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Kraken REST API

The Kraken REST API provides HTTP-based access to Kraken's spot and futures markets, account data, and trading operations. It covers public market data endpoints (no authentication required) and private endpoints for account management and order execution (HMAC-SHA512 authenticated). The API supports Spot, Futures, Custody, and Embed products, each with separate base URLs.

General
DeveloperKraken (Payward Inc.)
TypeREST API
LicenseCommercial API (free with Kraken account)
Documentationdocs.kraken.com
GitHubkrakenfx/api-go

Core Features

  • Public and private endpoints: public market data requires no authentication; private endpoints use HMAC-SHA512 signing.
  • Multiple product APIs: Spot, Futures, Custody, and Embed each have dedicated endpoints.
  • Tiered rate limiting: per-API-key counter with decay rates based on account tier (Intermediate or Pro).
  • Subaccount support: master accounts can manage subaccounts programmatically.
  • Earn and staking: endpoints for managing yield-generating positions.

Endpoints

Spot REST base URL: https://api.kraken.com/0/

CategoryEndpoints
PublicTicker, OHLC, order book, recent trades, spreads, asset pairs, system status
Private: AccountBalance, trade balance, open/closed orders, trade history, ledger entries
Private: TradingAdd order, amend order, cancel order, cancel all orders, batch orders
Private: FundingDeposit addresses, deposit methods, withdrawal info, withdraw funds
Private: EarnStaking and yield positions

Authentication

Spot REST authentication uses HMAC-SHA512:

  1. Generate a nonce (always-increasing unsigned 64-bit integer; millisecond UNIX timestamps recommended)
  2. Compute: SHA256(nonce + POST body data)
  3. Compute: HMAC-SHA512(URI path + SHA256 result, base64-decoded private key)
  4. Send API-Key header (public key) and API-Sign header (base64-encoded HMAC result)

The private key is never transmitted directly.


Rate Limits (Spot REST)

TierMax CounterDecay Rate
Intermediate200.5 per second
Pro201 per second

Ledger and trade history calls add 4 to the counter; all other private calls add 1. Order placement and cancellation use a separate trading rate limiter. Exceeding limits returns EAPI:Rate limit exceeded. Rate limits are shared across REST, WebSocket, and FIX for the same API key.


Tools and Resources


Ecosystem and Integrations

  • API keys are generated in Kraken account settings with configurable permissions (read-only, trading, funding).
  • Commercial redistribution of Kraken market data requires prior approval from marketdata@kraken.com.
  • Community SDKs available for Python, Go, C++, and Julia (listed in official documentation).

Generate API keys in your Kraken account settings and follow the REST quickstart to place your first programmatic order.

kraken rest api AI technology Hackathon projects

Discover innovative solutions crafted with kraken rest api AI technology, developed by our community members during our engaging hackathons.

BuildLane Horizon

BuildLane Horizon

BuildLane Horizon is a fully decoupled, multi-agent intelligence platform engineered to map competitive market transitions in real-time. Modern enterprise web data is locked behind aggressive anti-bot firewalls, and the data that is accessible is highly unstructured. BuildLane Horizon solves this by autonomously bypassing scraper blocks, extracting raw DOM matrices, and utilizing AI to synthesize that unstructured data into a persistent, relational cognitive memory graph. How it Works & Core Features Targeted Extraction: The engine utilizes asynchronous Python streams to safely pull raw HTML layouts from enterprise domains without triggering bot protections. Context Synthesis: Instead of standard regex scraping, we use AI to parse the complex DOM tree, identifying high-level strategic shifts like changes in monetization structures, evolving value propositions, and targeted engineering hiring trends. Graph Serialization: The system ingests the AI output and maps it into a vector/graph database, creating persistent relational chains (e.g., mapping a competitor's pivot toward Enterprise AI). The Architecture (Tech Stack) Extraction Engine: Bright Data Web Unlocker Cognitive Parser: Google Gemini 3.5 Flash Memory Graph Layer: Cognee Vector & Graph Memory Backend Framework: Python 3.11 + FastAPI Frontend Dashboard: React + Vite + Tailwind CSS v4 By strictly separating the extraction backend from the high-contrast React UI, BuildLane Horizon delivers a deterministic, real-time stream of actionable market intelligence rather than just raw, unformatted data.

