Sig·Software Engineer·Take-home Assignment·Senior

Senior

Apr 2026

Summary

SIG quant researcher take-home: three days to analyze a trading ledger with seven traders and come up with a strategy that actually makes money out-of-sample. Pretty open-ended, which I liked and also found stressful.

Questions Asked (1)

Given a trading ledger for a single asset traded by multiple traders, propose a strategy that maximizes return. Include exploratory analysis, signal identification from trader behavior, a clearly defined strategy rule, an out-of-sample backtest with return/Sharpe/drawdown metrics, and a written discussion of assumptions and risks.

Product Analytics & MetricsTechnical Trade-offsAdaptability & Ambiguity

Author's notes

Three days sounds like a lot until you realize how much they actually want.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a data science pipeline: explore the ledger to understand trader behavior, engineer features that capture predictive signals, define a simple rule-based strategy, and rigorously backtest it with proper out-of-sample validation. Emphasize that the goal is not to find a perfect strategy but to demonstrate a sound, reproducible process that balances return with risk management.

Pro tip: Show that you think like a software engineer: propose a modular, testable pipeline and discuss how you would handle data quality issues, look-ahead bias, and transaction costs—these details signal production readiness.

1. Exploratory Data Analysis

Examine the ledger's structure, data quality, and basic statistics. Visualize trade volume, PnL distribution, and trader activity over time to identify patterns and anomalies.

2. Signal Identification

Analyze trader behavior to find predictive signals, such as consistent profitability, contrarian vs. momentum trading, or clustering of trades. Use statistical tests or simple models to validate signals.

3. Strategy Definition

Formulate a clear, rule-based strategy that leverages the identified signals. Specify entry/exit conditions, position sizing, and risk controls in a way that is implementable and testable.

4. Backtesting and Evaluation

Split data into in-sample and out-of-sample periods. Backtest the strategy on out-of-sample data, computing return, Sharpe ratio, and maximum drawdown. Compare against a benchmark.

5. Discussion of Assumptions and Risks

Articulate key assumptions (e.g., no market impact, perfect execution) and risks (e.g., overfitting, regime changes). Suggest ways to mitigate these risks.

Key Points to Mention

Data quality checks: missing values, outliers, and timestamp consistency.
Feature engineering: rolling metrics (e.g., trader's recent PnL, win rate), trade frequency, and position changes.
Avoiding look-ahead bias: ensure signals use only past information.
Transaction costs and slippage: incorporate realistic frictions in backtest.
Out-of-sample validation: use walk-forward or time-series split to prevent overfitting.
Risk metrics: Sharpe ratio, maximum drawdown, and return distribution analysis.

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