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/ma_cross.py

@@ -0,0 +1,49 @@
+"""
+均线交叉因子。
+"""
+
+import pandas as pd
+
+from factors.base import BaseFactor
+
+
+class MACrossFactor(BaseFactor):
+    """
+    均线交叉信号。
+
+    返回：fast_ma / slow_ma - 1，正值表示短期均线在上方。
+    """
+
+    category = "technical"
+
+    def __init__(self, fast: int = 5, slow: int = 20):
+        self.fast = fast
+        self.slow = slow
+        self.name = f"ma_cross_{fast}_{slow}"
+
+    def calculate(self, df: pd.DataFrame) -> pd.Series:
+        ma_fast = df["close"].rolling(window=self.fast, min_periods=self.fast).mean()
+        ma_slow = df["close"].rolling(window=self.slow, min_periods=self.slow).mean()
+        return (ma_fast / ma_slow.replace(0, float("nan"))) - 1
+
+    def get_required_columns(self) -> list[str]:
+        return ["close"]
+
+
+class MADeviationFactor(BaseFactor):
+    """
+    价格偏离均线程度 = (close - ma) / ma * 100
+    """
+
+    category = "technical"
+
+    def __init__(self, period: int = 20):
+        self.period = period
+        self.name = f"ma_dev_{period}"
+
+    def calculate(self, df: pd.DataFrame) -> pd.Series:
+        ma = df["close"].rolling(window=self.period, min_periods=self.period).mean()
+        return (df["close"] - ma) / ma.replace(0, float("nan")) * 100
+
+    def get_required_columns(self) -> list[str]:
+        return ["close"]