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No gods, no kings, only NOPE - or divining the future with options flows. [Part 3: Hedge Winding, Unwinding, and the NOPE]

Hello friends!
We're on the last post of this series ("A Gentle Introduction to NOPE"), where we get to use all the Big Boy Concepts (TM) we've discussed in the prior posts and put them all together. Some words before we begin:
  1. This post will be massively theoretical, in the sense that my own speculation and inferences will be largely peppered throughout the post. Are those speculations right? I think so, or I wouldn't be posting it, but they could also be incorrect.
  2. I will briefly touch on using the NOPE this slide, but I will make a secondary post with much more interesting data and trends I've observed. This is primarily for explaining what NOPE is and why it potentially works, and what it potentially measures.
My advice before reading this is to glance at my prior posts, and either read those fully or at least make sure you understand the tl;drs:
https://www.reddit.com/thecorporation/collection/27dc72ad-4e78-44cd-a788-811cd666e32a
Depending on popular demand, I will also make a last-last post called FAQ, where I'll tabulate interesting questions you guys ask me in the comments!
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So a brief recap before we begin.
Market Maker ("Mr. MM"): An individual or firm who makes money off the exchange fees and bid-ask spread for an asset, while usually trying to stay neutral about the direction the asset moves.
Delta-gamma hedging: The process Mr. MM uses to stay neutral when selling you shitty OTM options, by buying/selling shares (usually) of the underlying as the price moves.
Law of Surprise [Lily-ism]: Effectively, the expected profit of an options trade is zero for both the seller and the buyer.
Random Walk: A special case of a deeper probability probability called a martingale, which basically models stocks or similar phenomena randomly moving every step they take (for stocks, roughly every millisecond). This is one of the most popular views of how stock prices move, especially on short timescales.
Future Expected Payoff Function [Lily-ism]: This is some hidden function that every market participant has about an asset, which more or less models all the possible future probabilities/values of the assets to arrive at a "fair market price". This is a more generalized case of a pricing model like Black-Scholes, or DCF.
Counter-party: The opposite side of your trade (if you sell an option, they buy it; if you buy an option, they sell it).
Price decoherence ]Lily-ism]: A more generalized notion of IV Crush, price decoherence happens when instead of the FEPF changing gradually over time (price formation), the FEPF rapidly changes, due usually to new information being added to the system (e.g. Vermin Supreme winning the 2020 election).
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One of the most popular gambling events for option traders to play is earnings announcements, and I do owe the concept of NOPE to hypothesizing specifically about the behavior of stock prices at earnings. Much like a black hole in quantum mechanics, most conventional theories about how price should work rapidly break down briefly before, during, and after ER, and generally experienced traders tend to shy away from playing earnings, given their similar unpredictability.
Before we start: what is NOPE? NOPE is a funny backronym from Net Options Pricing Effect, which in its most basic sense, measures the impact option delta has on the underlying price, as compared to share price. When I first started investigating NOPE, I called it OPE (options pricing effect), but NOPE sounds funnier.
The formula for it is dead simple, but I also have no idea how to do LaTeX on reddit, so this is the best I have:

https://preview.redd.it/ais37icfkwt51.png?width=826&format=png&auto=webp&s=3feb6960f15a336fa678e945d93b399a8e59bb49
Since I've already encountered this, put delta in this case is the absolute value (50 delta) to represent a put. If you represent put delta as a negative (the conventional way), do not subtract it; add it.
To keep this simple for the non-mathematically minded: the NOPE today is equal to the weighted sum (weighted by volume) of the delta of every call minus the delta of every put for all options chains extending from today to infinity. Finally, we then divide that number by the # of shares traded today in the market session (ignoring pre-market and post-market, since options cannot trade during those times).
Effectively, NOPE is a rough and dirty way to approximate the impact of delta-gamma hedging as a function of share volume, with us hand-waving the following factors:
  1. To keep calculations simple, we assume that all counter-parties are hedged. This is obviously not true, especially for idiots who believe theta ganging is safe, but holds largely true especially for highly liquid tickers, or tickers will designated market makers (e.g. any ticker in the NASDAQ, for instance).
  2. We assume that all hedging takes place via shares. For SPY and other products tracking the S&P, for instance, market makers can actually hedge via futures or other options. This has the benefit for large positions of not moving the underlying price, but still makes up a fairly small amount of hedges compared to shares.

Winding and Unwinding

I briefly touched on this in a past post, but two properties of NOPE seem to apply well to EER-like behavior (aka any binary catalyst event):
  1. NOPE measures sentiment - In general, the options market is seen as better informed than share traders (e.g. insiders trade via options, because of leverage + easier to mask positions). Therefore, a heavy call/put skew is usually seen as a bullish sign, while the reverse is also true.
  2. NOPE measures system stability
I'm not going to one-sentence explain #2, because why say in one sentence what I can write 1000 words on. In short, NOPE intends to measure sensitivity of the system (the ticker) to disruption. This makes sense, when you view it in the context of delta-gamma hedging. When we assume all counter-parties are hedged, this means an absolutely massive amount of shares get sold/purchased when the underlying price moves. This is because of the following:
a) Assume I, Mr. MM sell 1000 call options for NKLA 25C 10/23 and 300 put options for NKLA 15p 10/23. I'm just going to make up deltas because it's too much effort to calculate them - 30 delta call, 20 delta put.
This implies Mr. MM needs the following to delta hedge: (1000 call options * 30 shares to buy for each) [to balance out writing calls) - (300 put options * 20 shares to sell for each) = 24,000 net shares Mr. MM needs to acquire to balance out his deltas/be fully neutral.
b) This works well when NKLA is at $20. But what about when it hits $19 (because it only can go down, just like their trucks). Thanks to gamma, now we have to recompute the deltas, because they've changed for both the calls (they went down) and for the puts (they went up).
Let's say to keep it simple that now my calls are 20 delta, and my puts are 30 delta. From the 24,000 net shares, Mr. MM has to now have:
(1000 call options * 20 shares to have for each) - (300 put options * 30 shares to sell for each) = 11,000 shares.
Therefore, with a $1 shift in price, now to hedge and be indifferent to direction, Mr. MM has to go from 24,000 shares to 11,000 shares, meaning he has to sell 13,000 shares ASAP, or take on increased risk. Now, you might be saying, "13,000 shares seems small. How would this disrupt the system?"
(This process, by the way, is called hedge unwinding)
It won't, in this example. But across thousands of MMs and millions of contracts, this can - especially in highly optioned tickers - make up a substantial fraction of the net flow of shares per day. And as we know from our desk example, the buying or selling of shares directly changes the price of the stock itself.
This, by the way, is why the NOPE formula takes the shape it does. Some astute readers might notice it looks similar to GEX, which is not a coincidence. GEX however replaces daily volume with open interest, and measures gamma over delta, which I did not find good statistical evidence to support, especially for earnings.
So, with our example above, why does NOPE measure system stability? We can assume for argument's sake that if someone buys a share of NKLA, they're fine with moderate price swings (+- $20 since it's NKLA, obviously), and in it for the long/medium haul. And in most cases this is fine - we can own stock and not worry about minor swings in price. But market makers can't* (they can, but it exposes them to risk), because of how delta works. In fact, for most institutional market makers, they have clearly defined delta limits by end of day, and even small price changes require them to rebalance their hedges.
This over the whole market adds up to a lot shares moving, just to balance out your stupid Robinhood YOLOs. While there are some tricks (dark pools, block trades) to not impact the price of the underlying, the reality is that the more options contracts there are on a ticker, the more outsized influence it will have on the ticker's price. This can technically be exactly balanced, if option put delta is equal to option call delta, but never actually ends up being the case. And unlike shares traded, the shares representing the options are more unstable, meaning they will be sold/bought in response to small price shifts. And will end up magnifying those price shifts, accordingly.

NOPE and Earnings

So we have a new shiny indicator, NOPE. What does it actually mean and do?
There's much literature going back to the 1980s that options markets do have some level of predictiveness towards earnings, which makes sense intuitively. Unlike shares markets, where you can continue to hold your share even if it dips 5%, in options you get access to expanded opportunity to make riches... and losses. An options trader betting on earnings is making a risky and therefore informed bet that he or she knows the outcome, versus a share trader who might be comfortable bagholding in the worst case scenario.
As I've mentioned largely in comments on my prior posts, earnings is a special case because, unlike popular misconceptions, stocks do not go up and down solely due to analyst expectations being meet, beat, or missed. In fact, stock prices move according to the consensus market expectation, which is a function of all the participants' FEPF on that ticker. This is why the price moves so dramatically - even if a stock beats, it might not beat enough to justify the high price tag (FSLY); even if a stock misses, it might have spectacular guidance or maybe the market just was assuming it would go bankrupt instead.
To look at the impact of NOPE and why it may play a role in post-earnings-announcement immediate price moves, let's review the following cases:
  1. Stock Meets/Exceeds Market Expectations (aka price goes up) - In the general case, we would anticipate post-ER market participants value the stock at a higher price, pushing it up rapidly. If there's a high absolute value of NOPE on said ticker, this should end up magnifying the positive move since:
a) If NOPE is high negative - This means a ton of put buying, which means a lot of those puts are now worthless (due to price decoherence). This means that to stay delta neutral, market makers need to close out their sold/shorted shares, buying them, and pushing the stock price up.
b) If NOPE is high positive - This means a ton of call buying, which means a lot of puts are now worthless (see a) but also a lot of calls are now worth more. This means that to stay delta neutral, market makers need to close out their sold/shorted shares AND also buy more shares to cover their calls, pushing the stock price up.
2) Stock Meets/Misses Market Expectations (aka price goes down) - Inversely to what I mentioned above, this should push to the stock price down, fairly immediately. If there's a high absolute value of NOPE on said ticker, this should end up magnifying the negative move since:
a) If NOPE is high negative - This means a ton of put buying, which means a lot of those puts are now worth more, and a lot of calls are now worth less/worth less (due to price decoherence). This means that to stay delta neutral, market makers need to sell/short more shares, pushing the stock price down.
b) If NOPE is high positive - This means a ton of call buying, which means a lot of calls are now worthless (see a) but also a lot of puts are now worth more. This means that to stay delta neutral, market makers need to sell even more shares to keep their calls and puts neutral, pushing the stock price down.
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Based on the above two cases, it should be a bit more clear why NOPE is a measure of sensitivity to system perturbation. While we previously discussed it in the context of magnifying directional move, the truth is it also provides a directional bias to our "random" walk. This is because given a price move in the direction predicted by NOPE, we expect it to be magnified, especially in situations of price decoherence. If a stock price goes up right after an ER report drops, even based on one participant deciding to value the stock higher, this provides a runaway reaction which boosts the stock price (due to hedging factors as well as other participants' behavior) and inures it to drops.

NOPE and NOPE_MAD

I'm going to gloss over this section because this is more statistical methods than anything interesting. In general, if you have enough data, I recommend using NOPE_MAD over NOPE. While NOPE in theory represents a "real" quantity (net option delta over net share delta), NOPE_MAD (the median absolute deviation of NOPE) does not. NOPE_MAD simply answecompare the following:
  1. How exceptional is today's NOPE versus historic baseline (30 days prior)?
  2. How do I compare two tickers' NOPEs effectively (since some tickers, like TSLA, have a baseline positive NOPE, because Elon memes)? In the initial stages, we used just a straight numerical threshold (let's say NOPE >= 20), but that quickly broke down. NOPE_MAD aims to detect anomalies, because anomalies in general give you tendies.
I might add the formula later in Mathenese, but simply put, to find NOPE_MAD you do the following:
  1. Calculate today's NOPE score (this can be done end of day or intraday, with the true value being EOD of course)
  2. Calculate the end of day NOPE scores on the ticker for the previous 30 trading days
  3. Compute the median of the previous 30 trading days' NOPEs
  4. From the median, find the 30 days' median absolute deviation (https://en.wikipedia.org/wiki/Median_absolute_deviation)
  5. Find today's deviation as compared to the MAD calculated by: [(today's NOPE) - (median NOPE of last 30 days)] / (median absolute deviation of last 30 days)
This is usually reported as sigma (σ), and has a few interesting properties:
  1. The mean of NOPE_MAD for any ticker is almost exactly 0.
  2. [Lily's Speculation's Speculation] NOPE_MAD acts like a spring, and has a tendency to reverse direction as a function of its magnitude. No proof on this yet, but exploring it!

Using the NOPE to predict ER

So the last section was a lot of words and theory, and a lot of what I'm mentioning here is empirically derived (aka I've tested it out, versus just blabbered).
In general, the following holds true:
  1. 3 sigma NOPE_MAD tends to be "the threshold": For very low NOPE_MAD magnitudes (+- 1 sigma), it's effectively just noise, and directionality prediction is low, if not non-existent. It's not exactly like 3 sigma is a play and 2.9 sigma is not a play; NOPE_MAD accuracy increases as NOPE_MAD magnitude (either positive or negative) increases.
  2. NOPE_MAD is only useful on highly optioned tickers: In general, I introduce another parameter for sifting through "candidate" ERs to play: option volume * 100/share volume. When this ends up over let's say 0.4, NOPE_MAD provides a fairly good window into predicting earnings behavior.
  3. NOPE_MAD only predicts during the after-market/pre-market session: I also have no idea if this is true, but my hunch is that next day behavior is mostly random and driven by market movement versus earnings behavior. NOPE_MAD for now only predicts direction of price movements right between the release of the ER report (AH or PM) and the ending of that market session. This is why in general I recommend playing shares, not options for ER (since you can sell during the AH/PM).
  4. NOPE_MAD only predicts direction of price movement: This isn't exactly true, but it's all I feel comfortable stating given the data I have. On observation of ~2700 data points of ER-ticker events since Mar 2019 (SPY 500), I only so far feel comfortable predicting whether stock price goes up (>0 percent difference) or down (<0 price difference). This is +1 for why I usually play with shares.
Some statistics:
#0) As a baseline/null hypothesis, after ER on the SPY500 since Mar 2019, 50-51% price movements in the AH/PM are positive (>0) and ~46-47% are negative (<0).
#1) For NOPE_MAD >= +3 sigma, roughly 68% of price movements are positive after earnings.
#2) For NOPE_MAD <= -3 sigma, roughly 29% of price movements are positive after earnings.
#3) When using a logistic model of only data including NOPE_MAD >= +3 sigma or NOPE_MAD <= -3 sigma, and option/share vol >= 0.4 (around 25% of all ERs observed), I was able to achieve 78% predictive accuracy on direction.

Caveats/Read This

Like all models, NOPE is wrong, but perhaps useful. It's also fairly new (I started working on it around early August 2020), and in fact, my initial hypothesis was exactly incorrect (I thought the opposite would happen, actually). Similarly, as commenters have pointed out, the timeline of data I'm using is fairly compressed (since Mar 2019), and trends and models do change. In fact, I've noticed significantly lower accuracy since the coronavirus recession (when I measured it in early September), but I attribute this mostly to a smaller date range, more market volatility, and honestly, dumber option traders (~65% accuracy versus nearly 80%).
My advice so far if you do play ER with the NOPE method is to use it as following:
  1. Buy/short shares approximately right when the market closes before ER. Ideally even buying it right before the earnings report drops in the AH session is not a bad idea if you can.
  2. Sell/buy to close said shares at the first sign of major weakness (e.g. if the NOPE predicted outcome is incorrect).
  3. Sell/buy to close shares even if it is correct ideally before conference call, or by the end of the after-market/pre-market session.
  4. Only play tickers with high NOPE as well as high option/share vol.
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In my next post, which may be in a few days, I'll talk about potential use cases for SPY and intraday trends, but I wanted to make sure this wasn't like 7000 words by itself.
Cheers.
- Lily
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Wall Street Week Ahead for the trading week beginning June 29th, 2020

Good Saturday afternoon to all of you here on StockMarket. I hope everyone on this sub made out pretty nicely in the market this past week, and is ready for the new trading week ahead.
Here is everything you need to know to get you ready for the trading week beginning June 29th, 2020.

Fragile economic recovery faces first big test with June jobs report in the week ahead - (Source)

The second half of 2020 is nearly here, and now it’s up to the economy to prove that the stock market was right about a sharp comeback in growth.
The first big test will be the June jobs report, out on Thursday instead of its usual Friday release due to the July 4 holiday. According to Refinitiv, economists expect 3 million jobs were created, after May’s surprise gain of 2.5 million payrolls beat forecasts by a whopping 10 million jobs.
“If it’s stronger, it will suggest that the improvement is quicker, and that’s kind of what we saw in May with better retail sales, confidence was coming back a little and auto sales were better,” said Kevin Cummins, chief U.S. economist at NatWest Markets.
The second quarter winds down in the week ahead as investors are hopeful about the recovery but warily eyeing rising cases of Covid-19 in a number of states.
Stocks were lower for the week, as markets reacted to rising cases in Texas, Florida and other states. Investors worry about the threat to the economic rebound as those states move to curb some activities. The S&P 500 is up more than 16% so far for the second quarter, and it is down nearly 7% for the year. Friday’s losses wiped out the last of the index’s June gains.
“I think the stock market is looking beyond the valley. It is expecting a V-shaped economic recovery and a solid 2021 earnings picture,” said Sam Stovall, chief investment strategist at CFRA. He expects large-cap company earnings to be up 30% next year, and small-cap profits to bounce back by 140%.
“I think the second half needs to be a ‘show me’ period, proving that our optimism was justified, and we’ll need to see continued improvement in the economic data, and I think we need to see upward revisions to earnings estimates,” Stovall said.
Liz Ann Sonders, chief investment strategist at Charles Schwab, said she expects the recovery will not be as smooth as some expect, particularly considering the resurgence of virus outbreaks in sunbelt states and California.
“Now as I watch what’s happening I think it’s more likely to be rolling Ws,” rather than a V, she said. “It’s not just predicated on a second wave. I’m not sure we ever exited the first wave.”
Even without actual state shutdowns, the virus could slow economic activity. “That doesn’t mean businesses won’t shut themselves down, or consumers won’t back down more,” she said.

Election ahead

In the second half of the year, the market should turn its attention to the election, but Sonders does not expect much reaction to it until after Labor Day. RealClearPolitics average of polls shows Democrat Joe Biden leading President Donald Trump by 10 percentage points, and the odds of a Democratic sweep have been rising.
Biden has said he would raise corporate taxes, and some strategists say a sweep would be bad for business, due to increased regulation and higher taxes. Trump is expected to continue using tariffs, which unsettles the market, though both candidates are expected to take a tough stance on China.
“If it looks like the Senate stays Republican than there’s less to worry about in terms of policy changes,” Sonders said. “I don’t think it’s ever as binary as some people think.”
Stovall said a quick study shows that in the four presidential election years back to 1960, where the first quarter was negative, and the second quarter positive, stocks made gains in the second half.
Those were 1960 when John Kennedy took office, 1968, when Richard Nixon won; 1980 when Ronald Reagan’s was elected to his first term; and 1992, the first win by Bill Clinton. Coincidentally, in all of those years, the opposing party gained control of the White House.

Stimulus

The stocks market’s strong second-quarter showing came after the Fed and Congress moved quickly to inject the economy with trillions in stimulus. That unlocked credit markets and triggered a stampede by companies to restructure or issue debt. About $2 trillion in fiscal spending was aimed at consumers and businesses, who were in sudden need of cash after the abrupt shutdown of the economy.
Fed Chairman Jerome Powell and Treasury Secretary Steven Mnuchin both testify before the House Financial Services Committee Tuesday on the response to the virus. That will be important as markets look ahead to another fiscal package from Congress this summer, which is expected to provide aid to states and local governments; extend some enhanced benefits for unemployment, and provide more support for businesses.
“So much of it is still so fluid. There are a bunch of fiscal items that are rolling off. There’s talk about another fiscal stimulus payment like they did last time with a $1,200 check,” said Cummins.
Strategists expect Congress to bicker about the size and content of the stimulus package but ultimately come to an agreement before enhanced unemployment benefits run out at the end of July. Cummins said state budgets begin a new year July 1, and states with a critical need for funds may have to start letting workers go, as they cut expenses.
The Trump administration has indicated the jobs report Thursday could help shape the fiscal package, depending on what it shows. The federal supplement to state unemployment benefits has been $600 a week, but there is opposition to extending that, and strategists expect it to be at least cut in half.
The unemployment rate is expected to fall to 12.2% from 13.3% in May. Cummins said he had expected 7.2 million jobs, well above the consensus, and an unemployment rate of 11.8%.
As of last week, nearly 20 million people were collecting state unemployment benefits, and millions more were collecting under a federal pandemic aid program.
“The magnitude here and whether it’s 3 million or 7 million is kind of hard to handicap to begin with,” Cummins said. Economists have preferred to look at unemployment claims as a better real time read of employment, but they now say those numbers could be impacted by slow reporting or double filing.
“There’s no clarity on how you define the unemployed in the Covid 19 environment,” said Chris Rupkey, chief financial economist at MUFG Union Bank. “If there’s 30 million people receiving insurance, unemployment should be above 20%.

This past week saw the following moves in the S&P:

(CLICK HERE FOR THE FULL S&P TREE MAP FOR THE PAST WEEK!)

Major Indices for this past week:

(CLICK HERE FOR THE MAJOR INDICES FOR THE PAST WEEK!)

Major Futures Markets as of Friday's close:

(CLICK HERE FOR THE MAJOR FUTURES INDICES AS OF FRIDAY!)

Economic Calendar for the Week Ahead:

(CLICK HERE FOR THE FULL ECONOMIC CALENDAR FOR THE WEEK AHEAD!)

Percentage Changes for the Major Indices, WTD, MTD, QTD, YTD as of Friday's close:

(CLICK HERE FOR THE CHART!)

S&P Sectors for the Past Week:

(CLICK HERE FOR THE CHART!)

Major Indices Pullback/Correction Levels as of Friday's close:

(CLICK HERE FOR THE CHART!

Major Indices Rally Levels as of Friday's close:

(CLICK HERE FOR THE CHART!)

Most Anticipated Earnings Releases for this week:

(CLICK HERE FOR THE CHART!)

Here are the upcoming IPO's for this week:

(CLICK HERE FOR THE CHART!)

Friday's Stock Analyst Upgrades & Downgrades:

(CLICK HERE FOR THE CHART LINK #1!)
(CLICK HERE FOR THE CHART LINK #2!)

When Will The Economy Recover?

The economy is moving in the right direction, as many economic data points are coming in substantially better than what the economists expected. From May job gains coming in more than 10 million higher than expected and retail sales soaring a record 18%, how quickly the economy is bouncing back has surprised nearly everyone.
“As good as the recent economic data has been, we want to make it clear, it could still take years for the economy to fully come back,” explained LPL Financial Senior Market Strategist Ryan Detrick. “Think of it like building a house. You get all the big stuff done early, then some of the small things take so much longer to finish; I’m looking at you crown molding.”
Here’s the hard truth; it might take years for all of the jobs that were lost to fully recover. In fact, during the 10 recessions since 1950, it took an average of 30 months for lost jobs to finally come back. As the LPL Chart of the Day shows, recoveries have taken much longer lately. In fact, it took four years for the jobs lost during the tech bubble recession of the early 2000s to come back and more than six years for all the jobs lost to come back after the Great Recession. Given many more jobs were lost during this recession, it could takes many years before all of them indeed come back.
(CLICK HERE FOR THE CHART!)
The economy is going the right direction, and if there is no major second wave outbreak it could surprise to the upside. Importantly, this economic recovery will still be a long and bumpy road.

Nasdaq - Russell Spread Pulling the Rubber Band Tight

The Nasdaq has been outperforming every other US-based equity index over the last year, and nowhere has the disparity been wider than with small caps. The chart below compares the performance of the Nasdaq and Russell 2000 over the last 12 months. While the performance disparity is wide now, through last summer, the two indices were tracking each other nearly step for step. Then last fall, the Nasdaq started to steadily pull ahead before really separating itself in the bounce off the March lows. Just to illustrate how wide the gap between the two indices has become, over the last six months, the Nasdaq is up 11.9% compared to a decline of 15.8% for the Russell 2000. That's wide!
(CLICK HERE FOR THE CHART!)
In order to put the recent performance disparity between the two indices into perspective, the chart below shows the rolling six-month performance spread between the two indices going back to 1980. With a current spread of 27.7 percentage points, the gap between the two indices hasn't been this wide since the days of the dot-com boom. Back in February 2000, the spread between the two indices widened out to more than 50 percentage points. Not only was that period extreme, but ten months before that extreme reading, the spread also widened out to more than 51 percentage points. The current spread is wide, but with two separate periods in 1999 and 2000 where the performance gap between the two indices was nearly double the current level, that was a period where the Nasdaq REALLY outperformed small caps.
(CLICK HERE FOR THE CHART!)
To illustrate the magnitude of the Nasdaq's outperformance over the Russell 2000 from late 1998 through early 2000, the chart below shows the performance of the two indices beginning in October 1998. From that point right on through March of 2000 when the Nasdaq peaked, the Nasdaq rallied more than 200% compared to the Russell 2000 which was up a relatively meager 64%. In any other environment, a 64% gain in less than a year and a half would be excellent, but when it was under the shadow of the surging Nasdaq, it seemed like a pittance.
(CLICK HERE FOR THE CHART!)

Share Price Performance

The US equity market made its most recent peak on June 8th. From the March 23rd low through June 8th, the average stock in the large-cap Russell 1,000 was up more than 65%! Since June 8th, the average stock in the index is down more than 11%. Below we have broken the index into deciles (10 groups of 100 stocks each) based on simple share price as of June 8th. Decile 1 (marked "Highest" in the chart) contains the 10% of stocks with the highest share prices. Decile 10 (marked "Lowest" in the chart) contains the 10% of stocks with the lowest share prices. As shown, the highest priced decile of stocks are down an average of just 4.8% since June 8th, while the lowest priced decile of stocks are down an average of 21.5%. It's pretty remarkable how performance gets weaker and weaker the lower the share price gets.
(CLICK HERE FOR THE CHART!)

Nasdaq 2% Pullbacks From Record Highs

It's hard to believe that sentiment can change so fast in the market that one day investors and traders are bidding up stocks to record highs, but then the next day sell them so much that it takes the market down over 2%. That's exactly what happened not only in the last two days but also two weeks ago. While the 5% pullback from a record high back on June 10th took the Nasdaq back below its February high, this time around, the Nasdaq has been able to hold above those February highs.
(CLICK HERE FOR THE CHART!)
In the entire history of the Nasdaq, there have only been 12 periods prior to this week where the Nasdaq closed at an all-time high on one day but dropped more than 2% the next day. Those occurrences are highlighted in the table below along with the index's performance over the following week, month, three months, six months, and one year. We have also highlighted each occurrence that followed a prior one by less than three months in gray. What immediately stands out in the table is how much gray shading there is. In other words, these types of events tend to happen in bunches, and if you count the original occurrence in each of the bunches, the only two occurrences that didn't come within three months of another occurrence (either before or after) were July 1986 and May 2017.
In terms of market performance following prior occurrences, the Nasdaq's average and median returns were generally below average, but there is a pretty big caveat. While the average one-year performance was a gain of 1.0% and a decline of 23.6% on a median basis, the six occurrences that came between December 1999 and March 2000 all essentially cover the same period (which was very bad) and skew the results. Likewise, the three occurrences in the two-month stretch from late November 1998 through January 1999 where the Nasdaq saw strong gains also involves a degree of double-counting. As a result of these performances at either end of the extreme, it's hard to draw any trends from the prior occurrences except to say that they are typically followed by big moves in either direction. The only time the Nasdaq wasn't either 20% higher or lower one year later was in 1986.
(CLICK HERE FOR THE CHART!)

Christmas in July: NASDAQ’s Mid-Year Rally

In the mid-1980s the market began to evolve into a tech-driven market and the market’s focus in early summer shifted to the outlook for second quarter earnings of technology companies. Over the last three trading days of June and the first nine trading days in July, NASDAQ typically enjoys a rally. This 12-day run has been up 27 of the past 35 years with an average historical gain of 2.5%. This year the rally may have begun a day early, today and could last until on or around July 14.
After the bursting of the tech bubble in 2000, NASDAQ’s mid-year rally had a spotty track record from 2002 until 2009 with three appearances and five no-shows in those years. However, it has been quite solid over the last ten years, up nine times with a single mild 0.1% loss in 2015. Last year, NASDAQ advanced a solid 4.6% during the 12-day span.
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Tech Historically Leads Market Higher Until Q3 of Election Years

As of yesterday’s close DJIA was down 8.8% year-to-date. S&P 500 was down 3.5% and NASDAQ was up 12.1%. Compared to the typical election year, DJIA and S&P 500 are below historical average performance while NASDAQ is above average. However this year has not been a typical election year. Due to the covid-19, the market suffered the damage of the shortest bear market on record and a new bull market all before the first half of the year has come to an end.
In the surrounding Seasonal Patten Charts of DJIA, S&P 500 and NASDAQ, we compare 2020 (as of yesterday’s close) to All Years and Election Years. This year’s performance has been plotted on the right vertical axis in each chart. This year certainly has been unlike any other however some notable observations can be made. For DJIA and S&P 500, January, February and approximately half of March have historically been weak, on average, in election years. This year the bear market ended on March 23. Following those past weak starts, DJIA and S&P 500 historically enjoyed strength lasting into September before experiencing any significant pullback followed by a nice yearend rally. NASDAQ’s election year pattern differs somewhat with six fewer years of data, but it does hint to a possible late Q3 peak.
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STOCK MARKET VIDEO: Stock Market Analysis Video for Week Ending June 26th, 2020

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STOCK MARKET VIDEO: ShadowTrader Video Weekly 6.28.20

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Here are the most notable companies (tickers) reporting earnings in this upcoming trading week ahead-
  • $MU
  • $GIS
  • $FDX
  • $CAG
  • $STZ
  • $CPRI
  • $XYF
  • $AYI
  • $MEI
  • $UNF
  • $CDMO
  • $SCHN
  • $LNN
  • $CULP
  • $XELA
  • $KFY
  • $RTIX
  • $JRSH
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Below are some of the notable companies coming out with earnings releases this upcoming trading week ahead which includes the date/time of release & consensus estimates courtesy of Earnings Whispers:

Monday 6.29.20 Before Market Open:

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NONE.

Monday 6.29.20 After Market Close:

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Tuesday 6.30.20 Before Market Open:

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Tuesday 6.30.20 After Market Close:

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Wednesday 7.1.20 Before Market Open:

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Wednesday 7.1.20 After Market Close:

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NONE.

Thursday 7.2.20 Before Market Open:

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Thursday 7.2.20 After Market Close:

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NONE.

Friday 7.3.20 Before Market Open:

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NONE.

Friday 7.3.20 After Market Close:

([CLICK HERE FOR FRIDAY'S AFTER-MARKET EARNINGS TIME & ESTIMATES!]())
NONE.

Micron Technology, Inc. $48.49

Micron Technology, Inc. (MU) is confirmed to report earnings at approximately 4:00 PM ET on Monday, June 29, 2020. The consensus earnings estimate is $0.71 per share on revenue of $5.27 billion and the Earnings Whisper ® number is $0.70 per share. Investor sentiment going into the company's earnings release has 71% expecting an earnings beat The company's guidance was for earnings of $0.40 to $0.70 per share. Consensus estimates are for earnings to decline year-over-year by 29.00% with revenue increasing by 10.07%. Short interest has increased by 7.6% since the company's last earnings release while the stock has drifted higher by 8.0% from its open following the earnings release to be 0.9% below its 200 day moving average of $48.94. Overall earnings estimates have been revised lower since the company's last earnings release. On Thursday, June 11, 2020 there was some notable buying of 46,037 contracts of the $60.00 call expiring on Friday, July 17, 2020. Option traders are pricing in a 4.6% move on earnings and the stock has averaged a 8.4% move in recent quarters.

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General Mills, Inc. $59.21

General Mills, Inc. (GIS) is confirmed to report earnings at approximately 7:00 AM ET on Wednesday, July 1, 2020. The consensus earnings estimate is $1.04 per share on revenue of $4.89 billion and the Earnings Whisper ® number is $1.10 per share. Investor sentiment going into the company's earnings release has 69% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 25.30% with revenue increasing by 17.50%. Short interest has decreased by 9.4% since the company's last earnings release while the stock has drifted higher by 2.7% from its open following the earnings release to be 7.8% above its 200 day moving average of $54.91. Overall earnings estimates have been revised higher since the company's last earnings release. On Wednesday, June 24, 2020 there was some notable buying of 8,573 contracts of the $60.00 call expiring on Friday, July 17, 2020. Option traders are pricing in a 6.6% move on earnings and the stock has averaged a 3.0% move in recent quarters.

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FedEx Corp. $130.08

FedEx Corp. (FDX) is confirmed to report earnings at approximately 4:00 PM ET on Tuesday, June 30, 2020. The consensus earnings estimate is $1.42 per share on revenue of $16.31 billion and the Earnings Whisper ® number is $1.65 per share. Investor sentiment going into the company's earnings release has 61% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 71.66% with revenue decreasing by 8.41%. Short interest has increased by 10.4% since the company's last earnings release while the stock has drifted higher by 43.9% from its open following the earnings release to be 7.6% below its 200 day moving average of $140.75. Overall earnings estimates have been revised lower since the company's last earnings release. On Thursday, June 25, 2020 there was some notable buying of 1,768 contracts of the $145.00 call expiring on Thursday, July 2, 2020. Option traders are pricing in a 4.6% move on earnings and the stock has averaged a 7.7% move in recent quarters.

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Conagra Brands, Inc. $32.64

Conagra Brands, Inc. (CAG) is confirmed to report earnings at approximately 7:30 AM ET on Tuesday, June 30, 2020. The consensus earnings estimate is $0.66 per share on revenue of $3.24 billion and the Earnings Whisper ® number is $0.69 per share. Investor sentiment going into the company's earnings release has 66% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 83.33% with revenue increasing by 23.99%. Short interest has decreased by 38.3% since the company's last earnings release while the stock has drifted higher by 6.3% from its open following the earnings release to be 6.4% above its 200 day moving average of $30.68. Overall earnings estimates have been revised higher since the company's last earnings release. On Thursday, June 11, 2020 there was some notable buying of 3,239 contracts of the $29.00 put expiring on Thursday, July 2, 2020. Option traders are pricing in a 4.7% move on earnings and the stock has averaged a 10.8% move in recent quarters.

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Constellation Brands, Inc. $168.99

Constellation Brands, Inc. (STZ) is confirmed to report earnings at approximately 7:30 AM ET on Wednesday, July 1, 2020. The consensus earnings estimate is $1.91 per share on revenue of $1.97 billion and the Earnings Whisper ® number is $2.12 per share. Investor sentiment going into the company's earnings release has 53% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 13.57% with revenue decreasing by 13.69%. Short interest has increased by 20.8% since the company's last earnings release while the stock has drifted higher by 25.2% from its open following the earnings release to be 5.2% below its 200 day moving average of $178.34. Overall earnings estimates have been revised lower since the company's last earnings release. On Tuesday, June 9, 2020 there was some notable buying of 888 contracts of the $195.00 call expiring on Friday, October 16, 2020. Option traders are pricing in a 3.1% move on earnings and the stock has averaged a 5.7% move in recent quarters.

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Capri Holdings Limited $14.37

Capri Holdings Limited (CPRI) is confirmed to report earnings at approximately 6:30 AM ET on Wednesday, July 1, 2020. The consensus earnings estimate is $0.32 per share on revenue of $1.18 billion and the Earnings Whisper ® number is $0.34 per share. Investor sentiment going into the company's earnings release has 39% expecting an earnings beat The company's guidance was for earnings of $0.68 to $0.73 per share. Consensus estimates are for earnings to decline year-over-year by 49.21% with revenue decreasing by 12.20%. Short interest has increased by 35.1% since the company's last earnings release while the stock has drifted lower by 56.7% from its open following the earnings release to be 44.0% below its 200 day moving average of $25.67. Overall earnings estimates have been revised lower since the company's last earnings release. On Thursday, June 4, 2020 there was some notable buying of 11,042 contracts of the $17.50 put expiring on Friday, August 21, 2020. Option traders are pricing in a 10.8% move on earnings and the stock has averaged a 6.7% move in recent quarters.

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X Financial $0.92

X Financial (XYF) is confirmed to report earnings at approximately 5:00 PM ET on Tuesday, June 30, 2020. The consensus earnings estimate is $0.09 per share. Investor sentiment going into the company's earnings release has 25% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 55.00% with revenue increasing by 763.52%. Short interest has increased by 1.0% since the company's last earnings release while the stock has drifted lower by 1.2% from its open following the earnings release to be 37.7% below its 200 day moving average of $1.47. Overall earnings estimates have been unchanged since the company's last earnings release. The stock has averaged a 4.9% move on earnings in recent quarters.

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Acuity Brands, Inc. $84.45

Acuity Brands, Inc. (AYI) is confirmed to report earnings at approximately 8:40 AM ET on Tuesday, June 30, 2020. The consensus earnings estimate is $1.14 per share on revenue of $809.25 million and the Earnings Whisper ® number is $1.09 per share. Investor sentiment going into the company's earnings release has 42% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 51.90% with revenue decreasing by 14.60%. Short interest has increased by 48.5% since the company's last earnings release while the stock has drifted higher by 2.4% from its open following the earnings release to be 23.4% below its 200 day moving average of $110.25. Overall earnings estimates have been revised lower since the company's last earnings release. Option traders are pricing in a 9.2% move on earnings and the stock has averaged a 8.2% move in recent quarters.

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Methode Electronics, Inc. $30.02

Methode Electronics, Inc. (MEI) is confirmed to report earnings at approximately 7:00 AM ET on Tuesday, June 30, 2020. The consensus earnings estimate is $0.77 per share on revenue of $211.39 million. Investor sentiment going into the company's earnings release has 45% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 24.19% with revenue decreasing by 20.53%. Short interest has increased by 6.2% since the company's last earnings release while the stock has drifted lower by 1.7% from its open following the earnings release to be 9.0% below its 200 day moving average of $32.97. Overall earnings estimates have been revised lower since the company's last earnings release. Option traders are pricing in a 18.4% move on earnings and the stock has averaged a 8.1% move in recent quarters.

