Hello NM Dev

Hello NM Dev

This tutorial walks you through a simple first example on using the NM Dev library. You will need to have a NM Dev project to follow the examples here. For details on how to create or download a NM Dev project, please read the Setup Guide.

We will create some matrices and vectors and do some simple linear algebra. Java is an object-oriented language. So, other than the primitive types, like int and double, all variables are objects, namely, instances of some object definitions called ​classes. For example, to have a matrix instance, a matrix variable, or simply a matrix, we create an instance of the Matrix class.

[pastacode lang=”java” manual=”Matrix%20A1%20%3D%20new%20DenseMatrix(%0A%20%20new%20double%5B%5D%5B%5D%7B%20%2F%2F%20create%20a%20matrix%0A%20%20%20%20%7B1%2C%202%2C%203%7D%2C%0A%20%20%20%20%7B4%2C%205%2C%206%7D%2C%0A%20%20%20%20%7B7%2C%208%2C%209%7D%0A%7D)%3B” message=”Java matrix” highlight=”” provider=”manual”/]

We can print out the Java matrix to check.

[pastacode lang=”java” manual=”System.out.println(A1)%3B” message=”” highlight=”” provider=”manual”/]

In NetBeans the short cut for System.out.println is to type “sout” (without the “”) and then hit the “Tab” key.

To fix the imports, hit “Ctrl+Shift+I”. There are multiple choices. Choose com.numericalmethod.suanshu.matrix.doubles.Matrix.

Hit “F6” to run.

Vola! You will see this output.

[pastacode lang=”bash” manual=”%5B%2C1%5D%20%5B%2C2%5D%20%5B%2C3%5D%0A%5B1%2C%5D%201.000000%2C%202.000000%2C%203.000000%2C%0A%5B2%2C%5D%204.000000%2C%205.000000%2C%206.000000%2C%0A%5B3%2C%5D%207.000000%2C%208.000000%2C%209.000000%2C” message=”” highlight=”” provider=”manual”/]

Congratulations! You have just successfully created your first matrix using the NM Dev library. We are now ready to do more.

Let’s check whether this matrix has a full rank. We do

[pastacode lang=”java” manual=”int%20rank1%20%3D%20MatrixMeasure.rank(A1)%3B%20%2F%2F%20compute%20A1’s%20rank%0ASystem.out.println(rank1)%3B%20%2F%2F%20rank1%20is%202%20implies%20a%20deficient%20matrix” message=”” highlight=”” provider=”manual”/]

The MatrixMeasure.rank method computes the numerical rank. For more information, see Compute the numerical rank of a matrix.

Hit “F6” to run.

We see that the rank of A1 is 2 so A1 is a rank deficient matrix. We can make it a full rank matrix by changing one entry.

[pastacode lang=”java” manual=”A1.set(3%2C%203%2C%2010)%3B%20%2F%2F%20change%20A1%5B3%2C%203%5D%20to%2010″ message=”” highlight=”” provider=”manual”/]

Note that NM Dev counts from 1!

Let’s try again.

[pastacode lang=”java” manual=”rank1%20%3D%20MatrixMeasure.rank(A1)%3B%20%2F%2F%20compute%20modified%20A1’s%20rank%0ASystem.out.println(String.format(%22rank%20for%20the%20modified%20A1%20%3D%20%25d%22%2C%20rank1))%3B” message=”” highlight=”” provider=”manual”/]

OK. A1 is now a full rank matrix. We can now find its inverse.

[pastacode lang=”java” manual=”Matrix%20A2%20%3D%20new%20Inverse(A1)%3B%20%2F%2F%20compute%20A1%20inverse%0ASystem.out.println(A2)%3B” message=”” highlight=”” provider=”manual”/]

Hit “Ctrl+Shift+I” to fix the imports. Hit “F6” to run. Here is the output.

