Python for Finance, 3rd Edition

Chapter 19. Signals, Forecasts, and Portfolio Implementation

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Signals and forecasts translate research ideas into portfolio decisions. This chapter connects model outputs, such as scores, rankings, or predicted returns, to concrete weights and trades, taking into account rebalancing schedules, transaction costs, and capacity limits. It builds directly on the asset management foundations and portfolio-construction tools from Chapter 17 and Chapter 18, and it feeds the holdings and return histories used for systems and reporting in Chapter 20 and the small assetlib library in Chapter 21.

Throughout Part IV we continue to treat the efficient markets hypothesis (EMH) as a working baseline for liquid markets: the examples in this chapter are primarily didactic. They show how to move from hypothetical signals to portfolios without claiming that the particular signals used here deliver persistent outperformance. A careful discussion of what it means for markets to be “efficient”, and how to test ...