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Cluc4werk.py
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Cluc4werk.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
from pandas import DataFrame
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)
class Cluc4werk(IStrategy):
# Used for "informative pairs"
stake = 'BTC'
fiat = 'USD'
"""
PASTE OUTPUT FROM HYPEROPT HERE
"""
# Buy hyperspace params:
buy_params = {
'bbdelta-close': 0.00793,
'bbdelta-tail': 0.83802,
'close-bblower': 0.0034,
'closedelta-close': 0.00613,
'rocr-1h': 0.64081,
'volume': 21
}
# Sell hyperspace params:
sell_params = {
'sell-bbmiddle-close': 0.97703
}
# ROI table:
minimal_roi = {
"0": 0.0155,
"109": 0.01075,
"393": 0.00771,
"587": 0.00643,
"711": 0.00377,
"770": 0.00114,
"1039": 0
}
# Stoploss:
stoploss = -0.31742
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.31289
trailing_stop_positive_offset = 0.33275
trailing_only_offset_is_reached = True
"""
END HYPEROPT
"""
timeframe = '1m'
# Make sure these match or are not overridden in config
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.0
ignore_roi_if_buy_signal = True
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
"""
Idea is to have "STAKE/USD" and "COIN/USD" as informative pairs as they move inverse of COIN/STAKE.
For example, stake currency is BTC, whitelist is */BTC
Current pair being examined (metadata['pair']) is XLM/BTC
Be able to have informative pairs BTC/USD and XLM/USD available for use with some indicators for all pairs in the whitelist.
Ideally have this work gracefully with a change to the stake/whitelist in the config file.
If a desired informative pair does not exist (e.g. if exchange doesnt trade XLM/USD in this example), simply ignore those indicators without errors.
"""
coin, stake = metadata['pair'].split('/')
informative_pairs += [("ETH/USD", timeframe),
("BTC/USD", timeframe),
]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Set Up Bollinger Bands
mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
dataframe['lower'] = lower
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)
inf_tf = '1h'
"""
informative = self.dp.get_pair_dataframe(pair="ETH/USDT", timeframe="5m")
# ETH/USDT RSI based on 5m candles
informative['rsi'] = ta.RSI(informative, timeperiod=14)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '5m', ffill=True)
"""
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
informative['rocr'] = ta.ROCR(informative, 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['close'] * params['bbdelta-close']) &
dataframe['closedelta'].gt(dataframe['close'] * params['closedelta-close']) &
dataframe['tail'].lt(dataframe['bbdelta'] * params['bbdelta-tail']) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift())
) |
(
(dataframe['close'] < dataframe['ema_slow']) &
(dataframe['close'] < params['close-bblower'] * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * params['volume']))
)),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
params = self.sell_params
dataframe.loc[
#(dataframe['high'].le(dataframe['high'].shift(1))) &
#(dataframe['close'] > dataframe['bb_middleband']) &
(qtpylib.crossed_above((dataframe['close'] * params['sell-bbmiddle-close']),dataframe['bb_middleband'])) &
#(qtpylib.crossed_above(dataframe['close'],dataframe['bb_middleband'])) &
(dataframe['volume'] > 0)
,
'sell'
] = 1
return dataframe