"""
Chan 数据服务：用 ccxt 从交易所拉取 K 线，内存缓存 + CSV 落盘；
后台线程定期增量刷新，断线时记录 resume_since 以免漏 K；
配置中的基础周期（如 1m/1h）可合成 DERIVED_TIMEFRAME_PLAN 中的衍生周期。
"""
import asyncio
import csv
import json
import logging
import os
import threading
import time
from contextlib import asynccontextmanager
from datetime import datetime, timezone
from pathlib import Path
from typing import Dict, Iterable, List, Optional

import ccxt  # type: ignore
import ccxt.pro as ccxt_pro  # type: ignore
import pandas as pd  # type: ignore
from fastapi import FastAPI, HTTPException, Query, WebSocket, WebSocketDisconnect
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse
import uvicorn
from technical.util import resample_to_interval

# docker compose logs --tail=200
# docker compose down && docker compose build --no-cache && docker compose up -d

# 基础周期枚举顺序（用于衍生周期展示顺序）；仅允许集合内周期作为交易所直接拉取的 tf
TIMEFRAME_ORDER = ["1m", "1h", "1d", "1w"]
ALLOWED_TIMEFRAMES = set(TIMEFRAME_ORDER)
# 各基础周期一根 K 线的毫秒长度（用于历史分页与断线回退）
TIMEFRAME_TO_MS: Dict[str, int] = {
    "1m": 60_000,
    "1h": 3_600_000,
    "1d": 86_400_000,
    "1w": 604_800_000,
}
# 内存最大缓存的 K 线数量（超出部分从 CSV 按需读取，不限制则不限量）
MAX_CANDLES_IN_MEMORY: Dict[str, int] = {
    "1m": 129_600,   # 3 个月（3 * 30 * 24 * 60）
}
# 每个基础周期可派生出的合成周期列表（由该基础周期 K 线 resample 得到）
DERIVED_TIMEFRAME_PLAN: Dict[str, List[str]] = {
    "1m": ["2m", "3m", "4m", "5m", "10m", "15m", "20m", "25m", "30m", "45m"],
    "1h": ["2h", "3h", "4h", "5h", "6h", "7h", "8h", "9h", "10", "11h", "12h", "16h", "20h"],
    "1d": ["2d", "3d", "4d", "5d", "6d"],
    "1w": ["2w", "3w"],
}
CSV_FIELDNAMES = ["timestamp", "datetime", "open", "high", "low", "close", "volume"]
DEFAULT_LIMIT = 500
RECENT_CANDLE_LIMIT = 10
RECENT_FETCH_INTERVAL = 5  # 后台刷新循环休眠秒数
PERSIST_INTERVAL = 600  # 全量落盘周期（秒）
WS_UPDATE_CANDLE_COUNT = 2  # WebSocket 增量推送最近 K 线根数


logger = logging.getLogger("data_provider")
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
)


def to_utc_iso(timestamp_ms: int) -> str:
    """将毫秒时间戳格式化为 UTC ISO 字符串（末尾 Z）。"""
    dt = datetime.fromtimestamp(timestamp_ms / 1000, tz=timezone.utc)
    return dt.isoformat().replace("+00:00", "Z")


def parse_timestamp(value: Optional[object]) -> Optional[int]:
    """解析查询参数中的时间为 UTC 毫秒时间戳；支持数字或 ISO 字符串。"""
    if value is None:
        return None
    if isinstance(value, (int, float)):
        return int(value)
    if isinstance(value, str):
        text = value.strip()
        if not text:
            return None
        if text.isdigit():
            return int(text)
        if text.endswith("Z"):
            text = text[:-1] + "+00:00"
        try:
            dt = datetime.fromisoformat(text)
        except ValueError as exc:  # pragma: no cover - informative logging
            raise ValueError(f"无法解析时间字符串: {value}") from exc
        if dt.tzinfo is None:
            dt = dt.replace(tzinfo=timezone.utc)
        else:
            dt = dt.astimezone(timezone.utc)
        return int(dt.timestamp() * 1000)
    raise ValueError(f"不支持的时间格式: {value}")


def candle_to_dict(candle: Iterable[float]) -> Dict[str, float]:
    """ccxt OHLCV 单根 [ts, o, h, l, c, v] 转为内部字典结构。"""
    ts = int(candle[0])
    return {
        "timestamp": ts,
        "datetime": to_utc_iso(ts),
        "open": float(candle[1]),
        "high": float(candle[2]),
        "low": float(candle[3]),
        "close": float(candle[4]),
        "volume": float(candle[5]),
    }


def timeframe_to_minutes(tf: str) -> Optional[int]:
    """将如 15m、2h 转为「分钟数」，供 resample 与衍生周期计算。"""
    if not tf:
        return None
    unit = tf[-1]
    try:
        value = int(tf[:-1])
    except ValueError:
        return None
    multiplier = {
        "m": 1,
        "h": 60,
        "d": 1_440,
        "w": 10_080,
    }.get(unit)
    if multiplier is None:
        return None
    return value * multiplier


class WebSocketManager:
    """管理 WebSocket 连接及订阅，线程安全地广播 K 线更新。"""

    def __init__(self) -> None:
        self._subscriptions: Dict[tuple, set] = {}
        self._async_lock = asyncio.Lock()
        self._loop: Optional[asyncio.AbstractEventLoop] = None

    def set_loop(self, loop: asyncio.AbstractEventLoop) -> None:
        self._loop = loop

    async def connect(self, ws: WebSocket) -> None:
        await ws.accept()
        logger.info("WebSocket 客户端已连接")

    async def disconnect(self, ws: WebSocket) -> None:
        async with self._async_lock:
            for key in list(self._subscriptions):
                self._subscriptions[key].discard(ws)
                if not self._subscriptions[key]:
                    del self._subscriptions[key]
        logger.info("WebSocket 客户端已断开")

    async def subscribe(self, ws: WebSocket, symbol: str, timeframe: str) -> None:
        key = (symbol, timeframe)
        async with self._async_lock:
            self._subscriptions.setdefault(key, set()).add(ws)
        logger.info("WebSocket 订阅: %s %s", symbol, timeframe)

    async def unsubscribe(self, ws: WebSocket, symbol: str, timeframe: str) -> None:
        key = (symbol, timeframe)
        async with self._async_lock:
            if key in self._subscriptions:
                self._subscriptions[key].discard(ws)
                if not self._subscriptions[key]:
                    del self._subscriptions[key]

    def has_subscribers(self, symbol: str, timeframe: str) -> bool:
        """非异步快速检查（供同步线程调用）。"""
        return bool(self._subscriptions.get((symbol, timeframe)))

    async def broadcast(
        self, symbol: str, timeframe: str, candles: List[Dict], msg_type: str = "kline",
    ) -> None:
        key = (symbol, timeframe)
        async with self._async_lock:
            subscribers = list(self._subscriptions.get(key, set()))
        if not subscribers:
            return
        message = json.dumps(
            {"type": msg_type, "symbol": symbol, "timeframe": timeframe, "data": candles},
            ensure_ascii=False,
        )
        dead: list = []
        for ws in subscribers:
            try:
                await ws.send_text(message)
            except Exception:
                dead.append(ws)
        if dead:
            async with self._async_lock:
                for ws in dead:
                    self._subscriptions.get(key, set()).discard(ws)

    def broadcast_from_thread(
        self, symbol: str, timeframe: str, candles: List[Dict], msg_type: str = "kline",
    ) -> None:
        """供同步后台线程调用，将广播提交到 asyncio 事件循环。"""
        if self._loop is None or self._loop.is_closed():
            return
        asyncio.run_coroutine_threadsafe(
            self.broadcast(symbol, timeframe, candles, msg_type),
            self._loop,
        )


class DataProvider:
    """封装交易所连接、本地 CSV、内存缓存、断线恢复与衍生周期聚合。"""

    def __init__(self, config_path: Path) -> None:
        self.config_path = config_path
        self.config = self._load_config()
        self.exchange_name: str = self.config["exchange"]
        self.symbols: List[str] = self._load_symbols(self.config)
        self.timeframes: List[str] = self._validate_timeframes(self.config.get("timeframes"))
        self.data_dir = Path(self.config.get("data_dir", "./data")).expanduser()
        start = parse_timestamp(self.config.get("start_time"))
        if start is None:
            raise ValueError("配置文件必须包含 start_time 字段")
        self.start_time_ms: int = start
        # 按周期独立 start_time（从 start_time_per_tf 字段读取），未配置则回退到全局 start_time
        self.start_time_map: Dict[str, int] = {}
        start_per_tf = self.config.get("start_time_per_tf", {})
        if isinstance(start_per_tf, dict):
            for tf, ts_str in start_per_tf.items():
                tf_start = parse_timestamp(ts_str)
                if tf_start is not None:
                    self.start_time_map[tf] = tf_start
        self.exchange = self._init_exchange()
        self.data: Dict[str, Dict[str, List[Dict[str, float]]]] = {
            symbol: {tf: [] for tf in self.timeframes} for symbol in self.symbols
        }
        # 衍生周期 -> 用于合成的交易所基础周期（每个衍生只对应一个 base）
        self.derived_map: Dict[str, str] = {}
        for base_tf in self.timeframes:
            for derived_tf in DERIVED_TIMEFRAME_PLAN.get(base_tf, []):
                self.derived_map.setdefault(derived_tf, base_tf)
        # 衍生周期展示顺序：按 TIMEFRAME_ORDER 中的基础周期依次展开
        derived_order: List[str] = []
        for base_tf in TIMEFRAME_ORDER:
            if base_tf not in self.timeframes:
                continue
            for derived_tf in DERIVED_TIMEFRAME_PLAN.get(base_tf, []):
                if derived_tf in self.derived_map and derived_tf not in derived_order:
                    derived_order.append(derived_tf)
        self.available_timeframes: List[str] = list(self.timeframes) + derived_order
        self._lock = threading.RLock()
        self._ready = threading.Event()
        self._stop_event = threading.Event()
        self._persist_thread: Optional[threading.Thread] = None
        self._backfill_thread: Optional[threading.Thread] = None
        self._watch_tasks: List = []          # asyncio Task — 每个基础周期一个 watch
        self._watch_loop_task = None          # master watch 协程
        self._cold_start_done = False
        # 记录断线后需要从哪个 since 重新拉取（symbol -> timeframe -> since_ms）
        self._resume_since: Dict[str, Dict[str, int]] = {}
        # 恢复点持久化文件
        self._resume_file: Path = self.data_dir / "resume_since.json"
        # 尝试加载历史恢复点
        self._load_resume_since()
        self._update_callbacks: List = []

