| huseinzol05/Stock-Prediction-Models |
6,233 |
|
0 |
0 |
almost 3 years ago |
0 |
|
46 |
apache-2.0 |
Jupyter Notebook |
| Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations |
| jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction |
4,220 |
|
0 |
0 |
about 3 years ago |
0 |
|
47 |
agpl-3.0 |
Python |
| LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data |
| borisbanushev/stockpredictionai |
3,235 |
|
0 |
0 |
about 4 years ago |
0 |
|
320 |
|
|
| 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. |
| lilianweng/stock-rnn |
1,339 |
|
0 |
0 |
about 4 years ago |
0 |
|
20 |
|
Python |
| Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings. |
| scorpionhiccup/StockPricePrediction |
1,132 |
|
0 |
0 |
over 2 years ago |
0 |
|
10 |
mit |
Jupyter Notebook |
| Stock Price Prediction using Machine Learning Techniques |
| Ceruleanacg/Personae |
1,034 |
|
0 |
0 |
over 7 years ago |
0 |
|
8 |
mit |
Python |
| 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. |
| kaushikjadhav01/Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis |
460 |
|
0 |
0 |
about 2 years ago |
0 |
|
21 |
mit |
Python |
| 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 |
| NourozR/Stock-Price-Prediction-LSTM |
425 |
|
0 |
0 |
about 3 years ago |
0 |
|
3 |
|
Python |
| OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network |
| tencia/stocks_rnn |
405 |
|
0 |
0 |
almost 10 years ago |
0 |
|
5 |
apache-2.0 |
Python |
| Stock price prediction with LSTMs in TensorFlow |
| JordiCorbilla/stock-prediction-deep-neural-learning |
385 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
cc0-1.0 |
Jupyter Notebook |
| Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting |
