feat(data): 新增财务指标和涨跌停数据接口

- 财务指标: fina_indicator_vip 封装，166 字段，季度同步
- 涨跌停价格: stk_limit 封装，日频数据同步
- 配套单元测试和调度中心集成

docs/api/api.md

@@ -619,3 +619,69 @@ df = pro.stock_st(trade_date='20250813')
 172  600289.SH   ST信通   20250813   ST     风险警示板
 173  000929.SZ  *ST兰黄   20250813   ST     风险警示板
 174  000638.SZ  *ST万方   20250813   ST     风险警示板
+
+
+每日涨跌停价格
+接口：stk_limit
+描述：获取全市场（包含A/B股和基金）每日涨跌停价格，包括涨停价格，跌停价格等，每个交易日8点40左右更新当日股票涨跌停价格。
+限量：单次最多提取5800条记录，可循环调取，总量不限制
+积分：用户积2000积分可调取，单位分钟有流控，积分越高流量越大，请自行提高积分，具体请参阅积分获取办法
+
+
+
+输入参数
+
+名称	类型	必选	描述
+ts_code	str	N	股票代码
+trade_date	str	N	交易日期
+start_date	str	N	开始日期
+end_date	str	N	结束日期
+
+
+输出参数
+
+名称	类型	默认显示	描述
+trade_date	str	Y	交易日期
+ts_code	str	Y	TS股票代码
+pre_close	float	N	昨日收盘价
+up_limit	float	Y	涨停价
+down_limit	float	Y	跌停价
+
+
+接口示例
+
+
+pro = ts.pro_api()
+
+#获取单日全部股票数据涨跌停价格
+df = pro.stk_limit(trade_date='20190625')
+
+#获取单个股票数据
+df = pro.stk_limit(ts_code='002149.SZ', start_date='20190115', end_date='20190615')
+
+
+
+数据示例
+
+     trade_date    ts_code  up_limit  down_limit
+0      20190625  000001.SZ     15.06       12.32
+1      20190625  000002.SZ     30.94       25.32
+2      20190625  000004.SZ     25.15       20.57
+3      20190625  000005.SZ      3.49        2.85
+4      20190625  000006.SZ      6.14        5.02
+5      20190625  000007.SZ      7.74        6.34
+6      20190625  000008.SZ      4.28        3.50
+7      20190625  000009.SZ      6.36        5.20
+8      20190625  000010.SZ      3.51        3.17
+9      20190625  000011.SZ     10.58        8.66
+10     20190625  000012.SZ      5.16        4.22
+11     20190625  000014.SZ     10.98        8.98
+12     20190625  000016.SZ      4.81        3.93
+13     20190625  000017.SZ      5.15        4.21
+14     20190625  000018.SZ      1.44        1.30
+15     20190625  000019.SZ      8.09        6.62
+16     20190625  000020.SZ     12.21        9.99
+17     20190625  000021.SZ      9.30        7.61
+18     20190625  000023.SZ     14.61       11.95
+19     20190625  000025.SZ     23.08       18.88
+20     20190625  000026.SZ      8.66        7.08

docs/api/financial_api.md

@@ -509,3 +509,212 @@ df2 = pro.cashflow_vip(period='20181231',fields='')
 10 | 母公司调整前报表 | 母公司调整之前的原始财务报表数据
 11 | 目公司调整前合并报表 | 母公司调整之前合并报表原数据
 12 | 母公司调整前报表 | 母公司报表发生变更前保留的原数据
+
+
+财务指标数据
+接口：fina_indicator，可以通过数据工具调试和查看数据。
+描述：获取上市公司财务指标数据，为避免服务器压力，现阶段每次请求最多返回100条记录，可通过设置日期多次请求获取更多数据。
+权限：用户需要至少2000积分才可以调取，具体请参阅积分获取办法
+
+提示：当前接口只能按单只股票获取其历史数据，如果需要获取某一季度全部上市公司数据，请使用fina_indicator_vip接口（参数一致），需积攒5000积分。
+
+输入参数
+
+名称	类型	必选	描述
+ts_code	str	Y	TS股票代码,e.g. 600001.SH/000001.SZ
+ann_date	str	N	公告日期
+start_date	str	N	报告期开始日期
+end_date	str	N	报告期结束日期
+period	str	N	报告期(每个季度最后一天的日期,比如20171231表示年报)
+输出参数
+
+名称	类型	默认显示	描述
+ts_code	str	Y	TS代码
+ann_date	str	Y	公告日期
+end_date	str	Y	报告期
+eps	float	Y	基本每股收益
+dt_eps	float	Y	稀释每股收益
+total_revenue_ps	float	Y	每股营业总收入
+revenue_ps	float	Y	每股营业收入
+capital_rese_ps	float	Y	每股资本公积
+surplus_rese_ps	float	Y	每股盈余公积
+undist_profit_ps	float	Y	每股未分配利润
+extra_item	float	Y	非经常性损益
+profit_dedt	float	Y	扣除非经常性损益后的净利润（扣非净利润）
+gross_margin	float	Y	毛利
+current_ratio	float	Y	流动比率
+quick_ratio	float	Y	速动比率
+cash_ratio	float	Y	保守速动比率
+invturn_days	float	N	存货周转天数
+arturn_days	float	N	应收账款周转天数
+inv_turn	float	N	存货周转率
+ar_turn	float	Y	应收账款周转率
+ca_turn	float	Y	流动资产周转率
+fa_turn	float	Y	固定资产周转率
+assets_turn	float	Y	总资产周转率
+op_income	float	Y	经营活动净收益
+valuechange_income	float	N	价值变动净收益
+interst_income	float	N	利息费用
+daa	float	N	折旧与摊销
+ebit	float	Y	息税前利润
+ebitda	float	Y	息税折旧摊销前利润
+fcff	float	Y	企业自由现金流量
+fcfe	float	Y	股权自由现金流量
+current_exint	float	Y	无息流动负债
+noncurrent_exint	float	Y	无息非流动负债
+interestdebt	float	Y	带息债务
+netdebt	float	Y	净债务
+tangible_asset	float	Y	有形资产
+working_capital	float	Y	营运资金
+networking_capital	float	Y	营运流动资本
+invest_capital	float	Y	全部投入资本
+retained_earnings	float	Y	留存收益
+diluted2_eps	float	Y	期末摊薄每股收益
+bps	float	Y	每股净资产
+ocfps	float	Y	每股经营活动产生的现金流量净额
+retainedps	float	Y	每股留存收益
+cfps	float	Y	每股现金流量净额
+ebit_ps	float	Y	每股息税前利润
+fcff_ps	float	Y	每股企业自由现金流量
+fcfe_ps	float	Y	每股股东自由现金流量
+netprofit_margin	float	Y	销售净利率
+grossprofit_margin	float	Y	销售毛利率
+cogs_of_sales	float	Y	销售成本率
+expense_of_sales	float	Y	销售期间费用率
+profit_to_gr	float	Y	净利润/营业总收入
+saleexp_to_gr	float	Y	销售费用/营业总收入
+adminexp_of_gr	float	Y	管理费用/营业总收入
+finaexp_of_gr	float	Y	财务费用/营业总收入
+impai_ttm	float	Y	资产减值损失/营业总收入
+gc_of_gr	float	Y	营业总成本/营业总收入
+op_of_gr	float	Y	营业利润/营业总收入
+ebit_of_gr	float	Y	息税前利润/营业总收入
+roe	float	Y	净资产收益率
+roe_waa	float	Y	加权平均净资产收益率
+roe_dt	float	Y	净资产收益率(扣除非经常损益)
+roa	float	Y	总资产报酬率
+npta	float	Y	总资产净利润
+roic	float	Y	投入资本回报率
+roe_yearly	float	Y	年化净资产收益率
+roa2_yearly	float	Y	年化总资产报酬率
+roe_avg	float	N	平均净资产收益率(增发条件)
+opincome_of_ebt	float	N	经营活动净收益/利润总额
+investincome_of_ebt	float	N	价值变动净收益/利润总额
+n_op_profit_of_ebt	float	N	营业外收支净额/利润总额
+tax_to_ebt	float	N	所得税/利润总额
+dtprofit_to_profit	float	N	扣除非经常损益后的净利润/净利润
+salescash_to_or	float	N	销售商品提供劳务收到的现金/营业收入
+ocf_to_or	float	N	经营活动产生的现金流量净额/营业收入
+ocf_to_opincome	float	N	经营活动产生的现金流量净额/经营活动净收益
+capitalized_to_da	float	N	资本支出/折旧和摊销
+debt_to_assets	float	Y	资产负债率
+assets_to_eqt	float	Y	权益乘数
+dp_assets_to_eqt	float	Y	权益乘数(杜邦分析)
+ca_to_assets	float	Y	流动资产/总资产
+nca_to_assets	float	Y	非流动资产/总资产
+tbassets_to_totalassets	float	Y	有形资产/总资产
+int_to_talcap	float	Y	带息债务/全部投入资本
+eqt_to_talcapital	float	Y	归属于母公司的股东权益/全部投入资本
+currentdebt_to_debt	float	Y	流动负债/负债合计
+longdeb_to_debt	float	Y	非流动负债/负债合计
+ocf_to_shortdebt	float	Y	经营活动产生的现金流量净额/流动负债
+debt_to_eqt	float	Y	产权比率
+eqt_to_debt	float	Y	归属于母公司的股东权益/负债合计
+eqt_to_interestdebt	float	Y	归属于母公司的股东权益/带息债务
+tangibleasset_to_debt	float	Y	有形资产/负债合计
+tangasset_to_intdebt	float	Y	有形资产/带息债务
+tangibleasset_to_netdebt	float	Y	有形资产/净债务
+ocf_to_debt	float	Y	经营活动产生的现金流量净额/负债合计
+ocf_to_interestdebt	float	N	经营活动产生的现金流量净额/带息债务
+ocf_to_netdebt	float	N	经营活动产生的现金流量净额/净债务
+ebit_to_interest	float	N	已获利息倍数(EBIT/利息费用)
+longdebt_to_workingcapital	float	N	长期债务与营运资金比率
+ebitda_to_debt	float	N	息税折旧摊销前利润/负债合计
+turn_days	float	Y	营业周期
+roa_yearly	float	Y	年化总资产净利率
+roa_dp	float	Y	总资产净利率(杜邦分析)
+fixed_assets	float	Y	固定资产合计
+profit_prefin_exp	float	N	扣除财务费用前营业利润
+non_op_profit	float	N	非营业利润
+op_to_ebt	float	N	营业利润／利润总额
+nop_to_ebt	float	N	非营业利润／利润总额
+ocf_to_profit	float	N	经营活动产生的现金流量净额／营业利润
+cash_to_liqdebt	float	N	货币资金／流动负债
+cash_to_liqdebt_withinterest	float	N	货币资金／带息流动负债
+op_to_liqdebt	float	N	营业利润／流动负债
+op_to_debt	float	N	营业利润／负债合计
+roic_yearly	float	N	年化投入资本回报率
+total_fa_trun	float	N	固定资产合计周转率
+profit_to_op	float	Y	利润总额／营业收入
+q_opincome	float	N	经营活动单季度净收益
+q_investincome	float	N	价值变动单季度净收益
+q_dtprofit	float	N	扣除非经常损益后的单季度净利润
+q_eps	float	N	每股收益(单季度)
+q_netprofit_margin	float	N	销售净利率(单季度)
+q_gsprofit_margin	float	N	销售毛利率(单季度)
+q_exp_to_sales	float	N	销售期间费用率(单季度)
+q_profit_to_gr	float	N	净利润／营业总收入(单季度)
+q_saleexp_to_gr	float	Y	销售费用／营业总收入 (单季度)
+q_adminexp_to_gr	float	N	管理费用／营业总收入 (单季度)
+q_finaexp_to_gr	float	N	财务费用／营业总收入 (单季度)
+q_impair_to_gr_ttm	float	N	资产减值损失／营业总收入(单季度)
+q_gc_to_gr	float	Y	营业总成本／营业总收入 (单季度)
+q_op_to_gr	float	N	营业利润／营业总收入(单季度)
+q_roe	float	Y	净资产收益率(单季度)
+q_dt_roe	float	Y	净资产单季度收益率(扣除非经常损益)
+q_npta	float	Y	总资产净利润(单季度)
+q_opincome_to_ebt	float	N	经营活动净收益／利润总额(单季度)
+q_investincome_to_ebt	float	N	价值变动净收益／利润总额(单季度)
+q_dtprofit_to_profit	float	N	扣除非经常损益后的净利润／净利润(单季度)
+q_salescash_to_or	float	N	销售商品提供劳务收到的现金／营业收入(单季度)
+q_ocf_to_sales	float	Y	经营活动产生的现金流量净额／营业收入(单季度)
+q_ocf_to_or	float	N	经营活动产生的现金流量净额／经营活动净收益(单季度)
+basic_eps_yoy	float	Y	基本每股收益同比增长率(%)
+dt_eps_yoy	float	Y	稀释每股收益同比增长率(%)
+cfps_yoy	float	Y	每股经营活动产生的现金流量净额同比增长率(%)
+op_yoy	float	Y	营业利润同比增长率(%)
+ebt_yoy	float	Y	利润总额同比增长率(%)
+netprofit_yoy	float	Y	归属母公司股东的净利润同比增长率(%)
+dt_netprofit_yoy	float	Y	归属母公司股东的净利润-扣除非经常损益同比增长率(%)
+ocf_yoy	float	Y	经营活动产生的现金流量净额同比增长率(%)
+roe_yoy	float	Y	净资产收益率(摊薄)同比增长率(%)
+bps_yoy	float	Y	每股净资产相对年初增长率(%)
+assets_yoy	float	Y	资产总计相对年初增长率(%)
+eqt_yoy	float	Y	归属母公司的股东权益相对年初增长率(%)
+tr_yoy	float	Y	营业总收入同比增长率(%)
+or_yoy	float	Y	营业收入同比增长率(%)
+q_gr_yoy	float	N	营业总收入同比增长率(%)(单季度)
+q_gr_qoq	float	N	营业总收入环比增长率(%)(单季度)
+q_sales_yoy	float	Y	营业收入同比增长率(%)(单季度)
+q_sales_qoq	float	N	营业收入环比增长率(%)(单季度)
+q_op_yoy	float	N	营业利润同比增长率(%)(单季度)
+q_op_qoq	float	Y	营业利润环比增长率(%)(单季度)
+q_profit_yoy	float	N	净利润同比增长率(%)(单季度)
+q_profit_qoq	float	N	净利润环比增长率(%)(单季度)
+q_netprofit_yoy	float	N	归属母公司股东的净利润同比增长率(%)(单季度)
+q_netprofit_qoq	float	N	归属母公司股东的净利润环比增长率(%)(单季度)
+equity_yoy	float	Y	净资产同比增长率
+rd_exp	float	N	研发费用
+update_flag	str	N	更新标识
+接口用法
+
+
+pro = ts.pro_api()
+
+df = pro.fina_indicator(ts_code='600000.SH')
+或者
+
+
+df = pro.query('fina_indicator', ts_code='600000.SH', start_date='20170101', end_date='20180801')
+数据样例
+
+ts_code  ann_date  end_date   eps  dt_eps  total_revenue_ps  revenue_ps  \
+0  600000.SH  20180830  20180630  0.95    0.95            2.8024      2.8024   
+1  600000.SH  20180428  20180331  0.46    0.46            1.3501      1.3501   
+2  600000.SH  20180428  20171231  1.84    1.84            5.7447      5.7447   
+3  600000.SH  20180428  20171231  1.84    1.84            5.7447      5.7447   
+4  600000.SH  20171028  20170930  1.45    1.45            4.2507      4.2507   
+5  600000.SH  20171028  20170930  1.45    1.45            4.2507      4.2507   
+6  600000.SH  20170830  20170630  0.97    0.97            2.9659      2.9659   
+7  600000.SH  20170427  20170331  0.63    0.63            1.9595      1.9595   
+8  600000.SH  20170427  20170331  0.63    0.63            1.9595      1.9595  

