Daily Range Analysis of Nifty


Life is simple, its just not easy – Steve Maraboli

The above quote fits very well for plain Vanilla trading strategies. Straddles and Strangles can seem to be very easy strategies to trade, but only when one starts trading it, realizes how tough it is to implement and turn out to be profitable at the end of the day. Sometimes, one needs to adjust legs according to the market movements to avoid burning fingers on a trending day.

I truly believe that when the trading strategies are backed by data, the probability of it working in the long term increases. In this post, We will analyze the daily data of Nifty from 2011 to 2019. The aim of this post is to find the average daily range of Nifty and see if we can draw any meaningful conclusions from this analysis.

How to find range?

Range is the difference between High and Low of the day. The range tells us the trading area of instrument for a particular day. In our analysis, we have calculated daily range in terms of percentage using the following formula:

Range (H-L) = [(Today’s High – Today’ Low)/Today’s Open]*100

Let us first have a look at the distribution plot of Nifty daily range. We can simply see that most of the times the range is less than 3%.

We calculated the range in percentage using the Open of the day as base. This helped us to analyze range using open of the day because this is the only data that remains constant throughout the day. The table below concludes that more than 98% of the times, the daily range of Nifty < 3%.

Range(H-L) < 3.0%98.24%

Now can we place a strangle with legs at 1.5% from the Open of the day on both the sides to get theta decay for the day?

No, we can’t simply use range (H-L) metric and place the strangle because there will be many days when the range will be less than 3% but Nifty will breach the 1.5% mark on one of the legs. So, we used two more factors in this analysis i.e., daily (High – Open) range and daily (Open – Low) range to get worthwhile results.

Range(O-L) < 1.5%91.56%
Range(H-O) < 1.5%95.12%

But the above result does not tell a complete picture either because these are individual ranges. There might be days when range of one leg might breach and the other might not. So, we need to find the probability when range(O-L) and range(H-O) both are less than 1.5%.

Range(O-L) < 1.5% and Range(H-O) < 1.5%86.87%

We started the analysis with a good 98% probability of success and after introducing two different metrics, we saw it reduce to 86.87%.

Range analysis for different conditions

There are three different cases for which we will be analyzing the range for. This is done to see if we can improve the probability if we know these conditions beforehand.

Case I: When Today’s Open is between yesterday’s range(H-L)
Range(O-L) < 1.5% and Range(H-O) < 1.5%87.64%
Case II: When Today’s Open is greater than yesterday’s High
Range(O-L) < 1.5% and Range(H-O) < 1.5%87.85%
Case III: When Today’s Open is less than yesterday’s Low
Range(O-L) < 1.5% and Range(H-O) < 1.5%80.31%

As we can see from the results that the probability of not breaching 1.5% levels on both the sides of day Open is around 87% for the first two cases. But, it reduces to 80.31% for the third case, when Open is less than yesterday’s low. A simple reason behind this fall in probability is because when markets fall, it tends to fall more and breach the lower leg. This happens because fear tends to manifest itself faster than greed.


We started the analysis with a simple aim to use the daily range and see if trading strangles with a 3% spread is a good idea or not. We found out from the analysis that more than 86% of the time we can easily pocket the theta decay for the day.

The Strategy

We, at MarketScanner have built TSS options strategy using the daily range analysis of Nifty.

The Strategy is backtested since the inception of Nifty Weekly options and we have been Forward testing it live for the last 4 months.

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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