To fetch the current price and yesterdays close in a single dataframe use the data.history method with a frequency of '1d'. It's not stated explicitly in the documentation but the last row of returned data will always be todays data (as of when the method is called) and the second to the last row will be yesterdays data. The price field will return the last traded price as of when the method is called (ie the current price). So this will return a dataframe with two rows of prices and a column for each asset. The last row contains the current prices and the first row is yesterdays close prices.

    my_asset_list = symbols('AAPL', 'IBM')  
    prices = data.history(my_asset_list,  
                               fields='price',  
                               bar_count=2,  
                               frequency='1d'  
                               )

One subtlety is the current price timestamps reflect midnight and not the actual time (eg 9:31). This is a result of using the '1d' frequency. The data.history dataframe only knows days and not times. Typically this isn't an issue but something to take note of.

Check out the logs in the attached algo for verification.

I notice that 'price' and 'close' give the same result. Should not 'close' give the price from the previous days only? Why is it giving a price for the current day?
What's the difference between 'price' and 'close'?

The close field is the last traded price of either the day (if '1d' frequency is selected) or the minute bar (if '1m' frequency is chosen). If there are no trades in a given bar then the value will be nan. These nan values show up especially with lower volume stocks which may not be traded every minute.

The price field is simply the close price but forward filled to eliminate the nans. It is the last traded price and not simply the last traded price in a given bar. The price and close values will always be the same except where close is nan. It's generally a good idea to use price.

The way to think of the data.history method is that it will return all the history it can. Specifically, it will return data up through the minute it was called. Consider the following code called during market hours (ie not in before_trading_start).

    prices = data.history(my_asset_list,  
                               fields='price',  
                               bar_count=2,  
                               frequency='1d'  
                               )

This will return a dataframe with two rows (since bar_count=2). The last row (row -1) will always contain the most recent data. In this case, the most recent data is today's data and will be the most recent minute close price. The second to the last row (row -2) will always be the previous bar data. In this case, yesterday's close. Including the current bar data in history makes the method consistent for both '1d' and 1m' bars (one would typically not want to exclude the most recent bar when fetching minute data). Additionally, it eliminates the need for two function calls in many instances, one for history and one for current.

Below is the behavior for the current days open, high, low, close, price, and volume when using data.history. The last values for these (eg high[-1] ) are the current days values as of the minute the method was called.

open: this is the days open price and will be constant throughout the day
high: the days high up to this time
low: the days low up to this time
close: the close price as of the last minute. This will update every minute and is the same as the data.current close price
price: the same as close but forward filled in cases where the close is nan.
volume: cumulative volume for the day

Hope that helps.