Files

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claude_dev a62013cb9c docs(factor): 第一批实施计划——13任务TDD拆解(挂载/股票池/指标/存储/引擎/CLI/API/前端三页/NAS验证),含ST过滤偏差声明与spawn池规避设计 [nas]

2026-08-24 23:52:40 +08:00

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因子研究第一批（Alpha101+Alpha158 挂载与全A重验）实施计划

For agentic workers: REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (- [ ]) syntax for tracking.

Goal: 把 vnpy 自带的 Alpha101(100)+Alpha158(158) 因子表达式挂进 sanguo_factor 注册表，建成批量评估管线（全A 8.5 年 → IC 排行榜落盘）+ 前端「因子研究工作台」三页面，部署 NAS 验证。

Architecture: 评估引擎不走 AlphaDataset 的 spawn 进程池（其 prepare_data 会对每个表达式 pickle 整个 9M 行 DataFrame），改为直接调用 vnpy.alpha.dataset.utility.calculate_by_expression 在进程内逐因子计算（内存天然有界，无 spawn 路径问题）。股票池由 dbbardata 直读（sqlite3 分块查询 → polars），vwap 在加载层派生（turnover/volume）。指标全部 numpy/pandas 向量化（RankIC=秩相关逐行向量化）。结果落 factor_eval.db（eval_runs + eval_results 两表，metrics 按周期存 JSON）。现有单因子分析链路（AlphaLabSession/alphalens tears）零改动。

Tech Stack: Python 3.10 (venv310) / polars 1.42 / pandas / sqlite3 / FastAPI / Vue3 + TS + Element Plus + echarts

Spec: docs/superpowers/specs/2026-08-24-factor-batch1-alpha101-158.md 前端视觉唯一真相源: docs/factor_research/factor-workbench-mockup.html（用户已确认）+ frontend/src/styles/tokens.css

Global Constraints

测试一律用 venv310/bin/python -m pytest ...（系统 python 的 pandas 3.0.1 假红）
commit message 末尾必打标签：后端/数据链路改动 [vps]（VPS 运行时也触发），纯前端/CI/docker [nas]
不改：sanguo_factor/analyzer.py、alpha_lab.py 现有行为、routes.py 既有 /factor/list /factor/analyze 端点、vnpy_v4.4.0/ 任何文件
新 API 路由放新文件 sanguo_api/routes_factor.py（仿 routes_paper.py 模式），在 app.py 注册
前端页面样式 1:1 对齐 mockup：深空黑 --bg、荧光青 --brand、琥珀 --amber、红涨绿跌 --up/--down、近直角、mono 数字；读 frontend/src/styles/tokens.css 取变量名，不写死色值
评估口径（spec 定稿）：主板+创业板（代码前缀 60/00/30，剔科创68/北交/ETF）、2018-01-01~2026-06-30、RankIC 前瞻 1/5/10 日、退市股保留、不做行业中性化
⚠️ 对 spec 的一处有意识偏差：不实现 ST 过滤。本地无 point-in-time 名称史，用「当前名称」回溯过滤会引入前视偏差，比不过滤更糟。代码前缀过滤 + 120 交易日预热已控住主要污染。在最终报告向用户说明
dbbardata 列名：symbol, exchange, datetime, interval, volume, turnover, open_interest, open_price, high_price, low_price, close_price，datetime 为 ISO 字符串，日线 interval='d'（既有直查范式见 sanguo_backtest/ashare_engine.py:235）
NAS 容器名 sanguo_vnpy_v2；docker 全路径 /var/packages/Docker/target/usr/bin/docker
YAGNI（spec 明确不做）：行业中性化、因子合成、Alpha360、GTJA191、聚宽财务因子、相关性聚类去重代码化（跑完数据后再分析）

File Structure

sanguo_factor/
  data_adapter.py        [改] convert_bars_to_alpha_df 补 vwap 派生列
  alpha_datasets.py      [新] 挂载 vnpy Alpha101/158 表达式进注册表
  universe.py            [新] dbbardata 分块直读 → AlphaLab 格式 polars df（含 vwap/bar_idx）
  metrics.py             [新] 向量化 RankIC/ICIR/t/胜率/多空年化/换手/十分组
  eval_store.py          [新] factor_eval.db 读写（eval_runs/eval_results）
  batch_eval.py          [新] 批量评估引擎（进程内逐因子算 → 指标 → 落盘）
sanguo_api/
  schemas.py             [改] +FactorBatchEvalRequest
  routes_factor.py       [新] GET runs/leaderboard/detail + POST submit
  app.py                 [改] 注册 routes_factor + set_eval_db_path 接线
sanguo_orchestrator/
  runner.py              [改] +submit_batch_eval +_batch_eval_worker + _persist_factor_eval
scripts/factor_research/
  run_eval.py            [新] CLI（冒烟/全量，NAS docker exec 用）
frontend/src/
  api/factor.ts          [改] +评估相关类型与请求
  router/index.ts        [改] +3 路由
  views/Layout.vue       [改] 投研组 +2 菜单项 +面包屑
  views/factor/Leaderboard.vue        [新] IC 排行榜
  views/factor/LeaderboardDetail.vue  [新] 因子详情
  views/factor/BatchEval.vue          [新] 批量评估
tests/factor/
  test_alpha_datasets.py [新]
  test_vwap_adapter.py   [新]
  test_universe.py       [新]
  test_metrics.py        [新]
  test_eval_store.py     [新]
  test_batch_eval.py     [新]
tests/api/
  test_factor_eval_routes.py [新]
.gitea/workflows/ci-cd.yml [改] pytest 门禁加 tests/factor

Task 1: vwap 派生列（data_adapter）

Files:

Modify: sanguo_factor/data_adapter.py
Test: tests/factor/test_vwap_adapter.py

Interfaces:

Produces: convert_bars_to_alpha_df(bars) -> pl.DataFrame 输出多一列 vwap: Float64（volume>0 时 = turnover/volume，否则 null）。下游 Task 2/6 依赖「df 有 vwap 列即可被表达式引用」（calculate_by_expression 按 df.columns 自动注入变量名）。
Step 1: 写失败测试

# tests/factor/test_vwap_adapter.py
"""vwap 派生列:turnover/volume,volume=0 → null."""
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0")))

from datetime import datetime
from vnpy.trader.object import BarData, Interval
from sanguo_factor.data_adapter import convert_bars_to_alpha_df


def _bar(symbol, dt, close=10.0, volume=100.0, turnover=1000.0):
    return BarData(
        symbol=symbol, exchange="SSE", datetime=dt, interval=Interval.DAILY,
        open_price=9.9, high_price=10.1, low_price=9.8, close_price=close,
        volume=volume, turnover=turnover, open_interest=0, gateway_name="T",
    )


def test_vwap_derived():
    df = convert_bars_to_alpha_df([_bar("600000", datetime(2024, 1, 5))])
    assert df["vwap"][0] == 10.0  # 1000 / 100


def test_vwap_null_when_volume_zero():
    df = convert_bars_to_alpha_df([_bar("600000", datetime(2024, 1, 5), volume=0.0, turnover=0.0)])
    assert df["vwap"][0] is None


def test_empty_schema_has_vwap():
    df = convert_bars_to_alpha_df([])
    assert "vwap" in df.columns
    assert df.height == 0

Step 2: 跑测试确认失败

Run: venv310/bin/python -m pytest tests/factor/test_vwap_adapter.py -v Expected: FAIL（vwap 不在 columns）

Step 3: 最小实现

convert_bars_to_alpha_df 的空 schema dict 加 "vwap": pl.Float64；构造 dict 加：

"vwap": [(b.turnover / b.volume) if (b.turnover and b.volume) else None for b in bars],

docstring 补一行：vwap: turnover/volume 派生(volume=0 置 null),Alpha101 表达式依赖。

Step 4: 跑测试确认通过 + 回归

Run: venv310/bin/python -m pytest tests/factor -v Expected: 全 PASS（含既有 5 个测试文件）

Step 5: Commit

git add sanguo_factor/data_adapter.py tests/factor/test_vwap_adapter.py
git commit -m "feat(factor): data_adapter 补 vwap 派生列(turnover/volume,零量置null)——Alpha101 表达式 59 处引用 vwap,dbbardata 无此列 [vps]"

Task 2: 挂载 Alpha101/Alpha158 表达式（alpha_datasets）

Files:

Create: sanguo_factor/alpha_datasets.py
Test: tests/factor/test_alpha_datasets.py

Interfaces:

Consumes: vnpy.alpha.dataset.datasets.alpha_101.Alpha101 / alpha_158.Alpha158（构造只注册表达式不计算，空 df 即可实例化）
Produces:
- mount_alpha101() -> int、mount_alpha158() -> int、mount_all() -> dict[str, int]（幂等，已存在同名跳过）
- 注册后 list_factors("alpha101") 100 项、list_factors("alpha158") 158 项，factor 名保持 vnpy 原名（alpha1..alpha100、kmid/klen/roc_5/ma_20/...）
- 挂载入口必须在 sanguo_factor/__init__.py 导入即生效（from . import alpha_datasets 链上自动注册）——下游 /factor/list、批量评估、CLI 全靠它
Step 1: 写失败测试

# tests/factor/test_alpha_datasets.py
"""Alpha101/158 全量挂载:数量/幂等/类别/表达式可用性."""
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0")))

from sanguo_factor.alpha_datasets import mount_all
from sanguo_factor.registry import list_factors, get_factor, _REGISTRY


def test_mount_all_counts():
    counts = mount_all()
    assert counts == {"alpha101": 100, "alpha158": 158}


def test_categories_and_expression():
    mount_all()
    a101 = list_factors("alpha101")
    assert {f["name"] for f in a101} >= {"alpha1", "alpha2", "alpha100"}
    assert get_factor("alpha5")["expression"].count("vwap") >= 1  # alpha5 显式用 vwap
    a158 = list_factors("alpha158")
    names = {f["name"] for f in a158}
    assert {"kmid", "klen", "roc_5", "ma_20", "std_20", "wvma_20", "vwap_0"} <= names


def test_mount_idempotent():
    mount_all()
    n_before = len(_REGISTRY)
    mount_all()
    assert len(_REGISTRY) == n_before


def test_import_side_effect_registers():
    # sanguo_factor 包导入即挂载(下游 /factor/list 依赖)
    import sanguo_factor
    assert len(list_factors("alpha101")) == 100

Step 2: 跑测试确认失败

Run: venv310/bin/python -m pytest tests/factor/test_alpha_datasets.py -v Expected: FAIL（ModuleNotFoundError: sanguo_factor.alpha_datasets）

Step 3: 实现

# sanguo_factor/alpha_datasets.py
"""挂载 vnpy 自带 Alpha101/Alpha158 表达式因子到注册表.

vnpy 的 AlphaDataset 子类在 __init__ 里只做 add_feature 字符串注册(不触发计算),
用空 schema df 实例化即可零成本提取全部表达式。
"""
import sys
import os

_VNPY_SRC = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "vnpy_v4.4.0"))
if _VNPY_SRC not in sys.path:
    sys.path.insert(0, _VNPY_SRC)

from .registry import register_factor, list_factors, _REGISTRY

_ALPHA_SCHEMA = {
    "vt_symbol": __import__("polars").Utf8,
    "datetime": __import__("polars").Datetime,
    "open": __import__("polars").Float64,
    "high": __import__("polars").Float64,
    "low": __import__("polars").Float64,
    "close": __import__("polars").Float64,
    "volume": __import__("polars").Float64,
    "turnover": __import__("polars").Float64,
    "open_interest": __import__("polars").Float64,
    "vwap": __import__("polars").Float64,
}

_DUMMY_PERIOD = ("2018-01-01", "2018-01-02")


def _extract_expressions(dataset_cls) -> dict[str, str]:
    """实例化数据集类,提取其注册的全部 {name: expression}."""
    import polars as pl
    df = pl.DataFrame(schema=_ALPHA_SCHEMA)
    ds = dataset_cls(df, _DUMMY_PERIOD, _DUMMY_PERIOD, _DUMMY_PERIOD)
    return {name: str(expr) for name, expr in ds.feature_expressions.items()}


def _mount(dataset_cls, category: str) -> int:
    """把一个数据集类的表达式挂进注册表(已存在同名跳过,幂等)."""
    mounted = 0
    for name, expression in _extract_expressions(dataset_cls).items():
        if name in _REGISTRY:
            continue
        register_factor(name, expression, category)
        mounted += 1
    return mounted


def mount_alpha101() -> int:
    """挂载 WorldQuant Alpha101(100 个),category=alpha101."""
    from vnpy.alpha.dataset.datasets.alpha_101 import Alpha101
    return _mount(Alpha101, "alpha101")


def mount_alpha158() -> int:
    """挂载 Qlib Alpha158(158 个),category=alpha158."""
    from vnpy.alpha.dataset.datasets.alpha_158 import Alpha158
    return _mount(Alpha158, "alpha158")


def mount_all() -> dict[str, int]:
    """挂载全部内置数据集,返回各类新挂数量."""
    return {"alpha101": mount_alpha101(), "alpha158": mount_alpha158()}


