baostock

name	baostock
description	BaoStock is a free, open-source A-share securities data platform providing historical K-line, financial statements, macroeconomic indicators, and industry classification data via Python API, returning pandas DataFrames.

BaoStock

Overview

BaoStock is a free, open-source securities data platform for China A-shares. It provides historical quotes, financial indicators, macroeconomic data, and industry classification through a unified Python API, returning pandas DataFrames.

Key advantages: Free, stable, long history (since 1990), no rate limits or credits required.

Quick Start

Installation

pip install baostock

Basic Usage Pattern

import baostock as bs
import pandas as pd

# 1. Login (required)
lg = bs.login()

# 2. Query data
rs = bs.query_history_k_data_plus(
    "sh.600000",
    "date,code,open,high,low,close,volume,amount",
    start_date="2024-01-01",
    end_date="2024-12-31",
    frequency="d",
    adjustflag="3",
)

# 3. Collect results (pandas 2.0+ compatible)
rows = []
while (rs.error_code == "0") and rs.next():
    rows.append(rs.get_row_data())
df = pd.DataFrame(rows, columns=rs.fields)

# 4. Logout
bs.logout()

Stock Code Format

BaoStock uses exchange.code format (note the dot separator):

Exchange	Prefix	Example
Shanghai (SSE)	sh.	sh.600000 (SPDB)
Shenzhen (SZSE)	sz.	sz.000001 (Ping An Bank)

Parameter Conventions

• Date: YYYY-MM-DD (e.g., 2024-12-31)
• Stock code: sh.600000, sz.000001
• Return format: Iterate via rs.next() + rs.get_row_data(), assemble into DataFrame
• All return values are strings: Must manually pd.to_numeric() for numeric columns

Complete API Reference

I. Market Data

1. Historical K-line: query_history_k_data_plus()

The most important API — supports daily/weekly/monthly/minute bars with adjustment.

rs = bs.query_history_k_data_plus(
    code="sh.600000",       # Stock code
    fields="date,code,...", # Return fields (comma-separated)
    start_date="2024-01-01",# Start date, default 2015-01-01
    end_date="2024-12-31",  # End date, default latest trading day
    frequency="d",          # Frequency
    adjustflag="3",         # Adjustment type
)

frequency values:

Value	Frequency	Notes
"d"	Daily	Default
"w"	Weekly
"m"	Monthly
"5"	5-minute	Index does not support minute bars
"15"	15-minute
"30"	30-minute
"60"	60-minute

adjustflag values:

Value	Meaning	Method
"3"	Unadjusted	Default
"1"	Backward adjusted	Percentage change method
"2"	Forward adjusted	Percentage change method

Note: BaoStock uses the "percentage change adjustment method", which differs from other platforms (e.g., Tongdaxin).

Daily/Weekly/Monthly fields:

Field	Description	Type
date	Trading date	str
code	Stock code	str
open	Open price	str→float
high	High price	str→float
low	Low price	str→float
close	Close price	str→float
preclose	Previous close	str→float
volume	Volume (shares)	str→float
amount	Amount (CNY)	str→float
adjustflag	Adjustment flag	str
turn	Turnover rate (%)	str→float, empty string when suspended
tradestatus	Trading status	str, "1"=normal, "0"=suspended
pctChg	Price change (%)	str→float
peTTM	PE ratio (TTM)	str→float
pbMRQ	PB ratio (MRQ)	str→float
psTTM	PS ratio (TTM)	str→float
pcfNcfTTM	PCF ratio (TTM)	str→float
isST	Is ST stock	str, "1"=yes, "0"=no

Minute bar fields:

Field	Description
date	Date
time	Time (format YYYYMMDDHHmmssSSS)
code	Stock code
open	Open price
high	High price
low	Low price
close	Close price
volume	Volume
amount	Amount
adjustflag	Adjustment flag

Minute bars do not include index data or valuation metrics (peTTM, etc.).

2. Index K-line

Uses the same query_history_k_data_plus() API with index codes:

Index	Code
SSE Composite	sh.000001
SZSE Component	sz.399001
CSI 300	sh.000300
CSI 500	sh.000905
ChiNext Index	sz.399006

Index does not support minute bars, only daily/weekly/monthly.

