# source: https://raw.githubusercontent.com/kanaxe/freqtrade_kanaxe/b674562eaa4915192ec3a58cf3f86b64f3fba6e4/volume1m.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- from logging import NullHandler, fatal import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame, Series from freqtrade.strategy import IStrategy from freqtrade.strategy import (merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter) from freqtrade.exchange import timeframe_to_prev_date from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from technical.indicators import indicators from freqtrade.persistence import Trade from datetime import datetime class github_kanaxe_freqtrade_kanaxe__volume1m__20211115_132756(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_buy_trend, populate_sell_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". # ROI table: # minimal_roi = { # "0": 0.02106, # "74": 0.01537, # "123": 0.01027, # "137": 0 # } # # Stoploss: # stoploss = -0.03574 minimal_roi = {"25": 0.011} stoploss = -0.015 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.006 trailing_only_offset_is_reached = True # trailing_stop = True # trailing_stop_positive = 0.004 # Optimal timeframe for the strategy. timeframe = '1m' inf_15m = '15m' # informative tf # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. # use_sell_signal = True # sell_profit_only = True # ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 240 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } # Hyperoptable parameters # buy_rsi = IntParameter(low=1, high=30, default=30, space='buy', optimize=True, load=True) # sell_rsi = IntParameter(low=35, high=100, default=40, space='sell', optimize=True, load=True) # use_custom_sell = True # use_custom_stoploss = True def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_15m) for pair in pairs] return informative_pairs def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_15m = self.dp.get_pair_dataframe( pair=metadata['pair'], timeframe=self.inf_15m) stoch_rfast = ta.STOCHRSI(informative_15m, timeperiod=14) informative_15m['rfastd'] = stoch_rfast['fastd'] informative_15m['rfastk'] = stoch_rfast['fastk'] informative_15m['angle'] = ta.LINEARREG_ANGLE(informative_15m['close'], timeperiod=21) informative_15m['lr_middle'] = ta.LINEARREG(informative_15m['close'], timeperiod=25) informative_15m['atr'] = ta.ATR(informative_15m,timeperiod=7) informative_15m['lr_lower1.0'] = informative_15m['lr_middle'] - informative_15m['atr'] informative_15m['angle']=ta.LINEARREG_ANGLE(informative_15m['close'], timeperiod=5) informative_15m['volumeM'] = informative_15m['volume'].rolling(40).mean() return informative_15m def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class. """ dataframe['adx'] = ta.ADX(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['rsi'] = ta.RSI(dataframe, timeperiod=21) dataframe['mfi'] = ta.MFI(dataframe) stoch_fast = ta.STOCHF(dataframe, timeperiod=31) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] stoch_rfast = ta.STOCHRSI(dataframe, timeperiod=30) dataframe['rfastd'] = stoch_rfast['fastd'] dataframe['rfastk'] = stoch_rfast['fastk'] dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=5) dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=5) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['sar'] = ta.SAR(dataframe) dataframe['ohlc4']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 dataframe['hlc3']=(dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 dataframe['hl2']=(dataframe['high'] + dataframe['low'] ) / 2 dataframe['ol']=(dataframe['open'] / dataframe['low']) - 1 dataframe['cl']=dataframe['close'] / dataframe['low'] dataframe['ho']=(dataframe['high'] / dataframe['open']) - 1 dataframe['hma19'] = qtpylib.hma(dataframe['close'], 19) dataframe['hma8'] = qtpylib.hma(dataframe['hl2'], 