A quantitative crypto trading firm operates automated strategies across multiple digital asset markets. Its infrastructure continuously receives market data, maintains local order books, calculates trading signals, performs risk checks, and submits orders through exchange APIs. For its latency-sensitive strategies, network performance is a direct infrastructure requirement.<\/span><\/p>\n The trading stack originally operated on general-purpose cloud infrastructure. Compute capacity was sufficient, but network latency to several important exchange endpoints was inconsistent. Typical RTT ranged between approximately 8 and 20+ ms, with periodic latency spikes caused by network routing.<\/span><\/p>\n The trading team required:<\/span><\/p>\n For this workload, selecting a server by CPU specification alone was not enough. The network route to the actual exchange gateway was equally important.<\/span><\/p>\n Unihost locations were tested against the trading firm’s real exchange endpoints before the final deployment location was selected. The execution environment was then placed in the location providing the most suitable network route.<\/span><\/p>\n AMD Ryzen 9 9950X<\/b> Dedicated Server: 16 cores \/ 32 threads, high-frequency CPU, DDR5 RAM, NVMe storage, up to 10 Gbps connectivity.<\/span><\/p>\n Used for: market-data ingestion, order-book processing, strategy execution, risk calculations, Redis, exchange connectivity, and order execution.<\/span><\/p>\n The live trading server runs only latency-sensitive workloads. Backtesting, analytics, historical data processing, and research are handled by separate infrastructure so they cannot interfere with execution workloads.<\/span><\/p>\n Architecture: <\/span>Market data feeds – Dedicated Ryzen execution server – Order book processing – Strategy engine – Risk checks – Order execution – Exchange gateway<\/span><\/p>\n After the execution environment was moved to the selected location, measured round-trip latency to the firm’s primary exchange endpoints typically ranged between approximately 1.8 and 4.6 ms under normal network conditions. The previous environment typically produced 8 – 20+ ms RTT. Jitter also decreased, providing a more predictable network environment for latency-sensitive strategies. The improvement did not come simply from using a more powerful server. It resulted from combining high per-core CPU performance with location selection and tested routing to the actual exchange endpoints.<\/span><\/p>\n The next stage is a second execution node for redundancy and additional low-latency deployments near other exchange infrastructure. Results at a glance: 1.8 – 4.6 ms RTT, sub-5 ms exchange latency, lower jitter, dedicated high-frequency CPU, isolated trading environment.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" About the project A quantitative crypto trading firm operates automated strategies across multiple digital asset markets. Its infrastructure continuously receives market data, maintains local order books, calculates trading signals, performs risk checks, and submits orders through exchange APIs. For its latency-sensitive strategies, network performance is a direct infrastructure requirement. Challenge The trading stack originally operated […]<\/p>\n","protected":false},"author":7,"featured_media":8979,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49,38],"tags":[],"class_list":["post-8978","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-study","category-crypto","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"yoast_head":"\nChallenge<\/b><\/h3>\n
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Solution<\/b><\/h3>\n
Results<\/b><\/h3>\n
What’s next<\/b><\/h3>\n