Hello readers of NishaTrades: welcome back! I hope that you all have been keeping safe and healthy these past few months. Quarantine has wrought an unprecedented cloud of uncertainty over all of our lives, in both the personal and political scope, and in today’s post I wanted to share a couple ~trading~ takes with regards to the upcoming presidential election this Tuesday. Over the course of the past seven months, we’ve seen a wide range of unprecedented measures in response to the pandemic. From lockdown and the liquidity crisis to reopening and seemingly restored markets, this time period has certainly been pivotal. And now, we have a critical election ahead of us. The results will undoubtedly have important implications on the social, economic, and political direction of the US. It additionally portends a lot for financial markets! These are a couple learnings/thoughts of mine surrounding the next few weeks ahead: ​

Reflections on...

...equity markets:
The onset of the pandemic put a grinding halt to an otherwise consistently bullish stock market during Trump’s administration. This sentiment driven selloff was spurred in part by investor concern about companies’ ability to retain revenue and meet debt obligations amidst an entire reset of daily business operations. Into the summer however, we saw the market rebound and become further detached from the economic reality unfolding around us (though small caps traditionally remain more levered to the economy). Technology stocks buttressed the new summer daily highs, and further drove a wedge between value and growth opportunities.

Per Tastytrade research (linked below), stocks have historically rallied before the election, begun selling off during, and selling off even more post. Regardless of an incumbent win or not, we’ve additionally seen greater stock volatility around Democractic victories (~7.4% dips post-Democratic wins vs. 2.5% rises post-Republican wins). This makes intuitive sense as the stock market would favor a “pro-business” president; investor sentiment on company earning potential stands to weaken following a Democratic sweep, and the likely concomitant corporate taxation to follow. 

...credit markets:
From consistently buying safe assets like treasuries and agency MBS throughout the spring and summer, the Fed infused the credit markets with much needed funding. Ever since, much attention has not only been drawn towards tightening credit spreads, but also the improving fundamentals underlying investment grade credit opportunities. In response to attractive low rates amidst a relatively stable fixed income environment, companies alike have dedicated efforts towards deleveraging and cleaning up their balance sheets. 

Additionally, all eyes have been on Jerome Powell and rates, however it’s worth also noting the impacts that differing legislative and public stimulus plans will have on credit markets. A disruptive Democratic sweep of the executive and legislative branches, could mean a reversion to higher corporate taxes. Higher corporate taxes makes tax shields from debt (the amount less that a corporation has to pay in taxes of their income) more attractive. As a result, we’ll likely see continued corporate debt issuance. With regards to public stimulus, a Democratic executive and legislative branch could potentially mean a more stifled stalemate on recovery plans, though it's challenging to project how this potential semblance of cooperation will translate to the economy, and by virtue, markets too.  

In a side note about rates: Historically in elections, Fed changes to rates have appeared partisan; however, given prior guidance and the unique economic environment in which we’re operating, it’s unlikely that the Fed will deviate from anything other than its consistent zero-bound path. Per Tastytrade research, historically interest rates often fell before and rose after Democratic elections, and vice versa for Republican.

Trading Takeaways:
A common thread: the outcome is uncertain. And where there is uncertainty, there is also opportunity! Whether it be an equity market reaction or a credit selloff, regardless of asset class or fundamentals, we can expect and have already seen high volatility. Taking a quick glance at S&P 500 Futures, as expected, we can already see this! Futures expiring Monday 11/2 are trading at 44.82% (+ or - $44.8) volatility, while those expiring Wednesday 11/4 are at 59% (+ or - $117.40). The difference between the dollar standard deviations from Monday to Wednesday are indicative of the overwhelming uncertainty that the market already priced in. A Biden win offers up a lot of  potential for option sellers around a greater and more elongated volatility squeeze. Risk-hedging and directionally-neutral trades to consider could perhaps be short iron condors or call spreads, with the potential to calendarize as volatility tapers into the following months!

In conclusion:
Thanks for reading! This post drew inspiration moreso from my increasing interest around the connection between macro events and markets. If you’re similarly interested in exploring more, I particularly enjoyed + learned a lot from:

• https://www.tastytrade.com/tt/shows/small-stakes/episodes/trading-before-and-after-election-10-29-2020?utm_source=daily_recap&utm_medium=email&utm_campaign=tastytrade&mc_cid=c3ac4db1f6&mc_eid=1dddbc48a5
• https://www.cnbc.com/2020/10/26/contested-election-what-went-down-in-markets-during-bush-gore-battle.html

Stay safe, and vote! 

Welcome, back! In this post, I'd like to further expand on an earlier discussion on correlations, this time analyzing how it can be used in pairs trading! 

