VERSION MODIFIED BY REUNIWARE (InvestDataSystems@Yahoo.Com / 2021) THIS VERSION CONTAINS HYPEROPT SETTINGS FROM E0V1E (cf. https://discord.gg/Ayvcvs6N )
Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 10.3%, 3m: 5.0%, 5m: 3.4%, 61m: 2.8%
Interface Version
N/A
Startup Candles
200
Indicators
9
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from datetime import datetime, timedelta, timezone
from functools import reduce
from typing import List
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from pandas import DataFrame, Series
# --------------------------------
import logging
import pandas as pd
import numpy as np
from freqtrade.persistence import Trade
import time
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas_ta as pta
import talib.abstract as ta
import technical.indicators as ftt
from freqtrade.persistence import Trade, PairLocks
from freqtrade.strategy import (BooleanParameter, DecimalParameter,
IntParameter, stoploss_from_open, merge_informative_pair)
from skopt.space import Dimension, Integer
logger = logging.getLogger(__name__)
def bollinger_bands(stock_price, window_size, num_of_std):
rolling_mean = stock_price.rolling(window=window_size).mean()
rolling_std = stock_price.rolling(window=window_size).std()
lower_band = rolling_mean - (rolling_std * num_of_std)
return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)
def ha_typical_price(bars):
res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.
return Series(index=bars.index, data=res)
class ClucHAnix_BB_RPB_MOD_E0V1E_ROI(IStrategy):
"""
VERSION MODIFIED BY REUNIWARE (InvestDataSystems@Yahoo.Com / 2021)
THIS VERSION CONTAINS HYPEROPT SETTINGS FROM E0V1E (cf. https://discord.gg/Ayvcvs6N )
"""
class HyperOpt:
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] + params['roi_p4'] + params[
'roi_p5'] + params['roi_p6']
roi_table[params['roi_t6']] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] + params['roi_p4'] + \
params['roi_p5']
roi_table[params['roi_t6'] + params['roi_t5']] = params['roi_p1'] + params['roi_p2'] + params['roi_p3'] + \
params['roi_p4']
roi_table[params['roi_t6'] + params['roi_t5'] + params['roi_t4']] = params['roi_p1'] + params['roi_p2'] + \
params['roi_p3']
roi_table[params['roi_t6'] + params['roi_t5'] + params['roi_t4'] + params['roi_t3']] = params['roi_p1'] + \
params['roi_p2']
roi_table[params['roi_t6'] + params['roi_t5'] + params['roi_t4'] + params['roi_t3'] + params['roi_t2']] = \
params['roi_p1']
roi_table[
params['roi_t6'] + params['roi_t5'] + params['roi_t4'] + params['roi_t3'] + params['roi_t2'] + params[
'roi_t1']] = 0
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
return [
Integer(1, 15, name='roi_t6'),
Integer(1, 45, name='roi_t5'),
Integer(1, 90, name='roi_t4'),
Integer(45, 120, name='roi_t3'),
Integer(45, 180, name='roi_t2'),
Integer(90, 300, name='roi_t1'),
Real(0.005, 0.10, name='roi_p6'),
Real(0.005, 0.07, name='roi_p5'),
Real(0.005, 0.05, name='roi_p4'),
Real(0.005, 0.025, name='roi_p3'),
Real(0.005, 0.01, name='roi_p2'),
Real(0.003, 0.007, name='roi_p1'),
]
buy_params = {
"antipump_threshold": 0.133,
"buy_btc_safe_1d": -0.311,
"clucha_bbdelta_close": 0.04796,
"clucha_bbdelta_tail": 0.93112,
"clucha_close_bblower": 0.01645,
"clucha_closedelta_close": 0.00931,
"clucha_enabled": False,
"clucha_rocr_1h": 0.41663,
"cofi_adx": 8,
"cofi_ema": 0.639,
"cofi_enabled": False,
"cofi_ewo_high": 5.6,
"cofi_fastd": 40,
"cofi_fastk": 13,
"ewo_1_enabled": False,
"ewo_1_rsi_14": 45,
"ewo_1_rsi_4": 7,
"ewo_candles_buy": 13,
"ewo_candles_sell": 19,
"ewo_high": 5.249,
"ewo_high_offset": 1.04116,
"ewo_low": -11.424,
"ewo_low_enabled": True,
"ewo_low_offset": 0.97463,
"ewo_low_rsi_4": 35,
"lambo1_ema_14_factor": 1.054,
"lambo1_enabled": False,
"lambo1_rsi_14_limit": 26,
"lambo1_rsi_4_limit": 18,
