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Polygon.io VS NumPy

Compare Polygon.io VS NumPy and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Polygon.io logo Polygon.io

Polygon.io offers streaming realtime data for stocks/equities, ETFs, Indecies and Forex/Currencies including crypto currencies. Our Real-Time Stock Data APIs help you build the future on fintech.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Polygon.io Landing page
    Landing page //
    2023-08-18
  • NumPy Landing page
    Landing page //
    2023-05-13

Polygon.io features and specs

  • Comprehensive Data Coverage
    Polygon.io offers a wide range of financial data, including stocks, forex, and crypto, making it a one-stop solution for financial data needs.
  • Real-time Data
    The platform provides real-time data feeds, which are crucial for traders and financial analysts to make timely decisions.
  • Developer-friendly API
    Polygon.io has a well-documented and easy-to-use API, which simplifies the integration process for developers looking to access financial data.
  • Historical Data Access
    Users can access extensive historical data through the platform, enabling backtesting and historical analysis of financial instruments.
  • Customizable Subscription Plans
    Polygon.io offers various subscription tiers, allowing users to select the level of access that best fits their needs and budget.

Possible disadvantages of Polygon.io

  • Cost
    For some users, the subscription fees may be considered expensive, especially for smaller businesses or individual investors.
  • Data Limits on Free Tier
    The free access tier has limitations on data availability and usage, which might be restrictive for more demanding applications.
  • Learning Curve
    Despite being developer-friendly, there may still be a learning curve for users who are not familiar with APIs or need specific data integrations.
  • Dependence on Internet Connectivity
    As an online service, uninterrupted access to Polygon.io's data depends on a stable internet connection.
  • Potential Overwhelming Features
    With an extensive range of features and data sets, beginners might find the platform overwhelming without clear guidance or use-case examples.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Polygon.io videos

+ Add

Get Stock Pricing Data From The Polygon.io API For Algo-Trading Using Python

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to Polygon.io and NumPy)

Polygon.io

NumPy

Finance
100 100%
Finance
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Investing
100 100%
Investing
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Polygon.io and NumPy. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Polygon.io and NumPy

Polygon.io Reviews

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NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

NumPy might be a bit more popular than Polygon.io. We know about 122 links to it since March 2021 and only 85 links to Polygon.io. 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.

Polygon.io mentions (85)

  • Build an Unusual Options Activity Scanner With Python and Free Data
    Polygon.io gives you 5 API calls/minute on the free tier. Thatโ€™s rough for options scanning since you need one call per expiration per symbol. Iโ€™d only recommend this if youโ€™re scanning fewer than 20 symbols. - Source: dev.to / 3 months ago
  • Latency Wars: The Architecture Of A Real-Time Trading Game
    The market data will be streamed from polygon.io. All trades should be handled by the Game Engine, so in the simplest form, the architecture looks like this:. - Source: dev.to / 10 months ago
  • Driving Smarter Decisions: Using Share Price APIs for Data-Driven Marketing
    Here are some valuable resources for developers exploring share price API solutions: Alpha Vantage API: A free platform offering extensive stock market data, including historical trends and real-time updates. Yahoo Finance API: A widely used service providing comprehensive financial data. Polygon.io: A robust tool for real-time market data and aggregated information across various financial markets. IEX Cloud:... - Source: dev.to / over 1 year ago
  • The use of API on a web app, considered individual or commercial use?
    I am building a web app, and I would like to use the polygon.io API on the back-end to forecast the market sentiment. The individual upgrade is $200, while business upgrade would cost $2000. Would my use of the API considered personal or commercial? Source: over 2 years ago
  • ChatGPT is going to revolutionize the stock market (with data)
    It's worth mentioning that we use polygon.io to provide market information, which has the ability to specify time frames for data. Each ChatGPT call will have the appropriate information at the time it should. We also use a temperature of 0, as we want idempotent predictions. Source: about 3 years ago
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NumPy mentions (122)

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What are some alternatives?

When comparing Polygon.io and NumPy, you can also consider the following products

Alpha Vantage - Alpha Vantage offers free APIs in JSON and CSV formats for realtime and historical stock and forex data, digital/crypto currency data and over 50 technical indicators.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Twelve Data - The simplest and most effective way to access both realtime and historical stock, forex, cryptocurrency data, and over 100 technical indicators.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Financial Modeling Prep - Access all stocks discounted cash flow statements, market price, stock markets news, and learn more about Financial Modeling. Learn M&A, LBO, DCF, Comps, and Financial Statement Modeling thought concrete examples

OpenCV - OpenCV is the world's biggest computer vision library

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