# source: https://raw.githubusercontent.com/syuraj/freq-test/12243c6e3ebf9a245cca012b5d5e35b9e49aae91/user_data/strategies_backup/UTBot_Alerts_strat.py # %% # Import libraries from cmath import nan from functools import reduce from math import sqrt import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.persistence import Trade from freqtrade.strategy import (BooleanParameter, CategoricalParameter, stoploss_from_open, DecimalParameter, IntParameter, IStrategy, informative, merge_informative_pair) import talib.abstract as ta import pandas_ta as pta from technical import qtpylib # %% # custom indicators def UTBot_Alerts(dataframe, key_value=1, atr_period=3, ema_period=200): # Calculate ATR and xATRTrailingStop xATR = np.array(ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=atr_period)) nLoss = key_value * xATR src = dataframe['close'] # Initialize arrays xATRTrailingStop = np.zeros(len(dataframe)) xATRTrailingStop[0] = src[0] - nLoss[0] # Calculate xATRTrailingStop using vectorized operations mask_1 = (src > np.roll(xATRTrailingStop, 1)) & (np.roll(src, 1) > np.roll(xATRTrailingStop, 1)) mask_2 = (src < np.roll(xATRTrailingStop, 1)) & (np.roll(src, 1) < np.roll(xATRTrailingStop, 1)) mask_3 = src > np.roll(xATRTrailingStop, 1) xATRTrailingStop = np.where(mask_1, np.maximum(np.roll(xATRTrailingStop, 1), src - nLoss), xATRTrailingStop) xATRTrailingStop = np.where(mask_2, np.minimum(np.roll(xATRTrailingStop, 1), src + nLoss), xATRTrailingStop) xATRTrailingStop = np.where(mask_3, src - nLoss, xATRTrailingStop) mask_buy = (np.roll(src, 1) < xATRTrailingStop) & (src > np.roll(xATRTrailingStop, 1)) mask_sell = (np.roll(src, 1) > xATRTrailingStop) & (src < np.roll(xATRTrailingStop, 1)) pos = np.zeros(len(dataframe)) pos = np.where(mask_buy, 1, pos) pos = np.where(mask_sell, -1, pos) pos[~((pos == 1) | (pos == -1))] = 0 ema = np.array(ta.EMA(dataframe['close'], timeperiod=ema_period)) buy_condition_utbot = (xATRTrailingStop > ema) & (pos > 0) & (src > ema) sell_condition_utbot = (xATRTrailingStop < ema) & (pos < 0) & (src < ema) trend = np.where(buy_condition_utbot, 1, np.where(sell_condition_utbot, -1, 0)) trend = np.array(trend) dataframe['trend'] = trend return dataframe def optimize_trend_alert(dataframe, key_value=1, atr_period=3, ema_period=200): # Calculate ATR and xATRTrailingStop xATR = np.array(ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=atr_period)) nLoss = key_value * xATR src = dataframe['close'] # Initialize arrays xATRTrailingStop = np.zeros(len(dataframe)) xATRTrailingStop[0] = src[0] - nLoss[0] # Calculate xATRTrailingStop using vectorized operations mask_1 = (src > np.roll(xATRTrailingStop, 1)) & (np.roll(src, 1) > np.roll(xATRTrailingStop, 1)) mask_2 = (src < np.roll(xATRTrailingStop, 1)) & (np.roll(src, 1) < np.roll(xATRTrailingStop, 1)) mask_3 = src > np.roll(xATRTrailingStop, 1) xATRTrailingStop = np.where(mask_1, np.maximum(np.roll(xATRTrailingStop, 1), src - nLoss), xATRTrailingStop) xATRTrailingStop = np.where(mask_2, np.minimum(np.roll(xATRTrailingStop, 1), src + nLoss), xATRTrailingStop) xATRTrailingStop = np.where(mask_3, src - nLoss, xATRTrailingStop) # Calculate pos using vectorized operations mask_buy = (np.roll(src, 1) < xATRTrailingStop) & (src > np.roll(xATRTrailingStop, 1)) mask_sell = (np.roll(src, 1) > xATRTrailingStop) & (src < np.roll(xATRTrailingStop, 1)) pos = np.zeros(len(dataframe)) pos = np.where(mask_buy, 1, pos) pos = np.where(mask_sell, -1, pos) pos[~((pos == 1) | (pos == -1))] = 0 ema = np.array(ta.EMA(dataframe['close'], timeperiod=ema_period)) buy_condition_utbot = (xATRTrailingStop > ema) & (pos > 0) & (src > ema) sell_condition_utbot = (xATRTrailingStop < ema) & (pos < 0) & (src < ema) trend = np.where(buy_condition_utbot, 1, np.where(sell_condition_utbot, -1, 0)) trend = np.array(trend) dataframe['trend'] = trend return dataframe class Github_syuraj_freq_test__UTBot_Alerts_strat__20240129_002406(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 = '1h' # Can this strategy go short? can_short = True # risk_c = DecimalParameter(0.025, 0.01, 0.1, decimals=2, space='buy') # Minimal ROI designed for the strategy. minimal_roi = { # "0": 0.282, # "138": 0.179, # "310": 0.089, # "877": 0 # '0': 0.344, '260': 0.225, '486': 0.09, '796': 0 "0": 0.184, "416": 0.14, "933": 0.073, "1982": 0 # '0': 0.279, '154': 0.122, '376': 0.085, '456': 0 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.317 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.022 trailing_only_offset_is_reached = True # 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 # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 #leverage here leverage_optimize = True leverage_num = IntParameter(low=1, high=5, default=5, space='buy', optimize=leverage_optimize) # Strategy parameters parameters_yes = True parameters_no = False key_value_l = IntParameter(1, 50, default=2, space="protection", optimize=True) key_value_s = IntParameter(1, 50, default=4, space="protection", optimize=True) atr_period_l = IntParameter(10, 200, default=7, space="signal", optimize=True) atr_period_s = IntParameter(10, 200, default=10, space="signal", optimize=True) ema_period_l = IntParameter(10, 200, default=10, space="signal", optimize=True) ema_period_s = IntParameter(10, 200, default=50, space="signal", optimize=True) ema_period_l_exit = IntParameter(10, 200, default=10, space="signal", optimize=True) ema_period_s_exit = IntParameter(10, 200, default=50, space="signal", optimize=True) volume_check = IntParameter(10, 200, default=10, space="signal", optimize=True) volume_check_s = IntParameter(10, 200, default=50, space="signal", optimize=True) volume_check_exit = IntParameter(10, 200, default=50, space="signal", optimize=True) volume_check_exit_s = IntParameter(10, 200, default=50, space="signal", optimize=True) def custom_strategy(dataframe): dataframe = UTBot_Alerts(dataframe, key_value=2, atr_period=7, ema_period=100) # Calculate RSI and ADX rsi = ta.RSI(dataframe['close']) adx = ta.ADX(dataframe['high'], dataframe['low'], dataframe['close']) # Define conditions based on UTBot Alerts and additional indicators # ... (your custom conditions here) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not self.dp: # Don't do anything if DataProvider is not available. return dataframe L_optimize_trend_alert = optimize_trend_alert(dataframe=dataframe, key_value= self.key_value_l.value, atr_period= self.atr_period_l.value, ema_period=self.ema_period_l.value) dataframe['trend_l'] = L_optimize_trend_alert['trend'] S_optimize_trend_alert = optimize_trend_alert(dataframe=dataframe, key_value= self.key_value_s.value, atr_period= self.atr_period_s.value, ema_period=self.ema_period_s.value) dataframe['trend_s'] = S_optimize_trend_alert['trend'] # ADX dataframe['adx'] = ta.ADX(dataframe) # RSI # dataframe['rsi'] = ta.RSI(dataframe) # EMA dataframe['ema_l'] = ta.EMA(dataframe['close'], timeperiod=self.ema_period_l_exit.value) dataframe['ema_s'] = ta.EMA(dataframe['close'], timeperiod=self.ema_period_s_exit.value) # Volume Weighted dataframe['volume_mean'] = dataframe['volume'].rolling(self.volume_check.value).mean().shift(1) dataframe['volume_mean_exit'] = dataframe['volume'].rolling(self.volume_check_exit.value).mean().shift(1) dataframe['volume_mean_s'] = dataframe['volume'].rolling(self.volume_check_s.value).mean().shift(1) dataframe['volume_mean_exit_s'] = dataframe['volume'].rolling(self.volume_check_exit_s.value).mean().shift(1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['adx'] > self.adx_long_min.value) & # trend strength confirmation (dataframe['adx'] < self.adx_long_max.value) & # trend strength confirmation (dataframe['trend_l'] > 0) & (dataframe['volume'] > dataframe['volume_mean']) & (dataframe['volume'] > 0) ), 'enter_long'] = 1 dataframe.loc[ ( (dataframe['adx'] > self.adx_short_min.value) & # trend strength confirmation (dataframe['adx'] < self.adx_short_max.value) & # trend strength confirmation (dataframe['trend_s'] < 0) & (dataframe['volume'] > dataframe['volume_mean_s']) # volume weighted indicator ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [] conditions_short = [] dataframe.loc[:, 'exit_tag'] = '' exit_long = ( # (dataframe['close'] < dataframe['low'].shift(self.sell_shift.value)) & (dataframe['close'] < dataframe['ema_l']) & (dataframe['volume'] > dataframe['volume_mean_exit']) ) exit_short = ( # (dataframe['close'] > dataframe['high'].shift(self.sell_shift_short.value)) & (dataframe['close'] > dataframe['ema_s']) & (dataframe['volume'] > dataframe['volume_mean_exit_s']) ) conditions_short.append(exit_short) dataframe.loc[exit_short, 'exit_tag'] += 'exit_short' conditions_long.append(exit_long) dataframe.loc[exit_long, 'exit_tag'] += 'exit_long' if conditions_long: dataframe.loc[ reduce(lambda x, y: x | y, conditions_long), 'exit_long'] = 1 if conditions_short: dataframe.loc[ reduce(lambda x, y: x | y, conditions_short), 'exit_short'] = 1 return dataframe