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.
Postmake.io/revenue offers a simple calculator to help businesses quickly estimate their annual recurring revenue (ARR). Users input their number of customers, average revenue per customer (ARPU), and customer churn rate to calculate current ARR, ARR growth potential, and potential revenue loss due to churn. The tool aims to provide a straightforward way to understand these key metrics and their impact on overall revenue, facilitating better financial planning.
Hacker News users generally reacted positively to Postmake's revenue calculator. Several commenters praised its simplicity and ease of use, finding it a helpful tool for quick calculations. Some suggested potential improvements, like adding more sophisticated features for calculating recurring revenue or including churn rate. One commenter pointed out the importance of considering customer lifetime value (CLTV) alongside revenue. A few expressed skepticism about the long-term viability of relying on a third-party tool for such calculations, suggesting spreadsheets or custom-built solutions as alternatives. Overall, the comments reflected an appreciation for a simple, accessible tool while also highlighting the need for more robust solutions for complex revenue modeling.
The blog post explores two practical applications of the K programming language in data science. First, it demonstrates K's conciseness and efficiency for calculating quantiles on large datasets, outperforming Python's NumPy in both speed and code brevity. Second, it showcases K's ability to elegantly express the k-nearest neighbors algorithm, highlighting its expressive power for complex calculations within a limited space. The author argues that despite its steep learning curve, K's unique strengths make it a valuable tool for certain data science tasks where performance and compact code are paramount.
The Hacker News comments generally praise the elegance and conciseness of K for data manipulation, with several users highlighting its power and expressiveness, especially for exploratory analysis. Some express familiarity with K and APL, noting the steep learning curve but appreciating the resulting efficiency. A few commenters mention the practical limitations of K's proprietary nature and the scarcity of available learning resources compared to more mainstream languages like Python. Others suggest that the article serves as a good introduction to the paradigm shift required to think in array-oriented languages. The licensing costs and limited community support are pointed out as potential drawbacks, while the article's clarity and engaging examples are commended.
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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.