# source: https://raw.githubusercontent.com/willy50414z/binance/5b07824df69fb43e4e8b0e91b2e2b6529151a80c/com/willy/binance/freqtrade/strategy/AMRS(ATR-Driven%20Mean%20Reversion%20Short/AMRS_v20.py
import logging
from datetime import datetime

import talib.abstract as ta
from freqtrade.strategy import IStrategy
from pandas import DataFrame

logger = logging.getLogger(__name__)


class Github_willy50414z_binance__AMRS_v20__20260301_155655(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "15m"
    can_short = True
    process_only_new_candles = True
    startup_candle_count = 120

    # --- 交易性能參數 ---
    # V20 使用動態止損，這裡設一個較寬的物理底線
    stoploss = -0.03

    # 稍微放寬 ROI，讓 ATR 追蹤止損來決定出場
    minimal_roi = {
        "0": 0.10,
        "300": 0.05,
        "1440": 0.01
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # (保持原有的 ma7, ma25, rsi, atr 計算)
        dataframe['ma7'] = ta.SMA(dataframe, timeperiod=7)
        dataframe['ma25'] = ta.SMA(dataframe, timeperiod=25)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['atr_mean'] = dataframe['atr'].rolling(window=100).mean()

        # 為了 exit_trend 增加連漲/連跌計數
        dataframe['ma7_gt_prev'] = (dataframe['close'] > dataframe['ma7']).astype(int)
        dataframe['streak_ma7'] = dataframe['ma7_gt_prev'].rolling(window=10).sum()

        dataframe['volume_mean'] = dataframe['volume'].rolling(window=30).mean()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                    (dataframe['rsi'] < 35) &  # 超賣
                    (dataframe['close'] < dataframe['ma7']) &  # 價格在短期均線下
                    (dataframe['volume'] > dataframe['volume_mean'] * 1.5) &  # 爆量
                    (dataframe['atr'] > dataframe['atr_mean'] * 0.8)  # --- V20: 避開死水區 ---
            ),
            ['enter_short', 'enter_tag']
        ] = (1, 'v20_adaptive_entry')

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        硬性指標出場邏輯 (作為 custom_exit 的第一道過濾)
        """
        dataframe.loc[
            (
                # 1. 趨勢徹底反轉：價格連續 5 根 K 線站穩在 MA7 之上 (對於 Short 來說是危險)
                    (dataframe["streak_ma7"] >= 5) |

                    # 2. 乖離率過高：價格反彈超過 MA25 的 0.5% (強勢反彈預警)
                    (dataframe["close"] > dataframe["ma25"] * 1.005)
            ),
            ["exit_short", "exit_tag"]
        ] = (1, "v20_indicator_exit")

        return dataframe

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]

        # 計算 ATR 止損距離 (約為 1.5 倍 ATR)
        # 換算成百分比
        atr_stop_pct = (last_candle['atr'] * 1.5) / last_candle['close']
        atr_stop_pct = max(min(atr_stop_pct, 0.025), 0.01)  # 限制在 1%~2.5% 之間

        # 1. 獲利超過 0.5%：立刻大幅縮減風險 (Move to partial break-even)
        if 0.005 <= current_profit < 0.012:
            return 0.002  # 鎖住 0.2% 獲利

        # 2. 獲利超過 1.2%：鎖住 0.6%
        if 0.012 <= current_profit < 0.022:
            return 0.006

        # 3. 大行情追蹤：獲利超過 2.2% 啟動 0.5% 寬度的追蹤止損
        if current_profit >= 0.022:
            return 0.005

        # 初始動態止損
        return -atr_stop_pct

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]
        prev_candle = dataframe.iloc[-2]

        # --- V20 價格行為離場 ---
        # 只要當前收盤高於前一根 K 線高點，代表短期反彈確立
        if current_profit > 0.002:  # 只要有微利
            if last_candle['close'] > prev_candle['high']:
                return "v20_pa_reversal"

        # 安全防護：價格重回 MA25 上方
        if last_candle['close'] > last_candle['ma25'] * 1.003:
            return "ma25_protection"

        return None