Question

I have an OHLC price data set, that I have parsed from CSV into a Pandas dataframe and resampled to 15 min bars:

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 500047 entries, 1998-05-04 04:45:00 to 2012-08-07 00:15:00
Freq: 15T
Data columns:
Close    363152  non-null values
High     363152  non-null values
Low      363152  non-null values
Open     363152  non-null values
dtypes: float64(4)

I would like to add various calculated columns, starting with simple ones such as period Range (H-L) and then booleans to indicate the occurrence of price patterns that I will define – e.g. a hammer candle pattern, for which a sample definition:

def closed_in_top_half_of_range(h,l,c):
    return c > l + (h-l)/2

def lower_wick(o,l,c):
    return min(o,c)-l

def real_body(o,c):
    return abs(c-o)

def lower_wick_at_least_twice_real_body(o,l,c):
    return lower_wick(o,l,c) >= 2 * real_body(o,c)

def is_hammer(row):
    return lower_wick_at_least_twice_real_body(row["Open"],row["Low"],row["Close"]) \
    and closed_in_top_half_of_range(row["High"],row["Low"],row["Close"])

Basic problem: how do I map the function to the column, specifically where I would like to reference more than one other column or the whole row or whatever?

This post deals with adding two calculated columns off of a single source column, which is close, but not quite it.

And slightly more advanced: for price patterns that are determined with reference to more than a single bar (T), how can I reference different rows (e.g. T-1, T-2 etc.) from within the function definition?

Answer 1

The exact code will vary for each of the columns you want to do, but it’s likely you’ll want to use the map and apply functions. In some cases you can just compute using the existing columns directly, since the columns are Pandas Series objects, which also work as Numpy arrays, which automatically work element-wise for usual mathematical operations.

>>> d
    A   B  C
0  11  13  5
1   6   7  4
2   8   3  6
3   4   8  7
4   0   1  7
>>> (d.A + d.B) / d.C
0    4.800000
1    3.250000
2    1.833333
3    1.714286
4    0.142857
>>> d.A > d.C
0     True
1     True
2     True
3    False
4    False

If you need to use operations like max and min within a row, you can use apply with axis=1 to apply any function you like to each row. Here’s an example that computes min(A, B)-C, which seems to be like your “lower wick”:

>>> d.apply(lambda row: min([row['A'], row['B']])-row['C'], axis=1)
0    6
1    2
2   -3
3   -3
4   -7

Hopefully that gives you some idea of how to proceed.

Edit: to compare rows against neighboring rows, the simplest approach is to slice the columns you want to compare, leaving off the beginning/end, and then compare the resulting slices. For instance, this will tell you for which rows the element in column A is less than the next row’s element in column C:

d['A'][:-1] < d['C'][1:]

and this does it the other way, telling you which rows have A less than the preceding row’s C:

d['A'][1:] < d['C'][:-1]

Doing ['A"][:-1] slices off the last element of column A, and doing ['C'][1:] slices off the first element of column C, so when you line these two up and compare them, you’re comparing each element in A with the C from the following row.