3. All Securities on Date: query_all_stock()

rs = bs.query_all_stock(day="2024-12-31")
# Returns: code, tradeStatus columns

II. Basic Information

4. Stock Basic Info: query_stock_basic()

rs = bs.query_stock_basic(code="sh.600000")  # Single stock
rs = bs.query_stock_basic()                   # All stocks

Returns: code, code_name, ipoDate, outDate, type (1=stock, 2=index, 3=other, 4=convertible bond, 5=ETF), status (1=listed, 0=delisted)

5. Industry Classification: query_stock_industry()

rs = bs.query_stock_industry(date="2024-12-31")  # Specific date
rs = bs.query_stock_industry()                     # Latest

Returns: updateDate, code, code_name, industry, industryClassification

Uses CSRC industry classification standard.

6. SSE 50 Constituents: query_sz50_stocks()

rs = bs.query_sz50_stocks(date="2024-12-31")

Returns: updateDate, code, code_name

7. CSI 300 Constituents: query_hs300_stocks()

rs = bs.query_hs300_stocks(date="2024-12-31")

8. CSI 500 Constituents: query_zz500_stocks()

rs = bs.query_zz500_stocks(date="2024-12-31")

III. Dividends & Adjustment

9. Dividend Information: query_dividend_data()

rs = bs.query_dividend_data(
    code="sh.600000",
    year="2024",
    yearType="report",  # "report"=announcement year, "operate"=ex-dividend year
)

Returns:

Field	Description
dividPreNoticeDate	Pre-notice date
dividAgmPumDate	AGM announcement date
dividPlanAnnounceDate	Plan announcement date
dividPlanDate	Implementation announcement date
dividRegistDate	Record date
dividOperateDate	Ex-dividend date
dividPayDate	Payment date
dividStockMarketDate	Stock listing date
dividCashPsBeforeTax	Cash dividend per share (before tax)
dividCashPsAfterTax	Cash dividend per share (after tax)
dividStocksPs	Stock dividend per share
dividCashStock	Dividend & transfer info
dividReserveToStockPs	Reserve to stock per share

10. Adjustment Factor: query_adjust_factor()

rs = bs.query_adjust_factor(
    code="sh.600000",
    start_date="2015-01-01",
    end_date="2024-12-31",
)

Returns: code, dividOperateDate, foreAdjustFactor, backAdjustFactor, adjustFactor

IV. Quarterly Financial Data

All quarterly APIs share the same parameters:

rs = bs.query_xxx_data(code="sh.600000", year=2024, quarter=2)

• code: Stock code
• year: Fiscal year (int)
• quarter: Quarter (1/2/3/4)

11. Profitability: query_profit_data()

Returns:

Field	Description
code	Stock code
pubDate	Publication date
statDate	Statistics end date
roeAvg	ROE (average, %)
npMargin	Net profit margin (%)
gpMargin	Gross profit margin (%)
netProfit	Net profit (CNY)
epsTTM	Earnings per share
MBRevenue	Main business revenue (CNY)
totalShare	Total shares
liqaShare	Tradable shares

12. Operating Efficiency: query_operation_data()

Returns: NRTurnRatio (receivables turnover), NRTurnDays, INVTurnRatio (inventory turnover), INVTurnDays, CATurnRatio (current asset turnover), AssetTurnRatio (total asset turnover)

13. Growth: query_growth_data()

Returns: YOYEquity (equity YoY), YOYAsset (asset YoY), YOYNI (net income YoY), YOYEPSBasic (basic EPS YoY), YOYPNI (net income attributable to parent YoY)

14. Solvency: query_balance_data()

Returns: currentRatio, quickRatio, cashRatio, YOYLiability, liabilityToAsset, assetToEquity

15. Cash Flow: query_cash_flow_data()

Returns: CAToAsset, NCAToAsset, tangibleAssetToAsset, ebitToInterest, CFOToOR, CFOToNP, CFOToGr

16. DuPont Analysis: query_dupont_data()

Returns: dupontROE, dupontAssetSto498 (equity multiplier), dupontAssetTurn, dupontPnitoni, dupontNitogr, dupontTaxBurden, dupontIntburden, dupontEbittogr

17. Express Report: query_performance_express_report()

Returns: performanceExpPubDate, performanceExpStatDate, performanceExpRevenue, performanceExpDeductedNP, performanceExpNetProfit, performanceExpEPSChgPct, performanceExpROEChgPct, etc.

