claude_dev
853d35197e
治幸存者偏差全集: 000985=5128 全市场(解锁策略02小市值全市场池) + 000928~000937 中证800十行业 24~196只(解锁策略03行业轮动)。复用+泛化 000852/932000 csindex 公告回溯流程, 不动硬编码路径。9 宽基无回归, was_removed 治偏差填齐, 全表 14402 行/20 指数。
251 lines
11 KiB
Python
251 lines
11 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""migrate_constituent.py — 单元3: 合并成份股 -> constituent_unified_staging (全集型, 幂等)。
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设计 (spec §14, 治幸存者偏差选股池):
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- baostock (``bs_index_constituent_old`` 988 时点全集, 方案A 后的权威历史源)
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-> 聚合成全集 (hs300/zz500/sz50 -> 000300/000905/000016),
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in_current=最后时点成份, was_removed=历史入选过但已踢出
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- 深证/国证 _union.parquet (399001/399006/399005/399330) -> 直入 (akshare cni)
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- 中证1000/2000 _snapshot.parquet (000852/932000) -> 当前 (akshare csindex)
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- 新浪 _sina.parquet -> 丢弃 (baostock 300/500/50 已权威)
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- code 统一 6 位无前缀 (sh.600000 / 600519.SH -> 600519)
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幂等(可重跑, 月度 schtask 安全):
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- ``DROP TABLE IF EXISTS constituent_unified_staging`` + ``CREATE TABLE ...`` 每次重建
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- baostock 源表读取**自动适配**:
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* 方案A 后正常只有 ``bs_index_constituent_old``(权威全集)
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* 若将来重建了 live ``bs_index_constituent``(新时点增量), UNION ALL 两者去重, 兼容两种状态
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环境变量:
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- ``SANGUO_DB``: quant_trading.db 路径, 默认 ``C:\\sanguo_vnpy_v2\\data\\quant_trading.db``(VPS)
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- ``HIST``(模块常量, 测试可 monkeypatch): 深证/中证 parquet 目录
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"""
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import glob
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import os
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import re
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import sqlite3
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import pandas as pd
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HIST = r"C:\sanguo_vnpy_v2\data\index_const_hist"
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STAGING = "constituent_unified_staging"
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BS_MAP = {"hs300": "000300", "zz500": "000905", "sz50": "000016"}
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def norm_code(code):
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s = re.sub(r"^(sh|sz|SH|SZ)\.?", "", str(code))
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s = re.sub(r"\.(SH|SZ|sh|sz)$", "", s)
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return s.zfill(6) if s.isdigit() and len(s) <= 6 else s
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def _read_baostock_constituent(c: sqlite3.Connection) -> pd.DataFrame:
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"""读 baostock 成份股历史, 自动适配方案A后(_old) / 未来(live)两种状态。
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- 只有 ``bs_index_constituent_old``: 读它(方案A 后常态)
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- 只有 ``bs_index_constituent``: 读它(未来重建 live 表)
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- 两者都在: UNION ALL 后 drop_duplicates(兼容过渡期)
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- 都没有: 返回空 DataFrame(不崩, 由上层决定是否报错)
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返回字段: ``updateDate, index_code, code, code_name``。
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"""
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cur = c.execute(
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"SELECT name FROM sqlite_master WHERE type='table' AND "
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"name IN ('bs_index_constituent_old', 'bs_index_constituent')"
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)
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tables = {row[0] for row in cur.fetchall()}
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parts = []
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if "bs_index_constituent_old" in tables:
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parts.append(pd.read_sql(
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"SELECT updateDate, index_code, code, code_name "
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"FROM bs_index_constituent_old", c,
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))
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print("[baostock] read from bs_index_constituent_old (方案A 权威历史全集)")
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if "bs_index_constituent" in tables:
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parts.append(pd.read_sql(
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"SELECT updateDate, index_code, code, code_name "
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"FROM bs_index_constituent", c,
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))
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print("[baostock] read from bs_index_constituent (live 增量)")
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if not parts:
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print("[baostock] WARN: 既无 _old 也无 live 表, baostock 段产出 0 行")
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return pd.DataFrame(columns=["updateDate", "index_code", "code", "code_name"])
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df = pd.concat(parts, ignore_index=True).drop_duplicates()
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return df
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def _read_announce_union_aggregated(hist_dir: str,
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index_codes: list[str]) -> pd.DataFrame:
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"""读 *_announce_union.parquet, 聚合成全集型(index_code/code/code_name/
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in_current/was_removed/source)。
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announce_union schema:
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updateDate / index_code / code / code_name / adjust_type / notice_id / source
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adjust_type: add | remove | current | initial
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全集聚合(spec §14):
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ever_codes = announce_union 所有 distinct code(任意 adjust_type)
