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/factors/sentiment/sentiment_engine.py

@@ -0,0 +1,311 @@
+"""
+情绪因子计算引擎。
+
+全链路：数据获取 → Qwen 分析 → 因子计算 → 缓存管理。
+"""
+
+import json
+import os
+import time
+from datetime import datetime
+from pathlib import Path
+
+import pandas as pd
+
+from data.data_manager import DataManager
+from factors.sentiment.news_source import NewsSource, align_news_to_trading_days
+from factors.sentiment.qwen_client import QwenClient
+from factors.sentiment.sentiment_factor import (
+    NewsSentimentFactor,
+    SentimentMomentumFactor,
+    SentimentConfidenceFactor,
+)
+
+
+def _get_or_fetch_price(dm, ts_code, fallback_code="000001.SZ"):
+    """
+    获取交易日历（价格 DataFrame）。
+
+    1. 优先从 DB 缓存读取目标股票
+    2. 无缓存则 sync_daily 补齐
+    3. 补齐失败则 fallback 到备用股票（仅用作交易日历，不影响新闻分析）
+    """
+    import pandas as pd
+    from database.dao import get_latest_trade_date
+
+    # 检查 DB 缓存
+    if get_latest_trade_date(ts_code):
+        daily = dm.get_daily(ts_code)
+        if daily is not None and not daily.empty:
+            return daily.set_index("trade_date").sort_index()
+
+    # 无缓存 → 尝试补齐
+    print("  [SentimentEngine] {} 无 DB 缓存，尝试 sync_daily...".format(ts_code))
+    try:
+        n = dm.sync_daily(ts_code)
+        if n > 0:
+            daily = dm.get_daily(ts_code)
+            if daily is not None and not daily.empty:
+                return daily.set_index("trade_date").sort_index()
+    except Exception as e:
+        print("  [SentimentEngine] sync_daily 失败: {}".format(e))
+
+    # Fallback
+    print("  [SentimentEngine] 使用 {} 交易日历作为 fallback".format(fallback_code))
+    try:
+        fallback = dm.get_daily(fallback_code)
+        if fallback is not None and not fallback.empty:
+            return fallback.set_index("trade_date").sort_index()
+    except Exception:
+        pass
+
+    return None
+
+
+class SentimentEngine:
+    """
+    情绪因子计算引擎。
+
+    参数:
+        data_manager: DataManager 实例
+        qwen_client: QwenClient 实例（可选，默认从环境变量创建）
+        news_source: NewsSource 实例（可选）
+        cache_dir: 缓存目录
+    """
+
+    def __init__(
+        self,
+        data_manager: DataManager,
+        qwen_client: QwenClient | None = None,
+        news_source: NewsSource | None = None,
+        cache_dir: str | None = None,
+    ):
+        self._dm = data_manager
+        self._qwen = qwen_client or QwenClient()
+        self._news_source = news_source or NewsSource()
+        self._cache_dir = Path(cache_dir or os.path.join(
+            os.path.dirname(__file__), "..", "..", ".cache", "sentiment"
+        ))
+        self._cache_dir.mkdir(parents=True, exist_ok=True)
+
+    # ── 核心方法 ──────────────────────────────────────────
+
+    def compute(
+        self,
+        ts_code: str,
+        start: str | None = None,
+        end: str | None = None,
+        max_news: int = 20,
+    ) -> pd.DataFrame:
+        """
+        计算单只股票的情绪因子（含缓存）。
+
+        返回:
+            DataFrame (trade_date, news_sent_5, news_conf_5, sent_delta_5)
+        """
+        from database.dao import get_latest_trade_date
+
+        # 1. 获取交易日历：优先目标股票 DB 缓存，无则尝试补齐，失败则 fallback
+        price = _get_or_fetch_price(self._dm, ts_code)
+        if price is None:
+            return pd.DataFrame()
+        daily_idx = pd.to_datetime(price.index, format="%Y%m%d", errors="coerce")
+
+        # 2. 检查缓存
+        cache_key = "sent_{}_{}_{}".format(ts_code, start or "all", end or "now")
+        cached = self._load_cache(cache_key)
+        if cached is not None:
+            return cached
+
+        # 3. 获取新闻
+        max_n = max_news or int(os.getenv("SENTIMENT_MAX_NEWS_PER_STOCK", "30"))
+        news_df = self._news_source.fetch(ts_code, start=start, end=end, max_news=max_n)
+
+        # 4. 对齐到交易日
+        if not news_df.empty:
+            news_df = align_news_to_trading_days(news_df, daily_idx)
+
+        # 5. Qwen 情绪分析（有 API key 时才执行）
+        sentiment_df = self._analyze_news(news_df)
+
+        # 6. 计算因子
+        factor_dfs = {}
+        if not sentiment_df.empty:
+            for factor_cls, kwargs in [
+                (NewsSentimentFactor, {"window": 5, "sentiment_df": sentiment_df}),
+                (SentimentConfidenceFactor, {"window": 5, "sentiment_df": sentiment_df}),
+                (SentimentMomentumFactor, {"period": 5, "sentiment_df": sentiment_df}),
+            ]:
+                f = factor_cls(**kwargs)
+                series = f.calculate(price)
+                factor_dfs[f.name] = series
+
+        if factor_dfs:
+            result = pd.DataFrame(factor_dfs)
+        else:
+            result = pd.DataFrame(index=price.index)
+
+        # 7. 缓存
+        self._save_cache(cache_key, result)
+
+        return result
+
+    def compute_batch(
+        self,
+        ts_codes: list[str],
+        date: str | None = None,
+        max_news: int = 20,
+    ) -> dict[str, pd.DataFrame]:
+        """
+        批量计算多只股票的情绪因子。
+
+        返回:
+            {ts_code: factor_df}
+        """
+        results = {}
+        total = len(ts_codes)
+        for i, ts_code in enumerate(ts_codes):
+            try:
+                results[ts_code] = self.compute(
+                    ts_code, start=None if date is None else date, end=date,
