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Exocet92andGitHub
d9f1862fd9
A speed claim a reader cannot reproduce is a screenshot. This harness installs manifoldbt from PyPI like any user would, generates its own data, and gates every timing behind a parity check: a workload where the two engines disagree publishes nothing and fails the run. It lives here rather than in the engine repository because it benchmarks the published wheel, not the source. Anyone can fork this repository and press "Run workflow" to get the same table on their own runner. The workflow runs on demand, weekly, and on every published release, so a version that gets slower says so in public.
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
"""Deterministic synthetic OHLCV bars, identical for every engine.
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One generator, one seed, one fingerprint. Both engines receive the *same*
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DataFrame object; nothing about the data can differ between them.
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Two properties are deliberate, not incidental:
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* ``open == previous close`` (no overnight gap). A bar that gaps through a stop
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is the one place where two engines can legitimately disagree on the fill price
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while both being correct. Removing gaps removes that whole class of false
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parity failures, so a real semantic drift is the only thing left that can trip
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the gate.
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* the intrabar range is wide enough that percentage stops and targets actually
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trigger, otherwise the bracket workload would measure an empty branch.
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"""
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from __future__ import annotations
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import hashlib
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import numpy as np
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import pandas as pd
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DEFAULT_SEED = 20260816
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def make_ohlcv(
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rows: int,
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*,
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seed: int = DEFAULT_SEED,
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freq: str = "1min",
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start: str = "2020-01-01",
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vol: float = 3e-4,
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drift: float = 2e-7,
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) -> pd.DataFrame:
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"""A gap-free random walk of ``rows`` bars, reproducible from ``seed``."""
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rng = np.random.default_rng(seed)
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log_ret = rng.normal(drift, vol, size=rows)
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close = 100.0 * np.exp(np.cumsum(log_ret))
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open_ = np.empty(rows, dtype=np.float64)
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open_[0] = 100.0
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open_[1:] = close[:-1]
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# Intrabar excursion beyond the open/close body, as a fraction of price.
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wick = rng.uniform(0.2, 1.8, size=rows) * vol * close
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body_hi = np.maximum(open_, close)
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body_lo = np.minimum(open_, close)
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high = body_hi + wick
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low = np.maximum(body_lo - wick, 1e-8)
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return pd.DataFrame(
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{
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"timestamp": pd.date_range(start, periods=rows, freq=freq, tz="UTC"),
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"open": open_,
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"high": high,
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"low": low,
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"close": close,
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"volume": rng.uniform(100.0, 10_000.0, size=rows),
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}
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)
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def digest(df: pd.DataFrame) -> str:
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"""Short content fingerprint of the bars, recorded in the result envelope.
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Anyone re-running the harness can compare this before comparing timings: a
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different digest means a different dataset, which makes the numbers
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incomparable no matter how clean the machine was.
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"""
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h = hashlib.sha256()
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for column in ("open", "high", "low", "close", "volume"):
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h.update(np.ascontiguousarray(df[column].to_numpy(dtype=np.float64)).tobytes())
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return h.hexdigest()[:16]
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