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QuantConnect VS DataMelt

Compare QuantConnect VS DataMelt and see what are their differences

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Do-It-Yourself Data Analytics & Business Intelligence, Powered by AI sponsored
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Contents:
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  3. » Reviews
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QuantConnect logo 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.

DataMelt logo DataMelt

DataMelt (DMelt), a free mathematics and data-analysis software for scientists, engineers and students.
  • QuantConnect Landing page
    Landing page //
    2023-10-15
  • DataMelt Landing page
    Landing page //
    2019-07-18

DataMelt is a Java program for statistics, general data analysis and data visualization. The program is often termed "computational platform" since it can be used with different programming languages (Java, Python, Groovy..). DataMelt is not limited to a single programming language. The program is used for numeric computation, statistics, analysis of large data volumes ("big data") and scientific visualization. Full description: https://handwiki.org/wiki/Software:DataMelt

QuantConnect

Website
quantconnect.com
Pricing URL
Official QuantConnect Pricing
$ Details
-
Platforms
-
Release Date
-
Edit details

DataMelt

Website
datamelt.org
Pricing URL
-
$ Details
freemium
Platforms
Linux Windows Mac OSX Java Cross Platform
Release Date
2020 June
Edit details

QuantConnect features and specs

  • Comprehensive Data Access
    QuantConnect provides access to a wide range of financial data which is crucial for developing and testing trading algorithms. This includes equities, futures, FOREX, and cryptocurrencies, which allows users to backtest strategies with historical data.
  • Cloud-Based Development
    The platform is cloud-based, which means users can access their projects from anywhere and don't need to worry about the computational resources required for large backtesting tasks. This also facilitates easy collaboration.
  • Wide Language Support
    QuantConnect supports multiple programming languages including C#, Python, and F#. This allows developers to choose from different languages they are comfortable with while coding algorithms.
  • Lean Algorithm Framework
    The open-source Lean Algorithm Framework is at the core of QuantConnect, providing a robust and flexible foundation for algorithmic trading strategies which can be customized to meet specific needs.
  • Community and Collaboration
    QuantConnect has an active community where users can share ideas, collaborate on projects, and seek help from others which enhances learning and innovation.

Possible disadvantages of QuantConnect

  • Complexity for Beginners
    The platform may be overwhelming for beginners due to the vast array of features and the requirement for programming skills, which can be a steep learning curve for some users.
  • Pricing Structure
    While QuantConnect offers free access with certain limitations, advanced features and higher data allowances come at a cost. This pricing may be a barrier for casual or small-scale users.
  • Limited Asset Classes for Free Users
    Free users may face limitations in terms of the number of asset classes and data sources available, which could restrict the range of strategies they are able to develop and test.
  • Dependence on Internet Connection
    As a cloud-based platform, an active internet connection is required to develop and execute algorithms, which could be a problem for users with unreliable internet access.
  • Execution Latency
    Running algorithms on a cloud platform might introduce latency issues which can be a disadvantage if executing strategies that require ultra-low latency transaction speeds.

DataMelt features and specs

  • Versatility
    DataMelt supports a wide range of programming languages including Java, Jython, Groovy, and JRuby, making it versatile for users familiar with different coding environments.
  • Rich Libraries
    It offers a comprehensive set of scientific libraries for numerical computation, data analysis, and visualization, which can be beneficial for complex scientific research and data processing tasks.
  • Cross-Platform
    DataMelt is platform-independent, running on any operating system that supports Java, such as Windows, macOS, and Linux. This makes it accessible to a wide audience.
  • Integrated Development Environment
    DataMelt provides a powerful IDE that integrates coding, plotting, and visualization tools, streamlining the workflow for developers and researchers.
  • Free and Open Source
    The core functionality of DataMelt is available for free, which can be appealing to individuals and organizations looking for budget-friendly computational tools.

QuantConnect videos

+ Add

Difference between Quantopian Quantiacs Quantconnect

More videos:

  • Review - Step by Step Algorithmic Trading Guide with QuantConnect

DataMelt videos

No DataMelt videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to QuantConnect and DataMelt)

QuantConnect

DataMelt

Finance
100 100%
Finance
0% 0
Technical Computing
0 0%
Technical Computing
100% 100
Development
100 100%
Development
0% 0
Office & Productivity
0 0%
Office & Productivity
100% 100

Questions & Answers

As answered by people managing QuantConnect and DataMelt.

How would you describe the primary audience of your product?

DataMelt's answer:

students and data scientists

What's the story behind your product?

DataMelt's answer:

DataMelt has its roots in particle physics where data mining is a primary task. It was created as Software:jHepWork project in 2005 and it was initially written for data analysis for particle physics.

What makes your product unique?

DataMelt's answer:

Multiplatform. Supports multiple programming languages: Java, Python (Jython), Groovy, Ruby

Why should a person choose your product over its competitors?

DataMelt's answer:

Large database of examples and code snippets https://datamelt.org/code/

Who are some of the biggest customers of your product?

DataMelt's answer:

Students at universities and data scientists.

Which are the primary technologies used for building your product?

DataMelt's answer:

Java (JDK any new new release including JDK20)

User comments

Share your experience with using QuantConnect and DataMelt. 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 QuantConnect and DataMelt

QuantConnect Reviews

TradingView Alternatives For Budget Conscious Traders
QuantConnect is a quantitative trading platform where you can develop algorithms in Python. It’s gaining popularity for its collaborative environment and large data library that supports backtesting and live trading. QuantConnect is flexible and supports multiple asset classes so it’s good for algorithmic traders.
Source: medium.com

DataMelt Reviews

  1. J.Mayear
    Great 3D graphics

    I like this DataMelt analysis program since it has many 2D/3D visualisation and a massive number of practical examples

Social recommendations and mentions

Based on our record, QuantConnect seems to be more popular. It has been mentiond 9 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.

QuantConnect mentions (9)

  • I'm a dev, we're in 2023, what should i start with ?
    I use https://quantconnect.com/ to backtest new algos and discover new algos. They support C# and python. Source: over 3 years ago
  • Where can I Learn OOP for trading in python? I’ve been looking for some information, but I didn’t find anything, any help?
    Use quantconnect.com, their API forces you to use OOP there so it's a good practice. Source: about 4 years ago
  • Backtesting tools
    For stocks and crypto: QuantConnect and Backtrader For options: MesoSim and OptionNetExplorer. Source: about 4 years ago
  • what do you guys think about Joel Greenblatt and his magic formula of investing? backtests of his formula return on average above 20% per annum
    Only you can teach you how to do it. quantconnect.com has a lot of tutorials and other documentation that should be enough for you to learn from. I'm still learning the process of backtesting and I'm not aware of an "easy" way to perform this type of work. Source: about 4 years ago
  • What are some things you have automated, using python?
    Thanks for the pointer. quantconnect.com and interactive brokers. I have a little fantasy that I'll do this once I retire and hand over 1% of my nest egg to it; see how it does... Hand over some more, etc... Source: almost 5 years ago
View more

DataMelt mentions (0)

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

What are some alternatives?

When comparing QuantConnect and DataMelt, you can also consider the following products

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

LabPlot - LabPlot is a KDE-application for interactive graphing and analysis of scientific data.

TradingView - The best charting tool for crypto and stocks

SciDaVis - SciDAVis is a free application for Scientific Data Analysis and Visualization.

CloudQuant - Crowd based algorithmic trading development and backtesing for stock market trading.

RJS Graph - RJS Graph is an artificial intelligence-based data management platform that allows users or developers to organize the data by manipulating the binaries, scientific, mathematical, and other insights with accurate results.

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