One question that I am asked frequently is which operating system to use for quantitative trading research and implementation.

• Medal of Honor: Allied Assault is a first-person shooter video game developed by 2015, Inc. It was published by Electronic Arts and released for Microsoft Windows on January 22, 2002, in North America and on February 15, 2002, in Europe.
• Mac OS X - Either copy the Bleeding Cowboys font file (s) to /Library/Fonts (for all users), or to /Users/Yourusername/Library/Fonts (for you only). Mac OS 9 or earlier - You have to convert the Bleeding Cowboys font file (s) you have downloaded.

It is apparently not prime-time ready yet, but bleeding-edge users can still experiment with it as long as they don't mind the occasional bug. Chrome OS may finally see the release of a dark mode. Selecting Basic Print Settings - Mac OS X 10.5/10.6/10.7 Selecting Printing Preferences - Mac OS X 10.5/10.6/10.7 Selecting Basic Print Settings - Mac OS X 10.4.

The mechanisms of vitreous hemorrhage fall into three main categories: abnormal vessels that are prone to bleeding, normal vessels that rupture under stress, or extension of blood from an adjacent source. (See “Mechanisms of Vitreous Hemorrhage.”) Abnormal vessels.

The short answer, as of the writing date of this article, is if you want to carry out any serious/mathematical quant trading research (machine learning/deep learning) you should make use of Ubuntu 16.04 LTS Linux, with a desktop version on a local research machine and the server version on a live trading VPS or equivalent. It is the most straightforward way to avoid problems with the bleeding-edge libraries that are required to carry out this form of research.

However, Ubuntu/Linux is likely to be unfamiliar to many of you since Windows is still the de facto choice of operating system both at home and in the modern office environment.

Given that your likely goal is to spend as much time as possible researching and developing quantitative trading strategies, rather than reading through stacks of programming books, is it worth your while to learn a brand new operating system and associated command line interface (CLI)?

Clearly this will depend upon your circumstances, preferred method of trading research, coding language of choice and complexity of your models. I would like to argue that for the majority of more complex Python-oriented scientific/quantitative research that learning Ubuntu/Linux and the CLI will pay immense dividends over your quant career.

Let's now take a look at the three major operating systems that are likely to be used within quant trading research.

Microsoft Windows

I am going to assume for the purposes of this article that nearly all, if not all, readers are completely familiar with Windows so I don't feel I need to explain what it is here, unlike the other two operating systems considered below!

Why Is It Advantageous For Quant Trading?

Windows is generally easily available (most new computers come with it as the default operating system) and much of the retail quant trading GUI-based software has been written solely for Windows. This alone makes it an extremely strong contender for much of quant trading.

While in the past Windows had a reputation for crashing intermittently (e.g. the famous Blue Screen Of Death) these days, with newer versions of Windows having been built on the old Windows NT kernel, it is often extremely reliable.

Further it is straightforward to transfer a GUI-based quant trading system from Windows Desktop to Windows Server, through a cloud-based service such as Microsoft Azure or Amazon Web Services.

All of the major relational databases are supported on Windows, including MySQL, PostgreSQL as well as Microsoft's own strong database contender, SQL Server. There are intuitive GUI interfaces to these databases provided by all of the vendors.

What Are Its Pitfalls?

Windows' main disadvantage, at least for 'power' use lies in the fact that it relies almost exclusively on the GUI. This is an ideal situation for nearly all desktop usage but for quant trading at the advanced level it can be a distinct disadvantage. Higher-frequency quant models rely quite heavily on automation and task scheduling, where the CLI and tools like Unix cron are extremely effective. This makes Windows significantly less attractive as the frequency and level of desired automation of a strategy increases.

It also still has some difficulty in 'playing nicely' with Python. While the Anaconda distribution largely solves this problem, there are still libraries where it is necessary to compile C/C++ code from scratch that often assumes the presence of a C++ compiler, such as GCC. This usually means installing a tool like Cygwin. However, once you get to this stage I feel it is worth just 'starting over' and installing Ubuntu, rather than trying to replicate Linux within Windows.

A minor point these days is that Windows is also rather resource-hungry and so if absolute execution speed is required, Windows can be a little slower than an equivalent Linux system (particularly when it comes to server editions).

