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到底该怎么用期权赚钱

在數位資產量化交易中的應用初探

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When Ryan was sixteen, he decided to start a green power company. He had always been interested in renewable energy, and he believed that there was a lot of potential in the market. With some help from his dad, Ryan set up a small office in their basement and started working on his first prototype.

It took him a few 在數位資產量化交易中的應用初探 years to 在數位資產量化交易中的應用初探 perfect his design, but by the time he was twenty-one, his company was starting to take off. Customers were beginning 在數位資產量化交易中的應用初探 to see the value in using renewable energy, and Ryan’s business was growing rapidly.

Now, ten years later, Ryan’s startup is one of the leading providers of green power technology. They have dozens of patents and several thousand employees. And they’re still growing rapidly.

Aes Chile is now one of the leading providers of green power technology. They have dozens of patents and several thousand employees. And they’re still growing rapidly. Thanks to Ryan’s hard work and innovation, more and more people are using renewable energy, which is helping to reduce our carbon footprint and make the world a cleaner, 在數位資產量化交易中的應用初探 healthier place.

多空持仓比在数字资产量化交易中的应用初探

可以发现,lspr可以转化为空头账户平均持仓量与多头账户平均持仓量之比。这意味着: lspr越高,空方平均资金量越大;反之亦然。 例如,若此时lspr为3,就表示平均每个净空头用于做空的保证金为净多头用于做多的保证金的三倍。 通常而言,我们认为信息交易者具有资金、消息上的优势,能够在市场中获得超额收益。在资金量较小的交易者贪婪地买入或者恐惧地卖出时,藏在阴影中的信息交易者便可能会在lspr因子上露出马脚。

相比上述两个因子,lspr波动率的计算可能稍有复杂。主观上分析,若lspr波动率较低,那么意味着这一段时间内多空持仓陷入了焦灼的状态,双方相持难下;反之,若lspr波动率较高,则说明资金博弈层面上,资金量较小的交易者的资金在多空两边反复横跳,这种情形可以被描述为“躁动”。而“躁动”和“焦灼”分别对应了未来的哪一种行情,则需要定量地进行分析。下面,我们分别对过去72小时和24小时内的lspr计算其变异系数(Coefficient of Variation),作为其波动率的估计量。

图中对A, B, C, …, L这些人进行了分类。蓝色的节点被称为分裂点,橙色的节点被称为叶片。叶片的数量也就决定了这一棵决策树最终将样本分成了几类。在本文的算法中,考虑到样本数量不是很多,最大叶片数可以设置为5,即最多将样本分为5份,单个叶片包含最小样本量为总体的16%。

在训练集中,通过观察f_roc的分组结果,可以发现:f_roc大幅减少确实意味着未来72小时更可能上涨,而f_roc大幅上涨对于多头来说却不是什么好迹象。 如果当前lspr较3天前大幅上升,那么多头处于不利地位。若当前lspr较3天前大幅下降,那做多的预期收益可能会更高。不过,当变动率接近零时,这个因子对未来收益率的预测能力则几乎很弱 。详情请见下图: