Technical indicators look like chart-reading mysticism until you implement them — then they reveal themselves as small, deterministic functions over an OHLCV time series. This post takes two classics apart in Python: On-Balance Volume (the cumulative sum that turns volume into a running confirmation signal) and the Golden Cross (a moving-average crossover you can detect exactly with a sign change and a diff). Vectorized pandas implementations, the math behind each, the look-ahead-bias trap that makes naive backtests lie, and an honest accounting of why a lagging indicator is a description of the past, not a prediction of the future.
Data-Analysis
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Decoding Market Indicators with Code: OBV and the Golden Cross from Scratch -
Polars: The DataFrame That Replaced pandas How Polars — a Rust-backed, Arrow-native DataFrame library — achieves real multi-core performance, a query optimizer, and streaming execution that pandas structurally cannot match, and how to migrate to it without losing your mind.
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R for Data Analysis: A Practical Guide for Engineers A technically honest guide to R for experienced engineers and data practitioners — covering Tidyverse, ggplot2, R Markdown, Shiny, statistical modeling, and a frank R vs Python comparison.