import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter from functools import reduce import logging # ------------------------------------------------------------------------------------------------- # --- logger for parameter merging output, only remove if you remove it further down too! --------- logger = logging.getLogger(__name__) # ------------------------------------------------------------------------------------------------- class BcmbigzDevelop(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.038, # I feel lucky! "10": 0.028, "40": 0.015, "180": 0.018, # We're going up? } stoploss = -0.99 # effectively disabled. timeframe = '5m' inf_1h = '1h' # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = False # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.03 # Custom stoploss use_custom_stoploss = True # Run "populate_indicators()" only for new candle. process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { 'buy': 'market', 'sell': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } buy_params = { "buy_minimum_conditions": 1, ############# # Enable/Disable conditions "bzv7_buy_condition_0_enable": True, "bzv7_buy_condition_1_enable": True, "bzv7_buy_condition_2_enable": True, "bzv7_buy_condition_3_enable": True, "bzv7_buy_condition_4_enable": True, "bzv7_buy_condition_5_enable": True, "bzv7_buy_condition_6_enable": True, "bzv7_buy_condition_7_enable": True, "bzv7_buy_condition_8_enable": True, "bzv7_buy_condition_9_enable": True, "bzv7_buy_condition_10_enable": True, "bzv7_buy_condition_11_enable": True, "bzv7_buy_condition_12_enable": True, "bzv7_buy_condition_13_enable": True, "v6_buy_condition_0_enable": True, "v6_buy_condition_1_enable": True, "v6_buy_condition_2_enable": True, "v6_buy_condition_3_enable": True, "v8_buy_condition_0_enable": True, "v8_buy_condition_1_enable": True, "v8_buy_condition_2_enable": True, "v8_buy_condition_3_enable": True, "v8_buy_condition_4_enable": True, } sell_params = { ############# # Enable/Disable conditions "bzv7_sell_condition_0_enable": False, "v8_sell_condition_0_enable": True, "v8_sell_condition_1_enable": True, "sell_condition_0_enable": True, } # if you want to see which buy conditions were met # or if there is an trade exit override due to high RSI set to True # logger will output the buy and trade exit conditions cust_log_verbose = False ############################################################################ bzv7_sell_condition_0_enable = CategoricalParameter([True, False], default=False, space="sell", optimize=False, load=True) v8_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True) v8_sell_condition_1_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True) sell_condition_0_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True) # minimum conditions to match in buy buy_minimum_conditions = IntParameter(1, 2, default=1, space='buy', optimize=False, load=True) # Strategy: BigZ07 # Buy HyperParam bzv7_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True) bzv7_buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='buy', optimize=False, load=True) bzv7_buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='buy', optimize=False, load=True) bzv7_buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_volume_drop_2 = DecimalParameter(1, 10, default=3, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='buy', decimals=1, optimize=False, load=True) bzv7_buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=False, load=True) bzv7_buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='buy', decimals=2, optimize=False, load=True) # Strategy: CombinedBinHClucAndMADV6 v6_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) # Strategy: CombinedBinHClucV8 v8_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v8_sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space="sell", decimals=2, optimize=False, load=True) buy_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.015, space="buy", decimals=3, optimize=False, load=True) buy_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space="buy", decimals=2, optimize=False, load=True) buy_dip_threshold_2 = DecimalParameter(0.02, 0.4, default=0.28, space="buy", decimals=2, optimize=False, load=True) buy_dip_threshold_3 = DecimalParameter(0.25, 0.44, default=0.36, space="buy", decimals=2, optimize=False, load=True) buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.04, default=0.031, space="buy", optimize=False, load=True) buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space="buy", optimize=False, load=True) buy_bb40_tail_bbdelta = DecimalParameter(0.2, 0.4, default=0.264, space="buy", optimize=False, load=True) buy_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.992, space="buy", optimize=False, load=True) buy_bb20_volume = IntParameter(18, 36, default=29, space="buy", optimize=False, load=True) buy_rsi_diff = DecimalParameter(34.0, 60.0, default=50.48, space="buy", decimals=2, optimize=False, load=True) buy_min_inc = DecimalParameter(0.005, 0.05, default=0.01, space="buy", decimals=2, optimize=False, load=True) buy_rsi_1h = DecimalParameter(40.0, 70.0, default=67.0, space="buy", decimals=2, optimize=False, load=True) buy_rsi = DecimalParameter(30.0, 40.0, default=38.5, space="buy", decimals=2, optimize=False, load=True) buy_mfi = DecimalParameter(36.0, 65.0, default=36.0, space="buy", decimals=2, optimize=False, load=True) buy_volume_1 = DecimalParameter(1.0, 10.0, default=2.0, space="buy", decimals=2, optimize=False, load=True) buy_ema_open_mult_1 = DecimalParameter(0.01, 0.05, default=0.02, space="buy", decimals=3, optimize=False, load=True) sell_custom_roi_profit_1 = DecimalParameter( 0.01, 0.03, default=0.01, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space="sell", decimals=2, optimize=True, load=True) sell_custom_roi_profit_2 = DecimalParameter( 0.01, 0.20, default=0.04, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space="sell", decimals=2, optimize=True, load=True) sell_custom_roi_profit_3 = DecimalParameter( 0.15, 0.30, default=0.08, space="sell", decimals=2, optimize=True, load=True ) sell_custom_roi_rsi_3 = DecimalParameter(44.0, 58.0, default=56, space="sell", decimals=2, optimize=False, load=True) sell_custom_roi_profit_4 = DecimalParameter(0.3, 0.7, default=0.14, space="sell", decimals=2, optimize=True, load=True) sell_custom_roi_rsi_4 = DecimalParameter(44.0, 60.0, default=58, space="sell", decimals=2, optimize=False, load=True) sell_custom_roi_profit_5 = DecimalParameter(0.01, 0.1, default=0.04, space="sell", decimals=2, optimize=True, load=True) sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space="sell", decimals=3, optimize=True, load=True) sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=True, load=True) sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space="sell", decimals=3, optimize=True, load=True) sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=True, load=True) sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space="sell", decimals=2, optimize=True, load=True) sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space="sell", decimals=3, optimize=True, load=True) sell_custom_stoploss_1 = DecimalParameter( -0.15, -0.03, default=-0.05, space="sell", decimals=2, optimize=True, load=True ) def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool: return True dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() last_candle_1 = dataframe.iloc[-2].squeeze() if (sell_reason == 'roi'): # Looks like we can get a little have more if (last_candle['cmf'] < -0.1) & (last_candle['close'] > last_candle['ema_200_1h']): return False return True def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): return False dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() last_candle_2 = dataframe.iloc[-2].squeeze() if (last_candle is not None): if (last_candle['high'] > last_candle['bb_upperband']) & (last_candle['volume'] > (last_candle_2['volume'] * 1.5)): return 'sell_signal_1' if (current_profit > self.sell_custom_roi_profit_4.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_4.value ): return "roi_target_4" elif (current_profit > self.sell_custom_roi_profit_3.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_3.value ): return "roi_target_3" elif (current_profit > self.sell_custom_roi_profit_2.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_2.value ): return "roi_target_2" elif (current_profit > self.sell_custom_roi_profit_1.value) & ( last_candle["rsi"] < self.sell_custom_roi_rsi_1.value ): return "roi_target_1" elif ( (current_profit > 0) & (current_profit < self.sell_custom_roi_profit_5.value) & (last_candle["sma_200_dec"]) ): return "roi_target_5" elif ( (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value)) ): return "trail_target_1" elif ( (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value)) ): return "trail_target_2" return False def custom_stoplossv6(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Manage losing trades and open room for better ones. if (current_profit < 0) & (current_time - timedelta(minutes=240) > trade.open_date_utc): return 0.01 return 0.99 def