docs(data): 归档数据层验证产物 + 数据层总览README

- scripts/data_platform/_archive/legacy/: 归档20个独立探针/诊断/旧降级脚本(零引用验证)
- docs/archive/data/: 归档17个数据相关旧设计/plan/report(保留fusion spec作深读)
- docs/data-platform/README.md: 数据层单一权威记录(8节:架构/布局/源/管线/铁律/API/缺口/待办)
- 删除 _mootdx_depth_result.txt
- Phase2待办: 15m灌库链+旧回填import链(有测试/wrapper依赖,VPS schtask确认后归档)

scripts/data_platform/_archive/README.md

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+# 数据层归档脚本（_archive/）
+
+本目录存放**已完成使命的验证/诊断/一次性脚本**，不再参与日常增量管线。
+保留于 git 历史便于回溯；如需重跑，移动回 `scripts/data_platform/` 顶层即可。
+
+## legacy/ — 探针 / 诊断 / 旧降级 / 一次性验证（2026-07-29 归档）
+
+| 脚本 | 类型 | 说明 |
+|------|------|------|
+| `probe_*.py`（12） | 探针 | 数据源/库/接口一次性 smoke 验证（akshare 状态/成份股/dbbardata 唯一性/退市/ETF/基本面/涨跌停/unified schema 等） |
+| `dbbardata_probe.py` | 探针 | dbbardata 表结构与行数抽查 |
+| `run_with_diag.py` / `diag_daily_update.ps1` | 诊断 | 带诊断输出的运行包装 |
+| `test_mootdx_depth.py` / `test_baostock_daily_constituent_sample.py` | 一次性验证 | mootdx 深度 / baostock 日线成份股采样（非 tests/ 正式套件） |
+| `resume_5yr_watcher.py` | 一次性 | 5 年全市场下载断点续传 watcher（已完成） |
+| `fallback.py` / `realtime.py` | 旧降级 | 旧多源降级管理器（日线 akshare→腾讯 / 实时 新浪→东财→腾讯），方案 A 后由 bs_eod/xt_eod 接管 |
+
+归档前已验证：**零 import、无活跃 wrapper 引用**。
+
+## backfill_15m/ — 15min 一次性灌库链（Phase 2 待归档）
+
+⚠️ 未归档。`backfill_15min_baostock` 被 `tests/data/test_backfill_15min_hardening.py` 正式 import，
+`refresh_15min_daily` / `download_minute` / `download_15m_xtdata` / `raw_redownload` / `audit_data_layout`
+存在交叉引用或 ops wrapper 依赖，需 Phase 2 评估后统一处理。

scripts/data_platform/_archive/legacy/dbbardata_probe.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""dbbardata_probe.py — 只读探测 dbbardata 现状(schema + 抽样各 interval 覆盖)。
+
+走 symbol 索引,避免全表 COUNT(亿级慢)。回答:
+  - dbbardata 有哪些 interval(d/15m/5m)
+  - 个股日线(interval='d' 类)含不含退市股(000005/000023/600811/600074)
+  - 在市股(600519/000001)日线日期范围
+  - ETF(510300/159915)有没有(判断 dbbardata 是否覆盖 ETF)
+用于决定迁移单元5(dbbardata 个股日线切 baostock 源)的工作量。
+"""
+import sqlite3
+from pathlib import Path
+
+DB = Path(r"C:\sanguo_vnpy_v2\data\quant_trading.db")
+c = sqlite3.connect(str(DB), timeout=60)
+c.execute("PRAGMA busy_timeout = 60000")
+
+cols = [r[1] for r in c.execute("PRAGMA table_info(dbbardata)")]
+print(f"DB: {DB}")
+print(f"dbbardata cols({len(cols)}): {cols}")
+
+print("\n=== 抽样: 各 symbol 的 interval 覆盖 (走索引, 快) ===")
+samples = {
+    "退市": ["000005", "000023", "600811", "600074"],
+    "在市个股": ["600519", "000001"],
+    "ETF/基金": ["510300", "159915", "510050"],
+}
+for label, syms in samples.items():
+    print(f"\n[{label}]")
+    for sym in syms:
+        rows = c.execute(
+            "SELECT interval, COUNT(*), MIN(datetime), MAX(datetime) "
+            "FROM dbbardata WHERE symbol=? GROUP BY interval",
+            (sym,),
+        ).fetchall()
+        print(f"  {sym}: {rows}")
+
+c.close()

scripts/data_platform/_archive/legacy/diag_daily_update.ps1

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+# diag_daily_update.ps1 — 诊断 sanguo-daily-update 为何跑 2.5h
+Write-Output "=== NOW: $(Get-Date) ==="
+Write-Output "=== python 进程 (CPU秒/内存MB/启动时间) ==="
+Get-Process python -ErrorAction SilentlyContinue |
+  Select-Object Id, @{n='CPU_s'; e={[math]::Round($_.CPU, 1)}},
+    @{n='WS_MB'; e={[math]::Round($_.WS / 1MB, 0)}}, StartTime |
+  Format-Table -Auto
+Write-Output "=== schtask sanguo-daily-update (状态+执行的命令) ==="
+$i = Get-ScheduledTaskInfo -TaskName sanguo-daily-update
+Write-Output ("last={0}  result=0x{1:X}  next={2}" -f $i.LastRunTime, $i.LastTaskResult, $i.NextRunTime)
+Write-Output "--- Task Action (实际执行) ---"
+(Get-ScheduledTask -TaskName sanguo-daily-update).Actions |
+  Select-Object Execute, Arguments | Format-List
+Write-Output "=== daily_update.log tail 40 ==="
+Get-Content C:\sanguo_vnpy_v2\data\daily_update.log -Tail 40 -ErrorAction SilentlyContinue
+Write-Output "=== qfq/ 各年: 文件数 + 最新写入时间 ==="
+Get-ChildItem C:\sanguo_vnpy_v2\data\qfq -Directory -ErrorAction SilentlyContinue | ForEach-Object {
+  $files = Get-ChildItem $_.FullName -File -ErrorAction SilentlyContinue
+  $mx = ($files | Sort-Object LastWriteTime -Descending | Select-Object -First 1).LastWriteTime
+  Write-Output ("  {0}: files={1}  lastwrite={2}" -f $_.Name, $files.Count, $mx)
+}
+Write-Output "=== qfq/2026 最新写入 top 5 (看在不在写) ==="
+Get-ChildItem C:\sanguo_vnpy_v2\data\qfq\2026 -ErrorAction SilentlyContinue |
+  Sort-Object LastWriteTime -Descending | Select-Object -First 5 Name, LastWriteTime |
+  Format-Table -Auto

scripts/data_platform/_archive/legacy/fallback.py

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+#!/usr/bin/env python3
+"""多源降级管理器 - 日线(akshare→腾讯) + 实时(新浪→东财→腾讯)"""
+import pandas as pd
+import urllib.request
+import json
+import logging
+from datetime import datetime, timedelta
+from typing import Optional
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
+logger = logging.getLogger(__name__)
+
+
+class FallbackManager:
+    def __init__(self):
+        self._source_used = ""
+
+    def get_source_used(self) -> str:
+        return self._source_used
+
+    def get_daily(self, symbol: str, start_date: str, end_date: str) -> pd.DataFrame:
+        """获取日线数据，降级链：akshare → 腾讯K线"""
+        # 1. akshare
+        try:
+            df = self._fetch_akshare_daily(symbol, start_date, end_date)
+            if df is not None and not df.empty:
+                self._source_used = "akshare"
+                return df
+        except Exception as e:
+            logger.warning(f"akshare日线失败 {symbol}: {e}")
+
+        # 2. 腾讯K线
+        try:
+            df = self._fetch_tencent_daily(symbol, start_date, end_date)
+            if df is not None and not df.empty:
+                self._source_used = "tencent_kline"
+                return df
+        except Exception as e:
+            logger.warning(f"腾讯K线失败 {symbol}: {e}")
+
+        raise RuntimeError(f"所有日线数据源失败: {symbol} {start_date}~{end_date}")
+
+    def get_realtime(self, symbol: str) -> dict:
+        """获取实时行情，降级链：新浪→东财→腾讯"""
+        from realtime import get_realtime_quote
+        result = get_realtime_quote(symbol)
+        self._source_used = result.get("source", "unknown")
+        return result
+
+    def _fetch_akshare_daily(self, symbol: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
+        import akshare as ak
+        code = symbol.replace("SH", "").replace("SZ", "").replace("sh", "").replace("sz", "")
+        s = start_date.replace("-", "")
+        e = end_date.replace("-", "")
+        df = ak.stock_zh_a_hist(symbol=code, period="daily", start_date=s, end_date=e, adjust="")
+        if df is None or df.empty:
+            return None
+        df = df.rename(columns={"日期": "date", "开盘": "open", "收盘": "close",
+                                 "最高": "high", "最低": "low", "成交量": "volume",
+                                 "成交额": "amount"})
+        df["date"] = pd.to_datetime(df["date"]).dt.strftime("%Y-%m-%d")
+        for c in ["open", "high", "low", "close", "volume", "amount"]:
+            df[c] = pd.to_numeric(df[c], errors="coerce").fillna(0)
+        return df[["date", "open", "high", "low", "close", "volume", "amount"]]
+
+    def _fetch_tencent_daily(self, symbol: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
+        """腾讯K线API获取日线"""
+        code = symbol.replace("SH", "").replace("SZ", "").replace("sh", "").replace("sz", "")
+        if code.startswith(("6", "5", "1")):
+            prefix = "sh"
+        else:
+            prefix = "sz"
+        tq_symbol = f"{prefix}{code}"
+
+        days = (datetime.strptime(end_date, "%Y-%m-%d") - datetime.strptime(start_date, "%Y-%m-%d")).days + 10
+        url = f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={tq_symbol},day,{start_date},,{days},"
+        try:
+            import urllib.request, json as _json
+            opener = urllib.request.build_opener(urllib.request.ProxyHandler({}))
+            req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+            with opener.open(req, timeout=10) as r:
+                resp = _json.loads(r.read())
+            d = resp.get("data")
+            if not isinstance(d, dict):
+                return None
+            klines = d.get(tq_symbol, {}).get("day", [])
+            if not klines:
+                return None
+            df = pd.DataFrame(klines)
+            ncols = len(df.columns)
+            if ncols >= 7:
+                df.columns = ["date", "open", "close", "high", "low", "volume", "amount"][:ncols]
+            else:
+                df.columns = ["date", "open", "close", "high", "low", "volume"][:ncols]
+            if "amount" not in df.columns:
+                df["amount"] = 0.0
+            for c in ["open", "close", "high", "low", "volume", "amount"]:
+                df[c] = pd.to_numeric(df[c], errors="coerce").fillna(0)
+            df["date"] = pd.to_datetime(df["date"]).dt.strftime("%Y-%m-%d")
+            mask = (df["date"] >= start_date) & (df["date"] <= end_date)
+            return df.loc[mask, ["date", "open", "high", "low", "close", "volume", "amount"]]
+        except Exception as e:
+            logger.warning(f"腾讯K线请求失败: {e}")
+            return None

