# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Klassi.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 import helper_functions as helper 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): # # ابتدا ستون ConDi رو با مقدار پیش‌فرض 0 ایجاد می‌کنیم # dataframe['ConDi'] = 0 # # شرط برای همگرایی (هر دو به سمت بالا یا هر دو به سمت پایین) # dataframe.loc[ # ((dataframe1 > dataframe1.shift(1)) & (dataframe2 > dataframe2.shift(1))) | # ((dataframe1 < dataframe1.shift(1)) & (dataframe2 < dataframe2.shift(1))), # 'ConDi' # ] = 1 # # شرط برای واگرایی (یکی به سمت بالا و دیگری به سمت پایین) # dataframe.loc[ # ((dataframe1 > dataframe1.shift(1)) & (dataframe2 < dataframe2.shift(1))) | # ((dataframe1 < dataframe1.shift(1)) & (dataframe2 > dataframe2.shift(1))), # 'ConDi' # ] = -1 # return dataframe['ConDi'] 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__Klassi__20260111_210550(IStrategy): INTERFACE_VERSION = 3 can_short = True # Stoploss: stoploss = -0.33 max_open_trades = 6 # Max Open Trades entry_tags_limit = 2 # limit per entry tag max_leverage = 15.0 wave_length = 0 # ROI table: minimal_roi = { "0": 0.264, # "0": 0.219, "31": 0.093, # "24": 0.087, "52": 0.026, # "67": 0.024, "94": 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) -> None: 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 nmom = 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 = 0.11 #--> 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 = '1h' informative_timeframe2 = '4h' informative_timeframe3 = '1d' informative_pair= 'BTC/USDT:USDT' process_only_new_candles = True startup_candle_count = 200 plot_config = { "main_plot": { "kama": { "color": "#e392fe", "type": "line" }, "ema": { "color": "#f2751a", "type": "line" }, "kc_upper": { "color": "#5c0700", "type": "line" }, "kc_lower": { "color": "#263e0f", "type": "line" }, "ema2": { "color": "#ccde01" }, "kama_1h": { "color": "#be38f3", "type": "line" }, "kama_4h": { "color": "#61177c", "type": "line" }, "EMA_1h": { "color": "#d29d00", "type": "line" }, "EMA_4h": { "color": "#785700", "type": "line" }, "highBand": { "color": "#c0c0c0", "type": "line" }, "lowBand": { "color": "#aaaaaa", "type": "line" }, "vwap": { "color": "#3ef4e2" } }, "subplots": { "Trends": { "long_slope": { "color": "#ff2600", "type": "line" }, "short_slope": { "color": "#00f900", "type": "line" }, "long_kama_1h": { "color": "#00f900", "type": "line" }, "long_kama_4h": { "color": "#00f900", "type": "line" }, "short_kama_4h": { "color": "#ff2600", "type": "line" }, "long_kama": { "color": "#00f900", "type": "line" }, "short_kama": { "color": "#ff2600", "type": "line" }, "short_ema_1h": { "color": "#ff2600", "type": "line" }, "long_ema_1h": { "color": "#00f900", "type": "line" }, "long_ema_4h": { "color": "#00f900", "type": "line" }, "short_ema_4h": { "color": "#ff2600", "type": "line" }, "short_kama_1h": { "color": "#ff2600", "type": "line" }, "long_WM2": { "color": "#00f900", "type": "line" }, "short_WM2": { "color": "#ff2600", "type": "line" }, "short_adx": { "color": "#ff2600", "type": "line" }, "long_adx": { "color": "#00f900", "type": "line" }, "long_adx_1h": { "color": "#00f900", "type": "line" }, "short_adx_1h": { "color": "#ff2600", "type": "line" }, "long_adx_4h": { "color": "#00f900", "type": "line" }, "short_adx_4h": { "color": "#ff2600", "type": "line" } }, "WilliamR%": { "WAD": { "color": "#f1c9fe", "type": "line" }, "WM1": { "color": "#d357fe", "type": "line" }, "WM2": { "color": "#61177c", "type": "line" }, "WM0": { "color": "#b18cfe", "type": "line" } }, "POWER": { "kama_slope": { "color": "#ff4013", "type": "line" } }, "ADX": { "ADX": { "color": "#ffe2d6", "type": "line" }, "ADX_4h": { "color": "#ff6a00", "type": "line" }, "ADX_1h": { "color": "#ffc677", "type": "line" } }, "RSI": { "RSI_4h": { "color": "#d29d00", "type": "line" }, "RSI": { "color": "#fefcdd", "type": "line" }, "RSI_1h": { "color": "#ffd877", "type": "line" } }, "ATR%": { "atrpct": { "color": "#f8fadb", "type": "line" }, "atrpct_4h": { "color": "#669c35", "type": "line" }, "atrpct_1h": { "color": "#b1dd8c", "type": "line" } }, "v": { "volume_ema": { "color": "#00a3d7", "type": "line" } } } } 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) -> None: """ 