Quant MCP Tools – deterministic market analytics for AI agents

Quant MCP Tools gives MCP clients three finance tools that return structured, reproducible outputs instead of vague market commentary.

Use it when you want an AI agent to answer questions like:

• How correlated are these assets right now?
• Which names are more volatile over the last 30–90 days?
• Would this proposed trade violate sizing, concentration, wash-sale, or drawdown rules?

This Actor is a good fit for portfolio copilots, trading assistants, finance agents, and builders who want deterministic checks before an agent takes action.

What you get

1) Correlation matrix

Tool: get_correlation_matrix

Returns a Pearson correlation matrix from aligned daily returns.

Useful for:

• diversification checks
• portfolio overlap analysis
• pre-trade portfolio reasoning

2) Volatility analysis

Tool: get_volatility_analysis

Returns annualized close-to-close and Parkinson volatility from daily OHLC data.

Useful for:

• risk ranking
• screening unstable names
• dynamic sizing logic

3) Trade validation

Tool: validate_trade_constraints

Checks a proposed trade against practical guardrails such as:

• volatility-adjusted sizing
• sector concentration limits
• wash-sale lookbacks
• daily drawdown cooldowns

Useful for:

• go / no-go trade checks
• risk enforcement before execution
• finance workflows that need explicit guardrails

Why use this instead of letting the LLM guess?

LLMs are good at narrative. They are not automatically reliable for reproducible quantitative checks.

Quant MCP Tools gives your agent:

• hard numbers
• predictable logic
• structured JSON output
• finance-aware guardrails

MCP endpoint

Use this Actor as a remote MCP server:

https://atrokhym--quant-mcp-tools.apify.actor/mcp

Authenticate with either:

• Authorization: Bearer <APIFY_TOKEN>
• or ?token=<APIFY_TOKEN>

Connect from Codex / Claude / other MCP clients

If your client supports remote MCP over HTTP, use:

• URL: https://atrokhym--quant-mcp-tools.apify.actor/mcp
• Bearer token: your Apify API token

Example use cases

• “Check whether NVDA and TSLA have been highly correlated over the last 30 days.”
• “Estimate realized volatility for AAPL, MSFT, and BTC-USD before sizing a trade.”
• “Validate whether this proposed buy would breach concentration or drawdown rules.”
• “Add a deterministic finance tool layer to an autonomous research or trading agent.”

Pricing

This Actor uses Pay-per-Event pricing:

• correlation-matrix — $0.05
• volatility-analysis — $0.08
• trade-validation — $0.25

Example tool inputs

Correlation matrix

{
  "tickers": ["NVDA", "TSLA", "BTC-USD"],
  "lookback_days": 90
}

Volatility analysis

{
  "tickers": ["AAPL", "MSFT"],
  "lookback_days": 30
}

Trade validation

{
  "proposed_trade": {
    "ticker": "NVDA",
    "side": "buy",
    "requested_notional_usd": 2500
  },
  "portfolio_snapshot": {
    "account_equity_usd": 50000,
    "holdings": [
      { "ticker": "AAPL", "market_value_usd": 8000, "sector": "Technology" },
      { "ticker": "XOM", "market_value_usd": 4000, "sector": "Energy" }
    ]
  },
  "recent_realized_losses": [],
  "risk_state": {
    "current_daily_pnl_percent": -0.004
  }
}

Notes

• Market data is currently backed by yfinance
• Outputs are for analytics and risk workflows, not investment advice
• Best used as a deterministic tool layer inside a broader agent system