Registration is Free. Algo Development is FreeBacktesting is Free.

You get paid a portion of all trading profits using your algorithm using our capital.

We license your algo.

You maintain full ownership and rights.

At no point do you have to put up your own capital.

Start building your trading strategy now for free.

The Trading Strategy Incubator

Your trading strategies are your proprietary way to trade. You have extraordinary ideas that can be developed into a profitable trading strategy.

CloudQuant® provides you the platform to bring your ideas, your approaches to trading to life. You develop your trading strategy, choose the inputs, choose the parameters, choose the stocks, and run the backtests. Once you are happy with your algo we will fund (provide the capital) and run the strategy using our production platform, with dedicated professional traders to handle the execution, compliance oversight, and technology.

When there is profit, you will get paid a share as a licensing fee for your algorithm. When there is a loss, we lose, not you.

In short, we have the platform (technology, team, data, oversight, risk, and trading capital), you have the ideas. We put those together and partner with you to generate trading profits together.

Learn more about our Trading Strategy Incubator

Why Choose CloudQuant?

CloudQuant News

backtest chart

Conversations: What we wish we knew when we started AlgoTrading

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CloudQuant's portfolio managers and quantitative algo traders look back on their starts in Algorithmic Trading. This candid overview allows everyone to see the "Things We Wish We Knew When We Started AlgoTrading".  This is a short collection of the interviews with some of our amazing coders here in the office
CloudQuant AI

WealthTech Conference, June 28, 2018 in Chicago

The emergence of technologies in new fields such as artificial intelligence, big data & predictive analytics, and blockchain are changing the investment landscape forever.  Join CloudQuant's CEO for the conference including the discussion on AI in Trading and Investing.

Industry News & Blogs


AI & Machine Learning News. 14, January 2019

Machine learning AI News covering topics of AI Doctors, Creepy innovations we can expect to see in 2019 and where to find Free data and Alternative data.. of course!

AI & Machine Learning News. 07, January 2019

Machine learning AI News covering topics such as Chinese Data Labeling Factories, Why Excel Users Should Consider Learning Programming, Microsoft's AI blood test, AI helping to catch people who lie to the police, How to Trade Intel Stock and Its Potential 33% Upside and Experts pick the biggest tech trends of 2019
Happy Data-Driven Holidays

A Data-Driven Christmas

‘Twas the week before Christmas at the North Pole finishing all the project was the Head Quant’s Goal. Santa was wanting the newest naughty and nice list. Presents are needed for all good engineers and analysts. In California, they want a fire restoration tree and plant seeding robot. In Michigan, they want distribution channels for their new legal pot.

AI & Machine Learning News. 17, December 2018

Data Scientists and Quants entering the world of algorithmic trading are finding alpha in Alternative Data. China is in the lead for AI and Machine Learning. ML is being mentioned in Earnings Reports and Calls. Overfitting in the news. Is data science really sexy? What is artificial stupidity?
Trend Analysis in a Candlestick Market Data Chart

AI & Machine Learning News. 10, December 2018

The Business of Selling your Location, Apple Terms of Service Change, 6 favorite case studies, Python Data Visualization, AI game teaches people sign language, Anheuser-Busch AI, 8 takeaways from NIPS 2018, DeepMind Folds Protiens, AI ethics researchers call for facial recognition regulation
Numpy Memory Leak Fix

Fixing Python Memory Leaks

A few of our power users reported that long-running backtests would sometimes run out of memory. These power-users are the people who often find new trading strategies and so we wanted to work with them to improve the performance of our backtesting tools. Over the past couple of weeks, our senior engineer found that the problem wasn't in our code, but in one of the popular Python libraries that we use. We found the problem in numpy and numba. 

CIO Review 50 Most Promising FINTECH