# 模块导入即挂载(与 library.py 内置因子同一模式,下游 import sanguo_factor 即全量可见)
mount_all()

注：__import__("polars") 的写法太丑——实现时直接顶部 import polars as pl，schema 用 pl.Utf8 等（polars 是硬依赖，无需 lazy）。上面为计划示意，实现以顶部 import polars 为准。

sanguo_factor/__init__.py 末尾追加一行：

from . import alpha_datasets  # noqa: F401  挂载 Alpha101/158(导入即注册)

（先读该文件现有内容，追加不重排。）

Step 4: 跑测试确认通过

Run: venv310/bin/python -m pytest tests/factor/test_alpha_datasets.py tests/factor -v Expected: 全 PASS

Step 5: Commit

git add sanguo_factor/alpha_datasets.py sanguo_factor/__init__.py tests/factor/test_alpha_datasets.py
git commit -m "feat(factor): 挂载 vnpy Alpha101(100)+Alpha158(158) 全量表达式进注册表——空df实例化零成本提取,导入即注册,幂等 [vps]"

Task 3: 评估股票池加载（universe）

Files:

Create: sanguo_factor/universe.py
Test: tests/factor/test_universe.py

Interfaces:

Consumes: dbbardata 表结构（见 Global Constraints）
Produces:
- STOCK_PREFIXES = ("60", "00", "30")、WARMUP_BARS = 120
- load_universe_bars(vnpy_db: str, start: str, end: str, symbols: list[str] | None = None, limit: int | None = None) -> pl.DataFrame
  - start/end 为评估窗（YYYY-MM-DD）；内部自动加 lookback（start-300 天）与 forward 缓冲（end+45 天，供 10 日前瞻收益）
  - 返回列：vt_symbol(Utf8), datetime(Datetime, 已截断到日), open, high, low, close, volume, turnover, open_interest, vwap, bar_idx(Int64)
  - vwap：volume>0 → turnover/volume 否则 null
  - bar_idx：按 symbol 分组、datetime 升序的 0 起行号（预热/次新过滤用）
  - limit：随机抽样 N 只（random.Random(42) 固定种子，冒烟可复现）；symbols 显式给定时只查这些
- evaluation_filter(df: pl.DataFrame, start: str, end: str) -> pl.DataFrame：datetime ∈ [start, end] 且 bar_idx >= WARMUP_BARS（IC 评估行集）
Step 1: 写失败测试

# tests/factor/test_universe.py
"""股票池加载:前缀过滤/时间缓冲/vwap/bar_idx/显式symbols/limit抽样."""
import sqlite3
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0")))

import pytest
from sanguo_factor.universe import load_universe_bars, evaluation_filter, WARMUP_BARS

_DDL = """
CREATE TABLE dbbardata(
  symbol TEXT, exchange TEXT, datetime TEXT, interval TEXT,
  volume REAL, turnover REAL, open_interest REAL,
  open_price REAL, high_price REAL, low_price REAL, close_price REAL)
"""


def _mk_db(tmp_path, rows):
    db = str(tmp_path / "qt.db")
    conn = sqlite3.connect(db)
    conn.execute(_DDL)
    conn.executemany("INSERT INTO dbbardata VALUES(?,?,?,?,?,?,?,?,?,?,?)", rows)
    conn.commit(); conn.close()
    return db


def _row(sym, ex, day, close, volume=100.0, turnover=None):
    return (sym, ex, f"{day} 00:00:00", "d", volume,
            turnover if turnover is not None else close * volume, 0,
            close, close, close, close)


@pytest.fixture()
def db(tmp_path):
    rows = []
    # 600000:60根(2017-10~2017-12) + 评估窗内 3 根
    for i in range(60):
        rows.append(_row("600000", "SSE", f"2017-10-{(i % 28) + 1:02d}", 10.0 + i * 0.01))
    for d in ("2018-01-02", "2018-01-03", "2018-01-04"):
        rows.append(_row("600000", "SSE", d, 11.0))
    # 000001:只有 2 根(次新,bar_idx<WARMUP 应被 evaluation_filter 排除)
    for d in ("2018-01-02", "2018-01-03"):
        rows.append(_row("000001", "SZSE", d, 5.0))
    # 应剔除:科创68/北交8开头/ETF 510300
    rows.append(_row("688001", "SSE", "2018-01-02", 20.0))
    rows.append(_row("830001", "BJSE", "2018-01-02", 3.0))
    rows.append(_row("510300", "SSE", "2018-01-02", 4.0))
    # 非日线 interval 应忽略
    rows.append(("600000", "SSE", "2018-01-02 09:35:00", "15m", 1, 1, 0, 1, 1, 1, 1))
    return _mk_db(tmp_path, rows)


def test_load_filters_prefix_and_interval(db):
    df = load_universe_bars(db, "2018-01-01", "2018-01-31")
    syms = df["vt_symbol"].unique().to_list()
    assert set(syms) == {"600000.SSE", "000001.SZSE"}


def test_load_lookback_buffer_and_vwap(db):
    df = load_universe_bars(db, "2018-01-01", "2018-01-31")
    # lookback 生效:2017-10 的 bar 也在(因子窗口预热需要)
    assert str(df["datetime"].min()).startswith("2017-10")
    # vwap = turnover/volume
    row = df.filter(df["vt_symbol"] == "600000.SSE").sort("datetime").row(0, named=True)
    assert row["vwap"] == pytest.approx(10.0)


def test_bar_idx_per_symbol(db):
    df = load_universe_bars(db, "2018-01-01", "2018-01-31").sort(["vt_symbol", "datetime"])
    idx = df.filter(df["vt_symbol"] == "000001.SZSE")["bar_idx"].to_list()
    assert idx == [0, 1]


def test_evaluation_filter_warmup(db):
    df = load_universe_bars(db, "2018-01-01", "2018-01-31")
    ev = evaluation_filter(df, "2018-01-01", "2018-01-31")
    # 000001 只有2根 < WARMUP_BARS → 全排除;600000 保留
    assert set(ev["vt_symbol"].unique().to_list()) == {"600000.SSE"}
    assert ev.height == 3


def test_explicit_symbols(db):
    df = load_universe_bars(db, "2018-01-01", "2018-01-31", symbols=["000001"])
    assert set(df["vt_symbol"].unique().to_list()) == {"000001.SZSE"}


def test_limit_deterministic(db):
    df1 = load_universe_bars(db, "2018-01-01", "2018-01-31", limit=1)
    df2 = load_universe_bars(db, "2018-01-01", "2018-01-31", limit=1)
    assert set(df1["vt_symbol"].unique()) == set(df2["vt_symbol"].unique())

Step 2: 跑测试确认失败

Run: venv310/bin/python -m pytest tests/factor/test_universe.py -v Expected: FAIL（ModuleNotFoundError）

Step 3: 实现

# sanguo_factor/universe.py
"""评估股票池:dbbardata 分块直读 → AlphaLab 格式 polars DataFrame.

口径(spec):主板+创业板(前缀 60/00/30,剔科创68/北交/ETF),退市股保留,
120 交易日预热(次新 + 因子窗口)。不做 ST 过滤(无 point-in-time 名称史,
当前名回溯过滤=前视偏差,比不过滤更糟)。
"""
import random
import sqlite3
from datetime import datetime, timedelta

import polars as pl

STOCK_PREFIXES = ("60", "00", "30")
WARMUP_BARS = 120
_CHUNK = 300          # 分块 IN 查询每块 symbol 数(控瞬时内存)
_LOOKBACK_DAYS = 300  # start 前缓冲(覆盖最大 60 日窗口 + 节假日)
_FORWARD_DAYS = 45    # end 后缓冲(覆盖 10 日前瞻收益)

_COLS = "symbol, exchange, datetime, volume, turnover, open_interest, open_price, high_price, low_price, close_price"


def load_universe_bars(
    vnpy_db: str,
    start: str,
    end: str,
    symbols: list[str] | None = None,
    limit: int | None = None,
) -> pl.DataFrame:
    """读评估窗(含前后缓冲)全A日线 → AlphaLab 格式(含 vwap/bar_idx)."""
    lookback_start = (
        datetime.strptime(start, "%Y-%m-%d") - timedelta(days=_LOOKBACK_DAYS)
    ).strftime("%Y-%m-%d")
    forward_end = (
        datetime.strptime(end, "%Y-%m-%d") + timedelta(days=_FORWARD_DAYS)
    ).strftime("%Y-%m-%d")

    if symbols is None:
        conn = sqlite3.connect(vnpy_db, timeout=30)
        try:
            likes = " OR ".join(f"symbol LIKE '{p}%'" for p in STOCK_PREFIXES)
            cur = conn.execute(
                f"SELECT DISTINCT symbol FROM dbbardata "
                f"WHERE interval='d' AND datetime>=? AND datetime<=? AND ({likes})",
                (lookback_start, forward_end),
            )
            symbols = [r[0] for r in cur.fetchall()]
        finally:
            conn.close()

    if not symbols:
        return _empty_alpha_df()
    if limit is not None and limit < len(symbols):
        symbols = sorted(random.Random(42).sample(symbols, limit))

    chunks: list[pl.DataFrame] = []
    for i in range(0, len(symbols), _CHUNK):
        part = symbols[i : i + _CHUNK]
        ph = ",".join("?" * len(part))
        conn = sqlite3.connect(vnpy_db, timeout=30)
        conn.execute("PRAGMA busy_timeout=30000")
        try:
            cur = conn.execute(
                f"SELECT {_COLS} FROM dbbardata "
                f"WHERE interval='d' AND datetime>=? AND datetime<=? AND symbol IN ({ph})",
                (lookback_start, forward_end, *part),
            )
            rows = cur.fetchall()
        finally:
            conn.close()
        if rows:
            chunks.append(pl.DataFrame(
                rows,
                schema={"symbol": pl.Utf8, "exchange": pl.Utf8, "dt": pl.Utf8,
                        "volume": pl.Float64, "turnover": pl.Float64, "open_interest": pl.Float64,
                        "open": pl.Float64, "high": pl.Float64, "low": pl.Float64, "close": pl.Float64},
                orient="row",
            ))
    if not chunks:
        return _empty_alpha_df()

    df = pl.concat(chunks)
    df = (
        df.with_columns(
            pl.col("dt").str.slice(0, 10).str.to_datetime("%Y-%m-%d").alias("datetime"),
            (pl.col("symbol") + "." + pl.col("exchange")).alias("vt_symbol"),
            pl.when(pl.col("volume") > 0)
            .then(pl.col("turnover") / pl.col("volume"))
            .otherwise(None)
            .alias("vwap"),
        )
        .sort(["vt_symbol", "datetime"])
        .with_columns(pl.int_range(pl.len()).over("vt_symbol").alias("bar_idx"))
        .select(["vt_symbol", "datetime", "open", "high", "low", "close",
                 "volume", "turnover", "open_interest", "vwap", "bar_idx"])
    )
    return df


def evaluation_filter(df: pl.DataFrame, start: str, end: str) -> pl.DataFrame:
    """IC 评估行集:窗口内 + 预热期已过."""
    return df.filter(
        (pl.col("datetime") >= pl.lit(start).str.to_date())
        & (pl.col("datetime") <= pl.lit(end).str.to_date())
        & (pl.col("bar_idx") >= WARMUP_BARS)
    )


def _empty_alpha_df() -> pl.DataFrame:
    return pl.DataFrame(schema={
        "vt_symbol": pl.Utf8, "datetime": pl.Datetime,
        "open": pl.Float64, "high": pl.Float64, "low": pl.Float64, "close": pl.Float64,
        "volume": pl.Float64, "turnover": pl.Float64, "open_interest": pl.Float64,
        "vwap": pl.Float64, "bar_idx": pl.Int64,
    })