    def _load_config(self) -> Dict[str, object]:
        """读取 JSON 配置文件。"""
        if not self.config_path.exists():
            raise FileNotFoundError(f"未找到配置文件: {self.config_path}")
        with self.config_path.open("r", encoding="utf-8") as fp:
            return json.load(fp)

    def _load_symbols(self, config: Dict[str, object]) -> List[str]:
        """从 symbols 列表、逗号分隔字符串或单字段 symbol 解析交易对，去重保序。"""
        raw_symbols: List[str] = []
        symbols_value = config.get("symbols")
        if isinstance(symbols_value, list):
            raw_symbols = [str(item).strip() for item in symbols_value if isinstance(item, str) and item.strip()]
        elif isinstance(symbols_value, str) and symbols_value.strip():
            raw_symbols = [item.strip() for item in symbols_value.split(",") if item.strip()]
        symbol_single = config.get("symbol")
        if not raw_symbols and isinstance(symbol_single, str) and symbol_single.strip():
            raw_symbols = [symbol_single.strip()]
        if not raw_symbols:
            raise ValueError("配置文件必须提供 symbols（列表或逗号分隔字符串）或 symbol 字段")
        unique: List[str] = []
        for item in raw_symbols:
            if item not in unique:
                unique.append(item)
        return unique

    def _get_start_time(self, timeframe: str) -> int:
        """获取指定周期的 start_time（毫秒）。优先使用 start_time_per_tf，否则回退全局。"""
        return self.start_time_map.get(timeframe, self.start_time_ms)

    def _validate_timeframes(self, configured: Optional[Iterable[str]]) -> List[str]:
        """校验周期在允许集合内；未配置则默认 TIMEFRAME_ORDER 全部；顺序优先按 TIMEFRAME_ORDER。"""
        if not configured:
            return list(TIMEFRAME_ORDER)
        invalid = [tf for tf in configured if tf not in ALLOWED_TIMEFRAMES]
        if invalid:
            raise ValueError(f"不支持的时间周期: {invalid}. 允许值: {sorted(ALLOWED_TIMEFRAMES)}")
        unique = []
        seen = set()
        for tf in TIMEFRAME_ORDER:
            if tf in configured and tf not in seen:
                unique.append(tf)
                seen.add(tf)
        for tf in configured:
            if tf not in seen:
                unique.append(tf)
                seen.add(tf)
        return unique

    def _init_exchange(self):
        """实例化两个交易所实例：REST 用 ccxt，WebSocket 用 ccxt.pro。"""
        # REST 实例：供冷启动回填、历史补数、定时落盘等同步代码使用
        if not hasattr(ccxt, self.exchange_name):
            raise ValueError(f"不支持的交易所: {self.exchange_name}")
        exchange_class = getattr(ccxt, self.exchange_name)
        exchange = exchange_class({"enableRateLimit": True})
        if exchange.id == "binance":
            exchange.options.setdefault("defaultType", "future")
        exchange.load_markets()
        logger.info("已初始化交易所 %s (ccxt REST)", exchange.id)

        # WebSocket 实例：供实时 K 线监听使用
        if not hasattr(ccxt_pro, self.exchange_name):
            raise ValueError(f"不支持的交易所: {self.exchange_name} (ccxt.pro)")
        ws_exchange_class = getattr(ccxt_pro, self.exchange_name)
        self._ws_exchange = ws_exchange_class({"newUpdates": True})
        if self._ws_exchange.id == "binance":
            self._ws_exchange.options.setdefault("defaultType", "future")
        logger.info("已初始化交易所 %s (ccxt.pro, WebSocket 已启用)", self._ws_exchange.id)

        return exchange

    def _record_resume_for_timeframe(self, tf: str) -> None:
        """WebSocket 断线时为该周期所有 symbol 记录恢复点，避免 gap。"""
        for symbol in self.symbols:
            with self._lock:
                current = self.data.get(symbol, {}).get(tf, [])
            if current:
                last_ts = int(current[-1]["timestamp"])
            else:
                last_ts = self._get_start_time(tf)
            tf_ms = TIMEFRAME_TO_MS[tf]
            since_ms = max(self._get_start_time(tf), last_ts - tf_ms)
            existing = self._get_resume_since(symbol, tf)
            if existing is None:
                self._set_resume_since(symbol, tf, since_ms)

    async def _backfill_resume_data(self) -> None:
        """启动 WebSocket watch 前的历史补数，通过 REST 补齐 resume_since gap。"""
        loop = asyncio.get_running_loop()
        for symbol in self.symbols:
            for tf in self.timeframes:
                if self._stop_event.is_set():
                    return
                resume_since = self._get_resume_since(symbol, tf)
                if resume_since is None:
                    continue
                logger.info("WebSocket 启动前补数: %s %s since=%s", symbol, tf,
                            to_utc_iso(resume_since))
                try:
                    history = await loop.run_in_executor(
                        None, self._fetch_history, symbol, tf, resume_since,
                    )
                    with self._lock:
                        current = self.data.setdefault(symbol, {}).get(tf, [])
                        merged = self._merge_candles(tf, current, history)
                        self.data[symbol][tf] = merged
                    self._notify_update(symbol, tf)
                    self._clear_resume_since(symbol, tf)
                    self._trim(symbol, tf)
                except Exception as exc:
                    logger.error("启动前补数失败 %s %s: %s", symbol, tf, exc)

    async def _watch_single_tf(self, tf: str) -> None:
        """单个基础周期的 WebSocket 监听协程。

        循环调用 watch_ohlcv_for_symbols，新 K 线到达后从 exchange.ohlcvs 缓存
        读取最新数据合并到内存并广播。
        """
        pairs = [[symbol, tf] for symbol in self.symbols]
        backoff = 2.0

        while not self._stop_event.is_set():
            try:
                await self._ws_exchange.watch_ohlcv_for_symbols(pairs)

                # watch_ohlcv_for_symbols 更新了 exchange.ohlcvs 缓存，
                # 从缓存中读取每个 symbol 的最新 candle 合并到本地
                for symbol in self.symbols:
                    ohlcv_cache = self._ws_exchange.ohlcvs.get(symbol, {}).get(tf)
                    if ohlcv_cache is None:
                        continue
                    cached = list(ohlcv_cache)
                    if not cached:
                        continue

                    with self._lock:
                        current = self.data.setdefault(symbol, {}).get(tf, [])
                        if current and current[-1]["timestamp"] >= cached[-1][0]:
                            continue  # 没有新数据
                        merged = self._merge_candles(tf, current, cached)
                        self.data[symbol][tf] = merged

                    self._notify_update(symbol, tf)
                    self._trim(symbol, tf)

                backoff = 2.0  # 成功后重置

            except ccxt.NetworkError as exc:
                logger.warning("WebSocket 网络错误 (tf=%s): %s，%.1fs 后重连", tf, exc, backoff)
                self._record_resume_for_timeframe(tf)
                await asyncio.sleep(backoff)
                backoff = min(backoff * 1.5, 60.0)

            except ccxt.BaseError as exc:
                logger.error("WebSocket 交易所错误 (tf=%s): %s，%.1fs 后重连", tf, exc, backoff)
                self._record_resume_for_timeframe(tf)
                await asyncio.sleep(backoff)
                backoff = min(backoff * 1.5, 60.0)

            except asyncio.CancelledError:
                logger.info("WebSocket 监听 (tf=%s) 被取消", tf)
                break

            except Exception:
                logger.exception("WebSocket 监听 (tf=%s) 未预期错误", tf)
                self._record_resume_for_timeframe(tf)
                await asyncio.sleep(backoff)
                backoff = min(backoff * 1.5, 60.0)

        logger.info("WebSocket 监听 (tf=%s) 已退出", tf)

    async def _watch_loop(self) -> None:
        """主 WebSocket 监听协程：先启动 watch 任务，再并发补历史 gap。"""
        for tf in self.timeframes:
            task = asyncio.create_task(self._watch_single_tf(tf), name=f"watch-{tf}")
            self._watch_tasks.append(task)

        logger.info("WebSocket 监听已启动: %d 个时间周期, %d 个任务",
                    len(self.timeframes), len(self._watch_tasks))