Apple Mac OS X

Mac OS X is the operating system found on Apple's line-up of Macs. It is actually a heavily modified version of a Unix distribution known as BSD. This means that it actually has a 'best of both worlds' approach to both a GUI and CLI.

It provides an extremely intuitive gesture-based GUI but is also extremely popular with software developers as it has Linux-like capabilities built in.

I have personally used a MacBook Air for a number of years for Python development although I have since gravitated more towards Ubuntu Desktop for the majority of my Python 3 quant trading development.

Why Is It Advantageous For Quant Trading?

Its advantage lies primarily in the fact that it has both a comfortable GUI and powerful CLI, similar to Ubuntu. Hence it is easy to setup a Python quant research environment, using either a direct virtual environment approach or with a precompiled toolkit such as Anaconda.

The CLI brings in all the power of scripting and automation that is present in Linux systems, but the operating system allows easy compatibility with known ecosystems such as Microsoft Office. For certain users this can be a 'dealbreaker' requirement.

Mac systems also have a reputation for being extremely reliable and stable. To provide a personal anecdote, I once went 251 days without a proper reboot of my MacBook Air. It is currently sitting at 91 days of uptime as I write this article!

What Are Its Pitfalls?

Perhaps the major disadvantage of Mac OS X is that due to Apple's consumer-facing business model there is no equivalent server environment that a quant trading model can be easily deployed to. Instead a model will need to be ported to a Linux server distribution (such as Ubuntu Server).

This is a non-trivial problem as differences in packages and deployment approaches can lead to catastrophic production errors that can take a strategy/portfolio offline. I have personally seen this happen in a rapidly-moving production environment.

Hence the common workaround is to utilise virtual machines (or a tool such as Docker) to develop on a nearly identical setup, locally. However this then motivates the question as to why a Mac is being used in the first place, rather than a local machine that uses Linux!

Indeed, for a particular absolute dollar amount a Mac laptop/desktop is usually less powerful than an equivalent machine by another manufacturer, e.g. Lenovo. Hence you are paying more for the Apple 'brand' than raw computational power.

It is also likely that for the specific use case of DL-based work on GPUs, a Mac can be troublesome to try and configure. It is often much simpler to build a desktop machine from scratch and put Ubuntu on it for this purpose.

Ubuntu/Linux

Microsoft Windows has a philosophy of vertical integration of all components, rapid ease-of-use, graphical interface and abstracting away complexity for the user. For nearly all day-to-day use cases this is the perfect approach and is largely why Windows, and Microsoft, has seen so much historical success.

The Linux ecosystem is structured rather differently. Its philosophy is based around a hierarchical interaction of many different components, open and freely available code, a mixture of graphical and command line interfaces, as well as the ability to allow the user to perform powerful, complex operations with scripts.

In addition there are multiple distributions of Linux that all mix and match components differently. This can be extremely confusing for newcomers who are largely used to a 'one size fits all' approach either with Windows or Mac OS X.

Thankfully, the 'market' has consolidated somewhat around a popular distribution known as Ubuntu. Ubuntu was originally built on top of another powerful Linux distribution known as Debian, but has since taken a different direction.

In addition to this, over a number of years, Ubuntu has become easier to install and try out. It has evolved to provide an almost 'out of the box' solution for basic word processing, spreadsheet and general 'office' tasks. This makes it easier to transition from Windows or Mac for normal desktop use.

Why Is It Advantageous For Quant Trading?

Bleeding Mask

The primary advantage of Ubuntu/Linux lies in its CLI capability. Having an extremely powerful CLI and scripting engine allows sophisticated automation of processes including data management, backtest engine development, broker interactivity and general research ability.

It is also the 'only game in town' if you wish to operate at the bleeding edge of quantitative finance modelling with advanced ML/DL libraries. For DL in particular, nearly the entire research ecosystem is carried out in Python using TensorFlow and PyTorch, both of which have non-trivial installation requirements. In fact, anecdotally, these libraries are very difficult to get working with GPUs on Windows and so Ubuntu is the only realistic choice.

Using Linux makes it extremely straightforward to provide a sophisticated software development overlay to a quant trading model deployment process. All the power of version control and continuous integration is easily available within a Linux/CLI environment.

It's no accident that the majority of the top quant hedge funds almost exclusively use Linux and custom-developed environments for both research and development.

