Tag Archive for: Exit Strategy

Week 8 Recap: Structure, Restraint, and Quiet Progress

15th – 21st February

Week 8 felt different.

Not explosive.
Not dramatic.
Just steady.

After the turbulence of previous weeks, the focus coming into this one was simple: tighten execution, reduce noise, and behave like a professional.

The Plan

Going into the week, I set five clear rules:

  • Maximum 5 trades per day. Use the trade planner properly.

  • Only take true A+ zones.

  • Keep risk fixed at 1 percent. No oversizing. If resizing, it must be down, never up.

  • Validate structure on at least one timeframe higher before committing.

  • Reassess trailing stop placement relative to the timeframe of entry.

Nothing new. Nothing revolutionary.
Just better discipline.

The Reality

For the first time in a while, I felt genuine alignment between higher timeframe and lower timeframe structure.

Instead of marking up charts mechanically, I began to see how they overlapped.

A protected low on the higher timeframe could also serve as a shared protected low inside a lower timeframe zone. When those two lined up, the setup carried more weight. More confluence. More confidence.

That shift alone changed the quality of trades I was willing to take.

Fewer Trades, Better Decisions

I did not oversize once this week.

That matters more than it sounds.

Keeping risk fixed at 1 percent created emotional stability. There was no internal pressure to “make it back faster.” No temptation to lean heavier on volatile instruments.

Trade frequency also improved. I passed on many setups that I would have taken a few weeks ago. Patience is starting to feel less like restraint and more like strategy.

Ironically, I also identified multiple setups that went on to be great winners without me.

That is an important lesson.

There is a difference between patience and being too demanding on the pullback. If price does not retrace perfectly into your preferred level, sometimes the market simply moves without you. That is an area to refine moving forward. Not by lowering standards, but by avoiding greed in the entry refinement.

Performance Overview

In R terms, Week 8 closed +8.22R across 5 trading days.

In dollar terms, that translated to approximately +$5.18K.

After a difficult Week 7, that kind of rebound feels significant. Not because of the number itself, but because of how it was achieved.

  • No oversized positions

  • Reduced trade count

  • Better structural alignment

  • Cleaner execution

The process improved first. The results followed.

That is the order it should always be in.

Exit Strategy Experiments

One of the most valuable developments this week has been the start of structured exit testing.

I’ve begun comparing:

  • Fixed 1R

  • Partials

  • 1.5R targets

  • Full runners

  • Trailing scenarios

Instead of guessing, I’m running the data.

The goal is not to find the most exciting outcome.
It is to find the most consistent, repeatable one.

Over time, this testing should remove another layer of emotional decision making. Exits should be predefined, not improvised.

Bonus: A Milestone

Quietly, and slightly unbelievably, I passed three prop firm challenges this week.

Not one.
Not two.
Three.

Current funded capital now sits at $250K.

That is real progress.

It is easy to get distracted by daily PnL swings, but zooming out shows something else entirely. Structure is improving. Risk management is tightening. Emotional reactions are decreasing.

Funding is increasing.

The Bigger Picture

Week 8 was not about chasing big numbers.

It was about:

  • Respecting higher timeframe structure

  • Trusting confluence

  • Keeping risk consistent

  • Letting the edge play out

Ordinary discipline produced extraordinary stability.

And that is the direction this project needs to continue.

Trade well. Stay ordinary.

Should you take profit or let your trade run?

Should You Take 1R or Let It Run?

Most new traders focus almost entirely on entries. They refine confirmations, tweak structure rules, and optimise timing. But very quickly you realise something more important. Your exit strategy determines your expectancy.

Let’s walk through a clean example using simple numbers. No complicated formulas. Just clear logic.

We will assume the same core distribution throughout so every strategy is compared fairly.

The Starting Distribution

Across a large sample of trades:

  • 40% lose and hit full stop at -1R
  • 30% reach 1R but fail to extend further
  • 30% extend beyond 1R and can reach 1.5R

This is the raw behaviour of your system before deciding how to exit.

Now let’s compare four exit strategies using this same base data.

Strategy 1: Fixed 1R Take Profit

In this model you close the entire position at 1R. No partials. No trailing. No runners.

Using the base distribution:

  • 60% of trades reach at least 1R
  • 40% lose -1R

So expectancy is:

  • 60% × +1R = +0.60R
  • 40% × -1R = -0.40R

Total = +0.20R per trade

This is clean and efficient. Your edge here is accuracy. You monetise the fact that most trades reach 1R.

Strategy 2: 50% Partial at 1R, Runner to 1.5R

This is the classic hybrid approach.

When price hits 1R:

  • Close 50% for +0.5R
  • Move stop to break even

If the trade extends to 1.5R:

  • Remaining half earns +0.75R
  • Total win = +1.25R

If price reverses after 1R:

  • Remaining half stops at break even
  • Total win = +0.5R

Using our distribution:

  • 30% hit 1.5R → +1.25R
  • 30% stall after 1R → +0.5R
  • 40% lose → -1R

Now calculate:

  • 30% × 1.25R = +0.375R
  • 30% × 0.5R = +0.15R
  • 40% × -1R = -0.40R

Total = +0.125R per trade

Still profitable. But lower than the simple 1R model.

Why? Because only 30% of trades meaningfully extend. The runner frequency is not high enough to compensate for halving position size.

Strategy 3: Full Position Runner to 1.5R

Now we remove partials. The entire position aims for 1.5R.

If price reaches 1R but fails to continue, you move stop to break even and make nothing.

Distribution becomes:

  • 30% hit 1.5R → +1.5R
  • 30% reach 1R but reverse → 0R
  • 40% lose → -1R

Expectancy:

  • 30% × 1.5R = +0.45R
  • 30% × 0R = 0
  • 40% × -1R = -0.40R

Total = +0.05R per trade

You increased reward size but reduced realised wins. That trade off reduced expectancy.

Strategy 4: Structure Based Trailing

Now we remove the artificial 1.5R cap. Instead of targeting a fixed multiple, you trail behind structure and allow the market to decide.

To keep assumptions realistic, let’s use this distribution:

  • 40% lose → -1R
  • 30% reach 1R and then stop at break even → 0R
  • 20% trend moderately → +1.5R
  • 10% become strong runners → +2.5R

Now calculate:

  • 20% × 1.5R = +0.30R
  • 10% × 2.5R = +0.25R
  • 30% × 0R = 0
  • 40% × -1R = -0.40R

Total = +0.15R per trade

This improves on partials and fixed 1.5R runners, but still does not beat the simple 1R model under these conditions.

Comparing All Four

Using consistent assumptions:

  • Fixed 1R → +0.20R
  • Partials + 1.5R cap → +0.125R
  • Full 1.5R runner → +0.05R
  • Structure trailing → +0.15R

Under this distribution, the simplest strategy wins.

What This Teaches a New Trader

Risk reward ratio alone means nothing. A 1:1.5 target is not automatically superior to 1:1. What matters is how often price actually extends.

Your optimal exit depends on the behaviour of your market.

In rotational conditions:

  • Moves stall quickly
  • Pullbacks are deep
  • Extensions are limited

That profile favours harvesting 1R consistently.

In strong trending conditions:

  • Pullbacks are shallow
  • Structure stair steps cleanly
  • Large extensions are common

That profile favours structure based trailing and uncapped runners.

The mistake is using the same exit logic in both environments.

How to Decide With Data

Track one simple metric over your next 50 trades:

Maximum favourable excursion measured in R.

If most trades rarely exceed 1.5R before reversing, fixed 1R exits are likely optimal.

If a meaningful percentage regularly reach 2R or more, you may be capping your distribution too early.

The goal is not to maximise reward on a single trade. The goal is to optimise your overall distribution.

Sometimes the ordinary 1R is the most efficient solution.

Sometimes the market is offering a trend and you need to step aside and let it pay you.

The numbers will tell you which environment you are in.