momentum devandClaude Opus 4.8
f8f85b3963
MEASURED 2026-07-02: our entire bar history was IEX-only. IEX sees ~2-5% of consolidated
volume (SNDQ 7/01: IEX 188 bars/1.5M shares vs SIP 391 bars/60.1M — 39x) and misses whole
minutes on thin names. Every volume-based signal (RVOL, liquidity floors, volume bursts,
dollar-volume strata) and several research verdicts ("untradeable dust", "thin premarket")
were computed on that sample. Daily bars had the same defect. Historical SIP is FREE on our
keys (15-min embargo) — only real-time SIP needs the paid plan.
- client.get_daily_bars: feed param (None -> settings.feed; pass "sip" for consolidated).
- research/sip_backfill.py: re-fetch minute sessions (04:00-20:00 ET) + dailies from SIP
via the existing idempotent upserts; embargo-caps the end when refetching today;
refresh_rolling_volume rebuild after (this morning's 20d averages were IEX-scaled too).
CLI: full history < today, or --date D for one session.
- premarket_eval.py: nightly job now SIP-upgrades today's bars BEFORE the pm1 rebuild, so
every finished session lands on true volume automatically (live loop stays IEX real-time
by design — free-key SIP is embargoed 15 min).
Chain running: full backfill -> verification -> rebuild liquid/premarket/runner shadow
tracks + re-validate (their volume gates were IEX-calibrated; verdicts may change).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
85 lines
3.9 KiB
Python
85 lines
3.9 KiB
Python
"""Accumulate-then-re-validate the premarket (pm1) shadow track.
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The pre-registered verdict on 2026-07-01 was PROMISING-BUT-UNDERPOWERED: the forward moves are
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REAL (avg +11-14% upside, unlike the RTH levers' tight-stop artifacts) but n=21 is far too small
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for the +1R Wilson bound to conclude. The live loop ingests premarket bars every trading day, so
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this job rebuilds pm1 from the (growing) DB and appends a timestamped metrics row to a history
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file. Run it on a schedule (systemd timer) or by hand; watch `n` climb and the Wilson LB tighten
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until the locked bar is decisively cleared or failed.
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python -m research.premarket_eval
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"""
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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 import labeler
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from research.multi_schema import open_research_db
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load_dotenv(os.path.join(os.path.dirname(__file__), "..", ".env"))
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HIST = os.path.join(os.path.dirname(__file__), "..", "data", "premarket_eval_history.tsv")
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_HEADER = "utc_ts\tn\tdays\trate_1r\twilson_lb\tmedian_up_60m\tmedian_dd_60m\tbig_gap_1r\tpassed\n"
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def _metrics(con) -> dict:
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rows = con.execute(
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"SELECT l.max_upside_next_60m, l.max_drawdown_next_60m, "
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"CASE WHEN l.reached_1r_before_minus_1r THEN 1 ELSE 0 END, se.gap_pct "
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"FROM outcome_labels l JOIN setup_events se ON se.setup_id = l.setup_id "
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"WHERE se.setup_version=?", [labeler.PREMARKET_SETUP_VERSION]).fetchall()
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n = len(rows)
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days = con.execute("SELECT count(DISTINCT session_date) FROM setup_events WHERE setup_version=?",
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[labeler.PREMARKET_SETUP_VERSION]).fetchone()[0]
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if not n:
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return {"n": 0, "days": days, "rate_1r": 0.0, "wilson_lb": 0.0,
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"median_up_60m": 0.0, "median_dd_60m": 0.0, "big_gap_1r": 0.0, "passed": False}
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r1 = [r[2] for r in rows]
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big = [r[2] for r in rows if (r[3] or 0) >= 0.5]
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med_up = labeler._median([r[0] for r in rows]) or 0.0
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med_dd = labeler._median([r[1] for r in rows]) or 0.0
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rate_1r = sum(r1) / n
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rate_big = (sum(big) / len(big)) if big else 0.0
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passed = (med_up >= labeler.PREMARKET_PROMO_MEDIAN_FWDMAX
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and med_dd > labeler.PREMARKET_PROMO_MEDIAN_ADVERSE
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and rate_1r > labeler.PREMARKET_PROMO_MIN_1R
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and rate_big > labeler.PREMARKET_PROMO_MIN_1R)
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return {"n": n, "days": days, "rate_1r": rate_1r,
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"wilson_lb": labeler._wilson_lower(sum(r1), n),
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"median_up_60m": med_up, "median_dd_60m": med_dd,
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"big_gap_1r": rate_big, "passed": passed}
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def main() -> None:
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con = open_research_db("market")
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# upgrade today's bars to the SIP consolidated tape first (live ingest is IEX-only,
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# ~2-5% of true volume) so the pm1 rebuild + every downstream signal sees real volume
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try:
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from alpaca_paper.client import AlpacaPaperClient
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from alpaca_paper.settings import AlpacaPaperSettings
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from datetime import date as _date
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from research.sip_backfill import refetch_minute_session
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_client = AlpacaPaperClient(AlpacaPaperSettings.from_env())
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n, rows = refetch_minute_session(con, _client, _date.today())
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print(f"SIP refetch today: {n} symbols -> {rows} rows")
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except Exception as exc: # noqa: BLE001 — never let the refetch break the eval
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print(f"SIP refetch skipped: {exc}")
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labeler.build_premarket(con, rebuild=True) # rebuild from the grown DB
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m = _metrics(con)
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ts = datetime.now(timezone.utc).isoformat(timespec="seconds")
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row = (f"{ts}\t{m['n']}\t{m['days']}\t{m['rate_1r']:.3f}\t{m['wilson_lb']:.3f}\t"
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f"{m['median_up_60m']:.4f}\t{m['median_dd_60m']:.4f}\t{m['big_gap_1r']:.3f}\t{m['passed']}")
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new = not os.path.exists(HIST)
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with open(HIST, "a") as f:
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if new:
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f.write(_HEADER)
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f.write(row + "\n")
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print("appended:", row)
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labeler.validate_premarket(con)
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if __name__ == "__main__":
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main()
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