# source: https://raw.githubusercontent.com/stash86/MultiMA_TSL/a9808dbd05bd6bf004b72451e1a7ba3dd3df3ee6/user_data/strategies/Cenderawasih/Cenderawasih_30m_1d.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.strategy import IStrategy, informative from freqtrade.strategy import (merge_informative_pair, DecimalParameter, IntParameter, BooleanParameter, CategoricalParameter, stoploss_from_open) from pandas import DataFrame, Series from typing import Dict, List, Optional, Tuple, Union from functools import reduce from freqtrade.persistence import Trade from datetime import datetime, timedelta, timezone from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_minutes import talib.abstract as ta import math import pandas_ta as pta # from finta import TA as fta import logging from logging import FATAL def tv_wma(df, length = 9) -> 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 + df.shift(i) * weight tv_wma = (sum / norm) if norm > 0 else 0 return 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' """ h = 2 * tv_wma(dataframe[field], math.floor(length / 2)) - tv_wma(dataframe[field], length) tv_hma = tv_wma(h, math.floor(math.sqrt(length))) # dataframe.drop("h", inplace=True, axis=1) return tv_hma logger = logging.getLogger(__name__) class github_stash86_MultiMA_TSL__Cenderawasih_30m_1d__20230812_075916 (IStrategy): def version(self) -> str: return "Cenderawasih-v1d-30m" INTERFACE_VERSION = 3 # ROI table: minimal_roi = { "0": 100 } # Buy hyperspace params: buy_params = { "buy_length_hma": 130, "buy_offset_hma": 0.83, "buy_length_hma1a": 23, "buy_offset_hma1a": 16, "buy_length_hma1b": 6, "buy_offset_hma1b": 17, "buy_length_hma2": 63, "buy_offset_hma2": 0.85, "buy_length_hma3": 30, "buy_offset_hma3": 0.84, "buy_length_hma4": 39, "buy_offset_hma4": 0.9, "buy_rsi_1": 42, "buy_rsi_2": 48, "buy_min_red_2h": 6, "buy_min_red_2h_2": 18, "buy_min_red_2h_3": 17, "buy_min_red_2h_4": 11, "buy_max_red_2h": -4, } # Sell hyperspace params: sell_params = { "sell_length_ema": 1, "sell_offset_ema": 20, "sell_length_ema2": 102, "sell_offset_ema2": 0.87, "sell_length_ema2a": 2, "sell_offset_ema2a": 16, "sell_length_ema2b": 1, "sell_offset_ema2b": 19, "sell_length_ema3": 71, "sell_offset_ema3": 0.89, "sell_length_ema3a": 6, "sell_offset_ema3a": 17, "sell_length_ema4": 133, "sell_offset_ema4": 1.17, "sell_long_green3": 6, "sell_long_green3a": 17, "sell_long_green3b": 0, } buy_dummy = IntParameter(20, 70, default=61, optimize=True) buy_rsi_1 = IntParameter(20, 50, default=50, optimize=False) buy_rsi_2 = IntParameter(20, 50, default=50, optimize=False) # buy_rsi_3 = IntParameter(20, 50, default=50, optimize=True) # buy_rsi_4 = IntParameter(20, 50, default=50, optimize=False) # buy_long_red2_2 = IntParameter(1, 20, default=1, optimize=True) # buy_long_red2_3 = IntParameter(1, 20, default=1, optimize=False) # buy_long_red2_4 = IntParameter(1, 10, default=1, optimize=False) buy_min_red_2h = IntParameter(1, 30, default=1, optimize=False) buy_min_red_2h_2 = IntParameter(1, 30, default=1, optimize=False) buy_min_red_2h_3 = IntParameter(1, 30, default=1, optimize=False) buy_min_red_2h_4 = IntParameter(1, 30, default=1, optimize=False) buy_max_red_2h = IntParameter(-20, 20, default=1, optimize=False) # buy_max_red_2h_2 = IntParameter(-20, 20, default=1, optimize=True) # buy_max_red_2h_3 = IntParameter(-20, 20, default=1, optimize=True) # buy_max_red_2h_4 = IntParameter(-20, 20, default=1, optimize=True) # buy_min_red_3_2h = IntParameter(1, 50, default=5, optimize=True) # buy_min_red_4_2h = IntParameter(11, 60, default=15, optimize=True) # buy_lowest_open_rolling = IntParameter(1, 10, default=1, optimize=False) # buy_lowest_close_rolling = IntParameter(1, 10, default=1, optimize=False) optimize_buy_hma = False buy_length_hma = IntParameter(5, 150, default=6, optimize=optimize_buy_hma) buy_offset_hma = DecimalParameter(0.8, 1, default=1, decimals=2, optimize=optimize_buy_hma) optimize_buy_hma1a = False buy_length_hma1a = IntParameter(1, 30, default=6, optimize=optimize_buy_hma1a) buy_offset_hma1a = IntParameter(16, 20, default=20, optimize=optimize_buy_hma1a) optimize_buy_hma1b = False buy_length_hma1b = IntParameter(1, 30, default=6, optimize=optimize_buy_hma1b) buy_offset_hma1b = IntParameter(16, 20, default=20, optimize=optimize_buy_hma1b) # buy_red_candle_rolling = IntParameter(1, 5, default=2, optimize=False) optimize_buy_hma2 = False buy_length_hma2 = IntParameter(5, 150, default=6, optimize=optimize_buy_hma2) buy_offset_hma2 = DecimalParameter(0.8, 1, default=1, decimals=2, optimize=optimize_buy_hma2) # optimize_buy_hma2a = True # buy_length_hma2a = IntParameter(1, 30, default=6, optimize=optimize_buy_hma2a) # buy_offset_hma2a = IntParameter(16, 20, default=20, optimize=optimize_buy_hma2a) optimize_buy_hma3 = False buy_length_hma3 = IntParameter(5, 150, default=6, optimize=optimize_buy_hma3) buy_offset_hma3 = DecimalParameter(0.8, 1, default=1, decimals=2, optimize=optimize_buy_hma3) # optimize_buy_hma3b = True # buy_length_hma3b = IntParameter(1, 30, default=6, optimize=optimize_buy_hma3b) # buy_offset_hma3b = IntParameter(16, 20, default=20, optimize=optimize_buy_hma3b) optimize_buy_hma4 = False buy_length_hma4 = IntParameter(5, 150, default=6, optimize=optimize_buy_hma4) buy_offset_hma4 = DecimalParameter(0.8, 1, default=1, decimals=2, optimize=optimize_buy_hma4) optimize_sell_ema = False sell_length_ema = IntParameter(1, 30, default=6, optimize=optimize_sell_ema) sell_offset_ema = IntParameter(20, 24, default=20, optimize=optimize_sell_ema) optimize_sell_ema2 = False sell_length_ema2 = IntParameter(5, 150, default=6, optimize=optimize_sell_ema2) sell_offset_ema2 = DecimalParameter(0.8, 1, default=0.98, decimals=2, optimize=optimize_sell_ema2) optimize_sell_ema2a = False sell_length_ema2a = IntParameter(1, 30, default=6, optimize=optimize_sell_ema2a) sell_offset_ema2a = IntParameter(16, 20, default=20, optimize=optimize_sell_ema2a) optimize_sell_ema2b = False sell_length_ema2b = IntParameter(1, 50, default=6, optimize=optimize_sell_ema2b) sell_offset_ema2b = IntParameter(16, 20, default=20, optimize=optimize_sell_ema2b) optimize_sell_ema3 = False sell_length_ema3 = IntParameter(5, 150, default=6, optimize=optimize_sell_ema3) sell_offset_ema3 = DecimalParameter(0.8, 1, default=0.95, decimals=2, optimize=optimize_sell_ema3) optimize_sell_ema3a = False sell_length_ema3a = IntParameter(1, 30, default=6, optimize=optimize_sell_ema3a) sell_offset_ema3a = IntParameter(16, 20, default=20, optimize=optimize_sell_ema3a) optimize_sell_ema4 = False sell_length_ema4 = IntParameter(5, 150, default=6, optimize=optimize_sell_ema4) sell_offset_ema4 = DecimalParameter(1, 1.2, default=1, decimals=2, optimize=optimize_sell_ema4) # optimize_sell_rsi = False # sell_rsi_overbought = IntParameter(40, 90, default=50, optimize=True) # sell_long_green = IntParameter(1, 50, default=1, optimize=False) sell_long_green3 = IntParameter(5, 40, default=10, optimize=False) # sell_long_green6 = IntParameter(-5, 40, default=10, optimize=True) sell_long_green3b = IntParameter(-5, 40, default=10, optimize=False) # sell_long_red3 = IntParameter(0, 50, default=1, optimize=True) # Stoploss: stoploss = -0.99 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.15 trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False timeframe = '30m' inf_timeframe1 = '2h' process_only_new_candles = True startup_candle_count = 999 timeframe_minutes = timeframe_to_minutes(timeframe) timeframe_minutes_string = f"{timeframe_minutes}m" if int(timeframe_minutes) >= 60: timeframe_minutes_string = f"{timeframe_minutes//60}h" inf_timeframe1_minutes = timeframe_to_minutes(inf_timeframe1) inf_timeframe1_minutes_string = f"{inf_timeframe1_minutes}m" if int(inf_timeframe1_minutes) >= 60: inf_timeframe1_minutes_string = f"{inf_timeframe1_minutes//60}h" @informative('1d') def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['age_filter_ok'] = (dataframe['volume'].rolling(window=30, min_periods=30).min() > 0) drop_columns = ['open', 'high', 'low', 'close', 'volume'] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) return