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Trading & market data

Real-time market data infrastructure, high-performance trading interfaces, and the quantitative research stack that produces the strategies running on top of them. Built by people who have shipped automated trading systems and operated in crypto markets for years.

Market data and execution surfaces

  • Multi-exchange data ingestion. Adapters for major crypto venues (spot, perpetuals, options) producing a normalized internal feed. Order book, trades, open interest, funding, options chains.
  • Bandwidth-efficient transport. Binary encoding (CBOR) over WebSocket reduces wire bandwidth 40-60% compared to JSON for the same feed, with zero-copy decoding on the client.
  • Native compute kernels. Order book reconstruction, footprint, CVD, volume profile, TPO, VPIN, technical indicators, and net-position computation as native Rust kernels exposed to the application layer via Node-API. CPU-bound work runs at native speed; rendering thread stays unblocked.
  • GPU-accelerated rendering. WebGPU rendering pipeline drives a single canvas at 60 fps with multi-channel SDF text, cursor-anchored zoom, and sub-pixel-accurate panning. Custom WGSL shaders for heatmap, footprint, and volume profile.
  • Execution surfaces. Trade execution against exchange API keys (CEX) or against custom on-chain vault contracts (DEX). Vault factories deployed via Foundry; per-strategy isolation; programmatic limit-order management.
  • AI orchestration. Multi-analyst orchestration (bull / bear / risk / flow agents plus a judge), streaming responses over SSE. Bring-your-own-key for Anthropic, OpenAI, or local Ollama deployments.

Quantitative research and automated trading

For organizations building proprietary trading capability — desks, prop shops, treasury operations, structured-product issuers — we build the full research-to-execution pipeline: data archives, backtesting frameworks, strategy implementation, live execution, and the operational monitoring around them.

  • Research infrastructure. Tick-level historical data archives across major venues, point-in-time-correct funding and basis history, normalized cross-venue order book reconstruction. Notebook environments wired to the same data the live execution stack consumes.
  • Backtesting frameworks. Event-driven backtesters that replay normalized order book and trade events with realistic latency, slippage, and fill-probability models. Walk-forward analysis, parameter sensitivity, regime-conditional performance attribution.
  • Strategy implementation. From research signal to production strategy: position sizing, risk overlays, kill switches, drawdown stops, manual override surfaces. Strategy authors retain IP; we build the deployment and operations infrastructure.
  • Market microstructure analysis. Cross-venue lead-lag, queue position estimation, footprint and CVD divergence, VPIN-based regime detection, options-implied flow extraction. The metrics that drive entry and exit decisions on faster timeframes.
  • Alpha decay monitoring. Per-strategy live PnL attribution against backtest expectation, with automated alerts when realized performance diverges materially from expected.
  • Strategy publishing. Marketplace primitives for publishing, browsing, backtesting, and reviewing strategies. Authorship attribution, version pinning, and revenue-share accounting.

Delivery models

  • White-label terminal. The full terminal stack rebranded to the customer's identity, deployed on customer-owned or our infrastructure.
  • Embedded components. Individual surfaces (orderbook, heatmap, options chain, AI panel) embedded into the customer's existing application via component contracts.
  • Custom exchange adapters. New venue integrations (CEX, DEX, prediction markets, RWA platforms) on the same normalized feed.
  • Research stack. Historical data archives, backtesting framework, and notebook environment deployed onto customer-owned infrastructure under the customer's data-license terms.
  • Strategy deployment infrastructure. Operational runtime for customer-owned strategies — execution, risk, monitoring, reconciliation. Strategy IP remains customer property.
  • Vault-based execution agreements. On-chain vault deployments under joint operational agreements where execution authority is shared with the customer.
  • Intelligence-feed licensing. Raw normalized feed or computed-metric feed (CVD, VPIN, footprint deltas) under a separate data license.

Engagement

Trading infrastructure is operated as a real-time system with documented latency targets per surface. Strategy deployment work assumes the customer holds the strategy IP. We do not build "guaranteed return" or "no drawdown" systems. Deliverables include the running stack, deployment recipe, exchange-key management procedure, capacity sizing for the customer's expected user load, and a documented kill-switch procedure for any automated execution surface.

Defect liability for execution and risk surfaces follows the engagement models framework. Defects in code we wrote that affect order placement, position accounting, or risk-gate logic are corrected at our cost within the SOW cure window. Strategy IP and inventory remain customer property; liability is for infrastructure correctness, not strategy performance.