I am working on a LSTM model and trying to use a DataLoader to provide the data.  I am using stock price data and my dataset consists of:

Date (string)
Closing Price (float)
Price Change (float)

Right now I am just looking for a good example of LSTM using similar data so I can configure my DataSet and DataLoader correctly.
To test my DataLoader I have the following code:
    for i, d in enumerate(dataloader):
        print(i, d)

Using the following definition of dataloader  the test works.
    dataloader = DataLoader(pricedata,
                            batch_size=30,
                            shuffle=False,
                            num_workers=4)

This give me x batches  of size 30, which makes sense.
However, I need to use a sliding window of size n so, assuming there are k instances in the dataset, I would like k-n batches with n instance in each batch.
So I redefined dataloader as:
    dataloader = DataLoader(pricedata,
                            batch_sampler=torch.utils.data.sampler.SequentialSampler(pricedata),
                            shuffle=False,
                            num_workers=4)

With this change I get the following error message:
TypeError: 'int' object is not iterable
when the code hits the test:
for i, d in enumerate(dataloader):
Based on this I have two questions:

Is torch.utils.data.sampler.SequentialSampler the appropriate sampler to use for a sliding window?
Can anyone point me to a good example of configuring an LSTM using a DataLoader to load numeric data?  All the examples I have seen are for NLP.

Thanks