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Inspiration

The first problem with this challenge was the lack of knowledge each of us had about trading algorithms, greatly hindering our ability to begin to understand what strategies we should use to maximize profits. Beyond that initial goal, however, we sought to supplement our little trading bot with a helpful dashboard explaining what he's doing, and why. Financial literacy is an important skill to possess, and we believe we've created a way to help others learn along with us!

What it does

My Little Trader is a web application that shows real-time simulation data of an artificial stock market. We encourage users to customize the basic reversion strategy trading algorithm and see how altering parameters affect both their short term and long term position. Every step of this process is demonstrated in the flowchart diagram revealing the steps of the algorithm and how the bot does his thing.

How We Built It

My Little Trader bot was lovingly built in Python using Flask and Jupyter Notebooks. The back-end of it was constructed by Nathan. Leveraging the back-end ability, Olivia built the main decision algorithm so that our bot is smart enough to make decisions on its own. Ted did a majority of the data visualization and worked to build a predictive model. His contributions improved the basic bot's ability to trade efficiently. Last but not least, Tobias constructed the front-end and part of the back-end with Nathan.

Challenges We ran into

The initial stages were the most difficult because we were juggling between several datasets. We also focused more on aspects of the integrations rather than building working separate compartments initially which would have helped us reach our goals faster.

What comes next?

Cleaning up the repo, which would not be very complex, since all of the scripts are compartmentalized. Increasing the parameters that a user can change and implementing a basic SQL back-end database to track how different algorithms and parameters perform. In the end a drag & drop interface to build the logic flowcharts could help user without programming knowledge test out trading strategies.

Accomplishments that I'm proud of

Nathan: I I wrote the backend, which I had no experience doing before, I'm really proud of how well we integrated our components together and how the API design turned out. Tobias: I am very happy to have real-time data update using Javascript in the Flask app I built. Olivia: I am happy that our team was able to meet our goals and even exceed them easily with a little more time. Everyone worked hard to see the project take off. Ted: I wrote an LSTM and contributed to the logic behind the basic algorithm to buy and sell.

What I learned

Ted: I learned that descriptive statistic comes first Nathan : API design mostly, but a lot about finance too !

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