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.
This post provides a gentle introduction to stochastic calculus, focusing on the Ito Calculus. It begins by explaining Brownian motion and its unusual properties, such as non-differentiability. The post then introduces Ito's Lemma, a crucial tool for manipulating functions of stochastic processes, highlighting its difference from the standard chain rule due to the non-zero quadratic variation of Brownian motion. Finally, it demonstrates the application of Ito's Lemma through examples like geometric Brownian motion, used in option pricing, and illustrates its role in deriving the Black-Scholes equation.
HN users largely praised the clarity and accessibility of the introduction to stochastic calculus, especially for those without a deep mathematical background. Several commenters appreciated the author's approach of explaining complex concepts in a simple and intuitive way, with one noting it was the best explanation they'd seen. Some discussion revolved around practical applications, including finance and physics, and different approaches to teaching the subject. A few users suggested additional resources or pointed out minor typos or areas for improvement. Overall, the post was well-received and considered a valuable resource for learning about stochastic calculus.
This post provides a gentle introduction to stochastic calculus, focusing on the Ito integral. It explains the motivation behind needing a new type of calculus for random processes like Brownian motion, highlighting its non-differentiable nature. The post defines the Ito integral, emphasizing its difference from the Riemann integral due to the non-zero quadratic variation of Brownian motion. It then introduces Ito's Lemma, a crucial tool for manipulating functions of stochastic processes, and illustrates its application with examples like geometric Brownian motion, a common model in finance. Finally, the post briefly touches on stochastic differential equations (SDEs) and their connection to partial differential equations (PDEs) through the Feynman-Kac formula.
HN users generally praised the clarity and accessibility of the introduction to stochastic calculus. Several appreciated the focus on intuition and the gentle progression of concepts, making it easier to grasp than other resources. Some pointed out its relevance to fields like finance and machine learning, while others suggested supplementary resources for deeper dives into specific areas like Ito's Lemma. One commenter highlighted the importance of understanding the underlying measure theory, while another offered a perspective on how stochastic calculus can be viewed as a generalization of ordinary calculus. A few mentioned the author's background, suggesting it contributed to the clear explanations. The discussion remained focused on the quality of the introductory post, with no significant dissenting opinions.
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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.