Quant Research Take-Home: An Original Practice Brief

A research assignment becomes useful interview preparation when you can defend the entire experiment: what you predicted, what you knew at the time, what you compared against and what the result supports.

This is an original Quant Blueprint practice brief with a small downloadable dataset and a reference solution. Every observation is synthetic. It is not an employer-issued assignment, a profitable strategy or evidence that a particular firm uses take-home assessments.

The underlying skills have a clear connection to research work. Two Sigma's official research interview guide names practical data analysis, coding and statistics. This exercise gives you one bounded way to practice those skills and explain your choices.

The brief: test whether one known feature improves a forecast

Download the practice CSV. It contains 12 equally spaced prediction steps. At each step, predict the next interval's synthetic outcome using only information available at that step.

Your deliverables are a runnable script and a one-page report. Set yourself a practice limit, such as 90 minutes, then record unfinished work honestly. That timing is a study suggestion, not an employer rule.

The last column is deliberately tempting. In this toy dataset it equals the target, so a model using it could look perfect. Its values do not become legitimate inputs merely because they appear beside the other columns in a clean CSV.

Use steps 1–6 for training, 7–9 for validation and 10–12 as a final holdout. The outcome of each training step is available by the next step, before that next prediction. Labels cover one interval and do not overlap beyond its boundary under this contract.

Compare these two prespecified models:

1. A constant prediction equal to the training mean outcome.
2. Ordinary least squares with an intercept and x_at_prediction as its only input.

Use mean squared error, averaging squared prediction errors over the same evaluation rows. Do not turn these synthetic outcomes into returns, annualized Sharpe ratios or financial conclusions.

Write your protocol before fitting

Your protocol should state the target, information set, split boundaries, models and metric. Also state how you will treat malformed rows, missing values and repeated step identifiers. This file is complete; your script should verify that rather than rely on an assumption hidden in your notebook.

For this exercise, reject missing or nonfinite values, require steps 1 through 12 exactly once, and sort by step. Treat step as an ordering key rather than an extra model feature. Do not use the target or the future measurement to fill a feature.

There is no need to standardize this single feature for the closed-form calculation below. If you add learned preprocessing in a later version, fit it on the training partition. scikit-learn's pitfalls guide explains why test information must stay outside fitting transformations and feature choices.

Before opening the solution, answer three questions:

• What makes the future measurement invalid even if it predicts extremely well?
• Why should both models be evaluated on the same rows?
• What decision will validation inform, and which choices will remain frozen before final evaluation?

A complete reference implementation

Save the downloaded file as quant-research-practice.csv beside your script, or pass its path as the first command-line argument. This example uses Python's standard library.

import csv
import math
import sys

path = sys.argv[1] if len(sys.argv) > 1 else "quant-research-practice.csv"
with open(path, newline="") as handle:
    raw = list(csv.DictReader(handle))

rows = []
for row in raw:
    step = int(row["step"])
    x = float(row["x_at_prediction"])
    y = float(row["outcome_next"])
    future = float(row["future_measurement"])
    if not all(math.isfinite(v) for v in (x, y, future)):
        raise ValueError("nonfinite data")
    rows.append((step, x, y))  # exclude future from model inputs
rows.sort()
if [row[0] for row in rows] != list(range(1, 13)):
    raise ValueError("expected steps 1 through 12 exactly once")

train, validation, holdout = rows[:6], rows[6:9], rows[9:]
x_mean = sum(x for _, x, _ in train) / len(train)
y_mean = sum(y for _, _, y in train) / len(train)
ss_x = sum((x - x_mean) ** 2 for _, x, _ in train)
if ss_x == 0:
    raise ValueError("constant training feature")
slope = sum((x - x_mean) * (y - y_mean)
            for _, x, y in train) / ss_x
intercept = y_mean - slope * x_mean

def mse(partition, predict):
    return sum((y - predict(x)) ** 2
               for _, x, y in partition) / len(partition)

baseline = lambda x: y_mean
linear = lambda x: intercept + slope * x
assert math.isclose(y_mean, 0)
assert math.isclose(slope, 2)
assert math.isclose(intercept, 0)
for name, partition in [("validation", validation), ("holdout", holdout)]:
    print(name, "baseline:", mse(partition, baseline),
          "linear:", mse(partition, linear))
assert math.isclose(mse(validation, baseline), 19 / 3)
assert math.isclose(mse(validation, linear), 1)
assert math.isclose(mse(holdout, baseline), 9)
assert math.isclose(mse(holdout, linear), 1)

Check the calculations by hand

The six training features sum to zero, as do their outcomes. The squared features sum to 10, and the feature–outcome products sum to 20. The fitted intercept is therefore zero and the slope is 2.

On validation, the outcomes are [-1, 3, 3]. The baseline predicts zero, giving squared errors [1, 9, 9] and MSE 19/3, approximately 6.3333. The linear model predicts [-2, 2, 4], giving squared errors [1, 1, 1] and MSE 1.

These are checksums for a teaching exercise, not estimates of a real strategy's performance. Twelve designed observations cannot establish robust out-of-sample predictive value in a market.

If you want help connecting this kind of work to your own research background, book a free quant consultation to discuss target roles and preparation. For a broader audit of an existing portfolio project, use the research-project review checklist.

Submit a report that another person can audit

Use the following copyable structure. Fill it with your actual work, including failed attempts.

Question:
At each step, can the known feature improve next-interval forecasts
relative to a training-mean baseline?

Information boundary:
List allowed inputs and explain the excluded future measurement.

Data and checks:
State row counts, ordering, missing-value policy and duplicate policy.

Protocol:
State training/validation/holdout steps, model specifications and MSE.

Results:
Report both models on identical rows; include one hand calculation.

Sensitivity:
Describe one prespecified change, its result and its limitations.

Conclusion:
State what these synthetic data support and what remains untested.

Reproduction:
Give the script command, Python version and dataset filename.

A useful conclusion would say that the specified linear model has lower MSE than the specified constant baseline on the two synthetic evaluation partitions. It should also say that the small constructed dataset does not justify a real-world deployment claim.

Choose a follow-up that changes the reasoning

For a sensitivity exercise, increase only the final holdout outcomes by a fixed amount while keeping the original fit frozen. Explain how a level shift affects the constant and linear forecasts. You are now running a declared stress scenario, not preserving an untouched test. Save it separately from the original result.

For a harder variant, predict a multi-step outcome whose label extends across the split boundary. Identify which training labels overlap evaluation information. scikit-learn's time-series splitter supports an observation-count gap and describes equally spaced samples for comparable fold durations. A gap is a tool; its appropriate size follows from your label and availability contract. It does not automatically fix revised data, future features or an incorrectly defined universe.

For another coding follow-up, remove one timestamp or add a duplicated row. Make your validation reject the file and explain why silently accepting it would change the experiment. Then ask what policy a genuine irregular dataset would require.

Practice defending a limitation

Give a two-minute account of the question, information set, baseline, result and largest limitation. Ask a partner to challenge one choice: why MSE, why that split, why a linear model, or why the result might disappear on new data.

The aim is to make your experiment inspectable. A clear explanation of an invalid feature can demonstrate more judgment than a perfect score obtained by leaking the target.

Continue with the worked research statistics cases or the Python data tasks. For help choosing a preparation path suited to your experience, discuss research and interview preparation in a free consultation.