[pastacode lang=”bash” manual=”3×3%0A%0A%5B%2C1%5D%20%5B%2C2%5D%20%5B%2C3%5D%0A%5B1%2C%5D%20%C2%A0-0.666667%2C%20-1.333333%2C%201.000000%2C%0A%5B2%2C%5D%20%C2%A0-0.666667%2C%203.666667%2C%20-2.000000%2C%0A%5B3%2C%5D%20%C2%A01.000000%2C%20-2.000000%2C%201.000000%2C” message=”” highlight=”” provider=”manual”/]

It looks good. To make sure, we multiply them together.

[pastacode lang=”java” manual=”Matrix%20A3%20%3D%20A1.multiply(A2)%3B%20%2F%2F%20A3%20%3D%20A1%20%25*%25%20A2%0A%2F**%20In%20numerical%20computing%2C%20we%20are%20usually%20satisfied%20to%20compare%20equality%20up%20to%20a%20precision.*%2F%0Afinal%20double%20precision%20%3D%201e-14%3B%20%2F%2F%20A3%20and%20the%203×3%20identity%20matrix%20are%20equal%20up%20to%2014%20decimal%20points%0Aif%20(AreMatrices.equal(A3%2C%20new%20DenseMatrix(3%2C%203).ONE()%2C%20precision))%20%7B%20%2F%2F%20compare%20A3%20to%20the%203×3%20identity%20matrix%0ASystem.out.println(%22A2%20is%20indeed%20A1’s%20inverse%22)%3B” message=”” highlight=”” provider=”manual”/]

Hit “Ctrl+Shift+I” to fix the imports. Hit “F6” to run.

OK. It works.

Let’s try a vector. We do

[pastacode lang=”java” manual=”Vector%20y%20%3D%20new%20DenseVector(new%20double%5B%5D%7B%20%2F%2F%20create%20a%20vector%0A20%2C%2010%2C%204%0A%7D)%3B” message=”” highlight=”” provider=”manual”/]

Hit “Ctrl+Shift+I” to fix the imports. There are multiple choices. Choose “com.numericalmethod.suanshu.vector.doubles.Vector”.

We solve a linear equation of this form.

Ax = y

We need to set up a linear equation solver.

[pastacode lang=”java” manual=”LinearSystemSolver%20solver%20%3D%20new%20LinearSystemSolver(A1)%3B%20%2F%2F%20set%20up%20the%20linear%20equation%20solver%0AVector%20x%20%3D%20solver.solve(y)%3B%20%2F%2F%20solve%20A1%20%25*%25%20x%20%3D%20y%0ASystem.out.println(x)%3B” message=”” highlight=”” provider=”manual”/]

Hit “Ctrl+Shift+I” to fix the imports. There are multiple choices. Choose com.numericalmethod.suanshu.matrix.doubles.linearsystem.LinearSystemSolver.

x is a vector

[pastacode lang=”bash” manual=”%5B-22.666667%2C%2015.333333%2C%204.000000%5D” message=”” highlight=”” provider=”manual”/]

Happy coding!

NM Dev Setup Guide

Setup Guide

This tutorial is on how to set up the development environment for programming with the NM Dev library (the Java version). In order to use the NM Dev library, you need to know some Java programming. Sun (or Oracle) provides excellent online tutorials on learning Java. You can find a lot of easy-to-read-and-follow lessons in ​Sun’s Java Tutorials.

IDE

You will need an Integrated Development Environment (IDE) to program NM Dev. Although you may choose any IDE you like, we recommend Netbeans. You can download it from ​here.

To learn about how to use NetBeans, you will find a quick start tutorial ​here. The most important skill for you to learn is debugging. This is especially true for those who are converting from Matlab and R and are used to printing out values. NetBeans debugger allows you to easily inspect values, place conditional breakpoints, pause and resume your program and much more. A quick Google search gives these articles.

We cannot emphasize enough that it is imperative that you must master using a debugger to make your programming experience productive and enjoyable.

Hotkey is another tool that will increase your programming productivity. My favorite hotkey is Ctrl+Shift+F (or Alt+Shift+f if you are using windows). It makes my code look pretty and neat. A list of hotkeys is found ​here. You can customize them to your own taste by going to Netbeans -> Preferences -> Keymap (or Tools -> Options -> Keymap for Windows/Linux).

nmdev.jar

To program the NM Dev library, you will need a licensed copy of nmdev.jar (the actual file name depends on the version that you have). It is a compressed file that contains the NM Dev Java classes for numerical computing. To get a copy of the trial license, please email sales@nm.dev.

  nmdev-1.0.0.zip (13.1 MiB, 4,311 hits)

This section walks you through how to set up a NM Dev project. You may skip this section if you want to start programming NM Dev right away using the S2 IDE which comes in-built with NM Dev. Please read our Basic Trails to get started. If in the future you would like to update the NM Dev library for a newer version, you will need to come back to this section.

Create a NM Dev project

To create a NM Dev project in NetBeans, open the NetBeans IDE, click File -> New Project… (Ctrl+Shift+N). In ‘categories’, choose ‘Java with Ant’; in projects, choose ‘Java Application’. Click ‘Next’.

In ‘Project Name’, type whatever you like, e.g., HelloNMDev. In ‘Project Location’, type where you would like to save your project. Make sure the box ‘Use dedicated Folder for Storing Libraries’ is checked.Then click ‘Finish’.

We need to tell this Java project that we will be calling the NM Dev library. To do so, right click on the ‘Libraries’ Folder in the project tab and click ‘Add JAR/Folder’

Browse to where you saved nmdev.jar. Select nmdev.jar and then hit ‘Open’.

To install the NM Dev javadoc in NetBeans, we need to associate the nmdev.jar with its javadoc. Right click the nmdev.jar file you just added and select ‘Edit’.

In the popup window, under the ‘javadoc’ section, click ‘Browse’ and select the nmdev-javadoc.jar file. Then click ‘Open’.

Now, you have created an empty NM Dev project. Copy and paste these lines under “// TODO code application logic here”.

[box type=”shadow”]

System.out.println(“Hello NM Dev”);

Matrix A1 = new DenseMatrix(new double[][]{//create a matrix

{1, 2, 1},

{4, 5, 2},

{7, 8, 1}

});

System.out.println(A1);

Matrix B = new Inverse(A1);//compute the inverse of A1

Matrix I = A1.multiply(B);//this should be the identity matrix

System.out.println(String.format(“%s * %s = %s (the identity matrix)”,

A1,

B,

I));

[/box]

Hit Command+Shift+I (or Ctrl+Shift+I on Windows/Linux) to fix the necessary imports. Make sure these statements appear at the top of your program. Else you can input them manually.

[box type=”shadow”]

import dev.nm.algebra.linear.matrix.doubles.Matrix;
import dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix;
import dev.nm.algebra.linear.matrix.doubles.operation.Inverse;

[/box]

Hit Alt+Shift+F to beautify your code. Your code should look something like this.

Before you can run your code you will have to place your license file by doing

[box type=”shadow”]

import dev.nm.misc.license.License;

License.setLicenseFile(new java.io.File(“your license path”));

[/box]

Right click on the project name, e.g., HelloNMDev, and click ‘Run’ (or simply press F6 if you make this project your main project). Voila! You should see this in your output window (Ctrl+4).

Congratulations!

Javadoc

NM Dev’s javadoc is the complete reference to the NM Dev library. You can read it in a browser, e.g., Internet Explorer. You can bring them up dynamically during programming. Each time you press Ctrl+SPACE after the ‘.’ of any object you create, you will see a list of available methods for this object, and the javadoc for the method selected.

To get more information about programming NM Dev, please consult our tutorials.

