"""
SOL5mStrategy - 基于 EMA26_EMA52_Cross 的改进版

核心改进（相比原版）：
    ★ 去掉了反向交叉退出信号（原版中这是最大亏损来源，206笔亏-3021 USDT）
    ★ 加入 trailing stop 保护利润
    ★ 只靠 ROI + trailing stop + 硬止损 管理退出

逻辑：
    - 底层使用 1m K线（由 config 中 timeframe: "1m" 控制）
    - resample 到 30m 计算 EMA26/EMA52 交叉
    - 交叉后延迟 30 根 1m K线入场（等待确认）
    - ROI 从 15% 逐步递减
    - Trailing stop：盈利 6% 后激活，回撤 3% 退出
    - 硬止损 -15%（安全网）

使用命令：
    freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json \
        --strategy SOL5mStrategy --strategy-path ./user_data/Chan/strategies \
        --timerange=20250301-
"""

import logging
from datetime import datetime
from typing import Optional

import talib.abstract as ta
from pandas import DataFrame
from technical.util import resample_to_interval, resampled_merge

from freqtrade.strategy import IStrategy

logger = logging.getLogger(__name__)


class SOL5mStrategy(IStrategy):
    INTERFACE_VERSION: int = 3

    # === 基础配置 ===
    # 注意：实际 timeframe 由 config 文件中的 "timeframe": "1m" 控制
    # 这里不设置 timeframe，让 config 覆盖
    can_short = True
    startup_candle_count: int = 1600

    # 硬止损 -3%（超短线合理止损，配合更严格的入场过滤）
    stoploss = -0.03
    use_custom_stoploss = False

    # Trailing stop：盈利 3% 后激活，回撤 1.5% 退出
    trailing_stop = True
    trailing_stop_positive = 0.015      # 回撤 1.5% 触发退出
    trailing_stop_positive_offset = 0.03  # 盈利 3% 后才开始追踪
    trailing_only_offset_is_reached = True

    # ROI：从 6% 逐步递减（给盈利交易更多空间）
    minimal_roi = {
        "0": 0.06,   # 6% 立即止盈
        "60": 0.04,  # 60分钟后 4%
        "120": 0.03, # 120分钟后 3%
        "240": 0.02, # 240分钟后 2%
        "480": 0.01, # 480分钟后 1%
        "720": 0,    # 720分钟后不设止盈
    }

    order_types = {
        "entry": "market",
        "exit": "market",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    # resample 时间倍数
    time15 = 15
    time30 = 30
    time60 = 60

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """在 15m / 30m / 60m 级别计算 EMA26/52 交叉信号"""
        ticker = self.get_ticker_indicator()

        # resample 到更大时间框架
        dataframe_15m = resample_to_interval(dataframe, ticker * self.time15)
        dataframe_30m = resample_to_interval(dataframe, ticker * self.time30)
        dataframe_60m = resample_to_interval(dataframe, ticker * self.time60)

        # 在每个时间框架上计算指标
        dataframe_15m = self.add_indicators(dataframe_15m)
        dataframe_30m = self.add_indicators(dataframe_30m)
        dataframe_60m = self.add_indicators(dataframe_60m)
        dataframe = self.add_indicators(dataframe)

        # 合并回 1m dataframe
        dataframe = resampled_merge(dataframe, dataframe_15m)
        dataframe = resampled_merge(dataframe, dataframe_30m)
        dataframe = resampled_merge(dataframe, dataframe_60m)

        return dataframe

    def add_indicators(self, dataframe: DataFrame) -> DataFrame:
        """计算 EMA26/52 及其交叉信号，以及RSI和成交量过滤"""
        dataframe["ema26"] = ta.EMA(dataframe, timeperiod=26)
        dataframe["ema52"] = ta.EMA(dataframe, timeperiod=52)
        
        # RSI用于确认趋势强度
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        
        # 成交量均线用于确认成交量
        dataframe["volume_mean"] = dataframe["volume"].rolling(window=20).mean()

        # 上穿：本根 EMA26 > EMA52，上一根 EMA26 ≤ EMA52
        dataframe["ema26_cross_up_52"] = (
            (dataframe["ema26"] > dataframe["ema52"])
            & (dataframe["ema26"].shift(1) <= dataframe["ema52"].shift(1))
        )

        # 下穿：本根 EMA26 < EMA52，上一根 EMA26 ≥ EMA52
        dataframe["ema26_cross_down_52"] = (
            (dataframe["ema26"] < dataframe["ema52"])
            & (dataframe["ema26"].shift(1) >= dataframe["ema52"].shift(1))
        )

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """30m EMA26/52 交叉入场，延迟 15 根 1m K线，加入RSI和成交量确认"""
        ticker = self.get_ticker_indicator()
        time = self.time30
        delay = 15  # 减少延迟从30到15分钟
        cross_up = f"resample_{ticker * time}_ema26_cross_up_52"
        cross_down = f"resample_{ticker * time}_ema26_cross_down_52"
        
        # 获取当前时间框架的RSI和成交量
        rsi_col = "rsi"
        volume_col = "volume"
        volume_mean_col = "volume_mean"

        # 做多：30m EMA26 上穿 EMA52 + RSI > 50（确认上涨趋势）+ 成交量确认
        dataframe.loc[
            (dataframe[cross_up].shift(delay) == True) &
            (dataframe[rsi_col] > 50) &  # RSI确认上涨趋势
            (dataframe[volume_col] > dataframe[volume_mean_col]),  # 成交量确认
            ["enter_long", "enter_tag"],
        ] = (1, "ema26x52_long")

        # 做空：30m EMA26 下穿 EMA52 + RSI < 50（确认下跌趋势）+ 成交量确认
        dataframe.loc[
            (dataframe[cross_down].shift(delay) == True) &
            (dataframe[rsi_col] < 50) &  # RSI确认下跌趋势
            (dataframe[volume_col] > dataframe[volume_mean_col]),  # 成交量确认
            ["enter_short", "enter_tag"],
        ] = (1, "ema26x52_short")

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """不使用信号退出，完全依赖 ROI / trailing stop / 硬止损"""
        return dataframe

    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:
        return 1.0

    def get_ticker_indicator(self) -> int:
        """获取 timeframe 的分钟数"""
        return int(self.timeframe[:-1])