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Locations

London, UK · United Kingdom

Machine learning
AI
Data science
FinTech

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 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. 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. 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. 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 specific to their domain. A math student can work on the optimization part of the problem, a data scientist can work on the data analysis and prediction part of the problem and a developer can work on the writing superior algorithms. With our finance domain knowledge, we can ensemble and build on their solutions to come up with well-optimized strategies.


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