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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
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  • Positive Autocorrelation And Trend Trading Success.

    In his interesting article "The Main Cause of Failure of Some Popular Technical Trading Methods" Michael Harris says:

    "Indicator based trend following and classical chart patterns are two trading methods that were developed in mid 20th century using data from the equity markets mainly and worked well during an extended period of time in those markets due to the presence of autocorrelation. After 1998 things got harder because serial correlation in equity indices decreased due to arbitrage and by 2007 it was mostly gone rendering these methods largely ineffective.

    On the above daily chart of S&P 500 from 01/03/1950 to 12/19/2012 the bottom pane is the 1-Lag rolling 120-day autocorrelation of daily arithmetic returns [X(i+1)/X(i) - 1]. It suffices to observe that from 01/1950 to 04/1988 the autocorrelation was very high, especially after 07/1964 and through 04/1988, or for a period of 24 years, with very few and short periods of negative autocorrelation. That was a very good time for technical methods based on trend-following and chart patterns during which some traders might have gotten the impression that these methods are significant although their success was clearly due to the high autocorrelation in the equity markets, i.e. the fact that future prices quite often behaved like past prices. In other markets, like commodity futures and currencies, which started trading in the mid 1970s, these technical methods were later applied and expected to work because they were raised to causation rules by some authors but in reality they failed more often than they worked well and were basically responsible for the high rate of failure of retail traders and even some funds."

    I copied Michael's instructions and calculated the 1 lag rolling 120 day autocorrelation of the daily arithmetic returns of both on the S&P cash price series and the ratio adjusted futures contract.

    I attach al chart- the remainder of my charts can't be uploaded and refernce will have to be made to my website at the given link at Traders Place. Firstly note the less than perfect correlation between the futures and cash series.

    Secondly note the far more pronounced downturn in positive autocorrelation for the relevant period of the cash series over the ratio adjusted futures series.

    Thirdly note the chart of the equity curve of a simple dual moving average crossover system, 50: 200, trading the ratio adjusted futures contract for the period of its existence from 1982.

    My initial conclusion is that the use of serial correlation analysis, or at least the 1 lag rolling 6 month variety, is not of a great deal of value in explaining the success or otherwise of a trend following system. I prefer to define "trendiness" in other ways which I find more helpful.(click to enlarge)

    Sep 24 4:45 PM | Link | Comment!
  • (AAPL) Apple Inc: Have You Held Since The IPO?

    (NASDAQ:AAPL) Apple Inc: have you held since the IPO?

    If you had bought shares in the IPO of Apple at the IPO price of $22 in 1980 ($2.44 adjusted for corporate actions) and held them through to the present day, you would have achieved a compound annual growth rate close to 17%.

    $100 invested in 1980 would be worth around $17,000 today. Not including the sparse dividend payouts.

    But take a look at the first chart below: the top chart is the stock price since 1985. The chart immediately below that represents the peak to valley draw downs Apple has suffered during its tumultuous history. What would it have felt like holding Apple stock when it was suffering an 82% draw down from its equity high? The average of the 5 largest peak to valley percentage draw downs has been a whopping 65%. I wonder how many investors had the guts (or more probably the luck) to hold Apple over the years through good times and bad.

    (click to enlarge)Buy and Hold Equity Curve Log Scale

    (click to enlarge)Buy and Hold Linear Scale with Draw Down

    We all know the Apple story but let's re-visit it in conjunction with the share price chart. On December 12, 1980, Apple launched the Initial Public Offering of its stock to the investing public. When Apple went public, it generated more capital than any IPO since Ford Motor Company in 1956. But it was 5 years before the stock rose and remained above its initial offer price. After a dramatic rise in price through to the end of 1987, the stock did little overall for the next decade and by 1997 was 82% below its all time high. If you sold out at this stage (at an unadjusted $29) your return would have been a miserable 1.6% per annum over 17 years.

    What ailed Apple during these years? Founder Steve Jobs was ousted by Jon Sculley following a power struggle in 1985. Competition from Microsoft, who launched their Windows GUI and gained market share. Competition from other operating systems. A series of failed consumer products. How would the average investor have felt during this period? Would he have held the stock during this period of severe decline and lackluster performance? Research has show that the vast majority of stocks ever listed have ceased to exist - why should Apple have proved the exception? There seemed to be good reason to sell out and many did so.