+                    max_news=max_news,
+                )
+            except Exception as e:
+                print(f"[WARN] {ts_code} 情绪因子失败: {e}")
+            if (i + 1) % 10 == 0:
+                print(f"[SentimentEngine] {i + 1}/{total}")
+        return results
+
+    # ── 分析范围解析 ──────────────────────────────────────
+
+    def get_scope_stocks(self) -> list[str]:
+        """
+        根据 .env 配置解析分析范围。
+
+        优先级: custom > index > sector > all
+        """
+        scope_type = os.getenv("SENTIMENT_SCOPE_TYPE", "index")
+        all_stocks = self._dm.get_stock_list()
+
+        if scope_type == "custom":
+            codes = os.getenv("SENTIMENT_SCOPE_CUSTOM", "")
+            return [c.strip() for c in codes.split(",") if c.strip()]
+
+        if scope_type == "sector":
+            return self._get_sector_stocks(all_stocks)
+
+        if scope_type == "index":
+            return self._get_index_stocks()
+
+        # all
+        return list(all_stocks.index)
+
+    def _get_index_stocks(self) -> list[str]:
+        """根据指数成分股获取股票列表。"""
+        indexes_str = os.getenv("SENTIMENT_SCOPE_INDEXES", "000300")
+        indexes = [i.strip() for i in indexes_str.split(",") if i.strip()]
+        stocks = set()
+        import akshare as ak
+        for idx_code in indexes:
+            try:
+                df = ak.index_stock_cons(symbol=idx_code)
+                if df is not None and not df.empty:
+                    # 列名可能是 '品种代码' 或 'stock_code'
+                    col = next((c for c in df.columns if "代码" in c or "code" in c.lower()), df.columns[0])
+                    for code in df[col]:
+                        stocks.add(f"{code}.SZ" if code.startswith("0") or code.startswith("3") else f"{code}.SH")
+            except Exception as e:
+                print(f"  [WARN] 指数 {idx_code} 成分股获取失败: {e}")
+        return list(stocks)
+
+    def _get_sector_stocks(self, all_stocks: pd.DataFrame) -> list[str]:
+        """根据板块名称筛选股票。"""
+        sectors_str = os.getenv("SENTIMENT_SCOPE_SECTORS", "")
+        sectors = [s.strip() for s in sectors_str.split(",") if s.strip()]
+        if not sectors:
+            return list(all_stocks.index)
+        # 利用 AkShare 行业分类筛选
+        import akshare as ak
+        all_industry = ak.stock_board_industry_name_em()
+        matched = all_industry[all_industry["板块名称"].isin(sectors)]
+        stocks = set()
+        for _, row in matched.iterrows():
+            try:
+                df = ak.stock_board_industry_cons_em(symbol=row["板块名称"])
+                if df is not None and not df.empty:
+                    code_col = next((c for c in df.columns if "代码" in c), df.columns[0])
+                    for code in df[code_col]:
+                        stocks.add(code)
+            except Exception:
+                continue
+        return list(stocks)
+
+    # ── 新闻情绪分析 ──────────────────────────────────────
+
+    def _analyze_news(self, news_df: pd.DataFrame) -> pd.DataFrame:
+        """
+        对新闻列表执行情绪分析。
+
+        返回:
+            DataFrame (date, sentiment_score, confidence, impact_duration, key_topics, title, content)
+        """
+        if news_df.empty:
+            return pd.DataFrame()
+
+        # 检查是否有 Qwen API 配置
+        if not self._qwen.api_key and not self._qwen.local_base_url:
+            return pd.DataFrame()
+
+        results = []
+        for _, row in news_df.iterrows():
+            title = row.get("title", "")
+            content = row.get("content", "")
+            # 优先用标题+内容，文本过短时只用标题
+            text = f"{title}\n{content}" if len(str(content)) > 20 else title
+            if len(text.strip()) < 10:
+                continue
+
+            analysis = self._qwen.analyze_sentiment(text)
+            results.append({
+                "date": row["date"],
+                "title": title,
+                "sentiment_score": analysis.get("sentiment_score", 0.0),
+                "confidence": analysis.get("confidence", 0.0),
+                "impact_duration": analysis.get("impact_duration", "short"),
+                "key_topics": json.dumps(analysis.get("key_topics", [])),
+            })
+
+        if not results:
+            return pd.DataFrame()
+
+        return pd.DataFrame(results)
+
+    # ── 缓存管理 ──────────────────────────────────────────
+
+    def _load_cache(self, key: str) -> pd.DataFrame | None:
+        """加载缓存。缓存有效期 6 小时。"""
+        cache_file = self._cache_dir / f"{key.replace('/', '_')}.parquet"
+        if not cache_file.exists():
+            return None
+        mtime = cache_file.stat().st_mtime
+        if time.time() - mtime > 6 * 3600:
+            return None
+        try:
+            return pd.read_parquet(cache_file)
+        except Exception:
+            return None
+
+    def _save_cache(self, key: str, df: pd.DataFrame) -> None:
+        cache_file = self._cache_dir / f"{key.replace('/', '_')}.parquet"
+        try:
+            df.to_parquet(cache_file)
+        except Exception:
+            pass
+
+    def clear_cache(self) -> int:
+        """清空缓存，返回删除文件数。"""
+        count = 0
+        for f in self._cache_dir.glob("*.parquet"):
+            f.unlink()
+            count += 1
+        return count