research(autonomous): 2026-04-14 — automated research run
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| Title | File | Date | Summary |
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| OBV Divergence Accumulation | research/2026-04-14-obv-divergence.md | 2026-04-14 | OBV rising while price flat/down = multi-week institutional accumulation; qualitative 68% win rate; implemented as obv_divergence scanner |
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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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# Research: OBV Divergence as Multi-Week Accumulation Signal
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**Date:** 2026-04-14
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**Mode:** autonomous
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## Summary
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On-Balance Volume (OBV) divergence — price flat or falling while OBV trends up — is an established
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signal for detecting multi-week institutional accumulation. Academic evidence on volume-price
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causality is mixed at the mean, but commercial backtests consistently show divergence strategies
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outperforming simple momentum in qualitative studies. The signal is distinct from the existing
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`volume_accumulation` scanner (which detects single-day spikes) and uses already-integrated price
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and volume data, making it straightforward to implement.
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## Sources Reviewed
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- **ArrowAlgo OBV Guide**: OBV divergence strategy: price lower low + OBV higher low = 68% win
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rate, 12% avg annual return (4-year backtest on individual stocks). Breakout confirmation:
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Sharpe 1.4. No standalone significance without price structure confirmation.
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- **Vestinda OBV Backtesting**: OBV + Ichimoku reversal on RIOT: 25% win rate but 64% annual ROI
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(high-reward lottery approach); OBV + Ichimoku on BAC: 46% win rate, 5.5% annual ROI,
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35% outperformance vs. buy-and-hold. Confirms OBV is better as a filter than a trigger.
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- **StockCharts ChartSchool**: Canonical OBV definition (Granville 1963). Rising OBV during
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sideways/declining price = quiet accumulation. Bullish divergence entry: price at lower low,
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OBV at higher low. Failure modes: volume spikes from news events, standalone unreliability.
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- **NinjaTrader OBV Blog**: Three strategies (trend-following, divergence, breakout confirmation).
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Key: OBV crossing above EMA = bullish entry. No hard win-rate stats.
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- **ScienceDirect volume-return causality study**: Lagged volume coefficient insignificant at the
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mean (OLS), but quantile regressions show higher predictive power when informed trading is
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elevated. Suggests OBV works better in high-conviction accumulation regimes.
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- **TradingAgents codebase**: OBV calculation already exists in `technical_analyst.py:298-348`
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for per-stock analysis, not for scanning. Reuse is straightforward.
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## Fit Evaluation
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| Dimension | Score | Notes |
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|-----------|-------|-------|
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| Data availability | ✅ | Price + volume history via `y_finance.py`; scan cache already built by `volume_accumulation` scanner (shared `"default"` cache key) |
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| Complexity | moderate | OBV computation is a simple loop (~30 lines); divergence detection requires loading cached history per ticker; bulk scan is feasible within scanner timeout |
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| Signal uniqueness | low overlap | `volume_accumulation` detects single-day 2x+ spikes with same-day direction filter; OBV divergence detects sustained multi-week buying pressure during price consolidation — complementary, not redundant |
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| Evidence quality | qualitative | Commercial backtests: 68% win rate (divergence), Sharpe 1.4 (breakout); academic: mixed — volume-return causality illusive at mean but stronger in high-conviction regimes (ScienceDirect 2025) |
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## Recommendation
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**Implement** — meets all four auto-implement thresholds. Signal is complementary to existing
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`volume_accumulation` scanner, reuses cached data, and has qualitative-level evidence (same tier as
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`short_squeeze` at time of implementation). Weak academic backing is a known limitation; the
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signal should be treated as a discovery filter and validated with `/iterate` performance data.
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## Proposed Scanner Spec
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- **Scanner name:** `obv_divergence`
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- **Data source:** `tradingagents/dataflows/alpha_vantage_volume.py` (`download_volume_data` +
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`_records_to_dataframe`); reuses the `"default"` volume cache shared with `volume_accumulation`
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- **Signal logic:**
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1. Load 90d daily price+volume history from the shared cache
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2. Compute OBV: cumulative sum, add volume if close > prev_close, subtract if close < prev_close
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3. Bullish divergence: `price_change_pct (lookback_days ago) ≤ max_price_change_pct (default 2%)`
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AND `obv_pct_gain ≥ min_obv_pct_gain (default 8%)`, where obv_pct_gain is the OBV change
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over the lookback period normalized by `avg_daily_volume × lookback_days`
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4. Filter out stocks where price fell >5% (likely distribution, not accumulation)
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- **Priority rules:**
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- HIGH if `obv_pct_gain ≥ 20%` AND `price_change_pct ≤ 0` (clear divergence, price unchanged or down)
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- MEDIUM if `obv_pct_gain ≥ 8%` (mild divergence during consolidation)
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- **Context format:** `"OBV divergence: price {price_change_20d:+.1f}% over {lookback}d, OBV +{obv_pct_gain:.1f}% of avg vol — multi-week accumulation signal"`
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@ -10,6 +10,7 @@ from . import (
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market_movers, # noqa: F401
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minervini, # noqa: F401
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ml_signal, # noqa: F401
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obv_divergence, # noqa: F401
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options_flow, # noqa: F401
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reddit_dd, # noqa: F401
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reddit_trending, # noqa: F401
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"""OBV divergence scanner — detects multi-week accumulation via OBV/price divergence."""
