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Vnstock Documentation

website·Indexed 32 days ago

https://vnstocks.com

A Python library and data layer for stock market analysis and global asset information. It provides tools for AI Agents to perform evidence-based financial analysis, integrating with Harness Engineering and MCP servers to automate data collection and stock screening.

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What's inside Vnstock

  1. Overview of the Python Coding with AI Course by Vnstock

    The 'Python Coding with AI' course by Vnstock is designed for non-programmers to build tools for financial data analysis and automation using AI. The course focuses on practical application over theoretical programming, teaching users how to prompt AI to generate Python code for filtering stocks, identifying technical signals, and automating investment reports and alerts.
    Course Goals:
- Communicate with AI to write Python programs for financial analysis.
- Process real-world data (stock filtering, technical signals).
- Build custom automation tools for reporting and investment alerts.

Key Learning Areas:
- Using Google Colab for rapid prototyping.
- Prompt engineering for accurate code generation and debugging.
- Building price alert bots and analysis charts.
- Deploying applications on local machines or the cloud.

  2. Overview of Vnstock for stock data extraction

    Vnstock is a Python-based tool used to download comprehensive stock market data for free. It allows users to automate data retrieval with a few lines of code, making it suitable for both basic fundamental analysis and integration into complex machine learning models. Unlike paid services like FireAnt or WiData, Vnstock provides free access to data sourced from third-party providers.

  4. Overview of Huggingface Services for Python Web Apps

    Huggingface provides a suite of open-source tools to transition Python applications from local environments to professional web apps. Key services include:

    • Datasets: A powerful open data repository for storing and retrieving ML/AI data. It can be used as a structured non-SQL database for financial data (e.g., transaction history, financial reports) and supports Git for versioning and synchronization.
    • Spaces: A platform to convert Python code or Jupyter Notebooks into interactive web apps using libraries like Streamlit, Panel, or Flask. It supports deployment via Streamlit SDK or Docker and manages source code via Git repositories.
    • Models: A hub providing free access to open-source AI models (e.g., DeepSeek R1) that can be integrated into applications for trend forecasting, data analysis, and automated reporting.

  6. Overview of Vnstock Pipeline 2.0.1 features

    Vnstock Pipeline 2.0.1 introduces enhancements for handling large-scale intraday and streaming data, specifically designed for backtesting and real-time analysis. Key capabilities include:

    • Real-time Streaming: Support for WebSocket streaming during trading sessions for low-latency data retrieval.
    • Storage Optimization: Support for the Parquet format, reducing disk space usage by up to 75% compared to CSV and improving read/write speeds.
    • Selective Data Loading: Ability to filter and download only required data fields to save bandwidth and increase processing speed.
    • Data Manager: Automated organization of data files and folders for faster retrieval without manual configuration.
    • Data Loss Prevention: Automatic stitching/merging of continuous intraday data if a network connection is interrupted.

  7. Overview of the Vnstock ecosystem libraries

    The Vnstock ecosystem consists of several specialized libraries for financial data analysis in the Vietnamese market:

    • Vnstock: The core library for basic stock data downloading functions.
    • Vnstock Data: Provides advanced stock price data, corporate financial reports, macroeconomic data, and ETF information.
    • Vnstock TA: A technical analysis library featuring approximately 60 indicators that operate directly on data retrieved by Vnstock.
    • Vnstock News: An automated crawler and analyzer for news from Vietnamese financial press sites.
    • Vnstock Pipeline: A high-performance automated data flow architecture supporting intra-day data via WebSocket.

  8. Overview of the vnstock Python library

    The vnstock library is an open-source tool available on GitHub designed for analyzing Vietnamese stock market data. It is written in English and aims to provide users with a simple, fast, and unrestricted way to access financial data for systematic analysis.

  9. Overview of Pyecharts for Python data visualization

    Pyecharts is an open-source Python library used to create visually appealing and highly interactive charts. It acts as a Python wrapper for Apache ECharts, allowing data analysts to generate web-based visualizations without needing to write JavaScript code. In the context of the Vnstock TA library, Pyecharts is utilized to represent technical indicator data.

  10. Overview of Vnstock News 2.1.0 news loading improvements

    Vnstock News 2.1.0 optimizes the collection of financial news by automating data retrieval and allowing custom source configurations. Key features include:

    • Automated News Aggregation: Direct loading of news from 10 popular financial news sources.
    • Custom News Sources: Ability to add preferred news websites to the configuration for automatic scanning.
    • Sitemap and RSS Scanning: Implementation of Sitemap and RSS mechanisms to ensure stable and automatic collection of new articles, even from websites with complex structures or irregular update frequencies.
    • News Data Archiving: Support for building news datasets intended for market sentiment analysis or long-term research storage.

  12. Introduction to Google Antigravity 2.0 for Beginners

    Google Antigravity 2.0 is an AI programming tool designed for users familiar with web-based AI chats (like ChatGPT, Claude, or Gemini) who want to transition into 'Vibe Coding'. It focuses on an agent-centric experience where users provide requests and receive results without needing to install libraries or configure complex development environments. It is best suited for quick Q&A, data analysis, information summarization, and planning tasks that do not require building large-scale software systems.