# source: https://raw.githubusercontent.com/1amcord/freqtrade_settings/b078a9d66f5e88f6ad18d35066241aaf91c31032/strategies/RSIDivergence.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- from datetime import datetime from datetime import timedelta import pandas as pd import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from technical.indicators import atr from freqtrade.strategy import DecimalParameter, IntParameter, BooleanParameter, merge_informative_pair, stoploss_from_open from technical.util import resample_to_interval, resampled_merge from freqtrade.persistence import Trade import logging logger = logging.getLogger(__name__) rangeUpper = 60 rangeLower = 5 use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Elliot Wave Oscillator def EWO(dataframe, sma1_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.SMA(df, timeperiod=sma1_length) sma2 = ta.SMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif def valuewhen(dataframe, condition, source, occurrence): copy = dataframe.copy() copy['colFromIndex'] = copy.index copy = copy.sort_values( by=[condition, 'colFromIndex'], ascending=False).reset_index(drop=True) copy['valuewhen'] = np.where( copy[condition] > 0, copy[source].shift(-occurrence), copy[source]) copy['barrsince'] = copy['colFromIndex'] - \ copy['colFromIndex'].shift(-occurrence) copy.loc[ ( (rangeLower <= copy['barrsince']) & (copy['barrsince'] <= rangeUpper) ), "in_range"] = 1 copy['in_range'] = copy['in_range'].fillna(0) copy = copy.sort_values(by=['colFromIndex'], ascending=True).reset_index(drop=True) return copy['valuewhen'], copy['in_range'] class github_1amcord_freqtrade_settings__RSIDivergence__20220814_101522(IStrategy): INTERFACE_VERSION = 2 # Optimal timeframe for the strategy timeframe = '15m' inf_tf = '4h' # Buy hyperspace params: buy_params = { 'use_bull': True, 'use_hidden_bull': False, 'use_rsi_smma_buy': True, "ewo_high": 5.835, "low_rsi_buy": 40, "high_rsi_buy": 40, "high_smma_rsi_buy": 35, "low_adx_buy": 30, "high_adx_buy": 30, "low_stoch_buy": 20, "high_stoch_buy": 80, "low_osc_buy": 80, "high_osc_buy": 80, "ewo_rol": 7, } # Sell hyperspace params: sell_params = { 'low_rsi_sell': 65, 'low_ewo_sell': 0.5 } # ROI table: minimal_roi = { "0": 10, } # Stoploss: stoploss = -0.24 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.03 trailing_stop_positive_offset = 0.04 trailing_only_offset_is_reached = False use_custom_stoploss = False use_bull = BooleanParameter( default=buy_params['use_bull'], space='buy', optimize=True) use_hidden_bull = BooleanParameter( default=buy_params['use_hidden_bull'], space='buy', optimize=True) use_rsi_smma_buy = BooleanParameter( default=buy_params['use_rsi_smma_buy'], space='buy', optimize=True) # Protection fast_ewo = 5 slow_ewo = 35 ewo_high = DecimalParameter( 0, 7.0, default=buy_params['ewo_high'], space='buy', optimize=False) low_ewo_sell = DecimalParameter( -0.5, 3, decimals=1, default=sell_params['low_ewo_sell'], space='sell', optimize=True) ewo_rol = IntParameter( 0, 10, default=buy_params['ewo_rol'], space='buy', optimize=False) low_rsi_buy = IntParameter( 0, 100, default=buy_params['low_rsi_buy'], space='buy', optimize=False) low_rsi_sell = IntParameter( 40, 100, default=sell_params['low_rsi_sell'], space='sell', optimize=True) high_rsi_buy = IntParameter( 15, 60, default=buy_params['high_rsi_buy'], space='buy', optimize=True) high_smma_rsi_buy = IntParameter( 15, 60, default=buy_params['high_smma_rsi_buy'], space='buy', optimize=True) low_adx_buy = IntParameter( 0, 100, default=buy_params['low_adx_buy'], space='buy', optimize=False) high_adx_buy = IntParameter( 0, 