It looks like you are doing this in an algo. I'll give an example of how to do this in a notebook because it's easier to output and visualize what's happening.
The first issue is one should grab all the data (high, low, price) at once and then drop nans if any row is a nan. Applying dropna to each individually runs the risk of either misaligning the data, or worse, having different lengths for each set. So, again in a notebook, this would be
# Get minute pricing for our security
data = get_pricing(security,
start_date=period_start,end_date=period_end,
fields=['high', 'low', 'price'],
frequency='minute')
The second issue is how to make the resample method work as expected. The first is to align and label the data correctly. The second is applying the correct resampling method to each value. Typically the price is just the last price at the end of the bar but Tradestation may use the mean or median of the bar.
# Resample into 30 minute bars
# Use the max high, min low, and last price for resampling. There could be other choices for price.
# Use the `agg` method to apply different functions to each column.
# Typically bars are labeled by the last minute and not the first so choose label='right'
# To ensure the last data point is in the bar choose closed='right'
prices_30m = data.resample('5T', label='right', closed='right').agg({'high': 'max', 'low': 'min', 'price': 'last'})
Check out the attached notebook.
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