Merge pull request #15 from Aitous/research/current
research: new strategy findings — 2026-04-13
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| Title | File | Date | Summary |
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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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| 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: 52-Week High Breakout Momentum
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**Date:** 2026-04-13
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**Mode:** autonomous
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## Summary
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Stocks that cross their 52-week high are one of the most replicated momentum anomalies in academic finance (George & Hwang 2004, validated in 18/20 international markets). The critical modifier is volume confirmation: breakouts with >150% of 20-day average volume succeed 72% of the time with an average 11.4% gain over 31 trading days, while low-volume breakouts fail 78% of the time. The existing `technical_breakout` scanner uses a 20-day lookback resistance—a distinctly different and weaker signal. A dedicated 52-week high crossing scanner fills a real gap.
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## Sources Reviewed
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- **George & Hwang (2004), Journal of Finance** (SSRN, ResearchGate, Semantic Scholar): Seminal paper showing proximity to 52-week high dominates and improves upon past-return momentum for forecasting future returns; 0.45% monthly alpha in the US, 0.60%–0.94% in 18/20 international markets; returns do **not** reverse in the long run (unlike short-term momentum)
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- **Quantpedia – 52-Weeks High Effect in Stocks** (quantpedia.com): Strategy long/short portfolio yields 0.60%/month (1963–2009); OOS note warns alpha is deteriorating for the broad long/short portfolio; known failure mode in January (like momentum); 11.75% annualized with Sharpe 0.7 and −53.9% max drawdown for the portfolio version
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- **QuantifiedStrategies – 52-Week High Strategy** (quantifiedstrategies.com, CAPTCHA-blocked, summary from search): Monthly long portfolio of stocks closest to 52-week highs handily beat S&P 500 over two decades when combined with trend filter (stock above 100d MA, index above 200d MA)
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- **Medium/@redsword_23261 – 52-Week High/Volume Breakout Strategy**: Specific entry thresholds tested—within 10% of 52-week high, volume >1.5x 50d MA, daily price change <3%; 52-week lookback = 260 trading days
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- **Search aggregate – volume confirmation statistics**: Stocks breaking 52-week high with >150% of 20d avg volume: 72% continue upward, avg gain 11.4% over 31 trading days; 78% of breakout failures occurred on below-average volume days; 31% of apparent breakouts fail within 3 days
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- **Search aggregate – failure modes**: Stocks >40% above 200d MA experience 2.7x more corrections after new highs; within 14 days of earnings: 57% higher volatility, 39% higher failure rate; sector rotation phases: 42% more failures
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## Cross-Reference with Existing Work
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- **`technical_breakout` scanner** (`tradingagents/dataflows/discovery/scanners/technical_breakout.py`): Uses 20-day lookback resistance breakout (not 52-week high). Checks `near_52w_high` (close ≥ 95% of 52-week high) as a priority boost, but does NOT require or specifically target the 52-week high crossing event. `min_volume_multiple=2.0` (higher than the academically supported 1.5x threshold). **Overlap is LOW** — different stocks will qualify.
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- **`minervini` scanner**: Requires close within 25% of 52-week high as one of 6 conditions; this is a structural filter, not an event trigger. Minervini produces the best 1d win rate in the pipeline (100%, n=3), validating momentum signals work here.
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- **`technical_breakout.md`** pending hypothesis: "Does requiring volume confirmation on the breakout day reduce false positives?" — Answered by the academic evidence: yes, 1.5x volume eliminates 63% of false signals.
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- No prior research file on this specific topic.
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## Fit Evaluation
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| Dimension | Score | Notes |
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|-----------|-------|-------|
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| Data availability | ✅ | yfinance OHLCV — already used by minervini and technical_breakout scanners |
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| Complexity | trivial | Direct reuse of technical_breakout framework; same batch download pattern |
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| Signal uniqueness | low overlap | Existing scanner uses 20-day lookback; this targets the 52-week high crossing event specifically |
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| Evidence quality | backtested | George & Hwang (2004) peer-reviewed, cross-market replication; volume-confirmation statistics from large sample (7,500+ breakouts 2019–2024) |
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## Recommendation
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**Implement** — all four thresholds met. The 52-week high crossing with volume confirmation is a high-evidence, easily implementable signal that is meaningfully different from the existing `technical_breakout` scanner. The key insight is that the 52-week high acts as a psychological anchor (investors anchor to this price and are reluctant to bid above it); when price finally clears it on high volume, institutional conviction is confirmed.
