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# Stock Market Data
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AKShare provides comprehensive stock market data coverage with 325+ functions across two main categories: core STOCK data (120 functions) and advanced STOCK_FEATURE data (205 functions). This covers Chinese A-shares, Hong Kong stocks, US stocks, and various technical indicators and analysis tools.
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## Core Stock Data (STOCK Category)
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### Real-time Stock Quotes
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#### Chinese A-shares (Sina Finance)
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```python { .api }
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import akshare as ak
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# Real-time A-share market data
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def stock_zh_a_spot() -> pd.DataFrame:
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    """
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    Returns real-time A-share quotes from Sina Finance
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    Returns:
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        pd.DataFrame: Real-time market data with columns:
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            - 代码 (Code): Stock symbol
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            - 名称 (Name): Stock name  
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            - 最新价 (Latest): Current price
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            - 涨跌额 (Change): Price change amount
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            - 涨跌幅 (Pct_change): Percentage change
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            - 买入 (Bid): Bid price
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            - 卖出 (Ask): Ask price
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            - 成交量 (Volume): Trading volume
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            - 成交额 (Amount): Trading amount
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    """
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# Usage example
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df = ak.stock_zh_a_spot()
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print(df.head())
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#     代码    名称    最新价  涨跌额  涨跌幅   买入   卖出      成交量        成交额
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# 0  000001  平安银行  10.50   0.15   1.45  10.49  10.50   1234567  12950123456
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# 1  000002   万科A    8.92  -0.08  -0.89   8.91   8.92    987654   8800765432
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```
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#### Historical A-share Data
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```python { .api }
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import akshare as ak
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def stock_zh_a_hist(symbol: str = "000001", period: str = "daily",
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                    start_date: str = "19700101", end_date: str = "20500101",
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                    adjust: str = "") -> pd.DataFrame:
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    """
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    Returns historical daily A-share data
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    Parameters:
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        symbol: Stock code (e.g., "000001")
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        period: Data frequency ("daily", "weekly", "monthly")
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        start_date: Start date in YYYYMMDD format
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        end_date: End date in YYYYMMDD format
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        adjust: Adjustment type ("qfq": 前复权, "hfq": 后复权, "": 不复权)
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    Returns:
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        pd.DataFrame: Historical data with OHLCV columns
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    """
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# Get historical data for Ping An Bank
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hist_df = ak.stock_zh_a_hist(symbol="000001", period="daily", start_date="20240101", end_date="20241201")
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print(hist_df.head())
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#         日期    开盘    收盘    最高    最低      成交量        成交额
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# 0  2024-01-02  10.20  10.35  10.40  10.15   5678901  58590123456
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# 1  2024-01-03  10.35  10.28  10.42  10.25   4567890  46789012345
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```
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#### Minute-level Data
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```python { .api }
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import akshare as ak
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def stock_zh_a_hist_min_em(symbol: str = "000001", period: str = "5",
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                           start_date: str = "1979-09-01 09:32:00", 
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                           end_date: str = "2222-01-01 09:32:00",
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                           adjust: str = "") -> pd.DataFrame:
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    """
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    Returns minute-level A-share data
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    Parameters:
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        symbol: Stock code (e.g., "000001")
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        period: Time period ("1", "5", "15", "30", "60") 
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        start_date: Start datetime in "YYYY-MM-DD HH:MM:SS" format
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        end_date: End datetime in "YYYY-MM-DD HH:MM:SS" format
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        adjust: Adjustment type ("", "qfq", "hfq")
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    Returns:
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        pd.DataFrame: Minute-level OHLCV data
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    """
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# Get 5-minute data for recent trading
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minute_df = ak.stock_zh_a_hist_min_em(symbol="000001", period="5")
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```
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### US Stock Market Data
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#### Real-time US Stocks
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```python { .api }
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import akshare as ak
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def stock_us_spot() -> pd.DataFrame:
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    """
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    Returns real-time US stock quotes
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    Returns:
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        pd.DataFrame: US stock market data with columns:
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            - 代码 (Symbol): Stock symbol
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            - 名称 (Name): Company name
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            - 最新价 (Price): Current price
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            - 涨跌额 (Change): Price change
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            - 涨跌幅 (Pct_change): Percentage change
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            - 成交量 (Volume): Trading volume
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    """
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# Get US stock real-time data
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us_df = ak.stock_us_spot()
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print(us_df.head())
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#    代码      名称     最新价  涨跌额  涨跌幅      成交量
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# 0  AAPL    Apple    150.25   2.15   1.45   12345678
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# 1  MSFT  Microsoft  310.80  -1.20  -0.38    8765432
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```
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#### Historical US Stock Data
