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/agents/selection_agent.py

@@ -0,0 +1,195 @@
+"""
+SelectionAgent — 多因子股票打分。
+
+综合技术因子 + 基本面因子 + ML 预测值 → 股票综合得分排序。
+"""
+
+import numpy as np
+import pandas as pd
+
+from agents.base import BaseAgent
+
+
+class SelectionAgent(BaseAgent):
+    """股票打分 Agent。"""
+
+    name = "Selection"
+    description = "多因子股票打分与推荐"
+
+    def execute(
+        self,
+        date: str | None = None,
+        ts_codes: list[str] | None = None,
+        top_n: int = 15,
+        weighting: str = "equal",
+    ) -> dict:
+        """
+        对股票池打分排序。
+
+        参数:
+            date: 目标日期，None=最新
+            ts_codes: 股票池，None=指数成分股
+            top_n: 返回 Top N
+            weighting: 'equal' | 'ic_weighted' | 'ml'
+
+        返回:
+            {"date": ..., "top_picks": [...], "score_df": DataFrame}
+        """
+        from factors.registry import get_factor, list_factors
+
+        # 确定股票池
+        if ts_codes is None:
+            if self.sent:
+                ts_codes = self.sent.get_scope_stocks()
+            else:
+                stocks = self.dm.get_stock_list()
+                ts_codes = list(stocks.index[:100])
+
+        if not ts_codes:
+            return {"date": date or self._today(), "top_picks": [], "score_df": pd.DataFrame()}
+
+        # 选择核心因子（覆盖多个维度，减少计算量）
+        core_factors = [
+            "momentum_20", "momentum_60",
+            "rsi_14", "volatility_20",
+            "vol_ratio_5", "ma_dev_20",
+            "turnover_5", "amplitude_5",
+        ]
+        factor_objects = [get_factor(n) for n in core_factors]
+
+        self.log("打分 {} 只股票 (权重={})".format(len(ts_codes), weighting))
+
+        # 1. 筛选有 DB 缓存的股票（Orchestrator 已在 Step1 同步了全量范围）
+        from database.dao import get_latest_trade_date
+        available = []
+        for ts_code in sorted(ts_codes):
+            if get_latest_trade_date(ts_code):
+                available.append(ts_code)
+        self.log("缓存命中: {}/{} ({:.1f}%)".format(
+            len(available), len(ts_codes),
+            len(available) / len(ts_codes) * 100 if ts_codes else 0))
+
+        # 2. 打分（限制上限防止单次太慢）
+        score_limit = min(len(available), 300)
+        scores = {}
+        valid_count = 0
+        for i, ts_code in enumerate(available[:score_limit]):
+            try:
+                score = self._score_stock(ts_code, factor_objects, date, weighting)
+                if score is not None:
+                    scores[ts_code] = score
+                    valid_count += 1
+            except Exception:
+                continue
+            if (i + 1) % 50 == 0:
+                self.log("  进度: {}/{}".format(i + 1, score_limit))
+
+        self.log("有效评分: {}/{}".format(len(scores), score_limit))
+
+        if not scores:
+            return {"date": date or self._today(), "top_picks": [], "score_df": pd.DataFrame()}
+
+        # 排序
+        sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+        top = sorted_scores[:top_n]
+
+        # 取股票名称（ts_code 是 index）
+        stock_list = self.dm.get_stock_list() if self.dm else pd.DataFrame()
+        name_map = {}
+        if not stock_list.empty and "name" in stock_list.columns:
+            name_map = dict(zip(stock_list.index, stock_list["name"]))
+
+        top_picks = []
+        for ts_code, score in top:
+            # 尝试多种格式匹配名称
+            code_no_suffix = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
+            name = name_map.get(ts_code, name_map.get(code_no_suffix, ts_code))
+            top_picks.append({
+                "ts_code": ts_code,
+                "name": name,
+                "score": round(score, 4),
+            })
+
+        score_df = pd.DataFrame(
+            {"ts_code": list(scores.keys()), "score": list(scores.values())}
+        ).sort_values("score", ascending=False).reset_index(drop=True)
+
+        return {
+            "date": date or self._today(),
+            "top_picks": top_picks,
+            "score_df": score_df,
+            "universe_size": len(ts_codes),
+            "valid_scores": valid_count,
+        }
+
+    def _filter_cached_stocks(self, ts_codes: list[str], limit: int = 100) -> list[str]:
+        """筛选有 DB 日线缓存的股票，避免逐个调用 AkShare。"""
+        from database.dao import get_latest_trade_date
+        cached = []
+        for ts_code in ts_codes[:limit]:
+            latest = get_latest_trade_date(ts_code)
+            if latest:
+                cached.append(ts_code)
+        return cached
+
+    def _score_stock(
+        self,
+        ts_code: str,
+        factors: list,
+        date: str | None,
+        weighting: str,
+    ) -> float | None:
+        """对单只股票打分。"""
+        daily = self.dm.get_daily(ts_code)
+        if daily is None or daily.empty:
+            return None
+        daily = daily.set_index("trade_date").sort_index()
+
+        factor_df = self.fe.compute(ts_code, factors)
+        if factor_df is None or factor_df.empty:
+            return None
+
+        if date and date in factor_df.index:
+            row = factor_df.loc[date]
+        else:
+            row = factor_df.iloc[-1]  # 最新一天
+
+        if row.isna().all():
+            return None
+
+        if weighting == "ml" and self.ml_models:
+            return self._score_ml(ts_code, factor_df, date)
+
+        # 等权打分：标准化因子值后求和
+        row_clean = row.dropna()
+        if len(row_clean) < 3:
+            return None
+
+        # z-score 标准化
+        z = (row_clean - factor_df[row_clean.index].mean()) / factor_df[row_clean.index].std().replace(0, 1)
+        return float(z.mean())
+
+    def _score_ml(self, ts_code: str, factor_df: pd.DataFrame, date: str | None) -> float | None:
+        """ML 模型打分。"""
+        from models.features import FeatureEngine
+        fe = FeatureEngine(lookahead=5)
+
+        daily = self.dm.get_daily(ts_code)
+        if daily is None or daily.empty:
+            return None
+        daily = daily.set_index("trade_date")
+
+        try:
+            X, _ = fe.build(factor_df, daily, fit=False)
+            if X.empty:
+                return None
+            if date and date in X.index:
+                X = X.loc[[date]]
+            else:
+                X = X.iloc[[-1]]
+
+            model = self.ml_models.get("lightgbm") or list(self.ml_models.values())[0]
+            pred = model.predict(X)
+            return float(pred.iloc[0]) if len(pred) > 0 else None
+        except Exception:
+            return None