Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Kraken

Kraken, operated primarily under the corporate umbrella of Payward Inc., represents a highly evolved shared financial infrastructure layer and digital asset exchange. Founded in 2011, the platform provides unified liquidity, centralized risk management, and a globally compliant infrastructure to serve diverse client segments.

General
Provider / AuthorKraken
Founded2011
TypeHigh-Performance Digital Asset Exchange & Financial Infrastructure
Primary ArchitecturePolyglot Microservices (Rust gRPC backend, C++ matching engine)
Websitehttps://www.kraken.com/en-gb/lp/platform
Developer Documentationhttps://docs.kraken.com/
Open-Source Repositoryhttps://github.com/krakenfx

Start building with Kraken products

Kraken provides an institutional-grade trading ecosystem capable of sub-2 millisecond round-trip latencies and handling up to 15,000 algorithmic orders per second. The underlying architecture utilizes a highly optimized microservices topology, with the ultra-hot path matching engine engineered in C++ for deterministic execution and the backend primarily built in Rust. Furthermore, Kraken offers developers the "Ink" Layer-2 blockchain, a scaling solution built on the Optimism OP Stack that seamlessly bridges centralized exchange liquidity with decentralized finance (DeFi). We highly encourage you to check out the innovative Web3 apps and algorithmic trading bots created with this technology during lablab.ai hackathons!

Key Platform Features

  • Polyglot Microservices: Utilizes Rust and the Tokio asynchronous runtime for highly efficient network I/O and routing, while relying on Python (Django) with strict static type checking (mypy) for API layer logic.
  • Deterministic Latency & Colocation: Offers dedicated colocation services through partnerships with the Beeks Group, allowing institutional traders to physically host their servers in the same data centers as the matching engine.
  • Dynamic Rate Limiting: Employs a sophisticated mathematical decay model that assigns penalty points based on computational burden and order resting time, rewarding efficient algorithmic behavior and detering malicious spoofing.
  • Cryptographic Proof of Reserves: Uses a mathematically verifiable Merkle Tree data structure, independently attested by a CPA firm, enabling clients to cryptographically verify their included balances without compromising privacy.
  • The Ink Layer-2 Blockchain: An optimistic rollup anchored to Ethereum that provides deep integration with Kraken's centralized infrastructure, empowering frictionless transitions to decentralized applications (dApps) without relinquishing private key custody.

Libraries and SDKs

  • API Specifications (api-specs): Machine-readable open-source specifications utilizing OpenAPI for REST and AsyncAPI for WebSocket channels.
  • Golang Implementation (api-go): An officially maintained, highly concurrent Go module designed for rapid interaction with Spot and Derivatives API endpoints.
  • Python WebSocket Client (kraken-wsclient-py): A native Python client optimized for establishing robust, persistent WebSocket connections for real-time order book feeds.
  • python-kraken-sdk: A community-driven Python SDK featuring built-in handlers for automated nonce calculation, REST retries, and asynchronous handling of WebSocket feeds.
  • KrakenTools: A public utility toolkit offering mathematical helper functions for cryptographic payload signing and data structuring.

Boilerplates

  • Decentralized Web Wallet (wallet): A self-custodial wallet framework written entirely in TypeScript, foundational for Web3 integration.
  • Futures Trading Bot Template: A deeply documented template demonstrating complex algorithmic execution and advanced error handling.
  • Freqtrade Integration: An open-source crypto trading bot allowing developers to test and deploy machine learning models and statistical arbitrage strategies live against the Kraken API.
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kraken AI Technologies Hackathon projects

Discover innovative solutions crafted with kraken AI Technologies, 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.

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.

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.

