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socketify.py VS Portfolio Backtest

Compare socketify.py VS Portfolio Backtest and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

socketify.py logo socketify.py

Maybe the fastest web framework for Python and PyPy

Portfolio Backtest logo Portfolio Backtest

Create portfolio backtests quickly and easily by describing them in plain English. Our AI will find the relevant Stocks or ETFs and create the backtest for you.
  • socketify.py Landing page
    Landing page //
    2023-09-24
  • Portfolio Backtest Write you backtest in natural language
    Write you backtest in natural language //
    2025-06-04
  • Portfolio Backtest Return Summary
    Return Summary //
    2025-06-04
  • Portfolio Backtest Portfolio Value
    Portfolio Value //
    2025-06-04
  • Portfolio Backtest Portfolio Drawdowns
    Portfolio Drawdowns //
    2025-06-04
  • Portfolio Backtest Annual Returns
    Annual Returns //
    2025-06-04

I built Portfolio Backtest because I wanted an easy way to backtest hypothetical portfolios.

Alternatives required you to fill out long complicated forms to construct the backtest but I wanted something where you could just describe what you wanted in plain english.

Once created I wanted it to be able to track CAGR, portfolio value over, returns by year, drawdowns and a range of other metrics.

socketify.py

Website
github.com
$ Details
-
Release Date
-
Edit details

Portfolio Backtest

Website
portfoliobacktest.com
$ Details
freemium
Release Date
2025 June
Startup details
Country
Australia
State
NSW
City
Mortdale
Founder(s)
Rhys Diab
Employees
1 - 9
Edit details

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.

Portfolio Backtest features and specs

  • Easy Portfolio Backtesting
    Portfolio Backtest provides a straightforward interface for testing historical performance of investment portfolios, making it accessible to both beginner and intermediate investors who want to evaluate asset allocation strategies.
  • Multiple Asset Support
    The platform supports backtesting across a variety of asset classes including stocks, ETFs, and mutual funds, allowing users to construct diversified portfolios and analyze their historical behavior.
  • Visual Performance Charts
    The tool provides clear visual charts and graphs showing portfolio growth, drawdowns, and other performance metrics over time, making it easy to compare different allocation strategies at a glance.
  • Free to Use
    Portfolio Backtest offers free access to its core backtesting features, which lowers the barrier to entry for individual investors who want to test strategies without committing to expensive subscription-based tools.
  • Portfolio Comparison
    Users can compare multiple portfolio configurations side by side, enabling them to evaluate the impact of different asset allocations and rebalancing strategies on risk-adjusted returns.

Possible disadvantages of Portfolio Backtest

  • Limited Historical Data
    The platform may have limited historical data depth compared to more premium backtesting tools, which can restrict the ability to test strategies over very long time horizons or during specific market events.
  • Basic Analytics
    Compared to professional-grade tools like Portfolio Visualizer or QuantConnect, the analytics and risk metrics available on Portfolio Backtest can be relatively basic, lacking advanced measures such as factor exposure analysis or Monte Carlo simulations.
  • Limited Customization Options
    The tool may not offer extensive customization for rebalancing frequencies, tax-loss harvesting simulations, or custom benchmarks, which limits its usefulness for more sophisticated portfolio strategies.
  • Smaller User Community
    Portfolio Backtest has a smaller user base and community compared to more established platforms, meaning there are fewer tutorials, forums, and shared strategies available for new users seeking guidance.
  • Survivorship Bias Risk
    Like many free backtesting tools, Portfolio Backtest may be subject to survivorship bias in its asset database, as delisted or failed securities might not be included, potentially leading to overly optimistic backtest results.

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

Analysis of Portfolio Backtest

Overall verdict

  • Portfolio Backtest is a solid tool for investors who want to validate investment strategies using historical data before committing real capital, offering a practical way to assess risk and returns.

Why this product is good

  • Allows users to test investment strategies against historical market data to evaluate potential performance
  • Helps identify risk exposure and drawdown potential before investing real money
  • Provides data-driven insights that can improve decision-making and reduce emotional investing
  • Often includes portfolio comparison features to benchmark strategies against indices or other allocations
  • Accessible interface that doesn't require advanced coding or statistical expertise

Recommended for

  • DIY retail investors testing asset allocation strategies
  • Financial planners validating client portfolio recommendations
  • Quantitative hobbyists experimenting with factor-based or rules-based strategies
  • Long-term investors wanting to stress-test portfolios against historical downturns
  • Users comparing passive vs active investment approaches before committing funds

socketify.py videos

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Portfolio Backtest videos

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Create Portfolio Backtest With Plain Language

Category Popularity

0-100% (relative to socketify.py and Portfolio Backtest)

socketify.py

Portfolio Backtest

Websocket
100 100%
Websocket
0% 0
Finance
0 0%
Finance
100% 100
Python
100 100%
Python
0% 0
Investing
0 0%
Investing
100% 100

User comments

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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.

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

Portfolio Backtest mentions (0)

We have not tracked any mentions of Portfolio Backtest yet. Tracking of Portfolio Backtest recommendations started around Jun 2025.

What are some alternatives?

When comparing socketify.py and Portfolio Backtest, you can also consider the following products

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