There are 150 repositories under stock-price-prediction topic.
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
Strategies to Gekko trading bot with backtests results and some useful tools.
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
Stock Trading Bot using Deep Q-Learning
Use NLP to predict stock price movement associated with news
Introducing neural networks to predict stock prices
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
Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.
Courses, Articles and many more which can help beginners or professionals.
This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
Simple to use interfaces for basic technical analysis of stocks.
Web app to predict closing stock prices in real time using Facebook's Prophet time series algorithm with a multi-variate, single-step time series forecasting strategy.
Stock Market Analysis and Prediction is the project on technical analysis, visualization and prediction using data provided by Google Finance.
多因子指数增强策略/多因子全流程实现
Examples of python neural net and ML stock prediction methods with sample stock data.
Find your trading, investing edge using the most advanced web app for technical and fundamental research combined with real time sentiment analysis.
This tool should help discover different patterns based on similarity measures in historical (financial) data
Top paper collection for stock price prediction, quantitative trading. Covering top conferences and journals like KDD, WWW, CIKM, AAAI, IJCAI, ACL, EMNLP.
Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio. Project developed as a part of NSE-FutureTech-Hackathon 2018, Mumbai. Team : Semicolon
基于神经网络的通用股票预测模型 A general stock prediction model based on neural networks
Plain Stock Close-Price Prediction via Graves LSTM RNNs
The apex of my CSE tenure at UIET Kurukshetra University in 2018, This project focuses on Zerodha, involving live online trading in the NSE-BSE with real money, utilizing Artificial Intelligence techniques. The project employs Python programming, incorporating live trading bots, indicator screeners, and back testers through REST API and websockets.
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
Python3 project applying Gaussian process regression for forecasting stock trends
Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016
Simple Stock Investment Recommendation System based on Machine-Learning algorithms for prediction and Twitter Sentiment Analysis.