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ClucHAnix_BB_RPB_MOD_E0V1E_DYNAMIC_TB (1).py
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ClucHAnix_BB_RPB_MOD_E0V1E_DYNAMIC_TB (1).py
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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 ClucHAnix5m(IStrategy):
# Buy hyperspace params:
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": False,
"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 = 0.001
trailing_stop_positive_offset = 0.012
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(-0.500, -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_MOD2_ROI_DYNAMIC_TB(ClucHAnix5m):
process_only_new_candles = True
custom_info_trail_buy = dict()
# Trailing buy parameters
trailing_buy_order_enabled = True
trailing_expire_seconds = 300
# 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': True,
}
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