OneTick Machine Learning Framework (OneTickML) is a set of tools for data science and machine learning experiments with market data provided by OneTick and other data sources (e.g. odbc, csv) that makes it easy to:
create experiment pipelines
prepare features from tick-by-tick market data
manage experiments and track trained models
try any ML model and select the best
serve models to production
OneTickML is based on open source technologies MLFlow and Ray and on a pandas-like OneTick API onetick-py.
MLFlow is used to track experiment structure (config with a description of data feeds, data processing, sets of hyperparameters for training models, evaluators) and experiment results (trained model, best hyperparameters, prediction metrics).
Ray Cluster is used to run multiple experiments in parallel for hyperparameter optimization (using grid, random, bayesian, bohb or hyperopt search).
onetick-py is used to prepare features from tick-by-tick market data in a powerful and intuitive pandas-like OneTick API