src/data/api_wrappers/__init__.py

@@ -12,11 +12,13 @@ Available APIs:
     - api_namechange: Stock name change history (股票曾用名)
     - api_bak_basic: Stock historical list (股票历史列表)
     - api_stock_st: ST stock list (ST股票列表)
+    - api_stk_limit: Stock limit price (每日涨跌停价格)
 
 Example:
     >>> from src.data.api_wrappers import get_daily, get_stock_basic, get_trade_cal, get_bak_basic
     >>> from src.data.api_wrappers import get_pro_bar, sync_pro_bar, get_daily_basic, sync_daily_basic
     >>> from src.data.api_wrappers import get_stock_st, sync_stock_st
+    >>> from src.data.api_wrappers import get_stk_limit, sync_stk_limit
     >>> data = get_daily('000001.SZ', start_date='20240101', end_date='20240131')
     >>> pro_data = get_pro_bar('000001.SZ', start_date='20240101', end_date='20240131')
     >>> daily_basic = get_daily_basic(trade_date='20240101')
@@ -24,6 +26,7 @@ Example:
     >>> calendar = get_trade_cal('20240101', '20240131')
     >>> bak_basic = get_bak_basic(trade_date='20240101')
     >>> stock_st = get_stock_st(trade_date='20240101')
+    >>> stk_limit = get_stk_limit(trade_date='20240101')
 """
 
 from src.data.api_wrappers.api_daily import (
@@ -58,6 +61,12 @@ from src.data.api_wrappers.api_stock_st import (
     sync_stock_st,
     StockSTSync,
 )
+from src.data.api_wrappers.api_stk_limit import (
+    get_stk_limit,
+    sync_stk_limit,
+    preview_stk_limit_sync,
+    StkLimitSync,
+)
 from src.data.api_wrappers.api_trade_cal import (
     get_trade_cal,
     get_trading_days,
@@ -107,6 +116,11 @@ __all__ = [
     "get_stock_st",
     "sync_stock_st",
     "StockSTSync",
+    # Stock limit price
+    "get_stk_limit",
+    "sync_stk_limit",
+    "preview_stk_limit_sync",
+    "StkLimitSync",
 ]
 
 # =============================================================================
@@ -179,6 +193,17 @@ try:
         order=40,
     )
 
+    # 7. Stock Limit Price - 每日涨跌停价格
+    from src.data.api_wrappers.api_stk_limit import StkLimitSync
+
+    sync_registry.register_class(
+        name="stk_limit",
+        sync_class=StkLimitSync,
+        display_name="每日涨跌停价格",
+        description="股票每日涨跌停价格（涨停价、跌停价）",
+        order=50,
+    )
+
 except ImportError:
     # sync_registry 可能不存在（首次导入），忽略
     pass

src/data/api_wrappers/api_stk_limit.py

@@ -0,0 +1,224 @@
+"""Stock Limit Price (涨跌停价格) interface.
+
+Fetch daily limit up/down prices for all stocks from Tushare.
+This interface retrieves the upper and lower limit prices for stocks,
+which are typically available around 8:40 AM each trading day.
+"""
+
+from typing import override
+
+import pandas as pd
+
+from src.data.client import TushareClient
+from src.data.api_wrappers.base_sync import DateBasedSync
+
+
+def get_stk_limit(
+    trade_date: str | None = None,
+    start_date: str | None = None,
+    end_date: str | None = None,
+    ts_code: str | None = None,
+    client: TushareClient | None = None,
+) -> pd.DataFrame:
+    """Fetch stock limit prices from Tushare.
+
+    This interface retrieves daily limit up/down prices for stocks.
+    Each trading day, limit prices are available around 8:40 AM.
+    Supports fetching all stocks for a single date (preferred for efficiency)
+    or date range data for specific stocks.
+
+    Args:
+        trade_date: Specific trade date (YYYYMMDD format).
+            If provided, fetches all stocks for this date (most efficient).
+        start_date: Start date (YYYYMMDD format).
+            Used with end_date for date range queries.
+        end_date: End date (YYYYMMDD format).
+            Used with start_date for date range queries.
+        ts_code: Stock code filter (optional).
+            e.g., '000001.SZ', '600000.SH'
+        client: Optional TushareClient instance for shared rate limiting.
+            If None, creates a new client. For concurrent sync operations,
+            pass a shared client to ensure proper rate limiting.
+
+    Returns:
+        pd.DataFrame with columns:
+        - trade_date: Trade date (YYYYMMDD)
+        - ts_code: Stock code
+        - pre_close: Previous closing price
+        - up_limit: Upper limit price (涨停价)
+        - down_limit: Lower limit price (跌停价)
+
+    Example:
+        >>> # Get all stocks limit prices for a single date (most efficient)
+        >>> data = get_stk_limit(trade_date='20240625')
+        >>>
+        >>> # Get date range data
+        >>> data = get_stk_limit(start_date='20240101', end_date='20240131')
+        >>>
+        >>> # Get specific stock data
+        >>> data = get_stk_limit(ts_code='000001.SZ', start_date='20240101', end_date='20240131')
+    """
+    client = client or TushareClient()
+
+    # Build parameters
+    params = {}
+    if trade_date:
+        params["trade_date"] = trade_date
+    if start_date:
+        params["start_date"] = start_date
+    if end_date:
+        params["end_date"] = end_date
+    if ts_code:
+        params["ts_code"] = ts_code
+
+    # Fetch data
+    data = client.query("stk_limit", **params)  # type: ignore
+
+    # Rename date column if needed
+    if "date" in data.columns:
+        data = data.rename(columns={"date": "trade_date"})
+
+    return data
+
+
+class StkLimitSync(DateBasedSync):
+    """Stock Limit Price data batch sync manager.
+
+    Inherits from DateBasedSync, fetches data by date for all stocks.
+    Each API call retrieves limit prices for all stocks on a specific date.
+
+    Example:
+        >>> sync = StkLimitSync()
+        >>> results = sync.sync_all()  # Incremental sync
+        >>> results = sync.sync_all(force_full=True)  # Full reload
+        >>> preview = sync.preview_sync()  # Preview
+    """
+
+    table_name: str = "stk_limit"
+    default_start_date: str = "20180101"
+
+    # Table schema definition
+    TABLE_SCHEMA: dict[str, str] = {
+        "ts_code": "VARCHAR(16) NOT NULL",
+        "trade_date": "DATE NOT NULL",
+        "pre_close": "DOUBLE",
+        "up_limit": "DOUBLE",
+        "down_limit": "DOUBLE",
+    }
+
+    # Index definitions
+    TABLE_INDEXES: list[tuple[str, list[str]]] = [
+        ("idx_stk_limit_date_code", ["trade_date", "ts_code"]),
+    ]
+
+    # Primary key definition
+    PRIMARY_KEY: tuple[str, str] = ("ts_code", "trade_date")
+
+    @override
+    def fetch_single_date(self, trade_date: str) -> pd.DataFrame:
+        """Fetch limit prices for all stocks on a specific date.
+
+        Args:
+            trade_date: Trading date (YYYYMMDD)
+
+        Returns:
+            DataFrame with limit prices for all stocks on the date
+        """
+        # Use get_stk_limit to fetch all stocks for a single date
+        data = get_stk_limit(
+            trade_date=trade_date,
+            client=self.client,  # Pass shared client for rate limiting
+        )
+        return data
+
+
+def sync_stk_limit(
+    force_full: bool = False,
+    start_date: str | None = None,
+    end_date: str | None = None,
+    dry_run: bool = False,
+) -> pd.DataFrame:
+    """Sync stock limit prices to local DuckDB storage.
+
+    This is the main entry point for stock limit price data synchronization.
+
+    Args:
+        force_full: If True, force full reload from default_start_date
+        start_date: Manual start date override (YYYYMMDD)
+        end_date: Manual end date override (defaults to today)
+        dry_run: If True, only preview what would be synced without writing
+
+    Returns:
+        DataFrame with synced data
+
+    Example:
+        >>> # First sync (full load from default_start_date)
+        >>> result = sync_stk_limit()
+        >>>
+        >>> # Subsequent syncs (incremental - only new data)
+        >>> result = sync_stk_limit()
+        >>>
+        >>> # Force full reload
+        >>> result = sync_stk_limit(force_full=True)
+        >>>
+        >>> # Manual date range
+        >>> result = sync_stk_limit(start_date='20240101', end_date='20240131')
+        >>>
+        >>> # Dry run (preview only)
+        >>> result = sync_stk_limit(dry_run=True)
+    """
+    sync_manager = StkLimitSync()
+    return sync_manager.sync_all(
+        force_full=force_full,
+        start_date=start_date,
+        end_date=end_date,
+        dry_run=dry_run,
+    )
+
+
+def preview_stk_limit_sync(
+    force_full: bool = False,
+    start_date: str | None = None,
+    end_date: str | None = None,
+    sample_size: int = 3,
+) -> dict[str, object]:
+    """Preview stock limit price sync data volume and samples.
+
+    This is the recommended way to check what would be synced before
+    actually performing the synchronization.
+
+    Args:
+        force_full: If True, preview full sync from default_start_date
+        start_date: Manual start date override
+        end_date: Manual end date override (defaults to today)
+        sample_size: Number of sample days to fetch for preview (default: 3)
+
+    Returns:
+        Dictionary with preview information:
+        {
+            'sync_needed': bool,
+            'date_count': int,
+            'start_date': str,
+            'end_date': str,
+            'estimated_records': int,
+            'sample_data': pd.DataFrame,
+            'mode': str,  # 'full', 'incremental', or 'none'
+        }
+
+    Example:
+        >>> # Preview what would be synced
+        >>> preview = preview_stk_limit_sync()
+        >>>
+        >>> # Preview full sync
+        >>> preview = preview_stk_limit_sync(force_full=True)
+        >>>
+        >>> # Preview with more samples
+        >>> preview = preview_stk_limit_sync(sample_size=5)
+    """
+    sync_manager = StkLimitSync()
+    return sync_manager.preview_sync(
+        force_full=force_full,
+        start_date=start_date,
+        end_date=end_date,
+        sample_size=sample_size,
+    )

src/data/api_wrappers/base_sync.py

@@ -1324,52 +1324,6 @@ class DateBasedSync(BaseDataSync):
                 probe_desc = f"date={probe_date}, all stocks"
                 self._probe_table_and_cleanup(probe_data, probe_desc)
 
-        # 执行同步
-        combined = self._run_date_range_sync(sync_start, sync_end, dry_run)
-        if self._should_probe_table():
-            print(f"[{class_name}] Table '{self.table_name}' is empty, probing...")
-            # 使用最近一个交易日的完整数据进行探测
-            probe_date = get_last_trading_day(sync_start, sync_end)
-            if probe_date:
-                probe_data = self.fetch_single_date(probe_date)
-                probe_desc = f"date={probe_date}, all stocks"
-                self._probe_table_and_cleanup(probe_data, probe_desc)
-
-        # 执行同步
-        if self._should_probe_table():
-            print(f"[{class_name}] Table '{self.table_name}' is empty, probing...")
-            # 使用最近一个交易日的完整数据进行探测
-            probe_date = get_last_trading_day(sync_start, sync_end)
-            if probe_date:
-                probe_data = self.fetch_single_date(probe_date)
-                probe_desc = f"date={probe_date}, all stocks"
-                self._probe_table_and_cleanup(probe_data, probe_desc)
-        if self._should_probe_table():
-            print(f"[{class_name}] Table '{self.table_name}' is empty, probing...")
-            # 使用最近一个交易日的完整数据进行探测
-            probe_date = get_last_trading_day(sync_start, sync_end)
-            if probe_date:
-                probe_data = self.fetch_single_date(probe_date)
-                probe_desc = f"date={probe_date}, all stocks"
-                self._probe_table_and_cleanup(probe_data, probe_desc)
-
-        # 执行同步
-        storage = Storage()
-        if not storage.exists(self.table_name):
-            print(
-                f"[{class_name}] Table '{self.table_name}' doesn't exist, creating..."
-            )
-            # 获取样本数据以推断 schema
-            sample = self.fetch_single_date(sync_end)
-            if sample.empty:
-                # 尝试另一个日期
-                sample = self.fetch_single_date("20240102")
-            if not sample.empty:
-                self._ensure_table_schema(sample)
-            else:
-                print(f"[{class_name}] Cannot create table: no sample data available")
-                return pd.DataFrame()
-
         # 执行同步
         combined = self._run_date_range_sync(sync_start, sync_end, dry_run)
 

src/data/api_wrappers/financial_data/api_fina_indicator.py

@@ -0,0 +1,394 @@
+"""财务指标数据接口 (VIP 版本)
+
+使用 Tushare VIP 接口 (fina_indicator_vip) 获取财务指标数据。
+按季度同步，一次请求获取一个季度的全部上市公司数据。
+
+接口说明:
+- fina_indicator_vip: 获取某一季度全部上市公司财务指标数据
+- 需要 5000 积分才能调用
+- period 参数为报告期（季度最后一天，如 20231231）
+- 每次请求最多返回 100 条记录（需多次请求获取更多数据）
+
+使用方式:
+    # 同步财务指标数据
+    from src.data.api_wrappers.financial_data.api_fina_indicator import (
+        FinaIndicatorQuarterSync,
+        sync_fina_indicator
+    )
+
+    # 方式1: 使用类
+    syncer = FinaIndicatorQuarterSync()
+    syncer.sync_incremental()  # 增量同步
+    syncer.sync_full()  # 全量同步
+
+    # 方式2: 使用便捷函数
+    sync_fina_indicator()  # 增量同步
+    sync_fina_indicator(force_full=True)  # 全量同步
+"""
+
+from typing import Optional
+import pandas as pd
+
+from src.data.client import TushareClient
+from src.data.api_wrappers.base_financial_sync import (
+    QuarterBasedSync,
+    sync_financial_data,
+    preview_financial_sync,
+)
+
+
+class FinaIndicatorQuarterSync(QuarterBasedSync):
+    """财务指标季度同步实现。
+
+    使用 fina_indicator_vip 接口按季度获取全部上市公司财务指标数据。
+
+    表结构: financial_fina_indicator
+    主键: (ts_code, end_date)
+    """
+
+    table_name = "financial_fina_indicator"
+    api_name = "fina_indicator_vip"
+
+    # 目标报表类型：默认只同步合并报表（财务指标接口无需过滤 report_type）
+    TARGET_REPORT_TYPE = None
+
+    # 表结构定义 - 完整的财务指标字段
+    TABLE_SCHEMA = {
+        # 基础字段
+        "ts_code": "VARCHAR(16) NOT NULL",
+        "ann_date": "DATE",
+        "end_date": "DATE NOT NULL",
+        # 每股收益指标
+        "eps": "DOUBLE",
+        "dt_eps": "DOUBLE",
+        "total_revenue_ps": "DOUBLE",
+        "revenue_ps": "DOUBLE",
+        "capital_rese_ps": "DOUBLE",
+        "surplus_rese_ps": "DOUBLE",
+        "undist_profit_ps": "DOUBLE",
+        "extra_item": "DOUBLE",
+        "profit_dedt": "DOUBLE",
+        "gross_margin": "DOUBLE",
+        # 偿债能力指标
+        "current_ratio": "DOUBLE",
+        "quick_ratio": "DOUBLE",
+        "cash_ratio": "DOUBLE",
+        # 营运能力指标
+        "invturn_days": "DOUBLE",
+        "arturn_days": "DOUBLE",
+        "inv_turn": "DOUBLE",
+        "ar_turn": "DOUBLE",
+        "ca_turn": "DOUBLE",
+        "fa_turn": "DOUBLE",
+        "assets_turn": "DOUBLE",
+        # 盈利能力指标
+        "op_income": "DOUBLE",
+        "valuechange_income": "DOUBLE",
+        "interst_income": "DOUBLE",
+        "daa": "DOUBLE",
+        "ebit": "DOUBLE",
+        "ebitda": "DOUBLE",
+        "fcff": "DOUBLE",
+        "fcfe": "DOUBLE",
+        # 资本结构指标
+        "current_exint": "DOUBLE",
+        "noncurrent_exint": "DOUBLE",
+        "interestdebt": "DOUBLE",
+        "netdebt": "DOUBLE",
+        "tangible_asset": "DOUBLE",
+        "working_capital": "DOUBLE",
+        "networking_capital": "DOUBLE",
+        "invest_capital": "DOUBLE",
+        "retained_earnings": "DOUBLE",
+        # 每股指标
+        "diluted2_eps": "DOUBLE",
+        "bps": "DOUBLE",
+        "ocfps": "DOUBLE",
+        "retainedps": "DOUBLE",
+        "cfps": "DOUBLE",
+        "ebit_ps": "DOUBLE",
+        "fcff_ps": "DOUBLE",
+        "fcfe_ps": "DOUBLE",
+        # 销售能力指标
+        "netprofit_margin": "DOUBLE",
+        "grossprofit_margin": "DOUBLE",
+        "cogs_of_sales": "DOUBLE",
+        "expense_of_sales": "DOUBLE",
+        "profit_to_gr": "DOUBLE",
+        "saleexp_to_gr": "DOUBLE",
+        "adminexp_of_gr": "DOUBLE",
+        "finaexp_of_gr": "DOUBLE",
+        "impai_ttm": "DOUBLE",
+        "gc_of_gr": "DOUBLE",
+        "op_of_gr": "DOUBLE",
+        "ebit_of_gr": "DOUBLE",
+        # 投资回报率指标
+        "roe": "DOUBLE",
+        "roe_waa": "DOUBLE",
+        "roe_dt": "DOUBLE",
+        "roa": "DOUBLE",
+        "npta": "DOUBLE",
+        "roic": "DOUBLE",
+        "roe_yearly": "DOUBLE",
+        "roa2_yearly": "DOUBLE",
+        "roe_avg": "DOUBLE",
+        # 利润结构指标
+        "opincome_of_ebt": "DOUBLE",
+        "investincome_of_ebt": "DOUBLE",
+        "n_op_profit_of_ebt": "DOUBLE",
+        "tax_to_ebt": "DOUBLE",
+        "dtprofit_to_profit": "DOUBLE",
+        # 现金流量指标
+        "salescash_to_or": "DOUBLE",
+        "ocf_to_or": "DOUBLE",
+        "ocf_to_opincome": "DOUBLE",
+        # 资本支出指标
+        "capitalized_to_da": "DOUBLE",
+        # 杠杆与偿债能力指标
+        "debt_to_assets": "DOUBLE",
+        "assets_to_eqt": "DOUBLE",
+        "dp_assets_to_eqt": "DOUBLE",
+        "ca_to_assets": "DOUBLE",
+        "nca_to_assets": "DOUBLE",
+        "tbassets_to_totalassets": "DOUBLE",
+        "int_to_talcap": "DOUBLE",
+        "eqt_to_talcapital": "DOUBLE",
+        "currentdebt_to_debt": "DOUBLE",
+        "longdeb_to_debt": "DOUBLE",
+        "ocf_to_shortdebt": "DOUBLE",
+        "debt_to_eqt": "DOUBLE",
+        "eqt_to_debt": "DOUBLE",
+        "eqt_to_interestdebt": "DOUBLE",
+        "tangibleasset_to_debt": "DOUBLE",
+        "tangasset_to_intdebt": "DOUBLE",
+        "tangibleasset_to_netdebt": "DOUBLE",
+        "ocf_to_debt": "DOUBLE",
+        "ocf_to_interestdebt": "DOUBLE",
+        "ocf_to_netdebt": "DOUBLE",
+        "ebit_to_interest": "DOUBLE",
+        "longdebt_to_workingcapital": "DOUBLE",
+        "ebitda_to_debt": "DOUBLE",
+        # 营运周期指标
+        "turn_days": "DOUBLE",
+        "roa_yearly": "DOUBLE",
+        "roa_dp": "DOUBLE",
+        "fixed_assets": "DOUBLE",
+        # 利润质量指标
+        "profit_prefin_exp": "DOUBLE",
+        "non_op_profit": "DOUBLE",
+        "op_to_ebt": "DOUBLE",
+        "nop_to_ebt": "DOUBLE",
+        "ocf_to_profit": "DOUBLE",
+        # 流动性指标
+        "cash_to_liqdebt": "DOUBLE",
+        "cash_to_liqdebt_withinterest": "DOUBLE",
+        "op_to_liqdebt": "DOUBLE",
+        "op_to_debt": "DOUBLE",
+        "roic_yearly": "DOUBLE",
+        "total_fa_trun": "DOUBLE",
+        "profit_to_op": "DOUBLE",
+        # 单季度指标 (q_*)
+        "q_opincome": "DOUBLE",
+        "q_investincome": "DOUBLE",
+        "q_dtprofit": "DOUBLE",
+        "q_eps": "DOUBLE",
+        "q_netprofit_margin": "DOUBLE",
+        "q_gsprofit_margin": "DOUBLE",
+        "q_exp_to_sales": "DOUBLE",
+        "q_profit_to_gr": "DOUBLE",
+        "q_saleexp_to_gr": "DOUBLE",
+        "q_adminexp_to_gr": "DOUBLE",
+        "q_finaexp_to_gr": "DOUBLE",
+        "q_impair_to_gr_ttm": "DOUBLE",
+        "q_gc_to_gr": "DOUBLE",
+        "q_op_to_gr": "DOUBLE",
+        "q_roe": "DOUBLE",
+        "q_dt_roe": "DOUBLE",
+        "q_npta": "DOUBLE",
+        "q_opincome_to_ebt": "DOUBLE",
+        "q_investincome_to_ebt": "DOUBLE",
+        "q_dtprofit_to_profit": "DOUBLE",
+        "q_salescash_to_or": "DOUBLE",
+        "q_ocf_to_sales": "DOUBLE",
+        "q_ocf_to_or": "DOUBLE",
+        # 同比增长率指标 (*_yoy)
+        "basic_eps_yoy": "DOUBLE",
+        "dt_eps_yoy": "DOUBLE",
+        "cfps_yoy": "DOUBLE",
+        "op_yoy": "DOUBLE",
+        "ebt_yoy": "DOUBLE",
+        "netprofit_yoy": "DOUBLE",
+        "dt_netprofit_yoy": "DOUBLE",
+        "ocf_yoy": "DOUBLE",
+        "roe_yoy": "DOUBLE",
+        "bps_yoy": "DOUBLE",
+        "assets_yoy": "DOUBLE",
+        "eqt_yoy": "DOUBLE",
+        "tr_yoy": "DOUBLE",
+        "or_yoy": "DOUBLE",
+        # 单季度增长指标 (q_*_yoy, q_*_qoq)
+        "q_gr_yoy": "DOUBLE",
+        "q_gr_qoq": "DOUBLE",
+        "q_sales_yoy": "DOUBLE",
+        "q_sales_qoq": "DOUBLE",
+        "q_op_yoy": "DOUBLE",
+        "q_op_qoq": "DOUBLE",
+        "q_profit_yoy": "DOUBLE",
+        "q_profit_qoq": "DOUBLE",
+        "q_netprofit_yoy": "DOUBLE",
+        "q_netprofit_qoq": "DOUBLE",
+        # 其他指标
+        "equity_yoy": "DOUBLE",
+        "rd_exp": "DOUBLE",
+        "update_flag": "VARCHAR(1)",
+    }
+
+    # 索引定义（不要创建唯一索引）
+    # 注意：财务数据可能发生多次修正，不设置主键和唯一索引
+    TABLE_INDEXES = [
+        ("idx_financial_fina_indicator_ts_code", ["ts_code"]),
+        ("idx_financial_fina_indicator_end_date", ["end_date"]),
+        ("idx_financial_fina_indicator_ts_period", ["ts_code", "end_date"]),
+    ]
+
+    def __init__(self):
+        """初始化财务指标同步器。"""
+        super().__init__()
+        self._fields = None  # 默认返回全部字段
+
+    def fetch_single_quarter(self, period: str) -> pd.DataFrame:
+        """获取单季度的全部上市公司财务指标数据。
+
+        注意：fina_indicator_vip 接口每次请求最多返回 100 条记录，
+        需要通过 offset 参数循环获取该季度的全部数据。
+
+        Args:
+            period: 报告期，季度最后一天日期（如 '20231231'）
+
+        Returns:
+            包含该季度全部上市公司财务指标数据的 DataFrame
+        """
+        all_data = []
+        offset = 0
+        limit = 100  # API 限制每次最多返回 100 条
+
+        while True:
+            params = {
+                "period": period,
+                "limit": limit,
+                "offset": offset,
+            }
+
+            if self._fields:
+                params["fields"] = self._fields
+
+            df = self.client.query(self.api_name, **params)
+
+            if df.empty:
+                break
+
+            all_data.append(df)
+
+            # 如果返回的数据少于 limit，说明已经取完
+            if len(df) < limit:
+                break
+
+            offset += limit
+
+        if not all_data:
+            return pd.DataFrame()
+
+        return pd.concat(all_data, ignore_index=True)
+
+
+# =============================================================================
+# 便捷函数
+# =============================================================================
+
+
+def sync_fina_indicator(
+    force_full: bool = False,
+    dry_run: bool = False,
+) -> list:
+    """同步财务指标数据（便捷函数）。
+
+    Args:
+        force_full: 若为 True，强制全量同步
+        dry_run: 若为 True，仅预览不写入
+
+    Returns:
+        同步结果列表
+
+    Example:
+        >>> # 增量同步
+        >>> sync_fina_indicator()
+        >>>
+        >>> # 全量同步
+        >>> sync_fina_indicator(force_full=True)
+        >>>
+        >>> # 预览
+        >>> sync_fina_indicator(dry_run=True)
+    """
+    return sync_financial_data(FinaIndicatorQuarterSync, force_full, dry_run)
+
+
+def preview_fina_indicator_sync() -> dict:
+    """预览财务指标同步信息。
+
+    Returns:
+        预览信息字典
+    """
+    return preview_financial_sync(FinaIndicatorQuarterSync)
+
+
+def get_fina_indicator(period: str, fields: Optional[str] = None) -> pd.DataFrame:
+    """获取财务指标数据（原始接口，单季度）。
+
+    用于直接获取某个季度的数据，不进行同步管理。
+    注意：该接口每次最多返回 100 条记录，如需获取全部数据请使用同步功能。
+
+    Args:
+        period: 报告期，季度最后一天日期（如 '20231231'）
+        fields: 指定返回字段，默认返回全部字段
+
+    Returns:
+        包含财务指标数据的 DataFrame
+    """
+    client = TushareClient()
+
+    if fields is None:
+        fields = (
+            "ts_code,ann_date,end_date,eps,dt_eps,total_revenue_ps,revenue_ps,"
+            "capital_rese_ps,surplus_rese_ps,undist_profit_ps,extra_item,profit_dedt,"
+            "gross_margin,current_ratio,quick_ratio,cash_ratio,invturn_days,arturn_days,"
+            "inv_turn,ar_turn,ca_turn,fa_turn,assets_turn,op_income,valuechange_income,"
+            "interst_income,daa,ebit,ebitda,fcff,fcfe,current_exint,noncurrent_exint,"
+            "interestdebt,netdebt,tangible_asset,working_capital,networking_capital,"
+            "invest_capital,retained_earnings,diluted2_eps,bps,ocfps,retainedps,cfps,"
+            "ebit_ps,fcff_ps,fcfe_ps,netprofit_margin,grossprofit_margin,cogs_of_sales,"
+            "expense_of_sales,profit_to_gr,saleexp_to_gr,adminexp_of_gr,finaexp_of_gr,"
+            "impai_ttm,gc_of_gr,op_of_gr,ebit_of_gr,roe,roe_waa,roe_dt,roa,npta,roic,"
+            "roe_yearly,roa2_yearly,roe_avg,opincome_of_ebt,investincome_of_ebt,"
+            "n_op_profit_of_ebt,tax_to_ebt,dtprofit_to_profit,salescash_to_or,"
+            "ocf_to_or,ocf_to_opincome,capitalized_to_da,debt_to_assets,assets_to_eqt,"
+            "dp_assets_to_eqt,ca_to_assets,nca_to_assets,tbassets_to_totalassets,"
+            "int_to_talcap,eqt_to_talcapital,currentdebt_to_debt,longdeb_to_debt,"
+            "ocf_to_shortdebt,debt_to_eqt,eqt_to_debt,eqt_to_interestdebt,"
+            "tangibleasset_to_debt,tangasset_to_intdebt,tangibleasset_to_netdebt,"
+            "ocf_to_debt,ocf_to_interestdebt,ocf_to_netdebt,ebit_to_interest,"
+            "longdebt_to_workingcapital,ebitda_to_debt,turn_days,roa_yearly,roa_dp,"
+            "fixed_assets,profit_prefin_exp,non_op_profit,op_to_ebt,nop_to_ebt,"
+            "ocf_to_profit,cash_to_liqdebt,cash_to_liqdebt_withinterest,op_to_liqdebt,"
+            "op_to_debt,roic_yearly,total_fa_trun,profit_to_op,q_opincome,"
+            "q_investincome,q_dtprofit,q_eps,q_netprofit_margin,q_gsprofit_margin,"
+            "q_exp_to_sales,q_profit_to_gr,q_saleexp_to_gr,q_adminexp_to_gr,"
+            "q_finaexp_to_gr,q_impair_to_gr_ttm,q_gc_to_gr,q_op_to_gr,q_roe,q_dt_roe,"
+            "q_npta,q_opincome_to_ebt,q_investincome_to_ebt,q_dtprofit_to_profit,"
+            "q_salescash_to_or,q_ocf_to_sales,q_ocf_to_or,basic_eps_yoy,dt_eps_yoy,"
+            "cfps_yoy,op_yoy,ebt_yoy,netprofit_yoy,dt_netprofit_yoy,ocf_yoy,roe_yoy,"
+            "bps_yoy,assets_yoy,eqt_yoy,tr_yoy,or_yoy,q_gr_yoy,q_gr_qoq,q_sales_yoy,"
+            "q_sales_qoq,q_op_yoy,q_op_qoq,q_profit_yoy,q_profit_qoq,q_netprofit_yoy,"
+            "q_netprofit_qoq,equity_yoy,rd_exp,update_flag"
+        )
+
+    return client.query("fina_indicator_vip", period=period, fields=fields)

