Proprietary Trading Network (PTN) is a competitive network that receives signals from quant and deep learning machine learning trading systems to deliver comprehensive trading signals across various asset classes. It incentivizes correctness through blockchain technology and rewards top traders with innovative performance metrics. The network operates based on rules that ensure fair competition and risk control, allowing only the best traders and trading systems to compete.

Moon Dev AI Agents for Trading is an experimental project exploring the potential of artificial financial intelligence for trading and investing research. The project aims to develop AI agents to complement and potentially replace human trading operations by addressing common trading challenges such as emotional reactions, ego-driven decisions, inconsistent execution, fatigue effects, impatience, and fear & greed cycles. The project focuses on research areas like risk control, exit timing, entry strategies, sentiment collection, and strategy execution. It is important to note that this project is not a profitable trading solution and involves substantial risk of loss.

AI-CryptoTrader is a state-of-the-art cryptocurrency trading bot that uses ensemble methods to combine the predictions of multiple algorithms. Written in Python, it connects to the Binance trading platform and integrates with Azure for efficiency and scalability. The bot uses technical indicators and machine learning algorithms to generate predictions for buy and sell orders, adjusting to market conditions. While robust, users should be cautious due to cryptocurrency market volatility.

Black Marlin is a UCI compliant chess engine fully written in Rust by Doruk Sekercioglu. It supports Chess960 and features a variety of search algorithms, pruning techniques, and evaluation methods. Black Marlin is designed to be efficient and accurate, and it has been shown to perform well against other top chess engines.

HybridAGI is the first Programmable LLM-based Autonomous Agent that lets you program its behavior using a **graph-based prompt programming** approach. This state-of-the-art feature allows the AGI to efficiently use any tool while controlling the long-term behavior of the agent. Become the _first Prompt Programmers in history_ ; be a part of the AI revolution one node at a time! **Disclaimer: We are currently in the process of upgrading the codebase to integrate DSPy**

ShieldLM is a bilingual safety detector designed to detect safety issues in LLMs' generations. It aligns with human safety standards, supports customizable detection rules, and provides explanations for decisions. Outperforming strong baselines, ShieldLM is impressive across 4 test sets.

DOGE-AI is an autonomous AI agent designed to uncover waste and inefficiencies in government spending and policy decisions. It aims to make complex bills more understandable for the general public by analyzing and presenting government data in a user-friendly manner. The project focuses on creating a strong foundation for interacting with government data and empowering others to build innovative solutions for greater public engagement.

Council is an open-source platform designed for the rapid development and deployment of customized generative AI applications using teams of agents. It extends the LLM tool ecosystem by providing advanced control flow and scalable oversight for AI agents. Users can create sophisticated agents with predictable behavior by leveraging Council's powerful approach to control flow using Controllers, Filters, Evaluators, and Budgets. The framework allows for automated routing between agents, comparing, evaluating, and selecting the best results for a task. Council aims to facilitate packaging and deploying agents at scale on multiple platforms while enabling enterprise-grade monitoring and quality control.

LotteryAi is a lottery prediction artificial intelligence that uses machine learning to predict the winning numbers of any lottery game. It requires Python 3.x and specific libraries like numpy, tensorflow, keras, and art for installation. Users need a data file with past lottery results in a comma-separated format to train the model and generate predictions. The tool comes with no guarantee of accuracy in predicting lottery numbers and is meant for educational and research purposes only.

Effort is an example implementation of the bucketMul algorithm, which allows for real-time adjustment of the number of calculations performed during inference of an LLM model. At 50% effort, it performs as fast as regular matrix multiplications on Apple Silicon chips; at 25% effort, it is twice as fast while still retaining most of the quality. Additionally, users have the option to skip loading the least important weights.

ChatGPT Queue Prompts is a collection of prompt chains designed to enhance interactions with large language models like ChatGPT. These prompt chains help build context for the AI before performing specific tasks, improving performance. Users can copy and paste prompt chains into the ChatGPT Queue extension to process prompts in sequence. The repository includes example prompt chains for tasks like conducting AI company research, building SEO optimized blog posts, creating courses, revising resumes, enriching leads for CRM, personal finance document creation, workout and nutrition plans, marketing plans, and more.

This repository focuses on utilizing generative AI to bridge the gap between natural language questions and SQL queries, aiming to improve data consumption in enterprise data warehouses. It addresses challenges in SQL query generation, such as foreign key relationships and table joins, and highlights the importance of accuracy metrics like Execution Accuracy (EX) and Exact Set Match Accuracy (EM). The workshop content covers advanced prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning models, and security measures against prompt and SQL injections.

DeepSeek is a new experimental architecture for a large language model (LLM) powered internet-scale retrieval engine. Unlike current research agents designed as answer engines, DeepSeek aims to process a vast amount of sources to collect a comprehensive list of entities and enrich them with additional relevant data. The end result is a table with retrieved entities and enriched columns, providing a comprehensive overview of the topic. DeepSeek utilizes both standard keyword search and neural search to find relevant content, and employs an LLM to extract specific entities and their associated contents. It also includes a smaller answer agent to enrich the retrieved data, ensuring thoroughness. DeepSeek has the potential to revolutionize research and information gathering by providing a comprehensive and structured way to access information from the vastness of the internet.

