Top Reasons for Choosing Python to Build OpenAlgo Instead of Go, Rust, or EXE
- Bryan Downing
- Apr 8
- 6 min read
In the fast-paced world of algorithmic trading, where milliseconds can make or break a trade, choosing the right technology stack is critical. When they set out to build OpenAlgo, an open-source algorithmic trading framework tailored for Indian traders, we faced a barrage of questions about our choice of technology.
With Python as the core programming language, and the platform delivered as a web application rather than a standalone executable (EXE), some questioned our decisions. Why not leverage the speed of Go or Rust, or the standalone simplicity of an EXE? In this article, we’ll explain the reasoning behind our choices, the specific needs of our user base, and why Python emerged as the optimal choice despite the allure of newer, faster languages.
1. Understanding the Problem Space
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Before delving into the technical rationale, it’s important to consider the context in which OpenAlgo was built. Here’s what we aimed to achieve:
Accessibility for Indian Traders: Indian markets have a mix of retail and institutional participants, with a growing appetite for algorithmic trading. However, many traders lack the technical expertise to use complex tools or build algorithms from scratch.
Flexibility and Scalability: Our platform had to support traders who use diverse strategies, ranging from intraday scalping of Nifty and Bank Nifty to long-term options strategies on high-liquid stock derivatives.
Focus on Rapid Prototyping: The framework needed to allow traders to quickly design, backtest, and deploy trading strategies without navigating steep learning curves.
With these goals in mind, we evaluated the trade-offs of various technologies and architectures.
2. Why Python?
2.1 Ease of Use and Rapid Prototyping
Python has long been the language of choice for financial professionals, especially in algorithmic trading. Why? It’s simple, intuitive, and allows for rapid prototyping. For traders, time is of the essence. They need to move quickly from idea to execution without wading through verbose syntax or overly complex type systems.
Python’s readability and extensive libraries (like NumPy, pandas, and scikit-learn) make it perfect for:
Data analysis and preprocessing: Traders can import market data, clean it, and analyze it in just a few lines of code.
Backtesting strategies: Python integrates seamlessly with tools like Zipline, Backtrader, and OpenEngine (our event-driven backtesting engine).
Machine learning models: With libraries like TensorFlow and PyTorch, traders can easily incorporate predictive models into their strategies.
2.2 Extensive Ecosystem
Python’s ecosystem for algorithmic trading is unparalleled. From accessing market data with APIs to implementing machine learning models and connecting to broker platforms, Python has a library for virtually every need. Some notable examples include:
CCXT for cryptocurrency exchange APIs.
IB_insync for Interactive Brokers integration.
TA-Lib for technical analysis.
This rich ecosystem reduces development time and ensures traders can experiment with cutting-edge tools without reinventing the wheel.
2.3 Community and Accessibility
Python’s popularity means there’s a massive, active community of developers, traders, and data scientists using the language. This makes it easier for Indian traders, who may not always have access to advanced resources, to find tutorials, documentation, and forums to troubleshoot issues or learn new skills.
For OpenAlgo, our goal was to lower the barrier of entry. By choosing Python, we ensured that even non-programmers could start experimenting with algorithmic trading while experienced developers could push the platform to its limits.
3. Why Not Go or Rust?
Yes, Go and Rust are faster than Python. They offer better memory management, concurrency, and performance. However, they come with trade-offs that make them less ideal for OpenAlgo’s mission. Here’s why:
3.1 Complexity vs. Convenience
Go and Rust are excellent languages for performance-critical applications, but they require a higher level of expertise to use effectively. For example:
Rust’s steep learning curve: While Rust is lauded for its safety and performance, its syntax and strict compiler rules make it intimidating for beginners. For traders who need to prototype quickly, Rust’s complexity would slow them down.
Go’s limited flexibility: Go is simpler than Rust but lacks some of the high-level abstractions and libraries that make Python so versatile for financial applications.
The additional complexity in these languages is unnecessary for the majority of use cases in algorithmic trading. Python’s developer ergonomics far outweigh the performance benefits of Go or Rust for the kind of rapid experimentation traders demand.
