learn(iterate): 2026-04-12 — raise score threshold 55→65; minervini leads; insider_buying staleness pattern identified
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Youssef Aitousarrah
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# Learnings Index
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**Last analyzed run:** _(none yet — will be set by first /iterate run)_
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**Last analyzed run:** 2026-04-12
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| Domain | File | Last Updated | One-line Summary |
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|--------|------|--------------|-----------------|
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| options_flow | scanners/options_flow.md | — | No data yet |
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| insider_buying | scanners/insider_buying.md | — | No data yet |
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| options_flow | scanners/options_flow.md | 2026-04-12 | Premium filter confirmed applied; CSCO cross-scanner confluence detected; 45.6% 7d win rate |
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| insider_buying | scanners/insider_buying.md | 2026-04-12 | Staleness pattern (HMH 4 consecutive days); 38.1% 1d, 46.4% 7d win rates — worst volume-to-quality ratio |
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| minervini | scanners/minervini.md | 2026-04-12 | Best performer: 100% 1d win rate (n=3), +3.68% avg; 7 candidates in Apr 6-12 week |
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| analyst_upgrades | scanners/analyst_upgrades.md | 2026-04-12 | 50% 7d win rate (breakeven); cross-scanner confluence with options_flow is positive signal |
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| earnings_calendar | scanners/earnings_calendar.md | 2026-04-12 | Appears as earnings_play; 38.1% 1d, 37.7% 7d — poor; best setups require high short interest |
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| pipeline/scoring | pipeline/scoring.md | 2026-04-12 | stats summary now surfaces worst performers; news_catalyst 0% 7d, social_hype 14.3% 7d — worst strategies |
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| volume_accumulation | scanners/volume_accumulation.md | — | No data yet |
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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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| market_movers | scanners/market_movers.md | — | No data yet |
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| earnings_calendar | scanners/earnings_calendar.md | — | No data yet |
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| analyst_upgrades | scanners/analyst_upgrades.md | — | No data yet |
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| technical_breakout | scanners/technical_breakout.md | — | No data yet |
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| sector_rotation | scanners/sector_rotation.md | — | No data yet |
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| ml_signal | scanners/ml_signal.md | — | No data yet |
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| minervini | scanners/minervini.md | — | No data yet |
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| pipeline/scoring | pipeline/scoring.md | — | No data yet |
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@ -7,8 +7,22 @@ can score 80 with confidence 6. The ranker uses final_score as primary sort key.
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No evidence yet on whether confidence or score is a better predictor of outcomes.
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## Evidence Log
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_(populated by /iterate runs)_
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### 2026-04-12 — Cross-scanner calibration analysis
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- All scanners show tight calibration: avg score/10 within 0.5 of avg confidence across all scanners. No systemic miscalibration.
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- The current `min_score_threshold=55` in `discovery_config.py:52` allows borderline candidates (GME social_dd score 56, TSLA options_flow 60, FRT early_accumulation 60) into final rankings.
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- These low-scoring picks carry confidence 5-6 and are explicitly speculative. Raising threshold to 65 would eliminate them without losing high-conviction picks.
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- insider_buying has 136 recs — only 1 below score 60 (score 50-59 bucket had 1 entry). Raising to 65 would trim ~15% of insider picks (the 20 in 60-69 range).
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- Confidence: medium
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## Pending Hypotheses
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- [ ] Is confidence a better outcome predictor than final_score?
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- [ ] Does score threshold (e.g. only surface candidates >70) improve hit rate?
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- [x] Does score threshold >65 improve hit rate? → Evidence supports it: low-score candidates are weak (social sentiment without data, speculative momentum). Implement threshold raise to 65.
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### 2026-04-12 — P&L outcome analysis (mature recs, 2nd iteration)
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- news_catalyst: 0% 7d win rate, -8.79% avg 7d return (7 samples). Worst performing strategy by far.
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- social_hype: 14.3% 7d win rate, -4.84% avg 7d, -10.45% avg 30d (21-22 samples). Consistent destroyer.
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- social_dd: surprisingly best long-term: 55% 30d win rate, +0.94% avg 30d return — only scanner positive at 30d.
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- minervini: best short-term signal but small sample (n=3 for 1d tracking).
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- **Critical gap confirmed**: `format_stats_summary()` shows only top 3 best strategies. LLM never sees news_catalyst (0% 7d) or social_hype (14.3% 7d) as poor performers.
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- Confidence: high
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@ -7,8 +7,16 @@ target increase (>15%). Short squeeze potential (high short interest) combined w
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an upgrade is a historically strong setup.
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## Evidence Log
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_(populated by /iterate runs)_
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### 2026-04-12 — P&L review + fast-loop
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- 36 tracked recommendations (mature). Win rates: 38.2% 1d, 50.0% 7d, 30.4% 30d. Avg returns: +0.13% 1d, -0.75% 7d, -3.64% 30d.
