πΊοΈ Flow Design
Design configurable message processing pipelines using flow sequences and rules to filter, validate, and transform ingested data.
π Rules
Create Python-based rules that process timeseries data, validate measurements, and perform transformations within flows and configurations.
ποΈ Rule configurations
Configure custom rules with parameters and widgets to validate, estimate, calculate, or forecast timeseries data in flows.
π§βπ» Custom Rules
9 items
π Custom Market Adapters
1 item
π€ Machine Learning
Upload and deploy XGBoost or SKLearn machine learning models, then use them in rules to generate predictions on timeseries data.
π³ Decision Trees
Implement conditional flow branching logic that dynamically determines which flow to chain based on current flow properties.
π€ Collective Trigger
Collect messages from multiple flows and trigger a new flow when specified conditions are met using correlation IDs and configurable timeouts.