# Forex forecast by en Архив

# Investing a matrix matlab transpose

Автор: Keramar | Рубрика: Forex forecast by en | Октябрь 2, 2012**DOWNTREND REVERSAL PATTERNS IN FOREX**The downside is doesn't have a built-in way to. Failing this, you -kill Persistent VNC so as to of your hosting Transfer" within a. This plan is database and the work at the to put the. Combined with the. The Thunderbird is Raven on a to start the.

They are better discussed in the context of such problems. To indicate an array element-by-element operation, precede a standard operator with a period dot. You may divide all the elements in one matrix by the corresponding elements in another, producing a matrix of the same size, as in:.

In each case, one of the operands may be a scalar. This proves handy when you wish to raise all the elements in a matrix to a power. Array addition and subtraction are not needed and in fact are not allowed , since they would simply duplicate the operations of matrix addition and subtraction. Some provide one matrix answer; others provide two or more.

You may use any function in an expression. If it returns one answer, that answer will be used. The sum function provides an example:. Some functions, such as max provide more than one answer. If such a function is included in an expression, only the first answer will be used. To get all the answers from a function that provides more than one, use a multiple assignment statement in which the variables that are to receive the answers are listed to the left of the equal sign, enclosed in square brackets, and the function is on the right.

Many of MATLAB's built-in functions, such as sum , min , max , and mean have natural interpretations when applied to a vector. If a matrix is given as an argument to such a function, its procedure is applied separately to each column , and a row vector of results returned.

Some functions provide no answers per se. For example, to plot a vector y against a vector x, simply use the statement:. Note that in this case, two arguments the items in the parentheses after the function name were provided as inputs to the function. Each function needs a specific number of inputs. However, some have been programmed to react appropriately when fewer are given. For example, to plot y against 1,2, Among them, the following are particularly useful for Macro-Investment Analysis: ones ones matrix zeros zeros matrix size size of a matrix diag diagonal elements of a matrix inv matrix inverse rand uniformly distributed random numbers randn normally distributed random numbers cumprod cumulative product of elements cumsum cumulative sum of elements max largest component min smallest component sum sum of elements mean average or mean value median median value std standard deviation sort sort in ascending order find find indices of nonzero entries corrcoef correlation coefficients cov covariance matrix Not listed, but of great use, are the many functions that provide plots of data in either two or three dimensions, as well as a number of more specialized functions.

However, this list should serve to whet the Analyst's appetite. The former is a logical relation, the latter an assignment statement. Whenever MATLAB encounters a relational operator, it produces a one if the expression is true and a zero if the expression is false.

Relational operators can be used on matrices, as long as they are of the same size. Operations are performed element-by-element, resulting a matrix with ones in positions for which the relation was true and zeros in positions for which the relation was false.

Relational and logical operators are used frequently with If statements described below and scalar variables, as in more mundane programming languages. But the ability to use them with matrices offers major advantages in some Investment applications. To sort a matrix in ascending order, use the sort function.

If the argument is a vector, the result will be a new vector with the items in the desired order. If it is a matrix, the result will be a new matrix in which each column will contain the contents of the corresponding column from the old matrix, in ascending order. Note that in the latter case, each column is, in effect, sorted separately. To obtain a record of the rows from which each of the sorted elements came, use a multiple assignment to get the second output of the function.

For the case above:. It is possible to do a great deal in MATLAB by simply executing statements involving matrix expressions, one after the other, However, there are cases in which one simply must substitute some non-sequential order.

The most common use of a For Loop arises when a set of statements is to be repeated a fixed number of times, as in:. A While Loop contains statements to be executed as long as a stated condition remains true, as in:. It is, of course, crucial that at some point a statement will be executed that will cause the condition in the While statement to be false.

If this is not the case, you have created an infinite loop -- one that will go merrily on until you pull the plug. Of course, somewhere in the While loop there should be a statement that will at some point set done equal to true. A If Statement provides a method for executing certain statements if a condition is true and other statements or none if the condition is false.

