# Quant MCP Tools – Market Analytics & Trade Validation (`atrokhym/quant-mcp-tools`) Actor Deterministic market analytics and trade-risk validation for AI agents over MCP. - **URL**: https://apify.com/atrokhym/quant-mcp-tools.md - **Developed by:** [Andrew Trokhymenko](https://apify.com/atrokhym) (community) - **Categories:** AI, Developer tools - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 correlation-matrices This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 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 ` - or `?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 ```json { "tickers": ["NVDA", "TSLA", "BTC-USD"], "lookback_days": 90 } ```` #### Volatility analysis ```json { "tickers": ["AAPL", "MSFT"], "lookback_days": 30 } ``` #### Trade validation ```json { "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 # Actor input Schema ## `logLevel` (type: `string`): Logging verbosity. ## `cacheTtlSeconds` (type: `integer`): How long market-data responses are cached on disk. ## `defaultLookbackDays` (type: `integer`): Calendar-day lookback when explicit dates are omitted. ## `dataTimeoutSeconds` (type: `integer`): Timeout for upstream market data requests. ## Actor input object example ```json { "logLevel": "INFO", "cacheTtlSeconds": 43200, "defaultLookbackDays": 365, "dataTimeoutSeconds": 20 } ``` # Actor output Schema ## `results` (type: `string`): Default dataset items produced by the Actor run. ## `storage` (type: `string`): Default key-value store for the Actor run. ## `run` (type: `string`): Console URL for the completed Actor run. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("atrokhym/quant-mcp-tools").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("atrokhym/quant-mcp-tools").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call atrokhym/quant-mcp-tools --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=atrokhym/quant-mcp-tools", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Quant MCP Tools – Market Analytics & Trade Validation", "description": "Deterministic market analytics and trade-risk validation for AI agents over MCP.", "version": "0.1", "x-build-id": "c53ltzJCKCC0ccOMi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/atrokhym~quant-mcp-tools/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-atrokhym-quant-mcp-tools", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/atrokhym~quant-mcp-tools/runs": { "post": { "operationId": "runs-sync-atrokhym-quant-mcp-tools", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/atrokhym~quant-mcp-tools/run-sync": { "post": { "operationId": "run-sync-atrokhym-quant-mcp-tools", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "logLevel": { "title": "Log Level", "enum": [ "DEBUG", "INFO", "WARNING", "ERROR" ], "type": "string", "description": "Logging verbosity.", "default": "INFO" }, "cacheTtlSeconds": { "title": "Cache TTL (seconds)", "minimum": 0, "type": "integer", "description": "How long market-data responses are cached on disk.", "default": 43200 }, "defaultLookbackDays": { "title": "Default Lookback (days)", "minimum": 1, "type": "integer", "description": "Calendar-day lookback when explicit dates are omitted.", "default": 365 }, "dataTimeoutSeconds": { "title": "Data Fetch Timeout (seconds)", "minimum": 1, "type": "integer", "description": "Timeout for upstream market data requests.", "default": 20 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```