In computer markets, speed is the most valuable thing. A late box can ruin a plan that was making money. That is particularly true when using algo trading in commodity market, where spreads are incredibly narrow and prices change every tick. 

In late 2024, a major Asian exchange said that computers or high-frequency systems now handle more than 60% of all trades on its stock derivatives complex. This really shows how vital latency-sensitive technology has become.

Co-location is one of the services exchanges provide, as it allows the user to place their gear alongside the matching engine in its data centres, by paying for a rack space. Reducing the distance between the exchange hub and the reasoning that creates orders enables companies to reduce wire time in kilometres to metres.

Information is not passed through an ISP. Instead, it travels across a few meters of top-of-rack cable and slaps the order book in microseconds. At a monthly fee, exchanges typically provide hot backup power, rapid cool and cross-connection. They provide a resource at the institutional level to access the eligible people.

A settlement brief from 2024 about an old trade entry point at a major exchange showed that the tool lowered round-trip latency from around 400 microseconds to just 100 microseconds. There was a huge drop, which directly led to better fill rates and less slippage.

Even though the numbers themselves don't seem like much, saving just 300 microseconds means seeing a price change 300 microseconds before your competitors and responding faster. In high-frequency market making, these windows tell you whether to remove someone else's price or add your own.

These days, computer rooms in exchange data centres look like Formula 1 workshops. There are two UPS lines, low-jitter switches, FPGA-accelerated network cards, and NIC drivers that don't use the kernel. Each layer is there to help the strategy that is running on the algo trading software, which reads market data, figures out edges, and places orders. Because propagation lag is already close to the speed of light, hardware changes make many small gains when the software stack is only a few centimetres from the matching engine.

You can't optimise something that hasn't been timestamped. Here's what production desks do:

• Tracks every packet at the NIC with PTP-locked hardware that is accurate to less than 100 ns.

• The FPGA code in their algo trading software includes in-band data that lets them do hop-by-hop reviews.

• Top-of-rack switches have microburst analysers that show when buffer pressure is getting too high before it leads to tail delay.

Weekly delay heat map reports help engineering teams find regressions quickly and decide when to update hardware so that performance doesn't suffer.

In complex futures, where theoretical values change prices with every base tick, latency arbitrage becomes more critical. A government agency looked at profits for the fiscal year 2024 and found that algorithmic techniques brought in 97% of the money for foreign buyers and 96% for private desks in index futures and options.

When hedging needs micro-hedges and Greek recalculations, speed is even more critical. When desks are co-located, delta-hedging robots can change quotes before slower desks can compute new volumes, catching the edge without any help from a person.

Mobile execution gates are a good example of this in real life. An options trading app that only sends orders to a broker's cloud server can't compete with a rack-mounted FPGA that acts right away after each multicast tick.

Because of this, physical proximity is still the moat for professional vol traders, even though derivatives are easier for regular people to reach through mobile apps.

Most academics agree that stated depth and bid–ask gaps get smaller when latency distributions are tighter. This is because fast market makers compete on price instead of time. As more players are placed close to each other, stream two-sided quotes, the risk of adverse selection for inactive orders decreases. This brings in more liquidity.

Still, regulators monitor these benefits against structural problems like overcrowding, feedback loops, and different entry fees. Some safety measures exchanges use to keep microsecond arms races from turning into flash crashes are periodic stress tests, message caps, and predictable batch bids.

Within tens of microseconds, analytics engines built into co-located racks can pull full-depth multicast feeds, figure out inferred volumes, and make liquidity maps. Traders who look at a live order book often pay attention to complicated surface measures like skew, kurtosis, and OI ladders. Before the identical prints reach a cloud port hundreds of kilometres away, your system can analyse option chain data in-rack to find secret supply-demand imbalances and make smart order routing decisions.

It is not cheap to buy ultra-low latency tools. Costs quickly increase when you add monthly rack fees, dual cross-connects, FPGA acceleration licenses, and microwave backup lines. Risk teams must compare these hard costs to what they expect to be edge decay and regulatory audits. Periodic code approval, kill-switch hooks, and pre-trade risk screens are all things that exchanges need. Many have also used deterministic queuing or speed bumps to even out differences in latency. These can make the raw benefit of closeness less useful if you are slow to update your stack.

Moving servers from a trade stack to an exchange data centre is not enough; it needs careful strategy, hardware, software, and compliance planning. The tips below help companies get microsecond benefits without lowering their reliability or ability to meet legal requirements.

• Map Strategy to Latency Budget:

Find out how much processing speed affects profits before you buy gear. Test delays of 15, 50, and 100 microseconds and check the fill ratio, slack, and inventory risk. Spend on co-location only in cases where the small latency makes a big difference in the P&L.

• Optimise Holistically:

Since latency savings add up, any slow microservice cancels any hardware gains. Then, refactor key paths in C, remove context switches, and pin threads to CPU cores to keep things deterministic. Profile every layer, from the codec to the trash collector.

• Benchmark Continuously:

The only way to see production latency is through real-time packet dumps, not lab numbers. Put PTP timestamps in market-data handles, use taps to mirror traffic, and send measures to Grafana so engineers can find drift and regressions before they hurt the edge.

• Automate Roll-Backs:

Fixing the firmware on an exchange or updating the tick-size table can break binary protocols without notice. Set up circuit-breaker health checks, transactional configuration management, and blue-green clusters. This will ensure that failed deployments return to normal within milliseconds. It will keep the continuous quoting going and help you align with regulations.

• Document Change Control:

More and more, regulators want audit trails that are clear and consistent. Keep track of different versions of each plan and hash files, and store market-data replays. Make execution reports that are cryptographically signed so that you can prove best-execution duties across fragmented multi-venue processes and repeat order decisions.

Is co-location important for algorithmic trading? Undeniably yes, especially for high-frequency strategies where every nanosecond matters. Co-location places trading systems physically next to the exchange’s servers, slashing transmission delays and boosting execution speed.

This proximity directly impacts order fill rates, improves spread capture, and increases the likelihood of beating competitors to market opportunities. In today’s speed-driven markets, co-location isn’t a luxury, it’s a necessity for staying ahead.