8) dataframe['cci'] = (ta.CCI(dataframe, timeperiod=21)/2) dataframe['sma60']=ta.SMA(dataframe['close'], timeperiod=60) dataframe['vema']=ta.EMA(dataframe['volume'], timeperiod=34) dataframe['vwma'] = (ta.SMA(dataframe['close']*dataframe['volume'], timeperiod=9)/ta.SMA(dataframe['volume'], timeperiod=9)) dataframe['evwma'] = (ta.EMA(dataframe['close']*dataframe['volume'], timeperiod=9)/ta.EMA(dataframe['volume'], timeperiod=9)) dataframe['angle'] = ta.LINEARREG_ANGLE(dataframe['close'], timeperiod=9) dataframe['lr_middle'] = ta.LINEARREG(dataframe['close'], timeperiod=25) dataframe['atr'] = ta.ATR(dataframe,timeperiod=7) dataframe['lr_lower1.0'] = dataframe['lr_middle'] - dataframe['atr'] dataframe['var'] = ta.VAR(dataframe['ho'], timeperiod=7) dataframe['tsf'] = ta.TSF(dataframe['ho'], timeperiod=7) dataframe['stddev'] = ta.STDDEV(dataframe['ho'], timeperiod=7) dataframe['shom']= ta.SMA(dataframe['ho'], timeperiod=14) dataframe['vshom']=(ta.SMA(dataframe['ho']*dataframe['volume']*dataframe['lr_middle'], timeperiod=14)/ta.SMA(dataframe['volume']*dataframe['lr_middle'], timeperiod=14)) dataframe['solm']= ta.SMA(dataframe['ol'], timeperiod=14) dataframe['vsolm']= (ta.SMA(dataframe['ol']*dataframe['volume'], timeperiod=14)/ta.SMA(dataframe['volume'], timeperiod=14)) dataframe['eshom']= ta.EMA(dataframe['ho'], timeperiod=14) dataframe['evshom']=(ta.EMA(dataframe['ho']*(dataframe['tsf'])*dataframe['volume'], timeperiod=14)/ta.SMA(dataframe['volume']*(dataframe['tsf']), timeperiod=14)) dataframe['esolm']= ta.EMA(dataframe['ol'], timeperiod=14) dataframe['evsolm']= (ta.EMA(dataframe['ol']*dataframe['volume'], timeperiod=14)/ta.EMA(dataframe['volume'], timeperiod=14)) dataframe['volumeM'] = dataframe['volume'].rolling(20).mean() Percent = 0.4 # changeLONGSHORT = 1 dataframe['upsignal']=(ta.EMA(dataframe['close'],timeperiod=7))+((ta.EMA(dataframe['close'],timeperiod=7))*Percent/100) dataframe['downsignal']=(ta.EMA(dataframe['close'],timeperiod=7))-(ta.EMA(dataframe['close'],timeperiod=7)*Percent/100) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_15m = self.informative_15m_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # #print(" {} -- {}".format(dataframe['volume'], dataframe['volume'].rolling(21).mean())) dataframe.loc[ ( # (dataframe['upsignal'].shift(3) < dataframe['hma19'])& # (qtpylib.crossed_above(dataframe['close'] , dataframe['vwma'])) & # (qtpylib.crossed_below(dataframe['sar'] , dataframe['hma19']))& # (dataframe['ol'] > 1.015) # (dataframe['ol'] > 1.0312) & # ((dataframe['ol'] - dataframe['cl']) > 0.0512) & # (dataframe['mfi'] < 20) # (dataframe['rfastk'] < 1)& # (dataframe['plus_di'] < 2.5) # (dataframe['adx'] < 23)& # (dataframe['downsignal'] < dataframe['hma19']) # (dataframe['minus_di'] > dataframe['mfi'])& # (dataframe['minus_di'] >40)& # (dataframe['minus_di'] <49) #((dataframe['adx'] - dataframe['mfi']) < 3) # (qtpylib.crossed_above(dataframe['minus_di'], dataframe['plus_di'])) # (dataframe['angle'] < -50)& # (qtpylib.crossed_above(dataframe['vshom'], dataframe['vsolm'])) # ((dataframe['angle'] < -50) & # (qtpylib.crossed_above(dataframe['angle'], -70))) # | # ((dataframe['angle'] > 0 ) & # (qtpylib.crossed_above(dataframe['angle'], 15))) # (qtpylib.crossed_above(dataframe['close'], dataframe['lr_lower1.0'])) # (dataframe['rfastd'] < 10)& # (dataframe['lr_lower1.0'] < dataframe['downsignal'])& # (dataframe['angle'] < -80)& # (qtpylib.crossed_above(dataframe['rfastd'], dataframe['atr'])) # (dataframe['angle_1h'] < -80)& # (dataframe['angle'] < -80)& # (dataframe['macd'] 0) & (dataframe['volume'] > 0) & (dataframe['close'] > dataframe['close'].shift(1)) & (dataframe['volumeM_15m'] <= dataframe['volume']) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( # (dataframe['downsignal'] > dataframe['hma19'])& # (qtpylib.crossed_above(dataframe['close'], dataframe['hma19'])) # (qtpylib.crossed_above(dataframe['rfastd'], 89)) # ( # (dataframe['mfi'] > 70) # | # (dataframe['angle'] > 87)& # (qtpylib.crossed_below(dataframe['fastd'], 95)) # | # (dataframe['cci'] > 130) # | # ((dataframe['rfastk'] > 98) & (dataframe['minus_di'] < 25)) # ) # (((qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband']))& # (dataframe['rfastd'] > 97)) # | # ((qtpylib.crossed_above(dataframe['rfastk'], dataframe['rfastd']))& # (dataframe['rfastd'] > 97)) # ) # (dataframe['angle_15m'] > 75)& # (dataframe['angle'] > 85)& (dataframe['bb_upperband'] < dataframe['close'])& (dataframe['rfastd_15m'].shift(1) > 99)& (qtpylib.crossed_below(dataframe['rfastd'], dataframe['rfastd_15m'])) ), 'sell'] =1 return dataframe