Analysis of Nifty Weekly Change


Trading strangles is assumed to be quite difficult as it requires adjustment if the market starts to move in one direction. This adjustments also increases the transaction cost of the trade. We faced the same difficulty and thus decided to find a solution for the same.

What if we know how much Nifty moves on average every week. We can then easily trade strangles on weekly Nifty options. So, we took the daily data of Nifty from 2011 to 2019 and found how much it moved from Thursday to Thursday.

We considered the following three data-points for the analysis:

  1. Weekly Close-Close %Change
  2. Weekly Close-High %Change
  3. Weekly Close-Low %Change

The Tale of Averages

Let us find the average of these weekly returns.

Average of weekly close-close change(%)0.17%
Average of weekly close-high change(%)1.55%
Average of weekly close-low change(%)1.46%

What? Nifty on average moves barely 0.17% every week? This average values can be a bit misleading because the positive returns offsets the negative returns and thus bring the average down. This proves the fact that it is very easy to mislead people with statistics.

So to overcome the above problem, we have to take the average of absolute values of returns. This will tell us the real picture of weekly movement of Nifty. The correct average values are as follows:

Average of weekly close-close change(%)1.69%
Average of weekly close-high change(%)1.59%
Average of weekly close-low change(%)1.55%

The average value can be affected by the outliers. So, we also find the median values of the absolute returns to find which is the actual mid-point of the data.

Median of weekly close-close change(%)1.44%
Median of weekly close-high change(%)1.25%
Median of weekly close-low change(%)1.14%

Distribution of weekly returns

We must also have a look at the distribution of weekly returns. We can see that most of the values are between -3% to +3%. The distribution is also not a perfect normal distribution as it has outliers on both the sides.

A quick analysis of maximum and minimum values of our data points reveals that the outliers are at a far distance from the mean and median values. These weeks can turn out to be the most bleeding ones for the strangle trader.

Maximum Weekly C-C change (%)8.24%
Minimum Weekly C-C change (%)-5.90%
Maximum Weekly C-H (%)9.29%
Maximum Weekly C-L (%)15.59%

We can also have a look at the top 5 weeks with largest weekly change and analyse the best way to manage the trade in case such large moves happen.

DateWeekly %Change (top 5 +ve)
DateWeekly %Change (top 5 -ve)


Finally, we found how much percentage of weeks out of the total weeks gave a return of less than 3% and the results can give enough conviction to trade strangles for a regular income.

% weeks when returns  <= +/- 2%65%
% weeks when returns  <=  +/- 2.5%79%
% weeks when returns  <=  +/- 3%86%
% weeks when returns  <=  +/- 3.5%90%
% weeks when returns  <= +/- 4%94%

For all our data points, the average and median values are less than 2%. The probability of success increases as we increase the size of our strangle, but that comes at the cost of profit.

We can place the strangles 3% away from the Thursday close and expect a good 86% historical probability of success on our trade. However, there are some weeks where the returns were more than 3%, we just need to control the losses with sound risk management techniques like hedging it with OTM options to cap the losses when we are wrong. 

You can find the analysis report and data* here.

The next part of this analysis, where I used INDIAVIX data alongside Nifty to see if we can manage the outlier trades, can be found here.

Note: The close price of Nifty in the excel sheet may not exactly match with the adjusted close price of Nifty. This analysis is not any strategy. This analysis is just a base that can help you develop your own ideas or strategies.

That’s it for today.

Stay Tuned!

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About the author

Akshay Satpaise

Akshay Satpaise is an Electrical Engineer who loves data crunching. He has interests in personal finance, stock market and data analysis.

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