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No gods, no kings, only NOPE - or divining the future with options flows. [Part 2: A Random Walk and Price Decoherence]
tl;dr - 1) Stock prices move continuously because different market participants end up having different ideas of the future value of a stock. 2) This difference in valuations is part of the reason we have volatility. 3) IV crush happens as a consequence of future possibilities being extinguished at a binary catalyst like earnings very rapidly, as opposed to the normal slow way. I promise I'm getting to the good parts, but I'm also writing these as a guidebook which I can use later so people never have to talk to me again. In this part I'm going to start veering a bit into the speculation territory (e.g. ideas I believe or have investigated, but aren't necessary well known) but I'm going to make sure those sections are properly marked as speculative (and you can feel free to ignore/dismiss them). Marked as [Lily's Speculation]. As some commenters have pointed out in prior posts, I do not have formal training in mathematical finance/finance (my background is computer science, discrete math, and biology), so often times I may use terms that I've invented which have analogous/existing terms (e.g. the law of surprise is actually the first law of asset pricing applied to derivatives under risk neutral measure, but I didn't know that until I read the papers later). If I mention something wrong, please do feel free to either PM me (not chat) or post a comment, and we can discuss/I can correct it! As always, buyer beware. This is the first section also where you do need to be familiar with the topics I've previously discussed, which I'll add links to shortly (my previous posts: 1) https://www.reddit.com/thecorporation/comments/jck2q6/no_gods_no_kings_only_nope_or_divining_the_future/ 2) https://www.reddit.com/thecorporation/comments/jbzzq4/why_options_trading_sucks_or_the_law_of_surprise/ --- A Random Walk Down Bankruptcy A lot of us have probably seen the term random walk, maybe in the context of A Random Walk Down Wall Street, which seems like a great book I'll add to my list of things to read once I figure out how to control my ADD. It seems obvious, then, what a random walk means - when something is moving, it basically means that the next move is random. So if my stock price is $1 and I can move in $0.01 increments, if the stock price is truly randomly walking, there should be roughly a 50% chance it moves up in the next second (to $1.01) or down (to $0.99). If you've traded for more than a hot minute, this concept should seem obvious, because especially on the intraday, it usually isn't clear why price moves the way it does (despite what chartists want to believe, and I'm sure a ton of people in the comments will tell me why fettucini lines and Batman doji tell them things). For a simple example, we can look at SPY's chart from Friday, Oct 16, 2020: https://preview.redd.it/jgg3kup9dpt51.png?width=1368&format=png&auto=webp&s=bf8e08402ccef20832c96203126b60c23277ccc2 I'm sure again 7 different people can tell me 7 different things about why the chart shape looks the way it does, or how if I delve deeply enough into it I can find out which man I'm going to marry in 2024, but to a rationalist it isn't exactly apparent at why SPY's price declined from 349 to ~348.5 at around 12:30 PM, or why it picked up until about 3 PM and then went into precipitous decline (although I do have theories why it declined EOD, but that's for another post). An extremely clever or bored reader from my previous posts could say, "Is this the price formation you mentioned in the law of surprise post?" and the answer is yes. If we relate it back to the individual buyer or seller, we can explain the concept of a stock price's random walk as such:
Most market participants have an idea of an asset's truevalue (an idealized concept of what an asset is actually worth), which they can derive using models or possibly enough brain damage. However, an asset's value at any given time is not worth one value (usually*), but a spectrum of possible values, usually representing what the asset should be worth in the future. A naive way we can represent this without delving into to much math (because let's face it, most of us fucking hate math) is: Current value of an asset = sum over all (future possible value multiplied by the likelihood of that value)
In actuality, most models aren't that simple, but it does generalize to a ton of more complicated models which you need more than 7th grade math to understand (Black-Scholes, DCF, blah blah blah). While in many cases the first term - future possible value - is well defined (Tesla is worth exactly $420.69 billion in 2021, and maybe we all can agree on that by looking at car sales and Musk tweets), where it gets more interesting is the second term - the likelihood of that value occurring. [In actuality, the price of a stock for instance is way more complicated, because a stock can be sold at any point in the future (versus in my example, just the value in 2021), and needs to account for all values of Tesla at any given point in the future.] How do we estimate the second term - the likelihood of that value occurring? For this class, it actually doesn't matter, because the key concept is this idea: even with all market participants having the same information, we do anticipate that every participant will have a slightly different view of future likelihoods. Why is that? There's many reasons. Some participants may undervalue risk (aka WSB FD/yolos) and therefore weight probabilities of gaining lots of money much more heavily than going bankrupt. Some participants may have alternative data which improves their understanding of what the future values should be, therefore letting them see opportunity. Some participants might overvalue liquidity, and just want to GTFO and thereby accept a haircut on their asset's value to quickly unload it (especially in markets with low liquidity). Some participants may just be yoloing and not even know what Fastly does before putting their account all in weekly puts (god bless you). In the end, it doesn't matter either the why, but the what: because of these diverging interpretations, over time, we can expect the price of an asset to drift from the current value even with no new information added. In most cases, the calculations that market participants use (which I will, as a Lily-ism, call the future expected payoff function, or FEPF) ends up being quite similar in aggregate, and this is why asset prices likely tend to move slightly up and down for no reason (or rather, this is one interpretation of why). At this point, I expect the 20% of you who know what I'm talking about or have a finance background to say, "Oh but blah blah efficient market hypothesis contradicts random walk blah blah blah" and you're correct, but it also legitimately doesn't matter here. In the long run, stock prices are clearly not a random walk, because a stock's value is obviously tied to the company's fundamentals (knock on wood I don't regret saying this in the 2020s). However, intraday, in the absence of new, public information, it becomes a close enough approximation. Also, some of you might wonder what happens when the future expected payoff function (FEPF) I mentioned before ends up wildly diverging for a stock between participants. This could happen because all of us try to short Nikola because it's quite obviously a joke (so our FEPF for Nikola could, let's say, be 0), while the 20 or so remaining bagholders at NikolaCorporation decide that their FEPF of Nikola is $10,000,000 a share). One of the interesting things which intuitively makes sense, is for nearly all stocks, the amount of divergence among market participants in their FEPF increases substantially as you get farther into the future. This intuitively makes sense, even if you've already quit trying to understand what I'm saying. It's quite easy to say, if at 12:51 PM SPY is worth 350.21 that likely at 12:52 PM SPY will be worth 350.10 or 350.30 in all likelihood. Obviously there are cases this doesn't hold, but more likely than not, prices tend to follow each other, and don't gap up/down hard intraday. However, what if I asked you - given SPY is worth 350.21 at 12:51 PM today, what will it be worth in 2022? Many people will then try to half ass some DD about interest rates and Trump fleeing to Ecuador to value SPY at 150, while others will assume bull markets will continue indefinitely and SPY will obviously be 7000 by then. The truth is -- no one actually knows, because if you did, you wouldn't be reading a reddit post on this at 2 AM in your jammies. In fact, if you could somehow figure out the FEPF of all market participants at any given time, assuming no new information occurs, you should be able to roughly predict the true value of an asset infinitely far into the future (hint: this doesn't exactly hold, but again don't @ me). Now if you do have a finance background, I expect gears will have clicked for some of you, and you may see strong analogies between the FEPF divergence I mentioned, and a concept we're all at least partially familiar with - volatility. Volatility and Price Decoherence ("IV Crush") Volatility, just like the Greeks, isn't exactly a real thing. Most of us have some familiarity with implied volatility on options, mostly when we get IV crushed the first time and realize we just lost $3000 on Tesla calls. If we assume that the current price should represent the weighted likelihoods of all future prices (the random walk), volatility implies the following two things:
Volatility reflects the uncertainty of the current price
Volatility reflects the uncertainty of the future price for every point in the future where the asset has value (up to expiry for options)
[Ignore this section if you aren't pedantic] There's obviously more complex mathematics, because I'm sure some of you will argue in the comments that IV doesn't go up monotonically as option expiry date goes longer and longer into the future, and you're correct (this is because asset pricing reflects drift rate and other factors, as well as certain assets like the VIX end up having cost of carry). Volatility in options is interesting as well, because in actuality, it isn't something that can be exactly computed -- it arises as a plug between the idealized value of an option (the modeled price) and the real, market value of an option (the spot price). Additionally, because the makeup of market participants in an asset's market changes over time, and new information also comes in (thereby increasing likelihood of some possibilities and reducing it for others), volatility does not remain constant over time, either. Conceptually, volatility also is pretty easy to understand. But what about our friend, IV crush? I'm sure some of you have bought options to play events, the most common one being earnings reports, which happen quarterly for every company due to regulations. For the more savvy, you might know of expected move, which is a calculation that uses the volatility (and therefore price) increase of at-the-money options about a month out to calculate how much the options market forecasts the underlying stock price to move as a response to ER. Binary Catalyst Events and Price Decoherence Remember what I said about price formation being a gradual, continuous process? In the face of special circumstances, in particularly binary catalyst events - events where the outcome is one of two choices, good (1) or bad (0) - the gradual part gets thrown out the window. Earnings in particular is a common and notable case of a binary event, because the price will go down (assuming the company did not meet the market's expectations) or up (assuming the company exceeded the market's expectations) (it will rarely stay flat, so I'm not going to address that case). Earnings especially is interesting, because unlike other catalytic events, they're pre-scheduled (so the whole market expects them at a certain date/time) and usually have publicly released pre-estimations (guidance, analyst predictions). This separates them from other binary catalysts (e.g. FSLY dipping 30% on guidance update) because the market has ample time to anticipate the event, and participants therefore have time to speculate