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Daily GitHub Project Recommendation: CCXT - The Crypto Trading Powerhouse Connecting Over a Hundred Exchanges!

To all blockchain and quantitative trading enthusiasts, today, we’re introducing a star project on GitHub with 38,000+ Stars and 8,000+ Forks — ccxt/ccxt! It’s a powerful cryptocurrency trading library designed to simplify interactions with global crypto exchanges and payment processing services.

Project Highlights

The advent of CCXT (CryptoCurrency eXchange Trading Library) has completely transformed how developers, quantitative traders, and data scientists interact with complex cryptocurrency trading markets.

From a technical perspective, it provides a unified API interface supporting over 100+ mainstream cryptocurrency exchanges. This means you don’t need to learn different API documentation and SDKs for each exchange; with just one codebase, you can easily retrieve market data, manage accounts, and execute trades. What’s even better, it supports multiple mainstream programming languages like JavaScript/TypeScript, Python, C#, PHP, and Go, significantly lowering the development barrier and allowing your trading strategies to be easily deployed cross-platform. The project also offers an optional standardized data format, facilitating cross-exchange analysis and arbitrage.

From an application perspective, CCXT serves as the foundation for building various crypto trading applications. Whether you’re developing automated trading bots, conducting market data analysis and visualization, backtesting trading strategies, developing trading indicators, or simply needing to quickly fetch market quotes, CCXT provides powerful support. It’s not just a tool; it’s a highway connecting you to the global crypto market.

Technical Details / Use Cases

CCXT is primarily developed in TypeScript and then transpiled to Python, PHP, C#, and Go, ensuring functional consistency and high performance across all language versions. It offers comprehensive public APIs (for fetching market data, order books, trade history, candlesticks, etc.) and private APIs (for account management, balance inquiries, placing orders, deposits, and withdrawals, etc.). For users seeking ultimate speed and efficiency, the Python version also supports the orjson library to accelerate JSON parsing.

Furthermore, CCXT thoughtfully provides a Command-Line Interface (CLI), allowing you to perform queries and trading operations directly from the terminal without writing any code. This is particularly convenient for quickly validating ideas or executing urgent tasks.

How to Get Started / Links

Want to dive deeper or start using this project right away? Installation is straightforward, just use your preferred language’s package manager:

  • JavaScript/Node.js: npm install ccxt
  • Python: pip install ccxt
  • PHP: composer require ccxt/ccxt
  • C#: Install-Package ccxt (via NuGet)
  • Go: go install github.com/ccxt/ccxt/go/v4@latest

Alternatively, you can directly visit the GitHub repository for more information and example code:

GitHub Repository: https://github.com/ccxt/ccxt

Call to Action

With its exceptional compatibility and ease of use, CCXT has become an indispensable tool in the crypto trading space. If you’re looking for a reliable, efficient solution to integrate multiple crypto exchanges, or want to build your own trading robot, CCXT is definitely worth your time to explore.

Like, Fork, and share it with your friends! Contributions to make this project even more powerful are also welcome!

Daily GitHub Project Recommendation: Perplexica - Open-Source AI Search Engine that Deeply Understands Your Queries!

Today, we’re bringing you an exciting GitHub project — Perplexica. This is a powerful open-source AI-driven search engine designed to provide you with smarter, deeper answers than traditional search. If you’re tired of manually sifting through vast amounts of information, Perplexica might just change your search experience!

Perplexica’s core philosophy is to build an intelligent search engine that can truly understand user queries. It doesn’t just return keyword-matching results; instead, it leverages advanced machine learning algorithms, such as similarity search and embedding techniques, to delve deep into the internet and distill clear answers with cited sources for you. As an open-source alternative to the popular Perplexity AI, Perplexica promises greater transparency and privacy protection while ensuring powerful functionality. It doesn’t rely on outdated data indexes but integrates the SearxNG meta-search engine to ensure you always get the latest and most accurate information.

