In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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

Use NLP to predict stock price movement associated with news

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

How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube

Introducing neural networks to predict stock prices

Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting

Use Tensorflow to run CNN for predict stock movement. Hope to find out which pattern will follow the price rising.

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

Team Buffalox8 predicts directional movement of stock prices.

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.

Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

This is the coding challenge for "Predicting Stock Prices" by @Sirajology on Youtube

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

Using Transformer deep learning architecture to predict stock prices.

Contains all code related to using HMMs to predict stock market prices.

This is a submission for the "Predicting Stock Prices challenge" by @Sirajology on Youtube. [runner-up]

Predicting stock price using historical data of a company, using Neural networks (LSTM).

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Attempt to predict future stock prices based on Google Trends data.

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Try to predict stock price with LSTM、GAN and DRL, exploring the features of news and technical indicators，which help improving perfomance of predictions.

This project is about predicting stock prices with more accuracy using LSTM algorithm. For this project we have fetched real-time data from yfinance library.

Algorithmic Trading program, that uses Genetic Programming and Genetic Algorithms to predict stock prices.

A Streamlit based application to predict future Stock Price and pipeline to let anyone train their own multiple Machine Learning models on multiple stocks to generate Buy/Sell signals. This is a WIP and I will keep on adding new ideas to this in future.

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Networks to predict stock prices.

Simple ML Model to Predict NVDA Stock Price

1. First we fetch data of stocks in realtime from nse India website, perform basis data visualizations using python to analyze the stock. 2. Then we use machine learning LSTM technique to predict the future stock price and at last create an interactive web-app using Streamlit in python.

Predicting stock prices from Yahoo stock screener using scikit-learn and sending the predicitons via smtplib to a phone number.