# source: https://raw.githubusercontent.com/luukien2/cuncon/c9d1d82f764702794aee781c0c7c2fd4f60e7c58/chl.py # -------------------------------------- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame, DatetimeIndex, merge # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa class github_luukien2_cuncon__chl__20200315_032232(IStrategy): minimal_roi = { "0": 1.00 } stoploss = -0.05 timeframe = '5m' resample_factor = 12 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = Lopquethoi.resample(dataframe, self.timeframe, self.resample_factor) ################################################################################## dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['adx'] = ta.ADX(dataframe) dataframe['mfi'] = ta.MFI(dataframe) dataframe['mfi_smooth'] = ta.EMA(dataframe, timeperiod=11, price='mfi') dataframe['cci_smooth'] = ta.EMA(dataframe, timeperiod=11, price='cci') dataframe['rsi_smooth'] = ta.EMA(dataframe, timeperiod=11, price='rsi') ################################################################################## bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_middleband'] = bollinger['mid'] macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] ################################################################################## bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=1.6) dataframe['entry_bb_lowerband'] = bollinger['lower'] dataframe['entry_bb_upperband'] = bollinger['upper'] dataframe['entry_bb_middleband'] = bollinger['mid'] dataframe['bpercent'] = (dataframe['close'] - dataframe['bb_lowerband']) / ( dataframe['bb_upperband'] - dataframe['bb_lowerband']) * 100 dataframe['bsharp'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / ( dataframe['bb_middleband']) dataframe['bsharp_slow'] = ta.SMA(dataframe, price='bsharp', timeperiod=11) dataframe['bsharp_medium'] = ta.SMA(dataframe, price='bsharp', timeperiod=8) dataframe['bsharp_fast'] = ta.SMA(dataframe, price='bsharp', timeperiod=5) ################################################################################## dataframe['mfi_rsi_cci_smooth'] = (dataframe['rsi_smooth'] * 1.125 + dataframe['mfi_smooth'] * 1.125 + dataframe[ 'cci_smooth']) / 3 dataframe['mfi_rsi_cci_smooth'] = ta.TEMA(dataframe, timeperiod=21, price='mfi_rsi_cci_smooth') dataframe['candle_size'] = (dataframe['close'] - dataframe['open']) * ( dataframe['close'] - dataframe['open']) / 2 dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4 dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=200, price='close') dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=100, price='close') dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=50, price='close') return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # protection against pump dump # (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) # # & (dataframe['macd'] < dataframe['macdsignal']) # & (dataframe['macd'] > 0) # & (dataframe['low'] < dataframe['entry_bb_lowerband']) # & (dataframe['low'].shift(1) < dataframe['bb_lowerband'].shift(1)) # & (dataframe['low'].shift(2) < dataframe['bb_lowerband'].shift(2)) # & ( # (dataframe['close'] > dataframe['open']) # | (dataframe['CDLHAMMER'] == 100) # | (dataframe['CDLINVERTEDHAMMER'] == 100) # | (dataframe['CDLDRAGONFLYDOJI'] == 100) # ) # bottom curve detection # & (dataframe['mfi_rsi_cci_smooth'] < 0) # # | ( ( (dataframe['average'].shift(5) > dataframe['average'].shift(4)) & (dataframe['average'].shift(4) > dataframe['average'].shift(3)) & (dataframe['average'].shift(3) > dataframe['average'].shift(2)) & (dataframe['average'].shift(2) > dataframe['average'].shift(1)) & (dataframe['average'].shift(1) < dataframe['average'].shift(0)) & (dataframe['low'].shift(1) < dataframe['bb_middleband']) & (dataframe['cci'].shift(1) < -100) & (dataframe['rsi'].shift(1) < 