Full Quant Research Chain AI Prompt

Step 1: Hypothesis — state the economic or behavioral rationale for why this signal should predict returns. Write it down before looking at any return data. What would falsify this hypothesis?
Step 2: Data protocol — define the asset universe with point-in-time construction, the signal computation with exact timing lags, the data sources, and the three-way train/validate/test split. Do not touch the test set.
Step 3: Signal construction and training set IC — compute the signal and evaluate IC, ICIR, and quintile performance on the training set only. If IC is not promising (ICIR < 0.3), return to Step 1 before proceeding.
Step 4: Parameter selection on validation set — select any free parameters (lookback windows, thresholds) on the validation set. Document the parameter search space and all results, not just the best.
Step 5: Multiple testing adjustment — apply Bonferroni or BHY correction given the number of hypotheses tested in your research program. Require t-statistic > 3.0 for a new signal to be considered genuine.
Step 6: Transaction cost and capacity analysis — estimate annualized turnover, total cost per unit of turnover, and net IC after costs. Estimate AUM capacity before market impact exceeds the gross alpha.
Step 7: Final evaluation on test set — evaluate the fully specified signal on the test set exactly once. Report all metrics: IC, ICIR, quintile spreads, net Sharpe. Compare to the validation results — large divergence suggests overfitting.
Step 8: Research report — write a complete research memo: hypothesis and rationale, data and methodology, training and validation results, multiple testing adjustments, transaction cost analysis, test set results, risks and limitations, and recommendation (implement / further research / reject).