lm_code/test1.py

import os
import time
import uuid
import datetime
from dataclasses import dataclass

from tqdm import tqdm
from loguru import logger

from bitmart.api_contract import APIContract
from bitmart.lib.cloud_exceptions import APIException

from 交易.tools import send_dingtalk_message


@dataclass
class StrategyConfig:
    # =============================
    # 1m | ETH 永续 | 控止损≤5/日
    # =============================

    # ===== 合约 =====
    contract_symbol: str = "ETHUSDT"
    open_type: str = "cross"
    leverage: str = "30"  # 50 -> 30：显著降低1m噪声导致的连环止损与回撤波动

    # ===== K线与指标 =====
    step_min: int = 1
    lookback_min: int = 240
    ema_len: int = 36  # 30 -> 36：均值更稳，信号更挑剔
    atr_len: int = 14

    # ===== 动态阈值基础（自适应行情）=====
    entry_dev_floor: float = 0.0012  # 0.10% -> 0.12%：过滤小噪声进场
    tp_floor: float = 0.0006  # 0.05% -> 0.06%：更接近“净盈利”
    sl_floor: float = 0.0018  # 0.15% -> 0.18%：ETH 1m插针多，底线略放宽

    # 更挑剔、更少止损（进场更苛刻；止损不过度随波动放大）
    entry_k: float = 1.45  # 1.20 -> 1.45：减少进场频率
    tp_k: float = 0.65  # 0.60 -> 0.65：略抬止盈
    sl_k: float = 1.05  # 1.20 -> 1.05：配合sl_floor，避免高波动下止损无限变大

    # ===== 时间/冷却 =====
    max_hold_sec: int = 75  # 90/120 -> 75：1m回归不恋战
    cooldown_sec_after_exit: int = 20  # 10 -> 20：减少“刚出又进”连环单

    # ===== 下单/仓位 =====
    risk_percent: float = 0.004  # 0.005 -> 0.004：再压一点波动，更贴合止损≤5/日
    min_size: int = 1
    max_size: int = 5000

    # ===== 日内风控 =====
    daily_loss_limit: float = 0.02  # -2% 停机
    daily_profit_cap: float = 0.01  # +1% 封顶停机

    # ===== 危险模式过滤（1m ETH 更敏感）=====
    atr_ratio_kill: float = 0.0038  # 0.0045 -> 0.0038：更早暂停开仓
    big_body_kill: float = 0.010  # 0.012 -> 0.010：更敏感

    # ===== 轮询节奏 =====
    klines_refresh_sec: int = 10
    tick_refresh_sec: int = 1
    status_notify_sec: int = 60

    # =========================================================
    # ✅ 止损后同向入场加门槛（但不禁止同向重入）
    # =========================================================
    reentry_penalty_mult: float = 1.55  # 同向入场门槛×1.55：大幅降低连环止损概率
    reentry_penalty_max_sec: int = 180  # 罚时最长持续
    reset_band_k: float = 0.45  # dev回到更靠近均值才解除罚则
    reset_band_floor: float = 0.0006  # 最小复位带宽（0.06%）

    # =========================================================
    # ✅ 自动阈值：ATR/Price 分位数基准（更稳，不被短时噪声带跑）
    # =========================================================
    vol_baseline_window: int = 120
    vol_baseline_quantile: float = 0.65
    vol_scale_min: float = 0.80
    vol_scale_max: float = 1.60

    # =========================================================
    # ✅ 升级：止损后同方向 SL 放宽幅度与“止损时 vol_scale”联动
    # =========================================================
    post_sl_sl_max_sec: int = 90  # 只照顾“扫损后很快反弹”的窗口
    post_sl_mult_min: float = 1.02
    post_sl_mult_max: float = 1.16
    post_sl_vol_alpha: float = 0.20  # mult = 1 + alpha*(vol_scale_at_sl - 1)


class BitmartFuturesMeanReversionBot:
    def __init__(self, cfg: StrategyConfig):
        self.cfg = cfg

