增加大模型

.env.example

@@ -3,7 +3,8 @@ BINANCE_FAPI_BASE=https://fapi.binance.com
 TOP_N=30
 VOLUME_THRESHOLD=10000000
 CHANGE_THRESHOLD=5
-REFRESH_MINUTES=5
+# 今日自动刷新间隔（分钟），240=每4小时；另支持页脚「立即刷新今日」手动
+REFRESH_MINUTES=240
 HOST=0.0.0.0
 PORT=21450
 
@@ -24,3 +25,12 @@ CHART_KLINE_LIMIT=300
 CHART_CACHE_MINUTES=60
 FUNDING_HISTORY_LIMIT=90
 FUNDING_CACHE_MINUTES=30
+
+# 大模型解读（OpenAI 兼容，网关 http://op.bz121.com）
+LLM_BASE_URL=http://op.bz121.com
+LLM_API_KEY=sk-your-key-here
+LLM_MODEL=gemma4:e4b
+# 每个币种间隔秒数（默认 180 = 3 分钟）
+LLM_SYMBOL_INTERVAL_SEC=180
+# 服务启动后若已配置 KEY，自动对三日交集解读一轮
+LLM_AUTO_ON_STARTUP=true

README.md

@@ -10,7 +10,9 @@
 - 成交额排名 Top30（USDT 计价）
 - 高亮标记（不改变排名）：成交额 ≥ 1000万 USDT、|涨跌幅| ≥ 5%
 - 昨日周期：`[D-1 08:00, D 08:00)`
-- 今日周期：`[D 08:00, 当前)`，每 5 分钟后台刷新，Web 每 60 秒拉取
+- 今日周期：`[D 08:00, 当前)`，每 **4 小时**后台刷新 + 页脚手动刷新；K线/周期数据服务端 SQLite + 浏览器缓存
+- 数据统计：连续三日 Top30 交集（涨跌幅不限）
+- 大模型解读（`gemma4:e4b`）：每日 **08:05** 对三日交集逐币解读（每币 3 分钟），启动自动一轮；需配置 `LLM_API_KEY`
 
 ## 环境要求
 

backend/app/chart_image.py

@@ -0,0 +1,66 @@
+"""服务端生成日K+成交量 PNG，供大模型视觉解读。"""
+
+import io
+from datetime import datetime
+
+from .kline_store import get_daily_candles
+
+
+async def render_daily_chart_png_async(symbol: str, limit: int = 300) -> bytes:
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    import matplotlib.dates as mdates
+
+    candles, _ = await get_daily_candles(symbol, limit)
+    if not candles:
+        raise ValueError(f"no klines for {symbol}")
+
+    times = [datetime.fromtimestamp(c["time"] / 1000) for c in candles]
+    opens = [c["open"] for c in candles]
+    highs = [c["high"] for c in candles]
+    lows = [c["low"] for c in candles]
+    closes = [c["close"] for c in candles]
+    vols = [c.get("quote_volume") or c.get("volume") or 0 for c in candles]
+
+    fig, (ax1, ax2) = plt.subplots(
+        2, 1, figsize=(12, 7), gridspec_kw={"height_ratios": [3, 1]}, facecolor="#0d1118"
+    )
+    fig.subplots_adjust(hspace=0.08)
+
+    for i in range(len(candles)):
+        t = times[i]
+        o, h, l, cl = opens[i], highs[i], lows[i], closes[i]
+        color = "#0ecb81" if cl >= o else "#f6465d"
+        ax1.plot([t, t], [l, h], color=color, linewidth=0.8)
+        ax1.add_patch(
+            plt.Rectangle(
+                (mdates.date2num(t) - 0.3, min(o, cl)),
+                0.6,
+                abs(cl - o) or 0.001,
+                facecolor=color,
+                edgecolor=color,
+            )
+        )
+    ax1.set_facecolor("#0d1118")
+    ax1.tick_params(colors="#8b9cb3")
+    ax1.set_title(f"{symbol} 日K + 成交量", color="#e7ecf3", fontsize=14)
+    ax1.grid(True, alpha=0.2)
+
+    colors_vol = ["#0ecb81" if closes[i] >= opens[i] else "#f6465d" for i in range(len(candles))]
+    ax2.bar(times, vols, color=colors_vol, alpha=0.7, width=0.8)
+    ax2.set_facecolor("#0d1118")
+    ax2.tick_params(colors="#8b9cb3")
+    ax2.set_ylabel("成交额", color="#8b9cb3")
+    ax2.grid(True, alpha=0.2)
+
+    for ax in (ax1, ax2):
+        ax.xaxis.set_major_formatter(mdates.DateFormatter("%m-%d"))
+    fig.autofmt_xdate()
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=120, facecolor="#0d1118")
+    plt.close(fig)
+    buf.seek(0)
+    return buf.read()

backend/app/config.py

@@ -17,7 +17,7 @@ class Settings(BaseSettings):
     top_n: int = 30
     volume_threshold: float = 10_000_000
     change_threshold: float = 5.0
-    refresh_minutes: int = 5
+    refresh_minutes: int = 240
     host: str = "127.0.0.1"
     port: int = 21450
     db_path: str = str(ROOT_DIR / "data" / "monitor.db")
@@ -37,6 +37,11 @@ class Settings(BaseSettings):
     proxy_enabled: bool = False
     proxy_url: str = "socks5h://192.168.8.4:1081"
     proxy_for: str = "binance"  # binance | wecom | all
+    llm_base_url: str = "http://op.bz121.com"
+    llm_api_key: str = ""
+    llm_model: str = "gemma4:e4b"
+    llm_symbol_interval_sec: int = 180
+    llm_auto_on_startup: bool = True
 
 
 settings = Settings()

backend/app/db.py

@@ -96,6 +96,17 @@ def init_db() -> None:
                 mark_price REAL NOT NULL DEFAULT 0,
                 updated_at TEXT NOT NULL
             );
+
+            CREATE TABLE IF NOT EXISTS llm_interpretations (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                symbol TEXT NOT NULL,
+                batch_id TEXT NOT NULL,
+                content TEXT NOT NULL,
+                created_at TEXT NOT NULL
+            );
+
+            CREATE INDEX IF NOT EXISTS idx_llm_symbol_batch
+                ON llm_interpretations(symbol, batch_id);
             """
         )
 