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) -> None: """ Called at the start of the bot iteration (one 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 informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe1) informative['kama'] = ta.KAMA(informative, period= 50, fast_ema= 3, slow_ema=21) informative.loc[(informative['kama'].shift(1) > informative['kama']),'short_kama'] = 2 informative.loc[(informative['kama'].shift(1) < informative['kama']),'long_kama'] = 2 informative['kama_slope'] = (informative['kama']/informative['kama'].shift(1)) * 1000 - 1000 # kama_slope over 2 means a little trend has been started like a trap. use for not_buy informative['RSI'] = ta.RSI(informative, period=14) informative['rsi_ma'] = ta.SMA(informative['RSI'] , period = 5 ) informative['ATR'] = ta.ATR(informative, period= 14) informative['ADX'] = ta.ADX(informative, period= 21) informative.loc[(informative['ADX'].shift(1) < informative['ADX']),'long_adx'] = 6 informative.loc[(informative['ADX'].shift(1) > informative['ADX']),'short_adx'] = 6 informative['EMA'] = ta.EMA(informative, period=21) informative.loc[(informative['EMA'].shift(1) > informative['EMA']),'short_ema'] = 2.5 informative.loc[(informative['EMA'].shift(1) < informative['EMA']),'long_ema'] = 2.5 informative['chop'] = calculate_chop(informative, 14) informative['nominal'] = informative['close'] * informative['volume'] informative['HMA'] = hma_volume(informative, period= 7) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe1, ffill=True) informative2 = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe2) # داده‌های تایم‌فریم دوم informative2['kama'] = ta.KAMA(informative2, period= 50, fast_ema= 3, slow_ema=21) informative2.loc[(informative2['kama'].shift(1) > informative2['kama']),'short_kama'] = 3 informative2.loc[(informative2['kama'].shift(1) < informative2['kama']),'long_kama'] = 3 informative2['kama_slope'] = (informative2['kama']/informative2['kama'].shift(1)) * 1000 - 1000 # kama_slope over 2 means a little trend has been started like a trap. use for not_buy informative2['RSI'] = ta.RSI(informative2, period=14) informative2['ATR'] = ta.ATR(informative2, period=14) informative2['MFI'] = ta.MFI(dataframe, timeperiod=14) informative2.loc[(informative2['MFI'].shift(1) > informative2['MFI']),'short_mfi'] = 1 informative2.loc[(informative2['MFI'].shift(1) < informative2['MFI']),'long_mfi'] = 1 informative2['ADX'] = ta.ADX(informative2, period=21) informative2.loc[(informative2['ADX'].shift(1) > informative2['ADX']),'short_adx'] = 7 informative2.loc[(informative2['ADX'].shift(1) < informative2['ADX']),'long_adx'] = 7 informative2['EMA'] = ta.EMA(informative2, period=21) informative2.loc[(informative2['EMA'].shift(1) > informative2['EMA']),'short_ema'] = 3.5 informative2.loc[(informative2['EMA'].shift(1) < informative2['EMA']),'long_ema'] = 3.5 informative2['WAD'] = ta.WILLR(informative2, timeperiod=50) informative2['wad_sma'] = ta.SMA(informative2, period = 5 ) informative2['chop'] = calculate_chop(informative2, 14) informative2['nominal'] = informative2['close'] * informative2['volume'] informative2['HMA'] = hma_volume(informative2, period= 5) dataframe = merge_informative_pair(dataframe, informative2, self.timeframe, self.informative_timeframe2, ffill=True) informative3 = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe3) # داده‌های تایم‌فریم دوم informative3['kama'] = ta.KAMA(informative3, period= 50, fast_ema= 3, slow_ema=21) informative3.loc[(informative3['kama'].shift(1) > informative3['kama']),'short_kama'] = 4 informative3.loc[(informative3['kama'].shift(1) < informative3['kama']),'long_kama'] = 4 informative3['kama_slope'] = (informative3['kama']/informative3['kama'].shift(1)) * 1000 - 1000 # kama_slope over 2 means a little trend has been started like a trap. use for not_buy informative3['chop'] = calculate_chop(informative3, 14) 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) # ------------ SPREAD CALCULATOR TO CALC LEVERAGE ------------------------------ dataframe['spread'] = 100 * abs(dataframe['high'] - dataframe['low']) / dataframe['close'] dataframe['spread_avg'] = dataframe['spread'].rolling(window=200).mean() dataframe['spread_avg_small'] = ta.SMA(dataframe['spread'], timeperiod = 