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, stoploss_from_absolute, stoploss_from_open from freqtrade.persistence import Trade from datetime import datetime, timezone class roger22(IStrategy): INTERFACE_VERSION = 3 can_short = False minimal_roi = { "1440": 0.005, # After 24 hours, require at least a 0.5% ROI to sell "720": 0.01, # After 12 hours, require at least a 1% ROI to sell "480": 0.02, # After 8 hours, require at least 2% ROI to sell "240": 0.03, # After 4 hours, require at least a 3% ROI to sell "120": 0.04, # After 2 hours, require at least a 4% ROI to sell "60": 0.05, # After 1 hour, require at least a 5% ROI to sell "30": 0.06, # After 30 minutes, require at least a 6% ROI to sell "15": 0.07, # After 15 minutes, require at least a 7% ROI to sell "0": 0.10 # After 0 minutes, require at least a 10% ROI to sell } stoploss = -0.20 use_custom_stoploss = True trailing_stop = False timeframe = '15m' process_only_new_candles = True max_profits = {} def on_trade_update(self, trade: Trade, **kwargs): if trade.pair not in self.max_profits: self.max_profits[trade.pair] = 0 self.max_profits[trade.pair] = max(self.max_profits[trade.pair], trade.current_profit_ratio) 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) 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['cdlengulfing'] = ta.CDLENGULFING(dataframe) dataframe['ATR'] = ta.ATR(dataframe, timeperiod=150) 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['sma20'] > dataframe['sma50']) & # Short term trend indicator (dataframe['sma50'] > dataframe['sma200']) & # Long term trend indicator (dataframe['bb_percent'] < 0.2) & # 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.loc[bullish_conditions | candlestick_patterns, '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['sma50'] < dataframe['sma200']) & (dataframe['bb_percent'] > 0.9) & (dataframe['bb_width'] > 0.03) ) 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 ) macd_condition = ( (dataframe['macd'] < dataframe['macdsignal']) # MACD line below signal line ) sell_signal = bearish_conditions | candlestick_patterns & macd_condition dataframe.loc[sell_signal, 'sell'] = 1 return dataframe def calc_stop_loss_pct(self, current_rate: float, atr_multiplier: float) -> float: return -atr_multiplier * current_rate 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 > 0.05: # If drawdown from peak is greater than 5% atr_stoploss *= 0.8 # Tighten the stop loss by 20% elif peak_profit_drawdown > 0.1: # If drawdown from peak is greater than 10% atr_stoploss *= 0.6 # Tighten the stop loss by 40% if current_profit > 0.05: # If current profit is above 5% atr_stoploss *= 0.8 # Tighten the stop loss by 20% elif current_profit > 0.1: # If current profit is above 10% atr_stoploss *= 0.6 # Tighten the stop loss by 40% adjusted_stoploss = max(atr_stoploss, self.stoploss) return adjusted_stoploss