research(autonomous): 2026-04-15 — automated research run (#19)
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| Title | File | Date | Summary |
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| RSI(2) Mean Reversion Oversold Bounce | research/2026-04-15-rsi-mean-reversion.md | 2026-04-15 | Connors RSI(2)<10 + price above 200d SMA = 75-79% win rate over 25y backtest; only contrarian signal in pipeline; implemented as rsi_oversold scanner |
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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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# Research: RSI(2) Mean Reversion Oversold Bounce
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**Date:** 2026-04-15
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**Mode:** autonomous
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## Summary
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Larry Connors' 2-period RSI mean-reversion strategy surfaces stocks in uptrends (price
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above 200-day SMA) that have pulled back sharply enough to register RSI(2) < 10. The
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200-day SMA filter is the critical guard against catching falling knives — without it,
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the plain RSI < 30 rule fails in persistent downtrends. Academic evidence from Lehmann
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(1990) and Alpha Architect confirms weekly losers revert at 0.86–1.24% per week, with
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contrarian strategies generating >2% per month in abnormal returns. This is the only
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contrarian signal not represented anywhere in the current momentum-heavy pipeline.
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## Sources Reviewed
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- **QuantifiedStrategies (search results)**: RSI(2) strategy with 75–79% win rate over
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25-year backtest (2000–2025); lower RSI at entry → higher subsequent returns; profit
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factor ≈ 2.08 at best settings.
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- **Medium / FMZQuant — Larry Connors RSI2**: Exact rule: price above 200d SMA AND
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RSI(2) < 10 → buy; exit when RSI(2) > 90. Tested on DIA and individual equities.
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Described as "fairly aggressive short-term" with entry on close.
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- **StockCharts ChartSchool — RSI(2)**: Entry RSI(2) ≤ 5 (aggressive) or ≤ 10; exit
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on move above 5-day SMA or RSI(2) > 90. Volume filter: 20-day avg volume > 40k.
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Warns: "RSI(2) can remain oversold a long time in a bear" → SMA200 filter mandatory.
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- **Alpha Architect — Short-Term Return Reversal (Lehmann 1990)**: Weekly losers
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generate +0.86% to +1.24% per week in the subsequent week; contrarian strategies
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(buy losers, sell winners) produce >2%/month abnormal returns. Effect is strongest
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for liquid, actively-traded stocks.
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- **Alpha Architect — Combining Reversals + Momentum**: Reversal and momentum coexist
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at the 1-month horizon — reversal is dominant among low-turnover stocks, momentum
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among high-turnover. Filtering to high-liquidity names (min avg volume) reduces noise.
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- **WebSearch aggregate**: Connors 25-year backtest CAGR 8.2%, max drawdown 16%;
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performance degrades in prolonged bear markets (2008, Mar 2020) — SMA200 filter
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critical; best results when SPY itself is not in freefall.
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## Cross-Reference: Existing Pipeline
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- **No existing mean-reversion scanner.** All current scanners (minervini,
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high_52w_breakout, technical_breakout, obv_divergence, short_squeeze, insider_buying,
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options_flow, earnings_beat) are momentum- or event-driven. The RSI oversold bounce
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is fully orthogonal.
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- **technical_breakout** (scanners/technical_breakout.md): targets resistance breakouts,
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opposite signal direction. No overlap.
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- **obv_divergence**: detects flat price + rising OBV (accumulation). Partial overlap
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in that both can flag a beaten-down stock, but OBV divergence requires volume evidence
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of buying; RSI oversold can fire on pure price action.
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- **No prior research file** on mean reversion or RSI.
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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 + `download_ohlcv_cached` fully integrated; RSI(2) computable from close prices, 200d SMA from same data |
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| Complexity | trivial/moderate | RSI(2) is a 6-line calculation; same code pattern as `high_52w_breakout` which already uses `download_ohlcv_cached` |
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| Signal uniqueness | low overlap | Only contrarian scanner in the entire pipeline; orthogonal to all momentum signals |
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| Evidence quality | backtested | Connors 25-year backtest, 75–79% win rate; Lehmann (1990) academic paper; Alpha Architect reversal review |
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All four auto-implement thresholds pass → **implement**.
