# source: https://raw.githubusercontent.com/vaskosmihaylov/nfi-custom-strategies/12e08105cf52606720da79d44f3d11640eaa009a/user_data/strategies/GeneTrader/GeneStrategy_v2_Shorts.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np from functools import reduce import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import merge_informative_pair, DecimalParameter, stoploss_from_open, RealParameter,IntParameter,informative from pandas import DataFrame, Series from datetime import datetime, timedelta import math import logging from freqtrade.persistence import Trade import pandas_ta as pta from technical.indicators import RMI logger = logging.getLogger(__name__) # Elliot Wave Oscillator def ewo(dataframe, sma1_length=5, sma2_length=35): sma1 = ta.EMA(dataframe, timeperiod=sma1_length) sma2 = ta.EMA(dataframe, timeperiod=sma2_length) smadif = (sma1 - sma2) / dataframe['close'] * 100 return smadif def top_percent_change_dca(dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] #EWO def EWO(dataframe, ema_length=5, ema2_length=3): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif # Williams %R def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name="{0} Williams %R".format(period), ) return WR * -100 # VWAP bands def VWAPB(dataframe, window_size=20, num_of_std=1): 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 bollinger_bands(stock_price, window_size, 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) upper_band = rolling_mean + (rolling_std * num_of_std) return np.nan_to_num(rolling_mean), np.nan_to_num(upper_band) #Chaikin Money Flow def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: """Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: dataframe(pandas.Dataframe): dataframe containing ohlcv n(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. """ mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low']) mfv = mfv.fillna(0.0) # float 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 Series(cmf, name='cmf') def ha_typical_price(bars): res = (bars['ha_high'] + bars['ha_low'] + bars['ha_close']) / 3. return Series(index=bars.index, data=res) class Github_vaskosmihaylov_nfi_custom_strategies__GeneStrategy_v2_Shorts__20260214_164549(IStrategy): """ Github_vaskosmihaylov_nfi_custom_strategies__GeneStrategy_v2_Shorts__20260214_164549 - Short-only version Converted from GeneStrategy_v2.py (long strategy) to trade shorts. All entry/exit logic inverted for short positions. Date: 2026-01-28 """ INTERFACE_VERSION = 3 def version(self) -> str: return "2026-01-28 (Shorts)" # ROI table: minimal_roi = { "0": 100 } # Shorts only can_short = True # DCA position_adjustment_enable = True # Stoploss: stoploss = -0.99 # Safety net # Trailing stop: trailing_stop = False trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.10 trailing_only_offset_is_reached = True timeframe = '5m' # Make sure these match or are not overridden in config use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Custom stoploss use_custom_stoploss = True # Keep emergency stop a small buffer away from liquidation. emergency_liq_buffer = 0.03 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 } def is_support(self, row_data) -> bool: 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) # Protection (NFIX29) - inverted for shorts fast_ewo = 50 slow_ewo = 200 # NFINext44 - inverted for shorts sell_44_ma_offset = 1.018 # was buy_44_ma_offset = 0.982 sell_44_ewo = 18.143 # was -18.143 sell_44_cti = 0.8 # was -0.8 sell_44_r_1h = 75.0 # was -75.0 # NFINext37 - inverted for shorts sell_37_ma_offset = 1.02 # was 0.98 sell_37_ewo = -9.8 # was 9.8 sell_37_rsi = 44.0 # was 56.0 (inverted) sell_37_cti = 0.7 # was -0.7 # NFINext7 - inverted for shorts sell_ema_open_mult_7 = 0.030 sell_cti_7 = 0.89 # was -0.89 # Sell parameters (for short entries - inverted from buy params) sell_rmi = IntParameter(50.0, 70.0, default=55, space='sell', optimize=True) sell_cci = IntParameter(90.0, 135.0, default=126, space='sell', optimize=True) sell_srsi_fk = IntParameter(50.0, 70.0, default=58, space='sell', optimize=True) sell_cci_length = IntParameter(25.0, 45.0, default=42, space='sell', optimize=True) sell_rmi_length = IntParameter(8.0, 20.0, default=11, space='sell', optimize=True) sell_bb_width = DecimalParameter(0.065, 0.135, default=0.097, space='sell', optimize=True) sell_bb_delta = DecimalParameter(0.018, 0.035, default=0.028, space='sell', optimize=True) sell_roc_1h = IntParameter(-200.0, 