@@ -353,6 +364,87 @@ def save_funding_current_bulk(data: dict[str, dict[str, Any]]) -> None:
             )
 
 
+def save_llm_interpretation(symbol: str, batch_id: str, content: str) -> None:
+    with get_conn() as conn:
+        conn.execute(
+            """
+            INSERT INTO llm_interpretations (symbol, batch_id, content, created_at)
+            VALUES (?, ?, ?, ?)
+            """,
+            (symbol.upper(), batch_id, content, datetime.now().isoformat()),
+        )
+
+
+def get_llm_interpretations(batch_id: str | None = None, limit: int = 50) -> list[dict[str, Any]]:
+    with get_conn() as conn:
+        if batch_id:
+            rows = conn.execute(
+                """
+                SELECT symbol, batch_id, content, created_at
+                FROM llm_interpretations
+                WHERE batch_id = ?
+                ORDER BY id DESC
+                LIMIT ?
+                """,
+                (batch_id, limit),
+            ).fetchall()
+        else:
+            rows = conn.execute(
+                """
+                SELECT symbol, batch_id, content, created_at
+                FROM llm_interpretations
+                WHERE batch_id = (
+                    SELECT batch_id FROM llm_interpretations ORDER BY id DESC LIMIT 1
+                )
+                ORDER BY id ASC
+                LIMIT ?
+                """,
+                (limit,),
+            ).fetchall()
+    return [
+        {
+            "symbol": r["symbol"],
+            "batch_id": r["batch_id"],
+            "content": r["content"],
+            "created_at": r["created_at"],
+        }
+        for r in rows
+    ]
+
+
+def get_llm_interpretation(symbol: str, batch_id: str | None = None) -> dict[str, Any] | None:
+    sym = symbol.upper()
+    with get_conn() as conn:
+        if batch_id:
+            row = conn.execute(
+                """
+                SELECT symbol, batch_id, content, created_at
+                FROM llm_interpretations
+                WHERE symbol = ? AND batch_id = ?
+                ORDER BY id DESC LIMIT 1
+                """,
+                (sym, batch_id),
+            ).fetchone()
+        else:
+            row = conn.execute(
+                """
+                SELECT symbol, batch_id, content, created_at
+                FROM llm_interpretations
+                WHERE symbol = ?
+                ORDER BY id DESC LIMIT 1
+                """,
+                (sym,),
+            ).fetchone()
+    if not row:
+        return None
+    return {
+        "symbol": row["symbol"],
+        "batch_id": row["batch_id"],
+        "content": row["content"],
+        "created_at": row["created_at"],
+    }
+
+
 def was_pushed_today(period_start: str, period_end: str) -> bool:
     with get_conn() as conn:
         row = conn.execute(

