// Access Chinese and global financial data using the AkShare Python library
| name | akshare-finance-data |
| description | Access Chinese and global financial data using the AkShare Python library |
| metadata | {"openclaw":{"emoji":"💹","category":"domains","subcategory":"finance","keywords":["akshare","financial data","chinese stocks","market data","economic indicators","quantitative finance"],"source":"https://github.com/akfamily/akshare"}} |
AkShare is an open-source Python library providing free access to Chinese and global financial market data. It aggregates data from 50+ sources including Sina Finance, East Money, Tushare, Yahoo Finance, and central bank websites. No API key required for most functions. Essential for financial research, quantitative analysis, and economic studies involving Chinese market data.
pip install akshare --upgrade
# Verify
python -c "import akshare as ak; print(ak.__version__)"
import akshare as ak
import pandas as pd
# Real-time quotes for all A-shares
df = ak.stock_zh_a_spot_em()
print(df.head())
# Columns: 代码, 名称, 最新价, 涨跌幅, 成交量, 成交额, ...
# Historical daily data for a specific stock
df = ak.stock_zh_a_hist(symbol="000001", period="daily",
start_date="20200101", end_date="20261231")
print(df.columns)
# 日期, 开盘, 收盘, 最高, 最低, 成交量, 成交额, 振幅, 涨跌幅, 换手率
# Minute-level data
df = ak.stock_zh_a_hist_min_em(symbol="000001", period="5",
start_date="2026-01-01 09:30:00",
end_date="2026-03-10 15:00:00")
# ETF list
df = ak.fund_etf_spot_em()
# Open-end fund NAV history
df = ak.fund_open_fund_info_em(symbol="000001", indicator="单位净值走势")
# Fund manager information
df = ak.fund_manager_em(symbol="000001")
# China government bond yields
df = ak.bond_china_yield(start_date="20200101", end_date="20261231")
# Corporate bond issuance
df = ak.bond_cb_jsl() # Convertible bonds from jisilu.cn
# GDP quarterly data
df = ak.macro_china_gdp()
# CPI monthly data
df = ak.macro_china_cpi()
# PMI (Purchasing Managers' Index)
df = ak.macro_china_pmi()
# Money supply (M0, M1, M2)
df = ak.macro_china_money_supply()
# US economic data
df = ak.macro_usa_gdp() # US GDP
df = ak.macro_usa_cpi() # US CPI
df = ak.macro_usa_unemployment_rate() # US unemployment
# CNY exchange rates
df = ak.currency_boc_sina(symbol="美元", start_date="20200101", end_date="20261231")
# All major currency pairs
df = ak.fx_spot_quote()
# Chinese commodity futures
df = ak.futures_zh_daily_sina(symbol="RB0") # Rebar futures
# Gold and silver prices
df = ak.futures_foreign_commodity_realtime(symbol="黄金")
import akshare as ak
import pandas as pd
def build_stock_panel(symbols: list, start: str, end: str) -> pd.DataFrame:
"""Build a panel dataset of stock returns and fundamentals."""
panels = []
for symbol in symbols:
# Price data
price = ak.stock_zh_a_hist(symbol=symbol, period="daily",
start_date=start, end_date=end)
price = price.rename(columns={"日期": "date", "收盘": "close",
"涨跌幅": "return", "成交额": "volume"})
price["symbol"] = symbol
price["date"] = pd.to_datetime(price["date"])
# Financial statements (annual)
try:
fin = ak.stock_financial_analysis_indicator(symbol=symbol)
fin = fin[["日期", "净资产收益率(%)", "资产负债率(%)"]].rename(
columns={"日期": "report_date", "净资产收益率(%)": "roe",
"资产负债率(%)": "leverage"})
except Exception:
fin = pd.DataFrame()
panels.append(price[["date", "symbol", "close", "return", "volume"]])
panel = pd.concat(panels, ignore_index=True)
panel = panel.set_index(["symbol", "date"]).sort_index()
return panel
# Usage
symbols = ["000001", "600519", "000858", "601318", "000333"]
panel = build_stock_panel(symbols, "20200101", "20261231")
print(f"Panel: {panel.shape[0]} observations, {panel.index.get_level_values(0).nunique()} firms")
def event_study(symbol: str, event_date: str, window: int = 10):
"""Simple event study around a given date."""
# Get data with buffer
start = pd.to_datetime(event_date) - pd.Timedelta(days=window*3)
end = pd.to_datetime(event_date) + pd.Timedelta(days=window*3)
df = ak.stock_zh_a_hist(symbol=symbol, period="daily",
start_date=start.strftime("%Y%m%d"),
end_date=end.strftime("%Y%m%d"))
df["date"] = pd.to_datetime(df["日期"])
df["return"] = df["涨跌幅"].astype(float)
df = df.set_index("date").sort_index()
# Market return (CSI 300)
market = ak.stock_zh_index_daily(symbol="sh000300")
market["date"] = pd.to_datetime(market["date"])
market = market.set_index("date")
market["mkt_return"] = market["close"].pct_change() * 100
# Merge and compute abnormal returns
merged = df[["return"]].join(market[["mkt_return"]], how="inner")
merged["abnormal_return"] = merged["return"] - merged["mkt_return"]
# Event window
event_idx = merged.index.get_indexer([pd.to_datetime(event_date)], method="nearest")[0]
event_window = merged.iloc[event_idx-window:event_idx+window+1]
event_window["CAR"] = event_window["abnormal_return"].cumsum()
return event_window[["return", "mkt_return", "abnormal_return", "CAR"]]
| Issue | Solution |
|---|---|
| Data source temporarily unavailable | AkShare aggregates from web sources; retry or use try/except |
| Inconsistent column names across functions | Always check df.columns before processing |
| Date format varies (string vs datetime) | Standardize: pd.to_datetime(df["日期"]) |
| Some functions require specific symbol format | A-shares: 6-digit code; indices: sh000001; HK: 00700 |
| Rate limiting from upstream sources | Add time.sleep(1) between batch requests |