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madrid_ribbon

🏆 League #106 / 1928
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DerSalvador/freqtrade-helm-chart/chart/deployed_strategies/binance-michael-k8s-namespace/madrid_ribbon.py · first seen 2026-07-16 · repo updated 2026-04-16

Basics mode: futures timeframe: 15m interface version: 3
Settings stoploss: -0.1 has minimal roi trailing custom stoploss process only new candles: false
Indicators Bollinger_Bands EMA MACD RSI Stochastic talib technical
Concepts trailing
Methods custom_stoploss
6 related strategies (⧉ identical code, ≈ similar name)

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# --- 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 madrid_ribbon(IStrategy):
    INTERFACE_VERSION = 3
    '\n    Madrid Ribbon 001\n    author@: Hessebo\n    This strategy aims to follow emas.\n    How to use it?\n\n    '
    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 = True
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.1
    # 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 entry signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        #               qtpylib.crossed_above(dataframe['ema5'], dataframe['ema20'].shift(1)) &
        #                (dataframe['ema5'] > dataframe['ema_madrid']) &
        #               (dataframe['ha_open'] < dataframe['ha_close'].shift(12))  # green bar
        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
        # Enter Short
        #               qtpylib.crossed_above(dataframe['ema10'], dataframe['ema20']) &
        #                (dataframe['ha_open'] < dataframe['ha_close'].shift(12))  # red bar
        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 exit signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        #                (dataframe['ha_close'] < dataframe['ema20']) &
        #                (dataframe['ha_open'] > dataframe['ha_close'])  # red bar
        dataframe.loc[qtpylib.crossed_above(dataframe['ema10'], dataframe['ema100']), 'exit_long'] = 1
        #                (dataframe['ha_close'] > dataframe['ema20']) &
        #                (dataframe['ha_open'] < dataframe['ha_close'])  # Green bar
        dataframe.loc[qtpylib.crossed_below(dataframe['ema10'], dataframe['ema100']), 'exit_short'] = 1
        return dataframe