"lambo2_ema_14_factor": 0.981,
"lambo2_enabled": True,
"lambo2_rsi_14_limit": 39,
"lambo2_rsi_4_limit": 44,
"local_trend_bb_factor": 0.823,
"local_trend_closedelta": 19.253,
"local_trend_ema_diff": 0.125,
"local_trend_enabled": True,
"nfi32_cti_limit": -1.09639,
"nfi32_enabled": True,
"nfi32_rsi_14": 15,
"nfi32_rsi_4": 49,
"nfi32_sma_factor": 0.93391,
}
# Sell hyperspace params:
sell_params = {
# custom stoploss params, come from BB_RPB_TSL
"pHSL": -0.134,
"pPF_1": 0.02,
"pPF_2": 0.047,
"pSL_1": 0.02,
"pSL_2": 0.046,
'sell-fisher': 0.38414,
'sell-bbmiddle-close': 1.07634
}
# ROI table:
minimal_roi = {
"0": 0.10347601757573865,
"3": 0.050495605759981035,
"5": 0.03350898081823659,
"61": 0.0275218557571848,
"125": 0.011112591523667215,
"292": 0.005185372158403069,
"399": 0
}
# Stoploss:
stoploss = -0.99 # use custom stoploss
# Trailing stop:
trailing_stop = False
trailing_stop_positive = 0.3207
trailing_stop_positive_offset = 0.3849
trailing_only_offset_is_reached = False
"""
END HYPEROPT
"""
timeframe = '5m'
# Make sure these match or are not overridden in config
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
# Custom stoploss
use_custom_stoploss = True
process_only_new_candles = True
startup_candle_count = 200
order_types = {
'buy': 'market',
'sell': 'market',
'emergencysell': 'market',
'forcebuy': "market",
'forcesell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
}
# hard stoploss profit
pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', 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', load=True)
pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True)
# profit threshold 2, SL_2 is used
pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True)
pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True)
# buy param
# ClucHA
clucha_bbdelta_close = DecimalParameter(0.01,0.05, default=buy_params['clucha_bbdelta_close'], decimals=5, space='buy', optimize=True)
clucha_bbdelta_tail = DecimalParameter(0.7, 1.2, default=buy_params['clucha_bbdelta_tail'], decimals=5, space='buy', optimize=True)
clucha_close_bblower = DecimalParameter(0.001, 0.05, default=buy_params['clucha_close_bblower'], decimals=5, space='buy', optimize=True)
clucha_closedelta_close = DecimalParameter(0.001, 0.05, default=buy_params['clucha_closedelta_close'], decimals=5, space='buy', optimize=True)
clucha_rocr_1h = DecimalParameter(0.1, 1.0, default=buy_params['clucha_rocr_1h'], decimals=5, space='buy', optimize=True)
# lambo1
lambo1_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo1_ema_14_factor'], space='buy', optimize=True)
lambo1_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo1_rsi_4_limit'], space='buy', optimize=True)
lambo1_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo1_rsi_14_limit'], space='buy', optimize=True)
# lambo2
lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True)
lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True)
lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True)
# local_uptrend
local_trend_ema_diff = DecimalParameter(0, 0.2, default=buy_params['local_trend_ema_diff'], space='buy', optimize=True)
local_trend_bb_factor = DecimalParameter(0.8, 1.2, default=buy_params['local_trend_bb_factor'], space='buy', optimize=True)
local_trend_closedelta = DecimalParameter(5.0, 30.0, default=buy_params['local_trend_closedelta'], space='buy', optimize=True)
# ewo_1 and ewo_low
ewo_candles_buy = IntParameter(2, 30, default=buy_params['ewo_candles_buy'], space='buy', optimize=True)
ewo_candles_sell = IntParameter(2, 35, default=buy_params['ewo_candles_sell'], space='buy', optimize=True)
ewo_low_offset = DecimalParameter(0.7, 1.2, default=buy_params['ewo_low_offset'], decimals=5, space='buy', optimize=True)
