The training data and test data were input to the trained machine learning model, and the Confusion matrix was calculated. Since TP (True Positive) is a profit pattern, FP (False Positive) is a loss cut pattern, and TN (True Negative) and FN (False Negative) are patterns that do nothing, the number of TPs increases and the number of FPs increases. It can be said that it is preferable to reduce.

In fact, the 2nd model is compared to the 1st model ・ In the training data ... TP has improved and FP has decreased, so the probability of profitability has improved. Accuracy and F1-score are also improved. ・ In the test data ... TP decreased, FP decreased. Since Accuracy and F1-score are improved, the probability that the prediction by the machine learning model will be correct is increasing, but since TP is decreasing and FP is decreasing, which one can make a profit depends on the situation. (Should we evaluate that the probability of loss cut is decreasing)

5 Summary

--A two-step machine learning model was applied to predict future price movements of Bitcoin. --Accuracy and F1-score could be improved by applying the "two-step machine learning model". (Prediction accuracy has improved.) </ B> ――The "two-step machine learning model" is preferable to the "one-step machine learning model" because the profit probability is improved and the loss cut probability is reduced in the training data. --However, when applied to test data, the "two-step machine learning model" has a lower profit probability and loss cut probability than the "one-step machine learning model", so the "two-step machine learning model" and "one-step machine" Which of the "learning models" can be profitable depends on the situation. However, the probability of losing money is decreasing

\begin{eqnarray}
Label
=
\begin{cases}
1 & ( vol \geqq Vthu ) \\
0 & ( Vthd \lt b \lt Vthu ) \\
-1 & (vol \lt Vthd)
\end{cases}
\end{eqnarray}