https://github.com/yourdanov/stocks

Stocks analysis and forecasting tool.
https://github.com/yourdanov/stocks

Last synced: 10 days ago
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Stocks analysis and forecasting tool.

• Host: GitHub
• URL: https://github.com/yourdanov/stocks
• Owner: yourdanov
• Created: 2024-12-09T01:17:31.000Z (14 days ago)
• Default Branch: main
• Last Pushed: 2024-12-09T02:35:22.000Z (14 days ago)
• Last Synced: 2024-12-09T03:25:14.701Z (14 days ago)
• Language: Python
• Size: 13.7 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# **Stocks Analysis and Forecasting Application**

---

## **Overview**

This application is a comprehensive stock analysis and forecasting tool. It allows users to visualize historical data, forecast future stock prices using multiple advanced models, and interact with data through a sleek graphical user interface (GUI). 

### **Features**

1. **Real-Time Stock Price Fetching**: Fetches the latest stock prices using Yahoo Finance.

2. **Historical Data Visualization**: Displays historical stock data for the last 10 years.

3. **Forecasting Models**:

   - ARIMA (Auto-Regressive Integrated Moving Average)

   - ETS (Exponential Smoothing)

   - Random Forest

   - LSTM (Long Short-Term Memory)

4. **Interactive Graphs**:

   - Displays model forecasts for the next 12 months.

   - Hover functionality to show predicted price and date.

5. **Modern GUI**:

   - User-friendly interface..

   - Interactive graphs with zoom and pan capabilities.

6. **Multi-Model Comparison**:

   - Compare forecasts from different models on the same graph.

---

## **Application Workflow**

1. **Data Fetching**:

   - Fetches historical stock data from Yahoo Finance for analysis.

   - Updates real-time prices using intraday data.

2. **Data Preprocessing**:

   - Cleans and prepares data for models.

   - Handles missing data, converts formats, and ensures compatibility.

3. **Forecast Generation**:

   - Each model generates predictions for the next 12 months.

   - Predictions start from the current date.

4. **Visualization**:

   - Forecasts are displayed on a graph.

   - Users can view specific predictions by hovering over points on the graph.

---

## **Modules and Functions**

### **1. Data Fetching**

**Function**: `fetch_stock_data(stock_symbol, start_date, end_date)`

- Fetches historical stock data from Yahoo Finance.

- Returns a time-series dataset for analysis.

**Function**: `fetch_intraday_price(stock_symbol)`

- Fetches the latest stock price using intraday data.

---

### **2. Forecasting Models**

- **ARIMA (AutoRegressive Integrated Moving Average)**:

  - Provides statistical-based predictions.

  - Suitable for time-series data with trends.

- **ETS (Exponential Smoothing)**:

  - Uses exponential smoothing for seasonal data.

- **Random Forest**:

  - A machine learning model that incorporates lag features and technical indicators.

- **LSTM (Long Short-Term Memory)**:

  - A neural network-based model for sequential data.

---

### **3. Graphical User Interface (GUI)**

**Function**:

- Launches the main GUI window.

- Displays historical data and model forecasts.

- Features:

  - Interactive graph with hover functionality.

  - Buttons to show historical data and exit.

**Function**:

- Opens a new window displaying historical data for the last 10 years.

---

## **Dependencies**

### **Python Libraries**

- `yfinance`: Fetches stock data.

- `pandas`: Data manipulation.

- `matplotlib`: Visualization.

- `tensorflow`: Neural network models (LSTM).

- `statsmodels`: ARIMA and ETS models.

- `sklearn`: Random Forest and preprocessing tools.

- `mplcursors`: Adds hover functionality.

- `tkinter`: GUI framework.

### **Installation**

```bash

pip install yfinance pandas matplotlib tensorflow statsmodels scikit-learn mplcursors tk

```

---

## **How to Use**

1. **Launch the Application**:

   - Run the script using `python stocks_forecast.py`.

2. **Select Stock**:

   - The application fetches data for IBM stocks by default.

   - Modify the `stock_symbol` variable to analyze other stocks.

3. **View Forecasts**:

   - Observe forecasts from ARIMA, ETS, Random Forest, and LSTM models.

   - Hover over points on the graph to view predictions.

4. **Exit**:

   - Click the "Exit" button to close the application.

---

## **Customization**

### **Modify Stock Symbol**

Change the `stock_symbol` variable in the `__main__` section:

```python

stock_symbol = "AAPL"  # For Apple stock

```

### **Adjust Forecast Horizon**

Modify the `steps` parameter in model functions to increase/decrease the forecast period.

### **Add More Models**

Additional models can be implemented e.g., XGBoost, Prophet.

---

## **Future Improvements**

1. **Additional Models**: Add models like Prophet, XGBoost, and SARIMA.

2. **User Preferences**: Allow users to select models and forecast durations dynamically.

3. **Data Export**: Functionality to save forecasts as CSV or Excel files.

---

## **Known Issues**

1. **LSTM Predictions**:

   - Requires sufficient data for accurate predictions.

   - Training may be slow for larger datasets.

2. **ARIMA Limitations**:

   - May not handle non-stationary data well without preprocessing.

3. **Real-Time Price Delay**:

   - Intraday data from Yahoo Finance may have a delay of up to 30 minutes.

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---

## **Support**

For issues or suggestions, contact:

**Developer**: Atanas Youdanov  

**Email**: [email protected] 

**GitHub**: [GitHub Profile](https://github.com/yourdanov)

---