📈 AI Stock Simulator
Welcome to the AI Stock Simulator, a Streamlit-based web application that allows users to simulate stock trading using real-time data and AI-driven price predictions. Whether you're a beginner looking to understand the stock market or an enthusiast aiming to test your trading strategies, this simulator provides an interactive and educational experience.
Table of Contents
📈 AI Stock Simulator Table of Contents 🔍 Overview 🚀 Features 🛠️ Installation 💻 Usage 🎨 User Interface 🔧 Technologies Used 🤝 Contributing 📜 License 📫 Contact 🔍 Overview
The AI Stock Simulator is designed to provide users with a realistic stock trading experience without the financial risk. It integrates real-time stock data fetching, basic AI predictions using linear regression, and a simulated trading environment where users can buy and sell stocks to manage a virtual portfolio.
🚀 Features
Real-Time Stock Data: Fetches up-to-date stock prices and historical data using the yfinance library. AI-Based Predictions: Utilizes a simple linear regression model to predict future stock prices. Virtual Trading: Simulate buying and selling stocks with a virtual balance. Portfolio Management: Track your holdings, current portfolio value, and net worth. Dark Theme UI: Enhanced user experience with a custom dark-themed interface. Interactive Charts: Visualize stock price history with dynamic charts. Responsive Layout: Organized layout with sidebars and columns for seamless navigation. 🛠️ Installation
Follow these steps to set up the AI Stock Simulator on your local machine.
- Clone the Repository git clone https://github.com/jumpan786/stock-simulator.git cd ai-stock-simulator
- Create a Virtual Environment (Optional but Recommended) python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
- Install Dependencies Ensure you have Python 3.7+ installed.
pip install -r requirements.txt If a requirements.txt file is not provided, you can install the necessary packages manually:
pip install streamlit yfinance numpy pandas matplotlib scikit-learn 4. Run the Application streamlit run app.py Replace app.py with the name of your main Python file if different.
- Access the App Once the server starts, open your web browser and navigate to http://localhost:8501 to interact with the AI Stock Simulator.
💻 Usage
Enter Stock Symbol: Use the sidebar to input the stock ticker symbol (e.g., AAPL for Apple Inc.). Select View History: Choose the number of days of historical data to view (ranging from 30 to 365 days). Set Investment Amount: Specify the amount you wish to invest per transaction (between $10 and $1,000). View Stock Chart: The main section displays the selected stock's price history in a line chart. Trading Panel: See the current stock price and AI 7-day forecast. Buy Shares: Purchase shares based on your investment amount. Sell Shares: Sell shares based on your investment amount. Portfolio Overview: Monitor your holdings, including the number of shares, current value, total portfolio value, and net worth. Balance Management: Start with an initial balance of $10,000. Buy and sell stocks to manage and grow your virtual balance. 🎨 User Interface
The application features a dark-themed interface with the following components:
Sidebar: Contains controls for stock selection, historical data range, and investment amount. Main Area: Stock Chart: Visual representation of the stock's price history. Trading Panel: Interactive section for buying and selling stocks with AI predictions. Portfolio Display: Tabular view of your current holdings and financial summaries.
Replace with actual screenshot if available.
🔧 Technologies Used
Streamlit: For building the interactive web application. yfinance: To fetch real-time and historical stock data. NumPy: For numerical operations. Pandas: For data manipulation and analysis. Matplotlib: For plotting charts. Scikit-learn: For implementing the linear regression model. 🤝 Contributing
Contributions are welcome! If you'd like to enhance the AI Stock Simulator, follow these steps:
Fork the Repository Create a Feature Branch git checkout -b feature/YourFeature Commit Your Changes git commit -m "Add some feature" Push to the Branch git push origin feature/YourFeature Open a Pull Request Please ensure your code adheres to the existing style and includes appropriate documentation.
📫 Contact
For any questions, suggestions, or feedback, feel free to reach out:
Email: reswanth.rejipillai@mail.mcgill.ca GitHub: @jumpman786
