# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/madrid_ribbon_4h.py from technical.util import resample_to_interval, resampled_merge from freqtrade.strategy import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame from datetime import datetime import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_remiotore_ccxt_freqtrade__madrid_ribbon_4h__20260111_210550(IStrategy): """ Madrid Ribbon 001 author@: Hessebo This strategy aims to follow emas. How to use it? """ INTERFACE_VERSION: int = 3 minimal_roi = { "60": 0.01, "30": 0.03, "20": 0.04, "0": 0.05 } can_short = True stoploss = -0.10 timeframe = '4h' trailing_stop = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 use_custom_stoploss = True def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if (current_profit > 0.3): return 0.01 elif (current_profit > 0.1): return 0.015 elif (current_profit > 0.06): return 0.01 elif (current_profit > 0.02): return 0.05 elif (current_profit > 0.01): return 0.003 return 0.15 process_only_new_candles = False use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } use_custom_stoploss = False def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if (current_profit > 0.3): return 0.01 elif (current_profit > 0.1): return 0.015 elif (current_profit > 0.06): return 0.01 elif (current_profit > 0.02): return 0.05 elif (current_profit > 0.01): return 0.003 return 0.15 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. """ 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'] stoch = ta.STOCH( dataframe, fastk_period=14, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0, ) dataframe["slowd"] = stoch["slowd"] dataframe["slowk"] = stoch["slowk"] macd = ta.MACD( dataframe, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0, ) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] dataframe['ema_madrid'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema15'] = ta.EMA(dataframe, timeperiod=15) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema25'] = ta.EMA(dataframe, timeperiod=25) dataframe['ema30'] = ta.EMA(dataframe, timeperiod=30) dataframe['ema35'] = ta.EMA(dataframe, timeperiod=35) dataframe['ema40'] = ta.EMA(dataframe, timeperiod=40) dataframe['ema45'] = ta.EMA(dataframe, timeperiod=45) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema55'] = ta.EMA(dataframe, timeperiod=55) dataframe['ema60'] = ta.EMA(dataframe, timeperiod=60) dataframe['ema65'] = ta.EMA(dataframe, timeperiod=65) dataframe['ema70'] = ta.EMA(dataframe, timeperiod=70) dataframe['ema75'] = ta.EMA(dataframe, timeperiod=75) dataframe['ema80'] = ta.EMA(dataframe, timeperiod=80) dataframe['ema85'] = ta.EMA(dataframe, timeperiod=85) dataframe['ema90'] = ta.EMA(dataframe, timeperiod=90) dataframe['ema95'] = ta.EMA(dataframe, timeperiod=95) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) 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 """ dataframe.loc[ ( (dataframe["rsi"] > 51) & (dataframe['ema5'] > dataframe['ema10']) & (dataframe['ema10'] > dataframe['ema15']) & (dataframe['ema15'] > dataframe['ema20']) & (dataframe['ema20'] > dataframe['ema25']) & (dataframe['ema25'] > dataframe['ema30']) & (dataframe['ema30'] > dataframe['ema35']) & (dataframe['ema35'] > dataframe['ema40']) & (dataframe['ema40'] > dataframe['ema45']) & (dataframe['ema45'] > dataframe['ema50']) & (dataframe['ema50'] > dataframe['ema55']) & (dataframe['ema55'] > dataframe['ema60']) & (dataframe['ema60'] > dataframe['ema65']) & (dataframe['ema65'] > dataframe['ema70']) & (dataframe['ema70'] > dataframe['ema75']) & (dataframe['ema75'] > dataframe['ema80']) & (dataframe['ema80'] > dataframe['ema85']) & (dataframe['ema85'] > dataframe['ema90']) & (dataframe['ema90'] > dataframe['ema95']) & (dataframe['ema95'] > dataframe['ema100']) & (dataframe['ema100'] > dataframe['ema200']) & (dataframe['close'] > dataframe['bb_middleband']) ), 'enter_long'] = 1 dataframe.loc[ ( (dataframe["rsi"] < 51) & (dataframe['ema5'] < dataframe['ema10']) & (dataframe['ema10'] < dataframe['ema15']) & (dataframe['ema15'] < dataframe['ema20']) & (dataframe['ema20'] < dataframe['ema25']) & (dataframe['ema25'] < dataframe['ema30']) & (dataframe['ema30'] < dataframe['ema35']) & (dataframe['ema35'] < dataframe['ema40']) & (dataframe['ema40'] < dataframe['ema45']) & (dataframe['ema45'] < dataframe['ema50']) & (dataframe['ema50'] < dataframe['ema55']) & (dataframe['ema55'] < dataframe['ema60']) & (dataframe['ema60'] < dataframe['ema65']) & (dataframe['ema65'] < dataframe['ema70']) & (dataframe['ema70'] < dataframe['ema75']) & (dataframe['ema75'] < dataframe['ema80']) & (dataframe['ema80'] < dataframe['ema85']) & (dataframe['ema85'] < dataframe['ema90']) & (dataframe['ema90'] < dataframe['ema95']) & (dataframe['ema95'] < dataframe['ema100']) & (dataframe['ema100'] < dataframe['ema200']) & (dataframe['close'] < dataframe['bb_middleband']) ), 'enter_short'] = 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 """ dataframe.loc[ ( qtpylib.crossed_above(dataframe['ema10'], dataframe['ema100']) ), 'exit_long'] = 1 dataframe.loc[ ( qtpylib.crossed_below(dataframe['ema10'], dataframe['ema100']) ), 'exit_short'] = 1 return dataframe