feat: 初始化 ProStock 项目基础结构和配置

- 添加项目规则文档（开发规范、安全规则、配置管理）
- 实现数据模块核心功能（API 客户端、限流器、存储管理、配置加载）
- 添加 .gitignore 和 .kilocodeignore 配置
- 配置环境变量模板
- 编写 daily 模块单元测试

src/data/storage.py

@@ -0,0 +1,133 @@
+"""Simplified HDF5 storage for data persistence."""
+import os
+import pandas as pd
+from pathlib import Path
+from typing import Optional
+from src.data.config import get_config
+
+
+class Storage:
+    """HDF5 storage manager for saving and loading data."""
+
+    def __init__(self, path: Optional[Path] = None):
+        """Initialize storage.
+
+        Args:
+            path: Base path for data storage (auto-loaded from config if not provided)
+        """
+        cfg = get_config()
+        self.base_path = path or cfg.data_path
+        self.base_path.mkdir(parents=True, exist_ok=True)
+
+    def _get_file_path(self, name: str) -> Path:
+        """Get full path for an HDF5 file."""
+        return self.base_path / f"{name}.h5"
+
+    def save(self, name: str, data: pd.DataFrame, mode: str = "append") -> dict:
+        """Save data to HDF5 file.
+
+        Args:
+            name: Dataset name (also used as filename)
+            data: DataFrame to save
+            mode: 'append' or 'replace'
+
+        Returns:
+            Dict with save result
+        """
+        if data.empty:
+            return {"status": "skipped", "rows": 0}
+
+        file_path = self._get_file_path(name)
+
+        try:
+            with pd.HDFStore(file_path, mode="a") as store:
+                if mode == "replace" or name not in store.keys():
+                    store.put(name, data, format="table")
+                else:
+                    # Merge with existing data
+                    existing = store[name]
+                    combined = pd.concat([existing, data], ignore_index=True)
+                    combined = combined.drop_duplicates(subset=["ts_code", "trade_date"], keep="last")
+                    store.put(name, combined, format="table")
+
+            print(f"[Storage] Saved {len(data)} rows to {file_path}")
+            return {"status": "success", "rows": len(data), "path": str(file_path)}
+
+        except Exception as e:
+            print(f"[Storage] Error saving {name}: {e}")
+            return {"status": "error", "error": str(e)}
+
+    def load(self, name: str,
+             start_date: Optional[str] = None,
+             end_date: Optional[str] = None,
+             ts_code: Optional[str] = None) -> pd.DataFrame:
+        """Load data from HDF5 file.
+
+        Args:
+            name: Dataset name
+            start_date: Start date filter (YYYYMMDD)
+            end_date: End date filter (YYYYMMDD)
+            ts_code: Stock code filter
+
+        Returns:
+            DataFrame with loaded data
+        """
+        file_path = self._get_file_path(name)
+
+        if not file_path.exists():
+            print(f"[Storage] File not found: {file_path}")
+            return pd.DataFrame()
+
+        try:
+            with pd.HDFStore(file_path, mode="r") as store:
+                if name not in store.keys():
+                    return pd.DataFrame()
+
+                data = store[name]
+
+                # Apply filters
+                if start_date and end_date and "trade_date" in data.columns:
+                    data = data[(data["trade_date"] >= start_date) & (data["trade_date"] <= end_date)]
+
+                if ts_code and "ts_code" in data.columns:
+                    data = data[data["ts_code"] == ts_code]
+
+                return data
+
+        except Exception as e:
+            print(f"[Storage] Error loading {name}: {e}")
+            return pd.DataFrame()
+
+    def get_last_date(self, name: str) -> Optional[str]:
+        """Get the latest date in storage.
+
+        Args:
+            name: Dataset name
+
+        Returns:
+            Latest date string or None
+        """
+        data = self.load(name)
+        if data.empty or "trade_date" not in data.columns:
+            return None
+        return str(data["trade_date"].max())
+
+    def exists(self, name: str) -> bool:
+        """Check if dataset exists."""
+        return self._get_file_path(name).exists()
+
+    def delete(self, name: str) -> bool:
+        """Delete a dataset.
+
+        Args:
+            name: Dataset name
+
+        Returns:
+            True if deleted
+        """
+        file_path = self._get_file_path(name)
+        if file_path.exists():
+            file_path.unlink()
+            print(f"[Storage] Deleted {file_path}")
+            return True
+        return False