# source: https://raw.githubusercontent.com/ShabbirHasan1/MultiMA_TSL/832377c981e0cf673eaf46a9b2c9ed5202b6cefa/user_data/strategies/MultiMA_TSL/MultiMA_TSL4b.py import freqtrade.vendor.qtpylib.indicators as qtpylib from typing import Dict, List, Optional import numpy as np import talib.abstract as ta from freqtrade.strategy import IStrategy, informative from freqtrade.strategy import (merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, BooleanParameter, timeframe_to_minutes) from pandas import DataFrame, Series from functools import reduce from freqtrade.persistence import Trade, PairLocks from datetime import datetime, timedelta, timezone from freqtrade.exchange import timeframe_to_prev_date from technical.indicators import zema import math import pandas_ta as pta import logging import time logger = logging.getLogger(__name__) ########################################################################################################### ## MultiMA_TSL, modded by stash86, based on SMAOffsetProtectOptV1 (modded by Perkmeister) ## ## Based on @Lamborghini Store's SMAOffsetProtect strat, heavily based on @tirail's original SMAOffset## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## This strategy is available on https://patreon.com/stash86 ## ## ## ## Thanks to ## ## - Perkmeister, for their snippets for the sell signals and decaying EMA sell ## ## - ChangeToTower, for the PMax idea ## ## - JimmyNixx, for their snippet to limit close value from the peak (that I modify into 5m tf check) ## ## - froggleston, for the Heikinashi check snippet from Cryptofrog ## ## ## ## ## ## ## ########################################################################################################### # I hope you do enough testing before proceeding, either backtesting and/or dry run. # Any profits and losses are all your responsibility class github_ShabbirHasan1_MultiMA_TSL__MultiMA_TSL4b__20220901_015620(IStrategy): def version(self) -> str: return "v4b" INTERFACE_VERSION = 2 buy_params = { "base_nb_candles_buy_ema": 32, "base_nb_candles_buy_ema2": 73, "low_offset_ema": 1.014, "low_offset_ema2": 1.054, "base_nb_candles_buy_hma": 30, "low_offset_hma": 0.953, "base_nb_candles_buy_hma2": 66, "low_offset_hma2": 0.904, "base_nb_candles_buy_vwma": 26, "low_offset_vwma": 0.949, "base_nb_candles_buy_vwma2": 27, "low_offset_vwma2": 0.982, "base_nb_candles_buy_vwma3": 69, "low_offset_vwma3": 0.932, "base_nb_candles_buy_vwma4": 75, "low_offset_vwma4": 0.903, "rsi_buy_vwma": 66, "rsi_fast_buy_vwma": 34, "buy_rsi_4_vwma3": 63, "buy_rsi_vwma3": 62, "buy_rsx_4_vwma3": 69, "buy_rsx_vwma3": 58, "ewo_high": 5.9, "ewo_high2": 6.1, "ewo_low": -9.6, "ewo_low2": -16.8, "rsi_buy": 63, "rsi_buy2": 48, "rsx_buy": 64, "rsx_buy2": 53, "rsx_4_buy": 36, "rsx_4_buy2": 35, "base_nb_candles_ema_sell": 31, "high_offset_sell_ema": 1.0, "base_nb_candles_ema_sell2": 20, "high_offset_sell_ema2": 1.018, "buy_length_volatility": 11, "buy_max_volatility": 1.65, "buy_length_volatility2": 16, "buy_max_volatility2": 1.67, "rsi_btc_15m_2": 15, "rsi_btc_5m_2": 13, } sell_params = { "min_rsi_sell": 47, "min_rsi_sell_2": 43, "base_nb_candles_ema_sell3": 83, "high_offset_sell_ema3": 0.96, "base_nb_candles_ema_sell4": 126, "high_offset_sell_ema4": 0.901, "base_nb_candles_ema_sell5": 134, "high_offset_sell_ema5": 1.048, "base_nb_candles_ema_sell6": 47, "high_offset_sell_ema6": 1.05, "base_nb_candles_ema_sell7": 113, "high_offset_sell_ema7": 0.948, "base_nb_candles_ema_sell8": 16, "high_offset_sell_ema8": 0.979, } # ROI table: minimal_roi = { "0": 100 } stoploss = -0.119 dummy = IntParameter(20, 70, default=61, space='buy', optimize=False) optimize_buy_ema = False base_nb_candles_buy_ema = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_ema'], space='buy', optimize=optimize_buy_ema) low_offset_ema = DecimalParameter(0.9, 1.1, default=buy_params['low_offset_ema'], space='buy', optimize=optimize_buy_ema) optimize_buy_ema2 = False base_nb_candles_buy_ema2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_ema2'], space='buy', optimize=optimize_buy_ema2) low_offset_ema2 = DecimalParameter(0.9, 