Baostock

BaoStock (Free China A-Share Data Platform)

BaoStock is a free, open-source data platform for China A-share securities. No registration or API key is required, and it returns pandas.DataFrame.

Installation

pip install baostock --upgrade

Verify the installation:

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

Expected output: login success!.

Basic Usage

Each session must begin with bs.login() and end with bs.logout():

import baostock as bs
import pandas as pd

# Log in to the system
lg = bs.login()

# ... perform data queries here ...

# Log out of the system
bs.logout()

Use .get_data() to retrieve the DataFrame from query results:

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

Core API

1. query_all_stock — Get All Securities List

Retrieves all stock/index codes for a specified trading day.

# Get all securities codes for a specified date
rs = bs.query_all_stock(day="2024-01-02")
df = rs.get_data()
# Returns fields: code (securities code), tradeStatus (trading status), code_name (securities name)

• day — Date string YYYY-MM-DD (default: today). Returns an empty DataFrame on non-trading days.

2. query_history_k_data_plus — K-Line Data

Retrieves historical K-line data (OHLCV + indicators).

# Get daily K-line data for ICBC
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",       # Frequency: d (daily), w (weekly), m (monthly), 5/15/30/60 (minute)
    adjustflag="3"       # Adjustment: 1 (forward), 2 (backward), 3 (none, default)
)
df = rs.get_data()

Parameters:

• code — Stock code, format sh.600000 or sz.000001
• fields — Comma-separated field names (see below)
• start_date / end_date — YYYY-MM-DD format
• frequency — d (daily), w (weekly), m (monthly), 5/15/30/60 (minute). Indices have no minute-level data.
• adjustflag — 1 (forward adjustment), 2 (backward adjustment), 3 (no adjustment, default)

Available fields for daily data:

date (date), code (securities code), open (open price), high (high price), low (low price), close (close price), preclose (previous close price), volume (volume), amount (turnover), adjustflag (adjustment flag), turn (turnover rate), tradestatus (trading status), pctChg (percent change), peTTM (trailing P/E ratio), pbMRQ (P/B ratio), psTTM (trailing P/S ratio), pcfNcfTTM (trailing P/CF ratio), isST (is ST stock)

Available fields for minute data:

date (date), time (time), code (securities code), open (open price), high (high price), low (low price), close (close price), volume (volume), amount (turnover), adjustflag (adjustment flag)

3. query_trade_dates — Trading Calendar

# Get the trading calendar for a specified range
rs = bs.query_trade_dates(start_date="2024-01-01", end_date="2024-12-31")
df = rs.get_data()
# Returns fields: calendar_date (calendar date), is_trading_day (whether it is a trading day)

4. query_stock_industry — Industry Classification

# Get industry classification for all stocks
rs = bs.query_stock_industry()
df = rs.get_data()
# Returns fields: updateDate (update date), code (securities code), code_name (securities name), industry (industry), industryClassification (industry classification)

5. query_stock_basic — Stock Basic Information

# Get basic information for a specified stock
rs = bs.query_stock_basic(code="sh.601398")
df = rs.get_data()
# Returns fields: code (securities code), code_name (securities name), ipoDate (IPO date), outDate (delisting date), type (type), status (status)

• type — 1 stock, 2 index, 3 other
• status — 1 listed, 0 delisted

6. query_dividend_data — Dividend Information

# Get dividend data for a specified stock
rs = bs.query_dividend_data(code="sh.601398", year="2023", yearType="report")
df = rs.get_data()

• yearType — report (reporting period) or operate (implementation period)

7. Financial Data (Quarterly)

Profitability

# Get profitability indicators (ROE, net profit margin, gross margin, etc.)
rs = bs.query_profit_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

Operational Efficiency

# Get operational efficiency indicators (inventory turnover, accounts receivable turnover, etc.)
rs = bs.query_operation_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

Growth Capability

# Get growth indicators (YoY revenue growth, YoY net profit growth, etc.)
rs = bs.query_growth_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

Solvency

# Get solvency indicators (current ratio, quick ratio, etc.)
rs = bs.query_balance_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

Cash Flow

# Get cash flow data
rs = bs.query_cash_flow_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

DuPont Analysis

# Get DuPont analysis data (ROE decomposition: profit margin × asset turnover × equity multiplier)
rs = bs.query_dupont_data(code="sh.601398", year=2023, quarter=4)
df = rs.get_data()

8. Index Data

Index Constituent Stocks

# Get CSI 300 constituent stocks
rs = bs.query_hs300_stocks()
df = rs.get_data()

# Get SSE 50 constituent stocks
rs = bs.query_sz50_stocks()
df = rs.get_data()

# Get CSI 500 constituent stocks
rs = bs.query_zz500_stocks()
df = rs.get_data()

Full Example: Download Daily K-Line Data and Save as CSV

import baostock as bs
import pandas as pd

# Log in to the system
bs.login()

# Get Kweichow Moutai 2024 daily K-line data (backward adjusted)
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"  # Backward adjustment
)
df = rs.get_data()

