Weekly Trading Scorecard KPIs: Adherence, Quality, State, Review Depth

Why a Weekly Scorecard Beats Chasing PnL

A weekly scorecard focuses attention on the small, repeatable behaviors that compound into durable results. Outcome metrics like PnL fluctuate with market regimes and randomness. Process metrics reveal whether decisions are consistent, setups are selective, and reviews are deliberate. Research on expertise, self-monitoring, and deliberate practice shows that structured feedback loops improve skill acquisition when feedback is immediate, specific, and tied to controllable actions. A scorecard built on four core KPIs makes that loop concrete: adherence, quality, state, and review depth.

These KPIs target the sequence that produces edge. Adherence captures whether entries, exits, and risk follow the plan. Quality evaluates the setups selected relative to the playbook. State measures cognitive and physiological readiness that moderates performance under stress. Review depth ensures learning is extracted and the playbook evolves. Together they supply a balanced view that cannot be inferred from PnL alone.

The Four KPIs Defined

Adherence is the proportion of decisions aligned with the written plan. It includes entering only approved setups, using predefined risk, following stop and target rules, and respecting no-trade filters. It is scored as the count of plan-consistent decisions divided by total trade decisions for the week. The denominator should reflect choices, not candles, so partial fills or scaled exits still count as one decision unless they represent a distinct rule application.

Quality reflects the structural integrity of each trade relative to the trader’s playbook. It evaluates whether higher timeframe context, location, signal clarity, and expected R multiple were present when the trade was taken. Quality is not whether the trade won; it is whether the evidence matched a proven setup. Using a simple rubric with pre-agreed criteria keeps this KPI objective.

State captures readiness to execute under uncertainty. It integrates sleep sufficiency, stress load, focus, and energy. Many traders use a short pre-session check that includes a self-rating and a quick physiological proxy when available. The aim is not perfection but to detect when a suboptimal state should trigger reduced risk, fewer trades, or a pause. Findings from performance psychology and sports science consistently show that state influences reaction time, risk perception, and rule compliance.

Review depth measures how thoroughly lessons are extracted. A robust review links trades back to hypotheses, tags context and setup, annotates charts, assesses decision points, and writes a concise lesson or an implementation intention. The value is in updating the playbook and reinforcing cues that predict quality setups while pruning ambiguous ones.

Building Operational Definitions

Clarity beats granularity. Each KPI should be defined in one short paragraph and scored on a 0 to 100 scale. Use concrete anchors to reduce subjectivity.

For adherence, define the eligible decision set and the features that constitute compliance. A common approach is to include entry rule alignment, initial stop placement, position sizing within tolerance, and exit according to plan. Treat violating any hard rule as a miss for that decision. Weekly adherence becomes compliant decisions divided by total decisions.

For quality, tie the rubric to the playbook. Example anchors might include alignment with higher timeframe bias, location at a known level, valid trigger signal, and minimum expected R multiple. Weighting can be even unless testing shows certain criteria predict expectancy more strongly.

State benefits from pre-session and intra-session ratings. A simple daily form captures hours of sleep, perceived stress, motivation, and focus on a 1 to 5 scale, with an optional physiological measure if available. Translate the daily score to a 0 to 100 scale and average across trading days. Days intentionally skipped due to low state should not count against state; they should count in favor of adherence if the plan prescribes standing down.

Review depth can be anchored to completion of critical elements. A consistent structure is helpful: annotated chart, hypothesis and invalidation, post-trade narrative on decision points, and one specific lesson or change to the playbook. Completion quality can be judged by clarity and evidence rather than length.

Scoring and Weighting

Score each KPI on 0 to 100. A simple weight scheme places emphasis where execution risk is highest. An effective default is 40 for adherence, 30 for quality, 20 for state, and 10 for review depth. The rationale echoes research on error chains in complex tasks: most performance failures begin with rule violations, followed by poor selection, amplified by degraded state, and either corrected or repeated depending on review quality.

Compute the weighted weekly score as the sum of KPI scores times weights divided by 100. A week with 85 adherence, 70 quality, 60 state, and 90 review depth yields 76.5 overall. The absolute number matters less than its stability and direction relative to one’s baseline and market conditions.

Data Capture That Fits Real Trading

The scorecard is only as reliable as the underlying journal. The logging process must be short and frictionless. Most traders need four small inputs per trade: the setup tag, context notes, the decisive signal, and the rule compliance check. A daily state check takes under two minutes. Post-trade review can be batched on a single block at the end of the session.

Illustrative example: a trader takes a pullback long in an uptrend aligned with higher timeframe support. The entry is at a retest of a level tagged in pre-market prep, with a well-defined invalidation. The trader logs the setup tag, the higher timeframe bias, the entry and stop relative to the level, and whether position size matched the pre-committed risk. After the session, the chart is annotated, the hypothesis is evaluated, and a one-sentence lesson is recorded, such as tightening the trigger rule when volatility expands.

Calibrating Targets and Baselines

Collect four weeks of scores before setting targets. Baselines smooth out early noise and reveal where the constraint lies. Many traders discover that adherence lags quality because impulse entries or premature exits erode otherwise good selection. Others find quality lags because the playbook is too broad and invites marginal trades.