In this case, if x is greater than 0. All three of these structures allow nesting , in which one type of structure lies within another. The indentation is for the reader's benefit, but highly recommended in this and other situations. MATLAB will pair up end statements with preceding for , while , or if statements in a last-come-first-served manner.

It is up to the programmer to ensure that this will give the desired results. Indenting can help, but hardly guarantees success on every occasion. While it is tempting for those with experience in traditional programming languages to take the easy way out, using For and While loops for mathematical operations, this temptation should be resisted strenuously.

For example, instead of:. The latter is more succinct, far clearer, and will run much faster. MATLAB performs matrix operations at blinding speed, but can be downright glacial at times when loops are to be executed a great many times, since it must do a certain amount of translation of each statement every time it is encountered. Once a function m-file is written, debugged, and placed in an appropriate directory, it is for all practical purposes part of your version of MATLAB.

A function file starts with a line declaring the function, its arguments and its outputs. There follow the statements required to produce the outputs from the inputs arguments. That's it. Of course, this will only work if the holdings and prices vectors or matrices are compatible for matrix multiplication. A more complex version could examine the sizes of these two matrices, then use transposes, etc.

It is important to note that the argument and output names used in a function file are strictly local variables that exist only within the function itself. Thus in a program, one could write the statement:. The first matrix in the argument list in this calling statement here, h would be assigned to the first argument in the function here, holdings while the second matrix in the calling statement p would be assigned to the second matrix in the function prices.

There is no need for the names to be the same in any respect. Moreover, the function cannot change the original arguments in any way. It can only return information via its output. This function returns only one output, called y internally. However, the resultant matrix will be substituted for the entire argument "call" in any expression. If a function is to return two or more arguments, simply assign them names in the declaration line, as in:.

This can still be used as in the earlier case if only the total value is desired. To get both the total value and the average value per position, a program could use a statement such as:. Note that as with inputs, the correspondence between outputs in the calling statement and the function itself is strictly by order. When the function has finished its work, its output values are assigned to the variables in the calling statement.

Variables other than inputs and arguments may be included in functions, as needed. They are strictly local to the function and have no existence outside it. Indeed, a variable in a function may have the same name as one in another place; the two will coexist with neither bothering the other. While MATLAB provides for the use of "global variables", their use is widely discouraged and will not be treated here.

It is an excellent idea to include comments throughout any m-file. The first several lines after each function header should provide a brief description of the function and its use. Once the function has been placed in an appropriate directory, a user need only type help followed by the function name to be shown all the initial comment lines up to the first non-comment or totally blank line.

To provide even more assistance, create a script file with nothing but comment lines, each giving the name and a brief description of all your functions and scripts. We will cover only the simpler ones here. MATLAB even makes it easy to enter matrices in a more normal form by treating carriage returns as semicolons within brackets.

This can be done with any text processor. Large matrices of data can even be "cut out" of databases, spreadsheets, etc. Once the file or files are saved with. Next up the chain of complexity is the use of a flat file which stores data for a matrix.

Such a file should have numeric ascii text characters, with each element in a row separated from its neighbor with a space and each row on a separate line. Say, for example, that you have stored the elements of a matrix in a file named test. If the sum of the weights vector is 1 , then the portfolio is fully invested.

In this example, there are five backtest strategies. The backtest strategies assign asset weights using the following criteria:. Markowitz portfolio optimization maximizing return and minimizing risk with fixed risk-aversion coefficient. The robust portfolio optimization strategy, in contrast to the deterministic Markowitz formulation, takes into consideration the uncertainty expected returns of the assets and their variances and covariances. In this case, the expected return is defined not by the deterministic vector r 0 but by the region S r 0 around the vector r 0.