ATRIO Boardroom

ATRIO Boardroom

**Founders and family offices decide alone.** Big calls get either delegated to a single advisor (fast, single point of failure) or convened with a committee (slow, hard to schedule, hard to audit). ATRIO Boardroom is the middle option: an AI boardroom that holds a real debate, enforces a per-tenant mandate at machine speed, and replays every decision in six months. ## Try the live demo **URL:** http://45.77.52.54:8080 (Vultr, Frankfurt) Click **Demo founder** on the sign-in screen — one click, no email — then type a boardroom question. Watch 5 specialist AI agents stream real Gemini 2.5 reasoning live, ~25 s. Go to Treasury, propose a SHV-xStock buy, try to self-second-sign (API refuses), open a new tab as **Demo CEO**, second-authorise, trade executes against Kraken paper. Download the board-pack PDF. Open the audit log. Six minutes, full lifecycle. ## The wedge - **Debate**, not consensus-on-rails. Six personas with distinct system prompts, distinct model assignments (Gemini 2.5 Flash for specialists, 2.5 Pro for Counsel), and dissent-driven turn-taking. - **Enforce**, at the API. A per-tenant `Mandate v1` (permitted instruments, daily loss limit, single-instrument max, permitted side) is the only path to a treasury action. Two-party auth cannot be bypassed by the UI. - **Audit**, by default. Every turn, vote, model call, and state transition writes to an append-only log. Exportable as JSONL + manifest. ## Why this isn't slideware - **381 / 381** backend tests pass at **90.68 %** coverage - **24 / 24** demo-video structural + **14 / 14** OCR verification - **54 / 54** pitch-deck structural + **12 / 12** OCR verification - **5 / 5** live multi-agent debate against real Gemini in ~25 s (no mocks) - **19** real bugs found and root-caused during the sprint ## Sponsors used Vultr · Google Gemini · Featherless · Speechmatics · Kraken xStocks · LiveKit. License: Apache 2.0.

Apohara CONSILIUM — Agent Governance OS

Apohara CONSILIUM — Agent Governance OS

THE PROBLEM. Italian banks face dual regulatory urgency: DORA (mandatory since Jan 17 2025 for 22,000+ EU financial entities — UniCredit, Intesa Sanpaolo as G-SIBs) + EU AI Act Article 14 (enforceable Aug 2 2026, fines up to €35M or 7% of global revenue). When Banca d'Italia asks "why did your AI decide X?", the bank needs tamper-evident, third-party-signed audit evidence. Existing tools (Galileo $73M, Lakera $30M, Patronus $20M, Credo AI) don't generate court-grade compliance evidence from production runtime. THE SOLUTION. CONSILIUM is a 3-tier open-source platform: (A) OSS Apache-2.0 entry — 9-vendor adversarial LLM ensemble + 78-rule deterministic judge layer + INV-15 Z3 SMT formal proof (UNSAT in 10.08ms). (B) Governance OS core — 4-stage SOAR pipeline + 6 compliance framework dashboards (EU AI Act, NIST AI RMF, ISO 42001, SOC 2, GDPR, NIST 800-53) + HMAC-SHA256 verdict chain + STIX 2.1 export. (D) CAICEP module — RFC 3161 TSA-timestamped verdict chain via freetsa.org (live evidence today) + roadmap to court-admissible attestation Q3 2026 via eIDAS QTSP partnership (Actalis Italia). LIVE EVIDENCE. apohara.dev/consilium/verify — interactive demo: paste any prompt → 9-vendor decision. Click any of 3 demo verdicts → verify RFC 3161 timestamp against freetsa.org independently. api.apohara.dev shows 10+ SOAR endpoints live, /v1/verdicts/{hash}/verify-timestamp returns valid:true with real Freetsa.org-signed token (1312 bytes, signed 2026-05-19T12:21:50Z). BUSINESS VALUE. TAM AI governance $3.59B by 2033 (36% CAGR). SAM EU regulated industries $400-800M by 2027. Initial wedge: Italian G-SIBs + Milan Fintech District (200+ companies) = $15-30M ACV in 12 months. Revenue: OSS free + Cloud Pro $299-999/mo + Business $2-5K/mo + Enterprise+CAICEP $25-200K/year. Exit reference: Cisco acquired Robust Intelligence Aug 2024 (~$350M, 451 Research). Built solo by Pablo M. Suarez (UNT, Argentina) in 8 days for Milan AI Week 2026.

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