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Citadel·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Quant Engineer interview at Citadel that went deep into factor selection methodology. The IC/ICIR question was basically the whole conversation and they clearly wanted more than textbook definitions.

Questions Asked (1)

Q1

How do you use IC and ICIR to evaluate and select alpha factors, covering cross-sectional vs. time-series perspectives, horizon choice, IC decay, sector and regime robustness, ICIR thresholds, multicollinearity among high-IC factors, and how these metrics feed into portfolio construction?

Product Analytics & MetricsTechnical Trade-offsSystem Design
Author's notes

This was a single question that expanded into basically a 30-minute interrogation.

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AI HintsAI Generated

Suggested Approach

Start by defining IC and ICIR and their roles in alpha evaluation, then systematically address cross-sectional vs. time-series perspectives, horizon selection, IC decay, and robustness checks. Emphasize how these metrics guide factor selection and portfolio construction, including handling multicollinearity and setting ICIR thresholds. Conclude with practical implementation considerations for a software engineer at Citadel.

Pro tip: Demonstrate awareness that ICIR thresholds are regime-dependent and that high-IC factors may be redundant; show how you'd use clustering or PCA to manage multicollinearity while preserving interpretability.

1. Define IC and ICIR

Explain that IC is the correlation between factor values and forward returns, and ICIR is the mean IC divided by its standard deviation, measuring consistency. Highlight that ICIR is preferred for factor selection due to its risk-adjusted nature.

2. Choose perspective and horizon

Discuss cross-sectional IC (ranking stocks at a point in time) vs. time-series IC (for a single asset over time). Explain how horizon choice (e.g., daily, weekly) affects IC and ICIR, and the need to align with trading frequency and IC decay analysis.

3. Assess robustness

Describe evaluating IC/ICIR across sectors and market regimes (e.g., bull/bear) to ensure stability. Mention IC decay to determine optimal holding periods and avoid stale signals.

4. Set thresholds and handle multicollinearity

Propose ICIR thresholds (e.g., >0.5) but note they depend on strategy and universe. Address multicollinearity by clustering correlated factors or using orthogonalization to avoid redundancy in portfolio construction.

5. Integrate into portfolio construction

Explain how IC/ICIR inform factor weighting (e.g., IC-weighted or ICIR-weighted composites) and risk models. Emphasize combining factors with low correlation to improve diversification and Sharpe ratio.

Key Points to Mention

  • IC is the correlation between factor and forward returns; ICIR is mean IC / std dev of IC, measuring consistency.
  • Cross-sectional IC evaluates factor efficacy across assets at a given time; time-series IC evaluates for a single asset over time.
  • Horizon choice should match trading frequency and be informed by IC decay analysis.
  • Robustness checks: compute IC/ICIR by sector and regime to ensure stability.
  • ICIR thresholds are context-dependent; typical values range from 0.3 to 0.5 for daily factors.
  • Multicollinearity: use correlation clustering, PCA, or orthogonalization to select non-redundant factors.
  • Portfolio construction: weight factors by IC or ICIR, and combine low-correlation factors to enhance diversification.

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