实现注意：pl.lit(start).str.to_date() 与 datetime 列比较——若类型不匹配报错，改用 datetime.strptime(start, "%Y-%m-%d") python 对象做 pl.lit(...)。以测试通过为准，别死磕一种写法。

Step 4: 跑测试确认通过

Run: venv310/bin/python -m pytest tests/factor/test_universe.py -v Expected: 6 个全 PASS

Step 5: Commit

git add sanguo_factor/universe.py tests/factor/test_universe.py
git commit -m "feat(factor): 评估股票池加载器——dbbardata分块直读polars,前缀60/00/30,300天lookback+45天forward缓冲,vwap派生,bar_idx预热 [vps]"

Task 4: 向量化评估指标（metrics）

Files:

Create: sanguo_factor/metrics.py
Test: tests/factor/test_metrics.py

Interfaces:

Produces（全部操作 pandas DataFrame，index=日期 DatetimeIndex，columns=vt_symbol，值=因子/收益；NaN=缺失自动剔除）:
- TRADING_DAYS_PER_YEAR = 244
- rank_corr_rows(A: pd.DataFrame, B: pd.DataFrame) -> pd.Series：逐日 Spearman 秩相关（列自动取交集）
- factor_turnover(F: pd.DataFrame) -> float：1 - 相邻两日因子秩相关 均值
- long_short_annual_return(F, R) -> float | None：top10%−bottom10% 等权日收益均值复利年化
- decile_annual_returns(F, R) -> list[float | None]：十分组等权年化（D1 最低→D10 最高）
- monthly_ic(ic: pd.Series) -> list[dict]：[{"month": "2018-01", "ic": 0.05}, ...]
- classify(icir, t_stat) -> str："effective" | "watch" | "eliminated"（|ICIR|≥0.3 且 |t|≥2 → effective；否则 |t|≥1.5 → watch）
- summarize_factor(F, R1, R5, R10) -> dict：{"1": {...}, "5": {...}, "10": {...}, "turnover": float}，每周期含 count, ic_mean, ic_std, icir, t_stat, win_rate, ls_annual, monthly_ic, deciles, conclusion；单因子异常时返回 {"error": str} 由调用方包装
Step 1: 写失败测试

# tests/factor/test_metrics.py
"""向量化指标:已知输入的精确断言."""
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))

import numpy as np
import pandas as pd
import pytest

from sanguo_factor.metrics import (
    rank_corr_rows, factor_turnover, long_short_annual_return,
    decile_annual_returns, monthly_ic, classify, summarize_factor,
    TRADING_DAYS_PER_YEAR,
)


def _mat(values, cols=("A", "B", "C")):
    idx = pd.to_datetime(["2024-01-01", "2024-01-02", "2024-01-03"])
    return pd.DataFrame(values, index=idx, columns=list(cols), dtype=float)


def test_rank_corr_perfect_monotonic():
    F = _mat([[1, 2, 3], [3, 2, 1], [1, 3, 2]])
    R = _mat([[10, 20, 30], [30, 20, 10], [10, 30, 20]])
    ic = rank_corr_rows(F, R)
    assert (ic == 1.0).all()


def test_rank_corr_inverse():
    F = _mat([[1, 2, 3], [1, 2, 3]])
    R = _mat([[3, 2, 1], [30, 20, 10]])
    ic = rank_corr_rows(F, R)
    assert (ic == -1.0).all()


def test_rank_corr_nan_propagates_row():
    F = _mat([[1, 2, 3], [np.nan, 2, 3]])
    R = _mat([[1, 2, 3], [1, 2, 3]])
    ic = rank_corr_rows(F, R)
    assert not np.isnan(ic.iloc[0])
    assert np.isnan(ic.iloc[1])  # 2个有效值秩恒定 → 无方差 → nan


def test_turnover_zero_for_static():
    F = _mat([[1, 2, 3]] * 4)
    assert factor_turnover(F) == pytest.approx(0.0)


def test_turnover_full_for_shuffled():
    F = _mat([[1, 2, 3], [3, 2, 1]])  # 完全逆序 → 秩相关-1 → 换手=2
    assert factor_turnover(F) == pytest.approx(2.0)


def test_long_short_direction():
    F = _mat([[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]], cols=tuple("ABCDE"))
    R = _mat([[0.01, 0.02, 0.03, 0.04, 0.05],
              [0.01, 0.02, 0.03, 0.04, 0.05]], cols=tuple("ABCDE"))
    # 每日 top10%(1只)=E bottom10%=A → 日均 0.04
    expected = (1 + 0.04) ** TRADING_DAYS_PER_YEAR - 1
    assert long_short_annual_return(F, R) == pytest.approx(expected)


def test_decile_monotonic():
    F = _mat([list(range(1, 11))] * 3, cols=tuple("ABCDEFGHIJ"))
    R = _mat([[c / 100 for c in range(1, 11)]] * 3, cols=tuple("ABCDEFGHIJ"))
    dec = decile_annual_returns(F, R)
    assert len(dec) == 10
    assert all(d is not None for d in dec)
    assert dec == sorted(dec)  # D1最低收益 → D10最高收益 单调


def test_monthly_ic_shape():
    idx = pd.to_datetime(["2024-01-05", "2024-01-10", "2024-02-01"])
    out = monthly_ic(pd.Series([0.1, 0.2, -0.1], index=idx))
    assert out[0] == {"month": "2024-01", "ic": pytest.approx(0.15)}
    assert out[1] == {"month": "2024-02", "ic": pytest.approx(-0.1)}


def test_classify_rules():
    assert classify(0.5, 5.0) == "effective"
    assert classify(-0.4, -3.0) == "effective"   # 负 IC 强因子同样有效(反向)
    assert classify(0.1, 1.8) == "watch"
    assert classify(0.0, 0.5) == "eliminated"


def test_summarize_factor_structure():
    rng = np.random.default_rng(7)
    idx = pd.bdate_range("2024-01-01", periods=60)
    base = np.tile(np.arange(10.0, 90.0, 1.0), (60, 1))
    F = pd.DataFrame(base + rng.normal(0, 0.5, base.shape), index=idx)
    R1 = pd.DataFrame(-0.001 * base + rng.normal(0, 0.001, base.shape), index=idx, columns=F.columns)
    R5, R10 = R1 * 5, R1 * 10
    out = summarize_factor(F, R1, R5, R10)
    assert set(out) == {"1", "5", "10", "turnover"}
    p1 = out["1"]
    for key in ("count", "ic_mean", "ic_std", "icir", "t_stat", "win_rate",
                "ls_annual", "monthly_ic", "deciles", "conclusion"):
        assert key in p1
    assert p1["count"] == 60
    assert p1["ic_mean"] < 0  # 构造为负相关

Step 2: 跑测试确认失败

Run: venv310/bin/python -m pytest tests/factor/test_metrics.py -v Expected: FAIL（ModuleNotFoundError）

Step 3: 实现

# sanguo_factor/metrics.py
"""批量评估指标:全向量化(RankIC=秩相关逐行),不依赖 alphalens.

所有矩阵约定:pandas DataFrame,index=DatetimeIndex(日),columns=vt_symbol,
值=因子值或前瞻收益,NaN=缺失自动从当日截面剔除。
"""
import warnings

import numpy as np
import pandas as pd

TRADING_DAYS_PER_YEAR = 244


def _row_pearson(a: np.ndarray, b: np.ndarray, index: pd.Index) -> pd.Series:
    """逐行 Pearson(输入已是秩),全 NaN/无方差行 → NaN."""
    mask = ~(np.isnan(a) | np.isnan(b))
    n = mask.sum(axis=1)
    a0 = np.where(mask, a, np.nan)
    b0 = np.where(mask, b, np.nan)
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", RuntimeWarning)
        am = np.nanmean(a0, axis=1, keepdims=True)
        bm = np.nanmean(b0, axis=1, keepdims=True)
    ad = np.where(mask, a0 - am, 0.0)
    bd = np.where(mask, b0 - bm, 0.0)
    denom = np.sqrt((ad ** 2).sum(axis=1) * (bd ** 2).sum(axis=1))
    with np.errstate(invalid="ignore", divide="ignore"):
        ic = np.where(denom > 0, (ad * bd).sum(axis=1) / denom, np.nan)
    ic = np.where(n >= 3, ic, np.nan)  # <3 只无意义
    return pd.Series(ic, index=index)


def rank_corr_rows(A: pd.DataFrame, B: pd.DataFrame) -> pd.Series:
    """逐日 Spearman:先各自按行取秩再逐行 Pearson."""
    cols = A.columns.intersection(B.columns)
    idx = A.index.intersection(B.index)
    a = A.loc[idx, cols].rank(axis=1).to_numpy(dtype=float)
    b = B.loc[idx, cols].rank(axis=1).to_numpy(dtype=float)
    return _row_pearson(a, b, idx)


def factor_turnover(F: pd.DataFrame) -> float:
    """换手率 = 1 - 相邻两日因子秩相关均值."""
    if len(F) < 2:
        return 0.0
    corr = rank_corr_rows(F.iloc[1:], F.iloc[:-1]).replace([np.inf, -np.inf], np.nan).dropna()
    return float(1.0 - corr.mean()) if len(corr) else 0.0


def _quantile_mask(F: pd.DataFrame, lo_frac: float, hi_frac: float) -> pd.DataFrame:
    """按行把因子值分位选mask(基于升序秩/当日有效数)."""
    ranks = F.rank(axis=1, ascending=False)  # 1=最大
    n = ranks.notna().sum(axis=1)
    k = np.maximum((n * 0.1).round().astype(int), 1)
    if lo_frac == 0.0:
        return ranks.le(k, axis=0) & ranks.notna()
    return ranks.ge(n - k + 1, axis=0) & ranks.notna()


def long_short_annual_return(F: pd.DataFrame, R: pd.DataFrame) -> float | None:
    """多空年化:top10% - bottom10% 等权前瞻日收益均值,复利年化."""
    cols = F.columns.intersection(R.columns)
    idx = F.index.intersection(R.index)
    f, r = F.loc[idx, cols], R.loc[idx, cols]
    top = _quantile_mask(f, 0.0, 0.1)
    bot = _quantile_mask(f, 0.9, 1.0)
    daily = (r.where(top).mean(axis=1) - r.where(bot).mean(axis=1)).dropna()
    if daily.empty:
        return None
    return float((1.0 + daily.mean()) ** TRADING_DAYS_PER_YEAR - 1.0)


def decile_annual_returns(F: pd.DataFrame, R: pd.DataFrame) -> list[float | None]:
    """十分组(D1因子最低→D10最高)等权年化收益."""
    cols = F.columns.intersection(R.columns)
    idx = F.index.intersection(R.index)
    f, r = F.loc[idx, cols], R.loc[idx, cols]
    pct = f.rank(axis=1, ascending=True).div(f.notna().sum(axis=1), axis=0)
    out: list[float | None] = []
    for d in range(10):
        sel = (pct > d / 10) & (pct <= (d + 1) / 10)
        daily = r.where(sel).mean(axis=1).dropna()
        out.append(
            float((1.0 + daily.mean()) ** TRADING_DAYS_PER_YEAR - 1.0)
            if len(daily) else None
        )
    return out


def monthly_ic(ic: pd.Series) -> list[dict]:
    """IC 按月聚合(sparkline/详情图数据)."""
    s = ic.dropna()
    if s.empty:
        return []
    g = s.groupby(s.index.to_period("M").to_timestamp()).mean()
    return [{"month": t.strftime("%Y-%m"), "ic": round(float(v), 6)} for t, v in g.items()]


def classify(icir: float | None, t_stat: float | None) -> str:
    """结论信号灯:|ICIR|>=0.3 且 |t|>=2 有效;否则 |t|>=1.5 观察;其余淘汰."""
    if icir is None or t_stat is None:
        return "eliminated"
    if abs(icir) >= 0.3 and abs(t_stat) >= 2.0:
        return "effective"
    if abs(t_stat) >= 1.5:
        return "watch"
    return "eliminated"


def _period_stats(ic: pd.Series) -> dict:
    s = ic.dropna()
    n = len(s)
    if n < 2:
        return {"count": int(n), "ic_mean": None, "ic_std": None,
                "icir": None, "t_stat": None, "win_rate": None,
                "monthly_ic": monthly_ic(ic)}
    m = float(s.mean())
    sd = float(s.std())
    icir = m / sd if sd > 0 else None
    t_stat = m / (sd / n ** 0.5) if sd > 0 else None
    return {
        "count": int(n),
        "ic_mean": m,
        "ic_std": sd,
        "icir": icir,
        "t_stat": t_stat,
        "win_rate": float((s > 0).mean()),
        "monthly_ic": monthly_ic(ic),
    }


def summarize_factor(F: pd.DataFrame, R1: pd.DataFrame, R5: pd.DataFrame, R10: pd.DataFrame) -> dict:
    """单因子全指标:三周期 IC 族 + 换手 + 多空 + 十分组 + 结论."""
    out: dict = {"turnover": factor_turnover(F)}
    for p, R in (("1", R1), ("5", R5), ("10", R10)):
        ic = rank_corr_rows(F, R)
        stats = _period_stats(ic)
        stats["ls_annual"] = long_short_annual_return(F, R)
        stats["deciles"] = decile_annual_returns(F, R)
        stats["conclusion"] = classify(stats["icir"], stats["t_stat"])
        out[p] = stats
    return out

Step 4: 跑测试确认通过

Run: venv310/bin/python -m pytest tests/factor/test_metrics.py -v Expected: 全 PASS（test_rank_corr_nan_propagates_row 那条：两列秩无方差 → denom=0 → NaN，与实现一致）

Step 5: Commit

git add sanguo_factor/metrics.py tests/factor/test_metrics.py
git commit -m "feat(factor): 向量化评估指标——RankIC逐行秩相关/ICIR/t/胜率/多空年化/换手/十分组/月度IC/结论分级 [vps]"

Task 5: 评估结果存储（eval_store）

Files:

Create: sanguo_factor/eval_store.py
Test: tests/factor/test_eval_store.py

Interfaces:

Produces:
- init_db(path)
- create_run(path, label, universe, symbols_count, factors_total, start, end, params: dict) -> str（run_id 形如 ev_20260824_1530_a1b2）
- save_results(path, run_id, rows: list[dict])（rows 元素 = {factor, category, expression, metrics: dict}，UPSERT）
- finish_run(path, run_id, status, factors_done)
- list_runs(path) -> list[dict]（created_at 倒序）
- get_rows(path, run_id, category=None, search=None) -> list[dict]（metrics 已反序列化）
- get_detail(path, run_id, factor) -> dict | None
- default_eval_db_path() -> str：orchestrator backtest_results.db 同目录下 factor_eval.db；环境变量 SANGUO_FACTOR_EVAL_DB 可覆盖（NAS/VPS/本地三环境同一套代码）
Step 1: 写失败测试

# tests/factor/test_eval_store.py
"""eval_store:建库/建run/UPSERT落盘/查询过滤/finish."""
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))

from sanguo_factor import eval_store


def _rows():
    return [
        {"factor": "alpha2", "category": "alpha101", "expression": "(-1)*ts_corr(...)",
         "metrics": {"1": {"ic_mean": 0.05}, "turnover": 0.3}},
        {"factor": "kmid", "category": "alpha158", "expression": "(close-open)/open",
         "metrics": {"1": {"ic_mean": -0.02}, "turnover": 0.6}},
    ]


def test_roundtrip(tmp_path):
    db = str(tmp_path / "factor_eval.db")
    eval_store.init_db(db)
    run_id = eval_store.create_run(db, label="冒烟", universe="custom", symbols_count=50,
                                   factors_total=2, start="2024-01-01", end="2024-12-31",
                                   params={"periods": [1, 5, 10]})
    eval_store.save_results(db, run_id, _rows())
    eval_store.finish_run(db, run_id, "done", factors_done=2)

    runs = eval_store.list_runs(db)
    assert len(runs) == 1 and runs[0]["run_id"] == run_id and runs[0]["status"] == "done"

    rows = eval_store.get_rows(db, run_id)
    assert len(rows) == 2
    assert rows[0]["metrics"]["1"]["ic_mean"] == 0.05

    assert eval_store.get_detail(db, run_id, "kmid")["category"] == "alpha158"
    assert eval_store.get_detail(db, run_id, "nope") is None


def test_filter_by_category_and_search(tmp_path):
    db = str(tmp_path / "factor_eval.db")
    eval_store.init_db(db)
    run_id = eval_store.create_run(db, label="x", universe="all_a", symbols_count=1,
                                   factors_total=2, start="", end="", params={})
    eval_store.save_results(db, run_id, _rows())
    assert len(eval_store.get_rows(db, run_id, category="alpha101")) == 1
    assert len(eval_store.get_rows(db, run_id, search="corr")) == 1   # 搜表达式
    assert len(eval_store.get_rows(db, run_id, search="kmid")) == 1   # 搜因子名


def test_upsert_same_run(tmp_path):
    db = str(tmp_path / "factor_eval.db")
    eval_store.init_db(db)
    run_id = eval_store.create_run(db, label="x", universe="all_a", symbols_count=1,
                                   factors_total=1, start="", end="", params={})
    eval_store.save_results(db, run_id, [_rows()[0]])
    eval_store.save_results(db, run_id, [{**_rows()[0], "metrics": {"1": {"ic_mean": 0.09}}}] * 1)
    rows = eval_store.get_rows(db, run_id)
    assert len(rows) == 1 and rows[0]["metrics"]["1"]["ic_mean"] == 0.09

Step 2: 跑测试确认失败

Run: venv310/bin/python -m pytest tests/factor/test_eval_store.py -v Expected: FAIL（ModuleNotFoundError）

Step 3: 实现

# sanguo_factor/eval_store.py
"""批量评估结果落盘 factor_eval.db(eval_runs + eval_results).

metrics 按周期 {"1": {...}, "5": {...}, "10": {...}, "turnover": float} 存 JSON,
schema 平坦、加周期零迁移。
"""
import json
import os
import sqlite3
import uuid
from datetime import datetime

_SCHEMA = """
CREATE TABLE IF NOT EXISTS eval_runs(
  run_id TEXT PRIMARY KEY,
  label TEXT NOT NULL,
  universe TEXT NOT NULL,
  symbols_count INTEGER NOT NULL,
  factors_total INTEGER NOT NULL,
  start TEXT NOT NULL,
  end TEXT NOT NULL,
  status TEXT NOT NULL,
  created_at TEXT NOT NULL,
  finished_at TEXT,
  factors_done INTEGER NOT NULL DEFAULT 0,
  params_json TEXT NOT NULL
);
CREATE TABLE IF NOT EXISTS eval_results(
  run_id TEXT NOT NULL,
  factor TEXT NOT NULL,
  category TEXT NOT NULL,
  expression TEXT NOT NULL,
  metrics_json TEXT NOT NULL,
  PRIMARY KEY(run_id, factor)
);
"""


def default_eval_db_path() -> str:
    """SANGUO_FACTOR_EVAL_DB 覆盖;默认与 backtest_results.db 同目录."""
    env = os.environ.get("SANGUO_FACTOR_EVAL_DB")
    if env:
        return env
    from sanguo_data.config import load_config, find_config_path
    cfg = load_config(find_config_path())
    vnpy_db = cfg.data_paths.get("vnpy_db", "")
    return os.path.join(os.path.dirname(os.path.abspath(vnpy_db)), "factor_eval.db")


def _conn(path: str) -> sqlite3.Connection:
    conn = sqlite3.connect(path, timeout=30)
    conn.execute("PRAGMA busy_timeout=30000")
    return conn


def init_db(path: str) -> None:
    os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
    conn = _conn(path)
    try:
        conn.executescript(_SCHEMA)
        conn.commit()
    finally:
        conn.close()


def create_run(path, label, universe, symbols_count, factors_total, start, end, params) -> str:
    run_id = f"ev_{datetime.now():%Y%m%d_%H%M%S}_{uuid.uuid4().hex[:4]}"
    conn = _conn(path)
    try:
        conn.execute(
            "INSERT INTO eval_runs VALUES(?,?,?,?,?,?,?,?,?,?,0,?)",
            (run_id, label, universe, symbols_count, factors_total, start, end,
             "running", datetime.now().isoformat(timespec='seconds'), None,
             json.dumps(params, ensure_ascii=False)),
        )
        conn.commit()
    finally:
        conn.close()
    return run_id


def save_results(path, run_id, rows: list[dict]) -> None:
    conn = _conn(path)
    try:
        conn.executemany(
            "INSERT OR REPLACE INTO eval_results VALUES(?,?,?,?,?)",
            [(run_id, r["factor"], r["category"], r["expression"],
              json.dumps(r["metrics"], ensure_ascii=False)) for r in rows],
        )
        cur = conn.execute(
            "SELECT COUNT(*) FROM eval_results WHERE run_id=?", (run_id,))
        done = cur.fetchone()[0]
        conn.execute("UPDATE eval_runs SET factors_done=? WHERE run_id=?", (done, run_id))
        conn.commit()
    finally:
        conn.close()


def finish_run(path, run_id, status, factors_done) -> None:
    conn = _conn(path)
    try:
        conn.execute(
            "UPDATE eval_runs SET status=?, finished_at=?, factors_done=? WHERE run_id=?",
            (status, datetime.now().isoformat(timespec='seconds'), factors_done, run_id),
        )
        conn.commit()
    finally:
        conn.close()


def list_runs(path) -> list[dict]:
    conn = _conn(path)
    conn.row_factory = sqlite3.Row
    try:
        rows = conn.execute(
            "SELECT * FROM eval_runs ORDER BY created_at DESC").fetchall()
    finally:
        conn.close()
    return [dict(r) for r in rows]


def get_rows(path, run_id, category=None, search=None) -> list[dict]:
    q = "SELECT * FROM eval_results WHERE run_id=?"
    args: list = [run_id]
    if category:
        q += " AND category=?"
        args.append(category)
    if search:
        q += " AND (factor LIKE ? OR expression LIKE ?)"
        args.extend([f"%{search}%", f"%{search}%"])
    conn = _conn(path)
    conn.row_factory = sqlite3.Row
    try:
        rows = conn.execute(q, args).fetchall()
    finally:
        conn.close()
    return [{**dict(r), "metrics": json.loads(r["metrics_json"])} for r in rows]


def get_detail(path, run_id, factor) -> dict | None:
    conn = _conn(path)
    conn.row_factory = sqlite3.Row
    try:
        r = conn.execute(
            "SELECT * FROM eval_results WHERE run_id=? AND factor=?",
            (run_id, factor)).fetchone()
    finally:
        conn.close()
    if r is None:
        return None
    return {**dict(r), "metrics": json.loads(r["metrics_json"])}

Step 4: 跑测试确认通过

Run: venv310/bin/python -m pytest tests/factor/test_eval_store.py -v Expected: 全 PASS

Step 5: Commit

git add sanguo_factor/eval_store.py tests/factor/test_eval_store.py
git commit -m "feat(factor): 评估结果存储 factor_eval.db——runs/results两表,metrics按周期JSON,SANGUO_FACTOR_EVAL_DB可覆盖 [vps]"

Task 6: 批量评估引擎（batch_eval）

Files:

Create: sanguo_factor/batch_eval.py
Test: tests/factor/test_batch_eval.py

Interfaces:

Consumes: Task 2 mount_all()/get_factor、Task 3 load_universe_bars/evaluation_filter、Task 4 summarize_factor、Task 5 store API、vnpy calculate_by_expression
Produces:
- run_batch_eval(factor_names: list[str], start: str, end: str, eval_db: str, label: str, universe: str = "all_a", symbols: list[str] | None = None, limit: int | None = None, cfg=None, progress_cb=None) -> dict
- 返回 {"run_id", "factors_total", "factors_done", "errors", "elapsed_sec"}
- progress_cb(done: int, total: int, current: str) 每因子一次（CLI 打印用；API 侧暂不接，YAGNI）
- 单因子计算异常不中断：该因子落一行 metrics={"error": "..."} 继续
Step 1: 写失败测试

# tests/factor/test_batch_eval.py
"""批量引擎端到端:合成小库 → 2因子计算 → 指标落盘可查."""
import sqlite3
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0")))

import pytest

from sanguo_factor import alpha_datasets  # 挂载
from sanguo_factor.batch_eval import run_batch_eval
from sanguo_factor import eval_store

_DDL = """
CREATE TABLE dbbardata(
  symbol TEXT, exchange TEXT, datetime TEXT, interval TEXT,
  volume REAL, turnover REAL, open_interest REAL,
  open_price REAL, high_price REAL, low_price REAL, close_price REAL)
"""


