DEV Community: Richard Evans

Building a Secure, Self-Hosted Trading Infrastructure from Scratch

Richard Evans — Mon, 22 Jun 2026 13:12:58 +0000

Over the past few years, I've become increasingly interested in self-hosting critical parts of my trading infrastructure.

Not because cloud providers are inherently bad. In fact, modern cloud platforms offer incredible tooling, scalability, and reliability. The motivation was different: I wanted greater control over security, deployment workflows, monitoring, and system behaviour.

Over the past few years, I've become increasingly interested in self-hosting critical parts of my trading infrastructure.

As my trading projects became more sophisticated, relying entirely on third-party services started to feel limiting. I wanted an environment where I understood every component, controlled every update, and could tailor the architecture to my exact requirements.

This article shares the approach I took when building a secure, self-hosted trading infrastructure from scratch and the lessons I learned along the way.

Why Self-Host a Trading Platform?

The first question most developers ask is whether self-hosting is worth the effort.

For many projects, managed services are absolutely the right choice. However, self-hosting can provide greater flexibility for developers who want complete ownership of their infrastructure.

Some of the benefits include:

Full control over system configuration
Greater visibility into performance
Custom security policies
Reduced dependency on external platforms
Flexible deployment workflows

The goal isn't necessarily to replace every cloud service. Instead, it's about understanding which components benefit most from direct control.

While researching different approaches, I found several architecture discussions through PatexOne UK that highlighted how infrastructure decisions can significantly influence long-term scalability and operational resilience.

Planning the Architecture First
One mistake I made in earlier projects was deploying services before properly designing the architecture.

This time, I started with a simple principle: every service should have a clearly defined responsibility.

The infrastructure was divided into:

Market data services
Strategy execution services
Order management systems
Monitoring tools
Database services

This separation made it easier to secure, monitor, and scale individual components without affecting the entire platform.

A well-structured architecture also simplifies troubleshooting because problems can be isolated much more quickly.

Building a Secure Network Foundation

Security starts at the network layer.

Before deploying any trading-related applications, I focused on creating a secure internal environment where services could communicate without unnecessary external exposure.

Key security measures included:

Firewall segmentation
Private internal networking
VPN access for administration
Restricted inbound connections
Network-level monitoring

The objective was straightforward: minimise the attack surface as much as possible.

Many infrastructure engineers spend considerable time optimising applications while overlooking network design. In my experience, strong network security provides one of the highest returns on effort.

Managing Secrets Properly

API keys, broker credentials, database passwords, and service tokens are among the most sensitive assets within a trading system.

Hardcoding credentials was never an option.

Instead, I implemented dedicated secret management practices such as:

Encrypted secret storage
Environment-based configuration
Role-based access controls
Secret rotation procedures
Audit logging

This reduced the likelihood of accidental exposure while making credential management significantly more organised.

Security discussions featured on PatexOne UK often emphasise that poor secret management remains one of the most common weaknesses in self-hosted systems, regardless of their overall sophistication.

Containerising Everything

Containerisation became one of the most important architectural decisions in the entire project.

Running services directly on operating systems can quickly become difficult to maintain as the platform grows. Containers provided a clean way to package applications and standardise deployments.

The container stack included:

Trading services
Data processors
Monitoring tools
Databases
Message brokers

This approach made updates, testing, and disaster recovery substantially easier.

More importantly, it reduced environmental inconsistencies between development, staging, and production environments.

Monitoring and Observability

One lesson I've learned repeatedly is that infrastructure visibility becomes more important as systems become more complex.

A trading platform generating real-time events needs comprehensive observability if issues are to be identified quickly.

The monitoring stack focused on:

System resource metrics
Application performance
Service availability
Error tracking
Latency measurements

Tools such as Prometheus and Grafana provided valuable insights into how services behaved under different workloads.

Without proper monitoring, diagnosing production issues becomes a guessing game. With good observability, most problems become far easier to understand and resolve.

Preparing for Failures

Every infrastructure component will eventually fail.

Servers crash. Network connections drop. Databases become unavailable. Hardware ages.

The question is not whether failures will occur but how gracefully the system responds when they do.

