Stockastic is an ML-powered stock price prediction app built with Python and Streamlit. It utilizes machine learning models to forecast stock prices and help investors make data-driven decisions.
Stockastic is built with these core frameworks and modules:
- Streamlit - To create the web app UI and interactivity
- YFinance - To fetch financial data from Yahoo Finance API
- StatsModels - To build the ARIMA time series forecasting model
- Plotly - To create interactive financial charts
The app workflow is:
- User selects a stock ticker
- Historical data is fetched with YFinance
- ARIMA model is trained on the data
- Model makes multi-day price forecasts
- Results are plotted with Plotly
- Real-time data - Fetch latest prices and fundamentals
- Financial charts - Interactive historical and forecast charts
- ARIMA forecasting - Make statistically robust predictions
- Backtesting - Evaluate model performance
- Responsive design - Works on all devices
- Clone the repo
git clone https://github.com/user/stockastic.git- Install requirements
pip install -r requirements.txt- Change directory
cd streamlit_app- Run the app
streamlit run 00_😎_Main.pyThe app will be live at http://localhost:8501
Some potential features for future releases:
- More advanced forecasting models like LSTM
- Quantitative trading strategies
- Portfolio optimization and tracking
- Additional fundamental data
- User account system
This is not financial advice! Use forecast data to inform your own investment research. No guarantee of trading performance.