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ClucHAnix.py
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ClucHAnix.py
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open
from pandas import DataFrame, Series
from datetime import datetime
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(IStrategy):
"""
PASTE OUTPUT FROM HYPEROPT HERE
Can be overridden for specific sub-strategies (stake currencies) at the bottom.
"""
buy_params = {
'bbdelta-close': 0.01965,
'bbdelta-tail': 0.95089,
'close-bblower': 0.00799,
'closedelta-close': 0.00556,
'rocr-1h': 0.54904
}
# Sell hyperspace params:
sell_params = {
# custom stoploss params, come from BB_RPB_TSL
"pHSL": -0.35,
"pPF_1": 0.02,
"pPF_2": 0.05,
"pSL_1": 0.02,
"pSL_2": 0.04,
'sell-fisher': 0.38414,
'sell-bbmiddle-close': 1.07634
}
# ROI table:
minimal_roi = {
"0": 100
}
# 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 = '1m'
# 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 = 168
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)
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
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']
# 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
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_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3)
dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)
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)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
params = self.buy_params
dataframe.loc[
(
dataframe['rocr_1h'].gt(params['rocr-1h'])
) &
((
(dataframe['lower'].shift().gt(0)) &
(dataframe['bbdelta'].gt(dataframe['ha_close'] * params['bbdelta-close'])) &
(dataframe['closedelta'].gt(dataframe['ha_close'] * params['closedelta-close'])) &
(dataframe['tail'].lt(dataframe['bbdelta'] * params['bbdelta-tail'])) &
(dataframe['ha_close'].lt(dataframe['lower'].shift())) &
(dataframe['ha_close'].le(dataframe['ha_close'].shift()))
) |
(
(dataframe['ha_close'] < dataframe['ema_slow']) &
(dataframe['ha_close'] < params['close-bblower'] * dataframe['bb_lowerband'])
)),
'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