Foreshock β€” Continuous ICT Vendor Risk Monitoring

Foreshock β€” Continuous ICT Vendor Risk Monitoring

Under DORA Article 28, fintechs are legally accountable for continuous oversight of every critical ICT vendor. The leading indicators of a vendor going bad show up in public data weeks before they reach a security score or a questionnaire cycle. Leadership exits. Lawsuits. Hiring freezes. Sentiment collapse. GRC platforms watch paperwork. Security raters watch the attack surface. Neither one watches business health, and that is the gap Foreshock fills. Foreshock runs a daily unattended agent. It pulls signals across five query classes per vendor through Bright Data MCP, and for public companies it pulls SEC EDGAR 8-K filings straight from the source. Every signal gets appended to a Type-2 timestamped history and never overwritten, so the trend is always preserved. A Claude validator throws out the false positives (about 80% of candidates), and a CDC diff scores six weighted dimensions: leadership, legal, headcount, sentiment, news volume, and open roles. When several signals deteriorate at once, a convergence alert fires. AI then writes the risk summary the way a GRC analyst would, and every factual claim carries a citation that resolves to its source signal. A built-in citation audit confirms it, with zero unresolved across all vendors. One click exports a DORA Article 28 ICT Register PDF: cover page, fleet audit, per-vendor sections, the AI narrative with numbered sources, and a methodology appendix. No competitor ships that today. The same engine (watch, detect, score, alert, summarize, source) points at any entity where stale data defeats the purpose. Fintech vendors today. Competitors, suppliers, and acquisition targets next.

GTM Signal Intelligence

GTM Signal Intelligence

The GTM Intelligence Platform transforms raw public web signals into actionable B2B sales intelligence β€” detecting when companies adopt new enterprise software long before any public announcement. Stage 1 β€” Signal Collection runs four concurrent collectors: a real-time Certificate Transparency log stream, a multi-source DNS subdomain harvester from six free APIs, a Wayback Machine CDX crawler detecting new integration pages, and a GitHub activity monitor flagging commit spikes and customer mentions. Stage 2 β€” Bright Data Integration is the backbone of the web data layer. The main pipeline uses the Bright Data REST API (SERP zone) for budget-guarded SERP queries and page rendering. The Bright Data MCP server is used directly via SSE, calling search_engine and scrape_as_markdown tools for live web intelligence. Stage 3 β€” Parsing & Normalization routes signals through specialized parsers and a VendorFingerprinter with 36+ pre-computed patterns (Salesforce, HubSpot, Stripe, Okta, Snowflake, Datadog) to assign vendor hints and confidence scores. Stage 4 β€” Correlation Engine groups signals into DealCluster objects using a pandas 30-day rolling window and a NetworkX bipartite graph. A multi-factor scorer assigns each cluster a HIGH, MEDIUM, or LOW confidence tier. Stage 5 β€” AI Enrichment feeds each cluster into GPT-4o-mini, returning the suspected vendor, deal close date, optimal outreach window, and reasoning. Confidence is blended 60% LLM + 40% Stage 4. Stage 6 β€” Delivery exposes intelligence through a FastAPI REST API, React 18 dashboard, PostgreSQL storage, email via Resend, and Slack alerts for HIGH-tier deals. Deployed on Render via Docker. Launch Sniper is a companion module detecting unreleased competitor products via WHOIS registrations, USPTO trademark filings, and robots.txt changes β€” using the Bright Data MCP server for live scraping and search, then generating AI-powered counter-playbooks delivered via email and Slack.

SignalScope AI

SignalScope AI

SignalScope AI is a production-ready, autonomous web-intelligence platform that ingests live business and market signals, filters and normalizes them, and produces concise, structured impact analyses to support rapid decision-making. The system combines a lightweight Node.js backend that orchestrates web search and content retrieval with AI-driven summarization and a React + TypeScript frontend dashboard for real-time monitoring. SignalScope continually queries SERP and news sources, resolves article metadata and preview images, and applies normalization and sanitization logic to produce a clean live feed. For deeper analysis, the platform assembles the most relevant articles and sends them, together with a user-provided event, to a structured-AI workflow which returns JSON analyses describing likely business impact, sentiment, and recommended next steps. Built for low-latency operations, the backend uses short-lived in-memory caches to reduce external requests and protect scraping credentials; integrations with Bright Data and Groq (or other LLM/structured-AI providers) are modular so teams can swap providers or extend capabilities. The frontend (Vite + Tailwind) visualizes live signals, summarizations, and agent analyses, enabling filtering by category and quick drill-down into source articles. Operational features include configurable environment variables for API credentials, optional IP allowlisting, and server-side image resolution to avoid credential exposure in browsers. SignalScope is designed for enterprise use cases-monitoring product launches, competitor moves, regulatory developments, and market shocks while remaining extensible for custom extractors, additional data sources, and tailored analysis prompts.