dataframe @informative(inf_timeframe1) def populate_indicators_inf(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['pct_change'] = dataframe['close'].pct_change() drop_columns = ['open', 'high', 'low', 'close', 'volume'] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) return dataframe @informative(timeframe, 'BTC/{stake}', '{base}_{column}_{timeframe}') # @informative('15m', 'BTC/USDT', '{base}_{column}_{timeframe}') def populate_indicators_btc_inf(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) drop_columns = ['open', 'high', 'low', 'close', 'volume'] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['pct_change'] = dataframe['close'].pct_change() dataframe['vrsi'] = ta.RSI(dataframe['volume'], timeperiod=15) dataframe['vrsi_45'] = ta.RSI(dataframe['volume'], timeperiod=45) dataframe['close_mean_75'] = dataframe['close'].rolling(75).mean() dataframe['close_median_75'] = dataframe['close'].rolling(75).median() dataframe['close_mean_150'] = dataframe['close'].rolling(150).mean() dataframe['close_median_150'] = dataframe['close'].rolling(150).median() dataframe['close_mean_300'] = dataframe['close'].rolling(300).mean() dataframe['close_median_300'] = dataframe['close'].rolling(300).median() dataframe['mfi'] = ta.MFI(dataframe, 15) dataframe['mfi_45'] = ta.MFI(dataframe, 45) dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) if not self.optimize_buy_hma: dataframe['hma_offset_buy1'] = tv_hma(dataframe, int(self.buy_length_hma.value)) *self.buy_offset_hma.value if not self.optimize_buy_hma1a: dataframe['hma_offset_buy1a'] = tv_hma(dataframe, int(5 * self.buy_length_hma1a.value)) * 0.05 * self.buy_offset_hma1a.value if not self.optimize_buy_hma1b: dataframe['hma_offset_buy1b'] = tv_hma(dataframe, int(5 * self.buy_length_hma1b.value)) * 0.05 * self.buy_offset_hma1b.value if not self.optimize_buy_hma2: dataframe['hma_offset_buy2'] = tv_hma(dataframe, int(self.buy_length_hma2.value)) *self.buy_offset_hma2.value # if not self.optimize_buy_hma2a: # dataframe['hma_offset_buy2a'] = tv_hma(dataframe, int(5 * self.buy_length_hma2a.value)) * 0.05 * self.buy_offset_hma2a.value if not self.optimize_buy_hma3: dataframe['hma_offset_buy3'] = tv_hma(dataframe, int(self.buy_length_hma3.value)) *self.buy_offset_hma3.value # if not self.optimize_buy_hma3b: # dataframe['hma_offset_buy3b'] = tv_hma(dataframe, int(5 * self.buy_length_hma3b.value)) * 0.05 * self.buy_offset_hma3b.value if not self.optimize_buy_hma4: dataframe['hma_offset_buy4'] = tv_hma(dataframe, int(self.buy_length_hma4.value)) *self.buy_offset_hma4.value if not self.optimize_sell_ema: dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(5 * self.sell_length_ema.value)) * 0.05 * self.sell_offset_ema.value if not self.optimize_sell_ema2: dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.sell_length_ema2.value)) *self.sell_offset_ema2.value if not self.optimize_sell_ema2a: dataframe['ema_offset_sell2a'] = ta.EMA(dataframe, int(5 * self.sell_length_ema2a.value)) * 0.05 * self.sell_offset_ema2a.value if not self.optimize_sell_ema2b: dataframe['ema_offset_sell2b'] = ta.EMA(dataframe, int(5 * self.sell_length_ema2b.value)) * 0.05 * self.sell_offset_ema2b.value if not self.optimize_sell_ema3: dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.sell_length_ema3.value)) *self.sell_offset_ema3.value if not self.optimize_sell_ema3a: dataframe['ema_offset_sell3a'] = ta.EMA(dataframe, int(5 * self.sell_length_ema3a.value)) * 0.05 * self.sell_offset_ema3a.value if not self.optimize_sell_ema4: dataframe['ema_offset_sell4'] = ta.EMA(dataframe, int(self.sell_length_ema4.value)) *self.sell_offset_ema4.value return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.optimize_buy_hma: dataframe['hma_offset_buy1'] = tv_hma(dataframe, int(self.buy_length_hma.value)) *self.buy_offset_hma.value if self.optimize_buy_hma1a: dataframe['hma_offset_buy1a'] = tv_hma(dataframe, int(5 * self.buy_length_hma1a.value)) * 0.05 * self.buy_offset_hma1a.value if self.optimize_buy_hma1b: dataframe['hma_offset_buy1b'] = tv_hma(dataframe, int(5 * self.buy_length_hma1b.value)) * 0.05 * self.buy_offset_hma1b.value if self.optimize_buy_hma2: dataframe['hma_offset_buy2'] = tv_hma(dataframe, int(self.buy_length_hma2.value)) *self.buy_offset_hma2.value # if self.optimize_buy_hma2a: # dataframe['hma_offset_buy2a'] = tv_hma(dataframe, int(5 * self.buy_length_hma2a.value)) * 0.05 * self.buy_offset_hma2a.value if self.optimize_buy_hma3: dataframe['hma_offset_buy3'] = tv_hma(dataframe, int(self.buy_length_hma3.value)) *self.buy_offset_hma3.value # if self.optimize_buy_hma3b: # dataframe['hma_offset_buy3b'] = tv_hma(dataframe, int(5 * self.buy_length_hma3b.value)) * 0.05 * self.buy_offset_hma3b.value if self.optimize_buy_hma4: dataframe['hma_offset_buy4'] = tv_hma(dataframe, int(self.buy_length_hma4.value)) *self.buy_offset_hma4.value dataframe['enter_tag'] = '' add_check = ( dataframe['live_data_ok'] & dataframe['age_filter_ok_1d'] & (dataframe['close'] < dataframe['open']) ) mean_above_median_75 = np.where((dataframe['close_mean_75'] >= dataframe['close_median_75']), True, False) mean_above_median_150 = np.where((dataframe['close_mean_150'] >= dataframe['close_median_150']), True, False) close_above_median_150 = np.where((dataframe['close'] >= dataframe['close_median_150']), True, False) close_above_mean_150 = np.where((dataframe['close'] >= dataframe['close_mean_150']), True, False) mean_above_median_300 = np.where((dataframe['close_mean_300'] >= dataframe['close_median_300']), True, False) close_above_median_300 = np.where((dataframe['close'] >= dataframe['close_median_300']), True, False) close_above_mean_300 = np.where((dataframe['close'] >= dataframe['close_mean_300']), True, False) mean_75_above_mean_150 = np.where((dataframe['close_mean_75'] >= dataframe['close_mean_150']), True, False) mean_75_above_mean_300 = np.where((dataframe['close_mean_75'] >= dataframe['close_mean_300']), True, False) mean_150_above_mean_300 = np.where((dataframe['close_mean_150'] >= dataframe['close_mean_300']), True, False) median_75_above_median_150 = np.where((dataframe['close_median_75'] >= dataframe['close_median_150']), True, False) median_75_above_median_300 = np.where((dataframe['close_median_75'] >= dataframe['close_median_300']), True, False) median_150_above_median_300 = np.where((dataframe['close_median_150'] >= dataframe['close_median_300']), True, False) buy_offset_hma1a1a1a1a = ( ( (dataframe['close'] < dataframe['hma_offset_buy1b']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & mean_above_median_150 & close_above_median_150 & close_above_mean_150 & median_150_above_median_300 ) ) dataframe.loc[buy_offset_hma1a1a1a1a, 'enter_tag'] += 'hma1a1a1a1a ' conditions.append(buy_offset_hma1a1a1a1a) buy_offset_hma1a1a1a1b1 = ( ( (dataframe['close'] < dataframe['hma_offset_buy1a']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & mean_above_median_150 & close_above_median_150 & close_above_mean_150 & ~median_150_above_median_300 & mean_150_above_mean_300 ) ) dataframe.loc[buy_offset_hma1a1a1a1b1, 'enter_tag'] += 'hma1a1a1a1b1 ' conditions.append(buy_offset_hma1a1a1a1b1) buy_offset_hma1a1a1a2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & mean_above_median_150 & close_above_median_150 & ~close_above_mean_150 ) ) dataframe.loc[buy_offset_hma1a1a1a2, 'enter_tag'] += 'hma1a1a1a2 ' conditions.append(buy_offset_hma1a1a1a2) buy_offset_hma1a1a1b = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & mean_above_median_150 & ~close_above_median_150 ) ) dataframe.loc[buy_offset_hma1a1a1b, 'enter_tag'] += 'hma1a1a1b ' conditions.append(buy_offset_hma1a1a1b) buy_offset_hma1a1a2a = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & ~mean_above_median_150 & close_above_median_150 ) ) dataframe.loc[buy_offset_hma1a1a2a, 'enter_tag'] += 'hma1a1a2a ' conditions.append(buy_offset_hma1a1a2a) buy_offset_hma1a1a2b1 = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & ~mean_above_median_150 & ~close_above_median_150 & mean_150_above_mean_300 ) ) dataframe.loc[buy_offset_hma1a1a2b1, 