L.A.A.T.

Project Tracks: Education, Student life, beginner

Inspiration

Looking for different stock trading methods, I came across technical analysis. I found that successfully applying these methods can take hours of looking at charts and graphs, as well as learning about trading strategies and indicators. I found a couple platforms that do this technical analysis automatically. However, I had no control over my investment as the user. Plus, the subscriptions can get pretty expensive

What it does

It gets stock-price data from an API, then uses another API to perform technical analysis on that data, and then submits buy/sell orders to the market automatically. Throughout the process, however, the user selects from several methods, all of which are explained

How we built it

Built stock-related algorithms in python. Used flask as the framework for the webapp. Included user verification (and data storage into database) and graphing capabilities (with JavaScript). HTML was used for the webapp views/pages and some CSS to style it.

Challenges we ran into

The stock market is closed on weekends. I was forced to pivot and use historical data for the demo

Accomplishments that we're proud of

Obtaining data, analyzing it, running it through algorithms, and then integrating all that with the front end proved to be very challenging, but also rewarding

What we learned

Use of APIs and http requests flask frameworks, views, routing, user verification python

What's next for L.A.A.T.

Implementing live data streaming. This way, the algorithm will place orders based on real-time data, not just on historical ones. Improve the front-end with CSS (pretty bare-bones right now)

Built With

• alpaca-api
• bta-lib-api
• css3
• flask
• html5
• javascript
• python
• visual-studio-code