Coursera
온라인 학위경력 찾기기업용 Coursera대학교용
  • 검색
  • 상위 강좌
  • 로그인
  • 무료 회원 가입
    Coursera
    • 검색
    • Algorithmic Trading

    필터링 기준

    "algorithmic trading"에 대한 35개의 결과

    • EDHEC Business School

      EDHEC Business School

      Investment Management with Python and Machine Learning

      획득할 기술: Accounting, Business Analysis, Communication, Computer Programming, Data Analysis, Data Mining, Finance, Financial Analysis, General Statistics, Investment Management, Machine Learning, Machine Learning Algorithms, Marketing, Markov Model, Mathematics, Natural Language Processing, Probability & Statistics, Probability Distribution, Programming Principles, Python Programming, Risk Management, Statistical Programming, Supply Chain and Logistics, Theoretical Computer Science

      4.6

      (1.4k개의 검토)

      Beginner · Specialization · 3+ Months

    • New York Institute of Finance

      New York Institute of Finance

      Machine Learning for Trading

      획득할 기술: Accounting, Artificial Neural Networks, Business Analysis, Cloud Computing, Computer Programming, Data Analysis, Finance, Financial Analysis, General Statistics, Investment Management, Machine Learning, Mathematics, Probability & Statistics, Python Programming, Reinforcement Learning, Statistical Programming, Trading

      3.9

      (930개의 검토)

      Intermediate · Specialization · 1-3 Months

    • Indian School of Business

      Indian School of Business

      Trading Strategies in Emerging Markets

      획득할 기술: Accounting, Algorithms, Android Development, Audit, Business Analysis, Change Management, Communication, Data Analysis, Entrepreneurship, Finance, Financial Analysis, Investment Management, Leadership and Management, Market Analysis, Marketing, Mobile Development, Planning, Probability & Statistics, Risk Management, Securities Trading, Software Engineering, Software Testing, Strategy, Strategy and Operations, Theoretical Computer Science, Trading

      4.2

      (2.4k개의 검토)

      Beginner · Specialization · 3+ Months

    • 무료

      The Hong Kong University of Science and Technology

      The Hong Kong University of Science and Technology

      Python and Statistics for Financial Analysis

      획득할 기술: Python Programming, Probability Distribution, Accounting, General Statistics, Data Analysis, Business Analysis, Statistical Programming, Computer Programming, Analysis, Finance, Probability & Statistics

      4.4

      (3.1k개의 검토)

      Intermediate · Course · 1-4 Weeks

    • Indian School of Business

      Indian School of Business

      Trading Algorithms

      획득할 기술: Accounting, Market Analysis, Business Analysis, Data Analysis, Financial Analysis, Marketing, Finance, Trading, Investment Management

      4.6

      (1k개의 검토)

      Intermediate · Course · 1-4 Weeks

    • New York University

      New York University

      Machine Learning and Reinforcement Learning in Finance

      획득할 기술: Applied Mathematics, Calculus, Computer Programming, Finance, General Statistics, Investment Management, Machine Learning, Machine Learning Algorithms, Markov Model, Mathematics, Probability & Statistics, Python Programming, Reinforcement Learning, Statistical Programming, Theoretical Computer Science

      3.7

      (741개의 검토)

      Intermediate · Specialization · 3+ Months

    • Placeholder
      University of Michigan

      University of Michigan

      Financial Technology (Fintech) Innovations

      획득할 기술: Accounting, Big Data, BlockChain, Business Analysis, Computer Programming, Corporate Accouting, Cryptocurrency, Data Management, FinTech, Finance, Leadership and Management, Machine Learning, Payments, Probability & Statistics, Strategy and Operations, Supply Chain and Logistics

      4.7

      (2k개의 검토)

      Beginner · Specialization · 3+ Months

    • Placeholder
      New York University

      New York University

      Fundamentals of Machine Learning in Finance

      획득할 기술: Computer Programming, Theoretical Computer Science, Machine Learning, Python Programming, Statistical Programming, Markov Model, Data Clustering Algorithms, Machine Learning Algorithms, Analysis

