# source: https://raw.githubusercontent.com/xaemiphor/ft/5c5cea910160cfd228b0986f4a789765b3c89936/strategies-reference/xamie01_project-samuel/madrid_ribbon.py # --- Do not remove these libs --- 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_xaemiphor_ft__madrid_ribbon__20250124_203027(IStrategy): """ Madrid Ribbon 001 author@: Hessebo This strategy aims to follow emas. How to use it? """ INTERFACE_VERSION: int = 3 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = { "60": 0.01, "30": 0.03, "20": 0.04, "0": 0.05 } can_short = False # Optimal stoploss designed for the strategy # This attribute will be overridden if the config file contains "stoploss" stoploss = -0.10 # Optimal timeframe for the strategy timeframe = '15m' # trailing stoploss 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: # Calculate as `-desired_stop_from_open + current_profit` to get the distance between current_profit and initial price 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 # run "populate_indicators" only for new candle process_only_new_candles = False # Experimental settings (configuration will overide these if set) use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Optional order type mapping 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: # Calculate as `-desired_stop_from_open + current_profit` to get the distance between current_profit and initial price 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 # https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html 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"] # print(metadata) # print(dataframe.tail(20)) 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[ ( # qtpylib.crossed_above(dataframe['ema5'], dataframe['ema20'].shift(1)) & (dataframe["rsi"] > 51) & # (dataframe['ema5'] > dataframe['ema_madrid']) & (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']) # (dataframe['ha_open'] < dataframe['ha_close'].shift(12)) # green bar ), 'enter_long'] = 1 # Enter Short dataframe.loc[ ( # qtpylib.crossed_above(dataframe['ema10'], dataframe['ema20']) & (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']) # (dataframe['ha_open'] < dataframe['ha_close'].shift(12)) # red bar ), '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']) # (dataframe['ha_close'] < dataframe['ema20']) & # (dataframe['ha_open'] > dataframe['ha_close']) # red bar ), 'exit_long'] = 1 dataframe.loc[ ( qtpylib.crossed_below(dataframe['ema10'], dataframe['ema100']) # (dataframe['ha_close'] > dataframe['ema20']) & # (dataframe['ha_open'] < dataframe['ha_close']) # Green bar ), 'exit_short'] = 1 return dataframe