backend/app/llm_service.py

@@ -0,0 +1,174 @@
+"""大模型解读（OpenAI 兼容接口 + 图表图片）。"""
+
+import asyncio
+import base64
+import logging
+from datetime import datetime
+
+import httpx
+
+from .chart_image import render_daily_chart_png_async
+from .config import settings
+from .db import get_llm_interpretation, save_llm_interpretation
+from .stats import compute_three_day_stats
+
+logger = logging.getLogger(__name__)
+
+_interpret_lock = asyncio.Lock()
+_interpret_state: dict = {
+    "running": False,
+    "current_symbol": "",
+    "done": 0,
+    "total": 0,
+    "batch_id": "",
+    "last_error": "",
+}
+
+
+def get_interpret_state() -> dict:
+    return dict(_interpret_state)
+
+
+def _api_url() -> str:
+    base = settings.llm_base_url.rstrip("/")
+    if base.endswith("/v1"):
+        return f"{base}/chat/completions"
+    return f"{base}/v1/chat/completions"
+
+
+def _build_prompt(symbol: str, stats_row: dict | None) -> str:
+    lines = [
+        f"你是加密货币合约分析师。请根据附图（{symbol} 近300日K+成交量）及数据给出中文简析。",
+        "关注：趋势、关键支撑阻力、成交量变化、资金费率含义、未来1-3日可能节奏。",
+        "控制在 200-350 字，条理清晰，不要废话。",
+    ]
+    if stats_row:
+        t, y, b = stats_row.get("today", {}), stats_row.get("yesterday", {}), stats_row.get("daybefore", {})
+        lines.append(
+            f"\n三日均为成交额Top30交集："
+            f"\n今日 排名{t.get('rank')} 涨跌{t.get('price_change_pct_fmt')} 额{t.get('quote_volume_fmt')}"
+            f"\n昨日 排名{y.get('rank')} 涨跌{y.get('price_change_pct_fmt')} 额{y.get('quote_volume_fmt')}"
+            f"\n前日 排名{b.get('rank')} 涨跌{b.get('price_change_pct_fmt')} 额{b.get('quote_volume_fmt')}"
+            f"\n资金费率(当前)：{t.get('funding_rate_fmt', '—')}"
+        )
+    return "\n".join(lines)
+
+
+async def interpret_symbol(
+    symbol: str,
+    stats_row: dict | None = None,
+    batch_id: str | None = None,
+) -> str:
+    if not settings.llm_api_key.strip():
+        raise RuntimeError("LLM_API_KEY 未配置")
+
+    png = await render_daily_chart_png_async(symbol, settings.chart_kline_limit)
+    b64 = base64.standard_b64encode(png).decode("ascii")
+    prompt = _build_prompt(symbol, stats_row)
+
+    payload = {
+        "model": settings.llm_model,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/png;base64,{b64}"},
+                    },
+                ],
+            }
+        ],
+        "max_tokens": 800,
+        "temperature": 0.4,
+    }
+
+    headers = {
+        "Authorization": f"Bearer {settings.llm_api_key}",
+        "Content-Type": "application/json",
+    }
+
+    async with httpx.AsyncClient(timeout=120.0) as client:
+        resp = await client.post(_api_url(), json=payload, headers=headers)
+        if resp.status_code >= 400:
+            # 部分模型不支持 vision，降级纯文本
+            logger.warning("LLM vision failed %s, fallback text", resp.status_code)
+            payload["messages"] = [{"role": "user", "content": prompt + "\n(附图日K+成交量未能传入，请基于数据简析)"}]
+            resp = await client.post(_api_url(), json=payload, headers=headers)
+        resp.raise_for_status()
+        data = resp.json()
+
+    content = data["choices"][0]["message"]["content"]
+    bid = batch_id or datetime.now().strftime("%Y-%m-%d-%H%M")
+    save_llm_interpretation(symbol, bid, content)
+    return content
+
+
+async def run_interpretation_batch(
+    symbols: list[str] | None = None,
+    *,
+    batch_id: str | None = None,
+) -> dict:
+    global _interpret_state
+
+    if _interpret_lock.locked():
+        return {"ok": False, "message": "解读任务进行中"}
+
+    stats = compute_three_day_stats()
+    if not stats.get("ok"):
+        return {"ok": False, "message": stats.get("message", "统计数据未就绪")}
+
+    sym_list = symbols or stats.get("symbols") or [x["symbol"] for x in stats.get("items", [])]
+    if not sym_list:
+        return {"ok": False, "message": "三日交集为空"}
+
+    stats_map = {x["symbol"]: x for x in stats.get("items", [])}
+    bid = batch_id or datetime.now().strftime("%Y-%m-%d-%H%M")
+    interval = settings.llm_symbol_interval_sec
+
+    async with _interpret_lock:
+        _interpret_state.update(
+            {
+                "running": True,
+                "current_symbol": "",
+                "done": 0,
+                "total": len(sym_list),
+                "batch_id": bid,
+                "last_error": "",
+            }
+        )
+        for i, sym in enumerate(sym_list):
+            _interpret_state["current_symbol"] = sym
+            try:
+                await interpret_symbol(sym, stats_map.get(sym), bid)
+                logger.info("LLM interpreted %s (%d/%d)", sym, i + 1, len(sym_list))
+            except Exception as e:
+                _interpret_state["last_error"] = str(e)
+                logger.error("LLM %s failed: %s", sym, e)
+                save_llm_interpretation(sym, bid, f"[解读失败] {e}")
+            _interpret_state["done"] = i + 1
+            if i < len(sym_list) - 1:
+                await asyncio.sleep(interval)
+
+        _interpret_state["running"] = False
+        _interpret_state["current_symbol"] = ""
+
+    return {
+        "ok": True,
+        "batch_id": bid,
+        "count": len(sym_list),
+        "interval_sec": interval,
+    }
+
+
+def schedule_interpret_background(symbols: list[str] | None = None) -> None:
+    """后台启动解读，不阻塞请求。"""
+
+    async def _run():
+        try:
+            await run_interpretation_batch(symbols)
+        except Exception as e:
+            logger.error("Background LLM batch failed: %s", e)
+
+    asyncio.create_task(_run())

backend/app/main.py

@@ -2,17 +2,19 @@ import logging
 from contextlib import asynccontextmanager
 from pathlib import Path
 
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import FileResponse
+from fastapi import BackgroundTasks, FastAPI, HTTPException
+from fastapi.responses import FileResponse, Response
 from fastapi.staticfiles import StaticFiles
 
 from .config import ROOT_DIR, settings
 from .funding_store import get_funding_bundle
 from .kline_store import get_daily_candles, sync_daily_klines
-from .db import get_latest_snapshot, init_db, log_push, save_snapshot
+from .db import get_latest_snapshot, get_llm_interpretations, init_db, log_push, save_snapshot
 from .exceptions import BinanceRateLimitedError
 from .period_api import get_period_top30
 from .periods import get_daybefore_period, get_today_period, get_yesterday_period
+from .chart_image import render_daily_chart_png_async
+from .llm_service import get_interpret_state, run_interpretation_batch
 from .scheduler import job_finalize_yesterday, job_push_wecom, job_refresh_today, start_scheduler, startup_tasks, stop_scheduler
 from .stats import compute_three_day_stats
 from .aggregator import aggregate_period
@@ -165,6 +167,49 @@ async def api_funding_history(symbol: str, limit: int | None = None, refresh: bo
         raise HTTPException(502, "资金费率获取失败") from e
 