        # 补历史数据与 watch 任务并发进行，不阻塞实时数据接收
        try:
            await self._backfill_resume_data()
        except Exception:
            logger.exception("启动前补数失败")

        try:
            await asyncio.gather(*self._watch_tasks, return_exceptions=True)
        except asyncio.CancelledError:
            pass
        finally:
            logger.info("所有 WebSocket 监听任务已结束")

    def _data_file_path(self, symbol: str, timeframe: str) -> Path:
        """单交易对单周期的 CSV 路径：data_dir/tf/exchange_symbol_tf.csv。"""
        symbol_safe = symbol.replace("/", "_").replace(":", "_")
        return self.data_dir / timeframe / f"{self.exchange.id}_{symbol_safe}_{timeframe}.csv"

    def _load_local(self, symbol: str, timeframe: str) -> List[Dict[str, float]]:
        """启动时从磁盘加载已有 K 线，损坏行跳过，按时间排序。"""
        path = self._data_file_path(symbol, timeframe)
        if not path.exists():
            return []
        loaded: List[Dict[str, float]] = []
        with path.open("r", encoding="utf-8", newline="") as fp:
            reader = csv.DictReader(fp)
            for row in reader:
                try:
                    loaded.append(
                        {
                            "timestamp": int(row["timestamp"]),
                            "datetime": row.get("datetime") or to_utc_iso(int(row["timestamp"])),
                            "open": float(row["open"]),
                            "high": float(row["high"]),
                            "low": float(row["low"]),
                            "close": float(row["close"]),
                            "volume": float(row["volume"]),
                        }
                    )
                except (KeyError, ValueError):
                    logger.warning("忽略损坏的行: %s", row)
        loaded.sort(key=lambda item: item["timestamp"])
        logger.info("交易对 %s 时间周期 %s 加载本地K线数量: %s", symbol, timeframe, len(loaded))
        return loaded

    def _merge_candles(
        self,
        timeframe: str,
        base: List[Dict[str, float]],
        new_candles: Iterable[Iterable[float]],
    ) -> List[Dict[str, float]]:
        """按 timestamp 去重合并，新数据覆盖同时间戳旧数据。"""
        merged = {entry["timestamp"]: entry for entry in base}
        for candle in new_candles:
            entry = candle_to_dict(candle)
            merged[entry["timestamp"]] = entry
        ordered = list(sorted(merged.values(), key=lambda item: item["timestamp"]))
        logger.debug("时间周期 %s 合并后K线数量: %s", timeframe, len(ordered))
        return ordered

    def _trim(self, symbol: str, timeframe: str) -> None:
        """将内存缓存裁剪到 MAX_CANDLES_IN_MEMORY 上限（全量数据已在 CSV 中）。"""
        max_count = MAX_CANDLES_IN_MEMORY.get(timeframe)
        if max_count is None:
            return
        sym_data = self.data.get(symbol)
        if sym_data is None:
            return
        candles = sym_data.get(timeframe)
        if candles is None or len(candles) <= max_count:
            return
        sym_data[timeframe] = candles[-max_count:]

    def _write_to_disk(self, symbol: str, timeframe: str, data: List[Dict[str, float]]) -> None:
        """先写临时文件再 replace，避免写入中断导致 CSV 损坏。"""
        path = self._data_file_path(symbol, timeframe)
        path.parent.mkdir(parents=True, exist_ok=True)
        tmp_path = path.with_suffix(path.suffix + ".tmp")
        try:
            with tmp_path.open("w", encoding="utf-8", newline="") as fp:
                writer = csv.DictWriter(fp, fieldnames=CSV_FIELDNAMES)
                writer.writeheader()
                writer.writerows(data)
            os.replace(tmp_path, path)
        finally:
            if tmp_path.exists():
                try:
                    tmp_path.unlink()
                except OSError:
                    pass
        logger.info("交易对 %s 时间周期 %s 已写入磁盘 (%s 根K线)", symbol, timeframe, len(data))

    def _fetch_history(self, symbol: str, timeframe: str, since_ms: int) -> List[List[float]]:
        """从 since_ms 分页拉取直到接近当前时间。

        当 since_ms 早于交易所数据保留期时，先从最新数据往回探测有效起点，
        再向后分页补齐。"""
        results: List[List[float]] = []
        limit = 1500
        now_ms = self.exchange.milliseconds()
        tf_ms = TIMEFRAME_TO_MS[timeframe]

        # 先尝试直接拉取
        first_batch = self._fetch_ohlcv_retry(symbol, timeframe, since=since_ms, limit=limit)

        # 若返回空，或首根时间远晚于 since（交易所忽略了过老的 since），启用反向探测
        gap_ms = tf_ms * limit * 2
        first_skipped = (
            not first_batch
            or (first_batch[0][0] - since_ms > gap_ms and first_batch[-1][0] >= now_ms - tf_ms * 10)
        )
        if first_skipped:
            logger.info("since 过老被交易所跳过，启用反向探测 %s %s (since=%s)", symbol, timeframe, to_utc_iso(since_ms))
            # 反向探测：从当前时间往前翻页，找到实际最早可用的 K 线
            all_collected: List[List[float]] = []
            search_since = max(0, now_ms - tf_ms * limit)
            search_rounds = 0
            max_search = 200
            earliest_ts = None
            while search_rounds < max_search:
                search_rounds += 1
                batch = self._fetch_ohlcv_retry(symbol, timeframe, since=search_since, limit=limit)
                if not batch:
                    break
                all_collected = batch + all_collected
                first_ts = batch[0][0]
                if first_ts == search_since or first_ts <= since_ms:
                    earliest_ts = first_ts
                    break
                search_since = max(0, first_ts - tf_ms * limit)
                time.sleep(self.exchange.rateLimit / 1000 if self.exchange.rateLimit else 0.2)
            if earliest_ts is None and all_collected:
                earliest_ts = all_collected[0][0]
            if earliest_ts is not None:
                logger.info("反向探测找到最早 %s: %s (%s)", symbol, to_utc_iso(earliest_ts), timeframe)
            # 用收集到的数据作为起点，继续向前分页到 now
            results = all_collected
            if results:
                last_ts = results[-1][0]
                if last_ts < now_ms - tf_ms:
                    extra = self._fetch_ohlcv_paginate(symbol, timeframe, last_ts + tf_ms, now_ms, limit)
                    results.extend(extra)
            logger.info("交易对 %s 时间周期 %s 拉取历史K线数量: %s", symbol, timeframe, len(results))
            return results

        # 直接拉取成功，正常向后分页
        results = list(first_batch)
        last_ts = results[-1][0]
        if last_ts < now_ms - tf_ms:
            extra = self._fetch_ohlcv_paginate(symbol, timeframe, last_ts + tf_ms, now_ms, limit)
            results.extend(extra)
        logger.info("交易对 %s 时间周期 %s 拉取历史K线数量: %s", symbol, timeframe, len(results))
        return results

    def _fetch_ohlcv_retry(self, symbol: str, timeframe: str, since: int, limit: int) -> List[List[float]]:
        """带重试的单次 fetch_ohlcv，限频/网络错误时等待重试。"""
        max_attempts = 5
        for attempt in range(max_attempts):
            try:
                return self.exchange.fetch_ohlcv(symbol, timeframe=timeframe, since=since, limit=limit)
            except ccxt.RateLimitExceeded as exc:
                wait_s = (self.exchange.rateLimit / 1000 if self.exchange.rateLimit else 1) * (attempt + 1)
                logger.warning("触发频率限制，等待 %.1fs: %s", wait_s, exc)
                time.sleep(wait_s)
            except ccxt.BaseError as exc:
                logger.error("拉取K线失败 (%s, %s, since=%s): %s (attempt %s/%s)",
                             timeframe, symbol, since, exc, attempt + 1, max_attempts)
                time.sleep(3 * (attempt + 1))
        logger.error("拉取K线失败，已达最大重试次数: %s %s", symbol, timeframe)
        return []

    def _fetch_ohlcv_paginate(self, symbol: str, timeframe: str, start_ms: int, end_ms: int, limit: int) -> List[List[float]]:
        """从 start_ms 向后分页拉取到 end_ms。"""
        results: List[List[float]] = []
        tf_ms = TIMEFRAME_TO_MS[timeframe]
        fetch_since = start_ms
        max_rounds = 5000
        rounds = 0
        while fetch_since < end_ms and rounds < max_rounds:
            rounds += 1
            candles = self._fetch_ohlcv_retry(symbol, timeframe, since=fetch_since, limit=limit)
            if not candles:
                break
            results.extend(candles)
            last_ts = candles[-1][0]
            fetch_since = last_ts + tf_ms
            if last_ts >= end_ms - tf_ms:
                break
            time.sleep(self.exchange.rateLimit / 1000 if self.exchange.rateLimit else 0.2)
        return results

    def initialize(self) -> None:
        """快速启动：仅加载本地磁盘已有数据到内存，立即设 _ready 让服务可用。后台再补拉交易所数据。"""
        logger.info("开始初始化数据提供商（仅加载本地数据）")
        for symbol in self.symbols:
            for timeframe in self.timeframes:
                existing = self._load_local(symbol, timeframe)
                with self._lock:
                    self.data.setdefault(symbol, {})[timeframe] = existing
                self._trim(symbol, timeframe)
                logger.info("交易对 %s 时间周期 %s 已加载本地K线: %s 根", symbol, timeframe, len(existing))
        self._ready.set()
        logger.info("数据初始化完成，服务已就绪（后台将补拉交易所数据）")

    def _cold_start_backfill(self) -> None:
        """后台一次性回填：从交易所拉取每个 symbol/timeframe 的缺失历史，合并后写盘。"""
        logger.info("开始后台回填交易所数据")
        for symbol in self.symbols:
            for timeframe in self.timeframes:
                if self._stop_event.is_set():
                    return
                with self._lock:
                    existing = list(self.data.get(symbol, {}).get(timeframe, []))
                tf_ms = TIMEFRAME_TO_MS[timeframe]
                last_ts = existing[-1]["timestamp"] if existing else None

