import pandas as pd
import numpy as np


def estimate_volatility(prices, l):
    """Create an exponential moving average model of the volatility of a stock
    price, and return the most recent (last) volatility estimate.
    
    Parameters
    ----------
    prices : pandas.Series
        A series of adjusted closing prices for a stock.
        
    l : float
        The 'lambda' parameter of the exponential moving average model. Making
        this value smaller will cause the model to weight older terms less 
        relative to more recent terms.
        
    Returns
    -------
    last_vol : float
        The last element of your exponential moving averge volatility model series.
    
    """
    # TODO: Implement the exponential moving average volatility model and return the last value.

    return prices.ewm(alpha=(1-l)).mean()[-1]


def test_run(filename='data.csv'):
    """Test run get_most_volatile() with stock prices from a file."""
    prices = pd.read_csv(filename, parse_dates=[
                         'date'], index_col='date', squeeze=True)
    print("Most recent volatility estimate: {:.6f}".format(estimate_volatility(prices, 0.7)))
    # print(estimate_volatility(prices, 0.7))


if __name__ == '__main__':
    test_run()

[((32, 13, 33, 65), 'pandas.read_csv', 'pd.read_csv', (), '', True, 'import pandas as pd\n')]