P2P Market Trading
Case study
#Trading #Gambling
Online betting requires a solid basis of data, robust models, and objective evaluation of efficacy and risk.
🡒 Gambling is hard. We provide end-to-end expertise in the P2P markets. Deep knowledge of a market might be sufficient for some to develop a profitable approach, but our view is that data, objective analysis, and computerised implementation are the principal requirements. Removing/limiting human input in the betting process is generally best.
What we’ve done
We've been heavily involved in most aspects of P2P betting. This covers the direct collection of very large amounts of market data, the restructuring, storage and access of these, and sourcing of auxillary information. We have built predictive models for odds estimation and market neutral algorithms for automated trading. We also maintain our own trading platform. In addition, we have a range of tools for the testing of algorithmic "edges", to guard against spurious profitability and accurate evaluation of risk. This is also key for sensible staking strategies.
Technical aspects
Data is collected directly from P2P APIs or from web-scraped sources. A wide range of techniques are employed, covering: machine-learning and statistical predictive modelling; time-series modelling and more general probabilistic approaches. Simulation and computer-intensive inference are used extensively. All work in this area is bespoke and coded in-house.
Trading applications are bespoke and can be hosted on the cloud for low maintenance 24-hour trading. Estimation of edges, profit and risk are from a mix of theoretical calculations, historical trading scenarios and extensive simulation.
#Trading #Gambling
Online betting requires a solid basis of data, robust models, and objective evaluation of efficacy and risk.
🡒 Marketing claims require proof. While these might be quantified by consumer trials, their veracity ultimately rests on solid statistical analysis. We provide solid statistical analysis.
What we’ve done
We've been heavily involved in most aspects of P2P betting. This covers the direct collection of very large amounts of market data, the restructuring, storage and access of these, and sourcing of auxillary information. We have built predictive models for odds estimation and market neutral algorithms for automated trading. We also maintain our own trading platform. In addition, we have a range of tools for the testing of algorithmic "edges", to guard against spurious profitability and accurate evaluation of risk. This is also key for sensible staking strategies.
Technical aspects
Data is collected directly from P2P APIs or from web-scraped sources. A wide range of techniques are employed, covering: machine-learning and statistical predictive modelling; time-series modelling and more general probabilistic approaches. Simulation and computer-intensive inference are used extensively. All work in this area is bespoke and coded in-house.
Trading applications are bespoke and can be hosted on the cloud for low maintenance 24-hour trading. Estimation of edges, profit and risk are from a mix of theoretical calculations, historical trading scenarios and extensive simulation.