wildcat89
Reputation: 1285
Apply rolling as part of a column calcuation?
I'm looking to create a new column that finds the minimum offset value (i.e. number of rows back) of the minimum value in a specific window, only problem is the window size changes from row to row.
A way to find the minimum offset value from the current row using a static number, is to reverse the order of the column, and then apply the argmin function with a shift, like so:
df['initx1'] = df['Close'][::-1].rolling(20).apply(np.argmin, raw=True).shift(-(20 + 1))
...however, what I'd like to do is use a dynamic rolling number without having to resort to using a for loop, I want this done as a column calculation like the above. The psuedo-code of what I'm attempting looks like this:
df['initx1'] = df['Close'][::-1].rolling(df['dynamicWindowSize']).apply(np.argmin, raw=True).shift(-(df['dynamicWindowSize'] + 1))
...but of course it throws an error saying rolling should be an integer, even though the series in df['dynamicWindowSize'] ARE all integers, but I know it's because I'm passing in a series. Any ideas?
A reproducible dataset and code is provided below to work with:
import numpy as np
import pandas as pd
from datetime import datetime
# Create a reproducible, static dataframe.
# 1 minute SPY data. Skip to the bottom...
df = pd.DataFrame([
{
"time": "2021-10-26 9:30",
"open": "457.2",
"high": "457.29",
"low": "456.78",
"close": "456.9383",
"volume": "594142"
},
{
"time": "2021-10-26 9:31",
"open": "456.94",
"high": "457.07",
"low": "456.8",
"close": "456.995",
"volume": "194061"
},
{
"time": "2021-10-26 9:32",
"open": "456.99",
"high": "457.22",
"low": "456.84",
"close": "457.21",
"volume": "186114"
},
{
"time": "2021-10-26 9:33",
"open": "457.22",
"high": "457.45",
"low": "457.2011",
"close": "457.308",
"volume": "294158"
},
{
"time": "2021-10-26 9:34",
"open": "457.31",
"high": "457.4",
"low": "457.25",
"close": "457.32",
"volume": "172574"
},
{
"time": "2021-10-26 9:35",
"open": "457.31",
"high": "457.48",
"low": "457.18",
"close": "457.44",
"volume": "396668"
},
{
"time": "2021-10-26 9:36",
"open": "457.48",
"high": "457.6511",
"low": "457.44",
"close": "457.57",
"volume": "186777"
},
{
"time": "2021-10-26 9:37",
"open": "457.5699",
"high": "457.73",
"low": "457.5699",
"close": "457.69",
"volume": "187596"
},
{
"time": "2021-10-26 9:38",
"open": "457.7",
"high": "457.73",
"low": "457.54",
"close": "457.63",
"volume": "185570"
},
{
"time": "2021-10-26 9:39",
"open": "457.63",
"high": "457.64",
"low": "457.31",
"close": "457.59",
"volume": "164707"
},
{
"time": "2021-10-26 9:40",
"open": "457.59",
"high": "457.72",
"low": "457.46",
"close": "457.7199",
"volume": "167438"
},
{
"time": "2021-10-26 9:41",
"open": "457.72",
"high": "457.8",
"low": "457.68",
"close": "457.72",
"volume": "199951"
},
{
"time": "2021-10-26 9:42",
"open": "457.73",
"high": "457.74",
"low": "457.6",
"close": "457.62",
"volume": "152134"
},
{
"time": "2021-10-26 9:43",
"open": "457.6",
"high": "457.65",
"low": "457.45",
"close": "457.5077",
"volume": "142530"
},
{
"time": "2021-10-26 9:44",
"open": "457.51",
"high": "457.64",
"low": "457.4001",
"close": "457.61",
"volume": "122575"
},
{
"time": "2021-10-26 9:45",
"open": "457.61",
"high": "457.76",
"low": "457.58",
"close": "457.75",
"volume": "119886"
},
{
"time": "2021-10-26 9:46",
"open": "457.74",
"high": "457.75",
"low": "457.37",
"close": "457.38",
"volume": "183157"
},
{
"time": "2021-10-26 9:47",
"open": "457.42",
"high": "457.49",
"low": "457.37",
"close": "457.44",
"volume": "128542"
},
{
"time": "2021-10-26 9:48",
"open": "457.43",
"high": "457.49",
"low": "457.33",
"close": "457.44",
"volume": "154181"
},
{
"time": "2021-10-26 9:49",
"open": "457.43",
"high": "457.5898",
"low": "457.42",
"close": "457.47",
"volume": "163063"
},
{
"time": "2021-10-26 9:50",
"open": "457.45",
"high": "457.59",
"low": "457.44",
"close": "457.555",
"volume": "96229"
},
{
"time": "2021-10-26 9:51",