    Would any of us have had the luck or skill to predict the return of Steve Jobs as CEO and the subsequent soaring of the stock price through to the present day? Who can know the future? Not I, that's for sure. Where will Apple go from here? I don't know and nor does anybody else for sure. What do the analysts say and do they know more than the man on the street?

    I have been an analyst with an investment bank myself. Their job is to generate commission for the sales desk. Analysts may know a lot about the companies they follow. But they are no better at predicting the future than you are.

    So what do I recommend? I recommend ignoring the news and taking a skeptical approach by following the share price, using a simple rule based mechanical system. Why? Because on the whole, doing so is likely to produce better long term risk adjusted returns than either Buy and Hold or discretionary fundamental analysis. Let us apply one simple trend following system (amongst scores of possibilities) to Apple stock since 1985 and compare the risk/return statistics to Buy and Hold.

    Here is a bog standard, no frills, easy to understand trend following system. A Bollinger Band Breakout.

    I like it; I have traded it and will in all probability do so again. Long established masters of the universe can switch channels here; those newer to the game should stay tuned in and read on.

    I am told this system was described by Chuck LeBeau and David Lucas in their 1992 book: "Technical Traders Guide to Computer Analysis of the Futures Markets".

    It's dead easy to trade and you only require end of day data. A relatively slow system, so good for the hobby trader with a day job. Or anyone else who does not wish to sit glued to a screen all day.

    • Calculate simple moving average of the close for the period of your choice.
    • I shall use 80 days for this example.
    • Calculate the standard deviation of the close over the same 80 day period.
    • Surround the 80 day moving average with an upper and lower band.
    • The upper band equals the moving average plus 2 standard deviations.
    • The lower band equals the moving average minus two standard deviations.
    • Use whatever multiple or fraction of standard deviation you choose: I shall use 2 for this example.

    Trading Rules

    Long Entry

    If you have no position, when the price closes above the upper band, buy at the market on the open of trading on the following day.

    Long Exit

    If you are long and the price closes below the moving average, exit at the market on the open of trading on the following day.

    Short Entry

    If you have no position, when the price closes below the lower band, sell (go short) at the market on the open of trading on the following day.

    Short Exit

    If you are short, when the price closes above the moving average, buy at the market on the open of trading on the following day.


    If you like to use stops, you can use the current moving average level to place your stops in the market. And update them daily or periodically.

    Money Management

    Risk a small fixed percentage of your account value on initiation of each trade. Your "risk" is the difference between your entry price and the moving average. Excluding overnight movement, gaps, slippage and fast markets. Let's use 5% of capital on an account of say $300,000 trading just Apple. Position size = (Desired percentage Risk*Trading Equity)/(Entry Risk ). Let's apply this to a long position in Apple on 9th January 2012. Use the previous close of $422.40. The moving Average was $394.62. Risk is therefore $422.40 - $394.62 = $27.78. Position size is therefore: (0.05*300,000)/(27.78) = 539 shares. 539 shares assuming an entry price of 422.40 will cost $227,674. Although you have paid $227,674 for your shares your risk is "only" $14,957 since your predefined exit will be at $394.62.

    Here are the assumptions made in my test run.

    • Long only trades taken.
    • Risk per trade 5%
    • Slippage 7%
    • Commission $0.01 per share
    • No interest earned on unused balances
    • Starting Capital $300,000
    • Start Date 1st July 1985
    • End Date September 2013

    Below you will see the results of trading Apple shares on this basis:

    (click to enlarge)BBBO Log Scale

    (click to enlarge)BBBO Linear Scale and Draw Down

    Here is a table from which you can see the relative merits of Buy and Hold and the operation of this simple system for the period concerned:



