# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/dd7354ccfcf24d50f2a62294e623ccf0a033da2a/startegy%20test/5/Hammer/Hammer.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 import CategoricalParameter, DecimalParameter, IntParameter import Config class Github_hamidreza07_freqai_strategy__Hammer__20241005_141958(IStrategy): """ Trades based on detection of Github_hamidreza07_freqai_strategy__Hammer__20241005_141958-like candlestick patterns How to use it? > python3 ./freqtrade/main.py -s Github_hamidreza07_freqai_strategy__Hammer__20241005_141958 """ # Buy hyperspace params: buy_params = { "buy_bb_enabled": True, "buy_bb_gain": 0.08, "buy_ema_enabled": True, "buy_mfi": 47.0, "buy_mfi_enabled": False, "buy_sma_enabled": True, } pattern_strength = 90 buy_mfi = DecimalParameter(0, 50, decimals=0, default=47, space="buy") buy_bb_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.08, space="buy") buy_bb_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_sma_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_ema_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_mfi_enabled = CategoricalParameter([True, False], default=False, space="buy") sell_hold_enabled = CategoricalParameter([True, False], default=True, space="sell") # set the startup candles count to the longest average used (SMA, EMA etc) startup_candle_count = 20 # set common parameters minimal_roi = Config.minimal_roi trailing_stop = Config.trailing_stop trailing_stop_positive = Config.trailing_stop_positive trailing_stop_positive_offset = Config.trailing_stop_positive_offset trailing_only_offset_is_reached = Config.trailing_only_offset_is_reached stoploss = Config.stoploss timeframe = Config.timeframe process_only_new_candles = Config.process_only_new_candles use_exit_signal = Config.use_exit_signal exit_profit_only = Config.exit_profit_only ignore_roi_if_entry_signal = Config.ignore_roi_if_entry_signal order_types = Config.order_types 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) bollinger = qtpylib.weighted_bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_lowerband'] = bollinger['lower'] dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) # 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 # ------------------------------------ # Github_hamidreza07_freqai_strategy__Hammer__20241005_141958: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Github_hamidreza07_freqai_strategy__Hammer__20241005_141958: 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] # Built-in candlestick patterns are not very good, so let's look for hammer-like patterns # We don't care which ones, just that the shadow (up or down) is 2x the body dataframe['height'] = abs(dataframe['close']-dataframe['open']) dataframe['body'] = dataframe['height'].clip(lower=0.01) dataframe['top'] = dataframe[['close','open']].max(axis=1) dataframe['bottom'] = dataframe[['close','open']].min(axis=1) dataframe['upper_shadow'] = dataframe['high']-dataframe['top'] dataframe['lower_shadow'] = dataframe['bottom']-dataframe['low'] dataframe['upper_ratio'] = (dataframe['high']-dataframe['top'])/dataframe['body'] dataframe['upper_ratio'] = dataframe['upper_ratio'].clip(upper=10) dataframe['lower_ratio'] = (dataframe['bottom']-dataframe['low'])/dataframe['body'] dataframe['lower_ratio'] = dataframe['lower_ratio'].clip(upper=10) 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 """ conditions = [] # GUARDS AND TRENDS conditions.append(dataframe['volume'] > 0) if self.buy_sma_enabled.value: conditions.append(dataframe['close'] < dataframe['sma']) if self.buy_mfi_enabled.value: conditions.append(dataframe['mfi'] <= self.buy_mfi.value) if self.buy_ema_enabled.value: conditions.append(dataframe['close'] <= dataframe['ema10']) # green candle #conditions.append(dataframe['close'] > dataframe['open']) # potential gain > goal if self.buy_bb_enabled.value: conditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) # TRIGGERS # the built-in pattern recognition doesn't work well, so check manually # non-zero body conditions.append(dataframe['body'] > 0.01) # shadow ratio > 2 conditions.append( (dataframe['upper_ratio'] > 2) | (dataframe['lower_ratio'] > 2) ) # # Detected one of the patterns # conditions.append( # (dataframe['CDLHAMMER'] >= self.pattern_strength) | # (dataframe['CDLINVERTEDHAMMER'] >= self.pattern_strength) | # (dataframe['CDLHANGINGMAN'] >= self.pattern_strength) | # (dataframe['CDLSHOOTINGSTAR'] >= self.pattern_strength) # ) 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: # GUARDS AND TRENDS conditions.append(dataframe['volume'] > 0) # red candle #conditions.append(dataframe['close'] < dataframe['open']) # conditions.append( # (dataframe['rsi'] > self.rsi_limit) & # (dataframe['rsi'] > 0) # ) # # conditions.append(dataframe['close'] > dataframe['sma']) # # conditions.append(dataframe['mfi'] >= self.mfi_limit) # close is above EMA10 conditions.append(dataframe['close'] >= dataframe['ema10']) # TRIGGERS conditions.append(dataframe['body'] > 0.01) # upper or lower shadow ratio > 2 conditions.append( (dataframe['upper_ratio'] > 2) #(dataframe['upper_ratio'] > 2) | #(dataframe['lower_ratio'] > 2) ) # # Detected one of the patterns # conditions.append( # (dataframe['CDLHAMMER'] >= self.pattern_strength) | # (dataframe['CDLINVERTEDHAMMER'] >= self.pattern_strength) | # (dataframe['CDLHANGINGMAN'] >= self.pattern_strength) | # (dataframe['CDLSHOOTINGSTAR'] >= self.pattern_strength) # ) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe