# source: https://raw.githubusercontent.com/DerSalvador/nateemma-strategies/32f714b24b69c53504cd605ec21e3fd0728b1594/archived/Patterns.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter class Github_DerSalvador_nateemma_strategies__Patterns__20240413_232736(IStrategy): """ Trades based on detection of candlestick patterns How to use it? > python3 ./freqtrade/main.py -s Github_DerSalvador_nateemma_strategies__Patterns__20240413_232736 """ pattern_strength = 90 rsi_limit = 20 mfi_limit = 25 # flags to enable/disable each pattern buy_CDLHAMMER_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLINVERTEDHAMMER_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLDRAGONFLYDOJI_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLPIERCING_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLMORNINGSTAR_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDL3WHITESOLDIERS_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_CDL3LINESTRIKE_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLSPINNINGTOP_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLENGULFING_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDLHARAMI_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDL3OUTSIDE_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_CDL3INSIDE_enabled = CategoricalParameter([True, False], default=False, space="buy") # Sell hyperspace params: sell_params = { "sell_CDL3INSIDE_enabled": True, "sell_CDL3LINESTRIKE_enabled": True, "sell_CDL3OUTSIDE_enabled": True, "sell_CDLDARKCLOUDCOVER_enabled": False, "sell_CDLENGULFING_enabled": False, "sell_CDLEVENINGDOJISTAR_enabled": True, "sell_CDLEVENINGSTAR_enabled": True, "sell_CDLGRAVESTONEDOJI_enabled": True, "sell_CDLHANGINGMAN_enabled": True, "sell_CDLHARAMI_enabled": True, "sell_CDLSHOOTINGSTAR_enabled": True, "sell_CDLSPINNINGTOP_enabled": True, "sell_hold_enabled": False, } sell_CDL3LINESTRIKE_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLSPINNINGTOP_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLENGULFING_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLHARAMI_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDL3OUTSIDE_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDL3INSIDE_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLHANGINGMAN_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLSHOOTINGSTAR_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLGRAVESTONEDOJI_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLDARKCLOUDCOVER_enabled = CategoricalParameter([True, False], default=False, space="sell") sell_CDLEVENINGDOJISTAR_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_CDLEVENINGSTAR_enabled = CategoricalParameter([True, False], default=True, space="sell") sell_hold_enabled = CategoricalParameter([True, False], default=True, space="sell") if sell_hold_enabled.value: # ROI table: minimal_roi = { "0": 0.278, "39": 0.087, "124": 0.038, "135": 0 } # Trailing stop: trailing_stop = True trailing_stop_positive = 0.172 trailing_stop_positive_offset = 0.212 trailing_only_offset_is_reached = False # Stoploss: stoploss = -0.333 else: # ROI table: minimal_roi = { "0": 0.296, "26": 0.104, "36": 0.037, "65": 0 } # Stoploss: stoploss = -0.284 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.183 trailing_stop_positive_offset = 0.274 trailing_only_offset_is_reached = True # Optimal timeframe for the strategy timeframe = '5m' # run "populate_indicators" only for new candle process_only_new_candles = False # Experimental settings (configuration will overide these if set) use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = False # Optional order type mapping order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ # MFI dataframe['mfi'] = ta.MFI(dataframe) # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] # EMA - Exponential Moving Average dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ # GUARDS AND TRENDS gconditions = [] gconditions.append( (dataframe['rsi'] < self.rsi_limit) & (dataframe['rsi'] > 0) ) gconditions.append(dataframe['close'] < dataframe['sma']) gconditions.append(dataframe['mfi'] <= self.mfi_limit) # reset array for pattern conditions conditions = [] # Bullish candlestick patterns # ordered by strength if self.buy_CDL3WHITESOLDIERS_enabled.value: conditions.append(dataframe['CDL3WHITESOLDIERS'] >= self.pattern_strength) if self.buy_CDLMORNINGSTAR_enabled.value: conditions.append(dataframe['CDLMORNINGSTAR'] >= self.pattern_strength) if self.buy_CDL3LINESTRIKE_enabled.value: conditions.append(dataframe['CDL3LINESTRIKE'] >= self.pattern_strength) if self.buy_CDL3OUTSIDE_enabled.value: conditions.append(dataframe['CDL3OUTSIDE'] >= self.pattern_strength) if self.buy_CDLHAMMER_enabled.value: conditions.append(dataframe['CDLHAMMER'] >= self.pattern_strength) if self.buy_CDLINVERTEDHAMMER_enabled.value: conditions.append(dataframe['CDLINVERTEDHAMMER'] >= self.pattern_strength) if self.buy_CDLDRAGONFLYDOJI_enabled.value: conditions.append(dataframe['CDLDRAGONFLYDOJI'] >= self.pattern_strength) if self.buy_CDLPIERCING_enabled.value: conditions.append(dataframe['CDLPIERCING'] >= self.pattern_strength) if self.buy_CDLSPINNINGTOP_enabled.value: conditions.append(dataframe['CDLSPINNINGTOP'] >= self.pattern_strength) if self.buy_CDLENGULFING_enabled.value: conditions.append(dataframe['CDLENGULFING'] >= self.pattern_strength) if self.buy_CDLHARAMI_enabled.value: conditions.append(dataframe['CDLHARAMI'] >= self.pattern_strength) if self.buy_CDL3INSIDE_enabled.value: conditions.append(dataframe['CDL3INSIDE'] >= self.pattern_strength) # build the dataframe using the guard and pattern results # calculate intermediate result from guard condittions if gconditions: gr = reduce(lambda x, y: x & y, gconditions) pr = False if conditions: pr = reduce(lambda x, y: x | y, conditions) # dataframe.loc[(gr | pr), 'buy'] = 1 dataframe.loc[(gr & pr), 'buy'] = 1 else: if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'buy'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ conditions = [] # if hold, then don't set a sell signal if self.sell_hold_enabled.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: # Pattern Recognition - Bearish candlestick patterns if self.sell_CDLEVENINGSTAR_enabled.value: conditions.append(dataframe['CDLEVENINGSTAR'] >= self.pattern_strength) if self.sell_CDL3LINESTRIKE_enabled.value: conditions.append(dataframe['CDL3LINESTRIKE'] >= self.pattern_strength) if self.sell_CDLEVENINGDOJISTAR_enabled.value: conditions.append(dataframe['CDLEVENINGDOJISTAR'] >= self.pattern_strength) if self.sell_CDL3LINESTRIKE_enabled.value: conditions.append(dataframe['CDL3LINESTRIKE'] <= -self.pattern_strength) if self.sell_CDL3OUTSIDE_enabled.value: conditions.append(dataframe['CDL3OUTSIDE'] <= -self.pattern_strength) if self.sell_CDLHANGINGMAN_enabled.value: conditions.append(dataframe['CDLHANGINGMAN'] >= self.pattern_strength) if self.sell_CDLSHOOTINGSTAR_enabled.value: conditions.append(dataframe['CDLSHOOTINGSTAR'] >= self.pattern_strength) if self.sell_CDLGRAVESTONEDOJI_enabled.value: conditions.append(dataframe['CDLGRAVESTONEDOJI'] >= self.pattern_strength) if self.sell_CDLDARKCLOUDCOVER_enabled.value: conditions.append(dataframe['CDLDARKCLOUDCOVER'] >= self.pattern_strength) if self.sell_CDLSPINNINGTOP_enabled.value: conditions.append(dataframe['CDLSPINNINGTOP'] <= -self.pattern_strength) if self.sell_CDLENGULFING_enabled.value: conditions.append(dataframe['CDLENGULFING'] <= -self.pattern_strength) if self.sell_CDLHARAMI_enabled.value: conditions.append(dataframe['CDLHARAMI'] <= -self.pattern_strength) if self.sell_CDL3INSIDE_enabled.value: conditions.append(dataframe['CDL3INSIDE'] <= -self.pattern_strength) dataframe.loc[reduce(lambda x, y: x | y, conditions), 'sell'] = 1 return dataframe