custom_stoplossv8(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Manage losing trades and open room for better ones. if (current_profit < 0) & (current_time - timedelta(minutes=280) > trade.open_date_utc): return 0.01 elif (current_profit < self.sell_custom_stoploss_1.value): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if (last_candle is not None): if (last_candle['sma_200_dec']) & (last_candle['sma_200_dec_1h']): return 0.01 return 0.99 def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Manage losing trades and open room for better ones. if (current_profit > 0): return 0.99 else: trade_time_50 = trade.open_date_utc + timedelta(minutes=50) # Trade open more then 60 minutes. For this strategy it's means -> loss # Let's try to minimize the loss if (current_time > trade_time_50): try: number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-number_of_candle_shift].squeeze() # We are at bottom. Wait... if candle['rsi_1h'] < 40: return 0.99 if candle['open_1h'] > candle['ema_200_1h']: return 0.1 # Are we still sinking? if current_rate * 1.025 < candle['open']: return 0.01 except IndexError as error: # Whoops, set stoploss at 10% return 0.1 return 0.99 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) # EMA informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # RSI informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) informative_1h['bb_lowerband'] = bollinger['lower'] informative_1h['bb_middleband'] = bollinger['mid'] informative_1h['bb_upperband'] = bollinger['upper'] # for BinClucMad informative_1h['ema_100'] = ta.EMA(informative_1h, timeperiod=100) informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) # for BinClucMad informative_1h['sma_200_dec'] = informative_1h['sma_200'] < informative_1h['sma_200'].shift(20) # for BinClucMad ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h['ssl_down'] = ssl_down_1h informative_1h['ssl_up'] = ssl_up_1h informative_1h["ssl-dir"] = np.where(ssl_up_1h > ssl_down_1h, "up", "down") return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['volume_mean_slow_48'] = dataframe['volume'].rolling(window=48).mean() dataframe['volume_mean_slow_30'] = dataframe['volume'].rolling(window=30).mean() # EMA dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) # MACD dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # Chaikin A/D Oscillator dataframe['mfv'] = MFV(dataframe) dataframe['cmf'] = dataframe['mfv'].rolling(20).sum()/dataframe['volume'].rolling(20).sum() # for BinClucMad dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) # for BinClucMad dataframe['sma_5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20) dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14) bb_40 = qtpylib.bollinger_bands(dataframe["close"], window=40, stds=2) dataframe["lower"] = bb_40["lower"] dataframe["mid"] = bb_40["mid"] dataframe["bbdelta"] = (bb_40["mid"] - dataframe["lower"]).abs() dataframe["closedelta"] = (dataframe["close"] - dataframe["close"].shift()).abs() dataframe["tail"] = (dataframe["close"] - dataframe["low"]).abs() return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # reset additional dataframe rows dataframe.loc[:, "bzv7_buy_condition_0_enable"] = False dataframe.loc[:, "bzv7_buy_condition_1_enable"] = False dataframe.loc[:, "bzv7_buy_condition_2_enable"] = False dataframe.loc[:, "bzv7_buy_condition_3_enable"] = False dataframe.loc[:, "bzv7_buy_condition_4_enable"] = False dataframe.loc[:, "bzv7_buy_condition_5_enable"] = False dataframe.loc[:, "bzv7_buy_condition_6_enable"] = False dataframe.loc[:, "bzv7_buy_condition_7_enable"] = False dataframe.loc[:, "bzv7_buy_condition_8_enable"] = False dataframe.loc[:, "bzv7_buy_condition_9_enable"] = False dataframe.loc[:, "bzv7_buy_condition_10_enable"] = False dataframe.loc[:, "bzv7_buy_condition_11_enable"] = False dataframe.loc[:, "bzv7_buy_condition_12_enable"] = False dataframe.loc[:, "bzv7_buy_condition_13_enable"] = False dataframe.loc[:, "v6_buy_condition_0_enable"] = False dataframe.loc[:, "v6_buy_condition_1_enable"] = False dataframe.loc[:, "v6_buy_condition_2_enable"] = False dataframe.loc[:, "v6_buy_condition_3_enable"] = False dataframe.loc[:, "v8_buy_condition_0_enable"] = False dataframe.loc[:, "v8_buy_condition_1_enable"] = False dataframe.loc[:, "v8_buy_condition_2_enable"] = False dataframe.loc[:, "v8_buy_condition_3_enable"] = False dataframe.loc[:, "v8_buy_condition_4_enable"] = False dataframe.loc[:, "conditions_count"] = 0 # Strategy: v8 BUY conditions dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & ( ((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_1.value ) & ( ((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_2.value ) & dataframe["lower"].shift().gt(0) & dataframe["bbdelta"].gt(dataframe["close"] * self.buy_bb40_bbdelta_close.value) & dataframe["closedelta"].gt(dataframe["close"] * self.buy_bb40_closedelta_close.value) & dataframe["tail"].lt(dataframe["bbdelta"] * self.buy_bb40_tail_bbdelta.value) & dataframe["close"].lt(dataframe["lower"].shift()) & dataframe["close"].le(dataframe["close"].shift()) & (self.v8_buy_condition_0_enable.value == True) ), "v8_buy_condition_0_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & ( ((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_1.value ) & ( ((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_2.value ) & (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < self.buy_bb20_close_bblowerband.value * dataframe["bb_lowerband"]) & (dataframe["volume"] < (dataframe["volume_mean_slow_30"].shift(1) * self.buy_bb20_volume.value)) & (self.v8_buy_condition_1_enable.value == True) ), "v8_buy_condition_1_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] < dataframe["sma_5"]) & (dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"]) & (dataframe["ema_50"] > dataframe["ema_200"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & ( ((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_1.value ) & ( ((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_2.value ) & ( ((dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_3.value ) & (dataframe["rsi"] < dataframe["rsi_1h"] - self.buy_rsi_diff.value) & (self.v8_buy_condition_2_enable.value == True) ), "v8_buy_condition_2_enable", ] = 1 dataframe.loc[ ( (dataframe["sma_200"] > dataframe["sma_200"].shift(20)) & (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(16)) & ( ((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_1.value ) & ( ((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_2.value ) & ( ((dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_3.value ) & ( ((dataframe["open"].rolling(24).min() - dataframe["close"]) / dataframe["close"]) > self.buy_min_inc.value ) & (dataframe["rsi_1h"] > self.buy_rsi_1h.value) & (dataframe["rsi"] < self.buy_rsi.value) & (dataframe["mfi"] < self.buy_mfi.value) & (self.v8_buy_condition_3_enable.value == True) ), "v8_buy_condition_3_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] > dataframe["ema_100_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_100_1h"]) & ( ((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_1.value ) & ( ((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_2.value ) & ( ((dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"]) < self.buy_dip_threshold_3.value ) & (dataframe["volume"].rolling(4).mean() * self.buy_volume_1.value > dataframe["volume"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_ema_open_mult_1.value)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & (dataframe["close"] < (dataframe["bb_lowerband"])) & (self.v8_buy_condition_4_enable.value == True) ), "v8_buy_condition_4_enable", ] = 1 # Strategy: v6 BUY conditions dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < 0.99 * dataframe["bb_lowerband"]) & ( (dataframe["volume"] < (dataframe["volume_mean_slow_30"].shift(1) * 21)) | (dataframe["volume_mean_slow_30"] > (dataframe["volume_mean_slow_30"].shift(30) * 0.4)) ) & (self.v6_buy_condition_0_enable.value == True) ), "v6_buy_condition_0_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < 0.975 * dataframe["bb_lowerband"]) & ( (dataframe["volume"] < (dataframe["volume_mean_slow_30"].shift(1) * 20)) | (dataframe["volume_mean_slow_30"] > dataframe["volume_mean_slow_30"].shift(30) * 0.4) ) & (dataframe["rsi_1h"] < 15) # Don't buy if someone drop the market. & (dataframe["volume"] < (dataframe["volume"].shift() * 4)) & (self.v6_buy_condition_1_enable.value == True) ), "v6_buy_condition_1_enable", ] = 1 dataframe.loc[ ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.02)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & ( (dataframe["volume"] < (dataframe["volume"].shift() * 4)) | (dataframe["volume_mean_slow_30"] > dataframe["volume_mean_slow_30"].shift(30) * 0.4) ) & (dataframe["close"] < (dataframe["bb_lowerband"])) & (self.v6_buy_condition_2_enable.value == True) ), "v6_buy_condition_2_enable", ] = 1 dataframe.loc[ ( (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & (dataframe["volume"] < (dataframe["volume"].shift() * 4)) & (dataframe["close"] < (dataframe["bb_lowerband"])) & (self.v6_buy_condition_3_enable.value == True) ), "v6_buy_condition_3_enable", ] = 1 # Strategy: BigZ07 BUY