research(autonomous): 2026-04-14 — automated research run
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| social_dd | scanners/social_dd.md | 2026-04-14 | 57.1% 30d win rate (+1.41% avg 30d, n=26) — only scanner positive at 30d; eval horizon mismatch persists |
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| volume_accumulation | scanners/volume_accumulation.md | — | No data yet |
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| short_squeeze | scanners/short_squeeze.md | 2026-04-14 | 60% 7d win rate (n=11), best 7d performer; BUT 30% 30d — short-term signal only, degrades at 30d |
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| earnings_beat | scanners/earnings_beat.md | 2026-04-14 | New PEAD scanner: recent EPS beats ≥5% surprise; 15% annualized academic edge; distinct from earnings_calendar |
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## Research
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|-------|------|------|---------|
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| Short Interest Squeeze Scanner | research/2026-04-12-short-interest-squeeze.md | 2026-04-12 | High SI (>20%) + DTC >5 as squeeze-risk discovery; implemented as short_squeeze scanner |
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| 52-Week High Breakout Momentum | research/2026-04-13-52-week-high-breakout.md | 2026-04-13 | George & Hwang (2004) validated: 52w high crossing + 1.5x volume = 72% win rate, +11.4% avg over 31d; implemented as high_52w_breakout scanner |
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| PEAD Post-Earnings Drift | research/2026-04-14-pead-earnings-beat.md | 2026-04-14 | Bernard & Thomas (1989): 18% annualized PEAD; QuantPedia: 15% annualized (1987-2004); implemented as earnings_beat scanner (distinct from earnings_calendar's upcoming-only scope) |
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| reddit_dd | scanners/reddit_dd.md | — | No data yet |
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| reddit_trending | scanners/reddit_trending.md | — | No data yet |
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| semantic_news | scanners/semantic_news.md | — | No data yet |
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# Research: Post-Earnings Announcement Drift (PEAD)
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**Date:** 2026-04-14
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**Mode:** autonomous
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## Summary
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PEAD is one of finance's most-studied anomalies: stocks that beat earnings estimates
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continue drifting upward for days to weeks after the announcement. QuantPedia backtests
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(1987–2004) show 15% annualized returns; the effect is strongest in small-to-mid caps
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with >10% EPS surprise. Our pipeline has an `earnings_calendar` scanner that predicts
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upcoming earnings but nothing that captures the drift *after* a beat — this is the gap.
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## Sources Reviewed
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- **QuantPedia — Post-Earnings Announcement Effect**: Combined EAR+SUE strategy generates
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~12.5% abnormal returns p.a. (1987–2004); optimal hold ~60 trading days; effect strongest
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in small caps; most returns on long side; -11.2% max drawdown observed.
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- **Ball & Brown (1968) / Bernard & Thomas (1989)**: Foundational PEAD literature;
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B&T (1989) documented ~18% annualized abnormal returns; magnitude has declined since
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but effect persists — particularly in small caps.
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- **DayTrading.com PEAD guide**: Drift persists through approximately day 9 before
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plateauing; 5–20 day hold periods are optimal for tactical implementations.
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- **SSRN / Philadelphia Fed (PEAD.txt, 2021)**: NLP-enhanced PEAD achieves 8.01%
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drift over 1-year window; suggests signal is durable when combined with text signals.
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- **QuantConnect price+earnings momentum**: Combined momentum strategy showed mixed results
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(Sharpe -0.27) when using *price* momentum alongside earnings growth — not the same as
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surprise-based PEAD.
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- **Alpha Architect — 13F data quality warning**: 13F-based institutional signals have 45-day
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lag and data quality issues — screened out as alternative. PEAD is clearly superior for
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short-horizon plays.
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- **Finnhub API docs / finnhub-python**: `earnings_calendar(from_date, to_date)` returns
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`epsActual` and `epsEstimate` for all US stocks in the window. Surprise detection requires
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only a lookback call — no extra data sources needed.
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## Fit Evaluation
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| Dimension | Score | Notes |
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|-----------|-------|-------|
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| Data availability | ✅ | `finnhub_api.get_earnings_calendar()` already integrated; returns `epsActual` + `epsEstimate`; lookback call detects recent beats |
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| Complexity | moderate | ~3h: query past-14d earnings calendar, filter for beats, compute surprise%, sort by magnitude |
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| Signal uniqueness | low overlap | `earnings_calendar` scanner = UPCOMING earnings; PEAD scanner = RECENT beats + drift capture; different timing and signal |
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| Evidence quality | backtested | QuantPedia: 15% annualized returns (1987–2004); Bernard & Thomas (1989); 60+ years of academic literature |
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## Recommendation
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**Implement** — All auto-implement thresholds pass.
