The turtle trading strategys profitability in the current market - Literature review Example

? The turtle trading strategy’s profitability in the current market Table of Contents I. The Turtle Trading Rules 3 II. Literature Review 6 References 14 I. The Turtle Trading Rules This paper discusses and undertakes a literature review on the turtle trading rule in its original form, including what its profitability is, and what the advantages and disadvantages of the rule are. The turtle trading rules find their origin in a bet and an experiment set up by its originator, the legendary investor Richard Dennis, to show that he could churn out traders in a manner that is as efficient, as effective, and as quick as the manner in which turtles are grown in turtle farms that Dennis came across while on a trip to Singapore. It is a complete trading system that is based on mechanical trading hinged on market price signals. The following aspects of the trades were all accounted for and covered by the rules: what to buy and what to sell, or the markets; how much of a particular market to sell or to buy, or the size of the position; the timing of the buying and the selling; the stops, or the timing of bailing out of positions where the trader is in a losing proposition; the exits, or the timing of the bailing out of positions where the trader is winning; and the selling and buying hows, which comprise the tactics for the trading exercises. Moreover, the turtles trading system focused on a number of trading instruments, all of them markets that are considered liquid. In the Chicago Board of Trade, the turtles traded in 30 year and 10 year Treasury bonds and notes. In the New York Exchange for Sugar and Cocoa, the turtles traded in cotton, coffee, sugar and cocoa. In the Chicago Mercantile Exchange, the turtles focused on a select group of currencies, which included the Japanese Yen, the Canadian Dollar, the British Pound, and the Swiss Franc. The list also included the S&P 500 Index of Stocks, as well as the 90 day US treasuries. In the Comex, the focus were gold, copper and silver. Finally, in the New York Mercantile Exchange, the focus were on unleaded gas, heating oil, and crude oil (Faith 2003, pp. 7-11; Carr 2009; Lauer 2007; Adamu and Phelps 2010; Anderson n.d.; FinanceManila 2007; Stockopedia Features 2011; Carr 2009; Business Insider 2013; Kasera n.d.; Au.Tra.Sy 2010; Faith 2003; TradingBlox.com n.d.; Powerstocks Research n.d.; Palantir Technologies 2012; Kowalski 2013; Power 2012; Trading Blox n.d.). The turtles trading system takes its cue from the highs in the prices for 22 days and for 55 days. There are two parts to the trading rules. In system 1, the trader assumes a long position on a market whenever the price goes beyond the high for the preceding 20 days. Conversely, the trader takes a short position whenever the price goes down below the low for the past 20 days. In the case of the last breakout resulting in a trade that is a win, the breakout signals are ignored. That said, the trader would record an entry on day 55, in order not to miss out on major moves in the markets. The exit for system 1 is when the price is a low for 10 days when the position is long, and the high for 10 days when the position is short. In system 2, the benchmark is 55 days, taking a buy position when the market price goes up beyond the high for the past 55 days, and a sell position when the market price goes below the low for the past 55 days. The exit signal for this system 2 mode is when the price is the low for the past 20 days for the long position, and the high for the past 20 days for the short position (Stockopedia 2013; Business Insider 2013; Kowalski 2013; Kasera n.d.; Au.Tra.Sy 2010; Carr 2009). An example makes the trading rules for the turtle trading system clear. In an instance where the price of a stock, say Nokia, goes up to a level that exceeds a hypothetical 20-day range of 3.40 US dollars on the high side, then that is a signal to buy. When the time comes, on the other hand, that the stock price dips below the low for a ten-day period, then that is the time to sell the Nokia stock. It is easy to see just where this kind of trading strategy works. Where the prices of the market go up and down in a relatively slow fashion, in other words in markets where prices are not so volatile, this kind of strategy works wonders in providing a rule that can work to maximize profits and minimize losses, because there is a signal to dispose of the market when the prices go down a certain level that is predetermined. On the other hand, it is also clear that where prices are bound to a particular range, and where the market is highly volatile, then this kind of trading strategy can be less than optimal (Palantir 2012; Carr 2009; Lauer 2007; Adamu and Phelps 2010; Anderson n.d.; FinanceManila 2007). Other more simplified