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Kraken CLI

Kraken CLI is an open-source command-line execution engine for Kraken's markets, launched on March 11, 2026. Built under the MIT license, it gives developers and AI agents direct access to market data, account management, and order execution across spot, futures, xStocks, and forex through 151 commands. It also ships a built-in Model Context Protocol (MCP) server, making it directly usable in AI coding tools like Claude Code, Cursor, and Gemini CLI.

General
ReleasedMarch 11, 2026 (v0.3.2 as of April 2026)
DeveloperKraken (Payward Inc.)
TypeOpen-source CLI / MCP server
LicenseMIT
GitHubkrakenfx/kraken-cli
Documentationkraken.com/kraken-cli

Core Features

  • 151 commands covering market data, account, trading, funding, futures, and WebSocket streaming.
  • Paper trading: sandbox mode using live prices with no real funds required and no Kraken account needed.
  • Built-in MCP server: launch with kraken mcp to expose commands as tools in any MCP-compatible AI agent.
  • Real-time WebSocket streaming: live tickers, order books, trades, and Level 3 data from the command line.
  • 50 pre-built Agent Skills for common trading workflows, ready to use in any AI agent.
  • Dead man's switch: auto-cancels open orders on disconnect to prevent unintended exposure.
  • Least-privilege API keys: supports scoped API keys; secrets are never logged.
  • Binary verification: release binaries are signed with minisign for supply chain integrity.

Command Groups

GroupDescription
Market DataTicker, order book, OHLC, recent trades, spreads (public, no auth)
AccountBalances, open orders, trade history, ledgers, positions
TradingBuy/sell, batch orders, amend/cancel (spot, xStocks, forex)
FundingDeposits, withdrawals, wallet transfers
FuturesOrders, positions, paper trading, all 8 order types
WebSocketReal-time streaming for tickers, trades, order books, Level 3
MCP Serverkraken mcp starts the MCP server over stdio

Installation

curl --proto '=https' --tlsv1.2 -LsSf \
  https://github.com/krakenfx/kraken-cli/releases/latest/download/kraken-cli-installer.sh | sh

Alternatively, install via Rust: cargo install --git https://github.com/krakenfx/kraken-cli

Pre-built binaries for macOS and Linux are available on GitHub Releases.

Tools and Resources

Ecosystem and Integrations

  • MCP server integrates natively with Claude Code, Cursor, GitHub Copilot, Gemini CLI, Goose, and Codex.
  • Paper trading mode works without a Kraken account for development and testing.
  • Live trading requires a Kraken account and API keys with appropriate permissions.
  • Public market data commands require no authentication and no account.

Install the CLI and run kraken --help to explore all commands, or kraken mcp to start the MCP server for AI agent access.

Edit on GitHub

kraken Kraken cli AI technology Hackathon projects

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

Kraken Alpha Agent

Kraken Alpha Agent

Kraken Alpha Agent is an autonomous trading agent built for the Kraken Trading Performance track. The project focuses on xStocks strategy selection, risk-controlled execution, and transparent PnL reporting. The agent ranks xStocks candidates, runs a reproducible hackathon-window backtest, applies strict guardrails, and exposes the result through a public Vercel dashboard. The core demo shows the full workflow: signal ranking, dry-run execution, risk checks, backtest performance, and judge auditability. During the audited 13-19 May 2026 hackathon window, the backtest shows +19.18 USD PnL across 10 trades, 80% win rate, and 0.06% max drawdown. A 30-day baseline shows +33.56 USD across 138 trades with 53% win rate. Important transparency note: live xStocks execution was blocked at the Kraken venue/account level for the Cyprus EU account used in the hackathon. The project therefore reports xStocks PnL from reproducible backtests, not from live xStocks fills. A short crypto diagnostic session produced live fills outside the xStocks track, proving the engine, API key, IP setup, and execution pipeline work end-to-end. The system defaults to dry-run mode and requires triple opt-in before any live order. It also enforces 1.0x leverage, no shorting, reduce-only exits for futures, and strict rejection handling for Kraken responses. The goal is simple: make autonomous trading agents measurable, risk-aware, and auditable enough for judge verification.

