# source: https://raw.githubusercontent.com/songschadt/deadzone/3058757173ec79257a1d50c5dd301561ad1e6640/user_data/strategies/deadzone.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame # noqa from datetime import datetime # noqa from typing import Optional, Union # noqa import pandas_ta as pdta from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, stoploss_from_open) from freqtrade.exchange import timeframe_to_prev_date # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta import freqtrade.vendor.qtpylib.indicators as qtpylib ###### Bollinger band function def calc_BBLower(source, length, mult): basis = ta.SMA(source, int(length)) dev = mult * ta.STDDEV(source, int(length)) return basis - dev def calc_BBUpper(source, length, mult): basis = ta.SMA(source, int(length)) dev = mult * ta.STDDEV(source, int(length)) return basis + dev class Github_songschadt_deadzone__deadzone__20221003_151751(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Optimal timeframe for the strategy. timeframe = '15m' # Can this strategy go short? can_short: bool = True # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "60": 0.1, "30": 1, "0": 1, } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -1 # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.0 # Disabled / not configured # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False use_custom_stoploss = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 #______________________________________________________________________________ # START STRATEGY PARAMS #_____________________________________________________________________________ takeLong = True takeExtra = True takeShort = True # DAYS OF WEEEK FILTER. These days will trade #0 = Monday #6 = Sunday # the following list excludes saturday and sunday from trading # this can be optimized if dezired but currently is not in the optimization space active_days_of_week = [0,1,2,3,4] cooldownPeriod = IntParameter(0, 100, default=0, space="buy", optimize=False) #TP : RR riskLongMultip = DecimalParameter(0.1, 10, default=1.0, space="buy", decimals=1, optimize = True) riskShortMultip = DecimalParameter(0.1, 10, default=1.0, space="buy", optimize = False, decimals=1) ## ATR BAND PARAMS ##---------- sourceUpper = 'close' sourceLower = 'close' atrMultUpper = DecimalParameter(0.1, 3.5, default=2.0, space="buy", decimals=1) #ATR Long Stoploss atrMultLower = DecimalParameter(0.1, 3.5, default=2.0, space="buy", optimize= False, decimals=1) #ATR Shor tStoploss atrPeriod = IntParameter(10,20, default=14, optimize = False) # ATR length atrPeriods = [14] ## WAE Period bollinger_band_lengths = np.arange(17,24,1) bb_mults = np.arange(1,21,1) Github_songschadt_deadzone__deadzone__20221003_151751multipliers = np.around(np.arange(4.0,9.0,0.1), 1) ### uncomment for fixed values during single backtest. (dont use in optimmization) ###---------------------------- # bollinger_band_lengths = [20] # bb_mults = [2] # Github_songschadt_deadzone__deadzone__20221003_151751multipliers = [7.2] ###---------------------------- #Sensitivity = IntParameter(50, 250, default = 50, space = "buy", optimize = True) Sensitivity = CategoricalParameter([50, 250], default = 50, space = "buy", optimize = True) fastLength = IntParameter(1, 250, default = 25, space = "buy", optimize = False) #FastEMALength slowLength = IntParameter(1, 250, default = 150, space = "buy", optimize = False) #SlowEMALength bb_mult= IntParameter(bb_mults[0], bb_mults[-1], default=bb_mults[0], space="buy", optimize= False) # DeadZone Multiplier channelLength = IntParameter( bollinger_band_lengths[0], bollinger_band_lengths[-1], default = bollinger_band_lengths[0], space = "buy", optimize = True) # BB Length Github_songschadt_deadzone__deadzone__20221003_151751multiplier = DecimalParameter(Github_songschadt_deadzone__deadzone__20221003_151751multipliers[0], Github_songschadt_deadzone__deadzone__20221003_151751multipliers[-1], default=Github_songschadt_deadzone__deadzone__20221003_151751multipliers[0], space="buy", optimize= True, decimals=1) # DeadZone Multiplier #______________________________________________________________________________ # END STRATEGY PARAMS #______________________________________________________________________________ # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } @property def plot_config(self): return { "main_plot": {}, "subplots": { "Github_songschadt_deadzone__deadzone__20221003_151751": { "Github_songschadt_deadzone__deadzone__20221003_1517514.5": { "color": "#ffffff", "type": "line" }, "e1": { "color": "#0000ff", "type": "line" } } } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: ##### ## DEADZONE ##### for guy in self.Github_songschadt_deadzone__deadzone__20221003_151751multipliers: dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(guy)] = pdta.rma(ta.TRANGE(dataframe), 100) * guy ##### # MACD ##### macd = ta.MACD(dataframe, fastperiod=self.fastLength.value, slowperiod=self.slowLength.value) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] ##### ## ATR ##### for guy in self.atrPeriods: dataframe['atr_' + str(guy)] = ta.ATR(dataframe, guy) # dataframe['atr_' + str(guy) + '_long_stop'] = dataframe['close'] - (dataframe['atr_' + str(guy)] * self.atrMultLower.value) # dataframe['atr_' + str(guy) + '_short_stop'] = dataframe['close'] + (dataframe['atr_' + str(guy)] * self.atrMultUpper.value) # dataframe['atr_' + str(guy) + '_long_TP'] = (self.riskLongMultip.value * dataframe['atr_' + str(guy) + '_long_stop']) # dataframe['atr_' + str(guy) + '_short_TP'] = (self.riskShortMultip.value * dataframe['atr_' + str(guy) + '_short_stop']) ##### # Bollinger Bands ##### for bblen in self.bollinger_band_lengths: for mult in self.bb_mults: mod = str(bblen) mod2 = str(mult) #bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=bblen, stds=2) dataframe['bb_lowerband_' + mod + '_' + mod2] = calc_BBLower(dataframe['close'], bblen, mult) dataframe['bb_upperband_' + mod + '_' + mod2] = calc_BBUpper(dataframe['close'], bblen, mult) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ #dataframe['t1'] = (dataframe['macd'] - dataframe['macd'].shift(1)) * self.Sensitivity.value dataframe['e1'] = (dataframe['bb_upperband_' + str(self.channelLength.value) + '_' + str(self.bb_mult.value)] - dataframe['bb_lowerband_' + str(self.channelLength.value) + '_' + str(self.bb_mult.value)]) #dataframe['trendUown'] = dataframe['t1'] if dataframe['t1'] < 0 else 0 ## comment out before optimizing dataframe['bc1'] = (qtpylib.crossed_above(dataframe['e1'], dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)]) | (dataframe['e1'] > dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)])) dataframe['bc2'] = (((dataframe['macd'] - dataframe['macd'].shift(1)) * self.Sensitivity.value) > 0 ) dataframe['bc3'] = (dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)]) dataframe.loc[ ( (qtpylib.crossed_above(dataframe['e1'], dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)]) | (dataframe['e1'] > dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)])) & # longZoneCond (((dataframe['macd'] - dataframe['macd'].shift(1)) * self.Sensitivity.value) > 0 ) & #trendUp > 0 ########### ## Checks for active days of week will not trade if day of week is not in self.active_days_of_week ( (dataframe['date'].dt.dayofweek == 0 if 0 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 1 if 1 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 2 if 2 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 3 if 3 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 4 if 4 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 5 if 5 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 6 if 6 in self.active_days_of_week else False) ) & (dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)] != 0) & # DEADZONE != 0 (self.takeLong) #(dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_long'] = 1 # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info) dataframe.loc[ ( (qtpylib.crossed_above(dataframe['e1'], dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)]) | (dataframe['e1'] > dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)])) & # longZoneCond (((dataframe['macd'] - dataframe['macd'].shift(1)) * self.Sensitivity.value) < 0 ) & #trendDown < 0 ########### ## Checks for active days of week will not trade if day of week is not in self.active_days_of_week ( (dataframe['date'].dt.dayofweek == 0 if 0 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 1 if 1 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 2 if 2 