49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
"""量化练习公共库：数据获取(带缓存+重试) + 常用指标 + 回测指标。"""
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import os, time, pandas as pd, numpy as np
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CACHE = os.path.join(os.path.dirname(__file__), "..", "data", "cache")
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os.makedirs(CACHE, exist_ok=True)
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def _fetch(symbol, start, end, adjust, retries=6, wait=2.0):
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import akshare as ak
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last = None
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for i in range(retries):
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try:
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return ak.stock_zh_a_hist(symbol=symbol, period="daily",
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start_date=start, end_date=end, adjust=adjust)
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except Exception as e: # 网络间歇性抽风，重试
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last = e
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time.sleep(wait * (i + 1))
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raise last
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def get_daily(symbol="000001", start="20200101", end="20241231", adjust="qfq"):
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"""拉单只A股日线(前复权)，带本地缓存与自动重试。"""
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f = os.path.join(CACHE, f"{symbol}_{start}_{end}_{adjust}.csv")
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if os.path.exists(f):
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return pd.read_csv(f, parse_dates=["日期"])
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df = _fetch(symbol, start, end, adjust)
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df.to_csv(f, index=False, encoding="utf-8-sig")
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return df
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def add_returns(df, price_col="收盘"):
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df = df.copy()
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df["ret"] = df[price_col].pct_change().fillna(0.0)
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return df
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def net_value(ret):
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return (1 + pd.Series(ret).fillna(0.0)).cumprod()
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def stats(ret, periods=252):
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ret = pd.Series(ret).fillna(0.0)
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nv = net_value(ret); total = nv.iloc[-1] - 1
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ann = (1 + total) ** (periods / max(len(ret), 1)) - 1
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vol = ret.std() * np.sqrt(periods)
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sharpe = (ret.mean() * periods) / vol if vol > 0 else np.nan
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dd = (nv / nv.cummax() - 1).min()
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calmar = ann / abs(dd) if dd < 0 else np.nan
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return {"总收益": f"{total:.2%}", "年化": f"{ann:.2%}", "年化波动": f"{vol:.2%}",
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"夏普": f"{sharpe:.2f}", "最大回撤": f"{dd:.2%}", "卡玛": f"{calmar:.2f}"}
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def max_drawdown(nv):
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return (nv / nv.cummax() - 1).min()
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