30) ) | ( (dataframe['low'] < dataframe['bb_middleband']) & (dataframe['cci'] < -200) & (dataframe['rsi'] < 30) & (dataframe['mfi'] < 30) ) | ( (dataframe['mfi'] < 10) & (dataframe['cci'] < -150) & (dataframe['rsi'] < dataframe['mfi']) ) ) & (dataframe['close'] > dataframe) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( ( (dataframe['mfi_rsi_cci_smooth'] > 100) & (dataframe['mfi_rsi_cci_smooth'].shift(1) > dataframe['mfi_rsi_cci_smooth']) & (dataframe['mfi_rsi_cci_smooth'].shift(2) < dataframe['mfi_rsi_cci_smooth'].shift(1)) & (dataframe['mfi_rsi_cci_smooth'].shift(3) < dataframe['mfi_rsi_cci_smooth'].shift(2)) ) | ( Lopquethoi.eight_green_candles(dataframe) ) | ( (dataframe['cci'] > 200) & (dataframe['rsi'] > 70) ) ) ), 'sell'] = 1 return dataframe class Lopquethoi: @staticmethod def seven_green_candles(dataframe): return ( (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) & (dataframe['open'].shift(5) < dataframe['close'].shift(5)) & (dataframe['open'].shift(6) < dataframe['close'].shift(6)) & (dataframe['open'].shift(7) < dataframe['close'].shift(7)) ) @staticmethod def eight_green_candles(dataframe): return ( (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) & (dataframe['open'].shift(5) < dataframe['close'].shift(5)) & (dataframe['open'].shift(6) < dataframe['close'].shift(6)) & (dataframe['open'].shift(7) < dataframe['close'].shift(7)) & (dataframe['open'].shift(8) < dataframe['close'].shift(8)) ) @staticmethod def eight_red_candles(dataframe, shift=0): return ( (dataframe['open'].shift(shift) > dataframe['close'].shift(shift)) & (dataframe['open'].shift(1 + shift) > dataframe['close'].shift(1 + shift)) & (dataframe['open'].shift(2 + shift) > dataframe['close'].shift(2 + shift)) & (dataframe['open'].shift(3 + shift) > dataframe['close'].shift(3 + shift)) & (dataframe['open'].shift(4 + shift) > dataframe['close'].shift(4 + shift)) & (dataframe['open'].shift(5 + shift) > dataframe['close'].shift(5 + shift)) & (dataframe['open'].shift(6 + shift) > dataframe['close'].shift(6 + shift)) & (dataframe['open'].shift(7 + shift) > dataframe['close'].shift(7 + shift)) & (dataframe['open'].shift(8 + shift) > dataframe['close'].shift(8 + shift)) ) @staticmethod def four_green_one_red_candle(dataframe): return ( (dataframe['open'] > dataframe['close']) & (dataframe['open'].shift(1) < dataframe['close'].shift(1)) & (dataframe['open'].shift(2) < dataframe['close'].shift(2)) & (dataframe['open'].shift(3) < dataframe['close'].shift(3)) & (dataframe['open'].shift(4) < dataframe['close'].shift(4)) ) @staticmethod def four_red_one_green_candle(dataframe): return ( (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift(1) > dataframe['close'].shift(1)) & (dataframe['open'].shift(2) > dataframe['close'].shift(2)) & (dataframe['open'].shift(3) > dataframe['close'].shift(3)) & (dataframe['open'].shift(4) > dataframe['close'].shift(4)) ) @staticmethod def resample( dataframe, interval, factor): df = dataframe.copy() df = df.set_index(DatetimeIndex(df['date'])) ohlc_dict = { 'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last' } df = df.resample(str(int(interval[:-1]) * factor) + 'min', plotoschow=ohlc_dict) df['resample_sma'] = ta.SMA(df, timeperiod=25, price='close') df = df.drop(columns=['open', 'high', 'low', 'close']) df = df.resample(interval[:-1] + 'min') df = df.interpolate(method='time') df['date'] = df.index df.index = range(len(df)) dataframe = merge(dataframe, df, on='date', how='left') return dataframe