When online trading was just gaining popularity, it was quite basic. Most new traders worked from a single computer, and in the vast majority of cases, it was their home PC or laptop. The trading platform was launched manually, trades were opened and closed within a single session, and everything was simply shut down at night. This meant that the market kept moving while the trader was forced to rest, which increased the risk of losses or missed profits by overlooking important events affecting asset prices.
Then automated trading systems began to appear. At first, they were simple, automating individual actions but nothing more. Gradually, their logic became more complex, and trading strategies began to require a constant presence in the market. At the same time, trade copying services developed, where one account pulls dozens of others along with it. And all of this no longer fit into the “turn on - work - turn off” routine.
At some point, it became clear that trading happens around the clock and you need to trade around the clock as well. But home computers weren’t designed for this kind of operation. They shut down, update for no apparent reason, sometimes freeze, and so on. Moreover, home internet connections are also unreliable, and electricity is a whole other story (especially in regions with unstable power grids).
This is where interest in VPS came from; at first, it was a solution for “those in the know,” but later it became the standard. After all, it’s convenient and practical - a remote server that runs 24/7, isn’t affected by household factors, and doesn’t require a physical presence. This means your trading terminal is always running, your trading strategy operates continuously, and trades are executed at any time.
But, as is always the case, this brought a new problem - the problem of choice.
On hosting companies’ websites, everything looks fairly simple: there are a few pricing plans, a set of specifications, and some numbers. They all list CPU parameters, RAM, and disk space. And it seems like the differences are minimal, so you can just pick any option - the cheapest one - and start trading. But in practice, that’s where the biggest mistake lies. Over time, it becomes clear that one VPS performs smoothly and consistently for months and years, while another starts to slow down at the worst possible moment, causing trades to fail.
This is where the hardest part begins: figuring out which server is best suited for trading. Let’s discuss this in this article.
Network, Latency, and Server Location
In web trading, everything boils down to one thing: how quickly and smoothly the signal travels from your terminal to the broker and back. Everything else- the processor, memory, or hard drive - are secondary factors that have virtually no impact on the trading process. “Practically,” but there is still some influence, which we’ll discuss later.
But the main battleground is, of course, the network.
When an order is sent from the terminal, it travels through a chain of nodes to the broker’s server and back. This time is called latency. This time is measured in milliseconds and looks like an insignificant detail on paper, but in practice it matters enormously.
For example, if your server is located in the same data center as the broker’s equipment, the latency can be less than 1 millisecond. If within the same city, it’s already about 1–3 ms. If packet exchange occurs within Europe, the latency is typically 5–30 ms. However, when communicating across the ocean, between continents, the values can be quite different. For example, traffic latency between Europe and the U.S. can range from 80 to 120 ms, and sometimes even higher.
The difference between 5 and 50 ms isn’t always noticeable in manual trading, but in automated trading, it starts to have a real impact on order execution, especially if your strategy is sensitive to entry price.
We’ve covered that. But the latency figure itself is only part of the picture. Much more important is the metric that reflects how this latency behaves over time.
When the server shows a stable 20 ms, that’s a perfectly normal and even standard situation. But here’s another scenario: an average of 10 ms, but with periodic spikes up to 100–200 ms. Visually, both scenarios look similar if you look only at the average value; however, in real-world operation, the second scenario performs much worse. This is called connection instability.
Such spikes do not depend on your server’s parameters or your internet bandwidth. The problem here is not the data transfer speed, but the stability of the route along which this data is transmitted.
Micro-packet loss also occurs occasionally, at a rate of 0.5–1%. This is almost imperceptible in everyday tasks, as data transfer protocols like TCP were designed to compensate for such losses. However, in trading, this manifests as occasional delays during which an order doesn’t execute immediately - in other words, slippage occurs - which may prevent you from buying or selling positions in time, leading to a loss of profit.
But let’s not delve too deeply into technical networking terms in this article; you can read about those in other articles. What’s important for us here is that the location of your server directly affects all this behavior, and the farther your trading VPS is from the broker, the longer and more unstable the routes will be - given the accumulation of probabilities - since the longer the route, the more points where a problem can arise.
And even if each of them works normally, the overall probability of network instability increases.
From this, we can draw a simple conclusion: Proximity to the broker reduces latency and decreases the number of intermediate nodes, which makes the connection more predictable.
Of course, the ideal scenario is to set up a server in the same data center where your broker is hosted, but in reality, try to choose at least the same city or region. If that’s not possible, then choose the nearest location with a reliable network - usually a major network hub like Amsterdam or Frankfurt am Main.
