DRW·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

DRW ML Engineer interview threw a serious quant-heavy coding problem at me, basically a full portfolio analytics library in pandas. The scope was way bigger than I expected for a single question.

Questions Asked (1)

Given one or more CSV files with daily asset prices or returns and optional portfolio weights, write Python and pandas code to load, clean, and align the data by date, handle missing values and outliers with documented choices, compute simple and log returns from prices, generate portfolio return time series with support for fixed-weight and periodic rebalancing, calculate cumulative return, annualized return, annualized volatility, Sharpe ratio, and maximum drawdown, support multiple portfolios with a summary table and exportable CSV and plot-ready output, and include unit tests for edge cases like non-overlapping dates, zero weights, and NaNs. Also report algorithmic complexity and justify your design decisions.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This was a lot.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then outline a modular pipeline: data loading/cleaning, return computation, portfolio aggregation, performance metrics, and testing. Emphasize vectorized pandas operations, documented choices for missing data and outliers, and design trade-offs for rebalancing and scalability.

Pro tip: Proactively discuss how you'd handle non-overlapping dates and NaNs by aligning on a union of dates and forward-filling prices (not returns) before computing returns, and mention that you'd validate with unit tests for edge cases like zero weights and all-NaN columns.

1. Clarify requirements and assumptions

Ask about data frequency, date alignment expectations, missing value handling, outlier definition, rebalancing frequency, and output format. State assumptions explicitly (e.g., prices are adjusted close, dates are trading days).

2. Design modular pipeline

Outline functions for loading/cleaning, computing returns, aggregating portfolio returns, calculating metrics, and exporting results. Emphasize vectorization and avoiding loops for performance.

3. Detail data cleaning and alignment

Explain how to align dates using outer join, handle missing values (e.g., forward-fill prices, drop leading NaNs), and detect outliers (e.g., z-score or IQR) with documented rationale.

4. Implement portfolio and metrics

Describe fixed-weight vs. periodic rebalancing logic, compute cumulative and annualized returns, volatility, Sharpe ratio, and max drawdown. Support multiple portfolios via a summary table.

5. Testing and complexity

Write unit tests for edge cases (non-overlapping dates, zero weights, NaNs). Analyze algorithmic complexity (e.g., O(T*N) for T dates and N assets) and justify design choices like using pandas for vectorization.

Key Points to Mention

Use pandas DataFrame alignment with outer join on dates, then forward-fill prices to handle missing values, documenting that returns are not filled.
Compute simple returns as pct_change() and log returns as np.log(1 + simple_returns) or np.log(price).diff().
For portfolio returns, use matrix multiplication (weights dot returns) for fixed-weight; for periodic rebalancing, reset weights at rebalance dates and compound within periods.
Annualize using 252 trading days: annualized return = (1 + cumulative_return)^(252/T) - 1, annualized volatility = daily_std * sqrt(252), Sharpe = (annualized_return - risk_free) / annualized_volatility.
Maximum drawdown: compute cumulative returns, running max, drawdown = (cum - running_max) / running_max, then min.
Unit tests should cover non-overlapping dates (resulting in NaNs or empty), zero weights (portfolio return zero), and NaNs (ensure proper handling without propagation).
Algorithmic complexity: O(T*N) for T dates and N assets, dominated by return computation and portfolio aggregation; vectorized operations ensure efficiency.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.