Overview:

This Python application is designed for the analysis and prediction of market assets, including Cryptocurrencies and Stocks. It utilizes machine learning to make predictions on the price of an asset for the next day based on historical data. The application currently supports the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model for prediction. The analysis can be displayed in either a dashboard or in a Jupyter notebook using matplotlib. The application uses the Dash and Flask frameworks to create a localhost instance for the dashboard, and all data is stored in SQLite3 databases.

Features:

Download data from YahooFinance: The application is able to download data from YahooFinance to use for analysis and prediction.
SQLite3 database integration: The application seamlessly integrates with SQLite3 databases to store and retrieve data.
ML/AI integration with LSTM-RNN: The application utilizes the LSTM-RNN model for machine learning and artificial intelligence.
Matplotlib support: Matplotlib, specifically seaborn, is used to generate visualizations and graphs of the analysis results.
Dashboard support using Dash and Flask: The application creates a dashboard in a localhost instance using the Dash and Flask frameworks.

Summary:

This Python application is designed for the analysis and prediction of market assets, such as Cryptocurrencies and Stocks. It utilizes machine learning, specifically the LSTM-RNN model, to make predictions on the price of an asset for the next day. The application supports downloading data from YahooFinance, integration with SQLite3 databases, and the use of Matplotlib for visualizations. It also includes a dashboard feature using the Dash and Flask frameworks. Future expansions of the application will include more options for ML/AI models and the potential integration of Postgres4 for improved query capabilities.

Csynodinos

Asset Analyser