November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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自动化交易操作
｜
303
在此背景下，图
10-3
还展示了低于“全凯利”价值的股票头寸的演变。
风险确实
会随着
f*
值的降低而降低。
10.2
基于机器学习的交易策略
第
8
章介绍了
Oanda
交易平台，包括其
RESTful API
和
Python
程序包
tpqoa
。本部
分将结合基于机器学习预测市场价格走势的方法与通过
Oanda v20 RESTful API
获
取的历史数据，以回测
EUR/USD
货币对的算法交易策略。它使用向量化回测方法，
这一次考虑到把买卖差价作为部分交易成本。与第
4
章介绍的简单向量回测方法相
比，该方法还对交易策略的风险特征进行了更深入的分析。
10.2.1
向量回测
回测基于日内数据进行的，具体点就是基于
5
分钟长的柱线。在下面的代码中，我
们连接到
Oanda v20 API
，并获取一周的
10
分钟条形数据。图
10-4
对获取数据期间
的中间收盘价进行了可视化。
In [36]: import tpqoa
In [37]: %time api = tpqoa.tpqoa('../pyalgo.cfg')
CPU times: user 893 µs, sys: 198 µs, total: 1.09 ms
Wall time: 1.04 ms
In [38]: instrument = 'EUR_USD'
In [39]: raw = api.get_history(instrument,
start='2020-06-08', ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.