@pytest.fixture(scope="module")
def db(tmp_path_factory):
    """3 只 × ~400 交易日(2017-06~2018-12)合成库:一只单调涨/一只震荡/一只反着走."""
    p = tmp_path_factory.mktemp("b")
    db = str(p / "qt.db")
    conn = sqlite3.connect(db)
    conn.execute(_DDL)
    import pandas as pd
    days = pd.bdate_range("2017-06-01", "2018-12-28")
    for i, day in enumerate(days):
        d = day.strftime("%Y-%m-%d")
        rows = [
            ("600000", "SSE", f"{d} 00:00:00", "d", 100.0, 1_000_000.0, 0,
             10 + i * 0.01, 10 + i * 0.01, 10 + i * 0.01, 10 + i * 0.01),      # 单调涨
            ("000001", "SZSE", f"{d} 00:00:00", "d", 100.0, 500_000.0, 0,
             5, 5, 5, 5),                                                      # 平盘(截面另一端)
            ("300001", "SZSE", f"{d} 00:00:00", "d", 200.0, 900_000.0, 0,
             20 - i * 0.005, 20 - i * 0.005, 20 - i * 0.005, 20 - i * 0.005),  # 缓跌
        ]
        conn.executemany("INSERT INTO dbbardata VALUES(?,?,?,?,?,?,?,?,?,?,?)", rows)
    conn.commit(); conn.close()
    return db


def test_run_batch_eval_end_to_end(db, tmp_path, monkeypatch):
    eval_db = str(tmp_path / "factor_eval.db")
    out = run_batch_eval(
        factor_names=["ma_20", "roc_5"], start="2018-01-01", end="2018-06-30",
        eval_db=eval_db, label="t", symbols=None, cfg=None, vnpy_db_override=db,
    )
    assert out["factors_done"] == 2
    assert out["errors"] == []
    rows = eval_store.get_rows(eval_db, out["run_id"])
    assert {r["factor"] for r in rows} == {"ma_20", "roc_5"}
    # ma_20 = ts_mean(close,20)/close:涨股该值持续低(均价低于现价) 跌股高 → 与次日收益负相关为主
    m = eval_store.get_detail(eval_db, out["run_id"], "ma_20")["metrics"]
    assert "1" in m and "5" in m and "10" in m and "turnover" in m
    assert isinstance(m["1"]["ic_mean"], float)


def test_bad_factor_recorded_not_fatal(db, tmp_path):
    eval_db = str(tmp_path / "factor_eval.db")
    out = run_batch_eval(
        factor_names=["不存在的因子", "kmid"], start="2018-01-01", end="2018-03-31",
        eval_db=eval_db, label="t2", cfg=None, vnpy_db_override=db,
    )
    assert out["factors_done"] == 2
    assert len(out["errors"]) == 1
    detail = eval_store.get_detail(eval_db, out["run_id"], "不存在的因子")
    assert "error" in detail["metrics"]

注：测试用的 vnpy_db_override 参数是测试注入数据库路径的后门（生产 cfg 走 cfg.data_paths["vnpy_db"]）；实现签名里带上它，默认 None。

Step 2: 跑测试确认失败

Run: venv310/bin/python -m pytest tests/factor/test_batch_eval.py -v Expected: FAIL（ModuleNotFoundError）

Step 3: 实现

# sanguo_factor/batch_eval.py
"""批量评估引擎:全A bars → 进程内逐因子 calculate_by_expression → 指标 → 落盘.

不走 AlphaDataset.prepare_data(其 spawn 池对每个表达式 pickle 整个 DataFrame,
9M 行 × 258 因子的传输开销不可接受);calculate_by_expression 纯进程内 polars,
内存随单因子天然有界。现有单因子分析链路(analyzer/alphalens tears)零改动。
"""
import sys
import os
import time

_VNPY_SRC = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "vnpy_v4.4.0"))
if _VNPY_SRC not in sys.path:
    sys.path.insert(0, _VNPY_SRC)

import pandas as pd
import polars as pl

from .universe import load_universe_bars, evaluation_filter
from .registry import get_factor
from . import eval_store
from .metrics import summarize_factor


def _forward_return_matrices(close_wide: pd.DataFrame, periods=(1, 5, 10)) -> dict[int, pd.DataFrame]:
    out = {}
    for p in periods:
        out[p] = close_wide.shift(-p) / close_wide - 1.0
    return out


def run_batch_eval(
    factor_names: list[str],
    start: str,
    end: str,
    eval_db: str,
    label: str,
    universe: str = "all_a",
    symbols: list[str] | None = None,
    limit: int | None = None,
    cfg=None,
    progress_cb=None,
    vnpy_db_override: str | None = None,
) -> dict:
    """跑一轮批量评估,结果增量写入 eval_db,返回摘要."""
    from vnpy.alpha.dataset.utility import calculate_by_expression

    if cfg is None:
        from sanguo_data.config import load_config, find_config_path
        cfg = load_config(find_config_path())
    vnpy_db = vnpy_db_override or cfg.data_paths["vnpy_db"]

    t0 = time.time()
    bars = load_universe_bars(vnpy_db, start, end, symbols=symbols, limit=limit)
    if bars.height == 0:
        raise ValueError(f"股票池为空: vnpy_db={vnpy_db} window={start}~{end}")

    alpha_df = bars.select(["vt_symbol", "datetime", "open", "high", "low", "close",
                            "volume", "turnover", "open_interest", "vwap"])
    eval_rows = evaluation_filter(bars, start, end).select(["vt_symbol", "datetime"])

    close_wide = (
        bars.select(["datetime", "vt_symbol", "close"])
        .pivot(index="datetime", on="vt_symbol", values="close")
        .sort("datetime").to_pandas().set_index("datetime")
    )
    close_wide.index = pd.to_datetime(close_wide.index)
    rets = _forward_return_matrices(close_wide)

    universe_label = universe if symbols is None else "custom"
    eval_store.init_db(eval_db)
    run_id = eval_store.create_run(
        eval_db, label=label, universe=universe_label,
        symbols_count=bars["vt_symbol"].n_unique(), factors_total=len(factor_names),
        start=start, end=end, params={"limit": limit, "symbols": symbols[:20] if symbols else None},
    )

    errors: list[str] = []
    done = 0
    buffer: list[dict] = []
    for i, name in enumerate(factor_names):
        row = _eval_one(name, alpha_df, eval_rows, rets)
        if "error" in row["metrics"]:
            errors.append(name)
        buffer.append(row)
        done += 1
        if len(buffer) >= 20 or done == len(factor_names):
            eval_store.save_results(eval_db, run_id, buffer)
            buffer = []
        if progress_cb:
            progress_cb(done, len(factor_names), name)

    eval_store.finish_run(eval_db, run_id, "done", factors_done=done)
    return {
        "run_id": run_id,
        "factors_total": len(factor_names),
        "factors_done": done,
        "errors": errors,
        "elapsed_sec": round(time.time() - t0, 1),
        "symbols_count": int(bars["vt_symbol"].n_unique()),
    }


def _eval_one(name: str, alpha_df: pl.DataFrame, eval_rows: pl.DataFrame,
              rets: dict[int, pd.DataFrame]) -> dict:
    factor = get_factor(name)
    if factor is None:
        return {"factor": name, "category": "unknown", "expression": "",
                "metrics": {"error": f"因子未注册: {name}"}}
    try:
        res = calculate_by_expression(alpha_df, factor["expression"])
        f_long = res.join(eval_rows, on=["vt_symbol", "datetime"], how="inner")
        F = (
            f_long.pivot(index="datetime", on="vt_symbol", values="data")
            .sort("datetime").to_pandas().set_index("datetime")
        )
        F.index = pd.to_datetime(F.index)
        metrics = summarize_factor(F, rets[1], rets[5], rets[10])
        return {"factor": name, "category": factor["category"],
                "expression": factor["expression"], "metrics": metrics}
    except Exception as e:  # 单因子失败不拖垮整批
        return {"factor": name, "category": factor["category"],
                "expression": factor["expression"],
                "metrics": {"error": f"{type(e).__name__}: {e}"}}

实现注意：res 是 calculate_by_expression 返回的 datetime/vt_symbol/data 三列 df；join eval_rows（已含窗口+预热过滤）即完成行集裁剪。若 polars pivot 参数名对不上（on=/columns=），以 venv310/bin/python -c "import polars as pl; help(pl.DataFrame.pivot)" 查 1.42 签名为准。

Step 4: 跑测试确认通过

Run: venv310/bin/python -m pytest tests/factor/test_batch_eval.py -v Expected: 2 个全 PASS（首跑会触发 vnpy alpha import，几秒）

Step 5: Commit

git add sanguo_factor/batch_eval.py tests/factor/test_batch_eval.py
git commit -m "feat(factor): 批量评估引擎——进程内逐因子calculate_by_expression(弃spawn池整df pickle),单因子失败不中断,结果增量落盘 [vps]"

Task 7: CLI 脚本（scripts/factor_research/run_eval.py）

Files:

Create: scripts/factor_research/run_eval.py（目录新建，无 __init__.py，脚本以 python scripts/factor_research/run_eval.py 运行，顶部 sys.path 补 repo root——参考 scripts/diag_factor.py 的既有做法，先读它）

Interfaces:

Produces: CLI，参数 --start --end --categories alpha101 alpha158 --factors a,b --symbols 600000,000001 --limit 50 --label --db --write-every --list-categories --list-factors [category]
默认：--start 2018-01-01 --end 2026-06-30 --categories alpha101 alpha158 --label batch1
结束打印 Top 15（按 1D |icir| 降序）小表格 + run_id
Step 1: 实现（CLI 薄壳，核心逻辑全在 Task 6，无需单测——用 --help + --list-factors 手验）

#!/usr/bin/env python
"""批量因子评估 CLI(冒烟/全量;NAS docker exec / 本地直跑).

用法示例:
  冒烟(50只×2024×10因子):
    venv310/bin/python scripts/factor_research/run_eval.py \
      --start 2024-01-01 --end 2024-12-31 --limit 50 --label smoke-2024 \
      --factors alpha2,alpha6,alpha12,alpha18,kmid,klen,roc_5,ma_20,std_20,wvma_20
  全量(Alpha101+158 × 全A × 8.5年):
    venv310/bin/python scripts/factor_research/run_eval.py --label batch1-full
"""
import argparse
import os
import sys

sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "vnpy_v4.4.0")))


def main() -> int:
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--start", default="2018-01-01")
    ap.add_argument("--end", default="2026-06-30")
    ap.add_argument("--categories", nargs="*", default=["alpha101", "alpha158"])
    ap.add_argument("--factors", default="", help="逗号分隔,优先于 --categories")
    ap.add_argument("--symbols", default="", help="逗号分隔裸代码;空=全A")
    ap.add_argument("--limit", type=int, default=None, help="随机抽样 N 只(种子42)")
    ap.add_argument("--label", default="batch1")
    ap.add_argument("--db", default=None, help="factor_eval.db 路径;默认 default_eval_db_path()")
    ap.add_argument("--list-factors", default="", metavar="CATEGORY", help="列出类目因子后退出")
    args = ap.parse_args()

    from sanguo_factor.alpha_datasets import mount_all
    mount_all()
    from sanguo_factor.registry import list_factors
    from sanguo_factor.batch_eval import run_batch_eval
    from sanguo_factor.eval_store import default_eval_db_path, get_rows

    if args.list_factors:
        for f in list_factors(args.list_factors):
            print(f"{f['name']:16s} [{f['category']}]  {f['expression'][:80]}")
        return 0

    if args.factors:
        factor_names = [s.strip() for s in args.factors.split(",") if s.strip()]
    else:
        factor_names = [f["name"] for c in args.categories for f in list_factors(c)]
    if not factor_names:
        print("未找到因子(检查 --categories/--factors)", file=sys.stderr)
        return 1

    db_path = args.db or default_eval_db_path()
    symbols = [s.strip() for s in args.symbols.split(",") if s.strip()] or None
    print(f"[eval] {len(factor_names)} 因子 × {args.start}~{args.end} → {db_path}")

    def _cb(done: int, total: int, current: str) -> None:
        print(f"[eval] {done}/{total} {current}", flush=True)

    out = run_batch_eval(factor_names, args.start, args.end, db_path, label=args.label,
                         symbols=symbols, limit=args.limit, cfg=None, progress_cb=_cb)

    print(f"[eval] run_id={out['run_id']} done={out['factors_done']} "
          f"errors={len(out['errors'])} elapsed={out['elapsed_sec']}s symbols={out['symbols_count']}")
    if out["errors"]:
        print(f"[eval] 失败因子: {', '.join(out['errors'][:20])}")

    rows = get_rows(db_path, out["run_id"])
    scored = []
    for r in rows:
        p1 = (r.get("metrics") or {}).get("1") or {}
        if p1.get("icir") is not None:
            scored.append((abs(p1["icir"]), r["factor"], p1))
    scored.sort(reverse=True)
    print(f"\n{'因子':<14s} {'IC':>8s} {'ICIR':>8s} {'t':>8s} {'胜率':>7s} 结论")
    for _, name, p1 in scored[:15]:
        print(f"{name:<14s} {p1['ic_mean']:>8.4f} {p1['icir']:>8.3f} "
              f"{p1['t_stat']:>8.2f} {p1['win_rate']:>7.1%} {p1['conclusion']}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())

Step 2: 手验

Run: venv310/bin/python scripts/factor_research/run_eval.py --list-factors alpha101 | head -5 && venv310/bin/python scripts/factor_research/run_eval.py --help Expected: 列出 alpha1..alpha5；--help 正常

Step 3: Commit

git add scripts/factor_research/run_eval.py
git commit -m "feat(factor): 批量评估CLI——冒烟/全量/列因子,进度打印+Top15汇总表(NAS docker exec 冒烟入口) [vps]"

Task 8: API 端点 + orchestrator 接线

Files:

Modify: sanguo_api/schemas.py（追加 FactorBatchEvalRequest）
Create: sanguo_api/routes_factor.py
Modify: sanguo_api/app.py（注册路由 + set_eval_db_path 接线）
Modify: sanguo_orchestrator/runner.py（追加 submit_batch_eval/_batch_eval_worker/_persist_factor_eval）
Test: tests/api/test_factor_eval_routes.py

Interfaces:

Consumes: Task 5 eval_store、Task 6 run_batch_eval、Task 2 挂载
Produces:
- GET /factor/eval/runs → {"runs": [...]}
- GET /factor/eval/leaderboard?run_id=&period=1|5|10&category=&search=&sort=abs_icir|ic_mean|t_stat|ls_annual&order=desc → {"tiles": {"factors_total","effective","watch","top_ls_annual","top_ls_factor"}, "rows": [...]}（row = 扁平化所选周期：factor,category,expression,ic_mean,icir,t_stat,win_rate,ls_annual,turnover,conclusion,monthly_ic；run_id 缺省 = 最新 run）
- GET /factor/eval/detail?run_id=&factor= → {"factor","category","expression","metrics"}（全周期）
- POST /factor/eval/submit body {"categories":["alpha101"],"factors":[],"symbols":[],"start":"2018-01-01","end":"2026-06-30","label":"batch1"} → {"task_id": "factor_eval_xxxx"}
- Orchestrator: async submit_batch_eval(factor_names, start, end, symbols, label) -> task_id；完成时 _persist_factor_eval 落 backtest_results.db（type="factor_eval", statistics={"run_id", "factors_done", "errors"}）使任务中心可见
- set_eval_db_path(path) 由 app.py 在启动时调（路径 = dirname(orchestrator db_path)/factor_eval.db）
Step 1: 写失败测试

# tests/api/test_factor_eval_routes.py
"""/factor/eval/* 端点:leaderboard 扁平化/tiles/排序/详情/提交校验."""
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))

import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient

from sanguo_factor import eval_store
from sanguo_api import routes_factor


@pytest.fixture()
def client(tmp_path, monkeypatch):
    db = str(tmp_path / "factor_eval.db")
    eval_store.init_db(db)
    routes_factor.set_eval_db_path(db)
    app = FastAPI()
    app.include_router(routes_factor.router)
    return TestClient(app)


def _seed(db):
    run_id = eval_store.create_run(db, label="冒烟", universe="custom", symbols_count=50,
                                   factors_total=3, start="2024-01-01", end="2024-12-31", params={})
    eval_store.save_results(db, run_id, [
        {"factor": "alpha2", "category": "alpha101", "expression": "e2",
         "metrics": {"turnover": 0.2, "1": {"ic_mean": 0.05, "icir": 0.4, "t_stat": 3.0,
                     "win_rate": 0.6, "ls_annual": 0.12, "conclusion": "effective",
                     "monthly_ic": [{"month": "2024-01", "ic": 0.05}]},
                     "5": {}, "10": {}}},
        {"factor": "kmid", "category": "alpha158", "expression": "ek",
         "metrics": {"turnover": 0.5, "1": {"ic_mean": -0.01, "icir": -0.05, "t_stat": -0.4,
                     "win_rate": 0.45, "ls_annual": -0.02, "conclusion": "eliminated",
                     "monthly_ic": []}, "5": {}, "10": {}}},
        {"factor": "alpha6", "category": "alpha101", "expression": "e6",
         "metrics": {"turnover": 0.3, "1": {"ic_mean": 0.03, "icir": 0.2, "t_stat": 1.7,
                     "win_rate": 0.55, "ls_annual": 0.06, "conclusion": "watch",
                     "monthly_ic": []}, "5": {}, "10": {}}},
    ])
    eval_store.finish_run(db, run_id, "done", 3)
    return run_id


def test_leaderboard_default_latest_run(client):
    _seed(routes_factor._eval_db_path)
    r = client.get("/factor/eval/leaderboard")
    assert r.status_code == 200
    body = r.json()
    assert body["tiles"]["factors_total"] == 3
    assert body["tiles"]["effective"] == 1
    assert body["tiles"]["watch"] == 1
    assert body["tiles"]["top_ls_factor"] == "alpha2"
    # 默认 abs_icir desc: alpha2(0.4) > alpha6(0.2) > kmid(0.05)
    assert [x["factor"] for x in body["rows"]] == ["alpha2", "alpha6", "kmid"]
    row = body["rows"][0]
    for key in ("factor", "category", "expression", "ic_mean", "icir", "t_stat",
                "win_rate", "ls_annual", "turnover", "conclusion", "monthly_ic"):
        assert key in row


def test_leaderboard_filter_and_period(client):
    _seed(routes_factor._eval_db_path)
    r = client.get("/factor/eval/leaderboard", params={"category": "alpha158"})
    assert [x["factor"] for x in r.json()["rows"]] == ["kmid"]
    r = client.get("/factor/eval/leaderboard", params={"search": "alpha6"})
    assert [x["factor"] for x in r.json()["rows"]] == ["alpha6"]


def test_detail(client):
    _seed(routes_factor._eval_db_path)
    r = client.get("/factor/eval/detail", params={"factor": "kmid"})
    assert r.status_code == 200
    assert r.json()["category"] == "alpha158"
    assert "1" in r.json()["metrics"]
    assert client.get("/factor/eval/detail", params={"factor": "nope"}).status_code == 404


def test_runs_list(client):
    _seed(routes_factor._eval_db_path)
    r = client.get("/factor/eval/runs")
    assert len(r.json()["runs"]) == 1


def test_submit_validation_error(client):
    # categories 与 factors 双空 → 422/400
    r = client.post("/factor/eval/submit",
                    json={"start": "2018-01-01", "end": "2026-06-30"})
    assert r.status_code in (400, 422)

Step 2: 跑测试确认失败

Run: venv310/bin/python -m pytest tests/api/test_factor_eval_routes.py -v Expected: FAIL（no module routes_factor）

Step 3: 实现

sanguo_api/schemas.py 末尾追加：

class FactorBatchEvalRequest(BaseModel):
    """Factor batch evaluation request"""
    categories: list[str] = []
    factors: list[str] = []
    symbols: list[str] = []
    start: str
    end: str
    label: str = ""

sanguo_api/routes_factor.py：

"""因子批量评估 API:runs/leaderboard/detail/submit."""
from fastapi import APIRouter, HTTPException, Depends, Query

from .schemas import FactorBatchEvalRequest
from .auth import verify_token as verify_token_impl

router = APIRouter(dependencies=[Depends(verify_token_impl)])

_eval_db_path: str | None = None

_SORT_KEYS = {
    "abs_icir": lambda p: abs(p.get("icir") or 0.0),
    "ic_mean": lambda p: p.get("ic_mean") if p.get("ic_mean") is not None else float("-inf"),
    "t_stat": lambda p: abs(p.get("t_stat") or 0.0),
    "ls_annual": lambda p: p.get("ls_annual") if p.get("ls_annual") is not None else float("-inf"),
}


def set_eval_db_path(path: str) -> None:
    global _eval_db_path
    _eval_db_path = path


def _db() -> str:
    from sanguo_factor.eval_store import default_eval_db_path
    return _eval_db_path or default_eval_db_path()


def _latest_run_id(db: str) -> str | None:
    from sanguo_factor.eval_store import list_runs
    runs = list_runs(db)
    return runs[0]["run_id"] if runs else None


@router.get("/factor/eval/runs")
def eval_runs():
    from sanguo_factor.eval_store import list_runs
    return {"runs": list_runs(_db())}


@router.get("/factor/eval/leaderboard")
def eval_leaderboard(
    run_id: str | None = None,
    period: str = Query("1", pattern="^(1|5|10)$"),
    category: str | None = None,
    search: str | None = None,
    sort: str = Query("abs_icir", pattern="^(abs_icir|ic_mean|t_stat|ls_annual)$"),
    order: str = Query("desc", pattern="^(asc|desc)$"),
):
    from sanguo_factor.eval_store import get_rows
    db = _db()
    rid = run_id or _latest_run_id(db)
    if not rid:
        return {"tiles": {"factors_total": 0, "effective": 0, "watch": 0,
                          "top_ls_annual": None, "top_ls_factor": None}, "rows": []}
    rows = get_rows(db, rid, category=category, search=search)

    flat: list[dict] = []
    for r in rows:
        m = r.get("metrics") or {}
        p = m.get(period) or {}
        if "error" in m and not p:
            flat.append({"factor": r["factor"], "category": r["category"],
                         "expression": r["expression"], "error": m["error"],
                         "ic_mean": None, "icir": None, "t_stat": None, "win_rate": None,
                         "ls_annual": None, "turnover": m.get("turnover"),
                         "conclusion": "eliminated", "monthly_ic": []})
            continue
        flat.append({
            "factor": r["factor"], "category": r["category"], "expression": r["expression"],
            "ic_mean": p.get("ic_mean"), "icir": p.get("icir"), "t_stat": p.get("t_stat"),
            "win_rate": p.get("win_rate"), "ls_annual": p.get("ls_annual"),
            "turnover": m.get("turnover"), "conclusion": p.get("conclusion", "eliminated"),
            "monthly_ic": p.get("monthly_ic", []),
        })

    keyfn = _SORT_KEYS[sort]
    flat.sort(key=lambda x: (keyfn(x) is not None, keyfn(x)), reverse=(order == "desc"))
    for i, row in enumerate(flat, 1):
        row["rank"] = i

    scored = [x for x in flat if x.get("ic_mean") is not None]
    top_ls = max(scored, key=lambda x: (x.get("ls_annual") or float("-inf")), default=None)
    tiles = {
        "factors_total": len(flat),
        "effective": sum(1 for x in flat if x["conclusion"] == "effective"),
        "watch": sum(1 for x in flat if x["conclusion"] == "watch"),
        "top_ls_annual": top_ls["ls_annual"] if top_ls else None,
        "top_ls_factor": top_ls["factor"] if top_ls else None,
    }
    return {"tiles": tiles, "rows": flat}


@router.get("/factor/eval/detail")
def eval_detail(run_id: str | None = None, factor: str = ""):
    from sanguo_factor.eval_store import get_detail
    db = _db()
    rid = run_id or _latest_run_id(db)
    d = get_detail(db, rid, factor) if rid else None
    if d is None:
        raise HTTPException(status_code=404, detail="factor not found in run")
    return d


