### Forums

### Blogroll

### Search

### Archives

- August 2015
- July 2015
- June 2015
- May 2015
- April 2015
- March 2015
- February 2015
- January 2015
- December 2014
- November 2014
- October 2014
- September 2014
- August 2014
- July 2014
- June 2014
- May 2014
- April 2014
- December 2012
- November 2012
- September 2011
- June 2011
- May 2011
- April 2011
- March 2011
- February 2011
- July 2010
- July 2009
- May 2009

### Categories

- Algo Design Language (6)
- Algo Strategy Engine (2)
- Algorithmic Trading (11)
- Alternative Investment (1)
- ARFIMA (1)
- ARMA (4)
- Asian markets (2)
- Binary Options (1)
- Black Noise (2)
- CAPM (1)
- Cointegration (7)
- Commodity Futures (3)
- Correlation (1)
- Correlation Dimension (1)
- Correlation Integral (1)
- CrashMetrics (1)
- Day Trading (2)
- Derivatives (3)
- Direction Prediction (4)
- Dispersion (1)
- Econometrics (7)
- Economics (1)
- Econophysics (2)
- Education (1)
- Emerging Markets (1)
- eMini Futures (4)
- Equity Curve (2)
- Equity Futures (1)
- ETFs (6)
- Factor Models (1)
- Fat Tails (3)
- FIGARCH (2)
- Financial Engineering (5)
- Fixed Income Futures (1)
- Forecasting (17)
- Fourier Transforms (2)
- Fractional Brownian Motion (2)
- Fractional Cointegration (2)
- Fractional Integration (3)
- Futures (9)
- GARCH (1)
- Genetic Programming (1)
- Graduate Programs (1)
- Granger Causality (1)
- Hedge Funds (3)
- Henon Attractor (1)
- High Frequency Finance (6)
- High Frequency Trading (9)
- Hurst Exponent (2)
- Hybrid Products (1)
- Interactive Brokers (1)
- Interest Rate Models (1)
- Jobs (1)
- Johansen (2)
- Julia (2)
- Kalman Filter (2)
- Kelly Criterion (2)
- Latency (1)
- Logistic Attractor (1)
- Logit Regression (2)
- Long Memory (4)
- Long/Short (1)
- Machine Learning (4)
- Market Efficiency (2)
- Market Microstructure (4)
- Market Timing (3)
- Markov Model (1)
- Markov State Models (1)
- Mathematca (1)
- Mathematica (6)
- Matlab (8)
- Mean Reversion (5)
- Metals (1)
- Model Review (1)
- Modeling (4)
- Momentum (2)
- Money Management (4)
- Monte Carlo (1)
- Multifactor Models (2)
- Natural Gas Futures (1)
- Nearest Neighbor (1)
- Neural Networks (1)
- Nonlinear Classification (2)
- Nonlinear Dynamics (2)
- Optimal f (2)
- Options (7)
- Order Flow (2)
- Pairs Trading (5)
- Pattern Trading (1)
- Performance Testing (1)
- Pink Noise (2)
- Portfolio Management (3)
- Principal Components Analysis (1)
- Programming (2)
- Purchasing Power Parity (1)
- Quant/Traders (1)
- Random Forrests (1)
- Recruitment (1)
- REGARCH (3)
- Regime Shifts (4)
- Regime Switching (2)
- Regression (1)
- Risk Management (2)
- S&P500 Index (9)
- Scalping (2)
- Seasonal Effects (1)
- Signal Processing (2)
- Spline Methods (1)
- Spread Trading (2)
- Statistical Arbitrage (8)
- Stochastic Differential Equations (1)
- Stochastic Process Control (1)
- Stock Market (1)
- Strange Attractor (1)
- Strategy Development (4)
- Support Vector Machines (1)
- Systematic Strategies (6)
- Time Series Modeling (5)
- Toxic Flow (2)
- TradeStation (4)
- Trading (9)
- Trading Technologies (3)
- Uncategorized (10)
- Unit Roots (1)
- Van Tharp (1)
- Venture Capital (1)
- VIX Index (14)
- Volatility ETF Strategy (10)
- Volatility Modeling (28)
- volatility sign prediction forecasting Engle (3)
- White Noise (3)
- Yield Curve Modeling (1)

### Tag Cloud

ADL ARFIMA ARMA Models Black Noise Direction Prediction ETFs Financial engineering Forecasting Fourier Transforms Fractional Brownian Motion Fractional Cointegration Fractional Integration Futures GARCH High Frequency Trading Jump Diffusion Kalman Filter Kurtosis Long Memory Machine Learning Market Microstructure Market Timing Mathematica MultiFactor Models Natural Gas Option Pricing Options Pairs Trading REGARCH Regime Shifts Robustness S&P500 Index Signal Processing Skewness Smile Statistical Arbitrage Stochastic Volatility Strategy Toxic Flow Tradestation Trading VIX Volatility Volatility Dynamics White Noise

# 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
Comments Off on Algorithmic Trading

## 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
Comments Off on A Comparison of Programming Languages

## 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
Comments Off on ETF Pairs Trading with the Kalman Filter

## 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
Comments Off on Statistical Arbitrage Using the Kalman Filter

## 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
Comments Off on Developing Statistical Arbitrage Strategies Using Cointegration

## 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
Comments Off on High Frequency Trading with ADL – JonathanKinlay.com

## 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
Comments Off on Can Machine Learning Techniques Be Used To Predict Market Direction? The 1,000,000 Model Test.

## 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