 
+@app.get("/api/chart/{symbol}/daily.png")
+async def api_chart_daily_png(symbol: str, limit: int | None = None):
+    sym = symbol.upper().strip()
+    if not sym.endswith("USDT"):
+        raise HTTPException(400, "invalid symbol")
+    try:
+        png = await render_daily_chart_png_async(sym, limit or settings.chart_kline_limit)
+        return Response(content=png, media_type="image/png")
+    except ValueError as e:
+        raise HTTPException(404, str(e)) from e
+    except Exception as e:
+        logger.error("chart png %s failed: %s", sym, e)
+        raise HTTPException(502, "图表生成失败") from e
+
+
+@app.get("/api/llm/status")
+async def api_llm_status():
+    state = get_interpret_state()
+    return {
+        **state,
+        "enabled": bool(settings.llm_api_key.strip()),
+        "model": settings.llm_model,
+        "base_url": settings.llm_base_url,
+        "interval_sec": settings.llm_symbol_interval_sec,
+    }
+
+
+@app.get("/api/llm/interpretations")
+async def api_llm_interpretations(batch_id: str | None = None, limit: int = 50):
+    return {"items": get_llm_interpretations(batch_id, limit)}
+
+
+@app.post("/api/llm/interpret/run")
+async def api_llm_interpret_run(background_tasks: BackgroundTasks):
+    if not settings.llm_api_key.strip():
+        raise HTTPException(400, "LLM_API_KEY 未配置")
+    state = get_interpret_state()
+    if state.get("running"):
+        return {"ok": False, "message": "解读任务进行中", **state}
+    background_tasks.add_task(run_interpretation_batch)
+    return {"ok": True, "message": "已启动三日交集解读队列", **get_interpret_state()}
+
+
 @app.post("/api/chart/{symbol}/daily/refresh")
 async def api_chart_daily_refresh(symbol: str, limit: int | None = None):
     """强制从币安同步日 K 到本地库。"""

backend/app/scheduler.py

@@ -13,6 +13,8 @@ from .periods import get_daybefore_period, get_today_period, get_yesterday_perio
 from .state import get_today_cache, set_today_cache
 from .funding_store import prefetch_funding
 from .kline_store import prefetch_symbols
+from .llm_service import run_interpretation_batch, schedule_interpret_background
+from .stats import compute_three_day_stats
 from .wecom import build_markdown, send_wecom_markdown
 
 logger = logging.getLogger(__name__)
@@ -147,6 +149,18 @@ async def job_refresh_today() -> None:
         _restore_today_from_db()
 
 
+async def job_llm_interpret() -> None:
+    """08:05 对三日交集币种逐个大模型解读（每币间隔 3 分钟）。"""
+    logger.info("Job: LLM interpret three-day intersection")
+    if not settings.llm_api_key.strip():
+        logger.info("LLM_API_KEY not set, skip")
+        return
+    try:
+        await run_interpretation_batch()
+    except Exception as e:
+        logger.error("LLM job failed: %s", e)
+
+
 async def startup_tasks() -> None:
     init_db()
     now = now_shanghai()
@@ -194,6 +208,12 @@ async def startup_tasks() -> None:
             except Exception as e:
                 logger.error("Startup catch-up push failed: %s", e)
 
+    if settings.llm_api_key.strip() and settings.llm_auto_on_startup:
+        stats = compute_three_day_stats()
+        if stats.get("ok") and stats.get("symbols"):
+            logger.info("Startup: schedule one LLM interpret batch")
+            schedule_interpret_background()
+
 
 def start_scheduler() -> None:
     scheduler.add_job(
@@ -208,19 +228,25 @@ def start_scheduler() -> None:
         id="push_wecom",
         replace_existing=True,
     )
+    refresh_hours = max(1, settings.refresh_minutes // 60)
     scheduler.add_job(
         job_refresh_today,
-        CronTrigger(minute=f"*/{settings.refresh_minutes}", timezone="Asia/Shanghai"),
+        CronTrigger(hour=f"*/{refresh_hours}", minute=0, timezone="Asia/Shanghai"),
         id="refresh_today",
         replace_existing=True,
     )
+    scheduler.add_job(
+        job_llm_interpret,
+        CronTrigger(hour=8, minute=5, timezone="Asia/Shanghai"),
+        id="llm_interpret",
+        replace_existing=True,
+    )
     if not scheduler.running:
         scheduler.start()
     logger.info(
-        "Scheduler started (today=%s, yesterday=%s, every %d min)",
-        settings.today_data_mode,
-        settings.yesterday_data_mode,
-        settings.refresh_minutes,
+        "Scheduler started (today every %dh, LLM 08:05, interval %ds)",
+        refresh_hours,
+        settings.llm_symbol_interval_sec,
     )
 
 

backend/app/stats.py

@@ -1,4 +1,4 @@
-"""三日数据统计：连续三日 Top30 且 |涨跌|>=5%。"""
+"""三日数据统计：连续三日成交额 Top30 交集（不限制涨跌幅）。"""
 
 from typing import Any
 
@@ -15,7 +15,6 @@ def compute_three_day_stats() -> dict[str, Any]:
     yesterday_snap = get_latest_snapshot("yesterday")
     daybefore_snap = get_latest_snapshot("daybefore")
 
-    threshold = settings.change_threshold
     top_n = settings.top_n
 
     missing = []
@@ -31,7 +30,7 @@ def compute_three_day_stats() -> dict[str, Any]:
             "ok": False,
             "missing_periods": missing,
             "message": f"缺少快照：{', '.join(missing)}，请等待刷新或手动触发",
-            "criteria": _criteria_text(threshold, top_n),
+            "criteria": _criteria_text(top_n),
             "count": 0,
             "items": [],
             "periods": _period_meta(today_snap, yesterday_snap, daybefore_snap),
@@ -46,12 +45,6 @@ def compute_three_day_stats() -> dict[str, Any]:
 
     for sym in sorted(symbols):
         t, y, b = today_map[sym], yesterday_map[sym], daybefore_map[sym]
-        if not (
-            abs(t.get("price_change_pct", 0)) >= threshold
-            and abs(y.get("price_change_pct", 0)) >= threshold
-            and abs(b.get("price_change_pct", 0)) >= threshold
-        ):
-            continue
         qualified.append(
             {
                 "symbol": sym,
@@ -79,9 +72,10 @@ def compute_three_day_stats() -> dict[str, Any]:
 
     return {
         "ok": True,
-        "criteria": _criteria_text(threshold, top_n),
+        "criteria": _criteria_text(top_n),
         "count": len(qualified),
         "items": qualified,
+        "symbols": [q["symbol"] for q in qualified],
         "periods": _period_meta(today_snap, yesterday_snap, daybefore_snap),
         "summary": {
             "today_top30": len(today_map),
@@ -92,11 +86,8 @@ def compute_three_day_stats() -> dict[str, Any]:
     }
 
 
-def _criteria_text(threshold: float, top_n: int) -> str:
-    return (
-        f"连续三日成交额 Top{top_n} 且每日 |涨跌幅| ≥ {threshold:g}%"
-        f"（今日/昨日/前日三个完整切日周期）"
-    )
+def _criteria_text(top_n: int) -> str:
+    return f"连续三日成交额均位列 Top{top_n}（今日/昨日/前日交集，涨跌幅不限）"
 
 
 def _pick_fields(row: dict) -> dict:

backend/requirements.txt

@@ -4,3 +4,4 @@ httpx[socks]>=0.27.0
 apscheduler>=3.10.4
 python-dotenv>=1.0.1
 pydantic-settings>=2.6.0
+matplotlib>=3.8.0

web/app.js

@@ -1,5 +1,3 @@
-const REFRESH_MS = 60_000;
-
 const PERIOD_API = {
   today: "/api/today/top30",
   yesterday: "/api/yesterday/top30",
@@ -12,8 +10,13 @@ const tableState = {
   daybefore: { items: [], meta: {}, sortKey: "rank", sortDir: "asc" },
 };
 
+const PERIOD_LS_PREFIX = "ba_period_";
+const PERIOD_TTL_MS = 4 * 60 * 60 * 1000;
+
 let statsData = null;
 let currentView = "today";
+let llmPollTimer = null;
+let llmInterpretMap = {};
 
 const SORT_KEYS = {
   rank: (r) => Number(r.rank) || 0,
@@ -171,15 +174,49 @@ function setPeriodData(periodId, data) {
   renderPeriodTable(periodId);
 }
 
-async function loadPeriod(periodId) {
+function loadPeriodFromLS(periodId) {
+  try {
+    const raw = localStorage.getItem(PERIOD_LS_PREFIX + periodId);
+    if (!raw) return null;
+    const obj = JSON.parse(raw);
+    if (!obj?.data || Date.now() - (obj.ts || 0) > PERIOD_TTL_MS) return null;
+    return obj.data;
+  } catch {
+    return null;
+  }
+}
+
+function savePeriodToLS(periodId, data) {
+  try {
+    localStorage.setItem(
+      PERIOD_LS_PREFIX + periodId,
+      JSON.stringify({ ts: Date.now(), data })
+    );
+  } catch {
+    /* quota */
+  }
+}
+
+async function loadPeriod(periodId, force = false) {
   const tbody = ensurePeriodTable(periodId);
+  if (!force) {
+    const cached = loadPeriodFromLS(periodId);
+    if (cached?.items?.length) {
+      setPeriodData(periodId, cached);
+      if (periodId === "today") {
+        document.getElementById("status").textContent = "今日数据（浏览器缓存）";
+      }
+      return;
+    }
+  }
   if (tbody) tbody.innerHTML = '<tr><td colspan="7" class="loading">加载中…</td></tr>';
   try {
     const res = await fetch(PERIOD_API[periodId]);
     const data = await res.json();
+    savePeriodToLS(periodId, data);
     setPeriodData(periodId, data);
     if (periodId === "today") {
-      document.getElementById("status").textContent = "今日数据已刷新";
+      document.getElementById("status").textContent = force ? "今日数据已手动刷新" : "今日数据已加载";
     }
   } catch (e) {
     if (tbody) tbody.innerHTML = `<tr><td colspan="7" class="error">${e.message}</td></tr>`;
@@ -258,6 +295,7 @@ function renderStatsTable() {
         <th>昨日排名</th><th>昨日涨跌</th><th>昨日成交额</th>
         <th>前日排名</th><th>前日涨跌</th><th>前日成交额</th>
         <th>三日总成交额</th>
+        <th>AI解读</th>
       </tr></thead>
       <tbody id="stats-body"></tbody>
     </table>`;
@@ -271,30 +309,128 @@ function renderStatsTable() {
         const pct = x.price_change_pct ?? 0;
         return `<td class="${f === "pct" ? pctClass(pct) : ""}">${f === "pct" ? x.price_change_pct_fmt || "—" : f === "rank" ? x.rank ?? "—" : x.quote_volume_fmt || "—"}</td>`;
       };
+      const llm = llmInterpretMap[row.symbol];
+      const llmCell = llm
+        ? `<details class="llm-inline"><summary>AI解读</summary><div class="llm-text">${escapeHtml(llm.content)}</div><small>${llm.created_at?.slice(0, 19) || ""}</small></details>`
+        : '<span class="muted">—</span>';
       return `<tr class="row-highlight">
         <td><strong>${row.symbol}</strong></td>
         ${cell("today", "rank")}${cell("today", "pct")}${cell("today", "vol")}
         ${cell("yesterday", "rank")}${cell("yesterday", "pct")}${cell("yesterday", "vol")}
         ${cell("daybefore", "rank")}${cell("daybefore", "pct")}${cell("daybefore", "vol")}
         <td>${formatVol(row.total_quote_volume)}</td>
+        <td class="llm-col">${llmCell}</td>
       </tr>`;
     })
     .join("");
 }
 
+function escapeHtml(s) {
+  return String(s || "")
+    .replace(/&/g, "&amp;")
+    .replace(/</g, "&lt;")
+    .replace(/>/g, "&gt;")
+    .replace(/\n/g, "<br>");
+}
+
 function formatVol(v) {
   if (v >= 1e8) return (v / 1e8).toFixed(2) + "亿";
   if (v >= 1e4) return (v / 1e4).toFixed(2) + "万";
   return String(Math.round(v));
 }
 
+async function loadLlmInterpretations() {
+  try {
+    const res = await fetch("/api/llm/interpretations");
+    const data = await res.json();
+    llmInterpretMap = {};
+    for (const item of data.items || []) {
+      llmInterpretMap[item.symbol] = item;
+    }
+    renderLlmList(data.items || []);
+    if (statsData?.ok) renderStatsTable();
+  } catch {
+    /* ignore */
+  }
+}
+
+function renderLlmList(items) {
+  const el = document.getElementById("llm-interpret-list");
+  if (!el) return;
+  if (!items.length) {
+    el.innerHTML = '<p class="loading">暂无解读记录</p>';
+    return;
+  }
+  el.innerHTML = items
+    .map(
+      (it) => `
+    <article class="llm-card">
+      <h4>${it.symbol} <small>${it.batch_id}</small></h4>
+      <div class="llm-text">${escapeHtml(it.content)}</div>
+      <time>${(it.created_at || "").replace("T", " ").slice(0, 19)}</time>
+    </article>`
+    )
+    .join("");
+}
+
+async function refreshLlmStatus() {
+  try {
+    const res = await fetch("/api/llm/status");
+    const st = await res.json();
+    const label = document.getElementById("llm-model-label");
+    const text = document.getElementById("llm-status-text");
+    if (label) label.textContent = st.enabled ? st.model : "未配置";
+    if (!text) return;
+    if (st.running) {
+      text.textContent = `解读中 ${st.done}/${st.total} · 当前 ${st.current_symbol || "—"} · 批次 ${st.batch_id}`;
+      if (!llmPollTimer) {
+        llmPollTimer = setInterval(async () => {
+          await refreshLlmStatus();
+          await loadLlmInterpretations();
+          if (!(await fetch("/api/llm/status").then((r) => r.json())).running) {
+            clearInterval(llmPollTimer);
+            llmPollTimer = null;
+          }
+        }, 5000);
+      }
+    } else {
+      text.textContent = st.enabled
+        ? `就绪 · 每币 ${st.interval_sec}s · 最近批次 ${st.batch_id || "—"}`
+        : "请在 .env 配置 LLM_API_KEY";
+      if (llmPollTimer) {
+        clearInterval(llmPollTimer);
+        llmPollTimer = null;
+      }
+    }
+  } catch {
+    /* ignore */
+  }
+}
+
+async function runLlmInterpret() {
+  const btn = document.getElementById("btn-llm-run");
+  if (btn) btn.disabled = true;
+  try {
+    const res = await fetch("/api/llm/interpret/run", { method: "POST" });
+    const data = await res.json();
+    if (!data.ok) alert(data.message || "启动失败");
+    await refreshLlmStatus();
+  } catch (e) {
+    alert(e.message);
+  } finally {
+    if (btn) btn.disabled = false;
+  }
+}
+
 async function loadStats() {
   document.getElementById("stats-table-wrap").innerHTML =
     '<p class="loading">统计中…</p>';
   try {
     const res = await fetch("/api/stats/three-day");
     statsData = await res.json();
+    await loadLlmInterpretations();
     renderStatsTable();
+    await refreshLlmStatus();
   } catch (e) {
     document.getElementById("stats-table-wrap").innerHTML = `<p class="error">${e.message}</p>`;
   }
@@ -330,6 +466,10 @@ function switchView(view) {
 
   if (view === "stats") {
     if (!statsData) loadStats();
+    else {
+      refreshLlmStatus();
+      loadLlmInterpretations();
+    }
     return;
   }
 
@@ -358,10 +498,16 @@ document.querySelectorAll("[data-reset]").forEach((btn) => {
 document.getElementById("btn-refresh").addEventListener("click", async () => {
   document.getElementById("status").textContent = "刷新中…";
   await fetch("/api/refresh/today", { method: "POST" });
-  await loadPeriod("today");
+  await loadPeriod("today", true);
   if (currentView === "stats") await loadStats();
 });
 
+document.getElementById("btn-llm-run")?.addEventListener("click", runLlmInterpret);
+document.getElementById("btn-llm-refresh")?.addEventListener("click", async () => {
+  await loadLlmInterpretations();
+  await refreshLlmStatus();
+});
+
 document.getElementById("btn-reload-stats")?.addEventListener("click", () => {
   statsData = null;
   loadStats();
@@ -371,7 +517,3 @@ document.getElementById("btn-export-stats")?.addEventListener("click", exportSta
 loadPeriod("today");
 loadPeriod("yesterday");
 loadPeriod("daybefore");
-
-setInterval(() => {
-  if (currentView === "today") loadPeriod("today");
-}, REFRESH_MS);

web/charts.js

@@ -1,10 +1,13 @@
-/** 日 K + 成交量（Canvas 高清） */
+/** 日 K + 成交量（Canvas 高清）· 浏览器 localStorage 缓存 · 点击全屏 */
 
 const chartDataCache = new Map();
 const chartQueue = [];
 let chartQueueRunning = false;
 const CHART_FETCH_GAP_MS = 120;
 
+const LS_KLINE_PREFIX = "ba_kline_";
+const KLINE_TTL_MS = 60 * 60 * 1000;
+
 const COLORS = {
   bg: "#0d1118",
   grid: "#2a3548",
@@ -16,7 +19,40 @@ const COLORS = {
 };
 
 const MINI_SIZE = { w: 380, h: 100 };
-const MODAL_SIZE = { w: 1280, h: 720 };
+
+function modalSize() {
+  const fs = document.fullscreenElement;
+  if (fs) {
+    return {
+      w: Math.max(800, window.innerWidth - 48),
+      h: Math.max(480, window.innerHeight - 100),
+    };
+  }
+  return { w: 1280, h: 720 };
+}
+
+function loadKlineFromLS(symbol) {
+  try {
+    const raw = localStorage.getItem(LS_KLINE_PREFIX + symbol);
+    if (!raw) return null;
+    const obj = JSON.parse(raw);
+    if (!obj?.candles?.length || Date.now() - (obj.ts || 0) > KLINE_TTL_MS) return null;
+    return obj;
+  } catch {
+    return null;
+  }
+}
+
+function saveKlineToLS(symbol, candles, source) {
+  try {
+    localStorage.setItem(
+      LS_KLINE_PREFIX + symbol,
+      JSON.stringify({ ts: Date.now(), candles, source })
+    );
+  } catch {
+    /* quota */
+  }
+}
 
 function enqueueCharts(root) {
   root.querySelectorAll(".mini-chart[data-symbol]").forEach((box) => {
@@ -66,7 +102,7 @@ function drawCandlestickChart(canvas, candles, options = {}) {
   if (!canvas || !candles.length) return;
 
   const large = options.large === true;
-  const size = large ? MODAL_SIZE : MINI_SIZE;
+  const size = large ? modalSize() : MINI_SIZE;
   const volRatio = large ? 0.22 : 0.32;
   const pad = large
     ? { t: 16, r: 16, b: 28, l: 56 }
@@ -164,7 +200,7 @@ function drawCandlestickChart(canvas, candles, options = {}) {
 
 function drawEmptyChart(canvas, large = false) {
   if (!canvas) return;
-  const size = large ? MODAL_SIZE : MINI_SIZE;
+  const size = large ? modalSize() : MINI_SIZE;
   const { ctx, w, h } = setupCanvas(canvas, size.w, size.h);
   ctx.fillStyle = "#1a2332";
   ctx.fillRect(0, 0, w, h);
@@ -173,6 +209,32 @@ function drawEmptyChart(canvas, large = false) {
   ctx.fillText("暂无数据", w / 2 - 28, h / 2);
 }
 
+async function fetchKlines(symbol) {
+  let candles = chartDataCache.get(symbol);
+  let source = "memory";
+  if (candles) return { candles, source };
+
+  const ls = loadKlineFromLS(symbol);
+  if (ls) {
+    candles = ls.candles;
+    source = "browser";
+    chartDataCache.set(symbol, candles);
+    return { candles, source: ls.source || source };
+  }
+
+  const res = await fetch(`/api/chart/${symbol}/daily?limit=300`);
+  if (!res.ok) {
+    const err = await res.json().catch(() => ({}));
+    throw new Error(err.detail || res.statusText);
+  }
+  const data = await res.json();
+  candles = data.candles || [];
+  source = data.source || "db";
+  chartDataCache.set(symbol, candles);
+  saveKlineToLS(symbol, candles, source);
+  return { candles, source };
+}
+
 async function loadMiniChart(box) {
   const symbol = box.dataset.symbol;
   if (!symbol) return;
@@ -182,26 +244,22 @@ async function loadMiniChart(box) {
   if (status) status.textContent = "加载…";
 
   try {
-    let candles = chartDataCache.get(symbol);
-    let source = "cache";
-    if (!candles) {
-      const res = await fetch(`/api/chart/${symbol}/daily?limit=300`);
-      if (!res.ok) {
-        const err = await res.json().catch(() => ({}));
-        throw new Error(err.detail || res.statusText);
-      }
-      const data = await res.json();
-      candles = data.candles || [];
-      source = data.source || "db";
-      chartDataCache.set(symbol, candles);
-    }
+    const { candles, source } = await fetchKlines(symbol);
     if (!candles.length) throw new Error("无K线数据");
     drawCandlestickChart(canvas, candles, { large: false });
     box.dataset.loaded = "1";
     const srcLabel =
-      source === "db" ? "本地" : source === "db_stale" ? "本地(旧)" : source === "cache" ? "缓存" : "同步";
+      source === "browser"
+        ? "浏览器"
+        : source === "db"
+          ? "本地"
+          : source === "db_stale"
+            ? "本地(旧)"
+            : source === "cache"
+              ? "缓存"
+              : "同步";
     if (status) status.textContent = `${candles.length}日·${srcLabel}`;
-    box.title = `${symbol} 日K+量 ${candles.length}根 (${srcLabel})，点击放大`;
+    box.title = `${symbol} 日K+量 ${candles.length}根 (${srcLabel})，点击全屏`;
   } catch (e) {
     if (status) status.textContent = "—";
     box.title = `${symbol}: ${e.message}`;
@@ -211,6 +269,36 @@ async function loadMiniChart(box) {
   }
 }
 
