"""量化练习公共库：数据获取(带缓存+重试) + 常用指标 + 回测指标。"""
import os, time, pandas as pd, numpy as np

CACHE = os.path.join(os.path.dirname(__file__), "..", "data", "cache")
os.makedirs(CACHE, exist_ok=True)

def _fetch(symbol, start, end, adjust, retries=6, wait=2.0):
    import akshare as ak
    last = None
    for i in range(retries):
        try:
            return ak.stock_zh_a_hist(symbol=symbol, period="daily",
                                      start_date=start, end_date=end, adjust=adjust)
        except Exception as e:                       # 网络间歇性抽风，重试
            last = e
            time.sleep(wait * (i + 1))
    raise last

def get_daily(symbol="000001", start="20200101", end="20241231", adjust="qfq"):
    """拉单只A股日线(前复权)，带本地缓存与自动重试。"""
    f = os.path.join(CACHE, f"{symbol}_{start}_{end}_{adjust}.csv")
    if os.path.exists(f):
        return pd.read_csv(f, parse_dates=["日期"])
    df = _fetch(symbol, start, end, adjust)
    df.to_csv(f, index=False, encoding="utf-8-sig")
    return df

def add_returns(df, price_col="收盘"):
    df = df.copy()
    df["ret"] = df[price_col].pct_change().fillna(0.0)
    return df

def net_value(ret):
    return (1 + pd.Series(ret).fillna(0.0)).cumprod()

def stats(ret, periods=252):
    ret = pd.Series(ret).fillna(0.0)
    nv = net_value(ret); total = nv.iloc[-1] - 1
    ann = (1 + total) ** (periods / max(len(ret), 1)) - 1
    vol = ret.std() * np.sqrt(periods)
    sharpe = (ret.mean() * periods) / vol if vol > 0 else np.nan
    dd = (nv / nv.cummax() - 1).min()
    calmar = ann / abs(dd) if dd < 0 else np.nan
    return {"总收益": f"{total:.2%}", "年化": f"{ann:.2%}", "年化波动": f"{vol:.2%}",
            "夏普": f"{sharpe:.2f}", "最大回撤": f"{dd:.2%}", "卡玛": f"{calmar:.2f}"}

def max_drawdown(nv):
    return (nv / nv.cummax() - 1).min()