Market, company and capital context
One SDK covers stocks, ETFs, indices, financials, valuation, money flow, chip distribution, realtime quotes and financial news.
Use the official quantcoda package to bring China A-share stocks, ETFs, indices, financials and money flow into Python and pandas. The hosted API keeps public parameters and fields aligned across Python, REST and MCP.
Install the SDK, set your QuantCoda API key and query by security symbol and date. Results arrive as pandas DataFrames ready for validation, transformation and research.
# Python 3.9+ pip install quantcoda import quantcoda as qc qc.set_token("qc_live_your_key") df = qc.daily(symbol="600519.SH", start_date="20260101") print(df.head())
One SDK covers stocks, ETFs, indices, financials, valuation, money flow, chip distribution, realtime quotes and financial news.
Tabular responses become DataFrames for filtering, joins, statistics, visualization and research pipelines.
Python methods retain public symbol, date and filter parameters instead of introducing a second naming model.
Endpoint metadata ships with the package. Use qc.search(), qc.interface_info() and qc.list_categories() to discover capabilities without a network request.
Use one package for the research objects developers most often need to combine. Start with the China A-Share Market Data API, then move into dedicated China ETF data, stock financial data and money-flow data without changing authentication or symbol conventions.
Query daily bars, adjustments, indices, ETF NAV, fund size, tracking indices and holdings as pandas DataFrames.
Join statements, indicators, valuation and capital-flow records through exchange-qualified symbols and explicit dates.
Explore the China A-Share Market Data API coverage or go directly to the API documentation. For AI-led workflows, the same API key connects to the China Stock MCP Server.