MemLabs Trading Challenge #1

⚡ ACTIVE

Jan 1, 2026 to May 31, 2026 0 days left 6 participants

1 day bars Metric: Total Log Return Pass: Total log return > 0

Prize Pool

Free Annual MemLabs Subscription
MemLabs t-shirt for top 3 submissions
Winner is added to the Hall of Fame
Introduction to quant recruiters
MemLabs video interview (optional)

Overview

Build a systematic trading strategy using daily OHLCV data. Your strategy will be evaluated on its total log return over the competition period — the best compound rate of return wins.

Goal

Maximize the total log return, which represents the best compound rate of return over the competition period. Log return is preferred over simple return because it is additive across time and provides a more accurate picture of compounded growth.

Rules

1 Use only daily OHLCV data from the provided dataset. No external data sources or APIs permitted.
2 No lookahead bias. only use data available at the time of the trade signal.
3 Submissions must use Python and follow the provided strategy template.
4 You may submit as many strategies as you like. all submissions are kept on the leaderboard.
5 Strategies found to use data snooping will be disqualified.

Scoring

Total Log Return (Primary)

The main ranking metric used to rank all submissions.

Pass Criteria

Total log return > 0

Secondary Metrics

Win Rate

Percentage of trades that closed with a positive log return.

Ann. Sharpe Ratio

Annualised return divided by annualised volatility. Higher is better; above 1 is considered good.

Max Drawdown

The largest peak-to-trough decline in the equity curve. Expressed as a log return (negative).

Best Trade

The single highest log return achieved in one trade during the evaluation period.

Worst Trade

The single lowest log return (largest loss) in one trade during the evaluation period.

Live Leaderboard

6 participants