It is now appropriate to derive the simple algorithm giving rise to these curves from a consideration of familiar aspects in the psychology of the market participants. After all, it is precisely the dynamic interaction between the greed and fears of those who already hold the stock and those who wish to become owners that determines the price at which supply and demand find at least temporary equilibrium. One can approach the problem from both the supply side and the demand side with remarkably similar results.
First consider someone who already has a long position in a given stock.
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What will motivate this person the "owner" to sell? If the stock is held at a profit, the more substantial this profit is the greater the temptation to take it. If - not having taken such profit - the owner sees the price has now dropped back almost to the purchase price, his propensity to sell is at a minimum because he is still in profit - albeit small - and believes the stock will at least partially retrace the higher prices of recent memory.
On the other hand, if the owner has been holding the stock at a loss, his overriding desire is to "get out even". Thus, as the stock price approaches from below the price at which he bought, his propensity to sell reaches a maximum. It is thus the purchase price which becomes either a support or resistance level for that owner; he either dumps the stock on the market or witholds it depending on the direction from which the market price is approaching his purchase price. Now let's look at the demand side and consider the psychology of someone we'll call him the "accumulator" wishing to take a substantial long position.
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He starts his buying and the price starts to rise attracting other buyers. Not wishing to "chase the stock", our accumulator holds off on future purchases until the price drops back to the average price at which he had assembled his position thus far. He can then buy more without substantially changing his aggregate cost per share. Thus, as the market price approaches from above towards his average cost, his propensity to buy reaches a maximum. If his average cost happens to coincide with the average cost of the owner considered earlier, then it is little wonder that the price trend reverses at this point since the accumulator now starts to buy again while the owner has lost interest in selling.
Supply and demand are maximally out of balance and the price must therefore rise sharply, i. In reality, of course, there are many "owners" with a corresponding spectrum of purchase prices.
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Similarly, while there may in fact be only a single "accumulator" in the initial stages of a bull move e. Each has their own time horizon, price objective, risk aversion, etc. To model this situation mathematically is quite complex. While one can certainly construct a purchase price spectrum from price and volume historical data, the decomposition of this spectrum into "owners" and "accumulators" would be tentative at best.
Furthermore, at some point the psychology of the accumulator transitions into that of the owner. Above all, even if these complexities could be resolved, the modelling process would be multi-parametric time horizon, risk aversion, profit objective, stop loss points, etc. As a first approximation, we can restrict consideration to just the first moment of the purchase price spectrum - the mean.
That is, we simply ask "What is the average price at which this stock has been bought? Once this interval has been specified, the computation is trivial.
We have not yet specified the interval over which the averages are to be taken. While even a casual reader of the earlier articles can already deduce the answer from the examples given, the rationale requires some discussion which is best left to the next article in the series. In the same spirit of logical development, we now motivate the choice for this interval. Consider the familiar case of a stock which has been dormant for a long period of time, trading in a narrow price band on relatively low volume.
On a certain day it suddenly breaks out of the trading range on heavy volume and we ask where we might expect to find support during the inevitable pullback which follows when the buying spurt subsides. We have already said that the theoretical support will involve an averaging process from some initial instant called the "launch point" to the present. If the launch point includes days prior to the sudden breakout, the averaging will mix time periods of differing underlying psychology and thus would not be expected to yield useful results.
For, clearly, the breakout day marked the beginning of a shift in the psychology of those subsequently acquiring the stock. In other words by and large people were buying the stock for different reasons after the breakout than before. Similarly we can consider another familiar situtation where a stock has been in a long period of consolidation after an earlier bull move. Again, volume has shrunk to a mere shadow of previous levels.
Then, one day, the stock starts moving up and trading volume accelerates.
Here again the trend reversal is indicative of a reversal in underlyling psychology - else why would there have been a change in trend? Thus it is clear that if the average price is to be a meaningful measure of psychological boundary, the average must be taken over a period of homogeneous psychology, i.
This is the key to the Midas method. By one of those unfortunate detours in the progress of technical analysis, attention came to be focussed on moving averages, i. Our "message" is that instead of "moving" averages, one should take fixed or "anchored" averages, where the anchoring point is the point of trend reversal.
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By way of example, consider the Midas chart obv omitted to focus on price alone for Cypress Semiconductor. Here we have a five-fold hierarchy of theoretical support levels, where every member of the hierarchy is launched precisely from a trend reversal point.
Thus an otherwise bewilderingly complex set of zig-zags in the price history can be understood with respect to a single algorithmic prescription: support will be found at the volume-weighted average price taken over an interval subsequent to a reversal in trend. In Count Dracula's immortal words when his hapless ovenight guests wished to leave: "Ahh, if only life were so simple! Here we could achieve a good "fit" to the observed price zig-zags only by adjusting the launch points somewhat from their a priori values.
In the case of S2, is was a matter of determining where in the midst of an extended consolidation bottom the launch point should taken. With S3 and S4, it involved a displacement of a day or two after the actual reversal day a common situation in strongly trending stocks. S5 could be viewed actually as an S6 - launched from the first pullback to an S5 which is not shown. Having thus developed the rationale for the Midas method of technical analysis, we will henceforth get down to the computational nitty gritty. After the next article if not by now you should be generating your own Midas charts and forming your own conclusions!
This description has intentionally avoided mathematical detail in order to focus on the conceptual foundations. We now turn to the actual equations and show how they can be readily evaluated in a spreadsheet. Suppose the input data spans a consecutive period of N days. On any given day, say the i-th, we denote the high, low and volume as H i , L i and V i respectively. These are the data that are actually used by MIDAS; we do not require the open, close or calendar date. If the available input data only gives a single price - the close for example - then simply set the high and low equal to this price; if volume data is not available, set the volume for every day equal to the same value - say one share.
Having less than complete input data is less than ideal but not a fatal drawback. In a similar fashion, also compute the cumulative product of the daily price and daily volume. In the upper graph, plot P i as the y- coordinate versus cumvol i as the x coordinate. In the lower graph, use the same x coordinate i. Thus every day gives rise to a single x-y point in each of the two graphs. Connecting the sequential points in each graph thereby traces out curves of price vs.
An easy way of carrying out these calculations in practice is through the use of a spreadsheet. Below I show how this could be done in Lotus other spreadsheets will be similar if not identical.
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Simply enter the input data in the first three columns starting with the second row, and enter the cell formulas as shown in rows two and three. COPY the third row downward for as many days rows as there are input data.
Finally, using the graphing capabilities of the spreadsheet, create a pair of x-y type graphs. In both graphs, choose the x coordinate as the "cumvol" column G in the present example.
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