#计算机科学#Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
#自然语言处理#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.
#计算机科学#Use unsupervised and supervised learning to predict stocks
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
#区块链#Strategies to Gekko trading bot with backtests results and some useful tools.
翻译 - 带有回测结果和一些有用工具的Gekko交易机器人的策略。
#算法刷题#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
#计算机科学#Portfolio optimization with deep learning.
Use NLP to predict stock price movement associated with news
#计算机科学#Introducing neural networks to predict stock prices
#计算机科学#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 ...
#自然语言处理#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 financia...
#算法刷题#Courses, Articles and many more which can help beginners or professionals.
Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.
#计算机科学#Programs for stock prediction and evaluation
Simple to use interfaces for basic technical analysis of stocks.
Stock Market Analysis and Prediction is the project on technical analysis, visualization and prediction using data provided by Google Finance.
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.