We often get asked:
“Why did you build OpenAlgo in Python? Isn’t Go or Rust faster?”
“Why a web application and not a desktop EXE?”
These are valid questions, especially from tech-savvy traders. But the answers are simple:
OpenAlgo is designed for real-world traders and quant enthusiasts — not for institutional HFT or ultra-low-latency systems.
Let’s walk you through our choices in plain English.
What OpenAlgo Is (and Isn’t)
OpenAlgo is currently built as a single-user, broker-connected platform. That means:
- It focuses on one trader, one account
- It is not designed for multi-user or multi-account concurrency
- It prioritizes flexibility, usability, and reliability over ultra-complex architecture
So while performance is important, building it like an HFT engine wasn’t the goal. Making it accessible and practical for everyday traders was.
Why We Chose Python Over Go or Rust
| Why it matters | Python | Go / Rust |
|---|---|---|
| Easy to read and learn | ✅ | ❌ |
| Rich financial libraries | ✅ | ❌ |
| Fast development cycles | ✅ | ⚠️ |
| Works well for single-user systems | ✅ | ✅ |
| Advanced concurrency for HFT | ⚠️ Not ideal | ✅ Best suited |
Python Is Trader-Friendly
Python is widely used in the trading world because:
- It’s easy to read and write
- Most broker APIs and trading libraries are built in Python
- Traders without a deep coding background can still understand and customize strategies
If we had used Go or Rust, OpenAlgo would become harder to adopt, contribute to, or extend.
Python Is Fast Enough (And Tuned)
While Python isn’t the fastest language on paper, we’ve tuned OpenAlgo to perform close to broker-level execution speeds.
- We implemented HTTP connection pooling, reducing order execution time to 80–120 milliseconds
- Most broker APIs already operate within 50–80 milliseconds
- So OpenAlgo is already very close to optimal execution speed
Unless you’re building a high-frequency trading desk, Python’s performance is more than enough — especially with the right optimization.
Why We Built a Web App — Not a Desktop EXE
Another popular question:
“Why not build OpenAlgo as a desktop EXE? Wouldn’t that be faster?”
Let’s break it down:
Platform Independence
OpenAlgo runs on Windows, Mac, or Linux — whether you’re using your laptop or a cloud server.
A desktop EXE would lock us into Windows-only, which excludes Mac and Linux traders — or forces them to use complex workarounds.
Budget-Friendly Hosting
You can host OpenAlgo on affordable Linux-based servers like:
- DigitalOcean
- Linode
- Vultr
- OvhCloud
- Raspberry Pi
Windows servers are more expensive, and maintaining them remotely is harder for most users.
Use It from Anywhere
OpenAlgo is browser-based. That means:
- Access it from your PC, laptop, tablet, or even your phone
- Host it locally or on the cloud
- Monitor or control trades even when you’re not at your desk
Updates Are Simple (Git-Based)
While we don’t have an auto-update system yet, OpenAlgo supports easy manual updates using:
git pullNo need to reinstall or download new versions — just update your repo and restart.
Speed Difference Is Marginal
Yes, desktop EXEs may be a few milliseconds faster. But in practice:
- Most strategies run every few seconds or minutes
- The difference in UI load or code execution is negligible
- Browser-based tools allow multi-tab use, modern UIs, and remote access
Summary Comparison
| Feature | Web App (OpenAlgo) | Desktop EXE |
|---|---|---|
| Platform Support | ✅ Mac / Linux / Windows | ❌ Windows only |
| Hosting Options | ✅ Cheap Linux servers | ❌ Costly Windows servers |
| Access | ✅ Browser / Mobile | ❌ Single PC only |
| Updates | ✅ Git pull | ❌ Manual install |
| Remote Monitoring | ✅ Yes | ❌ No |
| Speed Difference | ⚠️ Small tradeoff | ✅ Slightly faster |
Will We Use Go, Rust, or Desktop Apps in the Future?
Yes — where it makes sense.
- We may build Go-based broker bridges for sub-50ms execution layers
- We may explore Rust plugins for handling real-time data or multi-account streaming
- A desktop app may come in the future for fully offline, local-only workflows
But right now, our goal is to keep OpenAlgo:
- Simple
- Fast enough
- Cross-platform
- Trader-friendly
🌌 OpenAlgo Mini FOSS Universe!
Explore the open-source ecosystem around OpenAlgo — built for traders, by traders. Whether you’re into Python, Node.js, Excel, Rust, or the Web — there’s something here for every kind of automation enthusiast.
🔗 GitHub Repositories:
🧠 OpenAlgo Core (Python Flask + Tailwind + DaisyUI)
https://github.com/marketcalls/openalgo
🐍 Python Library
https://github.com/marketcalls/openalgo-python-library
🟢 Node.js Library
https://github.com/marketcalls/openalgo-node
📊 Excel Add-in
https://github.com/marketcalls/OpenAlgo-Excel
🧩 Chrome Plugin
https://github.com/marketcalls/openalgo-chrome
⚡️ Fast Scalper (Rust + Tauri)
https://github.com/marketcalls/fastscalper-tauri
🌐 Web Portal (NextJS + ShadcnUI)
https://github.com/marketcalls/openalgo-webpage
🛠 Dive in, fork it, build with it. Let’s make algo trading more accessible, powerful, and open for everyone.
Final Thoughts
OpenAlgo is not built for bragging about nanosecond latency.
It’s built to help real traders go from idea to execution with:
- Minimal friction
- High reliability
- Easy customization
That’s why we chose Python.
That’s why we built it as a web app.
And that’s why OpenAlgo is helping more traders automate with confidence — without needing to become software engineers.
Go has fewer keywords (25 vs 39), no truthiness to grapple with, was built to be so simple interns could maintain it. Only time I have ever been in dependency hell writing golang was because someone flat out removed a repo. Go compiles and runs nearly instantly making the developer feedback loop extremely fast. Testing is built in so no holy wars about which testing library to use. You don’t need to write tests to make sure a string is really a string and someone didn’t pass you an array of strings instead, because of types. Python with it’s magic methods, list comprehensions, truthiness, loose (progressive as an afterthought) typing is much harder to wrap your head around or develop. It has a much worse package management and dependency situation. You obviously picked python because it was familiar not because it had better developer ergonomics or was easier to pick up.
Thanks for the detailed comment — I appreciate where you’re coming from. Go’s simplicity, static typing, fast compile times, and built-in testing definitely make it an attractive choice for many systems-level applications. And yes, Python isn’t perfect — its dynamic typing, sometimes chaotic packaging ecosystem, and “magic” can be hard for newcomers to navigate.
But here’s the thing: OpenAlgo isn’t a kernel or a compiler. It’s a trading automation platform built for retail traders, not Go developers. Our primary audience isn’t low-level systems programmers, it’s traders — people who are more likely to understand a for i in range(10) than a goroutine.
We picked Python because it’s the lingua franca of the trading and data analysis community. Whether you’re talking about backtesting, pandas, NumPy, yfinance, or even broker APIs — the ecosystem overwhelmingly favors Python.
If we built it in Go or Rust, we’d spend half our time explaining how to compile and build, or worse, writing bindings just to call a Python script for data analysis. With Python, everything from strategy logic to WebSocket streaming to Excel integration feels native and familiar.
So yes, we picked Python because it’s familiar — to our users. And that’s exactly why it was the right choice.