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## Recommendation
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**Implement** — Pipeline gap: zero mean-reversion coverage. RSI(2) with SMA200 trend
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filter is one of the most replicated mean-reversion signals in quant literature, data
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is fully available, and implementation is trivial following the `high_52w_breakout`
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template. Expected holding period: 3–7 days (exit when RSI(2) > 90 or closes above
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5-day SMA).
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## Proposed Scanner Spec
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- **Scanner name:** `rsi_oversold`
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- **Data source:** `tradingagents/dataflows/data_cache/ohlcv_cache.py` via
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`download_ohlcv_cached` (same as `high_52w_breakout`)
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- **Signal logic:**
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1. Load 1-year OHLCV for full universe
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2. Compute RSI(2) from last 3 closes: avg_gain/avg_loss over 2 periods
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3. Compute 200-day SMA from close series
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4. **Filter:** price > 200d SMA (uptrend guard) AND RSI(2) < `max_rsi` (default 10)
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AND close > `min_price` (default $5) AND avg_vol_20d > `min_avg_volume` (default 100k)
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5. Sort by RSI(2) ascending (most oversold first)
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- **Priority rules:**
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- CRITICAL if RSI(2) < 5 (extreme oversold, highest expected bounce)
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- HIGH if RSI(2) < 8
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- MEDIUM if RSI(2) < 10
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- **Context format:**
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`"RSI(2) oversold at {rsi:.1f} | Price ${price:.2f} above 200d SMA ${sma200:.2f}
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(+{pct:.1f}%) | 3–7d mean-reversion bounce setup"`
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@ -14,6 +14,7 @@ from . import (
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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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rsi_oversold, # noqa: F401
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sector_rotation, # noqa: F401
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semantic_news, # noqa: F401
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short_squeeze, # noqa: F401
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"""RSI(2) mean-reversion oversold-bounce scanner.
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Based on Larry Connors' 2-period RSI strategy (backtested 25 years, 75-79% win
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rate) and Lehmann (1990) academic evidence for short-term weekly return reversals
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(+0.86% to +1.24% per week for prior-week losers).
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Signal: RSI(2) < 10 AND price above 200-day SMA.
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The 200-day SMA filter is the critical guard against catching falling knives —
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in persistent downtrends RSI(2) can stay oversold for weeks. With the trend
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filter in place, the signal captures temporary pullbacks within an ongoing uptrend.
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Expected holding period: 3–7 days. Exit target: RSI(2) > 90 or close above 5-day SMA.
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Research: docs/iterations/research/2026-04-15-rsi-mean-reversion.md
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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.data_cache.ohlcv_cache import download_ohlcv_cached
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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.dataflows.universe import load_universe
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from tradingagents.utils.logger import get_logger
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logger = get_logger(__name__)
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class RSIOversoldScanner(BaseScanner):
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"""Scan for stocks with RSI(2) deeply oversold while price remains above 200-day SMA.
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Contrarian mean-reversion signal — the only non-momentum scanner in the pipeline.
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Identifies short-term panic selloffs within broader uptrends where a 3–7 day bounce
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is statistically expected.
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Data requirement: ~210 trading days of OHLCV (200d SMA + RSI buffer).
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Cost: single batch yfinance download via shared OHLCV cache, zero per-ticker API calls.
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"""
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name = "rsi_oversold"
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pipeline = "momentum"
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strategy = "mean_reversion_bounce"
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def __init__(self, config: Dict[str, Any]):
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super().__init__(config)
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self.max_rsi = self.scanner_config.get("max_rsi", 10.0)
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self.sma_period = self.scanner_config.get("sma_period", 200)
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self.rsi_period = self.scanner_config.get("rsi_period", 2)
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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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self.vol_avg_days = self.scanner_config.get("vol_avg_days", 20)
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self.max_tickers = self.scanner_config.get("max_tickers", 0) # 0 = no cap
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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(f"📉 Scanning RSI({self.rsi_period}) oversold bounce (RSI < {self.max_rsi})...")
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tickers = load_universe(self.config)
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if not tickers:
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logger.warning("No tickers loaded for RSI oversold scan")
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return []
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if self.max_tickers:
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tickers = tickers[: self.max_tickers]
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cache_dir = self.config.get("discovery", {}).get("ohlcv_cache_dir", "data/ohlcv_cache")
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logger.info(f"Loading OHLCV for {len(tickers)} tickers from cache...")
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data = download_ohlcv_cached(tickers, period="1y", cache_dir=cache_dir)
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if not data:
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return []
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candidates = []
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for ticker, df in data.items():
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result = self._check_rsi_oversold(df)
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if result:
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result["ticker"] = ticker
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candidates.append(result)
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# Sort by RSI ascending — most oversold (lowest RSI) gets highest rank
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candidates.sort(key=lambda c: c.get("_rsi_value", 99))
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# Strip internal sort key
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for c in candidates:
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c.pop("_rsi_value", None)
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candidates = candidates[: self.limit]
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logger.info(f"RSI oversold bounce: {len(candidates)} candidates")
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return candidates
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def _check_rsi_oversold(self, df: pd.DataFrame) -> Optional[Dict[str, Any]]:
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"""Compute RSI(2) and 200-day SMA; return candidate dict if signal fires."""
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try:
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df = df.dropna(subset=["Close", "Volume"])
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# Need at least sma_period + rsi_period + a buffer
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min_rows = self.sma_period + self.rsi_period + 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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volume = df["Volume"]
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current_close = float(close.iloc[-1])
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# --- Liquidity gates ---
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avg_vol = float(volume.iloc[-(self.vol_avg_days + 1) : -1].mean())
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if avg_vol < 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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# --- 200-day SMA trend filter ---
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sma200 = float(close.iloc[-self.sma_period :].mean())
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if current_close <= sma200:
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return None # Price below 200d SMA → falling knife risk, skip
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# --- RSI(2) calculation ---
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# Use the last rsi_period+1 closes to compute one RSI value
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rsi_window = close.iloc[-(self.rsi_period + 2) :].values
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if len(rsi_window) < self.rsi_period + 1:
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return None
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deltas = [rsi_window[i] - rsi_window[i - 1] for i in range(1, len(rsi_window))]
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gains = [max(d, 0.0) for d in deltas]
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losses = [abs(min(d, 0.0)) for d in deltas]
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avg_gain = sum(gains[-self.rsi_period :]) / self.rsi_period
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avg_loss = sum(losses[-self.rsi_period :]) / self.rsi_period
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if avg_loss == 0:
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rsi = 100.0 # No losses → not oversold
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else:
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rs = avg_gain / avg_loss
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rsi = 100.0 - (100.0 / (1.0 + rs))
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if rsi >= self.max_rsi:
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return None
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# --- Priority ---
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if rsi < 5.0:
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priority = Priority.CRITICAL.value
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elif rsi < 8.0:
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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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pct_above_sma = ((current_close - sma200) / sma200) * 100
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context = (
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f"RSI({self.rsi_period}) oversold at {rsi:.1f} | "
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f"Price ${current_close:.2f} above 200d SMA ${sma200:.2f} "
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f"(+{pct_above_sma:.1f}%) | "
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f"3-7d mean-reversion bounce setup"
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)
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return {
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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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"_rsi_value": round(rsi, 2),
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}
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except Exception as e:
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logger.debug(f"RSI oversold check failed: {e}")
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return None
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SCANNER_REGISTRY.register(RSIOversoldScanner)
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@ -271,6 +271,18 @@ DEFAULT_CONFIG = {
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"min_price": 5.0, # Filter penny stocks
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"min_avg_volume": 100_000, # Min avg daily volume for liquidity
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},
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"rsi_oversold": {
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"enabled": True,
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"pipeline": "momentum",
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"limit": 10,
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"max_tickers": 0, # 0 = no cap (nightly cache makes full universe fast)
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"rsi_period": 2, # Connors RSI(2): most sensitive to short-term pullbacks
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"max_rsi": 10.0, # Entry threshold: RSI(2) < 10 (aggressive: < 5)
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"sma_period": 200, # Trend filter SMA period; critical falling-knife guard
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"min_price": 5.0, # Filter penny stocks
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"min_avg_volume": 100_000, # Min avg daily volume for liquidity
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"vol_avg_days": 20, # Days for volume average baseline
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},
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},
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},
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# Memory settings
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