The Edge Behind the Edge: Debriefs, Statistics, and Journalling
(Dan Goldberg, Head of Risk)
The very first webinar I did for the Phase 1 guys, I spoke about keeping statistics around trading. I must have had a bee in my bonnet about something cause it did turn into a classic Goldberg Clarity Rant! (trademark incoming!). I think it probably would have been around me, asking someone for their data, their trade statistics, or details surrounding their trading. And they probably didn’t have them.
Whilst I do empathise with this arduous task, I also realise the absolute necessity of it. To be clear, I hate journaling, debriefs, and keeping records with a passion. So this is very much a case of do what I say, not what I do! Any of that drives me crazy. My eldest son used to hate all that self-reflective essay stuff during his degree, usually accompanied by his lowest marks! I hear my daughter talking about sections in her degree that require self-reflective essays, and I always get that shudder down my spine.
That said, I would find it hard to disagree with the benefits of these types of exercises.
Why would trading be any different?
Professionals should focus on feedback loops.
If you treat trading as a performance discipline rather than a prediction exercise, structured debriefs, statistical tracking, and journalling are not optional. They are the mechanism through which edge is measured, preserved, and improved.
Debriefs
A debrief is not a diary entry. It is a structured performance audit. After each session, answer three categories of questions:
Process (examples)
- Did I follow my setup criteria exactly?
- Did I size according to plan?
- Was the trade taken during optimal market conditions (volatility, session timing, liquidity)?
Risk Management (examples)
- Was risk predefined?
- Did I respect the stop?
- Did I cut winners short or let losers expand?
Control
- Was I calm and deliberate?
- Did I deviate due to boredom, frustration, or FOMO?
- Did prior trades influence this decision?
Keep it concise. Bullet points only. The goal is pattern detection, not emotional venting. And keep it light enough that the lift of doing it doesn’t become a burden that you just drop like a January gym joiner!
Over time, you will identify recurring failure clusters: things that you absolutely need to refine in order to improve. The list of errors we can make in trading are too long to even contemplate listing. But they will appear to you fairly quickly if you have a process to look for them.
Statistics
Track what affects expectancy.
At a minimum, maintain the following metrics:
- Core Performance Metrics
- Win rate
- Average win (R)
- Average loss (R)
- Expectancy ([(Win% × Avg Win) – (Loss% × Avg Loss)])
- Profit factor
- Maximum drawdown
If you trade structured setups, segment your stats by setup. Without segmentation, you cannot determine which strategy actually drives your P&L. Do the same if you trade multiple products.
You are identifying which environments and/or trades produce positive expectancy and which do not.
This standardises performance and removes emotional distortion. Once you do this consistently, several truths become visible, for example:
- You may be profitable despite a low win rate.
- Your problem may not be entries, but early profit-taking.
- A small subset of trades may drive most of your gains.
Journalling: Behavioural Forensics
Statistics tell you what happened. Journaling explains why.
Keep it structured:
- Pre-Session
- Market bias
- Key levels
- Planned setups
- Maximum risk for the day
- Intra-Session Notes
- Emotional state shifts
- Impulse urges
- Deviations from the plan
- Post-Session Reflection
Avoid generic statements like “need more discipline.” Replace them with operational language, for example:
- “Entered before confirmation candle closed.”
- “Increased size after two losses.”
- “Traded during lunch session despite the rule.”
Be precise.
There is a lot here to do. And more can be done. Weekly and Monthly Reviews. Export data on a weekly and monthly basis. Group the set-ups, identify highest expectancy, behavioural leaks, triggers, and so on. Then once you’ve done all this, guess what? You have to work on it. Pick one of the two of the most damaging things and eliminate them pronto. Then evaluate that!
Because data removes narrative.
Without tracking, you can blame market conditions, volatility, manipulation, or timing. With data, the responsibility becomes specific and measurable. It becomes your fault. And what’s worse, it becomes your fault if you do nothing about it. All things that are not good for the fragile human ego.
This all goes to making other processes easier, like upping size, taking more risk, and so on, because it becomes an evidence-based approach as we discussed last week. You should see things like removing low-quality trades, removing behavioral issues, and eliminating unforced errors.
Over months, this stabilises your equity curve or should!
Execution without review leads to randomness. Review without statistics leads to biased subjectivity. Statistics without behavioural journalling miss the human variable.
Combine all three.
Dan’s Weekly Wisdom:
“Precision creates change. Vague frustration does not.”