# --- 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 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 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 typing import Dict, List, Optional, Tuple, Union from pmdarima import auto_arima from pmdarima import model_selection from sklearn.metrics import mean_squared_error import time warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) '\n\n\n\n________/\\\\\\\\\\__/\\\\______________/\\\\________________/\\\\________/\\\\_____/\\\\\\\\\\___/\\\\\\_____/\\\\__________________________/\\\\______________________/\\\\\\\\_________________ \n _____/\\\\////////__\\/\\\\__________/\\\\\\\\_______________\\/\\\\_____/\\\\//____/\\\\///////\\\\_\\/\\\\\\___\\/\\\\________________________/\\\\\\____________________/\\\\/////\\\\_______________ \n ___/\\\\/___________\\/\\\\_________\\/////\\\\_______________\\/\\\\__/\\\\//______\\///______/\\\\__\\/\\\\/\\\\__\\/\\\\_____/\\\\_____________/\\\\/\\\\___________________/\\\\____\\//\\\\______________ \n __/\\\\_____________\\/\\\\_____________\\/\\\\_____/\\\\\\\\_\\/\\\\\\//\\\\_____________/\\\\//___\\/\\\\//\\\\_\\/\\\\__/\\\\\\\\\\\\______/\\\\/\\/\\\\________/\\\\\\\\_\\/\\\\_____\\/\\\\__/\\\\\\\\\\_ \n _\\/\\\\_____________\\/\\\\\\\\\\______\\/\\\\___/\\\\//////__\\/\\\\//_\\//\\\\___________\\////\\\\__\\/\\\\//\\\\/\\\\_\\////\\\\////_____/\\\\/__\\/\\\\______/\\\\//////__\\/\\\\_____\\/\\\\_\\/\\\\//////__ \n _\\//\\\\____________\\/\\\\/////\\\\_____\\/\\\\__/\\\\_________\\/\\\\____\\//\\\\_____________\\//\\\\_\\/\\\\_\\//\\\\/\\\\____\\/\\\\_______/\\\\\\\\\\\\\\\\__/\\\\_________\\/\\\\_____\\/\\\\_\\/\\\\\\\\\\_ \n __\\///\\\\__________\\/\\\\___\\/\\\\_____\\/\\\\_\\//\\\\________\\/\\\\_____\\//\\\\___/\\\\______/\\\\__\\/\\\\__\\//\\\\\\____\\/\\\\_/\\__\\///////////\\\\//__\\//\\\\________\\//\\\\____/\\\\__\\////////\\\\_ \n ____\\////\\\\\\\\\\_\\/\\\\___\\/\\\\_____\\/\\\\__\\///\\\\\\\\_\\/\\\\______\\//\\\\_\\///\\\\\\\\\\/___\\/\\\\___\\//\\\\\\____\\//\\\\\\_____________\\/\\\\_____\\///\\\\\\\\__\\///\\\\\\\\/____/\\\\\\\\\\_ \n _______\\/////////__\\///____\\///______\\///_____\\////////__\\///________\\///____\\/////////_____\\///_____\\/////______\\/////______________\\///________\\////////_____\\///////_____\\//////////__\n\n\n\n\n\n' pd.set_option('display.float_format', lambda x: '%.7f' % x) logger = logging.getLogger(__name__) class ARIMA_5(IStrategy): INTERFACE_VERSION = 3 # Stoploss: stoploss = -0.99 # Trailing stop: use_custom_stoploss = 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 ## Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} # Optimal timeframe for the strategy timeframe = '5m' startup_candle_count = 400 process_only_new_candles = True # Custom Entry last_entry_price = None order_types = { 'entry': 'limit', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': True } leverage_input = 20.0 can_short: bool = True position_adjustment_enable = True # Hyper-opt parameters base_nb_candles_buy = IntParameter(150, 200, default=184, space='buy', optimize=True, load=True) up = DecimalParameter(low=1.02, 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(5, 30, default=5, space='buy', optimize=True, load=True) window = IntParameter(10, 30, default=16, space='buy', optimize=True, load=True) window_1h = IntParameter(10, 30, default=8, space='buy', optimize=True, load=True) window_4h = IntParameter(2, 30, default=2, 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=1.5, decimals=2, space='buy', optimize=True, load=True) x_4h = DecimalParameter(low=1.2, high=1.75, default=1.3, decimals=2, space='buy', optimize=True, load=True) ### trailing stop loss optimiziation ### tsl_target4 = DecimalParameter(low=0.80, high=1.45, default=0.85, decimals=3, space='sell', optimize=True, load=True) ts4 = DecimalParameter(low=0.32, high=0.8, default=0.45, decimals=2, space='sell', optimize=True, load=True) tsl_target3 = DecimalParameter(low=0.55, high=1, default=0.5, decimals=3, space='sell', optimize=True, load=True) ts3 = DecimalParameter(low=0.25, high=0.35, default=0.3, decimals=3, space='sell', optimize=True, load=True) tsl_target2 = DecimalParameter(low=0.4, high=0.5, default=0.2, decimals=3, space='sell', optimize=True, load=True) ts2 = DecimalParameter(low=0.15, high=0.25, default=0.2, decimals=3, space='sell', optimize=True, load=True) tsl_target1 = DecimalParameter(low=0.35, high=0.6, default=0.4, decimals=3, space='sell', optimize=True, load=True) ts1 = DecimalParameter(low=0.3, high=0.45, default=0.3, decimals=3, space='sell', optimize=True, load=True) tsl_target0 = DecimalParameter(low=0.25, high=0.35, default=0.3, decimals=3, space='sell', optimize=True, load=True) ts0 = DecimalParameter(low=0.08, high=0.4, default=0.08, decimals=3, space='sell', optimize=True, load=True) sell_decr_pos = DecimalParameter(0.01, 0.5, decimals=3, default=0.32, space='sell', optimize=False, load=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}] ### Trailing Stop ### def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: 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 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 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 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 elif current_profit > self.tsl_target0.value: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} SWINGING FOR THE MOON!!!') return 0.99 return self.stoploss 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: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if exit_reason == 'roi' and last_candle['min_l'] > last_candle['max_l'] * 3: return False # Handle freak events if exit_reason == 'roi' and trade.calc_profit_ratio(rate) < 0.003: 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 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['decision'] = 0 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 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') # 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 | 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_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) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condition1 = (dataframe['decision'] == 1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(6) < dataframe['move_mean'].shift(6)) & (dataframe['min'] < dataframe['max']) & (dataframe['min_l'] < dataframe['max_l']) & (dataframe['max_l'] < dataframe['atr_pcnt']) & (dataframe['OHLC4'] < dataframe['sma_dn']) & (dataframe['sma_dn'].shift() > dataframe['sma_dn']) & (dataframe['volume'] > 0) dataframe.loc[condition1, 'enter_long'] = 1 dataframe.loc[condition1, 'enter_tag'] = 'Up Trend Soon below sma_dn' condition2 = (dataframe['decision'] == 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_dn']) & (dataframe['max_l'] < dataframe['atr_pcnt']) & (dataframe['volume'] > 0) dataframe.loc[condition2, 'enter_long'] = 1 dataframe.loc[condition2, 'enter_tag'] = 'Move Mean Fib below sma_dn' condition3 = (dataframe['decision'] == 1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(6) < dataframe['move_mean'].shift(6)) & (dataframe['min'] < dataframe['max']) & (dataframe['min_l'] < dataframe['max_l']) & (dataframe['OHLC4'] < dataframe['sma']) & (dataframe['volume'] > 0) dataframe.loc[condition3, 'enter_long'] = 1 dataframe.loc[condition3, 'enter_tag'] = 'Up Trend Soon below sma' condition4 = (dataframe['decision'] == 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['max_l'] < dataframe['atr_pcnt']) & (dataframe['volume'] > 0) dataframe.loc[condition4, 'enter_long'] = 1 dataframe.loc[condition4, 'enter_tag'] = 'Move Mean Fib below sma' condition5150 = (dataframe['decision'] == 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[condition5150, 'enter_long'] = 1 dataframe.loc[condition5150, 'enter_tag'] = 'Hope this works...' return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: condition5 = (dataframe['decision'] == -1) & (dataframe['move'] >= dataframe['move_mean']) & (dataframe['move'].shift(6) < dataframe['move_mean'].shift(6)) & (dataframe['min'] > dataframe['max']) & (dataframe['min_l'] > dataframe['max_l']) & (dataframe['volume'] > 0) dataframe.loc[condition5, 'exit_long'] = 1 dataframe.loc[condition5, 'exit_tag'] = 'Down Trend Soon' condition6 = (dataframe['decision'] == -1) & (dataframe['move'] >= dataframe['move_mean_x']) & (dataframe['move'].shift(3) >= dataframe['move_mean_x'].shift(3)) & (dataframe['min'] > dataframe['max']) & (dataframe['min_l'] > dataframe['max_l']) & (dataframe['volume'] > 0) dataframe.loc[condition6, 'exit_long'] = 1 dataframe.loc[condition6, 'exit_tag'] = 'Move Mean Fib' return dataframe 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: if self.leverage_input > max_leverage: return max_leverage return self.leverage_input 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 ) -> Union[Optional[float], Tuple[Optional[float], Optional[str]]]: if current_profit > self.sell_decr_pos.value and trade.nr_of_successful_exits == 0: return -(trade.stake_amount / 2), 'half_profit_5%' return None