momentum devandClaude Opus 4.8
c7bfb3512a
(a) learn_params.per_setup_report: permanent per-setup P&L/earliness breakdown
in the harness (forces all-setups so the ORB-vs-rest comparison is visible).
(b) nightly_tune.py + nightly_tune.sh: backfill latest bars, sweep ORB params
over accumulated gapper sessions, write the best PROFITABLE config to
data/learned_params.json. run_live_paper reads it on boot and applies the
tuned ready_score / min_bars (explicit env still wins); won't overwrite with
a losing or thin-sample config. Cron-scheduled after the close.
Verified: 75/75 tests; boot logs the learned params and applies them.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""Nightly self-tuning loop.
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Run after the close: backfill the latest bars, sweep the ORB strategy parameters
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over the accumulated gapper sessions, and write the best config to
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data/learned_params.json. run_live_paper.py reads that file on boot, so the
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strategy re-tunes itself as data grows — no manual intervention.
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PYTHONPATH=. /home/philip/.venvs/momentum/bin/python nightly_tune.py
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Only writes a config that is actually PROFITABLE on the sample (>0 P&L, >=5
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trades); otherwise it leaves the existing learned_params.json untouched so a thin
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or bad day can't degrade the live config.
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"""
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from __future__ import annotations
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import itertools
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import json
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import os
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from datetime import datetime, timezone
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from dotenv import load_dotenv
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from research.multi_schema import open_research_db
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load_dotenv()
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def main():
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# Tune the profitable setup only.
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os.environ["STRATEGY_SETUPS"] = "opening_range_break"
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# 1) accumulate the latest sessions (best-effort; never abort tuning on this)
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try:
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import backfill_history
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backfill_history.main()
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except Exception as exc: # noqa: BLE001
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print(f"backfill skipped: {exc}", flush=True)
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# imported after backfill so STRATEGY_SETUPS is already set for classify_setup
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from learn_params import load_sessions, run_combo
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con = open_research_db("market")
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sessions = load_sessions(con)
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print(f"tuning over {len(sessions)} gapper sessions", flush=True)
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if len(sessions) < 10:
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print("too few sessions — keeping current learned_params.json")
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return
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grid = {
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"warmup": [3, 6],
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"min_bars": [3, 5],
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"eval_every": [1],
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"ready": [55, 60, 65, 70],
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"gap_min": [0.05],
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"rvol_min": [2.0],
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}
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keys = list(grid)
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best = None
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for combo in itertools.product(*grid.values()):
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p = dict(zip(keys, combo))
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if p["warmup"] < p["min_bars"]:
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continue
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m = run_combo(sessions, **p)
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if m["trades"] < 5 or m["pnl"] <= 0:
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continue
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# score: profit, lightly favouring earlier entries on ties
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score = m["pnl"] - (m["entry_min"] * 50.0)
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if best is None or score > best[2]:
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best = (p, m, score)
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if best is None:
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print("no profitable ORB config on this sample — keeping current params")
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return
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p, m, _ = best
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out = {
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"min_bars": p["min_bars"],
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"ready_score_pct": float(p["ready"]),
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"setups": "opening_range_break",
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"as_of": datetime.now(timezone.utc).isoformat(),
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"sessions": len(sessions),
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"pnl": round(m["pnl"]),
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"win": round(m["win"], 3),
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"avg_R": round(m["avgR"], 3),
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"entry_min": round(m["entry_min"], 1),
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}
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path = os.path.join(os.environ.get("DATA_DIR", "./data"), "learned_params.json")
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os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
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with open(path, "w") as f:
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json.dump(out, f, indent=2)
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print(f"wrote {path}:\n{json.dumps(out, indent=2)}", flush=True)
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if __name__ == "__main__":
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main()
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