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+# /iterate
+
+Analyze recent discovery runs and P&L outcomes, update learning files in
+`docs/iterations/`, implement concrete code improvements in `tradingagents/`,
+then commit everything. In CI (`CI=true`), stop before git operations — the
+workflow handles branching and PR creation.
+
+---
+
+## Step 1: Determine What to Analyze
+
+Read `docs/iterations/LEARNINGS.md` to find the **Last analyzed run** date.
+
+Then scan `results/discovery/` for all run directories with dates AFTER the
+last analyzed date. Each run directory contains `discovery_result.json` and
+`tool_execution_logs.json`. Collect all unanalyzed runs.
+
+Also scan `data/recommendations/` for JSON files dated 5 or more days ago.
+Load each file and extract all recommendations. These are mature enough to
+have played out — analyze them regardless of `status` field value.
+For P&L analysis you need: `ticker`, `strategy_match`, `final_score`,
+`confidence`, `discovery_date`, `entry_price`, `status`.
+
+Set your analysis mode:
+- If unanalyzed runs exist → include **fast-loop** (output quality analysis)
+- If mature recommendations exist → include **P&L loop** (outcome analysis)
+- If neither → print "No new data to analyze since last run." and exit.
+
+## Step 2: Load Domain Context
+
+For each scanner that appears in the unanalyzed runs (check `strategy_match`
+field in discovery results), read the corresponding file from
+`docs/iterations/scanners/<scanner_name>.md`.
+
+Also read `docs/iterations/pipeline/scoring.md`.
+Also read `docs/iterations/LEARNINGS.md` for full index awareness.
+
+## Step 3: Analyze
+
+### Fast-Loop Analysis (output quality)
+For each unanalyzed run's `discovery_result.json`:
+- **Signal specificity**: Does each candidate have a concrete, specific thesis
+  or a generic one? Flag candidates with vague context.
+- **Scanner noise rate**: How many candidates per scanner? Scanners producing
+  10+ candidates with low scores (<65) are noisy.
+- **Confidence calibration**: Is confidence (1-10) consistent with score (0-100)?
+  A score of 85 with confidence 5 suggests miscalibration.
+- **Duplicate candidates**: Same ticker appearing from 2+ scanners — note as
+  confluence (positive) or redundancy (negative, if identical thesis).
+
+### P&L Loop Analysis (outcome analysis)
+For each mature recommendation:
+- Group by `strategy_match` (scanner). Compute per-scanner: how many
+  recommendations were made, what was the average final_score, and whether
+  `status` fields suggest positive or negative outcomes.
+- Flag scanners where final_score > 80 but outcomes appear poor — overconfident.
+- Flag scanners where final_score < 65 but outcomes appear good — undervalued.
+- Note patterns: do high-confidence (9-10) picks outperform low-confidence (6-7)?
+
+## Step 4: Write Learnings
+
+For each scanner that appeared in the analysis, update its domain file in
+`docs/iterations/scanners/<scanner_name>.md`:
+
+1. **Append to Evidence Log**: Add a dated entry with your specific observations.
+   Use this format:
+   ```
+   ### YYYY-MM-DD — <run date or "P&L review">
+   - What was observed
+   - What it implies
+   - Confidence: low / medium / high
+   ```
+2. **Update Current Understanding**: If your new evidence is medium or high
+   confidence AND contradicts or meaningfully extends the current understanding,
+   rewrite that section. Otherwise leave it unchanged.
+3. **Update Pending Hypotheses**: Check off any hypotheses that are now answered.
+   Add new ones that your analysis surfaced.
+
+Update `docs/iterations/LEARNINGS.md`:
+- Set **Last analyzed run** to today's date
+- Update the one-line summary and Last Updated date for each scanner you touched
+
+## Step 5: Implement Code Changes
+
+Based on your learnings, identify concrete improvements. For each improvement:
+
+**Translate one learning → one code change.** Examples:
+- "ML signal threshold is worse than a coin flip" → raise threshold in
+  `tradingagents/dataflows/discovery/scanners/ml_signal.py`
+- "Options flow premium filter is configured but not applied" → add the check
+- "Reddit DD LLM score computed but unused" → use it for priority assignment
+
+For each change:
+1. Read the relevant scanner file to understand current implementation
+2. Make the minimal change that encodes the learning
+3. Do not refactor surrounding code — change only what the learning motivates
+
+Implement all changes before committing.
+
+## Step 6: Commit (skip if CI=true)
+
+If the environment variable `CI` is set, stop here. The workflow handles git.
+
+Otherwise:
+```bash
+git add docs/iterations/ tradingagents/
+git commit -m "learn(iterate): $(date +%Y-%m-%d) — <one-line summary of key findings>"
+```
+
+Then check for an existing open PR on branch `iterate/current`:
+```bash
+EXISTING=$(gh pr list --head iterate/current --state open --json number --jq '.[0].number // empty')
+```
+
+If one exists: push to that branch and update the PR description with your findings appended.
+If none exists: create branch `iterate/current`, push, open PR against `main`.