| 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:
- Idea: High-level summary of the strategy.
- Rationale for data: Why these fields? What do they represent?
- Rationale for operators: How do they transform the data?
- 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.