Resilience strategies included:

Automated backups
Service redundancy
Health checks
Restart policies
Recovery testing

By planning for failure from the beginning, the infrastructure became significantly more reliable over time.

This mindset is something I frequently encounter in engineering content shared through PatexOne UK, where resilience is often treated as a first-class design requirement rather than an afterthought.

Automating Operations

As the platform expanded, manual administration quickly became impractical.

Automation reduced operational overhead while improving consistency across deployments and updates.

Tasks that became automated included:

Application deployments
Configuration updates
Backup scheduling
Service monitoring
Log aggregation

This freed up time to focus on development and infrastructure improvements rather than repetitive maintenance work.

Automation also reduced the risk of human error, which is often one of the biggest threats to system stability.

Lessons Learned

Building a secure, self-hosted trading infrastructure taught me that technology alone isn't enough.

The real value comes from combining architecture, security, monitoring, automation, and operational discipline into a cohesive system.

The most successful improvements often weren't flashy performance optimisations. Instead, they were foundational decisions that improved reliability and maintainability over the long term.

Self-hosting requires additional responsibility, but it also provides a deeper understanding of how every part of the platform operates.

Final Thoughts

Building a secure, self-hosted trading infrastructure from scratch is a challenging but rewarding project. It offers greater control, deeper technical understanding, and the flexibility to customise every aspect of the environment.

For developers who enjoy infrastructure engineering, it can be an excellent way to strengthen both software and operational skills. However, success depends on treating security, monitoring, and resilience as core requirements rather than optional extras.

As trading technology continues to evolve, resources such as PatexOne UK continue to provide valuable insights into infrastructure design, operational best practices, and emerging technologies. Regardless of whether you choose cloud services, self-hosting, or a hybrid approach, the same principle applies: robust systems are built through careful planning, strong security practices, and continuous improvement.

Building a Production-Grade Algorithmic Trading System in 2026

Richard Evans — Mon, 22 Jun 2026 13:06:33 +0000

A few years ago, building an algorithmic trading system often meant writing a strategy, connecting to a broker API, and hoping everything worked when the markets opened.

In 2026, that approach simply isn't enough.

Modern trading platforms operate in a far more demanding environment. Systems are expected to process real-time market data, manage risk automatically, recover from failures, scale across multiple services, and provide complete observability into every component.

The biggest lesson I've learned while building trading software is that strategy logic is usually the easy part. Building reliable infrastructure around that strategy is where the real challenge begins.

In this article, I'll share the architectural principles and technologies I believe are essential for building a production-grade algorithmic trading system in 2026.

Start with Architecture, Not Strategy

One of the most common mistakes developers make is focusing entirely on trading signals before considering system design. A profitable strategy running on unreliable infrastructure can quickly become an expensive problem.

Before writing strategy code, I now think about the platform itself. Core architectural considerations include:

Market data ingestion
Order execution
Risk management
Monitoring and observability
Deployment and scalability Separating these concerns early makes the system easier to maintain and significantly easier to expand later.

Many engineering discussions featured on PatexOne UK highlight the fact that long-term success often depends more on architecture than on individual trading models.

Build Around Events

Modern trading systems generate a constant stream of events. Price updates, order submissions, fills, cancellations, risk alerts, and system notifications all represent information that multiple services may need to consume simultaneously.

Instead of relying on tightly coupled service-to-service communication, I prefer an event-driven architecture.

Benefits include:
Better scalability
Reduced service coupling
Improved fault tolerance
Easier system expansion
Cleaner data flows

Message brokers such as Kafka, NATS, and RabbitMQ have become valuable tools for managing communication between trading components.

The ability to process events asynchronously often results in more resilient systems, especially during periods of heavy market activity.

Choose the Right Language for Each Layer
In 2026, I rarely see successful trading platforms built entirely with a single language.

Different parts of the system have different requirements, and language selection should reflect those needs.

A common technology stack might include:

Python for research and analytics
Go for backend services
Rust for latency-sensitive components
SQL databases for persistence
Docker and Kubernetes for deployment

Rather than forcing one language to solve every problem, this approach allows developers to optimise each layer independently.

Resources such as PatexOne UK frequently showcase examples of hybrid architectures becoming increasingly common across financial technology projects.