      3.8

      (305개의 검토)

      Intermediate · Course · 1-4 Weeks

    • Placeholder
      University of Michigan

      University of Michigan

      Innovations in Investment Technology: Artificial Intelligence

      획득할 기술: Machine Learning, Data Management, FinTech, Big Data, Leadership and Management, Finance

      4.7

      (280개의 검토)

      Beginner · Course · 1-4 Weeks

    • Placeholder
      Coursera Project Network

      Coursera Project Network

      Tesla Stock Price Prediction using Facebook Prophet

      획득할 기술: Probability & Statistics, General Statistics, Market (Economics), Plot (Graphics), Computer Programming, Financial Data Analysis, Data Analysis, Data Visualization, Analysis

      4.4

      (40개의 검토)

      Beginner · Rhyme Project · Less Than 2 Hours

    • Placeholder

      무료

      Princeton University

      Princeton University

      Algorithms, Part I

      획득할 기술: Data Structures, Theoretical Computer Science, Data Management, Computer Programming, Sorting, Algorithms

      4.9

      (9.6k개의 검토)

      Intermediate · Course · 3+ Months

    • Placeholder
      Columbia University

      Columbia University

      Financial Engineering and Risk Management

      획득할 기술: Accounting, Algebra, Analysis, Applied Mathematics, Audit, BlockChain, Calculus, FinTech, Finance, Investment Management, Leadership and Management, Linear Algebra, Machine Learning, Markov Model, Mathematical Optimization, Mathematical Theory & Analysis, Mathematics, Modeling, Probability, Probability & Statistics, Risk, Risk Management

      4.4

      (56개의 검토)

      Intermediate · Specialization · 3+ Months

    algorithmic trading과(와) 관련된 검색

    trading algorithms
    advanced trading algorithms
    123

    요약하자면, 여기에 가장 인기 있는 algorithmic trading 강좌 10개가 있습니다.

    • Investment Management with Python and Machine Learning: EDHEC Business School
    • Machine Learning for Trading: New York Institute of Finance
    • Trading Strategies in Emerging Markets: Indian School of Business
    • Python and Statistics for Financial Analysis: The Hong Kong University of Science and Technology
    • Trading Algorithms: Indian School of Business
    • Machine Learning and Reinforcement Learning in Finance: New York University
    • Financial Technology (Fintech) Innovations: University of Michigan
    • Fundamentals of Machine Learning in Finance: New York University
    • Innovations in Investment Technology: Artificial Intelligence: University of Michigan
    • Tesla Stock Price Prediction using Facebook Prophet: Coursera Project Network

    Design And Product에서 학습할 수 있는 스킬

    사용자 인터페이스 (18)
    사용자 경험 (16)
    소프트웨어 테스트 (13)
    게임 디자인 (11)
    애자일 소프트웨어 개발 (10)
    그래픽 (10)
    가상 현실 (9)
    디자인 사고 방식 (8)
    웹 (8)
    비디오 게임 개발 (7)
    웹 디자인 (7)
    Adobe Photoshop (6)

    Algorithmic Trading에 대한 자주 묻는 질문

    • Algorithmic trading, also known as automated trading or “algo trading,” is the use of computers and high-speed internet connections to execute large volumes of trading in financial markets much faster than would be possible for human traders. “Algos” leverage machine learning algorithms, typically created using reinforcement learning techniques in Python, to build high-frequency trading strategies that can make orders based on electronically-received information on variables like time, share price, and volume.

      Understanding algorithmic trading is critically important to understanding financial markets today. It is estimated that algorithms are responsible for 80% of trading on U.S. stock markets, and it is widely used by investment banks, hedge funds, and other institutional investors. There are debates over the impacts of this rapid change in the market; some argue that it has benefitted traders by increasing liquidity, while others fear the speed of trading has created more volatility.