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```python { .api }
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import akshare as ak
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def stock_us_daily(symbol: str, start_date: str = "19700101",
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                   end_date: str = "20500101") -> pd.DataFrame:
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    """
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    Returns historical daily US stock data
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    Parameters:
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        symbol: US stock symbol (e.g., "AAPL")
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        start_date: Start date in YYYYMMDD format
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        end_date: End date in YYYYMMDD format
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    Returns:
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        pd.DataFrame: Historical OHLCV data
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    """
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# Get Apple historical data
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aapl_df = ak.stock_us_daily(symbol="AAPL", start_date="20240101")
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```
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### Hong Kong Stock Market
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#### Real-time HK Stocks
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```python { .api }
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import akshare as ak
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def stock_hk_spot() -> pd.DataFrame:
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    """
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    Returns real-time Hong Kong stock quotes
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    Returns:
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        pd.DataFrame: HK stock market data
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    """
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# Get Hong Kong stock data
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hk_df = ak.stock_hk_spot()
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```
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#### Historical HK Stock Data
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```python { .api }
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import akshare as ak
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def stock_hk_daily(symbol: str, start_date: str = "19700101",
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                   end_date: str = "20500101") -> pd.DataFrame:
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    """
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    Returns historical Hong Kong stock data
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    Parameters:
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        symbol: HK stock code (e.g., "00700")
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    Returns:
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        pd.DataFrame: Historical data
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    """
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# Get Tencent historical data
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tencent_df = ak.stock_hk_daily(symbol="00700", start_date="20240101")
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```
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## Advanced Stock Features (STOCK_FEATURE Category)
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### East Money Enhanced Data
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#### Real-time Enhanced A-shares
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```python { .api }
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import akshare as ak
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def stock_zh_a_spot_em() -> pd.DataFrame:
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    """
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    East Money enhanced real-time A-share data
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    Returns:
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        pd.DataFrame: Enhanced market data with columns:
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            - 序号 (Index): Row number
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            - 代码 (Code): Stock code
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            - 名称 (Name): Stock name
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            - 最新价 (Latest): Current price
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            - 涨跌幅 (Pct_change): Percentage change  
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            - 涨跌额 (Change): Price change amount
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            - 成交量 (Volume): Trading volume
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            - 成交额 (Amount): Trading amount
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            - 振幅 (Amplitude): Price amplitude
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            - 最高 (High): Highest price
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            - 最低 (Low): Lowest price
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            - 今开 (Open): Opening price
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            - 昨收 (Prev_close): Previous close
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            - 量比 (Volume_ratio): Volume ratio
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            - 换手率 (Turnover): Turnover rate
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            - 市盈率-动态 (PE_dynamic): Dynamic P/E ratio
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            - 市净率 (PB): Price-to-book ratio
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            - 总市值 (Total_mv): Total market value
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            - 流通市值 (Float_mv): Floating market value
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            - 涨速 (Rise_speed): Rising speed
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            - 5分钟涨跌 (Change_5min): 5-minute change
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            - 60日涨跌幅 (Change_60d): 60-day change
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            - 年初至今涨跌幅 (Change_ytd): Year-to-date change
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    """
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# Get enhanced real-time data
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enhanced_df = ak.stock_zh_a_spot_em()
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print(enhanced_df.columns.tolist())
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```
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#### Historical Data with Adjustments
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```python { .api }
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import akshare as ak
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def stock_zh_a_hist(symbol: str, period: str = "daily",
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                    start_date: str = "19700101", 
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                    end_date: str = "20500101",
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                    adjust: str = "") -> pd.DataFrame:
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    """
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    Enhanced historical A-share data with adjustment options
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    Parameters:
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        symbol: Stock code
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        period: Data frequency ("daily", "weekly", "monthly")
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        start_date: Start date (YYYYMMDD)
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        end_date: End date (YYYYMMDD)
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        adjust: Price adjustment ("qfq"=forward, "hfq"=backward, ""=none)
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    Returns:
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        pd.DataFrame: OHLCV data with volume and amount
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    """
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# Get adjusted historical data
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adj_df = ak.stock_zh_a_hist(
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    symbol="000001", 
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    period="daily", 
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    start_date="20240101",
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    adjust="qfq"  # Forward adjusted prices
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)
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```
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### Special Market Segments
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#### STAR Market (科创板)
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```python { .api }
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import akshare as ak
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def stock_kc_a_spot_em() -> pd.DataFrame:
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    """
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    Real-time STAR Market (Science and Technology Innovation Board) data
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    Returns:
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        pd.DataFrame: STAR Market real-time quotes
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    """
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# Get STAR Market data
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star_df = ak.stock_kc_a_spot_em()
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```
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#### ChiNext Market (创业板)  
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```python { .api }
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import akshare as ak
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def stock_cy_a_spot_em() -> pd.DataFrame:
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    """
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    Real-time ChiNext Market data
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    Returns:
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        pd.DataFrame: ChiNext real-time quotes
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    """
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# Get ChiNext data  
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chinext_df = ak.stock_cy_a_spot_em()
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```
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### Stock Connect Data
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#### Hong Kong-Shanghai/Shenzhen Stock Connect
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```python { .api }
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import akshare as ak
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def stock_hsgt_fund_flow_summary_em() -> pd.DataFrame:
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    """
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    Stock Connect capital flow summary
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    Returns:
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        pd.DataFrame: Capital flow data between mainland and HK
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    """
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def stock_hsgt_individual_em(symbol: str) -> pd.DataFrame:
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    """
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    Individual stock data in Stock Connect
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Individual stock connect data
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    """
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# Get Stock Connect flow summary
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flow_summary = ak.stock_hsgt_fund_flow_summary_em()
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# Get individual stock connect data
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individual_data = ak.stock_hsgt_individual_em(symbol="000001")
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```
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### Capital Flow Analysis
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#### Sector Capital Flows
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```python { .api }
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import akshare as ak
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def stock_fund_flow_concept() -> pd.DataFrame:
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    """
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    Concept sector capital flow data
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    Returns:
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        pd.DataFrame: Capital flows by concept sectors
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    """
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def stock_fund_flow_industry() -> pd.DataFrame:
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    """
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    Industry sector capital flow data
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    Returns:
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        pd.DataFrame: Capital flows by industry sectors  
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    """
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# Get concept sector flows
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concept_flows = ak.stock_fund_flow_concept()
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# Get industry sector flows  
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industry_flows = ak.stock_fund_flow_industry()
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```
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### Margin Trading Data
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#### Margin Trading Statistics
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```python { .api }
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import akshare as ak
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def stock_margin_detail_em() -> pd.DataFrame:
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    """
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    Detailed margin trading data
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    Returns:
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        pd.DataFrame: Margin trading statistics by stock
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    """
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def stock_margin_summary_em() -> pd.DataFrame:
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    """
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    Margin trading summary data
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    Returns:
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        pd.DataFrame: Overall margin trading summary
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    """
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# Get margin trading details
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margin_detail = ak.stock_margin_detail_em()
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# Get margin trading summary
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margin_summary = ak.stock_margin_summary_em()
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```
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### Dragon-Tiger Board Data
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#### Large Transaction Tracking
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```python { .api }
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import akshare as ak
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def stock_lhb_detail_em(start_date: str, end_date: str) -> pd.DataFrame:
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    """
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    Dragon-Tiger Board detailed transaction data
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    Parameters:
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        start_date: Start date (YYYY-MM-DD)
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        end_date: End date (YYYY-MM-DD)
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    Returns:
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        pd.DataFrame: Large transaction details
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    """
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# Get Dragon-Tiger Board data
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lhb_data = ak.stock_lhb_detail_em(
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    start_date="2024-01-01", 
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    end_date="2024-12-01"
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)
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```
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### Technical Analysis Indicators
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#### Technical Indicators from Tonghuashun
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```python { .api }
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import akshare as ak
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def stock_zh_a_gdhs_detail_em(symbol: str) -> pd.DataFrame:
