momentum devandClaude Opus 4.8
007cd95c79
Snapshot before the DuckDB->PostgreSQL migration. data/, archive/, the 232MB rebuild zip, the Windows .venv, and .env are excluded via .gitignore. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
137 lines
4.3 KiB
Python
137 lines
4.3 KiB
Python
"""Signal scanning: derive gapper candidates and scanner snapshots.
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Reads minute/daily bars from the market database, computes gap %% and
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relative volume, and writes ranked rows into scanner_snapshots so the
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dashboard and research notebooks can replay what the scanner saw.
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"""
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from __future__ import annotations
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import uuid
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from dataclasses import dataclass
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from datetime import date, datetime, timezone
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SCANNER_VERSION = "gapper-v2"
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@dataclass
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class GapperCandidate:
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symbol: str
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price: float
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gap_pct: float
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cumulative_volume: int
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relative_volume: float
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vwap: float | None
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rank: int = 0
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def scan_gappers(
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con,
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session_date: date,
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min_gap_pct: float = 5.0,
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min_relative_volume: float = 2.0,
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price_min: float = 1.0,
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price_max: float = 20.0,
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limit: int = 25,
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) -> list[GapperCandidate]:
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"""Find symbols gapping up on elevated volume for a session."""
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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, vwap,
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SUM(volume) OVER (PARTITION BY symbol) AS cum_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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FROM minute_bars
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WHERE session_date = ?
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),
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prev AS (
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SELECT symbol, close AS prev_close,
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ROW_NUMBER() OVER (
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PARTITION BY symbol ORDER BY trade_date DESC
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) AS rn
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FROM daily_bars
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WHERE trade_date < ?
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),
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avg_vol AS (
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SELECT symbol, AVG(volume) AS adv
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FROM (
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SELECT symbol, volume,
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ROW_NUMBER() OVER (
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PARTITION BY symbol ORDER BY trade_date DESC
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) AS rn
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FROM daily_bars
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WHERE trade_date < ?
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)
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WHERE rn <= 20
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GROUP BY symbol
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)
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SELECT l.symbol, l.close, l.vwap, l.cum_volume,
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p.prev_close, a.adv
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FROM latest l
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JOIN prev p ON p.symbol = l.symbol AND p.rn = 1
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LEFT JOIN avg_vol a ON a.symbol = l.symbol
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WHERE l.rn = 1 AND l.close BETWEEN ? AND ?
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""",
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[session_date, session_date, session_date, price_min, price_max],
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).fetchall()
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candidates: list[GapperCandidate] = []
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for symbol, close, vwap, cum_volume, prev_close, adv in rows:
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if not prev_close or prev_close <= 0:
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continue
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gap_pct = (float(close) - float(prev_close)) / float(prev_close) * 100.0
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rvol = (float(cum_volume) / float(adv)) if adv and adv > 0 else 0.0
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if gap_pct >= min_gap_pct and rvol >= min_relative_volume:
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candidates.append(
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GapperCandidate(
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symbol=symbol,
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price=float(close),
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gap_pct=gap_pct,
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cumulative_volume=int(cum_volume),
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relative_volume=rvol,
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vwap=float(vwap) if vwap is not None else None,
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)
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)
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candidates.sort(key=lambda c: (c.gap_pct * max(c.relative_volume, 0.1)), reverse=True)
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for i, candidate in enumerate(candidates[:limit], start=1):
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candidate.rank = i
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return candidates[:limit]
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def store_scanner_snapshot(
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con, candidates: list[GapperCandidate], snapshot_time: datetime | None = None
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) -> int:
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"""Persist a scan into scanner_snapshots. Returns rows written."""
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ts = (snapshot_time or datetime.now(timezone.utc)).replace(tzinfo=None)
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rows = [
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(
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str(uuid.uuid4()),
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ts,
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c.symbol,
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c.rank,
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c.price,
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c.gap_pct,
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c.cumulative_volume,
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c.relative_volume,
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c.vwap,
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(c.price - c.vwap) / c.vwap * 100.0 if c.vwap else None,
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SCANNER_VERSION,
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)
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for c in candidates
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]
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if not rows:
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return 0
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con.executemany(
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"""
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INSERT INTO scanner_snapshots (
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id, snapshot_time, symbol, rank, price, gap_pct,
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cumulative_volume, relative_volume, vwap, distance_from_vwap,
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scanner_version
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""",
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rows,
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)
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return len(rows)
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