@router.post("/factor/eval/submit")
async def eval_submit(req: FactorBatchEvalRequest):
    if not req.categories and not req.factors:
        raise HTTPException(status_code=400, detail="categories 与 factors 至少给一个")
    from .routes import get_orchestrator
    orch = get_orchestrator()
    if orch is None:
        raise HTTPException(status_code=503, detail="orchestrator 未就绪")
    task_id = await orch.submit_batch_eval(
        factor_names=req.factors, categories=req.categories,
        start=req.start, end=req.end, symbols=req.symbols or None, label=req.label,
    )
    return {"task_id": task_id}

sanguo_orchestrator/runner.py（追加；_batch_eval_worker 必须模块级——spawn 进程池按引用 pickle）：

def _batch_eval_worker(factor_names: list, categories: list, start: str, end: str,
                       symbols, label: str, eval_db: str) -> dict:
    """进程池 worker:批量评估(spawn 环境,自行 load config/挂载因子)."""
    from sanguo_factor.alpha_datasets import mount_all
    mount_all()
    if not factor_names:
        from sanguo_factor.registry import list_factors
        factor_names = [f["name"] for c in categories for f in list_factors(c)]
    from sanguo_factor.batch_eval import run_batch_eval
    return run_batch_eval(factor_names, start, end, eval_db, label=label, symbols=symbols)

Orchestrator 类内追加（仿 submit_factor）：

    async def submit_batch_eval(self, factor_names: list, categories: list,
                                start: str, end: str, symbols, label: str) -> str:
        """Submit a factor batch evaluation task asynchronously"""
        from .eval_db_path import resolve_eval_db  # 或直接内联(见下)
        task_id = f"factor_eval_{uuid.uuid4().hex[:8]}"
        self._record_submit(task_id)
        self.pool.submit(task_id, "factor_eval")
        eval_db = resolve_eval_db(self.db_path)
        fut: Future = self.pool.submit_work(
            task_id, _batch_eval_worker, factor_names, categories,
            start, end, symbols, label or "batch", eval_db,
        )
        task = self.pool.get_task(task_id)
        task.start()
        await self._notify_stage(task_id, "批量评估中")
        asyncio.ensure_future(self._wait_future(task_id, fut))
        return task_id

resolve_eval_db 别单独建文件——放 runner.py 模块级函数：

def resolve_eval_db(backtest_db_path: str) -> str:
    """factor_eval.db 与 backtest_results.db 同目录(SANGUO_FACTOR_EVAL_DB 可覆盖)."""
    import os
    env = os.environ.get("SANGUO_FACTOR_EVAL_DB")
    if env:
        return env
    return os.path.join(os.path.dirname(os.path.abspath(backtest_db_path or ".")), "factor_eval.db")

完成回写（在 _wait_future 完成分支里既有 if getattr(result, "ic_summary", ...) 之后并列加）：

        # 批量评估结果摘要落任务中心(完整数据在 factor_eval.db)
        if isinstance(result, dict) and "run_id" in result:
            self._persist_factor_eval(task_id, result)

    def _persist_factor_eval(self, task_id: str, summary: dict) -> None:
        """批量评估摘要落 backtest_results.db(type=factor_eval)进任务列表."""
        from sanguo_backtest.result_store import save_result, BacktestResult
        try:
            save_result(BacktestResult(
                task_id=task_id, type="factor_eval", status="done",
                strategy=summary.get("label", "batch"),
                symbol=f"{summary.get('symbols_count', 0)}只",
                params={},
                start="", end="",
                statistics={"run_id": summary.get("run_id"),
                            "factors_done": summary.get("factors_done"),
                            "errors": summary.get("errors", [])[:20]},
                equity_curve=None, trades=None,
            ), db_path=self.db_path)
        except Exception as e:
            import logging
            logging.getLogger(__name__).warning("persist factor_eval %s failed: %s", task_id, e)

⚠️ 先读 _wait_future 实际代码再插入——上面片段是意图，插入点以「worker 返回 dict 且带 run_id」为判据，别破坏既有 FactorReport/BacktestResult 分支。同样先读 BacktestResult dataclass 字段（sanguo_backtest/result_store.py）确认字段名一致。

sanguo_api/app.py：import 区加 from .routes_factor import router as factor_eval_router, set_eval_db_path as set_factor_eval_db_path；include_router 区加一行；创建 app/orchestrator 初始化处（找到 orchestrator db_path 设定的地方）加：

from sanguo_orchestrator.runner import resolve_eval_db
set_factor_eval_db_path(resolve_eval_db(orchestrator_db_path))

先读 app.py 全文找现有 wiring 位置（routes_paper 的 set_db_path 怎么接就怎么接）。

Step 4: 跑测试确认通过 + 回归

Run: venv310/bin/python -m pytest tests/api/test_factor_eval_routes.py tests/api/test_factor_routes.py tests/factor -v Expected: 全 PASS（既有 /factor/list、/factor/analyze 测试不回归）

Step 5: Commit

git add sanguo_api/schemas.py sanguo_api/routes_factor.py sanguo_api/app.py sanguo_orchestrator/runner.py tests/api/test_factor_eval_routes.py
git commit -m "feat(api): 因子批量评估四端点(runs/leaderboard/detail/submit)+orchestrator任务接线——leaderboard服务端扁平化排序,worker落factor_eval.db+任务中心摘要 [vps]"

Task 9: 前端接线（api/router/menu）

Files:

Modify: frontend/src/api/factor.ts
Modify: frontend/src/router/index.ts:25-27 区域
Modify: frontend/src/views/Layout.vue:31-33（面包屑）与 :69（导航数组）与 :171-174（菜单）

Interfaces:

Produces（供 Task 10/11/12 消费的 TS API）:

// —— 追加到 frontend/src/api/factor.ts ——

export interface EvalRun {
  run_id: string
  label: string
  universe: string
  symbols_count: number
  factors_total: number
  factors_done: number
  start: string
  end: string
  status: string
  created_at: string
}

export interface MonthlyIcPoint { month: string; ic: number }

export interface LeaderboardRow {
  rank: number
  factor: string
  category: string
  expression: string
  ic_mean: number | null
  icir: number | null
  t_stat: number | null
  win_rate: number | null
  ls_annual: number | null
  turnover: number | null
  conclusion: string
  monthly_ic: MonthlyIcPoint[]
  error?: string
}

export interface LeaderboardTiles {
  factors_total: number
  effective: number
  watch: number
  top_ls_annual: number | null
  top_ls_factor: string | null
}

export interface EvalDetail {
  factor: string
  category: string
  expression: string
  metrics: Record<string, unknown>
}

export interface BatchEvalSubmit {
  categories: string[]
  factors: string[]
  symbols: string[]
  start: string
  end: string
  label: string
}

export async function getEvalRuns(): Promise<EvalRun[]> {
  const { data } = await apiClient.get<{ runs: EvalRun[] }>('/factor/eval/runs')
  return data.runs
}

export async function getLeaderboard(params: {
  run_id?: string
  period?: string
  category?: string
  search?: string
  sort?: string
  order?: string
}): Promise<{ tiles: LeaderboardTiles; rows: LeaderboardRow[] }> {
  const { data } = await apiClient.get('/factor/eval/leaderboard', { params })
  return data
}

export async function getEvalDetail(factor: string, runId?: string): Promise<EvalDetail> {
  const { data } = await apiClient.get<EvalDetail>('/factor/eval/detail', {
    params: { factor, ...(runId ? { run_id: runId } : {}) },
  })
  return data
}

export async function submitBatchEval(req: BatchEvalSubmit): Promise<string> {
  const { data } = await apiClient.post<{ task_id: string }>('/factor/eval/submit', req)
  return data.task_id
}

router/index.ts 在 factor/new 之前插三行：

      { path: 'factor/leaderboard', name: 'fc-leaderboard', component: () => import('@/views/factor/Leaderboard.vue') },
      { path: 'factor/leaderboard/:factor', name: 'fc-leaderboard-detail', component: () => import('@/views/factor/LeaderboardDetail.vue') },
      { path: 'factor/batch', name: 'fc-batch', component: () => import('@/views/factor/BatchEval.vue') },

Layout.vue：

面包屑数组（31-33 行区域）加：

  { match: /^\/factor\/leaderboard\/.+$/, group: '投研', title: '因子详情' },
  { match: /^\/factor\/leaderboard$/, group: '投研', title: 'IC 排行榜' },
  { match: /^\/factor\/batch$/, group: '投研', title: '批量评估' },

侧边导航数组（69 行区域）加：

  { label: 'IC 排行榜', group: '投研', path: '/factor/leaderboard' },
  { label: '批量评估', group: '投研', path: '/factor/batch' },

菜单（171-174）改为：

        <el-sub-menu index="factor">
          <template #title><span class="nav-label">投研</span></template>
          <el-menu-item index="/factor/leaderboard">IC 排行榜</el-menu-item>
          <el-menu-item index="/factor/batch">批量评估</el-menu-item>
          <el-menu-item index="/factor/new">因子分析</el-menu-item>
        </el-sub-menu>

注意：先 Read Layout.vue 全文确认数组结构/键名与上面一致再改（摘要行号可能漂移）。此 task 页面文件尚不存在，router 懒加载不会构建失败，但本 task 不 build，build 统一放 Task 12。

Step 1: 改四个文件（如上）
Step 2: Commit

git add frontend/src/api/factor.ts frontend/src/router/index.ts frontend/src/views/Layout.vue
git commit -m "feat(web): 因子工作台接线——eval API 客户端+3路由+投研组菜单/面包屑 [nas]"

Task 10: IC 排行榜页（Leaderboard.vue）

Files:

Create: frontend/src/views/factor/Leaderboard.vue

Interfaces:

Consumes: Task 9 的 getEvalRuns/getLeaderboard、路由 fc-leaderboard-detail（行点击 router.push(/factor/leaderboard/${factor}?run=${runId})）
视觉：逐区块对照 mockup（docs/factor_research/factor-workbench-mockup.html 视图①「IC 排行榜」）复刻——4 统计瓦片/筛选栏（run 下拉+类别 chips+搜索框+1D5D10D 切换）/主表格（IC 均值列内嵌横向色条 div 宽度=|ic_mean|×500%、红正绿负按 --up/--down）/IC 趋势 sparkline（inline SVG polyline，取 monthly_ic 后 24 点，正段青负段按 up/down）/信号灯（●--lamp-ok=effective ●--lamp-warn=watch ●--lamp-idle=eliminated）

完整组件骨架（样式类名从 mockup 对应区块取值，token 全走 CSS 变量）：

<script setup lang="ts">
import { ref, onMounted } from 'vue'
import { useRouter } from 'vue-router'
import { getEvalRuns, getLeaderboard, type EvalRun, type LeaderboardRow, type LeaderboardTiles } from '@/api/factor'

const router = useRouter()
const runs = ref<EvalRun[]>([])
const runId = ref<string>('')
const period = ref<'1' | '5' | '10'>('1')
const category = ref<string>('')        // ''=全部
const search = ref('')
const sort = ref('abs_icir')
const loading = ref(false)
const tiles = ref<LeaderboardTiles | null>(null)
const rows = ref<LeaderboardRow[]>([])

const CATEGORY_CHIPS = [
  { key: '', label: '全部' },
  { key: 'alpha101', label: 'Alpha101' },
  { key: 'alpha158', label: 'Alpha158' },
  { key: 'builtin', label: '内置' },
]

async function load() {
  loading.value = true
  try {
    const data = await getLeaderboard({
      ...(runId.value ? { run_id: runId.value } : {}),
      period: period.value,
      ...(category.value ? { category: category.value } : {}),
      ...(search.value ? { search: search.value } : {}),
      sort: sort.value,
    })
    tiles.value = data.tiles
    rows.value = data.rows
  } finally {
    loading.value = false
  }
}

function pct(v: number | null): string {
  return v == null ? '—' : `${(v * 100).toFixed(1)}%`
}
function num(v: number | null, digits = 3): string {
  return v == null ? '—' : v.toFixed(digits)
}
function sparkPath(points: { ic: number }[]): string {
  const pts = points.slice(-24)
  if (!pts.length) return ''
  const w = 72, h = 20
  const maxAbs = Math.max(0.02, ...pts.map((p) => Math.abs(p.ic)))
  return pts
    .map((p, i) => `${(i / Math.max(pts.length - 1, 1)) * w},${h / 2 - (p.ic / maxAbs) * (h / 2 - 2)}`)
    .join(' ')
}
const CONCLUSION: Record<string, { label: string; color: string }> = {
  effective: { label: '有效', color: 'var(--lamp-ok)' },
  watch: { label: '观察', color: 'var(--lamp-warn)' },
  eliminated: { label: '淘汰', color: 'var(--lamp-idle)' },
}

onMounted(async () => {
  runs.value = await getEvalRuns()
  if (runs.value.length) runId.value = runs.value[0].run_id
  await load()
})
</script>

template 结构（视觉细节照 mockup）：页面标题区 → 4 瓦片（tiles.factors_total/effective/watch/top_ls_annual+top_ls_factor）→ 筛选栏（el-select run / chips / el-input search（回车触发 load）/ 1D·5D·10D segmented）→ 表格（列：排名/因子名（副行表达式截断 48 字符）/类别 chip/IC 均值+色条/ICIR/t 值/胜率/多空年化（红正绿跌色）/换手/IC 趋势 svg/信号灯+结论字）→ 行 click 跳详情；loading 时 el-loading / 空态「暂无评估结果，去批量评估发起」链接 /factor/batch。

