BaoStock（免费A股数据平台）

---

name: baostock

description: BaoStock 免费A股数据平台 - 支持K线、财务数据、行业分类查询，无需注册即可使用。

version: 1.2.0

homepage: https://www.baostock.com

metadata: {"clawdbot":{"emoji":"📊","requires":{"bins":["python3"]}}}

---

# BaoStock（免费A股数据平台）

[BaoStock](https://www.baostock.com) 是一个免费的开源中国A股证券数据平台。无需注册或API Key，返回 `pandas.DataFrame`。

安装

pip install baostock --upgrade

验证安装：

python3 -c "import baostock as bs; lg = bs.login(); print(lg.error_msg); bs.logout()"

预期输出：`login success!`。

基本用法

每个会话必须以 `bs.login()` 开始，以 `bs.logout()` 结束：

import baostock as bs
import pandas as pd

# 登录系统
lg = bs.login()

# ... 在此执行数据查询 ...

# 登出系统
bs.logout()

使用 `.get_data()` 从查询结果中获取DataFrame：

rs = bs.query_all_stock()
df = rs.get_data()

核心API

1. query_all_stock — 获取全部证券列表

获取指定交易日的全部股票/指数代码。

# 获取指定日期所有证券代码
rs = bs.query_all_stock(day="2024-01-02")
df = rs.get_data()
# 返回字段: code(证券代码), tradeStatus(交易状态), code_name(证券名称)

2. query_history_k_data_plus — K线数据

获取历史K线数据（开高低收量 + 指标）。

# 获取工商银行日K线数据
rs = bs.query_history_k_data_plus(
    "sh.601398",
    "date,code,open,high,low,close,volume,amount,pctChg",
    start_date="2024-01-01",
    end_date="2024-06-30",
    frequency="d",       # 频率: d(日线), w(周线), m(月线), 5/15/30/60(分钟线)
    adjustflag="3"       # 复权: 1(后复权), 2(前复权), 3(不复权，默认)
)
df = rs.get_data()

**参数说明：**

**日线可用字段：**

`date`(日期), `code`(证券代码), `open`(开盘价), `high`(最高价), `low`(最低价), `close`(收盘价), `preclose`(昨收价), `volume`(成交量), `amount`(成交额), `adjustflag`(复权标志), `turn`(换手率), `tradestatus`(交易状态), `pctChg`(涨跌幅), `peTTM`(滚动市盈率), `pbMRQ`(市净率), `psTTM`(滚动市销率), `pcfNcfTTM`(滚动市现率), `isST`(是否ST)

**分钟线可用字段：**

`date`(日期), `time`(时间), `code`(证券代码), `open`(开盘价), `high`(最高价), `low`(最低价), `close`(收盘价), `volume`(成交量), `amount`(成交额), `adjustflag`(复权标志)

3. query_trade_dates — 交易日历

# 获取指定范围的交易日历
rs = bs.query_trade_dates(start_date="2024-01-01", end_date="2024-12-31")
df = rs.get_data()
# 返回字段: calendar_date(日历日期), is_trading_day(是否交易日)

4. query_stock_industry — 行业分类

# 获取全部股票行业分类
rs = bs.query_stock_industry()
df = rs.get_data()
# 返回字段: updateDate(更新日期), code(证券代码), code_name(证券名称), industry(行业), industryClassification(行业分类)

5. query_stock_basic — 股票基本信息

# 获取指定股票基本信息
rs = bs.query_stock_basic(code="sh.601398")
df = rs.get_data()
# 返回字段: code(证券代码), code_name(证券名称), ipoDate(上市日期), outDate(退市日期), type(类型), status(状态)

