Quantiacs Explained: Build Quant Strategies and Get Funded (2026 Review)
Quantiacs Explained: How to Build Trading Strategies and Get Funded
For aspiring quants, the dream is simple: write a profitable trading algorithm, get it funded with real money, and earn a share of the profits, all without risking your own capital. That’s exactly what Quantiacs set out to offer when it launched in 2014, positioning itself as a marketplace where freelance quants could develop strategies, compete for capital allocations, and receive a 10% cut of any net profits their algorithms generated.
Over a decade later, Quantiacs still operates, but its golden era appears to be behind it (read the disclaimer below before signing up for the platform: whilst there seem to be active competitions, we could not verify whether the company is still actively managed or has employees). In this review, you’ll learn how the platform works, what data and tools it provides, how the contest and funding model is structured, and the important caveats you should know before you invest your time.
What Quantiacs Offers
Quantiacs provides a cloud‑based development environment where you can research, build, and submit trading strategies. The entire workflow runs in a browser, with no local setup required. Here are the core features:
- Python & Jupyter with ML libraries – A fully managed Jupyter Notebook and JupyterLab environment. All popular libraries are available (pandas, NumPy, SciPy, scikit‑learn, PyTorch, TensorFlow, XGBoost, statsmodels, and everything else from the Anaconda ecosystem).
- Clean, survivorship‑bias‑free data – Daily market data is updated automatically, with no delisted or dead assets removed. Instruments include:
- Stocks (Nasdaq 100, S&P 500)
- Futures (continuous contracts since January 2006)
- Bitcoin futures (since January 2014)
- Top 10 cryptocurrencies by market cap
- Alternative datasets – Macroeconomic data from the Bureau of Labor Statistics, exchange rates and indicators from the IMF, and SEC‑based fundamentals (assets, liabilities, revenue).
- Step‑by‑step tutorials and templates – Pre‑built strategy templates help beginners get started quickly.
- Your code stays private – You retain full intellectual property (IP) ownership of your algorithms.
- Zero downside risk – You never risk your own money. If your strategy is funded, you earn 10% of the net profits without putting up a cent.
How Quantiacs Works
- Learn – Start with the provided tutorials and templates in Jupyter.
- Build & Test – Write your strategy in Python, backtest it with realistic constraints (transaction costs, slippage, etc.).
- Validate – Run out‑of‑sample tests and walk‑forward validation.
- Compete – Submit your algorithm to quarterly contests and track your ranking on the public leaderboard.
- Get Funded – Top strategies receive capital allocations of up to $1 million USD. The profit share is 10% of net profits for one year, as long as the strategy continues to generate new profits.
Contest Prizes and Rules
The flagship contest allocates $2 million USD across the top seven strategies, ranked by live Sharpe ratio:
| Place | Allocation | Profit Share |
|---|---|---|
| 1st | $1,000,000 | 10% |
| 2nd | $500,000 | 10% |
| 3rd | $250,000 | 10% |
| 4th | $100,000 | 10% |
| 5th – 7th | $50,000 each | 10% |
One prize per user, awarded to your highest‑scoring submission. Quantiacs claims to have allocated over $30 million USD to winning algorithms since its inception, mostly on futures markets.
You can submit up to 50 strategies in total, but only 15 can enter a single contest. If you don’t select which 15, the platform automatically picks the ones with the highest Sharpe. A correlation filter checks for uniqueness; duplicate or non‑original strategies may be disqualified.
Historical Context: A Pioneer in Crowdsourced Quant Finance
Quantiacs was founded in 2014 by Martin Froehler, Eric Hamer, and Alex Foster. At its peak, the platform attracted over 10,000 registered quant‑developers and raised several million dollars in capital from leading investors. A 2018 press release celebrated a 300% year‑over‑year growth in users and described Quantiacs as “the largest marketplace for trading algorithms.”
The model was genuinely innovative for its time—years before Numerai’s tournament format or **WorldQuant’s BRAIN platform.
It offered freelance quants a way to build strategies in Python, test them on institutional‑grade data, and earn performance fees without needing to raise capital themselves.
Current State (Mid‑2026): Proceed with Caution?
Despite its pioneering role, Quantiacs today shows clear signs of neglect. As of mid‑2026, the platform is still online, but it feels like a time capsule.
- Outdated website – Certain sections of the site feel as well as the company's YouTube channel feel abandoned. The contest page still lists competitions from as far back as 2021 and the videos on YouTube are at least 9 years old.
- Dead community – The user forums and social media presence are essentially silent. There’s no active community of quants discussing strategies or sharing results.
- No active management on LinkedIn – We could not find any recent activity from **Quantiacs' LinkedIn page.
- Founder departed in 2018 – Martin Froehler, the original CEO and public face of the company, left Quantiacs in 2018 and moved on to other ventures. We found no evidence of a sale or new ownership, and we could not fully verify the company’s current operations.
For these reasons, we do not endorse Quantiacs in any way. While the platform may still function, the lack of transparency around its management and the stale contest listings appear as red flags. Nevertheless, Quantiacs deserves credit for pioneering the crowdsourced quant model, and if anyone knows more about Quantiacs' state in 2026, please send us an email so we can update this blog post!
Looking for a Modern, Active Alternative?
If you’re drawn to the idea of building quantitative strategies in Python, competing for real cash prizes, and earning ongoing profit sharing, consider AlphaNova’s Competition 5.
It’s a walk‑forward, cross‑sectional signal‑forecasting challenge with cash prizes. Contests run year-round, and the scoring system rewards genuinely uncorrelated, overfit‑filtered signals. You can submit up to 10 entries, iterate rapidly with the local runner, and build a portfolio‑ready Python project.