FYI, I came up with a workaround to account for gaps in prices using collected realtime data as a backup; I've included code below for others.
Ensure your DB param includes a realtime database:
DB = ['usstock-minute', 'usstock-minute-realtime-polygon']
# Combine history and realtime prices
if 'MinuteOpenOpen' in prices.index: # account for having no realtime pricing for given params
# Merge realtime prices into history prices
history_prices = prices.copy()
history_prices = history_prices.drop(['MinuteOpenOpen', 'MinuteHighHigh', 'MinuteLowLow', 'MinuteCloseClose', 'MinuteVolumeClose'])
history_prices.loc['Open'] = history_prices.loc['Open'].fillna(prices.loc['MinuteOpenOpen']).values
history_prices.loc['High'] = history_prices.loc['High'].fillna(prices.loc['MinuteHighHigh']).values
history_prices.loc['Low'] = history_prices.loc['Low'].fillna(prices.loc['MinuteLowLow']).values
history_prices.loc['Close'] = history_prices.loc['Close'].fillna(prices.loc['MinuteCloseClose']).values
history_prices.loc['Volume'] = history_prices.loc['Volume'].fillna(prices.loc['MinuteVolumeClose']).values
prices = history_prices