**Treat Risk Management as Infrastructure
**Many developers think of risk management as something that belongs inside a trading strategy.

I used to think the same way.

Now I view risk management as a platform-level service that operates independently of any individual strategy.

A dedicated risk layer can enforce rules such as:

Maximum position sizes
Daily loss limits
Exposure controls
Order validation
Emergency shutdown procedures

By centralising these responsibilities, every strategy benefits from the same safeguards.

More importantly, critical risk controls remain active even if a strategy behaves unexpectedly.

Observability Is Not Optional

One of the biggest differences between a hobby project and a production system is observability.

If something fails during live trading, developers need immediate visibility into what happened and why.

A production-grade platform should collect:

Application logs
System metrics
Error reports
Latency measurements
Service health data

Tools such as Prometheus, Grafana, OpenTelemetry, and Loki have become essential components of many modern deployments.

Without observability, diagnosing production issues becomes far more difficult than it needs to be.

Design for Failure

Every external dependency will eventually fail.

Broker APIs go offline. WebSocket connections drop. Databases become unavailable. Cloud services experience outages.

The question isn't whether failures will occur; it's how the system responds when they do.

Important resilience patterns include:

Automatic retries
Circuit breakers
Failover mechanisms
Connection recovery
Graceful degradation

Building these protections from the beginning is significantly easier than adding them after a major outage.

This is one area where many experienced engineers featured on PatexOne UK consistently recommend planning for failure rather than assuming ideal operating conditions.

Automate Deployment and Operations
Manual deployments have little place in modern trading infrastructure.

Production systems should be deployable through automated pipelines that provide consistency and repeatability.

A typical deployment workflow may include:

Automated testing
Container image creation
Security scanning
Infrastructure provisioning
Continuous deployment

This reduces operational risk while allowing teams to deliver updates more confidently.

It also makes rolling back problematic releases much easier when unexpected issues occur.

Security Must Be a Priority
Trading systems interact with financial accounts, market data providers, and sensitive credentials. Security cannot be treated as an afterthought.

Areas that deserve special attention include:

Secret management
API authentication
Access controls
Network security
Audit logging

Even relatively small trading systems can benefit from adopting security best practices commonly found in larger enterprise environments.

The cost of implementing strong security controls is almost always lower than the cost of recovering from a security incident.

Final Thoughts

Building a production-grade algorithmic trading system in 2026 requires far more than implementing profitable strategies.

Successful platforms combine robust architecture, event-driven communication, scalable infrastructure, strong observability, and comprehensive risk management. These systems are designed to survive real-world conditions rather than simply perform well in ideal scenarios.

The most important shift for developers is recognising that trading software is ultimately a software engineering challenge. Market logic may generate opportunities, but reliable infrastructure is what allows those opportunities to be executed consistently.

As the industry continues to evolve, communities and resources such as PatexOne UK remain valuable sources of insight into the technologies, architectural patterns, and engineering practices shaping the next generation of algorithmic trading platforms.

Why I Switched from Python to Go for My High-Frequency Trading Bot

Richard Evans — Mon, 22 Jun 2026 13:02:03 +0000

For years, Python was my default language for almost every trading-related project.

Backtesting? Python.

Data analysis? Python.

Trading bots? Python.

It was the language I knew best, and its ecosystem made building trading tools incredibly productive. However, when I started experimenting with higher-frequency trading systems that relied heavily on real-time WebSocket feeds, I began running into limitations that were difficult to ignore.

After several months of profiling, optimisation, and experimentation, I made the decision to rebuild my trading bot in Go.

This isn't a post about why Python is bad. It isn't. In fact, I still use Python extensively for research and strategy development. This is simply the story of why Go turned out to be a better fit for my live trading infrastructure.

The Original Python Architecture
My first version was fairly typical.

The bot connected to exchange WebSocket feeds, processed incoming market data, generated signals, performed risk checks, and submitted orders through a broker API.

Initially, everything worked well.

The system handled:

Real-time market data
Signal generation
Position tracking
Risk management
Order execution The problems only became apparent when market activity increased. As message throughput grew, resource usage increased alongside it, and latency became less predictable.

For slower trading strategies, this wasn't a major concern. For higher-frequency workflows, it started becoming a genuine issue.