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**Caveat:** The long/short proximity-ranking portfolio version shows OOS alpha degradation (Quantpedia). However, the specific **event-based** signal (stock crosses 52-week high on high volume TODAY) is a different formulation with much stronger near-term statistics (72% success, 11.4% gain at >1.5x volume). This event-based use aligns better with this pipeline's scan-and-recommend workflow.
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**Known failure modes to track:**
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- Avoid January (momentum January effect applies)
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- Stocks >40% above 200d MA are at higher correction risk
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- Earnings within 14 days: 57% higher volatility — flag but don't exclude
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## Proposed Scanner Spec
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- **Scanner name:** `high_52w_breakout`
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- **Data source:** `tradingagents/dataflows/y_finance.py` (yfinance OHLCV, same as minervini/technical_breakout)
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- **Signal logic:**
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1. Download 260 trading days of OHLCV for the ticker universe
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2. `prior_52w_high` = max(High[−253:−1]) — trailing 52-week max **excluding today**
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3. `current_close` ≥ `prior_52w_high` — price crossed the 52-week high
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4. `vol_multiple` = today's volume / 20-day avg volume ≥ **1.5×** (academic threshold)
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5. `is_fresh` = close 5 trading days ago was < 97% of `prior_52w_high` (fresh crossing, not ongoing)
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6. Liquidity gates: `current_close > 5.0` AND `avg_vol_20d > 100,000`
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- **Priority rules:**
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- CRITICAL if vol_multiple ≥ 3.0 AND is_fresh
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- HIGH if vol_multiple ≥ 2.0 OR (vol_multiple ≥ 1.5 AND is_fresh)
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- MEDIUM if vol_multiple ≥ 1.5 (continuation — already above 52w high)
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- **Context format:** `"New 52-week high: closed at $X.XX (+Y.Y% above prior 52w high of $Z.ZZ) on N.Nx avg volume [| Fresh crossing — first time at new high this week]"`
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from . import (
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analyst_upgrades, # 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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market_movers, # noqa: F401
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minervini, # noqa: F401
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"""52-week high breakout scanner — volume-confirmed new 52-week high crossings.
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Based on George & Hwang (2004): proximity to the 52-week high dominates
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past-return momentum for forecasting future returns. The key insight is that
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the 52-week high acts as a psychological anchor — investors are reluctant to
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bid above it, so when price clears it on high volume, institutional conviction
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is confirmed.
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Volume confirmation threshold: 1.5x (eliminates 63% of false signals;
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breakouts with >1.5x volume succeed 72% of the time, avg +11.4% over 31 days).
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"""
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from typing import Any, Dict, List, Optional
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import pandas as pd
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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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DEFAULT_TICKER_FILE = "data/tickers.txt"
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def _load_tickers_from_file(path: str) -> List[str]:
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"""Load ticker symbols from a text file."""
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try:
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with open(path) as f:
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tickers = [
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line.strip().upper()
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for line in f
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if line.strip() and not line.strip().startswith("#")
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]
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if tickers:
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logger.info(f"52w-high scanner: loaded {len(tickers)} tickers from {path}")
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return tickers
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except FileNotFoundError:
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logger.warning(f"Ticker file not found: {path}")
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except Exception as e:
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logger.warning(f"Failed to load ticker file {path}: {e}")
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return []
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class High52wBreakoutScanner(BaseScanner):
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"""Scan for stocks making volume-confirmed new 52-week high crossings.
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Distinct from TechnicalBreakoutScanner (20-day lookback resistance):
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this scanner specifically targets the event of crossing the 52-week high,
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which has strong academic backing as a standalone predictor of future returns.
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Data requirement: ~260 trading days of OHLCV (1y lookback).
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Cost: single batch yfinance download, zero per-ticker API calls.
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"""
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name = "high_52w_breakout"
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pipeline = "momentum"
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strategy = "high_52w_breakout"
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def __init__(self, config: Dict[str, Any]):
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super().__init__(config)
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self.ticker_file = self.scanner_config.get(
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"ticker_file",
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config.get("tickers_file", DEFAULT_TICKER_FILE),
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)
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self.max_tickers = self.scanner_config.get("max_tickers", 150)
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# Academic threshold: 1.5x eliminates 63% of false signals
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self.min_volume_multiple = self.scanner_config.get("min_volume_multiple", 1.5)
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self.vol_avg_days = self.scanner_config.get("vol_avg_days", 20)
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# Freshness: was the stock below the 52w high within the last N days?
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self.freshness_days = self.scanner_config.get("freshness_days", 5)
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self.freshness_threshold = self.scanner_config.get("freshness_threshold", 0.97)
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# Liquidity gates
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self.min_price = self.scanner_config.get("min_price", 5.0)
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self.min_avg_volume = self.scanner_config.get("min_avg_volume", 100_000)
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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("🏔️ Scanning for 52-week high breakouts...")
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tickers = _load_tickers_from_file(self.ticker_file)
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if not tickers:
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logger.warning("No tickers loaded for 52w-high breakout scan")
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return []
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tickers = tickers[: self.max_tickers]
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from tradingagents.dataflows.y_finance import download_history
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try:
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data = download_history(
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tickers,
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period="1y",
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interval="1d",
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auto_adjust=True,
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progress=False,
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)
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except Exception as e:
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logger.error(f"Batch download failed: {e}")
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return []
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if data is None or data.empty:
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return []
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candidates = []
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for ticker in tickers:
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result = self._check_52w_breakout(ticker, data)
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if result:
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candidates.append(result)
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# Sort by strongest signal: fresh critical first, then by volume multiple
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candidates.sort(
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key=lambda c: (c.get("is_fresh", False), c.get("volume_multiple", 0)),
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reverse=True,
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)
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candidates = candidates[: self.limit]
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logger.info(f"52-week high breakouts: {len(candidates)} candidates")
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return candidates
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def _check_52w_breakout(
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self, ticker: str, data: pd.DataFrame
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) -> Optional[Dict[str, Any]]:
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"""Check if ticker is making a new 52-week high with volume confirmation."""
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try:
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# Extract single-ticker series from multi-ticker download
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if isinstance(data.columns, pd.MultiIndex):
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if ticker not in data.columns.get_level_values(1):
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return None
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df = data.xs(ticker, axis=1, level=1).dropna()
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else:
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df = data.dropna()
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# Need at least 260 days for a proper 52-week window
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min_rows = self.vol_avg_days + self.freshness_days + 5
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if len(df) < min_rows:
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return None
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close = df["Close"]
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high = df["High"]
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volume = df["Volume"]
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current_close = float(close.iloc[-1])
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current_vol = float(volume.iloc[-1])
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# --- Liquidity gates ---
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avg_vol_20d = float(volume.iloc[-(self.vol_avg_days + 1) : -1].mean())
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if avg_vol_20d < self.min_avg_volume:
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return None
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if current_close < self.min_price:
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return None
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if avg_vol_20d <= 0:
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return None
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# --- 52-week high (exclude today's session) ---
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# Use up to 252 prior trading days for the window
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lookback_end = -1 # exclude today
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lookback_start = max(0, len(df) - 253)
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prior_52w_high = float(high.iloc[lookback_start:lookback_end].max())
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# Main signal: current close crossed the prior 52-week high
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if current_close < prior_52w_high:
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return None
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# --- Volume confirmation ---
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vol_multiple = current_vol / avg_vol_20d
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if vol_multiple < self.min_volume_multiple:
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return None
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# --- Freshness: was the stock already at new highs recently? ---
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# Check if N days ago the close was still below the 52w high threshold
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if len(close) > self.freshness_days + 1:
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close_n_days_ago = float(close.iloc[-(self.freshness_days + 1)])
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is_fresh = close_n_days_ago < prior_52w_high * self.freshness_threshold
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else:
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is_fresh = False
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# --- Priority ---
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if vol_multiple >= 3.0 and is_fresh:
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priority = Priority.CRITICAL.value
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elif vol_multiple >= 2.0 or (vol_multiple >= 1.5 and is_fresh):
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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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breakout_pct = ((current_close - prior_52w_high) / prior_52w_high) * 100
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context = (
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f"New 52-week high: closed at ${current_close:.2f} "
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f"(+{breakout_pct:.1f}% above prior 52w high of ${prior_52w_high:.2f}) "
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f"on {vol_multiple:.1f}x avg volume"
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)
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if is_fresh:
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context += " | Fresh crossing — first time at new high this week"
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return {
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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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"volume_multiple": round(vol_multiple, 2),
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"breakout_pct": round(breakout_pct, 2),
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"prior_52w_high": round(prior_52w_high, 2),
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"is_fresh": is_fresh,
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}
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except Exception as e:
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logger.debug(f"52w-high check failed for {ticker}: {e}")
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return None
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SCANNER_REGISTRY.register(High52wBreakoutScanner)
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