5) # ------------ TREND INDICATORS ------------------------------------------------ dataframe['kama'] = ta.KAMA(dataframe, window=50, pow1=4, pow2=12, fillna=True) dataframe['kama_'] = ta.KAMA(dataframe['close'], timeperiod=7, fastlimit=0.02, slowlimit=0.666) dataframe.loc[(dataframe['kama'].shift(1) > dataframe['kama']),'short_kama'] = 1 dataframe.loc[(dataframe['kama'].shift(1) < dataframe['kama']),'long_kama'] = 1 dataframe['ema'] = ta.SMA(dataframe, timeperiod=13) dataframe.loc[(dataframe['ema'].shift(1) > dataframe['ema']),'down_ema'] = 2 dataframe.loc[(dataframe['ema'].shift(1) < dataframe['ema']),'long_ema'] = 2 dataframe['ema200'] = ta.EMA(dataframe, timeperiod = 200) dataframe['ema5'] = ta.EMA(dataframe, timeperiod = 5) dataframe['nominal'] = dataframe['close'] * dataframe['volume'] dataframe['HMA'] = hma_volume(dataframe, period= 9) typical_price = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 vwap = (typical_price * dataframe['volume']).cumsum() / dataframe['volume'].cumsum() dataframe['vwap'] = vwap dataframe['volume_ema'] = ta.EMA(dataframe['volume'], timeperiod=20) dataframe.loc[ (dataframe['vwap'] < dataframe ['vwap'].shift(1)), 'short_vwap'] = 1.5 dataframe.loc[ dataframe['vwap'] > dataframe ['vwap'].shift(1), 'long_vwap'] = 1.5 # ------------------------- POWER ------------------------------- dataframe['ADX'] = ta.ADX(dataframe, timeperiod=21) dataframe.loc[(dataframe['ADX'].shift(1) > dataframe['ADX']),'short_adx'] = 5 dataframe.loc[(dataframe['ADX'].shift(1) < dataframe['ADX']),'long_adx'] = 5 # ATR for setting dynamic stop loss and take profit dataframe['ATR'] = ta.ATR(dataframe, timeperiod=14) dataframe['atrpct'] = (dataframe['ATR']/dataframe['close'])*100 dataframe['atrpct_1h'] = (dataframe['ATR_1h']/dataframe['close'])* 100 dataframe['atrpct_4h'] = (dataframe['ATR_4h']/dataframe['close_4h'])*100 dataframe['band'] = np.minimum(dataframe['ATR'] * 0.3, dataframe['close'] * (0.3 / 100)) dataframe['vwap_upper'] = dataframe['vwap'] + 1.7 * dataframe['ATR'] dataframe['vwap_lower'] = dataframe['vwap'] - 1.7 * dataframe['ATR'] dataframe['diff_kamavwap']= dataframe['kama'] - dataframe['vwap'] dataframe['diff_kamavwap_sma'] = ta.EMA(dataframe['diff_kamavwap'], timeperiod = 7) dataframe['der_diff_kamavwap'] = dataframe['diff_kamavwap_sma'].diff()/dataframe['close'] dataframe['der_diff_kamavwap_sma'] = ta.EMA(dataframe['der_diff_kamavwap'], timeperiod = 7) dataframe['der_diff_kamavwap_slope'] = dataframe['der_diff_kamavwap_sma']/dataframe['der_diff_kamavwap_sma'].shift(1) dataframe['distance_index'] = dataframe['diff_kamavwap_sma']/dataframe['close'] dataframe['diff_kamavwap_slope'] = dataframe['diff_kamavwap'] / dataframe['diff_kamavwap'].shift(1) dataframe['diff_kamavwap_slope_sma'] = ta.SMA(dataframe['diff_kamavwap_slope'], timeperiod = 3) #--------------------------OSCILLATORS----------------------------- dataframe['RSI'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_ma'] = ta.SMA(dataframe['RSI'], timeperiod=8) dataframe['L_rsi_thres'] = (dataframe['distance_index'] * 100) + 25 dataframe['S_rsi_thres'] = (dataframe['distance_index'] * 100) + 75 dataframe['MFI'] = ta.MFI(dataframe, timeperiod=14) dataframe['rsi_mfi'] = (dataframe['MFI'] + dataframe['RSI']) / 2 dataframe['cci'] = ta.CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=self.cci_period) # محاسبه شاخص تراکم-توزیع ویلیامز (WAD) dataframe['WAD'] = ta.WILLR(dataframe, timeperiod=50) dataframe['wad_sma']= ta.SMA(dataframe['WAD'], timeperiod = 3) dataframe['WM0'] = ta.SMA(dataframe['WAD'], timeperiod= 15) dataframe['WM1'] = ta.SMA(dataframe['WAD'], timeperiod=50) dataframe['WM2'] = ta.SMA(dataframe['WAD'], timeperiod=200) dataframe['long_rsi_threshold'] = 25 + (50 + dataframe['WM1']) / 2 dataframe['short_rsi_threshold'] = 75 + (50 + dataframe['WM1']) / 2 dataframe['diff_rsi'] = dataframe['long_rsi_threshold'].diff() dataframe['chop'] = calculate_chop(dataframe, 14) dataframe['chop_ma'] = ta.SMA(dataframe['chop'], 21) #------------------Market Stability Index and indicators based on it ---------------------------- dataframe['MIndex']= Market_Stability_index(dataframe['close_5m'] ,last_halving_price=63000, next_halving_price= 179000, last_halving_date=(datetime(2024,4, 26)), next_halving_date=(datetime(2028, 