"""共享测试装置：合成确定性 A 股行情（含涨跌趋势与噪声，无外部依赖）。"""

from __future__ import annotations

from decimal import Decimal

import numpy as np
import pandas as pd


def synthetic_daily(drifts: dict[str, float], n: int = 320, base: float = 100.0) -> pd.DataFrame:
    """生成多股票日线长表。

    每股价格：p[j] = p[j-1] * (1 + drift + 0.012 * sin((j + i) * 0.8)) —— 确定性、
    趋势 + 微幅周期噪声；含 high/low/volume/amount 供各因子使用。
    """
    dates = pd.bdate_range("2024-01-01", periods=n)
    rows: list[dict] = []
    for i, (sym, drift) in enumerate(drifts.items()):
        price = float(base)
        for j, d in enumerate(dates):
            ret = drift + 0.012 * np.sin((j + i) * 0.8)
            prev = price
            price = price * (1 + ret)
            rows.append(
                {
                    "symbol": sym,
                    "trade_date": d.date(),
                    "open": float(prev),
                    "high": float(price * 1.008),
                    "low": float(min(prev, price) * 0.992),
                    "close": float(price),
                    "volume": float(1_000_000 + j * 1000 + i * 3000),
                    "amount": float(price * (1_000_000 + j * 1000 + i * 3000)),
                }
            )
    return pd.DataFrame(rows)


def bars_dataframe_to_daily_bars(daily: pd.DataFrame) -> list:
    """测试辅助：DataFrame → domain DailyBar 实体（供 service/仓储层路径测试）。"""
    from app.domain.entities.market import DailyBar

    return [
        DailyBar(
            symbol=r.symbol,
            trade_date=r.trade_date,
            open=Decimal(str(r.open)),
            high=Decimal(str(r.high)),
            low=Decimal(str(r.low)),
            close=Decimal(str(r.close)),
            volume=Decimal(str(r.volume)),
            amount=Decimal(str(r.amount)),
        )
        for r in daily.itertuples()
    ]