GridStrategyV2

import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter
from freqtrade.persistence import Trade
from datetime import datetime
import logging

logger = logging.getLogger(__name__)


class GridStrategyV2(IStrategy):
    """
    专业自适应网格 v2 —— 高胜率版

    相比 v1 的改进：
      1. EMA 金叉 + EMA200 斜率确认：只在趋势明确向上时入场
      2. RSI 中性区过滤：只在 30~55 入场（不追高、不在恐慌区买）
      3. 成交量确认：成交量高于20日均量，信号更可靠
      4. 棘轮止损：盈利超过 1×ATR 时止损拉到保本位（消灭大亏损）
      5. DCA 最多 3 档（v1 是 4 档），减少深度套牢敞口
      6. 目标：胜率 85%+，Sharpe > 1.2
    """

    INTERFACE_VERSION = 3
    timeframe = "1h"
    can_short = False

    position_adjustment_enable = True
    max_entry_position_adjustment = 4   # 保留 4 档 DCA，给价格回归留足空间

    minimal_roi = {
        "0": 0.10,
        "2880": 0.02,
        "5760": 0.005,
    }

    stoploss = -0.25            # 宽止损：让 DCA 有充足空间摊成本
    trailing_stop = False       # 由 custom_stoploss 接管
    use_custom_stoploss = True

    startup_candle_count = 210  # EMA200 需要预热

    # ── Hyperopt 参数 ────────────────────────────────────────────────
    # 网格间距：缩小上限，高胜率策略用更窄的格
    atr_mult    = DecimalParameter(0.5, 2.0, default=1.0, decimals=1, space="buy")
    adx_max     = IntParameter(20, 45,       default=32,              space="buy")
    bb_width_max= DecimalParameter(0.05, 0.25, default=0.15, decimals=2, space="buy")
    rsi_min     = IntParameter(25, 45,       default=35,              space="buy")
    rsi_max     = IntParameter(45, 68,       default=58,              space="buy")

    # 止盈更快：高胜率的核心 —— 小利润快速兑现，让更多交易在价格反转前出场
    tp_atr_mult = DecimalParameter(0.3, 1.5, default=0.6, decimals=1, space="sell")
    # 棘轮触发更早：尽快把止损拉到保本，消灭潜在大亏
    breakeven_atr = DecimalParameter(0.2, 1.0, default=0.4, decimals=1, space="sell")

    _atr_cache: dict = {}

    # ── 指标计算 ─────────────────────────────────────────────────────
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["atr"]      = ta.ATR(dataframe, timeperiod=14)
        dataframe["adx"]      = ta.ADX(dataframe, timeperiod=14)
        dataframe["rsi"]      = ta.RSI(dataframe, timeperiod=14)
        dataframe["ema50"]    = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema200"]   = ta.EMA(dataframe, timeperiod=200)
        dataframe["vol_ma"]   = dataframe["volume"].rolling(20).mean()

        upper, mid, lower = ta.BBANDS(dataframe["close"], timeperiod=20,
                                       nbdevup=2.0, nbdevdn=2.0)
        dataframe["bb_upper"] = upper
        dataframe["bb_mid"]   = mid
        dataframe["bb_lower"] = lower
        dataframe["bb_width"] = (upper - lower) / mid

        # EMA200 斜率：当前值 vs 10根K线前的值
        dataframe["ema200_slope"] = dataframe["ema200"] - dataframe["ema200"].shift(10)

        if len(dataframe) > 0:
            self._atr_cache[metadata["pair"]] = float(dataframe["atr"].iloc[-1])

        return dataframe

    # ── 入场信号（比 v1 严格得多）────────────────────────────────────
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                # 大方向：价格 > EMA200，EMA50 > EMA200（金叉），EMA200 本身向上
                (dataframe["close"]       > dataframe["ema200"]) &
                (dataframe["ema50"]       > dataframe["ema200"]) &
                (dataframe["ema200_slope"] > 0) &

                # 震荡市：ADX 低 + BB 宽度不过大
                (dataframe["adx"]       < self.adx_max.value) &
                (dataframe["bb_width"]  < self.bb_width_max.value) &

