CODEWITHSUNDEEP
pythonpandasstocksyield-returnvolatility
Spurious
Spurious

Reputation: 1995

Calculating the stock price volatility from a 3-columns csv

I am looking for a way to make the following code work:

import pandas

path = 'data_prices.csv'
data =  pandas.read_csv(path, sep=';')
data = data.sort_values(by=['TICKER', 'DATE'], ascending=[True, False])
data.columns

I have a 2 dimensional array with three columns, the data looks like this:

DATE;TICKER;PRICE
20151231;A UN Equity;41.81
20151230;A UN Equity;42.17
20151229;A UN Equity;42.36
20151228;A UN Equity;41.78
20151224;A UN Equity;42.14
20151223;A UN Equity;41.77
20151222;A UN Equity;41.22
20151221;A UN Equity;40.83
20151218;A UN Equity;40.1
20091120;PCG UN Equity;42.1
20091119;PCG UN Equity;41.53
20091118;PCG UN Equity;41.86
20091117;PCG UN Equity;42.23
20091116;PCG UN Equity;42.6
20091113;PCG UN Equity;41.93
20091112;PCG UN Equity;41.6
20091111;PCG UN Equity;42.01

Now, I want to calculate the x-day realized volatility where x came from an input field and x should not be bigger than the number of observations.

The steps that need to be taken:

Upvotes: 0

Views: 5790

Answers (1)

sk877
sk877

Reputation: 406

Apologies, it's not fully clear on the sort of output you're hoping for so I've assumed you want to enter a ticker and a period (x) and see the current volatility number. Below I have also made use of numpy, in case you don't have that library.

Essentially I've created a DataFrame of all the original data and then a new DF filtered for the given ticker (where the user only needs to type in the 'A' or 'PCG' part, because 'UN Equity' is assumed constant). In this new DF, after checking that your period (x) input is not too high, it will output the most recent annualised volatility value.

import numpy as np
import pandas as pd

data = pd.read_csv('dump.csv', sep=';')
data = data.sort_values(by=['TICKER','DATE'],ascending=[True,True])


def vol(ticker, x):
    df = pd.DataFrame(data)
    df['pct_chg'] = df.PRICE.pct_change()
    df['log_rtn'] = np.log(1 + df.pct_chg)

    df_filtered = df[df.TICKER==ticker+' UN Equity']

    max_x = len(df_filtered) - 1
    if x > max_x:
        print('Too many periods. Reduce x')

    df_filtered['vol'] = pd.rolling_std(df_filtered.log_rtn, window=x) * (255**0.5)

    print(df_filtered.vol.iloc[-1])

As an example, with an input of vol('PCG',6) the output is 0.187855386042

Probably not the most elegant and apologies if I've misunderstood your request.

Upvotes: 2

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