3.2 Performance Bottlenecks Are Rarely in Python
While it’s true that Python is slower than compiled languages, the bottlenecks in algorithmic trading are usually not in the language itself. Instead, they arise from:
Network latency: Delays in receiving market data or sending orders to brokers.
Data I/O: Reading and writing large datasets to disk or loading them into memory.
To mitigate Python’s performance limitations, we use Cython and Numba to speed up critical sections of code. Additionally, Python’s ability to interface with C and C++ libraries allows us to leverage high-performance tools when necessary.
3. Why a Web Application, Not an EXE?
Another common question is why OpenAlgo is delivered as a web application rather than a standalone EXE. Here’s why we made this decision:
4.1 Cross-Platform Compatibility
Desktop EXEs are typically tied to a specific operating system (e.g., Windows). By building OpenAlgo as a web application, we ensure it works seamlessly on any device with a browser, whether it’s Windows, macOS, or Linux. This is particularly important for Indian traders, many of whom use diverse setups.
4.2 Ease of Updates
A web application allows us to roll out updates instantly, without requiring users to download and install new versions. This is critical for an open-source project like OpenAlgo, where we’re constantly improving functionality and addressing user feedback.
4.3 Collaboration and Remote Access
Trading strategies often require collaboration between team members. A web-based platform makes it easy for users to share data, strategies, and performance metrics in real time. Additionally, traders can access their accounts and strategies from anywhere, even when they’re away from their primary trading station.
4.4 Scalability
Web applications are inherently more scalable than desktop EXEs. With OpenAlgo, traders can leverage cloud computing resources to backtest strategies on massive datasets or deploy their trading algorithms without worrying about local hardware limitations.
5. The Role of OpenEngine in OpenAlgo
At the heart of OpenAlgo is OpenEngine, our event-driven backtesting engine. Built in Python, OpenEngine allows traders to:
Simulate the behavior of their strategies under realistic market conditions.
Test strategies across historical data to evaluate performance and risk.
OpenEngine’s design emphasizes modularity and extensibility, making it easy to plug in custom indicators, risk management rules, and execution logic. By building OpenEngine in Python, we’ve ensured that it integrates seamlessly with the broader Python ecosystem, allowing traders to experiment with cutting-edge techniques like AI-driven sentiment analysis or order flow modeling.
6. A Decade of Experience: Why It Matters
As the creator of OpenAlgo, I bring over 15 years of experience in trading Indian markets and designing trading systems. My journey—from trading Nifty and Bank Nifty derivatives to publishing over 100 open-source trading systems—has taught me that robust trading frameworks and market understanding are the keys to success.
Python’s flexibility and extensive libraries align perfectly with this philosophy. While Go or Rust might offer marginal performance gains, they wouldn’t provide the developer productivity traders need to prototype, iterate, and succeed in volatile markets.
7. The Future of OpenAlgo
While Python remains the core of OpenAlgo, we’re not blind to its limitations. For performance-critical components, we’re exploring:
C++ extensions: To speed up execution where necessary.
Parallel processing frameworks: Like Dask and Ray, to handle larger datasets efficiently.
Hybrid architectures: Using Python for orchestration and Rust/Go for low-level execution.
However, our commitment to accessibility and community-driven development remains unchanged. Python will continue to be the primary language for OpenAlgo, ensuring that it remains a platform for traders of all skill levels.
8. Conclusion
At its core, OpenAlgo is about empowering traders. By choosing Python as our primary language and delivering the platform as a web application, we’ve prioritized accessibility, flexibility, and community support over raw performance. While Go and Rust are faster, they come with trade-offs that don’t align with the needs of our users. Similarly, a web-based architecture allows us to serve a diverse, geographically distributed user base more effectively than a desktop EXE ever could.
In the end, trading success isn’t about having the fastest code—it’s about having the right tools, frameworks, and understanding to navigate the markets. OpenAlgo represents our commitment to building those tools and sharing them with the world. Whether you’re a seasoned trader or just starting out, we invite you to join us on this journey. Together, we can push the boundaries of what’s possible in algorithmic trading.
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