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JonathanGenetic Programming, Machine Learning, Portfolio Construction, Portfolio Theory, Systematic StrategiesAlgorithmic Trading, Equity Portfolios, Genetic Programming, Machine Learning, Portfolio Theory, Quantitative Equity Strategy

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JonathanEquities, Genetic Programming, Hedge Funds, Machine Learning, Portfolio Construction, Portfolio Management, Portfolio TheoryEquity Portfolios, Genetic Programming, Hedge Fund, Portfolio Construction, Portfolio Risk, Portfolio Theory, Quantitative Equity Strategy

For many decades the principles of portfolio construction laid out by Harry Markovitz in the 1950s have been broadly accepted as one of the cornerstones of modern portfolio theory (as summarized, for example, in this Wikipedia article). The strengths and weakness of the mean-variance methodology are now widely understood and broadly accepted. But alternatives exist, one…

JonathanAlgorithmic Trading, Machine Learning, Mathematica, News Trading, Sentiment Analysis, Text Mining2 commentsAlgorithmic Trading, Classifier, Machine Learning, Machine Readable News, Mathematica, News, News Trading Algorithms, S&P 500 Index, Sentiment, Text Analysis

Text and sentiment analysis has become a very popular topic in quantitative research over the last decade, with applications ranging from market research and political science, to e-commerce. In this post I am going to outline an approach to the subject, together with some core techniques, that have applications in investment strategy. In the early days of the…

JonathanDynamic Time Warping, Economics, ETFs, Forecasting, Machine Learning, Mathematica, Modeling, Productivity, SPY, Wiener Process1 commentCorrelation, Difference Measures, Drift, Dynamic Time Warping, Economics, ETF, Forecasting, Machine Learning, Market Outlook, Mathematica, Productivity, SPY, Wiener Process

History does not repeat itself, but it often rhymes – Mark Twain You certainly wouldn’t know it from a reading of the CBOE S&P500 Volatility Index (CBOE:VIX), which printed a low of 11.44 on Friday, but there is a great deal of uncertainty about the prospects for the market as we move further into the…

JonathanETFs, Machine Learning, Nearest Neighbor, Neural Networks, Out-Of-Hours Trading, Random Forrests, SPY, Support Vector Machines, Systematic Strategies, Trading Systems1 commentGenetic Programming, Machine Learning, Nearest Neighbor, Neural Networks, Random Forest, SPY, Trading Systems

The SPDR S&P 500 ETF (SPY) is one of the widely traded ETF products on the market, with around $200Bn in assets and average turnover of just under 200M shares daily. So the likelihood of being able to develop a money-making trading system using publicly available information might appear to be slim-to-none. So, to give ourselves…

Spending 12-14 hours a day managing investors’ money doesn’t leave me a whole lot of time to sit around watching TV. And since I have probably less than 10% of the ad-tolerance of a typical American audience member, I inevitably turn to TiVo, Netflix, or similar, to watch a commercial-free show. Which means that I…

JonathanEconometrics, Machine Learning, Mean Reversion, Momentum, Performance Testing, Strategy Development, Systematic Strategies, Volatility ModelingMean Reversion, Momentum, Strategy Performance, Volatility

Building a winning strategy, like the one in the e-Mini S&P500 futures described here is only half the challenge: it remains for the strategy architect to gain an understanding of the sources of strategy alpha, and risk. This means identifying the factors that drive strategy performance and, ideally, building a model so that their relative…

JonathanAlgorithmic Trading, Futures, Machine Learning, S&P500 Index, TradingAdaptrade, Curve Fitting, EMini, Monte Caloe Simulation, Out of Sample testing, Robustness

In his post on Multi-Market Techniques for Robust Trading Strategies (http://www.adaptrade.com/Newsletter/NL-MultiMarket.htm) Michael Bryant of Adaptrade discusses some interesting approaches to improving model robustness. One is to use data from several correlated assets to build the model, on the basis that if the algorithm works for several assets with differing price levels, that would tend to…

JonathanGenetic Programming, Machine Learning, Systematic Strategies, Trading RuleGenetic Programming, Machine Learning, Systematic Trading, Trading Rules

Posted by androidMarvin: Genetic programming is an approach to letting the computer generate its own program code, rather than have a person write the program. It doesn’t specifically “find patterns” or rules within data structures. It starts with a number of randomly-constructed (as long as they are mathematically valid) sample programs, evaluates how close each…

JonathanAlgorithmic Trading, High Frequency Trading, Machine Learning, Market Efficiency, Nonlinear ClassificationAutomated Trading, Coffee Futures, Crude Oil, Daytrading, E-mini, Energy, Futures, Genetic Algorithms, Genetric Programming, Heating Oil, Machine leaning, Model Robustness, Natural Gas, Ten Year Futures, US Bond Futures

One of the frustrating aspects of research and development of trading systems is that there is never enough time to investigate all of the interesting trading ideas one would like to explore. In the early 1970’s, when a moving average crossover system was considered state of the art, it was relatively easy to develop profitable…

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