Neutral Trading Strategies

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Trading Strategy – Neutral and Volatile Strategies


Can we summaries the earlier discussions on Option Trading Strategies?

In our previous discussions, we covered Bullish and Bearish Strategies. We also discussed Covered Calls in detail. We now turn to Option Strategies which you can apply if you are Neutral or if you believe the market will turn Volatile.

What does Neutral mean?

Neutral means you believe that the index or scrip in question is likely to remain wherever it is, or that the movement is not likely to be significant. For example, if the Sensex is around 3,200 now and you believe that the Sensex will stay around this level in the next two weeks, you are said to be Neutral.

What does Volatile mean?

A volatile view will imply that you believe the market will definitely move either upwards or downwards, but you are not sure which way the movement will occur. You are however quite sure that the market will not stay where it is. In this sense, a Volatile view is quite the opposite of the Neutral view.

What strategies can be applied to these situations?

The most common strategies to both situations are Straddles and Strangles.

What is a Straddle?

A Straddle is a strategy where you buy a Call Option as well as a Put Option on the same underlying scrip (or index) for the same expiry date for the same strike price. For example, if you buy a Satyam July Call Strike Price 240 and also buy a Satyam July Put Strike Price 240, you have bought a Straddle.

As a buyer of both Call and Put, you will pay a Premium on both the transactions. If the Call costs Rs 12 and the Put Rs 9, your total cost will be Rs 21.

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When will I buy a Straddle?

You will buy a Straddle if you believe that Satyam will become volatile. Its current price is say Rs 240, but you think it will either rise or fall significantly. For example, you could believe that Satyam could rise right upto Rs 300 or fall upto Rs 200 in the next fortnight or so.

Why should it fluctuate so much?

There could be various situations which might warrant heavy movement. For example, during Budget time, a favourable proposal might impact the price favourably and if nothing favourable is proposed, the price could fall significantly. An Indian company could be considering collaborations with a major foreign company. If the collaboration were to happen, the price could rise, and if it were not to happen, the price could fall.

An Indian company might be expecting a huge order from a foreign company. The market might be awaiting news on this front. While a positive development might result in a price rise, a negative development might dampen the prices.

Some companies might face huge lawsuits. The decision could significantly impact prices any which direction.

In all these cases, you are sure that the price will either move up or move down, but you are not clear which way.

How will the Straddle help me?

Let us continue the above example. You have bought the Call and the Put and spent Rs 21. The current price and the strike price are the same Rs 240. Your profile will be determined as under:

Satyam Closing Price Profit on Call Profit on Put Initial Cost Net Profit
200 0 40 21 19
210 0 30 21 9
220 0 20 21 -1
230 0 10 21 -11
240 0 0 21 -21
250 10 0 21 -11
260 20 0 21 -1
270 30 0 21 9
280 40 0 21 19

Thus you make maximum profit if the price falls significantly to Rs 200 or rises significantly to Rs 280. You will make a maximum loss of Rs 21 (your initial cost) if the price remains wherever it currently is.

What are the other implications of Straddle?

As a buyer of the Straddle, you will pay initially for both the Call and the Put. You need not place any margins as you are a buyer of both Options. If time passes and the scrip remains at or around the same price (in this case Rs 240), you will find that the Option Premia of both the Call and the Put will decline (Time Value of Options decline with passage of time). Hence, you will suffer losses.

When will I sell a Straddle?

You bought a Straddle because you thought the scrip will become volatile. Conversely, the seller of the Straddle would believe that the scrip will act neutral. The seller will believe that the price of Satyam will stay around Rs 240 in the next fortnight or so. Accordingly, he will sell both the Call and the Put.

If the price indeed remains around Rs 240, he will make a maximum gain of Rs 21. If the price were to move up or down, he will make a lower gain as he will have to pay either on the Call (if it moves up) or on the Put (if it moves down).

What is the break even point of the Straddle?

The Straddle has two break even points viz. the Strike Price plus both Premia and the Strike Price minus both Premia. In the above example, the two break even points are Rs 261 (240 + 21) and Rs 219 (240 – 21). As seen earlier, the break even points are the same for the buyer and the seller.

What are the other implications for the seller?

As a seller, he will receive the Premia of Rs 21 on day one. He will have to place margins on both the Options and hence these requirements could be fairly high. If time passes and the scrip stays around Rs 240, the seller will be happy as the Option values will decline and he can buy back these Options at a lower level. On the other hand, if the scrip moves, he should be careful and think of closing out early.

What is a Strangle?

