ETF Platform Breakout Pattern Screener

Problem Addressed

ETF Platform Breakout Pattern Screener targets a concrete workflow gap: deciding whether an ETF has formed a valid platform breakout often requires manually checking resistance levels, breakout strength, and volume confirmation. This skill turns that judgment into a repeatable scan for portfolios or ETF watchlists.

How It Works

The skill uses an 80/20 framework: the first 80% of bars identify the platform high, and the last 20% validate whether the breakout is effective.
- Platform detection: finds a prior high that is touched multiple times, reducing false platforms caused by single spikes.
- Breakout validation: checks new highs, stability above the platform high, and volume ratio.
- Data pipeline: prefers Tencent Finance API, falls back to AKShare, and can append only missing trading days.
- Outputs: produces full_scan_result.json, scan_candidates.json, and HTML reports for review or email distribution.
Scoring covers platform quality, breakout strength, trend volume, and mid-segment suppression, then maps scores to ratings such as AA, A, and B+. The default scope covers 95 ETFs, with custom lists, --etf-list, or --all parameters available.

Scope and Caveats

It is suitable for research, screening, and reporting, not for direct order execution. A platform breakout is still a pattern hypothesis and can be invalidated by liquidity, sector events, or macro conditions. For production use, keep human review of candidates and verify underlying K-line data and scoring logic when data sources or report labels seem inconsistent.