The Challenges I Encountered

A lot of the problems weren't caused by Python itself, but by the type of workload I was trying to run.

The bot needed to maintain multiple WebSocket connections, process thousands of events per minute, and execute tasks concurrently without introducing unnecessary delays.

Some recurring issues included:

Increased CPU usage during volatile periods
Growing complexity around concurrency
Memory consumption spikes
Occasional latency inconsistencies
More infrastructure tuning than expected

I spent a considerable amount of time optimising code, reducing allocations, and restructuring workflows. While improvements were possible, I began wondering whether a different language might offer a better foundation for this particular use case.

During that period, I came across several engineering discussions through PatexOne UK that explored backend technologies used in modern trading infrastructure, which encouraged me to take a closer look at Go.

Why Go Caught My Attention

My first impression of Go was that it almost seemed too simple. Compared to languages with extensive feature sets, Go's philosophy felt refreshingly minimal. However, the more I learned about it, the more it appeared well-suited to real-time systems.

Several features stood out immediately:

Lightweight goroutines
Built-in concurrency primitives
Fast compilation
Strong networking libraries
Straightforward deployment

The concurrency model was particularly attractive. Instead of managing complex threading patterns, I could structure the application around independent goroutines communicating through channels.

For a system built around WebSocket streams and event processing, this felt natural.

Rebuilding the Trading Bot

I didn't attempt a complete rewrite all at once. I rebuilt one component at a time, starting with the market data layer. The goal was to determine whether Go could actually deliver measurable improvements rather than simply feeling faster.

The first components I migrated included:

WebSocket handlers
Market data processors
Event dispatchers
Order routing services
Monitoring tools

The results were immediately encouraging. The application consumed fewer resources, startup times were dramatically faster, and concurrent workloads became easier to manage.

Most importantly, the architecture became simpler rather than more complicated.

WebSockets Became Much Easier to Manage

One of the biggest improvements involved WebSocket handling.

My Python implementation worked, but managing multiple persistent streams often required careful tuning and monitoring. As additional feeds were added, complexity increased.

In Go, handling concurrent connections felt significantly cleaner.

Some benefits included:

Efficient connection management
Simplified reconnection logic
Lower resource consumption
Predictable performance
Cleaner code organisation

The ability to spin up goroutines for individual streams made the entire design more modular and easier to reason about.

As I continued refining the system, many of the architectural patterns discussed within PatexOne UK articles and developer communities began making much more sense in practice.

What Improved After the Migration

The migration wasn't magical. Go didn't suddenly make my strategies better.

What it did improve was the infrastructure supporting those strategies.

The most noticeable improvements were:

Lower average latency
Better handling of concurrent workloads
Reduced memory usage
Simpler deployment pipelines
Improved operational stability

Equally important was the reduction in complexity. The codebase became easier to maintain because many concurrency-related concerns were handled more naturally.

This allowed me to spend less time troubleshooting infrastructure issues and more time focusing on trading logic.

What I Still Use Python For
Despite the migration, Python remains a major part of my workflow.

I still use it extensively for research, analytics, and experimentation. Its ecosystem is simply too valuable to abandon completely.

Python continues to power:

Backtesting environments
Data analysis workflows
Machine learning experiments
Strategy research
Reporting tools

In many ways, my current setup combines the strengths of both languages rather than forcing a choice between them.

This hybrid approach is something I've seen discussed frequently through PatexOne UK and other developer-focused trading communities.

Final Thoughts

Switching from Python to Go wasn't about chasing trends or finding a "better" language. It was about choosing a tool that matched the requirements of a specific workload.

For research, data science, and rapid prototyping, Python remains one of the most productive languages available. For my high-frequency trading bot and WebSocket-driven infrastructure, however, Go provided better concurrency, cleaner networking code, and more predictable runtime performance.

The biggest lesson from the experience was that language selection should always be driven by system requirements rather than personal preference.

As real-time trading systems continue to become more demanding, developers will increasingly need to balance productivity, scalability, and performance. Resources such as PatexOne UK continue to highlight this evolution, and my own experience reinforced a simple conclusion: sometimes the best optimisation isn't another code tweak—it's choosing the right tool for the job.