scripts/data_platform/_archive/legacy/probe_akshare_status.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""probe_akshare_status.py — 探查 akshare 低频任务现状(sanguo-bs-akshare + sanguo-index)。
+
+看: 三表 static 各子目录文件数+最新mtime / 事件类数据有没有 / constituent_unified source 分布。
+"""
+import datetime
+import sqlite3
+from pathlib import Path
+
+BASE = Path(r"C:\sanguo_vnpy_v2\data")
+print("===== data/ 子目录 =====")
+if BASE.exists():
+    for p in sorted(BASE.iterdir()):
+        if p.is_dir():
+            n = sum(1 for _ in p.rglob("*") if _.is_file())
+            print(f"  {p.name}/ ({n} files)")
+        else:
+            print(f"  {p.name}")
+
+print("\n===== static/ 各表文件数 + 最新 mtime =====")
+for sub in ["balance", "income", "cashflow", "valuation", "financial_abstract"]:
+    d = BASE / "static" / sub
+    if not d.exists():
+        print(f"  static/{sub}: MISSING")
+        continue
+    files = list(d.glob("*.parquet"))
+    if not files:
+        print(f"  static/{sub}: 0 parquet")
+        continue
+    mt = max(f.stat().st_mtime for f in files)
+    print(f"  static/{sub}: {len(files)} parquet, latest mtime={datetime.datetime.fromtimestamp(mt):%Y-%m-%d %H:%M}")
+
+print("\n===== 事件类数据(龙虎榜/北向/两融/解禁/大宗/可转债/研报) =====")
+for sub in ["longhubang", "north_flow", "margin", "blockade", "block_trade", "convertible_bond", "research"]:
+    d = BASE / "events" / sub
+    if d.exists():
+        files = list(d.glob("*"))
+        print(f"  events/{sub}: {len(files)} files")
+    else:
+        print(f"  events/{sub}: MISSING")
+# data 根下找可能的 events/其它事件目录
+for cand in ["events", "akshare_events", "longhubang", "north"]:
+    d = BASE / cand
+    if d.exists():
+        print(f"  {cand}/ exists")
+
+print("\n===== constituent_unified source 分布(看 akshare 部分啥时点) =====")
+c = sqlite3.connect(str(BASE / "quant_trading.db"))
+try:
+    print("  source 分布:", c.execute("SELECT source, COUNT(*) FROM constituent_unified GROUP BY source").fetchall())
+    print("  per-index×source:")
+    for row in c.execute("SELECT index_code, source, COUNT(*) FROM constituent_unified GROUP BY index_code, source ORDER BY index_code"):
+        print("   ", row)
+finally:
+    c.close()
+print("\nPROBE DONE")

scripts/data_platform/_archive/legacy/probe_constituent.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""probe_constituent.py — 单元3 前置: 探查成份股两源 schema (写 migrate 前必须看清)。
+
+1. bs_index_constituent (DB, baostock 300/500/50 历史): cols/rows/distinct index_code/抽样
+2. data/index_const_hist/*.parquet (akshare cni 深证 union): 文件列表 + 每个 shape/cols/抽样
+"""
+import glob
+import os
+import sqlite3
+
+import pandas as pd
+
+DB = r"C:\sanguo_vnpy_v2\data\quant_trading.db"
+HIST = r"C:\sanguo_vnpy_v2\data\index_const_hist"
+
+c = sqlite3.connect(DB, timeout=60)
+c.execute("PRAGMA busy_timeout = 60000")
+cols = [r[1] for r in c.execute("PRAGMA table_info(bs_index_constituent)")]
+print("[bs_index_constituent] cols:", cols)
+print("  rows:", c.execute("SELECT COUNT(*) FROM bs_index_constituent").fetchone()[0])
+idx = [r[0] for r in c.execute(
+    "SELECT DISTINCT index_code FROM bs_index_constituent ORDER BY index_code")]
+print("  distinct index_code:", idx)
+print("  distinct updateDate count:", c.execute(
+    "SELECT COUNT(DISTINCT updateDate) FROM bs_index_constituent").fetchone()[0])
+print("  sample rows:", c.execute(
+    "SELECT * FROM bs_index_constituent LIMIT 3").fetchall())
+c.close()
+
+print("\n[index_const_hist parquets]")
+files = sorted(glob.glob(os.path.join(HIST, "*.parquet")))
+print("files:", [os.path.basename(f) for f in files])
+for f in files:
+    df = pd.read_parquet(f)
+    print(f"  {os.path.basename(f)}: shape={df.shape} cols={list(df.columns)}")
+    print(f"    head:\n{df.head(2).to_string()}")

scripts/data_platform/_archive/legacy/probe_dbbardata_unique.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""probe_dbbardata_unique.py — 单元4 merge 前置: 查 dbbardata UNIQUE 约束 + verify staging。
+
+INSERT OR REPLACE 依赖 dbbardata 有 UNIQUE(symbol,exchange,datetime,interval) 才能去重,
+否则插重复。vnpy DbBarData 通常有 UniqueConstraint, 此处确认。
+"""
+import sqlite3
+
+DB = r"C:\sanguo_vnpy_v2\data\quant_trading.db"
+c = sqlite3.connect(DB, timeout=120)
+c.execute("PRAGMA busy_timeout = 120000")
+
+print("dbbardata sql:", c.execute(
+    "SELECT sql FROM sqlite_master WHERE name='dbbardata'").fetchone())
+print("\ndbbardata indexes:")
+for idx in c.execute("PRAGMA index_list('dbbardata')").fetchall():
+    cols = c.execute(f"PRAGMA index_info('{idx[1]}')").fetchall()
+    print(f"  {idx}  cols={cols}")
+
+print("\n--- staging verify ---")
+print("staging rows:", c.execute(
+    "SELECT COUNT(*) FROM dbbardata_staging_daily").fetchone()[0])
+print("staging distinct symbol:", c.execute(
+    "SELECT COUNT(DISTINCT symbol) FROM dbbardata_staging_daily").fetchone()[0])
+for sym, label in [("000005", "退市"), ("000023", "退市"),
+                   ("600519", "在市"), ("510300", "ETF")]:
+    r = c.execute(
+        "SELECT COUNT(*), MIN(datetime), MAX(datetime) "
+        "FROM dbbardata_staging_daily WHERE symbol=?", (sym,)).fetchone()
+    print(f"  {sym}({label}): {r}")
+c.close()

scripts/data_platform/_archive/legacy/probe_delisted_baostock.py

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+# -*- coding: utf-8 -*-
+"""
+P0 Task3 退市股 K 线 baostock 接口探针(只读)
+硬约束:
+- 直连不走代理(baostock 服务端在境内)
+- 单进程单登录串行(防黑名单)
+- 只读探针:不灌库不下全量
+"""
+import sys
+import os
+import io
+
+# Windows 控制台 utf-8 + 强制 flush
+if sys.platform == "win32":
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace", write_through=True)
+    sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace", write_through=True)
+
+# 清代理(baostock 直连境内,走代理必挂)
+for k in ("http_proxy", "https_proxy", "all_proxy", "HTTP_PROXY", "HTTPS_PROXY", "ALL_PROXY"):
+    os.environ.pop(k, None)
+
+import baostock as bs
+import pandas as pd
+import time
+
+def section(title):
+    print("\n" + "=" * 60)
+    print(f"### {title}")
+    print("=" * 60, flush=True)
+
+def dump_rs(rs, label, max_rows=5):
+    """把 ResultData 读完并打印字段 + 前 N 行"""
+    rows = []
+    while (rs.error_code == '0') & rs.next():
+        rows.append(rs.get_row_data())
+    df = pd.DataFrame(rows, columns=rs.fields) if rows else pd.DataFrame(columns=rs.fields)
+    print(f"[{label}] error={rs.error_code} {rs.error_msg} | fields={rs.fields} | rows={len(df)}")
+    if len(df) > 0:
+        print(f"[{label}] head({max_rows}):")
+        print(df.head(max_rows).to_string())
+    return df
+
+# ============ 1. 登录 ============
+section("LOGIN")
+t0 = time.time()
+lg = bs.login()
+print(f"login: error_code={lg.error_code} error_msg={lg.error_msg} elapsed={time.time()-t0:.2f}s", flush=True)
+if lg.error_code != '0':
+    print("LOGIN_FAILED_ABORT")
+    sys.exit(1)
+
+# ============ 2. query_all_stock(某日全市场列表) ============
+section("query_all_stock day=2026-07-18")
+rs = bs.query_all_stock(day="2026-07-18")
+df_all = dump_rs(rs, "query_all_stock(2026-07-18)", max_rows=5)
+# 看字段含不含 status / 退市日
+if len(df_all) > 0:
+    print("\n字段分析:")
+    print(f"  columns = {list(df_all.columns)}")
+    print(f"  has_status = {'status' in df_all.columns}")
+    print(f"  has_outDate = {'outDate' in df_all.columns}")
+    # code 前缀分布
+    if 'code' in df_all.columns:
+        df_all['prefix'] = df_all['code'].str.split('.').str[0]
+        print(f"  code 前缀分布:\n{df_all['prefix'].value_counts().to_string()}")
+    # tradeStatus 分布(若有)
+    if 'tradeStatus' in df_all.columns:
+        print(f"  tradeStatus 分布:\n{df_all['tradeStatus'].value_counts().to_string()}")
+
+# ============ 3. query_all_stock 不同日期对比(取旧日,看是否还能查到已退市股) ============
+section("query_all_stock day=2021-01-04(旧日,看是否含现已退市)")
+rs_old = bs.query_all_stock(day="2021-01-04")
+df_old = dump_rs(rs_old, "query_all_stock(2021-01-04)", max_rows=3)
+# 对比两日 code 差集(2021 在但 2026 不在 = 期间退市的候选)
+if len(df_all) > 0 and len(df_old) > 0 and 'code' in df_all.columns:
+    s_new = set(df_all['code'])
+    s_old = set(df_old['code'])
+    only_old = sorted(s_old - s_new)
+    only_new = sorted(s_new - s_old)
+    print(f"\n2021 有 / 2026 无(候选退市): {len(only_old)} 只 | 前 10 示例: {only_old[:10]}")
+    print(f"2026 有 / 2021 无(期间上市): {len(only_new)} 只 | 前 10 示例: {only_new[:10]}")
+    # 存下来供后面 K 线验证用
+    candidate_delisted = only_old[:10]  # 前 10 只候选退市股
+else:
+    candidate_delisted = []
+
+# ============ 4. query_stock_basic(单只基本信息 —— 找 status + outDate 字段) ============
+section("query_stock_basic 字段探查(活跃股 + 已知退市股)")
+# 先打活跃股(确认字段集)
+active_codes = ["sh.600000", "sz.000001", "sz.000002"]
+# 已知退市股候选(内存里的 + 常见历史退市)
+# sh.600074 退市保千 / sh.600432 退市吉恩 / sz.002450 *ST康得新 / sz.000033 新都退 / sh.600632 *ST 神城
+# 另外从 only_old 候选里挑几只(2021 有 2026 无 = 确认退市)
+delisted_candidates = ["sh.600074", "sh.600432", "sz.002450", "sz.000033", "sh.600632"]
+# 如果上面候选 only_old 里有,优先用(那些是 baostock 自己承认 2021 存在过的)
+probe_codes = active_codes + delisted_candidates
+if candidate_delisted:
+    probe_codes = probe_codes + candidate_delisted[:5]
+
+found_delisted_samples = []
+for code in probe_codes:
+    rs2 = bs.query_stock_basic(code=code)
+    b = []
+    while (rs2.error_code == '0') & rs2.next():
+        b.append(rs2.get_row_data())
+    df2 = pd.DataFrame(b, columns=rs2.fields) if b else pd.DataFrame(columns=rs2.fields)
+    print(f"\n--- query_stock_basic({code}) error={rs2.error_code} {rs2.error_msg}")
+    print(f"  fields = {rs2.fields}")
+    if len(df2) > 0:
+        print(f"  data   = {df2.to_dict('records')}")
+        # 记录退市样本(outDate 非空 或 status=0)
+        rec = df2.iloc[0].to_dict()
+        status_val = str(rec.get('status', ''))
+        outdate_val = str(rec.get('outDate', ''))
+        if status_val == '0' or (outdate_val and outdate_val not in ('', 'nan', 'None')):
+            found_delisted_samples.append((code, rec))
+    else:
+        print("  (empty)")
+    time.sleep(0.3)  # 温柔一点
+
+print(f"\n找到的退市样本: {len(found_delisted_samples)}")
+for code, rec in found_delisted_samples:
+    print(f"  {code}: status={rec.get('status')} outDate={rec.get('outDate')} type={rec.get('type')}")
+
+# ============ 5. 退市股 K 线验证 ============
+section("query_history_k_data_plus 退市股 K 线")
+fields = "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST"
+
+# 优先用真实退市股(status=0),否则用候选
+kline_targets = []
+for code, rec in found_delisted_samples:
+    kline_targets.append((code, rec.get('outDate', '')))
+# 如果没找到 status=0 的退市股,直接用候选列表
+if not kline_targets:
+    for code in delisted_candidates + candidate_delisted[:3]:
+        kline_targets.append((code, ''))
+
+# 去重
+seen = set()
+kline_targets_uniq = []
+for code, outdate in kline_targets:
+    if code not in seen:
+        seen.add(code)
+        kline_targets_uniq.append((code, outdate))
+
+print(f"K 线验证标的({len(kline_targets_uniq)}): {kline_targets_uniq}")
+
+for code, outdate in kline_targets_uniq[:6]:
+    print(f"\n--- K 线 {code} (outDate={outdate}) ---")
+    rs4 = bs.query_history_k_data_plus(
+        code, fields,
+        start_date='2020-01-01', end_date='2026-07-18',
+        frequency="d", adjustflag="3"
+    )
+    k = []
+    while (rs4.error_code == '0') & rs4.next():
+        k.append(rs4.get_row_data())
+    df4 = pd.DataFrame(k, columns=rs4.fields) if k else pd.DataFrame(columns=rs4.fields)
+    print(f"  error={rs4.error_code} {rs4.error_msg} | rows={len(df4)}")
+    if len(df4) > 0:
+        # 字符串转日期比大小
+        try:
+            df4['date_dt'] = pd.to_datetime(df4['date'], errors='coerce')
+            maxd = df4['date_dt'].max()
+            mind = df4['date_dt'].min()
+            print(f"  date range: {mind.date()} ~ {maxd.date()}")
+            print(f"  tail(3):")
+            print(df4.drop(columns=['date_dt']).tail(3).to_string())
+        except Exception as e:
+            print(f"  date parse err: {e}")
+            print(df4.tail(3).to_string())
+    time.sleep(0.3)
+
+# ============ 6. 近 5 年退市股数量估计(小抽样,不扫全量) ============
+section("近 5 年退市股数量估计(基于 query_all_stock 差集 + 候选 basic)")
+if len(df_all) > 0 and len(df_old) > 0:
+    print(f"2021-01-04 全市场: {len(df_old)} 只")
+    print(f"2026-07-18 全市场: {len(df_all)} 只")
+    print(f"2021 在 2026 不在的差集(候选期间退市/暂停/更名): {len(candidate_delisted)} 只(实际 {len(set(df_old['code']) - set(df_all['code']))} 只)")
+    print(f"注:这差集是『上限』——含真退市 + 更名/合并/暂停 + 历史数据缺日等。精确退市数要逐只 query_stock_basic 看 status=0 + outDate。")
+
+# ============ 7. logout ============
+section("LOGOUT")
+bs.logout()
+print("DONE_PROBE")

scripts/data_platform/_archive/legacy/probe_delisted_ext.py

@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+"""P0 Task3 补充探针:早期退市股 K 线完整性 + 全市场 status=0 数量抽样估计"""
+import sys, os, io
+if sys.platform == "win32":
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace", write_through=True)
+for k in ("http_proxy","https_proxy","all_proxy","HTTP_PROXY","HTTPS_PROXY","ALL_PROXY"):
+    os.environ.pop(k, None)
+
+import baostock as bs
+import pandas as pd
+import time
+
+lg = bs.login()
+print(f"login: {lg.error_code} {lg.error_msg}")
+if lg.error_code != '0':
+    print("ABORT"); sys.exit(1)
+
+# === 1. 早期退市股用更早 start_date 验证 K 线完整性 ===
+print("\n=== A. 早期退市股 K 线(start_date=2010-01-01) ===")
+fields = "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST"
+early_delisted = [
+    ("sh.600432", "2018-07-13"),
+    ("sz.000033", "2017-07-07"),
+    ("sh.600632", "2004-11-18"),
+]
+for code, outdate in early_delisted:
+    rs = bs.query_history_k_data_plus(
+        code, fields,
+        start_date='2010-01-01', end_date='2026-07-18',
+        frequency="d", adjustflag="3"
+    )
+    k = []
+    while (rs.error_code == '0') & rs.next():
+        k.append(rs.get_row_data())
+    df = pd.DataFrame(k, columns=rs.fields) if k else pd.DataFrame(columns=rs.fields)
+    if len(df) > 0:
+        df['date_dt'] = pd.to_datetime(df['date'], errors='coerce')
+        mind = df['date_dt'].min(); maxd = df['date_dt'].max()
+        print(f"  {code} outDate={outdate}: rows={len(df)} range={mind.date()}~{maxd.date()} | maxdate==outDate? {str(maxd.date())==outdate}")
+        print(f"    tail(2):")
+        print(df.drop(columns=['date_dt']).tail(2).to_string())
+    else:
+        print(f"  {code} outDate={outdate}: rows=0 err={rs.error_msg}")
+    time.sleep(0.3)
+
+# === 2. 全市场 status=0 退市股数量抽样估计 ===
+# 从 2021-01-04 全市场 code 列表 + 扩展号段扫描 status=0
+print("\n=== B. 全市场退市股数量估计 ===")
+# 先用 query_all_stock 拿基准列表(2021-01-04)
+rs0 = bs.query_all_stock(day="2021-01-04")
+base_codes = []
+while (rs0.error_code == '0') & rs0.next():
+    base_codes.append(rs0.get_row_data()[0])
+print(f"  2021-01-04 all_stock code 数: {len(base_codes)}")
+
+# 对这些 code 全部查 basic,统计 status=0 + outDate 分布
+# 但 4687 只全扫会耗费 ~4687 query,Mac 单登录串行 ~30 分钟
+# 改为抽样:每 10 只取 1 只,共约 470 只
+sample_codes = base_codes[::10]
+print(f"  抽样步长=10, 抽样数: {len(sample_codes)}")
+
+status0_cnt = 0
+status0_samples = []
+ipo_cnt = 0
+for i, code in enumerate(sample_codes):
+    rs2 = bs.query_stock_basic(code=code)
+    b = []
+    while (rs2.error_code == '0') & rs2.next():
+        b.append(rs2.get_row_data())
+    if b:
+        df2 = pd.DataFrame(b, columns=rs2.fields)
+        rec = df2.iloc[0].to_dict()
+        if str(rec.get('status','')) == '0':
+            status0_cnt += 1
+            status0_samples.append(rec)
+        elif str(rec.get('status','')) == '1':
+            ipo_cnt += 1
+    if (i+1) % 50 == 0:
+        print(f"    progress {i+1}/{len(sample_codes)} | status0={status0_cnt} status1={ipo_cnt}", flush=True)
+    time.sleep(0.05)  # 50ms 间隔 = 20 qps,温柔
+
+print(f"\n  抽样结果: {len(sample_codes)} 只中 status=0 {status0_cnt} 只, status=1 {ipo_cnt} 只")
+print(f"  退市率(抽样): {status0_cnt/len(sample_codes)*100:.2f}%")
+print(f"  外推全市场(基于 2021-01-04 的 {len(base_codes)} 只): {int(status0_cnt/len(sample_codes)*len(base_codes))} 只 status=0")
+print(f"  注:2021-01-04 当日已退市的不会出现在列表里,所以这是『2021-01-04 还在交易,但之后退市』的估计")
+print(f"  status=0 样本前 10:")
+for rec in status0_samples[:10]:
+    print(f"    {rec.get('code')} {rec.get('code_name')} ipoDate={rec.get('ipoDate')} outDate={rec.get('outDate')}")
+
+bs.logout()
+print("DONE_PROBE_EXT")

scripts/data_platform/_archive/legacy/probe_early_delisted_kline.py

@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+"""最小探针:早期退市股 K 线完整性(3 query,瞬时)"""
+import sys, os, io
+if sys.platform == "win32":
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace", write_through=True)
+for k in ("http_proxy","https_proxy","all_proxy","HTTP_PROXY","HTTPS_PROXY","ALL_PROXY"):
+    os.environ.pop(k, None)
+
+import baostock as bs
+import pandas as pd
+
+lg = bs.login()
+print(f"login: {lg.error_code} {lg.error_msg}", flush=True)
+if lg.error_code != '0':
+    sys.exit(1)
+
+fields = "date,code,open,high,low,close,volume,amount,turn,pctChg,peTTM,pbMRQ,isST"
+early = [
+    ("sh.600432", "2018-07-13"),
+    ("sz.000033", "2017-07-07"),
+    ("sh.600632", "2004-11-18"),
+]
+for code, outdate in early:
+    rs = bs.query_history_k_data_plus(
+        code, fields,
+        start_date='2010-01-01', end_date='2026-07-18',
+        frequency="d", adjustflag="3"
+    )
+    k = []
+    while (rs.error_code == '0') & rs.next():
+        k.append(rs.get_row_data())
+    df = pd.DataFrame(k, columns=rs.fields) if k else pd.DataFrame(columns=rs.fields)
+    if len(df) > 0:
+        print(f"{code} outDate={outdate}: rows={len(df)} range={df['date'].min()}~{df['date'].max()}", flush=True)
+        print(f"  maxdate==outDate? {df['date'].max()==outdate} | tail:", flush=True)
+        print(df.tail(2).to_string(), flush=True)
+    else:
+        print(f"{code} outDate={outdate}: rows=0 err={rs.error_msg}", flush=True)
+
+bs.logout()
+print("DONE", flush=True)

scripts/data_platform/_archive/legacy/probe_etf.py

@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""ETF universe + 前复权日线探针(P0 Task2.1)。
+VPS 跑: C:\\Python310\\python.exe -X utf8 probe_etf.py
+"""
+import sys
+from xtquant import xtdata as xd
+
+
+def main():
+    etf = xd.get_stock_list_in_sector("沪深ETF") or []
+    fund = xd.get_stock_list_in_sector("沪深基金") or []
+    a = xd.get_stock_list_in_sector("沪深A股") or []
+    u = list(set(a + etf + fund))
+    print(f"A={len(a)} ETF={len(etf)} fund={len(fund)} union={len(u)}")
+    print(f"sample ETF: {etf[:5]}")
+    print(f"sample fund: {fund[:5]}")
+
+    # 抽样: 510300.SH(沪深300ETF) / 513050.SH(中概互联网ETF) / 159919.SZ(300ETF)
+    samples = ["510300.SH", "513050.SH", "159919.SZ"]
+    r = xd.get_market_data_ex([], samples, period="1d",
+                              start_time="20240101", end_time="20260721",
+                              dividend_type="front")
+    for sym in samples:
+        df = r.get(sym) if r else None
+        bars = 0 if df is None else len(df)
+        tail_close = None if df is None or not len(df) else float(df["close"].iloc[-1])
+        head_date = None if df is None or not len(df) else str(df.index[0])[:8]
+        tail_date = None if df is None or not len(df) else str(df.index[-1])[:8]
+        nan_close = None if df is None else bool(df["close"].isnull().any())
+        print(f"{sym}: bars={bars} date=[{head_date}~{tail_date}] tail_close={tail_close} nan_close={nan_close}")
+
+    sys.stdout.flush()
+
+
+if __name__ == "__main__":
+    main()

scripts/data_platform/_archive/legacy/probe_etf_v2.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""ETF 探针 v2: 先 download 再读,对比 dividend_type,确认 ETF 在 sector 中。"""
+import sys
+from xtquant import xtdata as xd
+
+
+def main():
+    etf = xd.get_stock_list_in_sector("沪深ETF") or []
+    fund = xd.get_stock_list_in_sector("沪深基金") or []
+    a = xd.get_stock_list_in_sector("沪深A股") or []
+    u = list(set(a + etf + fund))
+    print(f"A={len(a)} ETF={len(etf)} fund={len(fund)} union={len(u)}")
+
+    # 1) 检查目标 samples 是否在 universe
+    for sym in ("510300.SH", "513050.SH", "159919.SZ"):
+        print(f"  {sym} in A={sym in a} in ETF={sym in etf} in fund={sym in fund}")
+
+    # 2) 重叠分析: ETF 与 fund 是否相同
+    overlap = set(etf) & set(fund)
+    only_etf = set(etf) - set(fund)
+    only_fund = set(fund) - set(etf)
+    print(f"overlap(ETF&fund)={len(overlap)} only_etf={len(only_etf)} only_fund={len(only_fund)}")
+    if only_etf:
+        print(f"  only_etf sample: {list(only_etf)[:5]}")
+    if only_fund:
+        print(f"  only_fund sample: {list(only_fund)[:5]}")
+
+    # 3) 先 download 再读
+    samples = ["510300.SH", "513050.SH", "159919.SZ"]
+    print(f"\n=== download_history_data(1d, 20240101~20260721) ===")
+    for sym in samples:
+        try:
+            n = xd.download_history_data(sym, "1d", "20240101", "20260721")
+            print(f"  {sym} download returned: {n}")
+        except Exception as e:
+            print(f"  {sym} download err: {e}")
+
+    # 4) 读两种 dividend_type
+    for dt in ("front", "none"):
+        print(f"\n=== get_market_data_ex dividend_type={dt} ===")
+        r = xd.get_market_data_ex([], samples, period="1d",
+                                  start_time="20240101", end_time="20260721",
+                                  dividend_type=dt)
+        for sym in samples:
+            df = r.get(sym) if r else None
+            bars = 0 if df is None else len(df)
+            tail_close = None if df is None or not len(df) else float(df["close"].iloc[-1])
+            head_date = None if df is None or not len(df) else str(df.index[0])[:8]
+            tail_date = None if df is None or not len(df) else str(df.index[-1])[:8]
+            print(f"  {sym}: bars={bars} date=[{head_date}~{tail_date}] tail_close={tail_close}")
+
+    sys.stdout.flush()
+
+
+if __name__ == "__main__":
+    main()