                # 计算 fetch_since：优先从数据末尾补，但若数据量极少
                #（刷新线程抢先写入的少量近期数据），则回退到 start_time 做全量回填
                if last_ts is not None:
                    if len(existing) > RECENT_CANDLE_LIMIT * 3:
                        # 已有足够历史数据，仅增量补拉
                        fetch_since = (existing[-2]["timestamp"] if len(existing) >= 2
                                       else max(0, existing[-1]["timestamp"] - tf_ms))
                    else:
                        # 只有刷新线程抢先写入的少量数据，从 start_time 全量回填
                        fetch_since = self._get_start_time(timeframe)
                        logger.info("数据量不足 (%s 根)，从 start_time 回填 %s %s",
                                    len(existing), symbol, timeframe)
                else:
                    fetch_since = self._get_start_time(timeframe)
                try:
                    history = self._fetch_history(symbol, timeframe, fetch_since)
                    # 再次读取最新数据后合并，避免覆盖并发刷新线程写入的更新
                    with self._lock:
                        current = list(self.data.get(symbol, {}).get(timeframe, []))
                        merged = self._merge_candles(timeframe, current, history)
                        self.data[symbol][timeframe] = merged
                    self._write_to_disk(symbol, timeframe, merged)
                    self._trim(symbol, timeframe)
                    self._notify_update(symbol, timeframe)
                    logger.info("回填完成 %s %s: +%s 根新K线", symbol, timeframe, len(history))
                except Exception as exc:
                    logger.error("回填失败 %s %s: %s", symbol, timeframe, exc)
                    self._set_resume_since(symbol, timeframe, fetch_since)
        logger.info("后台回填全部完成")
        self._cold_start_done = True
        
    def resample_df(self, df: pd.DataFrame, interval: int) -> pd.DataFrame:
        """将基础周期 DataFrame 聚合为 interval 分钟周期（freqtrade technical.util）。"""
        return resample_to_interval(df, interval)
    
    def _save_resume_since(self) -> None:
        """将断线恢复点持久化到 resume_since.json（原子替换）。"""
        path = self._resume_file
        path.parent.mkdir(parents=True, exist_ok=True)
        tmp_path = path.with_suffix(path.suffix + ".tmp")
        with self._lock:
            snapshot = {
                symbol: {tf: int(since) for tf, since in tf_map.items()}
                for symbol, tf_map in self._resume_since.items()
            }
        try:
            with tmp_path.open("w", encoding="utf-8") as fp:
                json.dump(snapshot, fp, ensure_ascii=False, separators=(",", ":"))
            os.replace(tmp_path, path)
        finally:
            if tmp_path.exists():
                try:
                    tmp_path.unlink()
                except OSError:
                    pass
        logger.debug("恢复点已保存到磁盘: %s", path)
    
    def _load_resume_since(self) -> None:
        """启动时加载恢复点；与内存合并时取更早的 since，避免漏拉。"""
        path = self._resume_file
        if not path.exists():
            return
        try:
            with path.open("r", encoding="utf-8") as fp:
                raw = json.load(fp)
        except Exception as exc:
            logger.warning("恢复点文件读取失败，忽略: %s (%s)", path, exc)
            return
        if not isinstance(raw, dict):
            logger.warning("恢复点文件格式错误，忽略: %s", path)
            return
        loaded: Dict[str, Dict[str, int]] = {}
        for symbol, tf_map in raw.items():
            if not isinstance(tf_map, dict):
                continue
            per_symbol: Dict[str, int] = {}
            for timeframe, since in tf_map.items():
                try:
                    per_symbol[str(timeframe)] = int(since)
                except Exception:
                    continue
            if per_symbol:
                loaded[str(symbol)] = per_symbol
        if not loaded:
            return
        with self._lock:
            # 合并为更早的 since，避免遗漏
            for symbol, tf_map in loaded.items():
                cur = self._resume_since.setdefault(symbol, {})
                for timeframe, since in tf_map.items():
                    prev = cur.get(timeframe)
                    if prev is None or since < prev:
                        cur[timeframe] = since
        logger.info("已加载恢复点: %s", path)
    
    def _get_resume_since(self, symbol: str, timeframe: str) -> Optional[int]:
        """若曾断线，返回应从哪一毫秒起补拉该 symbol/tf。"""
        with self._lock:
            return self._resume_since.get(symbol, {}).get(timeframe)
    
    def _set_resume_since(self, symbol: str, timeframe: str, since_ms: int) -> None:
        """断线时写入恢复点（取更早的 since 以免漏数据），并持久化到磁盘。"""
        with self._lock:
            per_symbol = self._resume_since.setdefault(symbol, {})
            prev = per_symbol.get(timeframe)
            # 取更早的 since，避免跳过数据
            if prev is None or since_ms < prev:
                per_symbol[timeframe] = since_ms
                logger.warning(
                    "记录断线恢复点: %s %s since=%s (%s)",
                    symbol,
                    timeframe,
                    since_ms,
                    to_utc_iso(since_ms),
                )
        # 同步写盘
        self._save_resume_since()
    
    def _clear_resume_since(self, symbol: str, timeframe: str) -> None:
        """补数成功后清除该 symbol/tf 的恢复点。"""
        with self._lock:
            if symbol in self._resume_since and timeframe in self._resume_since[symbol]:
                del self._resume_since[symbol][timeframe]
                if not self._resume_since[symbol]:
                    del self._resume_since[symbol]
                logger.info("清除断线恢复点: %s %s", symbol, timeframe)
        # 同步写盘
        self._save_resume_since()
    
    def on_update(self, callback) -> None:
        """注册数据更新回调（签名: callback(symbol, timeframe)）。"""
        self._update_callbacks.append(callback)

    def _notify_update(self, symbol: str, timeframe: str) -> None:
        """通知所有回调：某 symbol/timeframe 数据已更新。"""
        for cb in self._update_callbacks:
            try:
                cb(symbol, timeframe)
            except Exception as exc:
                logger.error("数据更新回调异常: %s", exc)

    def start_background_workers(self) -> None:
        """启动后台线程：冷启动回填 + 周期性落盘。WebSocket 监听由 lifespan 异步启动。"""
        self._stop_event.clear()
        self._backfill_thread = threading.Thread(target=self._cold_start_backfill, name="backfill-loop", daemon=True)
        self._persist_thread = threading.Thread(target=self._persist_loop, name="persist-loop", daemon=True)
        self._backfill_thread.start()
        self._persist_thread.start()
        logger.info("后台线程已启动（回填 + 落盘）")

    def start_watch_tasks(self) -> None:
        """在当前 asyncio event loop 上启动 WebSocket 监听。必须在 lifespan 内调用。"""
        loop = asyncio.get_running_loop()
        self._watch_loop_task = loop.create_task(self._watch_loop(), name="watch-master")
        logger.info("WebSocket 监听主任务已创建")

    def stop(self) -> None:
        """停止后台线程和异步 WebSocket 监听任务。"""
        self._stop_event.set()

        # 取消 WebSocket watch 任务（它们检查 stop_event 后会退出）
        for task in getattr(self, '_watch_tasks', []):
            if not task.done():
                task.cancel()
        if hasattr(self, '_watch_loop_task') and self._watch_loop_task is not None:
            if not self._watch_loop_task.done():
                self._watch_loop_task.cancel()

        # Join 所有后台线程
        for thread, name in [
            (self._backfill_thread, "backfill"),
            (self._persist_thread, "persist"),
        ]:
            if thread:
                thread.join(timeout=5)
                if thread.is_alive():
                    logger.warning("后台线程 %s 未能在 5s 内结束", name)

        logger.info("数据提供商已停止")

    def _persist_loop(self) -> None:
        """每隔 PERSIST_INTERVAL 秒把内存快照写 CSV 并保存恢复点。"""
        while not self._stop_event.wait(PERSIST_INTERVAL):
            self._persist_all()

    def _persist_all(self) -> None:
        """在锁内复制 data 后落盘，避免长时间持锁。有限制的时间周期跳过不写（CSV 已有全量历史）。"""
        if not self._ready.is_set():
            return
        with self._lock:
            snapshot = {
                symbol: {tf: list(data) for tf, data in tf_map.items()}
                for symbol, tf_map in self.data.items()
            }
        for symbol, tf_map in snapshot.items():
            for timeframe, data in tf_map.items():
                if timeframe in MAX_CANDLES_IN_MEMORY:
                    continue  # 内存只有尾部 N 根，不覆盖 CSV 全量
                self._write_to_disk(symbol, timeframe, data)
        # 周期性也保存一次恢复点，保证一致性
        self._save_resume_since()

    def is_ready(self) -> bool:
        return self._ready.is_set()

    def wait_ready(self, timeout: Optional[float] = None) -> bool:
        return self._ready.wait(timeout)

    def get_available_timeframes(self) -> List[str]:
        return list(self.available_timeframes)

    def get_derived_timeframes(self) -> List[str]:
        return list(self.derived_map.keys())

    def _get_base_klines(
        self,
        symbol: str,
        timeframe: str,
        start_ms: Optional[int],
        end_ms: Optional[int],
        limit: Optional[int],
    ) -> List[Dict[str, float]]:
        """从内存读取已缓存的基础周期 K 线并按时间/limit 裁剪；内存不足时从 CSV 补充。"""
        with self._lock:
            candles = list(self.data.get(symbol, {}).get(timeframe, []))
        # 有限制的时间周期，若内存不够老则从 CSV 补充历史
        if timeframe in MAX_CANDLES_IN_MEMORY and candles and start_ms is not None:
            if start_ms < candles[0]["timestamp"]:
                csv_candles = self._load_local(symbol, timeframe)
                if csv_candles:
                    merged = {c["timestamp"]: c for c in csv_candles}
                    for c in candles:
                        merged[c["timestamp"]] = c
                    candles = sorted(merged.values(), key=lambda x: x["timestamp"])
        if start_ms is not None:
            candles = [row for row in candles if row["timestamp"] >= start_ms]
        if end_ms is not None:
            candles = [row for row in candles if row["timestamp"] <= end_ms]
        if limit:
            candles = candles[-limit:]
        return candles

    def get_klines(
        self,
        symbol: str,
        timeframe: str,
        start_time: Optional[object] = None,
        end_time: Optional[object] = None,
        limit: Optional[int] = None,
    ) -> List[Dict[str, float]]:
        """对外查询：基础周期直接返回；衍生周期从 derived_map 取 base，resample 后对齐时间戳再裁剪。"""
        if symbol not in self.symbols:
            raise HTTPException(status_code=404, detail=f"symbol {symbol} 不可用")
        self.wait_ready()
        start_ms = parse_timestamp(start_time)
        end_ms = parse_timestamp(end_time)
        if timeframe in self.timeframes:
            return self._get_base_klines(symbol, timeframe, start_ms, end_ms, limit)
        base_tf = self.derived_map.get(timeframe)
        if not base_tf:
            raise HTTPException(status_code=404, detail=f"{symbol} 时间周期 {timeframe} 不可用")
        target_minutes = timeframe_to_minutes(timeframe)
        if target_minutes is None:
            raise HTTPException(status_code=400, detail=f"不支持的时间周期: {timeframe}")
        target_ms = target_minutes * 60_000
        # 起点前移一根目标周期长度，保证首根合成 K 边界完整
        adjusted_start = None if start_ms is None else max(0, start_ms - target_ms)
        base_candles = self._get_base_klines(symbol, base_tf, adjusted_start, end_ms, None)
        if not base_candles:
            return []
        df = pd.DataFrame(base_candles)
        if df.empty:
            return []
        df = df.drop_duplicates(subset=["timestamp"], keep="last").sort_values("timestamp")
        df["date"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
        # resample_to_interval 按「分钟」目标周期聚合 OHLCV
        resampled = self.resample_df(df, target_minutes)
        if resampled is None or resampled.empty:
            return []
        # 统一得到毫秒 timestamp 列（resample 可能返回 date 或 DatetimeIndex）
        if "timestamp" in resampled.columns:
            resampled_df = resampled.copy()
        else:
            resampled_df = resampled.copy()
            if "date" in resampled_df.columns:
                dates = pd.to_datetime(resampled_df["date"], utc=True, errors="coerce")
                resampled_df["timestamp"] = (dates.astype("int64", copy=False) // 1_000_000).astype("int64")
            elif isinstance(resampled_df.index, pd.DatetimeIndex):
                idx = resampled_df.index
                if idx.tz is None:
                    idx = idx.tz_localize("UTC")
                else:
                    idx = idx.tz_convert("UTC")
                resampled_df["timestamp"] = (idx.astype("int64", copy=False) // 1_000_000).astype("int64")
            else:
                raise HTTPException(status_code=500, detail=f"聚合结果缺少 timestamp 列 ({timeframe})")
        resampled_df = resampled_df.dropna(subset=["timestamp"]).sort_values("timestamp")
        if start_ms is not None:
            resampled_df = resampled_df[resampled_df["timestamp"] >= start_ms]
        if end_ms is not None:
            resampled_df = resampled_df[resampled_df["timestamp"] <= end_ms]
        if resampled_df.empty:
            return []
        resampled_df["datetime"] = resampled_df["timestamp"].apply(to_utc_iso)
        for column in ["open", "high", "low", "close", "volume"]:
            if column not in resampled_df.columns:
                resampled_df[column] = 0.0
        resampled_df = resampled_df[["timestamp", "datetime", "open", "high", "low", "close", "volume"]]
        result = resampled_df.to_dict("records")
        if limit:
            result = result[-limit:]
        logger.debug(
            "衍生周期返回",
            extra={
                "symbol": symbol,
                "timeframe": timeframe,
                "base_timeframe": base_tf,
                "count": len(result),
            },
        )
        return result


def create_app(provider: DataProvider) -> FastAPI:
    """构造 FastAPI 应用：lifespan 内同步 initialize 并启动后台拉数；WebSocket 实时推送。"""
    ws_manager = WebSocketManager()

    def _on_data_update(symbol: str, base_tf: str) -> None:
        """后台刷新线程回调：广播基础及衍生周期更新给 WebSocket 订阅者。"""
        with provider._lock:
            base_data = list(provider.data.get(symbol, {}).get(base_tf, []))
        recent = base_data[-WS_UPDATE_CANDLE_COUNT:] if base_data else []
        if recent:
            ws_manager.broadcast_from_thread(symbol, base_tf, recent)
        for derived_tf, src_base in provider.derived_map.items():
            if src_base != base_tf or not ws_manager.has_subscribers(symbol, derived_tf):
                continue
            try:
                target_min = timeframe_to_minutes(derived_tf)
                if target_min is None:
                    continue
                now_ms = int(time.time() * 1000)
                window_ms = target_min * 60_000 * (WS_UPDATE_CANDLE_COUNT + 2)
                derived = provider.get_klines(
                    symbol, derived_tf, start_time=now_ms - window_ms, limit=WS_UPDATE_CANDLE_COUNT,
                )
                if derived:
                    ws_manager.broadcast_from_thread(symbol, derived_tf, derived)
            except Exception as exc:
                logger.debug("衍生周期广播失败 %s %s: %s", symbol, derived_tf, exc)

    @asynccontextmanager
    async def lifespan(app: FastAPI):
        loop = asyncio.get_running_loop()
        ws_manager.set_loop(loop)
        provider.on_update(_on_data_update)
        await loop.run_in_executor(None, provider.initialize)
        provider.start_background_workers()
        # 启动 WebSocket 实时监听（asyncio 后台任务）
        provider.start_watch_tasks()
        try:
            yield
        finally:
            provider.stop()

    app = FastAPI(title="Chan 数据提供商", version="1.0.0", lifespan=lifespan)

    app.add_middleware(
        CORSMiddleware,
        allow_origins=["*"],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )

    @app.get("/health")
    async def health() -> Dict[str, object]:
        """存活检查：交易所、交易对、基础/衍生周期、是否已完成冷启动。"""
        return {
            "status": "ok",
            "exchange": provider.exchange_name,
            "symbols": provider.symbols,
            "base_timeframes": provider.timeframes,
            "derived_timeframes": provider.get_derived_timeframes(),
            "timeframes": provider.get_available_timeframes(),
            "ready": provider.is_ready(),
        }

    @app.get("/timeframes")
    async def list_timeframes() -> Dict[str, List[str]]:
        """返回配置的基础周期与可合成的衍生周期列表。"""
        provider.wait_ready()
        return {
            "base_timeframes": provider.timeframes,
            "derived_timeframes": provider.get_derived_timeframes(),
            "timeframes": provider.get_available_timeframes(),
        }

    @app.get("/api/candles")
    async def api_candles(
        symbol: str = Query(..., description="如 BTC/USDT"),
        tf: str = Query("1m", description="时间周期"),
        start: Optional[int] = Query(None, description="开始时间戳(ms)"),
        end: Optional[int] = Query(None, description="结束时间戳(ms)"),
        limit: Optional[int] = Query(None, description="可选，限制返回数量"),
    ):
        """按交易对与时间周期返回 OHLCV；tf 支持配置的基础周期及衍生合成周期。"""
        data = provider.get_klines(symbol=symbol, timeframe=tf, start_time=start, end_time=end, limit=limit)
        return data

    homepage_path = Path(__file__).resolve().parent / "homepage.html"
    docs_path = Path(__file__).resolve().parent / "api_docs.html"

    @app.get("/", response_class=HTMLResponse)
    async def root():
        """服务主页。"""
        if homepage_path.exists():
            return HTMLResponse(content=homepage_path.read_text(encoding="utf-8"))
        return HTMLResponse(content="<h1>主页页面未找到</h1>", status_code=404)

    @app.get("/api-manual", response_class=HTMLResponse)
    async def api_manual():
        """数据服务 API 使用手册页面。"""
        derived_plan = DERIVED_TIMEFRAME_PLAN
        timeframe_ms = TIMEFRAME_TO_MS
        memory_limits = MAX_CANDLES_IN_MEMORY
        base_tfs = provider.timeframes
        derived_tfs = provider.get_derived_timeframes()
        all_tfs = provider.get_available_timeframes()
        symbols = provider.symbols

        # Build derived timeframe table rows
        derived_rows = ""
        for base, derived_list in derived_plan.items():
            present = "✓" if base in base_tfs else "✗"
            derived_rows += f"<tr><td><code>{base}</code></td><td>{present}</td><td>{', '.join(f'<code>{d}</code>' for d in derived_list)}</td></tr>"

        # Build memory limits table rows
        memory_rows = ""
        for tf in base_tfs:
            limit = memory_limits.get(tf)
            if limit is None:
                span = "unlimited"
            elif tf == "1m":
                span = f"{limit:,} candles (~3 months)"
            elif tf == "1h":
                span = f"{limit:,} candles (~1 year)"
            else:
                span = f"{limit:,} candles"
            memory_rows += f"<tr><td><code>{tf}</code></td><td>{span}</td></tr>"