100, default=buy_params['high_adx_buy'], space='buy', optimize=False) low_stoch_buy = IntParameter( 0, 100, default=buy_params['low_stoch_buy'], space='buy', optimize=False) high_stoch_buy = IntParameter( 0, 100, default=buy_params['high_stoch_buy'], space='buy', optimize=False) low_osc_buy = IntParameter( 0, 100, default=buy_params['low_osc_buy'], space='buy', optimize=False) high_osc_buy = IntParameter( 0, 100, default=buy_params['high_osc_buy'], space='buy', optimize=False) # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 50 osc = 'RSI' src = 'close' rsiLen = 14 lbL = 40 lbR = 5 @property def plot_config(self): return { # Main plot indicators (Moving averages, ...) 'main_plot': { f'sar_{self.inf_tf}': {'color': 'lightgreen'}, # 'resample_360_sar': {'color': 'lightblue'}, # 'fastd': {'color': 'lightblue'}, # f'sl_10_{self.atr_len.value}_{self.atr_multiplier_profit_10.value}': {'color': '#ffd700'}, # f'sl_5_{self.atr_len.value}_{self.atr_multiplier_profit_5.value}': {'color': '#fcf75e'}, # f'sl_0_{self.atr_len.value}_{self.atr_multiplier_profit.value}': {'color': '#fdfd96'}, # f'sl_below_0_{self.atr_len.value}_{self.atr_multiplier_no_profit.value}': {'color': '#c3b091'}, # 'sar_4h': {'color': 'blue'}, }, 'subplots': { # Subplots - each dict defines one additional plot "RSI": { 'RSI': {'color': 'red'}, 'SMMA': {'color': 'yellow'} } } } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, self.inf_tf) for pair in pairs] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Informative dataframe informative = self.dp.get_pair_dataframe( pair=metadata['pair'], timeframe=self.inf_tf) informative['sar'] = ta.SAR(informative) dataframe = merge_informative_pair( dataframe, informative, self.timeframe, self.inf_tf, ffill=True) # inf_long = resample_to_interval(dataframe, 360) # inf_long['sar'] = ta.SAR(inf_long) # dataframe = resampled_merge(dataframe, inf_long, fill_na=True) # for val in self.atr_len.range: # dataframe[f'atr_{val}'] = ta.ATR( # dataframe, period=self.atr_len.value) # dataframe['RSI'] = ta.RSI(dataframe[self.src], self.rsiLen) # dataframe['RSI'] = dataframe['RSI'].fillna(0) dataframe['RSI'] = ta.RSI(dataframe, timeperiod=14) stoch = ta.STOCH(dataframe, fastk_period=10, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) dataframe['slowk'] = stoch['slowk'] dataframe['slowd'] = stoch['slowd'] dataframe['osc'] = dataframe[self.osc] # SMMA dataframe['SMMA'] = ta.SMA( ta.RSI(dataframe, timeperiod=5).rolling(3).mean(), period=14 ) # plFound = na(pivotlow(osc, lbL, lbR)) ? false : true dataframe['min'] = dataframe['osc'].rolling(self.lbL).min() dataframe['prevMin'] = np.where(dataframe['min'] > dataframe['min'].shift( ), dataframe['min'].shift(), dataframe['min']) dataframe.loc[ ( (dataframe['osc'].shift(1) == dataframe['prevMin'].shift(1)) & (dataframe['osc'] != dataframe['prevMin']) ), 'plFound'] = 1 # phFound = na(pivothigh(osc, lbL, lbR)) ? false : true dataframe['max'] = dataframe['osc'].rolling(self.lbL).max() dataframe['prevMax'] = np.where(dataframe['max'] < dataframe['max'].shift( ), dataframe['max'].shift(), dataframe['max']) dataframe.loc[ ( (dataframe['osc'].shift(1) == dataframe['prevMax'].shift(1)) & (dataframe['osc'] != dataframe['prevMax']) ), 'phFound'] = 1 # ------------------------------------------------------------------------------ # Regular Bullish # Osc: Higher Low # oscHL = osc[lbR] > valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[1]) dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen( dataframe, 'plFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] > dataframe['valuewhen_plFound_osc']) & (dataframe['inrange_plFound_osc'] == 1) ), 'oscHL'] = 1 # Price: Lower