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Front end UI
Inspiration
GET RICH QUICK!
What it does
Do you want to get rich? Are you tired of the man holding you down? Then, WeakLink.Ai is for you! Our app comes equipped with predictive software to suggest the most beneficial stocks to buy based off your preferences. Simply said, a personal stock broker in your pocket.
How we built it
Weaklink.Ai front end is built using the Dash framework for python. The partnered transactions are preformed with the assistance of Standard Library where our back end calculation engine uses modern machine learning techniques to make decisions about the best time to buy or sell a specific stock. Confirmation is sent to the user's mobile device via Twilio. Upon confirmation the workflow will execute the buy or sell transaction. The back end engine was custom built in python by one of our engineers.
Challenges we ran into
It was difficult to scrape the web for precise data in a timely and financially efficient fashion. It was very challenging to integrate Blockstack into a full python environment. The front end design was reformatted several times. There was some learning curves adjusting to never before seen or used api. Finding financially efficient solutions to some api
Accomplishments that we're proud of
Despite the various challenges we are proud of our project. The front was more visually appealing than anticipated. The transition from back end calculations to visual inspection was relatively seamless. This was our first time working with each other and we had very good synergy, we were able to divide up the work and support one another along the way each taking part in touching each aspect of the project.
What we learned
The various api available as well as some of their limitations. We discovered that open source api is often more helpful than a closed source black box. We also learned a lot about data security via Blockstack. Lastly we learned about various ways to interpret and analyze stocks in a quantitative fashion.
What's next for WeakLink.Ai
There is a lot of work left for us. The most immediate priority would be to set up trend analysis based on historical data of the user followed by more customization options. A place for the user to describe their desires and our machine learning algorithm will take that information into account in order to recommend actions of the user which is in their best interest.
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