fix: TradingView K线图表并修复品种推荐为空。

- 行情页改用 Lightweight Charts 标准蜡烛图（红跌绿涨）
- 修复 fee_rates 缺 source 列导致推荐刷新失败
- 空缓存自动重试，持仓页实时兜底计算推荐列表

Co-authored-by: Cursor <cursoragent@cursor.com>

recommend_store.py

@@ -2,12 +2,16 @@
 from __future__ import annotations
 
 import json
+import logging
 import math
 from datetime import datetime
 from typing import Callable, Optional
 
+from fee_specs import ensure_fee_rates_schema
 from product_recommend import list_product_recommendations
 
+logger = logging.getLogger(__name__)
+
 RECOMMEND_CACHE_SQL = """
 CREATE TABLE IF NOT EXISTS product_recommend_cache (
     id INTEGER PRIMARY KEY CHECK (id = 1),
@@ -34,6 +38,22 @@ def rows_missing_max_lots(rows: list[dict]) -> bool:
     return any("max_lots" not in r for r in rows)
 
 
+def recommend_cache_needs_refresh(
+    cached: dict,
+    *,
+    capital: float = 0.0,
+) -> bool:
+    """是否需要重新拉行情计算推荐列表。"""
+    if recommend_cache_stale(cached.get("updated_at")):
+        return True
+    rows = cached.get("rows") or []
+    if rows_missing_max_lots(rows):
+        return True
+    if float(capital or 0) > 0 and not rows:
+        return True
+    return False
+
+
 def enrich_recommend_rows(
     rows: list[dict],
     capital: float,
@@ -81,10 +101,19 @@ def refresh_recommend_cache(
 ) -> list[dict]:
     """后台拉行情、筛选并写入数据库。"""
     ensure_recommend_tables(conn)
+    ensure_fee_rates_schema(conn)
     all_rows = list_product_recommendations(
         capital, quote_fn, max_margin_pct=max_margin_pct, trading_mode=trading_mode,
     )
     rows = filter_affordable_recommendations(all_rows)
+    if not rows and float(capital or 0) > 0:
+        logger.warning(
+            "recommend refresh: 0 affordable rows capital=%.2f total=%d no_price=%d blocked=%d",
+            float(capital or 0),
+            len(all_rows),
+            sum(1 for r in all_rows if r.get("status") == "no_price"),
+            sum(1 for r in all_rows if r.get("status") == "blocked"),
+        )
     now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     conn.execute(
         """INSERT INTO product_recommend_cache (id, capital, rows_json, updated_at)
@@ -142,9 +171,7 @@ def recommend_payload(
     pct = max(1.0, min(100.0, float(max_margin_pct or 30.0)))
     payload["capital"] = cap
     payload["max_margin_pct"] = pct
-    payload["rows"] = enrich_recommend_rows(
-        payload.get("rows") or [],
-        cap,
-        max_margin_pct=pct,
-    )
+    rows = payload.get("rows") or []
+    payload["rows"] = enrich_recommend_rows(rows, cap, max_margin_pct=pct)
+    payload["needs_refresh"] = recommend_cache_needs_refresh(payload, capital=cap)
     return payload