    Max DD


    STD Dev

    Winning Months

    Risk Adjusted Return

    Buy and Hold














    The system (at the given position size) results in a lower absolute CAGR. A number of important factors must be noted. Firstly, absolute return could be greatly increased by using a higher position size if desired. Secondly the risk adjusted return for the system is far higher than that of Buy and Hold, the number of winning months is far greater and the maximum peak to valley drawdown is very, very much more bearable. To find the risk adjusted return, divide the higher annualized standard deviation of monthly returns of the Buy and Hold by the much smaller std dev of the BBBO system results: 46.57 / 13.56 = 3.43. Now divide the higher CAGR of Buy and Hold by this figure to give you the risk adjusted return of Buy and Hold (IE what return you could expect from Buy and Hold for an equal std dev to that of the BBBO system): 20.89% / 3.43 = 6.09%

    Now let us take a cooler, more rational and more cautious approach to investing. Single stock risk is not worth taking. When the next Enron or Lehman hits the headlines you will be grateful you were investing and trading a large diversified portfolio. Would I want to risk 5% per trade? No, I would not. I would want to trade a big and diversified portfolio and risk a much smaller amount per trade. By doing so I would expect the CAGR to increase (certainly in risk adjusted terms) and the maximum drawdown to remain at similar levels or decrease.

    My belief is that successful investing is not complex and that the adoption of simple, rule based trading methods is likely to provide far better returns than conventional stock picking approaches based on discretionary decisions and fundamental analysis.

    Don't forget the above results are purely hypothetical. The potential investor considering mechanical systems must take a great deal of time and effort to devise and test his own strategies on a great variety of historical data and satisfy him or herself that the concept has reality and a prospect of success in live trading going forward.

    Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

    Tags: AAPL, long-ideas
    Sep 24 2:48 PM | Link | Comment!
  • Synthetic Data: Considering Volatility And Drift

    I have continued to work on the question of changing volatility in the futures markets over time, for the purposes of attempting to build realistic synthetic data series.

    It is my intention to mimic each class or sector separately and to have many different series for each "asset class" which try to incorporate typical volatilities for the underlying instruments.

    I will program series for stock market indices, grains, metals (precious and industrial) and so on in an effort to introduce as much realism as possible.

    In that regard I am taking a closer look at the entire history of each of the instruments in my portfolio. I set out on my website a few charts for Comex Silver.

    What has happened over time? Has the frequency in any particular "bin" of daily return ranges changed over time? Has there been a trend up or down? If so, will this trend continue or will it revert to some sort of mean? Fruitless to speculate perhaps but interesting to see nonetheless what has occurred since volume built up by around 1967 in the newly created Comex Silver contract.

    Again, I used a CSI Pertpetual contract and the log normal of daily price change. If you look at a price chart perhaps the most notable features are the huge spikes in 1979 (the Hunt cornering episode I think?) and again the gigantic run up in 2011.

    In terms of short term measures of volatility (3, 7 and 14 days rolling annualised standard deviation of daily returns), these two periods alone do not really stand out: there are many peaks in very short term measures of volatility and these peaks seem to have been increasing in magnitude in recent years. The longer term picture seems more moot and the picture less clear: the trend in the 100 day rolling average seems to have trended up over time, but the same can not really be said for the 500 day. And in all cases the period from 1989 to 2002 shows a lowering of volatility over all time frames.

    What of the future? Who knows? In may be safest to assume that volatility over the very long term is mean reverting. No doubt greater volatility has followed increased volumes, but what of volume? If silver is less traded for any reason in the future we might reasonably expect volume and volatility to drop off.

    Where does this leave us? Back where we started: we must probably rely on a "close to random walk" in terms of volume, price and volatility if we want to cover all possibilities. But there again we should probably combine such series with different assumptions in other series for the same class: assumptions that volatility will trend upwards perhaps, as will volume.

    And what of price? Certainly in terms of stock indices it may be reasonable to include both scenarios: price series where a continuation of the Enlightenment continues to drive economies and hence stock markets upwards. And price series which include no inherent return or "drift" - where stock indices remain stagnant and move sideways, perhaps over many, many years.

    The same with commodities. Oil and gas may be scarce resources but who says their price has to move up forever? Perhaps man will cease to rely on oil as nuclear fusion becomes a reality.

    My object in investigating synthetic data is to test trend following on as wide a range of possible future conditions as I can imagine.

    Jul 02 8:39 AM | Link | Comment!
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