TradingAgents/docs/iterations/research/2026-04-14-obv-divergence.md

Research: OBV Divergence as Multi-Week Accumulation Signal

Date: 2026-04-14 Mode: autonomous

Summary

On-Balance Volume (OBV) divergence — price flat or falling while OBV trends up — is an established signal for detecting multi-week institutional accumulation. Academic evidence on volume-price causality is mixed at the mean, but commercial backtests consistently show divergence strategies outperforming simple momentum in qualitative studies. The signal is distinct from the existing volume_accumulation scanner (which detects single-day spikes) and uses already-integrated price and volume data, making it straightforward to implement.

Sources Reviewed

• ArrowAlgo OBV Guide: OBV divergence strategy: price lower low + OBV higher low = 68% win rate, 12% avg annual return (4-year backtest on individual stocks). Breakout confirmation: Sharpe 1.4. No standalone significance without price structure confirmation.
• Vestinda OBV Backtesting: OBV + Ichimoku reversal on RIOT: 25% win rate but 64% annual ROI (high-reward lottery approach); OBV + Ichimoku on BAC: 46% win rate, 5.5% annual ROI, 35% outperformance vs. buy-and-hold. Confirms OBV is better as a filter than a trigger.
• StockCharts ChartSchool: Canonical OBV definition (Granville 1963). Rising OBV during sideways/declining price = quiet accumulation. Bullish divergence entry: price at lower low, OBV at higher low. Failure modes: volume spikes from news events, standalone unreliability.
• NinjaTrader OBV Blog: Three strategies (trend-following, divergence, breakout confirmation). Key: OBV crossing above EMA = bullish entry. No hard win-rate stats.
• ScienceDirect volume-return causality study: Lagged volume coefficient insignificant at the mean (OLS), but quantile regressions show higher predictive power when informed trading is elevated. Suggests OBV works better in high-conviction accumulation regimes.
• TradingAgents codebase: OBV calculation already exists in technical_analyst.py:298-348 for per-stock analysis, not for scanning. Reuse is straightforward.

Fit Evaluation

Dimension	Score	Notes
Data availability	✅	Price + volume history via y_finance.py; scan cache already built by volume_accumulation scanner (shared "default" cache key)
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
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
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)

Recommendation

Implement — meets all four auto-implement thresholds. Signal is complementary to existing volume_accumulation scanner, reuses cached data, and has qualitative-level evidence (same tier as short_squeeze at time of implementation). Weak academic backing is a known limitation; the signal should be treated as a discovery filter and validated with /iterate performance data.

Proposed Scanner Spec

• Scanner name: obv_divergence
• Data source: tradingagents/dataflows/alpha_vantage_volume.py (download_volume_data + _records_to_dataframe); reuses the "default" volume cache shared with volume_accumulation
• Signal logic:
  1. Load 90d daily price+volume history from the shared cache
  2. Compute OBV: cumulative sum, add volume if close > prev_close, subtract if close < prev_close
  3. Bullish divergence: price_change_pct (lookback_days ago) ≤ max_price_change_pct (default 2%) AND obv_pct_gain ≥ min_obv_pct_gain (default 8%), where obv_pct_gain is the OBV change over the lookback period normalized by avg_daily_volume × lookback_days
  4. Filter out stocks where price fell >5% (likely distribution, not accumulation)
• Priority rules:
  • HIGH if obv_pct_gain ≥ 20% AND price_change_pct ≤ 0 (clear divergence, price unchanged or down)
  • MEDIUM if obv_pct_gain ≥ 8% (mild divergence during consolidation)
• Context format: "OBV divergence: price {price_change_20d:+.1f}% over {lookback}d, OBV +{obv_pct_gain:.1f}% of avg vol — multi-week accumulation signal"