Elonmusk Twitter Notifier
AI driven e-mail notifier for tweets mentioning stock from Elon Musk 📈
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Stock Market Prediction Web App Using Machine Learning And Sentiment Analysis | 460 | 3 months ago | 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 | ||||||||||
| Stock Prediction Neural Network And Machine Learning Examples | 246 | 4 months ago | apache-2.0 | Python | ||||||
| Examples of python neural net and ML stock prediction methods with sample stock data. | ||||||||||
| Tf_deep_rl_trader | 186 | 2 years ago | 28 | Python | ||||||
| Trading Environment(OpenAI Gym) + PPO(TensorForce) | ||||||||||
| A Deep Learning Based Illegal Insider Trading Detection And Prediction Technique In Stock Market | 42 | 5 years ago | Python | |||||||
| Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns. My research paper (IEEE Big Data 2018) on this can be found here: https://arxiv.org/pdf/1807.00939.pdf | ||||||||||
| Elonmusk Twitter Notifier | 40 | 3 years ago | 2 | gpl-3.0 | Python | |||||
| AI driven e-mail notifier for tweets mentioning stock from Elon Musk 📈 | ||||||||||
| Stockmarketml | 32 | 6 years ago | 4 | mit | Jupyter Notebook | |||||
| Predicting stocks with ML. | ||||||||||
| Stocktwits Sentiment | 28 | a year ago | 14 | mit | Python | |||||
| Stocktwits market sentiment analysis in Python with Keras and TensorFlow. | ||||||||||
| Stock Market Probabilities Deep Learning | 20 | 6 years ago | Python | |||||||
| Tries to predict if a stock will rise or fall with a certain percentage through giving probabilities of what events it thinks will happen. | ||||||||||
| Stock Price Prediction | 15 | 7 years ago | mit | Python | ||||||
| A practice project for machine learning and stop price prediction | ||||||||||
| Harnet | 15 | 9 months ago | mit | Python | ||||||
| TensorFlow implementation of the HARNet model for realized volatility forecasting. | ||||||||||
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