and hedge on the event. In most binary catalyst events, we see rapid fluctuations in price, usually called a gap up or gap down, which is caused by participants rapidly intaking new information and changing their FEPF accordingly. This is for the most part an anticipated adjustment to the FEPF based on the expectation that earnings is a Very Big Deal (TM), and is the reason why volatility and therefore option premiums increase so dramatically before earnings. What makes earnings so interesting in particular is the dramatic effect it can have on all market participants FEPF, as opposed to let's say a Trump tweet, or more people dying of coronavirus. In lots of cases, especially the FEPF of the short term (3-6 months) rapidly changes in response to updated guidance about a company, causing large portions of the future possibility spectrum to rapidly and spectacularly go to zero. In an instant, your Tesla 10/30 800Cs go from "some value" to "not worth the electrons they're printed on". [Lily's Speculation] This phenomena, I like to call price decoherence, mostly as an analogy to quantum mechanical processes which produce similar results (the collapse of a wavefunction on observation). Price decoherence occurs at a widespread but minor scale continuously, which we normally call price formation (and explains portions of the random walk derivation explained above), but hits a special limit in the face of binary catalyst events, as in an instant rapid portions of the future expected payoff function are extinguished, versus a more gradual process which occurs over time (as an option nears expiration). Price decoherence, mathematically, ends up being a more generalizable case of the phenomenon we all love to hate - IV crush. Price decoherence during earnings collapses the future expected payoff function of a ticker, leading large portions of the option chain to be effectively worthless (IV crush). It has interesting implications, especially in the case of hedged option sellers, our dear Market Makers. This is because given the expectation that they maintain delta-gamma neutral, and now many of the options they have written are now worthless and have 0 delta, what do they now have to do? They have to unwind. [/Lily's Speculation] - Lily
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:
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.
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! --- 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). --- 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:
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).
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):
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.
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) = 24,000net 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:
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. --- 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:
How exceptional is today's NOPE versus historic baseline (30 days prior)?
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:
Calculate today's NOPE score (this can be done end of day or intraday, with the true value being EOD of course)
Calculate the end of day NOPE scores on the ticker for the previous 30 trading days
Compute the median of the previous 30 trading days' NOPEs
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:
The mean of NOPE_MAD for any ticker is almost exactly 0.
[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:
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.
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.
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).
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.
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:
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.
Sell/buy to close said shares at the first sign of major weakness (e.g. if the NOPE predicted outcome is incorrect).
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.
Only play tickers with high NOPE as well as high option/share vol.
--- 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
Going to keep this simple. EDIT: this isn’t simple and I should write a short story on this. I am generally risk averse. I hate losing $100 at the casino, I hate paying extra for guac at chipotles, I will return something or price match an item for a few dollars of savings. I am generally frugal. But, I somehow had no issues losing 10k in options... How I started I remember my first trades like they were yesterday. I was trading the first hydrogen run-up in 2014 (FCEL, BLDP, PLUG) and made a few hundred dollars over a couple weeks. I quickly progressed to penny stocks / biotech binary events and general stock market gambling mid-2014. I was making a few % here and there but the trend was down in total account value. I was the king of buying the peak in run-ups. I managed to make it out of 2014 close to break-even to slightly down. WSB Era March 2015 was my first option trade. It was an AXP - American Express - monthly option trade. I saw one of the regular option traders/services post a block of 10,000 calls that had been bought for 1.3 and I followed the trade with 10 call options for a total of $1300. I woke up the next day to an analyst upgrade on AXP and was up 50% on my position. I was addicted! I day-dreamed for days about my AXP over night success. I think around that time there was some sort of Buffet buyout of Heinz and an option trade that was up a ridiculous amount of %%%. I wanted to hit it BIG. I came up with the idea that all I needed to reach my goal was a few 100% over night gains/ 1k>2k>4k>8k> etc. I convinced myself that I would have no problems being patient for the exact criteria that I had set and worked on some other trades. Remember, the first win is always free. I was trading options pretty regularly from March 2015 until August 2016. During my best week I was up 20k and could feel the milli within reach. I can remember the exact option trade (HTZ) and I was trading weeklies on it. For those who have been in the market long enough, you will remember the huge drawdown of August 2015. I lost half my account value on QCOM calls (100 of them) that I followed at the beginning of July and never materialized. I watched them eventually go to 0. It was another 10,000 block that was probably a hedge or sold. In August 2015 there were some issues with China and all of us woke up to stocks gapping down huge. Unfortunately my idea of buying far dated calls during the following days/weeks after the crash went sideways. I quickly learned that an increase in volatility causes a rise in option prices and I was paying a premium for calls that were going to lose value very quickly (the infamous IV crush). I kept trading options into the end of 2015 and managed to maintain my account value positive but the trading fees for the year amounted to $30,000+. My broker was loving it. I tried all the services, all the strategies. I created rules for my option plays: 1. No earnings 2. Only follow the big buys at a discount (10,000 blocks or more). 3. No weekly options 4. Take profit right away 5. Take losses quickly 6. etc. I had a whole note book of option plays that I was writing down and following. I was paying for option services that all of you know about - remember, they make money on the services and not trading. I even figured out a loop-hole with my broker: if I didn’t have enough money in my account, I could change my ask price to .01 and then change it to market buy and I would only need to accept a warning ⚠️ for the order to go through. I was able to day trade the option and make money, who cares if I didnt have enough? After a few months of this, I got a call from my broker that told me to stop and that I would be suspended if I continued with this. By the way, I was always able to satisfy the debit on the account - so it wasn’t an issue of lack of funds. Lost it all. Started taking money from lines of credits, every penny that I earned and losing it quicker and quicker. I was a full on gambler but I was convinced that 8 trades would offset all the losses. I kept getting drawn in to the idea that I could hit a homerun and make it out a hero. I eventually hit rock bottom on some weekly expiring FSLR options that I bought hours before expiration and said to myself - what the f are you doing? I resolved to invest for the long term and stop throwing tendies away. The feeling was reinforced during the birth of my first born and I thought - what a loser this kid will think of me if he knew how much I was gambling and wasting my life. It was a really powerful moment looking at my kid and reflecting on this idea. I decided at that point I was going to save every penny I had and invest it on new issues with potential. Fall 2016 TTD, COUP and NTNX IPO ‘ed I decided I was going to throw every dollar at these and did so for the next few months. I eventually started using margin (up to 215%) and buying these for the next 6 months. They paid out and managed to make it over 100k within the year. The first 100k was hard but once I crossed it, I never fell below this magic number. 2017 - I did some day trading but it was mostly obsessing over the above issues. I did gamble on a few options here and there but never more than 1k. 2018 - SFIX was my big winner, I bought a gap up in June 2018 and my combined account value had crossed 400k by August 2018. I was really struggling at crossing the 500k account value and experienced 3 x 30-40% drawdowns over the next 2 years before I finally crossed the 500k barrier and have never looked back. I still made some mistakes over the next few months - AKAO & GSUM come to mind. Both of these resulted in 20k+ losses. Fortunately my winners were much bigger than my losers. I thought about giving up and moving to index funds - but i was doing well - just experiencing large drawdowns because of leverage. 2019 big winners were CRON SWAV STNE. 2017 / 2018 / 2019 all had six digit capital gains on my tax returns. At the beginning of 2020 I was still day trading on margin (180-220%) and got a call from my broker that they were tightening up my margin as my account was analyzed by the risk department and deemed too risky. Believe it or not this was right before the covid crash. I brought my margin down to 100-110% of account value and even though the drawdown from covid hit hard, I wasn’t wiped out. I stayed the course and bought FSLY / RH during the big march drawdown and this resulted in some nice gains over the next few months. I am constantly changing and testing my investment strategy but let me tell you that obsessing over 1 or 2 ideas and throwing every penny at it and holding for a few years is the best strategy. It may not work at some point but right now it does. I still day trade but I trade with 10k or less on each individual position. It allows me minimize my losses and my winners are 1-7%. I am able to consistently make between 3-700$/ a day on day trades using the above strategy. I still take losses and still dream about hitting it big with an option trade but dont feel the need to put it all on the line every month / week. I finally crossed into the two , club. I know people are going to ask for proof or ban but I am not earning anything for posting and the details about some of the trades should be proof enough that I kept a detailed journal of it all. I have way more to write but these are the highlights. Eventually I will share how I build a position in a story I love. I still sell buy and sell to early but I am working on improving. TL:DR - I gambled, lost it all and gambled some more lost more. I made it out alive. I have only sold calls/puts lately. The one common denominator in all successful people is how much they obsess over 1 or 2 ideas. Do the same. All the winners on this sub have gone all in on one idea (FSLY / TSLA ). Stick with new stories or ones that are changing and go all in...wait a second, I didnt learn anything.
AMZN Trade Retrospective: Collecting a $.37 Credit for the Potential to Make Another $50
There are different ways to trade in a choppy environment. Here’s a deep dive on how I attempted to use weekly options to trade a potential bounce in AMZN, and collected $.37 initially, for the possibility of making $50 more, even though the trade ended up being only an $.81 winner.
Last Thursday, 9/24, when $AMZN was trading at about $3000 a share, I was looking for a cheap way to play a bounce in the stock. During that time, my bias in the markets had begun to shift to a more bullish stance after seeing how the market had difficulty grinding lower. With that in mind, I wanted to play a potential bounce in tech. But I knew I didn’t want to pay a debit at all to play for a bounce that might not even happen, given how uncertain and choppy the markets had been, but I still wanted to set myself up to capture some large gains if AMZN did indeed bounce. Therefore, the strategy that made the most sense to me, was a Call broken wing butterfly. Given that I’m a very short-term options trader who loves trading weeklies, I was trying to look for a cheap butterfly for the upcoming week that I could put on for a net credit. After exploring the options chain, I came across the +1/-2/+1 3300/3350/3450 call broken wing butterfly for the Oct 2 series. This fly, at the time (on Sept 24), was trading for a total of $.37 credit. Meaning, by putting on that butterfly, I would get paid $.37, and the following scenarios could happen:
If AMZN decided to tank or hang out sideways and never get up close enough to the butterfly to expand the spread in my favor, then I’d walk away pocketing the $.37 credit
If AMZN slowly crept up to reach exactly 3350 by expiration, I’d not only get to keep the credit, but also be able to sell the butterfly back out for $50. Of course, it doesn’t need to reach exactly 3350 by expiration. If AMZN slowly worked its way up to near 3300, then the butterfly would expand very nicely as well.
If AMZN blew past 3400 by expiration, I’d see a loss, up to a maximum of $50 / spread (if $AMZN moves past 3450). That’s because the 3300/3350 long call vertical of the fly provides 50 points of coverage before I essentially start losing money from the 3350/3450 short vertical, up until that 3450 kicks in to cap off further upside losses.
So that is a rough outline of the potential scenarios that would happen with this trade. Given the choppy market conditions, I was ok with risking $50/spread (point #3), in order to not lose money if I’m wrong on direction (point #1), while at the same time, keeping myself open to the possibility of the butterfly expanding in my favor (point #2) for some potentially very large gains. But satisfying point #3 is tricky. I needed more data points suggesting that $AMZN wouldn’t surge higher early on in the trade. Because if $AMZN did surge higher early on in the trade, then while the 3300 long call would rise in value, those two 3350 short calls would also rise in value, and because there’d still be some time value left, they could be very juiced up and eat away at the profits of that 3300 long call, so much so that the 3450 long call won’t even be able to offset those losses, especially given how far out of the money that 3450 call is. AMZN on 9/24, daily timeframe Looking at the chart above on 9/24, we can see that AMZN was trading at around $3000/share. In order to reach $3300 (where the first long call of the broken wing butterfly is), the stock would need to
Breach the 38% fib retracement (~AMZN=3131) of the move from the 9/2 high to the 9/21 low,
Breach the 20MA and 50MA
Breach the 50% fib retracement (~AMZN=3211)
Breach the 61.8% fib retracement (~AMZN=3292)
before finally reaching the 3300 long call. All of these levels, I felt, should provide some resistance for AMZN to have to chew thru over the following week, before it even gets to the long call. And by that time, if AMZN did reach 3300, then the 3300 long call would still have a lot of extrinsic value left (somewhere around $20 on the last day), while the 3350 short calls would be very cheap (each around $5), so the entire spread could be roughly worth $10. Which would be great, because that means I’d be getting paid $.37 to make another $10. So with all of the above considered, I chose to take on that upside risk, for a chance to make potentially $50 (realistically I try to aim for just half of the max profit: $25, and start harvesting profits and peeling off the flies at around $5-$10), and that day on 9/24, entered the Oct2 3300/3350/3450 call broken wing butterfly for a $.37 credit. After entry, on Friday 9/25 and Monday 9/28, AMZN made steady progress upwards, from 3000 to 3175, breaching the 31.8% retracement and tagging the 20MA and 50MA from below. AMZN on 9/28, daily timeframe but this move wasn’t large and fast enough to expand the value of the 3350 short calls. In fact, theta did a great job draining those short calls, while the 3300 long call did a good job retaining its premium, so the butterfly had already expanded a bit in my favor, and I was sitting at about a small $1.00 profit.
However, on Tuesday and Wednesday, AMZN began to stall out. By the end of Wednesday 9/30, when it looked like AMZN was putting in a topping tail, I decided that AMZN might not be able to make it near 3300 by expiration Friday, so I wanted to take in a bit more credit while I still could, before theta drained more of that 3300 long call. At the time, the spread was trading for almost $2. That’s when I made a slight adjustment to the spread and sold the 3300/3310 call vertical. AMZN on 9/30, daily timeframe This essentially rolled the 3300 long call up to 3310, and I was able to collect a small $.44 credit for it. However, this adjustment did open me up to an additional $10 of risk to the upside, because now, the long call vertical portion of the butterfly is only $40 wide (instead of $50). Still, with only 2 days left for AMZN to go higher, I felt comfortable taking on a bit more upside risk knowing that theta is going to be working hard to drain those 3350 short calls if AMZN did decide to surge higher. And at that moment, I actually wanted AMZN to move more towards my fly. My deltas were still positive, and the risk graph showed that a move towards the short strikes of the fly would expand it by another $4-5 by Thursday. So after this adjustment, the trade stood at a $.81 credit, and the profit potential on the fly was now $40 instead of $50. Which is still pretty good.
