Showing 2 open source projects for "algorithmic trading python"

View related business solutions
  • No-code email and landing page creation Icon
    No-code email and landing page creation

    Make campaign creation fast and easy with Knak

    Built for speed and collaboration, Knak streamlines campaign production with modular templates, real-time editing, simple collaboration, and seamless integrations with leading MAPs like Adobe Marketo Engage, Salesforce Marketing Cloud, Oracle Eloqua, and more. Whether you're supporting global teams or launching fast-turn campaigns, Knak helps you go from brief to build in minutes—not weeks. Say goodbye to bottlenecks and hello to marketing agility.
    Learn More
  • Find out just how much your login box can do for your customer | Auth0 Icon
    Find out just how much your login box can do for your customer | Auth0

    With over 53 social login options, you can fast-track the signup and login experience for users.

    From improving customer experience through seamless sign-on to making MFA as easy as a click of a button – your login box must find the right balance between user convenience, privacy and security.
    Sign up
  • 1
    LlamaIndex

    LlamaIndex

    Central interface to connect your LLM's with external data

    LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when...
    Downloads: 3 This Week
    Last Update:
    See Project
  • 2
    ML for Trading

    ML for Trading

    Code for machine learning for algorithmic trading, 2nd edition

    On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. ...
    Downloads: 9 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB