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# Category Archives: Matlab

## Algorithmic Trading

MOVING FROM RESEARCH TO TRADING I have written recently about the comparative advantages of different programming languages in the context of research and trading (see here). My sense of it is that there is no single “ideal” programming language – … Continue reading

Posted in Algorithmic Trading, Interactive Brokers, Matlab, Time Series Modeling, TradeStation
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## A Comparison of Programming Languages

Towards the end of last year I wrote a post (see here) about the advent of modern programming languages, including the JIT compiled Julia and visual programming language ADL from Trading Technologies. My conclusion (based on a not very scientific … Continue reading

Posted in Algo Design Language, Algorithmic Trading, Julia, Mathematica, Matlab, Programming
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## ETF Pairs Trading with the Kalman Filter

I was asked by a reader if I could illustrate the application of the Kalman Filter technique described in my previous post with an example. Let’s take the ETF pair AGG IEF, using daily data from Jan 2006 to Feb 2015 … Continue reading

Posted in Cointegration, Matlab, Statistical Arbitrage
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## Statistical Arbitrage Using the Kalman Filter

One of the challenges with the cointegration approach to statistical arbitrage which I discussed in my previous post, is that cointegration relationships are seldom static: they change quite frequently and often break down completely. Back in 2009 I began experimenting … Continue reading

Posted in Kalman Filter, Matlab, Pairs Trading, Statistical Arbitrage
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## Developing Statistical Arbitrage Strategies Using Cointegration

In his latest book (Algorithmic Trading: Winning Strategies and their Rationale, Wiley, 2013) Ernie Chan does an excellent job of setting out the procedures for developing statistical arbitrage strategies using cointegration. In such mean-reverting strategies, long positions are taken in … Continue reading

Posted in Cointegration, Johansen, Matlab, Mean Reversion, Pairs Trading, Statistical Arbitrage, Strategy Development, Systematic Strategies
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## High Frequency Trading with ADL – JonathanKinlay.com

Trading Technologies’ ADL is a visual programming language designed specifically for trading strategy development that is integrated in the company’s flagship XTrader product. Despite the radically different programming philosophy, my experience of working with ADL has been delightfully easy and … Continue reading

Posted in Algo Design Language, Algorithmic Trading, Futures, High Frequency Trading, Latency, Market Microstructure, Mathematica, Matlab, Order Flow, S&P500 Index, Scalping, Toxic Flow, TradeStation, Trading Technologies
Tagged ADL, Futures, High Frequency Trading, Latency, Toxic Flow, Trading Technologies
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## Can Machine Learning Techniques Be Used To Predict Market Direction? The 1,000,000 Model Test.

During the 1990’s the advent of Neural Networks unleashed a torrent of research on their applications in financial markets, accompanied by some rather extravagant claims about their predicative abilities. Sadly, much of the research proved to be sub-standard and the … Continue reading

Posted in Direction Prediction, Forecasting, Logit Regression, Machine Learning, Matlab, Modeling, Nearest Neighbor, Neural Networks, Nonlinear Classification, Nonlinear Dynamics, Random Forrests, S&P500 Index, Support Vector Machines
Tagged Direction Prediction, Forecasting, Machine Learning, Nearest Neighbor, Neural Networks, Nonlinear Classification, Random Forrests, Support Vector Machines
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## Learning the Kalman Filter

Many people have heard of Kalman filtering, but regard the topic as mysterious. While it’s true that deriving the Kalman filter and proving mathematically that it is “optimal” under a variety of circumstances can be rather intense, applying the filter to a basic linear system is actually very easy. This Matlab file is intended to demonstrate that. Continue reading