src/data/api_wrappers/financial_data/api_financial_sync.py

@@ -7,6 +7,7 @@
 - income: 利润表 (已实现)
 - balance: 资产负债表 (已实现)
 - cashflow: 现金流量表 (已实现)
+- fina_indicator: 财务指标 (已实现)
 
 使用方式:
     # 同步所有财务数据（增量）
@@ -25,12 +26,15 @@
     # 只同步现金流量表
     sync_financial(data_types=["cashflow"])
 
+    # 只同步财务指标
+    sync_financial(data_types=["fina_indicator"])
+
     # 预览同步
     from src.data.api_wrappers.financial_data.api_financial_sync import preview_sync
     preview = preview_sync()
 """
 
-from typing import List, Dict, Optional
+from typing import List, Optional
 
 from src.data.api_wrappers.financial_data.api_income import (
     IncomeQuarterSync,
@@ -47,6 +51,11 @@ from src.data.api_wrappers.financial_data.api_cashflow import (
     sync_cashflow,
     preview_cashflow_sync,
 )
+from src.data.api_wrappers.financial_data.api_fina_indicator import (
+    FinaIndicatorQuarterSync,
+    sync_fina_indicator,
+    preview_fina_indicator_sync,
+)
 
 
 # 支持的财务数据类型映射
@@ -69,6 +78,12 @@ FINANCIAL_SYNCERS = {
         "preview_func": preview_cashflow_sync,
         "display_name": "现金流量表",
     },
+    "fina_indicator": {
+        "syncer_class": FinaIndicatorQuarterSync,
+        "sync_func": sync_fina_indicator,
+        "preview_func": preview_fina_indicator_sync,
+        "display_name": "财务指标",
+    },
 }
 
 
@@ -76,7 +91,7 @@ def sync_financial(
     data_types: Optional[List[str]] = None,
     force_full: bool = False,
     dry_run: bool = False,
-) -> Dict[str, List]:
+) -> dict[str, list]:
     """同步财务数据（调度函数）。
 
     根据指定的数据类型，调度对应的同步器执行同步。
@@ -157,7 +172,7 @@ def sync_financial(
     return results
 
 
-def preview_sync(data_types: Optional[List[str]] = None) -> Dict[str, Dict]:
+def preview_sync(data_types: Optional[List[str]] = None) -> dict[str, dict]:
     """预览财务数据同步信息。
 
     Args:
@@ -189,7 +204,7 @@ def preview_sync(data_types: Optional[List[str]] = None) -> Dict[str, Dict]:
     return previews
 
 
-def list_financial_types() -> List[Dict]:
+def list_financial_types() -> list[dict]:
     """列出所有支持的财务数据类型。
 
