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.

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

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

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

Predict stock with LSTM supporting pytorch, keras and tensorflow

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

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.

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.

stock predict with MLP,CNN,RNN,LSTM,Transformer and Transformer-LSTM

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).

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

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.

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

Predicting the stock market with sentiment analysis and LSTM techniques

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.

CNN+LSTM+Attention predict stock

Price Prediction Case Study predicting the Bitcoin price and the Google stock price using Deep Learning, RNN with LSTM layers with TensorFlow and Keras in Python. (Includes: Data, Case Study Paper, Code)

Predict stock prices using Long Short-Term Memory (LSTM) networks.

Stock Market Predictor with LSTM network. Web scraping and analyzing tools (ohlc, mean)

Predict stock prices using LSTM networks in PyTorch. This project covers data preprocessing, sliding window creation, model training with early stopping, and evaluation with RMSE/MAE/MAPE. Includes visualizations of training loss, predicted vs actual prices, and short-horizon forecasts.

stock predict by cnn and lstm

I used Python with RNN(LSTM) model to predict Tesla stock price, hope that I can make Elon Musk happy along the way.

Using past price data and sentiment analysis from news and other documents to predict the S&P500 index using a LSTM RNN. Idea replicated from https://arxiv.org/abs/1912.07700 and https://arxiv.org/abs/1010.3003.

Using Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to predict the stock prices of Google.

Predicting Indian stock prices using Stacked LSTM model. Analysing Reliance, Tata Steel, HDFC Bank, Infosys data. Data prep, EDA, hyperparameter tuning.

A hybrid model to predict the volatility of stock index with LSTM and GARCH-type input parameters

Sample code for using LSTMs to predict stock price movements