WorldQuant MCP

A Python MCP server for automating WorldQuant BRAIN alpha-research workflows, plus a local reference pipeline that converts documentation into LLM-friendly artifacts. Built as a personal research-engineering project.

Note: This is an independent, unofficial project and is not affiliated with or endorsed by WorldQuant. WorldQuant's platform documentation is not included in this repository — you supply your own PDF (see Reference ingestion). Use of the BRAIN API is subject to WorldQuant's own terms.

Status

Initial working scaffold in place.

Current capabilities:

• Load credentials from .env or WQ_BRAIN_* environment variables.

• Convert scanned or text-based PDFs into markdown and chunked JSON.

• Authenticate against api.worldquantbrain.com.

• Search local operator/reference material.

• Fetch platform operators, account details, and data fields.

• Turn text hypotheses into field proxies and expression templates.

• Ingest external research links or notes into mechanism summaries.

• Mutate seed expressions into nearby variants.

• Submit simulations and poll simulation progress.

• Simulate batches of expressions and rank the results.

• Fetch alpha details and submission status.

• Validate whether an alpha is ready to submit and summarize blockers.

• Compare candidate expressions against recent alphas and logged experiments.

• Persist local experiment memory for future agents.

• Submit alphas behind an explicit confirmation flag.

Related MCP server: Alpaca MCP Gold Standard

Reference ingestion

The first implemented component is the PDF ingestion pipeline.

.venv/Scripts/python.exe -m pip install -e .
wq-ingest-reference --input wq-ops.pdf

Generated artifacts (produced locally, git-ignored — not committed):

• src/worldquant_mcp/reference/data/wq-ops.md

• src/worldquant_mcp/reference/data/wq-ops.chunks.json

If the PDF has no text layer, the ingester falls back to OCR using in-process Python libraries so it still works on a clean Windows machine.

Running the MCP server

Install the package in editable mode:

.venv/Scripts/python.exe -m pip install -e .

Do not start the stdio MCP server manually if you want to use it from VS Code. VS Code should launch it for you from mcp.json.

Add this to your user-level mcp.json or workspace .vscode/mcp.json:

{
  "servers": {
    "worldquant-mcp": {
      "type": "stdio",
      "command": ".venv/Scripts/worldquant-mcp.exe",
      "env": {
        "WQ_BRAIN_EMAIL": "<your-email>",
        "WQ_BRAIN_PASSWORD": "<your-password>"
      }
    }
  }
}

After saving mcp.json, use the VS Code MCP server management UI to reload or start the server. Once it shows as running, the tools from this server become available to Copilot agents.

If you want to run the server yourself for debugging only, use one of these commands:

.venv/Scripts/worldquant-mcp.exe

Or run it via Python:

.venv/Scripts/python.exe -m worldquant_mcp

Timeout behavior

• Polling tools now default to bounded waits instead of open-ended multi-minute blocking.

• wait_simulation, batch_simulate, and parallel_mechanism_search default to 180 seconds.

• Override with timeout_seconds when you intentionally want a longer or shorter wait.

• Set WQ_BRAIN_MAX_POLL_SECONDS if you want to change the client-wide default used by direct code paths.

• 429 handling is now more robust in the shared client: temporary rate limits are retried with bounded backoff, and CONCURRENT_SIMULATION_LIMIT_EXCEEDED backs off more aggressively before failing.

• Tune WQ_BRAIN_RATE_LIMIT_MAX_RETRIES and WQ_BRAIN_RATE_LIMIT_MAX_WAIT_SECONDS if you want stricter or looser retry behavior.

Example session

Once the server is running, an MCP-aware agent calls the tools directly. Illustrative output below (metric values are synthetic and field/alpha names are placeholders — no real platform data is shown):

> healthcheck
{ "status": "ok", "authenticated": true }

> get_platform_operators
[ "rank", "zscore", "ts_delta", "ts_backfill", "ts_decay_linear", "group_zscore", ... ]

> create_simulation  expression="rank(<your_signal>)"  settings=USA/TOP3000/delay=1
{ "status": "COMPLETE", "alpha_id": "<id>",
  "is": { "sharpe": 1.21, "fitness": 0.74, "returns": 0.052, "turnover": 0.11 } }

> validate_alpha_submission_by_id  alpha_id="<id>"
{ "ready": false, "blocking": ["LOW_FITNESS"], "pending": ["SELF_CORRELATION"] }

The operator names above are part of BRAIN's public expression language; the pipeline never hard-codes proprietary data fields or alpha identifiers.

Implemented tools

• healthcheck

• get_account

• get_platform_operators

• search_local_reference

• get_reference_operators

• get_data_fields

• get_recent_alphas

• get_alpha

• get_alpha_submission

• create_simulation

• batch_simulate

• summarize_alpha_batch_by_id

• mutate_alpha_expression

• map_research_hypothesis

• ingest_research_material

• get_simulation

• wait_simulation

• analyze_alpha_novelty

• log_alpha_experiment

• list_alpha_experiments

• validate_alpha_submission_by_id

• submit_alpha_by_id

Research Notes

• docs/mcp-roadmap.md tracks pragmatic MCP upgrades for alpha research.

• docs/quant-journey.md is a running experiment log for future agents.

• src/worldquant_mcp/data/experiment_memory.json stores the same experiments in machine-readable form.