMA(dataframe, int(5 * self.sell_length_ema3.value))
if self.optimize_sell_ema4:
column_name = f"ema_{int(5 * self.sell_length_ema4.value)}"
if column_name not in dataframe.columns:
dataframe[column_name] = ta.EMA(dataframe, int(5 * self.sell_length_ema4.value))
sell_ema_1 = (
(dataframe['close'] > (dataframe[f"ema_{int(5 * self.sell_length_ema1.value)}"] * 0.05 * self.sell_offset_ema1.value))
)
dataframe.loc[sell_ema_1, 'exit_tag'] += 'l_e_u '
conditions.append(sell_ema_1)
sell_ema_2 = (
(dataframe['close'] < (dataframe[f"ema_{int(5 * self.sell_length_ema2.value)}"] * 0.05 * self.sell_offset_ema2.value))
)
dataframe.loc[sell_ema_2, 'exit_tag'] += 'l_e_d '
conditions.append(sell_ema_2)
sell_ema_3 = (
(dataframe['close'] < (dataframe[f"ema_{int(5 * self.sell_length_ema3.value)}"] * 0.05 * self.sell_offset_ema3.value))
)
dataframe.loc[sell_ema_3, 'exit_tag'] += 's_e_d '
conditions_short.append(sell_ema_3)
sell_ema_4 = (
(dataframe['close'] > (dataframe[f"ema_{int(5 * self.sell_length_ema4.value)}"] * 0.05 * self.sell_offset_ema4.value))
)
dataframe.loc[sell_ema_4, 'exit_tag'] += 's_e_u '
conditions_short.append(sell_ema_4)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions)
&
add_check,
'exit_long',
]= 1
if conditions_short:
dataframe.loc[
reduce(lambda x, y: x | y, conditions_short)
&
add_check,
'exit_short',
]= 1
return dataframe
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
return self.lev
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
if (current_time - timedelta(minutes=int(self.timeframe_minutes)) >= trade.open_date_utc):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
current_profit = trade.calc_profit_ratio(current_candle['close'])
if current_time - timedelta(minutes=int(self.timeframe_minutes * self.sell_clear_old_trade.value)) >= trade.open_date_utc:
if (current_profit >= (-0.01 * self.sell_clear_old_trade_profit.value)):
return "sell_old_trade"
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