Track experiment cycles with SQS scoring (0-100), JSONL journal, and
auto-generated leaderboard to prevent duplicate experiments and enable
data-driven strategy decisions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove 5 non-alpha features (earnings surprise, risk penalty, parse confidence,
direction clarity, LM sentiment) from composite score to eliminate double-counting
with hard gates and noise sources. Redistribute weights to 5 alpha features.
Add default-deny for unknown event types, no-follow-through early exit (D+1),
kill switch log-only mode, macro regime size scaler. Remove SUE gate (Gate 8).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Flip scoring weights so event/document quality is primary signal (55%)
and market confirmation is secondary (35%). Add research mode with
kill-switch cooldown/reset, veto gates for bad events, reduced portfolio
risk, and 4 diagnostic analysis scripts.
Phase A: Research mode kill-switch reset, risk reduction (0.5%/trade,
max 4 positions), bullish-only direction for all event types.
Phase B: 2 new sub-scorers (parse_confidence, direction_clarity),
4 veto gates (oneoff risk, parse confidence, unknown/bearish direction).
Phase C: signal_quality, event_type_decomposition, kill_switch_impact,
concurrent_position analysis scripts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace naive abs(reaction_day_return) fallback with a composite score
from 4 market microstructure features available at entry time:
1. Reaction quality (35%) — moderate positive return (PEAD zone) is
ideal; extreme positives penalized as "priced in"
2. Close strength (30%) — close near session high = buyers won
3. Volume conviction (20%) — 1.2-2x is healthy; >3x is exhaustion
4. Gap quality (15%) — small positive gap = orderly strength
Real data results (14 events, b1868603 snapshot):
- Score filters out 6 of 10 losers (DDOG -11.7%, META -9.1%, etc.)
- With threshold 0.5: return -2.63% → +0.27%, drawdown 4.24% → 0.86%
- Profit factor 0.44 → 1.16 (turns profitable)
- MSFT loss (-8.7%) is macro-driven, not predictable from stock features
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
I Luk Kim