PASTE OUTPUT FROM HYPEROPT HERE Can be overridden for specific sub-strategies (stake currencies) at the bottom.
Timeframe
5m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 3.3%, 10m: 2.3%, 40m: 1.0%
Interface Version
N/A
Startup Candles
168
Indicators
4
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from typing import Optional
from functools import reduce
from typing import List
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import pandas as pd
import pandas_ta as pta
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open, RealParameter, IntParameter, BooleanParameter
from pandas import DataFrame, Series
from datetime import datetime, timedelta, timezone
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 fahmibah(IStrategy):
"""
PASTE OUTPUT FROM HYPEROPT HERE
Can be overridden for specific sub-strategies (stake currencies) at the bottom.
"""
# hypered params
buy_params = {
"clucha_enabled": True,
"bbdelta_close": 0.01889,
"bbdelta_tail": 0.72235,
"close_bblower": 0.0127,
"closedelta_close": 0.00916,
"rocr_1h": 0.79492,
"lambo1_enabled": True,
"lambo1_ema_14_factor": 1.054,
"lambo1_rsi_4_limit": 18,
"lambo1_rsi_14_limit": 39,
}
# Sell hyperspace params:
sell_params = {
# custom stoploss params, come from BB_RPB_TSL
"pHSL": -0.10,
"pPF_1": 0.02,
"pPF_2": 0.03,
"pSL_1": 0.015,
"pSL_2": 0.025,
# sell signal params
'sell_fisher': 0.39075,
'sell_bbmiddle_close': 0.99754
}
# ROI table:
minimal_roi = {
"0": 0.033,
"10": 0.023,
"40": 0.01,
}
# Stoploss:
stoploss = -0.10 # 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 = False
sell_profit_only = False
ignore_roi_if_buy_signal = False
# Custom stoploss
use_custom_stoploss = True
process_only_new_candles = True
startup_candle_count = 168
order_types = {
'buy': 'limit',
'sell': 'limit',
'emergencysell': 'limit',
'forcebuy': "limit",
'forcesell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
}
# buy params ClucHA
clucha_enabled = BooleanParameter(default=buy_params['clucha_enabled'], space='buy', optimize=False)
rocr_1h = DecimalParameter(0.5, 1.0, default=0.54904, space='buy', decimals=5, optimize=False)
bbdelta_close = DecimalParameter(0.0005, 0.02, default=0.01965, space='buy', decimals=5, optimize=False)
closedelta_close = DecimalParameter(0.0005, 0.02, default=0.00556, space='buy', decimals=5, optimize=False)
bbdelta_tail = DecimalParameter(0.7, 1.0, default=0.95089, space='buy', decimals=5, optimize=False)
close_bblower = DecimalParameter(0.0005, 0.02, default=0.00799, space='buy', decimals=5, optimize=False)
# buy params lambo1
lambo1_enabled = BooleanParameter(default=buy_params['lambo1_enabled'], space='buy', optimize=False)
lambo1_ema_14_factor = DecimalParameter(0.5, 2.0, default=1.054, space='buy', decimals=3, optimize=True)
lambo1_rsi_4_limit = IntParameter(0, 50, default=buy_params['lambo1_rsi_4_limit'], space='buy', optimize=True)
lambo1_rsi_14_limit = IntParameter(0, 50, default=buy_params['lambo1_rsi_14_limit'], space='buy', optimize=True)
# 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)
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
return informative_pairs
# come from BB_RPB_TSL
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']
# 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
# Clucha
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['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_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)
# lambo1
dataframe['ema_14'] = ta.EMA(dataframe['ha_close'], timeperiod=14)
dataframe['rsi_4'] = ta.RSI(dataframe['ha_close'], timeperiod=4)
dataframe['rsi_14'] = ta.RSI(dataframe['ha_close'], timeperiod=14)
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)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'buy_tag'] = ''
lambo1 = (
bool(self.lambo1_enabled.value) &
(dataframe['ha_close'] < (dataframe['ema_14'] * self.lambo1_ema_14_factor.value)) &
(dataframe['rsi_4'] < self.lambo1_rsi_4_limit.value) &
(dataframe['rsi_14'] < self.lambo1_rsi_14_limit.value)
)
dataframe.loc[lambo1, 'buy_tag'] += 'lambo1_'
conditions.append(lambo1)
clucHA = (
bool(self.clucha_enabled.value) &
(dataframe['rocr_1h'].gt(self.rocr_1h.value)) &
((
(dataframe['lower'].shift().gt(0)) &
(dataframe['bbdelta'].gt(dataframe['ha_close'] * self.bbdelta_close.value)) &
(dataframe['closedelta'].gt(dataframe['ha_close'] * self.closedelta_close.value)) &
(dataframe['tail'].lt(dataframe['bbdelta'] * self.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.close_bblower.value * dataframe['bb_lowerband'])
))
)
dataframe.loc[clucHA, 'buy_tag'] += 'clucHA_'
conditions.append(clucHA)
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(), '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