        # ✅ 只从环境变量读（请务必更换曾经硬编码泄露过的 key）
        self.api_key = os.getenv("BITMART_API_KEY", "").strip()
        self.secret_key = os.getenv("BITMART_SECRET_KEY", "").strip()
        self.memo = os.getenv("BITMART_MEMO", "合约交易").strip()

        if not self.api_key or not self.secret_key:
            raise RuntimeError("请先设置环境变量 BITMART_API_KEY / BITMART_SECRET_KEY / BITMART_MEMO(可选)")

        self.contractAPI = APIContract(self.api_key, self.secret_key, self.memo, timeout=(5, 15))

        # 持仓状态: -1 空, 0 无, 1 多
        self.pos = 0
        self.entry_price = None
        self.entry_ts = None
        self.last_exit_ts = 0

        # 日内权益基准
        self.day_start_equity = None
        self.trading_enabled = True
        self.day_tag = datetime.date.today()

        # 缓存
        self._klines_cache = None
        self._klines_cache_ts = 0
        self._last_status_notify_ts = 0

        # ✅ 止损后“同向入场加门槛”状态
        self.last_sl_dir = 0  # 1=多止损，-1=空止损，0=无
        self.last_sl_ts = 0.0

        # ✅ 止损后“同方向 SL 联动放宽”状态
        self.post_sl_dir = 0
        self.post_sl_ts = 0.0
        self.post_sl_vol_scale = 1.0  # 记录止损时的 vol_scale

        self.pbar = tqdm(total=60, desc="运行中(秒)", ncols=90)

    # ----------------- 通用工具 -----------------
    def ding(self, msg, error=False):
        prefix = "❌bitmart：" if error else "🔔bitmart："
        if error:
            for _ in range(3):
                send_dingtalk_message(f"{prefix}{msg}")
        else:
            send_dingtalk_message(f"{prefix}{msg}")

    def set_leverage(self) -> bool:
        try:
            resp = self.contractAPI.post_submit_leverage(
                contract_symbol=self.cfg.contract_symbol,
                leverage=self.cfg.leverage,
                open_type=self.cfg.open_type
            )[0]
            if resp.get("code") == 1000:
                logger.success(f"设置杠杆成功：{self.cfg.open_type} + {self.cfg.leverage}x")
                return True
            logger.error(f"设置杠杆失败: {resp}")
            self.ding(f"设置杠杆失败: {resp}", error=True)
            return False
        except Exception as e:
            logger.error(f"设置杠杆异常: {e}")
            self.ding(f"设置杠杆异常: {e}", error=True)
            return False

    # ----------------- 行情/指标 -----------------
    def get_klines_cached(self):
        now = time.time()
        if self._klines_cache is not None and (now - self._klines_cache_ts) < self.cfg.klines_refresh_sec:
            return self._klines_cache

        kl = self.get_klines()
        if kl:
            self._klines_cache = kl
            self._klines_cache_ts = now
        return self._klines_cache

    def get_klines(self):
        try:
            end_time = int(time.time())
            start_time = end_time - 60 * self.cfg.lookback_min

            resp = self.contractAPI.get_kline(
                contract_symbol=self.cfg.contract_symbol,
                step=self.cfg.step_min,
                start_time=start_time,
                end_time=end_time
            )[0]

            if resp.get("code") != 1000:
                logger.error(f"获取K线失败: {resp}")
                return None

            data = resp.get("data", [])
            formatted = []
            for k in data:
                formatted.append({
                    "id": int(k["timestamp"]),
                    "open": float(k["open_price"]),
                    "high": float(k["high_price"]),
                    "low": float(k["low_price"]),
                    "close": float(k["close_price"]),
                })
            formatted.sort(key=lambda x: x["id"])
            return formatted
        except Exception as e:
            logger.error(f"获取K线异常: {e}")
            self.ding(f"获取K线异常: {e}", error=True)
            return None

    def get_last_price(self, fallback_close: float) -> float:
        """
        优先取更实时的最新价；若SDK不支持/字段不同，回退到K线close。
        """
        try:
            if hasattr(self.contractAPI, "get_contract_details"):
                r = self.contractAPI.get_contract_details(contract_symbol=self.cfg.contract_symbol)[0]
                d = r.get("data") if isinstance(r, dict) else None
                if isinstance(d, dict):
                    for key in ("last_price", "mark_price", "index_price"):
                        if key in d and d[key] is not None:
                            return float(d[key])

            if hasattr(self.contractAPI, "get_ticker"):
                r = self.contractAPI.get_ticker(contract_symbol=self.cfg.contract_symbol)[0]
                d = r.get("data") if isinstance(r, dict) else None
                if isinstance(d, dict):
                    for key in ("last_price", "price", "last", "close"):
                        if key in d and d[key] is not None:
                            return float(d[key])
        except Exception:
            pass

        return float(fallback_close)

