But that's not very useful information, because it doesn't at all describe the magnitude of the tail events we experienced during the month. This result emphasizes the outsized impact of that 20-MAD event at the beginning of the month. (Yeah SqueezeMetrics looks expensive to my pocket and I can't code. The charts are meant to provide just enough historical context to understand what the patterns visible in the weather maps really mean, and how events actually unfold. For all this talk of time-traveling, though, the PDF document is really nothing more than a point-in-time analysis of the day's data. At the top-right of every Chart page, and to the right on both the Dashboard and Research page, there is a download link to the ticker's PDF summary. Like gamma, dark is a simple ratio: Short off-exchange volume is the numerator, and all off-exchange volume is the denominator. This gives us a much clearer view of changes in option exposures, and isolates the impact that options have in individual stocks in a way that could never be done with one-dimensional data. A 5.00x move is a 5 MAD move. I have two questions since this was a lot to unwrap. The step-forward curve tells us exactly this: The wiggly blue-and-red curve is the mean 1-week forward return that we'd expect following the potential next-day returns on the x-axis. That's a very beautiful explanation and I'm very thankful to you for sharing this. But when you combine all this counterfactual modeling, you end up with a fairly high-confidence method for determining exactly what closing prices would have what kind of impact on subsequent forecasts. Et cetera. In order to get the context we need, we have to compare every day in the past month to the trailing historical daily moves at the time. Risk capital is money that can be lost without jeopardizing one's financial security or lifestyle. Likewise, if call gamma is 20,000 and put gamma is 10,000, 'gamma' is 2.00. Now, finally, let's talk about the tiny bit of data that gets squeezed out the other end of all this insanity. Functionality is as minimal as possible. So, e.g., if the gamma of all call open interest in a stock, across all expirations and strikes, adds up to 500,000 shares per 1.00% move; and the gamma of all of the same puts adds up to 1,000,000 shares per 1.00% move, then 'gamma' is 0.50. Because in the same way that change in price, denominated in percent, fails to capture the reality of the change; change in volatility denominated in vol points (like VIX), also misses the mark. I'm very knew to options, mostly doing directional on qqq or vix. I calculated the flip point with the data on Thursday of July 2, 2020 and I got the flip point of 380. As with gamma, dark becomes more powerful when viewed in the context of the other predictors. So if the 20% decline followed a period of 1.00% average returns, then that -20% move was a 20-MAD event (!). This is quite powerful already, but waitthere's more! click on the key to the right to hide and show. I just don't want to hold long puts on qqq or want to enter long puts on vix at a stupid time. This way, we should be able to immediately get a sense of whether there are historical patterns and where they occur. And so, a second set of weather maps tells us whether 1-week returns tend to land under, or over, the 1.00 MAD expected move. Comparatively bullish! See, of all of our predictors, one of them, dark, really can't be known until after the market closes, because FINRA's data doesn't update until around 5:30pm. HIRO translates millions of individual options trades to estimate the impact on an underlying stock's movement - in real time. These tell us that that particular combination of predictors has a notable historical pattern. If two stocks, A and B, both move 5.00% tomorrow, that's not very useful information on its own. This is the part where we have to do a bit of time-traveling. Are there any particularly bold, or extensive, red or blue blotches on the map? I.e., the mean absolute deviation (MAD) was 1.00%. The result can be expressed as a decimal (0.0 to 1.0) or as a percentage (like DIX), but in either case, it tells us the relative amount of trade volume marked "short" in off-exchange (dark) trading. Like 'price', 'volatility' conveniently tends to move within the domain [-1, +1]. This number tells us, better than anything else, how price has really been moving, and is comparable across any and all assets. Here are the columns in those spreadsheets, and some numerical methods for extending the data: This data is meant to be extensive and extensible. These are fast, interactive charts, designed to cram over ten years and 15 time-series onto just one x-axis and two y-axes. So a 'volatility' of +0.90 MAD means there was a huge relative increase in volatility, which is the perspective we need to go alongside 'price'. Why is volatility important? There are four predictors that form the basis of all of our data(a) price, (b) volatility, (c) gamma, and (d) darklet's address each of them in turn. Because if a stock is becoming more or less volatile, that gives us crucial context about how market participants are engaging with the stock. More on that here. This is why, if we only looked at percentages, we'd get the totally wrong idea. Because it rises and falls without regard to price, volatility, or gamma, it provides an uncorrelated signal that frequently tracks sentiment, whether or not that sentiment has been reflected in price. I'm very knew to options, mostly doing directional on qqq or vix. I think many expects market to be volatile at least till next March FOMC meetings. SpotGamma vs SqueezeMetrics vs Tier1Alpha. This humble page is for context and idea generation. Then (as if four dimensions of data plus returns wasn't enough), we want to weight the historical returns data to emphasize data that is both nearer in time, and nearer in space, to the current coordinates. Changes in price must be viewed in the context of volatility, or else they lose meaning. And since the 20% decline would substantially raise the trailing historical average daily move of the past month (1.00% 1.90%), then the next few