4,1))) dataframe['long_wad_threshold'] = (dataframe['MIndex'] + 70) * -1 dataframe['short_wad_threshold'] = (30 - dataframe['MIndex'] )* -1 #---------------------------------- # Keltner Channel Calculation dataframe['kc_middle'] = ta.EMA(dataframe['close'], timeperiod=20) dataframe['kc_upper'] = dataframe['kc_middle'] + (ta.ATR(dataframe, timeperiod=14) * 1.5) # Modified to 1.5x ATR for crypto dataframe['kc_lower'] = dataframe['kc_middle'] - (ta.ATR(dataframe, timeperiod=14) * 1.5) # --------------- DERIVITIVES -------------------------- dataframe['typical_price'] = typical_price dataframe['kama_slope'] = (dataframe['kama']/dataframe['kama'].shift(1)) * 1000 - 1000 # kama_slope over 2 means a little trend has been started like a trap. use for not_buy dataframe['der1_price'] = (dataframe['kama'] / dataframe['kama'].shift(1)) * 1000 - 1000 dataframe.loc[dataframe['der1_price'] > 10, 'der1_price'] = 10 dataframe.loc[dataframe['der1_price'] < - 10, 'der1_price'] = -10 dataframe['der1_price_sma'] = ta.SMA(dataframe['der1_price'], timeperiod = 5) dataframe['der2_price'] = (dataframe['der1_price_sma']/dataframe['der1_price_sma'].shift(1)) * 10000 - 10000 dataframe.loc[dataframe['der2_price'] > 10, 'der2_price'] = 10 dataframe.loc[dataframe['der2_price'] < -10, 'der2_price'] = -10 dataframe['der2_price_sma'] = ta.SMA(dataframe['der2_price'], timeperiod = 5) # ------------------ Wave length calculations ---------------------- dataframe['cross_result'] = cross(dataframe['close'], dataframe['vwap']) # ---------------------------------- COnvergence divergence ------------------- dataframe['condi_rsi_kama'] = ConDi(dataframe, dataframe['rsi_ma'], dataframe['ema']) dataframe['condi_wad_ema'] = ConDi(dataframe, dataframe['wad_sma'] , dataframe['ema']) dataframe['condi_rsi_hma'] = ConDi(dataframe, dataframe['rsi_ma_1h'], dataframe['HMA_1h']) dataframe['condi_wad_hma'] = ConDi(dataframe, dataframe['wad_sma_4h'], dataframe['HMA_4h']) dataframe['condi_ema_hma'] = ConDi(dataframe, dataframe['ema'], dataframe['HMA']) dataframe['p_mom'] = (dataframe['rsi_mfi'].shift(1) < self.pmom) & (dataframe['rsi_mfi'] > self.pmom) & (dataframe['ema5'].diff() > 0) dataframe['n_mom'] = (dataframe['rsi_mfi'].shift(1) > self.nmom) & (dataframe['rsi_mfi'] < self.nmom) & (dataframe['ema5'].diff() < 0) # dataframe['rwma'] = range_ma(dataframe['high'] - dataframe['low'], dataframe['close'], 20) # dataframe['rwma_band_upper'] = dataframe['rwma'] + dataframe['band'] # dataframe['rwma_band_lower'] = dataframe['rwma'] - dataframe['band'] dataframe['STOCH_K'], dataframe['STOCH_D'] = ta.STOCH(dataframe['high'], dataframe['low'], dataframe['close'], fastk_period=14, slowk_period=3, slowd_period=3, slowk_matype=0, slowd_matype=0) # ---------------------------------- BTC Dominance data ----------------------- #_-_____NOT CLEANED BELOW dataframe['atf'] = 0 dataframe.loc[( (dataframe['diff_kamavwap_slope']>1 ) & (dataframe['diff_kamavwap_slope'].shift(1)<1)), 'atf' ] = 1 dataframe.loc[( (dataframe['diff_kamavwap_slope']<1 ) & (dataframe['diff_kamavwap_slope'].shift(1)>1)), 'atf' ] = -1 dataframe['vwap_slope'] = (dataframe['vwap']/dataframe['vwap'].shift(1))*1000 - 1000 # dataframe['kamadiff_normalized']= (dataframe['kamadiff'] - dataframe['kamadiff'].min()) / (dataframe['kamadiff'].max() - dataframe['kamadiff'].min()) dataframe['is_wave'] = np.where( ((abs(dataframe['diff_kamavwap_slope'] - 1 ) < 0.01 )), 0 , 1) dataframe['vsi'] = calculate_vsi(dataframe) # Calculate new indicators and conditions for entry # Calculate Price Volatility Index (item 2) dataframe['price_volatility'] = abs(dataframe['close'] - dataframe['close'].shift(1)) / dataframe['volume_ema'] # Dynamic Slope for Entry Signals (item 3) dataframe['kama_vwap_slope'] = (dataframe['kama'] - dataframe['vwap']).diff() / dataframe['close'] dataframe['kama_vwap_slope_sma'] = ta.SMA(dataframe['kama_vwap_slope'], timeperiod=7) # Williams Accumulation Distribution (WAD) - Strengthen Signals (item 5) dataframe['wad_support'] = (dataframe['WAD'] > dataframe['WM1']) # Bullish support dataframe['wad_resistance'] = (dataframe['WAD'] < dataframe['WM1']) # Bearish resistance # Adding Dynamic ADX Condition for Strong Trend Confirmation