> ## Documentation Index
> Fetch the complete documentation index at: https://docs.quantspace.limex.pro/llms.txt
> Use this file to discover all available pages before exploring further.
# Statistical Factor Models Server
> Stock-Watson, complete-panel, and dynamic statistical factor extraction.
## 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.
```json theme={null}
{
"mcpServers": {
"statistical-factor-models": {
"url": "https://backend-production-c775c.up.railway.app/mcp/statistical-factor-models"
}
}
}
```
```json theme={null}
{
"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
```json theme={null}
{
"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
```json theme={null}
{
"request": {
"init_kwargs": {
"n_factors_max": 2,
"flow_series": {},
"flow_series_diff": {}
}
}
}
```
### Fit the created Stock-Watson extractor
```json theme={null}
{
"request": {
"handle": "",
"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:
1. Normalize webhook body or uploaded CSV into a numeric panel with rows as series and columns as observations.
2. Validate the selected algorithm: `complete-panel`, `stock-watson`, or `banbura-modugno`.
3. For complete-panel runs, call `complete-panel-factor-extractor-fit` directly or create a handle first with `complete-panel-factor-extractor-create`.
4. For Stock-Watson, call `swfactor-extractor-create` with explicit `flow_series` and `flow_series_diff`, then `swfactor-extractor-fit`.
5. 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.
6. 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](/quantx/servers/deployed/n8n-workflows) for the shared webhook and MCP session patterns.
Available n8n template:
* [Statistical Factor Models User Workflow JSON](/quantx/servers/deployed/workflows/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.
Configure this endpoint in Cursor, Claude Desktop, or a generic MCP client.
Build webhook and scheduled factor-model flows around this MCP endpoint.
Use health, result, artifact, environment, and table helper tools.
## Other Servers
Market-data retrieval, TA-Lib indicators, and dataframe exports.
Official and replicated Fama-French factors plus loadings and alpha estimation.
JumpModel and SparseJumpModel regime fitting, online prediction, and backtesting.
Wavelet-based mean reversion analysis for financial time series.
ExtremeHurst signal generation from OHLCV data.
Fundamental stock screening based on earnings yield and balance-sheet quality.
Volatility targeting, EWMA volatility, Monte Carlo bands, and risk diagnostics.