import warnings warnings.filterwarnings('ignore') 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 import freqtrade.vendor.qtpylib.indicators as qtpylib from technical import qtpylib from datetime import timedelta, datetime, timezone from pandas import DataFrame, Series 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 import warnings import math warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning) logger = logging.getLogger(__name__) class NOTankAi_15(IStrategy): exit_profit_only = True trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 # Adjust as needed position_adjustment_enable = True ignore_roi_if_entry_signal = True max_entry_position_adjustment = 2 max_dca_multiplier = 1 process_only_new_candles = True can_short = False use_exit_signal = True #startup_candle_count: int = 200 stoploss = -0.05 timeframe = "15m" # Add the confirm_trade_entry method def confirm_trade_entry(self, pair: str, current_price: float) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Momentum-based entry momentum = ta.MOM(dataframe, timeperiod=10).iloc[-1] if momentum > 0: # Trend-based filter: only enter if price is above the 50-period EMA ema = ta.EMA(dataframe, timeperiod=50).iloc[-1] if current_price > ema: # Volume-based entry filter: enter only if recent volume is 2x the average recent_volume = dataframe['volume'].iloc[-1] avg_volume = dataframe['volume'].rolling(10).mean().iloc[-1] if recent_volume > 2 * avg_volume: return True return False def confirm_trade_entry(self, pair: str, current_price: float) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Momentum-based entry momentum = ta.MOM(dataframe, timeperiod=10).iloc[-1] if momentum > 0: # Only enter if there's positive momentum return True # Volume-based entry filter recent_volume = dataframe['volume'].iloc[-1] avg_volume = dataframe['volume'].rolling(10).mean().iloc[-1] if recent_volume > 1.5 * avg_volume: # Enter only if volume is 50% higher than average return True return False # DCA position_adjustment_enable = True initial_safety_order_trigger = DecimalParameter(low=-0.02, high=-0.01, default=-0.015, decimals=3, space="entry", optimize=True) safety_order_step_scale = DecimalParameter(low=1.2, high=2.0, default=1.5, decimals=2, space="entry", optimize=True) DecimalParameter(low=-0.02, high=-0.01, default=-0.015, decimals=3, space="entry", optimize=True) DecimalParameter(low=-0.01, high=-0.005, default=-0.007, decimals=3, space="entry", optimize=True) max_safety_orders = IntParameter(1, 6, default=2, space="entry", optimize=True) safety_order_step_scale = DecimalParameter(low=1.05, high=1.25, default=1.1, decimals=2, space="entry", optimize=True) safety_order_volume_scale = DecimalParameter(low=1.1, high=1.5, default=1.3, decimals=1, space="entry", optimize=True) # Custom Functions increment = DecimalParameter( low=1.0005, high=1.002, default=1.001, decimals=4, space="entry", optimize=True, load=True ) last_entry_price = None # Protections cooldown_lookback = IntParameter(2, 48, default=1, space="protection", optimize=True) stop_duration = IntParameter(12, 200, default=4, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) minimal_roi = { "0": 0.07, # 7% return after 0 minutes "60": 0.05, # 5% return after 60 minutes "120": 0.03 # 3% return after 120 minutes } { "0": 0.05, # 5% return after 0 minutes "60": 0.04, # 4% return after 60 minutes "120": 0.02 # 2% return after 120 minutes } { "0": 0.03, # 3% return after 0 minutes "60": 0.02, # 2% return after 60 minutes "120": 0.01 # 1% return after 120 minutes } 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"}, }, }, } @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 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: return proposed_stake / self.max_dca_multiplier def custom_entry_price( self, pair: str, trade: Optional["Trade"], current_time: datetime, proposed_rate: float, entry_tag: Optional[str], side: str, **kwargs, ) -> float: dataframe, last_updated = self.dp.get_analyzed_dataframe( pair=pair, timeframe=self.timeframe ) entry_price = (dataframe["close"].iat[-1] + dataframe["open"].iat[-1] + proposed_rate) / 3 if proposed_rate < entry_price: entry_price = proposed_rate logger.info( f"{pair} Using Entry Price: {entry_price} | close: {dataframe['close'].iat[-1]} open: {dataframe['open'].iat[-1]} proposed_rate: {proposed_rate}" ) if self.last_entry_price is not None and abs(entry_price - self.last_entry_price) < 0.0005: entry_price *= self.increment.value logger.info( f"{pair} Incremented entry price: {entry_price} based on previous entry price : {self.last_entry_price}." ) self.last_entry_price = entry_price return entry_price def confirm_trade_exit(self, pair: str, trade: Trade, current_price: float) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Loosen ATR-based trailing stop to 2 * ATR atr = ta.ATR(dataframe, timeperiod=14).iloc[-1] trailing_stop_price = current_price - 2 * atr # Looser trailing stop if current_price <= trailing_stop_price: return True return False def confirm_trade_exit(self, pair: str, trade: Trade, current_price: float) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # ATR-based trailing stop atr = ta.ATR(dataframe, timeperiod=14).iloc[-1] trailing_stop_price = current_price - 1.5 * atr # Adjust trailing stop based on ATR if current_price <= trailing_stop_price: return True return False def confirm_trade_exit(self, pair: str, trade: Trade, current_price: float) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Loosen ATR-based trailing stop to 2 * ATR atr = ta.ATR(dataframe, timeperiod=14).iloc[-1] trailing_stop_price = current_price - 2 * atr # Looser trailing stop if current_price <= trailing_stop_price: return True return False def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # 1. Fixed take-profit logic (% profit target) fixed_profit_target = 0.07 # 7% profit target entry_price = trade.open_rate target_price = entry_price * (1 + fixed_profit_target) if rate >= target_price: return True # 3. ATR-based trailing stop logic atr = ta.ATR(dataframe, timeperiod=14).iloc[-1] trailing_stop_price = rate - 1.5 * atr # Exit if price drops by 2 ATR units if rate <= trailing_stop_price: return True # 5. Volume spike exit logic recent_volume = dataframe['volume'].iloc[-1] avg_volume = dataframe['volume'].rolling(10).mean().iloc[-1] if recent_volume > 2 * avg_volume: # Exit if recent volume is 2x the rolling average volume # Add Moving Average (SMA) exit logic ma = ta.SMA(dataframe, timeperiod=50).iloc[-1] # 50-period SMA if rate < ma: # Exit if the price drops below the 50-period SMA return True return False def confirm_trade_exit(self, pair: str, trade: Trade, current_price: float) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Loosen ATR-based trailing stop to 2 * ATR atr = ta.ATR(dataframe, timeperiod=14).iloc[-1] trailing_stop_price = current_price - 2 * atr # Looser trailing stop if current_price <= trailing_stop_price: return True return False def confirm_trade_exit(self, pair: str, trade: Trade, current_price: float) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # ATR-based trailing stop atr = ta.ATR(dataframe, timeperiod=14).iloc[-1] trailing_stop_price = current_price - 1.5 * atr # Adjust trailing stop based on ATR if current_price <= trailing_stop_price: return True return False def confirm_trade_exit(self, pair: str, trade: Trade, current_price: float) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # Loosen ATR-based trailing stop to 2 * ATR atr = ta.ATR(dataframe, timeperiod=14).iloc[-1] trailing_stop_price = current_price - 2 * atr # Looser trailing stop if current_price <= trailing_stop_price: return True return False def confirm_trade_exit( self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs, ) -> bool: if exit_reason == "partial_exit" and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} partial exit is below 0") self.dp.send_msg(f"{trade.pair} partial exit is below 0") return False if exit_reason == "trailing_stop_loss" and trade.calc_profit_ratio(rate) < 0: logger.info(f"{trade.pair} trailing stop price is below 0") self.dp.send_msg(f"{trade.pair} trailing stop price is below 0") return False return