dataframe['ADX'] = ta.ADX(dataframe, timeperiod=21) dataframe['adx_trend'] = (dataframe['ADX'] > 25) # Filter only strong trends # Bollinger Bands to filter high volatility breakouts dataframe['bb_upper'], dataframe['bb_middle'], dataframe['bb_lower'] = ta.BBANDS(dataframe['close'], timeperiod=20) dataframe['bb_width'] = (dataframe['bb_upper'] - dataframe['bb_lower']) / dataframe['bb_middle'] # Bollinger Width dataframe['low_volatility'] = dataframe['bb_width'] < 0.1 # Low volatility indication # Adding ATR as a Dynamic Volatility Filter dataframe['atr_threshold'] = dataframe['ATR'] < dataframe['ATR'].rolling(window=20).mean() # Low ATR indication # Volume Indicator for additional confirmation dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20) dataframe['high_volume'] = dataframe['volume'] > dataframe['volume_sma'] * 1.5 # Volume spike confirmation # Enhanced RSI Sensitivity dataframe['rsi_bullish'] = dataframe['RSI'] > 50 # Uptrend confirmation with RSI dataframe['rsi_bearish'] = dataframe['RSI'] < 50 # Downtrend confirmation with RSI dataframe.loc[ (dataframe['der_diff_kamavwap_sma'] < dataframe['kama_vwap_slope_sma']) ,'not_short'] = True dataframe.loc[ (dataframe['der_diff_kamavwap_sma'] > dataframe['kama_vwap_slope_sma']) ,'not_short'] = True 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: # --------------------------------GPT ------------------------- # Updated Long Entry Conditions # پیشرفته سازی سیگنال ورود Long # dataframe.loc[ # (dataframe['typical_price'] > dataframe['vwap']) & # (dataframe['long_vwap'] == 1.5) & # (dataframe['adx_trend']) & # (dataframe['wad_support']) & # (dataframe['chop'] < 68.1) & # (abs(dataframe['kama_vwap_slope_sma']) > 0.002) & # (dataframe['price_volatility'] < 0.1) & # (dataframe['high_volume']) & # (dataframe['rsi_bullish']) & # (dataframe['low_volatility']), # ['enter_long', 'enter_tag']] = (1, 'Advanced_kama_vwap_adx') # # پیشرفته سازی سیگنال ورود Short # dataframe.loc[ # (dataframe['typical_price'] < dataframe['vwap']) & # (dataframe['short_vwap'] == 1.5) & # (dataframe['adx_trend']) & # (dataframe['wad_resistance']) & # (dataframe['chop'] < 68.1) & # (abs(dataframe['kama_vwap_slope_sma']) > 0.002) & # (dataframe['price_volatility'] < 0.1) & # (dataframe['high_volume']) & # (dataframe['rsi_bearish']) & # (dataframe['low_volatility']), # ['enter_short', 'enter_tag']] = (1, 'Advanced_kama_vwap_adx') # ----------------------------- Based on RSI -------------------- # dataframe.loc[( # (dataframe['der_diff_kamavwap_sma'] > dataframe['kama_vwap_slope_sma'] ) & # (dataframe['der_diff_kamavwap_sma'].shift(1) < dataframe['kama_vwap_slope_sma'].shift(1) ) & # (dataframe['der_diff_kamavwap_sma'] > 0.002 ) & # (dataframe['short_vwap'] == 1.5) & # (dataframe['STOCH_D'] > dataframe['STOCH_K']) & # (dataframe['volume'] > 100 ) # ), ['enter_short', 'enter_tag']] = (1, 'der') # dataframe.loc[( # (dataframe['der_diff_kamavwap_sma'] < dataframe['kama_vwap_slope_sma'] ) & # (dataframe['der_diff_kamavwap_sma'].shift(1) > dataframe['kama_vwap_slope_sma'].shift(1) ) & # (dataframe['der_diff_kamavwap_sma'] < -0.002 ) & # (dataframe['STOCH_D'] < dataframe['STOCH_K']) & # (dataframe['long_vwap'] == 1.5) & # (dataframe['volume'] > 100 ) # ), ['enter_long', 'enter_tag']] = (1, 'der') # --------------------------------Trend Market Entries----------------------------------- # ----------------Based on kama, ema WAD (wad calculated based on MIndex) ------------ dataframe.loc[( (dataframe['ADX'] > 30) & # Ensure market is trending (dataframe['is_wave'] == 1 ) & (dataframe['condi_wad_ema'] == 1 ) & (dataframe['condi_rsi_kama'] == 1 ) & (dataframe['RSI'] < dataframe['long_rsi_threshold'] ) & #30 (dataframe['RSI_1h'] < 40 ) & (dataframe['wad_sma'] < dataframe['long_wad_threshold']) & (dataframe['close'] < dataframe['kama']) & (dataframe['long_vwap'] == 1.5) & (dataframe['volume'] > 1000) ), ['enter_long', 'enter_tag']] = (1, 'kemaMIx') dataframe.loc[( (dataframe['ADX'] > 30) & # Ensure market is trending (dataframe['is_wave'] == 1 ) & (dataframe['condi_wad_ema'] == 1 ) & (dataframe['condi_rsi_kama'] == 1 ) & (dataframe['RSI'] > dataframe['short_rsi_threshold']) & #70 (dataframe['wad_sma'] > dataframe['short_wad_threshold']) & (dataframe['close'] > dataframe['kama']) & (dataframe['short_vwap'] == 