The world's largest quant competition. Run by WorldQuant (top-tier hedge fund). You build "alphas" — predictive signals for equity markets — using their BRAIN pla…
Open now
No open WorldQuant IQC competitions in the feed right now.
Add the competitions calendar to your phone
Subscribe once — every open competition's deadline shows up in your native calendar and auto-updates daily as new ones open and old ones close. No account needed.
Outlook / other: add a calendar “from URL” using the copied link.
How to win on WorldQuant IQC
WorldQuant International Quant Championship (IQC) 2026
What Is It?
The world's largest quant competition. Run by WorldQuant (top-tier hedge fund). You build "alphas" — predictive signals for equity markets — using their BRAIN platform. No finance background needed.
2026 Key Facts
- Edition: 6th year running
- Prize pool: $100,000
- Format: 3 stages (Qualifier → Regional → Global Finals in Singapore)
- Team size: 1–4 members (must be from same university)
- Cost: Completely FREE
2026 Timeline
| Stage | Dates |
|---|---|
| Team formation window | Mar 17 – May 13, 2026 |
| Stage 1 (Qualifier) | Mar 17 – May 18, 2026 |
| Stage 2 (Regional/National) | TBD |
| Stage 3 (Global Finals, Singapore) | September 2026 |
Platform
- URL: worldquantbrain.com/iqc
- Uses the WorldQuant BRAIN simulation platform
- Sign up: platform.worldquantbrain.com/iqc/sign-up
How Alphas Work
An "alpha" is a mathematical expression using operators on market data that predicts future stock returns. Example:
rank(-ts_delta(close, 5)) # stocks that fell most in 5 days tend to rebound
- Points accumulate based on how good your alphas perform
- More alphas submitted = more chances to score points
- Alphas are tested on historical data
Scoring & Prizes
- Teams ranked by cumulative alpha score at end of each stage
- Top teams per region advance to next stage
- Cash prizes for top stage 2 + stage 3 finishers (split equally among team)
- Employment opportunities: High performers considered for BRAIN Research Consultant, internship, or full-time at WorldQuant
Scoring — The Exact Fitness Formula
Fitness = sqrt(abs(Returns) / max(Turnover, 0.125)) * Sharpe
Submission thresholds you must clear:
| Alpha Type | Min Fitness | Min Sharpe |
|---|---|---|
| Delay-0 (trade at close) | > 1.3 | > 2.0 |
| Delay-1 (trade next day) | > 1.0 | > 1.25 |
Self-correlation rule: A new alpha must have PNL correlation < 0.7 with already-submitted alphas, OR have Sharpe at least 10% higher than the correlated ones.
Scale: 2025 IQC had ~80,000 participants, 11,000 universities, 142 countries, 263,000+ alphas submitted.
How to Win — Strategy (Research-Verified)
Volume First
- On average: ~100 simulations needed before one submittable alpha
- One documented participant tested 1,103 alphas, submitted 28
- Submit every day. Consistency over bursts.
Start with Delay-1 (Beginners)
- Lower thresholds (Fitness > 1.0, Sharpe > 1.25 vs. 1.3/2.0)
- Less transaction cost sensitivity — more forgiving for early alphas
Reduce Turnover to Improve Fitness
Turnover is your enemy: it represents transaction costs which kill real returns.
# Bad: high-turnover price reversion
rank(-close / delay(close, 1)) # changes every day
# Better: neutralize by market to reduce turnover
rank(-close / delay(close, 1)) - market_mean # smoother, lower turnover
Neutralizing by subindustry (instead of just market) can greatly increase fitness even while reducing Sharpe.
Use Fundamental Data (Low Turnover Advantage)
Fundamental data (P/E, earnings, ROE, revenue) updates quarterly, not daily. This means:
- Naturally lower turnover → higher fitness
- Less picked-over by other competitors
- Combine price momentum + fundamental signals for decorrelated alphas
Build Decorrelated Portfolios
Your team's total score = basket of alphas. Decorrelated signals add more value than one strong signal submitted 5 times.
- Data source mixing: price/volume + fundamental + news/sentiment + options
- Signal type mixing: momentum alpha + reversion alpha together = lower portfolio correlation
- Timing mixing: short-horizon signals (1–5 day) + medium-horizon (20–60 day)
Most Effective Alpha Patterns (Documented)
- Price mean reversion — consistently top-performing class. Stocks that drop tend to rebound.
rank(-ts_delta(close, 5)) - Earnings momentum — exploit quarterly earnings release immediately
rank(ts_delta(earnings_per_share, 1)) - Volume-price divergence — price up + volume down = weak signal
rank(-correlation(volume, close, 10)) - Short-interest signal — high short interest correlates with future drops
- Residual momentum — momentum unexplained by beta and sector
Read "101 Formulaic Alphas"
Academic paper by Kakushadze & Tulchinsky. Contains 101 directly testable alpha formulas. Systematic work through this paper alone yields multiple submittable alphas. Search on Google Scholar.
Prevent Portfolio Overweighting
# Use clamping in denominators to prevent single-stock dominance
rank(signal) / (abs(rank(signal)) + epsilon)
What You Need to Know
- Basic Python or Excel-level math understanding
- Read BRAIN documentation thoroughly (all operators explained)
- Study: "101 Formulaic Alphas" paper — search Google Scholar
- Resources: worldquant.com/brain/forum — top performers share insights
Career Value
- WorldQuant actively recruits from IQC winners
- Strong IQC performance = credible quant finance resume signal
- Past winners placed at WorldQuant, Jane Street, Two Sigma, Citadel