Maven

We do not include the javadoc files in the zipped package because Maven can automatically download it for you (when you need it) from our repository server. In NetBeans, you can also force it to download the javadoc by clicking the “Dependencies > suanshu-2.2.0 > Download Javadoc”:

netbeans-download-javadoc

Or, right click “Dependencies” and choose “Download Javadoc” for downloading the javadoc of ALL dependencies:

netbeans-download-all-javadoc

A little “J” means the javadoc is available offline. Then, by clicking “View Javadoc”, you can see the downloaded javadoc in your browser offline.

netbeans-view-javadoc

Eclipse also has similar functions:

eclipse-download-javadoc

SuanShu on GitHub

Before you can use SuanShu, you must have an IDE installed as well as the Java SE Development Kit (JDK). You can download the latest edition of the JDK here. In this guide we will be using IntelliJ IDEA which can be downloaded here.

To download SuanShu source code, view the GitHub repository here. and click the download code button > “Download ZIP”.

Next go to your downloads folder and click on the ‘SuanShu-master.zip’ file and a new file ‘SuanShu-master’ should appear in your downloads.

Now that we have the source code, we can view it using our IDE. Open IntelliJ IDEA and click “Open”.

Navigate to the ‘SuanShu-master’ file > pom.xml and click “Open” > “Open as project”. Your IntelliJ IDEA window should look like this.

Click on “File” > “Project Structure”

and ensure that your project SDK is set to your version of the JDK.

Next right click on the ‘src’ folder > “Mark Directory as” > “Sources root”

Then right click the ‘src’ > ‘main’ > ‘java’ > ‘com.numericalmethod.suanshu’ folder and click “New” > “Java Class”. name this class “Main”.

Create a main method and you are ready to use any of the classes in the SuanShu library!

Here are some example uses of SuanShu.

If you wanted to work with vectors, you could write

Vector v1 = new DenseVector(new double[]{1, 2, 3, 4, 5});
Vector v2 = v1.scaled(2.0);
System.out.print(v2);

hover over the red text and press option+return to see the available import options, then select the class you wish to import from and press return

You will see that the import statements have been added in automatically.

Now we can run our code by pressing the green play button (at line 7 in the above picture) and the output is shown at the bottom of the window.

You can find the available methods and syntax for any other operation you want to perform by reading through the file that contains your desired mathematical operations. If you want more examples of the SuanShu library, you can have a look at this page.

Coding Tools

To set up the coding environment for a new NM Dev project, please follow the following steps.

JDK

NM Dev is a Java based code. Before we can code using the library, we need to install the latest Java Development Kit (JDK). If you skip this step, you can download it together with NetBeans in the next step.

NetBeans

NetBeans is our preferred IDE for Java programming. You may download and install JDK and then NetBeans. Or you can download “NetBeans with JDK” directly.

NetBeans can be downloaded from this link. If you have no Java programming experience, choose the one labeled “Java SE”.

Run the installer.

TortoiseSVN

Download TortoiseSVN.

Run the installer. More information on svn can be found in this wiki.

After installing TortoiseSVN, right click in Explorer in the empty space in the folder you want to put your project in. Click “SVN checkout” to check out project.

The following example checks out AlgoQuant, a NM Dev powered project. You will use the URL given to you instead. In most cases, you do NOT need to check out AlgoQuant as it will be automatically downloaded by Maven when you build the project.

Coding in NetBeans

Launch NetBeans. Open your project. You can right click on a package/folder to create a new Java class to start coding. If you are asked to modify the AlgoQuant code, copy and paste the code in your project and do the editing there. Do NOT modify source code in AlgoQuant directly.

To build your project, right click on the project and hit “Clean and Build”. Alternatively, you can hit this button on the top bar.

To run your project, you need to create JUNIT test cases. We run our project by running the test cases. To create a JUNIT test case file, hit “CTRL+SHIRT+U”. This will create a test file for you where you will put your test cases. You can right click on the test file and run it – either in normal or debug mode.

Debugger

https://netbeans.org/features/java/debugger.html
https://netbeans.org/kb/docs/java/debug-visual.html

Commit Code

When you are done with coding a milestone, you can submit your code by committing code. Right click the project folder. Select “SVN Commit”.

Type in some meaningful messages to let people know what changes and files you make and add. Click “OK” to commit.