ewo_high_offset = DecimalParameter(0.75, 1.5, default=buy_params['ewo_high_offset'], decimals=5, space='buy', optimize=True)
ewo_high = DecimalParameter(2.0, 15.0, default=buy_params['ewo_high'], space='buy', optimize=True)
ewo_1_rsi_14 = IntParameter(10, 100, default=buy_params['ewo_1_rsi_14'], space='buy', optimize=True)
ewo_1_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_1_rsi_4'], space='buy', optimize=True)
ewo_low_rsi_4 = IntParameter(1, 50, default=buy_params['ewo_low_rsi_4'], space='buy', optimize=True)
ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True)
# cofi
cofi_ema = DecimalParameter(0.6, 1.4, default=buy_params['cofi_ema'] , space='buy', optimize=True)
cofi_fastk = IntParameter(1, 100, default=buy_params['cofi_fastk'], space='buy', optimize=True)
cofi_fastd = IntParameter(1, 100, default=buy_params['cofi_fastd'], space='buy', optimize=True)
cofi_adx = IntParameter(1, 100, default=buy_params['cofi_adx'], space='buy', optimize=True)
cofi_ewo_high = DecimalParameter(1.0, 15.0, default=buy_params['cofi_ewo_high'], space='buy', optimize=True)
# nfi32
nfi32_rsi_4 = IntParameter(1, 100, default=buy_params['nfi32_rsi_4'], space='buy', optimize=True)
nfi32_rsi_14 = IntParameter(1, 100, default=buy_params['nfi32_rsi_4'], space='buy', optimize=True)
nfi32_sma_factor = DecimalParameter(0.7, 1.2, default=buy_params['nfi32_sma_factor'], decimals=5, space='buy', optimize=True)
nfi32_cti_limit = DecimalParameter(-1.2, 0, default=buy_params['nfi32_cti_limit'], decimals=5, space='buy', optimize=True)
buy_btc_safe_1d = DecimalParameter(-0.5, -0.015, default=buy_params['buy_btc_safe_1d'], optimize=True)
antipump_threshold = DecimalParameter(0, 0.4, default=buy_params['antipump_threshold'], space='buy', optimize=True)
ewo_1_enabled = BooleanParameter(default=buy_params['ewo_1_enabled'], space='buy', optimize=True)
ewo_low_enabled = BooleanParameter(default=buy_params['ewo_low_enabled'], space='buy', optimize=True)
cofi_enabled = BooleanParameter(default=buy_params['cofi_enabled'], space='buy', optimize=True)
lambo1_enabled = BooleanParameter(default=buy_params['lambo1_enabled'], space='buy', optimize=True)
lambo2_enabled = BooleanParameter(default=buy_params['lambo2_enabled'], space='buy', optimize=True)
local_trend_enabled = BooleanParameter(default=buy_params['local_trend_enabled'], space='buy', optimize=True)
nfi32_enabled = BooleanParameter(default=buy_params['nfi32_enabled'], space='buy', optimize=True)
clucha_enabled = BooleanParameter(default=buy_params['clucha_enabled'], space='buy', optimize=True)
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
informative_pairs += [("BTC/USDT", "1m")]
informative_pairs += [("BTC/USDT", "1d")]
return informative_pairs
############################################################################
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:
# Heikin Ashi Candles
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_20'] = ta.RSI(dataframe, timeperiod=20)
# CTI
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
# 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)
# Set Up Bollinger Bands
mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2)
dataframe['lower'] = lower
dataframe['mid'] = mid
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['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
# # ClucHA
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs()
dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()
dataframe['bb_lowerband'] = dataframe['lower']
dataframe['bb_middleband'] = dataframe['mid']
dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3)
dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)
# Elliot
dataframe['EWO'] = EWO(dataframe, 50, 200)
rsi = ta.RSI(dataframe)
dataframe["rsi"] = rsi
rsi = 0.1 * (rsi - 50)
dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
