# Market Microstructure & Manipulation MCP (`ryanclinton/market-microstructure-manipulation-mcp`) Actor Detect market manipulation, analyze order book dynamics, and optimize regulatory surveillance via advanced econometric and game-theoretic methods. - **URL**: https://apify.com/ryanclinton/market-microstructure-manipulation-mcp.md - **Developed by:** [ryan clinton](https://apify.com/ryanclinton) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage 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 ## Market Microstructure Manipulation MCP Server **Detect market manipulation, analyze order book dynamics, and optimize regulatory surveillance** via advanced econometric and game-theoretic methods. This MCP server orchestrates **14 Apify actors** across financial markets, SEC filings, insider trading, congressional stock disclosures, cryptocurrency, foreign exchange, and economic indicator sources. It applies **Hawkes process order book simulation**, **Bayesian Online Changepoint Detection (BOCPD)** for spoofing, **spectral transfer entropy** for cross-asset information flow, **MRR spread decomposition**, **Fama-French event studies** for insider flow, **LASSO Granger causality**, **Student-t HMM regime classification**, and **extensive-form game theory** for surveillance optimization. ### What data can you access? | Data Point | Source | Coverage | |------------|--------|----------| | Stock market data | Finnhub | US equities, quotes, financials | | SEC regulatory filings | EDGAR | 10-K, 10-Q, 8-K, and enforcement | | Insider transactions | SEC Form 4 | Officer and director trades | | Congressional stock trades | Congressional Tracker | Senate and House member disclosures | | Cryptocurrency markets | CoinGecko | 10,000+ coins and tokens | | Currency exchange rates | Exchange Rate Tracker | 150+ currency pairs | | ECB reference rates | ECB Exchange Rates | Euro reference rates | | Federal Reserve indicators | FRED | Interest rates, VIX, GDP | | Labor market data | BLS | CPI, unemployment, PPI | | Federal regulations | Federal Register | SEC/CFTC rulemakings | | Tech community signals | Hacker News | Market-relevant discussions | | Website monitoring | Website Change Monitor | Regulatory page changes | | Consumer complaints | CFPB | Financial product complaints | | Exchange rate history | Historical Rates | Long-term FX time series | ### MCP Tools | Tool | Price | Description | |------|-------|-------------| | `simulate_order_book_dynamics` | $0.10 | Simulate multivariate Hawkes-process order book dynamics with queue-reactive intensity kernels. Returns branching ratio, criticality index, and queue imbalance. | | `detect_spoofing_manipulation` | $0.15 | Detect layering, spoofing, wash trading, and momentum ignition via BOCPD with Dirichlet-multinomial prior. Returns alerts with confidence scores. | | `measure_cross_asset_information` | $0.12 | Measure directed information flow via spectral transfer entropy and Hasbrouck information share from Johansen VECM decomposition. | | `decompose_spread_components` | $0.10 | Decompose bid-ask spread into adverse selection, inventory cost, and order processing via MRR model. Computes Kyle lambda, Roll measure, and Amihud illiquidity. | | `identify_insider_abnormal_flow` | $0.12 | Identify abnormal trading flow around insider/congressional transactions via event study methodology. CAR [-5,+30] with significance testing. | | `discover_manipulation_causality` | $0.12 | Discover directed causal links via LASSO-penalized Granger causality with van de Geer debiasing. F-statistics, p-values, and network density. | | `classify_market_regimes` | $0.10 | Classify market regimes (calm, volatile, crisis, recovery) via Student-t HMM. Transition matrix, stationary distribution, and Viterbi path. | | `optimize_surveillance_strategy` | $0.20 | Optimize regulatory surveillance via extensive-form game theory. Nash equilibrium, budget allocation, detection probability, and deterrence effect. | ### Data Sources - **Finnhub Stock Search** -- Real-time and historical stock data, company financials, earnings, and market metrics for US equities - **SEC EDGAR Filing Search** -- Securities and Exchange Commission regulatory filings including enforcement actions and litigation releases - **SEC Insider Trading** -- Form 4 insider transaction database tracking officer and director buys, sells, and option exercises with exact dates and values - **Congressional Stock Tracker** -- STOCK Act disclosures from US Senate and House members revealing congressional trading activity - **CoinGecko Crypto Search** -- Cryptocurrency market data covering 10,000+ coins with prices, volumes, market caps, and trading pairs - **Exchange Rate Tracker** -- Live currency exchange rates for cross-asset correlation analysis and FX market microstructure - **Exchange Rate History** -- Historical exchange rate time series for long-term trend analysis and regime detection - **ECB Exchange Rates** -- European Central Bank daily reference rates for EUR crosses used in cross-asset information flow measurement - **FRED Economic Data** -- Federal Reserve economic indicators including FEDFUNDS, VIX, GDP, and other macro variables driving market regimes - **BLS Economic Data** -- Bureau of Labor Statistics data including CPI, unemployment, and PPI for macroeconomic regime context - **Federal Register** -- SEC and CFTC regulatory actions, proposed rules, and enforcement orders affecting market structure - **Hacker News Search** -- Technology community discussion signals relevant to market events, IPOs, and security incidents - **Website Change Monitor** -- Monitoring regulatory and corporate website changes that may precede market-moving announcements - **CFPB Complaints** -- Consumer Financial Protection Bureau complaints for financial product manipulation context ### How the scoring works Each tool applies a specific **quantitative finance algorithm** to market data assembled from multiple sources. **Hawkes Process Order Book:** Estimates multivariate intensity lambda_d(t) = mu_d + sum alpha exp(-beta(t-s)) via EM estimation. The branching ratio (spectral radius of alpha/beta matrix) indicates market criticality -- values approaching 1.0 signal instability. **BOCPD Spoofing Detection:** Maintains run length posterior P(r_t | x_{1:t}) with normal-inverse-gamma conjugate prior and constant hazard function. Changepoints are classified by manipulation pattern (layering, spoofing, wash trading, momentum ignition). **Spectral Transfer Entropy:** TE(f) = 1/(4pi) ln(S_Y(f)/S_{Y|X}(f)) computed from AR spectral estimates. Hasbrouck information share from Johansen VECM via Cholesky decomposition of innovation covariance. **MRR Spread Decomposition:** Madhavan-Richardson-Roomans model decomposes spread into adverse selection, inventory, and order processing. Kyle lambda via OLS on signed flow, Roll measure from return autocovariance, Amihud illiquidity ratio. **Event Study (CAR):** Cumulative Abnormal Return in [-5, +30] event window around insider/congressional transactions relative to expected return, with t-statistics for significance. | Metric | Interpretation | |--------|---------------| | Branching Ratio > 0.9 | Market near criticality, high self-excitation | | BOCPD Changepoint | Potential manipulation event detected | | Transfer Entropy > 0 | Directed information flow from source to target asset | | Kyle Lambda > 0 | Price impact per unit order flow (higher = less liquid) | | CAR significantly positive | Abnormal returns following insider trade | | Granger F-stat significant | Causal influence between market variables | ### How to connect this MCP server #### Claude Desktop Add to your `claude_desktop_config.json`: ```json { "mcpServers": { "market-microstructure-manipulation": { "url": "https://market-microstructure-manipulation-mcp.apify.actor/mcp" } } } ```` #### Programmatic (cURL) ```bash curl -X POST https://market-microstructure-manipulation-mcp.apify.actor/mcp \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_APIFY_TOKEN" \ -d '{"jsonrpc":"2.0","method":"tools/call","params":{"name":"detect_spoofing_manipulation","arguments":{"query":"AAPL"}},"id":1}' ``` #### Other MCP clients This server works with any MCP-compatible client including **Cursor**, **Windsurf**, **Cline**, and custom integrations. Point your client to `https://market-microstructure-manipulation-mcp.apify.actor/mcp`. ### Use cases for market microstructure intelligence #### Market surveillance Detect spoofing, layering, wash trading, and momentum ignition patterns across equities and crypto markets using BOCPD changepoint detection with manipulation pattern classification. #### Insider trading surveillance Identify abnormal trading flow around SEC Form 4 insider transactions and congressional stock disclosures. CAR event studies reveal whether insider trades precede significant price movements. #### Cross-asset lead-lag analysis Measure directed information flow between assets using spectral transfer entropy. Identify which markets lead and which follow, useful for price discovery attribution and front-running detection. #### Market quality assessment Decompose bid-ask spreads into adverse selection, inventory, and order processing components. Kyle lambda and Amihud illiquidity provide market quality benchmarks for venue comparison. #### Regime detection and forecasting Classify current market regime (calm, volatile, crisis, recovery) using Student-t HMM. Transition probabilities and expected regime durations support portfolio allocation and risk management decisions. #### Regulatory resource optimization Optimize surveillance resource allocation using extensive-form game theory. Nash equilibrium analysis determines optimal budget distribution across surveillance actions for maximum deterrence effect. ### How much does it cost? This MCP uses **pay-per-event** pricing. You are only charged when a tool is called. Tool costs range from **$0.10 to $0.20** depending on the number of actors involved. The Apify Free plan includes **$5 of monthly platform credits**, enough for approximately 35-50 market microstructure queries per month. | Usage Example | Estimated Cost | |--------------|----------------| | Single spoofing detection scan | $0.15 | | Order book + regime classification | $0.20 | | Full surveillance optimization | $0.20 | | Cross-asset information flow analysis | $0.12 | ### How it works 1. **Tool call received** -- Your MCP client sends a market query (ticker, asset class, or sector). 2. **Parallel actor execution** -- Up to 14 Apify actors run simultaneously across equities, crypto, FX, SEC filings, insider trades, congressional disclosures, and economic indicators. 3. **Algorithm application** -- The requested econometric or game-theoretic algorithm is applied: Hawkes process, BOCPD, transfer entropy, MRR decomposition, event study, Granger causality, HMM, or game theory. 4. **Pipeline integration** -- Complex tools like surveillance optimization run prerequisite algorithms (BOCPD spoofing detection and HMM regime classification) first, then feed results into the game-theoretic optimizer. 5. **Structured response** -- Results are returned as JSON with algorithm outputs, confidence metrics, alerts, and supporting market data. ### FAQ **Q: Does this access real-time order book data?** A: It uses market data from Finnhub, CoinGecko, and exchange rate sources. Order book simulation is based on collected price and volume data rather than live Level 2 order book feeds. **Q: What manipulation types can it detect?** A: The BOCPD detector classifies four manipulation patterns: layering (placing and canceling orders), spoofing (deceptive large orders), wash trading (self-dealing), and momentum ignition (triggering cascading stops). **Q: Is congressional trading data legal to analyze?** A: Yes. Congressional stock disclosures are public records under the STOCK Act. The data is fetched from public disclosure databases. **Q: How does the game-theoretic surveillance work?** A: It models the regulator-manipulator interaction as an extensive-form zero-sum game, computing Nash equilibrium action probabilities via fictitious play. The output tells regulators how to allocate finite surveillance resources for maximum deterrence. **Q: Is the data real-time?** A: Data is fetched live from each source at query time. Market data, SEC filings, and economic indicators reflect their current state. **Q: Is it legal to use this data?** A: All data sources are publicly available. See [Apify's guide on web scraping legality](https://blog.apify.com/is-web-scraping-legal/). ### Related MCP servers | MCP Server | Focus | |------------|-------| | [ryanclinton/investment-alternative-data-mcp](https://apify.com/ryanclinton/investment-alternative-data-mcp) | Alternative data for investment analysis | | [ryanclinton/knowledge-graph-causal-discovery-mcp](https://apify.com/ryanclinton/knowledge-graph-causal-discovery-mcp) | Causal discovery with Granger causality | | [ryanclinton/sovereign-debt-contagion-mcp](https://apify.com/ryanclinton/sovereign-debt-contagion-mcp) | Sovereign risk and contagion modeling | ### Integrations This MCP server runs on the **Apify platform** and integrates with the broader Apify ecosystem: - **Apify API** -- Call this MCP programmatically from any language via the Apify API - **Scheduling** -- Set up recurring manipulation scans on daily or hourly schedules - **Webhooks** -- Trigger alerts when spoofing is detected or regime transitions occur - **Integrations** -- Connect to Slack, Zapier, Make, or any webhook-compatible service for market surveillance notifications # Actor input Schema ## Actor input object example ```json {} ``` # 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("ryanclinton/market-microstructure-manipulation-mcp").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("ryanclinton/market-microstructure-manipulation-mcp").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 ryanclinton/market-microstructure-manipulation-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ryanclinton/market-microstructure-manipulation-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Market Microstructure & Manipulation MCP", "description": "Detect market manipulation, analyze order book dynamics, and optimize regulatory surveillance via advanced econometric and game-theoretic methods.", "version": "1.0", "x-build-id": "2QNxZXEhv8459hSrc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ryanclinton~market-microstructure-manipulation-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ryanclinton-market-microstructure-manipulation-mcp", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ryanclinton~market-microstructure-manipulation-mcp/runs": { "post": { "operationId": "runs-sync-ryanclinton-market-microstructure-manipulation-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ryanclinton~market-microstructure-manipulation-mcp/run-sync": { "post": { "operationId": "run-sync-ryanclinton-market-microstructure-manipulation-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": {} }, "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 }, 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