Numerical Method Inc. Ltd. URL: Presented by Ken Yiu.

Slides:

Advertisements

Similar presentations

Zhen Lu CPACT University of Newcastle MDC Technology Reduced Hessian Sequential Quadratic Programming(SQP)

Advertisements

Year 11 Mathematics What type of Maths courses are there in year 11? ► ATAR Courses: Examinable courses, which may be used towards a university.

Introduction to the CUDA Platform

October Interfaces to NAG, R and Q Morten Kromberg Dyalog LTD Dyalog’13.

Applications of Stochastic Processes in Asset Price Modeling Preetam D’Souza.

The Role of Technology in Quantitative Trading Research AlgoQuant Haksun Li

MATLAB Presented By: Nathalie Tacconi Presented By: Nathalie Tacconi Originally Prepared By: Sheridan Saint-Michel Originally Prepared By: Sheridan Saint-Michel.

Matlab Simulations of Markov Models Yu Meng Department of Computer Science and Engineering Southern Methodist University.

Workshop on Stochastic Differential Equations and Statistical Inference for Markov Processes Day 1: January 19 th, Day 2: January 28 th Lahore University.

A Workshop on Subject GRE / AGRE Maths in 9 Classes, II Hours each Day & Three mock tests for AGRE By: Satyadhar Joshi

How to prepare yourself for a Quants job in the financial market?   Strong knowledge of option pricing theory (quantitative models for pricing and hedging)

Computational statistics, course introduction Course contents  Monte Carlo Methods  Random number generation  Simulation methodology  Bootstrap  Markov.

Why attending this Program Sharpening the quantitative skills in   Pricing, hedging and risk measurement of derivative securities   Implementing risk.

Rating Systems Vs Machine Learning on the context of sports George Kyriakides, Kyriacos Talattinis, George Stefanides Department of Applied Informatics,

Financial Engineering Club Career Path and Prep. Entry Level Career Paths Type 1: Research based Background: Physics, Electrical Engineering, Applied.

Oct 30, 2001 Richard Fateman1 Do Math Making Computers Do Math Prof. Richard Fateman Fall, 2001 University of California, Berkeley.

Scientific Computing on MSR Summer School 2009 – Jurgen Van Gael.

Lagrange interpolation Gives the same results as Newton, but different method.

Background Required. Mathematical Courses Calculus I and II Multivariable Courses Linear Algebra Differential Equations (ODE and PDE’s) Probability Statistics.

Opportunities in Quantitative Finance in the Department of Mathematics.

Machine Learning Usman Roshan Dept. of Computer Science NJIT.

The Role of Technology in Quantitative Trading Research AlgoQuant Haksun Li

Numerical Grid Computations with the OPeNDAP Back End Server (BES)

Algorithmic Trading as a Science Haksun Li

Math LHCb Ivan Belyaev LHCb, CERN & ITEP/Moscow.

SuanShu Introduction Numerical Method Inc.Numerical Method Inc., Apr 2011.

COP 3530: Data Structures, Algorithms, & Applications Instructor: Kristian Linn Damkjer.

Mathematical Finance Seminar. What is Mathematical Finance Other Terms Financial Engineering Quantitative Finance Computational Finance Mathematical Finance.

FROM NOW TO THE FUTURE MATHEMATICS Return Home Return Home Mathematics: Where do I see my future? What am I good at? What do I enjoy?

Chapter 17 Boundary Value Problems. Standard Form of Two-Point Boundary Value Problem In total, there are n 1 +n 2 =N boundary conditions.

Basic Numerical methods and algorithms

Outline of a Course on Computational Intelligence Claudio Moraga University of Dortmund Germany JEP Bitola Workshop December 2003

Topic 1 modelling of sensors systems ETEC Calibration methods We have a RTD sensing the temperature. The integer of the sensor inside the PLC is.

Research, Development, Consulting, Training High Fidelity Modeling and Simulation Where we are going… …future plans.

Application of Differential Applied Optimization Problems.

1 Computer Programming (ECGD2102 ) Using MATLAB Instructor: Eng. Eman Al.Swaity Lecture (1): Introduction.

Filtering and Recommendation INST 734 Module 9 Doug Oard.

Database Systems Carlos Ordonez. What is “Database systems” research? Input? large data sets, large files, relational tables How? Fast external algorithms;

MATHEMATICS T THE NEW STPM SYLLABUS (CONTENT & SCHEME OF ASSESSMENT)

Monté Carlo Simulation  Understand the concept of Monté Carlo Simulation  Learn how to use Monté Carlo Simulation to make good decisions  Learn how.

Advanced Higher Mathematics Methods in Algebra and Calculus Geometry, Proof and Systems of Equations Applications of Algebra and Calculus AH.