On Thursday, AMZN showed some strength and closed above the 50% fib (3211), which meant that if on Friday, AMZN worked its way up to around 3300, the fly could potentially be worth $5-10. Things were looking good (on any continued bullishness, the next target for AMZN was the 61.8% fib retracement at ~3300). So I left the trade alone without making any more adjustments. AMZN on 10/1, daily timeframe
Unfortunately, on Thursday night, news broke out that Trump was diagnosed with Coronavirus, and the market fell lower. By the open, AMZN was already trading at around 3150, roughly 150 points below the fly. The spread had instantly lost all of its value, so I basically let it expire worthless and walked away pocketing the $.81 credit. https://preview.redd.it/mpwrkjpk6xq51.png?width=4096&format=png&auto=webp&s=8dd7f4da7b000b2266ab57a3c23c1863f9423704 While the trade did not work out as well as I had liked, the important thing to note is that I was able to get paid even when the trade didn’t go in my favor. With options, there are ways to trade an underlying to a certain target without ponying up a debit, albeit at the cost of introducing tail risk, while offering the possibility of very large upside. This may be a style of trading that one can consider employing when the outlook of the markets is uncertain, as long as the trader is willing to make the necessary adjustments to control risk. Which leads me to the following section:
What if AMZN decided to surge very early on during the trade? What if AMZN had surged to 3300 with 4-5 DTE, hence juicing up the short calls and causing the butterfly to take on large negative deltas?
Even though the position would be very theta positive, I would pony up the debit to cap off the upside risk by buying the 3400/3450 call vertical, hence turning the 3300/3350/3450 broken wing butterfly into the 3300/3350/3400 balanced butterfly. From there on out until expiration, I would look for ways to reduce the debit incurred from that adjustment.
But what if AMZN tanked afterwards? You could end up getting whipsawed.
I’d rather be safe than sorry and make the necessary adjustments to avoid getting run over, because I don’t like playing the hope card. I could always undo the adjustment and look for ways to collect back more credit (at the cost of introducing risk elsewhere), depending on my new directional bias on AMZN at the time.
Your maximum loss is so large, $5000. I’d never make that bet, I would never risk $5000 to make $5000.
This style of trading is not for everyone. There are different ways to perceive risk. I don't really think of risk as binary as “max gain vs max loss”. If the trade goes against me, I’m not going to open myself up to the possibility of eating the maximum loss. I’m going to manage that risk and make sure that I don’t lose any money at all on the trade. Basically, I’m not going to just put on the trade, walk away to the prayer room, and come back at expiration and hope that AMZN expired at 3350.
Why not just join thetagang and slap on iron condors / credit spreads in this environment? You could’ve collected more credit by selling a 50 point wide put vertical with your bounce thesis.
Different traders have different styles. I personally don’t like pure premium selling strategies. I’d rather have long options in front of the shorts to open myself up for some large upside and convexity in the P/L curve, rather than limit myself to the concavity of pure premium selling strategies. Having long options in front of the shorts also helps me sleep better at night.
It’s hard to read this. Is there a more visual explanation?
Here’s a video on it: https://www.youtube.com/watch?v=8uq76fZ3EME TL;DR - I used weekly options to trade a potential bounce in AMZN, and got paid $.37 initially to do so, for the possibility of making $50 more. While the trade did not pan out, I walked away pocketing $.81 for being wrong.
i'm new in this forex stuff (not even starting yet) & first time visiting Forex. But i've read that forex basically gambling (guessing either it goes up or down, and you got previous data as reference). I'm also read about foolproof gambling tricks that works in real life roulette. Basically it goes like this :
bet $1 on red - if you win, repeat step 1.
if you lose, bet $3. if you win, repeat step 1.
if you lose again, bet $6. if you win, repeat step 1.
if you lose again, bet $14. if you win repeat step 1.