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from typing import Any, Dict, List, Optional
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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 OBVDivergenceScanner(BaseScanner):
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"""Scan for OBV bullish divergence: price flat/falling while OBV trends up.
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Distinguishes multi-week institutional accumulation (sustained OBV rise
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during price consolidation) from the single-day spikes caught by the
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volume_accumulation scanner.
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"""
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name = "obv_divergence"
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pipeline = "momentum"
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strategy = "volume_divergence"
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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 OBV divergence (multi-week accumulation)...")
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lookback = self.scanner_config.get("lookback_days", 20)
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min_obv_pct = self.scanner_config.get("min_obv_pct_gain", 8.0)
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max_price_change = self.scanner_config.get("max_price_change_pct", 2.0)
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max_tickers = self.scanner_config.get("max_tickers", 2000)
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try:
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from tradingagents.dataflows.alpha_vantage_volume import (
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_records_to_dataframe,
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download_volume_data,
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)
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from tradingagents.dataflows.y_finance import _get_ticker_universe
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tickers = _get_ticker_universe(max_tickers=max_tickers)
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if not tickers:
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logger.info("No tickers in universe")
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return []
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# Reuse shared volume cache built by volume_accumulation scanner
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cache_key = self.scanner_config.get("volume_cache_key", "default")
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raw_data = download_volume_data(
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tickers=tickers,
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history_period_days=90,
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use_cache=True,
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cache_key=cache_key,
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)
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candidates = []
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for ticker, records in raw_data.items():
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result = self._detect_divergence(
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ticker, records, lookback, min_obv_pct, max_price_change, _records_to_dataframe
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)
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if result:
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candidates.append(result)
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# Sort by OBV gain descending; strongest divergences first
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candidates.sort(key=lambda x: x.get("_obv_pct_gain", 0), reverse=True)
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# Strip internal sort key before returning
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final = []
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for c in candidates[: self.limit]:
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c.pop("_obv_pct_gain", None)
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final.append(c)
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logger.info(f"Found {len(final)} OBV divergence candidates")
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return final
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except Exception as e:
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logger.warning(f"⚠️ OBV divergence scan failed: {e}")
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return []
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def _detect_divergence(
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self,
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ticker: str,
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records: List[Dict],
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lookback: int,
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min_obv_pct: float,
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max_price_change: float,
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records_to_df,
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) -> Optional[Dict[str, Any]]:
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"""Compute OBV and detect bullish price/OBV divergence."""
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try:
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hist = records_to_df(records)
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if hist.empty or len(hist) < lookback + 5:
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return None
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closes = hist["Close"].values
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volumes = hist["Volume"].values
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# Compute OBV as cumulative sum
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obv = [0.0]
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for i in range(1, len(closes)):
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if closes[i] > closes[i - 1]:
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obv.append(obv[-1] + volumes[i])
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elif closes[i] < closes[i - 1]:
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obv.append(obv[-1] - volumes[i])
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else:
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obv.append(obv[-1])
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current_obv = obv[-1]
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past_obv = obv[-(lookback + 1)]
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current_price = float(closes[-1])
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past_price = float(closes[-(lookback + 1)])
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if past_price <= 0:
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return None
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price_change_pct = ((current_price - past_price) / past_price) * 100
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# Skip clear distribution: price dropped hard
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if price_change_pct < -5.0:
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return None
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# Normalize OBV change by avg_vol × lookback to get a scale-free percentage
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avg_vol = float(hist["Volume"].mean())
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if avg_vol <= 0:
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return None
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obv_change = current_obv - past_obv
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obv_pct_gain = (obv_change / (avg_vol * lookback)) * 100
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# Bullish divergence: OBV rising while price flat or falling
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if price_change_pct <= max_price_change and obv_pct_gain >= min_obv_pct:
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if obv_pct_gain >= 20.0 and price_change_pct <= 0.0:
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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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return {
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"ticker": ticker,
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"source": self.name,
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"context": (
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f"OBV divergence: price {price_change_pct:+.1f}% over {lookback}d, "
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f"OBV +{obv_pct_gain:.1f}% of avg vol — multi-week accumulation signal"
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),
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"priority": priority,
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"strategy": self.strategy,
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"_obv_pct_gain": obv_pct_gain,
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}
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except Exception:
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pass
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return None
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SCANNER_REGISTRY.register(OBVDivergenceScanner)
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"compression_bb_width_max": 6.0, # Max Bollinger bandwidth for compression
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"compression_min_volume_ratio": 1.3, # Min volume ratio for compression
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},
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"obv_divergence": {
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"enabled": True,
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"pipeline": "momentum",
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"limit": 10,
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"lookback_days": 20, # Days to measure price/OBV change over
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"min_obv_pct_gain": 8.0, # Min OBV gain as % of avg_vol × lookback_days
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"max_price_change_pct": 2.0, # Max price change % over lookback (divergence window)
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"volume_cache_key": "default", # Shares cache with volume_accumulation scanner
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"max_tickers": 2000, # Universe size cap
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},
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"market_movers": {
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"enabled": False,
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"pipeline": "momentum",
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