Low # priceLL = low[lbR] < valuewhen(plFound, low[lbR], 1) dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen( dataframe, 'plFound', 'low', 1) dataframe.loc[ (dataframe['low'] < dataframe['valuewhen_plFound_low']), 'priceLL'] = 1 # bullCond = plotBull and priceLL and oscHL and plFound dataframe.loc[ ( (dataframe['priceLL'] == 1) & (dataframe['oscHL'] == 1) & (dataframe['plFound'] == 1) ), 'bullCond'] = 1 # plot( # plFound ? osc[lbR] : na, # offset=-lbR, # title="Regular Bullish", # linewidth=2, # color=(bullCond ? bullColor : noneColor) # ) # plotshape( # bullCond ? osc[lbR] : na, # offset=-lbR, # title="Regular Bullish Label", # text=" Bull ", # style=shape.labelup, # location=location.absolute, # color=bullColor, # textcolor=textColor # ) # //------------------------------------------------------------------------------ # // Hidden Bullish # // Osc: Lower Low # # oscLL = osc[lbR] < valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[1]) dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen( dataframe, 'plFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] < dataframe['valuewhen_plFound_osc']) & (dataframe['inrange_plFound_osc'] == 1) ), 'oscLL'] = 1 # # // Price: Higher Low # # priceHL = low[lbR] > valuewhen(plFound, low[lbR], 1) dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen( dataframe, 'plFound', 'low', 1) dataframe.loc[ (dataframe['low'] > dataframe['valuewhen_plFound_low']), 'priceHL'] = 1 # hiddenBullCond = plotHiddenBull and priceHL and oscLL and plFound dataframe.loc[ ( (dataframe['priceHL'] == 1) & (dataframe['oscLL'] == 1) & (dataframe['plFound'] == 1) ), 'hiddenBullCond'] = 1 # # plot( # plFound ? osc[lbR] : na, # offset=-lbR, # title="Hidden Bullish", # linewidth=2, # color=(hiddenBullCond ? hiddenBullColor : noneColor) # ) # # plotshape( # hiddenBullCond ? osc[lbR] : na, # offset=-lbR, # title="Hidden Bullish Label", # text=" H Bull ", # style=shape.labelup, # location=location.absolute, # color=bullColor, # textcolor=textColor # ) # # //------------------------------------------------------------------------------ # // Regular Bearish # // Osc: Lower High # # oscLH = osc[lbR] < valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[1]) dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen( dataframe, 'phFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] < dataframe['valuewhen_phFound_osc']) & (dataframe['inrange_phFound_osc'] == 1) ), 'oscLH'] = 1 # # // Price: Higher High # # priceHH = high[lbR] > valuewhen(phFound, high[lbR], 1) dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen( dataframe, 'phFound', 'high', 1) dataframe.loc[ (dataframe['high'] > dataframe['valuewhen_phFound_high']), 'priceHH'] = 1 # # bearCond = plotBear and priceHH and oscLH and phFound dataframe.loc[ ( (dataframe['priceHH'] == 1) & (dataframe['oscLH'] == 1) & (dataframe['phFound'] == 1) ), 'bearCond'] = 1 # # plot( # phFound ? osc[lbR] : na, # offset=-lbR, # title="Regular Bearish", # linewidth=2, # color=(bearCond ? bearColor : noneColor) # ) # # plotshape( # bearCond ? osc[lbR] : na, # offset=-lbR, # title="Regular Bearish Label", # text=" Bear ", # style=shape.labeldown, # location=location.absolute, # color=bearColor, # textcolor=textColor # ) # # //------------------------------------------------------------------------------ # // Hidden Bearish # // Osc: Higher High # # oscHH = osc[lbR] > valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[1]) dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen( dataframe, 'phFound', 'osc', 1) dataframe.loc[ ( (dataframe['osc'] > dataframe['valuewhen_phFound_osc']) & (dataframe['inrange_phFound_osc'] == 1) ), 