Portfolio Optimization Challenge

1 Competition Outline

Background

Three Sigma Investments manages systematic multi-asset portfolios and is expanding its quantitative research team. This competition mirrors a real internal research problem: allocating capital across a fixed universe of 40 liquid instruments (equities, rates, and commodities futures) to maximize risk-adjusted return under realistic transaction cost assumptions.

Target & Benchmark

Competitors must produce daily portfolio weights across the provided 40-instrument universe for an out-of-sample test window. Submissions are scored purely on the resulting portfolio's annualized Sharpe Ratio, net of a 3bps transaction cost charged on every rebalance. The benchmark to beat is Three Sigma's internal equal-risk-contribution baseline, which scores 1.14.

Dataset

Download Dataset

Scoring Rules

• Submissions are evaluated on a held-out 12-month period not included in the training data.
• Final score is the net-of-cost annualized Sharpe Ratio of your daily rebalanced portfolio.
• A 3bps transaction cost is applied to the notional value of every position change.
• Position weights must sum to 1.0 (fully invested, long-only) at every rebalance date.
• Leverage is not permitted; individual position weights are capped at 15%.
• Ties are broken by lower realized maximum drawdown over the test window.

2 Prize Structure

The prize pool is split across the top-ranked leaderboard finishers. All winners listed below also receive a guaranteed first-round interview with Three Sigma Investments.

3 About Three Sigma Investments

Three Sigma Investments is a systematic asset manager founded in 2011, managing approximately $6.2B across multi-asset macro and equity market-neutral strategies. The research team is split between signal-generation and portfolio construction, with a strong bias toward reproducible, benchmark-tested research over discretionary judgment.

4 Roles Being Hired

This competition is being used to source full-time candidates for:

• Quantitative Researcher — design and validate alpha signals and portfolio construction methods for the multi-asset macro book.
• Portfolio Optimization Engineer — build and maintain the production allocation and risk-budgeting systems that turn signals into tradable positions.

Both roles are full-time, on-site in New York, and report directly into the Head of Quantitative Research.

5 Fine Print & Rules

• This is NOT a real competition. This is just a placeholder for future competitions that may be hosted on SearchSpace.
• Open to all individual competitors 18 or older.
• All submitted code and models remain the intellectual property of the competitor; Meridian receives a non-exclusive license to evaluate submissions.
• Meridian employees and contractors are not eligible to compete.
• Cash prizes are subject to applicable tax withholding and reporting requirements.
• Meridian reserves the right to disqualify submissions that show evidence of look-ahead bias, data leakage, or benchmark data scraping.
• Guaranteed interviews are contingent on passing a standard identity and background verification.

6 Submission Instructions

Submit your solution as a single CSV of daily portfolio weights following the format described in the dataset README, along with the source code used to generate it (Python or R accepted).

Include a short (1-page max) write-up describing your modeling approach — this is not scored but is reviewed by the hiring panel for finalists.