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    """
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    Stock technical analysis details
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Technical analysis indicators
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    """
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def stock_technology_ths(symbol: str, indicator: str) -> pd.DataFrame:
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    """
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    Technical indicators from Tonghuashun
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    Parameters:
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        symbol: Stock code
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        indicator: Technical indicator type
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    Returns:
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        pd.DataFrame: Technical indicator data
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    """
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# Get technical analysis details
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tech_detail = ak.stock_zh_a_gdhs_detail_em(symbol="000001")
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# Get specific technical indicators
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rsi_data = ak.stock_technology_ths(symbol="000001", indicator="RSI")
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```
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## Market Information and Lists
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### Stock Lists and Categories
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#### Market Board Classification
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```python { .api }
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import akshare as ak
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def stock_board_concept_name_em() -> pd.DataFrame:
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    """
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    Concept board names and classifications
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    Returns:
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        pd.DataFrame: Concept board information
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    """
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def stock_board_industry_name_em() -> pd.DataFrame:
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    """
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    Industry board names and classifications
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    Returns:
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        pd.DataFrame: Industry classification data
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    """
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# Get concept boards
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concept_boards = ak.stock_board_concept_name_em()
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# Get industry boards
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industry_boards = ak.stock_board_industry_name_em()
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```
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#### Stock Information
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```python { .api }
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import akshare as ak
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def stock_info_a_code_name() -> pd.DataFrame:
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    """
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    A-share stock code and name mapping
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    Returns:
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        pd.DataFrame: Stock code to name mapping
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    """
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def stock_info_sz_name_code() -> pd.DataFrame:
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    """
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    Shenzhen stock exchange code-name mapping
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    Returns:
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        pd.DataFrame: SZ stock information
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    """
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# Get A-share stock list
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stock_list = ak.stock_info_a_code_name()
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# Get Shenzhen stock list
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sz_stocks = ak.stock_info_sz_name_code()
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```
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## Usage Patterns
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### Common Data Retrieval Workflow
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```python { .api }
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import akshare as ak
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import pandas as pd
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# Step 1: Get stock list
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stock_list = ak.stock_info_a_code_name()
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# Step 2: Get real-time data for all stocks
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realtime_data = ak.stock_zh_a_spot_em()
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# Step 3: Get historical data for specific stock
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symbol = "000001"
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historical_data = ak.stock_zh_a_hist(
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    symbol=symbol,
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    start_date="20240101", 
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    end_date="20241201",
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    adjust="qfq"
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)
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# Step 4: Analyze technical indicators
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tech_indicators = ak.stock_zh_a_gdhs_detail_em(symbol=symbol)
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# Step 5: Check capital flows
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if symbol in realtime_data['代码'].values:
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    flow_data = ak.stock_hsgt_individual_em(symbol=symbol)
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```
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### Error Handling and Data Validation
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```python { .api }
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import akshare as ak
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def get_stock_data_safely(symbol: str) -> pd.DataFrame:
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    """Safe stock data retrieval with error handling"""
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    try:
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        df = ak.stock_zh_a_hist(symbol=symbol)
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        if df.empty:
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            print(f"No data available for symbol {symbol}")
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            return pd.DataFrame()
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        return df
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    except Exception as e:
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        print(f"Error retrieving data for {symbol}: {e}")
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        return pd.DataFrame()
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# Usage
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data = get_stock_data_safely("000001")
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```
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### Data Frequency and Updates
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- **Real-time data**: Updated during trading hours (9:30-15:00 CST)
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- **Daily data**: Available after market close 
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- **Historical data**: Covers multiple years of trading history
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- **Minute data**: Available for recent trading periods
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- **Adjusted prices**: Forward (qfq) and backward (hfq) adjustment options
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The stock data capabilities in AKShare provide comprehensive coverage for equity market analysis, from basic price data to advanced technical indicators and market microstructure information.