The take-home is where many quant interviews are won or lost. You are handed a dataset and an open-ended question, given a few days, and judged less on the final accuracy number than on how you think: how you frame the problem, how carefully you avoid fooling yourself, and how honestly you report what you found. This lesson walks through a realistic example end to end and pulls together everything in this module, the NumPy and pandas work, the models, and above all the leakage-safe, time-aware validation that separates a credible submission from one that gets quietly rejected.
The brief: you are given daily closing prices for an instrument and asked to build a model that predicts whether the next day will be up or down, then to assess whether that prediction could trade profitably. We will frame it, explore the data, engineer backward-looking features, validate with a time-series split, pick a model honestly, evaluate once on a held-out test set, turn the predictions into a simple strategy, and present the result without overselling it. The edge we find is small and real, which is exactly what an honest financial result looks like.
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