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Review Process

A review process is a structured, recurring routine, daily, weekly and monthly, for examining your journalled trades and process metrics to extract lessons, judge decisions on quality rather than outcome, and decide concrete adjustments for the next period.

Quick answer: A review process is a structured, recurring routine, daily, weekly and monthly, for examining your journalled trades and process metrics to extract lessons, judge decisions on quality rather than outcome, and decide concrete adjustments for the next period.

In simple words

Keeping a journal is only half the job; the review is where it pays off. A review process is a regular habit of sitting down with your records and asking honest questions: did I follow my plan, which mistakes repeated, what actually made or lost money, and what one thing will I change? Done daily, weekly and monthly, it turns a pile of data into specific improvements. The key skill is judging your decisions by whether they were sound, not just by whether they won, so you learn the right lessons.

Purpose

This page describes how to run a disciplined review at each timescale, and warns against the trap of judging past decisions purely by their outcomes, so review produces genuine learning rather than misleading conclusions.

Visual explanation

Review Process

The review cycle: gather journal data, analyse process and outcomes, judge decision quality, decide one change, and apply it to the next period, then repeat.

The Deliberate-Practice Learning CyclePLANpick one focusDOapply it liveREVIEWgrade the processADJUSTrefine the ruleone smallimprovement / cycle

Professional explanation

Review is the analysis stage of the feedback loop

The journal records data, but data does not improve anyone; the review is where recorded experience is converted into lessons and changes. A structured review process closes the feedback loop that deliberate practice requires by regularly asking what happened, why, and what to do differently. Crucially, review is deliberate and scheduled rather than casual, because ad hoc reflection after a bad day is dominated by emotion and recency, whereas a set routine examines the full sample calmly. The output of every review should be concrete: not a vague resolution to do better, but a specific, testable change to the process for the next period, which the following review will then evaluate.

The three timescales: daily, weekly, monthly

Effective review operates at nested timescales. The daily review, done at the close, grades each trade for plan adherence and captures fresh lessons and emotional notes while memory is accurate. The weekly review aggregates the week: adherence rate, mistakes that repeated, win rate and average R by setup, and whether size or routine drifted, producing one or two focus points for the coming week. The monthly review takes the widest lens: equity curve and drawdown, expectancy per strategy over a larger sample, progress on prior focus points, and any structural change to rules or instruments. Each timescale sees patterns the others miss, which is why a complete review process uses all three rather than only reacting day to day.

Resulting: judging decisions by outcomes is the core error

Annie Duke's term resulting names the mistake of evaluating a decision solely by how it turned out. In a probabilistic game, a good decision can lose and a bad decision can win over any short sample, so grading your process by the scoreboard teaches false lessons: you punish sound trades that happened to lose and reward reckless trades that happened to win. A disciplined review therefore separates two questions, was the decision sound given what I knew, and what was the outcome, and grades primarily on the first. This is uncomfortable because outcomes are vivid and decisions abstract, but it is the single most important habit that distinguishes a review that improves you from one that misleads you.

Distinguishing process errors from variance

A central task of review is sorting losses into two bins: those caused by process errors, breaking rules, poor sizing, taking setups outside the plan, and those that are simply variance, sound trades that lost because outcomes are probabilistic. This distinction determines the correct response. Process errors demand a change, a tightened rule, more friction, a habit adjustment. Variance demands no change at all; over-correcting after a normal losing trade is itself a mistake that breeds inconsistency. Reviewing with this lens prevents the two opposite failures: ignoring real leaks because a lucky win masked them, and constantly tinkering with a sound process because normal losses are mistaken for flaws.

Pattern-finding across the sample

The unique power of aggregated review is surfacing patterns invisible in any single trade. Grouping trades by setup reveals which have positive expectancy and which quietly bleed. Grouping by time of day, day-to-expiry, instrument, or your emotional state at entry can expose that losses concentrate in specific conditions, the first thirty minutes, expiry days, trades taken while angry, or after a win. Grouping by discipline grade often shows that A-graded trades are near breakeven variance while C-graded impulsive trades cause the real damage. These patterns are the highest-value output of review, because each one points to a specific, high-leverage change, cutting a losing subset or adding a rule, that memory could never have identified.

Turning review into a single next action

Reviews fail when they produce insight but no change, so the discipline is to convert each review into at most one or two concrete, prioritised actions for the next period, and to check progress on them at the following review. Trying to fix everything at once overloads limited attention and changes so many variables that you cannot tell what worked; changing one thing at a time keeps your process interpretable, mirroring the consistency principle. The best reviews read like a short experiment log: last period I focused on not taking setups outside the checklist, adherence rose from 70 to 88 percent, this period I will focus on cutting first-thirty-minute trades. Improvement compounds through this steady, single-threaded iteration.

