Popular GitHub Repositories for Market Risk

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

? A curated list of awesome LLMs & deep learning strategies & tools in financial market.

The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.

Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

ARCH models in Python

DATAGEN: AI-driven multi-agent research assistant automating hypothesis generation, data analysis, and report writing. Now expanding into crypto market intelligence. Learn more: https://datagen.digital/.

:couple: Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral) :couple:

? / ? Agent Index - This is the agent index for LobeChat. It accesses index.json from this repository to display a list of available agents for LobeChat to the agent market.

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.

Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.

A wechat robot based on ChatGPT with no risk, very stable! ?

AI-Native Risk Intelligence Systems, OpenDeRisk——Your application system risk intelligent manager provides 7* 24-hour comprehensive and in-depth protection.

Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"