2026 Goal Setting: Fix the Problem, Not the To-Do List

Turn towards the pain points and the rest will take care of itself

There has been little time for respite to start the year as the markets have kicked off with a bang.

Themes already in motion through 2025 extended into a crescendo - most notably across silver, gold, and base metals stocks. Volatility is high and opportunity feels abundant. However, that’s exactly when it becomes easiest to lose perspective on the goals that were set so recently.

In my last post, I explored a different way to think about 2026 goal-setting. Instead of starting the year with new resolutions and endless to-do lists, the focus was on something harder: identifying the biggest problems currently limiting your growth — and dealing with that head-on.

That idea comes from Principles by Ray Dalio, and it has become the overarching game plan for how I’m thinking about the year ahead. It applies so cleanly to trading because it is a profession that requires constant confrontation with adversity and honest self-assessment.

The framework is simple:

1. Set ambitious goals.

2. Identify problems and don’t tolerate them.

3. Diagnose the root causes.

4. Design solutions.

5. Act.

My work on this format is unfinished. That became painfully obvious on Thursday, 5th February, when I experienced a near record-losing day. But instead of suppressing that discomfort, I’m turning towards it. Because the pain is information, and refusing to engage with it is how problems compound.

This post isn’t about fixing everything at once. It’s about identifying the constraints and showing how to address one area properly. The example today is “sizing” — the adjustment that positioned me for a record January. However, risk management deserves equal attention, particularly after the recent drawdown, but that discussion is for another time.

Start With the Real Problems

Before solutions, you need a clear view of where the friction actually is. For most traders, it isn’t a lack of effort. It’s misallocated effort.

When I stepped back and reviewed my results for 2025, four broad problem areas stood out. I suspect many readers will recognise versions of these.

The first is a misplaced focus on P/L goals. Money is a by-product of excellence. Fixating on a number can distort behaviour relative to the opportunity set in front of you. At the same time, completely avoiding a yardstick can lead to playing too small when conditions are favourable. This market has rewarded aggression and may be the best conditions I have seen since 2019 in Australia. Used properly, P/L can be a scoreboard against planned goals and not a fixed target.

The second is trade selection and overtrading. This shows up as participation without edge: trading names that lack liquidity or range, reacting after a move has already occurred, or failing to assess expected value explicitly before acting. Activity replaces selectivity.

The third is sizing. This is where intent and actual execution often diverge. Many traders recognise high-quality setups in real time but fail to express that conviction through size. For me, risk accumulates through too many positions instead of through a small number of well-chosen big bets.

Finally, risk management. Correlated overnight exposure tends to build during periods of overconfidence. Intraday losses aren’t always accepted in full and instead, stops become piecemeal. Other areas of weakness included ad hoc hedging decisions. Ultimately, more overnight risk leads to higher variance and many more of these showed up last year in stronger markets.

These problems are rarely surface-level issues. They’re symptoms.

Diagnosing the Root Causes

Once you identify the problems, the more uncomfortable step is asking why they persist.

For me, one root cause is the compulsion to be involved. Unconsciously, I seem to equate being across every catalyst with success. Participation feels productive. Selective aggression feels like restraint. Over time, this leads to mixed focus and weaker execution.

Another is insufficient EV formalisation. Most experienced traders know what a great setup looks like intuitively — but intuition isn’t the same as a decision framework. When expected value isn’t made explicit, sizing becomes inconsistent, and hindsight bias creeps in.

Sizing, and the stress that comes with it, also play a role. I tend to build risk through accumulation across positions rather than concentrated conviction in the best ones. This isn’t always about fear of loss or cowardice - it’s often about not systematically stressing oneself to higher levels of responsibility. Performance literature is clear that controlled stress is not harmful. Instead, it is a necessary input for adaptation and decision-making under pressure. Avoidance does the opposite. The root cause is staying within comfort thresholds or goals that no longer challenge capacity.

Finally, attachment to ideas is an undoing. The biggest losses are rarely “good trades, bad beats.” Instead, they are average trades that aren’t cut decisively when price action invalidates the thesis. Partial exits soften the emotional hit, but they also delay acceptance. That delay is expensive.

Blame your upbringing, private school, or the boss who made you insecure. Whatever it is, there is always a root cause behind the behaviour.

You don’t fix these by trying harder. You fix them by designing systems that remove discretion at the wrong moments.

Why Focus on Sizing?

You can’t fix everything at once. Dalio’s framework is explicit about this: find the constraint.

For me, sizing is the highest-leverage problem to address first through examining the data. This also serves as a forcing function. It exposes weak trade selection. It reveals whether conviction is real or rhetorical. It determines whether the best ideas and strategies can actually be scaled and matter to the P/L.

This is particularly relevant in Australia, where there are so many illiquid stocks and many crowded setups. I always want to be asking - can I add value here? Does this edge have multiple variables? If not, why am I bothering?

If sizing improves, many other issues improve with it.

Some of those examples where I could have scaled exponentially include:

• LYC Long on 11/7/25 due to MP deal with the DoD

• EOS Long on 5/8/25 due to HELW contract win

• DRO Short on 2/10/25 for gap-up mean reversion

• SYR long on 14/10/25 due to China export controls

• ARU Short on 21/10/25 with EXIM funding announcement.

These are just a few of the playbooks where liquidity, statistical edge, and information advantage justified materially more risk.

Designing a Solution: How to Size Like It Matters

The goal is not to be aggressive all the time. It’s to be aggressive when the opportunity warrants it.

That starts by defining risk buckets tied to playbook strategy, trade quality, and edge factors. High-grade setups deserve exponentially more risk than average ones. By default, this forces better trade selection — because you simply can’t size everything. The tool to synthesise this all together is a grader and risk management sheet.

As an example, to define exponential risk buckets, a simple process could look like:

A+: $25k risk

A: $15k risk

B: $5k risk

The next step is creating a deliberate pause between trigger and action through planning. This removes urgency. It creates space to ask: Is this truly unique? Can I calculate expected value? Does the liquidity support the intent?

Every strategy should be defined in advance — with default grades, default sizing, and clear invalidation points. Adjustments can still happen in real time using trader discretion, but they happen against a reference point and within constraints, not from impulse.

Mental preparation matters more than most traders admit. Visualise the pattern you want to see, the size you intend to deploy, and the point at which you cut before the open. This makes execution calmer and more decisive in real time.

Over time, historical data closes the loop. You don’t need perfection. You need enough structure to answer simple questions honestly: Do my highest-grade trades actually drive my returns?

As an example, January grades across multiple strategies looked like this: