Determine if closing price is a 5 minute high in pandas dataframe

Question

I have a pandas dataframe with 1 minute stock data.

                       Close
2013-09-23 09:30:00       NaN
2013-09-23 09:31:00    8.2500
2013-09-23 09:32:00    8.2500
2013-09-23 09:33:00    8.2800
2013-09-23 09:34:00    8.2725
2013-09-23 09:35:00    8.2850
2013-09-23 09:36:00    8.2700
2013-09-23 09:37:00    8.2827
2013-09-23 09:38:00    8.3100
2013-09-23 09:39:00    8.3200

I'm trying to make a function that returns TRUE when a given close is a 5 minute high

Something like (pseudo code):

stockdata["close"].apply(lambda x: x == max of last 5 rows)

How would you do this?

Answer 1

You can check when your column values equal the maximum of the last five rows determined with the rolling_max function (http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments):

>>> stockdata["Close"] == pd.rolling_max(stockdata["Close"], 5)
2013-09-23 09:30:00    False
2013-09-23 09:31:00    False
2013-09-23 09:32:00    False
2013-09-23 09:33:00    False
2013-09-23 09:34:00    False
2013-09-23 09:35:00     True
2013-09-23 09:36:00    False
2013-09-23 09:37:00    False
2013-09-23 09:38:00     True
2013-09-23 09:39:00     True
dtype: bool

If your are not certain you data are always nicely 1-minutly (so the 5 rows are not always equal to 5 mins), you can set the freq keyword in rolling_max to 1min.