However, sometimes the closest option turns out to be less stable. In this case, it’s better to choose a server a bit further away but with consistent ping, since a difference of 5–10 milliseconds isn’t as important as avoiding spikes.
The conclusion is simple: try to strike a balance between minimal latency and connection stability.
Processor and Hidden Limitations
The vCPU parameter seems fairly straightforward, as it appears to be simply a virtual counterpart to a physical CPU core. But in reality, things are a bit more complicated.
Virtual servers run on a shared infrastructure managed by a hypervisor, where a single physical processor is programmatically partitioned to handle tasks for multiple clients. As long as the overall load on the node remains low, each individual system runs quickly and stably, and the terminal operates smoothly enough. But as soon as the total combined load from all clients exceeds 100% of the physical hardware’s capacity, certain issues begin to arise.
In this case, the hypervisor essentially tells the processor: “Divide the clients’ tasks into pieces and execute one small piece of each client’s tasks at a time, switching between them.” During such periods, system response times may drop slightly, but for trading, this will be enough to notice the difference.
This situation does not arise on its own and is most often the result of the hosting provider’s policy. After all, if too many client virtual machines are placed on a single physical server, the very “CPU contention” effect begins. It turns out that as long as all clients are behaving calmly, everything looks normal, but as soon as several of them start actively using the CPU, the server’s resources begin to be shared among them.
This is overselling, when a provider sells more virtual cores than it can reliably provide at any given moment. From a business perspective, this makes sense - it’s their profit - but from the end-user’s perspective, especially those in the trading industry, it’s a major risk.
There’s another approach where the provider limits the number of virtual machines on a node and ensures the total load doesn’t exceed the hardware’s capacity. In this case, even during peak loads, the system behaves smoothly and stably. Yes, this is more expensive, but the result is predictable performance, which is incredibly important for clients. This is precisely the approach adopted by 3v-Hosting.
RAM and System Behavior
With RAM, things are a bit simpler than with the processor.
The terminal itself doesn’t require much, and in an idle state, it stays around 300–800 MB. Of course, if you’ve opened more charts or added indicators, that number will go up a bit. If an expert advisor is running, especially one with built-in logic, memory usage can easily reach 1–2 GB per terminal.
Beyond that, the cumulative effect starts to kick in: one terminal is barely noticeable, two are already noticeable, and with three or four, the system starts to run at full capacity, especially if the server is small.
Another point worth considering is the memory consumption of the operating system itself, since any OS takes up its own share of memory immediately after the server starts. For Windows, this averages 2–3 GB. As a result, on a server with 4 GB of RAM, less than half of the RAM is actually left for the trading terminals themselves.
Beyond that, it all comes down to available memory: if there’s enough, the system runs smoothly, windows open instantly, the terminal doesn’t lag, and responses are immediate. But as soon as memory starts to run low, the system swap file kicks in, and instead of RAM, the system begins using the disk - which is significantly slower than RAM - to store cached data.
Even the fastest NVMe drive cannot match RAM in terms of read/write speeds, which causes delays in the system, the interface starts to “hesitate,” and the terminal may even freeze at the most inopportune moment.
Therefore, the logic with RAM is simple: you always need a small buffer - not huge, but enough so that the system doesn’t hit a ceiling during any activity.
Drive (SSD and NVMe)
The situation with choosing a drive is different from that with the CPU or memory. Of course, it affects system performance, but it doesn’t directly impact the trades themselves, since data written to the drive is typically intended for long-term storage - i.e., data that has already been processed. The active data the system is currently working with is stored in RAM.
When it comes to drives, keep the following information in mind. The difference between drive types is measured by read/write speed, which is quantified either in specific units of read/write cycles over a period of time (I/O or io - from input/output) or in the volume of data that can be written to the drive per unit of time - which is more intuitive. For example, a standard HDD can write about 100–150 MB of data per second. For solid-state SSDs, this figure is already around 400–550 MB/s. NVMe drives, however, can operate an order of magnitude faster, at approximately 1500–3500 MB/s, or even faster, depending on the manufacturer and year of release, as technology continues to evolve.
These figures look impressive, but as we mentioned above, they are hardly noticeable in the terminal’s actual operation.
The terminal doesn’t constantly load the drive; most of the load on the drives comes from system startup, opening programs, and logging. During these moments, a fast drive ensures smoother operation, as the system boots up faster and applications open without delays.
