This article explains four practical Python methods—Pandas_datareader, yfinance, Tushare, and JoinQuant—to fetch A‑share market data and manage trading positions, while also providing QR‑code links to free Python learning resources.
This article shares practical experiments using Python libraries such as pandas_datareader, yfinance, tushare, and the JoinQuant platform to fetch historical stock data, highlighting connection requirements, limitations, and the ease of obtaining data without additional installations.
This article explains a simple investment timing strategy that adjusts regular contributions based on daily northbound capital flows, walks through the financial concepts, provides complete Python code using Tushare to fetch and process the data, and shows back‑tested results and practical tips.
This tutorial shows how to fetch ten years of stock fundamentals using Tushare, compute a probabilistic "temperature" metric with NumPy and SciPy, and visualize the temperature alongside closing prices in Matplotlib, providing a complete end‑to‑end Python data‑analysis workflow.
This tutorial walks through installing PostgreSQL, connecting it to Python via psycopg2 and SQLAlchemy, fetching millions of stock records with Tushare, storing and updating the data in a PostgreSQL database, and visualizing query results for quantitative finance strategies, all with complete code examples.
This tutorial shows how to combine Python's threading and Queue modules to build a producer‑consumer multithreaded downloader for Tushare stock data, dramatically reducing download time from hours to under one hour.
