📈 Stockastic

Predicting Stocks with ML

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.

🏗️ How It's Built

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:

1. User selects a stock ticker
2. Historical data is fetched with YFinance
3. ARIMA model is trained on the data
4. Model makes multi-day price forecasts
5. Results are plotted with Plotly

🎯 Key Features

• 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

🚀 Getting Started

Local Installation

1. Clone the repo

git clone https://github.com/user/stockastic.git

2. Install requirements

pip install -r requirements.txt

3. Change directory

cd streamlit_app

4. Run the app

streamlit run 00_😎_Main.py

The app will be live at http://localhost:8501

📈 Future Roadmap

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

⚖️ Disclaimer

This is not financial advice! Use forecast data to inform your own investment research. No guarantee of trading performance.