Gahow Wang
36cb125719
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>
70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
"""Full-window audit: every registered US strategy vs TrendRider V9.
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Runs the official engine path (run_market, same params as precompute) over
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the current 10y common window and writes the comparison table plus the
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head-to-head verdict (does V9 beat every other strategy on BOTH CAGR and
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Sharpe?) to data/research/results/tr9_full_comparison.json.
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PYTHONPATH=. uv run python -m research.tr9_audit
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"""
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from __future__ import annotations
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import json
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import os
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import pandas as pd
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from trader import (AGENT_REVIEW_STRATEGIES, STRATEGY_REGISTRY,
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strategy_supports_market)
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import research.tr9_lab as lab
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OUT = os.path.join("data", "research", "results", "tr9_full_comparison.json")
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V9_NAMES = ["trend_rider_v9", "trend_rider_v9_vt24", "trend_rider_v9_vt32", "trend_rider_v9_vt36",
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"trend_rider_v10", "trend_rider_v10_vt32", "trend_rider_v10_vt36"]
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NEW_NAMES = [n for n in V9_NAMES if "v10" in n]
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def main() -> None:
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us_names = [n for n in STRATEGY_REGISTRY
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if strategy_supports_market(n, "us") and n not in AGENT_REVIEW_STRATEGIES
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and not n.startswith("sim_")]
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end = "2026-10-07"
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print(f"auditing {len(us_names)} US strategies (window ...{end})")
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curves = lab.run(us_names, start="2017-12-26", end=end)
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rows = []
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for name, eq in curves.items():
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m = lab.metrics(eq)
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rows.append({"name": name, **m})
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df = pd.DataFrame(rows).sort_values("cagr", ascending=False)
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pd.set_option("display.width", 200)
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print(f"{'name':44s} {'CAGR':>8s} {'vol':>7s} {'Sharpe':>7s} {'MDD':>7s}")
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for _, r in df.iterrows():
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print(f"{r['name']:44s} {r['cagr']*100:7.1f}% {r['vol']*100:6.1f}% "
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f"{r['sharpe']:7.2f} {r['mdd']*100:6.1f}%")
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others = df[~df["name"].isin(NEW_NAMES)]
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best_cagr = others.loc[others["cagr"].idxmax()]
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best_sharpe = others.loc[others["sharpe"].idxmax()]
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print(f"\nexisting best CAGR : {best_cagr['name']} {best_cagr['cagr']*100:.1f}%")
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print(f"existing best Sharpe : {best_sharpe['name']} {best_sharpe['sharpe']:.2f}")
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verdict = {}
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for v9 in NEW_NAMES:
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row = df[df["name"] == v9].iloc[0]
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beat_cagr = bool((others["cagr"] < row["cagr"]).all())
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beat_sharpe = bool((others["sharpe"] < row["sharpe"]).all())
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verdict[v9] = {"cagr": float(row["cagr"]), "sharpe": float(row["sharpe"]),
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"mdd": float(row["mdd"]),
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"beats_all_cagr": beat_cagr, "beats_all_sharpe": beat_sharpe,
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"beats_all_both": beat_cagr and beat_sharpe}
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print(f"{v9}: CAGR {row['cagr']*100:.1f}% (beats all: {beat_cagr}), "
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f"Sharpe {row['sharpe']:.2f} (beats all: {beat_sharpe})")
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with open(OUT, "w") as fh:
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json.dump({"window": {"start": "2017-12-26", "end": end},
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"rows": rows, "verdict": verdict}, fh, indent=1)
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print("wrote", OUT)
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if __name__ == "__main__":
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main()
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