Crack the Bot - regression and confounder practice

Infer the hidden rule driving an algorithmic trader by running real regressions on its tape - and learn to say 'there is no rule' when the data says so.

You face five cases, one at a time. In each case a bot trades against a random price tape, and a hidden rule drives its BUY/SELL decisions: momentum (follows the last move), mean reversion (fades the last move), a 3-tick delayed reaction, trend-following on the 5-tick average, or no rule at all. The rule only fires part of the time - the fire rate drops from 100% in case 1 to 75% in case 4, and the final case is pure noise - so the signal gets buried deeper in randomness as you progress.

Each case gives you 12 ticks of tape for free and a 2-minute clock. You can pull 5 or 25 more ticks, but every 5 ticks costs 4 seconds off the clock, so more data costs time. Your main tool is regression: you can regress the bot's action on any of three candidate predictors - the previous tick's move, the move 3 ticks ago, or the average of the last 5 moves - and read back the slope, t-statistic, p-value, and sample size. A confounder-check panel also lets you pick a suspect predictor X, control for a second predictor Z, and predict whether the naive slope shrinks, grows, or holds once Z is in the model.

When you commit to a rule, you must prove it by calling the bot's next 3 moves. If you call 'no rule', there is nothing to predict - the call itself is the answer, and it is right only when the case truly has no rule. Pulling new ticks wipes your existing regression results, since old estimates no longer describe the visible sample.

Why quant interviews test this

This game is a compressed version of the signal-research loop that quant research and econ-consulting interviews probe constantly: given noisy data, when is an effect real? Expect direct questions on what a t-statistic and p-value mean, why testing many hypotheses inflates false positives, and how omitted-variable bias distorts a naive regression - including the exact identity (bias equals the partial coefficient on the omitted variable times the auxiliary slope) this game makes you use.

The 'call its next 3 moves' step mirrors how interviewers separate people who can recite statistics from people who can use them: a model you believe should make out-of-sample predictions you are willing to be graded on. And the no-rule case is the trading-floor version of the most valuable answer in any data interview - 'this is noise, and here is the calculation that says so'.

The Crack the Bot guide covers how scoring works, the strategy that wins, a worked example and the mistakes most players make.

More Statistics games

• Twenty Backtests - Decide which of a book of backtested strategies - if any - deserves funding, once you account for how many were tried to find the winner.
• All Statistics practice
• Every game guide