import logging import math from datetime import datetime from functools import reduce import numpy as np import pandas as pd import pandas_ta as pta import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import (DecimalParameter, IntParameter, RealParameter, informative, merge_informative_pair, stoploss_from_open) from freqtrade.strategy.interface import IStrategy from pandas import DataFrame, Series import freqtrade.vendor.qtpylib.indicators as qtpylib logger = logging.getLogger(__name__) # ============================================================================= # INDICATOR / UTILITY FUNCTIONS # ============================================================================= def ewo(dataframe: DataFrame, sma1_length: int = 5, sma2_length: int = 35) -> Series: """ Elliot Wave Oscillator (EWO) calcolato come: (EMA(sma1_length) - EMA(sma2_length)) / close * 100 """ sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) return (sma1 - sma2) / dataframe['close'] * 100 def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """ Calcolo del Williams %R: Mostra la posizione del close rispetto al range [low, high] degli ultimi 'period' candle. """ highest_high = dataframe['high'].rolling(window=period).max() lowest_low = dataframe['low'].rolling(window=period).min() wr = (highest_high - dataframe['close']) / (highest_high - lowest_low) * -100 return wr def chaikin_money_flow(dataframe: DataFrame, n: int = 20, fillna: bool = False) -> Series: """ Chaikin Money Flow (CMF). Misura l'ammontare di Money Flow Volume su un periodo 'n'. """ mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) \ / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # evitiamo division by zero mfv *= dataframe['volume'] cmf = mfv.rolling(n, min_periods=0).sum() / dataframe['volume'].rolling(n, min_periods=0).sum() if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return pd.Series(cmf, name='cmf') def bollinger_bands(stock_price: Series, window_size: int, num_of_std: float): """ Restituisce la banda mediana (media mobile) e la banda inferiore di Bollinger con deviazione standard = num_of_std. """ rolling_mean = stock_price.rolling(window=window_size).mean() rolling_std = stock_price.rolling(window=window_size).std() lower_band = rolling_mean - (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band) def ha_typical_price(bars: DataFrame) -> Series: """ Calcola il prezzo tipico delle candele Heikin Ashi: (ha_high + ha_low + ha_close) / 3 """ return (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3.0 def pmax(df: DataFrame, period: int, multiplier: int, length: int, MAtype: int, src: int): """ Calcolo del Pmax, una variante di SuperTrend. - period: ATR period - multiplier: fattore di moltiplicazione dell'ATR (diviso per 10 nel codice, es. 27 => 2.7) - length, MAtype: parametri per la media mobile - src: definisce su quali prezzi calcolare la MA """ period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) # Determina la "fonte" su cui calcolare la media (close, (high+low)/2, (H+L+O+C)/4, ecc.) if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 else: # default a src=3 => (H+L+O+C)/4 masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 # Calcolo della media in base a MAtype if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: # talib astratto non ha T3 nativo (dipende dalle versioni) mavalue = ta.T3(masrc, timeperiod=length) elif MAtype == 4: mavalue = ta.SMA(masrc, timeperiod=length) elif MAtype == 5: mavalue = VIDYA(df, length=length) elif MAtype == 6: mavalue = ta.TEMA(masrc, timeperiod=length) elif MAtype == 7: mavalue = ta.WMA(masrc, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) else: mavalue = ta.EMA(masrc, timeperiod=length) # default # Calcolo ATR df['atr'] = ta.ATR(df, timeperiod=period) df['basic_ub'] = mavalue + ((multiplier / 10) * df['atr']) df['basic_lb'] = mavalue - ((multiplier / 10) * df['atr']) basic_ub = df['basic_ub'].values basic_lb = df['basic_lb'].values final_ub = np.zeros(len(df)) final_lb = np.zeros(len(df)) for i in range(period, len(df)): final_ub[i] = (basic_ub[i] if (basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1]) else final_ub[i - 1]) final_lb[i] = (basic_lb[i] if (basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1]) else final_lb[i - 1]) df['final_ub'] = final_ub df['final_lb'] = final_lb pm_arr = np.zeros(len(df)) for i in range(period, len(df)): if pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]: pm_arr[i] = final_ub[i] elif pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]: pm_arr[i] = final_lb[i] elif pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]: pm_arr[i] = final_lb[i] elif pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]: pm_arr[i] = final_ub[i] else: pm_arr[i] = 0.0 pm_series = pd.Series(pm_arr, index=df.index) # Mark the trend direction up/down pmx = np.where( (pm_arr > 0.0), np.where( mavalue < pm_arr, 'down', 'up' ), np.NaN ) return pm_series, pmx def VWAPB(dataframe: DataFrame, window_size: int = 20, num_of_std: float = 1): """ Calcolo di VWAP + bande usando la rolling_std sulla serie della vwap. """ df = dataframe.copy() df['vwap'] = qtpylib.rolling_vwap(df, window=window_size) rolling_std = df['vwap'].rolling(window=window_size).std() df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std) df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std) return df['vwap_low'], df['vwap'], df['vwap_high'] def top_percent_change_dca(dataframe: DataFrame, length: int) -> Series: """ Calcola la variazione percentuale del current close dal massimo 'Open' nell'ultimo 'length' periodi: (rollingMax(Open, length) - Close) / Close Se length == 0, si limita a (Open - Close) / Close sul candle corrente. Restituisce una Serie con tali valori. """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] def is_support(row_data: pd.Series) -> bool: """ Esempio di funzione usata per determinare un 'support' su 5 candele (la logica la gestisci come preferisci). """ conditions = [] for row in range(len(row_data) - 1): if row < len(row_data) / 2: conditions.append(row_data[row] > row_data[row + 1]) else: conditions.append(row_data[row] < row_data[row + 1]) return reduce(lambda x, y: x & y, conditions) # ============================================================================= # PLACEHOLDER PER ALTRI INDICATORI # ============================================================================= def VIDYA(df: DataFrame, length: int = 10): """ Placeholder per la VIDYA (Variable Index Dynamic Average). Se la usi realmente, definisci l'algoritmo o importa una libreria che la calcola. """ return ta.SMA(df['close'], timeperiod=length) def vwma(df: DataFrame, length: int = 20): """ Placeholder per la VWMA (Volume Weighted Moving Average). """ return df['close'].rolling(length).apply( lambda x: np.average(x, weights=df['volume'].loc[x.index]) ) def zema(df: DataFrame, period: int = 9): """ Placeholder per la Zero Lag EMA. """ return ta.EMA(df['close'], timeperiod=period) # ============================================================================= # LA STRATEGIA # ============================================================================= class GeneStrategy(IStrategy): """ Strategia di esempio con molteplici segnali di acquisto e DCA. Include logica di trailing stop / vendite custom e parametri ottimizzabili, con correzione del calcolo delle percentuali per la parte di trailing. """ def version(self) -> str: return "2025-01-15-corrected" # ROI table minimal_roi = { "0": 100 } # Stoploss stoploss = -0.99 # uso custom stoploss -> vedi self.custom_stoploss # DCA position_adjustment_enable = True # Trailing stop trailing_stop = False trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.10 trailing_only_offset_is_reached = True timeframe = '5m' process_only_new_candles = True startup_candle_count = 168 order_types = { 'entry': 'market', 'exit': 'market', 'emergency_exit': 'market', 'force_entry': "market", 'force_exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } # ------------------------------------------------------------------------- # PARAMETRI OTTIMIZZABILI # ------------------------------------------------------------------------- fast_ewo = 50 slow_ewo = 200 # NFINext44 buy_44_ma_offset = 0.982 buy_44_ewo = -18.143 buy_44_cti = -0.8 buy_44_r_1h = -75.0 # NFINext37 buy_37_ma_offset = 0.98 buy_37_ewo = 9.8 buy_37_rsi = 56.0 buy_37_cti = -0.7 # NFINext7 buy_ema_open_mult_7 = 0.030 buy_cti_7 = -0.89 # Parametri a scopo dimostrativo buy_rmi = IntParameter(30, 50, default=45, space='buy', optimize=True) buy_cci = IntParameter(-135, -90, default=-126, space='buy', optimize=True) buy_srsi_fk = IntParameter(30, 50, default=42, space='buy', optimize=True) buy_cci_length = IntParameter(25, 45, default=42, space='buy', optimize=True) buy_rmi_length = IntParameter(8, 20, default=11, space='buy', optimize=True) buy_bb_width = DecimalParameter(0.065, 0.135, default=0.097, space='buy', optimize=True) buy_bb_delta = DecimalParameter(0.018, 0.035, default=0.028, space='buy', optimize=True) buy_roc_1h = IntParameter(-25, 200, default=13, space='buy', optimize=True) buy_bb_width_1h = DecimalParameter(0.3, 2.0, default=1.3, space='buy', optimize=True) # ClucHA is_optimize_clucha = False buy_clucha_bbdelta_close = DecimalParameter(0.0005, 0.02, default=0.001, space='buy', optimize=True) buy_clucha_bbdelta_tail = DecimalParameter(0.7, 1.0, default=1.0, space='buy', optimize=True) buy_clucha_close_bblower = DecimalParameter(0.0005, 0.02, default=0.008, space='buy', optimize=True) buy_clucha_closedelta_close = DecimalParameter(0.0005, 0.02, default=0.014, space='buy', optimize=True) buy_clucha_rocr_1h = DecimalParameter(0.5, 1.0, default=0.51, space='buy', optimize=True) # Local Uptrend buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.026, space='buy', optimize=True) buy_bb_factor = DecimalParameter(0.99, 0.999, default=0.995, space='buy', optimize=True) buy_closedelta = DecimalParameter(12.0, 18.0, default=13.1, space='buy', optimize=True) # Buy generali rocr_1h = DecimalParameter(0.5, 1.0, default=0.51, space='buy', optimize=True) rocr1_1h = DecimalParameter(0.5, 1.0, default=0.59, space='buy', optimize=True) bbdelta_close = DecimalParameter(0.0005, 0.02, default=0.001, space='buy', optimize=True) closedelta_close = DecimalParameter(0.0005, 0.02, default=0.014, space='buy', optimize=True) bbdelta_tail = DecimalParameter(0.7, 1.0, default=1.0, space='buy', optimize=True) close_bblower = DecimalParameter(0.0005, 0.02, default=0.008, space='buy', optimize=True) # Sell params sell_fisher = DecimalParameter(0.1, 0.5, default=0.5, space='sell', optimize=True) sell_bbmiddle_close = DecimalParameter(0.97, 1.1, default=1.067, space='sell', optimize=True) # Deadfish sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.06, space='sell', optimize=True) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.1, space='sell', optimize=True) sell_deadfish_bb_factor = DecimalParameter(0.9, 1.2, default=1.2, space='sell', optimize=True) sell_deadfish_volume_factor = DecimalParameter(1.0, 2.5, default=1.9, space='sell', optimize=True) # SMAOffset base_nb_candles_buy = IntParameter(8, 20, default=13, space='buy', optimize=True) base_nb_candles_sell = IntParameter(8, 50, default=44, space='sell', optimize=True) low_offset = DecimalParameter(0.985, 0.995, default=0.991, space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=1.007, space='sell', optimize=True) high_offset_2 = DecimalParameter(1.01, 1.02, default=1.01, space='sell', optimize=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.25, space='sell', optimize=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.5, space='sell', optimize=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.08, space='sell', optimize=True) sell_trail_profit_min_2 = DecimalParameter(0.04, 0.1, default=0.04, space='sell', optimize=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.08, space='sell', optimize=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.07, space='sell', optimize=True) # hard stoploss profit pHSL = DecimalParameter(-0.5, -0.04, default=-0.163, space='sell', optimize=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.02, default=0.01, space='sell', optimize=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.008, space='sell', optimize=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.04, 0.1, default=0.072, space='sell', optimize=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.054, space='sell', optimize=True) # ------------------------------------------------------------------------- # INFORMATIVA SU ALTRI TIMEFRAME # ------------------------------------------------------------------------- def informative_pairs(self): """ Definisce tutte le coppie/timeframe che vogliamo usare come dati informativi. """ pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] # Aggiungiamo anche BTC/USDT a 5m, se necessario. informative_pairs += [("BTC/USDT", "5m")] return informative_pairs # ------------------------------------------------------------------------- # POPULATE INDICATORS # ------------------------------------------------------------------------- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Calcolo di tutti gli indicatori necessari alla strategia. """ # =================================================== # 1) Otteniamo i dati informativi su BTC a 5m info_tf = '5m' informative_btc = self.dp.get_pair_dataframe('BTC/USDT', timeframe=info_tf) # shift(1) per evitare dati "futuri" informative_btc = informative_btc.copy().shift(1) dataframe['btc_close'] = informative_btc['close'] dataframe['btc_ema_fast'] = ta.EMA(informative_btc, timeperiod=20) dataframe['btc_ema_slow'] = ta.EMA(informative_btc, timeperiod=25) dataframe['down'] = (dataframe['btc_ema_fast'] < dataframe['btc_ema_slow']).astype('int') # =================================================== # 2) Calcolo di varie medie e volume for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) dataframe['cmf'] = chaikin_money_flow(dataframe, 20) # Bollinger bands su 20 periodi, std=2 bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'] # Bollinger 40 bollinger2_40 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=40, stds=2) dataframe['bb_lowerband2_40'] = bollinger2_40['lower'] dataframe['bb_middleband2_40'] = bollinger2_40['mid'] dataframe['bb_upperband2_40'] = bollinger2_40['upper'] # EMA dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # HeikinAshi heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # ClucHA dataframe['bb_delta_cluc'] = (dataframe['bb_middleband2_40'] - dataframe['bb_lowerband2_40']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() # SRSI stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] # Altre bande BB mid, lower = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['bb_lowerband'] = lower dataframe['bb_middleband'] = mid # is DIP bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe['bb_lowerband3'] = bollinger3['lower'] dataframe['bb_middleband3'] = bollinger3['mid'] dataframe['bb_upperband3'] = bollinger3['upper'] dataframe['bb_delta'] = (dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2'] dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3) dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28) # VWAP vwap_low, vwap_mid, vwap_high = VWAPB(dataframe, 20, 1) dataframe['vwap_lowerband'] = vwap_low dataframe['vwap_middleband'] = vwap_mid dataframe['vwap_upperband'] = vwap_high dataframe['vwap_width'] = ((vwap_high - vwap_low) / vwap_mid) * 100 dataframe['ema_vwap_diff_50'] = (dataframe['ema_50'] - dataframe['vwap_lowerband']) / dataframe['ema_50'] # DIP protection dataframe['tpct_change_0'] = top_percent_change_dca(dataframe, 0) dataframe['tpct_change_1'] = top_percent_change_dca(dataframe, 1) dataframe['tcp_percent_4'] = top_percent_change_dca(dataframe, 4) # EWO dataframe['ewo'] = ewo(dataframe, 50, 200) # usato in NFINext44 dataframe['EWO'] = ewo(dataframe, self.fast_ewo, self.slow_ewo) # EMA 16 dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) # Ema 12/26 dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) # Williams R dataframe['r_14'] = williams_r(dataframe, period=14) # Rebuy check dataframe['ema_5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema_10'] = ta.EMA(dataframe, timeperiod=10) # Pmax dataframe['pm'], dataframe['pmx'] = pmax(heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3) dataframe['source'] = (dataframe['high'] + dataframe['low'] + dataframe['open'] + dataframe['close']) / 4 dataframe['pmax_thresh'] = ta.EMA(dataframe['source'], timeperiod=9) dataframe['sma_75'] = ta.SMA(dataframe, timeperiod=75) # Fisher RSI rsi = ta.RSI(dataframe) dataframe["fisher"] = (np.exp(2 * (0.1 * (rsi - 50))) - 1) / (np.exp(2 * (0.1 * (rsi - 50))) + 1) # =================================================== # 3) Otteniamo i dati 1h per la coppia corrente inf_tf = '1h' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) inf_heikinashi = qtpylib.heikinashi(informative) informative['ha_close'] = inf_heikinashi['close'] informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168) informative['rsi_14'] = ta.RSI(informative, timeperiod=14) informative['cmf'] = chaikin_money_flow(informative, 20) sup_series = informative['low'].rolling(window=5, center=True)\ .apply(lambda row: is_support(row), raw=True).shift(2) informative['sup_level'] = np.where( sup_series, np.where(informative['close'] < informative['open'], informative['close'], informative['open']), float('NaN') ) informative['sup_level'] = pd.Series(informative['sup_level']).ffill() informative['roc'] = ta.ROC(informative, timeperiod=9) informative['r_480'] = williams_r(informative, period=480) # Bollinger bands (1h) bb_1h = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2) informative['bb_lowerband2'] = bb_1h['lower'] informative['bb_middleband2'] = bb_1h['mid'] informative['bb_upperband2'] = bb_1h['upper'] informative['bb_width'] = (informative['bb_upperband2'] - informative['bb_lowerband2']) / informative['bb_middleband2'] informative['r_84'] = williams_r(informative, period=84) informative['cti_40'] = pta.cti(informative["close"], length=40) dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50) # merge informativo dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe # ------------------------------------------------------------------------- # POPULATE ENTRY TREND (BUY) # ------------------------------------------------------------------------- def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Logica di acquisto: molti segnali, ognuno contrassegnato da un enter_tag. """ # Check se BTC ha fatto un "dump" nelle ultime 24 candele btc_dump = (dataframe['btc_close'].rolling(24).max() >= (dataframe['btc_close'] * 1.03)) # RSI check rsi_check = (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) # Esempio DIP signal dataframe.loc[ ( (dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) & (dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) & (dataframe['srsi_fk'] < self.buy_srsi_fk.value) & (dataframe['bb_delta'] > self.buy_bb_delta.value) & (dataframe['bb_width'] > self.buy_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) & (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value) & (dataframe['roc_1h'] < self.buy_roc_1h.value) & (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value) ), ['enter_long', 'enter_tag'] ] = (1, 'DIP signal') # Break signal dataframe.loc[ ( (dataframe['bb_delta'] > self.buy_bb_delta.value) & (dataframe['bb_width'] > self.buy_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000) & (dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value) & (dataframe['roc_1h'] < self.buy_roc_1h.value) & (dataframe['bb_width_1h'] < self.buy_bb_width_1h.value) ), ['enter_long', 'enter_tag'] ] = (1, 'Break signal') # Cluc_HA dataframe.loc[ ( (dataframe['rocr_1h'] > self.buy_clucha_rocr_1h.value) & (dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.buy_clucha_bbdelta_close.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.buy_clucha_closedelta_close.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.buy_clucha_bbdelta_tail.value) & (dataframe['ha_close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['close'] > (dataframe['sup_level_1h'] * 0.88)) & (dataframe['ha_close'] < dataframe['ha_close'].shift()) ), ['enter_long', 'enter_tag'] ] = (1, 'cluc_HA') # NFIX39 dataframe.loc[ ( (dataframe['ema_200'] > (dataframe['ema_200'].shift(12) * 1.01)) & (dataframe['ema_200'] > (dataframe['ema_200'].shift(48) * 1.07)) & (dataframe['bb_lowerband2_40'].shift().gt(0)) & (dataframe['bb_delta_cluc'].gt(dataframe['close'] * 0.056)) & (dataframe['closedelta'].gt(dataframe['close'] * 0.01)) & (dataframe['tail'].lt(dataframe['bb_delta_cluc'] * 0.5)) & (dataframe['close'].lt(dataframe['bb_lowerband2_40'].shift())) & (dataframe['close'].le(dataframe['close'].shift())) & (dataframe['close'] > dataframe['ema_50'] * 0.912) ), ['enter_long', 'enter_tag'] ] = (1, 'NFIX39') # NFIX29 dataframe.loc[ ( (dataframe['close'] > (dataframe['sup_level_1h'] * 0.72)) & (dataframe['close'] < (dataframe['ema_16'] * 0.982)) & (dataframe['EWO'] < -10.0) & (dataframe['cti'] < -0.9) ), ['enter_long', 'enter_tag'] ] = (1, 'NFIX29') # local_uptrend dataframe.loc[ ( (dataframe['ema_26'] > dataframe['ema_12']) & (dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value) & (dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) & (dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000) ), ['enter_long', 'enter_tag'] ] = (1, 'local_uptrend') # vwap dataframe.loc[ ( (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['tcp_percent_4'] > 0.053) & (dataframe['cti'] < -0.8) & (dataframe['rsi'] < 35) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag'] ] = (1, 'vwap') # insta_signal dataframe.loc[ ( (dataframe['bb_width_1h'] > 0.131) & (dataframe['r_14'] < -51) & (dataframe['r_84_1h'] < -70) & (dataframe['cti'] < -0.845) & (dataframe['cti_40_1h'] < -0.735) & (btc_dump == 1) ), ['enter_long', 'enter_tag'] ] = (1, 'insta_signal') # NFINext44 dataframe.loc[ ( (dataframe['close'] < (dataframe['ema_16'] * self.buy_44_ma_offset)) & (dataframe['ewo'] < self.buy_44_ewo) & (dataframe['cti'] < self.buy_44_cti) & (dataframe['r_480_1h'] < self.buy_44_r_1h) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag'] ] = (1, 'NFINext44') # NFINext37 dataframe.loc[ ( (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['close'] < dataframe['sma_75'] * self.buy_37_ma_offset) & (dataframe['ewo'] > self.buy_37_ewo) & (dataframe['rsi'] < self.buy_37_rsi) & (dataframe['cti'] < self.buy_37_cti) ), ['enter_long', 'enter_tag'] ] = (1, 'NFINext37') # NFINext7 dataframe.loc[ ( (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_ema_open_mult_7)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open'] / 100)) & (dataframe['cti'] < self.buy_cti_7) ), ['enter_long', 'enter_tag'] ] = (1, 'NFINext7') # NFINext32 dataframe.loc[ ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 46) & (dataframe['rsi'] > 19) & (dataframe['close'] < dataframe['sma_15'] * 0.942) & (dataframe['cti'] < -0.86) ), ['enter_long', 'enter_tag'] ] = (1, 'NFINext32') # sma_3 dataframe.loc[ ( (dataframe['bb_lowerband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['close'] * 0.059) & (dataframe['ha_closedelta'] > dataframe['close'] * 0.023) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * 0.24) & (dataframe['close'] < dataframe['bb_lowerband2_40'].shift()) & (dataframe['close'] < dataframe['close'].shift()) & (btc_dump == 0) ), ['enter_long', 'enter_tag'] ] = (1, 'sma_3') # WVAP dataframe.loc[ ( (dataframe['close'] < dataframe['vwap_lowerband']) & (dataframe['tpct_change_1'] > 0.04) & (dataframe['cti'] < -0.8) & (dataframe['rsi'] < 35) & (rsi_check) & (btc_dump == 0) ), ['enter_long', 'enter_tag'] ] = (1, 'WVAP') return dataframe # ------------------------------------------------------------------------- # POPULATE EXIT TREND (SELL) # ------------------------------------------------------------------------- def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Logica di uscita "base" (non personalizzata). """ dataframe.loc[ ( (dataframe['fisher'] > self.sell_fisher.value) & (dataframe['ha_high'] <= dataframe['ha_high'].shift(1)) & (dataframe['ha_high'].shift(1) <= dataframe['ha_high'].shift(2)) & (dataframe['ha_close'] <= dataframe['ha_close'].shift(1)) & (dataframe['ema_fast'] > dataframe['ha_close']) & ((dataframe['ha_close'] * self.sell_bbmiddle_close.value) > dataframe['bb_middleband']) & (dataframe['volume'] > 0) ), 'exit' ] = 0 return dataframe # ------------------------------------------------------------------------- # CUSTOM SELL # ------------------------------------------------------------------------- def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): """ Esempio di logica di vendita personalizzata, con trailing condizionale e correzione del calcolo di max_profit rispetto al prezzo di apertura. """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) # Calcolo del profitto massimo (decimale) # Se open_rate = 10 e max_rate = 10.5 => max_profit = 0.05 (cioè +5%) max_profit = (trade.max_rate - trade.open_rate) / trade.open_rate if last_candle is not None: # trail_target_1 if ( (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (max_profit > (current_profit + self.sell_trail_down_1.value)) ): return 'trail_target_1' # trail_target_2 elif ( (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)) ): return 'trail_target_2' # RSI-85 target elif (current_profit > 3.0) and (last_candle['rsi'] > 85): return 'RSI-85 target' # Sell Signal1 if ( (current_profit > 0) and (count_of_buys < 4) and (last_candle['close'] > last_candle['hma_50']) and (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) and (last_candle['rsi'] > 50) and (last_candle['volume'] > 0) and (last_candle['rsi_fast'] > last_candle['rsi_slow']) ): return 'sell signal1' # Sell Signal1 * 1.01 if ( (current_profit > 0) and (count_of_buys >= 4) and (last_candle['close'] > last_candle['hma_50'] * 1.01) and (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) and (last_candle['rsi'] > 50) and (last_candle['volume'] > 0) and (last_candle['rsi_fast'] > last_candle['rsi_slow']) ): return 'sell signal1 * 1.01' # Sell Signal2 if ( (current_profit > 0) and (last_candle['close'] > last_candle['hma_50']) and (last_candle['close'] > (last_candle[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) and (last_candle['volume'] > 0) and (last_candle['rsi_fast'] > last_candle['rsi_slow']) ): return 'sell signal2' # Esempio di stoploss "deadfish" if ( (current_profit < self.sell_deadfish_profit.value) and (last_candle['close'] < last_candle['ema_200']) and (last_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (last_candle['close'] > last_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.sell_deadfish_volume_factor.value) and (last_candle['cmf'] < 0.0) ): return "sell_stoploss_deadfish" return None # ------------------------------------------------------------------------- # CUSTOM STOPLOSS # ------------------------------------------------------------------------- def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ Stoploss dinamico basato su soglie di profitto (pPF_1, pPF_2) e sullo scaling lineare del trailing tra pSL_1 e pSL_2. """ HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL # Se per qualche motivo sl_profit >= current_profit, fallback if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) # ------------------------------------------------------------------------- # GESTIONE DCA (adjust_trade_position) # ------------------------------------------------------------------------- initial_safety_order_trigger = -0.018 max_safety_orders = 8 safety_order_step_scale = 1.2 safety_order_volume_scale = 1.4 def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): """ Logica DCA personalizzata. Se l'attuale profitto è sopra la soglia initial_safety_order_trigger, non eseguiamo DCA. Altrimenti valutiamo le condizioni e la scalatura di volume. """ if current_profit > self.initial_safety_order_trigger: return None dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() filled_buys = trade.select_filled_orders('buy') count_of_buys = len(filled_buys) # Evitiamo di continuare a comprare se le candele sono brutte (condizioni "waiting") if count_of_buys == 1 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']): return None elif count_of_buys == 2 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215): return None elif count_of_buys == 3 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215): return None elif count_of_buys == 4 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] < last_candle['open']) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']): return None elif (count_of_buys == 5) and ( (last_candle['cmf_1h'] < 0.00) and (last_candle['close'] < last_candle['open']) and (last_candle['rsi_14_1h'] < 30) and (last_candle['tpct_change_0'] > 0.018) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']) ): logger.info( f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) > 0, rsi_1h > 30, etc." ) return None elif (count_of_buys == 6) and ( (last_candle['cmf_1h'] < 0.00) and (last_candle['close'] < last_candle['open']) and (last_candle['rsi_14_1h'] < 30) and (last_candle['tpct_change_0'] > 0.018) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']) ): logger.info( f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) > 0, rsi_1h > 30, etc." ) return None elif (count_of_buys == 7) and ( (last_candle['cmf_1h'] < 0.00) and (last_candle['close'] < last_candle['open']) and (last_candle['rsi_14_1h'] < 30) and (last_candle['tpct_change_0'] > 0.018) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']) ): logger.info( f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) > 0, rsi_1h > 30, etc." ) return None elif (count_of_buys == 8) and ( (last_candle['cmf_1h'] < 0.00) and (last_candle['close'] < last_candle['open']) and (last_candle['rsi_14_1h'] < 30) and (last_candle['tpct_change_0'] > 0.018) and (last_candle['ema_vwap_diff_50'] < 0.215) and (last_candle['ema_5'] >= last_candle['ema_10']) ): logger.info( f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) > 0, rsi_1h > 30, etc." ) return None # Se abbiamo ancora safety order disponibili, calcoliamo lo stake if 1 <= count_of_buys <= self.max_safety_orders: # progressione step safety_order_trigger = abs(self.initial_safety_order_trigger) if self.safety_order_step_scale > 1: safety_order_trigger = abs(self.initial_safety_order_trigger) + ( abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale, (count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1) ) elif self.safety_order_step_scale < 1: safety_order_trigger = abs(self.initial_safety_order_trigger) + ( abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (1 - math.pow(self.safety_order_step_scale, (count_of_buys - 1))) / (1 - self.safety_order_step_scale) ) # Se l'attuale profitto è inferiore al trigger (es: -3.6%) if current_profit <= -safety_order_trigger: try: # stake iniziale stake_amount = filled_buys[0].cost # scale del volume stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, (count_of_buys - 1)) return stake_amount except Exception as exception: logger.info(f'Error obtaining stake amount for {trade.pair}: {str(exception)}') return None return None