The profit-taking ladder: banking gains without cutting the winners
Taking profit too early caps your best trades; taking it too late gives them all back. AXIO's answer is a ladder — partial exits that bank a portion while a trailing stop rides the rest — and a shadow baseline that measures whether it actually helps.
The hardest part of a winning trade is deciding when to leave. Exit at the first green and your average win is small. Hold for the target every time and you watch fully-profitable trades round-trip back to breakeven. AXIO's profit-taking engine is built around that tension.
Partial scale-outs
When a position reaches a set profit-in-risk multiple (its "R"), AXIO sells a portion at market — banking real, realised profit — and lets the remainder run. The runner is then managed on a looser chandelier trail: a stop that follows the highest price seen since the position opened, at a distance scaled to volatility, so it gives the move room to breathe while still protecting the open gain.
For aggressive-style positions the ladder has more than one rung, so a strong trend can bank profit two or three times on the way up while a core runner stays on.
Profit-lock
Once a winner is far enough into profit and still trading near its own peak, AXIO pulls the stop up to a tight band beneath the current price — locking in roughly where you'd have exited anyway, while leaving a little room for more. It's ratchet-only: it can raise the stop, never lower it.
Don't nibble
A rung won't fire unless the slice it would sell books a minimum profit net of an estimated round-trip fee. There's no point taking a three-dollar partial that barely clears friction — the runner keeps going and the rung re-checks next tick at a higher price. On a small account the minimum scales down proportionally so the ladder still works.
Measuring whether it helps
Every closed trade is scored against a shadow baseline — an estimate of what the old, simpler logic (one 50% scale-out, runner to a fixed target) would have realised on that same price path. Your Diagnostics page reports actual-versus-baseline, so we can tell whether the adaptive layer genuinely improved expectancy or just produced more small realised wins while quietly clipping the big ones. If it doesn't beat the baseline over a real sample, it doesn't stay.
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