✨ AI Search 🔒
💬 Forum

ZaratustraV5

🏆 League #62 / 1922
♡

remiotore/ccxt-freqtrade/strategies/ZaratustraV5.py · ★3 · ⑂2 · first seen 2026-07-16 · repo updated 2026-01-11 · ⬇ 2 downloads

Basics mode: futures timeframe: 5m interface version: 3 5m 15m 30m
Settings stoploss: -0.296 has minimal roi trailing
Indicators Bollinger_Bands RSI pandas_ta talib technical
Concepts trailing
Methods leverage plot_config
15 related strategies (⧉ identical code, ≈ similar name)

Each tile is a different kind of check — from an instant code lint to full sandboxed backtests and forward tests on recent data. Not sure what a check actually proves? See the FAQ →

Source

Download Raw 
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
# By Remiotore (Jorge F. F.)
# Espero poder darte una buena vida algún día...

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple
from freqtrade.strategy import (
    IStrategy,
    #Trade,
    #Order,
    #PairLocks,
    informative,
    #BooleanParameter,
    #CategoricalParameter,
    #DecimalParameter,
    #IntParameter,
    #RealParameter,
    #timeframe_to_minutes,
    #timeframe_to_next_date,
    #timeframe_to_prev_date,
    #merge_informative_pair,
    #stoploss_from_absolute,
    #stoploss_from_open,
)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib


class ZaratustraV5(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '5m'
    can_short = True
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset = 0.05
    inf_times = ["5m", "15m",]

    # ROI table:
    minimal_roi = {}

    # Stoploss:
    stoploss = -0.296

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.013
    trailing_stop_positive_offset = 0.071
    trailing_only_offset_is_reached = True
    
    def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float:
        return 10.0
    
    @property
    def plot_config(self):
        plot_config = {
            'main_plot' : {},
            'subplots' : {
                "RSI": {
                    'rsi_30m': {'color' : 'lightgrey'},
                    'rsi_15m': {'color' : 'grey'},
                    'rsi_5m' : {'color' : 'darkgrey'},
                },
                "PDI": {
                    'pdi_30m': {'color' : 'lightgrey'},
                    'pdi_15m': {'color' : 'grey'},
                    'pdi_5m' : {'color' : 'darkgrey'},
                },
                "MDI": {
                    'mdi_30m': {'color' : 'lightgrey'},
                    'mdi_15m': {'color' : 'grey'},
                    'mdi_5m' : {'color' : 'darkgrey'},
                },
            }
        }
        return plot_config
    
    @informative('5m')
    @informative('15m')
    @informative('30m')
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe)
        dataframe['pdi'] = ta.PLUS_DI(dataframe)
        dataframe['mdi'] = ta.MINUS_DI(dataframe)

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bbu'] = bollinger['upper']
        dataframe['bbm'] = bollinger['mid']
        dataframe['bbl'] = bollinger['lower']

        return dataframe
    
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                # RSI
                (dataframe['rsi_30m'] > 50) &
                (dataframe['rsi_15m'] > 50) &
                (dataframe['rsi_5m']  > 50) &
                # Directional Indicator
                (dataframe['pdi_30m'] > 25) &
                (dataframe['pdi_15m'] > 25) &
                (dataframe['pdi_5m']  > 25) &
                # Bollinger Bands
                (dataframe['close_30m'] > dataframe['bbm_30m']) &
                (dataframe['close_15m'] > dataframe['bbm_15m']) &
                (dataframe['close_5m']  > dataframe['bbm_5m'])
            ),
            ['enter_long', 'enter_tag']
        ] = (1, 'Bullish trend')

        dataframe.loc[
            (
                # RSI
                (dataframe['rsi_30m'] < 50) &
                (dataframe['rsi_15m'] < 50) &
                (dataframe['rsi_5m']  < 50) &
                # Directional Indicator
                (dataframe['mdi_30m'] > 25) &
                (dataframe['mdi_15m'] > 25) &
                (dataframe['mdi_5m']  > 25) &
                # Bollinger Bands
                (dataframe['close_30m'] < dataframe['bbm_30m']) &
                (dataframe['close_15m'] < dataframe['bbm_15m']) &
                (dataframe['close_5m']  < dataframe['bbm_5m'])
            ),
            ['enter_short', 'enter_tag']
        ] = (1, 'Bearish trend')
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe