# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/one.py # --- Do not remove these libs --- from email.policy import default from idna import valid_contextj from freqtrade.strategy.interface import IStrategy # type: ignore from typing import Dict, Optional, Union from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime import pandas as pd 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, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt import logging from datetime import datetime, timedelta logger = logging.getLogger(__name__) def calculate_chop(dataframe, period=14): df = dataframe.copy() high_n = df['high'].rolling(window=period).max() low_n = df['low'].rolling(window=period).min() # محاسبه محدوده واقعی (TR) tr1 = df['high'] - df['low'] tr2 = abs(df['high'] - df['close'].shift(1)) tr3 = abs(df['low'] - df['close'].shift(1)) tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1) # مجموع محدوده واقعی (Sum TR) برای دوره n sum_tr = tr.rolling(window=period).sum() # محاسبه CHOP chop = 100 * np.log10(sum_tr / (high_n - low_n)) / np.log10(period) # مقادیر پایین CHOP (زیر 38.2) نشان‌دهنده بازاری با روند قوی است. # مقادیر بالای CHOP (بالای 61.8) نشان‌دهنده بازاری خنثی (بی‌روند) و پرنوسان است. return chop # محاسبه‌ی میانگین وزنی محدوده (RWMA) def range_ma(range_vals, price_vals, period): weights = range_vals / range_vals.rolling(window=period).sum() weighted_sum = (price_vals * weights).rolling(window=period).sum() total_weights = weights.rolling(window=period).sum() return weighted_sum / total_weights def calculate_vsi(dataframe): # volume sensitivity index dataframe = dataframe.copy() # محاسبه تغییرات درصدی قیمت dataframe['price_change'] = dataframe['close'].pct_change() # محاسبه تغییرات درصدی حجم dataframe['volume_change'] = dataframe['volume'].pct_change() # محاسبه حساسیت به حجم dataframe['vsi'] = dataframe['price_change'] / dataframe['volume_change'] # نرمال‌سازی VSI dataframe['vsi_normalized'] = (dataframe['vsi'] - dataframe['vsi'].min()) / (dataframe['vsi'].max() - dataframe['vsi'].min()) return dataframe['vsi_normalized'] def ConDi(dataframe, dataframe1, dataframe2): dataframe['ConDi'] = 0 dataframe.loc[ ((dataframe1 > dataframe1.shift(1)) & (dataframe2 > dataframe2.shift(1))) | ((dataframe1 < dataframe1.shift(1)) & (dataframe2 < dataframe2.shift(1))), 'ConDi' ] = 1 dataframe.loc[ ((dataframe1 > dataframe1.shift(1)) & (dataframe2 < dataframe2.shift(1))) | ((dataframe1 < dataframe1.shift(1)) & (dataframe2 > dataframe2.shift(1))), 'ConDi' ] = -1 return dataframe['ConDi'] def hma_volume(dataframe, period ): wma_1 = dataframe['nominal'].rolling(period//2).sum()/dataframe['volume'].rolling(period//2).sum() wma_2 = dataframe['nominal'].rolling(period).sum()/dataframe['volume'].rolling(period).sum() diff = 2 * wma_1 - wma_2 hma = diff.rolling(int(np.sqrt(period))).mean() return hma def Market_Stability_index(current_price, last_halving_price=63000, next_halving_price= 179000, last_halving_date=(datetime(2024,4, 26)), next_halving_date=(datetime(2028, 4,1))): days_between_halvings= (next_halving_date-last_halving_date).days days_since_last_halving= (datetime.now()-last_halving_date).days real_BTC_price= last_halving_price + (next_halving_price-last_halving_price)* (days_since_last_halving/days_between_halvings) stability_index = ((real_BTC_price - current_price)/ current_price)*100 return stability_index def cross(dataframe1, dataframe2): cross_up = (dataframe1 > dataframe2) & (dataframe1.shift(1) < dataframe2.shift(1)) cross_down = (dataframe2 > dataframe1) & (dataframe2.shift(1) < dataframe1.shift(1)) if cross_up.iloc[-1]: return 1 elif cross_down.iloc[-1]: return -1 return 0 class Github_remiotore_freqtrade__one__20260111_210550(IStrategy): INTERFACE_VERSION = 3 can_short = True # Stoploss: stoploss = -0.05 max_open_trades = 1 # Max Open Trades entry_tags_limit = 1 # limit per entry tag max_leverage = 5.0 wave_length = 0 # ROI table: minimal_roi = { "0": 0.10, # "0": 0.219, "10": 0.05, "20": 0.03, "31": 0.02, # "24": 0.087, "51": 0.01, # "67": 0.024, "60": 0 # "164": 0 } custom_roi= { "0": 0.05, "60": 0 } # Leverage configuration leverage_configuration = {} # Create custom dictionary for storing run-time data custom_info = {} def __init__(self, config: Dict) -> NGithub_remiotore_freqtrade__one__20260111_210550: super().__init__(config) # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 12, "low_offset": 0.979, #--> Original Value is 0.915 "long_WAD": -80, "long_rsi": 30, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 72, "high_offset": 1.008, "short_WAD": -20, "short_rsi": 70 } # Variables: base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False) base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False) low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False) high_offset = DecimalParameter(0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=False) atr_period = IntParameter(5, 30, default=14, space='stoploss', optimize=False) atr_multiplier = DecimalParameter(1.0, 30.0, default=10.0, space='stoploss', optimize=False) fast_ewo = 50 slow_ewo = 200 short_WAD = IntParameter(-35, 0, default=sell_params['short_WAD'], space='sell', optimize= False) long_WAD = IntParameter(-100, -65, default= buy_params['long_WAD'], space= 'buy', optimize= False) short_rsi = IntParameter(51,100, default = sell_params['short_rsi'], space= 'sell', optimize= False) long_rsi = IntParameter(0,49, default = buy_params['long_rsi'], space= 'buy', optimize= False) ket_stead = DecimalParameter(0.0, 0.1 , default=0.001, space='buy', optimize=True) ichimoku_period1=50 ichimoku_period2=100 ichimoku_period3=300 BBperiod= 250 roc_period= 14 pmom = 66 # 66 nmom = 30 # 30 #for calculation of coral trend ema_fast_period=50 ema_slow_period=200 # cci_period=IntParameter(30, 200, default=150, space= "buy", optimize=True) cci_threshold=IntParameter(0, 200, default=200, space = "buy", optimize=False) cci_period= 15 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.03 #---> 0.002 is Original Value trailing_stop_positive_offset = 0.15 #--> 0.055 is Original Value trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.10 ignore_roi_if_entry_signal = True # Optimal timeframe for the strategy timeframe = '5m' informative_timeframe1 = '15m' informative_timeframe2 = '1h' informative_timeframe3 = '4h' informative_pair= 'BTC/USDT:USDT' process_only_new_candles = True startup_candle_count = 200 plot_config = { } use_custom_stoploss = False # For postition sizing # risk_per_trade = 0.02 # Risking 2% of total capital per trade # stop_loss_distance = abs(trade.entry_price - stop_loss) # position_size = (total_capital * risk_per_trade) / stop_loss_distance @property def protections(self): return [ { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 1, "stop_duration_candles": 288, "only_per_pair": True }, { "method": "LowProfitPairs", "lookback_period_candles": 12, "stop_duration": 144, "required_profit": -0.1, "only_per_pair": True, } ] def cleanup_cache(self): """ Cleanup the cache with stored dataframes if the last updated time has passed (with a margin of two minutes). """ outdatedkeys = [] currentdatetime = datetime.now() for key in self.custom_info['cache']: minutes = (currentdatetime - self.custom_info['cache'][key]['datetime']).total_seconds() / 60 if (minutes - 2) > timeframe_to_minutes(self.custom_info['cache'][key]['tf']): outdatedkeys.append(key) self.log(f"Removing cache storage for '{key}' because last update was {minutes} minutes ago") for key in outdatedkeys: del self.custom_info['cache'][key] def bot_start(self, **kwargs) -> NGithub_remiotore_freqtrade__one__20260111_210550: """ Called only once after bot instantiation. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ # Setup cache for dataframes self.custom_info['cache'] = {} # Setup removal of autolocks self.custom_info['remove-autolock'] = [] self.open_trades_tags = [] # Track open trades and their entry tags with timestamps self.trade_exit_times = [] # Track the exit times to prevent next trades # Call to super first super().bot_start() def bot_loop_start(self, current_time: datetime, **kwargs) -> NGithub_remiotore_freqtrade__one__20260111_210550: """ Called at the start of the bot iteration (Github_remiotore_freqtrade__one__20260111_210550 loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) :param current_time: datetime object, containing the current datetime :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ # Run the cleanup of cache once every five minutes if current_time.minute % 5 == 0 and current_time.second <= 10: self.cleanup_cache() # Check if there are pairs set for which the Auto lock should be reoved if len(self.custom_info['remove-autolock']) > 0: self.unlock_reason("Auto lock") # Sanity check to alert when removing the lock failed for pair in self.custom_info['remove-autolock']: lock = self.is_locked_until(pair) if lock: self.log( f"{pair} has still an active lock until {lock}, while it should have been removed!", level="ERROR" ) # Clear the list self.custom_info['remove-autolock'].clear() return super().bot_loop_start(current_time) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe1) for pair in pairs] # 1h informative_pairs += [(pair, self.informative_timeframe2) for pair in pairs] # 4h informative_pairs += [(pair, self.informative_timeframe3) for pair in pairs] # 1d informative_pairs += [(self.informative_pair, self.timeframe)] return informative_pairs def get_informative_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe1) 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 # 15 min informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe1) informative['ema_20'] = ta.EMA(informative['close'], timeperiod=20) informative['rsi'] = ta.RSI(informative['close'], timeperiod=7) informative['sar'] = ta.SAR(informative['high'], informative['low'], acceleration=0.02, maximum=0.2) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe1, ffill=True) # 1h informative2 = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe2) # داده‌های تایم‌فریم دوم informative2['nominal'] = informative2['close'] * informative2['volume'] informative2['HMA'] = hma_volume(informative2, period= 5) informative2['macd'], informative2['macdsignal'], informative2 ['macdhist'] = ta.MACD(informative2['close'], fastperiod=12, slowperiod=26, signalperiod=9) dataframe = merge_informative_pair(dataframe, informative2, self.timeframe, self.informative_timeframe2, ffill=True) # 4h informative3 = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe3) # داده‌های تایم‌فریم دوم informative3['macd'], informative3['macdsignal'], informative3 ['macdhist']= ta.MACD(informative3['close'], fastperiod=12, slowperiod=26, signalperiod=9) dataframe = merge_informative_pair(dataframe, informative3, self.timeframe, self.informative_timeframe3, ffill=True) # btc_price = self.dp.get_pair_dataframe(pair=self.informative_pair, timeframe=self.timeframe) # dataframe = merge_informative_pair(dataframe, btc_price, self.timeframe, self.timeframe, ffill=True) drop_columns = [f"{s}_{self.timeframe}" for s in ['date', 'open', 'high', 'low', 'volume']] drop_columns += [f"{s}_{self.informative_timeframe1}" for s in ['date', 'open', 'high', 'low', 'volume']] drop_columns += [f"{s}_{self.informative_timeframe2}" for s in ['date', 'open', 'high', 'low', 'volume']] drop_columns += [f"{s}_{self.informative_timeframe3}" for s in ['date', 'open', 'high', 'low', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) # ------------ 5m timeframe indicators ------------------------------ dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=7) dataframe['macd'], dataframe['macdsignal'], _ = ta.MACD(dataframe['close'], fastperiod=6, slowperiod=13, signalperiod=5) dataframe['ema_9'] = ta.EMA(dataframe['close'], timeperiod=9) dataframe['nominal'] = dataframe['close'] * dataframe['volume'] dataframe['HMA'] = hma_volume(dataframe, period= 5) dataframe['HMA12'] = ta.EMA(dataframe['HMA'], timeperiod=12) typical_price = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 vwap = (typical_price * dataframe['volume']).cumsum() / dataframe['volume'].cumsum() dataframe['vwap'] = vwap dataframe['ADX'] = ta.ADX(dataframe, timeperiod=21) dataframe['ATR'] = ta.ATR(dataframe, timeperiod=14) dataframe['vwap_upper'] = dataframe['vwap'] + dataframe['ATR'] dataframe['vwap_lower'] = dataframe['vwap'] - dataframe['ATR'] dataframe['OBV'] = ta.OBV(dataframe['close'], dataframe['volume']) dataframe['not_long'] = np.where(( (dataframe['macdhist_4h'] < 0 ) & (dataframe['macdhist_4h'].shift(1) > dataframe['macdhist_4h']) & (dataframe['macdhist_1h'] < 0 ) & (dataframe['macdhist_1h'].shift(1) > dataframe['macdhist_1h']) ),0 , 1) dataframe['not_short'] = np.where(( (dataframe['macdhist_4h'] > 0 ) & (dataframe['macdhist_4h'].shift(1) < dataframe['macdhist_4h']) & (dataframe['macdhist_1h'] > 0 ) & (dataframe['macdhist_1h'].shift(1) < dataframe['macdhist_1h']) ),0 , 1) return dataframe def get_open_trades(self): # Fetch all open trades return Trade.get_open_trades() def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # -------------------------- based on Con Di --------------------------- dataframe.loc[( # (dataframe['close'] > dataframe['vwap_upper']) & (dataframe['HMA12'] > dataframe['HMA_1h']) & (dataframe['HMA12'].shift(1) < dataframe['HMA_1h'].shift(1)) & (dataframe['ADX'] > 30) & (dataframe['not_long'] == 0) & (dataframe['OBV'] > dataframe['OBV'].shift(1)) ), ['enter_long', 'enter_tag']] = (1, 'long') dataframe.loc[( # (dataframe['close'] < dataframe['vwap_lower']) & (dataframe['HMA12'] < dataframe['HMA_1h']) & (dataframe['HMA12'].shift(1) > dataframe['HMA_1h'].shift(1)) & (dataframe['ADX'] > 30) & (dataframe['not_short'] == 0) & (dataframe['OBV'] < dataframe['OBV'].shift(1)) ), ['enter_short', 'enter_tag']] = (1, 'short') return dataframe def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, entry_tag: str, **kwargs) -> bool: # Limit the number of open trades with the same entry tag open_trades = self.get_open_trades() open_trades_with_tag = [trade for trade in open_trades if trade.enter_tag == entry_tag] if len(open_trades_with_tag) >= self.entry_tags_limit: # logger.warning(f"Reached limit of {self.entry_tags_limit} trades with entry tag {entry_tag}.") return False return True def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe # def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[str]: # dataframe, *_ = self.dp.get_analyzed_dataframe( # pair=pair, timeframe=self.timeframe # ) # current_candle = dataframe.iloc[-1].squeeze() # previous_candle = dataframe.iloc[-2] # Get the previous row for comparison 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: dataframe, *_ = self.dp.get_analyzed_dataframe( pair=pair, timeframe=self.timeframe ) current_candle = dataframe.iloc[-1].squeeze() leverage = self.max_leverage * (1 - float(current_candle['ATR']/100)) return leverage # return leverage