SkyEye is an AI-powered Ground Controlled Intercept (GCI) bot designed for the flight simulator Digital Combat Simulator (DCS). It serves as an advanced replacement for the in-game E-2, E-3, and A-50 AI aircraft, offering modern voice recognition, natural-sounding voices, real-world brevity and procedures, a wide range of commands, and intelligent battlespace monitoring. The tool uses Speech-To-Text and Text-To-Speech technology, can run locally or on a cloud server, and is production-ready software used by various DCS communities.

Pwnagotchi is an AI tool leveraging bettercap to learn from WiFi environments and maximize crackable WPA key material. It uses LSTM with MLP feature extractor for A2C agent, learning over epochs to improve performance in various WiFi environments. Units can cooperate using a custom parasite protocol. Visit https://www.pwnagotchi.ai for documentation and community links.

The Electronic Component Classifier is a project that uses machine learning and artificial intelligence to automate the identification and classification of electrical and electronic components. It features component classification into seven classes, user-friendly design, and integration with Flask for a user-friendly interface. The project aims to reduce human error in component identification, make the process safer and more reliable, and potentially help visually impaired individuals in identifying electronic components.

Proprietary Trading Network (PTN) is a competitive network that receives signals from quant and deep learning machine learning trading systems to deliver comprehensive trading signals across various asset classes. It incentivizes correctness through blockchain technology and rewards top traders with innovative performance metrics. The network operates based on rules that ensure fair competition and risk control, allowing only the best traders and trading systems to compete.

Composer Trade MCP is the official Composer Model Context Protocol (MCP) server designed for LLMs like Cursor and Claude to validate investment ideas through backtesting and trade multiple strategies in parallel. Users can create automated investing strategies using indicators like RSI, MA, and EMA, backtest their ideas, find tailored strategies, monitor performance, and control investments. The tool provides a fast feedback loop for AI to validate hypotheses and offers a diverse range of equity and crypto offerings for building portfolios.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, ib_insync, Cython, Numba, bottleneck, numexpr, jedi language server, jupyterlab-lsp, black, isort, and more. It does not include conda/mamba and relies on pip for package installation. The image is optimized for size, includes common command line utilities, supports apt cache, and allows for the installation of additional packages. It is designed for ephemeral containers, ensuring data persistence, and offers volumes for data, configuration, and notebooks. Common tasks include setting up the server, managing configurations, setting passwords, listing installed packages, passing parameters to jupyter-lab, running commands in the container, building wheels outside the container, installing dotfiles and SSH keys, and creating SSH tunnels.

FinRobot is an open-source AI agent platform designed for financial applications using large language models. It transcends the scope of FinGPT, offering a comprehensive solution that integrates a diverse array of AI technologies. The platform's versatility and adaptability cater to the multifaceted needs of the financial industry. FinRobot's ecosystem is organized into four layers, including Financial AI Agents Layer, Financial LLMs Algorithms Layer, LLMOps and DataOps Layers, and Multi-source LLM Foundation Models Layer. The platform's agent workflow involves Perception, Brain, and Action modules to capture, process, and execute financial data and insights. The Smart Scheduler optimizes model diversity and selection for tasks, managed by components like Director Agent, Agent Registration, Agent Adaptor, and Task Manager. The tool provides a structured file organization with subfolders for agents, data sources, and functional modules, along with installation instructions and hands-on tutorials.

This repository provides a hands-on lab for learning about Neo4j and Google Cloud Vertex AI. It is intended for data scientists and data engineers to deploy Neo4j and Vertex AI in a Google Cloud account, work with real-world datasets, apply generative AI, build a chatbot over a knowledge graph, and use vector search and index functionality for semantic search. The lab focuses on analyzing quarterly filings of asset managers with $100m+ assets under management, exploring relationships using Neo4j Browser and Cypher query language, and discussing potential applications in capital markets such as algorithmic trading and securities master data management.

Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, and more. It provides Interactive Broker connectivity via ib_async and includes major Python packages for statistical and time series analysis. The image is optimized for size, includes jedi language server, jupyterlab-lsp, and common command line utilities. Users can install new packages with sudo, leverage apt cache, and bring their own dot files and SSH keys. The tool is designed for ephemeral containers, ensuring data persistence and flexibility for quantitative analysis tasks.

Qbot is an AI-oriented automated quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It provides a full closed-loop process from data acquisition, strategy development, backtesting, simulation trading to live trading. The platform emphasizes AI strategies such as machine learning, reinforcement learning, and deep learning, combined with multi-factor models to enhance returns. Users with some Python knowledge and trading experience can easily utilize the platform to address trading pain points and gaps in the market.

This repository contains the Python source code for FINMEM, a Performance-Enhanced Large Language Model Trading Agent with Layered Memory and Character Design. It introduces FinMem, a novel LLM-based agent framework devised for financial decision-making, encompassing three core modules: Profiling, Memory with layered processing, and Decision-making. FinMem's memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. The framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. It presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.

This repository focuses on the application of Large Language Models (LLMs) in the field of finance. It provides insights and knowledge about how LLMs can be utilized in various scenarios within the finance industry, particularly in generating AI agents. The repository aims to explore the potential of LLMs to enhance financial processes and decision-making through the use of advanced natural language processing techniques.