sample pattern analysis algorithm technique

1 www.indexstrategyadvisors.com

Index Trend Reversal Supplement #3:

2 Sample Pattern Analysis Algorithm Technique

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Sample Pattern Analysis Algorithm Technique High Yield Bond ETFs vs. Oil

Date Barrels (millions) Catalyst

Nov. 18, 1985 1.1 Test sell

Sept. 27, 1990 5 Desert Storm test

Oct. 10, 1990 4 Desert Storm

Jan 16, 1991 17.3 Desert Storm

April 12, 1996 28 Deficit Reduction

Sept 2, 2005 11 Katrina/notice

June 23, 2011 30 Libya

Condition: Federal Oil Reserve Sales




Step 1: Determine Stochastic of the Asset

The Stochastic Fast indicator calculates the location of a current price in relation to its range over a period of bars. The default settings are to use the most recent 14 bars (input StochLength), the high and low of that period to establish a range (input PriceH and PriceL) and the close as the current price (input PriceC). This calculation is then indexed and plotted as FastK. A smoothed average of FastK, known as FastD, is also plotted. FastK and FastD plot as oscillators with values from 0 to 100. The direction of the Stochastics should confirm price movement. For example, rising Stochastics confirm rising prices. Stochastics can also help identify turning points when there are non-confirmations or divergences. For example, a new high in price without a new high in Stochastics may indicate a false breakout. Stochastics are also used to identify overbought and oversold conditions when the Stochastics reach extreme highs or lows. Additionally, FastK crossing above the smoother FastD can be a buy signal and vice versa.




Step 2: Back test trend reversal incidence and severity by purchasing the asset based on the Stochastic for each instance in history




Step 3: Determine the exact rate of change in volume in response to prior price reversals in the asset. Volume itself is less important because changes in the capital markets result in varying volume levels during comparable events over time. The degree to which volume elevates from a normalized level at the time of reversal (historically) is what we are looking for here. And of course-- how does this rate of change compare to the market (beta)?

The Volume Rate of Change indicator compares the most current bar’s volume to the volume of a bar in the past (default is 14 bars ago). The difference is calculated as a percentage and plotted as a histogram, and like an oscillator, fluctuates above and below a zero line. Volume can provide insight into the strength or weakness of a price trend. This indicator plots positive values above the zero line, and negative below. A positive value suggests there is enough market support to continue to drive price activity in the direction of the current trend. A negative value suggests there is a lack of support and prices may begin to become stagnant or reverse.




Step 3 (continued): import the data from the volume rate of change during the catalyst




Step 3 (continued): This is the short version of the volume rate of change data. The number we are focused on is highlighted in purple and the value is 83.33 in this example

This is the long version which contains all the variables associated with our risk factor. These data provide the algo with all of the surrounding factors regarding the rate of change –measuring exactly what transpired prior to the reversal. If you right click the image below select “size and position” then reset the image size to 100% you can see that the rate of change went parabolic when the announcement was made. This is set to a one minute interval, however as you know we can look at the same data on a per trade basis if we want/need to.




Step 4: Now we must begin to put the data in perspective. Here we interpolate the ETFs with the same reversal data from the indices we will employ to trigger buy/sell instructions for the ETF. In this scenario we would not use the S&P 500-- we would use the benchmarked Indices for each of the ETFs themselves. Unfortunately there is not enough room on this screen to show the list of Indices.




Step 5: The key to a profitable strategy is consistent execution. The reality of index trend reversals is that every professional trader in the world is looking to cash in on them. For this reason we have to be prepared to get our orders filled amidst abnormal conditions. This part of the algorithm looks back at the spreads of all the transactions for each asset during prior reversals. It also baselines the excess spread and will send ‘feeler’ bids to sniff out other algos. In the end the algo will generate and test a pre-determined price range for what we are willing to pay (when buying) or accept (when selling) that is used both in executing our trade and in confirming trend reversal patterns when the reversal happen now and in the future whether or not we trade them.




Step 6: Sorry I haven't explained floor trader pivots effectively. They are critical in the execution process to alert us as to what exact price levels markets are likely to turn, therefore we ‘look back’ and capture all pivots from prior reversals for all the securities we are analyzing. The algo will fire when we have a 70% or better match. This is both when buying and selling. I have our defaults set up for 3 levels in both directions. I have only seen R4 and S4 one or two times in 16 years therefore we isolate the largest probability distribution to maximize our system resources.




Step 7: Reversals happen when momentum stops -- then starts again in the opposite direction. We call the actual reversal the “moment of truth” – somewhat as a pun, because all speculation ends at this point. Institutions are known for creating the moment of truth- through their herding behavior of buying and selling. This input for the algo captures institutional selling and buying and stores it in a database. We then cross-reference these data when we are ready to compare prior reversals against each other and also when we are ready to trade. These data are grouped with the deltas on the options and the rate of change in volatility. This data set very effectively signals momentum.

Institution Accumulation and Distribution Counts trades over the number of shares specified by the Min. Shares input or number of trades in dollars specified by the Min. Dollars input within a the last number of ticks specified by the TickLength input in an attempt to identify markets under institutional accumulation/distribution. These markets may display a greater propensity for price movement as institutional buyers and sellers remain active.




Stochastics, Volume Rate of Change, Relative Strength to Index, Bid/Ask Volume ratio, Floor Trader

Pivots and Institutional buying/selling are 6 of our most useful indicators for running the ITRS

algorithms to identify risk and growth factors related to index trend reversals. We use a total of 33

indicators that each generate a critical factor. The table on the following page is a summary doc I used

a few years ago to explain to my trading team how we execute the NASDAQ 100 with our model trades

and the differences between them. It is the least technical document I have on ITRS. The point of the

document is that all trades are ‘rules based’ model trades-- both buys and sells.



End of supplement


sample pattern analysis algorithm technique

Economy & Finance

price trend

current bars volume

oil step

volume rate ofchange

current trend

exact rate of change

rate of change compare

variables rate of change