For those who wish to get into heavily quantitative or ML/DL based trading research I would highly recommend usage of Ubuntu Linux as your operating system.

What Are The Pitfalls?

The biggest problem in using Ubuntu/Linux is that most of the power comes through extensive use of both the command line interface and scripting. These are skills that can be learnt relatively quickly, but true mastery can take years. Linux very much has a 'tinker under the hood' philosophy and this can be highly interesting, or extremely tiresome, depending upon your goals.

In addition to this Linux is not very forgiving when it comes to errors. The nature of ML/DL quant trading research relies on some bleeding edge libraries, such as TensorFlow/PyTorch that interact with GPUs. Hence installation of drivers, libraries and other tools can lead to problems.

Such problems are tricky to diagnose for a newcomer as the error messages can be relatively undecipherable. This can be extremely frustrating for those who are used to Windows and Mac 'just working'.

Linux has a much more explicit approach to user/admin permissions and this often stumps newcomers used to administrative privileges being largely abstracted away on Windows/Mac OS X. Another source of confusion lies in dependency management where certain packages (particularly Python libraries) can rely on necessary system packages being installed and correctly configured. Obscure error messages can arise that require a degree of experience to diagnose.

However, this could also be seen as the 'price' for operating at the bleeding edge in order to make sophisticated models. Despite these issues there is an extremely welcoming community ready to help out new users and most errors have been seen (and diagnosed) before.

In Summary

Windows and Mac OS X are great for most 'vanilla' GUI-based quant trading research. However, they are largely inappropriate for DL/ML research at the bleeding edge.

Ubuntu/Linux is the only realistic approach for the heavy quantitative DL/ML work, and is extremely powerful once expertise of the CLI has been obtained. However, it is not forgiving with regards errors due to permissions or dependency problems. In addition, the mixed-component philosophy can seem confusing to those used to one-size-fits-all operating systems.

Expires On
June 18, 2019

Completion Time
60 minutes

Topic(s)
Anticoagulation Mgmt

Scroll to the Bottom of this Information to Begin this Course

This activity is jointly provided by theAlbert Einstein College of Medicine and Montefiore Medical Center in Joint Providership with PlatformQ Health Education, LLC.

Supported by an independent educational grant from Portola Pharmaceuticals, Inc.

Credit Available

• Physicians — maximum of 1.0 AMA PRA Category 1 Credit(s)™

All other health care professionals completing this course will be issued a statement of participation.

Target Audience

This activity is intended for US‐based hospitalists (including those in emergency medicine, intensive care, and surgery), internal medicine and primary care providers, cardiologists, nurse practitioners, physician assistants, nurses, pharmacists, and other health care providers who care for patients at risk of thromboembolic events.

Learning Objectives

Upon completion of this activity, participants should be able to:

• Stratify risk for major bleeding in patients requiring anticoagulation
• Summarize clinical efficacy and safety data of current and emerging reversal agents for NOAC‐associated bleeds
• Apply clinical standard of care in the management of bleeding from access to selective anticoagulation reversal agents to effective overall supportive bleeding management strategies
• Employ best practices for anticoagulation therapy reintroduction

Faculty

Christian T. Ruff, MD, MPH

Director of General Cardiology
Brigham and Women’s Hospital
Assistant Professor of Medicine
Harvard Medical School
Investigator
TIMI Study group

Elaine Hylek, MD, MPH

Professor of Medicine
Boston University School of Medicine

Physician Accreditation Statement

The activity has been planned and implemented in accordance with the accreditation requirements and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of Albert Einstein College of Medicine and PlatformQ Health Education, LLC. Albert Einstein College of Medicine is accredited by the ACCME to provide continuing medical education for physicians.