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current_codes = announce_union 中 adjust_type='current' 的 code
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(∪ <idx>_snapshot.parquet 兜底, 兼容旧 announce 无 current 行)
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in_current = code in current_codes
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was_removed = not in_current(曾经入选已踢)
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code_name = 优先 snapshot 当前名(announce 的历史名可能过时)
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缺 announce_union 文件 -> 返回空 DataFrame(上层走 snapshot 兜底)。
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"""
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cols = ["index_code", "code", "code_name", "in_current", "was_removed", "source"]
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rows = []
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for idx in index_codes:
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ann_path = os.path.join(hist_dir, f"{idx}_announce_union.parquet")
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if not os.path.exists(ann_path):
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continue
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ann = pd.read_parquet(ann_path)
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ann["code"] = ann["code"].apply(norm_code)
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# current 兜底: announce 内 current 行 + snapshot 文件
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current_codes = set(ann.loc[ann["adjust_type"] == "current", "code"])
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snap_path = os.path.join(hist_dir, f"{idx}_snapshot.parquet")
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snap_name_map: dict[str, str] = {}
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if os.path.exists(snap_path):
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snap = pd.read_parquet(snap_path)
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snap["code"] = snap["code"].apply(norm_code)
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current_codes |= set(snap["code"])
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snap_name_map = dict(zip(snap["code"], snap["code_name"]))
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# ever = distinct(code), name 取 announce 或 snapshot(优先 snapshot)
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ever = (
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ann[["code", "code_name"]]
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.drop_duplicates(subset=["code"])
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)
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for _, r in ever.iterrows():
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code = r["code"]
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in_cur = code in current_codes
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name = snap_name_map.get(code) or r["code_name"]
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rows.append({
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"index_code": idx, "code": code, "code_name": name,
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"in_current": int(in_cur), "was_removed": int(not in_cur),
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"source": "csindex_announce",
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})
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if not rows:
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return pd.DataFrame(columns=cols)
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return pd.DataFrame(rows, columns=cols)
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def migrate(db_path: str) -> None:
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"""(幂等)重建 constituent_unified_staging。
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Args:
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db_path: quant_trading.db 路径(测试可传 tmp sqlite; 生产读 ``SANGUO_DB``)。
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"""
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c = sqlite3.connect(db_path, timeout=60)
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try:
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c.execute("PRAGMA busy_timeout = 60000")
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# 1. baostock -> 全集(自动适配 _old / live / 两者皆在)
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df_bs = _read_baostock_constituent(c)
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df_bs["index_code"] = df_bs["index_code"].map(BS_MAP)
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df_bs["code"] = df_bs["code"].apply(norm_code)
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last_sets = {}
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for idx, grp in df_bs.groupby("index_code"):
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last_d = grp["updateDate"].max()
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last_sets[idx] = set(grp[grp["updateDate"] == last_d]["code"])
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pool = (df_bs.groupby(["index_code", "code"])["code_name"]
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.first().reset_index())
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pool["in_current"] = pool.apply(
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lambda r: r["code"] in last_sets.get(r["index_code"], set()), axis=1)
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pool["was_removed"] = ~pool["in_current"]
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pool["source"] = "baostock"
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print(f"[baostock] pool rows={len(pool)} (300/500/50 全集)")
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# 2. 深证 union (排除 *_announce_union.parquet, 不同 schema)
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deep = []
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for f in sorted(glob.glob(os.path.join(HIST, "*_union.parquet"))):
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if f.endswith("_announce_union.parquet"):
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continue
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d = pd.read_parquet(f)[["index_code", "code", "code_name",
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"in_current", "was_removed"]]
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d["source"] = "akshare_cni"
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deep.append(d)
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df_deep = pd.concat(deep, ignore_index=True) if deep else pd.DataFrame(
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columns=["index_code", "code", "code_name", "in_current", "was_removed", "source"])
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df_deep["code"] = df_deep["code"].apply(norm_code)
