Tag Archives: PCA

Many examples of PCA uses in finance with Matlab source code

 

Many examples of PCA uses in finance with Matlab source code. There are some examples use with Value at Risk applications.

Lots of examples here:

http://quant.stackexchange.com/questions/1020/equity-risk-model-using-pca <– See the answer by vonjd

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1358533 <– Meucci paper with example code at http://www.mathworks.com/matlabcentral/fileexchange/23271 also focus on portfolio

http://www.cs.princeton.edu/picasso/mats/PCA-Tutorial-Intuition_jp.pdf <– uses toy example so not useful

http://stackoverflow.com/questions/21242542/principal-components-calculated-using-different-functions-in-matlab <– good example how to use princomp

http://www.docstoc.com/docs/20298522/MATLAB-CODE-FOR-PCA <– little example with no explanation

http://www.mathfinance.cn/category/matlab/1/3/ –> Suggested another PCA framework at

http://www.nlpca.org/matlab.html –> this is an non linear PCA framework

http://books.google.ca/books?id=AxvgfuSedRAC&pg=PA302&lpg=PA302&dq=matlab+pca+finance&source=bl&ots=b_k8uf-abe&sig=8svs1dUkKvRhJf1y4Rbm_TpLBPw&hl=en&sa=X&ei=lqdOU8zVC4Si2AWMwIDABA&ved=0CEoQ6AEwAzgK#v=onepage&q=matlab%20pca%20finance&f=false  <– This has an exact example in this paid book Stochastic Simulation and Applications in Finance with MATLAB Programs

http://en.wikipedia.org/wiki/Principal_component_analysis

http://www.quantatrisk.com/tag/matlab/ <— good explanation of PCA with real world example and source code Best Example headline of (heatmap and different plots!)

Anxiety Detection Model for Stock Traders based on Principal Component Analysis <–This code breaks

http://www.di.ens.fr/~aspremon/PDF/INFORMS05sparsePCA.pdf <– no source code examples

http://www.mathworks.com/com/help/stats/princomp.html <— nice explanation from Mathworks of core function

http://matlab-trading.blogspot.ca/2012/12/using-pca-for-spread-trading.html –> spread trading with detailed description from http://www.cs.bham.ac.uk/~pxt/IDA/PCA.tutorial.pdf with code but not compatible with Matlab

http://www.jasonhsu.org/uploads/1/0/0/7/10075125/principal_components_analysis2.pdf <– no link to the source code

http://www.quantzone.org/?tag=pca –> refers to nlpca framework above

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Youtube video New Matlab 2013a enhancements in Stats with PCA FPGA and Simulink code generation to your C or C++ API

Youtube video New Matlab 2013a enhancements in Stats with PCA FPGA and Simulink code generation to your C or C++ API

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CUDA as a super computer exciting with libraries for neural net learning, genetic algos, FFT, PCA, BLAS

HI there

The QuantLabs.net rate increase is coming and scheduled for first week of January 2013, see details below but why wait?

The march to my high frequency trading system continues! I have listed below the latest accomplishments in the world of GPU, CUDA ,and Matlab. One major take away in all this is that if you choose CUDA, you will be happy to develop the new CUDA 5 math library. All in all, it is very powerful and mind blowingly fast!

1. HFT Youtube video Demo of $30 96 core Nvidia CUDA GPU with Microsoft Visual C++ for ultra fast quant analysis

https://quantlabs.net/blog/2012/11/hft-youtube-video-demo-of-30-96-core-nvidia-cuda-gpu-with-microsoft-visual-c-for-ultra-fast-quant-analysis-cuda/

2. How to get your Microsoft Windows Visual C++ CUDA sample files working with Nvidia Geforce CUDA GPU board

https://quantlabs.net/blog/2012/11/how-to-get-your-microsoft-windows-visual-c-cuda-sample-files-working-with-nvidia-geforce-cuda-gpu-board/

3. GPU CUDA 3rd party high level C++ library for math awesomeness with genetic algorithm, neural net learning, PCA, FFT, BLAS

https://quantlabs.net/blog/2012/11/free-gpu-cuda-3rd-party-high-level-c-library-for-math-awesomeness-with-genetic-algorith-neural-net-learning-pc-fft-blas/

4. Using CUDA GPU to build a HFT super computer. The debate of Windows versus Linux is also over!

https://quantlabs.net/blog/2012/11/using-cuda-gpu-to-build-a-hft-super-computer-the-debate-of-windows-versus-linux-is-also-over-linux-cuda-gpu-windows-hft/

CUDA is very valuable to any trading firm that is using it so I am making a huge investment in it. I have also confirmed CUDA software developers charge top dollar so my mindset is making me think that if I supply ready to drop code for a HFT system, I should charge top dollar for the  QuantLabs.net Premium membership. As a result, I am leaning that way as I am scheduling the first week of January 2013 for the rate increase. I am not sure if it will be 25% or 50%. Either way, you better get in on the membership action while it is very, very affordable right now.

–> GO HERE TO JOIN! <–

Membership benefits here.

Bryan
P.S. Tomorrow will be further secrets to be revealed about Matlab’s data analytical power.