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UniFirst Corporation $170.54

UniFirst Corporation (UNF) is confirmed to report earnings at approximately 8:00 AM ET on Wednesday, July 1, 2020. The consensus earnings estimate is $1.17 per share on revenue of $378.28 million and the Earnings Whisper ® number is $1.25 per share. Investor sentiment going into the company's earnings release has 44% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 52.44% with revenue decreasing by 16.63%. Short interest has decreased by 2.7% since the company's last earnings release while the stock has drifted higher by 14.1% from its open following the earnings release to be 8.4% below its 200 day moving average of $186.14. Overall earnings estimates have been revised lower since the company's last earnings release. The stock has averaged a 7.0% move on earnings in recent quarters.

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DISCUSS!

What are you all watching for in this upcoming trading week?
I hope you all have a wonderful weekend and a great trading week ahead StockMarket.
submitted by bigbear0083 to StockMarket [link] [comments]

Quant Mentorship and the State of Algotrading

Is anybody interested in small group, facilitated mentorship?

I run the tech at a prop firm, and my day to day includes moderating strategy development between our programmers and traders, so this would be similar -- except remote. I recently got approval to reach out to the community to build up a small group for idea generation purposes ("innovation by teaching"), so that's what I'm ultimately hoping to get out of this experiment

There's a lot of noise on this sub, and I believe it is due to three reasons, which I'll detail below:

  1. The posters and commenters are speaking at completely different levels of complexity, breaking the upvote visibility algorithm
  2. The contributors do not provide "opinionated" advice, because of workflow differences
  3. There is not a globally accepted method for posters to refer to their skill level, and commenters assume the poster is one-level lower than where they are at

#1: Most advice on this sub is well-meaning, but is often at the wrong level of complexity

For example, I'd often see expert advice downvoted and my first thought is that it's competition wanting to keep things hidden, but I've realized that it's simply at the wrong level of complexity for the discussion. This leads to the experts staying quiet, and and the noise drowning out the signal. (I have a hard time finding you guys!)

#2: Opinionated advice is not given, due to the workflow-specific methods

Non-traders have not seen what an end-to-end quant framework looks like, and the huge number of "opinions" at each level. For example, one workflow and list of opinions are:

  1. Data Cleaning and Storage: Flat files, binary files, relational, non-relational DBs (csv, parquet, hdf5, MySQL, SQLIte, arctic, kdb+, etc.)
  2. Backtesting: Cloud based, open-source, proprietary, machine-learning, or not (Quantopian, QuantConnect, backtrader, MATLAB, MetaTrader, TradeStation, etc.)
  3. Out-of-sample Validation (cross-validation, walk-forward testing, hold-out datasets, pseudo-multi-instrument datasets, brownian motion equity curves)
  4. Risk Assessment (period assessment, volatility regimes, various percentile drawdown vs. monte-carlo, correlation risks, etc.)
  5. Strategy Performance Assessment (Sharpe, Calmar, MAR, Skew, Kurtosis, etc.)
  6. Portfolio Assessment (covariance, correlations, arithmetic, geometry holding period returns, etc)
  7. Money Management (optimal f, fixed f, kelly criterion)
  8. Performance Monitoring (broker reconciliation, logging, strategy/portfolio knockouts)
  9. Execution Validation ("big red button", target portfolio vs. ideal portfolio, expected slippage assessment)

To be facetious, there's 453,600 (7*6*5*4*5*4*3*3*3) different ways of expressing this workflow. And this is why there's not many opinions going around, because it's more likely to be "wrong" given all the options. (I'm sure I made a "mistake" somewhere on this list)

#3: Quant Skill Map

Finally, the workflow above has a skill map that approximates to the following:

  1. Reading Comprehension: Unable to read to the end of books and long texts (just kidding)
  2. Data or Ability: No access to data, and no programming ability
  3. Backtesting: Access to data, no method to backtest in-sample
  4. Validation 1: Able to backtest in-sample, no technique to assess out-of-sample
  5. Validation 2: Able to assess out-of-sample, no technique to assess overfitting and robustness
  6. Robustness: Able to assess out-of-sample robustness, no method to assess risk
  7. Risk: Able to assess risk, no method to assess aggregation
  8. Portfolio 1: Able to aggregate, no method to determine weights
  9. Portfolio 2: Able to determine portfolio weights, no method for money management
  10. Money Management: Able to allocate capital effectively

All it takes to go from one level to the next may be as simple as a dataset or an opinionated answer, or it could be a face-to-face introduction with a high volume futures trader.

If you've read to the end, send me a PM with the skill level you are at per Point #3 and I'll point you in the right direction.
submitted by mosymo to algotrading [link] [comments]

Looking for some feedback on a University Project

Hi everyone,
I've been working on a project for my Bachelor Thesis in Finance with Python for quite a while now and I'd love to have some feedback from you.
The project focuses on Option Pricing using the Black Scholes Model for Plain Vanilla and Binary Options. It allows the user to perform a series of tasks like computing and plotting greeks, option payoffs and the implied volatility surface and skew.
The script requires Chrome and quite a few modules to properly work, and the code is macOS native, meaning that it may not work on other operating systems (sorry for that).
My go-to IDE is PyCharm, but I guess any other IDE will work fine.
Here you can find the link to the GitHub repository where the project is located.
I will leave also some links to resources about all the theory behind the computations I do in the project for the ones that are not familiar with this topic.
Options Theory)
Black-Scholes Model
Binary Options
Greeks)
Options Strategies
Implied Volatility
Volatility Smile (Skew)

If you have any question let me know.
Thank you!
submitted by rcrmlt to learnpython [link] [comments]

Against Buying Low: A Meditation On Our Favorite Fantasy Tactic, Which Might Not Actually Work

Warning: Novel-length post ahead.
It’s that time of the season when this forum—along with much of the fantasy basketball punditry across the web—is fixated on buying low. It seems like the majority of threads here, and columns across the fantasy basketball web, are focused on identifying good buy low candidates.
Which, to me, begged the question: does buying low actually work? And the answer was: I had no fucking idea. Sure, there was a wealth of anecdotal evidence, but that could lead you in very different directions, depending upon whether you had bought low last year on Danny Green, or on Chris Paul. There was no metric of how effective a strategy it is on the whole.
So I set out to create one.
There’s a lot of uncertainty involved in buying low on players, and so by necessity we should be making probabilistic predictions: talking in terms of the odds that something will happen, rather than a binary will-it-or-won’t-it position. Anyone who says “Player X will certainly bounce back from their slow start” or “Player Y certainly won’t” is talking out of their ass. The truth is, we can’t say for sure: NBA statistics are the product of a complex system, not quite as complex as, say, the weather, but complex nonetheless. I can state with a pretty high degree of confidence that Kawhi Leonard will post top 10 value over the rest of the season (much like a meteorologist can estimate with pretty high confidence what the high temperature will be tomorrow). But ask a meteorologist what the high temperature will be on January 20th, or ask me whether Paul Millsap will post top 25 value the rest of the season, and now neither of us is quite so confident. The meteorologist would be hard pressed to do better than just guessing the average January 20th temperature from the last 20 years. And I’d probably be best off trying to figure out guys similar to Paul Millsap who have started slow, and asking what percentage of them ended up posting top 25 value.
Now, of course I am not suggesting that a statistical model could give you all of the information you need to determine whether it’s a good idea to buy low on someone. Things like team situation, minutes, injury history, etc. matter of course, and there’s no easy way to build a statistical model to account for them. But it’s still essential that we have some sort of model to give us an overall understanding of how effective buying low is. When we’re thinking about buying low, we tend to start with the “inside view” (whether a guy’s coach likes him; whether he’s spending extra time working on his FT shooting; whether he’s in a contract year), but in reality, we should be looking first at the “outside view:” what % of all buy low candidates end up getting their shit together? That number should be our jumping-off point, and then, when we have it, we can adjust it for situation-specific info like their minutes, their team situation, etc. If we start by looking at the inside view, we can fool ourselves into thinking: “Everything’s set up for this guy to succeed; there’s probably a 90% chance he bounces back this year.” When, in reality, that’s probably a pretty terrible estimate, and the outside view shows us why.
[A note on the data (feel free to skip this): I have taken players who have started slow over the first 32 days of the previous 3 seasons (2013-14, 2014-15, and 2015-16). These are “struggling” players. In each year, I have limited the data set to players who were in the top 75 in final average value the previous year, per bbm, and I define “struggling” players as those who are underperforming their previous year’s ranking by at least (5 x round #). So if a player finished 32nd the year before, they would need to be at 47th (32 + [5 x 3]) or lower over the first 32 days of the following season in order to qualify as “struggling.” Of course, these distinctions are, to an extent, arbitrary, and you could slice the data somewhat differently (though I think the general principle would still hold). My rationale for cutting off the pool at top 75: players below that level generally aren’t “buy low” candidates, because their owners tend to cut them if they’re really struggling early in the season. As for determining what qualifies someone as “struggling,” I wanted to capture the fact that a 1st round pick who slides 10 spots is a much bigger disaster than a 6th round pick who slides 10 spots. I don’t think the method I came up with is perfect, but it does at least have a reasonably even distribution of “struggling” players in each round. Also, I tossed out anyone who played fewer than 50 games in their prior full season, or fewer than 10 games over their “struggling” month. I wanted this to reflect guys who are basically healthy; buying low on injured players is a whole other can of worms.]
There are 78 “struggling” starts over these 3 seasons. (And some guys lay claim to multiple slow starts; looking at you, Serge Ibaka.) In looking at end results, I have grouped players into 5 categories. Category 1 encompasses players who, for the remainder of the season, actually outperform the value they put up the previous year. So if a guy was ranked 30th in 2014-15, and then slides to 60th in the first month of 2015-16, but then posts 25th-ranked value for the rest of 2015-16, he’s in Category 1. A player in Category 2 did not match his value from the previous year, but came close enough to it over the rest of the season that he wouldn’t qualify as “struggling,” per the definition above. Players in Category 3 qualified as “not struggling” under a less stringent definition: 10 x round #, instead of 5 x round #. Category 4 encompasses players who improved upon their terrible start, but didn’t play well enough to qualify for Categories 1, 2, or 3. So a player who ranked 40th the year before, and then 120th over the first month of the season, and then 100th over the rest of the season, would fall into Category 4. Players in Category 5 went on to perform even worse for the rest of the season than they did in the first month.
In more basic terms, you can think of it like this: Buying low on a Category 1 player is an Excellent Decision, buying low on a Category 2 player a Good Decision, buying low on a Category 3 player a Neutral Decision, buying low on a Category 4 player a Bad Decision, and buying low on a Category 5 player is a Terrible Decision. So generally speaking, in a reasonably competitive league, in order for a buy low trade to actually help your team, you’ll need the guy you receive to fall into either Category 1 or Category 2.
[Aside: There’s no way for me to know how intelligent the other managers in your league are; of course, if you can get a struggling guy who was drafted in the 2nd round for waiver wire fodder, then go forth (and you don’t need this guide). But in my experience it’s usually hard to buy really low on medium-to-high draft picks. You can cite the sunk cost fallacy all you want, but on some level, owners’ aversion to selling low makes sense; your objective should always be to win your league, and if you sell a guy that you took in the 2nd round for someone whose ceiling is 6th round value, you may have improved the floor of your overall team, but you’ve almost certainly made yourself less likely to come in 1st than if you just held onto your 2nd round pick and prayed.]
So let’s take a look at the data. What percentage of struggling guys end up falling into each of our 5 categories?
The breakdown isn’t very encouraging:
Category # of Players
1 15
2 20
3 10
4 11
5 22
http://imgur.com/a/8yCKy
If you buy low on a random player, the single most likely outcome of these 5 is that he’s going to go on to play even worse than the month of games that made him a buy low candidate in the first place. There’s a 42.3% chance that buying low on him is going to be a Bad Decision or a Terrible Decision, and a 44.8% chance, roughly comparable, that it’s going to be a Good Decision or an Excellent Decision. And given the sort of value you usually have to give up to buy low, it’s likely that the Category 4 and 5 players are going to hurt you more than the Category 1 and 2 players are going to help you. (I.e., it’s unusual for someone who is top 75 to begin with, and then sucks for a month, to suddenly get it together and start playing significantly better than their baseline ability. But it’s not that unusual for someone to suck for a month, and then continue sucking for the next 4 months.)
“Okay,” you might say, “Buying low in aggregate isn’t an amazing idea, but given my fantasy basketball knowledge, I can determine which players are going to be Category 1 and Category 2 guys, rather than just buying low at random.”
This was basically what I believed about myself. So I thought about which factors might lead me to think a single particular player was a good “buy low” candidate. The first thing that came to mind was the round they would have been picked in, based off of value the previous year. Surely 1st and 2nd round picks are safer investments, more likely to sniff 1st or 2nd round value despite their slow starts than a 6th round pick would be likely to approach 6th round value if he starts out badly. Right?
Well, basically, no.
If you look at the breakdown, higher-round slow starters are just as likely to flame out as their lower-round counterparts. And they aren’t any more likely to exceed their value from the previous year, either.
Round Category 1 Category 2 Category 3 Category 4 Category 5
1 2 6 0 3 4
2 3 6 1 2 4
3 3 2 4 3 5
4 2 2 2 3 4
5 3 2 1 0 1
6-7 2 2 2 0 4
Round Category 1 Category 2 Category 3 Category 4 Category 5
1-3 8 14 5 8 13
4-7 7 6 5 3 9
Then I scrutinized the list of high-ranking players, and wondered if there was a better indicator than round value to demonstrate someone’s consistency or safety as a pick. After all, Danny Green posted 2nd round value in 2014-15, but no one was drafting him in the 2nd round in 2015-16. So how about sorting guys by their usage rates? Usage is a pretty solid indicator of how involved a player is in his team’s offense. It stands to reason that someone like LeBron, who has a sky-high usage every year, is a safer selection, relative to where he’s drafted, than someone like Danny Green, who touches the ball less and thus is more vulnerable if his team’s tactics evolve (like, say, to accommodate the arrival of LaMarcus Aldridge).
So I sorted the players in my data set into 3 categories: High Usage (someone with a usage rate above 26.5 in the full previous season); Medium Usage (someone with a usage rate between 20 and 26.5 in the full previous season); and Low Usage (someone with a usage rate under 20 in the full previous season).
Surprisingly, the correlation isn’t especially strong here either. It’s true that on the whole, high usage players are somewhat safer bets, and are less likely to end up in true disaster territory (Category 5) than low usage players, with the middle-usage guys falling in the middle. But it’s not a hugely strong correlation, and it’s also true that low usage players are more likely to fall into Category 1 than high usage players are.
Usage Category 1 Category 2 Category 3 Category 4 Category 5
High 5 9 1 5 4
Medium 2 9 6 2 8
Low 8 2 3 4 10
http://imgur.com/a/9bTFv http://imgur.com/a/ltLJT http://imgur.com/a/XS6IE
Next, I wondered whether position might be the better metric to look at. Sure, some of the benefits and drawbacks of position are captured by usage (with Cs and SGs tending to have lower usage rates than PGs and SFs), but perhaps there were other pieces of the puzzle that usage didn’t capture. Maybe centers, due to higher risk of injury and a more dramatic dropoff late in their careers, were at greater risk of not recovering from a slow start. Maybe point guards were more insulated due to their central role in a team’s offense.
But, running the numbers, there wasn’t much to glean here either. Perhaps big men are slightly riskier buy low candidates, but the upside seems to be greater as well.
Position Category 1 Category 2 Category 3 Category 4 Category 5
PG 2 5 2 3 3
SG 1 5 1 2 5
SF 4 3 2 2 3
PF 3 3 2 1 5
C 4 4 3 3 6
Then I looked at the fantasy category in which each buy low candidate was struggling the most. Maybe guys who start the year in a shooting slump are more likely to bounce back than guys who see a reduction in rebounds, for instance. I had expected that FG% would be the most common category here, but I was shocked by just how common it was. 34 out of 78 players (43.6% of them) had a greater dropoff in FG% than in any other category. The other very popular category was steals, which was the biggest problem for 13/78 players (16.7%).
Based on the data I’ve got here, there’s no category that seems to clearly suggest whether a player is a good buy low candidate. Even dropoffs in points and rebounds, which you would think would be indicators that a player has entered a less favorable team situation, don’t tell us much about how they’re likely to perform moving forward (though the sample is, admittedly, limited). This suggests that even players whose situations change dramatically usually find ways to make themselves useful from a fantasy perspective, whether it’s improving their efficiency (FG% and TOs) or focusing more on defensive stats. A dropoff in 3PT is probably the most concerning based on this data, but with just 6 players in that category, I’d be wary of drawing too firm a conclusion from that. On the whole, the category a player struggles in isn’t especially helpful in projecting their performance moving forward.
Fantasy Cat Category 1 Category 2 Category 3 Category 4 Category 5
3PT 1 1 0 1 3
AST 1 0 0 0 0
BLK 4 0 1 0 1
FG 6 11 3 4 10
FT 2 0 1 3 0
PTS 0 4 1 1 1
REB 0 1 1 1 0
STL 1 3 3 1 5
TO 0 0 0 0 2
So what about age? Finally, here, a pattern begins to emerge:
Age Category 1 Category 2 Category 3 Category 4 Category 5
24 and under 2 4 0 2 0
25 5 6 0 1 3
26-28 2 1 4 3 7
29-30 5 6 3 3 4
31 and above 1 3 3 2 8
Age Category 1 Category 2 Category 3 Category 4 Category 5
25 and under 7 10 0 3 3
26 and older 8 10 10 8 19
http://imgur.com/a/kZj1u
At first glance, this surprised me. Certainly, I expected there to be a significant dropoff as players aged into their 30’s, as this data includes guys from the Garnett/Pierce/Nowitzki generation as they gradually slipped from reliable early round players, to middle-rounders, to scrubs. So that piece of the aging curve (with the brutal track record for players 31+) is pretty much exactly what I expected.
It’s the younger half of the aging curve that initially confused me. I think the traditional understanding is that athletes in many sports, including basketball, tend to peak statistically around the age of 26-28. So I had thought that we would see the best buy low options in that age range. Instead, it skews significantly younger, with age 25 appearing to be the cutoff: guys 25 and younger are, on the whole, much stronger buy low candidates, and then once they hit 26, it’s no longer a good idea to buy low on them.
But the more I thought about it, the more this made sense. After all, when we talk about guys peaking in the 26-28 range, we’re talking about their absolute performance, whereas in fantasy we’re more concerned with their performance relative to the previous year, and hence relative to where they were likely to be drafted. And players almost always make the biggest gains in fantasy value between the ages of 22 and 25. Now, of course, some of this expectation of improvement is baked into guys’ average draft positions; players like Towns and Porzingis are going to be drafted above where they finished the previous year on the expectation that they will develop and improve. But it seems like this assumption of improvement often goes out the window when a young guy starts slow; people are often willing to cut the cord on underperforming young guys, whether due to a fear of the “sophomore slump” or a suspicion that, given their limited NBA track records, they may just not be good enough yet to justify their high ADP (see, for example, all of the people panicking about Towns’s somewhat slow start).
In my experience, people seem to be more willing to unload an underperforming young player at a reasonable price, than they would be willing to unload an underperforming 30-year-old with a solid fantasy track record of many years. But they shouldn’t be: buying low on young players is not only higher-upside, it’s also, surprisingly, a safer bet than buying low on veterans, or even buying low on guys in the 26-28 range, which I would have thought to be the safest age range of all.
So if there’s one thing you should look at when you consider buying low, it’s the dude’s birthday.
Now, is there an appropriate time for buying low on guys older than 25? Of course. It’s important not to lose sight of the fact that (for me, at least, and I suspect for many other players) fantasy sports are an all-or-nothing exercise, e.g., you’d rather give yourself a chance of coming in 1st and a chance of coming in 8th, instead of locking in a 4th place finish. So if your team is struggling, it makes sense to pursue volatile, high-risk-high-reward strategies. And that’s exactly what buying low is.
But on the whole, buying low is not an efficient way of generating value. Trades rarely are. The most reliable way to generate value is to draft well; then, trades should be used primarily to solidify punting strategies, not to generate value. Sure, occasionally you’ll strike it hot with a random buy low candidate, but overall, it’s not a reliable way to improve one’s team. Yes, if your team is already bad, you might as well leverage a high-risk strategy and give it a shot, but “buying low” is often touted as THE essential fantasy basketball strategy, and there’s not a lot of evidence to support that.
submitted by Rodekio to fantasybball [link] [comments]

