Software Alternatives, Accelerators & Startups
Register | Login
PL

Quantopian VS PyLint

Compare Quantopian VS PyLint and see what are their differences

bunq
bank of the Free! Mobile banking that makes life easy. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Quantopian logo Quantopian

Your algorithmic investing platform

PyLint logo PyLint

Pylint is a Python source code analyzer which looks for programming errors.
  • Quantopian Landing page
    Landing page //
    2023-07-27
  • PyLint Landing page
    Landing page //
    2023-09-22

Quantopian features and specs

  • Community Collaboration
    Quantopian provided a platform for users to share and collaborate on trading algorithms, enabling users to learn from each other and improve their strategies.
  • Access to Data
    Quantopian offered access to a wide range of financial data sets, which allowed users to develop and back-test their algorithms using historical data.
  • Comprehensive Development Environment
    It featured an integrated development environment (IDE) with tools for coding, testing, and back-testing trading strategies in Python, which was user-friendly and powerful.
  • Educational Resources
    Quantopian provided various educational resources, including lectures, tutorials, and a supportive community forum, which were beneficial for both beginners and experienced traders.
  • Competition and Incentives
    Quantopian organized contests that incentivized users to develop successful trading algorithms, with the potential to receive a live trading allocation from the company.

Possible disadvantages of Quantopian

  • Shutting Down Services
    Quantopian shut down its retail offering in 2020, which meant that users could no longer use their platform for developing and testing new algorithms.
  • Limited Live Trading Options
    Users found limited options for deploying their strategies into live trading. Quantopian allowed this only for algorithms selected for allocation, which reduced accessibility for many users.
  • Dependence on Platform
    Users who developed algorithms on Quantopian's platform were heavily dependent on it, and when it shut down, they had to transition to other platforms, which could be challenging.
  • Resource Limitations
    There were computational and resource limitations for users, which could restrict the complexity of the algorithms and back-testing users could perform without additional infrastructure.
  • Portfolio Selection Process
    The selection process for having algorithms licenced for live trading allocation was competitive and not transparent to many users, which could lead to frustration.

PyLint features and specs

  • Extensive Error Checking
    PyLint provides comprehensive checks for errors in Python code, including syntax errors, structural problems, and more complex issues like unused variables and undefined names.
  • Customizability
    PyLint allows users to configure which types of errors and warnings they want to check for through configuration files, making it adaptable to different coding standards and preferences.
  • Integration with Development Tools
    PyLint can be integrated with various IDEs and editors such as Visual Studio Code, PyCharm, and more, enhancing the development workflow by providing real-time feedback.
  • Code Quality Metrics
    It offers additional metrics and ratings for code quality, helping developers understand the complexity and maintainability of their code.
  • Code Refactoring Support
    PyLint suggests specific code improvements and refactorings, which can enhance the readability and performance of the code.

Possible disadvantages of PyLint

  • Performance Overhead
    Analyzing large codebases can be slow with PyLint, impacting performance and increasing the time taken for continuous integration pipelines to run.
  • False Positives
    PyLint can generate false positive warnings, particularly in complex or dynamically-typed code, which might lead to developers spending time investigating non-issues.
  • Steep Learning Curve
    The initial setup and configuration of PyLint can be challenging for new users who are not familiar with its extensive customization options.
  • Strictness
    PyLint is very strict by default, which might overwhelm developers, especially those working in less formal or rapid development environments, with a high volume of warnings and errors.
  • Compatibility Issues
    There might be compatibility issues with certain Python versions or specific coding patterns, leading to inaccurate linting results or the need for frequent adjustments to configurations.

Quantopian videos

+ Add

Algorithmic Trading with Python and Quantopian p. 1

More videos:

  • Review - Quantopian, simple strategies

PyLint videos

+ Add

Pylint Tutorial – How to Write Clean Python

More videos:

  • Tutorial - How to write pylint plugins

Category Popularity

0-100% (relative to Quantopian and PyLint)

Quantopian

PyLint

Finance
100 100%
Finance
0% 0
Code Analysis
0 0%
Code Analysis
100% 100
Tool
100 100%
Tool
0% 0
Code Coverage
0 0%
Code Coverage
100% 100

User comments

Share your experience with using Quantopian and PyLint. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Quantopian and PyLint

Quantopian Reviews

We have no reviews of Quantopian yet.
Be the first one to post

PyLint Reviews

7 best recommended IntelliJ IDEA Python plugins - Programmer Sought
As the name suggests, this plugin is a Python linter. It provides real-time and on-demand scanning of Python files with Pylint ideas from your Intellij. Pylint is an open source project, so it can be fully customized according to your needs. In addition, Pylint has a lot of documentation on the plugin website.
Source: www.programmersought.com

Social recommendations and mentions

Based on our record, PyLint seems to be more popular. It has been mentiond 13 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.

Quantopian mentions (0)

We have not tracked any mentions of Quantopian yet. Tracking of Quantopian recommendations started around Mar 2021.

PyLint mentions (13)

  • Nix-Powered Python Development
    These requirements are not too uncommon. I have seen many projects with similar setup, with alternatives such as tox instead of nox, or black and pylint instead of ruff, etc. - Source: dev.to / 7 days ago
  • Nix Flake Templates
    Use pylint and flake8 for linting and static analysis. - Source: dev.to / 8 days ago
  • The Cloud Resume Challenge - GCP :)
    I used Pylint to perform basic test on the code and for the security bit I used snyk SCM to check for vulnerabilities within my code and it's dependencies. - Source: dev.to / over 2 years ago
  • I'm being told that one of my projects on GitHub is poorly coded. Can anyone tell me why? The only thing I see ugly, not necessary wrong or poorly coded, is the two variables with the list of iPhone models, and the incredibly long if, elif, and else statements.
    Pylint - https://pylint.pycqa.org/en/latest/ Black - https://black.readthedocs.io/en/stable/. Source: over 2 years ago
  • API pull into pandas with formatting.
    Your code isn't PEP-8 compliant. Use black or autopep8 on your code to auto-format your code, or at least use pylint to check for issues, before asking anyone else to read your code. Source: almost 3 years ago
View more

What are some alternatives?

When comparing Quantopian and PyLint, you can also consider the following products

QuantConnect - QuantConnect provides a free algorithm backtesting tool and financial data so engineers can design algorithmic trading strategies. We are democratizing algorithm trading technology to empower investors.

SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.

quantra - A public API for quantitative finance made with Quantlib

PyCharm - Python & Django IDE with intelligent code completion, on-the-fly error checking, quick-fixes, and much more...

Backtrader - Backtrader is a complete and advanced python framework that is used for backtesting and trading.

Coverity Scan - Find and fix defects in your Java, C/C++ or C# open source project for free

Loading...