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>
91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
"""Synthetic minute-bar builders shared across the test suite."""
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from __future__ import annotations
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from datetime import datetime, timedelta
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import pandas as pd
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SESSION_DATE = datetime(2026, 6, 11).date()
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# Bars are stored UTC-naive throughout the pipeline (ingestion converts to UTC
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# then drops tzinfo). 13:30 UTC == 09:30 America/New_York in June (EDT), so this
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# fixture exercises the same timezone path as live data: the watcher converts
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# the bar timestamp UTC -> Eastern before the entry-cutoff check.
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SESSION_OPEN = datetime(2026, 6, 11, 13, 30) # 09:30 ET expressed in UTC
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def make_bars(specs: list[tuple[float, float, float, float, int]],
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start: datetime | None = None,
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symbol: str = "TEST") -> pd.DataFrame:
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"""Build a minute-bar frame from (open, high, low, close, volume) tuples."""
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start = start or SESSION_OPEN
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rows = []
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cum_pv = 0.0
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cum_v = 0
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for i, (o, h, l, c, v) in enumerate(specs):
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ts = start + timedelta(minutes=i)
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typical = (h + l + c) / 3.0
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cum_pv += typical * v
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cum_v += v
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rows.append(
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{
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"symbol": symbol,
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"timestamp": ts,
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"session_date": SESSION_DATE,
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"is_premarket": False,
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"is_regular_hours": True,
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"open": o,
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"high": h,
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"low": l,
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"close": c,
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"volume": v,
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"vwap": cum_pv / cum_v if cum_v else c,
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"quality_score": 1.0,
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}
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)
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return pd.DataFrame(rows)
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def bull_flag_bars(symbol: str = "TEST") -> pd.DataFrame:
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"""Gap-up + impulse + light pullback + breakout: should evaluate ready.
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Pair with previous_close=10.0 and avg_daily_volume=500_000 so the gap
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(~25%) and relative volume criteria pass.
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"""
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specs = [
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# base
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(12.50, 12.60, 12.45, 12.55, 60_000),
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(12.55, 12.65, 12.50, 12.60, 55_000),
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(12.60, 12.70, 12.55, 12.65, 50_000),
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# impulse leg on expanding volume
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(12.65, 12.95, 12.62, 12.92, 120_000),
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(12.92, 13.25, 12.90, 13.20, 150_000),
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(13.20, 13.55, 13.18, 13.50, 180_000),
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(13.50, 13.85, 13.48, 13.80, 200_000),
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# shallow pullback on lighter volume (holds above vwap)
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(13.80, 13.82, 13.62, 13.66, 70_000),
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(13.66, 13.70, 13.55, 13.60, 55_000),
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(13.60, 13.66, 13.52, 13.58, 45_000),
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# breakout bar through pullback high
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(13.58, 13.95, 13.56, 13.92, 220_000),
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(13.92, 14.05, 13.88, 14.00, 190_000),
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]
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return make_bars(specs, symbol=symbol)
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def fading_bars(symbol: str = "FADE") -> pd.DataFrame:
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"""Steady downtrend below vwap: should evaluate blocked."""
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specs = []
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price = 10.0
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for i in range(15):
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o = price
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c = price - 0.08
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specs.append((o, o + 0.02, c - 0.03, c, 40_000))
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price = c
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return make_bars(specs, symbol=symbol)
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def tiny_bars(symbol: str = "TINY") -> pd.DataFrame:
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"""Too few bars: should block with insufficient_data."""
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return make_bars([(5.0, 5.1, 4.9, 5.05, 10_000)] * 3, symbol=symbol)
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