Table of Contents
Many quant engineers claim backtesting experience. Few understand why backtests fail. Here's how to evaluate if a candidate actually knows how to build reliable backtesting frameworks.
Why Backtests Often Fail In Production
- ✦ Market conditions change
- ✦ Execution assumptions are unrealistic
- ✦ Liquidity is overestimated
- ✦ Transaction costs are underestimated
- ✦ Models are overfit to historical data
- ✦ Data quality issues go unnoticed
Common Backtesting Pitfalls
A qualified candidate can explain:
- ✦ Look-ahead bias (using future data that wasn't available)
- ✦ Survivorship bias (only testing on stocks that still exist)
- ✦ Transaction costs and slippage (trading costs kill edge)
- ✦ Overfitting (too many parameters, too much optimization)
- ✦ Out-of-sample validation (testing on unseen data)
- ✦ Walk-forward analysis (rolling optimization windows)
Interview Questions
Out-of-sample testing, walk-forward analysis, parameter sensitivity analysis, monte carlo simulation, trading cost sensitivity.
Fixed per-trade cost, percentage of volume (slippage model), market impact model (Almgren-Chriss), or historical realized spread.
What to Look For
Signs of real backtesting experience:
- ✦ Production backtesting system they built
- ✦ Understanding of when backtests fail (and why)
- ✦ Experience with realistic transaction cost modeling
- ✦ Walk-forward analysis results
- ✦ Documentation of assumptions and limitations
Evidence Worth Reviewing
- ✦ Research reports with methodology documentation
- ✦ Performance attribution analysis
- ✦ Validation and robustness reports
- ✦ Data quality controls
- ✦ Backtesting architecture diagrams
- ✦ Risk-adjusted performance evaluations
Hire Engineers Who Understand Backtesting
Backtesting is the foundation of quantitative trading. Bad backtests lead to bad strategies. Offline Pixel pre-vets quant engineers on backtesting expertise. Raise a request, talk to candidates, fund the project, and approve payment when the work is done.
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