25.0, default=-13, space='sell', optimize=True) sell_bb_width_1h = DecimalParameter(0.3, 2.0, default=1.3, space='sell', optimize=True) # ClucHA - inverted for shorts is_optimize_clucha = False sell_clucha_bbdelta_close = DecimalParameter(0.0005, 0.02, default=0.001, space='sell', optimize=True) sell_clucha_bbdelta_tail = DecimalParameter(0.7, 1.0, default=1.0, space='sell', optimize=True) sell_clucha_close_bbupper = DecimalParameter(0.0005, 0.02, default=0.008, space='sell', optimize=True) sell_clucha_closedelta_close = DecimalParameter(0.0005, 0.02, default=0.014, space='sell', optimize=True) sell_clucha_rocr_1h = DecimalParameter(0.5, 1.0, default=0.51, space='sell', optimize=True) # Local_Downtrend (inverted from uptrend) sell_ema_diff = DecimalParameter(0.022, 0.027, default=0.026, space='sell', optimize=True) sell_bb_factor = DecimalParameter(1.001, 1.01, default=1.005, space='sell', optimize=True) sell_closedelta = DecimalParameter(12.0, 18.0, default=13.1, space='sell', optimize=True) # Sell params (inverted from buy params) rocr_1h = DecimalParameter(0.5, 1.0, default=0.51, space='sell', optimize=True) rocr1_1h = DecimalParameter(0.5, 1.0, default=0.59, space='sell', optimize=True) bbdelta_close = DecimalParameter(0.0005, 0.02, default=0.001, space='sell', optimize=True) closedelta_close = DecimalParameter(0.0005, 0.02, default=0.014, space='sell', optimize=True) bbdelta_tail = DecimalParameter(0.7, 1.0, default=1.0, space='sell', optimize=True) close_bbupper = DecimalParameter(0.0005, 0.02, default=0.008, space='sell', optimize=True) # Buy params (for short exits - inverted from sell params) buy_fisher = DecimalParameter(-0.5, -0.1, default=-0.5, space='buy', optimize=True) buy_bbmiddle_close = DecimalParameter(0.9, 1.03, default=0.933, space='buy', optimize=True) # Bullfish (inverted from Deadfish) buy_bullfish_bb_width = DecimalParameter(0.03, 0.75, default=0.06, space='buy', optimize=True) buy_bullfish_profit = DecimalParameter(-0.15, -0.05, default=-0.1, space='buy', optimize=True) buy_bullfish_bb_factor = DecimalParameter(0.9, 1.2, default=1.2, space='buy', optimize=True) buy_bullfish_volume_factor = DecimalParameter(1.0, 2.5, default=1.9, space='buy', optimize=True) # SMAOffset (inverted for shorts) base_nb_candles_sell = IntParameter(8.0, 20.0, default=13, space='sell', optimize=True) base_nb_candles_buy = IntParameter(8.0, 50.0, default=44, space='buy', optimize=True) high_offset = DecimalParameter(1.005, 1.015, default=1.009, space='sell', optimize=True) low_offset = DecimalParameter(0.985, 0.995, default=0.993, space='buy', optimize=True) low_offset_2 = DecimalParameter(0.98, 0.99, default=0.99, space='buy', optimize=True) # Trailing (inverted logic for shorts) 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='buy', optimize=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.02, default=0.01, space='buy', optimize=True) pSL_1 = DecimalParameter(0.008, 0.02, default=0.008, space='buy', optimize=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.04, 0.1, default=0.072, space='buy', optimize=True) pSL_2 = DecimalParameter(0.02, 0.07, default=0.054, space='buy', optimize=True) def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: """ Fixed 3x leverage for all shorts """ return 3.0 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] informative_pairs += [("BTC/USDT", "5m"), ] return informative_pairs def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() filled_sells = trade.select_filled_orders('exit') count_of_sells = len(filled_sells) if (last_candle is not None): # Time-based exit for shorts (inverted logic) if (current_time - timedelta(minutes=30) > trade.open_date_utc) & (trade.open_date_utc + timedelta(minutes=1500) < current_time) & (last_candle['close'] > last_candle['ema_200']): return 'dlho_to_trva_short' # Trailing stops for shorts if (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.open_rate - trade.min_rate) / 100) > (current_profit + self.sell_trail_down_1.value)): return 'trail_target_1_short' elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.open_rate - trade.min_rate) / 100) > (current_profit + self.sell_trail_down_2.value)): return 'trail_target_2_short' elif (current_profit > 3) & (last_candle['rsi'] < 15): # Inverted from >85 return 'RSI-15 target' # Exit signals for profitable shorts if (current_profit > 0) & (count_of_sells < 