conditions dataframe.loc[ ( self.bzv7_buy_condition_0_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['rsi'] < 30) & (dataframe['close'] * 1.024 < dataframe['open'].shift(3)) & (dataframe['rsi_1h'] < 71) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), "bzv7_buy_condition_0_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_1_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.bzv7_buy_bb20_close_bblowerband_safe_1.value) & (dataframe['rsi_1h'] < 69) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & ((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_1_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_2_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] < dataframe['bb_lowerband'] * self.bzv7_buy_bb20_close_bblowerband_safe_2.value) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_2_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_3_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['rsi'] < self.bzv7_buy_rsi_3.value) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_3.value)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_3_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_4_enable.value & (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_1.value) & (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_4_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_5_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.bzv7_buy_macd_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_5_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_6_enable.value & (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_5.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.bzv7_buy_macd_2.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_6_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_7_enable.value & (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_2.value) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.bzv7_buy_macd_1.value)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_7_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_8_enable.value & (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_3.value) & (dataframe['rsi'] < self.bzv7_buy_rsi_1.value) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_8_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_9_enable.value & (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_4.value) & (dataframe['rsi'] < self.bzv7_buy_rsi_2.value) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_9_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_10_enable.value & (dataframe['rsi_1h'] < self.bzv7_buy_rsi_1h_4.value) & (dataframe['close_1h'] < dataframe['bb_lowerband_1h']) & (dataframe['hist'] > 0) & (dataframe['hist'].shift(2) < 0) & (dataframe['rsi'] < 40.5) & (dataframe['hist'] > dataframe['close'] * 0.0012) & (dataframe['open'] < dataframe['close']) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_10_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_11_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['hist'] > 0) & (dataframe['hist'].shift() > 0) & (dataframe['hist'].shift(2) > 0) & (dataframe['hist'].shift(3) > 0) & (dataframe['hist'].shift(5) > 0) & (dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(5) > dataframe['close']/200) & (dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(10) > dataframe['close']/100) & ((dataframe['bb_upperband'] - dataframe['bb_lowerband']) < (dataframe['close']*0.1)) & ((dataframe['open'].shift() - dataframe['close'].shift()) < (dataframe['close'] * 0.018)) & (dataframe['rsi'] > 51) & (dataframe['open'] < dataframe['close']) & (dataframe['open'].shift() > dataframe['close'].shift()) & (dataframe['close'] > dataframe['bb_middleband']) & (dataframe['close'].shift() < dataframe['bb_middleband'].shift()) & (dataframe['low'].shift(2) > dataframe['bb_middleband'].shift(2)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_11_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_12_enable.value & (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['bb_lowerband'] * 0.993) & (dataframe['low'] < dataframe['bb_lowerband'] * 0.985) & (dataframe['close'].shift() > dataframe['bb_lowerband']) & (dataframe['rsi_1h'] < 72.8) & (dataframe['open'] > dataframe['close']) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] < (dataframe['volume'].shift() * self.bzv7_buy_volume_drop_1.value)) & ((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_12_enable", ] = 1 dataframe.loc[ ( self.bzv7_buy_condition_13_enable.value & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['cmf'] < -0.435) & (dataframe['rsi'] < 