A Strangle is a slightly safer Strategy in the sense that you buy a Call and a Put but at different strike prices rather than one single strike price as in the case of a Straddle. For example, you could buy a Satyam Put Strike 220 and a Satyam Call Strike 260 at prices of Rs 5 and Rs 6 respectively. This would cost you Rs 11 and you would have a Volatile view on the scrip.

The lower cost would however imply a wider break even and you would make profit only if the Scrip moves up or down by a wider margin.

The profit potential is provided in this table:

Satyam Closing Price Profit on Call Profit on Put Initial Cost Net Profit
200 0 20 11 9
210 0 10 11 -1
220 0 0 11 -11
230 0 0 11 -11
240 0 0 11 -11
250 0 0 11 -11
260 0 0 11 -11
270 10 0 11 -1
280 20 0 11 9

The two break even points here would be worked out as lower strike minus the two premia and higher strike plus the two premia respectively. In this case, the break even points are Rs 209 (220 – 11) and Rs 271 (260 + 11).

We will discuss the finer points of these strategies in the next Article.

Options Trading Strategies

List of Top 6 Options Trading Strategies

  1. Long Call Options Trading Strategy
  2. Short Call Options Trading Strategy
  3. Long Put Options Trading Strategy
  4. Short Put Options Trading Strategy
  5. Long Straddle Options Trading Strategy
  6. Short Straddle Options Trading Strategy

Let us discuss each of them in detail –

#1 Long Call Options Trading Strategy

  • This is one of the option trading strategies for aggressive investors who are very bullish about a stock or an index.
  • Buying calls can be an excellent way to capture the upside potential with limited downside risk.
  • It is the most basic of all options trading strategies. It is comparatively an easy strategy to understand.
  • When you buy it means you are bullish on a stock or an index and you expect to rise in future.
Best time to Use: When you are very bullish on the stock or index.
Risk: Risk is limited to the Premium. (There is a maximum loss if market expires at or below the option strike price).
Reward: Reward is Unlimited
Breakeven: (Strike Price + Premium)

Let us now understand through this example how to fetch the data from the website and how to determine the Payoff schedule for Long Call Strategy.

How to download Options Data?

Step 1: Visit the stock exchange website

  1. Go to
  2. Select Equity Derivatives
  3. In the Search box put CNX Nifty
  4. The Current Nifty Index Price is given on the right-hand top corner. Note it down in your excel spreadsheet.
  5. Please note that in this example, we have taken NSE (National Stock Exchange, India). You may download a similar dataset for other international stock exchanges like NYSE, LSE, etc

Step 2: Find the Option Premium

The next step is to find the Premium. For this, you will have to select some of the data according to your requirements.

So In the case of the Long Put options trading strategy, we will select the following data.

  • Instrument Type:Index Options
  • Symbol: NIFTY
  • Expiry Date: Select the required expiry date.
  • Option Type: Call (For further examples we will select Put, for a Put option)
  • Strike Price: Select the required Strike Price. In this case, I have selected 7600.
  • Once all the information is selected you may click on Get Data. The premium price will be displayed then which you will require for further calculations.

Step 3: Populate the data set in Excel Spreadsheet

Once you have got the Current Nifty Index Price and the Premium data, you can proceed further to calculate your Input-output data as follows in an Excel Spreadsheet.

  • As you can see in the image above, we have filled the data for the Current Nifty index, Strike Price and Premium.
  • We then have calculated the Break-even point. Break-even point is nothing but the price that the stock must reach for the option buyers to avoid any loss if they exercise the option.
  • For Call Option, this is how we calculated the Break-even point:

Breakeven Point= Strike Price + Premium

Step 4: Create the Payoff Schedule

Next, we come to the Payoff schedule. This basically tells you how much profit you will make or how much will you lose at a specific Nifty index. Note that in case of options you are not obliged to exercise them and hence you are able to limit your loss to the amount of premium paid.

Long-Short Equity Strategy using Ranking: : Simple Trading Strategies Part 4

In the last post, we covered Pairs trading strategy and demonstrated how to leverage data and mathematical analysis to create and automate a trading strategy.

Long-Short Equity Strategy is a natural extension of Pairs Trading applied to a basket of stocks.

Underlying Principle

Long-Short equity strategy is both long and short stocks simultaneously in the market. Just like pairs trading identifies which stock is cheap and which is expensive in a pair, a Long-Short strategy will rank all stocks in a basket to identify which stocks are relatively cheap and expensive. It will then go long (buys) the top n equities based on the ranking, and short (sells) the bottom n for equal amounts of money(Total value of long position = Total value of short position).

Remember how we said that Pairs Trading is a market neutral strategy? So is a Long-Short strategy as the equal dollar volume long and short positions ensure that the strategy will remain market neutral (immune to market movements). The strategy is also statistically robust — by ranking stocks and entering multiple positions, you are making many bets on your ranking model rather than just a few risky bets. You are also betting purely on the quality of your ranking scheme.

What is a Ranking Scheme?

A ranking scheme is any model that can assign each stock a number based on how they are expected to perform, where higher is better or worse. Examples could be value factors, technical indicators, pricing models, or a combination of all of the above. For example, you could use a momentum indicator to give a ranking to a basket of trend following stocks: stocks with highest momentum are expected to continue to do well and get the highest ranks; stocks with lowest momentum will perform the worst and get lowest rans.

The success of this strategy lies almost entirely in the ranking scheme used — the better your ranking scheme can separate high performing stocks from low performing stocks, better the returns of a long-short equity strategy. It automatically follows that developing a ranking scheme is nontrivial.

What happens once you have a Ranking Scheme?

Once we have determined a ranking scheme, we would obviously like to be able to profit from it. We do this by investing an equal amount of money into buying stocks at the top of the ranking, and selling stocks at the bottom. This ensures that the strategy will make money proportionally to the quality of the ranking only, and will be market neutral.

Let’s say you are ranking m equities, have n dollars to invest, and want to hold a total of 2p positions (where m > 2p ). If the stock at rank 1 is expected to perform the worst and stock at rank m is expected to perform the best:

  • You take the stocks in position 1,…,p in the ranking, sell n/2p dollars worth of each stock
  • For each stock in position m−p,…,m in the ranking, buy n/2p dollars worth of each stock

Note: Friction Because of Prices Because stock prices will not always divide n/2p evenly, and stocks must be bought in integer amounts, there will be some imprecision and the algorithm should get as close as it can to this number. For a strategy running with n=100000 and p=500, we see that

This will cause big problems for stocks with prices > 100 since you can’t buy fractional stock. This is alleviated by trading fewer equities or increasing the capital.

Let’s run through a hypothetical example

We generate random stock names and a random factor on which to rank them. Let’s also assume our future returns are actually dependent on these factor values.

Now that we have factor values and returns, we can see what would happen if we ranked our equities based on factor values, and then entered the long and short positions.

Our strategy is to sell the basket at rank 1 and buy the basket at rank 10. The returns of this strategy are:

We’re basically putting our money on our ranking model being able to separate and spread high performing stocks from low performing stocks.

For the rest of this post, we’ll talk about how to evaluate a ranking scheme. The nice thing about making money based on the spread of the ranking is that it is unaffected by what the market does.

Let’s consider a real world example.

We load data for 32 stocks from different sectors in S&P500 and try to rank them.

Let’s start by using one month normalized momentum as a ranking indicator

Now we’re going to analyze our stock behavior and see how our universe of stocks work w.r.t our chosen ranking factor.

Analyzing data

Stock behavior

We look at how our chosen basket of stocks behave w.r.t our ranking model. To do this, let’s calculate one week forward return for all stocks. Then we can look at the correlation of 1 week forward return with previous 30 day momentum for every stock. Stocks that exhibit positive correlation are trend following and stocks that exhibit negative correlation are mean reverting.

All our stocks are mean reverting to some degree! (Obviously we choose the universe to be this way :) ) This tells us that if a stock ranks high on momentum score, we should expect it to perform poorly next week.

Correlation between Ranking due to Momentum Score and Returns

Next, we need to look at correlation between our ranking score and forward returns of our universe, i.e. how predictive of of forward returns is our ranking factor? Does a high relative rank predict poor relative returns or vice versa?

To do this, we calculate daily correlation between 30 day momentum and 1 week forward returns of all stocks.

Daily Correlation is quite noisy, but very slightly negative (This is expected, since we said all the stocks are mean reverting). Let’s also look at average monthly correlation of scores with 1 month forward returns.

We can see that the average correlation is slightly negative again, but varies a lot daily as well from month to month.

Average Basket Return

Now we compute the returns of baskets taken out of our ranking. If we rank all equities and then split them into nn groups, what would the mean return be of each group?

The first step is to create a function that will give us the mean return in each basket in a given the month and a ranking factor.

We calculate the average return of each basket when equities are ranked based on this score. This should give us a sense of the relationship over a long timeframe.

