stock-pool-experiment-runner

name	stock-pool-experiment-runner
description	Use when orchestrating a J-stock custom stock-pool experiment where a fixed entry condition is applied to a broad/full universe during a training window, good tickers are selected into one or more test universes, and those universes are evaluated out-of-sample against current and full-pool controls.

Stock Pool Experiment Runner

Coordinate the stock-pool selection workflow without collapsing the train/test boundary.

Atomic Tasks

Route work to these skills in order:

1. stock-pool-split-planner: define train windows, test windows, controls, pool sizes, and leakage rules.
2. stock-pool-entry-signal-scan: generate training-period entry-signal-analysis datasets from the broad/full universe.
3. stock-pool-ticker-selector: aggregate training signals by ticker and export selected universe JSON files.
4. stock-pool-oos-evaluation-plan: generate an approval-ready OOS evaluation manifest comparing selected, current, and full universes.
5. evaluation-batch-executor: execute only after the exact manifest is approved.
6. stock-pool-oos-review: summarize completed OOS results and leakage checks.

Use existing evaluation-batch-runner only when the user asks for a full batch workflow and a manifest approval boundary is needed.

Hard Rules

• Treat ticker selection as model training. Never select tickers using dates that overlap the test window.
• Keep entry condition, ranking strategy, entry filters, sizing, capacity, and exit strategy fixed across universe controls unless the experiment explicitly varies one of them.
• Compare at least: current production pool, broad/full pool, and trained selected pool of the same target size as the current pool.
• Do not edit production config to run experiments. Pass universe files explicitly through CLI args or manifests.
• Use G:/My Drive/AI-Stock-Sync/config.json as production source of truth when resolving production defaults.
• Use absolute date ranges in plans and reports.

Reporting

Before any long run, report:

• Train window and test window for each fold.
• Universe files for current, full, and selected pools.
• Pool sizes to export, usually including 140 plus sensitivity sizes such as 80 and 200.
• The exact command or manifest that will run next.
• The approval state when execution would start.