    @staticmethod
    def ema(values, n: int) -> float:
        k = 2 / (n + 1)
        e = values[0]
        for v in values[1:]:
            e = v * k + e * (1 - k)
        return e

    @staticmethod
    def atr(klines, n: int) -> float:
        if len(klines) < n + 1:
            return 0.0
        trs = []
        for i in range(-n, 0):
            cur = klines[i]
            prev = klines[i - 1]
            tr = max(
                cur["high"] - cur["low"],
                abs(cur["high"] - prev["close"]),
                abs(cur["low"] - prev["close"]),
            )
            trs.append(tr)
        return sum(trs) / len(trs)

    def is_danger_market(self, klines, price: float) -> bool:
        last = klines[-1]
        body = abs(last["close"] - last["open"]) / last["open"] if last["open"] else 0.0
        if body >= self.cfg.big_body_kill:
            return True

        a = self.atr(klines, self.cfg.atr_len)
        atr_ratio = (a / price) if price > 0 else 0.0
        if atr_ratio >= self.cfg.atr_ratio_kill:
            return True

        return False

    def atr_ratio_baseline(self, klines) -> float:
        """
        自动阈值基准：最近 window 根的 atr_ratio 分布的 quantile 作为“典型波动”
        """
        window = self.cfg.vol_baseline_window
        if len(klines) < (window + self.cfg.atr_len + 5):
            return 0.0

        ratios = []
        for i in range(-window, 0):
            sub = klines[:i] if i != 0 else klines
            a = self.atr(sub, self.cfg.atr_len)
            p = sub[-1]["close"]
            if p > 0 and a > 0:
                ratios.append(a / p)

        if not ratios:
            return 0.0

        ratios.sort()
        q = max(0.0, min(1.0, self.cfg.vol_baseline_quantile))
        idx = int(q * (len(ratios) - 1))
        return ratios[idx]

    def dynamic_thresholds(self, atr_ratio: float, base_ratio: float):
        """
        动态阈值：atr_ratio * vol_scale，并带 floor
        """
        if base_ratio <= 0:
            vol_scale = 1.0
        else:
            raw = atr_ratio / base_ratio
            vol_scale = max(self.cfg.vol_scale_min, min(self.cfg.vol_scale_max, raw))

        entry_dev = max(self.cfg.entry_dev_floor, self.cfg.entry_k * vol_scale * atr_ratio)
        tp = max(self.cfg.tp_floor, self.cfg.tp_k * vol_scale * atr_ratio)
        sl = max(self.cfg.sl_floor, self.cfg.sl_k * vol_scale * atr_ratio)
        return entry_dev, tp, sl, vol_scale

    # ----------------- 账户/仓位 -----------------
    def get_assets_available(self) -> float:
        try:
            resp = self.contractAPI.get_assets_detail()[0]
            if resp.get("code") != 1000:
                return 0.0
            data = resp.get("data")
            if isinstance(data, dict):
                return float(data.get("available_balance", 0))
            if isinstance(data, list):
                for asset in data:
                    if asset.get("currency") == "USDT":
                        return float(asset.get("available_balance", 0))
            return 0.0
        except Exception as e:
            logger.error(f"余额查询异常: {e}")
            return 0.0

    def get_position_status(self) -> bool:
        try:
            resp = self.contractAPI.get_position(contract_symbol=self.cfg.contract_symbol)[0]
            if resp.get("code") != 1000:
                return False

            positions = resp.get("data", [])
            if not positions:
                self.pos = 0
                return True

            p = positions[0]
            self.pos = 1 if p["position_type"] == 1 else -1
            return True
        except Exception as e:
            logger.error(f"持仓查询异常: {e}")
            self.ding(f"持仓查询异常: {e}", error=True)
            return False

    def get_equity_proxy(self) -> float:
        return self.get_assets_available()

    def refresh_daily_baseline(self):
        today = datetime.date.today()
        if today != self.day_tag:
            self.day_tag = today
            self.day_start_equity = None
            self.trading_enabled = True
            self.ding(f"新的一天({today})：重置日内风控基准")

