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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.

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TradeMind AI: Market Intelligence Platform

TradeMind AI: Market Intelligence Platform

TradeMind AI is an AI-powered market intelligence platform designed to simplify stock market analysis for traders, investors, and students. Modern traders often rely on multiple websites and tools to monitor market indices, institutional activity, news sentiment, company fundamentals, and technical trends. TradeMind AI solves this fragmentation problem by combining all of these insights into a single unified dashboard. The platform provides real-time tracking of major Indian market indices including NIFTY 50, BSE Sensex, NIFTY Bank, and India VIX using live market data. It also performs market breadth analysis by monitoring NIFTY constituents and identifying advances, declines, and overall market participation. TradeMind AI includes sector performance analytics, top gainers and losers tracking, personalized watchlists, and intraday charting capabilities to help users identify trends and opportunities quickly. One of the platform's key features is its financial news intelligence system. Market news is aggregated and automatically classified as positive, negative, or neutral using the VADER sentiment analysis engine, allowing users to understand market sentiment at a glance. The project also features an AI-powered market assistant capable of answering finance-related questions using live market context, news sentiment, and institutional flow information. This enables users to interact with market data conversationally and receive context-aware explanations rather than generic responses. The frontend is built using React and Vite, while the backend uses FastAPI. The platform integrates Yahoo Finance for market data, NewsAPI for financial news, VADER for sentiment analysis, and Fireworks AI models for conversational intelligence. The entire application is containerized using Docker and deployed using Vercel and Render. TradeMind AI aims to make financial intelligence more accessible, understandable, and actionable for everyone participating in the market.

SplitLLM  – Explainable Hybrid AI Routing

SplitLLM – Explainable Hybrid AI Routing

SplitLLM is a production-inspired hybrid AI routing platform designed to make large language model deployments faster, more cost-efficient, and fully explainable. Instead of sending every request to a single cloud model, SplitLLM first analyzes each prompt using a multi-stage reasoning pipeline to determine its complexity, confidence requirements, expected cost, and execution strategy. Based on this analysis, it intelligently routes requests to either lightweight local models or powerful cloud models, ensuring the right model is used for the right task. Unlike traditional routers that only decide where to send a request, SplitLLM explains why every routing decision was made. Every prediction includes transparent reasoning, confidence estimates, routing factors, telemetry, and performance metrics, enabling users and organizations to understand and trust the system's decisions. The platform includes modular routing, configurable budget policies, live telemetry, benchmarking tools, a reasoning profiler, CLI utilities, REST APIs, Docker support, automated testing, and CI/CD workflows. It is designed with production engineering principles, making it easy to deploy, monitor, and extend. Built with AMD's AI ecosystem in mind, SplitLLM supports efficient local inference workflows and hybrid deployment strategies that reduce unnecessary cloud usage while maximizing available compute resources. The architecture is modular, scalable, and optimized for future integration with additional models and routing strategies. Our vision is simple: AI should not always use the biggest model—it should use the smartest one. SplitLLM delivers intelligent, explainable, and efficient AI routing for modern applications while helping developers reduce operational costs, improve response times, and maintain high-quality outputs.

MAITS/BANDA

MAITS/BANDA

The platform is written in Python and operates in a multi-threaded architecture managed by a centralized Orchestrator. It simulates multi-agent algorithmic trading (Paper Trading mode) while enforcing strict operational constraints through specialized modules: Multi-Threaded Orchestrator: Manages individual, isolated Trading Units (bots), ensuring concurrency and stability. Security Sentinel: Processes and validates all trade intents before execution to prevent out-of-bounds order sizes or unauthorized operations. Data Optimizer: Calculates real-time financial metrics, risk exposure, and tracks critical analytics such as Max Drawdown for every active bot. BandConnector: Integrates directly with Band Protocol oracles to fetch decentralized price feeds. These feeds are used as a ground-truth reference to audit local exchange data, detect price anomalies, and prevent execution based on manipulated or stale data. Logging and Auditing (Логування та аудит) The system separates operational concerns into distinct file structures to maintain clean state tracking: Bot Logs: Isolated runtime logs for individual bots are written to /home/ubuntu/kucoin_prod/bots/logs/n_bot.log (where n is the bot ID). Analytical Ledger: Trade history and portfolio metrics are committed to JSON Lines format at state/trades_history.lsonl for each bot container. System Auditor: A standalone tool (auditor.py) evaluates historical logs and real-time state to compile performance reports.