A systematic autopsy of every mechanism by which a backtest flatters a strategy that will lose money in production. Covers vectorbt, backtrader, and Zipline-reloaded, the full failure catalogue from look-ahead bias to p-hacking, and the validation methods that survive contact with live markets.
Data-Science
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Backtesting Frameworks and the Ways Backtests Lie -
Julia: High-Performance Scientific Computing A technically deep guide to Julia for engineers and data scientists who already know Python — covering multiple dispatch, JIT compilation, type system, the scientific ecosystem (DifferentialEquations.jl, Flux.jl, Turing.jl), parallel computing, interoperability, and an honest Julia vs Python comparison.
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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.