> ## 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.
# Volatility Scaling Lab Server
> Volatility targeting, EWMA volatility, Monte Carlo bands, and risk diagnostics.
## Overview
The Volatility Scaling Lab Server supports volatility-scaling research workflows: EWMA volatility estimation, volatility-targeting data preparation, Monte Carlo band analysis, and performance/risk diagnostics.
## Connection
Add this server to your MCP client configuration.
```json theme={null}
{
"mcpServers": {
"volatility-scaling-lab": {
"url": "https://volatility-scaling-lab-production.up.railway.app/mcp/qca-open-loop-volatility"
}
}
}
```
```json theme={null}
{
"mcpServers": {
"volatility-scaling-lab": {
"url": "https://volatility-scaling-lab-production.up.railway.app/mcp/qca-open-loop-volatility",
"headers": {
"Authorization": "Bearer ${env:VOLATILITY_SCALING_LAB_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://volatility-scaling-lab-production.up.railway.app/mcp/qca-open-loop-volatility` |
| Health URL | `https://volatility-scaling-lab-production.up.railway.app/health` |
| MCP path | `/mcp/qca-open-loop-volatility` |
| Request envelope | `{"request": {...}}` |
| Auth | Optional bearer token when enabled for the endpoint |
## Best-Fit Workflows
* Load market and risk-free data for a ticker/date range.
* Compute EWMA volatility from returns or from ticker/date input.
* Analyze Monte Carlo percentiles and band widths.
* Compare a strategy volatility path against Monte Carlo confidence bands.
* Compute return, volatility, Sharpe, drawdown, and tracking-error metrics.
## Recommended Tools
* compute-ewma-volatility-series
* load-and-prepare-data
* analyze-monte-carlo-results
* compare-strategy-to-monte-carlo
* compute-annualized-return
* compute-annualized-volatility
* compute-sharpe-ratio
## Tools
| Tool | Description | Returns | |
| ------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | -------------- |
| `dual-ewmaestimator-update` | Updates the dual exponential weighted moving average (EWMA) estimator for volatility calculations using risky and index return values. Params: r\_risky: float (risky asset return), r\_index: float (index return) | `Any` | |
| `ewmavolatility-estimator-update` | Updates an EWMA volatility estimator with a new return value, computing the exponentially weighted moving average volatility. Params: return\_value (float): the latest return value to incorporate into the volatility estimate | `Any` | |
| `ewmavolatility-estimator-reset` | Resets the EWMA volatility estimator to its initial state, clearing any accumulated data and starting fresh. Params: none | `Any` | |
| `ewmavolatility-estimator-get-current-volatility` | Retrieves the current EWMA volatility estimate from the volatility estimator. Params: No parameters. | `Any` | |
| `dual-ewmaestimator-reset` | Resets the internal state of the DualEWMAEstimator volatility estimator. Params: none | `Any` | |
| `dual-ewmaestimator-get-current-volatilities` | Returns the current volatility estimates calculated by the DualEWMA estimator. Params: none | `Any` | |
| `compute-volatility-tracking-error` | Computes annualized volatility tracking error by measuring the mean absolute difference between daily volatility estimates and a target volatility, scaled by the square root of trading periods per year. Params: sigma\_hat\_index (np.ndarray): daily volatility estimates; sigma\_target (float): target daily volatility; periods\_per\_year (int, default=252): trading days per year | `float` | |
| `compute-annualized-return` | Calculates annualized return from daily returns using either arithmetic mean or geometric (CAGR) method. Params: Inputs: returns (np.ndarray of daily returns), periods\_per\_year (int, default 252 trading days), method ('arithmetic' or 'geometric') | `float` | |
| `compute-annualized-volatility` | Compute annualized volatility from daily returns. Takes an array of daily returns and returns a single float representing the annualized volatility, scaled by the number of trading days per year. Params: returns: np.ndarray (daily returns), periods\_per\_year: int (default=252) | `float` | |
| `compute-sharpe-ratio` | Computes the Sharpe ratio from daily returns by annualizing return and volatility. Returns a float ratio without risk-free rate subtraction. Params: returns (np.ndarray): daily returns array; periods\_per\_year (int, default=252): trading days per year | `float` | |
| `compute-maximum-drawdown` | Computes the maximum drawdown of a cumulative return series, returning the largest peak-to-trough decline as a positive percentage (e.g., 0.55 for 55% drawdown). Params: cumulative\_returns: np.ndarray - cumulative return series starting at 1.0 (e.g., np.array(\[1.0, 1.05, 0.98])) | `float` | |
