Kraken REST API

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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
Developer	Kraken (Payward Inc.)
Type	REST API
License	Commercial API (free with Kraken account)
Documentation	docs.kraken.com
GitHub	krakenfx/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/

Category	Endpoints
Public	Ticker, OHLC, order book, recent trades, spreads, asset pairs, system status
Private: Account	Balance, trade balance, open/closed orders, trade history, ledger entries
Private: Trading	Add order, amend order, cancel order, cancel all orders, batch orders
Private: Funding	Deposit addresses, deposit methods, withdrawal info, withdraw funds
Private: Earn	Staking and yield positions

Authentication

Spot REST authentication uses HMAC-SHA512:

Generate a nonce (always-increasing unsigned 64-bit integer; millisecond UNIX timestamps recommended)
Compute: SHA256(nonce + POST body data)
Compute: HMAC-SHA512(URI path + SHA256 result, base64-decoded private key)
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)

Tier	Max Counter	Decay Rate
Intermediate	20	0.5 per second
Pro	20	1 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

API Reference Center: full endpoint documentation for all products.
Official Go SDK: MIT-licensed, covers Spot and Derivatives REST and WebSocket.
Python SDK (community): full-featured community SDK with async support and built-in WebSocket handlers.
Kraken CLI: command-line access to REST endpoints without writing code.
REST Quickstart Examples: code samples for common operations.

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.

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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.

Aegis AI

As enterprises and industrial sectors rapidly deploy autonomous AI agents and edge robotics, they expose themselves to novel, critical attack vectors such as advanced prompt injections, data exfiltration, and model denial-of-service (DoS) poisoning. Traditional security perimeters are insufficient for inspecting these dynamic, semantic payloads. Aegis AI bridges this critical security gap as an enterprise-grade SecOps firewall and autonomous edge proxy. Engineered in Go and Python, Aegis AI delivers sub-millisecond local enforcement, ensuring high-speed security without compromising operational latency. The platform's architecture is built on four core pillars: Edge-Native Proxy: Leveraging Veea's Lobster Trap, I deployed a high-performance local proxy that intercepts and sanitizes traffic directly at the edge, a crucial requirement for real-time robotics and localized AI agents. Autonomous Fuzzing Engine: Powered by Gemini, Aegis features a self-healing, continuous testing pipeline. It autonomously red-teams AI agents, proactively identifying vulnerabilities and dynamically generating defensive rules before zero-day exploits can be weaponized. Real-time Semantic Filtering: The system deeply inspects inbound and outbound payloads to neutralize complex prompt injection attacks and prevent unauthorized data exfiltration. Human-in-the-Loop Governance: A dedicated CISO staging queue quarantines highly anomalous or critical security events for manual oversight, ensuring strict enterprise governance and compliance. By combining proactive autonomous defense with robust edge-level proxying, Aegis AI provides the foundational security layer necessary for the safe, scaled adoption of AI agents in mission-critical environments.

Neural Analytics

An AI-powered analytics platform that lets you upload CSV files and ask natural language questions. Supports multi-table querying with automatic JOINs, semantic schema search, and AI-generated insights with interactive visualizations. Drag-and-drop CSV upload — Upload up to 5 files (40 MB each) instantly Multi-table NL querying — Ask questions across multiple datasets with automatic JOINs Zero SQL knowledge required — LLM generates correct queries from plain English Semantic schema search — FAISS embeddings find relevant columns by semantic meaning Auto-fix on failure — LLM self-corrects failing queries in real-time Interactive visualizations — Auto-selects best chart type (bar, line, pie, histogram) AI summaries — 3-bullet findings generated from query results Data cleaning report — Shows nulls normalized, duplicates removed, type coercions

Isolated Agents SDK

The Isolated Agents SDK is a powerful security framework designed to solve one of the most pressing challenges in the modern AI ecosystem: the safe execution of autonomous agents on local hardware. As AI agents gain the ability to generate and execute code, browse the web, and interact with file systems, the risk of "prompt injection" or malfunctioning logic leading to system compromise has increased significantly. Our SDK provides a "security-first" abstraction layer that automatically wraps any Python-based AI agent—whether built with LangChain, AutoGPT, or custom logic—within a rootless Podman container. This ensures that the agent operates in a completely isolated environment with zero access to the host system unless explicitly granted. Key features include: Automatic Isolation: Move from raw Python execution to a secure container with a single function call (run_agent). Granular Policies: Define strict resource limits (CPU/Memory), network whitelists (e.g., only allow access to specific LLM APIs), and read-only file system access. Seamless Serialization: Uses advanced pickling techniques to transport complex agent states into the container without requiring manual Dockerfile management. Artifact Management: Automatically collect and map generated files, logs, and data back to the host system. By bridging the gap between high-level AI orchestration and low-level system security, the Isolated Agents SDK empowers developers to build and deploy innovative AI tools with total peace of mind.

AI Career Copilot

AI Career Copilot is a full-stack platform built for ML/AI engineers navigating the job market. It lets users upload their CV for ATS scoring and gap analysis, search live ML/AI job listings (updated every 6 hours via Adzuna), and paste any job description to get an instant match score, missing keywords, tailored CV bullets, and a cover letter. The platform includes a Kanban-style application tracker, an interview prep module that generates questions from the user's actual projects, and an auto-apply engine using Playwright that submits applications on LinkedIn Easy Apply, Greenhouse, and Lever. A unique Projects feature lets users upload a code ZIP, describe their contributions, and get AI-generated CV-ready bullet points — with optional one-click GitHub repo creation. Built with FastAPI, React 18, Groq LLaMA, and Playwright.

Work Pulse

Remote Work Compliance & Productivity Management System is a full-stack enterprise platform built to help organizations efficiently manage remote and hybrid teams. The system centralizes attendance tracking, desktop activity monitoring, task management, compliance enforcement, payroll processing, employee wellness tracking, and AI-powered productivity analytics within a single ecosystem. The platform consists of a React-based web admin portal, a mobile employee application, a lightweight Go-based desktop monitoring agent, a Node.js backend server, and a Python AI analytics service. The desktop agent automatically tracks attendance, idle time, active applications, and work activity while securely transmitting data to the backend in real time. Managers and administrators can monitor productivity trends, analyze workforce performance, generate payroll reports, manage projects and tasks, and receive AI-generated insights such as burnout detection, workload analysis, and productivity scoring. Employees can access attendance records, task updates, payroll summaries, notifications, and wellness tools through the mobile application. The system uses PostgreSQL for data management, RabbitMQ for background processing, WebSockets for live updates, and Docker for scalable deployment. The project demonstrates modern enterprise software architecture with secure authentication, RBAC authorization, REST APIs, real-time communication, AI integration, and cross-platform desktop monitoring.

SRE-Healer: Autonomous Remediation Agent

In today's fast-paced software development environments, engineers and Site Reliability Engineers (SREs) face a massive productivity bottleneck: alert fatigue and repetitive infrastructure debugging. When a system crashes or experiences performance degradation, developers are forced to stop building new features and instead spend hours digging through logs to find the root cause. Enter SRE-Healer, a next-generation autonomous remediation agent designed to turn ideas into impact faster by eliminating manual debugging. SRE-Healer operates on a Closed-Loop Autonomous Remediation paradigm: it continuously observes system metrics, reasons through the root causes using AI, executes safety-first fixes, and verifies the system's recovery—all without human intervention. By handling repetitive infrastructure issues in the background, SRE-Healer buys back valuable time for developers. Building a complex, multi-layered AI agent architecture usually takes weeks of rigorous coding. However, we brought SRE-Healer to life in record time by partnering with the IBM Bob IDE. Bob served as our core development partner throughout the hackathon. We utilized Bob's Plan and Code modes to architect the system logic, generate the FastAPI event gateway, and write the core diagnostic scripts. By establishing an AGENTS.md knowledge base via the /init command, Bob understood our exact project intent and safely generated boilerplate code. Furthermore, we integrated IBM Bob Shell as the core "reasoning engine" of SRE-Healer to analyze error logs and orchestrate remediation actions securely. SRE-Healer proves that with IBM Bob as a development partner, builders can quickly construct sophisticated autonomous systems that drastically reduce Mean Time to Remediation (MTTR) and empower teams to deliver software with greater efficiency and confidence.