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Key implementation notes:
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- Focus on small-to-mid cap stocks where PEAD effect is strongest (B&T 1989)
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- Minimum 5% surprise threshold to filter noise
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- CRITICAL at >20% surprise, HIGH at 10–20%, MEDIUM at 5–10%
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- Hold horizon: 7–14 days (primary drift window per DayTrading.com)
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- Declining US large-cap PEAD mitigated by: small-cap bias + significant surprise filter
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## Known Failure Modes
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- US large-cap PEAD has declined since 1989 (more efficient pricing); strategy most
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effective for small/mid caps and significant surprises (>10%)
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- SUE reversal after 3 quarters (price reverts on next earnings); this is beyond our
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30d evaluation window so not immediately harmful
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- Overlapping earnings: same ticker may appear in `earnings_calendar` (upcoming) and
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`earnings_beat` (recent); ranker should treat these as separate signals
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## Proposed Scanner Spec
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- **Scanner name:** `earnings_beat`
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- **Strategy:** `pead_drift`
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- **Pipeline:** `events`
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- **Data source:** `tradingagents/dataflows/finnhub_api.py` → `get_earnings_calendar(from_date, to_date, return_structured=True)`
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- **Signal logic:**
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- Query past `lookback_days` (default 14) of earnings calendar
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- Compute `surprise_pct = (epsActual - epsEstimate) / abs(epsEstimate) * 100`
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- Filter: `surprise_pct >= min_surprise_pct` (default 5.0%)
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- Filter: `epsEstimate != 0` and both fields not None
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- Sort by `surprise_pct` descending
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- **Priority rules:**
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- CRITICAL if `surprise_pct >= 20`
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- HIGH if `surprise_pct >= 10`
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- MEDIUM otherwise
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- **Context format:** `"Earnings beat Xd ago: actual $A vs est $B (+Z% surprise) — PEAD drift window open"`
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# Import all scanners to trigger registration
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from . import (
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analyst_upgrades, # noqa: F401
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earnings_beat, # noqa: F401
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earnings_calendar, # noqa: F401
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high_52w_breakout, # noqa: F401
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insider_buying, # noqa: F401
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"""Post-Earnings Announcement Drift (PEAD) scanner.
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Surfaces stocks that recently reported significant EPS beats, capturing
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the well-documented post-earnings drift effect: beaten stocks tend to
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continue drifting upward for 7–30 days after the announcement.
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Research basis: docs/iterations/research/2026-04-14-pead-earnings-beat.md
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Key insight: PEAD edge is strongest for small-to-mid caps with >10% EPS
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surprise (Bernard & Thomas 1989; QuantPedia 15% annualized, 1987-2004).
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Hold window: 7–14 days (primary drift window; effect plateaus ~day 9).
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"""
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from datetime import datetime, timedelta
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from typing import Any, Dict, List
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from tradingagents.dataflows.discovery.scanner_registry import SCANNER_REGISTRY, BaseScanner
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from tradingagents.dataflows.discovery.utils import Priority
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from tradingagents.utils.logger import get_logger
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logger = get_logger(__name__)
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class EarningsBeatScanner(BaseScanner):
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"""Scan for recent EPS beats to capture post-earnings drift (PEAD)."""
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name = "earnings_beat"
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pipeline = "events"
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strategy = "pead_drift"
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def __init__(self, config: Dict[str, Any]):
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super().__init__(config)
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self.lookback_days = self.scanner_config.get("lookback_days", 14)
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self.min_surprise_pct = self.scanner_config.get("min_surprise_pct", 5.0)
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def scan(self, state: Dict[str, Any]) -> List[Dict[str, Any]]:
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if not self.is_enabled():
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return []
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logger.info(
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f"📈 Scanning earnings beats (past {self.lookback_days}d, "
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f">={self.min_surprise_pct}% surprise)..."
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)
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try:
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from tradingagents.dataflows.finnhub_api import get_earnings_calendar
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to_date = datetime.now().strftime("%Y-%m-%d")
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from_date = (datetime.now() - timedelta(days=self.lookback_days)).strftime("%Y-%m-%d")
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earnings = get_earnings_calendar(
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from_date=from_date,
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to_date=to_date,
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return_structured=True,
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)
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if not earnings:
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logger.info("No recent earnings data found")
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return []
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today = datetime.now().date()
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candidates = []
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for event in earnings:
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ticker = event.get("symbol", "").upper().strip()
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if not ticker:
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continue
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eps_actual = event.get("epsActual")
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eps_estimate = event.get("epsEstimate")
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earnings_date_str = event.get("date", "")
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# Need both actual and estimate to compute surprise
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if eps_actual is None or eps_estimate is None:
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continue
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# Avoid division by zero; skip stub/loss estimates near zero
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if eps_estimate == 0:
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continue
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surprise_pct = ((eps_actual - eps_estimate) / abs(eps_estimate)) * 100
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if surprise_pct < self.min_surprise_pct:
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continue
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# Days since announcement
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try:
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earnings_date = datetime.strptime(earnings_date_str, "%Y-%m-%d").date()
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days_ago = (today - earnings_date).days
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except (ValueError, TypeError):
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days_ago = None
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# Priority by surprise magnitude
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if surprise_pct >= 20:
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priority = Priority.CRITICAL.value
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elif surprise_pct >= 10:
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priority = Priority.HIGH.value
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else:
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priority = Priority.MEDIUM.value
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days_ago_str = f"{days_ago}d ago" if days_ago is not None else "recently"
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context = (
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f"Earnings beat {days_ago_str}: actual ${eps_actual:.2f} vs "
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f"est ${eps_estimate:.2f} (+{surprise_pct:.1f}% surprise) "
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f"— PEAD drift window open"
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)
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candidates.append(
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{
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"ticker": ticker,
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"source": self.name,
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"context": context,
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"priority": priority,
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"strategy": self.strategy,
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"eps_surprise_pct": surprise_pct,
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"eps_actual": eps_actual,
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"eps_estimate": eps_estimate,
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"days_since_earnings": days_ago,
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}
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)
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# Sort by surprise magnitude (largest beats first)
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candidates.sort(key=lambda x: x.get("eps_surprise_pct", 0), reverse=True)
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candidates = candidates[: self.limit]
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logger.info(f"Earnings beats (PEAD): {len(candidates)} candidates")
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return candidates
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except Exception as e:
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logger.warning(f"⚠️ Earnings beat scanner failed: {e}")
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return []
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SCANNER_REGISTRY.register(EarningsBeatScanner)
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