Zack is a lightweight and simple backtesting engine written in Zig. Designed for clarity and ease of use, it emphasizes a straightforward API and avoids external dependencies. It's geared towards individual traders and researchers who prioritize understanding and modifying their backtesting logic. Zack loads historical market data, applies user-defined trading strategies coded in Zig, and provides performance metrics. While basic in its current form, the project aims to be educational and easily extensible, serving as a foundation for building more complex backtesting tools.
The GitHub repository introduces "Zack," a nascent backtesting engine implemented in the Zig programming language. Zack aims to provide a straightforward and efficient tool for evaluating trading strategies against historical market data. The project's primary focus is on simplicity and performance, leveraging Zig's low-level control and lack of hidden runtime overhead.
While still in its early stages of development, Zack offers basic functionality for loading historical price data in CSV format and executing trading strategies defined in Zig. The engine iterates through the historical data, feeding each price tick to the user-defined strategy function. This function receives the current market data and can then issue trading orders based on its internal logic. The engine tracks the performance of the strategy, including metrics like profit and loss, and facilitates analysis of the results.
Zack's core design philosophy centers around minimizing dependencies and maximizing performance. By utilizing Zig, the project avoids the complexities of garbage collection and other runtime systems that can introduce unpredictable latency. The codebase is designed to be compact and understandable, prioritizing clarity and maintainability. The choice of CSV for data input further simplifies integration with various data sources.
Although currently limited in features compared to more mature backtesting platforms, Zack's emphasis on simplicity and performance positions it as a potentially valuable tool for traders and developers interested in a lightweight and highly controllable backtesting environment. The project's use of Zig also makes it an interesting case study in leveraging the language's capabilities for performance-sensitive applications in the financial domain. Future development is expected to expand upon its current functionalities, potentially incorporating support for more complex order types, broader data sources, and advanced performance metrics.
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https://news.ycombinator.com/item?id=43712877
HN commenters generally praised Zack's simplicity and the choice of Zig as its implementation language. Several noted Zig's growing popularity for performance-sensitive tasks and appreciated the project's clear documentation and ease of use. Some discussed the benefits of using a compiled language like Zig for backtesting compared to interpreted languages like Python, highlighting potential performance gains. Others offered suggestions for improvements, such as adding support for more complex trading strategies and integrating with different data sources. A few commenters also expressed interest in exploring Zig further due to this project.
The Hacker News post "Zack: A Simple Backtesting Engine in Zig" generated a moderate number of comments, mostly focusing on the choice of Zig as the implementation language, its performance characteristics, and comparisons to other backtesting solutions.
Several commenters expressed interest in Zig and its potential for performance-sensitive applications like backtesting. They praised Zig's memory management and control, suggesting it could lead to significant speed improvements over garbage-collected languages. One commenter specifically highlighted Zig's suitability for tasks involving numerical computation and data manipulation, key aspects of backtesting. The potential for minimizing runtime surprises and predictable performance was also mentioned as an attractive feature of Zig in this context.
The discussion also touched upon the trade-offs between simplicity and features. While some appreciated Zack's minimalist approach, others questioned its long-term viability and scalability compared to more mature backtesting frameworks. One commenter pointed out the lack of support for more complex features like slippage and commission modeling, which are crucial for realistic backtesting. This led to a discussion about the project's intended scope and whether it aimed to be a fully-fledged solution or a foundational building block for more sophisticated tools.
Performance comparisons with existing backtesting engines, particularly those written in Python, were a recurring theme. While no concrete benchmarks were presented in the comments, there was a general expectation that a Zig implementation could offer substantial performance gains. However, some commenters cautioned against premature optimization and emphasized the importance of profiling and benchmarking to validate these assumptions.
Finally, a few comments delved into specific aspects of Zack's design and implementation. One commenter inquired about the handling of historical data and the potential for integration with existing market data providers. Another comment touched upon the challenges of parsing and processing large datasets efficiently in a backtesting context. The discussion also briefly explored the possibility of using WebAssembly as a deployment target for wider accessibility.
Overall, the comments reflected a generally positive reception towards Zack, driven primarily by the interest in Zig and its potential for performance improvement in backtesting. However, there were also pragmatic concerns about the project's current limitations and the need for further development to address real-world backtesting requirements.