if you lose again, bet $31. if you win, repeat step 1
so, can this be apply on forex trading? (there's lot ads about forex trading apps, thinking to try it) can't profit big, but seem cant lose either. might be a good strategy. any thought? edit 1 : what i mean in this forex is binary options, which some forex trading apps operates. edit 2 : it takes 5 unlucky trading before $55 account blown off. is that really common to get 5 unlucky trading in a row? edit 3 : here's the math (cnp from reply) some forex apps (like expert option or olymp trade) operate on binary option (this is unregulated securities?) where usually they give 80% return on trade. the math goes like this :
$1 trade and win = $0.80 profit
lose then $3 trade and win = $2.4 - $1 (lose) = $1.4 profit
lose then $6 trade and win = $4.8 - $4 (lose) = $0.8 profit
lose then $14 trade and win = $11.2 - $10 (lose) = $1.2 profit
lose then $31 trade and win = $24.8 - $24 (lose) = $0.8 profit
profit means got back your initial too
edit 4 : some reply said **binary options type forex trading apps** are scam & fraud. bummer. maybe trading via smartphone isnt easy as i thought. edit 5 : still, add some ability to reading indicator & chart could help avoiding 5 unlucky trading in a row. damn, if i'm a programmer, i'll make a trading bots based on this idea xD
Gentlemen, Ladies and those otherwise addressed - we know you've been waiting for a good thing, and the survey results are finally ready! The answers were collected from you all during August 2020 with 1428 unique replies. That's a participation of 0.5% of all subscribers! That's really not too bad, when you keep in mind how popular these kind of surveys are. But we here at /peloton want to show you that this is all about presenting the information in the subreddit to cater better to our audience! Updated after a few hours to include some more historical data the final edit that for some reason wasn't copied properly
Without further ado, let's get cracking on the response
You and Cycling
1. Where do you live?
Largely the same picture as ever, with the US leading the way, the UK in second and then a sliding scale of Europeans countries. Slovenia continues to pick its way up the pile for obvious reasons! World Map to demonstrate
2. What's your age?
Pretty much the same as last year, with the usual reddit demographics of majority 20 somethings dominating.
3. What's your gender?
More normality here for reddit.
4. How much of the men's season do you watch/follow?
March '18 (%)
August '18 (%)
WT Stage races
WT One day races
Non WT Stage races
Non WT One day races
Literally everything I can consume
Whilst GT following may be down (somehow), all the lower level stuff is up, which makes sense considering how desperate we have been for any racing during the season shutdown.
5. Do you maintain an interest in women's professional road racing?
Do you maintain an interest in women's professional road racing?
Still very much a half/half interest in women's cycling on the subreddit.
6. How much of the women's season do you follow?
The following is true for the half of you that follows womens cycling.
Just the biggest televised events
Most of the live televised/delayed coverage stuff
All televised racing
Down to .Pro & beyond
7. How long have you been watching cycling?
Under a year
25 years +
Simplified the years a little this time, but whilst we have a fair number of newbies, most people have picked the sport up since around 2013/14.
8. Do you have like/dislike feelings about WT teams?
Once more, 14.4% of people really don't have feelings on the subject. Of those that do:
So, the most popular team this year is Jumbo-Visma, followed by Quick-Step & Bora-hansgrohe. Least popular are Ineos & UAE. As per usual, no one cares about NTT & CCC, with nearly 81% of users rating NTT as meh. Pretty damning stuff. Lastly, we have the usual historical comparison of how teams have fared over time, normalised to respondents to that question on the survey. Things to note then, firstly that the Astana redemption arc is over, seeing them back in the negative, maybe Fulgsangs spring issues helped aid that? The petrodollar teams of UAE & Bahrain are stubbornly negative too, with Israel keeping up the Katusha negative streak. Meanwhile, at the top end, EF & Jumbo go from strength to strength, whilst some others like Sunweb are sliding over time - their transfer policies no doubt helping that.
10. Do you ride a bike regularly?
No, I don't
Still a fairly small group of racers out of all of us
11. Out of the sports you practice, is cycling your favourite?
A new addition to the survey prompted by a good point last time, just over half of us rate cycling as the favourite sport we actually do.
12. What other sports do you follow?
Association Football / Soccer
Track & Field
Esports (yes, this includes DotA)
Motorsports (Not including F1)
Football always tops the charts, and Formula 1 continues to rank extremely highly among our userbase. Those who have a little following below 5% include Sailing, Fencing, Surfing, Boxing & Ultra-Running. Other cycling disciplines
13. Out of the sports you follow, is cycling your favourite sport?
Good. Makes sense if you hang out here.
14. How often do you participate in a /Peloton Race Thread whilst watching a race?
I always participate in Race Threads during races
I follow Race Threads during races
I often participate in Race Threads during races
I rarely/never participate in Race Threads during races
Slightly less invested than before, reverting back to an older trade.
15. How do you watch Races?
Free Local TV
Desperately scrabbling for Youtube highlights
Paid Streaming services
Year on year, paid streaming services go up - the increasing availability of live content legally continues to improve, and so do the numbers on the survey.
