AlgoPole
Algorithms are changing the World! To support you in the digital transformation of your company, Algopole offers you the state of Art of Machine Learning, Artificial Intelligence and Algorithmic Trading. Continue Reading
Who We Are
We are fond on Maths, Statistics and Technology and founded in 2014 our own Fintech based on a new amazing work concept: according to your projects, we build up a team of best freelance consultants, experts on the subject we work on. We design and achieve innovative algorithms for various topics on Algorithmic Trading, Internet Of Things and Financial Markets. We deliver state of art solutions with easy implementation algorithms to comply with your needs in the new World of Data. We support you in your projects for the digital transformation of your company.
What We Do
We design Machine Learning Algorithms. AlgoPole architecture is based on Cloud Computing. We developed a proprietary API to speak between your system and our Machine Learning Algorithms. The API is compliant with GDPR to protect and secure your data. In the cloud, storage is based on a NoSQL Database (Mongo DB) and all our algorithms are written in Python. We use a highly reliable, scalable, low-cost infrastruture platform powered by Amazon Web Services.
How it works? We clean and enrich your data, then, according to your goals and your demands, we design customized Machine Learning Algorithms adapted to your business. Our algorithms are very fast and highly optimized, and we can compute, on demand or in real-time, various statistics according to your points of interest. Finally, calculation are sent back to your system and you could use them as you like.
Our efficient, reliable, scalable, secure, and cost-effective sytem in the cloud is delivered as an utility: on-demand, available in seconds, withpay-as-you-go pricing. You don't pay any development or consulting, only the time you consume the Machine Learning Algorithms: if don't use them, you don't pay anything. All designs, developments and consulting are free of charge.
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News
A machine-learning approach for dynamic pricing
In 1933, William R. Thompson published an article on a Bayesian model-based algorithm that would ultimately become known as Thompson sampling. This heuristic was largely ignored by the academic community until recently, when it became the subject of intense study, thanks in part to internet companies that successfully implemented it for online ad display.Thompson sampling chooses actions for addressing the exploration-exploitation in the multiarmed bandit problem to maximize performance and continually learn, acquiring new information to improve future performance.In a new study, “Online Network Revenue Management Using Thompson Sampling,” MIT Professor David Simchi-Levi and his team have now demonstrated that Thompson sampling can be used for a revenue management problem, where demand function is unknown.
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