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?
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:
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!
Welcome back! It’s been seven months away from Nisha Trades, and to say that I’m excited to be back is most definitely an understatement. During my final semester of senior year, I dedicated the time I would normally spend blogging towards an online “International Business” class. I truly enjoyed learning about the intricacies of global business and would highly recommend it to other high school students who (like me) are similarly interested in finance and the business world. Today, I’m back for a new post: cost basis reduction and Facebook!
Most early (and few seasoned) traders indulge in the comfortable “buy and hold” strategy -- the typical route of picking a stock, assuming a bullish position, buying it, and waiting until it goes up. While this strategy has idyllic promise, it means either waiting endlessly for a profit or suffering when the stock suddenly dips...a situation quite a few traders found themselves in after Facebook’s epic drop earlier this week. Cost-basis reduction allows you buy and hold onto a stock, but effectively reduce the amount of impact that downwards movement would have on your portfolio by selling premium against it every month .
A revolutionary social media platform started by Mark Zuckerberg, Facebook has certainly received its fair share of attention in recent years. Facebook came under intense scrutiny for its role in the United States 2016 election, Russian meddling, and privacy concerns in the U.S. and Europe. Consequently, Zuckerberg found himself accountable before the U.S. Congress, left to explain for his company’s impact on world affairs. On Thursday (7/26/18), Facebook was dealt yet another troubling blow. Following its latest earnings, Facebook’s stock price dropped nearly 20% -- one the biggest one-day-drops in United States history. Traders near and far, with long positions on Facebook were left distraught. It would be horrible to have bought and held Facebook for eternity only to have it drop massively. Thanks to cost-basis reduction, my portfolio didn’t have a long face over Facebook.
Facebook’s stock was under intense pressure in spring, causing for option prices to be (as expected) very high in such conditions. At this time, I was selling premium on puts and was ultimately assigned. This was one of the few occasions where I wanted to be assigned stock and keep it for a few years; it fit my portfolio and long-term outlook. In turn, I owned 100 shares of Facebook stock at $177.50.
But, of course, I wasn’t just holding onto my Facebook stock. Instead, I sold monthly premium in a variety of ways: strangles around earnings and call or put verticals (in either direction) on a monthly or weekly basis.
Overall, today my account shows a $1,400+ profit. Although the stock is at $174.89 today (as I write this post), after this massive pullback, I can still afford to absorb further downturn for another $14 or 8%. Had I not been doing cost-basis reduction (and essentially chipping away at the amount I originally paid for the stock), I would have incurred a loss of about $261.
Currently due to the fear in the market, IV is high and so is premium. Looking ahead in September, a 25 delta call (couldn’t find the 30 delta call) is priced around $2.60 which is about 1.5% of Facebook’s current price. So, in the meantime, I plan to continue my campaign of cost-basis reduction so that hopefully one day I can own this stock free and clear! :-)
To learn more about cost-basis reduction, you can read an earlier post entirely dedicated to this very concept here.
Thank you for reading! Find more NishaTrades on Twitter @nishatrades.
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:
Consider for example a simple FANGs portfolio in which we have short 30 delta put (that is, we have long bias in the four stocks). Here we have found the beta weighted delta for all four stocks
So if you sum up all the beta weighted deltas of individual stocks, you get the beta weighted delta of your portfolio -- in our example it's 347.05.
This single number gives us a bird's-eye view of the risk in our portfolio . Using this information, we can strategically position ourselves to neutralize our deltas and reduce risk. We could hedge our portfolio against a downturn by selling about 347 deltas worth of SPY which can be done by either straight selling 357 SPY stocks or selling say about 7 ATM calls of SPY (each ATM call is 50 delta).
Note that the beta weighted deltas constantly fluctuate with price so any and all adjustments should be made with the current prices. Most good trading platforms (like TastyWorks) provide these calculations for free so traders can focus on trading and not crunching numbers. Having a basic understanding of this concept will go a long way in managing your portfolio.
Thank you for reading! :)
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:
Strategy III: Buy long dated option and sell monthly calls against it
For this strategy, we did a diagonal spread. A diagonal or time-spread is when you buy a far month option while selling a close month option (to be further explained in future post).
In this strategy, we bought 10 ATM Dec calls at a strike price of $238 and paid $11.39 ($11,3900) for each call. Against each of these, we sold 10 calls at 30 delta in the monthly options about 30 days away. Towards the end of the expiration, we bought back the monthly calls and sold the 30 delta call the following month. Like the covered call, the diagonal spread is a similarly cyclical process.
This strategy is also called a poor man's covered call as you might have noticed that we bought a far month call option paying $11,3900 instead of buying the SPY stock itself for $23,800. At about half the price, we are control 10 times more units. Though this strategy comes with more leverage, it also comes with more risk. Nevertheless, the return on this strategy was 89.20% !!
After about 8 months, we can look at how the different strategies fared in the chart 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.
Note on commissions: for sake of simplicity, we are ignoring the price of transactions. But I'll note it that it costs about $6 to buy 100 units of SPY. Each option transaction is $1 per trade without any overheads. So you'll see that the cost of entry or exit is pretty negligible for our purposes here.
...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:
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)
Earnings trades come in two flavors: good and bad. But this doesn't mean our outcome needs to be a loss. By proactively repairing our bad trades, we can hope to either minimize the loss, scratch the trade, or make a profit!
I repeated by favorite Netflix earnings trade, this earnings cycle in October. This time the price stayed range-bound, making it a good trade.
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:
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! :)
As a child, many of us have endured the arduous labor that is chores. While all of us have done the dishes and cleaned our rooms, let’s admit it… we were always a little envious of our friends who received pocket money. (Trading makes up for it though, so it’s all good :)) Imagine your friend, king of the playground and loaded with $100. Devastated, he loses $50 by accident. Now consider his dad, who likely earns a couple of thousands per month. If he lost the same $50 bill, it would certainly hurt his pride but not his wallet, as much as his young son’s. If you could choose to be in the son or father’s position, which one would you choose? To lose half of your earnings or a minute fraction? This form of decision making is analogous to that of a trader’s. In this post, we will be learning about trading size in your portfolio.
One of the toughest things in trading or investing is figuring out the size of each trade. How much of your capital should be deployed in the markets and how much do you hold in cash? Let’s take a hypothetical portfolio of $10,000. If you have $10,000 in your trading account, how would you allocate this $10,000 in markets to make your money work for you?
Well, you could pick your one favorite stock (selection made on criteria that suits you) and buy every stock possible for the entire $10,000 and hope that the stock goes up in value and you profit from it. Most traders who start fresh are inclined to do this. While this is an easy way to execute, it’s neither prudent nor profitable to put all of your eggs in one basket. What do you think would happen if the stock took a 20% hit and your portfolio value falls to $8000? You can of course wait for the stock to rally to get back to your initial investment of $10,000. But just how easy is it for the stock to move up in terms of it’s current value so that you can break even? If the stock falls 20% in value, it needs to gain 25% from that point for you to make a profit. If the stock falls 50% then it needs to gain 100% (or literally double it’s value) for you to breakeven. The table and chart listed below shows the non-linear relationship between the gains need after a steep drop for your portfolio to recover:
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!
In an earlier blog post, I discussed the ability to figure out the expected move based on volatility. Though there are extensive data science books written solely to extol the value of forecasting stock prices using statistical models and machine learnings, I prefer to rely upon trusty volatility as basis for my predictions. Why? 1. It’s to-the-point and saves me the hassle of looking at the output of probability based forecasts from different statistical models. 2. There’s actual market participation. Volatility is driven by fear or greed in the market and can provide me with a more realistic and tangible understanding of expected movement.
While predicting the price of a stock in the future might be a good research exercise, it is also a great tool for finding trading opportunities. If a stock or underlying has moved quickly to an extreme, I can take the contrarian view and fade the move.
Let’s work with an example. The figure below shows different underlyings and their associated volatility tickers created by CBOE.
On Thursday evening Soybean (SOYB) had closed at $17.56. It’s volatility SIV had closed at 20.99. Based on this, the one day, one standard deviation move expected was $0.19. Here are the calculations:
But around noon the next day SOYB was trading at $17.98 which was more than twice the expected $0.19; SOYB had moved more than two standard deviations from the prior day’s expected move. The degree of confidence for a 2 SD move is about 95%, that is to say there was only 5% chance that market was expecting this kind of move! Given such an extreme move, as a contrarian, I’d fade the move to short soybeans. Since SOYB is thinly traded with wide bid-ask spreads, I’d short the futures /ZS or maybe even short a put along with it to add a positive theta component.
Here’s another exercise expected move with SPX and its volatility VIX. For now, let’s ignore the news surrounding the market: bulls vs. bears, political environment fueling the market, or how high it is. Let’s instead try to quantify it.
I’m choosing the date of 2/11/16 which printed the lowest point in recent (short) memory – about 500+ days go. If we took the closing prices of 2/11/16, we can figure the expected move till today and see that SPX has been hovering pretty close to this number:
Here are all the standard deviations using 507 days since 2/11/16 when SPX was at 1829.08 and VIX was at 28.14 as:
If we see the large jump in the price of the stock beyond the given SDs, we can take the contrarian view to fade the move.
In this post, we explored another way to look for trading opportunities- this time using the expected move!
Stay tuned for more posts from NishaTrades!
On May 30, I was afforded the incredible opportunity of speaking with Tom and Tony of Tastytrade on their ‘Future Stars” segment! Here’s a little bit about my experience:
My nerves, when riding up the elevator of TastyTrade HQ, were comparable to IV during the earnings season-- very high. My parents and I had arrived in Chicago three days earlier to soak up as much as we could of the windy city before heading back home after my TastyTrade interview. And yet despite seeing the beautiful campus of University of Chicago, the breathtaking views from the Chicago River at sunset, and the famous Portillo's hot dogs and Giordano's deep-dish pizza, it was the sheer joy of being in the TastyTrade studios that made our trip :)
From TT’s wooden communal table, I attempted to decipher each strategy of the mural looming before us, just as Tony, the trading giant (literally and figuratively) greeted us with a wide, welcoming grin. I had a great time talking to many of my favorite online personas. From Pete Mulmat, of the Hot Seat with Pete and WDIS Futures Edition, Beef, Frank “The Indoor Kid”, Dr. Data, and others, such as Josh and Jules, each member of the TastyTrade crew was approachable and insightful to talk to and learn from.
Tom and Tony’s smiles made the interview all the more fun! During the segment, I almost forgot we were being broadcasted to thousands (live!), as it felt more like a conversation than an interview. I enjoyed sharing about my passion for trading, favorite strategies, and my biggest takeaways from my experience as a whole. It was truly an honor to speak to my trading gurus :)
TastyTrade as been my dojo for investing since my first Buffalo Wild Wings trade and I am so grateful for the content they promote! A big thanks to my fellow TastyNation traders who reached out via my blog and Twitter; I really appreciated your encouragement and can’t wait share even more regarding my trading journey.
The “Future Stars” segment was such a rewarding experience and I know that it will be one to remember.
Stay tuned for more posts from NishaTrades!
Though it's tempting we cannot blindly enter the market. It's important to keep these factors in mind when opening a position:
Eighteen-year old trader, future connoisseur of options.
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