Practical example

Illustrative example (Indian market)

In a weekly review of thirty Nifty and Bank Nifty trades, a trader tallies plan adherence at 73 percent and lists the seven deviations. Applying the resulting discipline, they find four of their losing trades were A-graded, sound trades that simply lost, so they leave the process untouched there. But the three worst losses were all C-graded impulsive entries taken after an earlier loss, a clear revenge-trading pattern and a genuine process error. Rather than overhaul everything, they set one action: a rule to stop trading after two losses, plus closing the live profit box that triggers the impulse. The next weekly review checks whether the rule held and whether the C-graded cluster shrank, keeping the change measurable.

Grouping a month of trades by day-to-expiry, an NSE trader discovers their Bank Nifty expiry-day scalps have strongly negative expectancy from theta and slippage while their earlier-in-the-week trades are positive. The monthly review turns this single pattern into one decision, stop trading the expiry-day subset, which no daily reaction to individual trades had revealed.

Advantages

  • Converts journal data into concrete, testable improvements
  • Counters resulting by grading decisions on quality, not just outcome
  • Separates process errors that need fixing from variance that does not
  • Surfaces patterns across the sample that single trades hide
  • Produces prioritised single actions so improvement stays interpretable

Limitations

  • Review needs honest, complete journal data to draw on
  • Small samples make aggregated conclusions unreliable, so patience is required
  • Judging decision quality is harder than reading outcomes and can be done wrong
  • Emotional bias can distort review unless it is scheduled and structured
  • Review improves an existing process but cannot supply an edge that is absent

Why it matters in practice

  • The review is where improvement actually happens; the journal without review is inert data
  • A structured review is what lets a trader iterate deliberately rather than react emotionally

Common mistakes

  • Keeping a journal but rarely running a structured review
  • Resulting: grading decisions by whether they won rather than whether they were sound
  • Over-correcting after normal losing trades caused by variance, not error
  • Ignoring real process leaks because a lucky win masked them
  • Producing vague resolutions instead of one concrete, testable change
  • Reviewing only after bad days, when emotion and recency dominate

Professional usage

Structured review is standard practice on professional desks and in trading coaching. Regular sessions dissect trades against the playbook, distinguish execution errors from variance, and analyse performance in risk-normalised units over large samples so conclusions are statistically grounded. Reviews are scheduled rather than emotional, focus on decision quality independent of outcome, and end with specific, prioritised adjustments that the next review evaluates. The professional stance is explicitly iterative and humble: review improves the process incrementally and never claims that better review guarantees profit, only that it is how skill is built and leaks are found.

Key takeaways

  • Review is where journal data becomes improvement; schedule it, do not wing it
  • Run daily, weekly and monthly reviews, each revealing different patterns
  • Avoid resulting: judge decisions on quality given what you knew, not on outcome
  • Separate process errors that need a fix from variance that needs none
  • End every review with one concrete, prioritised action for the next period