6. query_dividend_data — 分红信息

# 获取指定股票分红数据
rs = bs.query_dividend_data(code="sh.601398", year="2023", yearType="report")
df = rs.get_data()

7. 财务数据（季度）

#### 盈利能力

# 获取盈利能力指标（ROE、净利润率、毛利率等）
rs = bs.query_profit_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

#### 营运能力

# 获取营运能力指标（存货周转率、应收账款周转率等）
rs = bs.query_operation_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

#### 成长能力

# 获取成长能力指标（营收同比增长、净利润同比增长等）
rs = bs.query_growth_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

#### 偿债能力

# 获取偿债能力指标（流动比率、速动比率等）
rs = bs.query_balance_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

#### 现金流

# 获取现金流数据
rs = bs.query_cash_flow_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

#### 杜邦分析

# 获取杜邦分析数据（ROE分解：利润率×资产周转率×权益乘数）
rs = bs.query_dupont_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

8. 指数数据

#### 指数成分股

# 获取沪深300成分股
rs = bs.query_hs300_stocks()
df = rs.get_data()

# 获取上证50成分股
rs = bs.query_sz50_stocks()
df = rs.get_data()

# 获取中证500成分股
rs = bs.query_zz500_stocks()
df = rs.get_data()

完整示例: Download Daily K-Line Data and Save as CSV

import baostock as bs
import pandas as pd

# 登录系统
bs.login()

# 获取贵州茅台2024年日K线数据（后复权）
rs = bs.query_history_k_data_plus(
    "sh.600519",
    "date,code,open,high,low,close,volume,amount,pctChg,peTTM",
    start_date="2024-01-01",
    end_date="2024-12-31",
    frequency="d",
    adjustflag="2"  # 后复权
)
df = rs.get_data()

# 保存到CSV文件
df.to_csv("kweichow_moutai_2024.csv", index=False)
print(df.head())

# 登出系统
bs.logout()

股票代码格式

使用技巧

---

进阶示例

批量下载多只股票数据

import baostock as bs
import pandas as pd

bs.login()

# 定义要下载的股票列表
stock_list = ["sh.600519", "sh.601398", "sz.000001", "sz.300750", "sh.601318"]

all_data = []
for code in stock_list:
    # 获取日K线数据（前复权）
    rs = bs.query_history_k_data_plus(
        code,
        "date,code,open,high,low,close,volume,amount,pctChg,turn,peTTM,pbMRQ",
        start_date="2024-01-01",
        end_date="2024-06-30",
        frequency="d",
        adjustflag="1"  # 前复权
    )
    df = rs.get_data()
    all_data.append(df)
    print(f"已下载 {code}，共 {len(df)} 条记录")

# 合并所有数据
combined = pd.concat(all_data, ignore_index=True)
combined.to_csv("multi_stock_baostock.csv", index=False)
print(f"合并总记录数: {len(combined)}")

bs.logout()

获取全市场股票列表并筛选

import baostock as bs
import pandas as pd

bs.login()

# Get all securities for a specified date
rs = bs.query_all_stock(day="2024-06-28")
df = rs.get_data()

# 筛选正常交易的股票（排除指数和停牌股）
stocks = df[df["tradeStatus"] == "1"]
# 筛选上海A股（sh.6开头）
sh_stocks = stocks[stocks["code"].str.startswith("sh.6")]
print(f"沪市A股: {len(sh_stocks)} 只")

# 筛选深圳主板（sz.00开头）
sz_main = stocks[stocks["code"].str.startswith("sz.00")]
print(f"深圳主板: {len(sz_main)} 只")

# 筛选创业板（sz.30开头）
gem = stocks[stocks["code"].str.startswith("sz.30")]
print(f"创业板: {len(gem)} 只")

bs.logout()

计算技术指标

import baostock as bs
import pandas as pd
import numpy as np

bs.login()

# 获取平安银行日K线数据
rs = bs.query_history_k_data_plus(
    "sz.000001",
    "date,close,volume",
    start_date="2024-01-01",
    end_date="2024-12-31",
    frequency="d",
    adjustflag="1"
)
df = rs.get_data()
df["close"] = df["close"].astype(float)
df["volume"] = df["volume"].astype(float)

# 计算均线
df["MA5"] = df["close"].rolling(5).mean()
df["MA10"] = df["close"].rolling(10).mean()
df["MA20"] = df["close"].rolling(20).mean()

# Calculate MACD
ema12 = df["close"].ewm(span=12, adjust=False).mean()
ema26 = df["close"].ewm(span=26, adjust=False).mean()
df["DIF"] = ema12 - ema26
df["DEA"] = df["DIF"].ewm(span=9, adjust=False).mean()
df["MACD"] = 2 * (df["DIF"] - df["DEA"])

# Calculate volume moving averages
df["VOL_MA5"] = df["volume"].rolling(5).mean()
df["VOL_MA10"] = df["volume"].rolling(10).mean()

