Akshare Finance 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"]]