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/bollinger.py

@@ -0,0 +1,53 @@
+"""
+布林带因子：价格在布林带中的位置。
+"""
+
+import pandas as pd
+
+from factors.base import BaseFactor
+
+
+class BollingerFactor(BaseFactor):
+    """
+    布林带位置 = (close - middle) / (upper - lower)
+
+    值在 0~1 之间：接近 0 表示在下轨，接近 1 表示在上轨。
+    """
+
+    category = "technical"
+
+    def __init__(self, period: int = 20, std: float = 2.0):
+        self.period = period
+        self.std = std
+        self.name = f"boll_{period}"
+
+    def calculate(self, df: pd.DataFrame) -> pd.Series:
+        middle = df["close"].rolling(window=self.period, min_periods=self.period).mean()
+        std = df["close"].rolling(window=self.period, min_periods=self.period).std()
+        upper = middle + self.std * std
+        lower = middle - self.std * std
+        band_width = upper - lower
+        return ((df["close"] - lower) / band_width.replace(0, float("nan"))).clip(0, 1)
+
+    def get_required_columns(self) -> list[str]:
+        return ["close"]
+
+
+class BollingerWidthFactor(BaseFactor):
+    """布林带宽度 = (upper - lower) / middle * 100"""
+
+    category = "technical"
+
+    def __init__(self, period: int = 20, std: float = 2.0):
+        self.period = period
+        self.std = std
+        self.name = f"boll_width_{period}"
+
+    def calculate(self, df: pd.DataFrame) -> pd.Series:
+        middle = df["close"].rolling(window=self.period, min_periods=self.period).mean()
+        std = df["close"].rolling(window=self.period, min_periods=self.period).std()
+        band_width = 2 * self.std * std
+        return band_width / middle.replace(0, float("nan")) * 100
+
+    def get_required_columns(self) -> list[str]:
+        return ["close"]