These algorithms were part of my submission for the 2024 UQ Algo-Jam hosted by the UQ Fintech Club and sponsored by IMC trading. We were given 9 implements to trade, with a maximum daily spend of $500K and a years worth of data for each implement to develop and test our algorithms against. Our algorithms would then be tested against the new data where the team with the highest profit would win the competition. Despite being a solo team and this being my first time coding trading algorithms, I really enjoyed the competition and came away with a 5th place in a tight finish. I was very happy with how similarly my algorithms performed on the training data and the unknown data, although going back I would focus more on EMAs rather than SMAs and implimenting more ML.
Note: My code exists solely within the algorithm.py file, all other data and code was written by UQ FinTech
| Performance Metric | Results |
|---|---|
| Total PNL | $2,029,440.92 |
| Average Daily Return | $5,565.37 |
| Standard Deviation of Returns | $5,283.17 |
| Sharpe Ratio | 1.0534 |
| ---------- | ---------- |
| Fintech Token Returns | $54,233.60 |
| Fun Drink Returns | $898,600.00 |
| Red Pens Returns | $80,400.00 |
| Thrifted Jeans Returns | $346,624.00 |
| UQ Dollar Returns | $48,370.44 |
| Coffee Returns | $218,400.00 |
| Coffee Beans Returns | $25,262.88 |
| Goober Eats Returns | $311,250.00 |
| Milk Returns | $46,300.00 |
Algorithms - algorithms.py
Run against competition data - simulation.py
Results (comp data) - simulation_results/returns_plot.png