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>
94 lines
4.2 KiB
Python
94 lines
4.2 KiB
Python
"""Record every TrendRider V9 search trial on the official window.
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The repo keeps an append-only trial log (DSR discipline): each searched
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configuration is recorded with its result. This script re-runs the trial
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set explored for V9 on the 2017-12-26..2026-09-30 engine window and appends
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JSONL records to data/research/results/trials_us.jsonl.
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PYTHONPATH=. uv run python -m research.tr9_trials [--dry-run]
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"""
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from __future__ import annotations
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import os
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import sys
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from research.lab.trials import append_trials
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import research.tr9_lab as lab
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M = lab.TrendRiderV9
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OUT = os.path.join("data", "research", "results", "trials_us.jsonl")
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PROTOCOL = "tr9-v1-2026-10-07"
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# (name, kwargs) — every configuration judged on the design window.
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TRIALS = [
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("v9_base(v7 replica)", {}),
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("v9_soxl", {"risk_on": ("TQQQ", "UPRO", "TECL", "SOXL")}),
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("v9_soxl_tna", {"risk_on": ("TQQQ", "UPRO", "TECL", "SOXL", "TNA")}),
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("v9_tmf", {"tmf": "TMF"}),
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("v9_sqqq", {"sqqq": "SQQQ"}),
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("v9_tmf_sqqq", {"tmf": "TMF", "sqqq": "SQQQ"}),
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("v9_park_qqq", {"pt_park_mix": {"SHY": 0.5, "QQQ": 0.5}}),
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("v9_ladder", {"pt_mode": "ladder"}),
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("v9_no_pt", {"pt_mode": "none"}),
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("v9_assetvt", {"vt_mode": "asset"}),
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("v9_ewma", {"vt_ewma": 20}),
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("v9_momdiv", {"mom_pick_divisor": True}),
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("v9_trail18", {"trail_pct": 0.18}),
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("v9_trail22", {"trail_pct": 0.22}),
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("v9_trail26", {"trail_pct": 0.26}),
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("vt36", {"target_vol": 0.36, "min_lev": 0.75}),
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("vt36_t18", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.18}),
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("vt36_t19", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.19}),
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("vt36_t20", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.20}),
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("vt36_t21", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.21}),
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("vt36_t22", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.22}),
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("vt36_t23", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.23}),
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("vt36_t24", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.24}),
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("vt36_b15", {"target_vol": 0.36, "min_lev": 0.75, "pt_band": 0.15}),
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("vt36_thr25", {"target_vol": 0.36, "min_lev": 0.75, "pt_threshold": 0.25}),
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("vt36_thr35", {"target_vol": 0.36, "min_lev": 0.75, "pt_threshold": 0.35}),
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("vt32_t18", {"target_vol": 0.32, "min_lev": 0.70, "trail_pct": 0.18}),
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("vt32_t20", {"target_vol": 0.32, "min_lev": 0.70, "trail_pct": 0.20}),
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("vt32_t22", {"target_vol": 0.32, "min_lev": 0.70, "trail_pct": 0.22}),
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("vt34_t20", {"target_vol": 0.34, "min_lev": 0.72, "trail_pct": 0.20}),
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("vt34_t22", {"target_vol": 0.34, "min_lev": 0.72, "trail_pct": 0.22}),
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("vt38_t20", {"target_vol": 0.38, "min_lev": 0.78, "trail_pct": 0.20}),
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("vt30_t20", {"target_vol": 0.30, "min_lev": 0.65, "trail_pct": 0.20}),
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("vt24_t20", {"target_vol": 0.24, "min_lev": 0.50, "trail_pct": 0.20}),
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("vt36_t20_w20", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.20, "trail_window": 20}),
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("vt36_t20_w40", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.20, "trail_window": 40}),
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("vt36_t22_r10", {"target_vol": 0.36, "min_lev": 0.75, "trail_pct": 0.22, "trail_reentry_days": 10}),
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]
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def main() -> None:
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dry = "--dry-run" in sys.argv
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names = []
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for i, (label, kw) in enumerate(TRIALS):
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name = f"tr9t{i:02d}_{label}"
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lab.register(name, lambda kw=kw, **_: M(**kw))
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names.append(name)
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curves = lab.run(names)
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rows = []
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for i, (label, kw) in enumerate(TRIALS):
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name = f"tr9t{i:02d}_{label}"
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if name not in curves:
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rows.append({"trial": label, "kwargs": kw, "error": "run failed"})
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continue
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m = lab.metrics(curves[name])
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rows.append({"trial": label, "kwargs": {k: str(v) for k, v in kw.items()},
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"cagr": m["cagr"], "vol": m["vol"], "sharpe": m["sharpe"], "mdd": m["mdd"],
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"window": "2017-12-26..2026-09-30", "capital": 10000.0,
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"fixed_fee": 2.0, "tx_cost": 0.001})
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df = lab.report(curves, "trials")
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if dry:
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print("dry run: not writing", OUT)
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return
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append_trials(OUT, rows, PROTOCOL)
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print(f"appended {len(rows)} trials to {OUT} (protocol {PROTOCOL})")
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if __name__ == "__main__":
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main()
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