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/backtest/strategies/factor_cross.py

@@ -0,0 +1,51 @@
+"""
+因子阈值交叉策略。
+
+通用策略：任意因子上穿/下穿阈值 → 交易信号。
+
+支持：
+- 上穿买入 (cross_up: close < MA → cross above MA → buy)
+- 下穿买入 (cross_down: RSI > 70 → cross below 30 → buy)
+"""
+
+import pandas as pd
+
+from backtest.base import BaseStrategy
+from backtest.signal import factor_to_threshold_signal
+
+
+class FactorCrossStrategy(BaseStrategy):
+    """
+    因子阈值交叉策略。
+
+    适用场景：
+    - 均线偏离度上穿 0 → 买入（趋势转多）
+    - 波动率下穿阈值 → 买入（波动收敛后突破）
+    """
+
+    category = "trend"
+
+    def __init__(
+        self,
+        factor_column: str,
+        buy_threshold: float,  # 因子大于此值买
+        sell_threshold: float | None = None,
+        cross_direction: str = "up",
+    ):
+        self.factor_column = factor_column
+        self.buy_threshold = buy_threshold
+        self.sell_threshold = sell_threshold
+        self.cross_direction = cross_direction
+        self.name = f"factor_cross_{factor_column}"
+
+    def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
+        if self.factor_column not in factor_df.columns:
+            raise ValueError(f"factor_df 缺少 '{self.factor_column}' 列")
+
+        factor = factor_df[self.factor_column]
+        return factor_to_threshold_signal(
+            factor,
+            buy_threshold=self.buy_threshold,
+            sell_threshold=self.sell_threshold,
+            cross_direction=self.cross_direction,
+        )