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.