4) & (last_candle['close'] < last_candle['hma_50']) & (last_candle['close'] < (last_candle[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (last_candle['rsi']<50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] < last_candle['rsi_slow']): return 'buy signal1_short' if (current_profit > 0) & (count_of_sells >= 4) & (last_candle['close'] < last_candle['hma_50'] * 0.99) & (last_candle['close'] < (last_candle[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) & (last_candle['rsi']<50) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] < last_candle['rsi_slow']): return 'buy signal1 * 0.99_short' if (current_profit > 0) & (last_candle['close'] < last_candle['hma_50']) & (last_candle['close'] < (last_candle[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (last_candle['volume'] > 0) & (last_candle['rsi_fast'] < last_candle['rsi_slow']): return 'buy signal2_short' # Bullfish protection (inverted from Deadfish) if ( (current_profit < self.buy_bullfish_profit.value) and (last_candle['close'] > last_candle['ema_200']) and (last_candle['bb_width'] < self.buy_bullfish_bb_width.value) and (last_candle['close'] < last_candle['bb_middleband2'] * self.buy_bullfish_bb_factor.value) and (last_candle['volume_mean_12'] < last_candle['volume_mean_24'] * self.buy_bullfish_volume_factor.value) and (last_candle['cmf'] > 0.0) ): return f"buy_stoploss_bullfish" def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if ( trade.liquidation_price is None or trade.liquidation_price <= 0 or trade.open_rate is None or trade.open_rate <= 0 or trade.leverage is None or trade.leverage <= 0 ): return 1.0 if trade.is_short: emergency_rate = trade.liquidation_price * (1.0 - self.emergency_liq_buffer) emergency_profit = ((trade.open_rate / emergency_rate) - 1.0) * trade.leverage else: emergency_rate = trade.liquidation_price * (1.0 + self.emergency_liq_buffer) emergency_profit = ((emergency_rate / trade.open_rate) - 1.0) * trade.leverage # If emergency threshold is already breached, force immediate protection. if current_profit <= emergency_profit: return 0.001 try: return stoploss_from_open(emergency_profit, current_profit, is_short=trade.is_short) except TypeError: return stoploss_from_open(emergency_profit, current_profit) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: info_tf = '5m' informative = self.dp.get_pair_dataframe('BTC/USDT', timeframe=info_tf) informative_btc = informative.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['up'] = (dataframe['btc_ema_fast'] > dataframe['btc_ema_slow']).astype('int') # Calculate all ma_buy values (for short exits) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{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 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']) ## BB 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) 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) # # Heikin Ashi Candles 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_upperband2_40'] - dataframe['bb_middleband2_40']).abs() dataframe['ha_closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() # SRSI hyperopt stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2) dataframe['srsi_fk'] = stoch['fastk'] dataframe['srsi_fd'] = stoch['fastd'] # Set Up Bollinger Bands (use upper band for shorts) mid, upper = bollinger_bands(ha_typical_price(dataframe), window_size=40, num_of_std=2) dataframe['upper'] = upper dataframe['mid'] = mid dataframe['bbdelta'] = (dataframe['upper'] - mid).abs() dataframe['closedelta'] = (dataframe['ha_close'] - dataframe['ha_close'].shift()).abs() dataframe['tail'] = (dataframe['ha_high'] - dataframe['ha_close']).abs() dataframe['bb_upperband'] = dataframe['upper'] dataframe['bb_middleband'] = dataframe['mid'] # DIP (inverted to PUMP for shorts) 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_upperband3'] - dataframe['bb_upperband2']) / dataframe['bb_upperband2']) 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, vwap_high = VWAPB(dataframe, 20, 1) dataframe['vwap_high'] = vwap_high dataframe['vwap_upperband'] = vwap_high dataframe['vwap_middleband'] = vwap dataframe['vwap_lowerband'] = vwap_low dataframe['vwap_width'] = ( (dataframe['vwap_upperband'] - dataframe['vwap_lowerband']) / dataframe['vwap_middleband'] ) * 100 # Diff dataframe['ema_vwap_diff_50'] = ( ( dataframe['vwap_upperband'] - dataframe['ema_50'] ) / dataframe['ema_50'] ) # Pump protection (inverted from dip) 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) # NFINEXT44 dataframe['ewo'] = ewo(dataframe, 50, 200) # SMA dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30) # RMI hyperopt for val in self.sell_rmi_length.range: dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4) # CCI hyperopt for val in self.sell_cci_length.range: dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val) #CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # NFIX39 dataframe['bb_delta_cluc'] = (dataframe['bb_upperband2_40'] - dataframe['bb_middleband2_40']).abs() # NFIX29 dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # local_downtrend dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) # insta_signal dataframe['r_14'] = williams_r(dataframe, period=14) # rebuy check if EMA is falling dataframe['ema_5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema_10'] = ta.EMA(dataframe, timeperiod=10) # Profit Maximizer - PMAX (NFINext37) 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) rsi = ta.RSI(dataframe) dataframe["rsi"] = rsi rsi = 0.1 * (rsi - 50) dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) 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(dataframe, timeperiod=14) informative['cmf'] = chaikin_money_flow(dataframe, 20) # Resistance levels (inverted from support) res_series = informative['high'].rolling(window = 5, center=True).apply(lambda row: not self.is_support(row), raw=True).shift(2) informative['res_level'] = Series(np.where(res_series, np.where(informative['close'] > informative['open'], informative['close'], informative['open']), float('NaN'))).ffill() informative['roc'] = ta.ROC(informative, timeperiod=9) informative['r_480'] = williams_r(informative, period=480) # Bollinger bands bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2) informative['bb_lowerband2'] = bollinger2['lower'] informative['bb_middleband2'] = bollinger2['mid'] informative['bb_upperband2'] = bollinger2['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) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Short entry signals (inverted from long entry signals) """ # BTC pump (inverted from dump) btc_pump = ( (dataframe['btc_close'].rolling(24).min() <= (dataframe['btc_close'] * 0.97 )) ) rsi_check = ( (dataframe['rsi_84'] > 40) & (dataframe['rsi_112'] > 40) ) # PUMP signal (inverted from DIP) dataframe.loc[ ((dataframe[f'rmi_length_{self.sell_rmi_length.value}'] > self.sell_rmi.value) & (dataframe[f'cci_length_{self.sell_cci_length.value}'] >= self.sell_cci.value) & (dataframe['srsi_fk'] > self.sell_srsi_fk.value) & (dataframe['bb_delta'] > self.sell_bb_delta.value) & (dataframe['bb_width'] > self.sell_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.sell_closedelta.value / 1000 ) & (dataframe['close'] > dataframe['bb_upperband3'] * self.sell_bb_factor.value)& (dataframe['roc_1h'] > self.sell_roc_1h.value) & (dataframe['bb_width_1h'] < self.sell_bb_width_1h.value) ), ['enter_short', 'enter_tag']] = (1, 'PUMP signal') # Break signal (inverted) dataframe.loc[ ((dataframe['bb_delta'] > self.sell_bb_delta.value) & (dataframe['bb_width'] > self.sell_bb_width.value) & (dataframe['closedelta'] > dataframe['close'] * self.sell_closedelta.value / 1000 ) & (dataframe['close'] > dataframe['bb_upperband3'] * self.sell_bb_factor.value)& (dataframe['roc_1h'] > self.sell_roc_1h.value) & (dataframe['bb_width_1h'] < self.sell_bb_width_1h.value) ), ['enter_short', 'enter_tag']] = (1, 'Break signal short') # cluc_HA (inverted) dataframe.loc[ ((dataframe['rocr_1h'] < self.sell_clucha_rocr_1h.value ) & (dataframe['bb_upperband2_40'].shift() > 0) & (dataframe['bb_delta_cluc'] > dataframe['ha_close'] * self.sell_clucha_bbdelta_close.value) & (dataframe['ha_closedelta'] > dataframe['ha_close'] * self.sell_clucha_closedelta_close.value) & (dataframe['tail'] < dataframe['bb_delta_cluc'] * self.sell_clucha_bbdelta_tail.value) & (dataframe['ha_close'] > dataframe['bb_upperband2_40'].shift()) & (dataframe['close'] < (dataframe['res_level_1h'] * 1.12)) & (dataframe['ha_close'] > dataframe['ha_close'].shift()) ), ['enter_short', 'enter_tag']] = (1, 'cluc_HA_short') # NFIX39 (inverted) dataframe.loc[ ((dataframe['ema_200'] < (dataframe['ema_200'].shift(12) * 0.99)) & (dataframe['ema_200'] < (dataframe['ema_200'].shift(48) * 0.93)) & (dataframe['bb_upperband2_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'].gt(dataframe['bb_upperband2_40'].shift())) & (dataframe['close'].ge(dataframe['close'].shift())) & (dataframe['close'] < dataframe['ema_50'] * 1.088) ), ['enter_short', 'enter_tag']] = (1, 'NFIX39_short') # NFIX29 (inverted) dataframe.loc[ ((dataframe['close'] < (dataframe['res_level_1h'] * 1.28)) & (dataframe['close'] > (dataframe['ema_16'] * 1.018)) & (dataframe['EWO'] > 10.0) & (dataframe['cti'] > 0.9) ), ['enter_short', 'enter_tag']] = (1, 'NFIX29_short') # local_downtrend (inverted from uptrend) dataframe.loc[ ((dataframe['ema_26'] < dataframe['ema_12']) & (dataframe['ema_12'] - dataframe['ema_26'] > dataframe['open'] * self.sell_ema_diff.value) & (dataframe['ema_12'].shift() - dataframe['ema_26'].shift() > dataframe['open'] / 100) & (dataframe['close'] > dataframe['bb_upperband2'] * self.sell_bb_factor.value) & (dataframe['closedelta'] > dataframe['close'] * self.sell_closedelta.value / 1000 ) ), ['enter_short', 'enter_tag']] = (1, 'local_downtrend') # vwap (inverted) dataframe.loc[ ( (dataframe['close'] > dataframe['vwap_high']) & (dataframe['tcp_percent_4'] > 0.053) & (dataframe['cti'] > 0.8) & (dataframe['rsi'] > 65) & (dataframe['rsi_84'] > 40) & (dataframe['rsi_112'] > 40) & (dataframe['volume'] > 0) ), ['enter_short', 'enter_tag']] = (1, 'vwap_short') # insta_signal (inverted) 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) & ( (dataframe['close'].rolling(48).min() <= (dataframe['close'] * 0.9 )) ) & (dataframe['btc_close'].rolling(24).min() <= (dataframe['btc_close'] * 0.97 )) ), ['enter_short', 'enter_tag']] = (1, 'insta_signal_short') # NFINext44 (inverted) dataframe.loc[ ((dataframe['close'] > (dataframe['ema_16'] * self.sell_44_ma_offset))& (dataframe['ewo'] > self.sell_44_ewo)& (dataframe['cti'] > self.sell_44_cti)& (dataframe['r_480_1h'] > self.sell_44_r_1h)& (dataframe['volume'] > 0) ), ['enter_short', 'enter_tag']] = (1, 'NFINext44_short') # NFINext37 (inverted) dataframe.loc[ ((dataframe['pm'] < dataframe['pmax_thresh'])& (dataframe['close'] > dataframe['sma_75'] * self.sell_37_ma_offset)& (dataframe['ewo'] < self.sell_37_ewo)& (dataframe['rsi'] > self.sell_37_rsi)& (dataframe['cti'] > self.sell_37_cti) ), ['enter_short', 'enter_tag']] = (1, 'NFINext37_short') # NFINext7 (inverted) dataframe.loc[ ((dataframe['ema_26'] < dataframe['ema_12'])& ((dataframe['ema_12'] - dataframe['ema_26']) > (dataframe['open'] * self.sell_ema_open_mult_7))& ((dataframe['ema_12'].shift() - dataframe['ema_26'].shift()) > (dataframe['open'] / 100))& (dataframe['cti'] > self.sell_cti_7) ), ['enter_short', 'enter_tag']] = (1, 'NFINext7_short') # newstrat52 (inverted) dataframe.loc[ ((dataframe['rsi_slow'] > dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] > 54) & (dataframe['rsi'] < 81) & (dataframe['close'] > dataframe['sma_15'] * 1.058) & (dataframe['cti'] > 0.86) ), ['enter_short', 'enter_tag']] = (1, 'NFINext32_short') # sma_3 (inverted) dataframe.loc[ ((dataframe['bb_upperband2_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_upperband2_40'].shift()) & (dataframe['close'] > dataframe['close'].shift()) & (btc_pump == 0) ), ['enter_short', 'enter_tag']] = (1, 'sma_3_short') # WVAP (inverted) dataframe.loc[ ((dataframe['close'] > dataframe['vwap_upperband']) & (dataframe['tpct_change_1'] > 0.04) & (dataframe['cti'] > 0.8) & (dataframe['rsi'] > 65) & (rsi_check) & (btc_pump == 0) ), ['enter_short', 'enter_tag']] = (1, 'WVAP_short') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Short exit signals (inverted from long exit signals) """ dataframe.loc[ (dataframe['fisher'] < self.buy_fisher.value) & (dataframe['ha_low'].ge(dataframe['ha_low'].shift(1))) & (dataframe['ha_low'].shift(1).ge(dataframe['ha_low'].shift(2))) & (dataframe['ha_close'].ge(dataframe['ha_close'].shift(1))) & (dataframe['ema_fast'] < dataframe['ha_close']) & ((dataframe['ha_close'] * self.buy_bbmiddle_close.value) < dataframe['bb_middleband']) & (dataframe['volume'] > 0), 'exit_short' ] = 1 return dataframe initial_safety_order_trigger = -0.018 max_safety_orders = 8 safety_order_step_scale = 1.2 safety_order_volume_scale = 1.4 def top_percent_change_dca(self, dataframe: DataFrame, length: int) -> float: """ Percentage change of the current close from the range maximum Open price :param dataframe: DataFrame The original OHLC dataframe :param length: int The length to look back """ if length == 0: return (dataframe['open'] - dataframe['close']) / dataframe['close'] else: return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close'] def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): if current_profit > self.initial_safety_order_trigger: return None # credits to reinuvader for not blindly