    def risk_kill_switch(self):
        self.refresh_daily_baseline()
        equity = self.get_equity_proxy()
        if equity <= 0:
            return

        if self.day_start_equity is None:
            self.day_start_equity = equity
            logger.info(f"日内权益基准设定：{equity:.2f} USDT")
            return

        pnl = (equity - self.day_start_equity) / self.day_start_equity
        if pnl <= -self.cfg.daily_loss_limit:
            self.trading_enabled = False
            self.ding(f"触发日止损：{pnl * 100:.2f}% -> 停机", error=True)

        if pnl >= self.cfg.daily_profit_cap:
            self.trading_enabled = False
            self.ding(f"达到日盈利封顶：{pnl * 100:.2f}% -> 停机")

    # ----------------- 下单 -----------------
    def calculate_size(self, price: float) -> int:
        """
        保守仓位估算：按 1张≈0.001ETH（沿用你原假设）
        """
        bal = self.get_assets_available()
        if bal < 10:
            return 0

        margin = bal * self.cfg.risk_percent
        lev = int(self.cfg.leverage)

        size = int((margin * lev) / (price * 0.001))
        size = max(self.cfg.min_size, size)
        size = min(self.cfg.max_size, size)
        return size

    def place_market_order(self, side: int, size: int) -> bool:
        """
        side:
          1 开多
          2 平空
          3 平多
          4 开空
        """
        if size <= 0:
            return False

        client_order_id = f"mr_{int(time.time())}_{uuid.uuid4().hex[:8]}"
        try:
            resp = self.contractAPI.post_submit_order(
                contract_symbol=self.cfg.contract_symbol,
                client_order_id=client_order_id,
                side=side,
                mode=1,
                type="market",
                leverage=self.cfg.leverage,
                open_type=self.cfg.open_type,
                size=size
            )[0]

            logger.info(f"order_resp: {resp}")

            if resp.get("code") == 1000:
                return True

            self.ding(f"下单失败: {resp}", error=True)
            return False

        except APIException as e:
            logger.error(f"API下单异常: {e}")
            self.ding(f"API下单异常: {e}", error=True)
            return False

        except Exception as e:
            logger.error(f"下单未知异常: {e}")
            self.ding(f"下单未知异常: {e}", error=True)
            return False

    def close_position_all(self):
        if self.pos == 1:
            ok = self.place_market_order(3, 999999)
            if ok:
                self.pos = 0
        elif self.pos == -1:
            ok = self.place_market_order(2, 999999)
            if ok:
                self.pos = 0

    # ----------------- 止损后机制（核心优化） -----------------
    def _reentry_penalty_active(self, dev: float, entry_dev: float) -> bool:
        """
        止损后同向入场加门槛：
        - 只要 dev 还没有回到中性区，就对“上次止损方向”的同向入场门槛提高
        - dev 回到 abs(dev) <= reset_band 后自动解除
        - 超过 max_sec 自动解除（避免一直卡住）
        """
        if self.last_sl_dir == 0:
            return False

        if (time.time() - self.last_sl_ts) > self.cfg.reentry_penalty_max_sec:
            self.last_sl_dir = 0
            return False

        reset_band = max(self.cfg.reset_band_floor, self.cfg.reset_band_k * entry_dev)
        if abs(dev) <= reset_band:
            self.last_sl_dir = 0
            return False

        return True

    def _post_sl_dynamic_mult(self) -> float:
        """
        止损后同方向 SL 放宽倍数与“止损时 vol_scale”联动：
        mult = 1 + alpha*(vol_scale_at_sl - 1)
        并做上下限裁剪 + 有效期控制
        """
        if self.post_sl_dir == 0:
            return 1.0

        if (time.time() - self.post_sl_ts) > self.cfg.post_sl_sl_max_sec:
            self.post_sl_dir = 0
            self.post_sl_vol_scale = 1.0
            return 1.0

        raw = 1.0 + self.cfg.post_sl_vol_alpha * (self.post_sl_vol_scale - 1.0)
        raw = max(1.0, raw)  # 不缩小止损，只放宽
        return max(self.cfg.post_sl_mult_min, min(self.cfg.post_sl_mult_max, raw))

