momentum devandClaude Opus 5.5
81e505b845
KITT on 2026-09-25 (+557%, 9.3M shares, the day's biggest mover) was benched as "gap 557% squeeze unverified (rvol 1.54)". Two separate faults: 1. The rvol baseline was the 20-day MEAN, and two 11-14M-share days on 09-01/09-02 put it at 1.66M against a 333k median. Squeeze verification (gap >= 150% needs rvol >= 5) now takes the stronger of the mean-baseline reading and the median-baseline one. Both are cumulative volume over a full day's baseline, not prorated, so they share the units the rvol >= 5 boundary was calibrated in. A reverse-split fake still reads ~0.01 on both. This applies in the watcher pre-filter, the trigger book's squeeze exception and the fast-path population gate. The ranking and the tod-rvol arm signal are unchanged. 2. A name's first ingest reached back only to ~09:15 (09:30 + 15 min, or the 20-minute refresh floor). KITT was found at 09:35, so its stored day began at 09:13 and missed the 09:00 squeeze and ~6M premarket shares. The first pass now covers the session from 04:00 ET. With both fixed, KITT's volume at 09:35 is ~20x its median day. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
"""Read-side queries over the research market database."""
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from __future__ import annotations
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from datetime import date
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import pandas as pd
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def query_session_symbols(
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con,
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session_date: date,
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price_min: float = 1.0,
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price_max: float = 20.0,
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limit: int = 50,
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) -> list[dict]:
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"""Symbols with minute bars today whose latest close is inside the band.
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Pass price_min=0 / price_max=inf to disable the band.
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"""
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rows = con.execute(
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"""
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WITH latest AS (
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SELECT symbol, close, volume,
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ROW_NUMBER() OVER (
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PARTITION BY symbol ORDER BY timestamp DESC
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) AS rn,
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COUNT(*) OVER (PARTITION BY symbol) AS bar_count
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FROM minute_bars
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WHERE session_date = ?
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)
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SELECT symbol, close AS last_price, bar_count
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FROM latest
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WHERE rn = 1 AND close BETWEEN ? AND ?
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ORDER BY bar_count DESC
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LIMIT ?
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""",
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[session_date, price_min, price_max, limit],
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).fetchall()
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return [
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{"symbol": r[0], "last_price": float(r[1]), "bar_count": int(r[2])}
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for r in rows
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]
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def query_minute_bars(con, symbol: str, session_date: date) -> pd.DataFrame:
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"""All of today's minute bars for a symbol, chronological."""
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return con.execute(
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"""
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SELECT symbol, timestamp, session_date, is_premarket, is_regular_hours,
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open, high, low, close, volume, vwap, quality_score
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FROM minute_bars
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WHERE symbol = ? AND session_date = ?
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ORDER BY timestamp ASC
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""",
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[symbol, session_date],
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).df()
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def query_previous_close(con, symbol: str, session_date: date) -> float | None:
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row = con.execute(
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"""
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SELECT close FROM daily_bars
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WHERE symbol = ? AND trade_date < ?
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ORDER BY trade_date DESC LIMIT 1
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""",
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[symbol, session_date],
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).fetchone()
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return float(row[0]) if row else None
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def query_median_daily_volume(con, symbol: str, session_date: date, days: int = 20) -> float | None:
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"""Median of the last ``days`` daily volumes — the spike-robust baseline for
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squeeze verification (one or two prior squeeze days can multiply the mean)."""
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row = con.execute(
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"""
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SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY volume) FROM (
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SELECT volume FROM daily_bars
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WHERE symbol = ? AND trade_date < ?
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ORDER BY trade_date DESC LIMIT ?
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) recent
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""",
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[symbol, session_date, days],
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).fetchone()
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return float(row[0]) if row and row[0] is not None else None
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def query_avg_daily_volume(con, symbol: str, session_date: date, days: int = 20) -> float | None:
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row = con.execute(
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"""
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SELECT AVG(volume) FROM (
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SELECT volume FROM daily_bars
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WHERE symbol = ? AND trade_date < ?
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ORDER BY trade_date DESC LIMIT ?
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)
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""",
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[symbol, session_date, days],
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).fetchone()
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return float(row[0]) if row and row[0] is not None else None
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