feat: data-layering 第二批消费层读DB(divergence/staleness/accumulation/collect_eval/strategy_review/mo_provider/multi_timeframe/chip_factors)

deploy/profile-scripts/multi_timeframe.py

@@ -12,8 +12,6 @@
 
 import json
 import os
-import urllib.request
-import urllib.error
 from datetime import datetime, date, timedelta
 from typing import Optional
 
@@ -23,11 +21,7 @@ DATA_DIR = "/home/hmo/web-dashboard/data"
 HISTORY_PATH = os.path.join(DATA_DIR, "price_history.json")
 # multi_tf_cache.json 已迁移到 DB (mtf_cache 表)
 
-# 腾讯API K线端点
-KLINE_URL = "http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={market}{code},{period},,,{count},qfq"
-
-# 腾讯实时行情端点（用于市场前缀判断）
-QUOTE_URL = "http://qt.gtimg.cn/q={market}{code}"
+# (2026-08-26 分层铁律：K线/行情读 DB stock_daily/live_prices，不再直连腾讯API)
 
 
 def _write_klines_to_db(code: str, daily: list, weekly: list, monthly: list, fundamentals: dict = None):
@@ -125,8 +119,42 @@ def _save_mtf_cache():
         pass
 
 
+def _aggregate_bars(daily_bars: list, period: str) -> list:
+    """把升序日K聚合为周K/月K（period: week/month）。
+
+    Args:
+        daily_bars: 升序日K列表 [{date,open,close,high,low,volume}]
+        period: "week" / "month"
+
+    Returns:
+        升序聚合K线 [{date,open,close,high,low,volume}]
+    """
+    from collections import OrderedDict
+    groups = OrderedDict()
+    for b in daily_bars:
+        try:
+            dt = datetime.strptime(b["date"], "%Y-%m-%d")
+        except Exception:
+            continue
+        if period == "week":
+            iso = dt.isocalendar()
+            key = (iso[0], iso[1])  # (年, ISO周)
+        else:  # month
+            key = (dt.year, dt.month)
+        if key not in groups:
+            groups[key] = dict(b)
+        else:
+            g = groups[key]
+            g["close"] = b["close"]
+            g["high"] = max(g["high"], b["high"])
+            g["low"] = min(g["low"], b["low"])
+            g["volume"] += b["volume"]
+            g["date"] = b["date"]
+    return list(groups.values())
+
+
 def fetch_kline(code: str, period: str = "day", count: int = 120) -> list:
-    """从腾讯API获取K线数据，优先使用本地缓存
+    """从 DB stock_daily 获取K线数据（2026-08-26 分层铁律：消费层不直连腾讯API）
 
     Args:
         code: 股票代码 (如 "300548")
@@ -151,61 +179,47 @@ def fetch_kline(code: str, period: str = "day", count: int = 120) -> list:
     if cached_klines and updated_at and (now - updated_at) < _KLINE_CACHE_TTL.get(period, 3600):
         return cached_klines
 
-    market = _market_prefix(code)
-    is_index = any(code.startswith(p) for p in ["sh", "sz", "hk"])
-    
-    # 指数代码已经自带前缀，API直接用code；普通股票需要加market前缀
-    api_code = code if is_index else f"{market}{code}"
-    url = f"http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={api_code},{period},,,{count},qfq"
+    # 读 DB stock_daily（2026-08-26 分层铁律：消费层不直连腾讯API）
+    # 指数代码（sh000001/sz399001）与港股（5位 00700）在 stock_daily 中直接以原 code 存储
+    raw_code = str(code).split("_")[0]
+
+    # 周/月K需要更多日K做聚合：周K≈5日/根，月K≈22日/根，留余量
+    need = count
+    if period in ("week", "month"):
+        need = count * (5 if period == "week" else 22) + 10
 
     try:
-        req = urllib.request.Request(url, headers=_user_agent())
-        with urllib.request.urlopen(req, timeout=10) as resp:
-            raw = json.loads(resp.read().decode("utf-8"))
+        import sqlite3
+        db = sqlite3.connect('/home/hmo/MoFin/data/mofin.db', timeout=5)
+        rows = db.execute(
+            "SELECT date, open, close, high, low, volume FROM stock_daily "
+            "WHERE code=? ORDER BY date DESC LIMIT ?", (raw_code, need)
+        ).fetchall()
+        db.close()
     except Exception as e:
         return {"error": str(e), "code": code, "period": period}
 
-    if not isinstance(raw, dict):
-        return {"error": f"API returned {type(raw).__name__}", "raw": str(raw)[:200]}
-
-    api_data = raw.get("data", {})
-    if not isinstance(api_data, dict):
-        return {"error": f"data field is {type(api_data).__name__}", "raw": str(api_data)[:200]}
-
-    # 指数代码已经自带前缀（sh000001/sz399001），直接用
-    # 普通股票代码需要加market前缀（sh600036/sz300750）
-    is_index = any(code.startswith(p) for p in ["sh", "sz", "hk"])
-    stock_key = code if is_index else f"{market}{code}"
-    stock_data = api_data.get(stock_key, {})
-
-    # 腾讯API的K线字段名: qfqday, qfqweek, qfqmonth
-    period_key = f"qfq{period}"
-    klines = stock_data.get(period_key, [])
-
-    if not klines:
-        # 尝试其他字段名
-        for k in stock_data:
-            if isinstance(stock_data[k], list) and len(stock_data[k]) > 0:
-                if isinstance(stock_data[k][0], list) and len(stock_data[k][0]) >= 6:
-                    klines = stock_data[k]
-                    break
-
-    result = []
-    for k in klines:
-        if len(k) >= 6:
-            try:
-                result.append({
-                    "date": str(k[0]),
-                    "open": float(k[1]),
-                    "close": float(k[2]),
-                    "high": float(k[3]),
-                    "low": float(k[4]),
-                    "volume": float(k[5]),
-                })
-            except (ValueError, IndexError):
+    bars = []
+    for r in reversed(rows):  # 升序（旧→新），与腾讯API输出时序一致
+        try:
+            if r[1] is None or r[2] is None:
                 continue
+            bars.append({
+                "date": str(r[0]),
+                "open": float(r[1]),
+                "close": float(r[2]),
+                "high": float(r[3] or r[2]),
+                "low": float(r[4] or r[2]),
+                "volume": float(r[5] or 0),
+            })
+        except (ValueError, TypeError):
+            continue
 
-    return result
+    if period == "day":
+        return bars[-count:]
+
+    # 周/月K：由日K聚合（open=首日open, close=末日close, high=max, low=min, volume=sum）
+    return _aggregate_bars(bars, period)[-count:]
 
 
 def calc_moving_averages(klines: list, windows: list = [5, 10, 20, 60]) -> dict: