Levent Gündogdu

Senior trading-systems engineer  /  C++ & CUDA, native Bayesian optimisation, low-latency execution, HPC

German citizen  -  based in Portugal, relocating to London

I build native, measurement-driven trading systems: tick-level backtesters, GPU-accelerated optimisers, real-time execution engines, and the observability that proves they work. For the last three years I have designed and run Fathom, an integrated systematic trading platform, on my own capital - trading my own book while building it.

Current work: Fathom

An integrated systematic trading platform: tick-level backtesting, GPU-accelerated Bayesian optimisation, a live C++ execution engine, 3D telemetry, and an autonomous monitoring loop. Self-directed, funded from personal capital.

Native execution engine
The live decision path, rewritten in single-host C++ under a measurement-first discipline. Shared-memory transport and journaled durability are the end state; CUDA-resident decisioning is the final step.
C++ is the live execution engine for all 13 trading tracks, cut over track-by-track after a shadow-validation burn, gated by a custom divergence comparator with per-track rollback. Eight shared C++ libraries. Measurement landed first: 10 per-hop latency histograms across the live path, 17M+ cross-host observations, single-digit-millisecond decision p99.
Native CUDA Bayesian optimisation engine
Gaussian-process Bayesian optimisation rebuilt from scratch in C++/CUDA to tune strategy parameters, against a PyTorch/BoTorch baseline.
Matern 5/2 GP, qLogEI acquisition, parallel multi-stream L-BFGS-B. 33x faster end-to-end than the BoTorch baseline on a V100 - 8.1 s to 243 ms per iteration at d=19, n=256. 121 unit tests plus 28 SciPy reference checks to machine epsilon. Runs as elastic GPU workers pulling jobs over NATS across a six-GPU, three-architecture fleet (RTX 4090 / Ada, V100 / Volta, P100 / Pascal) spanning the production cluster and an external worker host.
Tick-level backtester
A C++20 engine that replays historical market data tick-by-tick through the production strategy chain.
~11K lines of C++20, 133 unit tests, a 10-plugin ordered decision chain. Runs against a TimescaleDB market-data store with 365-day raw-tick retention.
MCP server + agentic operations
A Model Context Protocol server exposing the platform's internals to LLMs: two agents sweep state autonomously and publish findings, and the same tools drive LLM-written analyst tear sheets.
46 introspection tools across trading, analytics, market data, infrastructure, and experiments. Two agents (infrastructure and trading) run a local LLM, publishing deduplicated findings over NATS. The same tool layer generates daily and weekly tear sheets - execution-quality and portfolio metrics computed live (operator-invoked). Scope-based auth; fixture-tested tools.
Real-time 3D telemetry dashboard
A Swift / RealityKit dashboard that renders live platform state as procedural 3D geometry, fed by a custom binary WebSocket protocol.
~27,500 lines of Swift, 10 binary codec versions across 8 stream types. Radar webs, voxel grids of optimisation trials, gauge arcs, atlas-based glyph text. Profiled and optimised: dirty-flagged mesh caching, frame-budgeted scheduling, signpost instrumentation.
Platform & infrastructure
Not just the trading app - the whole stack, self-built and operated end-to-end as infrastructure-as-code.
6-node bare-metal Kubernetes (Cilium eBPF, kube-vip HA) + a dedicated GPU host, the whole stack as OpenTofu IaC across dev and prod: Rook/Ceph, three PostgreSQL clusters (one TimescaleDB) with offsite WAL archiving to S3 and point-in-time recovery, NATS HA, Vault, cert-manager TLS, GPU Operator. Self-hosted supply chain (OneDev CI, Harbor + Trivy CVE scanning), full OpenTelemetry to VictoriaMetrics / VictoriaLogs / Tempo.
Reliability & exchange connectivity
The correctness layer under a money-handling system: explicit failure semantics, reconciliation, broad venue connectivity, and a test suite that gates every change.
A durable ordering contract - at-least-once delivery, serializable transactions, retry-on-conflict - so concurrent writers can't corrupt order or position state; broker timeouts never assert an unknown outcome. Connectivity across four venues (Schwab, IB, Kraken, Binance) over REST and WebSocket, plus a from-scratch FIX 4.4 engine (session layer + order-entry / market-data) - also the integration path for the venues next on the roadmap (Lime, Alpaca; both FIX 4.2). Deep C++ test suites (133 simulator + 121 CUDA-optimiser cases), real fixtures over mocks, simulator-vs-live parity as a merge gate, and a 95,000-price grid scan that caught a floating-point broker-rejection bug.

Writing

Contact

levent@feature-it.com | send mail

linkedin.com/in/levent-guendogdu