How to Backtest a Dip-Buying Strategy (Step by Step)

"Buy the dip" isn't testable until you make it specific. A dip of how much? Measured over what window? When do you sell? A complete rule looks like: "Buy bitcoin every time it falls 3% from its recent high; sell when it's 3% above the price I bought at."

Every word there is a decision you can change later - the asset, the dip size, the exit. Writing it out is the real work; the test itself is mechanical.

Pick the asset and the time span. More history is better, and crucially it should cover different market conditions - a bull market, a crash, and a flat stretch - so you're not just measuring how the rule did in one lucky regime.

Daily candles are a sensible default for a dip-buying rule. Make sure the data is real and continuous, with no gaps that would distort the trades.

The backtest steps through that data day by day, checking your rule, and placing simulated buys and sells. The key discipline is that at each step the rule only sees the past - never future prices - so the result reflects decisions you could actually have made in real time.

With Premiss this is the whole interaction: type the rule in plain English, and it generates and runs the Python backtest on real candles, returning the trades and the result.

Don't stop at the headline return. Look at:

• How many trades it took - three lucky trades is not evidence; hundreds is more telling.
• The worst drawdown - how far the account fell from a peak. A great return you couldn't have stomached living through isn't tradeable.
• Whether the result holds across the whole period, or came from one explosive stretch.
• How it compares to simply buying and holding the asset over the same window.

Change one thing at a time: a 5% dip instead of 3%, a different exit, a different asset. If the strategy only works at exactly one setting, you've probably overfit to the past and it won't survive live.

A robust edge tends to keep working - not identically, but recognizably - as you nudge the inputs. That stability is far more reassuring than a single dazzling number.