# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/Zeus.py
# Github_DerSalvador_freqtrade_helm_chart__Zeus__20260115_122204 Strategy: First Generation of GodStra Strategy with maximum
# AVG/MID profit in USDT
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces entry exit roi --strategy Github_DerSalvador_freqtrade_helm_chart__Zeus__20260115_122204
# --- Do not remove these libs ---
import logging
from freqtrade.strategy import CategoricalParameter, DecimalParameter
from numpy.lib import math
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
# import talib.abstract as ta
import pandas as pd
import ta
from ta.utils import dropna
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce
import numpy as np

class Github_DerSalvador_freqtrade_helm_chart__Zeus__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    # *    1/43:     86 trades. 72/6/8 Wins/Draws/Losses. Avg profit  12.66%. Median profit  11.99%. Total profit  0.10894395 BTC ( 108.94Σ%). Avg duration 3 days, 0:31:00 min. Objective: -48.48793
    # "max_open_trades": 10,
    # "stake_currency": "BTC",
    # "stake_amount": 0.01,
    # "tradable_balance_ratio": 0.99,
    # "timeframe": "4h",
    # "dry_run_wallet": 0.1,
    # Buy hyperspace params:
    entry_params = {'entry_cat': '<R', 'entry_real': 0.0128}
    # Sell hyperspace params:
    exit_params = {'exit_cat': '=R', 'exit_real': 0.9455}
    # ROI table:
    minimal_roi = {'0': 0.564, '567': 0.273, '2814': 0.12, '7675': 0}
    # Stoploss:
    stoploss = -0.256
    entry_real = DecimalParameter(0.001, 0.999, decimals=4, default=0.11908, space='entry')
    entry_cat = CategoricalParameter(['>R', '=R', '<R'], default='<R', space='entry')
    exit_real = DecimalParameter(0.001, 0.999, decimals=4, default=0.59608, space='exit')
    exit_cat = CategoricalParameter(['>R', '=R', '<R'], default='>R', space='exit')
    # Buy hypers
    timeframe = '4h'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add all ta features
        dataframe['trend_ichimoku_base'] = ta.trend.ichimoku_base_line(dataframe['high'], dataframe['low'], window1=9, window2=26, visual=False, fillna=False)
        KST = ta.trend.KSTIndicator(close=dataframe['close'], roc1=10, roc2=15, roc3=20, roc4=30, window1=10, window2=10, window3=10, window4=15, nsig=9, fillna=False)
        dataframe['trend_kst_diff'] = KST.kst_diff()
        # Normalization
        tib = dataframe['trend_ichimoku_base']
        dataframe['trend_ichimoku_base'] = (tib - tib.min()) / (tib.max() - tib.min())
        tkd = dataframe['trend_kst_diff']
        dataframe['trend_kst_diff'] = (tkd - tkd.min()) / (tkd.max() - tkd.min())
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        IND = 'trend_ichimoku_base'
        REAL = self.entry_real.value
        OPR = self.entry_cat.value
        DFIND = dataframe[IND]
        # print(DFIND.mean())
        if OPR == '>R':
            conditions.append(DFIND > REAL)
        elif OPR == '=R':
            conditions.append(np.isclose(DFIND, REAL))
        elif OPR == '<R':
            conditions.append(DFIND < REAL)
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        IND = 'trend_kst_diff'
        REAL = self.exit_real.value
        OPR = self.exit_cat.value
        DFIND = dataframe[IND]
        # print(DFIND.mean())
        if OPR == '>R':
            conditions.append(DFIND > REAL)
        elif OPR == '=R':
            conditions.append(np.isclose(DFIND, REAL))
        elif OPR == '<R':
            conditions.append(DFIND < REAL)
        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
        return dataframe