1.1, default=buy_params['low_offset_ema2'], space='buy', optimize=optimize_buy_ema2) optimize_buy_hma = False base_nb_candles_buy_hma = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_hma'], space='buy', optimize=optimize_buy_hma) low_offset_hma = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_hma'], space='buy', optimize=optimize_buy_hma) optimize_buy_vwma = False base_nb_candles_buy_vwma = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_vwma'], space='buy', optimize=optimize_buy_vwma) low_offset_vwma = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_vwma'], space='buy', optimize=optimize_buy_vwma) optimize_buy_vwma2 = False base_nb_candles_buy_vwma2 = IntParameter(5, 80, default=buy_params['base_nb_candles_buy_vwma2'], space='buy', optimize=optimize_buy_vwma2) low_offset_vwma2 = DecimalParameter(0.9, 0.99, default=buy_params['low_offset_vwma2'], space='buy', optimize=optimize_buy_vwma2) optimize_buy_vwma3 = False base_nb_candles_buy_vwma3 = IntParameter(5, 80, default=6, space='buy', optimize=optimize_buy_vwma3) low_offset_vwma3 = DecimalParameter(0.9, 0.99, default=0.95, space='buy', optimize=optimize_buy_vwma3) optimize_buy_vwma4 = False base_nb_candles_buy_vwma4 = IntParameter(5, 80, default=6, space='buy', optimize=optimize_buy_vwma4) low_offset_vwma4 = DecimalParameter(0.9, 0.99, default=0.95, space='buy', optimize=optimize_buy_vwma4) optimize_rsi_buy_vwma = False rsi_buy_vwma = IntParameter(30, 70, default=50, space='buy', optimize=optimize_rsi_buy_vwma) rsi_fast_buy_vwma = IntParameter(30, 70, default=50, space='buy', optimize=optimize_rsi_buy_vwma) # Protection ewo_check_optimize = False ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, decimals = 1, space='buy', optimize=ewo_check_optimize) ewo_high = DecimalParameter(2.0, 12.0, default=6.0, decimals = 1, space='buy', optimize=ewo_check_optimize) ewo_low2 = DecimalParameter(-20.0, -8.0, default=-20.0, decimals = 1, space='buy', optimize=ewo_check_optimize) ewo_high2 = DecimalParameter(2.0, 12.0, default=6.0, decimals = 1, space='buy', optimize=ewo_check_optimize) rsi_buy_optimize = False rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize) rsi_buy2 = IntParameter(30, 70, default=50, space='buy', optimize=rsi_buy_optimize) buy_rsi_fast = IntParameter(0, 50, default=35, space='buy', optimize=False) optimize_rsx_buy = False rsx_buy = IntParameter(30, 70, default=50, space='buy', optimize=optimize_rsx_buy) rsx_buy2 = IntParameter(30, 70, default=50, space='buy', optimize=optimize_rsx_buy) optimize_rsx_4_buy = False rsx_4_buy = IntParameter(30, 70, default=50, space='buy', optimize=optimize_rsx_4_buy) rsx_4_buy2 = IntParameter(30, 70, default=50, space='buy', optimize=optimize_rsx_4_buy) buy_rsx_vwma3 = IntParameter(10, 70, default=50, optimize=False) buy_rsx_4_vwma3 = IntParameter(10, 70, default=50, optimize=False) buy_rsi_vwma3 = IntParameter(10, 70, default=50, optimize=False) buy_rsi_4_vwma3 = IntParameter(10, 70, default=50, optimize=False) distance_max_close = DecimalParameter(1.0, 1.2, default=1.06, decimals = 2, space='buy', optimize=False) distance_max_close2 = DecimalParameter(1.0, 1.2, default=1.09, decimals = 2, space='buy', optimize=False) optimize_buy_volatility = False buy_length_volatility = IntParameter(10, 200, default=72, space='buy', optimize=optimize_buy_volatility) buy_min_volatility = DecimalParameter(0, 0.5, default=0, decimals = 2, space='buy', optimize=False) buy_max_volatility = DecimalParameter(0.5, 2, default=1, decimals = 2, space='buy', optimize=optimize_buy_volatility) optimize_buy_volatility2 = False buy_length_volatility2 = IntParameter(10, 200, default=72, space='buy', optimize=optimize_buy_volatility2) buy_max_volatility2 = DecimalParameter(0.5, 2, default=1, decimals = 2, space='buy', optimize=optimize_buy_volatility2) fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=False) slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=False) optimize_sell_ema = False base_nb_candles_ema_sell = IntParameter(5, 80, default=20, space='buy', optimize=optimize_sell_ema) high_offset_sell_ema = DecimalParameter(0.99, 1.1, default=1.012, space='buy', optimize=optimize_sell_ema) min_rsi_sell = IntParameter(30, 100, default=50, space='sell', optimize=False) optimize_sell_ema2 = False base_nb_candles_ema_sell2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_sell_ema2) high_offset_sell_ema2 = DecimalParameter(0.99, 1.1, default=1.012, space='buy', optimize=optimize_sell_ema2) min_rsi_sell_2 = IntParameter(30, 100, default=50, space='sell', optimize=False) optimize_sell_ema3 = False base_nb_candles_ema_sell3 = IntParameter(5, 140, default=20, space='sell', optimize=optimize_sell_ema3) high_offset_sell_ema3 = DecimalParameter(0.9, 1.1, default=1.012, space='sell', optimize=optimize_sell_ema3) optimize_sell_ema4 = False base_nb_candles_ema_sell4 = IntParameter(5, 140, default=20, space='sell', optimize=optimize_sell_ema4) high_offset_sell_ema4 = DecimalParameter(0.9, 1.1, default=1.012, space='sell', optimize=optimize_sell_ema4) optimize_sell_ema5 = False base_nb_candles_ema_sell5 = IntParameter(5, 140, default=20, space='sell', optimize=optimize_sell_ema5) high_offset_sell_ema5 = DecimalParameter(0.9, 1.1, default=1.012, space='sell', optimize=optimize_sell_ema5) optimize_sell_ema6 = False base_nb_candles_ema_sell6 = IntParameter(5, 140, default=20, space='sell', optimize=optimize_sell_ema6) high_offset_sell_ema6 = DecimalParameter(0.9, 1.1, default=1.012, space='sell', optimize=optimize_sell_ema6) optimize_sell_ema7 = False base_nb_candles_ema_sell7 = IntParameter(5, 140, default=20, space='sell', optimize=optimize_sell_ema7) high_offset_sell_ema7 = DecimalParameter(0.9, 1.1, default=1.012, space='sell', optimize=optimize_sell_ema7) optimize_sell_ema8 = False base_nb_candles_ema_sell8 = IntParameter(5, 140, default=20, space='sell', optimize=optimize_sell_ema8) high_offset_sell_ema8 = DecimalParameter(0.9, 1.1, default=1.012, space='sell', optimize=optimize_sell_ema8) # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.018 use_custom_stoploss = False # Protection hyperspace params: protection_params = { "cooldown_lookback": 2, # value loaded from strategy } cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=False) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) return prot # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 288 age_filter = 30 @informative('1d') def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['age_filter_ok'] = (dataframe['volume'].rolling(window=self.age_filter, min_periods=self.age_filter).min() > 0) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # EWO dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) #RSX dataframe['rsx_14'] = pta.rsx(dataframe['close'], length=14) dataframe['rsx_4'] = pta.rsx(dataframe['close'], length=4) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] # Profit Maximizer - PMAX dataframe['pm'], df_pmx = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) df_source = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close'])/4 dataframe['pmax_thresh'] = ta.EMA(df_source, timeperiod=9) dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) if not self.optimize_buy_ema: dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value if not self.optimize_buy_ema2: dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value if not self.optimize_sell_ema: dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) * self.high_offset_sell_ema.value if not self.optimize_sell_ema2: dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell2.value)) * self.high_offset_sell_ema2.value if not self.optimize_sell_ema3: dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell3.value)) * self.high_offset_sell_ema3.value if not self.optimize_sell_ema4: dataframe['ema_offset_sell4'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell4.value)) * self.high_offset_sell_ema4.value if not self.optimize_sell_ema5: dataframe['ema_offset_sell5'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell5.value)) * self.high_offset_sell_ema5.value if not self.optimize_sell_ema6: dataframe['ema_offset_sell6'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell6.value)) * self.high_offset_sell_ema6.value if not self.optimize_sell_ema7: dataframe['ema_offset_sell7'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell7.value)) * self.high_offset_sell_ema7.value if not self.optimize_sell_ema8: dataframe['ema_offset_sell8'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell8.value)) * self.high_offset_sell_ema8.value if not self.optimize_buy_hma: dataframe['hma_offset_buy'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value if not self.optimize_buy_vwma: dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value if not self.optimize_buy_vwma2: dataframe['vwma_offset_buy2'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma2.value)) *self.low_offset_vwma2.value if not self.optimize_buy_vwma3: dataframe['vwma_offset_buy3'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma3.value)) *self.low_offset_vwma3.value if not self.optimize_buy_vwma4: dataframe['vwma_offset_buy4'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma4.value)) *self.low_offset_vwma4.value if not self.optimize_buy_volatility: df_std = dataframe['close'].rolling(int(self.buy_length_volatility.value)).std() dataframe["volatility"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility.value) if not self.optimize_buy_volatility2: df_std = dataframe['close'].rolling(int(self.buy_length_volatility2.value)).std() dataframe["volatility2"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility2.value) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.optimize_buy_ema: dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value if self.optimize_buy_ema2: dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value if self.optimize_sell_ema: dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) * self.high_offset_sell_ema.value if self.optimize_sell_ema2: dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell2.value)) * self.high_offset_sell_ema2.value if self.optimize_buy_volatility: df_std = dataframe['close'].rolling(int(self.buy_length_volatility.value)).std() dataframe["volatility"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility.value) if self.optimize_buy_volatility2: df_std = dataframe['close'].rolling(int(self.buy_length_volatility2.value)).std() dataframe["volatility2"] = (df_std > self.buy_min_volatility.value) & (df_std < self.buy_max_volatility2.value) dataframe.loc[:, 'buy_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'buy'] = 0 if self.optimize_buy_hma: dataframe['hma_offset_buy'] = tv_hma(dataframe, int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value buy_offset_hma = ( ( ( (dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) ) ) & (dataframe['rsi_fast'] < 30) ) dataframe.loc[buy_offset_hma, 'buy_tag'] += 'hma_1 ' if self.optimize_buy_vwma: dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value if self.optimize_buy_vwma2: dataframe['vwma_offset_buy2'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma2.value)) *self.low_offset_vwma2.value if self.optimize_buy_vwma3: dataframe['vwma_offset_buy3'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma3.value)) *self.low_offset_vwma3.value if self.optimize_buy_vwma4: dataframe['vwma_offset_buy4'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma4.value)) *self.low_offset_vwma4.value buy_offset_vwma = ( ( ( (dataframe['close'] < dataframe['vwma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < self.rsi_buy_vwma.value) & (dataframe['rsi_fast'] < self.rsi_fast_buy_vwma.value) ) ) ) dataframe.loc[buy_offset_vwma, 'buy_tag'] += 'vwma_1 ' conditions.append(buy_offset_vwma) buy_offset_vwma_2 = ( ( ( (dataframe['close'] < dataframe['vwma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) ) dataframe.loc[buy_offset_vwma_2, 'buy_tag'] += 'vwma_2 ' conditions.append(buy_offset_vwma_2) buy_offset_vwma_3 = ( ( ( ((dataframe['close'] < dataframe['vwma_offset_buy3']).rolling(2).min() > 0) & (dataframe['pm'] <= dataframe['pmax_thresh']) & ((dataframe['rsi'] < self.buy_rsi_vwma3.value)) & ((dataframe['rsi_fast'] < self.buy_rsi_4_vwma3.value)) & ((dataframe['rsx_14'] < self.buy_rsx_vwma3.value)) & ((dataframe['rsx_4'] < self.buy_rsx_4_vwma3.value)) ) ) ) dataframe.loc[buy_offset_vwma_3, 'buy_tag'] += 'vwma_3 ' conditions.append(buy_offset_vwma_3) buy_offset_vwma_4 = ( ( ( ((dataframe['close'] < dataframe['vwma_offset_buy4']).rolling(2).min() > 0) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) ) dataframe.loc[buy_offset_vwma_4, 'buy_tag'] += 'vwma_4 ' conditions.append(buy_offset_vwma_4) add_check = ( (dataframe['live_data_ok']) & (dataframe['age_filter_ok_1d']) & (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & ( ( (dataframe['close'] < dataframe['ema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe["volatility"]) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * self.distance_max_close.value)) & ( (dataframe['ewo'] < self.ewo_low.value) | ( (dataframe['ewo'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) ) ) & (dataframe['close'] < dataframe['ema_offset_sell']) & (dataframe['rsx_14'] < self.rsx_buy.value) & (dataframe['rsx_4'] < self.rsx_4_buy.value) ) | ( (dataframe['close'] < dataframe['ema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe["volatility2"]) & (dataframe['close'].rolling(288).max() >= (dataframe['close'] * self.distance_max_close2.value)) & ( (dataframe['ewo'] < self.ewo_low2.value) | ( (dataframe['ewo'] > self.ewo_high2.value) & (dataframe['rsi'] < self.rsi_buy2.value) ) ) & (dataframe['close'] < dataframe['ema_offset_sell2']) & (dataframe['rsx_14'] < self.rsx_buy2.value) & (dataframe['rsx_4'] < self.rsx_4_buy2.value) ) ) & (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ (add_check & reduce(lambda x, y: x | y, conditions)), 'buy' ]=1 dataframe.loc[ buy_offset_hma & buy_offset_vwma, 'buy' ]= 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_tag'] = '' conditions = [] if self.optimize_sell_ema3: dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell3.value)) * self.high_offset_sell_ema3.value if self.optimize_sell_ema4: dataframe['ema_offset_sell4'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell4.value)) * self.high_offset_sell_ema4.value if self.optimize_sell_ema5: dataframe['ema_offset_sell5'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell5.value)) * self.high_offset_sell_ema5.value if self.optimize_sell_ema6: dataframe['ema_offset_sell6'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell6.value)) * self.high_offset_sell_ema6.value if self.optimize_sell_ema7: dataframe['ema_offset_sell7'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell7.value)) * self.high_offset_sell_ema7.value if self.optimize_sell_ema8: dataframe['ema_offset_sell8'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell8.value)) * self.high_offset_sell_ema8.value sell_cond_1 = ( (dataframe['close'] > dataframe['ema_offset_sell']) & (dataframe['volume'] > 0) & (dataframe['rsi'] > self.min_rsi_sell.value) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) conditions.append(sell_cond_1) dataframe.loc[sell_cond_1, 'exit_tag'] += 'EMA_1 ' sell_cond_2 = ( (dataframe['close'] > dataframe['ema_offset_sell2']) & (dataframe['volume'] > 0) & (dataframe['rsi'] > self.min_rsi_sell_2.value) & (dataframe['pm'] > dataframe['pmax_thresh']) ) conditions.append(sell_cond_2) dataframe.loc[sell_cond_2, 'exit_tag'] += 'EMA_2 ' sell_cond_3 = ( (dataframe['close'] < dataframe['ema_offset_sell3']) & (dataframe['volume'] > 0) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) conditions.append(sell_cond_3) dataframe.loc[sell_cond_3, 'exit_tag'] += 'EMA_3 ' sell_cond_4 = ( (dataframe['close'] < dataframe['ema_offset_sell4']) & (dataframe['volume'] > 0) & (dataframe['pm'] > dataframe['pmax_thresh']) ) conditions.append(sell_cond_4) dataframe.loc[sell_cond_4, 'exit_tag'] += 'EMA_4 ' sell_cond_5 = ( ((dataframe['close'] > dataframe['ema_offset_sell5']).rolling(2).min() > 0) & (dataframe['volume'] > 0) & (dataframe['pm'] > dataframe['pmax_thresh']) ) conditions.append(sell_cond_5) dataframe.loc[sell_cond_5, 'exit_tag'] += 'EMA_5 ' sell_cond_6 = ( ((dataframe['close'] > dataframe['ema_offset_sell6']).rolling(2).min() > 0) & (dataframe['volume'] > 0) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) conditions.append(sell_cond_6) dataframe.loc[sell_cond_6, 