Trading Cycle Algorithm

Options Trading Strategies – Intermarket Analysis in Brief for Retail Asset Allocation

If you are trading a mix of Verticals, Calendars and Iron Condors across highly liquid indexes like the DJX, DIA, MNX, QQQQ, RUT, SMH, SPY and XSP, is your trading risk adequately diversified? No.

In choosing the MNX, QQQQ, SMH, SPY and XSP, there is a duplication of stock components in these Indexes: for example, AMAT (Applied Materials) is a component of all 5 Indexes.  Bear in mind the MNX and the QQQQ are both smaller versions of the Nasdaq100 Index, the only difference being the MNX is an European styled cash settled Index and the cubes (QQQQ) is an American style stock settled Index.  Another example, Apple (AAPL) is a component of the MNX/QQQQ and SPY/XSP – both the SPY and the XSP track the S&P 500, the SPY is American style stock settled and the XSP is European style cash settled.  Duplication is not diversification.  Even if you allocated capital to the smaller versions of the Dow: DJX, the European style cash settled version of the DIA which is the American style stock settled version.  Moreover, if you extended capital allocation to trade the RUT, thinking you are diversifying into small-cap stocks and away from large-caps, you just sunk more of your trading capital into equities.  Again, you cannot achieve diversification by adding more capital in the same asset class.  You need to learn how to trade options without concentration risk in stocks.  Do not confuse asset category (market capitalization) with asset class.

This is where there is a need to understand Intermarket relationships.  Intermarket analysis requires the simultaneous analysis of 4 main Asset Classes: Currencies (U.S. Dollar remains most liquid of all major traded currencies), Commodities, Bonds and Stocks.  Synchronizing the rotation of asset allocation within your own portfolio lies in getting a grip on how these four markets interrelate with each other.

Here’s the synopsis of the relationships.  Commodities lead bonds, bonds lead stocks and stocks lead commodities.  The cycle holds true at least in a normal inflationary/disinflationary environment.  Other than itself, Commodities affects 2 markets (Bonds and Stocks); effectively, impacting 3 out of the 4 Intermarket relationships.  Even if you do not trade Commodity ETFs as part of your portfolio, you need to track Commodities as a leading economic cycle indicator.  The futures/Mini Futures that you see on news headlines/trading screens are relevant only as daily gauges for stock market behaviour.  They are not a cycle indicator across Asset Classes.

So, you may already understand the criteria to define a “normal” economic cycle for the Directional Relationships to behave “ideally” (see below); BUT, how do you determine which Asset Class is driving the cycle? In other words, at a given point in the Intermarket cycle, how do you determine which Asset Class has the DOMINANT Relative Strength to trade? Follow the link below for a video-based course, to learn how Relative Strength – a rotational algorithmic measure is used to replace conventional Fundamental Analysis, as an asset allocation technique.

Moving on, here’s the Business Cycle in brief.  Bonds lead stocks, to trend in the same direction – except during deflation when bonds rise and stocks fall.  On average bonds are 18 months ahead of stocks in rising to their peak or falling to their bottoms; thereafter, stocks follow in the same direction.  If bonds have not broken down yet, this extends the gains in the stock market, acting as support for prevailing stock market levels.  The real risk begins to build 5-7 months after the bond market peaks or bottoms, thereafter the next 6 months stocks accelerate in the direction bonds have set.

Typically, commodities and bonds have an inverse relationship: as commodities rise, bonds falls but as commodities fall, bonds rise. Inflationary expectations affect bond prices. US Dollar movements which is tied into Monetary Policy changes affects commodity prices.  Commodities lead bonds 12–18 months in advance (it takes this long for Monetary Policy to come into effect) and 24–27 months before the economy fully absorbs the policy changes.

Now, the relationship between commodities and stocks. Stocks tend to lead commodities. Commodities are a hedge against inflation, with price inflation and higher inflation expectations occurring towards the end of the business cycle.

Money and company growth using credit (loans) takes time to make its way through the economic system, from making prices rise to raising expectations on inflation. Thus, commodities usually outperform at the end of the business cycle.

Rising bond prices generally raise stock prices in recovery, with falling commodity prices confirming an economic expansion phase is in play. As the expansion matures and begins to decelerate, watch for bonds to turn down first (as interest rates rise), followed by stocks.

Finally, it is after commodities outperform stocks and start turning down, this signals the end of an economic expansion with the probable start of activity decelerating, then slipping into an impending recession.