# 检测金叉/死叉信号
df["signal"] = 0
df.loc[(df["MA5"] > df["MA20"]) & (df["MA5"].shift(1) <= df["MA20"].shift(1)), "signal"] = 1   # 金叉
df.loc[(df["MA5"] < df["MA20"]) & (df["MA5"].shift(1) >= df["MA20"].shift(1)), "signal"] = -1  # 死叉

golden_cross = df[df["signal"] == 1]
death_cross = df[df["signal"] == -1]
print(f"金叉次数: {len(golden_cross)}, 死叉次数: {len(death_cross)}")
print("金叉日期:", golden_cross["date"].tolist())

bs.logout()

获取沪深300成分股并下载数据

import baostock as bs
import pandas as pd

bs.login()

# 获取沪深300成分股
rs = bs.query_hs300_stocks()
hs300 = rs.get_data()
print(f"沪深300共 {len(hs300)} 只成分股")

# 下载前10只成分股的日K线数据
for _, row in hs300.head(10).iterrows():
    code = row["code"]
    name = row["code_name"]
    rs = bs.query_history_k_data_plus(
        code,
        "date,code,close,pctChg,turn",
        start_date="2024-06-01",
        end_date="2024-06-30",
        frequency="d",
        adjustflag="1"
    )
    df = rs.get_data()
    print(f"{name}({code}): {len(df)} records")

bs.logout()

获取财务数据并分析

import baostock as bs
import pandas as pd

bs.login()

# 获取多只银行股的盈利能力数据
bank_codes = ["sh.601398", "sh.601939", "sh.601288", "sh.600036", "sh.601166"]
profit_data = []

for code in bank_codes:
    rs = bs.query_profit_data(code=code, year=2023, quarter=4)
    df = rs.get_data()
    if not df.empty:
        profit_data.append(df.iloc[0])

profit_df = pd.DataFrame(profit_data)
# 查看ROE和净利润率
print(profit_df[["code", "roeAvg", "npMargin", "gpMargin"]])

# 获取成长能力数据
growth_data = []
for code in bank_codes:
    rs = bs.query_growth_data(code=code, year=2023, quarter=4)
    df = rs.get_data()
    if not df.empty:
        growth_data.append(df.iloc[0])

growth_df = pd.DataFrame(growth_data)
# 查看营收增长率和净利润增长率
print(growth_df[["code", "YOYEquity", "YOYAsset", "YOYNI"]])

bs.logout()

完整示例: Simple Backtesting Framework

import baostock as bs
import pandas as pd
import numpy as np

bs.login()

# 获取平安银行2023年日K线数据（前复权）
rs = bs.query_history_k_data_plus(
    "sz.000001",
    "date,open,high,low,close,volume",
    start_date="2023-01-01",
    end_date="2023-12-31",
    frequency="d",
    adjustflag="1"
)
df = rs.get_data()
for col in ["open", "high", "low", "close", "volume"]:
    df[col] = df[col].astype(float)

# 双均线策略回测
df["MA5"] = df["close"].rolling(5).mean()
df["MA20"] = df["close"].rolling(20).mean()

initial_cash = 100000  # 初始资金：10万
cash = initial_cash
shares = 0             # 持有股数
trades = []            # 交易记录

for i in range(20, len(df)):
    # 金叉 buy signal
    if df["MA5"].iloc[i] > df["MA20"].iloc[i] and df["MA5"].iloc[i-1] <= df["MA20"].iloc[i-1]:
        if cash > 0:
            buy_price = df["close"].iloc[i]
            shares = int(cash / buy_price / 100) * 100  # 向下取整到手（100股）
            cost = shares * buy_price
            cash -= cost
            trades.append({"date": df["date"].iloc[i], "action": "BUY",
                          "price": buy_price, "shares": shares, "cash": cash})

    # 死叉 sell signal
    elif df["MA5"].iloc[i] < df["MA20"].iloc[i] and df["MA5"].iloc[i-1] >= df["MA20"].iloc[i-1]:
        if shares > 0:
            sell_price = df["close"].iloc[i]
            cash += shares * sell_price
            trades.append({"date": df["date"].iloc[i], "action": "SELL",
                          "price": sell_price, "shares": shares, "cash": cash})
            shares = 0

# 计算最终收益
final_value = cash + shares * df["close"].iloc[-1]
total_return = (final_value - initial_cash) / initial_cash * 100

print(f"初始资金: {initial_cash:.2f}")
print(f"最终组合价值: {final_value:.2f}")
print(f"总收益率: {total_return:.2f}%")
print(f"交易次数: {len(trades)}")
for t in trades:
    print(f"  {t['date']} {t['action']} {t['shares']} shares @ {t['price']:.2f}")

bs.logout()

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