Targets should be realistic and asymptotic. Moving adherence from 70 to 85 is feasible with simple environmental controls. Moving it from 90 to 95 often requires structural changes such as automating stops or removing hotkeys. Targets work best when tied to concrete behaviors and small weekly experiments. This mirrors the evidence that habit change succeeds when the environment is shaped to make the desired action easy and the undesired action hard.

Using the Scorecard to Drive Action

A score below target should map to a specific behavior change and a pre-commitment. If adherence is below 80, reduce position size by half the next week and require a screenshot of the checklist before each entry. If quality is below 70, prune the playbook to the top two setups and enforce a minimum evidence threshold. If state is below 65, add a minimum sleep rule and cap trades after a missed night. If review depth is below 85, block a fixed daily time and limit reviews to the essential elements to reduce friction.

Linking scores to small consequences increases salience and reduces present bias. Behavioral research supports this strategy: pre-commitments and implementation intentions bridge the gap between intention and action by specifying what, when, and how in advance.

Handling Low Sample Weeks and Variability

Low trade counts can distort scores. The solution is a rolling four-week average for the headline numbers while still reviewing the current week qualitatively. Maintain a note when N is small and treat swings cautiously. For quality, provide a confidence note such as number of trades per setup. For adherence, show both percentage and the raw numerator and denominator to keep perspective.

Market regimes also shift the base rate of valid setups. In thin or mean-reverting weeks, quality can stay high with fewer trades, while adherence can fall if boredom drives impulsive entries. The scorecard catches this by showing rising variance in adherence relative to quality. The response is to tighten the evidence threshold and accept smaller N rather than forcing trades.

Reducing Subjectivity and Drift

Subjective drift is common. Standards loosen slowly unless anchors are refreshed. Keep a small library of exemplar trades for each setup that define quality. Revisit them weekly before the session. For adherence, write and sign a short rules-of-engagement sheet that includes the few non-negotiables. For review depth, define done as a checklist of the critical artifacts instead of an open-ended narrative.

Blind re-scoring of a random subset of trades can reveal drift. Compare the new quality ratings with the original ones. If the correlation falls, tighten anchors or simplify the rubric. While formal inter-rater reliability is impractical for solo traders, self-audits maintain integrity.

Integrating State Without Excuses

State is diagnostic, not permissive. A low state score does not justify breaking rules; it justifies reducing risk, reducing frequency, or standing down. A small set of state triggers protects capital and confidence. For example, after less than six hours of sleep, risk per trade is capped at half. After two consecutive rule breaks, participation is paused and the day becomes review-only. Over time, these rules reduce the frequency of cascading errors that often follow fatigue or frustration.

Physiological measures such as heart rate variability can be useful if they are easy to capture and interpreted consistently, but they are not required. A simple subjective scale is sufficient when used honestly and consistently. The value comes from linking state to clear actions, not from biometric precision.

Review Depth That Actually Changes Behavior

Effective reviews are concise and decision-focused. The objective is to extract a lesson that changes the next set of decisions. A review that simply narrates price action adds little. A stronger review isolates the decisive moment, tests the hypothesis against the unfolding structure, and records the specific cue that will trigger action or restraint next time.

Many traders find value in a brief rehearsal of the lesson before the next session. Visualizing the cue and the intended response primes recognition and lowers reaction time. This is consistent with mental practice research showing that motor and cognitive schemas strengthen when rehearsed in context.

Tuesday Rhythm Tip

On Tuesday, run a quick mini-scorecard using Monday’s data. If adherence is already off target, shrink size and tighten triggers for the rest of the week rather than waiting for Friday. If quality is low, restrict to the single best setup until the score recovers. Early course correction avoids the end-of-week scramble and protects confidence.

A Simple Weekly Flow

Keep the mechanics light so the scorecard is sustainable.

Start each morning with a two-minute state check and a brief review of exemplar trades to anchor quality. During the session, tag each trade with setup, context, signal, and a yes or no adherence note. After the session, annotate charts and write a one-sentence lesson per trade. On Friday, compute KPI scores, apply weights, and write a short note identifying the single constraint for next week.

Example Targets and Adjustments

Suppose a trader’s four-week baseline is 78 overall, with adherence at 82, quality at 68, state at 72, and review depth at 88. The constraint is quality. The adjustment is to prune the playbook to two setups and require higher timeframe alignment plus a clear location for entry. After two weeks, quality rises to 76 and the overall score reaches 81 despite flat PnL. The improved selection reduces churn, and the scorecard confirms the change before PnL noise catches up.

Another trader shows high adherence and quality but low state due to inconsistent sleep. The intervention is a lights-out alarm and a rule that bans new trades after 90 minutes of screen time without a break. State climbs, and adherence improves further because fatigue-driven lapses decline. The review notes reflect fewer impulsive entries and more patience at key levels.

Common Pitfalls

A frequent pitfall is using the scorecard to justify more trading. High review depth can become a productivity trap when it expands instead of clarifying. Keep reviews constrained to the elements that change behavior. Another is averaging away lessons. If a week has two significant rule breaks, address them explicitly with pre-commitments even if the average score looks acceptable. A third is moving goalposts. Lock definitions for a quarter and change them only during a scheduled review.

Closing

A weekly scorecard turns abstract intentions into observable behaviors with clear feedback. Adherence confirms discipline, quality directs selectivity, state protects consistency under stress, and review depth converts experience into skill. The method is simple, the math is basic, and the leverage is high. PnL will always swing. A strong process can compound steadily if it is measured, reviewed, and refined every week.