Tag Archives: trading systems

Discussion system design, the best trading systems and what makes successful traders.

Discussion system design, the best trading systems and what makes successful traders

Another video of popular topic! I still say roll your own solution

 

 

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Matlab Production Server Architecture PDF with Excel, Database Integration, and Big Data for live trading systems

Matlab Production Server Architecture PDF with Excel, Database Integration, and Big Data for live trading systems
Check out these links:

http://www.allbookez.com/pdf/256kg22/

MATLAB in Production Systems, Database Integration, and Big Data download

http://www.allbookez.com/pdf/e4mda1/

Even the price list with the cost of this Production Server

http://www.allbookez.com/pdf/r2vpm/

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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!

Ernie Chan and Trading Systems and Methords books are best resources to learn mean reversion quant strategies with Matlab

Ernie Chan and Trading Systems and Methords books are best resources to learn mean reversion quant strategies with Matlab

I am looking mean reversion strategies and see how it can be applied within Matlab for my basic system. Learn more here on the components.
I have come to the conclusion that:

1. Ernie Chan’s set of books are excellent set of resources for this. They also include Matlab source code which covers a lot of scenarios.

2 PJ Kaufman’s book on Trading System and Methods has lots of example of mean reversion strategies.

I think these are adequate resource to implement into this system. I will post some videos on these so stay tuned
Learn more about mean reversion trading strategies through my FREE newsletter

Some sample source and spreadsheets from Trading System and Methods: 

full
spreadsheet, TSM Commodity Channel Index HPQ

NOTES:
Mean-Reverting Indicator
Use the ARIMA confi dence bands to determine overbought/oversold levels

…..along with a single 0.20 smoothing, a double smoothing, and a single error correction.
The error correction positions the trendline in the middle of the price move and may be
a good candidate for mean reversion trading….
spreadsheet TSM Comparison of exponentials MSFT


*******
A mean-reverting (fading) strategy can be created by trading when the excess kurtosis
crosses above zero, indicating an excessive move, then trading the opposite way
indicated by the skew. Volatility will also be important. The rules for mean reverting are:
• Buy when the excess kurtosis crosses above 0, skew < 0, and volatility > minimum level.
• Sell when excess kurtosis crosses below 0, skew > 0, and volatility > minimum level.
*******
program TSM Kurtosis and TSM Skewness

program TSM VIX Connors

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Complete list of open source automated trading systems or algorithmic trading platforms. Includes Java JBookTrader

Complete list of open source automated trading systems or algorithmic trading platforms

http://jbrkeith.blogspot.ca/2010/07/review-of-open-source-trading-software.html

Out of all these, it looks like JBookTrader is the most modern and up to date

I just tried importing stuff with JBookTrader. The included Word doc explaining how to set it up with Eclipse reminds me on why I really dislike Java and the whole ecosystem that comes with it. I would rather throw myself onto the highway than go through this horrid torture again. Now I know why people hate it as I don’t have the time to tinker with this set up.

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Youtube video High-Frequency Trading A Practical Guide to Algorithmic Strategies and Trading Systems quant book review

Youtube video High-Frequency Trading A Practical Guide to Algorithmic Strategies and Trading Systems quant book review

I talk about this book and it’s capability to make you think about strategies and how to implement them.

Learn more of what is going with me at:

http://quantlabs.net/membership.htm

 

http://www.amazon.com/High-Frequency-Trading-Practical-Algorithmic-Strategies/dp/0470563761

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Next HFT book to read: High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems

Next HFT book to read:

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems

http://www.amazon.com/High-Frequency-Trading-Practical-Algorithmic-Strategies/dp/0470563761/ref=sr_1_2?ie=UTF8&qid=1327081392&sr=8-2

This looks decent for my next step.

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Trading Systems Improves Market Liquidity

Trading Systems Improves Market Liquidity

 

The use of trading systems, in general, is an effective alternative to increase the liquidity of financial markets without, however, raise the speculative risk relatively.

http://engfinance.blogspot.com/2012/01/trading-systems-improve-market.html

==

Does any one know of a quantitative proof of such an assertion? It appears that this assertion wasn’t true in August 2008, for example.

 

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Your ideas for article about evaluating trading systems for quant development

Your ideas for article about evaluating trading systems for quant development

I am about to write an Ezine article about evaluating trading systems in general and particullar Bik Research’s S.ONE automated TA system (http://www.bikresearch.si/index.php?page=s-p-100).

Do you guys have any fresh questions that have to be answered about paper trading as evaluation tool?

