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Inspiration
We saw how tough it is for new investors, especially high school students, to jump into the stock market without getting overwhelmed. MoodCast was created to make trading simple and smart, using real-time data and AI to guide beginners with confidence.
What it does
MoodCast is a web app that forecasts stock prices for the upcoming 7 days for any stock listed on NASDAQ or NSE using AI models and up-to-date Yahoo Finance data, combined with Twitter sentiment analysis to capture market trends. It helps new investors make informed trades easily.
How we built it
We built MoodCast with Flask and WordPress for a user-friendly frontend, powered by Python for backend logic. For predictions, we used three ML models ARIMA to capture time-series trends, LSTM to catch complex patterns over time, and Linear Regression as a baseline.
We pulled fresh stock data from Yahoo Finance and used tweets using Tweepy for sentiment analysis, then combined all this to give clearer trading advice.
Challenges we ran into
Cleaning noisy Twitter data was tricky, and tuning our ML models for accuracy was challenging too. Handling Yahoo Finance API rate limits and syncing real-time stock data with sentiment scores tested our skills, but we optimized the pipelines to keep everything running smoothly.
Accomplishments that we're proud of
We’re thrilled to deliver accurate stock predictions with a sleek interface, blending technical (ML) and social (Twitter) insights. Getting real-time data to flow smoothly and building a tool that beginners love is a big win.
What we learned
We mastered integrating multiple ML models, processing live data, and handling API. We also learned how sentiment impacts markets and how to design an app that’s intuitive for new users all while coding as a team.
What's next for MoodCast
We plan to add more data sources (like Reddit sentiment), upgrade to a React frontend for a slicker UI to make MoodCast the go-to app for young investors.
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