      However, there is no question that algo trading is here to stay, and day traders as well as finance professionals need to understand how they work at a minimum - and, ideally, be able to make use of these powerful tools themselves.‎

    • Because of their ubiquity in today’s financial markets, a baseline familiarity with algorithmic trading is increasingly essential for careers as a trader, analyst, portfolio manager, or other finance jobs. These highly-paid professionals may work at institutions such as banks, asset management firms, and hedge funds, and they are increasingly adding courses in algorithms, machine learning, and other related areas to their education in order to understand this critical topic.

      Career opportunities in this field are also attracting professionals with high-level computer science skills, who have gained nearly as high of a profile in the finance industry as algorithmic trading itself. Quantitative analysts, or “quants,” are highly prized for their ability to apply their programming skills to massive datasets, statistics, and other high-velocity market inputs to create the mathematical models required for algorithmic trading and other financial engineering techniques.

      In a sense, then, algorithmic trading is where finance and programming meet, giving professionals with the ability to span these worlds the opportunity to create enormous value for their firms.‎

    • Absolutely. Coursera offers a wealth of courses and Specializations about relevant topics in both finance and computer science, including opportunities to learn specifically about algorithmic trading. These courses are offered by top-ranked schools from around the world such as New York University and the Indian School of Business, as well as leading companies like Google Cloud.

      In addition to being able to access a high-quality education remotely from anywhere in the world, learning online through Coursera offers other advantages. The ability to virtually attend lectures and complete coursework on a flexible schedule makes online courses ideal for working professionals in finance or computer programming that want to add algorithmic trading to their skillset. And the lower cost of online courses compared to on-campus alternatives means that this high-value education can be surprisingly affordable.‎

    • The skills and experience that you might need to already have before starting to learn algorithmic trading are generally financial in nature, covering areas like programming skills, knowledge of trading and financial markets, and a solid understanding of financial modeling and quantitative analysis. These are deep subjects that would involve having a fundamental basis of mathematics concepts, data science, and programming capabilities. You might also learn more about algorithmic trading in other ways, from studying online webinars, taking online courses, reading informative blogs, or watching video content. Conversely, you might also attend college to gain a degree in mathematics, computer science, or statistical analysis. Having a good education would be a good benefit before starting to learn algorithmic trading.‎

    • The kind of people that are best suited for work that involves algorithmic trading are people who are comfortable working with numbers, data, computer algorithms, and financial concepts. People working in algorithmic trading are known as ‘quants’, short for quantitative analysts, or financial quantitative analysts. A person who works as a quant uses knowledge, skills, and experience to help financial organizations generate profits while reducing risk. These quants must be able to analyze data, develop statistical scenarios, and implement complex mathematical models for banks, hedge funds, and investment firms to make smart decisions about pricing structures, investments, and risk management opportunities.‎

    • You might know if learning algorithmic trading is right for you if you have a sharp mind that can scan and analyze numbers in math, data, and financial areas quickly and decisively. You would most likely love aspects of technology and finance, working with programming languages, scrutinizing data, crunching numbers, and having a good grasp of principles with ratios and percentages. If you want to learn if algorithmic trading is right for you, then you might want to take online courses in statistical modeling, quantitative analyses, financial trading, computer programming and related areas to gauge your interest and capability for this subject.‎

    이 FAQ 콘텐츠는 정보 전달 목적만으로 사용할 수 있습니다. 학습자는 과정 및 기타 학점 정보가 개인적, 직업적 및 재정적 목표에 부합하는지 확인하기 위해 추가 조사를 수행하는 것이 좋습니다.
    살펴볼 만한 다른 주제
    Placeholder
    예술 & 인문학
    338개의 강좌
    Placeholder
    비즈니스
    1095개의 강좌
    Placeholder
    컴퓨터 공학
    668개의 강좌
    Placeholder
    데이터 과학
    425개의 강좌
    Placeholder
    정보 기술
    145개의 강좌
    Placeholder
    건강
    471개의 강좌
    Placeholder
    수학 및 논리
    70개의 강좌
    Placeholder
    자기개발
    137개의 강좌
    Placeholder
    물리 과학 및 공학
    413개의 강좌
    Placeholder
    사회 과학
    401개의 강좌
    Placeholder
    언어 학습
    150개의 강좌