Using some basic Google Finance functions, I created a spreadsheet to get prices for a few of my favorite ETFs in real-time (well, 20 min delayed). One of the core functions I used from Google Finance was "=GOOGLEFINANCE("SPY","CLOSE",TODAY()-500,TODAY())". This allows me to get the  historical closing prices for SPY for past 500 sessions.

Using the historical prices downloaded in Google sheets, I was able to use the "CORREL" function to find the correlations between two data sets i.e., historical prices of two different ETFs.

Since I was using multiple ETFs, I decided to construct a table to in order to obtain a birds-eye view over the entire collection of the ETFs. Here was the end result:

What does it mean?

• The top-most row has the most recent closing price, for example, SPY closing price was $292.03 (data in this post is as of 10/1/18)
• The second row displays the YTD gain (or loss) for the ETF. Here, SPY has rallied 8.62% this year
• The top right triangle lists the correlations between the different pairs of ETFs
• The bottom left triangle lists the sizing equivalence if we were to do a pairs trade

Let's use this table to look for opportunity and see if we can place an example pairs trade.

A closer look at the table highlights an opportunity in SPY (S&P 500)  versus XLK (Tech sector).  The table below shows the relevant stats:

 Here, I've highlighted only the portions relevant to the trade so we can focus on task at hand. Let's look them:

1. As discussed before, SPY is at $292.03 and has rallied 8.62% this year
2. Comparatively, XLK has rallied a whopping 15.86% and is currently trading at $75.81
3. The most important statistic here is that the two ETFs have a high positive correlation at 98.34%. This means both go up together or down together and the likelihood of this happening is pretty high. Usually I'd look at this number over different time frames in order to see where this correlation is currently, with respect to past, but we'll keep it simple here and accept the 98.34% correlation as a good indicator of high positive correlation. 
4. Given the discussion above, we can conclude that XLK has rallied relatively faster than SPY.  Therefore, we can look to fade (short) XLK against a long position in SPY. Since the prices of the two aren't the same, we'll have to size the trade appropriately. Here we gather from the table above that we could do a trade of one SPY ($292.03) against four of XLK (4 x $75.81 = $303.24). Not exact, but it's good enough for our trade!

(We can be little more precise by multiplying the IV (implied volatility) to the price and figuring out the ratio for sizing purposes, but we will use this in a future post.)

Thank you for reading! I hope this has been helpful in understanding pair trading through using correlations! If you're interested, you can find my Google Sheets here! 

Entering my sophomore year of high school, I decided to relinquish three weeks of summer vacation to take a course in "International Markets and Finance" at Brown. Despite doing what any fourteen-year-old would cringe at, I actually had a lot of fun and learned from my intelligent peers and professor. It was a great introduction into the world of finance and definitely gave me a new perspective at the trading world.  

During our coursework, we were assigned a paper trading project in order to make some virtual dough. After competing with other teams in our class, we were going to be evaluated on performance and strategy by our professor. 

Our class duration of three weeks was a tiny time frame to measure any kind of performance or skills, because success inevitably laid in selecting stocks and hoping it rallied. What was worse,  we couldn't short stocks or trade options, futures, or any derivatives. Given these shortcoming, I dipped into my bag of tricks and tips that I learnt from my trading gurus at TastyTrade and picked 'beta-weighting delta'.

So what exactly is 'beta-weighting delta'? 

Beta-weighting delta on a portfolio is  one number that can tell us as to how much our portfolio can move up if the index moved up 1 point -- or much our portfolio would move down if the index lost 1 point. While this didn't necessarily help in picking lucky stocks, it did help us watch the impact any of our picks made on our portfolio. While our team didn't win the stock-picking competition, our unique approach to portfolio management won us some accolades from my professor.

In a past post,  I previously discussed the important Greek metric: delta. Deltas of an option are the measure of how much the option price would move for a $1 move in the price of stock. While a bit different, the concept of the 'beta weighted delta' of a portfolio remains the same: it answers the question as to how much your portfolio would gain (or lose) for a $1 gain (or loss) in the index that you are beta-weighting against.

So what is beta? Beta is a measure of volatility for a specific stock. This is computed by your trading platform based on historic relations with the index. Different stocks have different betas.  Listed below are a few betas:

As you can see, Amazon's (AMZN) beta is 1.48 while that of Facebook (FB) is 0.71. The benchmark index SPY is 1.
Beta of the stock helps us infer the volatility of the stock compared to the index. So Amazon's at 1.48 means it is about 48% more volatile than SPY and if the index rallies up one point, Amazon could rally up 48 points. 

Using beta, we can beta weight all of our stocks against the index which in essence would compare a diverse group of stocks into some sort of equivalence using the index.  The formula and calculation of the beta weighted delta is as follows:​

Welcome back to Cost-basis-reduction- Part Two! In my previous post I had talked about this concept as a core philosophy of trading, investing, or of doing any part of business

In this post, we will look at putting this philosophy into practice. We'll look at some hard numbers gathered over past 8 months or so by a fellow trader. 

Let's assume that we have a bullish bias. There are a few routes or strategies we can take when deciding how we'd like to position ourselves: 

Let's look at three strategies as a part of our experiment: 
​
Strategy I:    Buy stock and hold
Here, we did the simplest of strategies which is to buy a stock and wait for a change in price, also known as buying and holding. Here the investor buys a stock and at  a time the investor deems appropriate, he/she sells the stock. 
In this example, we bought 100 units of SPY in third week of March 2017 for $238,  so the total cost on the trade was $23,800. Currently the stock is about $258.58 which is healthy 8.65% return with absolutely no sweat. Easy enough, right? But is that truly our best option?

Strategy II:    Buy stock and sell monthly calls against it (Covered Call)
In this strategy, we did the covered call strategy.  We bought  the same $238 stock and every month we will sold 30 delta calls for the following month. We chose 30 delta as it translates to a probability of about 50% probability for being ITM one month from now.  
On Mar 3rd we bought the stock at $238.25 and sold April 30 delta call at a strike price of $242 for $1.45 ($145). We bought back this same call for free on Apr 20th and sold a May $239 Call for $1.49 ($149). This cycle kept repeating every month. 
With every trade, we kept collecting premium by sell calls. And by collecting premium, we kept reducing our cost basis putting us at a gain of about 8.27%. 
This is slightly less than the 8.65% gain in previous strategy as there were months when SPY went up too fast causing us to buy the calls back at a loss.

Complete log of the trades is listed as below:​

The baseline is the white dotted line which is the price that we bought the SPY stock in March at $238.
In the Strategy II and III, we have reduced the cost basis of our stock considerably while in Strategy I we didn't do anything and we are still holding on to the stock hoping that it'll keep going up perpetually.  How would  Strategy I fare if there's a 2% or 5% pull back? There is no cushion room in Strategy I to recover from this kind of pullback.  However with Strategy II and III, both can withstand a mild to modest correction in stock price. 

The entire goal of the Strategy II or III is to reduce the cost basis down to zero so that in few years you own the stock free and clear. 

...and, now, the not so good earnings trades. 

In my last post I discussed all my 'good trades' that worked perfectly. Unfortunately, this is not always the case. There are times when you might sell premium 1 or 1.5 standard deviations away and the stock price moves more than your expected comfort level, resulting in a loss post-earnings. 

Despite this setback, I rarely close my position for a stop loss on earnings trades. Learning the defense mechanism to handle the loss (lower the loss or scratch the trade) or even turn the loser into a winner is an extremely valuable skill to have as a trader, and allows you keep your portfolio in the green.
​
I tackle bad earnings trades with a  two-pronged approach:

1. Roll the untested side in  (bring the untested side closer to the tested side)
2. Roll the tested side out to next options cycle to give myself duration to be correct

Let's look at my earnings trade with Netflix which went against me at first, but turned around as I patiently worked through it:

The earnings were supposed to be announced after market close on Mon, July 17th. ​The MMM (market maker expected move)  was around $11 for the options expiring that Friday (4 days away). With stock trading around $161, I opened a short strangle about 2 hours prior to close. The strikes I chose were 138 for puts and 185 for calls (this was more than twice the MMM expected move range).  I sold 5 strangles (5 each of calls and puts). NFLX is a kind of stock that is typically range bound when there is no earnings, but during during earnings, it has a tendency to move 2 to 3 standard deviations. Though I was going out far on either side, there was still enough premium in there for my comfort level.  

After the earnings announcement, NFLX opened around $176.12. Though it did not breach, the loss was significant. The first defense mechanism I used was to roll the short call (tested side) out in time to the following Friday while collecting additional credit. 
Typically, I'd roll the put (untested side) closer to the call but given NFLX's propensity to move fast around earnings, and even post earnings, I left the put side as it was.

On Friday, July 21st, NFLX opened at 182.72; the puts were expiring worthless, so I sold the puts for the following week (but a lot more closer to the price action). I skewed it a bit by selling only 2 puts against the 5 short calls as I expected retracement of the gap created during earnings. 

When the stock pulled back a little on July 27th, I sold some more puts (I was still skewed with more calls than puts). The stock had opened at 189.89 and went on to close at 182.68

Finally on another big move down on July 31st, I sold two more puts.  The stock had opened at 184.26 and closed at 181.66

Within the next two days, I had completely scratched the trade and went on to make a profit, equivalent to the profit I had initially expected on my original trade.

Here is a log of the trades I placed for this position and an equity curve showing my P/L. As you can see from the P/L chart and equity curve, through rolling I was able to extend duration and turn around a bad trade. (Click image to enlarge)

The third quarter earnings cycle just concluded couple weeks ago. In this two part post, I'd like to reflect on  some of my earnings trades - the good ones :-)

An earnings cycle which typically occurs four times a year, for four quarters, presents a good opportunity for premium sellers to get engaged and gain some money from the increased IV.  

