We provide the algorithms for automated trading in financial markets, which are based on the analysis and machine learning from a large number of historical data. Analyzed data may differ for different investment strategies, but they generally include the time of transaction volume, price and identification of traded assets.
We custom develop business strategy algorithms for clients and we always test the resulting predictive models on historical data, on which we verify their performance.
How we proceed
- Analysis of business strategies - examines business accruals, selected signals, reliability and homogeneity of the input data, transaction costs and theoretical profitability at various levels of probability.
- Analysis of historical data at various time intervals - searches for signals in various long time periods, creating a time map of market behavior.
- Creation of predictive models - the chosen algorithms learn to respond to the occurrence of signals. Acquired predictive models are verified on historical data.
Analysis of internal and external data can reveal commercial, financial and safety risks and minimize losses caused by them.
Analysis and predictive models
We analyze financial and transactional data and develop predictive models for the assessment of the solvency of clients, the creditworthiness of clients or potential business customers.