In QC, Is it possible to use functions with pandas series argument?

For example I would like to use function from mlfinlab packages `get_daily_vol`:

get_daily_vol(close, lookback=100)

where series is the pandas series with timestamp index and lookback is some number.

Now, I would like to calculate daily volatility for every bar (slice). Here is code that doesn't work:


import numpy as np
import mlfinlab as ml
import pandas as pd


class CalibratedResistanceAtmosphericScrubbers(QCAlgorithm):


def Initialize(self):
self.SetStartDate(2019, 1, 1) # Set Start Date
self.SetEndDate(2019, 3, 1)
self.SetCash(100000) # Set Strategy Cash
self.spy = self.AddEquity("SPY", Resolution.minute)
self.spy.SetDataNormalizationMode(DataNormalizationMode.Adjusted) # Raw, SplitAdjusted, TotalReturn
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Cash)

# init close prices
self.open = np.array([])
self.high = np.array([])
self.low = np.array([])
self.close = np.array([])
self.volume = np.array([])
self.lookback = max(self.periods)
self.SetWarmUp(self.lookback * 2)


def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock data
'''
if "SPY" not in data.Bars:
return

open_ = data["SPY"].Open
high_ = data["SPY"].High
low_ = data["SPY"].Low
close_ = data["SPY"].Close
volume_ = data["SPY"].Volume
self.open = np.append(self.open, close_)[-self.lookback*2:]
self.high = np.append(self.high, close_)[-self.lookback*2:]
self.low = np.append(self.low, close_)[-self.lookback*2:]
self.close = np.append(self.close, close_)[-self.lookback*2:]
self.volume = np.append(self.volume, close_)[-self.lookback*2:]
self.time = self.Time

if self.IsWarmingUp:
return

df = pd.DataFrame({'open': self.open, 'high': self.high, 'low': self.low, 'close': self.close, 'volume': self.volume})
# HERE I SHOULD SOMEHOW CREATE INDEX VECTOR WITH FOR DF WITH ALL PASSED CLOSE PRICES

# Compute volatility - THATS THE FUNCTION I NEED TO APPLY INE EVERY STEP
daily_vol = ml.util.get_daily_vol(self.close, lookback=self.volatility_lookback)