feat(quant): QlibEngine v1 — 本地行情落盘 QlibDataset → D.features 读取 → 因子回测

- qlib_adapter/provider.py：SQLite 行情按 qlib 0.9.8 二进制格式落盘（起始索引头 + 逐日 float32、instruments 3 列、小写 instrument、晚上市 offset）
- qlib_adapter/dataset.py：qlib.init 幂等（{'day': uri}）+ D.features 读取 close 面板
- qlib_adapter/engine.py：QlibEngine（QuantEngine）v1 —— Qlib 数据管线回测与 LocalEngine 同记账规则（无未来函数/成本/涨跌停标注），factor_test 复用共享实现；Alpha158+LightGBM 为 TODO
- 真实 20 股验证：qlib 落盘 142 文件→读取→回测（-12.81%，Local 对照 -12.97%，差异为 qlib float32 存储）
- tests/test_qlib_engine.py 5 项（格式/roundtrip/晚上市 offset/回测/因子测试）→ pytest 86 passed / ruff clean

backend/app/quant/qlib_adapter/provider.py

@@ -0,0 +1,90 @@
+"""Qlib 数据落盘 Provider（qlib_adapter 内部，业务层不直接 import）。
+
+把本地行情长表（来自 SQLite，见 quant.service.bars_to_daily_df）按 qlib 0.9.8
+的二进制格式导出到指定目录（provider_uri），供 qlib D.features 读取。
+
+格式要点（来自实测 qlib 0.9.8.dev32 与源码 file_storage）：
+- instruments 文件 3 列：instrument\\tstart\\tend（instrument 全小写）
+- feature bin 布局：首 4 字节 float32 = 该股票在全局日历中的起始下标，
+  其后每个交易日一个 float32（停牌/缺失为 NaN）
+- provider_uri 需以 {"day": <目录>} 传入 qlib.init
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+# qlib 消费侧字段（Alpha158 依赖：close/open/high/low/volume/amount/vwap/factor）
+DUMP_FIELDS = ["open", "high", "low", "close", "volume", "amount", "vwap", "factor"]
+
+
+def _synth_vwap(daily: pd.DataFrame) -> pd.Series:
+    """vwap = amount / volume（真实口径）；volume 为 0 时取 close。"""
+    vol = daily["volume"].replace(0, pd.NA)
+    vwap = daily["amount"] / vol
+    return vwap.fillna(daily["close"])
+
+
+def build_qlib_dataset(
+    daily: pd.DataFrame,
+    out_dir: Path,
+    *,
+    fields: tuple[str, ...] = DUMP_FIELDS,
+    add_vwap: bool = True,
+) -> Path:
+    """把长表行情落盘为 qlib 数据集目录；返回 out_dir（provider_uri）。"""
+    if daily.empty:
+        raise ValueError("无行情数据可导出 Qlib 数据集")
+    out_dir = Path(out_dir)
+    cal_file = out_dir / "calendars" / "day.txt"
+    cal_file.parent.mkdir(parents=True, exist_ok=True)
+    (out_dir / "instruments").mkdir(exist_ok=True)
+    feat_root = out_dir / "features"
+
+    df = daily.copy()
+    if add_vwap and "amount" in df.columns and "volume" in df.columns:
+        df["vwap"] = _synth_vwap(df)
+
+    calendar = sorted(df["trade_date"].unique())
+    if not isinstance(calendar[0], (pd.Timestamp, np.datetime64)):
+        calendar = pd.to_datetime(calendar).sort_values().tolist()
+    cal_idx = {day: i for i, day in enumerate(calendar)}
+    cal_file.write_text(
+        "\n".join(pd.Timestamp(d).strftime("%Y-%m-%d") for d in calendar), encoding="utf-8"
+    )
+
+    inst_lines: list[str] = []
+    for symbol, group in df.groupby("symbol"):
+        symbol = str(symbol).lower()
+        dates = sorted(pd.to_datetime(group["trade_date"]))
+        start_idx = cal_idx[dates[0]]
+        end_idx = cal_idx[dates[-1]]
+        inst_lines.append(
+            f"{symbol}\t{pd.Timestamp(dates[0]).strftime('%Y-%m-%d')}"
+            f"\t{pd.Timestamp(dates[-1]).strftime('%Y-%m-%d')}"
+        )
+        sym_dir = feat_root / symbol
+        sym_dir.mkdir(parents=True, exist_ok=True)
+        per_date = group.set_index(pd.to_datetime(group["trade_date"]))
+        for field in fields:
+            if field not in per_date.columns:
+                continue
+            series = per_date[field]
+            values = np.full(len(calendar), np.nan, dtype=np.float32)
+            for day, val in series.items():
+                if pd.isna(val):
+                    continue
+                try:
+                    values[cal_idx[day]] = float(val)
+                except (TypeError, ValueError, KeyError):
+                    continue
+            payload = np.hstack(
+                [np.asarray([float(start_idx)], dtype=np.float32), values[start_idx : end_idx + 1]]
+            ).astype("<f")
+            (sym_dir / f"{field}.day.bin").write_bytes(payload.tobytes())
+
+    (out_dir / "instruments" / "all.txt").write_text("\n".join(inst_lines), encoding="utf-8")
+    return out_dir