Skip to main content

Table 4 Parameters tested in the ML models and parameters leading to the best model

From: Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Model

Hyperparameters used

Hyperparameters of the model with the best performance in the validation sample

RF

Number of trees: 500, 1000, 1500

Bitcoin: 1500 trees, 50.0% of the variables sampled at each split

Percentage of variables sampled at each split: 50.0%, 33.3%

Litecoin: 1500 trees, 50.0% of the variables sampled at each split

Ethereum: 500 trees, 50.0% of the variables sampled at each split

RF-binary

Number of trees: 500, 1000, 1500

Bitcoin: 500 trees, 33.3% of the variables sampled at each split

Percentage of variables sampled at each split: 50.0%, 33.3%

Litecoin: 1000 trees, 33.3% of the variables sampled at each split

Ethereum: 1500 trees, 50.0% of the variables sampled at each split

SVM

Kernel: radial, linear, polynomial

Bitcoin: polynomial kernel, gammaā€‰=ā€‰0.20

Gamma: 0.05. 0.10, 0.20

Litecoin: radial kernel, gammaā€‰=ā€‰0.05

Ethereum: radial kernel, gammaā€‰=ā€‰0.10

SVM-binary

Kernel: radial, linear, polynomial

Bitcoin: radial kernel, gammaā€‰=ā€‰0.20

Gamma: 0.05. 0.10, 0.20

Litecoin: polynomial kernel, gammaā€‰=ā€‰0.10

Ethereum: radial kernel, gammaā€‰=ā€‰0.10

  1. This table presents all combinations of hyperparameters used in the experiments. The hyperparameters of the model with the best performance in the validation sample were then used to define the trading strategies in the test sample. For Random Forests (RF), the remaining hyperparameters were kept at the defaults of the randomForest R package. For Support Vector Machines (SVM), the remaining hyperparameters were kept at the defaults of the e1071 R package