DecisionTree

Here, we show model performance in predicting the bitcoin price and their hyper-parameters.

Prediction

Prices

DATE CLOSE
2024-12-04 94,837.67
2024-12-11 87,917.41
2024-12-18 96,537.70
2024-12-25 98,313.26

Charts

Model Performance

For scoring, Root Mean Squared Error was used.

Training data

Mean across all targets: 1,557.28
D+7 D+14 D+21 D+28
305.96 1,806.76 414.08 3,702.32

Test data

Mean across all targets: 15,824.84
D+7 D+14 D+21 D+28
3,231.14 18,001.30 19,412.95 22,653.99

Model Setup

Below are choices made for the model setup.

Exogenous Data

(Determined by Forward Selection and heuristically based on the model's assumptions)

param description value
include_indices Whether to include market indices. False
include_commodities Whether to include commodities. False
include_currencies Whether to include currencies. False

Feature Generator

(Determined by Grid Search)

param description value
ohlc_ratio Whether to add OHLC ratios of bitcoin price to the data. False
lags List of lags to use. None, if no lags are used. [7]

Feature Scaler

(Determined by heuristically, considering the model's assumptions)

param description value
log Whether to take the log of the data. False
diff Whether to difference the data. True
normalize Whether to normalize the data. True

Hyper-parameters

(Determined by Grid Search)

param description value
criterion Function for splitting the tree. squared_error
max_depth The maximum depth of the tree. 5
max_features The number of features to consider when looking for the best split. Either int, or log2 (=log2(num features)) or sqrt (= sqrt(num features)). log2
min_samples_split The minimum number of samples required to split an internal node. 5
min_samples_leaf The minimum number of samples required to be at a leaf node. 3