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AI Algorithms in Quantitative Finance | Numerical Method Inc.

Explore the application of artificial intelligence algorithms in quantitative finance with Numerical Method Inc. Develop an easy-to-use API for numerical algorithms and research consulting in quantitative and program trading. Features AlgoQuant and SuanShu providing tools for trading research, historical data, algebra, analysis, statistics, and optimization. Participate in a new project to design a Java library of AI algorithms for quantitative finance, with opportunities to enhance skills and potentially land a job offer.

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AI Algorithms in Quantitative Finance | Numerical Method Inc.

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  1. HKUST FYP 2011-2012Application ofArtificial Intelligence Algorithmsin Quantitative Finance Numerical Method Inc. Ltd. URL: http://www.numericalmethod.com Presented by Ken Yiu

  2. Who are we? • 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

  3. What do we do? SELL BUY 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

  4. AlgoQuant– the integrated trading research tool 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…

  5. SuanShu ( ) – the Java numerical library 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-thminmax optimization • Nelder-Mead optimization • unconstrained optimization • univariateoptimization • and many more…

  6. New Project • 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 JUnittestcasesfor proving the correctness of your code • Performance improvement by exploiting the power of multi-core CPUs and GPUs (if time permits)

  7. Benefits • 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

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

  9. Q & A

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