Initial commit: cc-cursor 全链路量化研究平台

7 Sprints 全部完成: Sprint 0: 基础设施 (DataManager + MariaDB) Sprint 1: 因子引擎 (34因子/12分类) Sprint 2: VectorBT 回测 (5策略+截面) Sprint 3: Optuna 优化 (+Walk-Forward) Sprint 4: ML 模型 (LightGBM+CatBoost) Sprint 5: Qwen 情绪因子 (三源新闻+日期对齐) Sprint 6: Agent 系统 (4Agent+日报.md/.html) 生产加固 (15项): Tushare双源fallback, SSH自动恢复, pool_pre_ping, save_daily先删后插, load_dotenv绝对路径, 日报5d/20d修复, RiskAgent改上证指数, 昨日对比+数据截止, mac_report utf8mb4, CLAUDE-*.md 9条已知Bug, demo全参数化, djapi数据源归一化, indexDatas API修正 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-07 15:59:05 +08:00
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+"""
+数据访问对象。
+
+提供 DataFrame 级别的读写操作，屏蔽底层 ORM/SQL 细节。
+"""
+
+import pandas as pd
+from sqlalchemy import text
+
+from database.connection import get_engine
+from database.models import StockBasic, StockDaily, StockFinancial, Report
+
+# DB 表列名，供 DataManager 在写入前筛选
+_DAILY_COLS = [
+    "ts_code", "trade_date", "open", "high", "low", "close",
+    "pre_close", "change", "pct_chg", "vol", "amount", "turnover_rate",
+]
+_FINA_COLS = [
+    "ts_code", "end_date", "eps", "bvps", "roe", "roe_diluted",
+    "net_profit_margin", "debt_to_assets", "current_ratio", "quick_ratio",
+    "total_revenue", "total_revenue_yoy", "net_profit", "net_profit_yoy",
+]
+
+
+def _df_to_db(df: pd.DataFrame, model_class, replace: bool = False) -> int:
+    """将 DataFrame 写入对应表，返回写入行数。"""
+    if df.empty:
+        return 0
+    engine = get_engine()
+    if_action = "replace" if replace else "append"
+    # 统一字符串列，避免 MySQL 类型问题
+    df = df.where(pd.notna(df), None)
+    rows = len(df)
+    df.to_sql(
+        model_class.__tablename__,
+        con=engine,
+        if_exists=if_action,
+        index=False,
+        method="multi",
+        chunksize=500,
+    )
+    return rows
+
+
+# ── StockBasic ─────────────────────────────────────────────
+
+def save_stock_list(df: pd.DataFrame) -> int:
+    """保存股票列表（replace 模式）。"""
+    cols = ["ts_code", "name", "area", "industry", "market", "list_date", "is_hs"]
+    df = df[[c for c in cols if c in df.columns]].copy()
+    return _df_to_db(df, StockBasic, replace=True)
+
+
+def query_stock_list() -> pd.DataFrame:
+    """查询全部股票列表。"""
+    engine = get_engine()
+    return pd.read_sql(f"SELECT * FROM {StockBasic.__tablename__}", con=engine).set_index("ts_code")
+
+
+# ── StockDaily ─────────────────────────────────────────────
+
+def save_daily(df: pd.DataFrame) -> int:
+    """批量写入日线数据。先删旧再插新，避免主键冲突。"""
+    cols = [
+        "ts_code", "trade_date", "open", "high", "low", "close",
+        "pre_close", "change", "pct_chg", "vol", "amount", "turnover_rate",
+    ]
+    df = df[[c for c in cols if c in df.columns]].copy()
+    if df.empty:
+        return 0
+    # 删除即将写入的日期的旧数据
+    engine = get_engine()
+    ts_codes = df["ts_code"].unique().tolist()
+    trade_dates = df["trade_date"].unique().tolist()
+    if ts_codes and trade_dates:
+        with engine.connect() as conn:
+            conn.execute(
+                text("DELETE FROM {} WHERE ts_code IN :codes AND trade_date IN :dates".format(
+                    StockDaily.__tablename__)),
+                {"codes": tuple(ts_codes), "dates": tuple(trade_dates)},
+            )
+            conn.commit()
+    return _df_to_db(df, StockDaily, replace=False)
+
+
+def query_daily(ts_code: str, start: str | None = None, end: str | None = None) -> pd.DataFrame:
+    """按股票代码和日期范围查询日线。"""
+    engine = get_engine()
+    table = StockDaily.__tablename__
+    sql = f"SELECT * FROM {table} WHERE ts_code = :ts_code"
+    params = {"ts_code": ts_code}
+    if start:
+        sql += " AND trade_date >= :start"
+        params["start"] = start
+    if end:
+        sql += " AND trade_date <= :end"
+        params["end"] = end
+    sql += " ORDER BY trade_date ASC"
+    df = pd.read_sql(text(sql), con=engine, params=params)
+    if not df.empty:
+        df["trade_date"] = df["trade_date"].astype(str)
+    return df
+
+
+def get_latest_trade_date(ts_code: str) -> str | None:
+    """获取某股票在数据库中的最新交易日。"""
+    engine = get_engine()
+    table = StockDaily.__tablename__
+    sql = f"SELECT MAX(trade_date) FROM {table} WHERE ts_code = :ts_code"
+    with engine.connect() as conn:
+        result = conn.execute(text(sql), {"ts_code": ts_code}).scalar()
+    return result
+
+
+# ── StockFinancial ─────────────────────────────────────────
+
+def save_financial(df: pd.DataFrame) -> int:
+    """批量写入财务数据（replace 模式：同报告期覆盖更新）。"""
+    cols = [
+        "ts_code", "end_date", "eps", "bvps", "roe", "roe_diluted",
+        "net_profit_margin", "debt_to_assets", "current_ratio", "quick_ratio",
+        "total_revenue", "total_revenue_yoy", "net_profit", "net_profit_yoy",
+    ]
+    df = df[[c for c in cols if c in df.columns]].copy()
+    return _df_to_db(df, StockFinancial, replace=True)
+
+
+def query_financial(ts_code: str) -> pd.DataFrame:
+    """查询某股票全部财务数据。"""
+    engine = get_engine()
+    table = StockFinancial.__tablename__
+    sql = f"SELECT * FROM {table} WHERE ts_code = :ts_code ORDER BY end_date DESC"
+    return pd.read_sql(text(sql), con=engine, params={"ts_code": ts_code})