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"""Deterministic synthetic OHLCV fixtures for worker tests.
Every object here carries ``_synthetic: true`` and must never be used as a
production fallback. Values are simple deterministic series so accounting
assertions in backtest tests can be hand-verified.
"""
import pandas as pd
_SYNTHETIC = {"_synthetic": True}
def synthetic_daily(symbol: str = "SH#600000", start="2024-01-02", days=20,
base=10.0, volume=1_000_000, extra_fields=True) -> pd.DataFrame:
dates = pd.bdate_range(start, periods=days)
rows = []
price = base
for i, d in enumerate(dates):
o = round(price, 2)
c = round(price * 1.02, 2) if i % 2 == 0 else round(price * 0.98, 2)
h = round(max(o, c) * 1.01, 2)
l = round(min(o, c) * 0.99, 2)
row = {
"date": d.strftime("%Y-%m-%d"),
"symbol": symbol,
"open": o, "high": h, "low": l, "close": c,
"volume": volume + i * 1000,
}
if extra_fields:
# raw provider-style extra fields preserved verbatim
row["amount"] = round((o + h + l + c) / 4 * (volume + i * 1000), 2)
row["turnover"] = round(0.5 + i * 0.01, 3)
rows.append(row)
price = c
df = pd.DataFrame(rows)
df.attrs["synthetic"] = True
return df
SYNTH = _SYNTHETIC
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