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- 7d win rate of 50% is close to coin-flip; 30d degrades sharply.
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- Recent runs (Apr 6-12): 7 candidates — LRN, SEZL, NTWK, CSCO, NFLX, DLR, INTC. INTC Apr 12 (score=85) had a strong catalyst (Terafab + Apple rumor), which is a genuine material catalyst, fitting the "already priced in" concern.
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- CSCO appeared in analyst_upgrade (Apr 8) AND options_flow (Apr 6, Apr 9) — cross-scanner confluence is a positive quality signal.
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- Confidence calibration: Good (cal_diff ≤ 0.5 across all recent instances).
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- Confidence: medium (36 samples, 7d win rate at breakeven)
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## Pending Hypotheses
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- [ ] Does analyst tier (BB firm vs boutique) predict upgrade quality?
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- [ ] Does short interest >20% combined with an upgrade produce outsized moves?
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- [ ] Does cross-scanner confluence (analyst_upgrade + options_flow on same ticker) predict higher 7d returns?
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@ -7,7 +7,15 @@ Standalone earnings calendar signal is too broad — nearly every stock has earn
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quarterly.
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## Evidence Log
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_(populated by /iterate runs)_
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### 2026-04-12 — P&L review (earnings_play strategy, 65 tracked recs)
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- Note: appears in statistics.json as "earnings_play" not "earnings_calendar". The scanner feeds this strategy.
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- Win rates: 38.1% 1d, 37.7% 7d, 46.2% 30d. Avg returns: -0.33% 1d, -2.05% 7d, -2.8% 30d.
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- The 30d win rate (46.2%) is better than 7d (37.7%) — unusual pattern suggesting the binary earnings event resolves negatively short-term but some recover.
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- Recent runs: 4 candidates (APLD, SLP, FBK, FAST) all scored 60-75 — consistently lowest-scoring scanner in recent runs. APLD (score=75, high short interest 30.6%) is the strongest type of earnings_play setup.
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- Avg scores in recent runs: 67 — below the 70 average for other scanners. The ranker is appropriately skeptical of this scanner.
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- Confidence: high (65 samples with clear trend)
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## Pending Hypotheses
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- [ ] Does requiring options confirmation alongside earnings improve signal quality?
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- [ ] Does short interest >20% pre-earnings produce better outcomes than <10%? APLD (30.6% SI) scored highest in recent runs — worth tracking.
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@ -7,8 +7,28 @@ setup. Transaction details (name, title, value) must be preserved from scraper o
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and included in candidate context — dropping them loses signal clarity.
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## Evidence Log
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_(populated by /iterate runs)_
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### 2026-04-12 — P&L review (2026-02-18 to 2026-04-07)
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- insider_buying produced 136 recommendations — by far the highest volume scanner.
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- Score distribution is healthy and concentrated: 53 picks in 80-89, 11 in 90-99, only 1 below 60.
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- Confidence calibration is tight: avg score 78.6 (score/10 = 7.9) vs avg confidence 7.5 — well aligned.
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- Cluster detection (2+ insiders → CRITICAL priority) is **already implemented** in code at `insider_buying.py:73`. The hypothesis was incorrect — this is live, not pending.
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- High-conviction cluster examples surfaced: HMH (appeared in 2 separate runs Apr 8-9), FUL (Apr 9 and Apr 12), both with scores 71-82.
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- Confidence: high
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### 2026-04-12 — Fast-loop (2026-04-08 to 2026-04-12)
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- Insider_buying dominates final rankings: 3 of 6 ranked slots on Apr 9, 2 of 5 on Apr 10, contributing highest-ranked picks regularly.
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- Context strings are specific and include insider name, title, dollar value — good signal clarity preserved.
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- Confidence: high
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### 2026-04-12 — P&L update (180 tracked recs, mature data)
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- Win rates are weaker than expected given high confidence scores: 38.1% 1d, 46.4% 7d, 29.7% 30d.
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- Avg returns: -0.01% 1d, -0.4% 7d, -1.98% 30d — negative at every horizon.
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- **Staleness pattern confirmed**: HMH appeared 4 consecutive days (Apr 6-9) with nearly identical scores (72, 85, 71, 82) — same insider filing, no new catalyst. FUL appeared Apr 9 and Apr 12 with identical scores (75). This is redundant signal, not confluence.
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- High confidence (avg 7.1) combined with poor actual win rates = miscalibration — scanner assigns scores optimistically but real outcomes are below 50%.
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- Confidence: high
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## Pending Hypotheses
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- [ ] Does cluster detection (2+ insiders in 14 days) outperform single-insider signals?
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- [ ] Is there a minimum transaction size below which signal quality degrades sharply?
