brain-feature-implementation

name	brain-feature-implementation
description	Implements WorldQuant Brain features from an idea markdown file. Downloads dataset and generates alpha expressions defined in the idea.
allowed-tools	["Read","RunTerminal","ManageTodoList"]

Brain Feature Implementation

Description

This skill automates the process of converting a WorldQuant Brain idea document (Markdown) into actionable Alpha expressions. It handles dataset downloading and code generation for each distinct idea pattern.

Scope of Work

• This skill operates exclusively by manipulating local CSV files using the provided Python scripts.
• Do NOT use any WorldQuant Brain MCP tools (e.g., brain-api).
• Do NOT write custom Python scripts (e.g. python -c ... or new .py files) to check data or generate expressions. You MUST use the scripts/implement_idea.py tool.
• Do not attempt to submit alphas or run simulations on the platform. Focus only on generating the expression files locally.

Instructions

1. Analyze the Idea Document

   • Read the provided markdown file.
   • Extract the following metadata:
     • Dataset ID (e.g., analyst15)
     • Region (e.g., GLB)
     • Delay (e.g., 1 or 0)
   • If any metadata is missing, ask the user to clarify.

2. Download Dataset

   • Execute the fetch script using the extracted parameters.
   • Locate Scripts:
     • Check your current working directory (ls -R or Get-ChildItem -Recurse).
     • Find the path to fetch_dataset.py. It is likely in brain-feature-implementation/scripts or scripts.
   • Run Command:
     • Change directory to the folder containing the script before running it.
     • Command:

       cd <PATH_TO_SCRIPTS_FOLDER> && python fetch_dataset.py --datasetid <ID> --region <REGION> --delay <DELAY>
       

   • Wait for the download to complete. The script will create a folder in ../data/.

3. Plan Implementation

   • Scan the markdown file for Feature Definitions or Formulas.
   • Look for patterns like Definition: <formula> or code blocks describing math.
   • Use the manage_todo_list tool to create a plan with one entry for each unique idea/formula found.
     • Title: The Idea Name or ID (e.g., "3.1.1 Estimate Stability Score").
     • Description: The specific template formula (e.g., template: "{st_dev} / abs({mean})").

4. Execute Implementation

   • For each item in the Todo List:
     • Construct the Template:
       • Use Python format string syntax {variable}.
       • The {variable} must match the suffix of the fields in the dataset (e.g., mean, st_dev, gro).
       • CRITICAL: Do NOT include the full prefix or horizon in the template. The script auto-detects these.
       • Correct Example: For anl15_gr_12_m_gro / anl15_gr_12_m_pe, use template: {gro} / {pe}.
       • Incorrect Example: {anl15_gr_12_m_gro} / {pe} (Includes prefix).
       • Incorrect Example: ${gro} / ${pe} (Shell syntax).
     • Determine Dataset Folder: {ID}_{REGION}_delay{DELAY} (e.g., analyst10_GLB_delay1).
     • Run Script:
       • Navigate to the folder containing implement_idea.py (as identified in step 2).
       • Command:

         cd <PATH_TO_SCRIPTS_FOLDER> && python implement_idea.py --template "<TEMPLATE_STRING>" --dataset "<DATASET_FOLDER_NAME>"
         

       • Note: The script ONLY accepts --template and --dataset. Do not pass any other arguments like --filters or --groupby.
       • Strict Rule: Do NOT use python -c or create temporary scripts to verify or process results. Trust the output of implement_idea.py.
     • Verify the output (number of expressions generated).
     • Mark the Todo item as completed.

5. Finalize Output

   • After all Todo items are completed, merge all generated expressions into a single file.
   • Run Merge Script:
     • Navigate to the folder containing scripts.
     • Command:

       cd <PATH_TO_SCRIPTS_FOLDER> && python merge_expression_list.py --dataset "<DATASET_FOLDER_NAME>"
       

   • This will create final_expressions.json in the dataset directory.
   • Report the total number of unique expressions and the path to the final file to the user.

Script Dependencies

This skill relies on the following scripts in its scripts/ directory:

• fetch_dataset.py: Downloads data from Brain API.
• implement_idea.py: Generates alpha expressions from templates.
• ace_lib.py & helpful_functions.py: Support libraries.