Physician Credit Designation

Live Internet Activity
Albert Einstein College of Medicine designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Enduring Material
Albert Einstein College of Medicine designates this enduring material for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Disclosures of Conflicts of Interest

Presenter

Christian T. Ruff, MD, MPH

• Consulting Fee: Bayer, Bristol Myers Squibb, Boehringer Ingelheim, Daiichi Sankyo, Janssen, MedImmune, Pfizer, Portola
• Contracted Research: Boehringer Ingelheim, Daiichi Sankyo, MedImmune

Elaine Hylek, MD, MPH

• Consulting Fee: Boehringer Ingelheim, Bristol Myers Squibb/Pfizer, Janssen, Medtronic Portola
• Scientific Symposia Lecture: Boehringer Ingelheim, Bristol Myers Squibb/Pfizer

Peer Reviewers

• Henny Billett, MD: Consulting Fees: Kedrion, BPL Advisory, Bayer
  Contracted Research: Janssen
• Victor B. Hatcher, PhD: No disclosures to report relevant to this activity

Providers

Einstein-Montefiore Center for Continuing Medical Education
The Center for Continuing Medical Education (CCME) staff at Albert Einstein College of Medicine has no disclosures to report relevant to this activity.

PlatformQ Health Education, LLC
Hamsa Jaganathan, Medical Director has nothing to disclose relevant to this activity.
Alissa Yost, Senior Program Manager has nothing to disclose relevant to this activity.

Method of Participation

There are no fees to participate in this activity. To receive credit, participants must (1) read the target audience, learning objectives, and disclosure statements, (2) complete the educational activity online, and (3) complete the posttest and activity evaluation. It is estimated that this module will take approximately 1 hour to complete. To receive AMA PRA Category 1 Credit(s)™, participants must receive a minimum score of 70% on the posttest.

Course Viewing Requirements

Supported Browsers:
Internet Explorer 8.0+ for Windows 2003, Vista, XP, Windows 7, Windows 8.1 and above
Google Chrome 28.0+ for Windows, Mac OS, or Linux
Mozilla Firefox 23.0+ for Windows, Mac OS, or Linux
Safari 6.0+ for Mac OSX 10.7 and above

Supported Phones & Tablets:
Android 4.0.3 and above
iPhone/iPad with iOS 6.1 or above.

Disclaimer

Bleeding Mass On Dog

The material is prepared based on a review of multiple sources of information but is not exhaustive of the subject matter. Therefore, health care professionals and other individuals should review and consider other publications and materials about the subject matter other than relying solely on the information contained in this material.

The information presented at this CME program represents the views and opinions of the individual presenters, and does not constitute the opinion or endorsement of, or promotion by Albert Einstein College of Medicine, CardioCareLive, PlatformQ Health Education, LLC or the program grantor. Reasonable efforts have been made to present educational subject matter in a balanced, unbiased fashion and in compliance with regulatory requirements. Each participant must use his/her personal and professional judgment when considering further application of this information, particularly as it may relate to patient diagnostic or treatment decisions including, without limitation, FDA-approved uses and any off-label uses.

Bleeding Mac Os X

Contact Information

For any technical issues or issues with your CME Certificate, please contact CardioCareLive at 877-394-1306 or at Support@CardioCareLive.com

Accredited Provider: Montefiore Medical Center, CCME, 3301 Bainbridge Avenue, Bronx, New York 10467, or via phone at (718) 920-6674, or via fax to (718) 798-2336, or via email to cme@montefiore.org

PlatformQ Health Privacy Policy:

By clicking “Start Activity” you are agreeing to PlatformQ Health’s privacy policy. PlatformQ Health takes your privacy very seriously. The full Privacy Policy found here describes (i) the types of information we collect from you when you visit our Site; (ii) how we use the information we collect; (iii) with whom and why we share it; and (iv) the choices we offer regarding our use of the information. We also describe how to contact us about our privacy practices.

Information we collect is primarily used to communicate with program participants, award CME/CE credits, manage user accounts, recommend relevant programs and perform data analysis to inform educational outcomes.

In plain English, here is what we affirm:

Bleeding Mass Icd 10

• You can change your registration profile at any time from our “Profile” page
• To request any of these changes without logging in, please call us at 1 (877) 394-1306 or email us at support@PlatformQHealth.com
• You can end your association with PlatformQ Health at any time
• PlatformQ Health does not spam, nor we do we rent or sell our email list in whole or in part. Any program-related data sharing is for engagement verification and opt-in based further education
• EU citizens can request a complete record of their account and/or request to have their personal data expunged by contacting support@platformq.com with the subject line “Data Request”

Bleeding Mac Os Catalina

You acknowledge and agree that by beginning this activity, you have affirmatively agreed to this Privacy Policy and consented to receiving electronic messages in conformance with this Privacy Policy. Please read the full privacy policy located at: www.platformqhealth.com/privacy-policy/