# Low-Volatility Strategy AI Agent Connect

> Identify and trade assets with the lowest historical volatility to capture risk-adjusted premiums.

## Overview
- **Category:** finance
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_hHiR6pDnkPi9ULQVGcVTEdzehMcpTtRwO5jfR2Mu/ai-agent-connect
- **Tags:** volatility, quantitative, portfolio-optimization, risk-management, finance

## Description

This MCP server provides quantitative tools to implement the low-volatility anomaly strategy. It allows AI agents to identify assets with minimal historical dispersion and manage portfolios using advanced weighting methods. Use `calculate_volatility_signals` to rank liquid assets by volatility and detect the low-vol spread. Use `generate_portfolio_weights` to allocate capital via equal or inverse-volatility weighting. Finally, use `analyze_strategy_performance` to evaluate Sharpe ratios and beta against market benchmarks.

## Tools

### generate_portfolio_weights
Determines the capital allocation for the selected assets using different weighting methodologies

### analyze_strategy_performance
Evaluates the risk-adjusted returns and risk characteristics of the low-volatility strategy

### calculate_volatility_signals
Computes historical volatility and generates the primary buy/sell/hold signals based on the low-volatility ranking

## Prompt Examples

**Prompt:** 
```
Calculate the volatility signals for these assets: [{'assetId': 'AAPL', 'close': 150, 'avgDailyVolume': 2000000}, {'assetId': 'TSLA', 'close': 200, 'avgDailyVolume': 5000000}].
```

**Response:** 
```
The volatility signals have been calculated. AAPL shows lower volatility than TSLA, making it a candidate for the low-volatility group.
```

**Prompt:** 
```
Generate portfolio weights for these signals using inverse volatility: [{'assetId': 'A', 'volatility': 0.1}, {'assetId': 'B', 'volatility': 0.2}].
```

**Response:** 
```
The weights are: Asset A: 0.666, Asset B: 0.333.
```

**Prompt:** 
```
Analyze the performance of a portfolio with returns [0.01, 0.02, -0.01] against a benchmark [0.005, 0.01, 0.005].
```

**Response:** 
```
The strategy achieved a positive Sharpe ratio and outperformed the benchmark with a higher cumulative return.
```

## Frequently Asked Questions

**How does the strategy identify assets to buy?**
The strategy uses `calculate_volatility_signals` to rank liquid assets by their historical volatility. It selects the assets with the lowest volatility for long positions.

**What weighting methods are supported?**
You can use `generate_portfolio_weights` to apply either equal weighting or inverse-volatility weighting to your selected assets.

**How is performance measured?**
Performance is evaluated using `analyze_strategy_performance`, which calculates the Sharpe ratio, low-vol beta, and cumulative excess returns relative to a benchmark.