中文版
因子流：
《“Know When to Hodl 'Em, Know When to Fodl 'Em”: An Investigation of Factor Based Investing in the Cryptocurrency Space》
利用11个数字货币的行情和发行数据，构造了动量、价值和caryy因子：
动量因子指标是
动量因子为前一周的回报
价值因子为当前市场价值与区块链中$-value链上交易的7天平均值的比值。
Carry因子被定义为7天内发行数字货币总量的负数，除以7天周期期发行的数字货币
三个因子都有效，动量因子表现最好，因子复合后能获得更高的风险调整后收益。
算法流：
CONTROL STRATEGY TO TRADE CRYPTOCURRENCIES
JOSEF KOKEŠ
Main goal of this article is to introduce strategy for automated trading on cryptocurrency exchange market. For this purpose we will use algorithm based of Floyd-Warshall algorithm
这篇文章的主要目的是介绍在数字货币交易市场下使用算法进行自动交易，介绍的算法基于的是最短路径弗洛伊德算法，这种算法的时间复杂度为O(N^3)，空间复杂度为O(N^2)
然而并没有给出比较有价值的内容，可以使用HDOJ 1217 Arbitrage(拟最短路，floyd算法)里面的题目和代码进行测试，可以使用floyd算法和SPFA算法进行测试
对于预测模型呢，
多因子选股策略主要步骤是因子池构建、检验因子有效性、剔除冗余因子、数据预处理、分类模型的构建、模型准确率及历史回测评估。
Tips：一般来说冗余因子剔除的方法如下：首先根据不同因子与模型形成期的股票
投资组合的收益率的相关性，对不同的因子的组合打分；接下来按照单只股票计
算各因子之间的得分相关系数矩阵；根据上述计算的得分相关系数矩阵计算整个
形成期的相关系数矩阵的平均值；最后设置一个选择阈值，用于剔除冗余因子。
技术面指标如动量、换手率、波动率等和其他指标如预期收益增长、宏观经济变量
等。
《Automated Bitcoin Trading via Machine Learning Algorithms》
使用xgboost具有自动随机选择因子的能力，可以取代在较多或者大量因子模型的因子有效性检验的问题。
作者：斯坦福大学 计算机学院的Isaac Madan Shauraya Saluja
特征：使用了过去5年的和比特币价格和支付相关的25个特征，
数据：使用了两种时间段，每日的记录，时间间隔为10分钟
结果：我们能够以98.7%的精确度预测出每日价格变化的迹象。
模型的假设是未来的价格可以看作是过去价格序列的组合，并把问题看作一个二分类问题，使用了模型有随机森林和广义线性模型（generalized linear models.）
先前的工作：Shah and Zhang使用了贝叶斯回归（Bayesian regression）获得了较好的收益。
他们的不足：没有探索比特币价格和其市值，挖矿速度上面的关系
结论：
第一个结论：
使用了广义线性回归模型，结果很不错，数据集价格点之间的较长时间间隔可能是导致价格波动不准的原因，使用SVM进行分类的效果比较差，原因可能是因为数据量不足的问题？
对随机森林而言，相对于广义二分线性回归模型在数据集上表现出了更高的精准度但是预测性不如广义线性回归模型。较低的预测是因为假阳性情况比较多，比实际情况表现出了更positive。
第二个结论：
10分钟级的数据比10s级的数据效果更好一些，表现在灵敏度和特异性比率，比10s级的数据更能反映趋势。RF相对于二分线性回归效果好的原因可能是因为RF使用非参数决策树，所以数据的离群值和线性可分性不受关注。
英文版：
《“Know When to Hodl 'Em, Know When to Fodl 'Em”: An Investigation of Factor Based Investing in the Cryptocurrency Space》
This paper uses momentum and distribution data from 11 digital currencies to construct momentum, value, and caryy factors.
The momentum is defined as prior week’s return
The value is defined as the ratio of current market value and the trailing 7 days average of $-valued on-chain transactions in its blockchain.
Carry is defined as negative of the sum total coin issuance over the precding 7days,divided by the coins outstanding at the beginning of that 7 day period
momentum as “the return we get if things keep changing the way they have”, value as the return we get “if things go back to where they were/some kind of fair equilibrium”, and carry as “the return we get if things don’t change at all”.
All three factors are effective, the momentum factor performs best, and the factor composite can obtain higher risk-adjusted returns.
《Automated Bitcoin Trading via Machine Learning Algorithms》
hypothesis
Specifically, with the idea that future price trends can be inferred directly from a linear combination of existing time series data
Dataset
Our data set consists of over 25 features relating to the Bitcoin price and payment network over the course of five years, recorded daily.
Result
Using this information we were able to predict the sign of the daily price change with an accuracy of 98.7%.
论文《Cryptocurrency price drivers: Wavelet coherence analysis revisited》（小波相干性分析）
google引用量 38
The hypothesis of this paper is that the relationship between online factors and prices depends on market mechanisms.
Using wavelet coherence to study the co-movement between the cryptocurrency price and its related factors
The main finding of this study is that the medium-term positive correlation between factors extracted from the Internet and prices is significantly enhanced when the price series is foamy; this explains why these relationships appear and disappear over time. The second finding is that the short-term relationship between the chosen factors and the price seems to be caused by specific market events (such as hacking/security vulnerabilities), and the impact of these factors on prices is not consistent over time intervals. In addition, the relationship between different cryptocurrencies was studied for the first time using wavelet coherence.
《Predicting Cryptocurrency Price Bubbles Using Social Media Data and Epidemic Modelling》
如题目所示，流行病