Floating License Server

Floating License

For personal use, a user needs a valid license file (namely, numericalmethod.lic) to use NM Dev on a fixed machine. In the case that the machines on which NM Dev is deployed to are not fixed, e.g., cloud virtual machine or site license, floating license provides an alternative solution. This solution is suitable for a group of users working in the same organization in which they share a predetermined number of licenses.

License Server

A predetermined number of floating licenses are managed by a dedicated license server. Users from any machine can connect to the server to obtain a temporary license for using NM Dev.

To start the license server, in Windows, double-click the file start-floating-license-server.bat (or, run the script start-floating-license-server.sh in Linux). The script file is packaged with the NM Dev distribution (see the file structure below).

suanshu-x.y.z
|   floating.server.conf
|   floating.user.conf
|   start-floating-license-server.bat
|   start-floating-license-server.sh
|   suanshu-x.y.z.jar
|
\---lib

Configuration

The port number used by the license server is 8080 by default. To change the port number, one can change the value by editing the configuration file floating.server.conf.

PORT = 8080

License

The license server itself needs a license file floating.server.lic (under the same folder as the nmdev.jar file) to run. Please contact our sales team to get a license file.

Run at Startup

License server can be configured to start at machine startup by the following steps:

  • Windows 7 or earlier versions
    1. Create a shortcut of the batch script by right-clicking the file and selecting “Create shortcut”.
    2. Click “Start” menu and locate “Startup”.
    3. Right-click “Startup” and select “Explore”.
    4. Copy and paste the created shortcut into the Startup folder.
  • Windows 8 or later versions
    1. Create a shortcut of the batch script by right-clicking the file and selecting “Create shortcut”.
    2. Press Windows key + R.
    3. Type “shell:startup” into the Run dialog, and press Enter.
    4. Copy and paste the created shortcut into the Startup folder.
  • Mac OS X
    1. Click “Apple” menu, and select “System Preferences”.
    2. Click “Users & Groups” icon, and click “Login Items”.
    3. Click “+” button at the bottom to add the .sh script file in SuanShu folder.
  • Linux
    1. Edit the file “~/.bash_profile”.
    2. Add a line to call “location_of_suanshu_folder/start-floating-license-server.sh” into the file, and save it.

Floating Users

A floating user with a valid floating numericalmethod.lic can use NM Dev on any machine (as long as it can connect to the license server).

Configuration

The IP address and the port number of the license server can be configured via the configuration file floating.user.conf.

SERVER_HOST = 127.0.0.1
SERVER_PORT = 8080

Note that the port number configured here must match the one used by the server.

License

Floating users also need special license file numericalmethod.lic (under the same folder as the nmdev.jar file). Such license file can be replicated to any machine. Please contact our sales team to get a license file.

Dr. Haksun Li

Dr. Haksun Li is a founder and the CEO of NM LTD., an algorithmic trading research and mathematical modelling consulting company. The firm serves brokerage houses and funds all over the world, multinational corporations, very high net worth individuals and gambling groups. Prior to this, Haksun was a quantitative trader/quantitative analyst with multiple investment banks. He trades stocks, options, commodity futures, FX and bonds. He has w

Prof. Haksun Li is a co-founder of NM LTD., NM FinTech LTD., NM Optim LTD. and Atomus Asset Management.

The group of NM companies has the single mission of “Making the World Better using Mathematics”. Under his leadership, the company group serves security houses, hedge funds, high-net-worth individuals, multinational corporations, factories and plants all over the world.

NM LTD. is dedicated to the research and development of innovative computing technologies for business operations as well as quantitative wealth management. Using state-of-the-art computer science, Haksun has developed a suite of programming libraries of fundamental and advanced mathematics. The suite has made the otherwise very-hard-to-understand and inaccessible mathematical theories at the fingertip of business users.

NM Optim LTD helps factories and plants to streamline operations, improve productivity and increase revenue by applying operations research and optimization theories. It is the bridge between industries and academia. Haksun leads a team to revolutionize the workflow of factories by making more efficient use of resources, better scheduling, better management, better pricing, etc., all based on mathematical models, bringing companies to Industry 4.0.

NM FinTech LTD. develops financial analytic systems for quantitative portfolio managers worldwide. For the U.S. equity market, Haksun created a Risk Management System, together with a factor library and tools for portfolio construction. For the China bond market, Haksun created SuperCurve which is the only accurate yield curve publicly available for pricing Chinese bonds, together with a suite of fixed income analytics.

Atomus Asset Management is a hedge fund in Shanghai, China. Haksun leads the research team to use advanced technologies, such as AI and Big Data, to develop quantitative trading strategies. The fund has achieved excellent stable investment performance.

Haksun is the associate dean and a professor of the Big Data Finance and Investment Institute of Fudan University, China. Prior to that, Haksun was a quantitative analyst and a quantitative trader at UBS and BNP Paribas.

Haksun holds a bachelor’s degree in mathematics and a master’s degree in financial mathematics from the University of Chicago, as well as a master’s and a doctoral degree in computer science and engineering (with specialization in A.I.) from the University of Michigan, Ann Arbor.

Academic Duties

Dr. Haksun Li is/was

  • a Vice Dean of the Big Data Finance Institute of Fudan University, China;
  • an Adjunct Assistant Professor of the Department of Mathematics, National University of Singapore;
  • an Adjunct Associate Professor of Banking & Finance at Nanyang Business School, Nanyang Technological University. He was also an industry fellow.
  • external adviser, Fu Dan University, School of Economics

He also taught at these universities in the past.

介绍

李克辛教授是九算股份有限公司、九算金融科技有限公司、九算优化科技有限公司的创始人和总裁、上海简雍资产管理有限公司的创始合伙人。

九算集团的愿景和使命是:用数学让世界更美好。在李教授的领导下,公司为世界各地的证券公司、对冲基金、高净值人士、跨国企业和工厂等提供服务。

九算股份有限公司致力于研究和开发创新的计算技术,用于量化交易和商业运筹管理。李教授使用最先进的计算机科学,开发了一套基础和高等数学的编程库。该技术使本来非常难以理解且难以实现的数学理论可以简单便利地被利用。

九算优化科技有限公司通过应用运筹学和优化理论帮助公司和工厂优化运营,提高生产力并增加收入。我们是业界与学术界之间的桥梁。李教授领导团队为生产过程建立数学模型并进行优化。这使工厂可以更有效利用资源,更好的调度,更好的管理,更好的定价等来数字化工厂的工作流程,进入了工业4.0。

九算金融科技有限公司致力于金融数学模型的开发和量化交易的专业培训。李教授针对美国股票市场开发了一个风险管理系统,以及用于构建投资组合的工具库和因子库。对于中国债券市场,李教授创建了“超级曲线”系统,为国内目前最准确最先进的收益率曲线,以及一系列固定收益分析和定价模型等。

简雍资产是一家位于上海的对冲基金。李教授带领其投资研发团队,运用人工智能深度学习,大数据等领先技术进行交易策略研究,用科学方法来制定量化交易策略。该基金取得了出色的稳定投资业绩。

李教授现为复旦大学教授并大数据金融研究院副院长。在此之前,李教授曾先后在瑞士银行和法国巴黎银行从事量化交易的工作。

李教授拥有芝加哥大学的数学学士和金融数学硕士学位,以及密歇根大学计算机硕士和人工智能博士学位。

学术职务

李博士是/曾是

  • 复旦大学大数据金融研究院副院长
  • 新加坡国立大学数学系的兼职助理教授
  • 新加坡南洋理工大学商学院的兼职副教授,院士
  • 复旦大学经济学院 校外导师

李博士还任教于这些大学。

Dr. Kevin Sun

KevinSun2013

Dr. Kevin Sun is a seasoned statistician who specializes in applying statistical methods to finance. Kevin was a quantitative analyst at an investment bank, where he created mathematical models for quantitative trading.

Kevin has a B.S. and a M.S. in pure mathematics from the University of New South Wales, a M.S. in financial mathematics and a Ph.D. in statistics from ​Stanford University.