1.5) & (dataframe['volume'] > 1000) ), ['enter_short', 'enter_tag']] = (1, 'kemaMIx') # -------- based on cross of EMA200 and KAMA50 --------------- # dataframe.loc[( # # (dataframe['atrpct'] > 1) & # (dataframe['ADX'] > 25) & # it was considered that adx>40 is a super strong trend, adx< 20 considered as trange market # # (dataframe['ADX_4h'] > 20) & # it was considered that adx>40 is a super strong trend, adx< 20 considered as trange market # # (dataframe['is_wave'] == 0) & # Ensure market is trending # (dataframe['kama_'] > dataframe['ema']) & # EMA and KAMA cross # (dataframe['kama_'].shift(1) < dataframe['ema'].shift(1)) & # (dataframe['chop_1h'] < 68.1) & # (dataframe['condi_rsi_kama'] == 1 ) & # (dataframe['condi_wad_ema'] == 1 ) & # (dataframe['close'] < dataframe['kc_upper']) & # For safety # (abs(dataframe['diff_kamavwap_slope'] - 1 ) > 0.01) & # (dataframe['volume'] > 10000) # ), ['enter_long', 'enter_tag']] = (1, 'L_k*ema') # dataframe.loc[( # # (dataframe['atrpct'] > 1) & # (dataframe['ADX'] > 25) & # it was considered that adx>40 is a super strong trend, adx< 20 considered as trange market # # (dataframe['ADX_4h'] > 20) & # it was considered that adx>40 is a super strong trend, adx< 20 considered as trange market # # (dataframe['is_wave'] == 0 ) & # Ensure market is trending # (dataframe['kama_'] < dataframe['ema']) & # EMA and KAMA cross # (dataframe['kama_'].shift(1) > dataframe['ema'].shift(1)) & # (dataframe['chop_1h'] < 68.1) & # (dataframe['condi_rsi_kama'] == 1 ) & # (dataframe['condi_wad_ema'] == 1 ) & # (dataframe['close'] > dataframe['kc_lower']) & # for safety # (abs(dataframe['diff_kamavwap_slope'] - 1 ) > 0.01) & # # (dataframe['short_vwap'] == 1.5) & # (dataframe['volume'] > 10000) # ), ['enter_short', 'enter_tag']] = (1, 'S_k*ema') # ------------------------ based on ketlner, for not strong trends ----------------- # dataframe.loc[( # (dataframe['short_vwap'] == 1.5) & # ENsure Market is Trending # ((dataframe['atrpct'] < 1) & (dataframe['atrpct_1h'] < 1)) & # # (dataframe['vsi'] > 0.4) & # (dataframe['close'] > dataframe['kc_upper']) & # Price above Keltner upper band (overbought) # abs((dataframe['diff_kamavwap_slope'] - 1) < self.ket_stead.value ) & # (dataframe['WAD'] > dataframe['short_wad_threshold']) & # (dataframe['condi_rsi_kama'] == 1 ) & # (dataframe['condi_wad_ema'] == 1 ) & # (dataframe['chop_4h'] < 68.1) & # (dataframe['volume'] > 1000) # ), ['enter_short', 'enter_tag']] = (1, 'ketlner') # dataframe.loc[( # (dataframe['long_vwap'] == 1.5) & # ENsure Market is Trending # ((dataframe['atrpct'] < 1) & (dataframe['atrpct_1h'] < 1)) & # # (dataframe['vsi'] > 0.4) & # (dataframe['close'] < (dataframe['kc_lower'])) & # Price below Keltner lower band (oversold) # (dataframe['WAD'] < dataframe['long_wad_threshold']) & # (dataframe['condi_rsi_kama'] == 1 ) & # (dataframe['condi_wad_ema'] == 1 ) & # abs((dataframe['diff_kamavwap_slope'] - 1) < self.ket_stead.value ) & # (dataframe['chop_4h'] < 68.1) & # (dataframe['volume'] > 10000) # ), ['enter_long', 'enter_tag']] = (1, 'ketlner') # ----------------based on Williams ------------------------------ # dataframe.loc[( # (dataframe['ATR_4h'] < 1) & # (dataframe['condi_wad_ema'] == 1 ) & # (dataframe['condi_rsi_kama'] == 1 ) & # (dataframe['RSI'] > 75 + (50 + dataframe['WM1']) / 2) & #70 # ((dataframe['WAD'] > dataframe['WM0']) | (dataframe['WAD'] > -2 )) & # (dataframe['WM1'] > dataframe['WM2']) & # (dataframe['chop_4h'] < 68.1) & # (dataframe['volume'] > 10000) # ), ['enter_short', 'enter_tag']] = (1, 'SW%') # dataframe.loc[( # (dataframe['ATR_4h'] < 1) & # (dataframe['condi_wad_ema'] == 1 ) & # (dataframe['condi_rsi_kama'] == 1 ) & # (dataframe['RSI'] < 25 + (50 + dataframe['WM1']) / 2 ) & #30 # (dataframe['WM1'] < dataframe['WM2']) & # ((dataframe['WAD'] > dataframe['WM0']) | (dataframe['WAD'] < -98 )) & # (dataframe['chop_4h'] < 68.2) & # (dataframe['volume'] > 10000) # ), ['enter_long', 'enter_tag']] = (1, 'LW%') # ------- based on atf --------------------------------- dataframe.loc[( (dataframe['atf'] == -1) & (dataframe['WAD'] > dataframe['short_wad_threshold']) & (abs(dataframe['diff_kamavwap_slope'] - 1) > 0.003) & # Increase slope threshold slightly for stronger