Baostock

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: atompilot
• Source: atompilot/baostock-skill
• License: MIT

Install and usage instructions live in the source repository linked above.