# source: https://raw.githubusercontent.com/wynnforthework/quant-strategies-knowledge/523670ea407d3e361fdf8adcd2c74819d2275e83/freqtrade-strategies/GodStra.py
# Github_wynnforthework_quant_strategies_knowledge__GodStra__20251007_112722 Strategy
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
#   {
#       "method": "AgeFilter",
#       "min_days_listed": 30
#   },
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
# IMPORTANT: Use Smallest "max_open_trades" for getting best results inside config.json

# --- Do not remove these libs ---
import logging
from functools import reduce

import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
# Add your lib to import here
# import talib.abstract as ta
import pandas as pd
from freqtrade.strategy import IStrategy
from numpy.lib import math
from pandas import DataFrame
# import talib.abstract as ta
from ta import add_all_ta_features
from ta.utils import dropna
from freqtrade.strategy.parameters import IntParameter, DecimalParameter, CategoricalParameter

# --------------------------------



class Github_wynnforthework_quant_strategies_knowledge__GodStra__20251007_112722(IStrategy):
    can_short: bool = True
    # 5/66:      9 trades. 8/0/1 Wins/Draws/Losses. Avg profit  21.83%. Median profit  35.52%. Total profit  1060.11476586 USDT ( 196.50Σ%). Avg duration 3440.0 min. Objective: -7.06960
    # +--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
    # |   Best |   Epoch |   Trades |    Win Draw Loss |   Avg profit |                        Profit |   Avg duration |   Objective |
    # |--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------|
    # | * Best |   1/500 |       11 |      2    1    8 |        5.22% |  280.74230393 USDT   (57.40%) |      2,421.8 m |    -2.85206 |
    # | * Best |   2/500 |       10 |      7    0    3 |       18.76% |  983.46414442 USDT  (187.58%) |        360.0 m |    -4.32665 |
    # | * Best |   5/500 |        9 |      8    0    1 |       21.83% | 1,060.11476586 USDT  (196.50%) |      3,440.0 m |     -7.0696 |

    INTERFACE_VERSION: int = 3

    # Define hyperoptable buy params so 'buy' space is present
    buy_oper: CategoricalParameter = CategoricalParameter(
        [">", "<", "=", "CA", "CB", ">I", "=I", "<I", ">R", "=R", "<R"],
        default="<",
        space="buy",
    )
    buy_int: IntParameter = IntParameter(0, 100, default=42, space="buy")
    buy_real: DecimalParameter = DecimalParameter(-1.0, 1.0, default=0.063, decimals=3, space="buy")

    # Minimal sell param to satisfy 'sell' space if included in default
    sell_oper: CategoricalParameter = CategoricalParameter(
        [">", "<", "=", "CA", "CB", ">I", "=I", "<I", ">R", "=R", "<R"],
        default="=R",
        space="sell",
    )
    sell_int: IntParameter = IntParameter(0, 100, default=98, space="sell")
    sell_real: DecimalParameter = DecimalParameter(0.0, 1.0, default=0.878, decimals=3, space="sell")

    # ROI table:
    minimal_roi = {
        "0": 0.3556,
        "4818": 0.21275,
        "6395": 0.09024,
        "22372": 0
    }

    # Stoploss:
    stoploss = -0.34549

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.22673
    trailing_stop_positive_offset = 0.2684
    trailing_only_offset_is_reached = True
    # Use 5m to align with config overrides in hyperopt
    timeframe = '5m'
    print('Add {\n\t"method": "AgeFilter",\n\t"min_days_listed": 30\n},\n to your pairlists in config (Under StaticPairList)')

    def dna_size(self, dct: dict):
        def int_from_str(st: str):
            str_int = ''.join([d for d in st if d.isdigit()])
            if str_int:
                return int(str_int)
            return -1  # in case if the parameter somehow doesn't have index
        return len({int_from_str(digit) for digit in dct.keys()})

