Quant Trading Signal Test Plan
This conversation was generated using Cook'd AI's career assistant and reviewed by Fallon Han from our editorial team for accuracy.
I want to be tested on a trading strategy idea and how to validate it scientifically. I am a master's student in applied math targeting systematic trading at a prop shop in Chicago on STEM OPT. Please make this feel like a real quant interview.
Great. Let's make it concrete. Suppose you propose a daily cross-sectional mean-reversion signal on liquid US equities using overnight gap, intraday range, and recent short-term returns. Before we talk implementation, explain the economic intuition, what you would expect the signal to exploit, and how you would test whether it is real rather than just overfit noise.
The intuition is that large overnight moves and stretched short-term price action may partially revert as liquidity normalizes and short-term excess demand fades. I would first define the universe, then build a clean signal and examine forward returns by decile. I would use an out-of-sample split and walk-forward testing to check stability, and I would compare against a simple benchmark like naive reversal on prior return only.
Good start, but I want more rigor. Walk me through the main backtesting pitfalls you would guard against. I am especially interested in data leakage, survivorship bias, look-ahead in corporate actions, and whether your signal could be just a proxy for volatility or market beta.
I would use point-in-time data and ensure prices, fundamentals, and corporate actions are aligned as of each decision time. I would include delisted names to avoid survivorship bias and make sure rebalancing uses only information available before the open or close, depending on the execution rule. To test if the signal is only a volatility proxy, I would neutralize exposures to sector, beta, size, and realized volatility, then see if the alpha remains. I would also check performance after transaction costs and slippage, since a high-turnover strategy can look strong before costs.
Now imagine the strategy looks great for two years, then suffers a deep drawdown with a 12 percent peak-to-trough loss. In a review meeting, how would you interpret that drawdown scientifically? What would you want to know before deciding whether the signal is broken?
I would first separate signal decay from normal regime variation. I would compare the drawdown period to the strategy's historical exposure profile, factor correlations, and market regime to see whether the loss came from one-off crowding, volatility spikes, or a shift in mean reversion dynamics. I would inspect whether the drawdown was concentrated in certain sectors, times of day, or names with poor liquidity. If the losses are explained by a known regime shift, I would reduce risk or adapt the signal. If the edge disappears across robust subperiods and the hit rate collapses even after controlling for costs, I would treat it as evidence that the alpha is no longer reliable.
Solid answer. Final question: if you had to improve the research process before presenting this to a Chicago prop shop, what would you change so your results are more credible to a skeptical interviewer?
I would pre-register the research rules as much as possible, keep the feature set simple, and document every test I ran so I can distinguish discovery from confirmation. I would use multiple holdout periods, stress-test with different transaction cost assumptions, and run permutation or placebo tests to see whether the edge survives randomization. I would also report not just Sharpe, but turnover, capacity, drawdown duration, and exposure stability, because a prop shop will care about whether the strategy can survive live trading.