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ZaratustraV19

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remiotore/ccxt-freqtrade/strategies/ZaratustraV19.py · ★3 · ⑂2 · first seen 2026-07-16 · repo updated 2026-01-11

Basics mode: futures timeframe: 5m interface version: 3
Settings stoploss: -0.2 has minimal roi trailing protections
Indicators ADX ATR Bollinger_Bands talib technical
Concepts ml risk_management trailing
Methods leverage plot_config protections
Other ml scikit-learn
15 related strategies (⧉ identical code, ≈ similar name)

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
from freqtrade.strategy import IStrategy
from datetime import datetime
from pandas import DataFrame
from typing import Dict, List
import talib.abstract as ta
from technical import qtpylib
from sklearn.preprocessing import MinMaxScaler



class ZaratustraV19(IStrategy):
    # Parameters
    INTERFACE_VERSION = 3
    timeframe = '5m'
    can_short = True
    use_exit_signal = False
    exit_profit_only = True
    
    # ROI table:
    
    minimal_roi = {
        "0": 0.5,
        "60": 0.45,
        "120": 0.4,
        "240": 0.3,
        "360": 0.25,
        "720": 0.2,
        "1440": 0.15,
        "2880": 0.1,
        "3600": 0.05,
        "7200": 0.02,
    }

    # Stoploss:
    stoploss = -0.20

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.013
    trailing_stop_positive_offset = 0.050
    trailing_only_offset_is_reached = True

    # Max Open Trades:
    max_open_trades = 10

    @property
    def protections(self):
        return [
            {
                "method": "CooldownPeriod",
                "stop_duration_candles": 6
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 6,
                "trade_limit": 2,
                "stop_duration_candles": 60,
                "required_profit": 0.02
            },
            {
                "method": "LowProfitPairs",
                "lookback_period_candles": 24,
                "trade_limit": 4,
                "stop_duration_candles": 2,
                "required_profit": 0.01
            }
        ]

    @property
    def plot_config(self):
        plot_config = {}
        plot_config['main_plot'] = {
            'tsf' : { 'color' : 'black' },
            'max' : { 'color' : 'green' },
            'min' : { 'color' : 'red' },

        }
        plot_config['subplots'] = {
            'DI': {
                'dx' : { 'color': 'yellow' },
                'adx': { 'color': 'orange' },
                'pdi': { 'color': 'green' },
                'mdi': { 'color': 'red' },
                'atr': { 'color': 'purple' },
            },
            'Regresion' : {
                'slope' : {},
            },
        }

        return plot_config

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['dx']    = ta.DX(dataframe)
        dataframe['adx']   = ta.ADX(dataframe)
        dataframe['pdi']   = ta.PLUS_DI(dataframe)
        dataframe['mdi']   = ta.MINUS_DI(dataframe)
        dataframe['max']   = ta.MAX(dataframe)
        dataframe['min']   = ta.MIN(dataframe)
        dataframe['tsf']   = ta.TSF(dataframe)
        dataframe['atr']   = MinMaxScaler(feature_range=(0, 100)).fit_transform(ta.ATR(dataframe).values.reshape(-1, 1))
        dataframe['slope'] = ta.LINEARREG_SLOPE(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']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ########################
        # Min & Max Conditions #
        ########################

        dataframe.loc[
            (
                qtpylib.crossed_above(dataframe['close'], dataframe['max'].shift(1)) &
                (dataframe['slope'] > dataframe['slope'].shift(1)) &
                (dataframe['slope'] > 0)
            ),
            ['enter_long', 'enter_tag']
        ] = (1, 'Long Max enter')

        dataframe.loc[
            (
                qtpylib.crossed_below(dataframe['close'], dataframe['min'].shift(1)) &
                (dataframe['slope'] < dataframe['slope'].shift(1)) &
                (dataframe['slope'] < 0)
            ),
            ['enter_short', 'enter_tag']
        ] = (1, 'Short Min enter')

        ##############################
        # Bollinger Bands Conditions #
        ##############################

        dataframe.loc[
            (
                qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband']) &
                (dataframe['slope'] > dataframe['slope'].shift(1)) &
                (dataframe['slope'] > 0)
            ),
            ['enter_long', 'enter_tag']
        ] = (1, 'Long Bollinger enter')

        dataframe.loc[
            (
                qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband']) &
                (dataframe['slope'] < dataframe['slope'].shift(1)) &
                (dataframe['slope'] < 0)
            ),
            ['enter_short', 'enter_tag']
        ] = (1, 'Short Bollinger enter')

        ##################################
        # TimeSeries Forecast Conditions #
        ##################################

        dataframe.loc[
            (
                qtpylib.crossed_above(dataframe['close'], dataframe['tsf']) &
                (dataframe['slope'] > dataframe['slope'].shift(1)) &
                (dataframe['slope'] > 0)
            ),
            ['enter_long', 'enter_tag']
        ] = (1, 'Long TimeSeries Forecast enter')

        dataframe.loc[
            (
                qtpylib.crossed_below(dataframe['close'], dataframe['tsf']) &
                (dataframe['slope'] < dataframe['slope'].shift(1)) &
                (dataframe['slope'] < 0)
            ),
            ['enter_short', 'enter_tag']
        ] = (1, 'Short TimeSeries Forecast enter')

        ####################################
        # Directional Indicator Conditions #
        ####################################

        dataframe.loc[
            (
                (dataframe['dx']  > dataframe['mdi']) &
                (dataframe['adx'] > dataframe['mdi']) &
                (dataframe['pdi'] > dataframe['mdi']) &
                (dataframe['atr'] > dataframe['mdi']) &
                (dataframe['slope'] > dataframe['slope'].shift(1)) &
                (dataframe['slope'] > 0)
            ),
            ['enter_long', 'enter_tag']
        ] = (1, 'Long DI enter')

        dataframe.loc[
            (
                (dataframe['dx']  > dataframe['pdi']) &
                (dataframe['adx'] > dataframe['pdi']) &
                (dataframe['mdi'] > dataframe['pdi']) &
                (dataframe['atr'] > dataframe['pdi']) &
                (dataframe['slope'] < dataframe['slope'].shift(1)) &
                (dataframe['slope'] < 0)
            ),
            ['enter_short', 'enter_tag']
        ] = (1, 'Short DI enter')
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe
    
    def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs,) -> float:
        return 10