--- 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 Financial Data Guide ## Overview 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. ## Installation ```bash pip install akshare --upgrade # Verify python -c "import akshare as ak; print(ak.__version__)" ``` ## Core Data Categories ### Stock Market Data (A-Shares) ```python 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") ``` ### Fund Data ```python # 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") ``` ### Bond Market ```python # 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 ``` ### Macroeconomic Indicators ```python # 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 ``` ### Foreign Exchange ```python # 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() ``` ### Futures and Commodities ```python # 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") ``` ## Research Workflow Example ### Financial Panel Data Construction ```python 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") ``` ### Event Study ```python 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"]] ``` ## Common Gotchas | 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 | ## References - [AkShare Documentation](https://akshare.akfamily.xyz/) - [AkShare GitHub](https://github.com/akfamily/akshare) - [AkShare API Reference](https://akshare.akfamily.xyz/data/stock/stock.html)