You work as a machine learning specialist at a hedge fund firm. Your firm is working on a new quant algorithm to predict when to enter and exit holdings in their portfolio. You are building a machine learning model to predict these entry and exit points in time. You have cleaned your data, and you are now ready to split the data into training and test datasets.
Which splitting technique is best suited to your model’s requirements?

Answer options:

A.Use k-fold cross validation to split the data.
B.Sequentially splitting the data
C.Randomly splitting the data
D.Categorically splitting the data by holding

Answer: B
Option A is incorrect. Using k-fold cross validation will randomly split your data. But you need to consider the time-series nature of your data when splitting. So randomizing the data would eliminate the time element of your observations, making the datasets unusable for predicting price changes over time.
Option B is correct. By sequentially splitting the data, you preserve the time element of your observations.
Option C is incorrect. Randomly splitting the data would eliminate the time element of your observations, making the datasets unusable for predicting price changes over time.
Option D is incorrect. If you split the data by a category such as the holding attribute, you would create imbalanced training and test dataset since some holdings would only be in the training dataset and others would only be in the test dataset.
Reference:
Please see the Amazon Machine Learning developer guide titled Splitting Your Data.