Statistical Factor Models Server

Overview

The Statistical Factor Models Server fits statistical factor models from numeric panel matrices. It covers Stock-Watson EM, complete-panel Bai-Ng style extraction, and Banbura-Modugno dynamic factor extraction.

Connection

Add this server to your MCP client configuration.

Cursor
Bearer auth

{
  "mcpServers": {
    "statistical-factor-models": {
      "url": "https://backend-production-c775c.up.railway.app/mcp/statistical-factor-models"
    }
  }
}

{
  "mcpServers": {
    "statistical-factor-models": {
      "url": "https://backend-production-c775c.up.railway.app/mcp/statistical-factor-models",
      "headers": {
        "Authorization": "Bearer ${env:STATISTICAL_FACTOR_MODELS_MCP_TOKEN}"
      }
    }
  }
}

Restart the client after changing MCP configuration. The server tools appear automatically after the connection is established.

Transport

Property	Value
Protocol	MCP over Streamable HTTP
MCP URL	`https://backend-production-c775c.up.railway.app/mcp/statistical-factor-models`
Health URL	`https://backend-production-c775c.up.railway.app/health`
MCP path	`/mcp/statistical-factor-models`
Request envelope	`{"request": {...}}`
Auth	Optional bearer token when enabled for the endpoint

Best-Fit Workflows

Fit a balanced panel with no missing values.
Fit a missing-value panel with Stock-Watson EM.
Fit a missing-value panel with a dynamic/state-space factor model.
Use explicit mixed-frequency mappings for Stock-Watson runs.

Recommended Tools

complete-panel-factor-extractor-fit
swfactor-extractor-create -> swfactor-extractor-fit
bmfactor-extractor-fit

Tools

Tool	Description	Returns
`swfactor-extractor-fit`	Fit the Stock-Watson EM factor model on a 2D panel `X` with shape `(N, T)`, where rows are series and columns are time points. Missing observations may be passed as `null`/NaN. Every row of `X` must already be stationary / `I(0)`; if the underlying series are `I(1)`, transform them first, for example with first differences or log differences. Preferred agent path: first create a handle with `swfactor-extractor-create`, pass exactly one of `n_factors` or `n_factors_max` in `init_kwargs`, and always pass explicit `flow_series` and `flow_series_diff` there, even when both are empty dicts. Then call `swfactor-extractor-fit` with that `handle`. Do not pass `n_factors_max` directly to `swfactor-extractor-fit`, and do not rely on direct/singleton SW calls to fill `flow_series` or `flow_series_diff` for you. For mixed-frequency setup, ordinary MAR rows and end-of-period stock-style rows should not go into `flow_series` or `flow_series_diff`; use `flow_series[i]=M` for Type C / Type D rows that decompose into `M` higher-frequency `I(0)` pieces, and use `flow_series_diff[i]=M` only for Type E rows built as stationary transforms of averaged `I(1)` levels. Classify rows by the stationary series actually passed into `X`, not by the raw source column. Example: if `X` rows are adjusted log-returns for `[SPY_adj_close, QQQ_adj_close, IWM_adj_close, ...]` and only `SPY` and `QQQ` are quarter-end rows on a monthly grid, use `flow_series={0:3,1:3}` and `flow_series_diff={}`. When the subtask is only to prepare SW mixed-frequency mappings, return only those two arguments.	`Any`
`complete-panel-factor-extractor-fit`	Fit the complete-panel Bai-Ng style factor model on a balanced 2D numeric panel `X` with shape `(N, T)`. Missing values are not allowed. Every row of `X` must already be stationary / `I(0)`; if the underlying series are `I(1)`, transform them first, for example with first differences or log differences. Fast happy path: call this tool directly with `{"request":{"X":[[1.0,2.0,3.0],[1.5,2.5,3.5]]}}`. If you need custom rank settings, first create a handle with `complete-panel-factor-extractor-create` and pass exactly one of `n_factors` or `n_factors_max` in `init_kwargs`.	`Any`
`bmfactor-extractor-fit`	Fit the Banbura-Modugno dynamic factor model on a 2D panel `X` with shape `(N, T)`, where rows are series and columns are time points. Missing observations may be passed as `null`/NaN. Every row of `X` must already be stationary / `I(0)`; if the underlying series are `I(1)`, transform them first, for example with first differences or log differences. Use this when you want a dynamic/state-space model rather than the simpler SW extractor. Fast happy path: call this tool directly with `{"request":{"X":[[1.0,null,3.0],[2.0,2.5,3.5]]}}`. If you need custom rank settings, first create a handle with `bmfactor-extractor-create` and pass exactly one of `n_factors` or `n_factors_max` in `init_kwargs`.	`Any`

Examples

Fit a balanced panel directly

{
  "request": {
    "X": [
      [
        1.0,
        2.0,
        3.0,
        4.0
      ],
      [
        1.5,
        2.5,
        3.5,
        4.5
      ],
      [
        2.0,
        3.0,
        4.0,
        5.0
      ]
    ]
  }
}

Create a Stock-Watson extractor

{
  "request": {
    "init_kwargs": {
      "n_factors_max": 2,
      "flow_series": {},
      "flow_series_diff": {}
    }
  }
}

Fit the created Stock-Watson extractor

{
  "request": {
    "handle": "<handle-from-create>",
    "X": [
      [
        1.0,
        null,
        3.0,
        4.0
      ],
      [
        2.0,
        2.5,
        null,
        4.5
      ],
      [
        0.5,
        1.0,
        1.5,
        2.0
      ]
    ]
  }
}

n8n Workflow

This server can be exposed as an n8n Webhook for panel factor extraction. The workflow can accept JSON or uploaded CSV, validate rank settings, build the numeric panel X, route by algorithm, call the MCP tool, and respond with a compact fit summary. Recommended n8n MCP chain:

Normalize webhook body or uploaded CSV into a numeric panel with rows as series and columns as observations.
Validate the selected algorithm: complete-panel, stock-watson, or banbura-modugno.
For complete-panel runs, call complete-panel-factor-extractor-fit directly or create a handle first with complete-panel-factor-extractor-create.
For Stock-Watson, call swfactor-extractor-create with explicit flow_series and flow_series_diff, then swfactor-extractor-fit.
For Banbura-Modugno, call bmfactor-extractor-create / bmfactor-extractor-fit when a configured handle is needed, or call bmfactor-extractor-fit directly for the simple path.
If the fit returns result_id, call result_get before responding from n8n.

Keep panel data and model parameters in the webhook body or Code-node output. Keep credentials in n8n Credentials, not in exported workflow JSON. See n8n Workflows for the shared webhook and MCP session patterns. Available n8n template:

Statistical Factor Models User Workflow JSON - public panel-fit webhook at POST https://n8n.limex.pro/webhook/stat-factor-model-fit.

Notes

Primary fitting tools expect X as a JSON array of arrays with shape (N, T): rows are series, columns are time points.
Input rows should already be stationary / I(0).
Use null for missing observations in the Stock-Watson and dynamic factor tools.
For Stock-Watson mixed-frequency work, create a handle with explicit flow_series and flow_series_diff first.

Client setup

Configure this endpoint in Cursor, Claude Desktop, or a generic MCP client.

n8n workflows

Build webhook and scheduled factor-model flows around this MCP endpoint.

Shared tools

Use health, result, artifact, environment, and table helper tools.

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Overview

MCP Setup

Dataloader Server

Fama-French Replicate Server