Seems like we are able to separate high performers from low performers with very small success.

Spread Consistency

Of course, that’s just the average relationship. To get a sense of how consistent this is, and whether or not we would want to trade on it, we should look at it over time. Here we’ll look at the monthly spreads for the first two years. We can see a lot of variation, and further analysis should be done to determine whether this momentum score is tradeable.

Finally, lets look at the returns if we had bought the last basket and sold the first basket every month (assuming equal capital allocation to each security)

We see that we have a very faint ranking scheme that only mildly separates high performing stocks from low performing stocks. Besides, this ranking scheme has no consistency and varies a lot month to month.

Finding the correct ranking scheme

To execute a long-short equity, you effectively only have to determine the ranking scheme. Everything after that is mechanical. Once you have one long-short equity strategy, you can swap in different ranking schemes and leave everything else in place. It’s a very convenient way to quickly iterate over ideas you have without having to worry about tweaking code every time.

The ranking schemes can come from pretty much any model as well. It doesn’t have to be a value based factor model, it could be a machine learning technique that predicted returns one-month ahead and ranked based on that.

Choice and Evaluation of a Ranking Scheme

The ranking scheme is where a long-short equity strategy gets its edge, and is the most crucial component. Choosing a good ranking scheme is the entire trick, and there is no easy answer.

A good starting point is to pick existing known techniques, and see if you can modify them slightly to get increased returns. We’ll discuss a few starting points here:

  • Clone and Tweak: Choose one that is commonly discussed and see if you can modify it slightly to gain back an edge. Often times factors that are public will have no signal left as they have been completely arbitraged out of the market. However, sometimes they lead you in the right direction of where to go.
  • Pricing Models: Any model that predicts future returns can be a factor. The future return predicted is now that factor, and can be used to rank your universe. You can take any complicated pricing model and transform it into a ranking.
  • Price Based Factors (Technical Indicators): Price based factors, like we discussed today, take information about the historical price of each equity and use it to generate the factor value. Examples could be moving average measures, momentum ribbons, or volatility measures.
  • Reversion vs. Momentum: It’s important to note that some factors bet that prices, once moving in a direction, will continue to do so. Some factors bet the opposite. Both are valid models on different time horizons and assets, and it’s important to investigate whether the underlying behavior is momentum or reversion based.
  • Fundamental Factors (Value Based): This is using combinations of fundamental values like P.E ratio, dividend etc. Fundamental values contain information that is tied to real world facts about a company, so in many ways can be more robust than prices.

Ultimately, developing predictive factors is an arms race in which you are trying to stay one step ahead. Factors get arbitraged out of markets and have a lifespan, so it’s important that you are constantly doing work to determine how much decay your factors are experiencing, and what new factors might be used to take their place.

Additional Considerations

  • Rebalancing Frequency

Every ranking system will be predictive of returns over a slightly different timeframe. A price-based mean reversion may be predictive over a few days, while a value-based factor model may be predictive over many months. It is important to determine the timeframe over which your model should be predictive, and statistically verify that before executing your strategy. You don’t want to overfit by trying to optimize the rebalancing frequency — you will inevitably find one that is randomly better than others, but not necessary because of anything in your model. Once you have determined the timeframe on which your ranking scheme is predictive, try to rebalance at about that frequency so you’re taking full advantage of your models.

  • Capital Capacity and Transaction Costs

Every strategy has a minimum and maximum amount of capital it can trade before it stops being profitable. The minimum threshold is usually set by transaction costs.

Trading many equities will result in high transaction costs. Say that you want to purchase 1000 equities, you will incur a few thousand dollars in costs per rebalance. Your capital base must be high enough that the transaction costs are a small percentage of the returns being generated by your strategy. For example, if your capital is 100,000$ and your strategy makes 1% per month(1000$) , then all of these returns will be taken up by transaction costs.. You would need to be running the strategy on millions of dollars for it to be profitable over 1000 equities.

The minimum capacity is quite high as such, and dependent largely on the number of equities traded. However, the maximum capacity is also incredibly high, with long-short equity strategies capable of trading hundreds of millions of dollars without losing their edge. This is true because the strategy rebalances relatively infrequently, and the total dollar volume is divided by the number of equities traded. Therefore dollar-volume per equity is quite low and you don’t have to worry about impacting the market by your trades. Let’s say you’re trading 1000 equities with 100,000,000$. If you rebalance your entire portfolio every month, you are only trading 100,000 dollar-volume per month for each equity, which isn’t enough to be a significant market share for most securities.

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