inf_tf = '1h'
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
inf_heikinashi = qtpylib.heikinashi(informative)
informative['ha_close'] = inf_heikinashi['close']
informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
### BTC protection
dataframe['btc_1m']= self.dp.get_pair_dataframe('BTC/USDT', timeframe='1m')['close']
btc_1d = self.dp.get_pair_dataframe('BTC/USDT', timeframe='1d')[['date', 'close']].rename(columns={"close": "btc"}).shift(1)
dataframe = merge_informative_pair(dataframe, btc_1d, '1m', '1d', ffill=True)
# Pump strength
dataframe['zema_30'] = ftt.zema(dataframe, period=30)
dataframe['zema_200'] = ftt.zema(dataframe, period=200)
dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30']
#NOTE: dynamic offset
dataframe['perc'] = ((dataframe['high'] - dataframe['low']) / dataframe['low']*100)
dataframe['avg3_perc'] = ta.EMA(dataframe['perc'], 3)
dataframe['norm_perc'] = (dataframe['perc'] - dataframe['perc'].rolling(50).min())/(dataframe['perc'].rolling(50).max()-dataframe['perc'].rolling(50).min())
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'buy_tag'] = ''
dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_buy.value))
dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] = ta.EMA(dataframe, timeperiod=int(self.ewo_candles_sell.value))
is_btc_safe = (
(pct_change(dataframe['btc_1d'], dataframe['btc_1m']).fillna(0) > self.buy_btc_safe_1d.value) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
is_pump_safe = (
(dataframe['pump_strength'] < self.antipump_threshold.value)
)
lambo1 = (
bool(self.lambo1_enabled.value) &
(dataframe['close'] < (dataframe['ema_14'] * self.lambo1_ema_14_factor.value)) &
(dataframe['rsi_4'] < int(self.lambo1_rsi_4_limit.value)) &
(dataframe['rsi_14'] < int(self.lambo1_rsi_14_limit.value))
)
dataframe.loc[lambo1, 'buy_tag'] += 'lambo1_'
conditions.append(lambo1)
lambo2 = (
bool(self.lambo2_enabled.value) &
(dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) &
(dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) &
(dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value))
)
dataframe.loc[lambo2, 'buy_tag'] += 'lambo2_'
conditions.append(lambo2)
local_uptrend = (
bool(self.local_trend_enabled.value) &
(dataframe['ema_26'] > dataframe['ema_14']) &
(dataframe['ema_26'] - dataframe['ema_14'] > dataframe['open'] * self.local_trend_ema_diff.value) &
(dataframe['ema_26'].shift() - dataframe['ema_14'].shift() > dataframe['open'] / 100) &
(dataframe['close'] < dataframe['bb_lowerband2'] * self.local_trend_bb_factor.value) &
(dataframe['closedelta'] > dataframe['close'] * self.local_trend_closedelta.value / 1000 )
)
dataframe.loc[local_uptrend, 'buy_tag'] += 'local_uptrend_'
conditions.append(local_uptrend)
nfi_32 = (
bool(self.nfi32_enabled.value) &
(dataframe['rsi_20'] < dataframe['rsi_20'].shift(1)) &
(dataframe['rsi_4'] < self.nfi32_rsi_4.value) &
(dataframe['rsi_14'] > self.nfi32_rsi_14.value) &
(dataframe['close'] < dataframe['sma_15'] * self.nfi32_sma_factor.value) &
(dataframe['cti'] < self.nfi32_cti_limit.value)
)
dataframe.loc[nfi_32, 'buy_tag'] += 'nfi_32_'
conditions.append(nfi_32)
ewo_1 = (
bool(self.ewo_1_enabled.value) &
(dataframe['rsi_4'] < self.ewo_1_rsi_4.value) &
(dataframe['close'] < (dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] * self.ewo_low_offset.value)) &
(dataframe['EWO'] > self.ewo_high.value) &
(dataframe['rsi_14'] < self.ewo_1_rsi_14.value) &
(dataframe['close'] < (dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value))
)
dataframe.loc[ewo_1, 'buy_tag'] += 'ewo1_'
conditions.append(ewo_1)
ewo_low = (
bool(self.ewo_low_enabled.value) &
(dataframe['rsi_4'] < self.ewo_low_rsi_4.value) &
(dataframe['close'] < (dataframe[f'ma_buy_{self.ewo_candles_buy.value}'] * self.ewo_low_offset.value)) &
(dataframe['EWO'] < self.ewo_low.value) &
(dataframe['close'] < (dataframe[f'ma_sell_{self.ewo_candles_sell.value}'] * self.ewo_high_offset.value))
)
dataframe.loc[ewo_low, 'buy_tag'] += 'ewo_low_'
conditions.append(ewo_low)
cofi = (
bool(self.cofi_enabled.value) &
(dataframe['open'] < dataframe['ema_8'] * self.cofi_ema.value) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) &
(dataframe['fastk'] < self.cofi_fastk.value) &
(dataframe['fastd'] < self.cofi_fastd.value) &
(dataframe['adx'] > self.cofi_adx.value) &
(dataframe['EWO'] > self.cofi_ewo_high.value)
)
dataframe.loc[cofi, 'buy_tag'] += 'cofi_'
conditions.append(cofi)
clucHA = (
bool(self.clucha_enabled.value) &
(dataframe['rocr_1h'].gt(self.clucha_rocr_1h.value)) &
((
(dataframe['lower'].shift().gt(0)) &
(dataframe['bbdelta'].gt(dataframe['ha_close'] * self.clucha_bbdelta_close.value)) &
(dataframe['ha_closedelta'].gt(dataframe['ha_close'] * self.clucha_closedelta_close.value)) &
(dataframe['tail'].lt(dataframe['bbdelta'] * self.clucha_bbdelta_tail.value)) &
(dataframe['ha_close'].lt(dataframe['lower'].shift())) &
(dataframe['ha_close'].le(dataframe['ha_close'].shift()))
) |
(
(dataframe['ha_close'] < dataframe['ema_slow']) &
(dataframe['ha_close'] < self.clucha_close_bblower.value * dataframe['bb_lowerband'])
))
)
dataframe.loc[clucHA, 'buy_tag'] += 'clucHA_'
conditions.append(clucHA)
dataframe.loc[
# is_btc_safe & # broken?
# is_pump_safe &
reduce(lambda x, y: x | y, conditions),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
params = self.sell_params
dataframe.loc[
(dataframe['fisher'] > params['sell-fisher']) &
(dataframe['ha_high'].le(dataframe['ha_high'].shift(1))) &
(dataframe['ha_high'].shift(1).le(dataframe['ha_high'].shift(2))) &
(dataframe['ha_close'].le(dataframe['ha_close'].shift(1))) &
(dataframe['ema_fast'] > dataframe['ha_close']) &
((dataframe['ha_close'] * params['sell-bbmiddle-close']) > dataframe['bb_middleband']) &
(dataframe['volume'] > 0)
,
'sell'
] = 1
return dataframe
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:
trade.sell_reason = sell_reason + "_" + trade.buy_tag
return True
def pct_change(a, b):
return (b - a) / a
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 ClucHAnix_BB_RPB_MOD_E0V1E_ROI_DYNAMIC_TB(ClucHAnix_BB_RPB_MOD_E0V1E_ROI):
process_only_new_candles = True
custom_info_trail_buy = dict()
# Trailing buy parameters
trailing_buy_order_enabled = True
trailing_expire_seconds = 1800
# If the current candle goes above min_uptrend_trailing_profit % before trailing_expire_seconds_uptrend seconds, buy the coin
trailing_buy_uptrend_enabled = True
trailing_expire_seconds_uptrend = 90
min_uptrend_trailing_profit = 0.02
debug_mode = True
trailing_buy_max_stop = 0.01 # stop trailing buy if current_price > starting_price * (1+trailing_buy_max_stop)
trailing_buy_max_buy = 0.002 # buy if price between uplimit (=min of serie (current_price * (1 + trailing_buy_offset())) and (start_price * 1+trailing_buy_max_buy))
init_trailing_dict = {
'trailing_buy_order_started': False,
'trailing_buy_order_uplimit': 0,
'start_trailing_price': 0,
'buy_tag': None,
'start_trailing_time': None,
'offset': 0,
'allow_trailing': False,
}
def trailing_buy(self, pair, reinit=False):
# returns trailing buy info for pair (init if necessary)
if not pair in self.custom_info_trail_buy:
self.custom_info_trail_buy[pair] = dict()
if (reinit or not 'trailing_buy' in self.custom_info_trail_buy[pair]):
self.custom_info_trail_buy[pair]['trailing_buy'] = self.init_trailing_dict.copy()
return self.custom_info_trail_buy[pair]['trailing_buy']
def trailing_buy_info(self, pair: str, current_price: float):
# current_time live, dry run
current_time = datetime.now(timezone.utc)
if not self.debug_mode:
return
trailing_buy = self.trailing_buy(pair)
duration = 0
try:
duration = (current_time - trailing_buy['start_trailing_time'])
except TypeError:
duration = 0
finally:
logger.info(
f"pair: {pair} : "
f"start: {trailing_buy['start_trailing_price']:.4f}, "
f"duration: {duration}, "
f"current: {current_price:.4f}, "
f"uplimit: {trailing_buy['trailing_buy_order_uplimit']:.4f}, "
f"profit: {self.current_trailing_profit_ratio(pair, current_price)*100:.2f}%, "
f"offset: {trailing_buy['offset']}")
def current_trailing_profit_ratio(self, pair: str, current_price: float) -> float:
trailing_buy = self.trailing_buy(pair)
if trailing_buy['trailing_buy_order_started']:
return (trailing_buy['start_trailing_price'] - current_price) / trailing_buy['start_trailing_price']
else:
return 0
def trailing_buy_offset(self, dataframe, pair: str, current_price: float):
# return rebound limit before a buy in % of initial price, function of current price
# return None to stop trailing buy (will start again at next buy signal)
# return 'forcebuy' to force immediate buy
# (example with 0.5%. initial price : 100 (uplimit is 100.5), 2nd price : 99 (no buy, uplimit updated to 99.5), 3price 98 (no buy uplimit updated to 98.5), 4th price 99 -> BUY
current_trailing_profit_ratio = self.current_trailing_profit_ratio(pair, current_price)
last_candle = dataframe.iloc[-1]
adapt = abs((last_candle['norm_perc']))
default_offset = 0.003 * (1 + adapt) #NOTE: default_offset 0.003 <--> 0.006
#default_offset = adapt*0.01
trailing_buy = self.trailing_buy(pair)
if not trailing_buy['trailing_buy_order_started']:
return default_offset
# example with duration and indicators
# dry run, live only
last_candle = dataframe.iloc[-1]
current_time = datetime.now(timezone.utc)
trailing_duration = current_time - trailing_buy['start_trailing_time']
if trailing_duration.total_seconds() > self.trailing_expire_seconds:
if ((current_trailing_profit_ratio > 0) and (last_candle['buy'] == 1)):
# more than 1h, price under first signal, buy signal still active -> buy
return 'forcebuy'
else:
# wait for next signal
return None
elif (self.trailing_buy_uptrend_enabled and (trailing_duration.total_seconds() < self.trailing_expire_seconds_uptrend) and (current_trailing_profit_ratio < (-1 * self.min_uptrend_trailing_profit))):
# less than 90s and price is rising, buy
return 'forcebuy'
if current_trailing_profit_ratio < 0:
# current price is higher than initial price
return default_offset
trailing_buy_offset = {
0.06: 0.02,
0.03: 0.01,
0: default_offset,
}
for key in trailing_buy_offset:
if current_trailing_profit_ratio > key:
return trailing_buy_offset[key]
return default_offset
# end of trailing buy parameters
# -----------------------------------------------------
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = super().populate_indicators(dataframe, metadata)
self.trailing_buy(metadata['pair'])
return dataframe
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool:
val = super().confirm_trade_entry(pair, order_type, amount, rate, time_in_force, **kwargs)
if val:
if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
val = False
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if(len(dataframe) >= 1):
last_candle = dataframe.iloc[-1].squeeze()
current_price = rate
trailing_buy = self.trailing_buy(pair)
trailing_buy_offset = self.trailing_buy_offset(dataframe, pair, current_price)
if trailing_buy['allow_trailing']:
if (not trailing_buy['trailing_buy_order_started'] and (last_candle['buy'] == 1)):
# start trailing buy
trailing_buy['trailing_buy_order_started'] = True
trailing_buy['trailing_buy_order_uplimit'] = last_candle['close']
trailing_buy['start_trailing_price'] = last_candle['close']
trailing_buy['buy_tag'] = last_candle['buy_tag']
trailing_buy['start_trailing_time'] = datetime.now(timezone.utc)
trailing_buy['offset'] = 0
self.trailing_buy_info(pair, current_price)
logger.info(f'start trailing buy for {pair} at {last_candle["close"]}')
elif trailing_buy['trailing_buy_order_started']:
if trailing_buy_offset == 'forcebuy':
# buy in custom conditions
val = True
ratio = "%.2f" % ((self.current_trailing_profit_ratio(pair, current_price)) * 100)
self.trailing_buy_info(pair, current_price)
logger.info(f"price OK for {pair} ({ratio} %, {current_price}), order may not be triggered if all slots are full")
elif trailing_buy_offset is None:
# stop trailing buy custom conditions
self.trailing_buy(pair, reinit=True)
logger.info(f'STOP trailing buy for {pair} because "trailing buy offset" returned None')
elif current_price < trailing_buy['trailing_buy_order_uplimit']:
# update uplimit
old_uplimit = trailing_buy["trailing_buy_order_uplimit"]
self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'] = min(current_price * (1 + trailing_buy_offset), self.custom_info_trail_buy[pair]['trailing_buy']['trailing_buy_order_uplimit'])
self.custom_info_trail_buy[pair]['trailing_buy']['offset'] = trailing_buy_offset
self.trailing_buy_info(pair, current_price)
logger.info(f'update trailing buy for {pair} at {old_uplimit} -> {self.custom_info_trail_buy[pair]["trailing_buy"]["trailing_buy_order_uplimit"]}')
elif current_price < (trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy)):
# buy ! current price > uplimit && lower thant starting price
val = True
ratio = "%.2f" % ((self.current_trailing_profit_ratio(pair, current_price)) * 100)
self.trailing_buy_info(pair, current_price)
logger.info(f"current price ({current_price}) > uplimit ({trailing_buy['trailing_buy_order_uplimit']}) and lower than starting price price ({(trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_buy))}). OK for {pair} ({ratio} %), order may not be triggered if all slots are full")
elif current_price > (trailing_buy['start_trailing_price'] * (1 + self.trailing_buy_max_stop)):
# stop trailing buy because price is too high
self.trailing_buy(pair, reinit=True)
self.trailing_buy_info(pair, current_price)
logger.info(f'STOP trailing buy for {pair} because of the price is higher than starting price * {1 + self.trailing_buy_max_stop}')
else:
# uplimit > current_price > max_price, continue trailing and wait for the price to go down
self.trailing_buy_info(pair, current_price)
logger.info(f'price too high for {pair} !')
else:
logger.info(f"Wait for next buy signal for {pair}")
if (val == True):
self.trailing_buy_info(pair, rate)
self.trailing_buy(pair, reinit=True)
logger.info(f'STOP trailing buy for {pair} because I buy it')
return val
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = super().populate_buy_trend(dataframe, metadata)
if self.trailing_buy_order_enabled and self.config['runmode'].value in ('live', 'dry_run'):
last_candle = dataframe.iloc[-1].squeeze()
trailing_buy = self.trailing_buy(metadata['pair'])
if (last_candle['buy'] == 1):
if not trailing_buy['trailing_buy_order_started']:
open_trades = Trade.get_trades([Trade.pair == metadata['pair'], Trade.is_open.is_(True), ]).all()
if not open_trades:
logger.info(f"Set 'allow_trailing' to True for {metadata['pair']} to start trailing!!!")
# self.custom_info_trail_buy[metadata['pair']]['trailing_buy']['allow_trailing'] = True
trailing_buy['allow_trailing'] = True
initial_buy_tag = last_candle['buy_tag'] if 'buy_tag' in last_candle else 'buy signal'
dataframe.loc[:, 'buy_tag'] = f"{initial_buy_tag} (start trail price {last_candle['close']})"
else:
if (trailing_buy['trailing_buy_order_started'] == True):
logger.info(f"Continue trailing for {metadata['pair']}. Manually trigger buy signal!!")
dataframe.loc[:,'buy'] = 1
dataframe.loc[:, 'buy_tag'] = trailing_buy['buy_tag']
# dataframe['buy'] = 1
return dataframe