signals (dataframe['RSI'] > 70) & # Add RSI to confirm overbought conditions for short (dataframe['ADX'] > 25) & # Only enter when ADX confirms trend strength (dataframe['short_vwap'] == 1.5) & (dataframe['volume'] > 1000) & (dataframe['volume'] > dataframe['volume'].rolling(window=10).mean()) # Confirm abnormal volume ), ['enter_short', 'enter_tag']] = (1, 'ATF') dataframe.loc[( (abs(dataframe['diff_kamavwap_slope'] - 1) > 0.003) & (dataframe['atf'] == 1) & (dataframe['WAD'] < dataframe['long_wad_threshold']) & (dataframe['RSI'] < 30) & # Add RSI to confirm oversold conditions for long (dataframe['ADX'] > 25) & # Only enter when ADX confirms trend strength (dataframe['long_vwap'] == 1.5) & (dataframe['volume'] > 1000) & (dataframe['volume'] > dataframe['volume'].rolling(window=10).mean()) # Confirm abnormal volume ), ['enter_long', 'enter_tag']] = (1, 'ATF') # ----------------------------- Based on derivatives -------------------- dataframe.loc[( (dataframe['der_diff_kamavwap_slope'] < 1 ) & (dataframe['der_diff_kamavwap_slope'].shift(1) > 1) & (dataframe['der_diff_kamavwap_sma'] > 0.001) & (dataframe['WAD'] > dataframe['short_wad_threshold']) & # (dataframe['is_wave'] == 1 ) & (dataframe['condi_wad_ema'] == 1 ) & (dataframe['condi_rsi_kama'] == 1 ) & (dataframe['short_vwap'] == 1.5) & (dataframe['STOCH_D'] > dataframe['STOCH_K']) & (dataframe['volume'] > 1000 ) ), ['enter_short', 'enter_tag']] = (1, 'der2') dataframe.loc[( (dataframe['der_diff_kamavwap_slope'] > 1 ) & (dataframe['der_diff_kamavwap_slope'].shift(1) < 1) & (dataframe['der_diff_kamavwap_sma'] < -0.001) & (dataframe['WAD'] < dataframe['long_wad_threshold']) & # (dataframe['is_wave'] == 1 ) & (dataframe['condi_wad_ema'] == 1 ) & (dataframe['STOCH_D'] < dataframe['STOCH_K']) & (dataframe['condi_rsi_kama'] == 1 ) & (dataframe['long_vwap'] == 1.5) & (dataframe['volume'] > 1000 ) ), ['enter_long', 'enter_tag']] = (1, 'der2') # -------------------------------------- GPT suggest -------------------------------------------------------------- # # # Entry Conditions for Long # dataframe.loc[ # (dataframe['typical_price'] > dataframe['vwap']) & # Market trending up # (dataframe['typical_price'].shift(1) < dataframe['vwap'].shift(1)) & # (dataframe['long_vwap'] == 1.5) & # Long VWAP signal # (dataframe['wad_support']) & # WAD support # (dataframe['chop'] < 68.1) & # Low choppiness, potential trend # (abs(dataframe['kama_vwap_slope_sma']) > 0.002) & # Uptrend slope # (dataframe['price_volatility'] < 0.1), # Filter low volatility # ['enter_long', 'enter_tag']] = (1, 'kama_vwap') # # Entry Conditions for Short # dataframe.loc[ # (dataframe['typical_price'] < dataframe['vwap']) & # Market trending down # (dataframe['typical_price'].shift(1) > dataframe['vwap'].shift(1)) & # (dataframe['short_vwap'] == 1.5) & # Short VWAP signal # (dataframe['wad_resistance']) & # WAD resistance # (dataframe['chop'] < 68.1) & # Low choppiness, potential trend # (abs(dataframe['kama_vwap_slope_sma']) > 0.002) & # Downtrend slope # (dataframe['price_volatility'] < 0.1), # Filter low volatility # ['enter_short', 'enter_tag']] = (1, 'kama_vwap') # --------------------------- Advanced RSI ------------------------------- # ----------------------------based on RSI, WAD and WAD.mean() for range markets ---------------------------------------- dataframe.loc[( (dataframe['short_rsi_threshold'] > dataframe['S_rsi_thres']) & (dataframe['diff_rsi'] < 0) & (dataframe['diff_rsi'].shift(1) > 0) & (dataframe['close'] > dataframe['kama_']) & (dataframe['RSI'] > 70) & (dataframe['ADX'] > 25) & # Add ADX filter for stronger trends (dataframe['ATR'] > dataframe['ATR'].rolling(window=14).mean()) & # Filter for volatility (dataframe['short_vwap'] == 1.5) & (dataframe['STOCH_D'] > dataframe['STOCH_K']) & (dataframe['volume'] > 1000) ), ['enter_short', 'enter_tag']] = (1, 'rsi') dataframe.loc[( (dataframe['long_rsi_threshold'] < dataframe['L_rsi_thres']) & (dataframe['diff_rsi'] > 0) & (dataframe['diff_rsi'].shift(1) < 0) & (dataframe['close'] < dataframe['kama_']) & (dataframe['RSI'] < 30) & (dataframe['ADX'] > 25) & # Add ADX filter for stronger trends (dataframe['ATR'] > dataframe['ATR'].rolling(window=14).mean()) & # Filter for volatility (dataframe['long_vwap'] == 1.5) & (dataframe['STOCH_D'] < dataframe['STOCH_K']) & (dataframe['volume'] > 1000) ), ['enter_long', 'enter_tag']] = (1, 'rsi') # -------------------------- based on Con Di --------------------------- # dataframe.loc[( # (dataframe['condi_wad_hma'] == -1) & # (dataframe['condi_rsi_hma'] == -1) & # (dataframe['condi_ema_hma'] == -1) & # (dataframe['close'] < dataframe['HMA']) & # (dataframe['ADX'] > 25) & # تایید روند قوی با ADX # (dataframe['STOCH_D'] > dataframe['STOCH_K']) & # (dataframe['RSI'] > 70) & # تایید حالت اشباع خرید برای Short # (dataframe['volume'] > 1500) # # (dataframe['volume'] > dataframe['volume'].rolling(window=10).mean()) # بررسی حجم غیرعادی # ), ['enter_short', 'enter_tag']] = (1, 'condis') # dataframe.loc[( # (dataframe['condi_wad_hma'] == 1) & # (dataframe['condi_rsi_hma'] == 1) & # (dataframe['condi_ema_hma'] == 1) & # (dataframe['close'] > dataframe['HMA']) & # (dataframe['ADX'] > 25) & # تایید روند قوی با ADX # (dataframe['RSI'] < 30) & # تایید حالت اشباع فروش برای Long # (dataframe['STOCH_D'] < dataframe['STOCH_K']) & # (dataframe['volume'] > 1500) # # (dataframe['volume'] > dataframe['volume'].rolling(window=10).mean()) # بررسی حجم غیرعادی # ), ['enter_long', 'enter_tag']] = (1, 'condis') # ---------------------------------kosh---------------------------------- dataframe.loc[( (dataframe['p_mom'] == True) & (dataframe['p_mom'].shift(1) == False) & (dataframe['STOCH_D'] < dataframe['STOCH_K']) & (dataframe['WAD'] < -51) ), ['enter_long', 'enter_tag']] = (1, 'rmi') dataframe.loc[( (dataframe['n_mom'] == True) & (dataframe['n_mom'].shift(1) == False) & (dataframe['STOCH_D'] > dataframe['STOCH_K']) & (dataframe['WAD'] > -49) ), ['enter_short', 'enter_tag']] = (1, 'rmi') # ---------------------------------STOCH---------------------------------- # dataframe.loc[( # (dataframe['p_mom'] == True) & # (dataframe['p_mom'].shift(1) == False) & # (dataframe['WAD'] < -51) # ), ['enter_long', 'enter_tag']] = (1, 'rmi') # dataframe.loc[( # (dataframe['n_mom'] == True) & # (dataframe['n_mom'].shift(1) == False) & # (dataframe['WAD'] > -49) # ), ['enter_short', 'enter_tag']] = (1, 'rmi') 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: dataframe.loc[( (dataframe['enter_short'] == 1 ) ), ['exit_long', 'exit_tag']] = (1, 'Lopp_signal') dataframe.loc[( (dataframe['enter_long'] == 1 ) ), ['exit_short', 'exit_tag']] = (1, 'Sopp_signal') 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 # if any(tag in trade.enter_tag for tag in ['lrsi', 'srsi']) and current_profit >= 0.02: # return 'condidit' if any(tag in trade.enter_tag for tag in ['ketlner']) and (current_candle['close'] < current_candle['kc_lower']) and (current_candle['kama_slope'] > 1) and (previous_candle['kama_slope'] < 1): return "ketlner_did_it!!" if any(tag in trade.enter_tag for tag in ['ketlner']) and (current_candle['close'] > current_candle['kc_upper']) and (current_candle['kama_slope'] < 1) and (previous_candle['kama_slope'] > 1): return "ketlner_did_it!!" # if any(tag in trade.enter_tag for tag in ['Satf']) and (current_candle['kama_slope'] > previous_candle['kama_slope']): # return 'satf ended' # if any(tag in trade.enter_tag for tag in ['Latf']) and (current_candle['kama_slope'] < previous_candle['kama_slope']): # return 'latf ended' # if any(tag in trade.enter_tag for tag in ['S_vwap']) and (current_candle['typical_price'] > current_candle['vwap']): # return 'svwap ended' # if any(tag in trade.enter_tag for tag in ['L_vwap']) and (current_candle['typical_price'] < current_candle['vwap']): # return 'lvwap ended' # if any(tag in trade.enter_tag for tag in ['L_k*ema']) and (current_candle['kama_'] < current_candle['ema']) and (current_profit < 0): # return "Lprevent!!" # if any(tag in trade.enter_tag for tag in ['S_k*ema']) and (current_candle['kama_'] > current_candle['ema']) and (current_profit < 0): # return "Sprevent!!" 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() # dataframe['spread'] = 1000 * abs(dataframe['high'] - dataframe['low']) / dataframe['close'] # # بقیه کدهای محاسبه leverage # dataframe['spread_avg'] = dataframe['spread'].rolling(window=100).mean() # leverage = self.max_leverage * (1.0 - float(current_candle['atr_pct'])) return 15.0 # return leverage