scripts/data_platform/_archive/legacy/probe_fundamentals_panel.py

@@ -0,0 +1,62 @@
+"""Smoke + timing: get_fundamentals_df fields= short-circuit + ThreadPool.
+
+ASCII-only (VPS GBK console safe). Run on VPS:
+  C:\\Python310\\python.exe -X utf8 probe_fundamentals_panel.py
+"""
+import os
+import sys
+import time
+
+os.environ.pop("http_proxy", None)
+os.environ.pop("https_proxy", None)
+os.environ.pop("all_proxy", None)
+
+sys.path.insert(0, r"C:\sanguo_vnpy_v2")
+
+from sanguo_portfolio.providers.local_unified_provider import LocalUnifiedProvider
+
+DB = r"C:\sanguo_vnpy_v2\data\quant_trading.db"
+DATA = r"C:\sanguo_vnpy_v2\data"
+
+p = LocalUnifiedProvider({"db_path": DB, "data_dir": DATA})
+
+# candidate pool: pull a few hundred codes from constituent_unified (000985 = full mkt)
+try:
+    codes = p.get_index_stocks("000985", "2024-06-03")
+except Exception as exc:
+    print("get_index_stocks failed:", exc)
+    codes = []
+codes = codes[:300] if codes else []
+jq = [c if "." in c else c + ".XSHE" for c in codes]
+print("pool size:", len(jq))
+if not jq:
+    sys.exit(0)
+
+date = "2024-06-03"
+
+# warm caches once (first hit pays file open) to measure steady-ish state? No -
+# measure COLD first-rebalance (the real pain): fields=None full read.
+t0 = time.time()
+df_none = p.get_fundamentals_df(jq, date=date)
+t_none = time.time() - t0
+
+# fresh provider to drop per-instance caches, measure fields= short-circuit cold
+p2 = LocalUnifiedProvider({"db_path": DB, "data_dir": DATA})
+t0 = time.time()
+df_fld = p2.get_fundamentals_df(jq, date=date, fields=["market_cap", "eps"])
+t_fld = time.time() - t0
+
+print("fields=None   : %6.2fs  rows=%d cols=%d" % (t_none, len(df_none), len(df_none.columns)))
+print("fields=[mkt,ep]: %6.2fs  rows=%d cols=%d" % (t_fld, len(df_fld), len(df_fld.columns)))
+if t_fld > 0:
+    print("speedup       : %.1fx" % (t_none / t_fld))
+
+# correctness: market_cap + eps match between the two
+import pandas as pd
+common = [c for c in df_fld.columns if c in df_none.columns]
+for col in ("market_cap", "eps"):
+    a = df_none[col].reindex(df_fld.index)
+    b = df_fld[col]
+    mask = a.notna() & b.notna()
+    diff = (a[mask] - b[mask]).abs().max() if mask.any() else 0.0
+    print("match %-12s: max_diff=%.6g" % (col, diff))

scripts/data_platform/_archive/legacy/probe_limit_status.py

@@ -0,0 +1,54 @@
+"""Smoke: get_limit_status_batch on real dbbardata (window query + detection).
+
+ASCII-only (VPS GBK console safe). Run on VPS:
+  C:\\Python310\\python.exe -X utf8 probe_limit_status.py
+"""
+import collections
+import os
+import sys
+
+for k in ("http_proxy", "https_proxy", "all_proxy"):
+    os.environ.pop(k, None)
+
+sys.path.insert(0, r"C:\sanguo_vnpy_v2")
+from sanguo_portfolio.providers.local_unified_provider import LocalUnifiedProvider
+
+DB = r"C:\sanguo_vnpy_v2\data\quant_trading.db"
+DATA = r"C:\sanguo_vnpy_v2\data"
+p = LocalUnifiedProvider({"db_path": DB, "data_dir": DATA})
+
+DATE = "2024-06-03"
+try:
+    codes = p.get_index_stocks("000985", DATE)
+except Exception as exc:
+    print("get_index_stocks failed:", exc)
+    codes = []
+jq = [c if "." in c else c + ".XSHE" for c in codes[:800]]
+print("pool:", len(jq), "date:", DATE)
+
+out = p.get_limit_status_batch(jq, DATE)
+cnt = collections.Counter()
+examples = {"up": [], "down": [], "paused": []}
+for k, v in (out or {}).items():
+    if v is None:
+        cnt["none"] += 1
+        continue
+    if v["is_limit_up"]:
+        cnt["up"] += 1
+        if len(examples["up"]) < 5:
+            examples["up"].append(k)
+    elif v["is_limit_down"]:
+        cnt["down"] += 1
+        if len(examples["down"]) < 5:
+            examples["down"].append(k)
+    if v["is_paused"]:
+        cnt["paused"] += 1
+        if len(examples["paused"]) < 5:
+            examples["paused"].append(k)
+    if not (v["is_limit_up"] or v["is_limit_down"] or v["is_paused"]):
+        cnt["normal"] += 1
+
+print("counts:", dict(cnt))
+print("limit_up examples:", examples["up"])
+print("limit_down examples:", examples["down"])
+print("paused examples:", examples["paused"])

scripts/data_platform/_archive/legacy/probe_unified_aw.py

@@ -0,0 +1,134 @@
+# -*- coding: utf-8 -*-
+"""UnifiedProvider + all_weather 数据链路诊断探针(VPS 跑, 快速版)。
+
+每步带时间戳 + flush, 超时也能看卡哪。慢步骤降样本。
+定位: equity 重复日 / B_mean=0 / 数据缺失。
+"""
+import sys
+import os
+import sqlite3
+import time
+from collections import Counter
+
+try:
+    sys.stdout.reconfigure(line_buffering=True)
+except Exception:
+    pass
+
+t0 = time.time()
+
+
+def step(name):
+    print(f"\n=== {name}  [+{time.time()-t0:.1f}s]", flush=True)
+
+
+def line(k, v):
+    print(f"[{k}] {v}", flush=True)
+
+
+VPS_ROOT = r"C:\sanguo_vnpy_v2"
+DB = os.path.join(VPS_ROOT, "data", "quant_trading.db")
+
+os.environ.setdefault("DEFAULT_DATA_PROVIDER", "jqdata")
+from unittest.mock import MagicMock
+if "jqdatasdk" not in sys.modules:
+    _m = MagicMock()
+    _m.utils.assert_auth = lambda f: f
+    sys.modules["jqdatasdk"] = _m
+
+sys.path.insert(0, VPS_ROOT)
+
+step("STEP0 环境")
+line("python", sys.version.split()[0])
+line("PKG", os.path.isdir(os.path.join(VPS_ROOT, "sanguo_portfolio")))
+line("DB", os.path.exists(DB))
+conn = sqlite3.connect(DB)
+tabs = [r[0] for r in conn.execute("SELECT name FROM sqlite_master WHERE type='table'")]
+line("tables", tabs)
+
+step("STEP0.5 混合 datetime 检测(单只抽样, 不全表 COUNT)")
+# 单只 600519 抽样看格式(走索引, 快)
+sample = conn.execute(
+    "SELECT datetime FROM dbbardata WHERE symbol='600519' AND exchange='SSE' "
+    "AND interval='d' ORDER BY datetime DESC LIMIT 5"
+).fetchall()
+line("600519 最近5条 datetime", [r[0] for r in sample])
+# DISTINCT 对比(单只, 索引内)
+d_raw = conn.execute(
+    "SELECT COUNT(DISTINCT datetime) FROM dbbardata "
+    "WHERE symbol='600519' AND exchange='SSE' AND interval='d'"
+).fetchone()[0]
+d_sub = conn.execute(
+    "SELECT COUNT(DISTINCT substr(datetime,1,10)) FROM dbbardata "
+    "WHERE symbol='600519' AND exchange='SSE' AND interval='d'"
+).fetchone()[0]
+line("DISTINCT datetime(原始)", d_raw)
+line("DISTINCT substr(datetime,1,10)(按日)", d_sub)
+line("重复日数(原始-按日)", d_raw - d_sub)
+
+step("STEP1 get_trade_days 重复日期(equity_curve 重复 bug 根因)")
+from sanguo_portfolio.providers import LocalUnifiedProvider
+p = LocalUnifiedProvider({})
+days = p.get_trade_days(start_date="2024-01-02", end_date="2024-03-31")
+strs = [str(d)[:10] for d in days]
+line("trade_days total", len(days))
+line("unique dates", len(set(strs)))
+dup = [d for d, c in Counter(strs).items() if c > 1]
+line("DUP dates count", len(dup))
+line("DUP sample", dup[:5])
+
+step("STEP2 成分股(constituent_unified 覆盖)")
+for idx in ["000300", "399101", "399001", "000852"]:
+    try:
+        s = p.get_index_stocks(idx)
+        line(f"index_stocks {idx}", len(s))
+    except Exception as e:
+        line(f"index_stocks {idx} ERR", repr(e))
+
+step("STEP3 fundamentals 600519(单股, 关键字段)")
+fdf = p.get_fundamentals_df(["600519.XSHG"], date="2024-03-29")
+cols_chk = [
+    "code", "market_cap", "circulating_market_cap", "pe_ratio", "pb_ratio",
+    "ps_ratio", "pcf_ratio", "eps", "roe", "roa", "gross_profit_margin",
+    "net_profit_margin", "inc_revenue_year_on_year", "roic",
+]
+for c in cols_chk:
+    if c in fdf.columns:
+        line(f"  {c}", fdf[c].iloc[0])
+    else:
+        line(f"  {c}", "MISSING_COL")
+
+step("STEP4 _trend_mean 小样本复算(hs300 前40, B_mean=0 根因)")
+import numpy as np
+hs300 = p.get_index_stocks("000300")
+line("hs300 size", len(hs300))
+# 只取前 40 只做 fundamentals(提速), top20 by circ_mktcap
+sample40 = hs300[:40]
+fdf2 = p.get_fundamentals_df(sample40, date="2024-03-29")
+line("fdf2 shape", fdf2.shape)
+if "circulating_market_cap" in fdf2.columns:
+    line("circ_mktcap nonNaN", int(fdf2["circulating_market_cap"].notna().sum()))
+fdf2s = fdf2.sort_values("circulating_market_cap", ascending=False, na_position="last")
+blst = list(fdf2s.index)[:20]
+line("blst(20)", blst)
+df = p.get_price(blst, end_date="2024-03-29", frequency="1d", fields=["close"], count=10, panel=False)
+line("trend get_price isNone", df is None)
+if df is not None:
+    line("trend get_price shape", df.shape)
+    line("trend cols", list(df.columns))
+    line("time dtype", df["time"].dtype if "time" in df.columns else "NO_TIME")
+    print(df.head(3).to_string(), flush=True)
+    try:
+        pivot = df.pivot(index="time", columns="code", values="close")
+        line("pivot shape", pivot.shape)
+        if len(pivot) >= 2:
+            change = (pivot.iloc[-1] / pivot.iloc[0] - 1) * 100
+            arr = np.nan_to_num(change.to_numpy())
+            line("B_mean manual", float(np.mean(arr)))
+            line("change nonZero count", int((arr != 0).sum()))
+        else:
+            line("pivot rows<2", len(pivot))
+    except Exception as e:
+        line("pivot ERR", repr(e))
+
+step("DONE")

scripts/data_platform/_archive/legacy/probe_unified_schema.py

@@ -0,0 +1,130 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""probe_unified_schema.py — 探查方案A 新权威数据层 schema (LocalUnifiedProvider 实现依赖)。
+
+输出 constituent_unified / dbbardata('d') / bs_adjust_factor / valuation_baostock 的
+列结构 + 样本 + 覆盖, 供 spec §6 使用层 provider 实现参考。
+"""
+import sqlite3
+from pathlib import Path
+
+import pandas as pd
+
+BASE = Path(r"C:\sanguo_vnpy_v2")
+DB = BASE / "data" / "quant_trading.db"
+VAL_DIR = BASE / "data" / "valuation_baostock"
+
+
+def section(title):
+    print(f"\n===== {title} =====")
+
+
+# 1. constituent_unified
+section("constituent_unified schema")
+c = sqlite3.connect(str(DB))
+try:
+    info = c.execute("PRAGMA table_info(constituent_unified)").fetchall()
+    print("columns:", [(r[1], r[2]) for r in info])
+    print("count/index:",
+          c.execute("SELECT COUNT(*), COUNT(DISTINCT index_code) FROM constituent_unified").fetchone())
+    print("per-index (total / in_current / was_removed):")
+    for row in c.execute("SELECT index_code, COUNT(*), SUM(in_current), SUM(was_removed) "
+                         "FROM constituent_unified GROUP BY index_code ORDER BY index_code"):
+        print("  ", row)
+    print("sample 300:")
+    for row in c.execute("SELECT * FROM constituent_unified WHERE index_code LIKE '%300%' LIMIT 3"):
+        print("  ", row)
+finally:
+    c.close()
+
+# 2. dbbardata('d') raw 个股 + ETF
+section("dbbardata('d') sample")
+c = sqlite3.connect(str(DB))
+try:
+    print("columns:", [r[1] for r in c.execute("PRAGMA table_info(dbbardata)").fetchall()])
+    for sym, lbl in [("600519", "个股在市"), ("000005", "退市"), ("510300", "ETF")]:
+        r = c.execute("SELECT COUNT(*), MIN(datetime), MAX(datetime) FROM dbbardata "
+                      "WHERE symbol=? AND interval='d'", (sym,)).fetchone()
+        print(f"  {sym}({lbl}): {r}")
+    print("sample 600519 last 3:")
+    for row in c.execute("SELECT symbol,exchange,datetime,volume,turnover,open_price,close_price "
+                         "FROM dbbardata WHERE symbol='600519' AND interval='d' "
+                         "ORDER BY datetime DESC LIMIT 3"):
+        print("  ", row)
+finally:
+    c.close()
+
+# 3. bs_adjust_factor
+section("bs_adjust_factor schema")
+c = sqlite3.connect(str(DB))
+try:
+    tabs = [r[0] for r in c.execute("SELECT name FROM sqlite_master WHERE type='table' "
+                                    "AND name LIKE '%adjust%'").fetchall()]
+    print("adjust tables:", tabs)
+    if "bs_adjust_factor" in tabs:
+        print("columns:", [r[1] for r in c.execute("PRAGMA table_info(bs_adjust_factor)").fetchall()])
+        print("count:", c.execute("SELECT COUNT(*) FROM bs_adjust_factor").fetchone())
+        print("sample 600519:")
+        for row in c.execute("SELECT * FROM bs_adjust_factor WHERE code LIKE '%600519%' LIMIT 3"):
+            print("  ", row)
+finally:
+    c.close()
+
+# 4. valuation_baostock parquet
+section("valuation_baostock parquet")
+print("years:", sorted(p.name for p in VAL_DIR.glob("*.parquet")) if VAL_DIR.exists() else "DIR MISSING")
+p2026 = VAL_DIR / "2026.parquet"
+if p2026.exists():
+    df = pd.read_parquet(p2026)
+    print("columns:", list(df.columns))
+    print("shape:", df.shape)
+    print("sample 600519:")
+    sub = df[df["symbol"].astype(str).str.contains("600519")] if "symbol" in df.columns else df.head(0)
+    print(sub.head(3).to_dict("records"))
+
+# 5. static akshare 三表 + valuation (market_cap/total_share 来源 + 文件格式)
+from collections import Counter
+section("static akshare (格式 + market_cap/total_share 来源)")
+for sub in ["valuation", "balance", "income"]:
+    d = BASE / "data" / "static" / sub
+    if not d.exists():
+        print(f"{sub}: DIR MISSING")
+        continue
+    allf = list(d.iterdir())
+    exts = Counter(p.suffix for p in allf)
+    print(f"{sub}: {len(allf)} files, ext分布={dict(exts)}")
+    matches = sorted([p for p in allf if "600519" in p.name])
+    if not matches:
+        print("  600519 无文件")
+        continue
+    f0 = matches[0]
+    with open(f0, "rb") as fh:
+        head = fh.read(8)
+    print(f"  600519 file={f0.name} magic={head[:4]!r}")
+    if head[:4] == b"PAR1":
+        df = pd.read_parquet(f0)
+        print(f"  parquet cols({len(df.columns)}):", list(df.columns)[:20])
+        if sub == "valuation":
+            hit = [c for c in df.columns if any(k in str(c) for k in ("市值", "股本"))]
+            print("  市值/股本列:", hit, "sample:", df.head(1).to_dict("records"))
+        if sub == "balance":
+            hit = [c for c in df.columns if any(k in str(c) for k in ("TOTAL_SHARES", "TOTAL_SHARE", "总股本", "实收资本"))]
+            print("  股本相关列:", hit)
+    else:
+        print(f"  非 parquet (magic={head[:4]!r})")
+
+# 6. 复权因子构造验证 (600519 foreAdjustFactor 语义)
+section("复权因子 foreAdjustFactor 语义验证")
+c = sqlite3.connect(str(DB))
+try:
+    rows = c.execute("SELECT dividOperateDate, foreAdjustFactor FROM bs_adjust_factor "
+                     "WHERE code='sh.600519' ORDER BY dividOperateDate").fetchall()
+    print("600519 除权事件数:", len(rows))
+    print("前3:", rows[:3])
+    print("后3:", rows[-3:])
+    print("语义推断: foreAdjustFactor 递增=累计前复权因子; 最新事件后=1.0; "
+          "qfq[t]=raw[t]*factor[t]")
+finally:
+    c.close()
+
+print("\nPROBE DONE")

scripts/data_platform/_archive/legacy/realtime.py

@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+"""实时行情三源降级 - 新浪→东财→腾讯"""
+import urllib.request
+import json
+import re
+import logging
+from datetime import datetime
+from typing import Optional
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
+logger = logging.getLogger(__name__)
+
+HEADERS_SINA = {
+    "User-Agent": "Mozilla/5.0", "Referer": "https://finance.sina.com.cn",
+    "Accept-Language": "zh-CN,zh;q=0.9"
+}
+HEADERS_EM = {"Referer": "https://www.eastmoney.com"}
+FETCHED_AT = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def _fetch_url(url: str, headers: dict = None, timeout: int = 10) -> str:
+    req = urllib.request.Request(url, headers=headers or {})
+    try:
+        with urllib.request.urlopen(req, timeout=timeout) as r:
+            charset = "gbk" if "sina" in url or "sinajs" in url else "utf-8"
+            return r.read().decode(charset, errors="replace")
+    except Exception:
+        return ""
+
+
+def _parse_sina(raw: str, symbol: str) -> Optional[dict]:
+    m = re.search(r'"([^"]*)"', raw)
+    if not m:
+        return None
+    parts = m.group(1).split(",")
+    if len(parts) < 32:
+        return None
+    try:
+        prev_close = float(parts[2]) if parts[2] else 0
+        current = float(parts[3]) if parts[3] else 0
+        return {
+            "symbol": symbol, "name": parts[0],
+            "current": round(current, 2), "prev_close": round(prev_close, 2),
+            "open": round(float(parts[1]), 2) if parts[1] else 0,
+            "high": round(float(parts[4]), 2) if parts[4] else 0,
+            "low": round(float(parts[5]), 2) if parts[5] else 0,
+            "volume": int(float(parts[8])) if parts[8] else 0,
+            "amount": round(float(parts[9]), 2) if parts[9] else 0,
+            "timestamp": f"{parts[30]} {parts[31]}" if len(parts) > 31 else "",
+            "source": "sina", "fetched_at": FETCHED_AT,
+        }
+    except (ValueError, IndexError):
+        return None
+
+
+def _parse_tencent(raw: str, symbol: str) -> Optional[dict]:
+    m = re.search(r'"([^"]*)"', raw)
+    if not m:
+        return None
+    fields = m.group(1).split("~")
+    if len(fields) < 35:
+        return None
+    try:
+        current = float(fields[3])
+        prev_close = float(fields[4])
+        if current <= 0:
+            return None
+        return {
+            "symbol": symbol, "name": fields[1],
+            "current": round(current, 2), "prev_close": round(prev_close, 2),
+            "open": round(float(fields[5]), 2),
+            "high": round(float(fields[33]), 2) if fields[33] else 0,
+            "low": round(float(fields[34]), 2) if fields[34] else 0,
+            "volume": int(float(fields[6])) if fields[6] else 0,
+            "amount": round(float(fields[37]) * 10000, 2) if fields[37] else 0,
+            "timestamp": fields[30][:8] + " " + fields[30][8:] if fields[30] else "",
+            "source": "tencent", "fetched_at": FETCHED_AT,
+        }
+    except (ValueError, IndexError):
+        return None
+
+
+def _parse_eastmoney(raw: str, symbol: str) -> Optional[dict]:
+    try:
+        obj = json.loads(raw)
+        d = obj.get("data", {}) or {}
+        if not d.get("f43"):
+            return None
+        return {
+            "symbol": symbol, "name": d.get("f58", ""),
+            "current": round(d["f43"] / 100, 2),
+            "prev_close": round(d["f60"] / 100, 2),
+            "open": round(d["f46"] / 100, 2),
+            "high": round(d["f44"] / 100, 2),
+            "low": round(d["f45"] / 100, 2),
+            "volume": d.get("f47", 0),
+            "amount": round(d.get("f48", 0) / 1e8, 2),
+            "timestamp": "",
+            "source": "eastmoney", "fetched_at": FETCHED_AT,
+        }
+    except Exception:
+        return None
+
+
+def _get_prefix(code: str) -> str:
+    code = re.sub(r"[^0-9]", "", code)
+    if code.startswith(("60", "68", "51", "58", "11")):
+        return "sh", code
+    return "sz", code
+
+
+def _em_secid(code: str) -> str:
+    code = re.sub(r"[^0-9]", "", code)
+    m = 1 if code.startswith(("60", "68")) else 0
+    return f"{m}.{code}"
+
+
+def get_realtime_quote(code: str) -> dict:
+    """获取实时行情，三源降级：新浪→东财→腾讯"""
+    prefix, clean = _get_prefix(code)
+    symbol = f"{prefix}{clean}"
+
+    # 1. 新浪
+    raw = _fetch_url(f"http://hq.sinajs.cn/list={symbol}", HEADERS_SINA)
+    if raw:
+        data = _parse_sina(raw, symbol)
+        if data and data["current"] > 0:
+            logger.info(f"新浪成功: {symbol} = {data['current']}")
+            return data
+
+    # 2. 东财
+    secid = _em_secid(code)
+    raw = _fetch_url(
+        f"http://push2.eastmoney.com/api/qt/stock/get?secid={secid}"
+        f"&fields=f43,f44,f45,f46,f47,f48,f57,f58,f60,f169,f170",
+        HEADERS_EM)
+    if raw:
+        data = _parse_eastmoney(raw, symbol)
+        if data and data["current"] > 0:
+            logger.info(f"东财成功: {symbol} = {data['current']}")
+            return data
+
+    # 3. 腾讯
+    raw = _fetch_url(f"http://qt.gtimg.cn/q={symbol}")
+    if raw:
+        data = _parse_tencent(raw, symbol)
+        if data and data["current"] > 0:
+            logger.info(f"腾讯成功: {symbol} = {data['current']}")
+            return data
+
+    return {"error": f"所有数据源均无法获取 {code}", "symbol": symbol, "fetched_at": FETCHED_AT}
+
+
+if __name__ == "__main__":
+    import sys
+    code = sys.argv[1] if len(sys.argv) > 1 else "600519"
+    result = get_realtime_quote(code)
+    for k, v in result.items():
+        print(f"  {k}: {v}")

