# source: https://raw.githubusercontent.com/RoboticAutomations/freqtradestrategies/52c4bd40414645f079a980cf4486dc7af4061efb/AIRA%20-%20open%20code%20v2/strategies/arima-statistical/ARIMA_MTF_CS.py # directory_url: https://github.com/RoboticAutomations/freqtradestrategies/blob/main/AIRA - open code v2/strategies/arima-statistical/ # User: RoboticAutomations # Repository: freqtradestrategies # -------------------- # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List, Optional, Tuple, Union from functools import reduce from pandas import DataFrame import warnings import pandas as pd # -------------------------------- import talib.abstract as ta from talib import MA_Type import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, informative from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter, BooleanParameter from freqtrade.exchange import date_minus_candles import technical.indicators as ftt import math import logging from scipy.signal import find_peaks, find_peaks_cwt import warnings from math import ceil from datetime import datetime, timezone, timedelta from pmdarima import auto_arima from pmdarima import model_selection from sklearn.metrics import mean_squared_error from statsmodels.tsa.stattools import acf import time warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning) pd.set_option('display.float_format', lambda x: '%.7f' % x) logger = logging.getLogger(__name__) class Github_RoboticAutomations_freqtradestrategies__ARIMA_MTF_CS__20260915_233254(IStrategy): INTERFACE_VERSION = 3 # Stoploss: stoploss = -0.08 # Trailing stop: use_custom_stoploss = True trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # Initialize dicts for arima storage last_run_time = {} arima_model = {} last_run_time_1h = {} arima_model_1h = {} last_run_time_4h = {} arima_model_4h = {} # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False can_short = True ## Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } order_types = { "entry": "limit", "exit": "limit", "emergency_exit": "market", "stoploss": "market", "stoploss_on_exchange": True, "stoploss_on_exchange_interval": 60, "stoploss_on_exchange_limit_ratio": 0.99 } # Optimal timeframe for the strategy timeframe = '15m' startup_candle_count = 480 process_only_new_candles = True # Custom Entry last_entry_price = None position_adjustment_enable = True # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=-12.0, space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=8.0, space='buy', optimize=True) ewo_high_2 = DecimalParameter(-6.0, 12.0, default=-8, space='buy', optimize=True) # Hyper-opt parameters base_nb_candles_buy = IntParameter(150, 200, default=180, space='buy', optimize=True) up = DecimalParameter(low=1.020, high=1.025, default=1.02, decimals=3 ,space='buy', optimize=True, load=True) dn = DecimalParameter(low=0.983, high=0.987, default=0.984, decimals=3 ,space='buy', optimize=True, load=True) increment = DecimalParameter(low=1.0005, high=1.001, default=1.0007, decimals=4 ,space='buy', optimize=True, load=True) atr_length = IntParameter(10, 30, default=14, space='buy', optimize=True, load=True) window = IntParameter(10, 30, default=21, space='buy', optimize=True, load=True) window_1h = IntParameter(10, 30, default=21, space='buy', optimize=True, load=True) window_4h = IntParameter(10, 30, default=5, space='buy', optimize=True, load=True) x = DecimalParameter(low=1.2, high=1.75, default=1.6, decimals=2 ,space='buy', optimize=True, load=True) x_1h = DecimalParameter(low=1.2, high=1.75, default=1.6, decimals=2 ,space='buy', optimize=True, load=True) x_4h = DecimalParameter(low=1.2, high=1.75, default=1.6, decimals=2 ,space='buy', optimize=True, load=True) predicted_profit_percentage = DecimalParameter(low=0.5, high=1, default=1, decimals=3, space='buy', optimize=True, load=True) predicted_profit_percentage_4h = DecimalParameter(low=0.5, high=2, default=1.5, decimals=3, space='buy', optimize=True, load=True) predicted_profit_percentage_4h_big = DecimalParameter(low=0.5, high=3, default=2, decimals=3, space='buy', optimize=True, load=True) ### trailing stop loss optimiziation ### tsl_target3 = DecimalParameter(low=0.10, high=0.15, default=0.15, decimals=2, space='sell', optimize=True, load=True) ts3 = DecimalParameter(low=0.025, high=0.04, default=0.035, decimals=3, space='sell', optimize=True, load=True) tsl_target2 = DecimalParameter(low=0.06, high=0.10, default=0.1, decimals=3, space='sell', optimize=True, load=True) ts2 = DecimalParameter(low=0.015, high=0.03, default=0.02, decimals=3, space='sell', optimize=True, load=True) tsl_target1 = DecimalParameter(low=0.04, high=0.08, default=0.06, decimals=3, space='sell', optimize=True, load=True) ts1 = DecimalParameter(low=0.01, high=0.016, default=0.013, decimals=3, space='sell', optimize=True, load=True) tsl_target0 = DecimalParameter(low=0.03, high=0.06, default=0.04, decimals=3, space='sell', optimize=True, load=True) ts0 = DecimalParameter(low=0.005, high=0.012, default=0.01, decimals=3, space='sell', optimize=True, load=True) moon = IntParameter(80, 