Popular GitHub Repositories for Market Cycles

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

ValueCell is a community-driven, multi-agent platform for financial applications.

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

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

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

: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.

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.

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:

Korea Investment & Securities Open API Github

? / ? 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

Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution.

Free self-hosted market-making bot for crypto projects & token issuers. Volume, spread, liquidity, price ranges, and dynamic order books — all in one.

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.

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models