November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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122
｜
第
4
章
定义一个列表对象，以选择以后要绘制的列。
根据动量策略参数得出市场头寸。
计算出结果最终策略的对数收益。
将列名称追加到列表对象。
绘制所有相关列，来将策略的绩效与基准工具的绩效进行比较。
图 4-10：苹果股票的盘中总表现和五种动量策略（最后 1，3，5，7 个和 9 个回报）
图
4-11
展示了标准普尔
500
指数相同五种策略的表现。同样，所有五种策略配置均
优于指数，并且都显示出正收益（不包括交易费用）。
4.3.2
方法通用化
“动量回测类”介绍了一个包含
MomVectorBacktester
类的
Python
模块，该模块允
许对基于动量的策略进行一些更标准化的回测。该类具有以下属性：
•
symbol
：要使用的
RIC
（路透金融工具编码）。
•
start
：所选数据的开始日期。
掌握向量化回测
｜
123
图 4-11：标普 500 指数的日内总表现和五种动量策略（最后 1，3，5，7 和 9 个回报）
•
end
：所选数据的结束日期。
•
amount
：初始投资的金额。
•
tc
：用于每笔交易的比例交易成本。
与
SMAVectorBacktester
类相比，该类引入了两个重要的概括：在回测阶段开始时
要投资的固定金额和成比例的交易成本，使其在成本上更接近于真实的市场。特别
是在时间序列动量策略的背景下，交易成本的增加非常重要 ...
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