trading ai agent

trading ai agent

Most trading bots do the obvious — NVIDIA earnings beat, buy NVIDIA. By the time they act, the edge is gone. This agent thinks differently. It uses second-order reasoning — when NVIDIA beats earnings, it asks who else benefits? and trades ARM (chip architecture) and TSMC (manufacturer) before the crowd catches up. What it does: Monitors 6 live RSS news feeds continuously Uses Groq LLaMA 3.3 70B to reason about market implications Passes every trade through 10 intelligence layers: sentiment scoring, confidence thresholds, volatility checks, position sizing, stop-loss, brokerage fee simulation, portfolio awareness, and more Executes trades autonomously on Kraken xStocks Logs every decision with full reasoning — why it bought, why it skipped, what sentiment score triggered it Tech Stack: Groq LLaMA 3.3 70B · Kraken xStocks · Yahoo Finance (live prices) · Flask + JS dashboard · Persistent JSON storage Live results at time of recording: Portfolio $10,058 · P&L +$58.84 after fees · 4 active positions (NVDA, ARM, AAPL, AMD) Built solo by Varun — AI Agent Olympics · Milan AI Week 2026 🔗 github.com/AVarun3204/trading-agent

ATLAS - Enterprise Multi-Agent Governance

ATLAS - Enterprise Multi-Agent Governance

ATLAS is an enterprise multi-agent system where every agentic decision is inspected, signed, and auditable. THE PROBLEM Goldman Sachs CIO said publicly: "We don't know what controls we need for agentic AI." Enterprise LLM agents make decisions affecting databases, APIs, financial records. There is no infrastructure that makes these decisions inspectable, auditable, and compliant. WHAT ATLAS DELIVERS - 29/29 scientific test suite PASS in under 1 second - All 5 sponsors integrated end-to-end (real API calls, not mocked): · Speechmatics for voice transcription · Featherless for open-source model routing (MiniMax-M2.5, DeepSeek-V3.2, Kimi-K2.5, Llama-3.3-70B) · Google Gemini 2.0 Flash for orchestration and synthesis · Vultr for infrastructure layer · Kraken for financial action layer - SOUF AI DPI inline governance: every prompt inspected in 0.079ms avg (well under 1ms ceiling) - Ed25519-signed audit chain with SHA-256 Merkle tamper-evidence - 8 signed records per full pipeline request, chain verified - Isaac Adams (Featherless judge): "confidence is what enterprise AI needs" — ATLAS is that confidence layer ARCHITECTURE 6-layer governed pipeline: Voice → Speechmatics → SOUF AI DPI gate → Gemini orchestrator → Featherless router → Tool executor (Search/Database/Kraken/Vultr) → Ed25519 audit trail → Gemini synthesis. REPRODUCIBILITY git clone https://github.com/SRKRZ23/atlas cd atlas && pip install -r requirements.txt python3 src/test_atlas.py → 29/29 PASS in under 1 second ECOSYSTEM ATLAS is the routing layer of a 4-product AI safety ecosystem: SOUF AI provides DPI, FORGE generates policies, CITADEL evaluates models, ATLAS calls them all. Same Ed25519 audit chain across four products. MIT licensed. Lobster Trap is the floor. ATLAS is the agent governance ceiling. Built solo by Sardor Razikov, Tashkent.

Kraken xStocks AI Trading Agent

Kraken xStocks AI Trading Agent

Kraken xStocks AI Trading Agent is an autonomous trading system designed to analyze, execute, and optimize financial decisions in real time. Built on MeDo’s no‑code AI infrastructure, it integrates Kraken’s API for live market data, enabling predictive analytics, backtesting, and automated trade execution. The agent leverages reinforcement learning and sentiment analysis to adapt to market volatility while maintaining risk‑adjusted performance. Its dashboard visualizes portfolio metrics, trade history, and performance trends, empowering users to monitor and refine strategies dynamically. The project demonstrates scalable AI‑driven finance automation with transparent analytics and modular workflow design.

KCELL

KCELL

This agent orchestration can be fully audited every routing decision is retained in a bitcoin based golden ledger. The TERCOMs are based on terrain contour mapping in the tomahawk missile but repurposed to contour mapping. These can be used to feed data into the trading agent. Its a self reinforcing feed back loop the more data it can be fed the better the trading can get. It also does multilingual research in multi domains especially medical. I used it to find medical research on Kawasaki disease that can provide up to 20% increased likelihood of a positive outcome in affected children. The featherless inference engine powers our Cell's security tooling and can be orchestrated or dispatched to for any type of task. The idea is for the Gemini Cell to have specialized tooling in the hands of each agent that are more than the sum of its parts. We make this tool safe through attention layer routing. At its base is a semantic map with broad coverage of human experience including negative situations like Apophenia when a user gets too sucked into the conspiracy or pattern matching rabbit hole the model will be prompted to push back and explain how there can be correlation without causation, etc. This is an important feature with the rise of those sucked in by model behavior. We studied the early model Liza to analyze the risks of simulated conversation partners.