    Coursera Footer

    경력을 시작하거나 쌓기

    • Google 데이터 분석가
    • Google 프로젝트 관리
    • Google UX 디자인
    • Google IT 지원
    • IBM 데이터 과학
    • IBM 데이터 분석가
    • Excel & R을 사용한 IBM 데이터 분석
    • IBM 사이버 보안 분석가
    • IBM 데이터 엔지니어링
    • IBM 풀스택 클라우드 개발자
    • Facebook 소셜 미디어 마케팅
    • Facebook 마케팅 분석
    • Salesforce 영업 개발 담당자
    • Salesforce 영업 운영
    • Intuit 부기
    • Google 클라우드 자격증: 클라우드 아키텍트 취득 준비
    • Google 클라우드 자격증: 클라우드 데이터 엔지니어 취득 준비
    • 경력 시작
    • 수료증 취득 준비
    • 경력 쌓기

    인기 있는 주제 찾아보기

    • 무료 강좌
    • 언어 학습
    • 파이썬
    • Java
    • 웹 디자인
    • SQL
    • Cursos Gratis
    • Microsoft Excel
    • 프로젝트 관리
    • 사이버 보안
    • 인사
    • 데이터 과학 무료 강좌
    • 영어 말하기
    • 콘텐츠 작성
    • 풀스택 웹 개발
    • 인공 지능
    • C 프로그래밍
    • 커뮤니케이션 기술
    • 블록체인
    • 모든 강좌 보기

    인기 강좌 및 문서

    • 데이터 과학 팀을 위한 기술
    • 데이터 기반 의사 결정
    • 소프트웨어 엔지니어링 기술
    • 엔지니어링 팀을 위한 소프트 스킬
    • 경영 기술
    • 마케팅 기술
    • 영업 팀을 위한 기술
    • 제품 관리자 기술
    • 금융을 위한 기술
    • 영국에서 인기 있는 데이터 과학 강좌
    • Beliebte Technologiekurse in Deutschland
    • 인기 있는 사이버 보안 자격증
    • 인기 있는 IT 자격증
    • 인기 있는 SQL 자격증
    • 마케팅 관리자 커리어 가이드
    • 프로젝트 관리자 커리어 가이드
    • Python 프로그래밍 기술
    • 웹 개발자 커리어 가이드
    • 데이터 분석가 기술
    • UX 설계자를 위한 기술

    온라인으로 학위 또는 자격증 취득

    • MasterTrack® 자격증
    • 전문 자격증
    • 대학교 수료증
    • MBA 및 경영학 학위
    • 데이터 과학 학위
    • 컴퓨터 공학 학위
    • 데이터 분석 학위
    • 공중 보건 학위
    • 사회 과학 학위
    • 관리 학위
    • 유럽 일류 대학의 학위
    • 석사 학위
    • 학사 학위
    • 성적 기반 경로를 제공하는 학위
    • 이학사 강좌
    • 학사 학위란 무엇인가요?
    • 석사 학위를 취득하는 데 얼마나 오래 걸리나요?
    • 온라인 MBA를 들을 만한 가치가 있나요?
    • 대학원 등록금을 지불하는 7가지 방법
    • 모든 자격증 보기

    Coursera

    • 소개
    • 제공 내용
    • 리더십
    • 직업
    • 카탈로그
    • Coursera Plus
    • 전문 자격증
    • MasterTrack® 자격증
    • 학위
    • 기업용 Coursera
    • 정부용
    • 캠퍼스용
    • 파트너가 되기
    • 코로나바이러스감염증-19 대응

    커뮤니티

    • 학습자
    • 파트너
    • 개발자
    • 베타 테스터
    • 번역가
    • 블로그
    • 기술 블로그
    • 지도 센터

    기타

    • 보도 자료
    • 투자자
    • 조건
    • 개인정보 보호
    • 도움말
    • 접근성
    • 문의하기
    • 문서
    • 디렉토리
    • 계열사
    어디에서나 학습
    앱스토어에서 다운로드하기구글 플레이에서 받기
    Placeholder
    © 2022 Coursera Inc. All rights reserved.
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder