We are building a platform to crowd-source trading strategies. We restructure trading into a data and math problem by abstracting out finance knowledge - for example, a problem to predict buy/sell sigWe are building a platform to crowd-source trading strategies. We restructure trading into a data and math problem by abstracting out finance knowledge - for example, a problem to predict buy/sell signal on a stock can be re-framed as a 0/1 classification problem on time series data.
This allows anyone with data and/or math skills to analyze historical data to discover patterns and build models without any experience in finance - opening up a big pool of users for us.
To make this interesting for users, we turn problems into online competition, QuantQuest, where our community of students, data scientists, developers, quants etc solve them for cash prizes, profit shares and other rewards.
Anyone can use our free tutorials (medium.com/auquan) and open source backtesting toolbox (bitbucket.org/auquan/auquantoolbox) to learn about and solve these trading problems.
Profitable strategies created by our users are used by our trading partners to trade live in the markets. We charge these trading firms a percentage cut in the profits and reward our users by splitting this fee equally between the user and Auquan.
Why are we doing this?
As more trading and investment management firms around the world switch to quantitative style of trading, the demand for quantitative researchers to discover and implement newer trading ideas is also on the rise.
To quote in numbers, total Assets Under Management in the hedge fund industry are ~ 3000B$. Of these, almost 400B$ are now managed quantitatively, and this number has been growing at a very fast pace.
Our solution serves this demand with latent talent - skilled people with analytical backgrounds who can draw on skills from their respective fields to collaboratively design highly successful trading strategies.
What is unique about our approach?
Most companies crowdsourcing strategies ask users to submit full trading strategies that can be further optimized and used to trade out of the box. We believe this limits the scope of users who can actually solve these problems, especially in countries similar to India which have a big community of analytical-oriented, quantitatively trained people but trading culture is almost non-existent and very few people have any knowledge of finance.
By asking people to only solve a part of the problem, we motivate them to use what they know, draw on their existing skills, allowing a larger set of users with very specific problem solving skills to work on problems specificmore
CEO Ex-Derivatves Trader, Math Fanatic, Founder@Auquan
CTO Full Stack Software Developer at Gusto, enjoy debugging more than coding.