How to do Random Walk using NSEPython in Indian Stock Market

Markov Model – Application of Markov Chain in Stock Market How to do Random Walk using NSEPython in Indian Stock Market

How to do Random Walk using NSEPython in Indian Stock Market

In Our Last Chapter, We have discussed getting the Transition Matrix/ Initial Probability Matrix using python. Now, We shall dive into more detail into Markov Chain and its relation with matrix theory more theoretically.

The Random Walk

Let’s do a random walk using out NSEPython Library and use the index_history() function. But instead of getting 100 days, Let’s get ∞ days –

				
					symbol = "NIFTY 50"
days = 9204
end_date = datetime.datetime.now().strftime("%d-%b-%Y")
end_date = str(end_date)

start_date = (datetime.datetime.now()- datetime.timedelta(days=days)).strftime("%d-%b-%Y")
start_date = str(start_date)

df=index_history("NIFTY 50",start_date,end_date)
df["state"]=df["CLOSE"].astype(float).pct_change()

df['state']=df['state'].apply(lambda x: 'Upside' if (x > 0.001) else ('Downside' if (x<=0.001) else 'Consolidation'))
print(df.tail())

Output –

				
					#
        Index Name	INDEX_NAME	HistoricalDate OPEN HIGH    LOW	    CLOSE   state
6267	Nifty 50	Nifty 50	26 Apr 1996	1133.17	1133.17	1106.29	1123.63	Upside
6268	Nifty 50	Nifty 50	25 Apr 1996	1157.94	1160.16	1110.61	1120.82	Downside
6269	Nifty 50	Nifty 50	24 Apr 1996	1136.97	1145.11	1126.77	1145.11	Upside
6270	Nifty 50	Nifty 50	23 Apr 1996	1090.04	1100.51	1090.04	1095.82	Downside
6271	Nifty 50	Nifty 50	22 Apr 1996	1136.28	1136.28	1102.83	1106.93	Upside

As We theoretically can not have ∞ days of data, We took the best that we could. The Nifty 50 index was launched on 22 April 1996. So there are roughly 9204 days from the start date to the end date (4th July as We’re writing), but not including the end date.

Now, As We are supposedly “walking” randomly from one day to another, We need to consider our 9204 days of walking as ∞ steps.

The Stationary Distribution

So, After ∞ steps, Let’s analyse the probabilities of the states.

				
					df['state'].value_counts(normalize=True)

Output –

				
					Downside         0.574298
Upside           0.425542
Consolidation    0.000159
Name: state, dtype: float64

So, After ∞ steps, The Probability of having a “Downside” state day is 57.4%.

This probability distribution has a special name – “The Stationary Distribution” or “The Equilibrium State”. It simply means that, This probability distribution does not change with time with respect to this Markov Chain. So, We found our Stationary State but this method does not sound very efficient right?

Notes on the Hypothetical Markov Chain

This Markov Chain is an example model with random values as a probability which can be seen in the weighted arrows!

Random Walking in this Markov Chain is comparatively similar. Here is an amazing tutorial here with full code for a similar Markov chain.

Here are the results of iterating this assumptive Markov Chain with 1,00,000 random walks –

				
					Downside         0.35191
Upside           0.21245
Consolidation    0.43564
Name: state, dtype: float64

So, We managed to find a Stationary State. But this method is not efficient as We do not know if there is another Stationary State. Well, A better way to approach this problem is Linear Algebra.

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