I  bought an automated trading software (NinjaTrader) for programming my own statistical-technical system. There’s a free version that I’d advice to you to get it, I’m pretty sure that once you see how the backtesting-Optimize module works you’ll come out with good questions.

1

There is some general information on testing trading ideas and systems here: <http://www.verticalsolutions.com/papers/> and a tool for ranking trading systems here: <http://www.verticalsolutions.com/tools/system_ranking.html>. There are also some papers on using control charts to help evaluate trading systems in the Papers section.

 

 

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Quant analytics: good ways to test experimental automated trading systems – preferably, using a distribution-free technique ie. permutation tests

Any ideas on some good ways to test experimental automated trading systems – preferably, using a distribution-free technique (e.g. permutation tests) ?.

——

I wonder what are u want to test? What is the hypothesis?

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is your question about technical side — which platform/software to use, or about testing methodologies?

—–

As in optimization, or robustness evaluation?

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I am primarily interested in the statistical evaluation methodologies that one uses to test the validity or (statistical) “significance” of a trading rule. A monte carlo approach ostensibly, seems like a sensible approach – (typically, an MC approach involves generating a random TS and testing the rule on the series) however, my trading rules are not based directly off a financial time series, so it is difficult to see how monte carlo framework fits in my particular case.

The question then remains, how to test a rule in a robust way – so that one can ascertain that success (or otherwise) is not due to random events.

——

first of all I would suggest considering if the amount of data used in the test is statistically significant. That is, if it covers different market conditions and is simply long enough to generate at least 100 trades. Then I’d pay attention to the distribution of trade results — ideally they should be evenly distribute with no excessive outliers. In other words, if all your profit is generated by a couple of trades while other trades are around 0 I would be in doubt if the trading rule is really based on a repeating local market inefficiency and not on some occasionally caught random process.

As to MC method — I personally do not use it as I do not build mathematical models for trading systems, so either my systems provide more or less equal returns for any given period (and in this case it makes no sense to MC), or vice versa — they do use some significant information which could be simply lost when MCing. So, in both cases MC makes no sense for me. However there are a lot of people around who build their systems on mathematical models, and for them, I suppose, MC is crucial to test.

It’s a bit difficult to go any further because we don’t have any idea not only about your trading rules (and no one expects you to publish them for sure), but also about your approach: if it’s based only on abstract mathematical models or on some market facts. Your comment that your rules are not based directly on financial time series makes me think that you follow the latter approach. However any more detailed information from your side could help.

—–

thanks for your detailed response. Despite coming from a mathematical background, I believe that trying to create a parsimonous mathematical model that explains all (or even the majority) of market behaviour, is fundamentally and intrisically flawed (but thats a topic for a philosophical debate in itself).

I’m already doing most of what you recommend in your reply – including introducing a few “sample statistics” of my own. Like you, (on some reflection), a MC approach does not make sense for my approach either.

I suppose, what I’m trying to get at, is a methodology for determining how much better a particular rule is – compared to a trading rule comprising of ‘randomly generated signals’ – or perhaps a ‘trivial rule’ such as one based on SMA, Fibonnaci (or similar).

The (obvious?) approach I suppose, would be to propose null hypotheses stating that all the generated trades (from all rules being tested) are drawn from the same population (ergo; similar mean and distribution properties) and then test to see if those null hypotheses can be rejected.

Coming to think of it, that may well be the most appropriate approach (its sometimes good to think out aloud!). I would be interested though, if someone is using an alternative approach to testing whether a specific rule’s performance is largely due to chance, or due to somehow encapsulating some salient information regarding market behaviour.

 

 

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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!

Accounting question regarding automated trading systems

Accounting question regarding automated trading systems

Could trading strategies and/or trading algorithms be capitalized as identifiable intangible assets on the balance sheet? While I am on the fence with seemingly discretionary strategies that may be written down and explained, I would think hard-coded trading algorithms would fit an intangible asset description since it would be possible to sell/separate them from the company. Please offer your ideas, and if you have industry experience dealing with this question, and how it is usually handled (reported or not, amortized or not, how valuation of such assets work, etc.)

Thanks in advance,

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, I think one of the primary issues would be if you sell the system vs use the system. In that sense, is a trading system really any different from an inventory system for a retailer?

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I don’t see why not. There are some reasonably well-understood practices for valuing other kinds of intellectual property, for example when doing a company valuation for sale. I don’t know whether you can or must report the values of intangible assets in any given industry – you’d need to ask a tax lawyer on that one. The question is what you’re trying to get from it.

 

 

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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!