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add all ta features
        dataframe = dropna(dataframe)
        dataframe = add_all_ta_features(
            dataframe, open="open", high="high", low="low", close="close", volume="volume",
            fillna=True)
        # dataframe.to_csv("df.csv", index=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = list()
        # Use hyperoptable simple gate to ensure presence of 'buy' space
        OPR = self.buy_oper.value
        INT = self.buy_int.value
        REAL = self.buy_real.value
        IND = 'trend_ichimoku_base'
        CRS = 'volatility_kcc'
        DFIND = dataframe[IND]
        DFCRS = dataframe[CRS]
        if OPR == ">":
            conditions.append(DFIND > DFCRS)
        elif OPR == "=":
            conditions.append(np.isclose(DFIND, DFCRS))
        elif OPR == "<":
            conditions.append(DFIND < DFCRS)
        elif OPR == "CA":
            conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
        elif OPR == "CB":
            conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
        elif OPR == ">I":
            conditions.append(DFIND > INT)
        elif OPR == "=I":
            conditions.append(DFIND == INT)
        elif OPR == "<I":
            conditions.append(DFIND < INT)
        elif 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

        # Simple mirrored short entry using inverse relation
        s_conditions = list()
        if OPR == ">":
            s_conditions.append(DFIND < DFCRS)
        elif OPR == "=":
            s_conditions.append(np.isclose(DFIND, DFCRS))
        elif OPR == "<":
            s_conditions.append(DFIND > DFCRS)
        elif OPR == "CA":
            s_conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
        elif OPR == "CB":
            s_conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
        elif OPR == ">I":
            s_conditions.append(DFIND < INT)
        elif OPR == "=I":
            s_conditions.append(DFIND == INT)
        elif OPR == "<I":
            s_conditions.append(DFIND > INT)
        elif OPR == ">R":
            s_conditions.append(DFIND < REAL)
        elif OPR == "=R":
            s_conditions.append(np.isclose(DFIND, REAL))
        elif OPR == "<R":
            s_conditions.append(DFIND > REAL)
        if s_conditions:
            dataframe.loc[reduce(lambda x, y: x & y, s_conditions), 'enter_short'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = list()
        OPR = self.sell_oper.value
        INT = self.sell_int.value
        REAL = self.sell_real.value
        IND = 'trend_kst_diff'
        CRS = 'volume_mfi'
        DFIND = dataframe[IND]
        DFCRS = dataframe[CRS]
        if OPR == ">":
            conditions.append(DFIND > DFCRS)
        elif OPR == "=":
            conditions.append(np.isclose(DFIND, DFCRS))
        elif OPR == "<":
            conditions.append(DFIND < DFCRS)
        elif OPR == "CA":
            conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
        elif OPR == "CB":
            conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
        elif OPR == ">I":
            conditions.append(DFIND > INT)
        elif OPR == "=I":
            conditions.append(DFIND == INT)
        elif OPR == "<I":
            conditions.append(DFIND < INT)
        elif 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

        # Mirrored short exit
        x_conditions = list()
        if OPR == ">":
            x_conditions.append(DFIND < DFCRS)
        elif OPR == "=":
            x_conditions.append(np.isclose(DFIND, DFCRS))
        elif OPR == "<":
            x_conditions.append(DFIND > DFCRS)
        elif OPR == "CA":
            x_conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
        elif OPR == "CB":
            x_conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
        elif OPR == ">I":
            x_conditions.append(DFIND < INT)
        elif OPR == "=I":
            x_conditions.append(DFIND == INT)
        elif OPR == "<I":
            x_conditions.append(DFIND > INT)
        elif OPR == ">R":
            x_conditions.append(DFIND < REAL)
        elif OPR == "=R":
            x_conditions.append(np.isclose(DFIND, REAL))
        elif OPR == "<R":
            x_conditions.append(DFIND > REAL)
        if x_conditions:
            dataframe.loc[reduce(lambda x, y: x & y, x_conditions), 'exit_short'] = 1
        return dataframe

    def leverage(self, pair, current_time, current_rate, proposed_leverage, max_leverage, entry_tag, side, **kwargs) -> float:
        try:
            stop = abs(float(self.stoploss)) if getattr(self, "stoploss", None) is not None else None
            base = 0.05 / stop if stop and stop > 0 else (proposed_leverage or 1.0)
        except Exception:
            base = proposed_leverage or 1.0
        base = max(1.0, min(float(base), float(max_leverage)))
        return base