days of 1.00% returns would be a mere 0.53 MAD. Feeble-minded humans can't see in four dimensions, but 100 lines of code named Robot Jim can. The conceptual link between short sales and investor buying activitythe key to understanding what "short volume" really meanshas never been clearly drawn before [now]. And that brings us to Every security in our database can be exported as a spreadsheet document (CSV). But some of you will want to test your own signals, create your own moving averages, look for your own patterns, and run your own backtests. This includes (a) the distribution, (b) the weather maps, and (c) the step-forward analysis. Return to the example above: The average daily move of a stock, before that 20% decline, was always 1.00%. But as we said, the subsequent 20-MAD event raised the average daily move over the past month to 1.90%. As with the 'price' data, the x-axis is standardized to MAD returns, so a +1.00 MAD gain would be a positive return that matches the average expected weekly move in the stock, and a -2.00 MAD return would be a weekly loss twice the expected move. Probably because high gamma means a lot of calls have been bought, and high dark means customers are buying tons of shares in off-exchange transactions. And so, in order to make it possible to backtest our data in the most conservative way possible (such that you are never accidentally including that 1.5 hours of lookahead bias), we lag the dark data by a full day. And the same can be said of our fourth and final predictor: Dark pool short volume, as reported by FINRA in its Reg SHO daily files, is the basis for the Dark Index (DIX), and the subject of the paper, "Short is Long." This means that we can't even take an educated guess at what dark will be until after 4pm. This fourth axis is the final dimension of the data. Summary: 'Price' is the 1-month sum of daily MAD returns, with a rolling window of 1-month volatility (defined as average daily moves) as its denominator. Well, usually this, too, is denominated in percent moves. I.e., "there is half as much call gamma as put gamma." It just takes a bit of counterfactual modeling. I.e., does volatility tend to increase (orange) or decrease (purple)? It searches through the top 1000 securities by dollar volume and finds the best bullish and bearish opportunities (50 from each category), then sorts them according to Robot Jim's return forecast (MEAN). Because we have the luxury, now, of viewing gamma in the context of both (a) 'price' and (b) 'volatility'. The need for each of these four dimensions as inputs is what drives the presentation and visualization of the data (it's not easy to think in 4-D), as well as the algorithmic methods with which we derive probability distributions. But we really need to know about more than directionmagnitude matters just as much. So, e.g., if the gamma of all call open interest in a stock, across all expirations and strikes, adds up to 500,000 shares per 1.00% move; and the gamma of all of the same puts adds up to 1,000,000 shares per 1.00% move, then 'gamma' is 0.50. By default, the range on the x-axis is -10 to +5 MAD for consistency (and because most stocks have left-skewed returns), but the axis is extended whenever higher-magnitude returns need to be shown. Since, a month prior, MAD was 1.00%, and suddenly, MAD became 1.90%, all we really need to do to describe the change in volatility is to difference these (1.90 - 1.00). By way of example, see how the upper-right corner of AAPL's gamma, dark weather map not only corresponds to strongly positive returns (blue), but also an increase in volatility (orange)! And that's all there is to it. We can't do what Robot Jim does, because we can only see in three dimensions, but we can come pretty close to what he does, by taking multiple cross-sections of pair-wise predictors and their historical returns data. Started with holding overnight, but giving up on that, can't handle time decay. This means that any given coordinate pair can be associated with price-up, vol-up; price-up, vol-down; price-down, vol-up; or price-down, vol-down. Since the daily PDF documents do the most the express that multidimensionality, let's talk about those next. A 5 MAD move is big. Perhaps 1-week returns are bullish when dark is high and gamma is lowor maybe it's the opposite! If we don't, it's garbage-in, garbage-out. Consider each of our four predictors as an axis on a 4-D scatter plot, "color" every historical data point according to its 1-week return, and then search for the historical combination of data coordinates that most closely resembles the current scenario. If volatility is decreasing while price rises, that's a very different situation from volatility increasing as price rises, and you would never want to mistake one for the other. So, e.g., if a stock (priced at $100) that usually moves 1.00% per day starts off a month with a nasty one-day, 20% decline ($100 $80), then gradually claws back 1.00% per day for the next 20 market days ( $97.61), the "monthly return" is -2.38%. Still there maybe some basic guidance that can be learned from these services. Does AAPL perform better with a strategy that focuses on the price, volatility relationship, or on the gamma, dark relationship? By design, the critically important concept of "zero GEX" is completely preserved here (as gamma = 1.00), while removing all other confounding factors. This weighted distribution, containing 42 discrete historical events (two months of market-day data are being sampled here), their weights, and their 1-week return, is then plotted as a histogram, smoothed with kernel density estimation, and consulted for its mean and median expected returnswhich are plotted on the x-axis in green and orange, respectively. For context, this is converted into both percent and spot prices on the upper x-axis. But what if we want to get a contextual sense of how a stock has moved over not the past day, but the past month? If you have the need to add some complexity to this process, or to apply your own sorting and weighting schemes, there's an API call (/latest) that returns all of the most recent day's data, in JSON or CSV format, for your nerdy pleasure. Not doing spread at this moment because of low capital. 