Step 1: 写组件（对照 mockup 逐区块复刻）
Step 2: 手验：cd frontend && npm run dev 登录后访问 /factor/leaderboard（空态可见、筛选交互正常、无 console 报错）
Step 3: Commit

git add frontend/src/views/factor/Leaderboard.vue
git commit -m "feat(web): IC排行榜页——瓦片+筛选chips+全指标表格+IC趋势sparkline+信号灯,终端风1:1 mockup [nas]"

Task 11: 因子详情页（LeaderboardDetail.vue）

Files:

Create: frontend/src/views/factor/LeaderboardDetail.vue

Interfaces:

Consumes: getEvalDetail、route param factor、query run；周期切换本地态（默认 1）
Produces: 图表用 echarts（平台既有模式：import * as echarts from 'echarts' 或 composables/useChart.ts + @/utils/echartsDark 的 darkTitle/darkTooltip/darkGrid/darkAxis，参考 frontend/src/components/charts/EquityChart.vue 的用法）

页面结构（对照 mockup 视图②）：

返回链接 ← IC 排行榜 + 因子名标题 + 类别 chip + 信号灯
表达式卡片：--panel 底、青色 mono、可展开全文
6 格指标条（当前周期）：IC 均值 / ICIR / t 值 / 胜率 / 多空年化 / 换手率 + 参评天数（metrics[period].count）
月度 IC 柱状 + 累计 IC 折线（双 y 或同图双系列 echarts：柱=月度 IC 正红负绿、线=累计 IC 青 #00e5ff）
十分组年化收益柱状（D1–D10，色阶从 --down 渐到 --up）
tears 入口：跳既有 /factor/new（提示「 Tears 报告请从因子分析页发起」按钮）——v1 不做深链
周期切换（1D/5D/10D）重渲染 3/4/5 区块

<script setup lang="ts">
import { ref, onMounted, watch, nextTick } from 'vue'
import { useRoute, useRouter } from 'vue-router'
import { getEvalDetail } from '@/api/factor'

const route = useRoute()
const router = useRouter()
const period = ref<'1' | '5' | '10'>('1')
const detail = ref<Awaited<ReturnType<typeof getEvalDetail>> | null>(null)
const error = ref('')

async function load() {
  try {
    detail.value = await getEvalDetail(String(route.params.factor),
      route.query.run ? String(route.query.run) : undefined)
  } catch (e) {
    error.value = '因子不存在或无评估数据'
  }
}

function metrics() {
  return (detail.value?.metrics?.[period.value] ?? {}) as Record<string, any>
}
// 月度IC/累计IC 与 十分组 两个 chart 实例:echarts init 于 ref 容器,
// period 变化 watch 后 setOption 重绘(数据源 metrics().monthly_ic / .deciles)
onMounted(load)
watch(period, async () => { await nextTick(); /* setOption 重绘两张图 */ })
</script>

Step 1: 写组件
Step 2: 手验：dev server 从排行榜点行进入，三区块渲染、周期切换重绘、无 console 报错
Step 3: Commit

git add frontend/src/views/factor/LeaderboardDetail.vue
git commit -m "feat(web): 因子详情页——表达式卡+6格指标+月度/累计IC双系列图+十分组柱状,echarts终端暗色 [nas]"

Task 12: 批量评估页 + 前端构建门禁（BatchEval.vue + build + CI 扩）

Files:

Create: frontend/src/views/factor/BatchEval.vue
Modify: .gitea/workflows/ci-cd.yml:57（pytest 命令加 tests/factor）

Interfaces:

Consumes: submitBatchEval、getFactors（类目清单）、既有任务中心路由（提交后跳 /backtest/history）
页面结构（对照 mockup 视图③）：01 股票池（3 卡：全A '' /自定义 symbols textarea——中证800 预设 v1 置灰「未就绪」）→ 02 因子集（按 category 分组勾选卡：alpha101/alpha158/builtin，显示每类因子数，全选/清空）→ 03 时间窗（el-date-picker 起止，默认 2018-01-01 / 2026-06-30；提示文案「全量约 4400 只 × 8.5 年，预计 1–3 小时，完成后结果进 IC 排行榜」）→ 提交按钮（校验：因子集非空）→ 成功 toast + 跳任务中心
CI：pytest tests/data_platform tests/portfolio tests/factor -q（tests/factor 全是纯逻辑小测，进 CI 门禁防回归）
Step 1: 写组件（如上结构，el-card 分步布局 + 终端风覆盖样式照平台既有表单页如 backtest/New.vue 的覆盖写法）
Step 2: 完整构建验证（必做，别只 dev）

Run: cd frontend && npm run build Expected: vue-tsc + vite build 零 error（warning 可容忍）；失败先修再提交

Step 3: 后端全量回归 + CI 文件改

Run: venv310/bin/python -m pytest tests/factor tests/api -q Expected: 全 PASS

Step 4: Commit

git add frontend/src/views/factor/BatchEval.vue .gitea/workflows/ci-cd.yml
git commit -m "feat(web): 批量评估页——股票池/因子集/时间窗三步提交进任务中心;CI门禁加tests/factor [nas]"

Task 13: 部署 NAS + 冒烟 + 全量 + 验证

Files: 无新文件（操作任务）

Step 1: push 触发 CI

git push origin master

盯 http://192.168.2.154:3000/sanguo/sanguo_vnpy_v2/actions：test（pytest 含新 tests/factor）→ nas-deploy → nas-verify 全绿。红了就地修（Mac runner 睡眠会导致不跑——确认 caffeinate 在跑）。

Step 2: NAS 容器内冒烟（50 只 × 2024 × 10 因子）

ssh sanguo-nas '/var/packages/Docker/target/usr/bin/docker exec sanguo_vnpy_v2 \
  python scripts/factor_research/run_eval.py \
  --start 2024-01-01 --end 2024-12-31 --limit 50 --label smoke-2024 \
  --factors alpha2,alpha6,alpha12,alpha18,kmid,klen,roc_5,ma_20,std_20,wvma_20 \
  --db /volume1/stock/sanguo_vnpy_v2/data/factor_eval.db'

Expected: [eval] run_id=ev_... done=10 errors=0，Top15 表格打印，|IC| 普遍 < 0.1。记下 elapsed 秒数（全量外推依据）。

冒烟 db 路径显式给 /volume1/stock/sanguo_vnpy_v2/data/factor_eval.db（容器挂载卷内、API 容器同进程可见的持久位置）。若容器内该目录不存在，先 docker exec ... mkdir -p。default_eval_db_path() 在容器里指向哪要先验证：docker exec sanguo_vnpy_v2 python -c "from sanguo_factor.eval_store import default_eval_db_path; print(default_eval_db_path())"——如果和上面不一致，统一以 SANGUO_FACTOR_EVAL_DB=/volume1/stock/sanguo_vnpy_v2/data/factor_eval.db 环境变量注入容器（改 NAS 容器 env 或 app.py 启动处，优先不动 app.py，用 NAS 侧 env）。

Step 3: API 验证

TOKEN=$(curl -s -X POST http://192.168.2.154:8000/api/v1/auth/login -H 'Content-Type: application/json' \
  -d '{"username":"admin","password":"<从 .claude/gitea-config.json 或询问用户>"}' | python3 -c 'import json,sys;print(json.load(sys.stdin)["token"])')
curl -s "http://192.168.2.154:8000/api/v1/factor/eval/runs" -H "Authorization: Bearer $TOKEN"
curl -s "http://192.168.2.154:8000/api/v1/factor/eval/leaderboard" -H "Authorization: Bearer $TOKEN" | python3 -m json.tool | head -40

Expected: runs 含 smoke-2024（status=done）；leaderboard tiles.factors_total=10、rows 10 行、无 error 字段。

Step 4: 前端页面验证（chrome-devtools MCP）

打开 http://192.168.2.154:8000/（或前端实际端口，问 runbook/nas-verify 用的地址）→ 登录 → 投研→IC 排行榜：冒烟 run 可选、瓦片数字、表格 10 行、信号灯正常 → 点行进详情：三图表渲染 → 批量评估页：表单结构完整。截图留档。

Step 5: 全量评估（走 API 任务中心，非 docker exec——长任务铁律）

curl -s -X POST http://192.168.2.154:8000/api/v1/factor/eval/submit \
  -H "Authorization: Bearer $TOKEN" -H 'Content-Type: application/json' \
  -d '{"categories":["alpha101","alpha158"],"factors":[],"symbols":[],
       "start":"2018-01-01","end":"2026-06-30","label":"batch1-full"}'

Expected: {"task_id":"factor_eval_..."}；任务中心出现任务、stage=批量评估中。预计 1–3 小时（以冒烟实测外推），期间 Monitor 轮询：

# 每 10 分钟查一次进度(eval_runs.factors_done 递增 = 活着)
ssh sanguo-nas '/var/packages/Docker/target/usr/bin/docker exec sanguo_vnpy_v2 python -c "
import sqlite3; c=sqlite3.connect(\"/volume1/stock/sanguo_vnpy_v2/data/factor_eval.db\");
print(c.execute(\"SELECT run_id,factors_done,factors_total,status FROM eval_runs ORDER BY created_at DESC LIMIT 1\").fetchone())"'

Step 6: 全量结果验收

完成后 leaderboard：tiles.factors_total=258、effective/watch 有分布（全 0 effective = 数据或口径有问题要排查）；抽 3 个知名因子（alpha101 的 alpha6/alpha12、alpha158 的 ma_20）人工核对 IC 符号量级是否合理（量价反转类因子 A 股 RankIC 1D 通常 |0.03|~|0.1|）。

Step 7: 收尾汇报
git log --oneline 列出全部 commit（确认每条有标签）
向用户汇报：冒烟/全量耗时、Top 因子表、有效/观察/淘汰分布、ST 过滤偏差说明、待推 VPS 清单（按 CLAUDE.md 铁律，等用户原话「推vps」）
更新 memory factor-session-mainline.md

Self-Review 结论

Spec 覆盖：挂载(T2)/vwap(T1)/批量脚本(T6,7)/冒烟先行(T13S2)/缓存扩容→不需要（批量路径绕开 AlphaLabSession 的 50 只上限，spec 第 5 条「股票池缓存扩容」由新路径天然满足，零改动）/新 API(T8)/前端三页(T9-12)/验收标准 5 条对应 T13 S2-S6
偏差（已声明）：ST 过滤不做（前视偏差理由）；「SQLite 或 CSV+markdown」取 SQLite 单方案
类型一致性：metrics/period key "1"/"5"/"10" 全链一致；run_id 贯穿 store/API/前端 query
排序稳定性：leaderboard 默认 abs_icir desc，rank 服务端生成

89 KiB Raw Blame History Unescape Escape

因子研究第一批（Alpha101+Alpha158 挂载与全A重验）实施计划

Global Constraints

File Structure

Task 1: vwap 派生列（data_adapter）

Task 2: 挂载 Alpha101/Alpha158 表达式（alpha_datasets）

Task 3: 评估股票池加载（universe）

Task 4: 向量化评估指标（metrics）

Task 5: 评估结果存储（eval_store）

Task 6: 批量评估引擎（batch_eval）

Task 7: CLI 脚本（scripts/factor_research/run_eval.py）

Task 8: API 端点 + orchestrator 接线

Task 9: 前端接线（api/router/menu）

Task 10: IC 排行榜页（Leaderboard.vue）

Task 11: 因子详情页（LeaderboardDetail.vue）

Task 12: 批量评估页 + 前端构建门禁（BatchEval.vue + build + CI 扩）

Task 13: 部署 NAS + 冒烟 + 全量 + 验证

Self-Review 结论

89 KiB

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