Quant Research Projects: A Review Checklist

A strong quant research project shows that you can ask a precise question, design a test, and explain the limits of the result. A complicated model and a high backtest return do not establish those skills on their own.

Use this checklist before putting a project on your CV or discussing it in an interview. It works for a simple forecasting experiment as well as a larger research portfolio.

If you need a project to attempt before reviewing your own, use the research take-home practice brief. It supplies a small synthetic dataset, a baseline, a worked implementation and a report template. For shorter implementation exercises, try the Python data tasks.

1. State exactly what is being predicted

Write one sentence containing the target, horizon, universe, and information available at decision time. “Predict returns with machine learning” leaves too much unspecified.

A more useful project question is: “Using information available at the end of each trading day, does a simple signal improve next-day return forecasts for this fixed universe relative to a no-signal baseline?” This is an illustrative design, not a trading recommendation.

Then define when the prediction is made and when a hypothetical trade could actually execute. A closing price is not available before that close has occurred.

2. Audit the data before choosing the model

Record where the data came from, the date range, adjustments, missing-value handling, and licensing constraints. Ask whether the universe includes securities that later disappeared. A present-day list applied to the past can introduce survivorship bias.

For each input, write its availability time. Publication dates, revision dates, and observation dates can be different. A clean table is not necessarily information you could have known then.

Deliverable: a data dictionary and a short description of the availability assumptions.

3. Put learned preprocessing inside the training window

Scaling, imputation, feature selection, and hyperparameter tuning can all leak evaluation information. Fit them on training data, then apply the fitted transformation to the held-out period.

For time-ordered data, evaluate on later observations. If labels overlap across time, account for that overlap when designing the split. A random split is not automatically appropriate for a prediction you intend to make forward in time.

The scikit-learn guide to common pitfalls illustrates why preprocessing must be isolated from the test set; its time-series split documentation describes a time-ordered splitter and its assumptions.

4. Beat a meaningful baseline

Compare the model against a simple alternative using the same universe, dates, and information set. Depending on the task, that might be the historical mean, a linear model, a lagged value, or a no-trade policy.

Report absolute performance and the difference from the baseline. A result is easier to assess when you can explain what the added complexity buys.

Interview question to practice: if a simpler model performs nearly as well, why would you keep the complex one?

5. Separate prediction from trading performance

Forecast accuracy is not the same as implementable profit. If you introduce a strategy, state the position-sizing rule, turnover, transaction-cost assumptions, execution delay, and any borrowing constraints.

Show at least a low-cost and a higher-cost scenario. If the result disappears after small plausible costs, that is a finding to discuss. Do not hide it by reporting only the most favorable backtest.

6. Test how fragile the result is

Change one assumption at a time: sample period, parameter choice, rebalance frequency, or universe. Explain which conclusions survive. Keep a record of the experiments you tried; selecting the best of many attempts changes how convincing the final result is.

Leave a final holdout untouched until the research choices are settled. Once you use its results to redesign the model, it is no longer an untouched test.

7. Write a result you can defend

Use this illustrative CV pattern:

Built a reproducible forecasting pipeline for [defined target] using [dataset]; compared [model] with [baseline] under a time-ordered evaluation; investigated [specific source of uncertainty].

Replace the brackets with your actual work. Add numbers only when you can explain their calculation and evaluation period. Never borrow a result from another project or imply a simulated portfolio was live capital.

8. Prepare the interview explanation

Give a two-minute explanation with five parts: question, data, method, evidence, limitation. Then prepare for these follow-ups:

• Why this target and horizon?
• What was known at prediction time?
• Where could leakage enter?
• What baseline was hardest to beat?
• What failed, and what did you change?
• What would you test next with more time?

A clear explanation of a failed hypothesis can demonstrate better judgment than a polished chart with no defensible experiment behind it.

For shorter questions about sampling, regression and uncertainty, work through the quant research statistics interview cases. To turn your project into an application entry, use the quant résumé examples, keeping every result tied to work you actually did.

For the transition from academic work, read PhD to quant research. To balance project work with interview preparation, follow the four-week study plan. Book a free quant consult to discuss your research background, target roles and the mentoring and preparation support available through Quant Blueprint.