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print(f"[深证 union] rows={len(df_deep)}")
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# 3. 中证 announce_union 全集(优先, 自动发现 *_announce_union.parquet); 缺则回退 snapshot
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# G1+G2 扩展: 不再硬编码 000852/932000, glob 整个 HIST 目录自动发现新指数
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announce_files = sorted(glob.glob(os.path.join(HIST, "*_announce_union.parquet")))
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ANNOUNCE_IDX = [
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os.path.basename(f).split("_announce_union.parquet")[0]
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for f in announce_files
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]
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print(f"[中证 announce] 发现 {len(ANNOUNCE_IDX)} 个指数: {ANNOUNCE_IDX}")
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df_csi = _read_announce_union_aggregated(HIST, ANNOUNCE_IDX)
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if df_csi.empty:
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# 回退: 旧 snapshot-only 路径(announce_union 未生成时)
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snap = []
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for f in [os.path.join(HIST, "000852_snapshot.parquet"),
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os.path.join(HIST, "932000_snapshot.parquet")]:
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if os.path.exists(f):
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d = pd.read_parquet(f)[["index_code", "code", "code_name"]]
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d["in_current"] = True
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d["was_removed"] = False
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d["source"] = "akshare_csindex"
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snap.append(d)
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df_csi = pd.concat(snap, ignore_index=True) if snap else pd.DataFrame(
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columns=["index_code", "code", "code_name", "in_current", "was_removed", "source"])
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df_csi["code"] = df_csi["code"].apply(norm_code)
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print(f"[中证 snapshot 回退] rows={len(df_csi)}")
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else:
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print(f"[中证 announce_union] rows={len(df_csi)}, "
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f"indices={df_csi['index_code'].nunique()}")
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# 合并 + 去重 (同 index+code+source)
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all_df = pd.concat([pool, df_deep, df_csi], ignore_index=True)
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all_df = all_df.drop_duplicates(["index_code", "code", "source"])
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print(f"\n[TOTAL] constituent_unified: {len(all_df)} rows, "
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f"{all_df['index_code'].nunique()} indices")
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print("\n各指数分布:")
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print(all_df.groupby("index_code").agg(
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n=("code", "count"), src=("source", "first"),
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in_cur=("in_current", "sum"), removed=("was_removed", "sum")))
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# 写 staging(幂等: DROP+CREATE)
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c.execute(f"DROP TABLE IF EXISTS {STAGING}")
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c.execute(f"""CREATE TABLE {STAGING} (
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index_code TEXT, code TEXT, code_name TEXT, source TEXT,
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in_current INTEGER, was_removed INTEGER)""")
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work = all_df[["index_code", "code", "code_name", "source",
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"in_current", "was_removed"]].copy()
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work["in_current"] = work["in_current"].astype(int)
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work["was_removed"] = work["was_removed"].astype(int)
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c.executemany(f"INSERT INTO {STAGING} VALUES (?,?,?,?,?,?)",
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work.itertuples(index=False, name=None))
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c.commit()
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n = c.execute(f"SELECT COUNT(*) FROM {STAGING}").fetchone()[0]
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# 抽样验证
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print(f"\n[staging] {STAGING}: {n} rows")
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print("sample 300:", c.execute(
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"SELECT COUNT(*), SUM(in_current), SUM(was_removed) FROM "
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f"{STAGING} WHERE index_code='000300'").fetchone())
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print("sample 399001:", c.execute(
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"SELECT COUNT(*), SUM(in_current), SUM(was_removed) FROM "
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f"{STAGING} WHERE index_code='399001'").fetchone())
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print("sample 000852:", c.execute(
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"SELECT COUNT(*), SUM(in_current), SUM(was_removed) FROM "
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f"{STAGING} WHERE index_code='000852'").fetchone())
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print("sample 932000:", c.execute(
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"SELECT COUNT(*), SUM(in_current), SUM(was_removed) FROM "
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f"{STAGING} WHERE index_code='932000'").fetchone())
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finally:
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c.close()
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print("\nMIGRATE STAGING DONE (未 rename, 验证 OK 后单独合并)")
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if __name__ == "__main__":
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_db = os.environ.get(
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"SANGUO_DB", r"C:\sanguo_vnpy_v2\data\quant_trading.db"
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)
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migrate(_db)
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