Frequently asked questions

What is a trading review process?
It is a structured, recurring routine, daily, weekly and monthly, for examining your journalled trades and process metrics to extract lessons, judge decisions on quality rather than outcome, and decide concrete adjustments for the next period. It is the analysis stage that turns journal data into improvement.
How often should I review my trading?
Use three timescales: a daily review at the close to grade trades and capture fresh lessons, a weekly review to aggregate adherence and setup performance, and a monthly review of your equity curve, expectancy and structural changes. Each timescale reveals patterns the others miss.
What is resulting in trading?
Resulting, a term from Annie Duke, is judging a decision solely by its outcome. Because a good decision can lose and a bad one can win in the short run, resulting teaches false lessons, so disciplined review grades decisions on whether they were sound given what you knew.
How do I judge a trade if the outcome is noisy?
Ask two separate questions: was the decision sound given the information at the time, and what was the result. Grade primarily on the first. A well-reasoned trade that lost gets a good process grade, and a reckless trade that won gets a poor one.
How do I tell a process error from bad luck?
Sort each loss into two bins: process errors, such as breaking a rule, mis-sizing or taking a setup outside your plan, and variance, sound trades that simply lost. Process errors demand a change; variance demands none, and over-correcting for it breeds inconsistency.
What should a weekly review cover?
Your plan-adherence rate and the specific deviations, mistakes that repeated, win rate and average R by setup, whether size or routine drifted, and one or two focus points for the coming week. It aggregates the week into a small number of actionable conclusions.
What should a monthly review cover?
The widest lens: your equity curve and drawdown, expectancy per strategy over a larger sample, progress on the prior month's focus points, and any structural change to rules, instruments or sizing. The bigger sample makes conclusions about edge more reliable than weekly data.
Why should review be scheduled rather than casual?
Because ad hoc reflection, usually after a bad day, is dominated by emotion and recency and examines a biased slice of trades. A scheduled review looks at the full sample calmly, which is what allows honest pattern-finding rather than emotional over-reaction.
What patterns should I look for in review?
Group trades by setup, time of day, day-to-expiry, instrument, discipline grade and emotional state, and look for where losses concentrate. Common findings are that impulsive off-plan trades cause most damage, or that a specific subset like expiry-day trades has negative expectancy.
Why shouldn't I change my strategy after every loss?
Because most individual losses are variance, not error, and over-correcting for normal losing trades destroys the consistency that lets an edge show through. Change the process only when review identifies a genuine, repeated process error, not after a single sound trade that lost.
How many changes should come out of a review?
At most one or two concrete, prioritised actions. Changing many variables at once overloads attention and makes it impossible to tell what worked. Single-threaded iteration keeps your process interpretable and lets the next review measure the change's effect.
How do I stop a review from being just venting?
Structure it around data and specific questions, schedule it away from the emotional aftermath of a bad day, grade decisions on quality, and require a concrete written action as the output. A review that ends without a testable next step has not done its job.
Why does resulting feel so natural?
Because outcomes are vivid and immediate while decision quality is abstract, and hindsight bias makes results feel inevitable. Overcoming resulting takes deliberate effort to reconstruct what you knew at the time, which is exactly why pre-trade reasoning in the journal is so valuable.
Can I over-review my trading?
Yes, in the sense of over-reacting: reviewing too reactively and changing the process after every loss creates churn and inconsistency. Effective review is regular but calm, distinguishes variance from error, and changes little unless the evidence over a sample warrants it.
What sample size do I need for review conclusions?
Per-trade lessons are immediate, but conclusions about a setup's edge need a meaningful sample, often dozens of trades, because variance dominates small samples. Treat weekly conclusions as tentative and lean on monthly and larger samples for judgments about whether an approach works.
How does review connect to my trading journal?
The journal supplies the raw data, including the pre-trade reasoning that lets you judge decision quality, and the review analyses that data to produce lessons and changes. The journal without review is inert; the review without a journal has nothing reliable to analyse.
Should reviews focus on winners or losers?
Both. Losers reveal process errors and variance, but winners can hide luck, rule-breaking that paid off, or oversizing. Reviewing winners with the same scrutiny prevents you from mistaking lucky or reckless wins for evidence that your process is sound.
What is the output of a good review?
A short, specific, prioritised action for the next period, framed like an experiment: what you will change, why, and how you will measure it. The following review then checks whether the change helped, so improvement compounds through steady iteration.
Does reviewing guarantee I will improve?
No. Review is how deliberate improvement happens, but it depends on honest data, correct interpretation and an underlying edge to refine. It raises the odds and speed of improvement and helps you find leaks, without guaranteeing better results, which stay uncertain.
How long should a review take?
A daily review can be a few minutes of grading and notes, a weekly review perhaps half an hour of aggregation, and a monthly review longer. The value is in consistency and structure, not duration, so a short scheduled review beats an occasional marathon one.
What is the first habit to build for reviewing?
Start with a short daily review that grades each trade for plan adherence and separates losses into error or variance. That single habit builds the discipline of judging decisions on quality, which is the foundation the weekly and monthly reviews rest on.

Voice search & related questions

Natural-language questions people ask about Review Process.

How do I review my trades properly?
Sit down on a schedule with your journal and ask what you followed, what repeated, and what actually made or lost money, then pick one thing to change for next time.
What is resulting in trading?
It is judging a decision only by whether it won. Since good trades can lose, that teaches the wrong lessons, so grade your decisions on whether they were sound.
How often should I review my trading?
Daily to grade each trade, weekly to spot repeating mistakes, and monthly to look at the bigger picture. Each level shows patterns the others miss.
Should I change my strategy after a losing day?
Usually not. Most single losses are just variance. Change your process only when review shows a real, repeated mistake, not after one trade that lost.
How do I know if a loss was a mistake or bad luck?
Ask if you broke a rule or mis-sized. If yes, it is a process error to fix. If you followed a sound plan and it lost, that is just variance.
What should come out of a review?
One clear thing to change next time, written down, that you can measure. A review with no concrete action has not really helped you.

Sources & references

Last reviewed 12 July 2026. Educational content only — not investment advice. Markets and rules change; verify current conventions with SEBI, NSE/BSE and your broker.

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Educational content only — not investment advice. Examples use illustrative numbers and simplified models. Risk-management techniques reduce but never remove risk, and trading derivatives involves substantial risk of loss. See our Risk Disclosure and SEBI Disclaimer.