Backtesting Python Interview Guide
Backtesting Python interview guide for signal generation, position timing, costs, leakage, metrics, examples, and realistic caveats.
Candidates writing or explaining small strategy backtests.
Backtest timing is the first check
A Python backtest must define when signals are observed, when trades are placed, and which prices are used. Wrong timing can create lookahead bias.
Costs and constraints matter
Transaction costs, slippage, turnover, position limits, and liquidity assumptions can change a strategy from attractive to unusable.
Concrete example
If a signal is computed from today close, the backtest should not assume execution at the same close unless that assumption is explicitly justified.
Metrics need context
Return, Sharpe, drawdown, turnover, hit rate, and exposure each answer different questions. Choose metrics that match the strategy goal.
Common mistakes
Candidates often present a toy backtest as proof. Strong answers discuss leakage, costs, sample selection, and robustness.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.