## Context

The user clarified the real priority: make money by following trends, especially by recovering quickly when a hard veto blocks a move that later repairs into a valid trend.

GPT-Pro also already narrowed the next worthwhile work:
- execution timing
- entry-specific exit treatment
- optional high-zone shadow observation

The practical constraint is equally clear:
- keep it small-team sized
- do not build a broad research platform
- do not change RC1 production behavior before the evidence is stronger

## Goals / Non-Goals

**Goals**
- Turn blocked glued weak rebounds into a reproducible observation dataset.
- Compare delayed reentry paths by subtype in a narrow shadow scope.
- Show whether the pain point is:
- no real followthrough
- execution timing slippage
- exit family clipping the reentry too early
- Keep outputs lightweight and easy to rerun.

**Non-Goals**
- Promote any followthrough branch to formal alpha in this phase.
- Relax default hard blocks in RC1.
- Introduce a new online monitoring subsystem.
- Rework all sell families.

## Decisions

### 1. Research stays outside the RC1 path
This phase adds a standalone script and report only. No change to default branch config, governance, or forward pipeline behavior.

### 2. Candidate observation is rebuilt from actual strategy logic
Instead of inferring from static thresholds alone, the script replays the strategy state machine and captures every `buy_block_glued_high_weak_rebound` decision directly.

### 3. Reentry analysis stays subtype-specific
The script evaluates three isolated probe lanes:
- `mid_zone_very_weak_b1`
- `high_zone_weak_b1`
- `ql_rebound_weak_followthrough`

This keeps evidence clean and avoids mixed-lane conclusions.

### 4. Exit-treatment evidence is observational first
Before touching sell logic, the script measures:
- realized reentry return
- next-open vs same-close execution delta
- post-entry max-close opportunity
- post-exit followthrough left on the table

## Risks / Trade-offs

- [Small sample sizes] accepted; the goal is to avoid broad, unjustified strategy edits.
- [Fetched raw history may change with new market data] accepted; this is a living research report, not a golden baseline.
- [Internal strategy hooks are reused] accepted; this keeps the review aligned with the real engine path.

## Migration Plan

1. Add the lean review script and helper functions.
2. Add a focused regression test for known block/reentry mapping.
3. Generate outputs and publish the review report.
4. Record findings in OpenSpec acceptance and workspace memory.