16. Where else do you follow races live (in addition to watching them)?
We can safely say that most of us were wrong about this one. That's not a lot of confidence in Richie Porte either, the man who was to finish on the third spot of the podium. Alexander Foliforov (0,23%) had just a tiny number of votes less, and that man wasn't even in the race.
24. What for you was the defining cycling moment of the previous decade?
We had a lot of brilliant suggestions, but these were the clear five favourites when we tabulated the results.
2018 Giro - Chris Froome Solo Attack
2016 TDF - Chris Froome Running up Ventoux
2019 TDF - Landslides, First Columbian Winner, Pinot's bitter abandon - This was one race for the ages
2016 Paris-Roubaix - Mostly known for Tom Boonen losing. Also, some guy called Mat won.
2019 AGR - MvdP with his incredible catch-up for the win.
Honorable mentions go to the Giro 2018, which had Tom Dumoulin winning, and of almost identical fascination to many of you - Tom Dumoulin going on someones porta-potty in the middle of the stage. Little bit of recency bias perhaps, but that's better than ignoring that this was for the last decade and firmly insisting Tom Boonens 2005 WC win was the biggest thing. Special shoutout to almost all the Danes present in /peloton who voted for Mads Pedersens WC win last year. It's an understandable reaction.
25. Any suggestions for the Survey?
Could you add a section on rider popularity, same as for the teams?
Ask how people became interested in cycling
Ask how people watch cycling (e.g. TV Channels/Streaming etc.)
If you could be an animal for one day, which one would it be?
Would you wear a facemask while watching a cycling race live?
Which race do you look forward to see the most every year?
Favourite riders of your own country?
How many bikes do you own?
We promise to feature one of these suggestions in the next survey Suggestions
Always have a “no” or “not interested” option
We will try to implement this. But it will also skew results. About the Survey
More questions about womens cycling would be nice.
Less questions about womens cycling
The subscribers are torn on Women's cycling, nearly a 50/50 split there as the survey showed - The moderators at /peloton are firmly in the "more cycling is better" basket, and we will continue to get as good coverage of womens cycling as possible.
Are you trying to give the moderators PTSD? Because this is how you give the moderators PTSD.
26. Any suggestions for the sub?
More stationary fitness bike related content
ALSJFLKAJSLDKJAØLSJKD:M:CSAM)=#/()=#=/")¤=/)! - Your moderator seems to be out of function. Please stand by while we find you a new moderator
The Weekly threads are great for these types of questions, where several people can contribute and build up once it is understood which information is relevant.
Allow limited doping talk in result threads.
Our experience is that "limited" will never be so, if we're going to moderate it fairly. Moderating is not a popularity contest, but believe it or not, we're actually trying to be as fair as possible. and for that, we need rules that are not subjective. Unless you have a stationary exercise bike.
Written original content is always great - recaps, old race reviews or interesting rider bios, etc.
More non-race threads
Try and do some AMAs with pro cyclists, coaches, trainers, etc
All of these are good suggestions, but remember that all of you can also contribute - The mods are sometimes stretched thin, specially in the middle of hectic race schedules. It's easier if one of you has a way to contact a rider or a person of interest and can facilitate the initial communication.
Standardize major event thread titles for better search.
We've worked on this! The Official Standard is now as follows: [Race Thread] 202x Race Name – Stage X (Class)
A wiki that explains how races work. Roles of diff riders/support staff. History of racing.
This sounds as a nice community project for the after-season, and hopefully many of you subscribers can contribute.
Tidy up the sidebar!
Come with suggestions on how to tidy it up!
Don't assume everyone reading is a man, "thanks, bro". But that goes for all of Reddit. I know you can't fix that.
We have chastised all the mods. They are now perfectly trained in gender-neutral pronouns. Be well, fellow being.
Have a buy you a beer link for the mods for all the work you do.
If we can implement this for hard liquor, you know we will.
Remove the spoiler rule during grand tours. It kills the hype.
The spoiler rule is one that is discussed frequently - in general - some users absolutely hate it, but a majority love it. Perhaps we'll include a question in the next survey to see how this divide is exactly.
Lose the spoiler tag when it is for serious things; Lambrecht death, Jakobsen fall.
We actually do - whenever there is a matter of life or death, we think public information is more important than a spoiler rule. But at the same time, we try to collect all the different posts into one main thread, so to keep things focused and letting very speculative posts meet with hard evidence from other sources.
Less downvoting of opinions that differ from the fashionable consensus.
This is a tough ask of the internet. While we can agree that voting should be done accordingly to what insights they bring, not subjective opinions, it is very hard to turn that type of thinking around. We can ask of you, our subscribers, that you please think twice about hitting that downvote button, and only do so because of you think a post is factually incorrect, not because it differs with your own subjective opinion. That's the primary analysis of the survey! Feel free to contribute with how you experience things here!
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