        # Pre-build config JSON example to avoid f-string escaping issues
        import json as _json
        _config_obj = {
            "exchange": "binance",
            "symbols": ["BTC/USDT:USDT", "ETH/USDT:USDT"],
            "start_time": "2024-01-01T00:00:00Z",
            "start_time_per_tf": {"1m": "2026-01-01T00:00:00Z"},
            "timeframes": ["1m", "1h", "1d", "1w"],
            "data_dir": "./data",
        }
        config_example = _json.dumps(_config_obj, indent=2, ensure_ascii=False)

        return f"""<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Data Provider — API Manual</title>
    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet">
    <link href="https://cdn.jsdelivr.net/npm/bootstrap-icons@1.8.1/font/bootstrap-icons.css" rel="stylesheet">
    <style>
        body {{
            font-family: "Helvetica Neue", Arial, "PingFang SC", "Microsoft YaHei", sans-serif;
            background: #f8f9fa; color: #333; padding: 0;
        }}
        .hero {{
            background: linear-gradient(135deg, #1a1a2e 0%, #16213e 50%, #0f3460 100%);
            color: #fff; padding: 48px 0 40px; margin-bottom: 32px;
        }}
        .hero h1 {{ font-size: 2rem; font-weight: 700; margin: 0; }}
        .hero .badge {{ font-size: 0.8rem; vertical-align: middle; margin-left: 10px; }}
        .hero p {{ opacity: 0.85; margin: 8px 0 0; font-size: 0.95rem; }}
        .nav-pills .nav-link {{ color: #495057; border-radius: 6px; padding: 6px 16px; font-size: 0.9rem; }}
        .nav-pills .nav-link.active {{ background: #0d6efd; }}
        .section {{ display: none; }}
        .section.active {{ display: block; }}
        .card {{ border: 1px solid #e9ecef; box-shadow: 0 1px 3px rgba(0,0,0,0.04); margin-bottom: 20px; }}
        .card-header {{ background: #fff; border-bottom: 1px solid #e9ecef; font-weight: 600; font-size: 0.95rem; padding: 12px 18px; }}
        pre {{ background: #1e1e2e; color: #cdd6f4; border-radius: 8px; padding: 16px; font-size: 0.85rem; line-height: 1.6; overflow-x: auto; }}
        pre code {{ color: inherit; }}
        .method-badge {{ display: inline-block; padding: 2px 8px; border-radius: 4px; font-size: 0.75rem; font-weight: 700; margin-right: 8px; width: 52px; text-align: center; }}
        .method-GET {{ background: #d1fae5; color: #065f46; }}
        .method-WS  {{ background: #ede9fe; color: #5b21b6; }}
        .endpoint-path {{ font-family: monospace; font-size: 0.9rem; }}
        .table {{ font-size: 0.875rem; }}
        .table code {{ background: #f1f3f5; padding: 1px 5px; border-radius: 3px; font-size: 0.82rem; }}
        .status-ready {{ color: #198754; }}
        .status-notready {{ color: #dc3545; }}
        .param-required {{ color: #dc3545; font-weight: 600; }}
        .toc-item {{ cursor: pointer; }}
        footer {{ border-top: 1px solid #dee2e6; margin-top: 48px; padding: 20px 0; color: #868e96; font-size: 0.85rem; text-align: center; }}
        .endpoint-card {{ border-left: 3px solid #0d6efd; }}
        .endpoint-card.ws {{ border-left-color: #7c3aed; }}
    </style>
</head>
<body>

<div class="hero">
    <div class="container">
        <h1>Data Provider <span class="badge bg-success">v1.0.0</span></h1>
        <p>Cryptocurrency OHLCV data microservice — REST + WebSocket API for candlestick (K-line) data</p>
        <p style="font-size:0.82rem; opacity:0.7;">Exchange: {provider.exchange_name} &middot; Symbols: {len(symbols)} &middot; Timeframes: {len(all_tfs)} &middot; Status: <span class="{'status-ready' if provider.is_ready() else 'status-notready'}">{'Ready' if provider.is_ready() else 'Initializing…'}</span></p>
    </div>
</div>

<div class="container" style="max-width: 960px;">

    <!-- Tabs -->
    <ul class="nav nav-pills mb-4" id="manualTabs" role="tablist">
        <li class="nav-item"><button class="nav-link active" data-section="overview">Overview</button></li>
        <li class="nav-item"><button class="nav-link" data-section="rest">REST API</button></li>
        <li class="nav-item"><button class="nav-link" data-section="ws">WebSocket</button></li>
        <li class="nav-item"><button class="nav-link" data-section="timeframes">Timeframes</button></li>
        <li class="nav-item"><button class="nav-link" data-section="config">Configuration</button></li>
        <li class="nav-item"><button class="nav-link" data-section="examples">Code Examples</button></li>
    </ul>

    <!-- ==================== OVERVIEW ==================== -->
    <div class="section active" id="section-overview">
        <div class="card">
            <div class="card-header">What is Data Provider?</div>
            <div class="card-body">
                <p><strong>Data Provider</strong> is a standalone microservice that fetches, caches, and serves cryptocurrency OHLCV (Open/High/Low/Close/Volume) candlestick data from the Binance Futures exchange via CCXT.</p>
                <p>It decouples data acquisition from analysis — consumers request historical or real-time market data through a simple REST or WebSocket API instead of talking to exchange APIs directly.</p>
                <hr>
                <h6>Key Features</h6>
                <div class="row">
                    <div class="col-md-6">
                        <ul class="small">
                            <li>Fetches base OHLCV at <b>1m, 1h, 1d, 1w</b> from Binance</li>
                            <li>Synthesizes <b>{len(derived_tfs)} derived timeframes</b> by resampling</li>
                            <li>Dual storage: in-memory cache + CSV persistence</li>
                            <li>Background refresh every <b>{RECENT_FETCH_INTERVAL}s</b></li>
                        </ul>
                    </div>
                    <div class="col-md-6">
                        <ul class="small">
                            <li>REST endpoint at <code>/api/candles</code></li>
                            <li>WebSocket real-time push at <code>/ws</code></li>
                            <li>Automatic disconnect recovery</li>
                            <li>Fully Dockerized, single JSON config file</li>
                        </ul>
                    </div>
                </div>
            </div>
        </div>

        <div class="card">
            <div class="card-header">Architecture</div>
            <div class="card-body">
                <pre style="background:#f8f9fa; color:#333; text-align:center; font-size:0.8rem; line-height:1.4;">
┌───────────────────────┐
│   Binance Futures     │
│   (ccxt driver)       │
└──────────┬────────────┘
           │ fetch_ohlcv()
┌──────────┴────────────┐
│    DataProvider        │
│                        │
│  · In-memory cache     │
│  · CSV persistence     │
│  · Background refresh  │
│  · Derived timeframe   │
│    resample engine     │
└──────┬────────┬────────┘
       │        │
  REST │        │ WebSocket
       │        │
┌──────┴────────┴────────┐
│  Consumers (Flask app, │
│  browser, scripts…)    │
└────────────────────────┘</pre>
            </div>
        </div>

        <div class="card">
            <div class="card-header">Endpoints at a Glance</div>
            <div class="card-body p-0">
                <table class="table table-hover mb-0">
                    <thead class="table-light"><tr><th>Method</th><th>Path</th><th>Description</th></tr></thead>
                    <tbody>
                        <tr><td><span class="method-badge method-GET">GET</span></td><td class="endpoint-path">/</td><td>Service info &amp; health summary</td></tr>
                        <tr><td><span class="method-badge method-GET">GET</span></td><td class="endpoint-path">/health</td><td>Structured health check (JSON)</td></tr>
                        <tr><td><span class="method-badge method-GET">GET</span></td><td class="endpoint-path">/timeframes</td><td>List available base &amp; derived timeframes</td></tr>
                        <tr><td><span class="method-badge method-GET">GET</span></td><td class="endpoint-path">/api/candles</td><td>Fetch OHLCV candles</td></tr>
                        <tr><td><span class="method-badge method-WS">WS</span></td><td class="endpoint-path">/ws</td><td>Real-time K-line streaming</td></tr>
                        <tr><td><span class="method-badge method-GET">GET</span></td><td class="endpoint-path">/api-manual</td><td>This page</td></tr>
                    </tbody>
                </table>
            </div>
        </div>

        <div class="card">
            <div class="card-header">Data Model</div>
            <div class="card-body">
                <p class="small">Every candle object (REST response, WebSocket payload) has this structure:</p>
                <table class="table table-bordered table-sm">
                    <thead class="table-light"><tr><th>Field</th><th>Type</th><th>Example</th></tr></thead>
                    <tbody>
                        <tr><td><code>timestamp</code></td><td>int</td><td><code>1704067200000</code></td></tr>
                        <tr><td><code>datetime</code></td><td>string</td><td><code>"2024-01-01T00:00:00Z"</code></td></tr>
                        <tr><td><code>open</code></td><td>float</td><td><code>42314.0</code></td></tr>
                        <tr><td><code>high</code></td><td>float</td><td><code>44266.0</code></td></tr>
                        <tr><td><code>low</code></td><td>float</td><td><code>42207.9</code></td></tr>
                        <tr><td><code>close</code></td><td>float</td><td><code>44230.2</code></td></tr>
                        <tr><td><code>volume</code></td><td>float</td><td><code>206424.144</code></td></tr>
                    </tbody>
                </table>
            </div>
        </div>
    </div>

