This chapter discusses, including detailed mathematical descriptions, trading strategies involving cryptocurrencies. Unlike more traditional assets such as stocks and bonds, there are no evident “fundamentals” for cryptoassets based on which one could build “fundamental” trading strategies such as value-based strategies, so cryptocurrency trading strategies tend to rely on trend data mining via machine learning techniques. The chapter discusses such strategies based on artificial neural networks with the input data based on technical indicators such as exponential moving averages and standard deviations, and relative strength index (RSI), and with the output data predicting certain probabilities pertaining to future returns, and also Twitter sentiment based strategies, where based on a learning vocabulary of pertinent keywords a predictive model is built using naïve Bayes conditional independence assumption with the Bernoulli or multinomial distribution by analyzing tweets containing such keywords.


Cryptocurrency Cryptoassets Cryptocurrency trading Machine learning Artificial neural network (ANN) Technical indicators Exponential moving average Exponential standard deviation Relative strength index (RSI) Input layer Output layer Twitter sentiment Learning vocabulary Keywords Bayes’ theorem Conditional independence assumption Bernoulli distribution Multinomial distribution Training data Bitcoin (BTC) Blockchain Ethereum (ETH) Trend data mining Activation function Sentiment analysis 


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Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Quantigic Solutions LLCStamfordUSA
  2. 2.Universidad del CEMABuenos AiresArgentina

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