# source: https://raw.githubusercontent.com/XQuant42/ft_pantheon/9cc88e804b9f553d3e9fe90539030b18cb8c0b2e/user_data/strategies/MultiMA_TSL3_Mod.py import freqtrade.vendor.qtpylib.indicators as qtpylib from typing import Dict, List import numpy as np import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import (merge_informative_pair, DecimalParameter, IntParameter, RealParameter,BooleanParameter, timeframe_to_minutes) from pandas import DataFrame, Series from functools import reduce from freqtrade.persistence import Trade from datetime import datetime, timedelta from freqtrade.exchange import timeframe_to_prev_date from technical.indicators import zema, VIDYA ########################################################################################################### ## MultiMA_TSL, modded by stash86, based on SMAOffsetProtectOptV1 (modded by Perkmeister) ## ## Based on @Lamborghini Store's SMAOffsetProtect strat, heavily based on @tirail's original SMAOffset## ## ## ## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ## ## ## ## Thanks to ## ## - Perkmeister, for their snippets for the sell signals and decaying EMA sell ## ## - ChangeToTower, for the PMax idea ## ## - JimmyNixx, for their snippet to limit close value from the peak (that I modify into 5m tf check) ## ## - froggleston, for the Heikinashi check snippet from Cryptofrog ## ## - Uzirox, for their pump detection code ## ## ## ## ## ########################################################################################################### # I hope you do enough testing before proceeding, either backtesting and/or dry run. # Any profits and losses are all your responsibility class Github_XQuant42_ft_pantheon__MultiMA_TSL3_Mod__20251123_060325(IStrategy): INTERFACE_VERSION = 2 DATESTAMP = 0 SELLMA = 1 SELL_TRIGGER=2 # Buy hyperspace params: buy_params = { "buy_rsi_fast_max": 98, "buy_rsi_fast_min": 36, "buy_rsi_max": 79, "buy_rsi_min": 24, "ewo_high": 0.546, "ewo_high2": 8.497, "ewo_low": -14.239, "ewo_low2": -15.614, "fast_ewo": 12, "pmax_pct_max": 83.754, "pmax_pct_min": 20.09, "slow_ewo": 150, "volume_pct_max": 8.721, "volume_pct_min": 0.247, "buy_condition_ema_enable": True, # value loaded from strategy "close_pct_max": 0.06785, "close_pct_min": 0.01121, } # Sell hyperspace params: sell_params = { "base_nb_candles_ema_sell": 65, "base_nb_candles_ema_sell2": 49, "high_offset_sell_ema": 1.074, } # Protection hyperspace params: protection_params = { "cooldown_lookback": 39, "low_profit_lookback": 29, "low_profit_min_req": -0.03, "low_profit_stop_duration": 52, } # ROI table: minimal_roi = { "0": 100 } stoploss = -0.15 use_custom_stoploss = True # Trailing stoploss (not used) trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.018 # Buy hyperspace params: """optimize_buy_ema = False # Not used base_nb_candles_buy_ema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_ema) low_offset_ema = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=optimize_buy_ema) base_nb_candles_buy_ema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_ema) low_offset_ema2 = DecimalParameter(0.9, 1.1, default=0.958, space='buy', optimize=optimize_buy_ema) optimize_buy_trima = False # Not used base_nb_candles_buy_trima = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_trima) low_offset_trima = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_trima) base_nb_candles_buy_trima2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_trima) low_offset_trima2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_trima) optimize_buy_zema = False # Not used base_nb_candles_buy_zema = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_zema) low_offset_zema = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_zema) base_nb_candles_buy_zema2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_zema) low_offset_zema2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_zema) optimize_buy_hma = False # Not used base_nb_candles_buy_hma = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_hma) low_offset_hma = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_hma) base_nb_candles_buy_hma2 = IntParameter(5, 80, default=20, space='buy', optimize=optimize_buy_hma) low_offset_hma2 = DecimalParameter(0.9, 0.99, default=0.958, space='buy', optimize=optimize_buy_hma)""" buy_condition_enable_optimize = False # Not used buy_condition_ema_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) """buy_condition_trima_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) buy_condition_zema_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize) buy_condition_hma_enable = BooleanParameter(default=True, space='buy', optimize=buy_condition_enable_optimize)""" ewo_check_optimize = True ewo_low = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=ewo_check_optimize) ewo_high = DecimalParameter(0.0, 12.0, default=6.0, space='buy', optimize=ewo_check_optimize) ewo_low2 = DecimalParameter(-20.0, -8.0, default=-20.0, space='buy', optimize=ewo_check_optimize) ewo_high2 = DecimalParameter(2.0, 12.0, default=6.0, space='buy', optimize=ewo_check_optimize) fast_ewo = IntParameter(10, 50, default=50, space='buy', optimize=True) slow_ewo = IntParameter(100, 200, default=200, space='buy', optimize=True) pct_optimize = True pmax_pct_min = DecimalParameter(1.00, 100.00, default=1, space='buy', optimize=pct_optimize) pmax_pct_max = DecimalParameter(1.00, 100.00, default=1, space='buy', optimize=pct_optimize) volume_pct_min = DecimalParameter(0.01, 20, default=0.01, space='buy', optimize=pct_optimize) volume_pct_max = DecimalParameter(0.01, 20, default=0.01, space='buy', optimize=pct_optimize) high_precision_pct_optimize = False # Optimise this setting individually close_pct_min = RealParameter(0.0001, 0.1, default=0.01, space='buy', optimize=high_precision_pct_optimize) close_pct_max = RealParameter(0.0001, 0.1, default=0.01, space='buy', optimize=high_precision_pct_optimize) buy_rsi_optimize = True buy_rsi_min = IntParameter(0, 100, default=1, space='buy', optimize=buy_rsi_optimize) buy_rsi_max = IntParameter(0, 100, default=100, space='buy', optimize=buy_rsi_optimize) buy_rsi_fast_min = IntParameter(0, 100, default=1, space='buy', optimize=buy_rsi_optimize) buy_rsi_fast_max = IntParameter(0, 100, default=100, space='buy', optimize=buy_rsi_optimize) # Sell hyperspace params: optimize_sell_ema = True base_nb_candles_ema_sell = IntParameter(5, 80, default=20, space='sell', optimize=True) high_offset_sell_ema = DecimalParameter(0.99, 1.1, default=1.012, space='sell', optimize=True) base_nb_candles_ema_sell2 = IntParameter(5, 80, default=20, space='sell', optimize=True) # Protection hyperspace params: cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=True) low_profit_optimize = True low_profit_lookback = IntParameter(2, 60, default=20, space="protection", optimize=low_profit_optimize) low_profit_stop_duration = IntParameter(12, 200, default=20, space="protection", optimize=low_profit_optimize) low_profit_min_req = DecimalParameter(-0.05, 0.05, default=-0.05, space="protection", decimals=2, optimize=low_profit_optimize) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.low_profit_lookback.value, "trade_limit": 1, "stop_duration": int(self.low_profit_stop_duration.value), "required_profit": self.low_profit_min_req.value }) return prot # Optimal timeframe for the strategy. timeframe = '5m' # storage dict for custom info custom_info = { } # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 400 def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) < 1): return False last_candle = dataframe.iloc[-1] if(self.custom_info[pair][self.DATESTAMP] != last_candle['date']): # new candle, update EMA and check sell # smoothing coefficients sell_ema = self.custom_info[pair][self.SELLMA] if(sell_ema == 0): sell_ema = last_candle['ema_sell'] emaLength = 32 alpha = 2 /(1 + emaLength) # update sell_ema sell_ema = (alpha * last_candle['close']) + ((1 - alpha) * sell_ema) self.custom_info[pair][self.SELLMA] = sell_ema self.custom_info[pair][self.DATESTAMP] = last_candle['date'] if((last_candle['close'] > (sell_ema * self.high_offset_sell_ema.value)) & (last_candle['buy_copy'] == 0)): if self.config['runmode'].value in ('live', 'dry_run'): self.custom_info[pair][self.SELL_TRIGGER] = 1 return False buy_tag = 'empty' if hasattr(trade, 'buy_tag') and trade.buy_tag is not None: buy_tag = trade.buy_tag else: trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) buy_signal = dataframe.loc[dataframe['date'] < trade_open_date] if not buy_signal.empty: buy_signal_candle = buy_signal.iloc[-1] buy_tag = buy_signal_candle['buy_tag'] if buy_signal_candle['buy_tag'] != '' else 'empty' return f'New Sell Signal ({buy_tag})' return False #credit to Perkmeister for this custom stoploss to help the strategy ride a green candle when the sell signal triggered def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: sl_new = 1 if(self.custom_info[pair][self.SELL_TRIGGER] == 1): if self.config['runmode'].value in ('live', 'dry_run'): sl_new = 0.001 if (current_profit > 0.2): sl_new = 0.05 elif (current_profit > 0.1): sl_new = 0.03 elif (current_profit > 0.06): sl_new = 0.02 elif (current_profit > 0.03): sl_new = 0.01 return sl_new def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if(len(dataframe) < 1): return False last_candle = dataframe.iloc[-1].squeeze() if ((rate > last_candle['close'])) : return False self.custom_info[pair][self.DATESTAMP] = last_candle['date'] self.custom_info[pair][self.SELLMA] = last_candle['ema_sell'] return 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: self.custom_info[pair][self.SELL_TRIGGER] = 0 return True def get_ticker_indicator(self): return int(self.timeframe[:-1]) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Parabolic SAR dataframe['sar'] = ta.SAR(dataframe) # EWO #dataframe['ema_delta'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) - ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value # EWO delta? Not used anyway dataframe['ewo'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # Heiken Ashi heikinashi = qtpylib.heikinashi(dataframe) heikinashi["volume"] = dataframe["volume"] dataframe['ha_up'] = (heikinashi['close'] > heikinashi['open']).astype('int') dataframe['ha_down'] = (heikinashi['open'] > heikinashi['close']).astype('int') # Profit Maximizer - 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 = HA(dataframe, 4) if self.config['runmode'].value in ('live', 'dry_run'): # Exchange downtime protection dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0) else: dataframe['live_data_ok'] = True # Check if the entry already exists if not metadata["pair"] in self.custom_info: # Create empty entry for this pair {datestamp, sellma, sell_trigger} self.custom_info[metadata["pair"]] = ['', 0, 0] dataframe['24hr_high'] = (dataframe['high'].rolling(window=288, min_periods= 288).max()) dataframe['smooth_high'] =ta.EMA(dataframe['24hr_high'], timeperiod=2) dataframe['high_rising'] = (dataframe['smooth_high'] > dataframe['smooth_high'].shift()).astype('int') dataframe['high_falling'] = (dataframe['smooth_high'] < dataframe['smooth_high'].shift()).astype('int') dataframe['24hr_low'] = (dataframe['low'].rolling(window=288, min_periods= 