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Survivorship Bias: What Works, What Fails, and Why?

Alex Alexander

A few successful examples do not establish an edge, especially when losing trades can be ignored or explained away after the fact. I would want a dated journal, consistent rules, realistic fees, and enough observations to understand drawdowns. What minimum evidence would make you comfortable testing this approach with small size? For a fair comparison, I would also track expectancy, average loss, maximum drawdown, and the number of trades rather than focusing on win rate alone. This matters because a high win rate can still hide poor risk control. In your experience, what specific rule or metric best separates disciplined use of this algorithmic trading & quant topic from guesswork?

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    To separate disciplined algorithmic trading from guesswork, the best metric is Expectancy combined with a strict maximum drawdown limit. Expectancy calculates your average dollar win multiplied by your win probability, minus your average loss multiplied by your loss probability. A high win rate means nothing if a single outlier loss wipes out your account.

    When testing any quantitative model on a prop firm evaluation, survivorship bias is a huge trap. Backtests often ignore realistic slippage, commissions, and daily loss limits. Always forward-test with micro size in live market conditions and track your trade count alongside maximum drawdown.

    How do you filter out curve-fitted strategies in your own journals? Share your process below!

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