Expand recommend table with gap, daily stats, and client-side sorting

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

recommend_trend.py

@@ -64,6 +64,68 @@ def _direction_from_closes(bars: list) -> str:
     return TREND_RANGE
 
 
+def _bar_ohlcv(bar: dict) -> tuple[float, float, float, float, float]:
+    o, h, l, c = _bar_ohlc(bar)
+    v = float(bar.get("v") or bar.get("volume") or 0)
+    return o, h, l, c, v
+
+
+def compute_daily_quote_stats(bars: list) -> dict:
+    """从日线提取：跳空、昨收、今开、昨涨跌、昨振幅、成交量。"""
+    empty = {
+        "gap": "",
+        "gap_label": "—",
+        "gap_pct": None,
+        "prev_close": None,
+        "today_open": None,
+        "yesterday_change": None,
+        "yesterday_change_pct": None,
+        "yesterday_amplitude_pct": None,
+        "volume": None,
+    }
+    if len(bars) < 2:
+        return empty
+
+    t_o, _, _, _, t_v = _bar_ohlcv(bars[-1])
+    y_o, y_h, y_l, y_c, y_v = _bar_ohlcv(bars[-2])
+    if y_c <= 0:
+        return empty
+
+    prev_close = round(y_c, 4)
+    today_open = round(t_o, 4) if t_o > 0 else None
+
+    gap, gap_label, gap_pct = "none", "否", 0.0
+    if today_open is not None and today_open > y_c:
+        gap, gap_label = "up", "跳空高开"
+        gap_pct = (today_open - y_c) / y_c * 100
+    elif today_open is not None and today_open < y_c:
+        gap, gap_label = "down", "跳空低开"
+        gap_pct = (today_open - y_c) / y_c * 100
+
+    if len(bars) >= 3:
+        _, _, _, p_c, _ = _bar_ohlcv(bars[-3])
+        base = p_c if p_c > 0 else y_o
+    else:
+        base = y_o if y_o > 0 else y_c
+
+    y_change = y_c - base if base > 0 else None
+    y_change_pct = (y_change / base * 100) if y_change is not None and base > 0 else None
+    y_amp = ((y_h - y_l) / base * 100) if base > 0 and y_h >= y_l else None
+    vol = y_v if y_v > 0 else (t_v if t_v > 0 else None)
+
+    return {
+        "gap": gap,
+        "gap_label": gap_label,
+        "gap_pct": round(gap_pct, 2) if gap != "none" else 0.0,
+        "prev_close": prev_close,
+        "today_open": today_open,
+        "yesterday_change": round(y_change, 4) if y_change is not None else None,
+        "yesterday_change_pct": round(y_change_pct, 2) if y_change_pct is not None else None,
+        "yesterday_amplitude_pct": round(y_amp, 2) if y_amp is not None else None,
+        "volume": int(vol) if vol is not None else None,
+    }
+
+
 def analyze_daily_trend(bars: list, *, overlap_threshold: float = OVERLAP_RANGE_THRESHOLD) -> dict:
     """根据近一周日线判断走势；最近三天重叠度≥阈值视为震荡。"""
     empty = {
@@ -120,6 +182,7 @@ def _normalize_daily_bars(raw: list) -> list:
                 "h": float(row[2]),
                 "l": float(row[3]),
                 "c": float(row[4]),
+                "v": float(row[5]) if len(row) > 5 and row[5] else 0.0,
             })
         elif isinstance(row, dict) and row.get("d"):
             out.append({
@@ -128,6 +191,7 @@ def _normalize_daily_bars(raw: list) -> list:
                 "h": float(row.get("h", 0) or 0),
                 "l": float(row.get("l", 0) or 0),
                 "c": float(row.get("c", 0) or 0),
+                "v": float(row.get("v", 0) or 0),
             })
     return out
 
@@ -181,17 +245,70 @@ def fetch_week_daily_bars(
     return bars[-DAILY_LOOKBACK:] if bars else []
 
 
+def analyze_product_daily(
+    symbol: str,
+    *,
+    fetch_fn: Callable[[str, str], list] | None = None,
+) -> dict:
+    """拉取主力合约一周日线：走势 + 跳空/量价统计。"""
+    sym = (symbol or "").strip()
+    if not sym:
+        out = analyze_daily_trend([])
+        out.update(compute_daily_quote_stats([]))
+        return out
+    bars = fetch_week_daily_bars(sym, fetch_fn=fetch_fn)
+    out = analyze_daily_trend(bars)
+    out.update(compute_daily_quote_stats(bars))
+    return out
+
+
 def analyze_product_trend(
     symbol: str,
     *,
     fetch_fn: Callable[[str, str], list] | None = None,
 ) -> dict:
-    """拉取主力合约一周日线并分析走势。"""
-    sym = (symbol or "").strip()
-    if not sym:
-        return analyze_daily_trend([])
-    bars = fetch_week_daily_bars(sym, fetch_fn=fetch_fn)
-    return analyze_daily_trend(bars)
+    return analyze_product_daily(symbol, fetch_fn=fetch_fn)
+
+
+GAP_SORT_RANK = {"up": 2, "down": 1, "none": 0, "": -1}
+TREND_SORT_RANK = {
+    TREND_BREAK_LONG: 0,
+    TREND_BREAK_SHORT: 0,
+    TREND_LONG: 1,
+    TREND_SHORT: 2,
+    TREND_RANGE: 3,
+    "": 9,
+}
+
+
+def recommend_sort_key(row: dict, sort_by: str = "trend", *, desc: bool = True) -> tuple:
+    """可排序字段：trend / gap / volume / amplitude。"""
+    key = (sort_by or "trend").strip().lower()
+    if key == "gap":
+        primary = GAP_SORT_RANK.get(row.get("gap") or "", -1)
+        secondary = abs(float(row.get("gap_pct") or 0))
+    elif key == "volume":
+        primary = float(row.get("volume") or 0)
+        secondary = 0.0
+    elif key == "amplitude":
+        primary = float(row.get("yesterday_amplitude_pct") or 0)
+        secondary = 0.0
+    else:
+        primary = TREND_SORT_RANK.get(row.get("trend") or "", 9)
+        secondary = -(int(row.get("max_lots") or 0))
+
+    if desc:
+        return (-primary, -secondary, row.get("name") or "")
+    return (primary, secondary, row.get("name") or "")
+
+
+def sort_recommend_rows(
+    rows: list[dict],
+    *,
+    sort_by: str = "trend",
+    desc: bool = True,
+) -> list[dict]:
+    return sorted(rows, key=lambda r: recommend_sort_key(r, sort_by, desc=desc))
 
 
 def trend_sort_key(row: dict) -> tuple:
@@ -213,4 +330,4 @@ def trend_sort_key(row: dict) -> tuple:
 
 
 def sort_recommend_by_trend(rows: list[dict]) -> list[dict]:
-    return sorted(rows, key=trend_sort_key)
+    return sort_recommend_rows(rows, sort_by="trend", desc=True)