'enter_tag'] += 'hma1a1a2b1 ' conditions.append(buy_offset_hma1a1a2b1) buy_offset_hma1a1a2b2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & ~mean_above_median_150 & ~close_above_median_150 & ~mean_150_above_mean_300 ) ) dataframe.loc[buy_offset_hma1a1a2b2, 'enter_tag'] += 'hma1a1a2b2 ' conditions.append(buy_offset_hma1a1a2b2) buy_offset_hma1a1b = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & close_above_median_300 & ~close_above_mean_300 ) ) dataframe.loc[buy_offset_hma1a1b, 'enter_tag'] += 'hma1a1b ' conditions.append(buy_offset_hma1a1b) buy_offset_hma1a2a = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & ~close_above_median_300 & close_above_mean_300 ) ) dataframe.loc[buy_offset_hma1a2a, 'enter_tag'] += 'hma1a2a ' conditions.append(buy_offset_hma1a2a) buy_offset_hma1a2b1 = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & ~close_above_median_300 & ~close_above_mean_300 & mean_above_median_150 ) ) dataframe.loc[buy_offset_hma1a2b1, 'enter_tag'] += 'hma1a2b1 ' conditions.append(buy_offset_hma1a2b1) buy_offset_hma1a2b2a1 = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & ~close_above_median_300 & ~close_above_mean_300 & ~mean_above_median_150 & mean_150_above_mean_300 & mean_75_above_mean_150 ) ) dataframe.loc[buy_offset_hma1a2b2a1, 'enter_tag'] += 'hma1a2b2a1 ' conditions.append(buy_offset_hma1a2b2a1) buy_offset_hma1a2b2a2b = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & ~close_above_median_300 & ~close_above_mean_300 & ~mean_above_median_150 & mean_150_above_mean_300 & ~mean_75_above_mean_150 & ~mean_75_above_mean_300 ) ) dataframe.loc[buy_offset_hma1a2b2a2b, 'enter_tag'] += 'hma1a2b2a2b ' conditions.append(buy_offset_hma1a2b2a2b) buy_offset_hma1a2b2b = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & mean_above_median_300 & ~close_above_median_300 & ~close_above_mean_300 & ~mean_above_median_150 & ~mean_150_above_mean_300 ) ) dataframe.loc[buy_offset_hma1a2b2b, 'enter_tag'] += 'hma1a2b2b ' conditions.append(buy_offset_hma1a2b2b) buy_offset_hma1b = ( ( (dataframe['close'] < dataframe['hma_offset_buy1']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 30) & (dataframe['rsi'] < self.buy_rsi_1.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h.value)) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] < (-0.01 * self.buy_max_red_2h.value)) & ~mean_above_median_300 ) ) dataframe.loc[buy_offset_hma1b, 'enter_tag'] += 'hma1b ' conditions.append(buy_offset_hma1b) buy_offset_hma2a1a1b = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & mean_above_median_150 & ~close_above_median_150 ) ) dataframe.loc[buy_offset_hma2a1a1b, 'enter_tag'] += 'hma2a1a1b ' conditions.append(buy_offset_hma2a1a1b) buy_offset_hma2a1a2a = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & ~mean_above_median_150 & close_above_median_150 ) ) dataframe.loc[buy_offset_hma2a1a2a, 'enter_tag'] += 'hma2a1a2a ' conditions.append(buy_offset_hma2a1a2a) buy_offset_hma2a2a1 = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & mean_above_median_300 & ~close_above_median_300 & close_above_mean_300 & mean_above_median_150 ) ) dataframe.loc[buy_offset_hma2a2a1, 'enter_tag'] += 'hma2a2a1 ' conditions.append(buy_offset_hma2a2a1) buy_offset_hma2a2a2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & mean_above_median_300 & ~close_above_median_300 & close_above_mean_300 & ~mean_above_median_150 ) ) dataframe.loc[buy_offset_hma2a2a2, 'enter_tag'] += 'hma2a2a2 ' conditions.append(buy_offset_hma2a2a2) buy_offset_hma2a2b1a2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & mean_above_median_300 & ~close_above_median_300 & ~close_above_mean_300 & mean_above_median_150 & close_above_median_150 & ~close_above_mean_150 ) ) dataframe.loc[buy_offset_hma2a2b1a2, 'enter_tag'] += 'hma2a2b1a2 ' conditions.append(buy_offset_hma2a2b1a2) buy_offset_hma2a2b2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & mean_above_median_300 & ~close_above_median_300 & ~close_above_mean_300 & ~mean_above_median_150 ) ) dataframe.loc[buy_offset_hma2a2b2, 'enter_tag'] += 'hma2a2b2 ' conditions.append(buy_offset_hma2a2b2) buy_offset_hma2b1a1a2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & ~mean_above_median_300 & close_above_median_300 & close_above_mean_300 & mean_above_median_150 & close_above_median_150 & ~close_above_mean_150 ) ) dataframe.loc[buy_offset_hma2b1a1a2, 'enter_tag'] += 'hma2b1a1a2 ' conditions.append(buy_offset_hma2b1a1a2) buy_offset_hma2b1b = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & ~mean_above_median_300 & close_above_median_300 & ~close_above_mean_300 ) ) dataframe.loc[buy_offset_hma2b1b, 'enter_tag'] += 'hma2b1b ' conditions.append(buy_offset_hma2b1b) buy_offset_hma2b2a = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & ~mean_above_median_300 & ~close_above_median_300 & close_above_mean_300 ) ) dataframe.loc[buy_offset_hma2b2a, 'enter_tag'] += 'hma2b2a ' conditions.append(buy_offset_hma2b2a) buy_offset_hma2b2b1b = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & ~mean_above_median_300 & ~close_above_median_300 & ~close_above_mean_300 & mean_above_median_150 & ~mean_75_above_mean_150 ) ) dataframe.loc[buy_offset_hma2b2b1b, 'enter_tag'] += 'hma2b2b1b ' conditions.append(buy_offset_hma2b2b1b) buy_offset_hma2b2b2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 30) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 50) & (dataframe['rsi'] < self.buy_rsi_2.value) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_2.value)) & ~mean_above_median_300 & ~close_above_median_300 & ~close_above_mean_300 & ~mean_above_median_150 ) ) dataframe.loc[buy_offset_hma2b2b2, 'enter_tag'] += 'hma2b2b2 ' conditions.append(buy_offset_hma2b2b2) buy_offset_hma3a1 = ( ( (dataframe['close'] < dataframe['hma_offset_buy3']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 50) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 70) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_3.value)) & mean_above_median_300 & close_above_median_300 ) ) dataframe.loc[buy_offset_hma3a1, 'enter_tag'] += 'hma3a1 ' conditions.append(buy_offset_hma3a1) buy_offset_hma3a2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy3']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 50) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 70) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_3.value)) & mean_above_median_300 & ~close_above_median_300 ) ) dataframe.loc[buy_offset_hma3a2, 'enter_tag'] += 'hma3a2 ' conditions.append(buy_offset_hma3a2) # buy_offset_hma3b1 = ( # ( # (dataframe['close'] < dataframe['hma_offset_buy3']) # & # (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 50) # & # (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 70) # & # (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_3.value)) # & # ~mean_above_median_300 # & # (dataframe['close'] > dataframe['close_median_300']) # ) # ) # dataframe.loc[buy_offset_hma3b1, 'enter_tag'] += 'hma3b1 ' # conditions.append(buy_offset_hma3b1) buy_offset_hma3b2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy3']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 50) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] < 70) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_3.value)) & ~mean_above_median_300 & ~close_above_median_300 ) ) dataframe.loc[buy_offset_hma3b2, 'enter_tag'] += 'hma3b2 ' conditions.append(buy_offset_hma3b2) buy_offset_hma4a1a1a = ( ( (dataframe['close'] < dataframe['hma_offset_buy4']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 70) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_4.value)) & mean_above_median_300 & close_above_median_300 & close_above_mean_300 & mean_150_above_mean_300 & mean_75_above_mean_150 ) ) dataframe.loc[buy_offset_hma4a1a1a, 'enter_tag'] += 'hma4a1a1a ' conditions.append(buy_offset_hma4a1a1a) # buy_offset_hma4a1a1b = ( # ( # (dataframe['close'] < dataframe['hma_offset_buy4']) # & # (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 70) # & # (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_4.value)) # & # mean_above_median_300 # & # close_above_median_300 # & # close_above_mean_300 # & # mean_150_above_mean_300 # & # ~mean_75_above_mean_150 # ) # ) # dataframe.loc[buy_offset_hma4a1a1b, 'enter_tag'] += 'hma4a1a1b ' # conditions.append(buy_offset_hma4a1a1b) # buy_offset_hma4a1a2 = ( # ( # (dataframe['close'] < dataframe['hma_offset_buy4']) # & # (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 70) # & # (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_4.value)) # & # mean_above_median_300 # & # close_above_median_300 # & # close_above_mean_300 # & # ~mean_150_above_mean_300 # ) # ) # dataframe.loc[buy_offset_hma4a1a2, 'enter_tag'] += 'hma4a1a2 ' # conditions.append(buy_offset_hma4a1a2) buy_offset_hma4a2 = ( ( (dataframe['close'] < dataframe['hma_offset_buy4']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 70) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_4.value)) & mean_above_median_300 & ~close_above_median_300 ) ) dataframe.loc[buy_offset_hma4a2, 'enter_tag'] += 'hma4a2 ' conditions.append(buy_offset_hma4a2) buy_offset_hma4b = ( ( (dataframe['close'] < dataframe['hma_offset_buy4']) & (dataframe[f"btc_rsi_{self.timeframe_minutes_string}"] >= 70) & (dataframe[f"pct_change_{self.inf_timeframe1_minutes_string}"] > (-0.01 * self.buy_min_red_2h_4.value)) & ~mean_above_median_300 ) ) dataframe.loc[buy_offset_hma4b, 'enter_tag'] += 'hma4b ' conditions.append(buy_offset_hma4b) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions) & add_check, 'enter_long', ]= 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.optimize_sell_ema: dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(5 * self.sell_length_ema.value)) * 0.05 * self.sell_offset_ema.value if self.optimize_sell_ema2: dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.sell_length_ema2.value)) *self.sell_offset_ema2.value if self.optimize_sell_ema2a: dataframe['ema_offset_sell2a'] = ta.EMA(dataframe, int(5 * self.sell_length_ema2a.value)) * 0.05 * self.sell_offset_ema2a.value if self.optimize_sell_ema2b: dataframe['ema_offset_sell2b'] = ta.EMA(dataframe, int(5 * self.sell_length_ema2b.value)) * 0.05 * self.sell_offset_ema2b.value if self.optimize_sell_ema3: dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.sell_length_ema3.value)) *self.sell_offset_ema3.value if self.optimize_sell_ema3a: dataframe['ema_offset_sell3a'] = ta.EMA(dataframe, int(5 * self.sell_length_ema3a.value)) * 0.05 * self.sell_offset_ema3a.value if self.optimize_sell_ema4: dataframe['ema_offset_sell4'] = ta.EMA(dataframe, int(self.sell_length_ema4.value)) *self.sell_offset_ema4.value dataframe['exit_tag'] = '' conditions = [] mean_above_median_75 = np.where((dataframe['close_mean_75'] >= dataframe['close_median_75']), True, False) mean_above_median_150 = np.where((dataframe['close_mean_150'] >= dataframe['close_median_150']), True, False) close_above_median_150 = np.where((dataframe['close'] >= dataframe['close_median_150']), True, False) close_above_mean_150 = np.where((dataframe['close'] >= dataframe['close_mean_150']), True, False) mean_above_median_300 = np.where((dataframe['close_mean_300'] >= dataframe['close_median_300']), True, False) close_above_median_300 = np.where((dataframe['close'] >= dataframe['close_median_300']), True, False) close_above_mean_300 = np.where((dataframe['close'] >= dataframe['close_mean_300']), True, False) mean_75_above_mean_150 = np.where((dataframe['close_mean_75'] >= dataframe['close_mean_150']), True, False) mean_75_above_mean_300 = np.where((dataframe['close_mean_75'] >= dataframe['close_mean_300']), True, False) mean_150_above_mean_300 = np.where((dataframe['close_mean_150'] >= dataframe['close_mean_300']), True, False) median_75_above_median_150 = np.where((dataframe['close_median_75'] >= dataframe['close_median_150']), True, False) median_75_above_median_300 = np.where((dataframe['close_median_75'] >= dataframe['close_median_300']), True, False) median_150_above_median_300 = np.where((dataframe['close_median_150'] >= dataframe['close_median_300']), True, False) strong_uptrend = ( mean_75_above_mean_150 & mean_75_above_mean_300 & mean_150_above_mean_300 ) medium_uptrend = ( mean_75_above_mean_150 & mean_75_above_mean_300 & ~mean_150_above_mean_300 ) weak_uptrend = ( mean_75_above_mean_150 & ~mean_75_above_mean_300 & ~mean_150_above_mean_300 ) strong_downtrend = ( ~mean_75_above_mean_150 & ~mean_75_above_mean_300 & ~mean_150_above_mean_300 ) medium_downtrend = ( ~mean_75_above_mean_150 & ~mean_75_above_mean_300 & mean_150_above_mean_300 ) weak_downtrend = ( ~mean_75_above_mean_150 & mean_75_above_mean_300 & mean_150_above_mean_300 ) sell_ema_1a1 = ( (dataframe['close'] > dataframe['ema_offset_sell']) # & # mean_above_median_300 # & # mean_above_median_150 ) conditions.append(sell_ema_1a1) dataframe.loc[sell_ema_1a1, 'exit_tag'] += 'EMA_up ' # sell_ema_1a2 = ( # (dataframe['close'] > dataframe['ema_offset_sell']) # & # mean_above_median_300 # & # ~mean_above_median_150 # ) # conditions.append(sell_ema_1a2) # dataframe.loc[sell_ema_1a2, 'exit_tag'] += 'EMA_up_a2 ' # sell_ema_1b1 = ( # (dataframe['close'] > dataframe['ema_offset_sell']) # & # ~mean_above_median_300 # & # mean_above_median_150 # ) # conditions.append(sell_ema_1b1) # dataframe.loc[sell_ema_1b1, 'exit_tag'] += 'EMA_up_b1 ' # sell_ema_1b2 = ( # (dataframe['close'] > dataframe['ema_offset_sell']) # & # ~mean_above_median_300 # & # ~mean_above_median_150 # ) # conditions.append(sell_ema_1b2) # dataframe.loc[sell_ema_1b2, 'exit_tag'] += 'EMA_up_b2 ' sell_ema_2a1 = ( (dataframe['close'] < dataframe['ema_offset_sell2']) & mean_above_median_300 & mean_above_median_150 ) conditions.append(sell_ema_2a1) dataframe.loc[sell_ema_2a1, 'exit_tag'] += 'EMA_down_a1 ' sell_ema_2a2 = ( (dataframe['close'] < dataframe['ema_offset_sell2a']) & mean_above_median_300 & ~mean_above_median_150 ) conditions.append(sell_ema_2a2) dataframe.loc[sell_ema_2a2, 'exit_tag'] += 'EMA_down_a2 ' sell_ema_2b = ( (dataframe['close'] < dataframe['ema_offset_sell2']) & ~mean_above_median_300 ) conditions.append(sell_ema_2b) dataframe.loc[sell_ema_2b, 'exit_tag'] += 'EMA_down_b ' sell_ema_2bb = ( (dataframe['close'] < dataframe['ema_offset_sell2b']) & strong_uptrend ) conditions.append(sell_ema_2bb) dataframe.loc[sell_ema_2bb, 'exit_tag'] += 'EMA_down_b2 ' sell_ema_3a1 = ( ((dataframe['close'] < dataframe['ema_offset_sell3a']).rolling(2).min() > 0) & mean_above_median_300 & mean_above_median_150 ) conditions.append(sell_ema_3a1) dataframe.loc[sell_ema_3a1, 'exit_tag'] += 'EMA_down_2a1 ' sell_ema_3a2 = ( ((dataframe['close'] < dataframe['ema_offset_sell3']).rolling(2).min() > 0) & mean_above_median_300 & ~mean_above_median_150 ) conditions.append(sell_ema_3a2) dataframe.loc[sell_ema_3a2, 'exit_tag'] += 'EMA_down_2a2 ' sell_ema_3b = ( ((dataframe['close'] < dataframe['ema_offset_sell3']).rolling(2).min() > 0) & ~mean_above_median_300 ) conditions.append(sell_ema_3b) dataframe.loc[sell_ema_3b, 'exit_tag'] += 'EMA_down_2b ' sell_ema_4 = ( (dataframe['close'] > dataframe['ema_offset_sell4']).rolling(2).min() > 0 ) conditions.append(sell_ema_4) dataframe.loc[sell_ema_4, 'exit_tag'] += 'EMA_up_2 ' sell_long_green3a = ( (dataframe['pct_change'].rolling(3).sum() > (0.01 * self.sell_long_green3.value)) & ~strong_uptrend & ~strong_downtrend & ~medium_downtrend ) conditions.append(sell_long_green3a) dataframe.loc[sell_long_green3a, 'exit_tag'] += 'green_3 ' sell_long_green3c = ( (dataframe['pct_change'].rolling(3).sum() > (0.01 * self.sell_long_green3b.value)) & strong_downtrend ) conditions.append(sell_long_green3c) dataframe.loc[sell_long_green3c, 'exit_tag'] += 'green_3_strong_down ' # dataframe.loc[strong_uptrend, 'exit_tag'] += 'strong_up ' # dataframe.loc[medium_uptrend, 'exit_tag'] += 'medium_up ' # dataframe.loc[weak_uptrend, 'exit_tag'] += 'weak_up ' # dataframe.loc[strong_downtrend, 'exit_tag'] += 'strong_down ' # dataframe.loc[medium_downtrend, 'exit_tag'] += 'medium_down ' # dataframe.loc[weak_downtrend, 'exit_tag'] += 'weak_down ' add_check = ( (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions) & add_check, 'exit_long' ] = 1 return dataframe