+let chartModalSymbol = "";
+
+function closeChartModal() {
+  const modal = document.getElementById("chart-modal");
+  if (!modal) return;
+  modal.classList.add("hidden");
+  if (document.fullscreenElement) {
+    document.exitFullscreen?.().catch(() => {});
+  }
+}
+
+function openChartModal(symbol) {
+  const candles = chartDataCache.get(symbol);
+  if (!candles?.length) return;
+
+  chartModalSymbol = symbol;
+  const modal = document.getElementById("chart-modal");
+  modal.classList.remove("hidden");
+  document.getElementById("chart-modal-title").textContent =
+    `${symbol} · 日K + 成交量（${candles.length} 根）`;
+  const canvas = document.getElementById("chart-modal-canvas");
+  drawCandlestickChart(canvas, candles, { large: true });
+
+  const inner = modal.querySelector(".chart-modal-inner");
+  const req = inner.requestFullscreen || inner.webkitRequestFullscreen;
+  if (req) {
+    req.call(inner).catch(() => {});
+  }
+}
+
 function setupChartModal() {
   let modal = document.getElementById("chart-modal");
   if (!modal) {
@@ -221,33 +309,33 @@ function setupChartModal() {
       <div class="chart-modal-inner">
         <button type="button" class="chart-modal-close" aria-label="关闭">×</button>
         <h3 id="chart-modal-title"></h3>
-        <p class="chart-modal-hint">日K + 成交量 · 300根 · 滚轮可缩放页面</p>
+        <p class="chart-modal-hint">日K + 成交量 · 300根 · 点击全屏 · Esc 退出</p>
         <div class="chart-modal-canvas-wrap">
           <canvas id="chart-modal-canvas"></canvas>
         </div>
       </div>`;
     document.body.appendChild(modal);
-    modal.querySelector(".chart-modal-close").onclick = () =>
-      modal.classList.add("hidden");
+    modal.querySelector(".chart-modal-close").onclick = closeChartModal;
     modal.addEventListener("click", (e) => {
-      if (e.target === modal) modal.classList.add("hidden");
+      if (e.target === modal) closeChartModal();
     });
     document.addEventListener("keydown", (e) => {
-      if (e.key === "Escape") modal.classList.add("hidden");
+      if (e.key === "Escape") closeChartModal();
+    });
+    document.addEventListener("fullscreenchange", () => {
+      if (!chartModalSymbol) return;
+      const canvas = document.getElementById("chart-modal-canvas");
+      const candles = chartDataCache.get(chartModalSymbol);
+      if (canvas && candles?.length) {
+        drawCandlestickChart(canvas, candles, { large: true });
+      }
     });
   }
 
   document.body.addEventListener("click", (e) => {
     const box = e.target.closest(".mini-chart[data-symbol]");
     if (!box || box.dataset.loaded !== "1") return;
-    const symbol = box.dataset.symbol;
-    const candles = chartDataCache.get(symbol);
-    if (!candles) return;
-    modal.classList.remove("hidden");
-    document.getElementById("chart-modal-title").textContent =
-      `${symbol} · 日K + 成交量（${candles.length} 根）`;
-    const canvas = document.getElementById("chart-modal-canvas");
-    drawCandlestickChart(canvas, candles, { large: true });
+    openChartModal(box.dataset.symbol);
   });
 }
 

web/index.html

@@ -9,7 +9,7 @@
 <body>
   <header class="site-header">
     <h1>币安 U本位合约 · 成交额排名</h1>
-    <p class="subtitle">北京时间 08:00 切日 · Top30 · 日K+成交量+资金费率</p>
+    <p class="subtitle">北京时间 08:00 切日 · Top30 · 今日每4小时自动刷新+手动 · 08:05 AI解读三日交集</p>
   </header>
 
   <nav class="main-nav" id="main-nav">
@@ -22,7 +22,7 @@
   <main id="view-today" class="view-panel active">
     <section class="panel">
       <div class="panel-head">
-        <h2>今日周期 <span class="live">实时</span></h2>
+        <h2>今日周期 <span class="live">4h+手动</span></h2>
         <span class="period" id="today-period">—</span>
         <span class="updated" id="today-updated"></span>
         <div class="panel-actions">
@@ -78,6 +78,18 @@
       <p class="stats-desc" id="stats-desc"></p>
       <div class="table-wrap" id="stats-table-wrap"></div>
     </section>
+    <section class="panel llm-panel">
+      <div class="panel-head">
+        <h2>大模型解读 <span class="llm-model" id="llm-model-label">—</span></h2>
+        <span class="updated" id="llm-status-text">—</span>
+        <div class="panel-actions">
+          <button type="button" class="btn-secondary" id="btn-llm-run">开始解读</button>
+          <button type="button" class="btn-secondary" id="btn-llm-refresh">刷新解读</button>
+        </div>
+      </div>
+      <p class="stats-desc">每日 08:05（北京时间）自动对「三日 Top30 交集」逐币解读，每币约 3 分钟；启动时也会自动跑一轮（需配置 LLM_API_KEY）。</p>
+      <div id="llm-interpret-list" class="llm-list"></div>
+    </section>
   </main>
 
   <footer>

web/style.css

@@ -339,6 +339,80 @@ button:hover {
   overflow: auto;
 }
 
+.chart-modal-inner:fullscreen {
+  width: 100vw;
+  height: 100vh;
+  max-width: none;
+  max-height: none;
+  border-radius: 0;
+  display: flex;
+  flex-direction: column;
+}
+
+.chart-modal-inner:fullscreen .chart-modal-canvas-wrap {
+  flex: 1;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  overflow: hidden;
+}
+
+.llm-panel {
+  margin-top: 1rem;
+}
+
+.llm-model {
+  font-size: 0.75rem;
+  color: var(--accent);
+  font-weight: normal;
+}
+
+.llm-list {
+  display: grid;
+  gap: 0.75rem;
+  max-height: 420px;
+  overflow-y: auto;
+}
+
+.llm-card {
+  background: #121a26;
+  border: 1px solid var(--border);
+  border-radius: 8px;
+  padding: 0.75rem 1rem;
+}
+
+.llm-card h4 {
+  margin: 0 0 0.5rem;
+  font-size: 0.95rem;
+}
+
+.llm-card h4 small {
+  color: var(--muted);
+  font-weight: normal;
+}
+
+.llm-text {
+  font-size: 0.88rem;
+  line-height: 1.55;
+  color: var(--text);
+  white-space: pre-wrap;
+}
+
+.llm-inline summary {
+  cursor: pointer;
+  color: var(--accent);
+  font-size: 0.85rem;
+}
+
+.llm-col {
+  max-width: 280px;
+  font-size: 0.82rem;
+}
+
+.muted {
+  color: var(--muted);
+}
+
 .chart-modal-inner h3 {
   margin: 0 0 0.25rem;
   font-size: 1.15rem;