To summarize: if you use a single terminal for your work, a standard SSD will be more than sufficient at this point. If you have multiple terminals or additional services, NVMe provides a significant margin for the system’s performance.
And the last thing we haven’t discussed yet is disk capacity. There are no restrictions here, as all well-known trading terminals do not require a large amount of disk storage. Ultimately, choose a VPS with enough disk space to install the OS itself and a small buffer - usually at least 40 GB for a Windows VPS.
So, how many resources do you really need?
So, if we put together everything we’ve discussed above, the picture looks like this. Let’s consider everything using the example of a server running Windows Server, since historically, the vast majority of trading terminals have been developed specifically for Windows.
So, a single terminal doesn’t require a powerful server, and in most cases, a server with 2 vCPUs and 4–6 GB of RAM will suffice. As for storage, a standard SSD with a capacity of about 40–60 GB will be more than enough to keep the system running smoothly.
However, as soon as a second terminal is added, the load begins to grow non-linearly, and at this point it makes sense to look for a server with 4 vCPUs and 6–8 GB of RAM.
But when it comes to multiple terminals, bots, or the use of parallel strategies, the requirements become less predictable, as the load starts to fluctuate. And in such moments, it’s not so much the quantity of resources that matters as their availability and stability, so that the CPU isn’t shared with neighbors, memory doesn’t run out, and the system doesn’t go into swap. Still, we believe that 6–8 vCPUs and 8–12 GB of RAM will be sufficient here.
This brings us to the main point of the entire article: Numbers alone don’t tell the whole story.
You could choose a server with 4 vCPUs and 8 GB of RAM, but on an overloaded node, it will perform worse than a more modest plan from a reputable provider. The same goes for memory - if it’s pushed to the limit, the system will slow down even with minor spikes in load.
The disk and its type remain a secondary consideration. A simple SSD already handles all basic tasks, and NVMe provides a speed buffer but doesn’t directly impact transactions.
Ultimately, it all comes down to a simple approach. Don’t chase after maximum specs; instead, choose a configuration that matches your workload with a little headroom. And look not just at the numbers, but at how the server performs in real-world use.
Reliability and Uptime
We’ve discussed the specific physical parameters of the server, so now let’s look at the final important parameter: uptime. It doesn’t depend on a specific server configuration but rather on the hosting provider in the broadest sense.
Uptime is measured as the ratio of the server’s operational time over a given period, usually a year or a month, and it looks simple. A 99.9% uptime claimed by a provider translates to roughly 40–45 minutes of downtime per month. A 99.99% uptime is already about 4–5 minutes. It might seem like a difference of a hundredth of a percent, but in absolute terms, the difference is huge.
Again, the numbers alone don’t tell the whole story. For example, a single short outage for maintenance or a switchover once a month is barely noticeable. It’s a different story when the server goes down regularly, even if only briefly. Such situations create a constant risk because it’s impossible to predict exactly when it will happen.
In online trading, this is immediately noticeable, since even a brief outage can occur precisely during a period of active market movement. That’s why traders have started moving from home PCs to hosting services.
A standard hosting infrastructure operates quite differently: servers may not be restarted for months or even years, and this is their normal operating mode. And if a shutdown does occur, it is planned well in advance and is usually related to server maintenance or network device reconfigurations.
This is ensured by very specific measures, such as backup power, redundant network channels, air conditioning and dust control, as well as the use of backup power channels and UPS units. The user doesn’t see any of this, but they do see the result: their server running stably around the clock, year after year.
Conclusions
Ultimately, choosing a VPS for Forex trading isn’t just about comparing prices across different hosting companies; it’s about finding a predictable environment where everything works the same way today, tomorrow, and a month from now.
It’s important to remember that the processor isn’t important in and of itself, but rather how it’s shared among neighboring VPSs. Memory isn’t about its capacity, but whether there’s sufficient reserve. And the disk isn’t about the type, but whether it becomes a bottleneck during rare moments of high load. Moreover, all of this takes a back seat if the network is unstable or the server regularly “freezes” due to issues on the provider’s end.
In trading, a slightly higher latency isn’t as scary as its unpredictability - after all, it’s precisely this unpredictability that breaks your trading strategy at the worst possible moment.
Therefore, the approach here is quite simple: you should focus not on the maximum server specifications, but on the stability of the entire system as a whole. If the system behaves predictably and stably, requires no attention, and doesn’t throw up any surprises during trading, then you’ve made the right choice.
