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A-TEAM INSIGHT BLOGS
THE SIX KEY TECHNOLOGY CHALLENGES FACING THE TRADING STACK
17 July 2023
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By Dave Carson, Head of Field Engineering at DiffusionData.
The challenges I want to discuss are those that are affecting every trading
participant today, including exchanges, data providers, banks and hedge funds.
These include:
Controlling the Firehose of Data
With the rise of electronic trading, data volumes have spiralled. As technology
continues to advance, so too does the speed at which automations submit
orders/quotes etc. However, not every application needs to receive all market
data. In response, Messaging platforms moved away from socket-based TCP to
multicast to keep up with data volumes – no need for ACKs.
What this means is that platforms are just overloading downstream clients with
data, data that they may not be interested in or even asked for. How do you
solve this problem? By structuring data in a structured, hierarchical topic tree
enables clients to subscribe at their relevant level, meaning that they only get
the content they need. In addition, the texture of the delivery of the data can
be modified, for example it can be throttled for websites or delayed for
non-paying users.
Another challenge is that different users have different needs, but using
personalization technology, you can cater for a global userbase from single data
source – all performed in real-time.
This is all about controlling the firehose. Depending on what a trader is
looking at, personalized content can be made available. This saves CPU, NIC
utilization and network bandwidth. By controlling the firehose everybody wins.
Integration Challenges
As more feeds are built (including internal systems), data needs to be sent from
A to B and onto C and maybe back to A. Therefore, all these systems need to be
able to talk to each other – a technical challenge every time. The problem is
compounded when each engineer tasked with deploying integration brings their own
style – leading to a plethora of distinct applications being deployed to
production. They behave differently and can create daily challenges and issues,
especially if the person responsible for a specific set of code leaves the
company. The whole approach is, frankly, unsupportable and not a strategy.
The solution is to deploy a framework that can integrate back-end data sources
and external systems, using consistent boilerplate code. By deploying such a
framework, this industry-wide problem is resolved as adapters will look and
behave the same. This saves time in production support as every adapter shares
90% of the code, which is tested and production ready. Not only does this
mitigate the risk of bugs every time a new adapter is built, but the user also
benefits from a consistent upgrade/downgrade process, configuration,
administration and monitoring. By using a framework, developers can build once
and deploy many.
Disaster Recovery and Data Replication
Sending data from A to B, from site to site, or cloud to site etc, creates
challenges and it can be something as simple as keeping Disaster Recovery (DR)
up-to-date. Any institution that is mandated must do DR tests as part its
compliance, it must have an updated DR site that is ready in the case of
failure.
Keeping DR up-to-date with such high data volumes is not an easy task. Every DR
test that I have been involved in takes weeks of planning, you tend to do it
over the weekend, you get your results, and then if it fails, you have to
analyse why it failed and how do we make sure it doesn’t happen again. That’s
the process. I am aware of many DR tests failing! This will resonate with a lot
of people. Many DR sites will be out-of-date. For example, changes can be made
in production over the weekend and they may not be replicated to the DR site.
To solve this there needs to be a focus on streaming data reliably, securely and
efficiently. It is possible to ensure that the DR site is ready to go at all
times. This involves replication between sites, such that all that has to be
done to flip the site is to flip a switch on the load balancer. Clients will
still receive a seamless experience. One month you can be running on site A, the
next month site B. You don’t actually have a DR site, you have two primaries and
they’re always ready. They’re not sitting there getting stale. Many trading
firms are seeking to implement this sensible Flip/Flop approach.
Auto Scaling and Handling Peak Events
Operational costs are always a focus for financial institutions and indeed all
companies. To reduce costs there is need for platform intelligence which can
reduce operational complexity and cost, manage increased scale of real-time data
delivery to meet market demands, and assure data access control and security.
The key to success is deploying a platform that can easily and reliably scale up
and down as required and also deliver data with optimal efficiency using data
deltas and compression algorithms to reduce bandwidth usage. Thereby reducing
both CapEx and OpEx costs.
Development teams are now, more than ever, seeking a low-code approach to
building their next generation event-driven applications. For the finance
industry to continue its digital transformation, driven by AI, machine learning,
and cloud architectures, an intelligent event data platform that can
autonomously react to market activity in real-time will be pivotal to the
evolution of their infrastructures. This has to be the case if you want to
deliver a hyper-personalized experience.
Controlling Access to Data/Entitlements
Every exchange and data provider control access to data it provides to trading
firms. Data consumers must be able to report and monitor who is using what data,
and ensure only permissioned users get the data. However, data providers may
visit a trading firm to verify how said firm is enforcing access to data? This
can be quite hard to do.
This requires a platform which can talk to any permissioning system and monitor
who is using what data in real-time and when the user subscribes/unsubscribes.
In addition, permission changes need to take place in real-time. This
information can be electronically collected and reported which can then be used
as compliance evidence. In essence, what seems a complicated procedure becomes a
file sharing exercise.
Resiliency and Monitoring
How do you make sure there is no single point of failure? Clustering technology
is key here. Node failure doesn’t impact data flows since the cluster replicates
data/configuration/sessions across nodes. If you haven’t got a clustering
strategy, you should have.
How do you monitor the sheer number of disparate systems I have discussed? A
monitoring API that can monitor any measurable metric is crucial, be it network,
operating system, data or client/user related. And because it’s an API it should
be able to integrate with any monitoring system. Integrating with any monitoring
system is a challenge for financial services, which goes back to my earlier
point about integration challenges.
Final Words
So that’s the here and now and some of the challenges financial institutions are
trying to solve. But just looking to the future briefly, and especially in the
European space, Consolidated Tape (CT) is a huge challenge. It may change
regulations which creates new issues, but on the whole, I think CT is a move in
the right direction.
The other main challenge is the move to the cloud which is well underway.
Everyone is looking to define their strategies. How do organizations deploy a
cloud agnostic approach? Nobody wants to be coupled to a particular player. Many
of the issues institutions might have in their cloud deployments, I believe are
touched upon in the trading stack challenges I have highlighted. The move to the
cloud won’t be easy, but taking the best practice approach I have outlined will
certainly help.
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