scripts/data_platform/_archive/legacy/resume_5yr_watcher.py

@@ -0,0 +1,68 @@
+"""5yr 15min 回填冷却后自动续跑 watcher。
+
+baostock 被限流/冷却时登录会卡死或失败。本 watcher 每 30min 探测一次 baostock，
+可登就启动 backfill_15min_baostock（marker-based resume 自动续跑缺口），断路器触发
+或登录失败就继续等。backfill 成功（exit 0，全部缺口填完）则退出。
+
+ detached 启动：
+   nohup ./venv311/bin/python3 scripts/data_platform/resume_5yr_watcher.py \
+     > /Users/chufeng/data_cache/stock/logs/daily_update/resume_watcher.log 2>&1 &!
+"""
+import os
+import subprocess
+import time
+
+ROOT = "/Users/chufeng/.openclaw/sanguo_projects/sanguo_vnpy_v2"
+PY = os.path.join(ROOT, "venv311/bin/python3")
+SCRIPT = os.path.join(ROOT, "scripts/data_platform/backfill_15min_baostock.py")
+PROBE_INTERVAL = 1800  # 30min
+
+# 探测脚本：SIGALRM 给 bs.login() 套 15s 闹钟，避免卡死
+_PROBE = r'''
+import signal
+def _h(*a): raise TimeoutError()
+signal.signal(signal.SIGALRM, _h)
+signal.alarm(15)
+try:
+    import baostock as bs
+    lg = bs.login()
+    ok = lg.error_code == "0"
+    try: bs.logout()
+    except Exception: pass
+    print("LOGIN_OK" if ok else "LOGIN_FAIL")
+except Exception as e:
+    print("LOGIN_ERR", type(e).__name__, e)
+finally:
+    signal.alarm(0)
+'''
+
+
+def baostock_up() -> bool:
+    try:
+        r = subprocess.run([PY, "-c", _PROBE], capture_output=True, text=True, timeout=30)
+        return "LOGIN_OK" in r.stdout
+    except Exception:
+        return False
+
+
+def main() -> None:
+    round_ = 0
+    while True:
+        round_ += 1
+        if baostock_up():
+            print(f"[round {round_}] baostock 可登，启动回填（marker-resume 续跑缺口）", flush=True)
+            env = dict(os.environ)
+            env["STOCK_ROOT"] = "/Users/chufeng/data_cache/stock"
+            env["BS_START_DATE"] = "20210101"
+            rc = subprocess.call([PY, SCRIPT], env=env, cwd=ROOT)
+            if rc == 0:
+                print(f"[round {round_}] 回填完成（exit 0），退出 watcher", flush=True)
+                return
+            print(f"[round {round_}] 回填 exit={rc}（断路器/失败），等 {PROBE_INTERVAL}s 再试", flush=True)
+        else:
+            print(f"[round {round_}] baostock 不可登（冷却中），等 {PROBE_INTERVAL}s", flush=True)
+        time.sleep(PROBE_INTERVAL)
+
+
+if __name__ == "__main__":
+    main()

scripts/data_platform/_archive/legacy/run_with_diag.py

@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+"""monkey-patch engine 关键方法加诊断, 跑回测看 ETF cancel 根因(不改源码)。"""
+import sys, os, logging
+os.environ.setdefault("DEFAULT_DATA_PROVIDER", "jqdata")
+from unittest.mock import MagicMock
+m = MagicMock(); m.utils.assert_auth = lambda f: f
+sys.modules.setdefault("jqdatasdk", m)
+logging.basicConfig(level=logging.WARNING, format="%(message)s")
+
+from bullet_trade.core import engine as eng
+
+_orig_calc = eng.BacktestEngine._calculate_order_amount
+def calc(self, order, cp):
+    r = _orig_calc(self, order, cp)
+    print(f"[ENG_DIAG] {order.security} cp={cp} amount={r} tgt_val={getattr(order,'_target_value',None)} is_tgt={getattr(order,'_is_target_value',None)} order_amt={getattr(order,'amount',None)}", flush=True)
+    return r
+eng.BacktestEngine._calculate_order_amount = calc
+
+_orig_bp = eng.BacktestEngine._resolve_base_exec_price
+def bp(self, security, current_dt, fq_mode):
+    r = _orig_bp(self, security, current_dt, fq_mode)
+    print(f"[ENG_DIAG_BP] {security} dt={current_dt} fq={fq_mode} -> {r}", flush=True)
+    return r
+eng.BacktestEngine._resolve_base_exec_price = bp
+
+sys.argv = ['runner', '--provider', 'unified', '--max-pool', '30', '--start', '2024-01-02', '--end', '2024-01-31', '--cash', '1000000']
+from sanguo_portfolio.runner_backtest import main
+main()

scripts/data_platform/_archive/legacy/test_baostock_daily_constituent_sample.py

@@ -0,0 +1,215 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""小样本验证脚本: 测试日K线和成分股下载 (各 10 只/1 指数)
+
+验证项:
+  1. 日K线 10 只: parquet 生成 + 字段齐全 + 行数合理 + 日期范围对
+  2. 成分股 1 指数 1 年: parquet 生成 + 成分股数量合理
+  3. query 计数器工作
+
+用法:
+  python test_baostock_daily_constituent_sample.py
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+# 颜色输出
+GREEN = "\033[92m"
+RED = "\033[91m"
+RESET = "\033[0m"
+
+
+def run_test(name: str, cmd: list, expected_checks: dict):
+    """运行测试脚本并验证结果"""
+    print(f"\n{'=' * 60}")
+    print(f"测试: {name}")
+    print(f"命令: {' '.join(cmd)}")
+    print(f"{'=' * 60}")
+
+    result = subprocess.run(cmd, capture_output=True, text=True)
+
+    if result.returncode not in [0, 2]:  # 0=完成, 2=断路器(可接受)
+        print(f"{RED}❌ 失败: returncode={result.returncode}{RESET}")
+        print("STDOUT:", result.stdout[-500:] if len(result.stdout) > 500 else result.stdout)
+        print("STDERR:", result.stderr[-500:] if len(result.stderr) > 500 else result.stderr)
+        return False
+
+    print(f"{GREEN}✅ 完成: returncode={result.returncode}{RESET}")
+
+    # 检查输出
+    output = result.stdout + result.stderr
+
+    for check_key, check_pattern in expected_checks.items():
+        if check_pattern in output:
+            print(f"{GREEN}✅ {check_key}: 找到'{check_pattern}'{RESET}")
+        else:
+            print(f"{RED}❌ {check_key}: 未找到'{check_pattern}'{RESET}")
+            return False
+
+    return True
+
+
+def check_parquet_files(out_dir: Path, pattern: str, min_count: int):
+    """检查 parquet 文件生成"""
+    print(f"\n检查 parquet 文件: {out_dir}/{pattern}")
+    parquet_files = list(out_dir.rglob(pattern))
+
+    if len(parquet_files) >= min_count:
+        print(f"{GREEN}✅ 找到 {len(parquet_files)} 个 parquet 文件 (≥{min_count}){RESET}")
+        # 显示前几个文件
+        for f in parquet_files[:3]:
+            print(f"  - {f.name}")
+        return True
+    else:
+        print(f"{RED}❌ 只找到 {len(parquet_files)} 个 parquet 文件 (<{min_count}){RESET}")
+        return False
+
+
+def check_parquet_fields(file_path: Path, required_fields: list):
+    """检查 parquet 字段齐全"""
+    import pandas as pd
+
+    print(f"\n检查字段: {file_path}")
+    try:
+        df = pd.read_parquet(file_path)
+        missing_fields = [f for f in required_fields if f not in df.columns]
+
+        if missing_fields:
+            print(f"{RED}❌ 缺失字段: {missing_fields}{RESET}")
+            print(f"实际字段: {list(df.columns)}")
+            return False
+        else:
+            print(f"{GREEN}✅ 字段齐全: {len(required_fields)} 个必选字段都在{RESET}")
+            print(f"字段列表: {list(df.columns)}")
+            return True
+    except Exception as e:
+        print(f"{RED}❌ 读 parquet 失败: {e}{RESET}")
+        return False
+
+
+def check_parquet_rowcount(file_path: Path, min_rows: int, max_rows: int):
+    """检查 parquet 行数合理"""
+    import pandas as pd
+
+    print(f"\n检查行数: {file_path}")
+    try:
+        df = pd.read_parquet(file_path)
+        row_count = len(df)
+
+        if min_rows <= row_count <= max_rows:
+            print(f"{GREEN}✅ 行数合理: {row_count} 行 (期望 {min_rows}-{max_rows}){RESET}")
+            return True
+        else:
+            print(f"{RED}❌ 行数异常: {row_count} 行 (期望 {min_rows}-{max_rows}){RESET}")
+            return False
+    except Exception as e:
+        print(f"{RED}❌ 读 parquet 失败: {e}{RESET}")
+        return False
+
+
+def main():
+    """主测试流程"""
+    print(f"\n{'=' * 60}")
+    print("BaoStock 脚本小样本验证")
+    print(f"{'=' * 60}")
+
+    tests_passed = 0
+    tests_failed = 0
+
+    # ======================= 测试 1: 日K线 10 只 =======================
+    print(f"\n{'#' * 60}")
+    print("# 测试 1: 日K线下载 (10 只股票)")
+    print(f"{'#' * 60}")
+
+    daily_cmd = [
+        sys.executable,
+        "scripts/data_platform/baostock_daily_fullmarket_download.py",
+        "--limit", "10",
+        "--start", "2020-01-01",
+        "--end", "2020-12-31"
+    ]
+
+    daily_checks = {
+        "登录成功": "baostock 登录成功",
+        "全市场A股": "全市场 A 股",
+        "处理10只": "limit=10 截断",
+        "query计数": "query 总计:",
+    }
+
+    if run_test("日K线下载", daily_cmd, daily_checks):
+        tests_passed += 1
+
+        # 检查 parquet 文件
+        daily_out_dir = Path("data/daily_baostock")
+        if check_parquet_files(daily_out_dir, "*.parquet", 10):
+            tests_passed += 1
+
+            # 检查第一个文件的字段和行数
+            first_parquet = list(daily_out_dir.glob("*.parquet"))[0]
+            daily_fields = "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,psTTM,pcfNcfTTM,pbMRQ,isST".split(",")
+
+            if check_parquet_fields(first_parquet, daily_fields):
+                tests_passed += 1
+
+                # 2020-01-01~2020-12-31 约 244 个交易日
+                if check_parquet_rowcount(first_parquet, 200, 300):
+                    tests_passed += 1
+                else:
+                    tests_failed += 1
+            else:
+                tests_failed += 1
+        else:
+            tests_failed += 1
+    else:
+        tests_failed += 1
+
+    # ======================= 测试 2: 成分股 1 指数 1 年 =======================
+    print(f"\n{'#' * 60}")
+    print("# 测试 2: 成分股下载 (1 指数 1 年)")
+    print(f"{'#' * 60}")
+
+    constituent_cmd = [
+        sys.executable,
+        "scripts/data_platform/baostock_constituent_download.py",
+        "--indices", "hs300",
+        "--start", "2020-01-01",
+        "--end", "2020-12-31"
+    ]
+
+    constituent_checks = {
+        "登录成功": "baostock 登录成功",
+        "快照日期": "快照日期列表:",
+        "每周一": "每周一快照",
+    }
+
+    if run_test("成分股下载", constituent_cmd, constituent_checks):
+        tests_passed += 1
+
+        # 检查 parquet 文件
+        constituent_out_dir = Path("data/constituent_baostock")
+        if check_parquet_files(constituent_out_dir, "hs300_*.parquet", 50):  # 2020年约52个周一
+            tests_passed += 1
+        else:
+            tests_failed += 1
+    else:
+        tests_failed += 1
+
+    # ======================= 测试结果汇总 =======================
+    print(f"\n{'=' * 60}")
+    print("测试结果汇总")
+    print(f"{'=' * 60}")
+    print(f"通过: {tests_passed}")
+    print(f"失败: {tests_failed}")
+
+    if tests_failed == 0:
+        print(f"\n{GREEN}✅ 所有测试通过!{RESET}\n")
+        return 0
+    else:
+        print(f"\n{RED}❌ 有 {tests_failed} 个测试失败{RESET}\n")
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/data_platform/_archive/legacy/test_mootdx_depth.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""mootdx 分钟K线历史深度测试 —— 周一开盘后跑(非交易时段通达信全服务器返空)
+
+目的:对比 miniQMT 模拟端 1m/5m/15m 统一只给 ~12 个月(2025-07-16 起),
+看通达信公共行情服务器能给多深的分钟历史。
+
+用法:
+  venv311/bin/python scripts/data_platform/test_mootdx_depth.py
+结果写 scripts/data_platform/_mootdx_depth_result.txt 并打印。
+
+注意:
+- 频率值表(mootdx 0.11.7 实测): 0=5m 1=15m 2=30m 3=60m 4=日 8=1分钟 9=日线
+- bars 返回【不复权】原始价;offset 硬上限 800,更深历史靠 start 分页
+- 非交易时段(收盘后/周末)通达信服务器 quotes+bars 全频率返空,连日线都不给
+"""
+import socket
+import sys
+import datetime
+from mootdx.quotes import Quotes
+
+_TDX_SERVERS = [
+    ('119.97.185.59', 7709), ('124.70.133.119', 7709), ('116.205.183.150', 7709),
+    ('123.60.73.44', 7709),  ('116.205.163.254', 7709), ('121.36.225.169', 7709),
+    ('123.60.70.228', 7709), ('124.71.9.153', 7709),    ('110.41.147.114', 7709),
+    ('124.71.187.122', 7709),
+]
+
+
+def _probe(ip, port, timeout=2.0):
+    try:
+        with socket.create_connection((ip, port), timeout=timeout):
+            return True
+    except Exception:
+        return False
+
+
+def _n(x):
+    """统一求长度, 规避空 DataFrame 的 bool 歧义坑"""
+    if x is None:
+        return 0
+    try:
+        return len(x)
+    except Exception:
+        return 0
+
+
+def find_server(symbol='600519'):
+    """遍历服务器, 返回第一个能返回日线 bars 的(交易日内才有)"""
+    for ip, port in _TDX_SERVERS:
+        if not _probe(ip, port):
+            continue
+        try:
+            c = Quotes.factory(market='std', server=(ip, port))
+            d = c.bars(symbol=symbol, frequency=9, offset=5)
+            if _n(d) > 0:
+                return ip, c
+        except Exception:
+            pass
+    return None, None
+
+
+def test_depth(c, symbol, freq, name, max_pages=200):
+    """start 分页翻到底, 找最早/最新 datetime. 200页: 1m≈20月/5m≈8年/15m≈24年"""
+    start = 0
+    total = 0
+    pages = 0
+    earliest = None
+    latest = None
+    while pages < max_pages:
+        try:
+            df = c.bars(symbol=symbol, frequency=freq, offset=800, start=start)
+        except Exception:
+            break
+        n = _n(df)
+        if n == 0:
+            break
+        total += n
+        try:
+            ft = str(df.iloc[0]['datetime'])
+            lt = str(df.iloc[-1]['datetime'])
+            if earliest is None or ft < earliest:
+                earliest = ft
+            if latest is None or lt > latest:
+                latest = lt
+        except Exception:
+            pass
+        if n < 800:
+            break
+        start += n
+        pages += 1
+    return name, total, earliest, latest, pages
+
+
+def main():
+    out = ['mootdx 深度测试 @ %s' % datetime.datetime.now()]
+    ip, c = find_server()
+    if c is None:
+        out.append('!!! 没有服务器返回日线数据 —— 非交易时段(周末/收盘后)通达信全服务器返空')
+        out.append('!!! 请周一 09:30 开盘后重跑此脚本')
+        msg = '\n'.join(out)
+        print(msg)
+        with open('_mootdx_depth_result.txt', 'w') as f:
+            f.write(msg)
+        sys.exit(1)
+
+    out.append('server: %s' % ip)
+    out.append('')
+    for sym in ['600519']:  # 茅台(2001上市, 老股, 测深度上限最佳)
+        out.append('=== %s (茅台) ===' % sym)
+        for fr, nm in [(8, '1分钟'), (0, '5分钟'), (1, '15分钟')]:
+            name, total, earliest, latest, pages = test_depth(c, sym, fr, nm)
+            out.append('  %-6s: %7d 根 | 最早=%s | 最新=%s | 翻%d页'
+                       % (name, total, earliest, latest, pages))
+    out.append('')
+    out.append('对比: miniQMT 模拟端 1m/5m/15m 统一 ~12 个月(2025-07-16 起)')
+    msg = '\n'.join(out)
+    print(msg)
+    with open('_mootdx_depth_result.txt', 'w') as f:
+        f.write(msg)
+
+
+if __name__ == '__main__':
+    main()