docs: land capacity-study evidence and share the rank-map helper

Brings the durable artifacts of research/portfolio-capacity-rebalancing onto
main so the rationale for raising the count cap lives with the code that cites
it. The matrix runner, the research simulator hooks and the study's unit tests
are deliberately left behind; they remain at tag research/portfolio-capacity-final.

Corrects conclusions that were reached on EV per trade and are now superseded:
the findings doc's decisions 1 (keep cap 10) and 4 (run the risk-floor A/B) are
struck through and answered in a new correction section, and the research README
and phase-A matrix entries are updated to match. The frozen specification itself
is untouched -- its recorded SHA-256 f1e37783 still verifies.

effective-risk-floor-ab.md is retained but marked CLOSED/NEGATIVE: the study it
proposes is already answered by cap15 vs cash_unbounded (-0.753pp CAGR while
EV/trade rises), and its EV-based pass rule would have shipped it.

scripts/research_rankings.py replaces a fourth copy of the historical rank-map
helper; run_research_matrix, run_execution_recovery_matrix and
run_daily_reentry_matrix now share it. The shared version adds a duplicate
observation guard and a deterministic symbol tie-break the copies lacked.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

scripts/run_execution_recovery_matrix.py

@@ -55,6 +55,11 @@ ROOT = Path(__file__).resolve().parents[1]
 if str(ROOT) not in sys.path:
     sys.path.insert(0, str(ROOT))
 
+from scripts.research_rankings import (  # noqa: E402
+    _live_universe_rank_map,
+    _period_percentiles,
+)
+
 # Must match Phase A cache when reusing research-cands.pkl
 CACHE_VERSION = "research-matrix-v1-daily-prod"
 
@@ -104,66 +109,6 @@ def _parse_args() -> argparse.Namespace:
     return p.parse_args()
 
 
-def _period_percentiles(
-    observations: list[dict], value_key: str
-) -> dict[tuple[str, str], float]:
-    by_period: dict[tuple, list[dict]] = {}
-    for row in observations:
-        if row.get(value_key) is None:
-            continue
-        period = tuple(row["ranking_period"])
-        by_period.setdefault(period, []).append(row)
-    result: dict[tuple[str, str], float] = {}
-    for group in by_period.values():
-        ordered = sorted(
-            group, key=lambda row: (float(row[value_key]), str(row["symbol"]))
-        )
-        denominator = len(ordered) - 1
-        for rank, row in enumerate(ordered):
-            result[(str(row["symbol"]), str(row["date"]))] = round(
-                rank / denominator * 100.0 if denominator > 0 else 100.0,
-                2,
-            )
-    return result
-
-
-def _live_universe_rank_map(
-    observations: list[dict],
-    benchmark_closes: dict[date, float],
-    momentum_weight: float,
-) -> dict[tuple[str, str], dict[str, float | None]]:
-    raw_pct = _period_percentiles(observations, "momentum")
-    residual_pct = _period_percentiles(observations, "residual_momentum")
-    vol_pct = _period_percentiles(observations, "vol_6m")
-    benchmark_ords = sorted(value.toordinal() for value in benchmark_closes)
-    residual_start_ord = benchmark_ords[251] if len(benchmark_ords) >= 252 else None
-    ranks: dict[tuple[str, str], dict[str, float | None]] = {}
-    for row in observations:
-        identity = (str(row["symbol"]), str(row["date"]))
-        asof_ord = date.fromisoformat(identity[1]).toordinal()
-        momentum_pct = (
-            residual_pct.get(identity)
-            if residual_start_ord is not None and asof_ord >= residual_start_ord
-            else raw_pct.get(identity)
-        )
-        volatility_pct = vol_pct.get(identity)
-        strategy_rank = (
-            round(
-                momentum_pct * momentum_weight
-                + volatility_pct * (1.0 - momentum_weight),
-                2,
-            )
-            if momentum_pct is not None and volatility_pct is not None
-            else momentum_pct
-        )
-        ranks[identity] = {
-            "momentum_percentile": momentum_pct,
-            "volatility_percentile": volatility_pct,
-            "strategy_rank": strategy_rank,
-        }
-    return ranks
-
-
 def _window(arm: dict, name: str) -> dict | None:
     for row in arm.get("windows") or []:
         if row.get("window") == name: