For years I focused on entries. I had rules, frameworks, indicators, and models for when to get into a trade.
But when it came to exits, I realized something uncomfortable:
I didn’t really know how to exit a position.
I had rules for entries and feelings for exits.
Sometimes I tightened stops too early and watched the market run without me.
Other times I held too long and watched open profit evaporate.
There was no system—just intuition under pressure.
That led me to a simple hypothesis:
Thesis: If trailing stops are systematic, consistent, and adaptive, the equity curve will take care of itself.
This is the start of documenting that journey.
Machine A and Machine B
To make exits systematic, I split my trading system into two conceptual machines.
Machine A decides what to trade.
Machine B decides how to manage risk after entry.
Machine A handles entries, direction, and baseline risk.
Machine B handles trailing stops and exit decisions, independent of my discretionary judgment.
This separation was a turning point. Instead of improvising exits, I began treating them as an engineering problem.
Why This Matters
Most trading education focuses on entries.
Most trading books focus on entries.
Most traders obsess over entries.
But exits shape the equity curve.
If trailing stops are inconsistent, emotional, or arbitrary, the distribution of outcomes becomes noise.
If trailing stops are systematic and adaptive, the equity curve becomes predictable.
That is the problem I’m trying to solve.
In future posts, I’ll document how I’m building a machine-learned trailing stop engine, the data traps I ran into (futures rollovers, RTH vs ETH, indicator distortions), and how I’m turning Machine B into a production system.