# source: https://raw.githubusercontent.com/IvPalmer/Master-Trader/c3a837bc9907fdcab2f25cc43d250faabaf2e408/ft_userdata/user_data/strategies/ElliotV5.py
# directory_url: https://github.com/IvPalmer/Master-Trader/blob/main/ft_userdata/user_data/strategies/
# User: IvPalmer
# Repository: Master-Trader
# --------------------from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, stoploss_from_absolute, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt

buy_params = {
    "base_nb_candles_buy": 17,
    "ewo_high": 3.34,
    "ewo_low": -17.457,
    "low_offset": 0.978,
    "rsi_buy": 60
}

sell_params = {
    "base_nb_candles_sell": 39,
    "high_offset": 1.011
}

def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['close'] * 100
    return emadif


class Github_IvPalmer_Master_Trader__ElliotV5__20260923_203754(IStrategy):
    INTERFACE_VERSION = 3

    minimal_roi = {
        "0": 0.215, "40": 0.132, "87": 0.086, "201": 0.03
    }

    stoploss = -0.08  # MAE data: 95% of winners recovered from -4.95% or less

    @property
    def protections(self):
        return [
            {"method": "CooldownPeriod", "stop_duration_candles": 5},
            {"method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 3, "stop_duration_candles": 12, "only_per_pair": True},
            {"method": "LowProfitPairs", "lookback_period_candles": 72, "trade_limit": 2, "stop_duration_candles": 72, "required_profit": -0.02},
            {"method": "MaxDrawdown", "lookback_period_candles": 576, "max_allowed_drawdown": 0.20, "stop_duration_candles": 72, "trade_limit": 1},
        ]

    base_nb_candles_buy = IntParameter(
        5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
    base_nb_candles_sell = IntParameter(
        5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
    low_offset = DecimalParameter(
        0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
    high_offset = DecimalParameter(
        0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)

    fast_ewo = 50
    slow_ewo = 200
    ewo_low = DecimalParameter(-20.0, -8.0,
                               default=buy_params['ewo_low'], space='buy', optimize=True)
    ewo_high = DecimalParameter(
        2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)
    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)

    trailing_stop = True
    trailing_stop_positive = 0.005
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False

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

    timeframe = '5m'
    informative_timeframe = '1h'
    process_only_new_candles = True
    startup_candle_count = 200
    use_custom_stoploss = True

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        """Time-based exit tightening (5m strategy: 93% of winners close in <50min)."""
        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600

        if trade_duration > 8:
            return -0.001  # Force close after 8h
        elif trade_duration > 4:
            time_sl = stoploss_from_open(-0.03, current_profit)
            if time_sl != -0.99:
                return time_sl
            else:
                return -0.001  # already past -3%, force close now
        elif trade_duration > 2:
            atr_sl = self._atr_stoploss(pair, current_rate, multiplier=2.0)
            if atr_sl is not None:
                return atr_sl

        # Default: defer to strategy-level stoploss (-8%)
        return -1

    def _atr_stoploss(self, pair: str, current_rate: float, multiplier: float = 3.0):
        """ATR-based dynamic stoploss."""
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if len(dataframe) < 1:
            return None
        atr = dataframe.iloc[-1]['regime_atr_14']
        if np.isnan(atr) or atr <= 0:
            return None
        stoploss_price = current_rate - (atr * multiplier)
        return stoploss_from_absolute(stoploss_price, current_rate=current_rate,
                                       is_short=False, leverage=1.0)

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    def get_informative_indicators(self, metadata: dict):
        dataframe = self.dp.get_pair_dataframe(
            pair=metadata['pair'], timeframe=self.informative_timeframe)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        # --- Regime Detection ---
        # ATR regime — detect crash/high volatility periods
        dataframe['regime_atr_14'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['regime_atr_sma_50'] = dataframe['regime_atr_14'].rolling(50).mean()
        dataframe['regime_volatile'] = (dataframe['regime_atr_14'] > 2.0 * dataframe['regime_atr_sma_50']).astype(int)

        # ADX regime — detect trending vs ranging
        dataframe['regime_adx_14'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['regime_trending'] = (dataframe['regime_adx_14'] > 35).astype(int)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(
            (
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                (dataframe['EWO'] > self.ewo_high.value) &
                (dataframe['rsi'] < self.rsi_buy.value) &
                (dataframe['volume'] > 0) &
                (dataframe['regime_volatile'] == 0) &
                (dataframe['regime_trending'] == 0)
            )
        )
        conditions.append(
            (
                (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                (dataframe['EWO'] < self.ewo_low.value) &
                (dataframe['volume'] > 0) &
                (dataframe['regime_volatile'] == 0) &
                (dataframe['regime_trending'] == 0)
            )
        )
        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 = []
        conditions.append(
            (
                (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                (dataframe['volume'] > 0)
            )
        )
        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'exit_long'
            ]=1
        return dataframe