November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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118
｜
第
4
章
时间序列动量不关注横截面中证券的相对收益，它完全关注证券自身的过去收
益……我们在几乎所有我们研究的工具中对时间序列动量的发现似乎都对“随
机游走”假设提出了挑战。它的最基本形式意味着知道过去的价格是涨还是跌，
并不意味着将来价格会涨还是跌。
4.3.1
基础入门
给定美元（
XAU =
）的黄金价格的日末收盘价：
In [74]: data = pd.DataFrame(raw['XAU='])
In [75]: data.rename(columns={'XAU=': 'price'}, inplace=True)
In [76]: data['returns'] = np.log(data['price'] / data['price'].shift(1))
最简单的时间序列动量策略是：如果最后一次收益为正，则购买股票，如果最后收
益为负，则出售股票。使用
NumPy
和
pandas
可以很容易地将其形式化。只需将最后
可用收益的迹象作为市场头寸即可。图
4-7
说明了此策略的绩效。该策略的性能确
实大大落后于基本工具：
In [77]: data['position'] = np.sign(data['returns'])
In [78]: data['strategy'] = data['position'].shift(1) * data['returns']
In [79]: data[['returns', 'strategy']].dropna().cumsum(
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