Graph

Overview

Graph represents a lazy chain of research logic.

price = Panel.from_domain(price_df, domain, name="price")
signal = rank(zscore(price), name="signal")

The raw input is a Panel. The derived signal is a Graph.

Responsibility

Graph manages:

• Logic-chain outputs
• Dependency collection
• DAG validation
• Topological ordering
• Graph specifications
• Runtime delegation
• Materialized output access

Validation rejects cycles, duplicate node names, invalid parent types, and operation nodes with an invalid number of parents.

Graph does not store raw input frames and does not contain domain-specific operation methods.

Output

Before execution, output access raises an error:

signal.output

After execution, Graph.output is a panel:

signal.compute()
signal_panel = signal.output

Computing a downstream graph also populates outputs for evaluated intermediate graphs.

Multi-Output Graphs

strategy = Graph(outputs=[signal, prediction])
strategy.compute()
outputs = strategy.output

For a multi-output graph, output is a mapping from output name to panel.