# source: https://raw.githubusercontent.com/willy50414z/binance/5b07824df69fb43e4e8b0e91b2e2b6529151a80c/com/willy/binance/freqtrade/strategy/AMRS(ATR-Driven%20Mean%20Reversion%20Short/AMRS_v24_TrendCatcherShort.py
import logging
from datetime import datetime
import talib.abstract as ta
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import numpy as np

logger = logging.getLogger(__name__)


class Github_willy50414z_binance__AMRS_v24_TrendCatcherShort__20260301_155655(IStrategy):
    """
    AMRS v23 - 趨勢捕捉者 (空頭優化版)
    優化方向：
    1. 增加交易樣本：放寬硬性 ATR 與 Volume 限制。
    2. 延長持倉時間：移除敏感的 ema_slope 離場，改用 RSI 超賣反彈離場。
    3. 動態風險控制：調整階梯止損，讓利潤有空間成長。
    """
    INTERFACE_VERSION = 3
    timeframe = "15m"
    can_short = True
    process_only_new_candles = True
    startup_candle_count = 120

    # 物理止損放寬，交給 custom_stoploss 處理精細化操作
    stoploss = -0.04
    minimal_roi = {
        "0": 0.20,  # 極高目標，交給追蹤止損決定
        "120": 0.05,
        "360": 0.02,
        "720": 0.01  # 12小時沒動靜再保本走人
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 基礎指標
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['atr_pct'] = (dataframe['atr'] / dataframe['close']) * 100

        # 移動平均線
        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)

        # 相對波動率：當前波動是否高於近期平均
        dataframe['volatility_avg'] = dataframe['atr_pct'].rolling(window=30).mean()

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                # 1. 趨勢過濾：大趨勢向下
                    (dataframe['close'] < dataframe['ema_200']) &
                    (dataframe['ema_fast'] < dataframe['ema_slow']) &

                    # 2. 相對波動率：只要當前波動不低於平均的 80% 即可 (放寬)
                    (dataframe['atr_pct'] > dataframe['volatility_avg'] * 0.8) &

                    # 3. RSI 高位勾頭：做空的核心點
                    (dataframe['rsi'] > 50) &
                    (dataframe['rsi'] < dataframe['rsi'].shift(1)) &

                    # 4. 成交量：只要有溫和放量即可 (1.1x)
                    (dataframe['volume'] > dataframe['volume'].rolling(20).mean() * 1.1)
            ),
            'enter_short'
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                # 改進：只有在 RSI 從超賣區(<30) 向上反彈超過 35 時，才認為下跌動能暫時消失
                    (dataframe['rsi'] > 35) &
                    (dataframe['rsi'].shift(1) < 30) &
                    (dataframe['close'] > dataframe['ema_fast'])
            ),
            'exit_short'
        ] = 1

        return dataframe

    # 2. 強化追蹤止損邏輯
    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        # --- 獲利保護：大幅縮緊 ---

        # 獲利超過 2%：鎖定 1.2%
        if current_profit > 0.02:
            return 0.012

        # 獲利超過 1%：鎖定 0.5%
        if current_profit > 0.01:
            return 0.005

        # 獲利超過 0.4%：立即保本 (Break-even)
        # 這能解決「平均利潤過低」的問題，將微利轉化為保本
        if current_profit > 0.004:
            return 0.001

        # --- 虧損控制：時間止損 ---
        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600

        # 如果進場 2 小時後還在虧損，說明反轉沒發生，強制縮減止損至 1%
        if trade_duration > 2 and current_profit < 0:
            return -0.01

        return self.stoploss