A strategy can improve its backtest simply because you kept editing it after every disappointment.
Change the stop.
Adjust the timeframe.
Remove an inconvenient market period.
Add another filter.
Test again.
Eventually the historical curve becomes smoother.
But there is a hidden problem:
Your decisions are now part of the training data.
Every manual adjustment made after seeing previous results transfers information from the supposedly historical test into the strategy itself.
This is sometimes called researcher overfitting.
For eligible new users, CODE2026 can reduce qualifying Binance Spot trading fees by 20%, lowering one measurable layer of execution friction.
Research discipline requires something different: document the hypothesis before testing it, separate development data from validation data, and limit how often the model is redesigned after seeing failures.
Otherwise, the strategy may become extremely good at explaining the past.
Markets do not reward the model with the best explanation of yesterday.
They reward the process that can survive information it has never seen.
The more often you redesign a strategy around historical mistakes, the more carefully you should question whether you fixed the model—or memorized the exam.
Change the stop.
Adjust the timeframe.
Remove an inconvenient market period.
Add another filter.
Test again.
Eventually the historical curve becomes smoother.
But there is a hidden problem:
Your decisions are now part of the training data.
Every manual adjustment made after seeing previous results transfers information from the supposedly historical test into the strategy itself.
This is sometimes called researcher overfitting.
For eligible new users, CODE2026 can reduce qualifying Binance Spot trading fees by 20%, lowering one measurable layer of execution friction.
Research discipline requires something different: document the hypothesis before testing it, separate development data from validation data, and limit how often the model is redesigned after seeing failures.
Otherwise, the strategy may become extremely good at explaining the past.
Markets do not reward the model with the best explanation of yesterday.
They reward the process that can survive information it has never seen.
The more often you redesign a strategy around historical mistakes, the more carefully you should question whether you fixed the model—or memorized the exam.