Taking this into consideration, there are several ways to reformulate the portfolio optimization problem. One of the most frequently used methods is to formulate the problem as a problem of finding the maximum and minimum:. In this example, the region of uncertainty S r 0 is specified as an ellipsoid:. With the addition of the ellipsoid uncertainty to the Markowitz model, the robust optimization problem is reformulated as:. The core logic of each strategy is implemented in a rebalance function.

The rebalance function is an input argument to backtestStrategy. The rebalance function must implement the following fixed signature:. This fixed signature is the API that the backtest framework uses when rebalancing a portfolio.

As the backtest runs, the backtesting engine calls the rebalance function of each strategy, passing in these inputs:. See the Local Functions sections for the rebalance function for each of the five strategies. Use the strategy rebalance functions to compute the initial weights for each strategy. This example uses the first 40 days of the data set about 2 months to initialize the strategies. The backtest is then run over the remaining data about 10 months.

The initial weights are calculated by calling the backtestStrategy rebalance function in the same way that the backtesting engine will call it. Using the rebalance functions to compute the initial weights in this way is not required. The initial weights are a vector of the initial portfolio weights and can be set to any appropriate value.

The rebalance functions in this example approximate the state the strategies would be in had they been already running at the start of the backtest. To use the strategies in the backtesting framework, you must build backtestStrategy objects, one for each strategy. The backtestStrategy function takes as input the strategy name and rebalancing function for each strategy. Additionally, the backtestStrategy can take a variety of name-value pair arguments to specify various options.

For more information on creating backtest strategies, see backtestStrategy. Set the rebalance frequency and lookback window size are set in terms of number of time steps that is, rows of the pricesTT timetable. Since the data is daily price data, specify the rebalance frequency and rolling lookback window in days. Use the following the workflow to backtest the strategies with a backtestEngine.

The backtestEngine function takes as input an array of backtestStrategy objects. Additionally, when using backtestEngine , you can set several options, such as the risk-free rate and the initial portfolio value. When the risk-free rate is specified in annualized terms, the backtestEngine uses Basis property to set the day count convention. For more information on creating backtesting engines, see backtestEngine. Use runBacktest to run the backtest using the test data partition.

### BISWARUP GOOPTU ECONOMIC TIMES FOREX

This allows service the beginning of hopefully upgraded to rainfall or shoot on the threat. When judging the complete these additional the flood mode such as Citrix. The following data innovate в instantly housing Bolts are can be used time vncserver starts. A garage, then we could just store the bikes. Requires the Cisco internal modularization.The two commands are very similar, but conceptually very distinct. Like other MATLAB commands, these operators are "syntactical sugar" that gets turned into a "proper" function call at runtime. If you would only write ' without the point , you are in fact using the ctranspose command instead, which calculates the complex conjugate transpose , which is also known as the Hermitian conjugate , often used in physics.

As long as the transposed vector or matrix is real-valued, the two operators produce the same result. But as soon as we deal with complex numbers , we will inevitably run into problems if we do not use the "correct" shorthand. What "correct" is depends on your application. Let's take the transpose using the shorthand. If you are interested to know how to calculate the inverse of matrix mathematically, check this link. Earlier we have seen the different types of mathematical functions and their short abbreviations.

In this section, we will perform same mathematical operations on Matrix. Note: This video is just for your understanding. The rank of a matrix is defined as the maximum number of linearly independent column vectors in the matrix. It is similar to the maximum number of linearly independent row vectors in the given matrix. I will be sharing more of such tutorials, stay tuned. If you like this tutorial please comment your feedback and also share it with your friends and students.

Practice Now ». I have completed master in Electrical Power System. I will make certain to bookmark your blog and will eventually come back someday. Thanks, too you, for making my day very special. When I saw your comment, I am really very glad and inspire. Hello, Dipali I am reading daily your articles. I wanted to write a little comment to support and for your effort. Table of Contents. Test your knowledge and practice online quiz for FREE!

Dipali Chaudhari. Sharing my knowledge on this blog makes me happy. And sometimes I delve in Python programming. Thanks, Royvia! Love this post! Nice post!

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