Alpha Explanation Workflow
This manual provides a step-by-step workflow for analyzing and explaining a WorldQuant BRAIN alpha expression. By following this guide, you can efficiently gather the necessary information to understand the logic and potential strategy behind any alpha.

Step 1: Deconstruct the Alpha Expression
The first step is to break down the alpha expression into its fundamental components: data fields and operators.

For example, given the expression 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 brain-platform-mcp tool get_datafields to get detailed information about each data field.

Tool Usage: xml <use_mcp_tool> <server_name>brain-platform-mcp</server_name> <tool_name>get_datafields</tool_name> <arguments> { "instrument_type": "EQUITY", "region": "ASI", "delay": 1, "universe": "MINVOL1M", "data_type": "VECTOR", "search": "shrt3_bar" } </arguments> </use_mcp_tool>

Tips for effective searching:

Specify Parameters: Always provide as much information as you know, including instrument_type, region, delay, universe, and data_type (MATRIX or VECTOR).
Iterate: If you don't find the data field on your first try, try different combinations of parameters. The ASI region, for example, has two universes: MINVOL1M and ILLIQUID_MINVOL1M.
Check Data Type: Be sure to check if the data is a MATRIX (one value per stock per day) or a VECTOR (multiple values per stock per day). This is crucial for understanding how the data is used.
Example Data Field Information:

oth423_find: A matrix data field from the "Fundamental Income and Dividend Model" dataset in the ASI region. It represents a "Find score," likely indicating fundamental attractiveness.
shrt3_bar: A vector data field from the "Securities Lending Files Data" dataset in the ASI region. It provides a vector of ratings (1-10) indicating the demand to borrow a stock, which is a proxy for short-selling interest.
Step 3: Understand the Operators
Use the brain-platform-mcp tool get_operators to get a list of all available operators and their descriptions.

Tool Usage: xml <use_mcp_tool> <server_name>brain-platform-mcp</server_name> <tool_name>get_operators</tool_name> <arguments> {} </arguments> </use_mcp_tool> The output of this command contains a wealth of information. For your convenience, a table of the most common operators is included in the Appendix of this manual.

Step 4: Consult Official Documentation
For more complex topics, the official BRAIN documentation is an invaluable resource. Use the get_documentations tool to see a list of available documents, and get_documentation_page to read a specific page.

Example: To understand vector data fields better, I consulted the "Vector Data Fields ðŸ¥‰" document (vector-datafields). This revealed that vector data contains multiple values per instrument per day and must be aggregated by a vector operator before being used with other operators.

Step 5: Broaden Understanding with External Research (Must Call the arxiv_api.py script to get the latest research papers)
For cutting-edge ideas and inspiration, you can search for academic papers on arXiv using the provided arxiv_api.py script.

Workflow:

Identify Keywords: Based on your analysis of the alpha, identify relevant keywords. For our example, these were: "short interest", "fundamental analysis", "relative value", and "news sentiment".
Run the Script: Use the with-wrappers script to avoid SSL errors.

python arxiv_api.py "your keywords here" -n 10
Step 6: Synthesize and Explain
Once you have gathered all the necessary information, structure your explanation in a clear and concise format. The following template is recommended:

Idea: A high-level summary of the alpha's strategy.
Rationale for data used: An explanation of why each data field was chosen and what it represents.
Rationale for operators used: A step-by-step explanation of how the operators transform the data to generate the final signal.
Further Inspiration: Ideas for new alphas based on your research.
Troubleshooting
SSL Errors: If you encounter a CERTIFICATE_VERIFY_FAILED error when running python scripts that access the internet, use the AI to help you change or make script to execute your command.
Appendix A: Understanding Vector Data
Vector Data is a distinct type of data field where the number of events recorded per day, per instrument, can vary. This is in contrast to standard matrix data, which has a single value for each instrument per day.

For example, news sentiment data is often a vector because a stock can have multiple news articles on a single day. To use this data in most BRAIN operators, it must first be aggregated into a single value using a vector operator.