Bullish

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  Indicator shows to buy MSFT

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  The low on MSFT as showed in the indicator

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  The sell point for MSFT as stated in the indicator

Inspiration

With the job market being at an all time low, many people are unable to find jobs that are able to support them with their day to day needs. This gave us the idea to look into other ways that people make money and one of which was the stock market.

What it does

As of right now, our AI agent analysis 40 different stocks in the market and creates a detailed plan on how to trade it, looking for VWAP, Volume, RSI. The agent then shows when to enter the trade along with a stop loss price and a take profit price along with the approximate window in which these criterias will hit.

How we built it

The MarketRadar project was built as a lightweight, static web application using vanilla HTML, CSS, and JavaScript. It relies on a small serverless function to fetch delayed USD quotes, and it is designed for deployment on static hosting platforms like Vercel or Netlify. The core market scanning logic is implemented entirely from scratch in app.js, including functions that simulate realistic price candles for tickers such as AAPL and MSFT, compute over a dozen technical indicators, and generate trading signals by applying custom rule sets to the calculated indicator values. The result is a fully functional, self-contained market scanner that requires no build step and can be run directly by opening index.html in a browser.

Challenges we ran into

A challenge we ran into when working on this AI agent was getting the data from the markets and having it accurately predict how it should be traded. Since there is no guarenteed, we added a stop loss value to help trades in the case that the AI is incorrect, there is a buffer to not loss too much as that is always a risk in the market.

Accomplishments that we're proud of

Something that we are really proud of is the fact that we were actually able to turn this idea into a reality. This AI agent was built not only for this project but but we can also continue to develope it and improve it for our use and others to use.

What we learned

We learned through making our AI agent how to gain a better understanding on the market along with how to read it better when to enter trades and what we should be doing when trading. The amount of research done when building this agent had taught us skills that puts us above others who aren't as invested as us.

What's next for Bullish

The next steps for Bullish is looking have the AI agent look into many more stocks to gain a large varity data and more styles of trades. We are also looking to add more fetures to make it easier to use along with opening a domain to open this to the general public to use.

Built With

• browser
• css
• github
• html
• javascript
• stooq-delayed-market-quote-api
• technical-analysis-indicators
• vercel
• vercel-serverless-functions