# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/MACD_23.py Skip to content Why GitHub? Team Enterprise Explore Marketplace Pricing Search Sign in Sign up p-zombie / freqtrade Public 3812 Code Issues 3 Pull requests 1 Actions Projects Wiki Security Insights freqtrade/user_data/strategies/CombinedBinHClucAndMADV6.py / @joaorafaelm joaorafaelm add strats Latest commit e9533ab on 14 Jun History 1 contributor 248 lines (210 sloc) 13.2 KB import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair def SSLChannels(dataframe, length = 7): df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] class Github_remiotore_ccxt_freqtrade__MACD_23__20260111_210550(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.029, # I feel lucky! "10": 0.021, "40": 0.005, } stoploss = -0.99 # effectively disabled. timeframe = '5m' inf_1h = '1h' use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_signal = False trailing_stop = False trailing_only_offset_is_reached = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 use_custom_stoploss = True process_only_new_candles = False startup_candle_count: int = 200 order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if (current_profit < 0) & (current_time - timedelta(minutes=240) > trade.open_date_utc): return 0.01 return 0.99 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14) ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h['ssl_down'] = ssl_down_1h informative_1h['ssl_up'] = ssl_up_1h return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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['ema_slow'] = ta.EMA(dataframe, timeperiod=50) dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean() dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # strategy ClucMay72018 (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.99 * dataframe['bb_lowerband']) & # Guard is on, candle should dig not so hard (0,99) (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & # Try to exclude pumping (dataframe['volume'] > 0) ) | ( # strategy ClucMay72018 (dataframe['close'] < dataframe['ema_slow']) & (dataframe['close'] < 0.975 * dataframe['bb_lowerband']) & # Guard is off, candle should dig hard (0,975) (dataframe['volume'] < (dataframe['volume'].shift() * 4)) & # Don't buy if someone drop the market. (dataframe['rsi_1h'] < 15) & # Buy only at dip (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & # Try to exclude pumping (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( # strategy MACD Low buy (dataframe['close'] > dataframe['ema_200']) & (dataframe['close'] > dataframe['ema_200_1h']) & (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.02)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * 4)) & # Don't buy if someone drop the market. (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(30) * 0.4) & # Try to exclude pumping (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( # strategy MACD Low buy (dataframe['ema_26'] > dataframe['ema_12']) & ((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * 0.03)) & ((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) & (dataframe['volume'] < (dataframe['volume'].shift() * 4)) & # Don't buy if someone drop the market. (dataframe['close'] < (dataframe['bb_lowerband'])) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ) | ( (dataframe['close'] < dataframe['sma_5']) & (dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) & (dataframe['ema_slow'] > dataframe['ema_200']) & (dataframe['ema_50_1h'] > dataframe['ema_200_1h']) & (dataframe['rsi'] < dataframe['rsi_1h'] - 43.276) & (dataframe['volume'] > 0) ), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] > dataframe['bb_middleband'] * 1.01) & # Don't be gready, sell fast (dataframe['volume'] > 0) # Make sure Volume is not 0 ) , 'sell' ] = 1 return dataframe © 2021 GitHub, Inc. Terms Privacy Security Status Docs Contact GitHub Pricing API Training Blog About Loading complete