###### Is the last day of the train set D and the first day of the test set D+n with n>0? Splitting without considering the time series structure is wrong.
###### Is there no leakage? Unfortunately, there's no automated way to check if the dataset is leaked. This step is validated if the features of date d are built as follows:
###### Is There a plot showing your cross-validation? As usual, all plots should have named axis and a title. If you chose a Time Series Split the plot should look like this:
##### **The pipeline shouldn't be trained once and predict on all data points!** As explained: The signal has to be generated with the chosen cross validation: train the model on the train set of the first fold, then predict on its validation set; train the model on the train set of the second fold, then predict on its validation set, etc ... Then, concatenate the predictions on the validation sets to build the machine learning signal.
##### The transformed machine learning signal (long only, long short, binary, ternary, stock picking, proportional to probability or custom ) is multiplied by the return between d+1 and d+2. As a reminder, the signal at date d predicts wether the return between d+1 and d+2 is increasing or decreasing. Then, the PnL of date d could be associated with date d, d+1 or d+2. This is arbitrary and should impact the value of the PnL.
##### You invest the same amount of money every day. One exception: if you invest 1$ per day per stock the amount invested every day may change depending on the strategy chosen. If you take into account the different values of capital invested every day in the calculation of the PnL, the step is still validated.
