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Strategy Lab / Invest / QUALITYCOMPOUND

QUALITYCOMPOUND QualityCompound v1.0 - Fundamentals-Led Compounding

A fundamentally strong, reasonably-valued business in a confirmed long-term weekly uptrend compounds shareholder value better than chasing pure price action. Version v1 - status ACTIVE - weekly evaluation cadence.

Equity
$9997.00
Closed Trades
0
Win Rate
n/a
Evidence Status
Insufficient Evidence
N=0 closed trades

Setup Score: 0.35xFundamentals + 0.25xValuation + 0.20xTrendConfirmation + 0.10xRelativeStrength(vs SPY, 26-week) + 0.10xRegime.

Trades this app's own real, already-computed fundamentals/valuation methodology (scoring.score_fundamentals/score_valuation) - the exact functions behind the live public Invest-horizon score, not an invented second quality metric.

Deliberately NO max-holding-period exit - only exits on a genuine thesis break (fundamentals collapse, confirmed 3-week trend break below the 50-week SMA) or the stop.

Known limitations:

  • No real Protected-NBBO/executable-quote feed - synthetic liquidity-tier spreads used instead (same substitution as every other lab).
  • Entries/exits fill at the next COMPLETED WEEK's first available open (no true weekly-open microstructure feed exists) - strictly no-look-ahead, but this lab is weekly-bar-driven, not daily/intraday.
  • No quarterly-fundamentals-HISTORY feed (only a trailing-twelve-months snapshot from Finnhub/Yahoo) - 'is the business's quality trend improving' is read from price action (trend/relative-strength), never fabricated from invented historical fundamentals. Disclosed per-engine.
  • No sector-relative valuation feed - score_valuation() uses a flat ~20 fair-P/E assumption, the exact same simplification the live public Invest-horizon composite score already uses (weight_profiles.py).
  • Regime is a single broad-market weekly SPY-trend read (Supportive/Neutral/Adverse), not swing_lab's fuller breadth/volatility/panic-rebound machinery - a long-horizon book doesn't need to react to short-term volatility spikes.
  • No pairwise correlation matrix - concentration risk is managed via a simple max-3-positions-per-subsector cap instead (YAGNI at an 8-position-max concentrated book).
  • HistoricalTwin/adaptive component weights share the exact same statistics engine as Day-Trade/Swing-Trade Labs (bullyeah_engine.adaptive_learning) - stays observer-only until 30+ closed episodes exist, per that module's own gating rule.

This is a research hypothesis, not a proven profitable methodology.

Evidence (Insufficient Evidence):

Needs at least 15 completed trades AND 15 independent entry days before any read is meaningful (spec section 39.1) - 0 trades so far. No significance testing applied above that floor either - see module docstring for what's deferred (PBO, Deflated Sharpe, bootstrap CIs, walk-forward holdout).