# source: https://raw.githubusercontent.com/eZ3KIEL29640/FT_strat/205cd67a9c07a0988a88f0f39c8f44e54bdac7ab/TankAi.py import logging from functools import reduce import datetime import talib.abstract as ta import pandas_ta as pta import logging import numpy as np import pandas as pd #from murrey_math import calculate_murrey_math_levels import freqtrade.vendor.qtpylib.indicators as qtpylib from technical import qtpylib from datetime import timedelta, datetime, timezone from pandas import DataFrame, Series from technical import qtpylib from typing import Optional from freqtrade.strategy.interface import IStrategy from technical.pivots_points import pivots_points from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_minutes from freqtrade.persistence import Trade from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, RealParameter, merge_informative_pair) from scipy.signal import argrelextrema from typing import Optional from functools import reduce logger = logging.getLogger(__name__) class Github_eZ3KIEL29640_FT_strat__TankAi__20240719_093104(IStrategy): exit_profit_only = True ### No selling at a loss use_custom_stoploss = True trailing_stop = False position_adjustment_enable = True ignore_roi_if_entry_signal = True position_adjustment_enable = True max_entry_position_adjustment = 2 max_dca_multiplier = 2.5 process_only_new_candles = True can_short = False use_exit_signal = True startup_candle_count: int = 200 stoploss = -0.99 timeframe = '30m' minimal_roi = { "0": 0.50, "60": 0.45, "120":0.40 } plot_config = { "main_plot": {}, "subplots": { "extrema": { "&s-extrema": { "color": "#f53580", "type": "line" }, "&s-minima_sort_threshold": { "color": "#4ae747", "type": "line" }, "&s-maxima_sort_threshold": { "color": "#5b5e4b", "type": "line" } }, "min_max": { "maxima": { "color": "#a29db9", "type": "line" }, "minima": { "color": "#ac7fc", "type": "line" }, "maxima_check": { "color": "#a29db9", "type": "line" }, "minima_check": { "color": "#ac7fc", "type": "line" } } } } # protections cooldown_lookback = IntParameter(24, 48, default=12, space="protection", optimize=True) stop_duration = IntParameter(12, 200, default=5, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) #trailing stop loss optimiziation tsl_target5 = DecimalParameter(low=0.3, high=0.4, default=0.3, decimals=2, space='sell', optimize=True, load=True) ts5 = DecimalParameter(low=0.04, high=0.06, default=0.05,decimals=2, space='sell', optimize=True, load=True) tsl_target4 = DecimalParameter(low=0.18, high=0.3, default=0.2, decimals=2, space='sell', optimize=True, load=True) ts4 = DecimalParameter(low=0.03, high=0.05, default=0.045, decimals=3, space='sell', optimize=True, load=True) tsl_target3 = DecimalParameter(low=0.12, high=0.18, default=0.15, decimals=2,space='sell', optimize=True, load=True) ts3 = DecimalParameter(low=0.025, high=0.04, default=0.035, decimals=3,space='sell', optimize=True, load=True) # tsl_target2 = DecimalParameter(low=0.07, high=0.12, default=0.1, decimals=2,space='sell', optimize=True, load=True) # ts2 = DecimalParameter(low=0.015, high=0.03, default=0.02, decimals=2,space='sell', optimize=True, load=True) # tsl_target1 = DecimalParameter(low=0.04, high=0.07, default=0.06, decimals=3,space='sell', optimize=True, load=True) # ts1 = DecimalParameter(low=0.01, high=0.016, default=0.013, decimals=3,space='sell', optimize=True, load=True) # tsl_target0 = DecimalParameter(low=0.02, high=0.05, default=0.040, decimals=3 ,space='sell', optimize=True, load=True) # ts0 = DecimalParameter(low=0.008, high=0.015, default=0.012, decimals=3,space='sell', optimize=True, load=True) ### protections ### @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) if self.use_stop_protection.value: prot.append({ "method": "StoplossGuard", "lookback_period_candles": 24 * 3, "trade_limit": 2, "stop_duration_candles": self.stop_duration.value, "only_per_pair": False }) return prot ### Dollar Cost Averaging ### # This is called when placing the initial order (opening trade) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: # We need to leave most of the funds for possible further DCA orders # This also applies to fixed stakes return proposed_stake / self.max_dca_multiplier def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) filled_entries = trade.select_filled_orders(trade.entry_side) count_of_entries = trade.nr_of_successful_entries trade_duration = (current_time - trade.open_date_utc).seconds / 60 # Take Profit if current_profit > 0.15 and trade.nr_of_successful_exits == 0: # Take half of the profit at +15% return -(trade.stake_amount / 4) if current_profit > 0.20 and trade.nr_of_successful_exits == 1: # Take half of the profit at +15% return -(trade.stake_amount / 3) # Profit Based DCA if current_profit > -0.0155 and trade.nr_of_successful_entries == 1: return None if current_profit > -0.045 and trade.nr_of_successful_entries == 2: return None if current_profit > -0.105 and trade.nr_of_successful_entries == 3: return None if current_profit > -0.15 and trade.nr_of_successful_entries == 4: return None if current_profit > -0.20 and trade.nr_of_successful_entries == 5: return None try: # This returns first order stake size stake_amount = filled_entries[0].cost # This then calculates current safety order size if count_of_entries == 1: stake_amount = stake_amount * 1 elif count_of_entries == 2: stake_amount = stake_amount * 1 elif count_of_entries == 3: stake_amount = stake_amount * 1 elif count_of_entries == 4: stake_amount = stake_amount * 1 elif count_of_entries == 5: stake_amount = stake_amount * 1 else: stake_amount = stake_amount return stake_amount except Exception as exception: return None return None def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() trade_duration = (current_time - trade.open_date_utc).seconds / 60 if current_candle['rsi'] < 80: for stop5 in self.tsl_target5.range: if (current_profit > stop5): for stop5a in self.ts5.range: self.dp.send_msg(f'*** {pair} *** Profit: {current_profit} - lvl5 {stop5}/{stop5a} activated') return stop5a for stop4 in self.tsl_target4.range: if (current_profit > stop4): for stop4a in self.ts4.range: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl4 {stop4}/{stop4a} activated') return stop4a for stop3 in self.tsl_target3.range: if (current_profit > stop3): for stop3a in self.ts3.range: self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl3 {stop3}/{stop3a} activated') return stop3a # for stop2 in self.tsl_target2.range: # if (current_profit > stop2): # for stop2a in self.ts2.range: # self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl2 {stop2}/{stop2a} activated') # return stop2a # if trade_duration < 600: # print(f"{pair}: 2nd trailing stop engaged {trade_duration} elapsed") # for stop1 in self.tsl_target1.range: # if (current_profit > stop1): # for stop1a in self.ts1.range: # self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl1 {stop1}/{stop1a} activated') # return stop1a # if trade_duration < 360: # print(f"{pair} 1st trailing stop engaged {trade_duration} elapsed") # for stop0 in self.tsl_target0.range: # if (current_profit > stop0): # for stop0a in self.ts0.range: # self.dp.send_msg(f'*** {pair} *** Profit {current_profit} - lvl0 {stop0}/{stop0a} activated') # return stop0a else: for stop0 in self.tsl_target0.range: if (current_profit > stop0): self.dp.send_msg(f'*** {pair} *** Profit {current_profit} SWINGING FOR THE MOON!!!') return 0.99 return self.stoploss def feature_engineering_expand_all(self, dataframe, period, **kwargs): dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-rocr-period"] = ta.ROCR(dataframe, timeperiod=period) dataframe["%-cmf-period"] = chaikin_mf(dataframe, periods=period) dataframe["%-chop-period"] = qtpylib.chopiness(dataframe, period) dataframe["%-linear-period"] = ta.LINEARREG_ANGLE( dataframe['close'], timeperiod=period) dataframe["%-atr-period"] = ta.ATR(dataframe, timeperiod=period) dataframe["%-atr-periodp"] = dataframe["%-atr-period"] / \ dataframe['close'] * 1000 return dataframe def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs): dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-obv"] = ta.OBV(dataframe) dataframe["dpo"] = pta.dpo(dataframe['close'], length=40, centered=False) dataframe["%-dpo"] = dataframe["dpo"] # Williams R% dataframe['%-willr14'] = pta.willr(dataframe['high'], dataframe['low'], dataframe['close']) # VWAP vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1) 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 dataframe = dataframe.copy() dataframe['%-dist_to_vwap_upperband'] = get_distance(dataframe['close'], dataframe['%-vwap_upperband']) dataframe['%-dist_to_vwap_middleband'] = get_distance(dataframe['close'], dataframe['%-vwap_middleband']) dataframe['%-dist_to_vwap_lowerband'] = get_distance(dataframe['close'], dataframe['%-vwap_lowerband']) dataframe['%-tail'] = (dataframe['close'] - dataframe['low']).abs() dataframe['%-wick'] = (dataframe['high'] - dataframe['close']).abs() dataframe['%-rawclose'] = dataframe['close'] dataframe["%-pct-change"] = dataframe["close"].pct_change() dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-raw_price"] = dataframe["close"] dataframe["%-raw_open"] = dataframe["open"] dataframe["%-raw_low"] = dataframe["low"] dataframe["%-raw_high"] = dataframe["high"] heikinashi = qtpylib.heikinashi(dataframe) dataframe['%-ha_open'] = heikinashi['open'] dataframe['%-ha_close'] = heikinashi['close'] dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['%-ha_high'] = heikinashi['high'] dataframe['%-ha_low'] = heikinashi['low'] dataframe['%-ha_closedelta'] = (heikinashi['close'] - heikinashi['close'].shift()) dataframe['%-ha_tail'] = (heikinashi['close'] - heikinashi['low']) dataframe['%-ha_wick'] = (heikinashi['high'] - heikinashi['close']) dataframe['%-HLC3'] = (heikinashi['high'] + heikinashi['low'] + heikinashi['close'])/3 murrey_math_levels = calculate_murrey_math_levels(dataframe) for level, value in murrey_math_levels.items(): dataframe[level] = value dataframe["%-+3/8"] = dataframe["[+3/8]P"] dataframe["%-+2/8"] = dataframe["[+2/8]P"] dataframe["%-+1/8"] = dataframe["[+1/8]P"] dataframe["%-8/8"] = dataframe["[8/8]P"] dataframe["%-7/8"] = dataframe["[7/8]P"] dataframe["%-6/8"] = dataframe["[6/8]P"] dataframe["%-5/8"] = dataframe["[5/8]P"] dataframe["%-4/8"] = dataframe["[4/8]P"] dataframe["%-3/8"] = dataframe["[3/8]P"] dataframe["%-2/8"] = dataframe["[2/8]P"] dataframe["%-1/8"] = dataframe["[1/8]P"] dataframe["%-0/8"] = dataframe["[0/8]P"] dataframe["%--1/8"] = dataframe["[-1/8]P"] dataframe["%--2/8"] = dataframe["[-2/8]P"] dataframe["%--3/8"] = dataframe["[-3/8]P"] dataframe['ema_2'] = ta.EMA(dataframe, timeperiod=2) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[+3/8]P']) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[+2/8]P']) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[+1/8]P']) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[8/8]P']) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[4/8]P']) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[0/8]P']) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[-1/8]P']) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[-2/8]P']) dataframe['%-distema2'] = get_distance(dataframe['ema_2'], dataframe['[-3/8]P']) dataframe['%-entrythreshold4'] = (dataframe['%-tail'] - dataframe['[0/8]P']) dataframe["%-entrythreshold5"] = (dataframe['%-tail'] - dataframe['[-1/8]P']) dataframe["%-entrythreshold6"] = (dataframe['%-tail'] - dataframe['[-2/8]P']) dataframe["%-entrythreshold7"] = (dataframe['%-tail'] - dataframe['[-3/8]P']) dataframe["%-exitthreshold4"] = (dataframe['%-wick'] - dataframe['[8/8]P']) dataframe["%-exitthreshold5"] = (dataframe['%-wick'] - dataframe['[+1/8]P']) dataframe["%-exitthreshold6"] = (dataframe['%-wick'] - dataframe['[+2/8]P']) dataframe["%-exitthreshold7"] = (dataframe['%-wick'] - dataframe['[+3/8]P']) dataframe['mmlextreme_oscillator'] = 100 * ((dataframe['close'] - dataframe["[-3/8]P"]) / (dataframe["[+3/8]P"] - dataframe["[-3/8]P"])) dataframe['%-mmlextreme_oscillator'] = dataframe['mmlextreme_oscillator'] # Calculate the percentage change between the high and open prices for each 5-minute candle dataframe['%-perc_change'] = (dataframe['high'] / dataframe['open'] - 1) * 100 # Create a custom indicator that checks if any of the past 100 5-minute candles' high price is 3% or more above the open price dataframe['%-candle_1perc_50'] = dataframe['%-perc_change'].rolling(50).apply(lambda x: np.where(x >= 1, 1, 0).sum()).shift() dataframe['%-candle_2perc_50'] = dataframe['%-perc_change'].rolling(50).apply(lambda x: np.where(x >= 2, 1, 0).sum()).shift() dataframe['%-candle_3perc_50'] = dataframe['%-perc_change'].rolling(50).apply(lambda x: np.where(x >= 3, 1, 0).sum()).shift() dataframe['%-candle_5perc_50'] = dataframe['%-perc_change'].rolling(50).apply(lambda x: np.where(x >= 5, 1, 0).sum()).shift() dataframe['%-candle_-1perc_50'] = dataframe['%-perc_change'].rolling(50).apply(lambda x: np.where(x <= -1, -1, 0).sum()).shift() dataframe['%-candle_-2perc_50'] = dataframe['%-perc_change'].rolling(50).apply(lambda x: np.where(x <= -2, -1, 0).sum()).shift() dataframe['%-candle_-3perc_50'] = dataframe['%-perc_change'].rolling(50).apply(lambda x: np.where(x <= -3, -1, 0).sum()).shift() dataframe['%-candle_-5perc_50'] = dataframe['%-perc_change'].rolling(50).apply(lambda x: np.where(x <= -5, -1, 0).sum()).shift() # Calculate the percentage of the current candle's range where the close price is dataframe['%-close_percentage'] = (dataframe['close'] - dataframe['low']) / (dataframe['high'] - dataframe['low']) dataframe['%-body_size'] = abs(dataframe['open'] - dataframe['close']) dataframe['%-range_size'] = dataframe['high'] - dataframe['low'] dataframe['%-body_range_ratio'] = dataframe['%-body_size'] / dataframe['%-range_size'] dataframe['%-upper_wick_size'] = dataframe['high'] - dataframe[['open', 