pead-screener

PEAD screening identifies post-earnings-announcement drift candidates by analyzing weekly candle patterns to detect red candle pullbacks and subsequent breakouts. It computes composite scores to classify the stage of the drift for structured review.

You can screen post-earnings drift candidates by running Mode A, which fetches earnings events from an FMP earnings calendar. It applies minimum gap and market-cap filters, computes weekly candles, and generates structured JSON and Markdown reports.

Yes, Mode B accepts earnings-trade-analyzer JSON outputs to perform weekly candle analysis. It grades the inputs, scans for red candle pullbacks and breakouts, and computes composite scores for post-earnings drift candidates.

You need the requests library installed to run the PEAD screener. The Skill uses it to fetch earnings calendar data in Mode A and processes weekly candle patterns to output Top-N reports with API usage metrics.

Automating post-earnings drift screening is best handled by computing weekly candle patterns to identify breakouts and red candle pullbacks. This Skill generates composite scores, stage classifications, and structured JSON and Markdown reports for consistent review.

Limitations include relying on FMP earnings calendar data availability in Mode A and requiring pre-processed earnings-trade-analyzer JSON outputs for Mode B. Screening accuracy depends on the quality of weekly candle computation and market-cap filter inputs.