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. 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. If we don't, it's garbage-in, garbage-out. Yay, bullish! 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. This means that we can't even take an educated guess at what dark will be until after 4pm. 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. 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. Are there any particularly bold, or extensive, red or blue blotches on the map? HIRO translates millions of individual options trades to estimate the impact on an underlying stock's movement - in real time. 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. 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. 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. I.e., "there is half as much call gamma as put gamma." But maybe manageable). 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)! Now, finally, let's talk about the tiny bit of data that gets squeezed out the other end of all this insanity. 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. A 5.00x move is a 5 MAD move. 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. Feeble-minded humans can't see in four dimensions, but 100 lines of code named Robot Jim can. 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. Any time 'gamma' is under 1.00, puts are relatively more important; any time 'gamma' is over 1.00, calls are relatively more important. The conceptual link between short sales and investor buying activitythe key to understanding what "short volume" really meanshas never been clearly drawn before [now]. 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. 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). Since the daily PDF documents do the most the express that multidimensionality, let's talk about those next. Not doing spread at this moment because of low capital. Still there maybe some basic guidance that can be learned from these services. 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. 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. Well, usually this, too, is denominated in percent moves. In this case, the subsequent mean return over the period would be not -0.45 MAD, but +0.26 MAD. Functionality is as minimal as possible. Fast charts, no frills. 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. 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 is why, if we only looked at percentages, we'd get the totally wrong idea. And that's all there is to it. The most bearish outcome would be a 4.00% next-day loss, which would predict further losses (-1.0 MAD). And that brings us to Every security in our database can be exported as a spreadsheet document (CSV). 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." 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. A 1.00x move is a 1 MAD move. Because if a stock is becoming more or less volatile, that gives us crucial context about how market participants are engaging with the stock. 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. Not doing spread at this moment because of low capital. Would you recommend any? Comparatively bullish! Does AAPL perform better with a strategy that focuses on the price, volatility relationship, or on the gamma, dark relationship? 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]. This is quite powerful already, but waitthere's more! Changes in price must be viewed in the context of volatility, or else they lose meaning. Started with holding overnight, but giving up on that, can't handle time decay. I.e., the mean absolute deviation (MAD) was 1.00%. 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. 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. I.e., does volatility tend to increase (orange) or decrease (purple)? 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. 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. This number tells us, better than anything else, how price has really been moving, and is comparable across any and all assets. 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. 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. I calculated the flip point with the data on Thursday of July 2, 2020 and I got the flip point of 380. I just don't want to hold long puts on qqq or want to enter long puts on vix at a stupid time. 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). 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). Movement in price is usually denominated in dollars, points, or percent. I understand that these gamma exposure calculations are sort of made up. Return to the example above: The average daily move of a stock, before that 20% decline, was always 1.00%. 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. If two stocks, A and B, both move 5.00% tomorrow, that's not very useful information on its own. Risk capital is money that can be lost without jeopardizing one's financial security or lifestyle. Stock B moved 0.50x its average expected move. 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. 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. 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%. 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. 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'. I.e., "there is half as much call gamma as put gamma." This fourth axis is the final dimension of the data. I think many expects market to be volatile at least till next March FOMC meetings. It will generally move between -1 and +1. As juxtaposition, imagine that the 20% decline followed a period of 5.00% average daily moves instead. 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. Started with holding overnight, but giving up on that, can't handle time decay. 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). 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. These tell us that that particular combination of predictors has a notable historical pattern. More on that here. But as we said, the subsequent 20-MAD event raised the average daily move over the past month to 1.90%. 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. 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). But we really need to know about more than directionmagnitude matters just as much. 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? 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.
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