← Write-Ups Execution Competition ↗

Part 1: Market Prediction

Dataset

Set Variables Time Range
Training 14 hidden vars (A-N, Y1, Y2) 1-80000
Test Same vars excluding Y1/Y2 80005-96000
Late Addition O-P columns (~80% N/A) 1 day before deadline

Objective

Achieve highest R-squared score in out-of-sample forecasts.

Evaluation: Weighted average of Y1 and Y2 R-squared scores

Details: Algorithm Infrastructure

Part 2: Basketball Trading

Materials Provided

  1. Variable documentation explaining factors influencing home team win probability
  2. JSON file with example game event sequences
  3. Base code for trading strategy

Objective

Achieve highest P&L based on trading strategy.

Trading Mechanics

Action Home Wins Home Loses
Buy at 50 +50 profit -50 loss
Short at 50 -50 loss +50 profit

Contracts pay 100 if home team wins.

Strategy: Possession-Based Model

Component Description
Outcomes 4 per possession: [0, 1, 2, 3] points
Scoring Score per second using team averages
Weights Public basketball statistics
Adjustments Individual player game fitness

Concepts Explored: Bayesian updating, reinforcement learning, time-remaining probability

Details: Algorithm