TheoryApplication Discrete Continuous - - Stochastic differential equations - - Ito’s formula - - Derivation of the Black-Scholes equation - - Markov processes.

The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Introduction Course Outline.

© The MathWorks, Inc. ® ® Monte Carlo Simulations using MATLAB Vincent Leclercq, Application engineer

SuanShu Introduction Numerical Method Inc.Numerical Method Inc., Jan 2013.

ECE 103 Engineering Programming Chapter 55 C Math Library Herbert G. Mayer, PSU CS Status 6/4/2014 Initial content copied verbatim from ECE 103 material.

Introduction to GAMS, Netlib, Numerical Recipes CS 3414.

Texas A&M University, Department of Aerospace Engineering AN EMBEDDED FUNCTION TOOL FOR MODELING AND SIMULATING ESTIMATION PROBLEMS IN AEROSPACE ENGINEERING.

Linear Algebra Libraries: BLAS, LAPACK, ScaLAPACK, PLASMA, MAGMA

Stats Term Test 4 Solutions. c) d) An alternative solution is to use the probability mass function and.

Project NExT CAS Panel Session University of Wisconsin, Madison July 30, 2008 Mathematica in the Military: Using CAS tools at the United States Military.

Anders Nielsen Technical University of Denmark, DTU-Aqua Mark Maunder Inter-American Tropical Tuna Commission An Introduction.

GPU VSIPL: Core and Beyond Andrew Kerr 1, Dan Campbell 2, and Mark Richards 1 1 Georgia Institute of Technology 2 Georgia Tech Research Institute.

Linear Algebra Libraries: BLAS, LAPACK, ScaLAPACK, PLASMA, MAGMA Shirley Moore CPS5401 Fall 2013 svmoore.pbworks.com November 12, 2012.

Nonlinear Adaptive Kernel Methods Dec. 1, 2009 Anthony Kuh Chaopin Zhu Nate Kowahl.

1 Seattle University Master’s of Science in Business Analytics Key skills, learning outcomes, and a sample of jobs to apply for, or aim to qualify for,

Applications of Stochastic Processes in Asset Price Modeling Preetam D’Souza.

Project Dow: Extending EclipseTrader Emmanuel Sotelo Fall 2008.

Martin Kruliš by Martin Kruliš (v1.0)1.

S5.40. Module Structure 30% practical tests / 70% written exam 3h lectures / week (except reading week) 3 x 2h of computer labs (solving problems practicing.

Progress Report— 11/06 宗慶.

An Introduction to Maple

Communication and Coding Theory Lab(CS491)

How do we find the best linear regression line?

Time Complexity and Parallel Speedup to Compute the Gamma Summarization Matrix Carlos Ordonez, Yiqun Zhang University of Houston, USA 1.

Introduction to algo quant, an integrated trading research tool

Introduction CSE 541.

What is Artificial Intelligence?

Presentation transcript:

Numerical Method Inc. Ltd. URL: Presented by Ken Yiu

Numerical Method Inc. is a company specializing in mathematical modeling and algorithms, esp. in quantitative finance Provide an easy-to-use, object- oriented and extensible API for numerical algorithms Research consulting in quantitative trading & program trading

BUY SELL Start with a vague trading intuition Using mathematics, turn the idea into a quantitative model for analysis Implement the model on computer systems for back-testing, refinement and systematic trading

Features historical data sources, Yahoo!, FX rates trading signal library strategy template in-sample strategy calibration out-sample back-testing performance analysis and many more…

Algebra matrix elementary operations, decomposition & factorization vector and vector space dense & sparse matrix solvers complex number continued fraction interval arithmetic Analysis / Calculus polynomial & Jenkins-Traub root finding special functions univariate/multivariate differentiation finite difference numerical integration interpolation Statistics OLS & logistic regression GLM regression & model selection signal processing & filters descriptive & ranking statistics linear random number generators various random distributions univariate/multivariate timeseries analysis hypothesis testing Brownian motion stochastic differentiation equations SDE integration cointegration Markov chain hidden Markov model Optimization least P-th minmax optimization Nelder-Mead optimization unconstrained optimization univariate optimization and many more…

Objective: To design and implement a Java library of common AI algorithms such as genetic algorithms, artificial neural networks, support vector machines, etc. for quantitative finance, esp. high frequency data Scope & Deliverables: Easy-to-read & extensible implementation of the API Comprehensive Javadoc documentation Extensive JUnit testcases for proving the correctness of your code Performance improvement by exploiting the power of multi-core CPUs and GPUs (if time permits)

Study AI basics using your FYP credits Excellent opportunity for students who plan to do research in AI or get into finance Practice your Java programming skill Improve your object-oriented programming & API design skills Get a job offer if you are good

Group size: 2~4 Interested in AI Strong self-learning capability Self-motivated & detail-oriented Proficient in Java and OO programming