brain-explain-alphas

name	brain-explain-alphas
description	Provides a step-by-step workflow for analyzing and explaining WorldQuant BRAIN alpha expressions. Use this when the user asks to explain a specific alpha expression, what a datafield does, or how operators work together. Includes steps for data field lookup, operator analysis, and external research.

Alpha Explanation Workflow

This manual provides a workflow for analyzing and explaining a WorldQuant BRAIN alpha expression. For the full detailed workflow and examples, see reference.md.

Step 1: Deconstruct the Alpha Expression

Break down the alpha expression into its fundamental components: data fields and operators. Example: quantile(ts_regression(oth423_find,group_mean(oth423_find,vec_max(shrt3_bar),country),90))

• Data Fields: oth423_find, shrt3_bar
• Operators: quantile, ts_regression, group_mean, vec_max

Step 2: Analyze Data Fields

Use the get_datafields tool to get details about each data field.

• Identify: Instrument Type, Region, Delay, Universe, Data Type (Matrix/Vector).
• Note: Vector data requires aggregation (e.g., vec_max).

Step 3: Understand the Operators

Use the get_operators tool to understand what each operator does.

Step 4: Consult Official Documentation

Use get_documentations and read_specific_documentation for deep dives into concepts (e.g., vector data handling).

Step 5: Synthesize and Explain

Structure the explanation:

1. Idea: High-level summary of the strategy.
2. Rationale for data: Why these fields? What do they represent?
3. Rationale for operators: How do they transform the data?
4. Further Inspiration: Potential improvements.

Appendix: Vector Data

Vector data records multiple events per day per instrument (e.g., news). It requires aggregation (like vec_mean, vec_sum) to become a matrix value usable by other operators.