executing safety orders # Obtain pair dataframe. dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) # Only sell when it seems it's dropping back down (for shorts) last_candle = dataframe.iloc[-1].squeeze() filled_sells = trade.select_filled_orders('entry') count_of_sells = len(filled_sells) # DCA logic for shorts (inverted conditions) if count_of_sells == 1 and (last_candle['tpct_change_0'] > 0.018) and (last_candle['close'] > last_candle['open']) : return None elif count_of_sells == 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_sells == 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_sells == 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_sells == 5 and (last_candle['cmf_1h'] > 0.00) and (last_candle['close'] > last_candle['open']) and (last_candle['rsi_14_1h'] > 70) 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']): logger.info(f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to drop below 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to drop below 70") return None elif count_of_sells == 6 and (last_candle['cmf_1h'] > 0.00) and (last_candle['close'] > last_candle['open']) and (last_candle['rsi_14_1h'] > 70) 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']): logger.info(f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to drop below 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to drop below 70") return None elif count_of_sells == 7 and (last_candle['cmf_1h'] > 0.00) and (last_candle['close'] > last_candle['open']) and (last_candle['rsi_14_1h'] > 70) 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']): logger.info(f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to drop below 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to drop below 70") return None elif count_of_sells == 8 and (last_candle['cmf_1h'] > 0.00) and (last_candle['close'] > last_candle['open']) and (last_candle['rsi_14_1h'] > 70) 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']): logger.info(f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to drop below 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to drop below 70") return None if 1 <= count_of_sells <= self.max_safety_orders: safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_sells) 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_sells - 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_sells - 1))) / (1 - self.safety_order_step_scale)) if current_profit <= (-1 * abs(safety_order_trigger)): try: # This returns first order stake size stake_amount = filled_sells[0].cost # This then calculates current safety order size stake_amount = stake_amount * math.pow(self.safety_order_volume_scale,(count_of_sells - 1)) amount = stake_amount / current_rate return stake_amount except Exception as exception: logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') return None return None # PMAX def pmax(df, period, multiplier, length, MAtype, src): period = int(period) multiplier = int(multiplier) length = int(length) MAtype = int(MAtype) src = int(src) mavalue = 'MA_' + str(MAtype) + '_' + str(length) atr = 'ATR_' + str(period) pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype) pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype) if src == 1: masrc = df["close"] elif src == 2: masrc = (df["high"] + df["low"]) / 2 elif src == 3: masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4 if MAtype == 1: mavalue = ta.EMA(masrc, timeperiod=length) elif MAtype == 2: mavalue = ta.DEMA(masrc, timeperiod=length) elif MAtype == 3: 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(df, timeperiod=length) elif MAtype == 8: mavalue = vwma(df, length) elif MAtype == 9: mavalue = zema(df, period=length) 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 final_ub = np.full(len(df), 0.00) basic_lb = df['basic_lb'].values final_lb = np.full(len(df), 0.00) 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.full(len(df), 0.00) for i in range(period, len(df)): pm_arr[i] = ( final_ub[i] if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i]) else final_lb[i] if ( pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i]) else final_lb[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i]) else final_ub[i] if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] < final_lb[i]) else 0.00) pm = Series(pm_arr) # Mark the trend direction up/down # 1 = up, -1 = down, 0 = no trend pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), -1, 1), 0) return pm, pmx