Retail traders can keep reading about the economics of inter–market analysis and asset diversification. Though, they will not solve these key issues, every option trader trading with USD $25-$50K or less, must deal with for retail asset allocation purposes:

• How much capital is adequate to sufficiently diversify risk away from any one Asset Class?

… if you can afford to diversify …

• How do you practically reconcile the multiple and continually dynamic macro-economic relationships, to trade in the relevant asset class?

Where can I learn how to trade options profitably using Intermarket analysis with retail asset allocation methods? Follow the link below, entitled “Consistent Results” to see a profitable retail option trader’s portfolio that is set up to cycle in and cycle out of Intermarket relationships, between asset classes.

Why is it possible? I’m using optionable ETFs (Commodity, Currency, Emerging Market and REIT), as well as optionable broad based/sector Equity Indexes, to trade the volatilities of each respective asset class. I do not need to trade Commodities and Currencies directly.  Remember, volatility can be added to/reduced from the portfolio, as not all Asset Classes or Sectors or Individual Companies or Countries move up/down in value ALL at the same time; and/or, ALL at the same rate.

About the Author

Please see Consistent Results http://www.homeoptionstrading.com/consistent_results/.

Here’s the summary for month-end July 2009 …

❑ Return: Profit/Start of Year Cash Balance = UP +115%! That’s +16.43% Return per Month!

❑ Win/Loss Probability = 90.20%. 9 Wins per 1 Loss. Average Win/Average Loss = $3.66 Won per $1 Loss.

❑ Performance Ratio = (Win/Loss Probability) x (Average Win/Average Loss) = 90.20% x $3.66 = 3.30.

❑ Positive Expectancy = $1,316 per trade.

Preview an original 55 hour video-based course for online options trading from home, at http://www.homeoptionstrading.com/original_curriculum.html

Purchase the curriculum and receive a $800 options basic course as a Bonus!

Algorithm $114.71 Algorithm. Analysis of algorithms, Profiling (computer programming), Program optimization, List of algorithms, Complexity class, Abstract machine, Algorithm characterizations, Algorithm examples, Algorithmic composition, Garbage In, Garbage Out, Highlevel synthesis, Algorithmic trading, Introduction to Algorithms, List of algorithm general topics, List of terms relating to algorithms and data structures, Randomized algorithm, Quantum algorithm, False cognate, Decidability (logic), Axiom, Imperative programming, Termination analysis Author: Miller, Frederic P./ Vandome, Agnes F./ McBrewster, John Binding Type: Paperback Number of Pages: 174 Publication Date: 2009/11/03 Language: English Dimensions: 5.98 x 9.01 x 0.40 inches

RamerDouglasPeucker Algorithm $58.94 High Quality Content by WIKIPEDIA articles The DouglasPeucker algorithm is an algorithm for reducing the number of points in a curve that is approximated by a series of points. The initial form of the algorithm was independently suggested in 1972 by Urs Ramer and 1973 by David Douglas and Thomas Peucker. (See the References for more details.) This algorithm is also known under the following names: the RamerDouglasPeucker algorithm, the iterative endpoint fit algorithm or the splitandmerge algorithm.The purpose of the algorithm is, given a curve composed of line segments, to find a similar curve with fewer points. The algorithm defines dissimilar based on the maximum distance between the original curve and the simplified curve. The simplified curve consists of a subset of the points that defined the original curve. Author: Surhone, Lambert M./ Tennoe, Mariam T./ Henssonow, Susan F. Binding Type: Paperback Number of Pages: 70 Publication Date: 2010/08/20 Language: English Dimensions: 5.98 x 9.00 x 0.17 inches

Prims Algorithm $86.03 High Quality Content by WIKIPEDIA articles In computer science, Prims algorithm is an algorithm that finds a minimum spanning tree for a connected weighted undirected graph. This means it finds a subset of the edges that forms a tree that includes every vertex, where the total weight of all the edges in the tree is minimized. Prims algorithm is an example of a greedy algorithm. The algorithm was developed in 1930 by Czech mathematician Vojt ch Jarnik and later independently by computer scientist Robert C. Prim in 1957 and rediscovered by Edsger Dijkstra in 1959. Therefore it is also sometimes called the DJP algorithm, the Jarnik algorithm, or the PrimJarnik algorithm. The only spanning tree of the empty graph (with an empty vertex set) is again the empty graph. The following description assumes that this special case is handled separately. The algorithm continuously increases the size of a tree, one edge at a time, starting with a tree consisting of a single vertex, until it spans all vertices. Author: Surhone, Lambert M./ Tennoe, Mariam T./ Henssonow, Susan F. Binding Type: Paperback Number of Pages: 104 Publication Date: 2010/10/07 Language: English Dimensions: 6.00 x 9.02 x 0.25 inches