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FREE GPU CUDA 3rd party high level C++ library for math awesomeness with genetic algorith,, neural net learning, PC, FFT, BLAS

FREE GPU CUDA 3rd party high level C++ library for math awesomeness with genetic algorithm, neural net learning, PCA, FFT, BLAS

I stlll have to verify this is free but there is a download button with no limitations. I will try to post a video on this.

Key is ensure it is compatible for both Windows and Linux which this does

http://www.accelereyes.com/arrayfire/c/examples_2financial_2blackscholes_8cpp-example.htm

After further digging, this is actually an expensive option:

http://www.accelereyes.com/products/arrayfire_licensing

Learn more if I implement this into HFT platform

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NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Meetups of Ernie Chan’s Pitfall of Backtesting, Monte Carlo primer, Beta analysis, PCA, Bayesian, and MCMC research models coming

Hi there

I have added two online Meetups for later this year.

1. Monte Carlo primer on Nov 5 at 7pm EST. Details here.

2. I made a video on this speaker of Dr. Ernie Chan’s Pitfalls of Backtesting. Go here. This Meetup happens on Dec 11 at 6:30 EST.

Also, I am preparing so many more strategy development and models with R script code walkthrough videos on Markov Chain Monte Carlo (MCMC),  PCA, and Bayesian. These are what the big boys use in their quant trading analysis and research for forecasting.

For my Premium Membership, I also added 4 new Beta and Fixed Income vs Asian Currency analysis R script models with videos each:

1. 4 myths on beta

2. Calculate beta with CAPM

3, Sophisticated beta analysis with advanced management and portfolio tools

4. Asian currency return analysis against the US Treasury 10 year bond

This increases the value for my Premium Membership as I add more live webinar demos on all these. Interested in joining my membership? Join –> here <– to get immediate access to so much. Benefits listed here.

Got questions or comment, let me know.

Thanks

Bryan

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R URLS in Mean reversion, Statistical Arbitrage, Event arbitrage, Market Inefficiency, CAPM, Bayesian, PCA, Markov Chain Monte Carlo

R URLS in Mean reversion, Statistical Arbitrage, Event arbitrage, Market Inefficiency, CAPM, Bayesian, PCA, Markov Chain Monte Carlo

This has been posted in the PremiumMembership section. This saves people tonnes of time which R script works and which ones don’t. Also, there will be a R source code walkthrough of each coming over the next few weeks.

Get instant access to this now!

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Youtube video request Seeking PCA, Markov, data mining examples for automated trading model profits

Youtube video request Seeking PCA, Markov, data mining examples for automated trading model profits

Join my membership here.

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Improve your trading strategies with R code for usage of profit off tick data patterns, trade direction, downside risk, trade signals, PCA

Improve your trading strategies with R code for usage of profit off tick data patterns, trade direction, downside risk, trade signals, PCA
Hi there,

My Algorithm, Modelling, and Strategy Development courses just keep getting bigger and better. In the next few weeks, I’ll be posting R source code to teach you exactly how to:

Are you looking to get started on quant trading as soon as possible? I’m just about to post some crucial source code in R. Here’s what I’ll be including:

* Better Data: Get your hands on high frequency market data. Includes realized volatility, the bid/ask spread, trade direction, and calendar patterns with tick patterns

* How To Use Performance Analytics: display relative performance, calculate downside risk, show relative return risk, and compare distributions

* How To Prepare For All Outcomes: Estimate parameters for back testing, create trading signals, and then evaluate back-testing performance

* Principal Component Analysis: How to use it for financials within R.

And that’s just the tip of a growing iceberg for Premium members. Accelerate your learning right now:
–>
http://quantlabs.net/dlg/sell.php?prodData=m%2C3
<–

Get even more benefits including our HFT and Algo Development courses, software tool kits, and more!
–>
http://quantlabs.net/quant-member-benefits/
<–
Good trading,

Bryan

P.S. Remember: R is totally free as it’s open source. You don’t need expensive proprietary software packages to work with it. Volatility forecasting, pairs trading, cointegration, estimating, simulation, and much more. It’s all coming ASAP!

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Quant analytics: PCA for alpha generation

Quant analytics: PCA for alpha generation

I am curious if anyone has had success applying principal component analysis in the context of alpha generation (and not in the context of risk, as PCA is typically applied). For instance, I like the straightforward “absorption ratio” introduced by Kritzman, et al. here:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1633027

I suspect that tracking the evolution of portfolio risk concentration may yield forward-looking clues on the portfolio’s performance. For instance, perhaps a factor mimicking portfolio may lose efficacy if its risk becomes highly concentrated during the life of the trade (in this hypothetical context, the “absorption ratio” is used as a timing parameter for factor rotation).

As an aside, outside of alpha generation, I think any “real-time” systemic risk metrics are highly relevant in today’s market. Particularly in the US equity space, where investors have enjoyed a respite (of sorts) from macro-driven Eurozone news since mid-Dec (coinciding with the ECB’s 3-yr LTRO announcement). The abrupt declines over the last hour of trading on May 22 (purportedly driven by comments from the former Greece PM regarding the country’s potential exit from the euro) is a sharp reminder to me just how quickly the market can switch back to the high correlation environments of 2H11. Monitoring systematic risk may give active investors sufficient time to adjust their level of activeness/leverage in open positions.

 

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