288).min()) dataframe['smooth_low'] =ta.EMA(dataframe['24hr_low'], timeperiod=2) dataframe['low_rising'] = (dataframe['smooth_low'] > dataframe['smooth_low'].shift()).astype('int') dataframe['low_falling'] = (dataframe['smooth_low'] < dataframe['smooth_low'].shift()).astype('int') dataframe['24hr_delta'] = (dataframe['24hr_high'] - dataframe['24hr_low']) dataframe['smooth_delta'] =ta.EMA(dataframe['24hr_delta'], timeperiod=2) dataframe['delta_rising'] = (dataframe['smooth_delta'] > dataframe['smooth_delta'].shift()).astype('int') dataframe['pmax_high_delta'] = (dataframe['24hr_high'] - dataframe['pmax_thresh']) dataframe['smooth_pmax_high'] =ta.EMA(dataframe['pmax_high_delta'], timeperiod=2) dataframe['pmax_low_delta'] = (dataframe['pmax_thresh'] - dataframe['24hr_low']) dataframe['smooth_pmax_low'] =ta.EMA(dataframe['pmax_low_delta'], timeperiod=2) dataframe['pmax_pct'] = (dataframe['pmax_thresh'] - dataframe['24hr_low']) / (dataframe['24hr_high'] - dataframe['24hr_low']) * 100 dataframe['pmax_pct_rising'] = (dataframe['pmax_pct'] > dataframe['pmax_pct'].shift()).astype('int') dataframe['smooth_volume'] =ta.EMA(dataframe['volume'], timeperiod=2) dataframe['smooth_volume_slow'] =ta.EMA(dataframe['volume'], timeperiod=12) dataframe['volume_pct'] =(dataframe['volume']).pct_change() dataframe['smooth_volume_pct'] =ta.EMA(dataframe['volume_pct'], timeperiod=2) dataframe['volume_pct_rising'] = (dataframe['volume_pct'] > dataframe['volume_pct'].shift()).astype('int') dataframe['smooth_volume_pct_rising'] =ta.EMA(dataframe['volume_pct_rising'], timeperiod=2) dataframe['close_pct'] =(dataframe['close']).pct_change() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] """dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value dataframe['ema_offset_buy2'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema2.value)) *self.low_offset_ema2.value""" dataframe['ema_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_ema_sell.value)) dataframe.loc[:, 'buy_tag'] = '' dataframe.loc[:, 'buy_copy'] = 0 dataframe.loc[:, 'buy'] = 0 if (self.buy_condition_ema_enable.value): buy_offset_ema = ( ( #(dataframe['pm'] <= dataframe['pmax_thresh']) #& #(dataframe['ha_up'].rolling(self.ha_rolling_up.value).sum() == self.ha_rolling_up.value) #& #(qtpylib.crossed_above(dataframe['HA_Close'].shift(self.ha_rolling_up.value -1 ), dataframe['HA_Open'].shift(self.ha_rolling_up.value + 1))) #& #(dataframe['ha_down'].shift(self.ha_rolling_up.value).rolling(self.ha_rolling_down.value).sum() == self.ha_rolling_down.value) #& (qtpylib.crossed_below(dataframe['sar'], dataframe['pmax_thresh'])) & (dataframe['pmax_thresh'] > dataframe['pm']) & (dataframe['pmax_thresh'] > dataframe['sar']) #& #(dataframe['high_rising'] == 1) ) ) dataframe.loc[buy_offset_ema, 'buy_tag'] += 'ema ' conditions.append(buy_offset_ema) """if (self.buy_condition_zema_enable.value): dataframe['zema_offset_buy'] = zema(dataframe, int(self.base_nb_candles_buy_zema.value)) *self.low_offset_zema.value dataframe['zema_offset_buy2'] = zema(dataframe, int(self.base_nb_candles_buy_zema2.value)) *self.low_offset_zema2.value buy_offset_zema = ( ( (dataframe['close'] < dataframe['zema_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) ) | ( (dataframe['close'] < dataframe['zema_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) ) ) dataframe.loc[buy_offset_zema, 'buy_tag'] += 'zema ' conditions.append(buy_offset_zema) if (self.buy_condition_hma_enable.value): dataframe['hma_offset_buy'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma.value)) *self.low_offset_hma.value dataframe['hma_offset_buy2'] = qtpylib.hull_moving_average(dataframe['close'], window=int(self.base_nb_candles_buy_hma2.value)) *self.low_offset_hma2.value buy_offset_hma = ( ( ( (dataframe['close'] < dataframe['hma_offset_buy']) & (dataframe['pm'] <= dataframe['pmax_thresh']) & (dataframe['rsi'] < 35) ) | ( (dataframe['close'] < dataframe['hma_offset_buy2']) & (dataframe['pm'] > dataframe['pmax_thresh']) & (dataframe['rsi'] < 30) ) ) & (dataframe['rsi_fast'] < 30) ) dataframe.loc[buy_offset_hma, 'buy_tag'] += 'hma ' conditions.append(buy_offset_hma)""" add_check = ( (dataframe['live_data_ok']) & (dataframe['pmax_pct'] > self.pmax_pct_min.value) & (dataframe['volume_pct'] > self.volume_pct_min.value) & (dataframe['close_pct'] > self.close_pct_min.value) & (dataframe['rsi'] > self.buy_rsi_min.value) & (dataframe['rsi_fast'] > self.buy_rsi_fast_min.value) & (dataframe['pmax_pct'] < self.pmax_pct_max.value) & (dataframe['volume_pct'] < self.volume_pct_max.value) & (dataframe['close_pct'] < self.close_pct_max.value) & (dataframe['rsi'] < self.buy_rsi_max.value) & (dataframe['rsi_fast'] < self.buy_rsi_fast_max.value) & (dataframe['ewo'] > self.ewo_high.value) & #(dataframe['open'] < dataframe['ema_offset_buy']) #& #(dataframe['buy_low_rolling'].shift().rolling(self.buy_smooth_ha_rolling.value).sum() == self.buy_low_rolling.value) #& #(dataframe['delta_rising'].rolling(5).sum() == self.buy_smooth_ha_rolling.value) #& #(dataframe['close'] > (dataframe['ema_sell'] * self.high_offset_sell_ema.value)) #& #(dataframe['close'].rolling(288).max() < (dataframe['close'] * 1.10 )) #& #(dataframe['Smooth_HA_O'].shift(1) < dataframe['Smooth_HA_H'].shift(1)) #& #(dataframe['rsi_fast'] > self.buy_rsi_fast.value) #& #(dataframe['rsi_84'] > 60) #& #(dataframe['rsi_112'] > 60) #& #(dataframe['ewo'] > self.ewo_high.value) #& #( # ( # (dataframe['close'] > dataframe['pmax_thresh']) # & # (dataframe['pm'] > dataframe['pmax_thresh']) # & # ( # (dataframe['ewo'] < self.ewo_low.value) # | # ( # (dataframe['ewo'] > self.ewo_high.value) # & # (dataframe['rsi'] < self.rsi_buy.value) # ) # ) # ) # | # ( # (dataframe['close'] > dataframe['pmax_thresh']) # & # (dataframe['pm'] > dataframe['pmax_thresh']) # & # ( # (dataframe['ewo'] < self.ewo_low2.value) # | # ( # (dataframe['ewo'] > self.ewo_high2.value) # & # (dataframe['rsi'] < self.rsi_buy2.value) # ) # ) # ) #) #& (dataframe['volume'] > 0) ) if conditions: dataframe.loc[ (add_check & reduce(lambda x, y: x | y, conditions)), ['buy_copy','buy'] ]=(1,1) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'sell'] = 0 return dataframe # Elliot Wave Oscillator def EWO(dataframe, sma1_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.EMA(df, timeperiod=sma1_length) sma2 = ta.EMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif # 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 = f'MA_{MAtype}_{length}' atr = f'ATR_{period}' pm = f'pm_{period}_{multiplier}_{length}_{MAtype}' pmx = f'pmX_{period}_{multiplier}_{length}_{MAtype}' # MAtype==1 --> EMA # MAtype==2 --> DEMA # MAtype==3 --> T3 # MAtype==4 --> SMA # MAtype==5 --> VIDYA # MAtype==6 --> TEMA # MAtype==7 --> WMA # MAtype==8 --> VWMA # MAtype==9 --> zema 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 pmx = np.where((pm_arr > 0.00), np.where((mavalue < pm_arr), 'down', 'up'), np.NaN) return pm, pmx # smoothed Heiken Ashi def HA(dataframe, smoothing=None): df = dataframe.copy() df['HA_Close']=(df['open'] + df['high'] + df['low'] + df['close'])/4 df.reset_index(inplace=True) ha_open = [ (df['open'][0] + df['close'][0]) / 2 ] [ ha_open.append((ha_open[i] + df['HA_Close'].values[i]) / 2) for i in range(0, len(df)-1) ] df['HA_Open'] = ha_open df.set_index('index', inplace=True) df['HA_High']=df[['HA_Open','HA_Close','high']].max(axis=1) df['HA_Low']=df[['HA_Open','HA_Close','low']].min(axis=1) if smoothing is not None: sml = abs(int(smoothing)) if sml > 0: df['Smooth_HA_O']=ta.EMA(df['HA_Open'], sml) df['Smooth_HA_C']=ta.EMA(df['HA_Close'], sml) df['Smooth_HA_H']=ta.EMA(df['HA_High'], sml) df['Smooth_HA_L']=ta.EMA(df['HA_Low'], sml) return df def pump_warning(dataframe, perc=15): df = dataframe.copy() df["change"] = df["high"] - df["low"] df["test1"] = (df["close"] > df["open"]) df["test2"] = ((df["change"]/df["low"]) > (perc/100)) df["result"] = (df["test1"] & df["test2"]).astype('int') return df['result']