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>

finance/data/sources/tushare_source.py

@@ -0,0 +1,139 @@
+"""
+Tushare 数据源封装。
+
+与 AkShareSource 保持相同接口，作为平替/备份数据源。
+"""
+
+import os
+import time
+
+import pandas as pd
+import tushare as ts
+
+# 确保 .env 已加载（无论从哪个路径导入）
+import config.settings  # noqa: F401
+
+
+class TushareSource:
+    """Tushare 数据源。"""
+
+    def __init__(self, token: str | None = None):
+        token = token or os.getenv("TUSHARE_TOKEN", "")
+        if not token:
+            self._pro = None
+        else:
+            ts.set_token(token)
+            self._pro = ts.pro_api()
+
+    @property
+    def available(self) -> bool:
+        return self._pro is not None
+
+    def _ensure_pro(self):
+        if self._pro is None:
+            raise RuntimeError("Tushare token 未配置，请在 .env 中设置 TUSHARE_TOKEN")
+
+    # ── 股票列表 ──────────────────────────────────────────
+
+    def fetch_stock_list(self) -> pd.DataFrame:
+        """获取 A 股股票列表。"""
+        self._ensure_pro()
+        fields = "ts_code,name,area,industry,market,list_date,is_hs"
+        df = self._pro.stock_basic(
+            exchange="", list_status="L",
+            fields=fields,
+        )
+        if df is None or df.empty:
+            return pd.DataFrame()
+        return df[["ts_code", "name"]]
+
+    # ── 日线数据 ──────────────────────────────────────────
+
+    def fetch_daily(
+        self, ts_code: str, start: str, end: str | None = None
+    ) -> pd.DataFrame:
+        """
+        获取单只股票日线行情。
+
+        参数:
+            ts_code: 如 '000001.SZ'
+            start: 'YYYYMMDD'
+            end: 'YYYYMMDD'
+        """
+        self._ensure_pro()
+        end = end or time.strftime("%Y%m%d")
+
+        df = self._pro.daily(
+            ts_code=ts_code,
+            start_date=start,
+            end_date=end,
+            fields="ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount,turnover_rate",
+        )
+        if df is None or df.empty:
+            return pd.DataFrame()
+
+        # 前复权因子
+        try:
+            adj = self._pro.adj_factor(ts_code=ts_code, start_date=start, end_date=end)
+            if adj is not None and not adj.empty:
+                df = df.merge(adj[["trade_date", "adj_factor"]], on="trade_date", how="left")
+                for col in ["open", "high", "low", "close", "pre_close"]:
+                    if col in df.columns and "adj_factor" in df.columns:
+                        df[col] = (df[col] * df["adj_factor"]).round(2)
+                df = df.drop(columns=["adj_factor"])
+        except Exception:
+            pass
+
+        df["trade_date"] = df["trade_date"].astype(str)
+        return df
+
+    # ── 指数日线 ──────────────────────────────────────────
+
+    def fetch_index_daily(
+        self, ts_code: str, start: str, end: str | None = None
+    ) -> pd.DataFrame:
+        """
+        获取指数日线行情。
+
+        Tushare index_daily 接口。
+        """
+        self._ensure_pro()
+        end = end or time.strftime("%Y%m%d")
+        df = self._pro.index_daily(
+            ts_code=ts_code,
+            start_date=start, end_date=end,
+            fields="ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount",
+        )
+        if df is None or df.empty:
+            return pd.DataFrame()
+        df["trade_date"] = df["trade_date"].astype(str)
+        return df
+
+    # ── 财务数据 ──────────────────────────────────────────
+
+    def fetch_financial(self, ts_code: str) -> pd.DataFrame:
+        """获取单只股票核心财务指标。"""
+        self._ensure_pro()
+        fields = (
+            "ts_code,end_date,roe,roa,grossprofit_margin,netprofit_margin,"
+            "debt_to_assets,eps,dt_eps,bps,pe,pb,"
+            "total_revenue,revenue_yoy,n_income,n_income_yoy"
+        )
+        df = self._pro.fina_indicator(
+            ts_code=ts_code,
+            fields=fields,
+        )
+        if df is None or df.empty:
+            return pd.DataFrame()
+
+        df = df.rename(columns={
+            "grossprofit_margin": "gross_profit_margin",
+            "netprofit_margin": "net_profit_margin",
+            "dt_eps": "eps_diluted",
+            "bps": "bvps",
+            "revenue_yoy": "total_revenue_yoy",
+            "n_income": "net_profit",
+            "n_income_yoy": "net_profit_yoy",
+        })
+        df["end_date"] = df["end_date"].astype(str)
+        return df