Python for Finance, 3rd Edition

Chapter 26. Algorithmic Trading in the Real World

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Algorithmic trading is not an end in itself; it is a way of structuring investment decisions as repeatable workflows that must justify their complexity, costs, and risks. This chapter steps back from implementation details and asks when algorithmic trading is a worthwhile exercise in practice, how fragile small statistical edges can be after real-world frictions, and why even highly sophisticated hedge funds often struggle to deliver sustained benchmark outperformance.

This chapter closes Part V by connecting the EMH baseline from Chapter 22 with the backtesting, engine, and deployment work of the previous chapters. The goal is not to dismiss algorithmic trading, but to frame it realistically: gross signals are only the starting point; implementation costs, slippage, model decay, competition, and operational complexity often consume a large share of the apparent opportunity. The examples translate trading frictions into return-hurdle calculations, ...