versions of the trading rules simply focus on buying on breakouts and selling when the prices reach a threshold low. In one version, when the price exceeds the high over a 40-day period, that is a signal to buy. On the other hand, when prices go below the low recorded over a period of 20 days, that is a signal to sell. This is a trend-following strategy precisely because the objective is to look for trends in the market, in this case price trends over a period of time, 20, 40 and 55 days, and then making decisions to either buy or sell the market based on the trends. The distillation of some of the key insights from the turtles trading rules by one of the turtles, Michael Covel, includes the following: that one ought to focus on price rather than on market news; that traders ought to allow for flexible arrangements with regard to the chosen signals and the signal parameters, and one arrives at ideal parameters through market testing; exits and entries are to be planned meticulously, making sure to follow strategies for cutting losses and reaping profits in a sensible and predictable manner; market volatility determines position sizes, with sizes going up as volatility of the markets goes down, and vice versa; the largest exposure on one trade is capped at two percent of the total account; drawdowns are the inevitable consequence of the trades, and the flipside of large profits is also very big drawdowns- this is another way of saying that the risk-reward profile for trades follows the market dynamics, in that the bigger the rewards, the bigger are the risks as well for trades, with drawdowns representing the risks of losses from trading (Carr 2009; Lauer 2007; Adamu and Phelps 2010; Anderson n.d.; FinanceManila 2007). II. Literature Review A study confirms the profitability of using the turtle trading system in some futures markets, notably in US Treasury bonds, in the corn and gold COMEX, in the British pound, and in the S&P 500 index. In the first scenario where the turtle trading rules are used, but no reinvestment of profits were made, profits of up to 51 percent were made, with the highest profits accruing to corn, gold and US T bonds trades, and smaller profits made with the British pound and S&P 500 trades. On the other hand, where the trades were optimized making use of money management methods that included reinvestment, returns of up to 2,000 percent were recorded for corn, and up to 100 percent for T bonds, with lesser returns realized for the other futures markets (Anderson and Faff 2004, pp. 1067- 1073). On the other hand, comparisons with other mechanical trading models in the literature reveal that some enhanced trading models, making use of coevolution and grammatical evolution techniques or GE for instance, offer better returns than stock turtle trading system rules. In coevolution models, entry and exit rules are coevolved for exit and entry for long and short positions, for instance. In GE models, solutions are arrived at and then iterated for fitness with certain rules and predicted outcomes, until such a point is reached when the outcomes are optimal, in this case the returns are optimal. For both of these models, there are scenarios where the achieved returns are on par, or greater, than the returns achieved for mechanical trading models based on the stock or unmodified turtle trading rules. That said, the refined models do not take away from the success of the turtle trading rules, given that the returns for the turtle trading system are comparable, and at a high level to begin with (Adamu and Phelps 2010). The table below details profitability for different futures trades making use of the turtle trading system (Anderson and Faff 2004, pp. 1067- 1073): Table Source: Anderson and Faff 2004, p. 1070 Going deeper into the above study, the finding with regard to comparing the returns from the turtle trading system with trading models that leverage mathematical modeling for futures trading is that again, mathematical models used in conjunction with the turtle trading system enhance returns. One such set of mathematical models derive their basis and meaning from so-called gaming mathematics. One such gaming mathematics-derived trading system is called the optimal f approach. In the optimal f approach, which improves on the turtle trading rules by mathematically determining the fund allocation for every traded futures contract, as well as the number of contracts to be traded at any given point in time. Here the findings are that for different futures contracts, while the turtle trading system was able to generate outstanding returns for certain trading instruments, the returns for the optimal f gaming mathematics-based rules are better. The conclusion, at any rate, as far as the turtle trading system alone is concerned, is that it results in excellent returns on investment, if followed