18. Forecast Report: query_forecast_report()

Returns: profitForcastExpPubDate, profitForcastExpStatDate, profitForcastType (increase/decrease/turnaround/first loss/continued profit/continued loss/slight increase/slight decrease/uncertain), profitForcastAbstract, profitForcastChgPctUp, profitForcastChgPctDwn

V. Macroeconomic Data

19. Deposit Rates: query_deposit_rate_data()

rs = bs.query_deposit_rate_data(start_date="2020-01-01", end_date="2024-12-31")

Returns: pubDate, depositType, depositRate

20. Loan Rates: query_loan_rate_data()

rs = bs.query_loan_rate_data(start_date="2020-01-01", end_date="2024-12-31")

Returns: pubDate, loanType, loanRate

21. Reserve Requirement Ratio: query_required_reserve_ratio_data()

rs = bs.query_required_reserve_ratio_data(start_date="2020-01-01", end_date="2024-12-31")

Returns: pubDate, effectiveDate, deposit (large institution), smallDeposit (small institution)

22. Money Supply (Monthly): query_money_supply_data_month()

rs = bs.query_money_supply_data_month(start_date="2020-01", end_date="2024-12")

Returns: statYear, statMonth, m0Month, m0YOY, m0ChainRelative, m1Month, m1YOY, m1ChainRelative, m2Month, m2YOY, m2ChainRelative

23. Money Supply (Annual): query_money_supply_data_year()

rs = bs.query_money_supply_data_year(start_date="2020", end_date="2024")

Returns: statYear, m0, m1, m2

Common Pitfalls

1. All return values are strings

BaoStock returns all fields as strings. You must convert manually:

df["close"] = pd.to_numeric(df["close"], errors="coerce")

2. Empty string for turnover rate when suspended

Turnover rate returns "" on suspended days. Direct float() conversion will fail:

df["turn"] = pd.to_numeric(df["turn"], errors="coerce")  # "" → NaN

3. pandas 2.0+ compatibility

BaoStock internally uses DataFrame.append(), which was removed in pandas 2.0+. You must manually collect data:

# BAD: rs.get_data() crashes on pandas 2.0+
df = rs.get_data()

# GOOD: Manual collection
rows = []
while (rs.error_code == "0") and rs.next():
    rows.append(rs.get_row_data())
df = pd.DataFrame(rows, columns=rs.fields)

4. login/logout prints to stdout

BaoStock's login() and logout() print messages to stdout. Suppress with:

import os, contextlib
with open(os.devnull, "w") as devnull:
    with contextlib.redirect_stdout(devnull):
        bs.login()

5. Minute bar time field format

The time field in minute bars is YYYYMMDDHHmmssSSS (17-digit string):

# "20240102093500000" → "2024-01-02 09:35:00"
t = "20240102093500000"
dt = f"{t[:4]}-{t[4:6]}-{t[6:8]} {t[8:10]}:{t[10:12]}:{t[12:14]}"

6. Adjustment method differs from other platforms

BaoStock uses the percentage change adjustment method, which produces slightly different absolute prices compared to Tongdaxin's fixed-point method.

7. Connection loss requires re-login

After network interruption or long idle time, BaoStock connections may expire. Add retry + re-login logic:

try:
    rs = bs.query_history_k_data_plus(...)
except Exception:
    bs.logout()
    bs.login()
    rs = bs.query_history_k_data_plus(...)  # Retry

8. Index does not support minute bars

Index codes (e.g., sh.000001) return empty data for minute frequencies without error.

9. Limited ETF support

BaoStock has limited ETF data coverage. Consider using pytdx or tushare for ETF quotes.

Official Resources

• Website: https://www.baostock.com
• API Docs: https://www.baostock.com/mainContent?file=pythonAPI.md
• GitHub: https://github.com/baostock/baostock
• PyPI: pip install baostock