     Returns:

tests/test_stk_limit.py

@@ -0,0 +1,246 @@
+"""Tests for stock limit price API wrapper."""
+
+import pytest
+import pandas as pd
+from unittest.mock import patch, MagicMock
+
+from src.data.api_wrappers.api_stk_limit import (
+    get_stk_limit,
+    sync_stk_limit,
+    preview_stk_limit_sync,
+    StkLimitSync,
+)
+
+
+class TestStkLimit:
+    """Test suite for stk_limit API wrapper."""
+
+    @patch("src.data.api_wrappers.api_stk_limit.TushareClient")
+    def test_get_by_date(self, mock_client_class):
+        """Test fetching data by trade_date."""
+        # Setup mock
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.query.return_value = pd.DataFrame(
+            {
+                "ts_code": ["000001.SZ", "000002.SZ"],
+                "trade_date": ["20240625", "20240625"],
+                "pre_close": [10.0, 20.0],
+                "up_limit": [11.0, 22.0],
+                "down_limit": [9.0, 18.0],
+            }
+        )
+
+        # Test
+        result = get_stk_limit(trade_date="20240625")
+
+        # Assert
+        assert not result.empty
+        assert len(result) == 2
+        assert "ts_code" in result.columns
+        assert "trade_date" in result.columns
+        assert "up_limit" in result.columns
+        assert "down_limit" in result.columns
+        mock_client.query.assert_called_once_with("stk_limit", trade_date="20240625")
+
+    @patch("src.data.api_wrappers.api_stk_limit.TushareClient")
+    def test_get_by_date_range(self, mock_client_class):
+        """Test fetching data by date range."""
+        # Setup mock
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.query.return_value = pd.DataFrame(
+            {
+                "ts_code": ["000001.SZ", "000001.SZ"],
+                "trade_date": ["20240624", "20240625"],
+                "pre_close": [10.0, 10.5],
+                "up_limit": [11.0, 11.55],
+                "down_limit": [9.0, 9.45],
+            }
+        )
+
+        # Test
+        result = get_stk_limit(start_date="20240624", end_date="20240625")
+
+        # Assert
+        assert not result.empty
+        assert len(result) == 2
+        mock_client.query.assert_called_once_with(
+            "stk_limit", start_date="20240624", end_date="20240625"
+        )
+
+    @patch("src.data.api_wrappers.api_stk_limit.TushareClient")
+    def test_get_by_stock_code(self, mock_client_class):
+        """Test fetching data by stock code."""
+        # Setup mock
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.query.return_value = pd.DataFrame(
+            {
+                "ts_code": ["000001.SZ"],
+                "trade_date": ["20240625"],
+                "pre_close": [10.0],
+                "up_limit": [11.0],
+                "down_limit": [9.0],
+            }
+        )
+
+        # Test
+        result = get_stk_limit(ts_code="000001.SZ", trade_date="20240625")
+
+        # Assert
+        assert not result.empty
+        assert len(result) == 1
+        assert result.iloc[0]["ts_code"] == "000001.SZ"
+        mock_client.query.assert_called_once_with(
+            "stk_limit", trade_date="20240625", ts_code="000001.SZ"
+        )
+
+    @patch("src.data.api_wrappers.api_stk_limit.TushareClient")
+    def test_empty_response(self, mock_client_class):
+        """Test handling empty response."""
+        # Setup mock
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.query.return_value = pd.DataFrame()
+
+        # Test
+        result = get_stk_limit(trade_date="20240625")
+
+        # Assert
+        assert result.empty
+
+    @patch("src.data.api_wrappers.api_stk_limit.TushareClient")
+    def test_shared_client(self, mock_client_class):
+        """Test passing shared client for rate limiting."""
+        # Setup mock
+        shared_client = MagicMock()
+        shared_client.query.return_value = pd.DataFrame(
+            {
+                "ts_code": ["000001.SZ"],
+                "trade_date": ["20240625"],
+                "pre_close": [10.0],
+                "up_limit": [11.0],
+                "down_limit": [9.0],
+            }
+        )
+
+        # Test
+        result = get_stk_limit(trade_date="20240625", client=shared_client)
+
+        # Assert
+        assert not result.empty
+        shared_client.query.assert_called_once()
+        # Verify new client was not created
+        mock_client_class.assert_not_called()
+
+
+class TestStkLimitSync:
+    """Test suite for StkLimitSync class."""
+
+    @patch("src.data.api_wrappers.api_stk_limit.TushareClient")
+    @patch("src.data.api_wrappers.base_sync.Storage")
+    @patch("src.data.api_wrappers.base_sync.sync_trade_cal_cache")
+    def test_fetch_single_date(
+        self, mock_sync_cal, mock_storage_class, mock_client_class
+    ):
+        """Test fetch_single_date method."""
+        # Setup mock
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.query.return_value = pd.DataFrame(
+            {
+                "ts_code": ["000001.SZ", "000002.SZ"],
+                "trade_date": ["20240625", "20240625"],
+                "pre_close": [10.0, 20.0],
+                "up_limit": [11.0, 22.0],
+                "down_limit": [9.0, 18.0],
+            }
+        )
+
+        mock_storage = MagicMock()
+        mock_storage_class.return_value = mock_storage
+        mock_storage.exists.return_value = True
+        mock_storage.load.return_value = pd.DataFrame()
+
+        # Test
+        sync = StkLimitSync()
+        result = sync.fetch_single_date("20240625")
+
+        # Assert
+        assert not result.empty
+        assert len(result) == 2
+        mock_client.query.assert_called_once_with("stk_limit", trade_date="20240625")
+
+    def test_table_schema(self):
+        """Test table schema definition."""
+        sync = StkLimitSync()
+
+        # Assert table configuration
+        assert sync.table_name == "stk_limit"
+        assert "ts_code" in sync.TABLE_SCHEMA
+        assert "trade_date" in sync.TABLE_SCHEMA
+        assert "pre_close" in sync.TABLE_SCHEMA
+        assert "up_limit" in sync.TABLE_SCHEMA
+        assert "down_limit" in sync.TABLE_SCHEMA
+        assert sync.PRIMARY_KEY == ("ts_code", "trade_date")
+
+
+class TestSyncFunctions:
+    """Test suite for sync convenience functions."""
+
+    @patch.object(StkLimitSync, "sync_all")
+    def test_sync_stk_limit(self, mock_sync_all):
+        """Test sync_stk_limit convenience function."""
+        # Setup mock
+        mock_sync_all.return_value = pd.DataFrame(
+            {
+                "ts_code": ["000001.SZ"],
+                "trade_date": ["20240625"],
+                "up_limit": [11.0],
+                "down_limit": [9.0],
+            }
+        )
+
+        # Test
+        result = sync_stk_limit(force_full=True)
+
+        # Assert
+        assert not result.empty
+        mock_sync_all.assert_called_once_with(
+            force_full=True,
+            start_date=None,
+            end_date=None,
+            dry_run=False,
+        )
+
+    @patch.object(StkLimitSync, "preview_sync")
+    def test_preview_stk_limit_sync(self, mock_preview):
+        """Test preview_stk_limit_sync convenience function."""
+        # Setup mock
+        mock_preview.return_value = {
+            "sync_needed": True,
+            "date_count": 10,
+            "start_date": "20240601",
+            "end_date": "20240610",
+            "estimated_records": 5000,
+            "sample_data": pd.DataFrame(),
+            "mode": "incremental",
+        }
+
+        # Test
+        result = preview_stk_limit_sync()
+
+        # Assert
+        assert result["sync_needed"] is True
+        assert result["mode"] == "incremental"
+        mock_preview.assert_called_once_with(
+            force_full=False,
+            start_date=None,
+            end_date=None,
+            sample_size=3,
+        )
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])