                # RSI 中性区：不追高（< rsi_max），不在极度恐慌区（> rsi_min）
                (dataframe["rsi"] > self.rsi_min.value) &
                (dataframe["rsi"] < self.rsi_max.value) &

                # 入场位置：价格在 BB 中轨以下（不在上半区追入）
                (dataframe["close"] < dataframe["bb_mid"]) &

                # 成交量确认
                (dataframe["volume"] > dataframe["vol_ma"] * 0.8) &
                (dataframe["volume"] > 0)
            ),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe

    # ── 棘轮止损 ─────────────────────────────────────────────────────
    def custom_stoploss(self, pair, trade, current_time: datetime,
                        current_rate, current_profit, after_fill, **kwargs):
        """
        棘轮机制：
        - 亏损中：维持原始止损 (-20%)
        - 盈利超过 breakeven_atr × ATR：止损拉到 -0.5%（近似保本）
        - 盈利超过 2 × breakeven_atr × ATR：止损追踪到当前盈利的 -50%
        """
        atr = self._atr_cache.get(pair)
        if atr is None or trade.open_rate == 0:
            return self.stoploss

        be_threshold = (self.breakeven_atr.value * atr) / trade.open_rate

        if current_profit >= be_threshold * 2:
            # 追踪止损：盈利超过2倍阈值，止损锁在当前盈利的一半以上
            return max(-current_profit * 0.5, -0.005)
        elif current_profit >= be_threshold:
            # 已盈利超过1个ATR，止损拉到保本
            return -0.005
        else:
            return self.stoploss  # 还在初期，维持原止损

    # ── 动态止盈 ─────────────────────────────────────────────────────
    # OKX 现货手续费（实际费率，按档位调整这个值）
    FEE_RATE = 0.001  # 0.10% 单边（挂单价）

    def custom_exit(self, pair, trade, current_time, current_rate,
                    current_profit, **kwargs):
        atr = self._atr_cache.get(pair)
        if atr is None or trade.open_rate == 0:
            return None

        filled = trade.nr_of_successful_entries

        # 动态最小止盈：必须覆盖所有入场手续费 + 出场手续费 + 0.2% 净利润缓冲
        # current_profit 已由 Freqtrade 扣除手续费，这里额外加 0.2% 安全垫
        # 确保每笔交易在任意 DCA 档位下都真正盈利
        fee_buffer = self.FEE_RATE * filled + 0.002   # 已入场次数 × 入场费 + 净利缓冲
        atr_target = (self.tp_atr_mult.value * atr) / trade.open_rate
        profit_target = max(atr_target, fee_buffer)

        if current_profit >= profit_target:
            return f"grid_tp_lvl{filled}_{current_profit:.3f}"

        return None

    # ── 网格加仓逻辑 ─────────────────────────────────────────────────
    def adjust_trade_entry(
        self,
        trade: Trade,
        current_time,
        current_rate: float,
        current_profit: float,
        min_stake,
        max_stake: float,
        current_entry_rate: float,
        current_exit_rate: float,
        current_entry_profit: float,
        current_exit_profit: float,
        **kwargs,
    ):
        filled = trade.nr_of_successful_entries
        if filled >= (self.max_entry_position_adjustment + 1):
            return None

        atr = self._atr_cache.get(trade.pair)
        if atr:
            grid_step = (self.atr_mult.value * atr) / current_rate
            grid_step = max(grid_step, 0.012)
            grid_step = min(grid_step, 0.08)
        else:
            grid_step = 0.03

        threshold = -(grid_step * filled)
        if current_profit > threshold:
            return None

        # 仓位递增：1x → 1.5x → 2x → 2.5x
        multiplier = 1.0 + max(filled - 1, 0) * 0.5
        stake = trade.stake_amount * multiplier
        stake = max(stake, min_stake or 0)
        stake = min(stake, max_stake)

        logger.info(
            f"[GridV2] {trade.pair} 第{filled}次加仓 "
            f"间距={grid_step:.2%} 当前={current_profit:.2%} 加仓={stake:.2f}U ({multiplier}x)"
        )
        return stake