live: commit the TrendRider V9/V10 registrations the monitor already runs

trader.py registers trend_rider_v9 (+ vt24/vt32/vt36) and trend_rider_v10
(+ vt32/vt36) from strategies/trend_rider_v9.py, and bridge.py labels them.
These were left uncommitted by the 2026-10-07 research session while the
monitor ran them from the working tree (state files data/trader_us_trend_rider_v9*/v10*).
research/tr9_*.py is the research trail CLAUDE.md cites.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

research/tr9_audit.py

@@ -0,0 +1,69 @@
+"""Full-window audit: every registered US strategy vs TrendRider V9.
+
+Runs the official engine path (run_market, same params as precompute) over
+the current 10y common window and writes the comparison table plus the
+head-to-head verdict (does V9 beat every other strategy on BOTH CAGR and
+Sharpe?) to data/research/results/tr9_full_comparison.json.
+
+    PYTHONPATH=. uv run python -m research.tr9_audit
+"""
+from __future__ import annotations
+
+import json
+import os
+
+import pandas as pd
+
+from trader import (AGENT_REVIEW_STRATEGIES, STRATEGY_REGISTRY,
+                    strategy_supports_market)
+
+import research.tr9_lab as lab
+
+OUT = os.path.join("data", "research", "results", "tr9_full_comparison.json")
+V9_NAMES = ["trend_rider_v9", "trend_rider_v9_vt24", "trend_rider_v9_vt32", "trend_rider_v9_vt36",
+            "trend_rider_v10", "trend_rider_v10_vt32", "trend_rider_v10_vt36"]
+NEW_NAMES = [n for n in V9_NAMES if "v10" in n]
+
+
+def main() -> None:
+    us_names = [n for n in STRATEGY_REGISTRY
+                if strategy_supports_market(n, "us") and n not in AGENT_REVIEW_STRATEGIES
+                and not n.startswith("sim_")]
+    end = "2026-10-07"
+    print(f"auditing {len(us_names)} US strategies (window ...{end})")
+    curves = lab.run(us_names, start="2017-12-26", end=end)
+    rows = []
+    for name, eq in curves.items():
+        m = lab.metrics(eq)
+        rows.append({"name": name, **m})
+    df = pd.DataFrame(rows).sort_values("cagr", ascending=False)
+    pd.set_option("display.width", 200)
+    print(f"{'name':44s} {'CAGR':>8s} {'vol':>7s} {'Sharpe':>7s} {'MDD':>7s}")
+    for _, r in df.iterrows():
+        print(f"{r['name']:44s} {r['cagr']*100:7.1f}% {r['vol']*100:6.1f}% "
+              f"{r['sharpe']:7.2f} {r['mdd']*100:6.1f}%")
+
+    others = df[~df["name"].isin(NEW_NAMES)]
+    best_cagr = others.loc[others["cagr"].idxmax()]
+    best_sharpe = others.loc[others["sharpe"].idxmax()]
+    print(f"\nexisting best CAGR   : {best_cagr['name']} {best_cagr['cagr']*100:.1f}%")
+    print(f"existing best Sharpe : {best_sharpe['name']} {best_sharpe['sharpe']:.2f}")
+    verdict = {}
+    for v9 in NEW_NAMES:
+        row = df[df["name"] == v9].iloc[0]
+        beat_cagr = bool((others["cagr"] < row["cagr"]).all())
+        beat_sharpe = bool((others["sharpe"] < row["sharpe"]).all())
+        verdict[v9] = {"cagr": float(row["cagr"]), "sharpe": float(row["sharpe"]),
+                       "mdd": float(row["mdd"]),
+                       "beats_all_cagr": beat_cagr, "beats_all_sharpe": beat_sharpe,
+                       "beats_all_both": beat_cagr and beat_sharpe}
+        print(f"{v9}: CAGR {row['cagr']*100:.1f}% (beats all: {beat_cagr}), "
+              f"Sharpe {row['sharpe']:.2f} (beats all: {beat_sharpe})")
+    with open(OUT, "w") as fh:
+        json.dump({"window": {"start": "2017-12-26", "end": end},
+                   "rows": rows, "verdict": verdict}, fh, indent=1)
+    print("wrote", OUT)
+
+
+if __name__ == "__main__":
+    main()