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On Some Practical Issues when Using AlgoQuant to Compute the Markowitz Efficient Frontier

Markowitz suggests in his Nobel Prize-winning paper Markowitz(1952) that when one selects a portfolio, he/she should consider both the return and the risk of the portfolio. Most of us, if not all, are risk-averse. Risk-averse means that if there are two  portfolios with the same return, but different risks (in this article by risk we mean the standard …

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Solving the “Corner Solution Problem” of Portfolio Optimization

Many portfolio optimization methods (e.g., Markowitz/Modern Portfolio Theory in 1952) face the well-known predicament called the “corner portfolio problem”. When short selling is allowed, they usually give efficient allocation weighting that is highly concentrated in only a few assets in the portfolio. This means that the portfolio is not as diversified as we would like, which …

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Mean-Variance Portfolio Optimization When Means And Covariances Are Unknown

[Lai, Xing and Chen, 2010], in the paper “Mean-Variance Portfolio Optimization When Means And Covariances Are Unknown”, proposed a ground breaking method to do portfolio optimization. In what follows we summarize their idea and use it to implement a periodic rebalancing strategy based on the AlgoQuant framework. Harry Markowitz won the Nobel prize for his …

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Bloomberg Tick-By-Tick Data Download

Bloomberg maintains tick-by-tick historical data for only 140 days. However, you may want to backtest your strategies with a longer history. In this case, you can archive these tickdata by yourself and do backtesting with the archived data. Since version 0.2, AlgoQuant supports downloading tick-by-tick data from Bloomberg and saving them as CSV files via the Bloomberg Java …

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The Role of Technology in Quantitative Trading Research

I posted my presentation titled “The Role of Technology in Quantitative Trading Research” presented in HKU-HKUST-Stanford Conference in Quantitative Finance. Dec 9, 2011. Workshop On Recent Developments Of Financial Mathematics (REDFIN2011). Dec 13, 2011. You can find the powerpoint here. Abstract: There needs a technology to streamline the quantitative trading research process. Typically, quants/traders, from …

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Mean Reversion vs. Trend Following

AlgoQuant 0.0.5 just got released! This release is particularly exciting because you no longer need a license to use AlgoQuant. AlgoQuantCore now disappears forever. The source of the entire AlgoQuant project is now available: http://www.numericalmethod.com/trac/numericalmethod/browser/algoquant Maybe even more exciting is that we ship this release with two quantitative trading strategies: one mean reversion strategy and …

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Strategy Optimization

Trading strategy optimization is an important aspect and a challenging problem in algorithmic trading. It requires determining a set of optimal solutions with respect to multiple objectives, where the objective functions are often multimodal, non-convex, and non-smooth. Moreover, the objective functions are subject to various constraints of which many are typically non-linear and discontinuous. Conventional …

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The Right (and Wrong) Way to Run an Algorithmic Trading Group

I would like to share with you the unique vision that Numerical Method Inc. has about running an algorithmic trading group. To get an edge over competing funds, we emphasize on 1) the research process and 2) technology rather than on hiring more intelligent people. Currently, the majority of the quant funds run like cheap …

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