ProStock/src/data/sync.py

"""数据同步调度中心模块。

该模块作为数据同步的调度中心，统一管理各类型数据的同步流程。

【重要规范 - sync.py 职责范围】
本模块**仅包含每日更新的数据接口**，季度/低频数据不应放入此文件：

✅ 本模块包含的同步逻辑（每日更新）：
    - api_daily.py: 日线数据同步 (DailySync 类)
    - api_daily_basic.py: 每日指标数据同步 (DailyBasicSync 类)
    - api_bak_basic.py: 历史股票列表同步 (BakBasicSync 类)
    - api_pro_bar.py: Pro Bar 数据同步 (ProBarSync 类)
    - api_stock_basic.py: 股票基本信息同步
    - api_trade_cal.py: 交易日历同步

❌ 不应包含的同步逻辑（季度/低频更新）：
    - financial_data/: 财务数据（利润表、资产负债表、现金流量表等）
      使用方式：
          from src.data.api_wrappers.financial_data.api_financial_sync import sync_financial
          sync_financial()

    - api_namechange.py: 股票名称变更（不频繁）
      使用方式：
          from src.data.api_wrappers import sync_namechange
          sync_namechange(force=True)

使用方式：
    # 预览同步（检查数据量，不写入）
    from src.data.sync import preview_sync
    preview = preview_sync()

    # 同步所有每日更新数据（不包括财务数据、namechange）
    from src.data.sync import sync_all_data
    result = sync_all_data()

    # 强制全量重载
    result = sync_all_data(force_full=True)
"""

from typing import Optional, Dict, Union, Any

import pandas as pd

from src.data.api_wrappers import sync_all_stocks
from src.data.api_wrappers.api_daily import sync_daily, preview_daily_sync
from src.data.api_wrappers.api_pro_bar import sync_pro_bar
from src.data.api_wrappers.api_bak_basic import sync_bak_basic
from src.data.api_wrappers.api_daily_basic import sync_daily_basic
from src.data.api_wrappers.api_stock_st import sync_stock_st


def preview_sync(
    force_full: bool = False,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
    sample_size: int = 3,
    max_workers: Optional[int] = None,
) -> dict[str, Any]:
    """预览日线同步数据量和样本（不实际同步）。

    这是推荐的方式，可在实际同步前检查将要同步的内容。

    Args:
        force_full: 若为 True，预览全量同步（从 20180101）
        start_date: 手动指定起始日期（覆盖自动检测）
        end_date: 手动指定结束日期（默认为今天）
        sample_size: 预览用样本股票数量（默认: 3）
        max_workers: 工作线程数（默认: 10）

    Returns:
        包含预览信息的字典：
        {
            'sync_needed': bool,
            'stock_count': int,
            'start_date': str,
            'end_date': str,
            'estimated_records': int,
            'sample_data': pd.DataFrame,
            'mode': str,  # 'full', 'incremental', 'partial', 或 'none'
        }

    Example:
        >>> # 预览将要同步的内容
        >>> preview = preview_sync()
        >>>
        >>> # 预览全量同步
        >>> preview = preview_sync(force_full=True)
        >>>
        >>> # 预览更多样本
        >>> preview = preview_sync(sample_size=5)
    """
    return preview_daily_sync(
        force_full=force_full,
        start_date=start_date,
        end_date=end_date,
        sample_size=sample_size,
    )


def sync_all(
    force_full: bool = False,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
    max_workers: Optional[int] = None,
    dry_run: bool = False,
) -> dict[str, pd.DataFrame]:
    """同步所有股票的日线数据。

    这是日线数据同步的主要入口点。

    Args:
        force_full: 若为 True，强制从 20180101 完整重载
        start_date: 手动指定起始日期（YYYYMMDD）
        end_date: 手动指定结束日期（默认为今天）
        max_workers: 工作线程数（默认: 10）
        dry_run: 若为 True，仅预览将要同步的内容，不写入数据

    Returns:
        映射 ts_code 到 DataFrame 的字典

    Example:
        >>> # 首次同步（从 20180101 全量加载）
        >>> result = sync_all()
        >>>
        >>> # 后续同步（增量 - 仅新数据）
        >>> result = sync_all()
        >>>
        >>> # 强制完整重载
        >>> result = sync_all(force_full=True)
        >>>
        >>> # 手动指定日期范围
        >>> result = sync_all(start_date='20240101', end_date='20240131')
        >>>
        >>> # 自定义线程数
        >>> result = sync_all(max_workers=20)
        >>>
        >>> # Dry run（仅预览）
        >>> result = sync_all(dry_run=True)
    """
    return sync_daily(
        force_full=force_full,
        start_date=start_date,
        end_date=end_date,
        max_workers=max_workers,
        dry_run=dry_run,
    )


def sync_all_data(
    force_full: bool = False,
    max_workers: Optional[int] = None,
    dry_run: bool = False,
) -> dict[str, Any]:
    """同步所有每日更新的数据类型。

    【重要】本函数仅同步每日更新的数据，不包含季度/低频数据。

    该函数按顺序同步以下每日更新的数据类型：
    1. 交易日历 (sync_trade_cal_cache)
    2. 股票基本信息 (sync_all_stocks)
    3. 日线数据 (sync_daily)
    4. Pro Bar 数据 (sync_pro_bar)
    5. 每日指标数据 (sync_daily_basic)
    6. 历史股票列表 (sync_bak_basic)
    7. ST股票列表 (sync_stock_st)

    【不包含的同步（需单独调用）】
    - 财务数据: 利润表、资产负债表、现金流量表（季度更新）
      使用: from src.data.api_wrappers.financial_data.api_financial_sync import sync_financial
      调用: sync_financial()

    - 名称变更 (namechange): 股票曾用名（低频更新）
      使用: from src.data.api_wrappers import sync_namechange
      调用: sync_namechange(force=True)

    Args:
        force_full: 若为 True，强制所有数据类型完整重载
        max_workers: 日线数据同步的工作线程数（默认: 10）
        dry_run: 若为 True，仅显示将要同步的内容，不写入数据

    Returns:
        映射数据类型到同步结果的字典

    Example:
        >>> result = sync_all_data()
        >>>
        >>> # 强制完整重载
        >>> result = sync_all_data(force_full=True)
        >>>
        >>> # Dry run
        >>> result = sync_all_data(dry_run=True)
    """
    results: dict[str, Any] = {}

    print("\n" + "=" * 60)
    print("[sync_all_data] Starting full data synchronization...")
    print("=" * 60)

    # 1. Sync trade calendar (always needed first)
    print("\n[1/7] Syncing trade calendar cache...")
    try:
        from src.data.api_wrappers import sync_trade_cal_cache

        sync_trade_cal_cache()
        results["trade_cal"] = pd.DataFrame()
        print("[1/7] Trade calendar: OK")
    except Exception as e:
        print(f"[1/7] Trade calendar: FAILED - {e}")
        results["trade_cal"] = pd.DataFrame()

    # 2. Sync stock basic info
    print("\n[2/7] Syncing stock basic info...")
    try:
        sync_all_stocks()
        results["stock_basic"] = pd.DataFrame()
        print("[2/7] Stock basic: OK")
    except Exception as e:
        print(f"[2/7] Stock basic: FAILED - {e}")
        results["stock_basic"] = pd.DataFrame()

    # 3. Sync daily market data
    # print("\n[3/7] Syncing daily market data...")
    # try:
    #     # 确保表存在
    #     from src.data.api_wrappers.api_daily import DailySync
    #
    #     DailySync().ensure_table_exists()
    #
    #     daily_result = sync_daily(
    #         force_full=force_full,
    #         max_workers=max_workers,
    #         dry_run=dry_run,
    #     )
    #     results["daily"] = daily_result
    #     total_daily_records = (
    #         sum(len(df) for df in daily_result.values()) if daily_result else 0
    #     )
    #     print(
    #         f"[3/7] Daily data: OK ({total_daily_records} records from {len(daily_result)} stocks)"
    #     )
    # except Exception as e:
    #     print(f"[3/7] Daily data: FAILED - {e}")
    #     results["daily"] = pd.DataFrame()

    # 4. Sync Pro Bar data
    print("\n[4/7] Syncing Pro Bar data (with adj, tor, vr)...")
    try:
        # 确保表存在
        from src.data.api_wrappers.api_pro_bar import ProBarSync

        ProBarSync().ensure_table_exists()

        pro_bar_result = sync_pro_bar(
            force_full=force_full,
            max_workers=max_workers,
            dry_run=dry_run,
        )
        results["pro_bar"] = pro_bar_result
        total_pro_bar_records = (
            sum(len(df) for df in pro_bar_result.values()) if pro_bar_result else 0
        )
        print(
            f"[4/7] Pro Bar data: OK ({total_pro_bar_records} records from {len(pro_bar_result)} stocks)"
        )
    except Exception as e:
        print(f"[4/7] Pro Bar data: FAILED - {e}")
        results["pro_bar"] = pd.DataFrame()

    # 5. Sync daily basic indicators
    print(
        "\n[5/7] Syncing daily basic indicators (PE, PB, turnover rate, market value)..."
    )
    try:
        # 确保表存在
        from src.data.api_wrappers.api_daily_basic import DailyBasicSync

        DailyBasicSync().ensure_table_exists()

        daily_basic_result = sync_daily_basic(force_full=force_full, dry_run=dry_run)
        results["daily_basic"] = daily_basic_result
        print(f"[5/7] Daily basic: OK ({len(daily_basic_result)} records)")
    except Exception as e:
        print(f"[5/7] Daily basic: FAILED - {e}")
        results["daily_basic"] = pd.DataFrame()

    # 6. Sync stock historical list (bak_basic)
    print("\n[6/7] Syncing stock historical list (bak_basic)...")
    try:
        # 确保表存在
        from src.data.api_wrappers.api_bak_basic import BakBasicSync

        BakBasicSync().ensure_table_exists()

        bak_basic_result = sync_bak_basic(force_full=force_full)
        results["bak_basic"] = bak_basic_result
        print(f"[6/7] Bak basic: OK ({len(bak_basic_result)} records)")
    except Exception as e:
        print(f"[6/7] Bak basic: FAILED - {e}")
        results["bak_basic"] = pd.DataFrame()

    # 7. Sync ST stock list
    print("\n[7/7] Syncing ST stock list...")
    try:
        # 确保表存在
        from src.data.api_wrappers.api_stock_st import StockSTSync

        StockSTSync().ensure_table_exists()

        stock_st_result = sync_stock_st(force_full=force_full)
        results["stock_st"] = stock_st_result
        print(f"[7/7] ST stock list: OK ({len(stock_st_result)} records)")
    except Exception as e:
        print(f"[7/7] ST stock list: FAILED - {e}")
        results["stock_st"] = pd.DataFrame()

    # Summary
    print("\n" + "=" * 60)
    print("[sync_all_data] Sync Summary")
    print("=" * 60)
    for data_type, data in results.items():
        if isinstance(data, dict):
            # 日线和 Pro Bar 返回的是 dict[str, DataFrame]
            total_records = sum(len(df) for df in data.values())
            print(f"  {data_type}: {len(data)} stocks, {total_records} total records")
        else:
            # daily_basic 和 bak_basic 返回的是 DataFrame
            print(f"  {data_type}: {len(data)} records")
    print("=" * 60)
    print("\nNote: namechange is NOT in auto-sync. To sync manually:")
    print("  from src.data.api_wrappers import sync_namechange")
    print("  sync_namechange(force=True)")

    return results


if __name__ == "__main__":
    print("=" * 60)
    print("Data Sync Module")
    print("=" * 60)
    print("\nUsage:")
    print("  # Sync all data types at once (RECOMMENDED)")
    print("  from src.data.sync import sync_all_data")
    print("  result = sync_all_data()  # Incremental sync all")
    print("  result = sync_all_data(force_full=True)  # Full reload")
    print("")
    print("  # Or sync individual data types:")
    print("  from src.data.sync import sync_all, preview_sync")
    print("  from src.data.api_wrappers import sync_daily_basic, sync_bak_basic")
    print("")
    print("  # Preview before sync (recommended)")
    print("  preview = preview_sync()")
    print("")
    print("  # Dry run (preview only)")
    print("  result = sync_all(dry_run=True)")
    print("")
    print("  # Actual sync")
    print("  result = sync_all()  # Incremental sync")
    print("  result = sync_all(force_full=True)  # Full reload")
    print("")
    print("  # bak_basic sync")
    print("  result = sync_bak_basic()  # Incremental sync")
    print("  result = sync_bak_basic(force_full=True)  # Full reload")
    print("\n" + "=" * 60)

    # Run sync_all_data by default
    print("\n[Main] Running sync_all_data()...")
    result = sync_all_data()
    print("\n[Main] Sync completed!")
    print(f"Total data types synced: {len(result)}")