scrupulously (Anderson and Faff 2004, pp. 1068-1069) The same conclusion about the profitability of trades making use of the turtle trading system is reached in the study by Anderson (n.d.)where he did various simulations, some making use of the original form of the turtle trading system, and some making use of derivations of the system combined with optimal fixed fraction trading. In those simulations for corn and Treasury bonds, the original turtle trading system yielded positive returns of 450 percent for corn. The optimal fixed fraction trading improvement over the original turtle trades determined ideal sizes for the trading portfolios. At differing sizes of the portfolio, the returns on corn and T bills trades differed, and were also positive (Anderson n.d., pp. 1-14). In another paper the optimal fixed fraction trading enhancement to the turtle trading rules is presented together with the improvements in returns over the original trading rules. Here again, the findings are that the returns for optimal f trading techniques are larger than those achieved making use of the turtle trading system, even as the latter also produced good returns (Anderson 2003, pp. 1-15). The returns for various sizes of the trading accounts showed dramatic improvements in margins making use of different trading sizes In the table below, the yields/returns are given in terms of the final balance of the accounts at the end of the trades, following the turtle trading rules together with the optimal fixed fraction trading enhancements to the conduct of the trade (Anderson 2003, pp. 1-15): Table Source: Anderson 2003, p. 12 Ten-year tracking of trades making use of various currency pairs in combination with the turtles trading system rules yielded returns that were along the lines of 20 percent or so compounded annually, with different currency pairs showing variations in returns over a ten-year period. This indicates that the turtles trading rules are effective for currency trading, and have been proven to yield positive returns over a large period of time, with consistent application of the rules. Comparison with gaming math models such as optimal f, which impose rules-based determination of the amount to be invested in different contracts, and the amount of contracts to be traded at any given time, yields results along the lines of the turtle trading rules providing good and comparable returns (Mechanical Forex 2010; Anderson 2003). Meanwhile, simulation runs for yen trades making use of the turtles trading system and using the yen as the anchor currency versus the US dollar as the counterpart trading currency, were made. The results are that the returns on the use of the turtles trading system with such a currency pair and arrangement yielded cumulative profits in excess of 1,000 percent from the end of 1981 all the way to the end of 2004. Again, these are simulated runs, but the basic message is that with the discipline imposed by the turtles trading system, and making use of currency trades as an example, there are scenarios where the turtles trading system excels at providing spectacular returns on trades over sufficiently long periods of time. The idea is that the time horizon for the trades has to be sufficiently large, and the rules have to be followed with absolute consistency, once the parameters are set. (Kum 2004; Turtle Talk 2005; Sure Fire Thing 2011; Seleznov 2009). On the other hand, observations in the academic literature are that the turtle trading system has become somewhat less efficient in generating returns as compared to 20 years ago, when returns were better and more predictable (Krawczuk 2009, pp. 63-67). Other studies meanwhile, while affirming the positive returns for the turtle trading system, argue that improvements to the timing of buys and sells in the trading rules, through algorithmic means can improve returns (Adamu and Phelps 2010). Meanwhile, another study validating the positive returns from turtle trades qualifies that turtle trades work well in trending markets where prices either trend up or down, but do not work so well in markets where prices either move in a narrow band or go back to a particular band of values over time. Moreover, this validation study shows that turtle trades work better in some types of markets and commodities than in others. This is typified in the Profit and Loss curves for various investment vehicles where turtle trading rules were applied (Kasera n.d., pp. 12-13): Plots source: Kasera n.d.. p. 12 On the other hand, while for individual markets the returns are different, for the overall population of markets, taking the aggregate returns using the turtle trading rules, the total returns are positive over time (Kasera n.d., p. 14): Plot Source: Kasera n.d., p.14 In the context of momentum trading, we see that the turtle trading system's rules for entering on