"""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