90, default=85, space='sell', optimize=True) @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 5 }, { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 6, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.02 }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "required_profit": 0.01 } ] def custom_entry_price(self, pair: str, trade: Optional['Trade'], current_time: datetime, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs) -> float: dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) entry_price = (dataframe['close'].iat[-1] + dataframe['open'].iat[-1] + proposed_rate + proposed_rate) / 4 logger.info(f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}") # Check if there is a stored last entry price and if it matches the proposed entry price if self.last_entry_price is not None and abs(entry_price - self.last_entry_price) < 0.0001: # Tolerance for floating-point comparison entry_price *= self.increment.value # Increment by 0.2% logger.info(f"{pair} Incremented entry price: {entry_price} based on previous entry price : {self.last_entry_price}.") # Update the last entry price self.last_entry_price = entry_price return entry_price def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: # Handle freak events if exit_reason == 'roi' and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} ROI is below 0") self.dp.send_msg(f'{trade.pair} ROI is below 0') return False if exit_reason == 'partial_exit' and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} partial exit is below 0") self.dp.send_msg(f'{trade.pair} partial exit is below 0') return False if exit_reason == 'trailing_stop_loss' and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} trailing stop price is below 0") self.dp.send_msg(f'{trade.pair} trailing stop price is below 0') return False return True @informative('4h') def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair_4h = metadata['pair'] current_time_4h = time.time() dataframe['OHLC4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 size_4h = len(dataframe) - 10 train_4h, test_4h = model_selection.train_test_split(dataframe['OHLC4'], train_size=size_4h) # Initialize values for the current pair if not already done if pair_4h not in self.last_run_time_4h: self.last_run_time_4h[pair_4h] = current_time_4h logger.info(f"Initial ARIMA 4h Model Training for {pair_4h}") # Fit ARIMA model start_time_4h = time.time() self.arima_model_4h[pair_4h] = auto_arima(train_4h, start_p=1, start_q=1, start_P=1, start_Q=1, max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=False, stepwise=True, suppress_warnings=True, D=10, max_D=20, error_action='ignore') fitting_time_4h = time.time() - start_time_4h logger.info(f"{pair_4h} - ARIMA 4h Model fitted in {fitting_time_4h:.2f} seconds") # Check if it's time to retrain for the current pair if current_time_4h - self.last_run_time_4h[pair_4h] >= 14400: # Check if an 4 hours has passed logger.info(f"Auto Fitting 4h ARIMA Model for {pair_4h}") self.last_run_time_4h[pair_4h] = current_time_4h # Fit ARIMA model start_time_4h = time.time() self.arima_model_4h[pair_4h] = auto_arima(train_4h, start_p=1, start_q=1, start_P=1, start_Q=1, max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=False, stepwise=True, suppress_warnings=True, D=10, max_D=20, error_action='ignore') fitting_time_4h = time.time() - start_time_4h logger.info(f"{pair_4h} - ARIMA 4h Model fitted in {fitting_time_4h:.2f} seconds") # Use the previously fitted ARIMA model for forecasting if self.arima_model_4h[pair_4h] is not None: start_time_4h = time.time() future_forecast_4h, conf_int_4h = self.arima_model_4h[pair_4h].predict(n_periods=test_4h.shape[0], return_conf_int=True) inference_time_4h = time.time() - start_time_4h timeleft_4h = current_time_4h - self.last_run_time_4h[pair_4h] if timeleft_4h <= 43200 and timeleft_4h != 0: logger.info(f"{pair_4h} - ARIMA 4h Model re-optimized in {timeleft_4h:.2f} seconds") # Extract upper and lower confidence intervals lower_confidence_4h, upper_confidence_4h = conf_int_4h[:, 0], conf_int_4h[:, 1] logger.info(f"{pair_4h} - Inference time: {inference_time_4h:.2f} seconds | " \ f"Current Price: {dataframe['OHLC4'].iloc[-1]:.7f} | 4h Future Forecast: {future_forecast_4h.iloc[-1]:.7f}") dataframe['rmse'] = 0 dataframe['accuracy_perc'] = 0 dataframe['reward'] = 0 dataframe['rmse'] = np.sqrt(mean_squared_error(test_4h, future_forecast_4h)) dataframe['accuracy_perc'] = 100 * (1 - (dataframe['rmse'].iloc[-1] / dataframe['OHLC4'].iloc[-1])) dataframe['reward'] = ((future_forecast_4h.iloc[-1] / dataframe['OHLC4'].iloc[-1]) - 1) * 100 rmse_4h = dataframe['rmse'].iloc[-1] accuracy_perc_4h = dataframe['accuracy_perc'].iloc[-1] reward_4h = dataframe['reward'] .iloc[-1] # Apply rolling window operation to the 'OHLC4' column rolling_window_4h = dataframe['OHLC4'].rolling(self.window_4h.value) # Calculate the peak-to-peak value on the resulting rolling window data ptp_value_4h = rolling_window_4h.apply(lambda x: np.ptp(x)) # Assign the calculated peak-to-peak value to the DataFrame column dataframe['move'] = ptp_value_4h / dataframe['OHLC4'] dataframe['move_mean'] = dataframe['move'].mean() dataframe['move_mean_x'] = dataframe['move'].mean() * self.x_4h.value move_4h = '{:.2f}'.format(dataframe['move'].iloc[-1] * 100) move_mean_4h = '{:.2f}'.format(dataframe['move_mean'].iloc[-1] * 100) if future_forecast_4h.iloc[-1] > dataframe['OHLC4'].iloc[-1]: direction_4h = 'Up' dataframe['decision'] = 1 else: direction_4h = 'Down' dataframe['decision'] = -1 logger.info(f"{pair_4h} - Test RMSE: {rmse_4h:.3f} | Accuracy: {accuracy_perc_4h:.2f}% | Potential Profit: {move_4h}% | Avg. Profit: {move_mean_4h}% | 4h Trend: {direction_4h}") dataframe['arima_predictions'] = pd.Series(future_forecast_4h) dataframe['lower_confidence'] = pd.Series(lower_confidence_4h) dataframe['upper_confidence'] = pd.Series(upper_confidence_4h) dataframe['atr'] = ta.ATR(dataframe, timeperiod=self.atr_length.value) dataframe['vol_z_score'] = (dataframe['volume'] - dataframe['volume'].rolling(window=30).mean()) / dataframe['volume'].rolling(window=30).std() dataframe['vol_anomaly'] = np.where(dataframe['vol_z_score'] > 3, 1, 0) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['sma'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] dataframe['sma_up'] = dataframe['sma'] * self.up.value dataframe['sma_dn'] = dataframe['sma'] * self.dn.value # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # Calculate Bollinger Bands dataframe['bb_upperband'], dataframe['bb_middleband'], dataframe['bb_lowerband'] = ta.BBANDS(dataframe['close'], timeperiod=20) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] return dataframe @informative('1h') def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair_1h = metadata['pair'] current_time_1h = time.time() dataframe['OHLC4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 size_1h = len(dataframe) - 10 train_1h, test_1h = model_selection.train_test_split(dataframe['OHLC4'], train_size=size_1h) # Initialize values for the current pair if not already done if pair_1h not in self.last_run_time_1h: self.last_run_time_1h[pair_1h] = current_time_1h logger.info(f"Initial ARIMA 1h Model Training for {pair_1h}") # Fit ARIMA model start_time_1h = time.time() self.arima_model[pair_1h] = auto_arima(train_1h, start_p=1, start_q=1, start_P=1, start_Q=1, max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=False, stepwise=True, suppress_warnings=True, D=10, max_D=20, error_action='ignore') fitting_time_1h = time.time() - start_time_1h logger.info(f"{pair_1h} - ARIMA 1h Model fitted in {fitting_time_1h:.2f} seconds") # Check if it's time to retrain for the current pair if current_time_1h - self.last_run_time_1h[pair_1h] >= 3600: # Check if an 1 hour has passed logger.info(f"Auto Fitting ARIMA 1h Model for {pair_1h}") self.last_run_time_1h[pair_1h] = current_time_1h # Fit ARIMA model start_time_1h = time.time() self.arima_model[pair_1h] = auto_arima(train_1h, start_p=1, start_q=1, start_P=1, start_Q=1, max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=False, stepwise=True, suppress_warnings=True, D=10, max_D=20, error_action='ignore') fitting_time_1h = time.time() - start_time_1h logger.info(f"{pair_1h} - ARIMA 1h Model fitted in {fitting_time_1h:.2f} seconds") # Use the previously fitted ARIMA model for forecasting if self.arima_model[pair_1h] is not None: start_time_1h = time.time() future_forecast_1h, conf_int_1h = self.arima_model[pair_1h].predict(n_periods=test_1h.shape[0], return_conf_int=True) inference_time_1h = time.time() - start_time_1h # print(self.last_run_time_1h) timeleft_1h = current_time_1h - self.last_run_time_1h[pair_1h] if timeleft_1h <= 14400 and timeleft_1h != 0: logger.info(f"{pair_1h} - ARIMA 1h Model re-optimized in {timeleft_1h:.2f} seconds") # Calculate autocorrelation and add to dataframe acf1 = acf(dataframe['OHLC4'], fft=False)[1] dataframe['acf1'] = acf1 # Assuming you want to use the autocorrelation value for the last candle acf1_last = dataframe['acf1'].iloc[-1] logger.info(f"Autocorrelation for {pair_1h}: {acf1_last:.6f}") # Extract upper and lower confidence intervals lower_confidence_1h, upper_confidence_1h = conf_int_1h[:, 0], conf_int_1h[:, 1] logger.info(f"{pair_1h} - Inference time: {inference_time_1h:.2f} seconds | " \ f"Current Price: {dataframe['OHLC4'].iloc[-1]:.7f} | 1h Future Forecast: {future_forecast_1h.iloc[-1]:.7f}") dataframe['rmse'] = 0 dataframe['accuracy_perc'] = 0 dataframe['reward'] = 0 dataframe['rmse'] = np.sqrt(mean_squared_error(test_1h, future_forecast_1h)) dataframe['accuracy_perc'] = 100 * (1 - (dataframe['rmse'].iloc[-1] / dataframe['OHLC4'].iloc[-1])) dataframe['reward'] = ((future_forecast_1h.iloc[-1] / dataframe['OHLC4'].iloc[-1]) - 1) * 100 rmse_1h = dataframe['rmse'].iloc[-1] accuracy_perc_1h = dataframe['accuracy_perc'].iloc[-1] reward_1h = dataframe['reward'] .iloc[-1] # Apply rolling window operation to the 'OHLC4' column rolling_window_1h = dataframe['OHLC4'].rolling(self.window_1h.value) # 21 hours # Calculate the peak-to-peak value on the resulting rolling window data ptp_value_1h = rolling_window_1h.apply(lambda x: np.ptp(x)) # Assign the calculated peak-to-peak value to the DataFrame column dataframe['move'] = ptp_value_1h / dataframe['OHLC4'] dataframe['move_mean'] = dataframe['move'].mean() dataframe['move_mean_x'] = dataframe['move'].mean() * self.x_1h.value move_1h = '{:.2f}'.format(dataframe['move'].iloc[-1] * 100) move_mean_1h = '{:.2f}'.format(dataframe['move_mean'].iloc[-1] * 100) if future_forecast_1h.iloc[-1] > dataframe['OHLC4'].iloc[-1]: direction_1h = 'Up' dataframe['decision'] = 1 else: direction_1h = 'Down' dataframe['decision'] = -1 logger.info(f"{pair_1h} - Test RMSE: {rmse_1h:.3f} | Accuracy: {accuracy_perc_1h:.2f}% | Potential Profit: {move_1h}% | Avg. Profit: {move_mean_1h}% | 1h Trend: {direction_1h}") dataframe['arima_predictions'] = pd.Series(future_forecast_1h) dataframe['lower_confidence'] = pd.Series(lower_confidence_1h) dataframe['upper_confidence'] = pd.Series(upper_confidence_1h) dataframe['atr'] = ta.ATR(dataframe, timeperiod=self.atr_length.value) dataframe['vol_z_score'] = (dataframe['volume'] - dataframe['volume'].rolling(window=30).mean()) / dataframe['volume'].rolling(window=30).std() dataframe['vol_anomaly'] = np.where(dataframe['vol_z_score'] > 3, 1, 0) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['sma'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] dataframe['sma_up'] = dataframe['sma'] * self.up.value dataframe['sma_dn'] = dataframe['sma'] * self.dn.value # Calculate Bollinger Bands dataframe['bb_upperband'], dataframe['bb_middleband'], dataframe['bb_lowerband'] = ta.BBANDS(dataframe['close'], timeperiod=20) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pair = metadata['pair'] current_time = time.time() dataframe['OHLC4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 size = len(dataframe) - 10 train, test = model_selection.train_test_split(dataframe['OHLC4'], train_size=size) # Initialize values for the current pair if not already done if pair not in self.last_run_time: self.last_run_time[pair] = current_time logger.info(f"Initial ARIMA {self.timeframe} Model Training for {pair}") # Fit ARIMA model with error handling and performance tracking start_time = time.time() try: model = auto_arima(train, start_p=1, start_q=1, start_P=1, start_Q=1, max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=False, stepwise=True, suppress_warnings=True, D=10, max_D=20, error_action='ignore') # Validate model if not hasattr(model, 'order'): raise ValueError("Model fitting failed - no order attribute") # Store model and track performance self.arima_model[pair] = model fitting_time = time.time() - start_time logger.info(f"{pair} - ARIMA {self.timeframe} Model fitted in {fitting_time:.2f} seconds") logger.info(f"Model Order: {model.order} | AIC: {model.aic():.2f}") # Track historical performance if not hasattr(self, 'model_history'): self.model_history = {} if pair not in self.model_history: self.model_history[pair] = [] # Calculate in-sample performance preds = model.predict_in_sample() rmse = np.sqrt(mean_squared_error(train, preds)) self.model_history[pair].append({ 'timestamp': current_time, 'order': model.order, 'aic': model.aic(), 'rmse': rmse, 'fitting_time': fitting_time }) except Exception as e: logger.error(f"ARIMA model fitting failed for {pair}: {str(e)}") # Use previous model if available if pair in self.arima_model: logger.warning(f"Using previous model for {pair}") return else: raise ValueError(f"Initial model fitting failed for {pair}") # Check if it's time to retrain for the current pair if current_time - self.last_run_time[pair] >= 3600: # Check if an hour has passed logger.info(f"Auto Fitting ARIMA {self.timeframe} Model for {pair}") self.last_run_time[pair] = current_time # Fit ARIMA model start_time = time.time() self.arima_model[pair] = auto_arima(train, start_p=1, start_q=1, start_P=1, start_Q=1, max_p=5, max_q=5, max_P=5, max_Q=5, seasonal=False, stepwise=True, suppress_warnings=True, D=10, max_D=20, error_action='ignore') fitting_time = time.time() - start_time logger.info(f"{pair} - ARIMA {self.timeframe} Model fitted in {fitting_time:.2f} seconds") # Use the previously fitted ARIMA model for forecasting if self.arima_model[pair] is not None: start_time = time.time() future_forecast, conf_int = self.arima_model[pair].predict(n_periods=test.shape[0], return_conf_int=True) inference_time = time.time() - start_time timeleft = current_time - self.last_run_time[pair] if timeleft <= 3600 and timeleft != 0: logger.info(f"{pair} - ARIMA {self.timeframe} Model re-optimized in {timeleft:.2f} seconds") # Extract upper and lower confidence intervals lower_confidence, upper_confidence = conf_int[:, 0], conf_int[:, 1] logger.info(f"{pair} - Inference time: {inference_time:.2f} seconds | " \ f"Current Price: {dataframe['OHLC4'].iloc[-1]:.7f} | {self.timeframe} Future Forecast: {future_forecast.iloc[-1]:.7f}") dataframe['rmse'] = 0 dataframe['accuracy_perc'] = 0 dataframe['reward'] = 0 dataframe['rmse'] = np.sqrt(mean_squared_error(test, future_forecast)) dataframe['accuracy_perc'] = 100 * (1 - (dataframe['rmse'].iloc[-1] / dataframe['OHLC4'].iloc[-1])) dataframe['reward'] = ((future_forecast.iloc[-1] / dataframe['OHLC4'].iloc[-1]) - 1) * 100 rmse = dataframe['rmse'].iloc[-1] accuracy_perc = dataframe['accuracy_perc'].iloc[-1] reward = dataframe['reward'] .iloc[-1] # Apply rolling window operation to the 'OHLC4' column rolling_window = dataframe['OHLC4'].rolling(self.window.value) # 5.25 hrs # Calculate the peak-to-peak value on the resulting rolling window data ptp_value = rolling_window.apply(lambda x: np.ptp(x)) # Assign the calculated peak-to-peak value to the DataFrame column dataframe['move'] = ptp_value / dataframe['OHLC4'] dataframe['move_mean'] = dataframe['move'].mean() dataframe['move_mean_x'] = dataframe['move'].mean() * self.x.value move = '{:.2f}'.format(dataframe['move'].iloc[-1] * 100) move_mean = '{:.2f}'.format(dataframe['move_mean'].iloc[-1] * 100) if future_forecast.iloc[-1] > dataframe['OHLC4'].iloc[-1]: direction = 'Up' dataframe['decision'] = 1 else: direction = 'Down' dataframe['decision'] = -1 logger.info(f"{pair} - Test RMSE: {rmse:.3f} | Accuracy: {accuracy_perc:.2f}% | Potential Profit: {move}% | Avg. Profit: {move_mean}% | {self.timeframe} Trend: {direction}") dataframe['arima_predictions'] = pd.Series(future_forecast) dataframe['lower_confidence'] = pd.Series(lower_confidence) dataframe['upper_confidence'] = pd.Series(upper_confidence) dataframe['atr'] = ta.ATR(dataframe, timeperiod=self.atr_length.value) dataframe['atr_pcnt'] = (ta.ATR(dataframe, timeperiod=self.atr_length.value) / dataframe['OHLC4']) dataframe['vol_z_score'] = (dataframe['volume'] - dataframe['volume'].rolling(window=30).mean()) / dataframe['volume'].rolling(window=30).std() dataframe['vol_anomaly'] = np.where(dataframe['vol_z_score'] > 3, 1, 0) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['sma'] = dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] dataframe['sma_up'] = dataframe['sma'] * self.up.value dataframe['sma_dn'] = dataframe['sma'] * self.dn.value dataframe['max_l'] = dataframe['OHLC4'].rolling(120).max() / dataframe['OHLC4'] - 1 dataframe['min_l'] = abs(dataframe['OHLC4'].rolling(120).min() / dataframe['OHLC4'] - 1) dataframe['max'] = dataframe['OHLC4'].rolling(4).max() / dataframe['OHLC4'] - 1 dataframe['min'] = abs(dataframe['OHLC4'].rolling(4).min() / dataframe['OHLC4'] - 1) dataframe['rsi_overbought'] = (dataframe['rsi'] > 70).astype('int') dataframe['volume_increase'] = (dataframe['volume'] > dataframe['volume'].shift()).astype('int') dataframe['price_below_sma'] = (dataframe['close'] < dataframe['sma']).astype('int') dataframe['price_drop'] = (dataframe['close'] < dataframe['close'].shift()).astype('int') dataframe['williamr'] = ta.WILLR(dataframe, timeperiod=10) # Calculate Bollinger Bands dataframe['bb_upperband'], dataframe['bb_middleband'], dataframe['bb_lowerband'] = ta.BBANDS(dataframe['close'], timeperiod=20) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ dataframe['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(dataframe) # values [0, 100] dataframe['CDLDOJISTAR'] = ta.CDLDOJISTAR(dataframe) # values [0, -100, 100] dataframe['CDLTAKURI'] = ta.CDLTAKURI(dataframe) # values [0, 100] dataframe['CDLTRISTAR'] = ta.CDLTRISTAR(dataframe) # values [0, -100, 100] dataframe['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(dataframe) # values [0, -100, 100] dataframe['CDLMARUBOZU'] = ta.CDLMARUBOZU(dataframe) # values [0, -100, 100] dataframe['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(dataframe) dataframe['CDL2CROWS'] = ta.CDL2CROWS(dataframe) # values [0, 100] dataframe['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(dataframe) # values [0, -100, 100] dataframe['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(dataframe) # values [0, 100] dataframe['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(dataframe) # values [0, -100, 100] dataframe['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(dataframe) # values [0, 100] dataframe['CDLTHRUSTING'] = ta.CDLTHRUSTING(dataframe) # values [0, 100] # # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] # # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] bollinger_bullish_divergence = ( (dataframe['close'].shift(1) > dataframe['close']) & (dataframe['bb_lowerband'].shift(1) <= dataframe['bb_lowerband']) ) bollinger_bearish_divergence = ( (dataframe['close'].shift(1) < dataframe['close']) & (dataframe['bb_upperband'].shift(1) >= dataframe['bb_upperband']) ) macd_bullish_divergence = ( (dataframe['close'].shift(1) > dataframe['close']) & (dataframe['macd'].shift(1) <= dataframe['macd']) ) macd_bearish_divergence = ( (dataframe['close'].shift(1) < dataframe['close']) & (dataframe['macd'].shift(1) >= dataframe['macd']) ) long_condition = ( (dataframe['close'] > dataframe['bb_lowerband']) & (qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) & (dataframe['rsi'] < 50) & (bollinger_bullish_divergence | macd_bullish_divergence) ) dataframe.loc[long_condition, 'enter_long'] = 1 dataframe.loc[long_condition, 'enter_tag'] = 'L - Bullish Divergence' short_condition = ( (dataframe['close'] < dataframe['bb_upperband']) & (qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal'])) & (dataframe['rsi'] > 50) & (bollinger_bearish_divergence | macd_bearish_divergence) ) dataframe.loc[short_condition, 'enter_short'] = 1 dataframe.loc[short_condition, 'enter_tag'] = 'S - Bearish Divergence' condition1 = ( (dataframe['decision_1h'] == 1) & (dataframe['EWO_4h'] > self.ewo_high.value) & (dataframe['move_1h'] >= dataframe['move_mean_1h']) & (dataframe['move_1h'].shift(5) < dataframe['move_mean_1h'].shift(5)) & (dataframe['volume_1h'] > 0) ) dataframe.loc[condition1, 'enter_long'] = 1 dataframe.loc[condition1, 'enter_tag'] = 'EWO High 1ne' condition2 = ( (dataframe['decision_4h'] == 1) & (dataframe['EWO_4h'] > self.ewo_high_2.value) & (dataframe['move_4h'] >= dataframe['move_mean_4h']) & (dataframe['move_4h'].shift(5) < dataframe['move_mean_4h'].shift(5)) & (dataframe['volume_4h'] > 0) ) dataframe.loc[condition2, 'enter_long'] = 1 dataframe.loc[condition2, 'enter_tag'] = 'EWO High 2wo' condition3 = ( (dataframe['decision_4h'] == 1) & (dataframe['EWO_4h'] < self.ewo_low.value) & (dataframe['move_4h'] <= dataframe['move_mean_4h']) & (dataframe['move_4h'].shift(5) > dataframe['move_mean_4h'].shift(5)) & (dataframe['volume_4h'] > 0) ) dataframe.loc[condition3, 'enter_long'] = 1 dataframe.loc[condition3, 'enter_tag'] = 'EWO Low-Low' condition4 = ( (dataframe['decision'] == 1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(5) < dataframe['move_mean'].shift(5)) & (qtpylib.crossed_above(dataframe['macd_4h'], dataframe['macdsignal_4h'])) & (dataframe['rsi_4h'] < 50) & (dataframe['volume'] > 0) ) dataframe.loc[condition4, 'enter_long'] = 1 dataframe.loc[condition4, 'enter_tag'] = 'Up Trend Soon 15m (4h MACD + RSI)' condition5 = ( (dataframe['decision_1h'] == 1) & (dataframe['move_1h'] >= dataframe['move_mean_1h']) & (dataframe['move_1h'].shift(2) < dataframe['move_mean_1h'].shift(2)) & (dataframe['volume'] > 0) ) dataframe.loc[condition5, 'enter_long'] = 1 dataframe.loc[condition5, 'enter_tag'] = 'Up Trend Soon 1h' condition6 = ( (dataframe['decision_4h'] == 1) & (dataframe['move_4h'] >= dataframe['move_mean_4h']) & (dataframe['move_4h'].shift(2) < dataframe['move_mean_4h'].shift(2)) & (dataframe['volume_4h'] > 0) ) dataframe.loc[condition6, 'enter_long'] = 1 dataframe.loc[condition6, 'enter_tag'] = 'Up Trend Soon 4h' condition7 = ( (dataframe['decision_4h'] == 1) & (dataframe['move'] >= dataframe['move_mean_x']) & (dataframe['min'] < dataframe['max']) & (dataframe['min_l'] < dataframe['max_l']) & (dataframe['max_l'] < dataframe['atr_pcnt']) & (dataframe['OHLC4'] < dataframe['sma']) & (dataframe['volume'] > 0) ) dataframe.loc[condition7, 'enter_long'] = 1 dataframe.loc[condition7, 'enter_tag'] = 'Move Mean Fib below sma' condition8 = ( (dataframe['decision_4h'] == 1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['min'] < dataframe['max']) & (dataframe['OHLC4'] > dataframe['sma']) & (dataframe['sma_up'].shift() < dataframe['sma']) & (dataframe['volume'] > 0) ) dataframe.loc[condition8, 'enter_long'] = 1 dataframe.loc[condition8, 'enter_tag'] = 'Hope this works...' condition9 = ( (dataframe['decision_1h'] == -1) & (dataframe['decision_4h'] == -1) & (dataframe['move'] <= dataframe['move_mean_x']) & (dataframe['max'] < dataframe['min']) & (dataframe['min_l'] > dataframe['max_l']) & (dataframe['max_l'] > dataframe['atr_pcnt']) & (dataframe['OHLC4'] > dataframe['sma_up']) & (dataframe['volume'] > 0) ) dataframe.loc[condition9, 'enter_short'] = 1 dataframe.loc[condition9, 'enter_tag'] = 'Move Mean Fib above sma_up' condition10 = ( (dataframe['decision_1h'] == -1) & (dataframe['decision_4h'] == -1) & (dataframe['move'] <= dataframe['move_mean']) & (dataframe['move'].shift(6) > dataframe['move_mean'].shift(6)) & (dataframe['max'] < dataframe['min']) & (dataframe['min_l'] > dataframe['max_l']) & (dataframe['OHLC4'] > dataframe['sma']) & (dataframe['volume'] > 0) ) dataframe.loc[condition10, 'enter_short'] = 1 dataframe.loc[condition10, 'enter_tag'] = 'Down Trend Soon above sma' condition11 = ( (dataframe['reward'] >= 0.08) & (dataframe['reward_4h'] >= abs(self.predicted_profit_percentage_4h_big.value)) & (dataframe['accuracy_perc'] >= 0.9) & (dataframe['accuracy_perc_4h'] >= 98.0) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(6) < dataframe['move_mean'].shift(6)) & (dataframe['min'] < dataframe['max']) & (dataframe['min'] != 0) & (dataframe['volume'] > 0) & (dataframe['OHLC4'] < dataframe['sma']) ) dataframe.loc[condition11, 'enter_long'] = 1 dataframe.loc[condition11, 'enter_tag'] = 'Big Up Trend 4h' condition12 = ( (dataframe['reward'] <= -0.10) & (dataframe['reward_4h'] <= -abs(self.predicted_profit_percentage_4h_big.value)) & (dataframe['accuracy_perc'] >= 98.0) & (dataframe['accuracy_perc_4h'] >= 98.0) & (dataframe['move'] <= dataframe['move_mean']) & (dataframe['move'].shift(6) > dataframe['move_mean'].shift(6)) & (dataframe['max'] < dataframe['min']) & (dataframe['max'] != 0) & (dataframe['volume'] > 0) ) dataframe.loc[condition12, 'enter_short'] = 1 