We are living in a low volatility environment (with VIX hovering at a range of 10 to 12 for a good part of the year). Unfortunately, in a low volatility environment the opportunities for us premium sellers to be successful is extremely low.  

Here is my go-to recipe for earnings trades:

• I try to stay delta neutral as far as I can.  Being delta neutral means that I don't have any directional bias even if stock goes up or down after an earnings announcement. I can never truly have a good sense or intuition about the direction of a stock, as I cannot predict how the stock will move after earnings.  The nature of the announcement has no bearing on direction. A good announcement can dip the stock price while bad news could send the stock soaring high.
• However, there are times when I do take a directional bet on the stock and go long or go short. My directional assumptions are not predicted by any technical or fundamental analysis, but rather dictated based on my portfolio's beta-weighted delta (to be further explained in future post). (As you might be noticing, delta is key) At a high level, my beta-weighted delta tells me the impact a one dollar move in the stock market would have on my portfolio.  So if, for example,  I currently have a huge negative beta weighted delta and if a highly correlated stock, like GOOG, AAPL, AMZN, were to be announcing their earnings, I might take a position in direction opposite to my portfolio. That is, I'll neutralize my negative deltas in portfolio by adding some positive deltas (or going long) in GOOG or AAPL, or AMZN.
• Watch the IV numbers across the different options cycle. The nearest options chain would have a really high IV compared to the subsequent option chains later in time. This difference between front and back option cycle can give you an idea of how inflated the IV is. The whole game is to sell inflated premium prior to announcement and buy it back after announcement once the IV collapses.
• I never buy premium going into earnings (as studies and research done by TastyTrade has shown that it's something that'll never work over a long haul). While there may be a few successes here and there, I don't consider it a sustainable strategy.
• In terms of money management, I try to stay small, with none of my positions exceeding 2% to 5% of the portfolio size. Given various accounts, strangles are naked/uncovered positions which have theoretically undefined risk and can't be placed in a retirement type account. The buying power reduction of one naked put is around 20% of the price of the stock. So, if NFLX is trading at $160, one short put would need a buying power of about 20% of $16,000 which would be about $3,200. Adding an extra short call wouldn't increase the required buying power as the risk is only one sided. So one can view the buying power reduction on a short strangle as around 20% of the stock price (might vary slightly given the volatility, market conditions, interest rates etc, but it's a good rough estimate to use) For these reasons, I try to stay small in my positions. 
• My favorite delta neutral strategy is to sell strangles at one standard deviation. That is, sell a 16 delta call and sell a 16 delta put in the option cycle nearest to the earnings.  While all the research on TastyTrade has shown that the 16 delta option provides the best bang for my buck, during earnings I like to go a little further away primarily for two reasons:
  • by going further away from 16 to 10 delta, the buying power reduction is a bit less
  • in my experience 16 delta positions would need a lot more active management, which is not easy during the school year :(
• The "MMM" market maker move on the TOS platform gives a quick and easy reference number that's a one standard deviation expected move (this can also be easily calculated! - see earlier post on this topic).  Given the "MMM" number, I like to add 25% or 50% buffer and sell strangles.

Here are a  couple of trades from this earning cycle. Please click on the image to see details of the trade:

I've posted 5 successful earnings trades above. Or as Tom (at TastyTrade) would call it "winner, winner, chicken dinner".
That was easy, right...?  Well, not so fast. 

Things worked out well on these trades, but there are trades that don't always go the way you'd like them to. That's why  the topic of this blog post is 'Good Trades'! 

In the next post, I'll highlight some 'Bad Trades' and show how I try to repair trades gone astray. 

Thank you for reading! :) 
-Nisha

Alternatively, you could spread your risk and allocate this $10,000 across several different stocks in a manner that exposure of risk to your portfolio is spread across these different stocks. For example, we could allocate $250 per trade/investment and buy or sell 15 to 20 stocks/options. This would be a bit more diversified compared to the previous example but if all the stocks selected are highly correlated to each other, then the impact of the market move in a direction would have same impact as if the portfolio had just one stock as in previous example.


So, a better way of portfolio allocation would be is to add some diversification to your portfolio in terms of having some long positions, some short positions, having a mix of stocks, ETFs, commodities, options that have low correlation between each other.  Advanced traders would add diversification in terms of strategies so at that one can benefit from the changes in volatility or direction or time.
 
Personally, in this extremely low IV environment I'd  like to keep 50% of my capital in cash and deploy the other 50% in the market, diversifying it through various underlyings and strategies. The trick is to diversify as much as possible and  trade small, but trade often, as TastyTrade would say.

​Stay tuned for more posts from NishaTrades! 

Stay tuned for more posts from NishaTrades!