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HKUST FYP 2011-2012 Application of Artificial Intelligence Algorithms in Quantitative Finance

HKUST FYP 2011-2012 Application of Artificial Intelligence Algorithms in Quantitative Finance. Numerical Method Inc. Ltd. URL: http://www.numericalmethod.com Presented by Ken Yiu. Who are we?.

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HKUST FYP 2011-2012 Application of Artificial Intelligence Algorithms in Quantitative Finance

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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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