Inspiration
Although we don't have any previous trading experience, we wanted to try our hand at trading thanks to the Optiver challenge! We thought that this was an opportunity which we simply couldn't pass up.
What it does
Our algorithm monitors the running average share price for each instrument, selling when price is higher than the running average, and buying when the price is low. In both cases, going over the limit to take advantage of the market fluctuation if necessary.
How we built it
We used the built-in functions accessible to us through the optiBook, while using numpy for arithmetic operations, and some mat.plotly when needing to visualise strategy changes as we made them.
Challenges we ran into
We don't have much background knowledge about trading, trading patterns, or trading strategies. We had to do some reading and staring at graphs before making some common-sense deductions on how to approach the problem.
Accomplishments that we're proud of
We've got a bit of a better understanding of how trading works! It was really exciting to be putting our code and predictions into a (fictional-) real environment to battle it out.
What we learned
- It's unpopular to own
PHILIPS_B - When traders make big mistakes, the market can really freak out
- When the market makers leave the market, weird things can happen
- The market will not (see: very unlikely) regulate itself ...Plus, a few trading basics!
What's next for The Big Short
Selling all of our shares at the right moment and closing our account, all to retire as millionaires (hopefully)
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