45 lines
1.8 KiB
JSON
45 lines
1.8 KiB
JSON
{
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"max_active": 5,
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"hypotheses": [
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{
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"id": "insider_buying-min-txn-100k",
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"scanner": "insider_buying",
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"title": "Raise min_transaction_value to $100K",
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"description": "Hypothesis: filtering to insider purchases \u2265$100K (vs. current $25K) produces higher-quality picks by excluding routine small-lot grants and focusing on high-conviction, out-of-pocket capital deployment. Research (Lakonishok & Lee 2001; Cohen et al. 2012) shows large-value insider buys predict forward returns; small ones do not.",
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"branch": "hypothesis/insider_buying-min-txn-100k",
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"pr_number": 529,
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"status": "running",
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"priority": 3,
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"expected_impact": "medium",
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"hypothesis_type": "implementation",
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"created_at": "2026-04-10",
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"min_days": 21,
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"days_elapsed": 3,
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"picks_log": [
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"2026-04-10",
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"2026-04-13",
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"2026-04-14"
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],
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"baseline_scanner": "insider_buying",
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"conclusion": null
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},
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{
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"id": "social_dd-ranker-suppression",
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"scanner": "social_dd",
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"title": "Does ranker suppression cause us to miss social_dd 30d winners?",
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"description": "social_dd shows 60% 30d win rate (+2.32% avg) but only 41.7% 7d (-1.92%). Hypothesis: the ranker and recommendation system evaluate at 7d horizon, unfairly penalizing a slow-win scanner. Most picks (22/25) already score >=65, so score suppression is not the primary issue \u2014 horizon mismatch is.",
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"branch": null,
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"pr_number": null,
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"status": "concluded",
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"priority": 0,
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"expected_impact": "medium",
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"hypothesis_type": "statistical",
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"created_at": "2026-04-13",
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"min_days": 0,
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"days_elapsed": 0,
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"picks_log": [],
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"baseline_scanner": "social_dd",
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"conclusion": "statistical"
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}
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]
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} |