    <!-- ==================== REST API ==================== -->
    <div class="section" id="section-rest">

        <h5 class="mb-3"><span class="method-badge method-GET">GET</span> /health</h5>
        <div class="card endpoint-card">
            <div class="card-body">
                <p class="small">Health check — use <code>ready</code> to determine if the service finished cold-start initialization.</p>
                <p><strong>Response</strong> <span class="text-muted small">200 OK</span></p>
                <pre><code>{{
  "status": "ok",
  "exchange": "binance",
  "symbols": ["BTC/USDT:USDT", "ETH/USDT:USDT"],
  "base_timeframes": ["1m", "1h", "1d", "1w"],
  "derived_timeframes": ["2m","3m","4m","5m",...],
  "timeframes": ["1m","2m","3m",...],
  "ready": true
}}</code></pre>
                <button class="btn btn-sm btn-outline-secondary" onclick="fetch('/health').then(r=>r.json()).then(d=>alert(JSON.stringify(d,null,2)))">Try it</button>
            </div>
        </div>

        <h5 class="mb-3 mt-4"><span class="method-badge method-GET">GET</span> /api/candles</h5>
        <div class="card endpoint-card">
            <div class="card-body">
                <p class="small">The primary data endpoint. Returns OHLCV candlestick data for a given symbol and timeframe.</p>
                <p><strong>Query Parameters</strong></p>
                <table class="table table-bordered table-sm">
                    <thead class="table-light"><tr><th>Param</th><th>Type</th><th>Required</th><th>Default</th><th>Description</th></tr></thead>
                    <tbody>
                        <tr><td><code>symbol</code></td><td>string</td><td><span class="param-required">Yes</span></td><td>—</td><td>Trading pair, e.g. <code>BTC/USDT:USDT</code></td></tr>
                        <tr><td><code>tf</code></td><td>string</td><td>No</td><td><code>1m</code></td><td>Timeframe — any base or derived</td></tr>
                        <tr><td><code>start</code></td><td>int</td><td>No</td><td>—</td><td>Start time, UTC milliseconds</td></tr>
                        <tr><td><code>end</code></td><td>int</td><td>No</td><td>—</td><td>End time, UTC milliseconds</td></tr>
                        <tr><td><code>limit</code></td><td>int</td><td>No</td><td>—</td><td>Max candles to return</td></tr>
                    </tbody>
                </table>
                <p><strong>Response</strong> <span class="text-muted small">200 OK — array of candle objects</span></p>
                <pre><code>[
  {{
    "timestamp": 1704067200000,
    "datetime": "2024-01-01T00:00:00Z",
    "open": 42314.0,
    "high": 44266.0,
    "low": 42207.9,
    "close": 44230.2,
    "volume": 206424.144
  }}
]</code></pre>
                <p class="small text-muted">At least one of <code>start</code>/<code>end</code> or <code>limit</code> should be provided. If none given, all in-memory candles are returned (subject to memory limits).</p>
            </div>
        </div>

        <h5 class="mb-3 mt-4"><span class="method-badge method-GET">GET</span> /timeframes</h5>
        <div class="card endpoint-card">
            <div class="card-body">
                <p class="small">Lists all available timeframes: base (fetched from exchange) and derived (synthesized by resampling).</p>
                <pre><code>{{
  "base_timeframes": ["1m","1h","1d","1w"],
  "derived_timeframes": ["2m","3m","4m","5m",...],
  "timeframes": ["1m","2m","3m",...]
}}</code></pre>
            </div>
        </div>

        <h5 class="mb-3 mt-4"><span class="method-badge method-GET">GET</span> /</h5>
        <div class="card endpoint-card">
            <div class="card-body">
                <p class="small">Root endpoint — returns service name, exchange, symbols, timeframes, and ready status. Same data as <code>/health</code>.</p>
            </div>
        </div>
    </div>

    <!-- ==================== WEBSOCKET ==================== -->
    <div class="section" id="section-ws">

        <div class="card endpoint-card ws">
            <div class="card-header"><span class="method-badge method-WS">WS</span> /ws — Real-time K-line Streaming</div>
            <div class="card-body">
                <p class="small">Connect to receive live incremental candle updates. Subscribe to specific symbol/timeframe pairs; receive a full snapshot followed by real-time push.</p>

                <h6 class="mt-3">Connection URL</h6>
                <pre><code>ws://localhost:80/ws</code></pre>
                <p class="small text-muted">Replace <code>localhost:80</code> with the server address if connecting remotely.</p>

                <h6 class="mt-3">Client → Server Messages</h6>
                <table class="table table-bordered table-sm">
                    <thead class="table-light"><tr><th>Action</th><th>Payload</th><th>Description</th></tr></thead>
                    <tbody>
                        <tr>
                            <td><code>subscribe</code></td>
                            <td><pre style="margin:0;padding:4px 8px;font-size:0.78rem;"><code>{{"action":"subscribe","symbol":"BTC/USDT:USDT","timeframe":"1m"}}</code></pre></td>
                            <td>Subscribe to a symbol/timeframe. Server replies with <code>subscribed</code>, then a <code>snapshot</code> of recent candles.</td>
                        </tr>
                        <tr>
                            <td><code>unsubscribe</code></td>
                            <td><pre style="margin:0;padding:4px 8px;font-size:0.78rem;"><code>{{"action":"unsubscribe","symbol":"BTC/USDT:USDT","timeframe":"1m"}}</code></pre></td>
                            <td>Stop receiving updates for this pair.</td>
                        </tr>
                        <tr>
                            <td><code>ping</code></td>
                            <td><pre style="margin:0;padding:4px 8px;font-size:0.78rem;"><code>{{"action":"ping"}}</code></pre></td>
                            <td>Keepalive ping. Server replies with <code>pong</code>.</td>
                        </tr>
                    </tbody>
                </table>

                <h6 class="mt-3">Server → Client Messages</h6>
                <table class="table table-bordered table-sm">
                    <thead class="table-light"><tr><th>Type</th><th>Description</th></tr></thead>
                    <tbody>
                        <tr><td><code>subscribed</code></td><td>Confirms subscription: <code>{{"type":"subscribed","symbol":"...","timeframe":"..."}}</code></td></tr>
                        <tr><td><code>snapshot</code></td><td>Full recent candles on subscribe: <code>{{"type":"snapshot","symbol":"...","timeframe":"...","data":[...]}}</code></td></tr>
                        <tr><td><code>kline</code></td><td>Incremental candle updates (last 2 candles): <code>{{"type":"kline","symbol":"...","timeframe":"...","data":[...]}}</code></td></tr>
                        <tr><td><code>pong</code></td><td>Ping response.</td></tr>
                        <tr><td><code>unsubscribed</code></td><td>Unsubscribe confirmation.</td></tr>
                        <tr><td><code>error</code></td><td>Error message: <code>{{"type":"error","message":"..."}}</code></td></tr>
                    </tbody>
                </table>

                <h6 class="mt-3">Python Client Example</h6>
                <pre><code>import asyncio, json
import websockets

async def listen():
    async with websockets.connect("ws://localhost:80/ws") as ws:
        # Subscribe to BTC 1m candles
        await ws.send(json.dumps({{
            "action": "subscribe",
            "symbol": "BTC/USDT:USDT",
            "timeframe": "1m"
        }}))
        while True:
            msg = json.loads(await ws.recv())
            print(f"[{{msg['type']}}] {{msg.get('symbol','')}} {{msg.get('timeframe','')}}")
            if msg['type'] == 'kline':
                for candle in msg.get('data', []):
                    print(f"  o={{candle['open']}} h={{candle['high']}} "
                          f"l={{candle['low']}} c={{candle['close']}}")

asyncio.run(listen())</code></pre>
                <p class="small text-muted">Requires: <code>pip install websockets</code>. Subscribe to multiple symbol/timeframe pairs by sending additional subscribe messages.</p>
            </div>
        </div>
    </div>

    <!-- ==================== TIMEFRAMES ==================== -->
    <div class="section" id="section-timeframes">

        <div class="card">
            <div class="card-header">Base & Derived Timeframes</div>
            <div class="card-body">
                <p class="small"><strong>Base</strong> timeframes are fetched directly from the exchange. <strong>Derived</strong> timeframes are synthesized on-the-fly by aggregating base candles — no extra API calls needed.</p>
            </div>
            <div class="card-body p-0">
                <table class="table table-hover mb-0">
                    <thead class="table-light"><tr><th>Base Timeframe</th><th>Configured</th><th>Derived Timeframes Synthesized</th></tr></thead>
                    <tbody>{derived_rows}</tbody>
                </table>
            </div>
        </div>

        <div class="card">
            <div class="card-header">Memory Limits</div>
            <div class="card-body">
                <p class="small">To bound memory usage, only a sliding window is held in RAM for high-frequency timeframes. Data beyond the window is served from CSV on disk lazily.</p>
            </div>
            <div class="card-body p-0">
                <table class="table table-hover mb-0">
                    <thead class="table-light"><tr><th>Timeframe</th><th>Max Candles in Memory</th></tr></thead>
                    <tbody>{memory_rows}</tbody>
                </table>
            </div>
        </div>

        <div class="card">
            <div class="card-header">CSV Storage</div>
            <div class="card-body">
                <p class="small">Data is persisted under <code>data/&lt;timeframe&gt;/</code> as one CSV file per symbol (e.g. <code>data/1m/BTC_USDT_USDT.csv</code>). Schema: <code>timestamp,datetime,open,high,low,close,volume</code>.</p>
            </div>
        </div>
    </div>

    <!-- ==================== CONFIGURATION ==================== -->
    <div class="section" id="section-config">