dataframe.loc[condition12, 'enter_tag'] = 'Big Down Trend 4h' condition13 = ( (dataframe['reward'] >= self.predicted_profit_percentage.value) & (dataframe['reward_4h'] >= self.predicted_profit_percentage_4h.value) & (dataframe['accuracy_perc'] >= 0.9) & (dataframe['accuracy_perc_4h'] >= 98.0) & (dataframe['decision_4h'] == 1) & (dataframe['volume'] > 0) & (dataframe['williamr'] <= -90) & (dataframe['williamr'].shift(10) <= -85) ) dataframe.loc[condition13, 'enter_long'] = 1 dataframe.loc[condition13, 'enter_tag'] = 'W Up Trend Soon' condition14 = ( (dataframe['reward'] <= -abs(self.predicted_profit_percentage.value)) & (dataframe['reward_4h'] <= -abs(self.predicted_profit_percentage_4h.value)) & (dataframe['accuracy_perc'] >= 0.9) & (dataframe['accuracy_perc_4h'] >= 98.0) & (dataframe['decision_4h'] == -1) & (dataframe['williamr'] <= -10) & (dataframe['williamr'].shift(10) <= -25) & (dataframe['volume'] > 0) ) dataframe.loc[condition14, 'enter_short'] = 1 dataframe.loc[condition14, 'enter_tag'] = 'W Down Trend Soon' return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condition1 = ( (dataframe['decision'] == -1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(5) < dataframe['move_mean'].shift(5)) & (qtpylib.crossed_below(dataframe['macd_4h'], dataframe['macdsignal_4h'])) & (dataframe['rsi_4h'] > 50) & (dataframe['volume'] > 0) ) dataframe.loc[condition1, 'exit_long'] = 1 dataframe.loc[condition1, 'exit_tag'] = 'Down Trend Soon 15m (4h MACD + RSI)' condition2 = ( (dataframe['decision_1h'] == -1) & (dataframe['move_1h'] >= dataframe['move_mean_1h']) & (dataframe['move_1h'].shift(2) < dataframe['move_mean_1h'].shift(2)) & (dataframe['volume'] > 0) ) dataframe.loc[condition2, 'exit_long'] = 1 dataframe.loc[condition2, 'exit_tag'] = 'Down Trend Soon 1h' condition3 = ( (dataframe['decision_4h'] == -1) & (dataframe['move_4h'] >= dataframe['move_mean_4h']) & (dataframe['move_4h'].shift(2) < dataframe['move_mean_4h'].shift(2)) & (dataframe['volume'] > 0) ) dataframe.loc[condition3, 'exit_long'] = 1 dataframe.loc[condition3, 'exit_tag'] = 'Down Trend Soon 4h' condition4 = ( (dataframe['decision'] == 1) & (dataframe['move'] <= dataframe['move_mean']) & (dataframe['move'].shift(5) > dataframe['move_mean'].shift(5)) & (dataframe['volume'] > 0) ) dataframe.loc[condition4, 'exit_short'] = 1 dataframe.loc[condition4, 'exit_tag'] = 'Up Trend Soon 15m' condition5 = ( (dataframe['decision_4h'] == 1) & (dataframe['move_4h'] <= dataframe['move_mean_4h']) & (dataframe['move_4h'].shift(5) > dataframe['move_mean_4h'].shift(5)) & (dataframe['volume'] > 0) ) dataframe.loc[condition5, 'exit_short'] = 1 dataframe.loc[condition5, 'exit_tag'] = 'Up Trend Soon 4h' if self.use_exit_signal: dataframe.loc[ ( (dataframe['close_1h'] < dataframe['bb_middleband_1h']) | (dataframe['close_4h'] < dataframe['bb_middleband_4h']) | (dataframe['rsi_1h'] > 70) ), 'exit_long' ] = 1 dataframe.loc[ ( (dataframe['close_1h'] > dataframe['bb_middleband_1h']) | (dataframe['close_4h'] > dataframe['bb_middleband_4h']) | (dataframe['rsi_1h'] < 30) ), 'exit_short' ] = 1 return dataframe # # ADD BEST RMSE & MAPE TO DECIDE TREND UP OR DOWN OR ENTRY SIGNAL - correct values for arima paramters depeding on the timeframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """Dynamic leverage adjustment based on market conditions""" # Get volatility measure dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) atr = dataframe['atr'].iloc[-1] volatility_factor = min(max(atr / current_rate, 0.5), 2.0) # Adjust base leverage based on volatility base_leverage = min(5 / volatility_factor, max_leverage) if max_leverage is None: raise ValueError('Max leverage cannot be None') if not isinstance(max_leverage, (int, float)): raise ValueError('Max leverage must be a number') base_leverage = 5 proposed_leverage = 3 dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() bullish_patterns = ['CDLRISEFALL3METHODS', 'CDLMORNINGDOJISTAR', 'CDLTAKURI', 'CDL3STARSINSOUTH', 'CDLHAMMER', 'CDLINVERTEDHAMMER', 'CDLDRAGONFLYDOJI', 'CDLPIERCING', 'CDLMORNINGSTAR', 'CDL3WHITESOLDIERS'] bearish_patterns = ['CDLTHRUSTING', 'CDLSTALLEDPATTERN', 'CDL2CROWS', 'CDLHANGINGMAN', 'CDLSHOOTINGSTAR', 'CDLGRAVESTONEDOJI', 'CDLDARKCLOUDCOVER', 'CDLEVENINGDOJISTAR', 'CDLEVENINGSTAR'] bullish_bearish_patterns = ['CDLSEPARATINGLINES', 'CDLHARAMICROSS', 'CDLMARUBOZU', 'CDLLONGLEGGEDDOJI', 'CDLTRISTAR', 'CDLDOJISTAR', 'CDL3LINESTRIKE', 'CDLSPINNINGTOP', 'CDLENGULFING', 'CDLHARAMI', 'CDL3OUTSIDE', 'CDL3INSIDE'] pattern_found = False if side == 'long': for pattern in bullish_patterns: pattern_value = current_candle.get(pattern) if pattern_value is not None and pattern_value == 100: # Bullish signal base_leverage = max(1.0, min(base_leverage * 1.5, max_leverage)) print(f'Bullish Pattern: {pattern} Value: {pattern_value} Adjusted Leverage: {base_leverage}') self.dp.send_msg(f'Found {pattern} pattern for {pair} Leverage updated to {base_leverage}') pattern_found = True break elif side == 'short': for pattern in bearish_patterns: pattern_value = current_candle.get(pattern) if pattern_value is not None and pattern_value == 100: # Bearish signal base_leverage = max(1.0, min(base_leverage * 1.5, max_leverage)) print(f'Bearish Pattern: {pattern} Value: {pattern_value} Adjusted Leverage: {base_leverage}') self.dp.send_msg(f'Found {pattern} pattern for {pair} Leverage updated to {base_leverage}') pattern_found = True break if not pattern_found and side == 'long': for pattern in bullish_bearish_patterns: pattern_value = current_candle.get(pattern) if pattern_value is not None and pattern_value == 100: # Bullish signal base_leverage = max(1.0, min(base_leverage * 2, max_leverage)) print(f'Bullish Pattern: {pattern} Value: {pattern_value} Adjusted Leverage: {base_leverage}') self.dp.send_msg(f'Found {pattern} bullish pattern for {pair} Leverage updated to {base_leverage}') pattern_found = True break elif not pattern_found and side == 'short': for pattern in bullish_bearish_patterns: pattern_value = current_candle.get(pattern) if pattern_value is not None and pattern_value == -100: # Bearish signal base_leverage = max(1.0, min(base_leverage * 2, max_leverage)) print(f'Bearish Pattern: {pattern} Value: {pattern_value} Adjusted Leverage: {base_leverage}') self.dp.send_msg(f'Found {pattern} bearish pattern for {pair} Leverage updated to {base_leverage}') pattern_found = True break # Apply maximum and minimum limits if any pattern is found if pattern_found: adjusted_leverage = max(min(base_leverage, max_leverage), 1.0) # Apply max and min limits print(f'Base Leverage: {proposed_leverage}') print(f'Adjusted Leverage: {adjusted_leverage}') return adjusted_leverage # Return the adjusted leverage # If no pattern matches, return the proposed_leverage print(f'No Pattern Matched for {pair}. Using Proposed Leverage: {proposed_leverage}') self.dp.send_msg(f'No Pattern Matched for {pair}. Using Proposed Leverage: {proposed_leverage}') return proposed_leverage def adjust_trade_position(self, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: """ Adjusts trade position size based on leverage and market conditions. """ dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) if len(dataframe) > 2: last_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() signal_name = 'enter_long' if not trade.is_short else 'enter_short' prior_date = date_minus_candles(self.timeframe, 1, current_time) if last_candle.get(signal_name) == 1 and previous_candle.get(signal_name) != 1 \ and trade.nr_of_successful_entries < 2 \ and trade.orders[-1].order_date_utc < prior_date: proposed_leverage = trade.leverage side = 'long' if not trade.is_short else 'short' default_max_leverage = 20 adjusted_leverage = self.leverage(trade.pair, current_time, current_rate, proposed_leverage, default_max_leverage, trade.enter_tag, side) new_position_size = trade.stake_amount * (adjusted_leverage / proposed_leverage) # Ensure new_position_size adheres to limits if min_stake is not None: new_position_size = max(new_position_size, min_stake) new_position_size = min(new_position_size, max_stake) self.dp.send_msg(f"Adjusting position size for {trade.pair}. Previous Leverage: {proposed_leverage} for {trade.pair}, " f"Adjusted Leverage for {trade.pair}: {adjusted_leverage}, Previous Stake for {trade.pair}: {trade.stake_amount}, " f"New Stake: {new_position_size}") return new_position_size return None def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: adjusted_leverage = getattr(self, 'adjusted_leverage', 1) dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if current_candle['max_l'] > 0.0035: if (current_profit > self.tsl_target3.value): self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl3 {self.tsl_target3.value}/{self.ts3.value} activated') logger.info(f'*** {pair} *** Profit {current_profit} - lvl3 {self.tsl_target3.value}/{self.ts3.value} activated') return self.ts3.value * adjusted_leverage if (current_profit > self.tsl_target2.value): self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl2 {self.tsl_target2.value}/{self.ts2.value} activated') logger.info(f'*** {pair} *** Profit {current_profit} - lvl2 {self.tsl_target2.value}/{self.ts2.value} activated') return self.ts2.value * adjusted_leverage if (current_profit > self.tsl_target1.value): self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl1 {self.tsl_target1.value}/{self.ts1.value} activated') logger.info(f'*** {pair} *** Profit {current_profit} - lvl1 {self.tsl_target1.value}/{self.ts1.value} activated') return self.ts1.value * adjusted_leverage if (current_profit > self.tsl_target0.value): self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl0 {self.tsl_target0.value}/{self.ts0.value} activated') logger.info(f'*** {pair} *** Profit {current_profit} - lvl0 {self.tsl_target0.value}/{self.ts0.value} activated') return self.ts0.value * adjusted_leverage else: if (current_profit > self.tsl_target0.value): self.dp.send_msg(f'*** {pair} *** Profit {current_profit} SWINGING FOR THE MOON!!!') return 0.99 * adjusted_leverage return self.stoploss def EWO(dataframe, ema_length=6, ema2_length=42): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif