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Backtrader VS Scikit-learn

Compare Backtrader VS Scikit-learn and see what are their differences

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Backtrader logo Backtrader

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

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Backtrader Landing page
    Landing page //
    2021-09-30
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Backtrader features and specs

  • Versatility
    Backtrader supports a wide variety of data sources and formats, as well as different types of financial instruments, allowing for extensive backtesting and live trading capabilities.
  • Community and Documentation
    The platform has a strong community and comprehensive documentation, making it easier for new users to get started and for experienced users to troubleshoot and optimize their strategies.
  • Python Integration
    Written in Python, Backtrader allows users to leverage Python's extensive ecosystem of libraries for data analysis, machine learning, and other financial computations.
  • Open Source
    As an open-source project, users can modify and extend the platform to meet their specific trading and testing needs without restrictions, and contribute to its development.
  • Flexibility in Strategy Design
    Backtrader offers a flexible and intuitive framework to design complex trading strategies, enabling users to test multiple strategies with different parameters efficiently.

Possible disadvantages of Backtrader

  • Steep Learning Curve
    Despite its flexibility, new users may find Backtrader's extensive features and options overwhelming, requiring a significant amount of time to learn and effectively utilize.
  • Performance Issues
    For very large datasets, Backtrader might experience performance bottlenecks or require additional optimization, as Python is not the fastest language for high-frequency backtesting.
  • Limited Technical Support
    As a community-driven open-source project, Backtrader might lack the formal technical support and customer service that comes with commercial trading platforms.
  • Complexity in Live Trading
    Transitioning from backtesting to live trading can require significant additional setup and potential custom development, especially in integrating broker APIs.
  • Outdated Resources
    Some educational materials and tutorials may be outdated, leading to confusion due to interface or feature updates that are not well-documented.

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Backtrader videos

+ Add

Backtrader Python Review

More videos:

  • Review - Algorithmic Trading with Python and Backtrader (Part 1)
  • Review - Backtrader Live Forex Trading with Interactive Brokers (Part 1)

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Category Popularity

0-100% (relative to Backtrader and Scikit-learn)

Backtrader

Scikit-learn

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

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Backtrader and Scikit-learn

Backtrader Reviews

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Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Scikit-learn seems to be a lot more popular than Backtrader. While we know about 31 links to Scikit-learn, we've tracked only 3 mentions of Backtrader. 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.

Backtrader mentions (3)

  • My reality of trading and how i wish i had never started.
    I do like what I see and hear about backtrader.com. I would say they are a notable exception to my general rule of not trusting or using backtesting frameworks. However, I still think it is important to understand how the framework you are using works. So if you are using backtrader for backtesting you still need to put in the time to understand the backtesting engine. Source: about 2 years ago
  • My reality of trading and how i wish i had never started.
    What about backtrader.com? And I feel like it would be step 2 after you at least have something to backtrade and test haha. Source: about 2 years ago
  • I need to know what can go wrong with my 'masterplan'
    Backtesting is basically applying your strategy on historical price data to see if it makes money. I've used Backtrader it works decently well: https://backtrader.com/. Source: over 3 years ago

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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What are some alternatives?

When comparing Backtrader and Scikit-learn, you can also consider the following products

quantra - A public API for quantitative finance made with Quantlib

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

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.

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

Quantopian - Your algorithmic investing platform

NumPy - NumPy is the fundamental package for scientific computing with Python

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