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What does a backtest fundamentally tell you?
How a strategy will perform in the future How a strategy's exact rules would have behaved in a past period Whether a strategy is profitable in all market conditions The maximum amount a strategy can lose
Frage 2 von 15
A backtest enters trades at a candle's close based on an indicator computed from that same closed candle. Suspicious?
Yes — that's lookahead bias No — the candle is closed, so its data was available at decision time Yes — indicators must only use data from a week earlier No — but only if the timeframe is daily
Frage 3 von 15
What is out-of-sample (OOS) data?
Data from a different exchange than the one you trade Data excluded because of gaps or bad quality History the strategy was never tuned on, reserved for honest evaluation Simulated data generated by Monte Carlo
Frage 4 von 15
Which is the classic symptom of a curve-fit (overfit) strategy?
It trades too rarely Great in-sample results that collapse on data it never saw It only goes long, never short Its equity curve is too smooth in live trading
Frage 5 von 15
What does walk-forward analysis do?
Repeatedly optimizes on one window and tests on the next unseen window, rolling through time Runs the same backtest many times to average out randomness Tests a strategy on multiple coins simultaneously Projects the equity curve forward using regression
Frage 6 von 15
The Sharpe ratio measures…
Total return divided by number of trades Return earned per unit of volatility (risk-adjusted return) The percentage of winning trades How fast a strategy recovers from drawdowns
Frage 7 von 15
Why 'deflate' a Sharpe ratio?
To account for inflation eroding returns To convert it from daily to annual units Because the best of many tried configurations has an inflated Sharpe by selection luck Because exchanges overstate volume
Frage 8 von 15
After a 50% drawdown, what return is needed just to get back to break-even?
50% 75% 100% 150%
Frage 9 von 15
A strategy wins only 35% of the time. Can it be profitable?
No — you need at least 50% Yes — if average winners are enough larger than average losers Only in bull markets Only with leverage
Frage 10 von 15
'Risk 1% per trade' means…
Position size is 1% of account equity The trade's stop-loss distance implies losing at most 1% of equity if hit You trade once per day at most Fees must stay under 1% of the position
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The main practical use of Monte Carlo simulation on a backtest's trades is…
Predicting next month's return Estimating the range of outcomes and worst-case drawdowns beyond the single historical path Removing losing trades from the record Optimizing indicator parameters faster
Frage 12 von 15
A trend-following strategy 'suddenly stopped working'. The most common boring explanation is…
The exchange changed its API The market shifted into a ranging regime, where trend systems bleed by design Other traders copied the strategy The indicator library has a bug
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Moving a strategy from daily to 15-minute bars typically…
Reduces the impact of fees because trades are smaller Multiplies trade count, making fees and slippage a much larger share of returns Has no effect on costs Guarantees more profit due to more opportunities
Frage 14 von 15
Bitcoin bought in January 2018 eventually recovered to strong gains. What does that history hide?
Nothing — the return is the whole story A multi-year, roughly 80% drawdown the holder had to sit through That the data before 2020 is unreliable That fees consumed most of the gains
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You test your held-out (OOS) data, tweak the strategy because results disappointed, and re-test on the same held-out data. What happened?
Good practice — iteration improves strategies The held-out data has effectively become in-sample; its evidential value is gone Nothing, as long as the tweak was small The strategy is now walk-forward validated