ExitIQ — AI Exit Advisor

Inspiration

When the market goes up we always wonder whether we want to sell part of our assets to make some profits. During the time of hesitation the market changes so fast and we miss the opportunity of selling and the regret always go on .....

What it does

AI-powered investment exit advisor that tells retail investors whether to hold, trim, or sell, using portfolio data, market signals, macro conditions, and sentiment.

How we built it

Use fastest path Tech Stack - Frontend: Use Streamlit; Backend Logic: Python;

Challenges we ran into

Need to implement the application on Streamlit Cloud free tier. FRED API — The notebook env restricts outbound HTTPS to fred.stlouisfed.org. The deployed app should work fine (different network), but if FRED still fails, macro signals will show "unavailable" gracefully. yfinance — The notebook env blocks pip installs to external indexes; the Streamlit app installs it at startup via subprocess (cached). First cold-start may take ~30s. Won't able to version control the deployment script from Zerve canvas. _Difficult to navigate through the files system from Zerve UI

Accomplishments that we're proud of

Build a full stack prototype that can help retail investors make trading decisions.

What we learned

Versioned control all the code created to avoid missing features.

What's next for ExitIQ — AI Exit Advisor

Upgrades When Ready OpenAI GPT-4 reasoning layer — pass the signals dict to GPT to generate a natural language explanation Google Trends via pytrends for search-volume momentum Reddit sentiment via PRAW (r/wallstreetbets, r/investing) Multi-position portfolio view — loop the engine across a holdings list Alerts/email — trigger when sell pressure crosses a threshold

Built With

• fredapi
• numpy
• pandas
• python
• scikit-learn
• streamlit
• ta
• yfinance