# source: https://raw.githubusercontent.com/kalebCard/MemeHunter/3fc6cb2e1a7fbb8cca56e6c66a0da411deab1027/user_data/strategies/MemeHunterFuturesV5_Flat1x.py
# directory_url: https://github.com/kalebCard/MemeHunter/blob/main/user_data/strategies/
# User: kalebCard
# Repository: MemeHunter
# --------------------"""
Github_kalebCard_MemeHunter__MemeHunterFuturesV5_Flat1x__20261013_014419 - Versión para prueba de sensibilidad de apalancamiento (PB-66).
Fija el apalancamiento en 1.0x plano (sin apalancamiento adicional ni sizing asimétrico)
para medir la robustez de la frontera eficiente del motor base sobre la muestra completa.
"""

from datetime import datetime
from typing import Optional
import logging

from freqtrade.strategy import IStrategy
from freqtrade.persistence import Trade
from pandas import DataFrame
import pandas_ta as ta
import numpy as np

LOG = logging.getLogger(__name__)


class Github_kalebCard_MemeHunter__MemeHunterFuturesV5_Flat1x__20261013_014419(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '4h'
    can_short = True

    minimal_roi = {
        "0": 0.602,
        "1704": 0.141,
        "2255": 0.047,
        "7483": 0
    }

    stoploss = -0.10

    trailing_stop = True
    trailing_stop_positive = 0.040
    trailing_stop_positive_offset = 0.120
    trailing_only_offset_is_reached = True

    process_only_new_candles = True
    startup_candle_count: int = 200

    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False,
        'stoploss_on_exchange_interval': 60
    }

    order_time_in_force = {
        'entry': 'GTC',
        'exit': 'GTC'
    }

    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:
        # PB-66: Apalancamiento plano 1.0x estricto
        return 1.0

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema_50'] = ta.ema(dataframe['close'], length=50)
        dataframe['ema_200'] = ta.ema(dataframe['close'], length=200)
        dataframe['dist_ema_50'] = (dataframe['close'] - dataframe['ema_50']) / dataframe['ema_50']

        dataframe['rsi'] = ta.rsi(dataframe['close'], length=14)

        macd = ta.macd(dataframe['close'], fast=12, slow=26, signal=9)
        dataframe['macd'] = macd['MACD_12_26_9']
        dataframe['macdsignal'] = macd['MACDs_12_26_9']
        dataframe['macdhist'] = macd['MACDh_12_26_9']

        dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)

        upper_wick = dataframe['high'] - dataframe[['close', 'open']].max(axis=1)
        candle_body = (dataframe['close'] - dataframe['open']).abs()
        candle_range = dataframe['high'] - dataframe['low']

        vol_sma20 = ta.sma(dataframe['volume'], length=20)
        vol_ratio = dataframe['volume'] / vol_sma20.replace(0, np.nan)

        is_wick_dominant = (upper_wick >= candle_body * 1.8) & (upper_wick >= candle_range * 0.45)
        is_volume_spike = vol_ratio >= 1.4
        is_closing_weak = dataframe['close'] <= (dataframe['low'] + candle_range * 0.35)

        dataframe['trapped_longs_signal'] = (is_wick_dominant & is_volume_spike & is_closing_weak).astype(int)

        # Sin filtro de calendario para mantener consistencia con PB-61
        dataframe['seasonal_short_allowed'] = True

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'enter_long'] = 0
        dataframe.loc[:, 'enter_short'] = 0

        short_trapped_conditions = (
            (dataframe['seasonal_short_allowed']) &
            (dataframe['trapped_longs_signal'].rolling(window=3).max() == 1) &
            (dataframe['close'] < dataframe['low'].shift(1)) &
            (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) &
            (dataframe['volume'] > 0)
        )
        dataframe.loc[short_trapped_conditions, 'enter_short'] = 1
        dataframe.loc[short_trapped_conditions, 'enter_tag'] = 'short_trapped_auction'

        short_structure_shift = (
            (dataframe['seasonal_short_allowed']) &
            (dataframe['dist_ema_50'].rolling(window=3).max() >= 0.12) &
            (dataframe['close'] < dataframe['low'].shift(1)) &
            (dataframe['rsi'] < 68) &
            (dataframe['rsi'].shift(1) >= 65) &
            (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) &
            (dataframe['volume'] > 0)
        )
        dataframe.loc[short_structure_shift, 'enter_short'] = 1
        dataframe.loc[short_structure_shift, 'enter_tag'] = 'short_structure_shift'

        short_macro_conditions = (
            (dataframe['seasonal_short_allowed']) &
            (dataframe['close'] < dataframe['ema_200']) &
            (dataframe['macd'] < dataframe['macdsignal']) &
            (dataframe['macd'] < 0) &
            (dataframe['rsi'] <= 44) &
            (dataframe['rsi'] >= 22) &
            (dataframe['volume'] > 0)
        )
        dataframe.loc[short_macro_conditions, 'enter_short'] = 1
        dataframe.loc[short_macro_conditions, 'enter_tag'] = 'short_macro_breakdown'

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[:, 'exit_long'] = 0
        dataframe.loc[:, 'exit_short'] = 0

        exit_short = (
            (dataframe['close'] > dataframe['ema_200']) |
            ((dataframe['macd'] > dataframe['macdsignal']) & (dataframe['rsi'] > 58))
        )
        dataframe.loc[exit_short, 'exit_short'] = 1

        return dataframe