# We've got you covered

We are here to guide you in making tough decisions with your hard earned money. Drop us your details and we will reach you for a free one on one discussion with our experts.

or

Call us on: +917410000494

- The expectation
- the logic of Pair Trading
- Tracking pairs - PTM1 ,C1 (Pair Trading Method 1,Chapter 1)
- Pair Stats - PTM1, C1
- Pre Trade Setup - PTM1, C3
- The Density Curve - PTM1, C4
- The Pair Trade - PTM1, C5
- Straight Line Equation -PTM2,C1 (Pair trade method 2,chapter 1)
- Linear Regression - PTM2, C2
- The Error Ratio - PTM2, C3
- The ADF Test - PTM2, C4
- How to Identify Trade?
- Taking a Live Example - 1
- Taking a Live example - 2
- Calender Spread explained!!!
- Study of Momentum Portfolio

In the chapter 10 of Future Trading module,I have briefly explained about calender spreads.. Calendar spreads have traditionally been dealt with using a price-based approach. This is how it works:

- Calculate the fair market value of your current month contract
- Calculate the fair market value of the mid-month contract
- You should look for relative mispricing of the contracts

You can either sell the mid-month contracts and buy the current contract, or you can buy the current contract and sell it. This is an example for a Calendar Spread.

- TCS Futures to be bought after 28th june 2018 @ 1846
- TCS Futures to be sold after 28 th July 2018 @ 1851

You can buy and sell futures of the same stock but contracts with different expiries, as shown above. This is where you can expect to make the difference in prices between the two contracts. Calendar spreads are very risky so the amount you can make from calendar spreads is small. This is something that you might like if you're a trader who is not afraid of risk.

This method of creating a calendar spread works well.

If you don't know what I'm talking about, I suggest that you read Chapter 10 of the Futures Trading module. This will give you a brief overview of the classic calendar spreads approach. It is a solid foundation upon which other types of calendar spreads can be built.

Let's get to work.

After you've read the chapters about pair trading, understanding the calendar spread logic should be easy. This simplified approach assumes the current price for futures is an indication of all market information. This information could include news about the stock, corporate actions, fair value and any other relevant information.

If the assumption above is true, then we could use the price as a trigger to find opportunities to setup a calendar spread trade. This simplifies the entire process. Calendar spreads are low-risk strategies so don't expect to make big bucks with this strategy. You can buy-sell the same asset simultaneously, so you eliminate the directional risk. Therefore, it makes sense to increase the leverage. Calendar spread trades are also very short term, and most trades close within one day. This is unlike pair trades. This will be explained by an example.

Start by downloading the daily closing stock prices for the near-month and next-month contracts.

Calculate the daily historical difference between the contracts to generate a time series. Calculate the standard deviation and the mean of the time series. The range of the difference can be calculated using the standard deviation and mean data. When the difference between two contracts moves to mean plus/minus 1 standard deviation, a trading signal is generated. The trade is closed when it falls to mean.

You get the idea.

To illustrate calendar spreads, I used the SBIN example.

(IMAGE 1)

Next, calculate the difference between these two contracts. It is recommended to subtract the near-month contract's price from the current month contract. Because the 'cost to carry' means that the futures price for Near month contracts is always higher than those of the current month. This is explained in detail in Chapter 10 of the futures module.

The difference is then calculated and time series data generated as shown below.

(IMAGE 2

Now I'll calculate the standard deviation and mean for this time series. I will use the mean to estimate how much difference is acceptable on an 'every day' basis, while the standard deviation will provide me with an indication of variation in the difference. Here's the snapshot

(IMAGE 3).

Excel allows you to calculate the standard deviation and mean using the functions '=Average()' and ‘=stdev()’.

The mean of 1.227 means that the difference between the contracts should be at least 1.227. This means that there are no trade opportunities when the spread or difference between the contracts hovers around a similar value.

To calculate the spread, we now use the standard deviation and the mean values

- Upper range =

1.227 + 0.4935 = 1.7205 - Lower Range =

1.227 = 0.4935 = 0.7335

Although I said that the spread could hover around 1.2227, I didn't calculate vicinity which is extremely important. It allows us to determine the range (vicinity), within the which the spread may fluctuate on daily basis.

Spreads that exceed the upper limit of 1.7205 indicate either an increase in the value of the next month contract or a decrease in the value the current month contract.

Arbitrage involves buying and selling assets in both the cheaper and more expensive markets. would therefore trade to to buy the next month's contract .

If the spread falls below 0.7335 (lower range value), it means that the current month is more expensive than the near month. The trade is to **buy the next month contract** and sell the current month.

Let's look at this logic and see if SBIN gave us any opportunities in the 200 trading days since then.

We can draw the following conclusion if we keep the above points in mind.

- When the spread is greater than 1.7205, you can sell the spread. Sell spread, buy the current contract, and sell the next month contract.
- Spreads that shrink below 0.7335 are worth buying. Spread means to buy the next month contract and then sell the current month contract.

It may be difficult to decide which contract you should buy or which one to sell after a signal arrives. Instead, think of the near-month contract. Buy spread is to buy the next month, and sell spread is to sell the month.

I will now search the excel sheet for historical opportunities. First, I will look for the sell spread opportunities. To do this, I will simply apply a filter to remove all values above 1.7205. Here are the results.

(IMAGE 4).

You can see that the spread has increased beyond 1.7205 on six occasions. These occasions had a trigger to buy, which meant that the spread would return to the mean.

Here's how the spread actually behaved.

(CHART)

You will notice that signals are generated around the month's end, which is likely due to expiry dynamics. Every trade, even the small ones, has produced a profit and was closed the next day.

Let's see how the buy spread trades performed. Here are the results.

(IMAGE 5)

Nearly 28 trades are available here, and not all are successful. The losses are almost as small as the profits. Let me do the exact calculation as I did for the short trades.

This example should give you an idea of how to create a calendar spread. This is a much simpler and more intuitive approach than the traditional calendar spreads.

Here are my thoughts on Calendar spreads. This will double up as the key takeaways from this chapter.

- Calendar spreads have small expected profits and small losses
- You can take full advantage of leverage by eliminating any directional risk
- SBIN was successful in short trades, but not in long trades. This means that I would only be looking for short opportunities in SBI. Also, it is important to backtest each futures contract's P&L profile and determine which contracts you can be long or short.
- Because the P&L is low, trading costs should be minimal. .
- Trades are usually closed within one or two days
- Expiry dynamics means that trades often originate around expiry.

Consider this: If you can backtest this across all futures contracts in equity and commodities, you will have at most a signal or two every day!

Please post your queries below..