Project highlights include:

  • Intelligent Understanding and Answering: No longer just a list of links, Perplexica directly provides answers with sources.
  • Local LLM Support: You can choose to use local large language models like Qwen, DeepSeek, Llama, Mistral, for a more private search experience.
  • Multi-dimensional Focus Modes: Offers various modes such as “All,” “Writing Assistant,” “Academic Search,” “YouTube Search,” “Wolfram Alpha Search,” and “Reddit Search” to meet professional search needs in different scenarios.
  • API Integration Capability: Developers can easily embed its powerful features into their own applications.
  • Always Up-to-Date: Get real-time information via SearxNG, bidding farewell to outdated search results.

Perplexica is primarily built with TypeScript, and its architectural design balances flexibility and efficiency. Whether you are a student, researcher, developer, or just a casual user seeking more efficient search, Perplexica can offer you customized intelligent search solutions. Especially for developers who prioritize data privacy, wish to use local LLMs, or need to integrate AI search capabilities into their own applications, Perplexica is undoubtedly a highly attractive option. Currently, the project has garnered 24595 stars, which is sufficient proof of its potential and popularity!

Want to experience Perplexica’s powerful features firsthand? The project’s installation is very user-friendly, and the official recommendation is to use Docker for quick deployment. Detailed installation guides and API documentation are provided in the repository, allowing you to get started easily.

Explore Now: https://github.com/ItzCrazyKns/Perplexica

Don’t hesitate, click the link to explore how Perplexica redefines your search experience! If you find this project useful, please give it a Star; your support is the biggest motivation for the open-source community. We look forward to your contributions and feedback!

Daily GitHub Project Recommendation: MarkItDown - Microsoft’s AI Document Preprocessing Powerhouse!

Today’s recommended project comes from tech giant Microsoft, a Python tool called MarkItDown. If you frequently work with various document formats, especially when preparing data for LLMs (Large Language Models) or text analysis, then MarkItDown is absolutely your efficiency booster! It can effortlessly convert various complex file formats into structured Markdown, enabling AI to better understand your content.

Project Highlights

The core value of MarkItDown lies in its powerful “format unification” capability. Imagine this: whether it’s PDF reports, Word documents, Excel spreadsheets, PowerPoint presentations, or even text within images (via OCR) or audio transcripts, it can transform them into a Markdown format that is extremely friendly to LLMs.

  • Compatibility King: The project supports input formats that cover almost all types encountered in our daily work, from common Office documents and PDFs to HTML, CSV, and even YouTube links and ZIP files! This means you can convert virtually any form of digital information into unified Markdown.
  • AI-Friendly Design: The project specifically highlights the benefits of converting content to Markdown. Markdown, with its concise markup and well-structured representation, helps LLMs better understand and process document information, and also performs exceptionally well in terms of token efficiency, making it a crucial part of building AI applications.
  • Structure Preservation: Unlike simple text extraction, MarkItDown is dedicated to preserving the document’s original key structures such as headings, lists, tables, and links during conversion. This is vital for subsequent semantic understanding, information extraction, and knowledge graph construction.

Technical Details and Use Cases

This lightweight Python tool (with 74,328 Stars and 4,126 Forks) not only offers a clean command-line interface but also supports Python API calls, making it easy to integrate into your automated workflows. It even supports plugin extensions, allowing integration with advanced services like Azure Document Intelligence or leveraging LLMs for image description, further enhancing document processing capabilities.

  • Use Cases: Whether you are a data scientist, AI researcher, or a developer who needs to automate the processing of large volumes of heterogeneous documents, MarkItDown can help you efficiently convert unstructured or semi-structured data into a high-quality, unified format consumable by LLMs, greatly simplifying the data preprocessing workflow.

How to Get Started

Getting started is very simple. You just need to ensure a Python 3.10+ environment, then install via pip:

pip install 'markitdown[all]'

Afterward, you can perform conversions via the command line or Python code, for example:

markitdown your_document.pdf > output.md

To explore more features or start your first conversion, please visit the project’s GitHub repository:https://github.com/microsoft/markitdown

Call to Action

If you find MarkItDown helpful in your work, consider giving it a star, exploring its codebase, or even contributing your efforts. Community involvement is the driving force behind the project’s continuous development!