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  • $300
  • 365 Days
  • Profile photo of mahesh chanduka

Course Description

This course introduces students to quantitative investment. A “quant” portfolio manager or a trader usually starts with an intuition or a vague trading idea. Using mathematics, s/he turns the intuition into a mathematical trading model for analysis, backtesting, and refinement. When the quantitative investment model proves to be likely profitable after passing rigorous statistical tests, the portfolio manager implements the model on a computer system for automatic execution. In short, quantitative investment is the process where ideas are turned into mathematical models and then coded into computer programs for systematic trading. It is a science where mathematics and computer science meet. In this course, students study investment strategies from the popular academic literature and learn the fundamental mathematics and IT aspects of this emerging field. After satisfactorily completing this course, the students will have an overview of the necessary quantitative, computing, and programming skills in quantitative investment.

Recommended For

Portfolio managers who wish to apply their mathematical and statistical strengths in the trading arena
Algorithmic traders who seek a deeper appreciation of mathematics and programming
Regulators, risk managers and auditors who need a good understanding of the nature of quantitative analysis
Anyone who aspires to become a quantitative trader

Preferred Background

Some experience in trading is preferred but not essential
University level mathematics and statistics
Programming experience





Required software for this course: AlgoQuant

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Lecture Handouts & Notes

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Dr. Haksun Li is a founder and the CEO of Numerical Method ( NM LTD.), an algorithmic trading research and mathematical modeling consulting company. The firm serves brokerage houses and funds all over the world, multinational corporations, very high net worth individuals and gambling groups. Prior to this, Haksun was a quantitative trader/quantitative analyst with multiple investment banks. He trades stocks, options, commodity futures, FX and bonds. He has worked in New York, London, Tokyo, Singapore and Hong Kong.

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Course Curriculum

Technical analysis: linear trading rules FREE 03:18:00
Definition of Quantitative Trading; The Quantitative Trading Research Process; A Mathematical Analysis of Linear Technical Indicators;
Programming a hidden Markov chain, a trend following strategy FREE 03:00:00
The Role of Technology in Quantitative Trading; Basic Math Programming in Java; Strategy Programming;
Trading basket construction 02:44:00
Paris Trading; Sample Pairs Trading Strategy; Cointegration; Stochastic Spread;
Programming a cointegration model; basket creation; parameter sensitivity analysis 01:01:00
Observations and Hidden State Process; Parameter Estimation; Likelihood Function; Log-Likelihood; Nelder-Mead; Marginal Likelihood; The Q-Function; EM Intuition; Expectation-Maximization Algorithm; Kalman Filter; Conceptual Diagram; A Linear Discrete System; Observations and Noises; Discrete System Diagram; Computing the ‘Best’ State Estimate; Predicted (a Priori) State Estimation; Predicted (a Priori) Variance; Minimize Posteriori Variance; A Trading Algorithm;
Optimal trading strategies 00:56:00
The ideas of this lecture are how to find a stochastic description of the asset we want to trade, how to find a strategy that applies to the asset so that the expected performance is maximized, how to model the asset you want to trade. Mean Reversion and Trend Following Trading.
Programming a trading strategy; parameter calibration 01:43:00
AlgoQuant Framework; Backtesting; Historical P&L; Optimized P&L; Sensitivity Analysis of Parameters; Backtesting Using Recommended Parameters; Monte Carlo Simulation; Bootstrapping; Computer Power;
Portfolio optimization & risk management 01:15:00
Basket of strategies; How to allocate capital to a basket of strategies or assets; Portfolio allocation problem; Portfolio Statistics - Performance measure; Sharpe ratio; Potential for gains; Performance Measure Requirements; Omega Definition and Advantages; Intuitions; Affine Invariant; Numerator Integrals; Denumerator Integrals; Options Intuition; Sharpe-Omega and Moments;
Portfolio Optimization 00:33:00
Optimization for Sharpe Ratio; Optimization s.t. Constraints; Optimization Methods: Penalty Method, Optimization fог Omega, Optimization Fог Sharpe Ratio, Threshold Accepting Algorithm;
Risk Management 01:15:00
Types of Risk; VaR Definition; VaR in Layman Term; VaR Computations; Historical Simulations; Variance-CoVariance; Monte Carlo Simulation; Fat Tails; QQ Plot; Asymptotic Properties; Extreme Value Theory; Convergence; Fisher-Tippett Theorem; Maximum Domain of Attraction; Hill Estimator;

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