The competition results are out – I finished in 129th place. The main reason was a 90k penalty in Round 5 caused by untested code that malfunctioned, resulting in a 260k loss on berries. While the final ranking wasn’t ideal, I’m genuinely satisfied with this journey. Looking back, I’ve grown tremendously through this experience. Having […]
Round 4 results were decent. Algo profits are shown in the following figure, though unfortunately there were no trading opportunities for diving gear this round. Manual trading brought in 90K, bringing me back to the top 40+ rankings. For Round 5, the website lists 20 traders, but the data only includes ten—not sure why. Analyzing […]
I did a quick analysis of each trader’s total profits over the three days of Round 4. (1) Left chart: Shows individual trader profits broken down by product category (2) Right chart: Displays their total overall profits (3) Units: All values are in K shells (thousands) Posting this now to help with everyone’s analysis.
For Day 4’s manual challenge, I approached it in two parts: Part 1: News InterpretationI evaluated each product news item based on two key dimensions: 1. Severity (impact magnitude)2. Timeliness (urgency) Detailed analysis: Product 1: Gradual decline with rumors of big buyer→ Probability: High chance of small drop, low chance of surge→ Severity: Low, Timeliness: […]
Day 3’s results were quite satisfying – 150k from algo trading (30k bananas/pearls, 17k berries, 60k+ pairs, 40k+ diving gear) plus 20k+ from manual, bringing me back to top 50. Day 4 introduced several new products:⦁ Picnic Basket (PB)⦁ Three components: DIP(D), BAGUETTE(B), UKULELE(U)The conversion rule: 4D + 2B + 1U + basket → 1PB […]
First, the distribution from the previous round appeared roughly symmetrical, so we estimated both the median and mean at the midpoint. In the last round:⦁ Profit range was approximately 9685 to 10500⦁ (Since exact numbers weren’t available, we used 10500 as placeholder) First-level logic:⦁ Median = (9685 + 10500)/2 = 9867.5⦁ Settlement price = (9685 […]
The second product in Round 3 is diving gear (DG). Priced at 100k with only 50 units available, DG exhibits extreme volatility with swings easily exceeding hundreds – catching just one wave could yield profits of tens to hundreds of thousands, potentially propelling traders to the top of the leaderboard and causing significant ranking chaos. […]
Round 3 introduced two new products: mayberries (MB) and diving gear (DG). Let’s first examine the theoretical returns for MB market making. The bid-ask spread is around 4.x, with total profit potential similar to pearls and bananas, suggesting market making is viable. However, MB’s position limit of 250 is disproportionately large compared to average trade […]
Round 2 was extremely time-constrained. For the algorithmic portion, my approach was essentially as described in my previous post. The manual trading component, however, proved more tricky. The manual trading mechanism worked as follows: ⦁ Each participant submits a price between 9000 and 11000 ⦁ If your price is below the median of all submissions, […]
The first round results were decent. In Round 2, two new products were introduced – let’s call them C and PC. Looking at their price movements, they showed high correlation, which was confirmed by calculating their price change correlation. This strongly suggested pair trading potential. I analyzed historical trading data to estimate theoretical market making […]