triggers of market highs and exiting on triggers of market lows, making use of moving peaks and throughs over a period of time as signals for trades, has aspects that fit with momentum trading principles. The idea behind momentum trading, as explained in the academic literature, is that there is the observed phenomenon of returns for traded instruments continuing over a period of time in the short term as well as in the medium term, whereas in the long term the tendency observed is for prices to reverse. We can surmise that where markets trend upward or downward very swiftly, within the time horizon being observed under the turtle trading system, that the turtle trading rules reflect aspects of momentum trading, of following through with expected quick upward movements on the prices of the traded instruments (Hong and Stein 1999, pp. 2143-2147; Lee and Swaminathan 2000; Anderson 2003). The overall assessment is that he turtles trading system works and has been shown to yield outstanding returns on trades over long periods of time. On the other hand, some of its shortcomings relate to the rules being not so effective in volatile markets that are marked by price movements within a range. In the normal case, it has been noted that the profitable trade signals rate is just half, equal to tossing a coin. Moreover, there is the risk that the total risks are not fully stated and are in fact often understated, especially in cases where the market has low volatility. Highs as well as lows in prices, on the other hand, are seen as not being very sophisticated markers for signaling trades, and can fail as effective signals in that regard (Datta 2010; Kum 2004; Turtle Talk 2005; Sure Fire Thing 2011; Stockopedia Features 2011; Carr 2009; Business Insider 2013; Kasera n.d.; Au.Tra.Sy 2010; Faith 2003; TradingBlox.com n.d.; Powerstocks Research n.d.; Palantir Technologies 2012; Kowalski 2013; Power 2012; Trading Blox n.d.). Kasera notes, meanwhile, that the turtle trading rules have several advantages relating to its versatility in being applicable to a number of markets; provides for mechanisms to adequately size positions; and makes use of a fast and efficient computational algorithm. Its disadvantages, meanwhile, include limitations in effectiveness in some markets where price movements revert to a mean, rather than trends upwards or downwards; and successful trades are outnumbered by unsuccessful trades, even though in terms of value successful trades offer greater returns compared to the size of losses in unsuccessful trades (Kasera n.d., p. 20). References Adamu, Kamal and Phelps, Steve, 2010. Coevolution of Technical Trading Rules for High Frequency Trading. Proceedings of the World Congress on Engineering. [Online] Available at: http://www.iaeng.org/publication/WCE2010/WCE2010_pp96-101.pdf [Accessed 11 March 2013] Anderson, John, n.d. Taking a Peek Inside the Turtle's Shell. Queensland University of Technology School of Economics and Finance. [Online] Available at: http://e-m-h.org/Ande.pdf [Accessed 11 March 2013] Anderson, John and Faff, Robert 2004. Maximizing futures returns using fixed fraction asset allocation. Applied Financial Economics 14. Available at: http://finance.martinsewell.com/money-management/AndersonFaff2004.pdf [Accessed 16 March 2013] Anderson, John 2003. Optimal f and Portfolio Return Optimization in US Futures Markets. Queensland University of Technology Discussion Paper No. 133, January 2003. Available at: http://eprints.qut.edu.au/511/1/Anderson_133.pdf#page=13&zoom=auto,0,540 [Accessed 16 March 2013] Au.Tra.Sy, 2010. Were the turtles just lucky?. Au.Tra.Sy Blog. [Online] Available at: http://www.automated-trading-system.com/turtles-just-lucky/ [Accessed 11 March 2013] Business Insider, 2013. Turtle Trading Rules: Trend Following Investing Based on 20 amp. BusinessInsider.com. 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[Online} Available at: http://www.palantir.com/2010/10/turtle-breakout-trading-strategy-simulation/ [Accessed 11 March 2013] Power, Benjamin, 2012. Interview: Turtle trader Curtis Faith on intuition and trading from the gut. Global Growth Investor. [Online} Available at: http://globalgrowthinvestor.com/544/interview-turtle-trader-curtis-faith-on-intuition-and-trading-from-the-gut/ [Accessed 11 March 2013] Powerstocks Research, n.d. The Turtle Trend Trading System. Powerstocks Research. [Online} Available at: http://powerstocks.co.za/turtletrader.php [Accessed 11 March 2013] Seleznov, Mark 2009. Technical Analysis on Turtle Trading. Seeking Alpha. [Online} Available at: http://seekingalpha.com/instablog/510046-mark-seleznov/35447-technical-analysis-on-turtle-trading [Accessed 11 March 2013] Stockopedia Features, 2011. Turtle Trading Rules: Trend Following Investing Based on 20 and 55 Day Highs. Stockopedia. 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