True 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 if current_profit > 0.25 and trade.nr_of_successful_exits == 0: return -(trade.stake_amount / 4) if current_profit > 0.40 and trade.nr_of_successful_exits == 1: return -(trade.stake_amount / 3) if current_profit > -0.15 and trade.nr_of_successful_entries == 1: return None if current_profit > -0.3 and trade.nr_of_successful_entries == 2: return None if current_profit > -0.6 and trade.nr_of_successful_entries == 3: return None try: stake_amount = filled_entries[0].cost 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 else: stake_amount = stake_amount return stake_amount except Exception as exception: return None return None def leverage( self, pair: str, current_time: "datetime", current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs, ) -> float: window_size = 50 dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) historical_close_prices = dataframe["close"].tail(window_size) historical_high_prices = dataframe["high"].tail(window_size) historical_low_prices = dataframe["low"].tail(window_size) base_leverage = 10 rsi_values = ta.RSI(historical_close_prices, timeperiod=14) atr_values = ta.ATR( historical_high_prices, historical_low_prices, historical_close_prices, timeperiod=14 ) macd_line, signal_line, _ = ta.MACD( historical_close_prices, fastperiod=12, slowperiod=26, signalperiod=9 ) sma_values = ta.SMA(historical_close_prices, timeperiod=20) current_rsi = rsi_values[-1] if len(rsi_values) > 0 else 50.0 current_atr = atr_values[-1] if len(atr_values) > 0 else 0.0 current_macd = ( macd_line[-1] - signal_line[-1] if len(macd_line) > 0 and len(signal_line) > 0 else 0.0 ) current_sma = sma_values[-1] if len(sma_values) > 0 else 0.0 dynamic_rsi_low = ( np.nanmin(rsi_values) if len(rsi_values) > 0 and not np.isnan(np.nanmin(rsi_values)) else 30.0 ) dynamic_rsi_high = ( np.nanmax(rsi_values) if len(rsi_values) > 0 and not np.isnan(np.nanmax(rsi_values)) else 70.0 ) dynamic_atr_low = ( np.nanmin(atr_values) if len(atr_values) > 0 and not np.isnan(np.nanmin(atr_values)) else 0.002 ) dynamic_atr_high = ( np.nanmax(atr_values) if len(atr_values) > 0 and not np.isnan(np.nanmax(atr_values)) else 0.005 ) long_increase_factor = 1.5 long_decrease_factor = 0.5 short_increase_factor = 1.5 short_decrease_factor = 0.5 volatility_decrease_factor = 0.8 if side == "long": if current_rsi < dynamic_rsi_low: base_leverage *= long_increase_factor elif current_rsi > dynamic_rsi_high: base_leverage *= long_decrease_factor if current_atr > (current_rate * 0.03): base_leverage *= volatility_decrease_factor if current_macd > 0: base_leverage *= long_increase_factor if current_rate < current_sma: base_leverage *= long_decrease_factor elif side == "short": if current_rsi > dynamic_rsi_high: base_leverage *= short_increase_factor elif current_rsi < dynamic_rsi_low: base_leverage *= short_decrease_factor if current_atr > (current_rate * 0.03): base_leverage *= volatility_decrease_factor if current_macd < 0: base_leverage *= short_increase_factor if current_rate > current_sma: base_leverage *= short_decrease_factor adjusted_leverage = max(min(base_leverage, max_leverage), 1.0) return adjusted_leverage 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"] dataframe["%-willr14"] = pta.willr(dataframe["high"], dataframe["low"], dataframe["close"]) 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"] dataframe["%-perc_change"] = (dataframe["high"] / dataframe["open"] - 1) * 100 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() ) 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 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"] 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) 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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["rsi"] = ta.RSI(dataframe) dataframe["DI_values"] = ta.PLUS_DI(dataframe) - ta.MINUS_DI(dataframe) dataframe["DI_cutoff"] = 0 maxima = np.zeros(len(dataframe)) minima = np.zeros(len(dataframe)) maxima[argrelextrema(dataframe["close"].values, np.greater, order=5)] = 1 minima[argrelextrema(dataframe["close"].values, np.less, order=5)] = 1 dataframe["maxima"] = maxima dataframe["minima"] = minima dataframe["&s-extrema"] = 0 min_peaks = argrelextrema(dataframe["close"].values, np.less, order=5)[0] max_peaks = argrelextrema(dataframe["close"].values, np.greater, order=5)[0] dataframe.loc[min_peaks, "&s-extrema"] = -1 dataframe.loc[max_peaks, "&s-extrema"] = 1 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["close"].rolling(window=10).min() dataframe["maxima_sort_threshold"] = dataframe["close"].rolling(window=10).max() 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(4).apply(lambda x: int((x != 1).all()), raw=True).fillna(0) ) dataframe["minima_check"] = ( dataframe["minima"].rolling(4).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: # Condições para entrada long df.loc[ ( (df["DI_catch"] == 1) # Condição DI_catch & (df["maxima_check"] == 1) # Condição maxima_check & (df["&s-extrema"] < 0) # Condição extrema & (df["minima"].shift(1) == 1) # Condição minima anterior & (df["volume"] > 0) # Volume maior que 0 & (df["rsi"] < 30) # RSI abaixo de 30 (condição adicional para limitar entradas) ), ["enter_long", "enter_tag"], ] = (1, "Minima") df.loc[ ( (df["minima_check"] == 0) # Condição minima_check & (df["volume"] > 0) # Volume maior que 0 & (df["rsi"] < 30) # RSI abaixo de 30 (condição adicional para limitar entradas) ), ["enter_long", "enter_tag"], ] = (1, "Minima Full Send") df.loc[ ( (df["DI_catch"] == 1) # Condição DI_catch & (df["minima_check"] == 0) # Condição minima_check & (df["minima_check"].shift(5) == 1) # Condição minima_check anterior & (df["volume"] > 0) # Volume maior que 0 & (df["rsi"] < 30) # RSI abaixo de 30 (condição adicional para limitar entradas) ), ["enter_long", "enter_tag"], ] = (1, "Minima Check") # Condições para entrada short df.loc[ ( (df["DI_catch"] == 1) # Condição DI_catch & (df["minima_check"] == 1) # Condição minima_check & (df["&s-extrema"] > 0) # Condição extrema & (df["maxima"].shift(1) == 1) # Condição maxima anterior & (df["volume"] > 0) # Volume maior que 0 & (df["rsi"] > 70) # RSI acima de 70 (condição adicional para limitar entradas) ), ["enter_short", "enter_tag"], ] = (1, "Maxima") df.loc[ ( (df["maxima_check"] == 0) # Condição maxima_check & (df["volume"] > 0) # Volume maior que 0 & (df["rsi"] > 70) # RSI acima de 70 (condição adicional para limitar entradas) ), ["enter_short", "enter_tag"], ] = (1, "Maxima Full Send") df.loc[ ( (df["DI_catch"] == 1) # Condição DI_catch & (df["maxima_check"] == 0) # Condição maxima_check & (df["maxima_check"].shift(5) == 1) # Condição maxima_check anterior & (df["volume"] > 0) # Volume maior que 0 & (df["rsi"] > 70) # RSI acima de 70 (condição adicional para limitar entradas) ), ["enter_short", "enter_tag"], ] = (1, "Maxima Check") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[((df["maxima_check"] == 0) & (df["volume"] > 0)), ["exit_long", "exit_tag"]] = ( 1, "Maxima Check", ) df.loc[ ( (df["DI_catch"] == 1) & (df["&s-extrema"] > 0) & (df["maxima"].shift(1) == 1) & (df["volume"] > 0) ), ["exit_long", "exit_tag"], ] = (1, "Maxima") df.loc[((df["maxima_check"] == 0) & (df["volume"] > 0)), ["exit_long", "exit_tag"]] = ( 1, "Maxima Full Send", ) df.loc[((df["minima_check"] == 0) & (df["volume"] > 0)), ["exit_short", "exit_tag"]] = ( 1, "Minima Check", ) df.loc[ ( (df["DI_catch"] == 1) & (df["&s-extrema"] < 0) & (df["minima"].shift(1) == 1) & (df["volume"] > 0) ), ["exit_short", "exit_tag"], ] = (1, "Minima") df.loc[((df["minima_check"] == 0) & (df["volume"] > 0)), ["exit_short", "exit_tag"]] = ( 1, "Minima Full Send", ) return df def top_percent_change(dataframe: DataFrame, length: int) -> float: 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): 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 = 3.125 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 = [] 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) 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) 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) 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