This guide shows how to quickly set up AKTools via Docker, retrieve high‑quality historical and real‑time stock data, upgrade AKShare, and create a flexible Gin‑based API to serve the data for quantitative analysis or stock‑screening applications.
This article walks through a Pandas data‑analysis issue where a user needs to filter stock codes between 10 and 12, explains the type‑comparison error, and provides a corrected solution with code snippets and best‑practice tips for asking Python questions.
This article demonstrates two Python approaches—using high-level requests and low-level HTTPConnection—to fetch intraday stock data from Eastmoney, providing complete code examples, URL discovery steps, and practical tips for data‑mining enthusiasts.
This guide shows how to download historical Kweichow Moutai stock data from NetEase Finance as a CSV file, handle encoding issues, and use Python's pandas and matplotlib libraries to filter the data and create both volume and OHLC line charts for a selected month.
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 guide walks you through downloading Kweichow Moutai's historical stock data as a CSV file, handling encoding issues, and using Python's pandas and matplotlib libraries to create line charts of trading volume and OHLC prices for clear visual analysis.
This article walks through the step-by-step process of extracting individual and batch stock minute-level data from Eastmoney using Python, covering URL analysis, parameter discovery, handling pagination, constructing CSV output, and extending the script to fetch arbitrary dates via cookie manipulation.
This guide explains how to use Python, pandas, and requests to fetch minute‑by‑minute K‑line data for Chinese stocks from Eastmoney's API, generate the required secid, handle different market codes, and continuously save the data until market close.
This article provides a Python tutorial that explains how to generate Eastmoney‑specific security IDs, request minute‑level K‑line data for a given stock code via the Eastmoney API, process the response into a pandas DataFrame, and continuously fetch and save data until market close.
This tutorial guides readers through analyzing web pages, using Python to write network programs, applying regular expressions, and operating Excel to scrape all listed company stock data for a specified date range, save it by stock code, and optionally store results in databases.
This tutorial compares static Matplotlib/Seaborn visualizations with interactive Plotly charts for stock price data, explains when to choose each library, and provides step‑by‑step Python code for fetching data, creating scatter and 3D surface plots, and installing Plotly in offline mode.
This tutorial explains how to collect the names and trading information of every stock listed on the Shanghai and Shenzhen exchanges using Python 3.5, requests, BeautifulSoup and regular expressions, then store the results in a file through a clear three‑step process of fetching the stock list, retrieving each stock's page, parsing the HTML and writing the data.
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.