'close']].max(axis=1) dataframe['%-upper_wick_range_ratio'] = dataframe['%-upper_wick_size'] / dataframe['%-range_size'] lookback_period = 10 dataframe['%-max_high'] = dataframe['high'].rolling(50).max() dataframe['%-min_low'] = dataframe['low'].rolling(50).min() dataframe['%-close_position'] = (dataframe['close'] - dataframe['%-min_low']) / (dataframe['%-max_high'] - dataframe['%-min_low']) dataframe['%-current_candle_perc_change'] = (dataframe['high'] / dataframe['open'] - 1) * 100 # Lazy Bear Impulse Macd dataframe['%-hi'] = ta.SMA(dataframe['high'], timeperiod = 28) dataframe['%-lo'] = ta.SMA(dataframe['low'], timeperiod = 28) dataframe['%-ema1'] = ta.EMA(dataframe['%-HLC3'], timeperiod = 28) dataframe['%-ema2'] = ta.EMA(dataframe['%-ema1'], timeperiod = 28) dataframe['%-d'] = dataframe['%-ema1'] - dataframe['%-ema2'] dataframe['%-mi'] = dataframe['%-ema1'] + dataframe['%-d'] dataframe['%-md'] = np.where(dataframe['%-mi'] > dataframe['%-hi'], dataframe['%-mi'] - dataframe['%-hi'], np.where(dataframe['%-mi'] < dataframe['%-lo'], dataframe['%-mi'] - dataframe['%-lo'], 0)) dataframe['%-sb'] = ta.SMA(dataframe['%-md'], timeperiod = 8) dataframe['%-sh'] = dataframe['%-md'] - dataframe['%-sb'] # WaveTrend using OHLC4 or HA close - 3/21 ap = (0.333 * (heikinashi['high'] + heikinashi['low'] + heikinashi["close"])) dataframe['esa'] = ta.EMA(ap, timeperiod = 9) dataframe['d'] = ta.EMA(abs(ap - dataframe['esa']), timeperiod = 9) dataframe['%-wave_ci'] = (ap-dataframe['esa']) / (0.015 * dataframe['d']) dataframe['%-wave_t1'] = ta.EMA(dataframe['%-wave_ci'], timeperiod = 12) dataframe['%-wave_t2'] = ta.SMA(dataframe['%-wave_t1'], timeperiod = 4) # 200 SMA and distance dataframe['%-200sma'] = ta.SMA(dataframe, timeperiod = 200) dataframe['%-200sma_dist'] = get_distance(heikinashi["close"], dataframe['%-200sma']) return dataframe def feature_engineering_standard(self, dataframe, **kwargs): dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7 dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25 return dataframe def set_freqai_targets(self, dataframe, **kwargs): dataframe["&s-extrema"] = 0 min_peaks = argrelextrema( dataframe["open"].values, np.less, order=self.freqai_info["feature_parameters"]["label_period_candles"] ) max_peaks = argrelextrema( dataframe["close"].values, np.greater, order=self.freqai_info["feature_parameters"]["label_period_candles"] ) for mp in min_peaks[0]: dataframe.at[mp, "&s-extrema"] = -1 for mp in max_peaks[0]: dataframe.at[mp, "&s-extrema"] = 1 dataframe["minima"] = np.where(dataframe["&s-extrema"] == -1, 1, 0) dataframe["maxima"] = np.where(dataframe["&s-extrema"] == 1, 1, 0) dataframe['&s-extrema'] = dataframe['&s-extrema'].rolling( window=5, win_type='gaussian', center=True).mean(std=0.5) dataframe['&s-extrema10'] = dataframe['&s-extrema'].rolling( window=10, win_type='gaussian', center=True).mean(std=0.5) dataframe['&s-extrema15'] = dataframe['&s-extrema'].rolling( window=15, win_type='gaussian', center=True).mean(std=0.5) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = ta.RSI(dataframe) dataframe = self.freqai.start(dataframe, metadata, self) murrey_math_levels = calculate_murrey_math_levels(dataframe) for level, value in murrey_math_levels.items(): dataframe[level] = value dataframe['mmlextreme_oscillator'] = 100 * ((dataframe['close'] - dataframe["[4/8]P"]) / (dataframe["[+3/8]P"] - dataframe["[-3/8]P"])) dataframe["DI_catch"] = np.where( dataframe["DI_values"] > dataframe["DI_cutoff"], 0, 1, ) dataframe["minima_sort_threshold"] = dataframe["&s-minima_sort_threshold"] dataframe["maxima_sort_threshold"] = dataframe["&s-maxima_sort_threshold"] dataframe['min_threshold_mean'] = dataframe["minima_sort_threshold"].expanding().mean() dataframe['max_threshold_mean'] = dataframe["maxima_sort_threshold"].expanding().mean() dataframe['maxima_check'] = dataframe['maxima'].rolling(12).apply(lambda x: int((x != 1).all()), raw=True).fillna(0) dataframe['minima_check'] = dataframe['minima'].rolling(12).apply(lambda x: int((x != 1).all()), raw=True).fillna(0) pair = metadata['pair'] if dataframe['maxima'].iloc[-3] == 1 and dataframe['maxima_check'].iloc[-1] == 0: self.dp.send_msg(f'*** {pair} *** Maxima Detected - Potential Short!!!' ) if dataframe['minima'].iloc[-3] == 1 and dataframe['minima_check'].iloc[-1] == 0: self.dp.send_msg(f'*** {pair} *** Minima Detected - Potential Long!!!' ) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: # df.loc[ # ( # (df["do_predict"] == 1) & # (df["DI_catch"] == 1) & # (df["maxima_check"] != 1) & # (df["&s-extrema"] > df["&s-extrema"].shift(1)) & # (df["&s-extrema"] < df["minima_sort_threshold"]) & # (df["&s-extrema"].shift(1) < df["minima_sort_threshold"].shift(1)) & # (df["&s-extrema"].shift(2) < df["minima_sort_threshold"].shift(2)) & # (df["close"].shift(1) < df["open"].shift(1)) & # (df["close"].shift(2) < df["open"].shift(2)) & # (df["maxima"] == 1) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'Extrema < Minima Threshold 3rd Descending') # df.loc[ # ( # (df["do_predict"] == 1) & # (df["DI_catch"] == 1) & # (df["maxima_check"] == 1) & # (df["&s-extrema"] > df["&s-extrema"].shift(1)) & # (df["&s-extrema"] < df["minima_sort_threshold"]) & # (df["&s-extrema"].shift(1) < df["minima_sort_threshold"].shift(1)) & # (df["&s-extrema"].shift(2) < df["minima_sort_threshold"].shift(2)) & # (df["close"] > df["open"]) & # (df["close"].shift(1) < df["open"].shift(1)) & # (df["maxima"] != 1) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['enter_long', 'enter_tag']] = (1, 'Extrema < Minima Threshold Quick Reversal') df.loc[ ( (df["do_predict"] == 1) & # Guard: tema is raising (df["DI_catch"] == 1) & (df["maxima_check"] == 1) & (df["&s-extrema"] < 0) & (df["minima"].shift(1) == 1) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'Minima') df.loc[ ( # (df["do_predict"] == 1) & # Guard: tema is raising # (df["DI_catch"] == 1) & # (df["maxima_check"] == 1) & # (df["&s-extrema"] < 0) & (df["minima_check"] == 0) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'Minima Full Send') df.loc[ ( (df["do_predict"] == 1) & # Guard: tema is raising (df["DI_catch"] == 1) & (df["minima_check"] == 0) & (df["minima_check"].shift(5) == 1) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'Minima Check') return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[ ( (df["do_predict"] == 1) & # Guard: tema is raising (df["maxima_check"] == 0) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'Maxima Check') df.loc[ ( (df["do_predict"] == 1) & # Guard: tema is raising (df["DI_catch"] == 1) & (df["&s-extrema"] > 0) & (df["maxima"].shift(1) == 1) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'Maxima') df.loc[ ( # (df["do_predict"] == 1) & # Guard: tema is raising # (df["DI_catch"] == 1) & # (df["&s-extrema"] > 0) & (df["maxima_check"] == 0) & (df['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (1, 'Maxima Full Send') # df.loc[ # ( # (df["do_predict"] == 1) & # Guard: tema is raising # (df["DI_catch"] == 0) & # (df['volume'] > 0) # Make sure Volume is not 0 # ), # ['exit_long', 'exit_tag']] = (1, 'Outlier') return df def top_percent_change(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 chaikin_mf(df, periods=20): close = df['close'] low = df['low'] high = df['high'] volume = df['volume'] mfv = ((close - low) - (high - close)) / (high - low) mfv = mfv.fillna(0.0) mfv *= volume cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum() return Series(cmf, name='cmf') 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 get_distance(p1, p2): return abs((p1) - (p2)) def calculate_murrey_math_levels(df, window_size=64): #df = df.iloc[-window_size:] # Calculate rolling 64-bar maximum and minimum values rolling_max_H = df['high'].rolling(window=window_size).max() rolling_min_L = df['low'].rolling(window=window_size).min() max_H = rolling_max_H min_L = rolling_min_L range_HL = max_H - min_L def calculate_fractal(v2): fractal = 0 if 25000 < v2 <= 250000: fractal = 100000 elif 2500 < v2 <= 25000: fractal = 10000 elif 250 < v2 <= 2500: fractal = 1000 elif 25 < v2 <= 250: fractal = 100 elif 12.5 < v2 <= 25: fractal = 12.5 elif 6.25 < v2 <= 12.5: fractal = 12.5 elif 3.125 < v2 <= 6.25: fractal = 6.25 elif 1.5625 < v2 <= 3.125: fractal = 3.125 elif 0.390625 < v2 <= 1.5625: fractal = 1.5625 elif 0 < v2 <= 0.390625: fractal = 0.1953125 return fractal def calculate_octave(v1, v2, mn, mx): range_ = v2 - v1 sum_ = np.floor(np.log(calculate_fractal(v1) / range_) / np.log(2)) octave = calculate_fractal(v1) * (0.5 ** sum_) mn = np.floor(v1 / octave) * octave if mn + octave > v2: mx = mn + octave else: mx = mn + (2 * octave) return mx def calculate_x_values(v1, v2, mn, mx): dmml = (v2 - v1) / 8 x_values = [] # Calculate the midpoints of each segment midpoints = [mn + i * dmml for i in range(8)] for i in range(7): x_i = (midpoints[i] + midpoints[i + 1]) / 2 x_values.append(x_i) finalH = max(x_values) # Maximum of the x_values is the finalH return x_values, finalH def calculate_y_values(x_values, mn): y_values = [] for x in x_values: if x > 0: y = mn else: y = 0 y_values.append(y) return y_values def calculate_mml(mn, finalH, mx): dmml = ((finalH - finalL) / 8) * 1.0699 mml = (float([mx][0]) * 0.99875) + (dmml * 3) # mml = (float([mx]) * 0.99875) + (dmml * 3) ml = [] for i in range(0, 16): calc = mml - (dmml * (i)) ml.append(calc) murrey_math_levels = { "[-3/8]P": ml[14], "[-2/8]P": ml[13], "[-1/8]P": ml[12], "[0/8]P": ml[11], "[1/8]P": ml[10], "[2/8]P": ml[9], "[3/8]P": ml[8], "[4/8]P": ml[7], "[5/8]P": ml[6], "[6/8]P": ml[5], "[7/8]P": ml[4], "[8/8]P": ml[3], "[+1/8]P": ml[2], "[+2/8]P": ml[1], "[+3/8]P": ml[0] } return mml, murrey_math_levels for i in range(len(df)): mn = np.min(min_L.iloc[:i + 1]) mx = np.max(max_H.iloc[:i + 1]) x_values, finalH = calculate_x_values(mn, mx, mn, mx) y_values = calculate_y_values(x_values, mn) finalL = np.min(y_values) mml, murrey_math_levels = calculate_mml(finalL, finalH, mx) # Add Murrey Math levels to the DataFrame at each time step for level, value in murrey_math_levels.items(): df.at[df.index[i], level] = value return df def PC(dataframe, in1, in2): df = dataframe.copy() pc = ((in2-in1)/in1) * 100 return pc