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Dividend Data VS socketify.py

Compare Dividend Data VS socketify.py and see what are their differences

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Real-time stock scanner, 50+ indicators, 800K+ economic series, SEC filings and insider trades. Institutional-grade data in one browser tab. Free to start. featured
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Contents:
  1. ยป Base Details
  2. ยป Reviews
  3. ยป Alternatives

Dividend Data logo Dividend Data

Stock data in your spreadsheet, automatically.

socketify.py logo socketify.py

Maybe the fastest web framework for Python and PyPy
  • Dividend Data
    Image date //
    2026-03-04
  • Dividend Data 2
    2 //
    2026-03-06
  • Dividend Data 3
    3 //
    2026-03-06
  • Dividend Data 4
    4 //
    2026-03-06

Dividend Data brings 30+ years of stock market data for 80,000+ tickers directly into your Google Sheets and Microsoft Excel spreadsheets โ€” no API keys, no coding, no copying and pasting.

Built for dividend & fundamental investors, it gives you instant access to dividends, yields, payout ratios, growth rates, financial statements, earnings, ratios, price history, and 100+ metrics through simple custom formulas.

Just type a formula. The data appears live.

What makes it different:

โ€ข Free tier with 2,500 monthly credits โ€” no trial expiration โ€ข 16 custom functions covering everything dividend investors need โ€ข 30+ years of historical data โ€ข Works in both Google Sheets and Microsoft Excel โ€ข Built by a dividend investor, for dividend investors

Used by fundamental investors who want institutional-grade data without the institutional price tag.

  • socketify.py Landing page
    Landing page //
    2023-09-24

Dividend Data features and specs

No features have been listed yet.

socketify.py features and specs

  • High Performance
    Socketify.py is designed for high scalability and performance, leveraging an efficient event loop and native extensions to handle a large number of concurrent connections efficiently.
  • WebSocket Support
    The library provides built-in support for WebSockets, making it suitable for real-time applications where persistent connections between client and server are necessary.
  • Asynchronous I/O
    Socketify.py is built on top of asynchronous I/O paradigms, allowing non-blocking operations that can improve the throughput of networked applications.
  • Ease of Use
    The library offers a clean and straightforward API with examples and documentation, which lowers the barrier to entry for developers who are new to network programming in Python.
  • Python Integration
    Being a Python library, socketify.py integrates well with existing Python applications and can be included as part of larger, multi-component systems.

Possible disadvantages of socketify.py

  • Limited Adoption
    As a relatively new or niche library, socketify.py might have a smaller user base and community compared to more established frameworks like Flask or Django, which could result in fewer community resources and third-party integrations.
  • Learning Curve
    For developers who are accustomed to synchronous programming paradigms, adapting to the asynchronous programming model of socketify.py may require an initial learning investment.
  • Documentation Depth
    While there is documentation, it might not be as extensive or comprehensive as those of more mature libraries, potentially requiring more experimentation or source code reading to fully grasp advanced features.
  • Potential Stability Issues
    Being less established, there might be undiscovered bugs or stability issues in production environments compared to long-standing Python networking libraries.
  • Ecosystem Limitations
    The library might lack some of the extensive third-party plugins or tools available in more popular frameworks, which could limit its extensibility.

Analysis of Dividend Data

Overall verdict

  • Dividend Data (dividenddata.com) is a useful free resource for investors tracking UK and international dividend-paying stocks, offering solid data coverage for the price, though it lacks some of the polish and advanced analytics of premium paid platforms.

Why this product is good

  • Provides comprehensive dividend history, yield, and payment date information for a wide range of listed companies, particularly strong on UK equities
  • Free to access, making it accessible for retail investors without subscription costs
  • Includes forecast dividend data and ex-dividend date calendars, useful for planning income strategies
  • Simple, straightforward interface that is easy to navigate for basic dividend lookups
  • Aggregates data that would otherwise require checking multiple company reports or exchange filings

Recommended for

  • UK-focused income investors seeking dividend yield and payment date information
  • Retail investors building dividend income portfolios on a budget
  • Casual investors who want quick reference data without paying for premium research tools
  • People tracking ex-dividend dates to time purchases or avoid missing payments
  • Investors who want a supplementary free tool alongside other more detailed brokerage or analytics platforms

Analysis of socketify.py

Overall verdict

  • Socketify.py is a solid choice for developers seeking a high-performance web framework in Python, particularly for I/O-bound applications requiring speed comparable to frameworks in compiled languages, thanks to its use of uWebSockets under the hood.

Why this product is good

  • Built on uWebSockets, providing significant performance improvements over traditional Python web frameworks
  • Supports WebSockets natively, making it suitable for real-time applications
  • Lightweight and minimalistic design reduces overhead
  • Compatible with ASGI, allowing integration with existing Python async ecosystem
  • Active development and growing community support on GitHub
  • Good for building high-throughput APIs and services

Recommended for

  • Developers building real-time applications like chat apps or live notifications
  • Projects requiring high concurrency and low latency in Python
  • Teams looking to replace slower WSGI-based frameworks with something faster
  • Applications needing WebSocket support without heavy framework overhead
  • Microservices architectures where performance is critical
  • Python developers wanting an alternative to Node.js for performance-sensitive tasks

Category Popularity

0-100% (relative to Dividend Data and socketify.py)

Dividend Data

socketify.py

Trading
100 100%
Trading
0% 0
Python
0 0%
Python
100% 100
Finance
100 100%
Finance
0% 0
Web Development
0 0%
Web Development
100% 100

User comments

Share your experience with using Dividend Data and socketify.py. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, socketify.py seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Dividend Data mentions (0)

We have not tracked any mentions of Dividend Data yet. Tracking of Dividend Data recommendations started around Mar 2026.

socketify.py mentions (2)

  • Show HN: Python framework is faster than Golang Fiber
    These "benchmarks" are useless, they're not testing anything real world except the performance of uWebsockets. There are copy errors all over the place. And then an advertisement: https://github.com/cirospaciari/socketify.py#briefcase-comme... Is this a professional framework that produces proper, real-world benchmarks and... - Source: Hacker News / over 3 years ago
  • This is how I started the development of the fastest ASGI and WSGI Server in TechEmPower Benchmarks
    After starting the project called socketify.py at https://github.com/cirospaciari/socketify.py, I got pretty good results and reviews, but many people asked if socketify.py could be used to create a WSGI and ASGI server. WSGI and ASGI have a lot of overhead, that's is why I choose not to use them in the first place, but adding an ASGI and WSGI server allows a lot of code already written to run faster! Source: over 3 years ago

What are some alternatives?

When comparing Dividend Data and socketify.py, you can also consider the following products

DailyStockMarketData.com - Daily stock market data in a convenient CSV

FinViz - Stock screener for investors and traders, financial visualizations.

Main Street Data - Stock KPIs, Fundamentals, and Revenue/Expense Segmentation

Market Data - Stock Market API, Spreadsheet Add-ons, Get Data Anywhere

Robinhood - Free stock trading service.

Wellfound - Where the startup world goes to find whatโ€™s next.

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