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 3 if 3 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 4 if 4 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 5 if 5 in self.active_days_of_week else False) | (dataframe['date'].dt.dayofweek == 6 if 6 in self.active_days_of_week else False) ) & (dataframe['Github_songschadt_deadzone__deadzone__20221003_151751' + str(self.Github_songschadt_deadzone__deadzone__20221003_151751multiplier.value)] != 0) & # DEADZONE != 0 (self.takeShort) #(dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ ## put these in here so they can be hyperopted. will slow down optimization runs... dataframe['exit_long'] = 0 dataframe['exit_short'] = 0 # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info) """ dataframe.loc[ ( (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) & # Signal: RSI crosses above buy_rsi (dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising (dataframe['volume'] > 0) # Make sure Volume is not 0 ),v 'exit_short'] = 1 """ return dataframe def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # Look up trade Open candle. trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) trade_candle = dataframe.loc[dataframe['date'] == trade_date] trade_entry_price = trade.open_rate atrLongStop =trade.open_rate - (trade_candle['atr_' + str(self.atrPeriod.value)] * self.atrMultLower.value) atrShortStop =trade.open_rate + (trade_candle['atr_' + str(self.atrPeriod.value)] * self.atrMultUpper.value) longRisk = trade_candle['close'] - atrLongStop #longRisk = -1* (trade_candle['atr_' + str(self.atrPeriod.value)] * self.atrMultLower.value) longTp = trade_entry_price + (self.riskLongMultip.value * longRisk) shortRisk = trade_candle['close'] - atrShortStop #shortRisk = (trade_candle['atr_' + str(self.atrPeriod.value)] * self.atrMultUpper.value) shortTp = trade_entry_price + (self.riskShortMultip.value * shortRisk) if ((len(longTp) > 0) and (current_rate >= longTp.item()) and (trade.is_short == False)): return 'long_TP_hit_' + str(longTp.item()) if ((len(shortTp) > 0) and (current_rate <= shortTp.item()) and (trade.is_short == True)): return 'short_TP_hit_' + str(shortTp.item()) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() # Look up trade Open candle. trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) trade_candle = dataframe.loc[dataframe['date'] == trade_date] ##### for trailing stop # but should use ATR of current candle #atrLongStop = trade.open_rate - (trade_candle['atr_' + str(self.atrPeriod.value)] * self.atrMultLower.value) #atrShortStop = trade.open_rate + (trade_candle['atr_' + str(self.atrPeriod.value)] * self.atrMultUpper.value) atrLongStop = -1 * (trade_candle['atr_' + str(self.atrPeriod.value)] * self.atrMultLower.value) / trade.open_rate atrShortStop = -1* (trade_candle['atr_' + str(self.atrPeriod.value)] * self.atrMultUpper.value) / trade.open_rate # Sell if price is < TP point # Use parabolic sar as absolute stoploss price #stoploss_price = dataframe['close'] - (dataframe['atr'] * self.atrMultiUpper.value) # Convert absolute price to percentage relative to current_rate ########### ## this is a trailing stop. ########### # if ((len(atrLongStop) > 0) and (atrLongStop.item() < current_rate) and (trade.is_short == False)): # # return (atrLongStop.item() / current_rate) - 1 # # if ((len(atrShortStop) > 0) and (atrShortStop.item() > current_rate) and (trade.is_short == True)): # # return (atrShortStop.item() / current_rate) - 1 ########### ## END trailing stop ########### if ((len(atrLongStop) > 0) and (trade.is_short == False)): ##print("Stoploss percent is:" + str(float(atrLongStop.item()))) ##print('stoploss from open: ' + str(stoploss_from_open(float(atrLongStop.item()), current_profit))) ##print("current_profit is: " + str(current_profit)) return stoploss_from_open(float(atrLongStop.item()), current_profit) if ((len(atrShortStop) > 0) and (trade.is_short == True)): ##print("Stoploss percent is:" + str(float(atrShortStop.item()))) ##print('stoploss from open: ' + str(stoploss_from_open(float(atrShortStop.item()), current_profit))) ##print("current_profit is: " + str(current_profit)) return stoploss_from_open(float(atrShortStop.item()), current_profit) # return maximum stoploss value, keeping current stoploss price unchanged return 1