'oscHH'] = 1 # # // Price: Lower High # # priceLH = high[lbR] < valuewhen(phFound, high[lbR], 1) dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen( dataframe, 'phFound', 'high', 1) dataframe.loc[ (dataframe['high'] < dataframe['valuewhen_phFound_high']), 'priceLH'] = 1 # # hiddenBearCond = plotHiddenBear and priceLH and oscHH and phFound dataframe.loc[ ( (dataframe['priceLH'] == 1) & (dataframe['oscHH'] == 1) & (dataframe['phFound'] == 1) ), 'hiddenBearCond'] = 1 # # plot( # phFound ? osc[lbR] : na, # offset=-lbR, # title="Hidden Bearish", # linewidth=2, # color=(hiddenBearCond ? hiddenBearColor : noneColor) # ) # # plotshape( # hiddenBearCond ? osc[lbR] : na, # offset=-lbR, # title="Hidden Bearish Label", # text=" H Bear ", # style=shape.labeldown, # location=location.absolute, # color=bearColor, # textcolor=textColor # )""" # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # dataframe['ADX'] = ta.ADX(dataframe, timeperiod=14) # Average price # dataframe['avg_price'] = ((dataframe['high'] + dataframe['low'])/2) # dataframe['sar'] = ta.SAR(dataframe) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.use_bull.value: conditions.append( ( (dataframe['bullCond'] > 0) & # (dataframe[f'sar_{self.inf_tf}'] < dataframe['close']) & (dataframe['EWO'] > dataframe['EWO'].shift(1).rolling(self.ewo_rol.value).max()) & # (dataframe['resample_360_sar'] < dataframe['close']) & # (dataframe['valuewhen_plFound_osc'] > self.low_osc_buy.value) & # (dataframe['valuewhen_plFound_osc'] < self.high_osc_buy.value) & # (dataframe['EWO'] > self.ewo_high.value) & (dataframe['RSI'] < self.high_rsi_buy.value) & # (dataframe['RSI'] > self.low_rsi_buy.value) & # (dataframe['ADX'] > self.low_adx_buy.value) & # (dataframe['ADX'] < self.high_adx_buy.value) & # (dataframe['slowk'] < self.high_stoch_buy.value) & # (dataframe['slowk'] > self.low_stoch_buy.value) & (dataframe['volume'] > 0) ) ) if self.use_hidden_bull.value: conditions.append( ( (dataframe['hiddenBullCond'] > 0) & # (dataframe[f'sar_{self.inf_tf}'] < dataframe['close']) & (dataframe['EWO'] > dataframe['EWO'].shift(1).rolling(self.ewo_rol.value).max()) & # (dataframe['resample_360_sar'] < dataframe['close']) & # (dataframe['valuewhen_plFound_osc'] > self.low_osc_buy.value) & # (dataframe['valuewhen_plFound_osc'] < self.high_osc_buy.value) & (dataframe['RSI'] < self.high_rsi_buy.value) & # (dataframe['RSI'] > self.low_rsi_buy.value) & # (dataframe['slowk'] < self.high_stoch_buy.value) & # (dataframe['slowk'] > self.low_stoch_buy.value) & # (dataframe['ADX'] > self.low_adx_buy.value) & # (dataframe['ADX'] < self.high_adx_buy.value) & (dataframe['volume'] > 0) ) ) if self.use_rsi_smma_buy.value: conditions.append( ( # (dataframe[f'sar_{self.inf_tf}'] < dataframe['close']) & (dataframe['EWO'] > dataframe['EWO'].shift(1).rolling(self.ewo_rol.value).max()) & (dataframe['RSI'] < self.high_smma_rsi_buy.value) & (qtpylib.crossed_above(dataframe['RSI'], dataframe['SMMA'])) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] conditions.append( (dataframe['EWO'] > self.low_ewo_sell.value) & (dataframe['EWO'] < dataframe['EWO'].shift(1)) & # (dataframe['RSI'] > self.low_rsi_sell.value) & # (qtpylib.crossed_below(dataframe['RSI'], dataframe['SMMA'])) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 # dataframe.to_csv('user_data/csvs/%s_%s.csv' % # (self.__class__.__name__, metadata["pair"].replace("/", "_"))) return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 if (current_profit > 0.2): sl_new = 0.05 elif (current_profit > 0.1): sl_new = 0.03 elif (current_profit > 0.06): sl_new = 0.02 elif (current_profit > 0.03): sl_new = 0.01 return sl_new