> ## 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.
# Wavelet Mean Reversion Server
> Wavelet-based mean reversion analysis for financial time series.
## Overview
The Wavelet Mean Reversion Server fetches market data, analyzes returns or signals with wavelet decomposition, and generates synthetic data for experiments.
## Connection
Add this server to your MCP client configuration.
```json theme={null}
{
"mcpServers": {
"wavelet-mean-reversion": {
"url": "https://wavelet-mean-reversion-production.up.railway.app/mcp/waveletmeanreversion"
}
}
}
```
```json theme={null}
{
"mcpServers": {
"wavelet-mean-reversion": {
"url": "https://wavelet-mean-reversion-production.up.railway.app/mcp/waveletmeanreversion",
"headers": {
"Authorization": "Bearer ${env:WAVELET_MEAN_REVERSION_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://wavelet-mean-reversion-production.up.railway.app/mcp/waveletmeanreversion` |
| Health URL | `https://wavelet-mean-reversion-production.up.railway.app/health` |
| MCP path | `/mcp/waveletmeanreversion` |
| Request envelope | `{"request": {...}}` |
| Auth | Optional bearer token when enabled for the endpoint |
## Best-Fit Workflows
* Fetch Yahoo market data.
* Compute returns for a ticker.
* Run wavelet decomposition and mean reversion analysis.
* Generate synthetic mean-reverting or trending data.
* Backtest simple wavelet mean-reversion strategies.
## Recommended Tools
* yahoo-data-fetcher-fetch
* yahoo-data-fetcher-fetch-returns
* wavelet-analyzer-analyze
* wavelet-analysis-result-summary
* wavelet-analysis-result-get-statistics-dataframe
## Tools
| Tool | Description | Returns |
| -------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------- |
| `main` | Main example demonstrating wavelet mean reversion analysis and trading strategy. | `Any` |
| `yahoo-data-fetcher-fetch` | Fetches historical stock data from Yahoo Finance for one or more tickers. Params: ticker (str or list): stock symbol(s); start\_date (str, optional): start date; end\_date (str, optional): end date; period (str, default='max'): time period; interval (str, default='1d'): data frequency | `Any` |
| `wavelet-mean-reversion-strategy-generate-signals` | Generate trading signals. | `Any` |
| `wavelet-analyzer-decompose` | Decomposes a signal into wavelet components for mean reversion analysis. Params: signal: np.ndarray (input signal to decompose) | `Any` |
| `wavelet-analyzer-analyze` | Analyzes a signal using wavelet decomposition to detect mean reversion patterns. Outputs analysis results for further processing in trading strategies. Params: signal: np.ndarray - input signal data to analyze (no default provided) | `Any` |
| `yahoo-data-fetcher-fetch-returns` | Fetches historical stock returns from Yahoo Finance for a given ticker symbol, supporting optional date ranges, return period, and log return calculation. Params: ticker (str), start\_date (str, optional), end\_date (str, optional), period (str, default='max'), log\_returns (bool, default=True) | `Any` |
| `yahoo-data-fetcher-fetch-multiple-series` | Fetches historical stock price data for multiple tickers from Yahoo Finance, returning the specified column over the given date range. Params: tickers (list of str), start\_date (str, optional), end\_date (str, optional), period (str, default='max'), column (str, default='Close') | `Any` |
| `wavelet-mean-reversion-strategy-backtest-simple` | Simple backtest of the strategy. | `Any` |
| `config-from-dict` | Create configuration from dictionary. | `Any` |
| `wavelet-mean-reversion-strategy-fit` | Fit the strategy by analyzing the signal. | `Any` |
| `synthetic-data-generator-generate-mean-reverting` | Generates synthetic mean-reverting time series data using Ornstein-Uhlenbeck process parameters. Params: N (default: 1000): number of data points; theta (default: 0.1): mean reversion speed; mu (default: 0.0): long-term mean; sigma (default: 1.0): volatility; seed (default: 42): random seed for reproducibility | `Any` |
| `synthetic-data-generator-generate-trending` | Generates synthetic trending financial data using a mean-reverting model with configurable parameters for length, drift, and volatility. Params: Inputs: N (int, default 1000), drift (float, default 0.01), volatility (float, default 1.0), seed (Optional\[int], default 42) | `Any` |
| `wavelet-visualizer-plot-trading-signals` | Plot trading signals on price chart. | `Any` |
| `wavelet-visualizer-plot-backtest-results` | Plot backtest performance. | `Any` |
| `synthetic-data-generator-generate-complex-signal` | Generates a synthetic complex signal for wavelet analysis, producing N data points with optional random seed and component breakdown. Params: N (int, default=2000): number of data points; seed (int, optional, default=42): random seed; include\_components (bool, default=False): whether to return individual signal components | `Any` |
| `synthetic-data-generator-add-noise` | Adds random noise to a signal array using a configurable noise level and optional seed for reproducibility. Params: signal (np.ndarray): input signal to add noise to; noise\_level (float, default 0.1): amplitude of added noise; seed (Optional\[int], default None): random seed for reproducibility | `Any` |
| `config-to-dict` | Convert configuration to dictionary. | `Any` |
| `wavelet-visualizer-plot-complete-analysis` | Create comprehensive analysis visualization. | `Any` |
| `wavelet-visualizer-plot-acf-analysis` | Plot autocorrelation function for each scale. | `Any` |
| `wavelet-visualizer-plot-monte-carlo-results` | Plot Monte Carlo simulation results. | `Any` |
| `wavelet-decomposition-result-get-scale-signal` | Extracts the signal for a specific wavelet scale from decomposition results. Params: scale\_name (str): name of the wavelet scale to extract signal from (e.g., 'D1', 'D2') | `Any` |
| `trading-signal-to-dataframe` | Convert signals to DataFrame. | `Any` |
| `wavelet-analysis-result-summary` | Generates a concise summary of wavelet analysis results, providing key metrics and insights from the wavelet decomposition and mean reversion analysis. Params: none | `Any` |
| `wavelet-analysis-result-get-statistics-dataframe` | Retrieves statistical data from wavelet analysis results as a pandas DataFrame, providing summary metrics for mean reversion analysis. Params: none | `Any` |
| `main-main` | Run all examples. | `Any` |
| `validate-time-series` | Validates and converts time series data to a 1D numpy array, ensuring it has at least 4 points and contains no NaN or inf values. Params: data (Union\[np.ndarray, pd.Series]): Time series data to validate | `np.ndarray` |
| `compare-with-bollinger-bands` | Compares a wavelet-based mean-reverting component with traditional Bollinger Bands, returning metrics for both approaches. Params: signal: Original signal array; wavelet\_mr\_component: Wavelet-derived mean-reverting component; window: Bollinger Band window (default 20); num\_std: Std deviation multiplier (default 2.0) | `Dict[str, np.ndarray]` |
| `example-synthetic-data` | Example using synthetic data. | `Any` |
| `example-real-market-data` | Example using real market data. | `Any` |
| `example-monte-carlo-backtest` | Example with Monte Carlo backtesting. | `Any` |
| `example-multiple-tickers` | Example analyzing multiple tickers. | `Any` |
| `example-custom-configuration` | Example using custom configuration. | `Any` |
| `default-config` | Preset factory: create Config via ObjectStore (handles) with default deps (none); returns a handle. | `object` |
| `monte-carlo-default` | Preset factory: create MonteCarloBacktester via ObjectStore (handles) with default deps (none); returns a handle. | `object` |
| `statistical-analyzer-default` | Preset factory: create StatisticalAnalyzer via ObjectStore (handles) with default deps (none); returns a handle. | `object` |
| `wavelet-analyzer-default` | Preset factory: create WaveletAnalyzer via ObjectStore (handles) with default deps (none); returns a handle. | `object` |
| `wavelet-strategy-default` | Preset factory: create WaveletMeanReversionStrategy via ObjectStore (handles) with default deps (analyzer); returns a handle. | `object` |
| `wavelet-visualizer-default` | Preset factory: create WaveletVisualizer via ObjectStore (handles) with default deps (none); returns a handle. | `object` |
| `yahoo-fetcher-default` | Preset factory: create YahooDataFetcher via ObjectStore (handles) with default deps (none); returns a handle. | `object` |
## Examples
### Fetch returns for one ticker
```json theme={null}
{
"request": {
"ticker": "AAPL",
"start_date": "2024-01-01",
"end_date": "2024-06-30",
"period": "1y",
"log_returns": true
}
}
```
### Analyze a signal
```json theme={null}
{
"request": {
"signal": [
101.0,
100.5,
100.9,
100.1,
99.8,
100.2
]
}
}
```
## Notes
* Instance-style tools can use handles when you need configured analyzers or strategies.
* Use Parallax ExtremeHurst when you need the ExtremeHurst signal engine rather than wavelet decomposition.
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.
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.