# Save to CSV file
df.to_csv("kweichow_moutai_2024.csv", index=False)
print(df.head())

# Log out of the system
bs.logout()

Stock Code Format

• Shanghai: sh.600000, sh.601398
• Shenzhen: sz.000001, sz.300750
• Beijing: bj.430047
• Indices: sh.000001 (SSE Composite Index), sh.000300 (CSI 300)

Usage Tips

• No registration or API key required — just call bs.login() to get started.
• Sessions may time out after prolonged inactivity — simply call bs.login() again.
• Not thread-safe — for parallel downloads, use multiprocessing (multi-process), not threading (multi-thread).
• Data coverage: A-shares from 1990 to present.
• Financial data is provided quarterly, with approximately a 2-month delay after the reporting period ends.
• Documentation: http://baostock.com/baostock/index.php/Python_API%E6%96%87%E6%A1%A3

Advanced Examples

Batch Download Data for Multiple Stocks

import baostock as bs
import pandas as pd

bs.login()

# Define the list of stocks to download
stock_list = ["sh.600519", "sh.601398", "sz.000001", "sz.300750", "sh.601318"]

all_data = []
for code in stock_list:
    # Get daily K-line data (forward adjusted)
    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"  # Forward adjustment
    )
    df = rs.get_data()
    all_data.append(df)
    print(f"Downloaded {code}, {len(df)} records")

# Merge all data
combined = pd.concat(all_data, ignore_index=True)
combined.to_csv("multi_stock_baostock.csv", index=False)
print(f"Total merged records: {len(combined)}")

bs.logout()

Get Full Market Stock List and Filter

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()

# Filter actively trading stocks (exclude indices and suspended stocks)
stocks = df[df["tradeStatus"] == "1"]
# Filter Shanghai A-shares (starting with sh.6)
sh_stocks = stocks[stocks["code"].str.startswith("sh.6")]
print(f"Shanghai A-shares: {len(sh_stocks)} stocks")

# Filter Shenzhen main board (starting with sz.00)
sz_main = stocks[stocks["code"].str.startswith("sz.00")]
print(f"Shenzhen main board: {len(sz_main)} stocks")

# Filter ChiNext board (starting with sz.30)
gem = stocks[stocks["code"].str.startswith("sz.30")]
print(f"ChiNext board: {len(gem)} stocks")

bs.logout()

Calculate Technical Indicators

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

bs.login()

# Get Ping An Bank daily K-line data
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)

# Calculate moving averages
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()

# Detect golden cross / death cross signals
df["signal"] = 0
df.loc[(df["MA5"] > df["MA20"]) & (df["MA5"].shift(1) <= df["MA20"].shift(1)), "signal"] = 1   # Golden cross
df.loc[(df["MA5"] < df["MA20"]) & (df["MA5"].shift(1) >= df["MA20"].shift(1)), "signal"] = -1  # Death cross

golden_cross = df[df["signal"] == 1]
death_cross = df[df["signal"] == -1]
print(f"Golden crosses: {len(golden_cross)}, Death crosses: {len(death_cross)}")
print("Golden cross dates:", golden_cross["date"].tolist())

bs.logout()

Get CSI 300 Constituent Stocks and Download Data

import baostock as bs
import pandas as pd

bs.login()

# Get CSI 300 constituent stocks
rs = bs.query_hs300_stocks()
hs300 = rs.get_data()
print(f"CSI 300 has {len(hs300)} constituent stocks")

# Download daily K-line data for the first 10 constituent stocks
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()

Get Financial Data and Analyze

import baostock as bs
import pandas as pd

bs.login()

# Get profitability data for multiple bank stocks
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)
# View ROE and net profit margin
print(profit_df[["code", "roeAvg", "npMargin", "gpMargin"]])

# Get growth capability data
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)
# View revenue growth rate and net profit growth rate
print(growth_df[["code", "YOYEquity", "YOYAsset", "YOYNI"]])

bs.logout()

Full Example: Simple Backtesting Framework

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

bs.login()

# Get Ping An Bank 2023 daily K-line data (forward adjusted)
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)

# Dual moving average strategy backtest
df["MA5"] = df["close"].rolling(5).mean()
df["MA20"] = df["close"].rolling(20).mean()

initial_cash = 100000  # Initial capital: 100,000
cash = initial_cash
shares = 0             # Shares held
trades = []            # Trade log

for i in range(20, len(df)):
    # Golden cross 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  # Round down to lots (100 shares)
            cost = shares * buy_price
            cash -= cost
            trades.append({"date": df["date"].iloc[i], "action": "BUY",
                          "price": buy_price, "shares": shares, "cash": cash})

    # Death cross 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

# Calculate final returns
final_value = cash + shares * df["close"].iloc[-1]
total_return = (final_value - initial_cash) / initial_cash * 100

print(f"Initial capital: {initial_cash:.2f}")
print(f"Final portfolio value: {final_value:.2f}")
print(f"Total return: {total_return:.2f}%")
print(f"Number of trades: {len(trades)}")
for t in trades:
    print(f"  {t['date']} {t['action']} {t['shares']} shares @ {t['price']:.2f}")

bs.logout()

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