2022 TENTEN CAPITAL LLC DBA SPOTGAMMA Futures, foreign currency and options trading contains substantial risk and is not for every investor. Movement in price is usually denominated in dollars, points, or percent. The decline would be a 4 MAD event instead (not nearly as big a deal). Rather than refer to this monthly return, we want to have some metric that considers each of the daily moves that comprise the past month. The only reason this comes up, of course, is that we believe we can derive a number for each of the other three predictors during the day, and before the market close. A 1.00x move is a 1 MAD move. Fast charts, no frills. Movement in price is rarely, if ever, denominated in what really mattersthe price change in relation to historical volatility, i.e., the price change relative to how much it usually moves. In the example above, the most bullish outcome for the stock would be a 2.00% next-day loss, which would predict subsequent strength (+0.4 MAD). In this case, the subsequent mean return over the period would be not -0.45 MAD, but +0.26 MAD. An investor could potentially lose all or more than the initial investment. To really get a feel for the history of a stock, and how it relates to the data, we need some good old-fashioned time-series charts. But maybe manageable). So, in the same way that 'price' records the change in price as a function of volatility, 'volatility' records the change in volatility as a function of volatility. It will generally move between -1 and +1. Any time 'gamma' is under 1.00, puts are relatively more important; any time 'gamma' is over 1.00, calls are relatively more important. Not doing spread at this moment because of low capital. With the availability of mean, median, and vol returns, split by predictor pair, the efficacy of innumerable mix-and-match strategies can be evaluated. The SpotGamma HIRO Indicator analyzes millions of options trades daily, monitoring every single options trade taking place in many of the market's most active US stocks, indices and ETFs. I.e., 'volatility' is +0.90 MAD. In this context, the mean return over the period, despite 20 straight days of +0.53 MAD returns, would be a lousy -0.45 MAD. Yay, bullish! Stock A moved 5.00x its average expected move. This means that, for any day in history, our signal can be assumed to be able to be traded during the day, or at the close, because we're not depending on that 5:30pm data. Started with holding overnight, but giving up on that, can't handle time decay. I understand that these gamma exposure calculations are sort of made up. Since there are four data axes, there are six possible pairs of predictors, and each of these predictors is plotted, on a fully normalized basis [-1, 1]. As juxtaposition, imagine that the 20% decline followed a period of 5.00% average daily moves instead. For example, if price is currently up, volatility is currently down, gamma is somewhere in the middle, and dark is extremely high; then we want to look for every historical analog, where price was up, vol was down, gamma was middling, and dark was high. The most bearish outcome would be a 4.00% next-day loss, which would predict further losses (-1.0 MAD). In the case of price and volatility, this is a matter of imagining how the paths of price and volatility returns would change given a range of closing prices (pretty easy); but in the case of gamma, it's a matter of computing thousands of simulated changes in price and implied volatility for every option with any open interest, updating open interest as new data becomes available, and then decaying every option's time value by a day while you're at it (not so easy). I.e., we should be able to know the day's closing forecast (future expected mean) before the 4pm close. But, like we said before, daily percent moves just aren't able to provide enough context on their own. with each of the others. Now we have two predictors, 'price' and 'volatility', that both use the same units (MAD), are measuring nothing more than simple averages on a 1-month period, move in the domain [-1, 1], and can be used on any asset. Only risk capital should . The data on the charts is described below, with their spreadsheet column names in parentheses. Ever since our 2016 paper on gamma exposure (GEX), people have been eager to replicate and extend the concept. I.e., "there is half as much call gamma as put gamma." Here, though, we're peeling away any and every layer of complexity to the computation and revealing a simple ratio: The gamma of all call open interest to the gamma of all put open interest. Would you recommend any? With the two conversion factors ("1MAD_x") and implied volatility (IV), every mean return, median return, and volatility can be placed in the context of historical and implied vol. And while we may know this intuitively, and we may have a sense for the way volatility impacts true returns, we have to declare all of this explicitly when we're attempting to use numerical methods. If we know, however, that Stock A has been moving 1.00% per day, on average; and Stock B has been moving 10.00% per day, on average; then we're able to place those percentage moves in context: Another way to express the multiple of the expected move is in "mean absolute deviation" (MAD). Specifically, what needs to be done is to compute, for all possible future closing prices of a stock, the future price, volatility, and gamma values. At this point, we ought to mention the API, because if you plan on doing any such extensive testing, you'll want batch processing and programmatic access. I think many expects market to be volatile at least till next March FOMC meetings. Stock B moved 0.50x its average expected move. A call-to-put ratio, but measured in gamma. Why the simplification?
Rinvoq Side Effects Liver, North Zone Yuva Fogsi 2023, Photo & Picture Resizer Resize Downsize Adjust, Kendo-numerictextbox Default Value, How Much Is An Eisenhower Dollar Worth, Electromagnetic Parking Sensors, Eco Friendly Civil Engineering Projects, Gbdt Machine Learning,