"open": "457.56",
"high": "457.61",
"low": "457.31",
"close": "457.4217",
"volume": "110380"
},
{
"time": "2021-10-26 9:52",
"open": "457.42",
"high": "457.56",
"low": "457.42",
"close": "457.47",
"volume": "107518"
},
{
"time": "2021-10-26 9:53",
"open": "457.475",
"high": "457.51",
"low": "457.4",
"close": "457.48",
"volume": "78062"
},
{
"time": "2021-10-26 9:54",
"open": "457.49",
"high": "457.57",
"low": "457.42",
"close": "457.46",
"volume": "133883"
},
{
"time": "2021-10-26 9:55",
"open": "457.47",
"high": "457.56",
"low": "457.45",
"close": "457.51",
"volume": "98998"
},
{
"time": "2021-10-26 9:56",
"open": "457.51",
"high": "457.54",
"low": "457.43",
"close": "457.43",
"volume": "110237"
},
{
"time": "2021-10-26 9:57",
"open": "457.43",
"high": "457.65",
"low": "457.375",
"close": "457.65",
"volume": "98794"
},
{
"time": "2021-10-26 9:58",
"open": "457.66",
"high": "457.69",
"low": "457.35",
"close": "457.45",
"volume": "262154"
},
{
"time": "2021-10-26 9:59",
"open": "457.45",
"high": "457.47",
"low": "457.33",
"close": "457.4",
"volume": "74685"
},
{
"time": "2021-10-26 10:00",
"open": "457.41",
"high": "457.48",
"low": "457.18",
"close": "457.38",
"volume": "166617"
},
{
"time": "2021-10-26 10:01",
"open": "457.39",
"high": "457.7",
"low": "457.39",
"close": "457.5",
"volume": "265649"
},
{
"time": "2021-10-26 10:02",
"open": "457.51",
"high": "457.57",
"low": "457.39",
"close": "457.53",
"volume": "131947"
},
{
"time": "2021-10-26 10:03",
"open": "457.53",
"high": "457.54",
"low": "457.4",
"close": "457.51",
"volume": "80111"
},
{
"time": "2021-10-26 10:04",
"open": "457.51",
"high": "457.62",
"low": "457.5",
"close": "457.6101",
"volume": "117174"
},
{
"time": "2021-10-26 10:05",
"open": "457.621",
"high": "457.64",
"low": "457.51",
"close": "457.58",
"volume": "168758"
},
{
"time": "2021-10-26 10:06",
"open": "457.58",
"high": "457.64",
"low": "457.46",
"close": "457.61",
"volume": "84076"
},
{
"time": "2021-10-26 10:07",
"open": "457.62",
"high": "457.7401",
"low": "457.62",
"close": "457.66",
"volume": "125156"
},
{
"time": "2021-10-26 10:08",
"open": "457.665",
"high": "457.69",
"low": "457.5",
"close": "457.67",
"volume": "116919"
},
{
"time": "2021-10-26 10:09",
"open": "457.69",
"high": "457.72",
"low": "457.5",
"close": "457.57",
"volume": "102551"
},
{
"time": "2021-10-26 10:10",
"open": "457.56",
"high": "457.75",
"low": "457.56",
"close": "457.7",
"volume": "109165"
},
{
"time": "2021-10-26 10:11",
"open": "457.7",
"high": "457.725",
"low": "457.63",
"close": "457.66",
"volume": "146209"
},
{
"time": "2021-10-26 10:12",
"open": "457.665",
"high": "457.88",
"low": "457.64",
"close": "457.86",
"volume": "210620"
},
{
"time": "2021-10-26 10:13",
"open": "457.855",
"high": "457.96",
"low": "457.83",
"close": "457.95",
"volume": "159975"
},
{
"time": "2021-10-26 10:14",
"open": "457.95",
"high": "458.02",
"low": "457.93",
"close": "457.95",
"volume": "152042"
},
{
"time": "2021-10-26 10:15",
"open": "457.96",
"high": "458.15",
"low": "457.96",
"close": "458.08",
"volume": "146047"
},
{
"time": "2021-10-26 10:16",
"open": "458.085",
"high": "458.17",
"low": "457.99",
"close": "458.15",
"volume": "100732"
},
{
"time": "2021-10-26 10:17",
"open": "458.17",
"high": "458.33",
"low": "458.155",
"close": "458.245",
"volume": "235072"
},
{
"time": "2021-10-26 10:18",
"open": "458.25",
"high": "458.29",
"low": "458.14",
"close": "458.16",
"volume": "422002"
},
{
"time": "2021-10-26 10:19",
"open": "458.17",
"high": "458.2801",
"low": "458.1699",
"close": "458.28",
"volume": "114611"
},
{
"time": "2021-10-26 10:20",
"open": "458.29",
"high": "458.39",
"low": "458.24",
"close": "458.37",
"volume": "241797"
},
{
"time": "2021-10-26 10:21",
"open": "458.37",
"high": "458.42",
"low": "458.31",
"close": "458.345",
"volume": "124824"
},
{
"time": "2021-10-26 10:22",
"open": "458.33",
"high": "458.49",
"low": "458.33",
"close": "458.47",
"volume": "132125"
}
])
# Convert df to numeric and time to datetime re: the .csv to .json
# converter tool I used online...
df[['open','high','low','close','volume']] = df[['open','high','low','close','volume']].apply(pd.to_numeric)
df['time'] = pd.to_datetime(df['time'])
# Define an initial x1 value. This is the minimum offset index number for the min
# of the last 20 rows. So, looking at the previous 20 rows, INCLUDING the current
# one, if the minimum close value happened 5 bars ago, this number would be 5.
# If the current bar holds the lowest close over the last 20 bars, this number
# would be 0. This is not important to the question as this part works as is.
df['initx1'] = df['close'][::-1].rolling(20).apply(np.argmax, raw=True).shift(-(20 + 1))
# Part of the strategy I'm wanting to emulate requires to get the second last initx1
# value and to add 2 to that number, so that's what I'm doing here
df['x1'] = df['initx1'].shift(2) + 2
# 'dynamicWindowSize' is called 'x2' and is just 1 + x1
df['x2'] = df['x1'] + 1
# To mitigate the error "trying to set rolling on an empty sequence" later,
# just drop nan rows from x1 and x2 now. Will only be the top 20 rows anyway.
df = df.dropna(subset=['x1', 'x2'])
# Convert x2 to integer
df['x2'] = df['x2'].astype(int)
# Inspect
print(df)
# This is the pseudo code of what I want. It doesn't work now obviously,
# but the rolling window size just needs to be the value in x2 for each
# row
df['lfOffset'] = df['close'][::-1].rolling(df['x2']).apply(np.argmin, raw=True).shift(-(df['x2'] + 1))
# # I found an answer from https://stackoverflow.com/questions/49955008/python-pandas-rolling-mean-with-window-value-in-another-column
# # that I tried modifying for my purposes here, but also doesn't work.
# def get_lfOffset_min(x):
# win = df.loc[:,'x2'].iloc[x.shape[0]-1].astype('int')
# win = max(win, 0)
# return pd.Series(x).rolling(window = win).apply(np.argmin, raw=True).shift(-(win + 1)) #.mean().iloc[-1]
# df.loc[:,'lfOffset'] = df.loc[:,'close'].expanding().apply(get_lfOffset_min)
Upvotes: 1
Views: 291
Answers (1)
Anton
Reputation: 606
Normally one can not look at two columns inside of the window function (the function supplied to .apply()). Since Pandas 1.3 there is an exception: if one uses Numba and specifies method="table", the whole dataframe is passed to the function. It is passed as an array, which makes the code somewhat ugly, but it does the job.
import numba
import math
@numba.njit
def compute_offset(arr):
window_size = arr[-1, 1]
min_ = math.inf
min_idx = None
window_start = max(arr.shape[0] - window_size, 0)
for i in range(arr.shape[0] - 1, window_start - 1, -1):
if arr[i, 0] < min_:
min_ = arr[i, 0]
min_idx = i
result = np.empty(3)
result[:2] = arr[-1, :2]
result[2] = arr.shape[0] - 1 - min_idx # the offset
return result
offset = (
df[["close", "x2"]]
.assign(offset=-1)
.expanding(method="table")
.apply(compute_offset, raw=True, engine='numba')
.offset
)
df_processed = df.join(offset)
Here are the first 20 rows of the result computed from the provided input:
Upvotes: 3
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