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Anthony FJ Garner
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Anthony Garner is a British national based in London. He left investment banking in 1992 in favour of a long cherished aim to work for himself and since 1995 has been trading financial markets for his own account, as well as having established, run or acted as consultant to a number of hedge... More
My company:
Malplaquet LLC
My blog:
Anthony FJ Garner
My book:
A Practical Guide to ETF Trading Systems
  • Arguments In Favour Of The Use Of Synthetic Data In Backtesting 0 comments
    Apr 30, 2013 5:35 AM

    There is precious little agreement in academia as to almost any aspect of markets. There is no need to list or re-iterate the many views out there.

    Much has been made of black swan events and there apparently increasing frequency in modern markets. Much has been made of the apparent change in markets over the decades which make it increasingly dangerous to rely on actual past market data beyond about the year 2000. We can not predict the future, we can not rely on the past. What should we do therefore to test the robustness or otherwise of our chosen algorithmic trading system?

    Many have noted that when presented with well constructed synthetic data series, few are able to tell the difference between these and real price series. Trends surface in synthetic data - even the most crude simulated data will show you this. Simulated data can ape changing volatility; changing correlations between simulated data streams will presumably manifest itself without the need for design. Synthetic data can manifest long periods of mean reversion, price shock and any other aspect of market reality.

    So ask yourselves this question: if the future is unknowable and unpredictable, may there not be some value in pitting your favoured approach to markets against totally unseen and randomly generated data which is more likely to represent the limitless possibilities which lie ahead of us than the (by definition) limited market conditions we have already experienced.

    And consider this: if your system is unable to profit from synthetic data, will it be any more capable of coping with what real markets throw at it tomorrow and ever after?

    It is just possible that the use of synthetic data in back testing might reveal some interesting "truths" which some of us may be unwilling to acknowledge.

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