        <div class="card">
            <div class="card-header">config.json</div>
            <div class="card-body">
                <pre><code>{config_example}</code></pre>
            </div>
            <div class="card-body p-0">
                <table class="table table-hover mb-0">
                    <thead class="table-light"><tr><th>Field</th><th>Type</th><th>Description</th></tr></thead>
                    <tbody>
                        <tr><td><code>exchange</code></td><td>string</td><td>CCXT exchange ID. Tested with <code>binance</code> (Binance Futures).</td></tr>
                        <tr><td><code>symbols</code></td><td>string[]</td><td>Trading pairs in Binance Futures format: <code>BTC/USDT:USDT</code>. Also accepts comma-separated string.</td></tr>
                        <tr><td><code>start_time</code></td><td>string</td><td>Global earliest backfill date, ISO 8601 UTC. Used as fallback for timeframes not listed in <code>start_time_per_tf</code>.</td></tr>
                        <tr><td><code>start_time_per_tf</code></td><td>object</td><td><em>Optional.</em> Per-timeframe override. Keys are timeframe strings (e.g. <code>"1m"</code>), values are ISO 8601 UTC dates. Useful for limiting high-frequency data volume (e.g. 1m from recent date, longer timeframes from earlier).</td></tr>
                        <tr><td><code>timeframes</code></td><td>string[]</td><td>Base timeframes to fetch. Supported: <code>1m</code>, <code>1h</code>, <code>1d</code>, <code>1w</code>.</td></tr>
                        <tr><td><code>data_dir</code></td><td>string</td><td>Directory for CSV storage (created if missing).</td></tr>
                    </tbody>
                </table>
            </div>
        </div>

        <div class="card">
            <div class="card-header">Environment Variables</div>
            <div class="card-body p-0">
                <table class="table table-hover mb-0">
                    <thead class="table-light"><tr><th>Variable</th><th>Default</th><th>Description</th></tr></thead>
                    <tbody>
                        <tr><td><code>CONFIG_PATH</code></td><td><code>config.json</code></td><td>Path to the JSON config file.</td></tr>
                        <tr><td><code>UVICORN_HOST</code></td><td><code>0.0.0.0</code></td><td>Host to bind.</td></tr>
                        <tr><td><code>UVICORN_PORT</code></td><td><code>80</code></td><td>Port to bind.</td></tr>
                        <tr><td><code>UVICORN_LOG_LEVEL</code></td><td><code>info</code></td><td>Log level: <code>debug</code>, <code>info</code>, <code>warning</code>, <code>error</code>.</td></tr>
                    </tbody>
                </table>
            </div>
        </div>
    </div>

    <!-- ==================== EXAMPLES ==================== -->
    <div class="section" id="section-examples">

        <div class="card">
            <div class="card-header">cURL — Fetch Last 500 5-Minute Candles</div>
            <div class="card-body">
                <pre><code>curl "http://localhost:80/api/candles?symbol=BTC/USDT:USDT&tf=5m&limit=500"</code></pre>
            </div>
        </div>

        <div class="card">
            <div class="card-header">cURL — Fetch Date Range</div>
            <div class="card-body">
                <pre><code># 4-hour candles for ETH, Jan–Mar 2024
curl "http://localhost:80/api/candles?symbol=ETH/USDT:USDT&tf=4h&start=1704067200000&end=1711929600000"</code></pre>
            </div>
        </div>

        <div class="card">
            <div class="card-header">Python — requests</div>
            <div class="card-body">
                <pre><code>import requests

# Check service is ready
resp = requests.get("http://localhost:80/health")
meta = resp.json()
print(f"Ready: {{meta['ready']}}")

# Fetch candles
params = {{
    "symbol": "BTC/USDT:USDT",
    "tf": "1h",
    "limit": 200,
}}
resp = requests.get("http://localhost:80/api/candles", params=params)
candles = resp.json()
for c in candles[:3]:
    print(c["datetime"], c["open"], c["close"])</code></pre>
            </div>
        </div>

        <div class="card">
            <div class="card-header">JavaScript — WebSocket</div>
            <div class="card-body">
                <pre><code>const ws = new WebSocket("ws://localhost:80/ws");

ws.onopen = () => {{
  ws.send(JSON.stringify({{
    action: "subscribe",
    symbol: "BTC/USDT:USDT",
    timeframe: "1m"
  }}));
}};

ws.onmessage = (event) => {{
  const msg = JSON.parse(event.data);
  if (msg.type === "snapshot") {{
    console.log("Initial data:", msg.data.length, "candles");
  }} else if (msg.type === "kline") {{
    console.log("Update:", msg.data);
  }}
}};

// Keepalive every 30s
setInterval(() => ws.send(JSON.stringify({{action: "ping"}})), 30000);</code></pre>
            </div>
        </div>

        <div class="card">
            <div class="card-header">JavaScript — fetch (browser)</div>
            <div class="card-body">
                <pre><code>const params = new URLSearchParams({{
  symbol: "BTC/USDT:USDT",
  tf: "5m",
  limit: "500"
}});
const resp = await fetch(`/api/candles?${{params}}`);
const candles = await resp.json();
console.log(`Got ${{candles.length}} candles`);</code></pre>
            </div>
        </div>
    </div>

    <footer>
        Data Provider v1.0.0 &middot; Chan Project &middot; Served from {provider.exchange_name}
    </footer>
</div>

<script>
// Simple tab switcher
document.querySelectorAll('#manualTabs .nav-link').forEach(btn => {{
    btn.addEventListener('click', () => {{
        document.querySelectorAll('#manualTabs .nav-link').forEach(b => b.classList.remove('active'));
        document.querySelectorAll('.section').forEach(s => s.classList.remove('active'));
        btn.classList.add('active');
        document.getElementById('section-' + btn.dataset.section).classList.add('active');
    }});
}});
</script>
</body>
</html>"""

    @app.websocket("/ws")
    async def websocket_endpoint(ws: WebSocket):
        """WebSocket 实时 K 线推送。

        客户端发送 JSON:
            {"action": "subscribe",   "symbol": "BTC/USDT:USDT", "timeframe": "1m"}
            {"action": "unsubscribe", "symbol": "BTC/USDT:USDT", "timeframe": "1m"}
            {"action": "ping"}
        服务端推送:
            {"type": "subscribed",   "symbol": "...", "timeframe": "..."}
            {"type": "snapshot",     "symbol": "...", "timeframe": "...", "data": [...]}
            {"type": "kline",        "symbol": "...", "timeframe": "...", "data": [...]}
            {"type": "pong"}
            {"type": "error",        "message": "..."}
        """
        await ws_manager.connect(ws)
        try:
            while True:
                raw = await ws.receive_text()
                try:
                    msg = json.loads(raw)
                except json.JSONDecodeError:
                    await ws.send_text(json.dumps({"type": "error", "message": "invalid JSON"}))
                    continue

                action = msg.get("action", "")
                symbol = str(msg.get("symbol", "")).strip()
                timeframe = str(msg.get("timeframe", "")).strip()

                if action == "ping":
                    await ws.send_text(json.dumps({"type": "pong"}))

                elif action == "subscribe":
                    if not symbol or not timeframe:
                        await ws.send_text(json.dumps(
                            {"type": "error", "message": "需要 symbol 和 timeframe 字段"}
                        ))
                        continue
                    await ws_manager.subscribe(ws, symbol, timeframe)
                    await ws.send_text(json.dumps(
                        {"type": "subscribed", "symbol": symbol, "timeframe": timeframe},
                        ensure_ascii=False,
                    ))
                    try:
                        snapshot = provider.get_klines(symbol, timeframe, limit=DEFAULT_LIMIT)
                        if snapshot:
                            await ws.send_text(json.dumps(
                                {"type": "snapshot", "symbol": symbol, "timeframe": timeframe, "data": snapshot},
                                ensure_ascii=False,
                            ))
                    except Exception as exc:
                        await ws.send_text(json.dumps({"type": "error", "message": str(exc)}))

                elif action == "unsubscribe":
                    await ws_manager.unsubscribe(ws, symbol, timeframe)
                    await ws.send_text(json.dumps(
                        {"type": "unsubscribed", "symbol": symbol, "timeframe": timeframe},
                        ensure_ascii=False,
                    ))

                else:
                    await ws.send_text(json.dumps({"type": "error", "message": f"未知 action: {action}"}))

        except WebSocketDisconnect:
            pass
        finally:
            await ws_manager.disconnect(ws)

    @app.get("/api/docs", response_class=HTMLResponse, include_in_schema=False)
    async def api_docs():
        """返回自定义 API 文档页面。"""
        if docs_path.exists():
            return HTMLResponse(content=docs_path.read_text(encoding="utf-8"))
        return HTMLResponse(content="<h1>API 文档页面未找到</h1>", status_code=404)

    return app


def build_app() -> FastAPI:
    """默认入口：从环境变量 CONFIG_PATH（或 config.json）加载配置并创建 FastAPI app。"""
    config_path = Path(os.getenv("CONFIG_PATH", "config.json"))
    provider = DataProvider(config_path)
    return create_app(provider)


app = build_app()


def main() -> None:
    """直接运行本模块时启动 uvicorn（监听 UVICORN_HOST / UVICORN_PORT）。"""
    host = os.getenv("UVICORN_HOST", "0.0.0.0")
    port = int(os.getenv("UVICORN_PORT", "80"))

    uvicorn.run(app, host=host, port=port, log_level=os.getenv("UVICORN_LOG_LEVEL", "info"))


if __name__ == "__main__":
    main()