| `metrics-to-dataframe` | Converts a dictionary of strategy metrics into a comparison DataFrame, organizing strategies as rows and metrics as columns. Params: metrics\_dict: Dictionary mapping strategy names to their metrics (e.g., \{'Strategy A': \{'Sharpe Ratio': 1.2, 'Volatility': 0.05}}) | `pd.DataFrame` | |
| `load-and-prepare-data` | Loads and prepares financial data for backtesting, including asset prices and risk-free rates, aligned to a trading calendar and transformed into returns. Params: start\_date (str, default '2000-06-06'), end\_date (str | None, defaults to today when omitted), cash\_rate\_convention (str, default 'calendar\_day'), ticker (str, default 'IVV') | `pd.DataFrame` |
| `compute-ewma-volatility-series` | Compute the EWMA volatility series for returns. Accepts direct returns, tabular/stored payloads with return\_column, or ticker + date range to load data inside the tool. Params: halflife (default 126), annualize (default true), periods\_per\_year (default 252). | `pd.DataFrame` | |
| `analyze-monte-carlo-results` | Analyze Monte Carlo simulation results to compute summary statistics including final, steady-state, and early-period percentiles and band widths. Params: df: pd.DataFrame - simulation results (required) | `Dict[str, float]` | |
| `compare-strategy-to-monte-carlo` | Compares a strategy's running annualized volatility to Monte Carlo confidence bands and returns key statistics. Params: strategy\_sigma\_hat\_ann: np.ndarray of strategy's volatility estimates; mc\_df: pd.DataFrame with p10, p50, p90 columns | `Dict[str, float]` | |
| `plot-cumulative-returns` | Generates a line plot comparing cumulative returns across multiple trading strategies over time. Saves the figure if a path is provided. Params: Inputs: results\_dict (dict of strategy name to DataFrame), save\_path (optional save location), title (plot title, default: 'Cumulative Returns Comparison') | `None` | |
| `plot-weights` | Plots strategy weights over time from a DataFrame, showing risky and cash allocations with optional title and saving capability. Params: Inputs: results\_df (pd.DataFrame with 'w' and 'c' columns), strategy\_name (optional title), save\_path (optional save location) | `None` | |
| `plot-controller-states` | Generates a plot showing controller states (kappa) over time from a results DataFrame, with optional leverage controller visualization. Params: results\_df (pd.DataFrame): DataFrame containing 'kappa' and optional 'kappa\_lev' columns; strategy\_name (str): Strategy name for plot title; save\_path (Optional\[str]): Path to save the figure | `None` | |
| `plot-drawdown` | Generates a drawdown plot comparing multiple trading strategies over time, showing percentage drawdowns from running maximums. Params: Inputs: results\_dict (dict of strategy names to DataFrames with 'R\_index' column), save\_path (optional path to save figure), title (plot title, default 'Drawdown Comparison') | `None` | |
| `create-metrics-table-markdown` | Generates a markdown table summarizing performance metrics for different strategies and saves it to a file. Params: metrics\_dict: Dict\[str, Dict\[str, float]] - strategy metrics (e.g., \{'Strategy A': \{'Sharpe Ratio': 1.2, 'Win Rate': 0.65}}); filename: str - output path (default: 'results/metrics\_table.md') | `None` | |
## Examples
### Compute EWMA volatility from market data
```json theme={null}
{
"request": {
"ticker": "SPY",
"start_date": "2020-01-01",
"halflife": 252,
"annualize": true
}
}
```
### Load aligned market and cash-rate data
```json theme={null}
{
"request": {
"ticker": "SPY",
"start_date": "2020-01-01",
"cash_rate_convention": "calendar_day"
}
}
```
### Analyze Monte Carlo output
```json theme={null}
{
"request": {
"df": {
"rows": [
{
"step": 1,
"p10": 0.1,
"p50": 0.15,
"p90": 0.2,
"target": 0.15
},
{
"step": 2,
"p10": 0.11,
"p50": 0.15,
"p90": 0.19,
"target": 0.15
}
]
}
}
}
```
## Notes
* `compute-ewma-volatility-series` is the preferred one-step tool for requests that start from a ticker and date range.
* When `end_date` is omitted, data-loading tools use the current date on the server.
* Large tabular outputs can be returned as result handles.
Configure this endpoint in Cursor, Claude Desktop, or a generic MCP client.
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.
Stock-Watson, complete-panel, and dynamic statistical factor extraction.
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.