
Grade process before P&L
Start with a process scorecard, not the return. Did you write the thesis, the invalidation trigger, and the sizing rule before entry? Did you follow t...
Post-trade reviews are how you turn outcomes into a better investing process. This framework helps you audit a closed position without hindsight bias: what you believed at entry, what evidence changed, whether sizing and execution rules were followed, and what was luck vs repeatable edge. You will end each review with one concrete update to your playbook (a trigger, a sizing guardrail, or a checklist item) and a scheduled follow-up so the lesson is actually used. KeepRule is for investment education, not advice—always do your own research.

Pick the smallest next action now: test your bias pattern, run a scenario, or copy a prompt before making a portfolio move.

Start with a process scorecard, not the return. Did you write the thesis, the invalidation trigger, and the sizing rule before entry? Did you follow t...

Write the “expected path” you were betting on (drivers, time window, key evidence) and then document what actually happened. The goal is not to defend...

Every review must produce exactly one actionable update you will reuse: a tighter entry condition, a clearer invalidation threshold, a sizing cap, a “...
Start with a process scorecard, not the return. Did you write the thesis, the invalidation trigger, and the sizing rule before entry? Did you follow the plan (entries, adds, trims, stop conditions), or did you improvise because the price moved? A good outcome with a broken process is still a failure—because it trains the wrong habit.
Write the “expected path” you were betting on (drivers, time window, key evidence) and then document what actually happened. The goal is not to defend yourself—it is to learn why reality differed: wrong thesis, right thesis but wrong timing, correct thesis but wrong position size, or a noisy market that overwhelmed fundamentals. This builds calibration over time.
Every review must produce exactly one actionable update you will reuse: a tighter entry condition, a clearer invalidation threshold, a sizing cap, a “no-trade” filter, or a forced waiting period. Avoid rewriting your entire strategy after one trade. Small, consistent rule upgrades compound faster than dramatic pivots driven by emotion or recency.
Ask: if you ran the same decision 20 times, would the process still make sense? Identify what was repeatable (evidence quality, valuation discipline, risk control) versus what was luck (macro tailwind, sentiment timing, unexpected news). If the win relied on luck, your takeaway should be humility and tighter guardrails—not bigger size next time.
Capture the review in a simple template: thesis snapshot, what changed, process score, and the single rule update. Then schedule a follow-up (7–30 days) to verify you actually applied the new rule on the next decision. Learning is only real when it changes behavior, not when it creates a clever explanation.

Run a lightweight review after every meaningful exit (or major trim) while details are fresh. Then do a monthly batch review to spot patterns across multiple decisions: repeat errors, repeated strengths, and drift in position sizing or checklist adherence. The batch review is where small process upgrades become obvious.
Judging the trade only by the return. Markets can reward bad discipline for a while and punish good decisions in the short run. A high-quality review starts with process (thesis clarity, evidence, sizing, execution rules) and only then looks at outcome. Otherwise you train yourself to chase what “worked,” not what was sound.
You can template the structure (fields, checklist, prompts) and automate data capture (dates, sizing, notes). But the two most important parts still need explicit human judgment: whether the thesis was valid and whether rule adherence was real. Automation should support clarity, not replace accountability.
Treat major reviews as “decision exits.” When you reaffirm a holding, add capital, or change the thesis, run the same process: what was the original expectation, what evidence changed, and did sizing remain inside your policy? Long-term investing still produces decisions that can be audited—just on a slower cadence.
Reconstruct the best-available snapshot (what you thought the driver was, what you feared, and what would have changed your mind) and mark it clearly as reconstructed. Then make the rule upgrade about documentation: require a one-sentence thesis + one invalidation trigger before any new position or add. The fastest win is making the next decision auditable.
Choose your latest closed position, complete the review template, and add one rule improvement for the next cycle.