    # ----------------- 交易逻辑 -----------------
    def in_cooldown(self) -> bool:
        return (time.time() - self.last_exit_ts) < self.cfg.cooldown_sec_after_exit

    def maybe_enter(self, price: float, ema_value: float, entry_dev: float):
        if self.pos != 0:
            return
        if self.in_cooldown():
            return

        dev = (price - ema_value) / ema_value if ema_value else 0.0
        size = self.calculate_size(price)
        if size <= 0:
            return

        penalty_active = self._reentry_penalty_active(dev, entry_dev)

        # 基础阈值
        long_th = -entry_dev
        short_th = entry_dev

        # 若罚则生效：对“上次止损方向”的同向阈值提高
        if penalty_active:
            if self.last_sl_dir == 1:
                long_th = -entry_dev * self.cfg.reentry_penalty_mult
            elif self.last_sl_dir == -1:
                short_th = entry_dev * self.cfg.reentry_penalty_mult

        logger.info(
            f"enter_check: price={price:.2f}, ema={ema_value:.2f}, dev={dev * 100:.3f}% "
            f"(entry_dev={entry_dev * 100:.3f}%, long_th={long_th * 100:.3f}%, short_th={short_th * 100:.3f}%) "
            f"size={size}, penalty={penalty_active}, last_sl_dir={self.last_sl_dir}"
        )

        if dev <= long_th:
            if self.place_market_order(1, size):  # 开多
                self.pos = 1
                self.entry_price = price
                self.entry_ts = time.time()
                self.ding(f"✅开多：dev={dev * 100:.3f}% size={size} entry={price:.2f}")

        elif dev >= short_th:
            if self.place_market_order(4, size):  # 开空
                self.pos = -1
                self.entry_price = price
                self.entry_ts = time.time()
                self.ding(f"✅开空：dev={dev * 100:.3f}% size={size} entry={price:.2f}")

    def maybe_exit(self, price: float, tp: float, sl: float, vol_scale: float):
        if self.pos == 0 or self.entry_price is None or self.entry_ts is None:
            return

        hold = time.time() - self.entry_ts

        if self.pos == 1:
            pnl = (price - self.entry_price) / self.entry_price
        else:
            pnl = (self.entry_price - price) / self.entry_price

        # ✅ 同方向止损后：在有效期内放宽 SL（与止损时 vol_scale 联动）
        sl_mult = 1.0
        if self.post_sl_dir == self.pos and self.post_sl_dir != 0:
            sl_mult = self._post_sl_dynamic_mult()
        effective_sl = sl * sl_mult

        if pnl >= tp:
            self.close_position_all()
            self.ding(f"🎯止盈：pnl={pnl * 100:.3f}% price={price:.2f} tp={tp * 100:.3f}%")
            self.entry_price, self.entry_ts = None, None
            self.last_exit_ts = time.time()

        elif pnl <= -effective_sl:
            # 记录止损方向
            sl_dir = self.pos  # 1=多止损，-1=空止损

            self.close_position_all()
            self.ding(
                f"🛑止损：pnl={pnl * 100:.3f}% price={price:.2f} "
                f"sl={sl * 100:.3f}% effective_sl={effective_sl * 100:.3f}%(×{sl_mult:.2f})",
                error=True
            )

            # ✅ 开启：同向入场加门槛
            self.last_sl_dir = sl_dir
            self.last_sl_ts = time.time()

            # ✅ 开启：同向 SL 联动放宽（记录止损时 vol_scale）
            self.post_sl_dir = sl_dir
            self.post_sl_ts = time.time()
            self.post_sl_vol_scale = float(vol_scale)

            self.entry_price, self.entry_ts = None, None
            self.last_exit_ts = time.time()

        elif hold >= self.cfg.max_hold_sec:
            self.close_position_all()
            self.ding(f"⏱超时：hold={int(hold)}s pnl={pnl * 100:.3f}% price={price:.2f}")
            self.entry_price, self.entry_ts = None, None
            self.last_exit_ts = time.time()

    def notify_status_throttled(self, price: float, ema_value: float, dev: float, bal: float,
                                atr_ratio: float, base_ratio: float, vol_scale: float,
                                entry_dev: float, tp: float, sl: float):
        now = time.time()
        if (now - self._last_status_notify_ts) < self.cfg.status_notify_sec:
            return
        self._last_status_notify_ts = now

        direction_str = "多" if self.pos == 1 else ("空" if self.pos == -1 else "无")
        penalty_active = self._reentry_penalty_active(dev, entry_dev)

        sl_mult = 1.0
        if self.pos != 0 and self.post_sl_dir == self.pos:
            sl_mult = self._post_sl_dynamic_mult()

        msg = (
            f"【BitMart {self.cfg.contract_symbol}｜1m均值回归(自动阈值+止损智能)】\n"
            f"方向：{direction_str}\n"
            f"现价：{price:.2f}\n"
            f"EMA{self.cfg.ema_len}：{ema_value:.2f}\n"
            f"dev：{dev * 100:.3f}%（entry_dev={entry_dev * 100:.3f}%）\n"
            f"ATR比：{atr_ratio * 100:.3f}%  基准：{base_ratio * 100:.3f}%  vol_scale={vol_scale:.2f}\n"
            f"tp/sl：{tp * 100:.3f}% / {sl * 100:.3f}%（postSL×{sl_mult:.2f}, sl@scale={self.post_sl_vol_scale:.2f}）\n"
            f"止损同向加门槛：{'ON' if penalty_active else 'OFF'}（last_sl_dir={self.last_sl_dir}）\n"
            f"可用余额：{bal:.2f} USDT  杠杆：{self.cfg.leverage}x\n"
            f"超时：{self.cfg.max_hold_sec}s 冷却：{self.cfg.cooldown_sec_after_exit}s"
        )
        self.ding(msg)

    def action(self):
        if not self.set_leverage():
            self.ding("杠杆设置失败，停止运行", error=True)
            return

        while True:
            now_dt = datetime.datetime.now()
            self.pbar.n = now_dt.second
            self.pbar.refresh()

            klines = self.get_klines_cached()
            if not klines or len(klines) < (self.cfg.ema_len + 5):
                time.sleep(1)
                continue

            last_k = klines[-1]
            closes = [k["close"] for k in klines[-(self.cfg.ema_len + 1):]]
            ema_value = self.ema(closes, self.cfg.ema_len)

            price = self.get_last_price(fallback_close=float(last_k["close"]))
            dev = (price - ema_value) / ema_value if ema_value else 0.0

            # 自动阈值
            a = self.atr(klines, self.cfg.atr_len)
            atr_ratio = (a / price) if price > 0 else 0.0
            base_ratio = self.atr_ratio_baseline(klines)
            entry_dev, tp, sl, vol_scale = self.dynamic_thresholds(atr_ratio, base_ratio)

            # 日内风控
            self.risk_kill_switch()

            # 刷新仓位
            if not self.get_position_status():
                time.sleep(1)
                continue

            # 停机：平仓+不再开仓
            if not self.trading_enabled:
                if self.pos != 0:
                    self.close_position_all()
                time.sleep(5)
                continue

            # 危险市场：不新开仓（允许已有仓按 tp/sl/超时 退出）
            if self.is_danger_market(klines, price):
                logger.warning("危险模式：高波动/大实体K，暂停开仓")
                self.maybe_exit(price, tp, sl, vol_scale)
                time.sleep(self.cfg.tick_refresh_sec)
                continue

            # 先出场再入场
            self.maybe_exit(price, tp, sl, vol_scale)
            self.maybe_enter(price, ema_value, entry_dev)

            # 状态通知（限频）
            bal = self.get_assets_available()
            self.notify_status_throttled(
                price, ema_value, dev, bal,
                atr_ratio, base_ratio, vol_scale,
                entry_dev, tp, sl
            )

            time.sleep(self.cfg.tick_refresh_sec)


if __name__ == "__main__":
    """
    Windows PowerShell:
      setx BITMART_API_KEY "你的key"
      setx BITMART_SECRET_KEY "你的secret"
      setx BITMART_MEMO "合约交易"
    重新打开终端再运行。

    Linux/macOS:
      export BITMART_API_KEY="你的key"
      export BITMART_SECRET_KEY="你的secret"
      export BITMART_MEMO="合约交易"
    """
    cfg = StrategyConfig()
    bot = BitmartFuturesMeanReversionBot(cfg)
    bot.action()

# 9208.96