22) & (dataframe['volume_mean_slow_48'] > dataframe['volume_mean_slow_48'].shift(48) * self.bzv7_buy_volume_pump_1.value) & (dataframe['volume_mean_slow_48'] * self.bzv7_buy_volume_pump_1.value < dataframe['volume_mean_slow_48'].shift(48)) & (dataframe['volume'] > 0) ), "bzv7_buy_condition_13_enable", ] = 1 # count the amount of conditions met dataframe.loc[:, "conditions_count"] = ( dataframe["bzv7_buy_condition_0_enable"].astype(int) + dataframe["bzv7_buy_condition_1_enable"].astype(int) + dataframe["bzv7_buy_condition_2_enable"].astype(int) + dataframe["bzv7_buy_condition_3_enable"].astype(int) + dataframe["bzv7_buy_condition_4_enable"].astype(int) + dataframe["bzv7_buy_condition_5_enable"].astype(int) + dataframe["bzv7_buy_condition_6_enable"].astype(int) + dataframe["bzv7_buy_condition_7_enable"].astype(int) + dataframe["bzv7_buy_condition_8_enable"].astype(int) + dataframe["bzv7_buy_condition_9_enable"].astype(int) + dataframe["bzv7_buy_condition_10_enable"].astype(int) + dataframe["bzv7_buy_condition_11_enable"].astype(int) + dataframe["bzv7_buy_condition_12_enable"].astype(int) + dataframe["bzv7_buy_condition_13_enable"].astype(int) + dataframe["v6_buy_condition_0_enable"].astype(int) + dataframe["v6_buy_condition_1_enable"].astype(int) + dataframe["v6_buy_condition_2_enable"].astype(int) + dataframe["v6_buy_condition_3_enable"].astype(int) + dataframe["v8_buy_condition_0_enable"].astype(int) + dataframe["v8_buy_condition_1_enable"].astype(int) + dataframe["v8_buy_condition_2_enable"].astype(int) + dataframe["v8_buy_condition_3_enable"].astype(int) + dataframe["v8_buy_condition_4_enable"].astype(int) ) # append the minimum amount of conditions to be met conditions.append(dataframe["conditions_count"] >= self.buy_minimum_conditions.value) conditions.append(dataframe["volume"].gt(0)) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "buy"] = 1 # verbose logging enable only for verbose information or troubleshooting if self.cust_log_verbose == True: for index, row in dataframe.iterrows(): if row["buy"] == 1: buy_cond_details = f"count={int(row['conditions_count'])}/bzv7_1={int(row['bzv7_buy_condition_1_enable'])}/bzv7_2={int(row['bzv7_buy_condition_2_enable'])}/bzv7_3={int(row['bzv7_buy_condition_3_enable'])}/bzv7_4={int(row['bzv7_buy_condition_4_enable'])}/bzv7_5={int(row['bzv7_buy_condition_5_enable'])}/bzv7_6={int(row['bzv7_buy_condition_6_enable'])}/bzv7_7={int(row['bzv7_buy_condition_7_enable'])}/bzv7_8={int(row['bzv7_buy_condition_8_enable'])}/bzv7_9={int(row['bzv7_buy_condition_9_enable'])}/bzv7_10={int(row['bzv7_buy_condition_10_enable'])}/bzv7_11={int(row['bzv7_buy_condition_11_enable'])}/bzv7_12={int(row['bzv7_buy_condition_12_enable'])}/bzv7_13={int(row['bzv7_buy_condition_13_enable'])}/bzv7_0={int(row['bzv7_buy_condition_0_enable'])}/v6_0={int(row['v6_buy_condition_0_enable'])}/v6_1={int(row['v6_buy_condition_1_enable'])}/v6_2={int(row['v6_buy_condition_2_enable'])}/v6_3={int(row['v6_buy_condition_3_enable'])}/v8_0={int(row['v8_buy_condition_0_enable'])}/v8_1={int(row['v8_buy_condition_1_enable'])}/v8_2={int(row['v8_buy_condition_2_enable'])}/v8_3={int(row['v8_buy_condition_3_enable'])}/v8_4={int(row['v8_buy_condition_4_enable'])}" logger.info(f"{metadata['pair']} - candle: {row['date']} - buy condition - details: {buy_cond_details}") return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # dataframe.loc[:, "sell"] = 0 if self.bzv7_sell_condition_0_enable.value: conditions.append( ( (dataframe["close"] > dataframe["bb_middleband"] * 1.01) & (dataframe["volume"] > 0) # Don't be gready, sell fast # Make sure Volume is not 0 ) ) if self.v8_sell_condition_0_enable.value: conditions.append( ( (dataframe["close"] > dataframe["bb_upperband"]) & (dataframe["close"].shift(1) > dataframe["bb_upperband"].shift(1)) & (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2)) & (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2)) & (dataframe["volume"] > 0) ) ) if self.v8_sell_condition_1_enable.value: conditions.append(((dataframe["rsi"] > self.v8_sell_rsi_main.value) & (dataframe["volume"] > 0))) if self.sell_condition_0_enable.value: conditions.append( ( (dataframe['close'] < dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_50']) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1 return dataframe # SSL Channels def SSLChannels(dataframe, length = 7): df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] # Chaikin Money Flow Volume def MFV(dataframe): df = dataframe.copy() N = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low']) M = N * df['volume'] return M