# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/roger27.py import numpy as np import pandas as pd import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import IStrategy from freqtrade.persistence import Trade class Github_remiotore_freqtrade__roger27__20260111_210550(IStrategy): INTERFACE_VERSION = 3 can_short = False minimal_roi = { "0": 0.50 } stoploss = -0.25 use_custom_stoploss = True use_custom_take_profit = True trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.05 trailing_only_offset_is_reached = False timeframe = '15m' process_only_new_candles = True max_profits = {} exit_profit_only = False def on_trade_update(self, trade: Trade, **kwargs): if trade.pair not in self.max_profits: self.max_profits[trade.pair] = 0 if trade.current_profit_ratio > 0: self.max_profits[trade.pair] = max(self.max_profits[trade.pair], trade.current_profit_ratio) else: self.max_profits[trade.pair] = 0 def on_trade_close(self, trade: Trade, **kwargs): self.max_profits.pop(trade.pair, None) def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ Populates new indicators for given strategy Args: dataframe (pd.DataFrame): dataframe for the given pair metadata (dict): metadata for the given pair Returns: pd.DataFrame: dataframe with the defined indicators """ dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200) dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma20'] = ta.SMA(dataframe, timeperiod=20) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) 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['bb_percent'] = \ (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband']) dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'] dataframe['cdl3inside'] = ta.CDL3INSIDE(dataframe) dataframe['cdl3outside'] = ta.CDL3OUTSIDE(dataframe) dataframe['cdl3starsinsouth'] = ta.CDL3STARSINSOUTH(dataframe) dataframe['cdlhammer'] = ta.CDLHAMMER(dataframe) dataframe['cdlinvertedhammer'] = ta.CDLINVERTEDHAMMER(dataframe) dataframe['cdl3blackcrows'] = ta.CDL3BLACKCROWS(dataframe) dataframe['cdl3whitesoldiers'] = ta.CDL3WHITESOLDIERS(dataframe) dataframe['cdl3linestrike'] = ta.CDL3LINESTRIKE(dataframe) dataframe['cdlgravestonedoji'] = ta.CDLGRAVESTONEDOJI(dataframe) dataframe['cdlshootingstar'] = ta.CDLSHOOTINGSTAR(dataframe) dataframe['cdl3inside'] = ta.CDL3INSIDE(dataframe) dataframe['cdl3outside'] = ta.CDL3OUTSIDE(dataframe) dataframe['cdl3starsinsouth'] = ta.CDL3STARSINSOUTH(dataframe) dataframe['cdlhammer'] = ta.CDLHAMMER(dataframe) dataframe['cdlinvertedhammer'] = ta.CDLINVERTEDHAMMER(dataframe) dataframe['cdlengulfing'] = ta.CDLENGULFING(dataframe) dataframe['ATR'] = ta.ATR(dataframe, timeperiod=4) dataframe['ATR_stoploss'] = dataframe['ATR'] * 6.5 macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] return dataframe def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ Populate rules for the "buy" signal Args: dataframe (pd.DataFrame): dataframe for the given pair metadata (dict): metadata for the given pair Returns: pd.DataFrame: dataframe with the defined indicators """ bullish_conditions = ( (dataframe['ema50'] > dataframe['ema200']) & # Long term trend indicator (dataframe['bb_percent'] < 0.15) & # Price is near the lower Bollinger Band (dataframe['bb_width'] < 0.05) # Bollinger Bands are narrow ) candlestick_patterns = ( (dataframe['cdl3inside'] == 100) | # 3 Inside Up (dataframe['cdl3outside'] == 100) | # 3 Outside Up (dataframe['cdl3starsinsouth'] == 100) | # 3 Stars In The South (dataframe['cdlhammer'] == 100) | # Hammer (dataframe['cdlinvertedhammer'] == 100) | # Inverted Hammer (dataframe['cdl3whitesoldiers'] == 100) | # 3 White Soldiers (dataframe['cdl3linestrike'] == 100) | # 3 Line Strike (dataframe['cdlgravestonedoji'] == 100) | # Gravestone Doji (dataframe['cdlshootingstar'] == 100) # Shooting Star ) macd_condition = ( (dataframe['macd'] > dataframe['macdsignal']) # MACD line above signal line ) dataframe.loc[bullish_conditions | (candlestick_patterns & macd_condition), 'buy'] = 1 return dataframe def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ Populate rules for the "sell" signal Args: dataframe (pd.DataFrame): dataframe for the given pair metadata (dict): metadata for the given pair Returns: pd.DataFrame: dataframe with the defined indicators """ bearish_conditions = ( (dataframe['ema20'] < dataframe['ema50']) & (dataframe['bb_percent'] > 0.85) & (dataframe['bb_width'] > 0.05) ) candlestick_patterns = ( (dataframe['cdl3blackcrows'] == -100) | # 3 Black Crows (dataframe['cdl3whitesoldiers'] == 100) | # 3 White Soldiers (dataframe['cdl3linestrike'] == -100) | # 3 Line Strike (dataframe['cdlgravestonedoji'] == -100) | # Gravestone Doji (dataframe['cdlshootingstar'] == -100) | # Shooting Star (dataframe['cdl3inside'] == -100) | # 3 Inside Down (dataframe['cdl3outside'] == -100) | # 3 Outside Down (dataframe['cdl3starsinsouth'] == -100) | # 3 Stars In The South (dataframe['cdlhammer'] == -100) | # Hammer (dataframe['cdlinvertedhammer'] == -100) # Inverted Hammer ) macd_condition = ( (dataframe['macd'] < dataframe['macdsignal']) # MACD line below signal line ) dataframe.loc[bearish_conditions & candlestick_patterns & macd_condition, 'sell'] = 1 return dataframe def calc_stop_loss_pct(self, current_rate: float, atr_multiplier: float) -> float: return -atr_multiplier * current_rate def calculate_dynamic_take_profit(self, dataframe: pd.DataFrame, trade: Trade) -> float: entry_atr = dataframe.loc[dataframe['date'] == trade.open_date_utc, 'ATR'].iloc[0] atr_multiplier = 6.5 take_profit_level = trade.open_rate + (entry_atr * atr_multiplier) return take_profit_level def custom_take_profit(self, pair: str, trade: 'Trade', current_profit: float, current_rate: float, **kwargs) -> float: dynamic_take_profit = self.calculate_dynamic_take_profit(self.dataframe, trade) adjusted_take_profit = max(dynamic_take_profit, self.minimal_roi[0]) return adjusted_take_profit def custom_stoploss(self, pair: str, trade: 'Trade', current_profit: float, current_rate: float, **kwargs) -> float: max_profit = self.max_profits.get(pair, current_profit) peak_profit_drawdown = max_profit - current_profit atr_stoploss = self.calc_stop_loss_pct(current_rate, 6.5) if peak_profit_drawdown > 0.02: # If drawdown from peak is greater than 5% atr_stoploss *= 0.7 # Tighten the stop loss by 30% elif peak_profit_drawdown > 0.1: # If drawdown from peak is greater than 10% atr_stoploss *= 0.5 # Tighten the stop loss by 50% if current_profit > 0.10: # If current profit is above 5% atr_stoploss *= 0.7 # Tighten the stop loss by 30% elif current_profit > 0.05: # If current profit is above 10% atr_stoploss *= 0.5 # Tighten the stop loss by 50% adjusted_stoploss = max(atr_stoploss, self.stoploss) return adjusted_stoploss