'exit_tag'] += 'EMA_6 ' sell_cond_7 = ( ((dataframe['close'] < dataframe['ema_offset_sell7']).rolling(2).min() > 0) & (dataframe['volume'] > 0) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) conditions.append(sell_cond_7) dataframe.loc[sell_cond_7, 'exit_tag'] += 'EMA_7 ' sell_cond_8 = ( ((dataframe['close'] < dataframe['ema_offset_sell8']).rolling(3).min() > 0) & (dataframe['volume'] > 0) & (dataframe['pm'] > dataframe['pmax_thresh']) ) conditions.append(sell_cond_8) dataframe.loc[sell_cond_8, 'exit_tag'] += 'EMA_8 ' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'sell' ]=1 return dataframe # 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 # PMAX def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = f'MA_{MAtype}_{length}' atr = f'ATR_{period}' pm = f'pm_{period}_{multiplier}_{length}_{MAtype}' pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}' # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) df[atr] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + ((multiplier/10) * df[atr]) df['basic_lb'] = mavalue - ((multiplier/10) * df[atr]) basic_ub = df['basic_ub'].values final_ub = np.full(len(df), 0.00) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.00) for i in range(period, len(df)): final_ub[i] = basic_ub[i] if ( basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1] final_lb[i] = basic_lb[i] if ( basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1] df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if ( pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00) pm = Series(pm_arr) # Mark the trend direction up/down pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx # smoothed Heiken Ashi def HA(dataframe, smoothing=None): df = dataframe.copy() df['HA_Close']=(df['open'] + df['high'] + df['low'] + df['close'])/4 df.reset_index(inplace=True) ha_open = [ (df['open'][0] + df['close'][0]) / 2 ] [ ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df)-1) ] df['HA_Open'] = ha_open df.set_index('index', inplace=True) df['HA_High']=df[['HA_Open','HA_Close','high']].max(axis=1) df['HA_Low']=df[['HA_Open','HA_Close','low']].min(axis=1) if smoothing is not None: sml = abs(int(smoothing)) if sml > 0: df['Smooth_HA_O']=ta.EMA(df['HA_Open'], sml) # df['Smooth_HA_C']=ta.EMA(df['HA_Close'], sml) df['Smooth_HA_H']=ta.EMA(df['HA_High'], sml) df['Smooth_HA_L']=ta.EMA(df['HA_Low'], sml) return df def pump_warning(dataframe, perc=15): # NOTE: segna "1" se c'รจ un pump df = dataframe.copy() df["change"] = df["high"] - df["low"] df["test1"] = (df["close"] > df["open"]) df["test2"] = ((df["change"]/df["low"]) > (perc/100)) df["result"] = (df["test1"] & df["test2"]).astype('int') return df['result'] # Volume Weighted Moving Average def vwma(dataframe, length = 10): """Indicator: Volume Weighted Moving Average (VWMA)""" # Calculate Result pv = dataframe['close'] * dataframe['volume'] vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length)) vwma = vwma.fillna(0, inplace=True) return vwma def tv_wma(dataframe, length = 9, field="close") -> DataFrame: """ Source: Tradingview "Moving Average Weighted" Pinescript Author: Unknown Args : dataframe : Pandas Dataframe length : WMA length field : Field to use for the calculation Returns : dataframe : Pandas DataFrame with new columns 'tv_wma' """ norm = 0 sum = 0 for i in range(1, length - 1): weight = (length - i) * length norm = norm + weight sum = sum + dataframe[field].shift(i) * weight dataframe["tv_wma"] = (sum / norm) if norm > 0 else 0 return dataframe["tv_wma"] def tv_hma(dataframe, length = 9, field="close") -> DataFrame: """ Source: Tradingview "Hull Moving Average" Pinescript Author: Unknown Args : dataframe : Pandas Dataframe length : HMA length field : Field to use for the calculation Returns : dataframe : Pandas DataFrame with new columns 'tv_hma' """ dataframe["h"] = 2 * tv_wma(dataframe, math.floor(length / 2), field) - tv_wma(dataframe, length, field) dataframe["tv_hma"] = tv_wma(dataframe, math.floor(math.sqrt(length)), "h") # dataframe.drop("h", inplace=True, axis=1) return dataframe["tv_hma"]