Skip to main content

​
Overview

The Fama-French Replicate Server provides official Fama-French 5-factor daily data, replicated proxy factors from the hosted cache, and loadings / alpha estimation against factor returns.

​
Connection

Add this server to your MCP client configuration. 
{
  "mcpServers": {
    "fama-french-replicate": {
      "url": "https://fama-french-factors-production.up.railway.app/mcp/fama_french_replicate"
    }
  }
}
Restart the client after changing MCP configuration. The server tools appear automatically after the connection is established.

​
Transport

PropertyValue
ProtocolMCP over Streamable HTTP
MCP URLhttps://fama-french-factors-production.up.railway.app/mcp/fama_french_replicate
Health URLhttps://fama-french-factors-production.up.railway.app/health
MCP path/mcp/fama_french_replicate
Request envelope{"request": {...}}
AuthOptional bearer token when enabled for the endpoint

​
Best-Fit Workflows

  • Fetch official Fama-French factors.
  • Fetch replicated proxy factors from the hosted cache.
  • Fetch the latest available proxy factor row.
  • Regress returns on factor series to estimate loadings and alpha.
  • get-official-ff-factors
  • get-proxy-ff-factors
  • get-proxy-ff-factors-last
  • get-loadings-and-alpha

​
Tools

ToolDescriptionReturns
get-official-ff-factorsDownload and process Fama-French 5-factor research data.pd.DataFrame
get-proxy-ff-factorsFetch replicated (proxy) Fama-French 5-factor data computed by our internal methodology.pd.DataFrame
get-proxy-ff-factors-lastFetch replicated (proxy) Fama-French 5-factor data for the latest available trading date.pd.DataFrame
get-loadings-and-alphaPerforms time-series regression of asset or portfolio returns on factor returns to estimate factor exposures (loadings) and abnormal returns (alpha). Returns a Series containing the estimated loadings and alpha. Params: factors: pd.DataFrame of factor returns (e.g., ‘MKT’, ‘SMB’, ‘HML’, ‘RMW’, ‘CMA’), rets: pd.Series of asset/portfolio excess returns, from_date/to_date: optional date range filters (e.g., ‘2020-01-01’)pd.Series

​
Examples

​
Fetch official factors

{
  "request": {
    "from_date": "2024-01-01",
    "to_date": "2024-12-31"
  }
}

​
Fetch proxy factors

{
  "request": {
    "from_date": "2026-04-01",
    "to_date": "2026-04-21"
  }
}

​
Fetch the latest proxy row

{
  "request": {}
}

​
Estimate loadings and alpha

{
  "request": {
    "factors": "$last",
    "rets": "$prev",
    "from_date": "2024-01-01",
    "to_date": "2024-12-31"
  }
}

​
Notes

  • Proxy-factor tools read from the hosted factor cache.
  • Use Dataloader when you need raw OHLCV data rather than factor data.

Client setup

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

Shared tools

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

​
Other Servers

Dataloader

Market-data retrieval, TA-Lib indicators, and dataframe exports.

Statistical Factor Models

Stock-Watson, complete-panel, and dynamic statistical factor extraction.

Jump Models

JumpModel and SparseJumpModel regime fitting, online prediction, and backtesting.

Wavelet Mean Reversion

Wavelet-based mean reversion analysis for financial time series.

Parallax ExtremeHurst

ExtremeHurst signal generation from OHLCV data.

EP Ratio Screener

Fundamental stock screening based on earnings yield and balance-sheet quality.

Volatility Scaling Lab

Volatility targeting, EWMA volatility, Monte Carlo bands, and risk diagnostics.