Can she squirt? The tale of Taylor Swift and her vibrator (I meant to post this yesterday)

This is how messed up my mind is (and why the title works):
Vibrator -> "Good Vibrations" -> The Beach Boys -> "Smile" Album -> Volatility Smile
Why Taylor Swift? She is innocent and naive just like most retail.
Now that we have that out of the way let's talk options....
So one of things that came out of the 87' crash was what we call the volatility smile which is simply the skew of puts and calls relative to their moniness. So what the fuck does that mean? Prior to the crash a modified black scholes model was used to price options; the problem is that black scholes assumes that volatility is constant - it isn't. When the black swan event occurred vol shot up and everyone lost their asses as both puts and calls were not skewed and thus always under priced.
The smile name is derived because the volatility surface resembles a smile or smirk where OTM puts and calls are skewed higher than ATM puts and calls. Here is an example:
http://www.optionsideacentral.com/wp-content/uploads/2013/10/AAPL-Skew.gif
You can also see the vol skewness on think or swim or ib under the "implied vol" section and see how as you get closer and closer to the ATM vol contracts and then goes up again.
the "smirk" (or the look I give a chick after I am caught looking at here clevage)
You will see that most smiles are askew to the downside (google volatility smile mother fucker can't do everything for you) - why is this? Isn't it wrong? Max loss to the downside is zero and max loss to the upside is infinity so why is it like this?
Volatility on the downside skew is greater than the upside because:
  1. They are insurance of a black swan event
  2. If the skew is noticeably higher then they are getting pounded by buyers
  3. Puts are more expensive to trade
So what brought this lesson on? This question:
What's happening with options prices on SPY? looking at sep 26 calls $202 strike. options price was trading higher today when SPY was 200.60 than it is now, when it's 201.21. will things normalize after this volatility? I'm red on my calls when i feel like i should be well in the money
Skew is the reason - with a binary event volatility is raised significantly more on the wings (further OTM options) then the ATM (regardless the whole vol surface moves) so when the market doesn't move the skew flattens taking out the higher vol component even though the stock delta may be rising.
saavy???
submitted by midgetginger to options [link] [comments]

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