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/technical/macd.py

@@ -0,0 +1,47 @@
+"""
+MACD 因子。
+"""
+
+import pandas as pd
+
+from factors.base import BaseFactor
+
+
+class MACDFactor(BaseFactor):
+    """
+    MACD 系列因子。
+
+    返回 DIF/DEA/HIST 三个值。使用 calculate() 返回 HIST（柱），
+    单独方法获取 DIF/DEA。
+    """
+
+    category = "technical"
+
+    def __init__(self, fast: int = 12, slow: int = 26, signal: int = 9):
+        self.fast = fast
+        self.slow = slow
+        self.signal = signal
+        self.name = f"macd_{fast}_{slow}_{signal}"
+
+    def _ema(self, series: pd.Series, span: int) -> pd.Series:
+        return series.ewm(span=span, min_periods=span).mean()
+
+    def calculate(self, df: pd.DataFrame) -> pd.Series:
+        """返回 MACD 柱（DIF - DEA）。"""
+        ema_fast = self._ema(df["close"], self.fast)
+        ema_slow = self._ema(df["close"], self.slow)
+        dif = ema_fast - ema_slow
+        dea = self._ema(dif, self.signal)
+        return dif - dea
+
+    def dif(self, df: pd.DataFrame) -> pd.Series:
+        ema_fast = self._ema(df["close"], self.fast)
+        ema_slow = self._ema(df["close"], self.slow)
+        return ema_fast - ema_slow
+
+    def dea(self, df: pd.DataFrame) -> pd.Series:
+        dif = self.dif(df)
+        return self._ema(dif, self.signal)
+
+    def get_required_columns(self) -> list[str]:
+        return ["close"]