Risch Algorithm $90.81 High Quality Content by WIKIPEDIA articles The Risch algorithm, named after Robert H. Risch, is an algorithm for the calculus operation of indefinite integration (i.e., finding antiderivatives). The algorithm transforms the problem of integration into a problem in algebra. It is based on the form of the function being integrated and on methods for integrating rational functions, radicals, logarithms, and exponential functions. Risch, who developed the algorithm in 1968, called it a decision procedure, because it is a method for deciding if a function has an elementary function as an indefinite integral; and also, if it does, determining it. The Risch algorithm is described (in more than 100 pages) in Algorithms for Computer Algebra by Keith O. Geddes, Stephen R. Czapor and George Labahn. The RischNorman algorithm, a faster but less powerful technique, was developed in 1976. Author: Surhone, Lambert M./ Tennoe, Mariam T./ Henssonow, Susan F. Binding Type: Paperback Number of Pages: 144 Publication Date: 2010/09/10 Language: English Dimensions: 6.00 x 9.02 x 0.34 inches

KirkpatrickSeidel Algorithm $68.51 High Quality Content by WIKIPEDIA articles The KirkpatrickSeidel algorithm, called by its authors the ultimate planar convex hull algorithm is an algorithm for computing the convex hull of a set of points in the plane, with O(n log h) time complexity, where n is the number of input points and h is the number of points in the hull. Thus, the algorithm is outputsensitive: its running time depends on both the input size and the output size. Another outputsensitive algorithm, the gift wrapping algorithm, was known much earlier, but the KirkpatrickSeidel algorithm has an asymptotic running time that is significantly smaller and that always improves on the O(n log n) bounds of nonoutputsensitive algorithms. The KirkpatrickSeidel algorithm is named after its inventors, David G. Kirkpatrick and Raimund Seidel. Author: Surhone, Lambert M./ Timpledon, Miriam T./ Marseken, Susan F. Binding Type: Paperback Number of Pages: 98 Publication Date: 2010/08/13 Language: English Dimensions: 5.98 x 9.00 x 0.23 inches

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MarkCompact Algorithm $86.03 High Quality Content by WIKIPEDIA articles In computer science, a markcompact algorithm is a type of garbage collection algorithm used to reclaim unreachable memory. Markcompact algorithms can be regarded as a combination of the marksweep algorithm and Cheneys copying algorithm. First, reachable objects are marked, then a compacting step relocates the reachable (marked) objects towards the beginning of the heap area. Compacting garbage collection is used by Microsofts Common Language Runtime and by the Glasgow Haskell Compiler. Author: Surhone, Lambert M./ Tennoe, Mariam T./ Henssonow, Susan F. Binding Type: Paperback Number of Pages: 104 Publication Date: 2010/10/06 Language: English Dimensions: 6.00 x 9.02 x 0.25 inches

SteinhausJohnsonTrotter Algorithm $74.88 High Quality Content by WIKIPEDIA articles The SteinhausJohnsonTrotter algorithm or JohnsonTrotter algorithm is an algorithm that generates permutations by transposing elements.The algorithm is set up with the idea that only one set of neighbors needs to swap positions and that there need only be one swap to generate the next permutation. To accommodate this, there needs to be an extra data element added: direction of mobility (ie direction of the swap). This direction is either left or right, but is initialized to the left. Author: Surhone, Lambert M./ Tennoe, Mariam T./ Henssonow, Susan F. Binding Type: Paperback Number of Pages: 92 Publication Date: 2010/10/07 Language: English Dimensions: 6.00 x 9.02 x 0.22 inches

Remez Algorithm $58.94 High Quality Content by WIKIPEDIA articles The Remez algorithm (sometimes also called Remes algorithm, Remez/Remes exchange algorithm, or simply Exchange algorithm), published by Evgeny Yakovlevich Remez in 1934 is an iterative algorithm used to find simple approximations to functions, specifically, approximations by functions in the Chebyshev space which are the best in the uniform norm L sense. A typical example of a Chebyshev space is the subspace of polynomials of order n in the space of real continuous functions on an interval, C a, b]. The polynomial of best approximation within a given subspace is defined to be the one that minimizes the maximum absolute difference between the polynomial and the function. Author: Surhone, Lambert M./ Tennoe, Mariam T./ Henssonow, Susan F. Binding Type: Paperback Number of Pages: 72 Publication Date: 2010/09/09 Language: English Dimensions: 6.00 x 9.02 x 0.17 inches