# Crypto Macro Correlation Tracker (`gochujang/crypto-macro-correlation`) Actor Compute rolling correlations between BTC/ETH/SOL and traditional markets (S\&P 500, Gold, DXY, US 10Y). Get 7d/30d/90d Pearson correlations, relative performance, and a risk-on/off signal. No API key required. Flat $0.005/run. - **URL**: https://apify.com/gochujang/crypto-macro-correlation.md - **Developed by:** [Hojun Lee](https://apify.com/gochujang) (community) - **Categories:** Developer tools, Automation, News - **Stats:** 2 total users, 1 monthly users, 66.7% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Crypto Macro Correlation Tracker > Compute **rolling correlations between BTC/ETH/SOL and S&P 500, Gold, DXY, US 10Y bonds**. Get 7d/30d/90d Pearson correlations, relative performance, and a market regime signal (risk-on/off/decoupling). No API key required. **$0.005/run flat**. --- ### What It Does Crypto's relationship with traditional markets shifts constantly. When BTC correlates strongly with S&P 500, it's trading as a risk asset. When it uncouples or tracks inversely to DXY, it signals a different macro regime. This actor computes these correlations in real-time so you can: - **Track regime shifts** — Is BTC acting as a risk asset, hedge, or independent asset? - **Position sizing** — Use correlation to assess portfolio diversification - **Macro timing** — Align crypto entries with macro tailwinds (dovish Fed, weak DXY) - **Dashboards** — Feed correlation data into trading system alerts ### Example Output ```json { "crypto_asset": "btc", "performance": { "current_price": 95230, "change_7d_pct": 3.2, "change_30d_pct": 12.8, "change_90d_pct": 45.1 }, "correlations": { "sp500": { "7d": {"correlation": 0.72, "strength": "strong"}, "30d": {"correlation": 0.58, "strength": "moderate"}, "90d": {"correlation": 0.41, "strength": "moderate"} }, "gold": { "7d": {"correlation": 0.18, "strength": "weak"}, "30d": {"correlation": 0.22, "strength": "weak"}, "90d": {"correlation": 0.35, "strength": "weak"} }, "dxy": { "7d": {"correlation": -0.65, "strength": "strong"}, "30d": {"correlation": -0.48, "strength": "moderate"}, "90d": {"correlation": -0.31, "strength": "weak"} } } } ```` ### SUMMARY Key-Value Store ```json { "risk_signal": "risk_on", "risk_description": "BTC behaving as risk asset — correlates with equities, inverse DXY", "macro_performance": { "sp500": {"change_7d_pct": 1.2, "change_30d_pct": 4.5}, "gold": {"change_7d_pct": -0.8, "change_30d_pct": 2.1}, "dxy": {"change_7d_pct": -1.1, "change_30d_pct": -3.2} } } ``` ### Risk Signals | Signal | Condition | Meaning | |--------|-----------|---------| | `risk_on` | BTC-SP500 corr >0.5, BTC-DXY corr <-0.3 | BTC trading as risk asset | | `decoupling` | BTC-SP500 corr <0.2, BTC-DXY corr >0 | BTC acting independently of macro | | `mixed` | Neither clear pattern | No dominant regime | ### Macro Assets Available | Code | Asset | Ticker | |------|-------|--------| | `sp500` | S\&P 500 | `^GSPC` | | `gold` | Gold Futures | `GC=F` | | `dxy` | US Dollar Index | `DX-Y.NYB` | | `us10y` | US 10-Year Treasury | `^TNX` | | `vix` | Volatility Index | `^VIX` | ### Correlation Strength Guide | Value | Strength | |-------|----------| | ≥ 0.8 | Very strong | | 0.6–0.8 | Strong | | 0.4–0.6 | Moderate | | 0.2–0.4 | Weak | | < 0.2 | Negligible | ### Input Options | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `cryptoAssets` | string\[] | `["btc","eth"]` | Crypto assets: `btc`, `eth`, `sol` | | `macroAssets` | string\[] | All 5 | Macro assets: `sp500`, `gold`, `dxy`, `us10y`, `vix` | | `lookbackDays` | integer | `90` | Total lookback period (30–365 days) | | `correlationWindows` | string\[] | `["7","30","90"]` | Rolling window sizes in days | ### Pricing - **$0.005 per run** (flat, regardless of assets or windows) - Ideal for daily scheduling to track regime shifts ### Data Sources - [Yahoo Finance](https://finance.yahoo.com) — S\&P 500, Gold, DXY, US 10Y, VIX (free) - [CoinGecko](https://api.coingecko.com) — BTC, ETH, SOL prices (free) # Actor input Schema ## `cryptoAssets` (type: `array`): Crypto assets to analyze (btc, eth, sol). ## `macroAssets` (type: `array`): Traditional market assets: sp500, gold, dxy, us10y, vix. ## `lookbackDays` (type: `integer`): Total historical period to analyze (30-365 days). ## `correlationWindows` (type: `array`): Rolling window sizes in days for correlation computation. ## Actor input object example ```json { "cryptoAssets": [ "btc", "eth" ], "macroAssets": [ "sp500", "gold", "dxy", "us10y", "vix" ], "lookbackDays": 90, "correlationWindows": [ "7", "30", "90" ] } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("gochujang/crypto-macro-correlation").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("gochujang/crypto-macro-correlation").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 gochujang/crypto-macro-correlation --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gochujang/crypto-macro-correlation", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Crypto Macro Correlation Tracker", "description": "Compute rolling correlations between BTC/ETH/SOL and traditional markets (S&P 500, Gold, DXY, US 10Y). Get 7d/30d/90d Pearson correlations, relative performance, and a risk-on/off signal. No API key required. Flat $0.005/run.", "version": "0.1", "x-build-id": "nc4BuPR1jVBoMzOdl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gochujang~crypto-macro-correlation/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gochujang-crypto-macro-correlation", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/gochujang~crypto-macro-correlation/runs": { "post": { "operationId": "runs-sync-gochujang-crypto-macro-correlation", "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/gochujang~crypto-macro-correlation/run-sync": { "post": { "operationId": "run-sync-gochujang-crypto-macro-correlation", "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": { "cryptoAssets": { "title": "Crypto Assets", "type": "array", "description": "Crypto assets to analyze (btc, eth, sol).", "default": [ "btc", "eth" ], "items": { "type": "string" } }, "macroAssets": { "title": "Macro Assets", "type": "array", "description": "Traditional market assets: sp500, gold, dxy, us10y, vix.", "default": [ "sp500", "gold", "dxy", "us10y", "vix" ], "items": { "type": "string" } }, "lookbackDays": { "title": "Lookback Days", "minimum": 30, "maximum": 365, "type": "integer", "description": "Total historical period to analyze (30-365 days).", "default": 90 }, "correlationWindows": { "title": "Correlation Windows", "type": "array", "description": "Rolling window sizes in days for correlation computation.", "default": [ "7", "30", "90" ], "items": { "type": "string" } } } }, "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 }, 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