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Landing Page
INSPIRATION
Our inspiration stems from the challenges individuals face in stock trading due to a lack of accessible, data-driven decision-making tools. Observing the complexities and uncertainties in the financial markets, we recognized the need for a solution that empowers investors with comprehensive risk analysis and insights. This led us to develop a platform that demonstrates portfolio risk assessment, providing users with intuitive, data-backed evaluations to make informed investment decisions confidently.
WHAT IT DOES
Our platform simplifies portfolio risk assessment by allowing users to input their stock holdings and investment amounts, generating real-time risk analysis using GARCH volatility modeling and Monte Carlo simulations. It measures portfolio volatility, forecasts the 5% worst-case scenario through thousands of simulated market conditions, and calculates expected annualized returns based on historical data. By providing a user-friendly, data-driven approach, it helps investors make informed decisions, reduce uncertainty, and better manage risk.
HOW WE BUILT IT
We built the front end using React.js and the back end with Django, integrating a Monte Carlo model in Python to analyze stock investments. Our implementation includes calculating GARCH volatility to enhance risk assessment.
Users can search for stocks and specify their investment amounts, with a fuzzy search feature powered by Fuse.js to improve the selection experience using data from a JSON file. Once a stock is selected, our system fetches real-time market data via Yahoo’s yfinance API and applies our Monte Carlo model to compute key financial metrics, including Risk, Maximum Return, Maximum Loss, and Value at Risk (VaR). To enhance user understanding, we leverage OpenAI’s API to generate AI-driven explanations of the results.
Additionally, users can build diversified portfolios by adding multiple stocks with custom investment amounts. To ensure an efficient development workflow, we used Git for version control, branching off from the main repository to work independently and merging changes strategically to accelerate progress.
CHALLENGES WE RAN INTO
One of the challenges we faced was efficiently sharing and managing code using Git, ensuring smooth collaboration and version control. Connecting the back end to the front end required overcoming issues with API integration, data formatting, and real-time updates to ensure seamless communication between the two. Implementing Monte Carlo simulations and GARCH volatility modeling presented computational challenges, requiring optimization to handle large datasets and thousands of simulations efficiently. Choosing the right statistical models for risk assessment was another key challenge, as we had to balance accuracy, interpretability, and performance. Additionally, ensuring the system provided meaningful insights to users without overwhelming them with complex financial data required careful consideration of user experience and interface design.
ACCOMPLISHMENTS THAT WE’RE PROUD OF
We gained experience in practicing professional and conventional code management through Git, improving collaboration and version control. Through statistical modeling, we were able to accurately assess portfolio risk and implement a Monte Carlo simulation to predict worst-case scenarios. Integrating the front end with the back end was a key learning experience, requiring us to efficiently handle data processing, API responses, and user interaction to ensure a seamless and responsive platform.
WHAT WE LEARNED
We gained experience in sharing and managing code through Git, improving our workflow and collaboration. This project was our first time working with real back-end development and REST APIs, requiring us to learn how to troubleshoot and optimize performance. Selecting the correct statistical models was a key challenge, as we had to balance accuracy and efficiency in predicting portfolio risk. Integrating GARCH volatility modeling and Monte Carlo simulations gave us a deeper understanding of financial risk analysis and how real-world market conditions impact investment decisions.
WHAT'S NEXT FOR STOCK BUDDY
The web application will incorporate a user authentication system, enabling account-based access that retains individual search history and portfolio data. This feature will allow users to track their investment analysis over time, providing a personalized experience. Additionally, the platform aims to expand functionality by integrating financial news, offering real-time market insights relevant to users’ portfolios. These enhancements will improve user engagement by combining historical data retention with dynamic market intelligence, supporting informed investment decision-making.
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