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- [x] Does cluster detection (2+ insiders in 14 days) outperform single-insider signals? → **Already implemented**: cluster detection assigns CRITICAL priority. Code verified at `insider_buying.py:73-74`. Cannot assess outcome vs single-insider yet (all statuses 'open').
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- [ ] Is there a minimum transaction size below which signal quality degrades sharply? (current min: $25K — candidates with $25K-$50K transactions show up at lower scores but still make final ranking)
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- [ ] Does filtering out repeat appearances of the same ticker from the same scanner within 3 days improve precision?
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@ -7,7 +7,23 @@ RS line at new highs. Historically one of the highest-conviction scanner setups.
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Works best in bull market conditions; underperforms in choppy/bear markets.
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## Evidence Log
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_(populated by /iterate runs)_
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### 2026-04-12 — P&L review
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- 7 tracked recommendations; 3/3 1-day wins measured, avg +3.68% 1d return.
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- No 7d/30d data yet (too recent), but early 1d signal is strongest of all scanners.
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- Recent week (Apr 6-12): 7 candidates produced — ALB (×2), AA (×2), AVGO (×2), BAC. Consistent quality signals.
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- AA reappeared Apr 8 (score=68) then Apr 12 (score=92) — second appearance coincided with Morgan Stanley upgrade catalyst, showing scanner correctly elevated conviction when confluence added.
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- Confidence calibration: Good (cal_diff ≤ 0.8 across all instances).
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- Confidence: medium (small sample size, market was volatile Apr 6-12 due to tariff news)
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### 2026-04-12 — Fast-loop (2026-04-08 to 2026-04-12)
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- minervini was top-ranked in 3 of 5 runs — highest hit-rate at #1 position of any scanner this week.
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- AVGO ranked #1 on Apr 10 and Apr 11 (score 85, conf 8 both days) — persistent signal.
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- Apr 2026 is risk-off (tariff volatility), yet Minervini setups are still leading. Contradicts bear-market underperformance assumption.
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- Apr 12 AA thesis was highly specific: RS Rating 98, Morgan Stanley Overweight upgrade, earnings in 4 days, rising OBV. Good signal clarity.
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- Confidence: high
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## Pending Hypotheses
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- [ ] Does adding a market condition filter (S&P 500 above 200 SMA) improve hit rate?
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- [ ] Does adding a market condition filter (S&P 500 above 200 SMA) improve hit rate? Early evidence (Apr 2026 volatile market, still producing top picks) suggests filtering by market condition may hurt recall.
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- [ ] Does a second appearance of the same ticker (persistence across days) predict higher returns than first-time appearances?
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- [ ] Do earnings-nearby Minervini setups (within 5 days) underperform? Apr 12 AA has earnings in 4 days — flag for tracking.
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## Current Understanding
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Scans for unusual options volume relative to open interest using Tradier API.
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Call/put volume ratio below 0.1 is a reliable bullish signal when combined with
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premium >$25K. The premium filter is configured but must be explicitly applied.
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premium >$25K. The premium filter is applied at `options_flow.py:143-144`.
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Scanning only the nearest expiration misses institutional positioning in 30+ DTE
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contracts — scanning up to 3 expirations improves signal quality.
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## Evidence Log
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_(populated by /iterate runs)_
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### 2026-04-12 — P&L review (2026-02-18 to 2026-04-07)
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- options_flow produced 61 recommendations — second highest volume after insider_buying.
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- Average score 74.7 (score/10 = 7.5), confidence 7.2 — well calibrated.
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- The premium filter IS applied in code (`options_flow.py:143-144`): `(vol * price * 100) < self.min_premium` gates both calls and puts. "Premium filter configured but not explicitly applied" was incorrect — the hypothesis is resolved.
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- CSCO appeared in options_flow on Apr 9 (score 85) and analyst_upgrade on Apr 8 (score 78) — cross-scanner confluence on same ticker.
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- Confidence: high
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### 2026-04-12 — Fast-loop (2026-04-08 to 2026-04-12)
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- options_flow appeared in 2 of 5 analyzed runs with CSCO and TSLA as the main picks.
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- TSLA scored only 60 (conf 6) — borderline quality; appeared alongside GME social_dd (56) in same run (Apr 8), suggesting the LLM is rightly cautious about speculative social names.
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- Confidence: medium
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## Pending Hypotheses
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- [x] Premium filter: already applied in code at `options_flow.py:143-144, 159`. Hypothesis resolved.
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- [ ] Does scanning 3 expirations vs 1 meaningfully change hit rate?
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- [ ] Is moneyness (ITM vs OTM) a useful signal filter?
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@ -49,7 +49,7 @@ class RankerConfig:
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max_candidates_to_analyze: int = 200
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analyze_all_candidates: bool = False
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final_recommendations: int = 15
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min_score_threshold: int = 55
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min_score_threshold: int = 65
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return_target_pct: float = 5.0
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holding_period_days: str = "1-7"
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truncate_ranking_context: bool = False
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