HMM-LSTM-RNN-CNN.Rmd

---
title: "HMM, LSTM, RNN, CNN Statistical Models"
author: "®γσ, Lian Hu"
date: "4/1/2021"
output: html_document
---

**Draft**

> **如何缓解量化交易中存在的"低信噪比"现象？**
金融市场的数据信噪比特别低,得到信息更多的背后是得到了更多的噪音。举例来说：假如获得了100个单位信息，但是其中有90单位信息是噪音，剩下的10单位是我们真正想要获得的信息。噪音会导致构建的模型变得容易过拟合，这也是很多回测非常好的策略实盘失效的原因。如果能缓解量化交易中存在的"低信噪比"，就可以进一步模型的精度。<br><br>[*首先，你得有指标度量信噪比。然后，下单数量应该与信噪比成正比*]{style="color:RoyalBlue"}

引用：[如何缓解量化交易中存在的"低信噪比"现象？](https://www.zhihu.com/question/534058888)



# 参考文献

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- [Airport Dash](https://rpubs.com/nabiilahardini/ts-flight)
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- [Using the `forecastHybrid` package](https://cran.r-project.org/web/packages/forecastHybrid/vignettes/forecastHybrid.html)
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- [如何缓解量化交易中存在的"低信噪比"现象？](https://www.zhihu.com/question/534058888)
- [R语言隐马尔可夫模型HMM连续序列重要性重抽样CSIR估计随机波动率模型SV分析股票收益率时间序列](http://tecdat.cn/r%e8%af%ad%e8%a8%80%e9%9a%90%e9%a9%ac%e5%b0%94%e5%8f%af%e5%a4%ab%e6%a8%a1%e5%9e%8bhmm%e8%bf%9e%e7%bb%ad%e5%ba%8f%e5%88%97%e9%87%8d%e8%a6%81%e6%80%a7%e9%87%8d%e6%8a%bd%e6%a0%b7csir%e4%bc%b0%e8%ae%a1)
- [一文搞懂HMM（隐马尔可夫模型）](https://www.cnblogs.com/skyme/p/4651331.html)
- [PYTHON用RNN循环神经网络：LSTM长期记忆、GRU门循环单元、回归和ARIMA对COVID-19新冠疫情新增人数时间序列预测](http://tecdat.cn/python%e7%94%a8rnn%e5%be%aa%e7%8e%af%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%ef%bc%9alstm%e9%95%bf%e6%9c%9f%e8%ae%b0%e5%bf%86%e3%80%81gru%e9%97%a8%e5%be%aa%e7%8e%af%e5%8d%95%e5%85%83%e3%80%81%e5%9b%9e)
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- [PYTHON用于NLP的SEQ2SEQ模型实例:用KERAS实现神经网络机器翻译](http://tecdat.cn/python%e7%94%a8%e4%ba%8enlp%e7%9a%84seq2seq%e6%a8%a1%e5%9e%8b%e5%ae%9e%e4%be%8b%e7%94%a8keras%e5%ae%9e%e7%8e%b0%e7%a5%9e%e7%bb%8f%e6%9c%ba%e5%99%a8%e7%bf%bb%e8%af%91)
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- [PYTHON中利用长短期记忆模型LSTM进行时间序列预测分析 – 预测电力负荷数据](http://tecdat.cn/python%e4%b8%ad%e5%88%a9%e7%94%a8%e9%95%bf%e7%9f%ad%e6%9c%9f%e8%ae%b0%e5%bf%86%e6%a8%a1%e5%9e%8blstm%e8%bf%9b%e8%a1%8c%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e9%a2%84%e6%b5%8b%e5%88%86%e6%9e%90)
- [PYTHON在KERAS中使用LSTM解决序列问题](http://tecdat.cn/python%e5%9c%a8keras%e4%b8%ad%e4%bd%bf%e7%94%a8lstm%e8%a7%a3%e5%86%b3%e5%ba%8f%e5%88%97%e9%97%ae%e9%a2%98)
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