Established seller since Seller Inventory FW Seller Inventory LIE Perry J. Publisher: Wiley , This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Synopsis About this title Learn to trade using technical analysis, market indicators, simple portfolio analysis, generally successful trading techniques, and common sense with this straightforward, accessible book.
From the Back Cover : A one-of-a-kind guide to technical trading "In trading, timing is everything, and now is the time to read this excellent and easy-to-follow book on technical trading systems by Perry Kaufman. Buy New Learn more about this copy. Customers who bought this item also bought.
Stock Image. Published by Wiley. Seller Rating:. Published by Wiley Seller Image. New Paperback or Softback Quantity Available: New Quantity Available: 1. Published by Wiley , New York Published by John Wiley and 38; Sons New Quantity Available: 2. Because this utilization of the moving average line not only produces objective trading signals but also quantifies risk, it is considered to be not only a technical indicator but also a mechanical trading system, albeit the most simplistic one imaginable. Since buy and sell signals are generated whenever the moving average line is violated, it is known as a stop-and-reverse trading system.
Although a day simple moving average is by no means the most successful mechanical trading system, it clearly illustrates what technicians mean when they speak of objective, mathematical indicators. It is this objectivity of trading signals derived from mathematical technical analysis that makes mathematical technical analysis the indispensable foundation of the vast majority of mechanical trading systems.
Although this book in no way negates the validity of such fundamental tools in system development,9 I do argue that an inherent limitation in using such tools is that they require an indepth understanding of a particular market or trading instrument. By contrast, mathematical technical indicators do not require any particular specialized knowledge of the underlying fundamentals affecting a particular market on the part of system developers. This absence of expertise thereby allows traders to apply their system as readily to Asian equities or live cattle, soybeans or foreign exchange, sugar or natural gas.
Although obvious benefits gained by participating in diverse markets will be examined in detail later, for now let me suggest that diversification into various low to negatively correlated asset classes increases the likelihood of improved rates of return on investment and often reduces the severity of peakto-valley drawdowns in equity. The problem is that there is no universally accepted definition of what separates long, intermediate, and short-term traders. For the sake of simplicity and consistency, I will designate some time parameters to each of these terms.
As used in this book, long-term traders are those who attempt to profit from trends lasting anywhere from 1 to 6 months. Intermediate-term traders are those who hold trades from 10 days to 1 month, and short-term traders are those holding positions for less than 10 days. Both types of trigger events can be used to produce successful trading systems because they capitalize on recurring psychological conditions in the market.
Psychological Significance of Price Triggers: Horizontal Support and Resistance Levels To understand why technical analysis works in terms of market psychology, let us examine the heating oil futures market, which began trading on Nymex during the late s.
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Dispelling Myths and Defining Terms 7 The late s and early s marked a strong uptrend in energy prices. Finally, what happens if the buying pressure becomes strong enough to satiate the selling represented by all of these trader types? This is why old resistance, once broken, becomes new support and old support becomes new resistance. Psychological Significance of Price Triggers: Horizontal Support and Resistance Levels: Corrections Another example of market psychology in relation to price triggers is the tendency of trends to experience temporary, countertrend reversals within the context of the larger dominant market trend.
Such minor countertrend reversals are called corrections, retracements, or pullbacks and typically are measured from the lowest low of the prior trend to the most recent highest high in bull market trends, or from the highest high of the prior trend to the most recent lowest low in bear market Dispelling Myths and Defining Terms 9 trends. The strength or weakness of the dominant market trend can be determined by the severity or mildness of these corrections.
The psychology behind market corrections is as follows. Hedgers and short-term countertrend traders establish countertrend positions into logical price target areas that are often long-term support or resistance levels, as discussed above. Trend-following traders also may exit with profits at these logical price trigger levels. As the market returns from its highs or lows, intermediate and short-term trend-followers take profits, accelerating the correction.
The most infamous example of a correction against the dominant market trend was the crash of Dividing this price move by 50 percent we get Adding The ultimate low print of the so-called crash of was in fact Consequently, I contend that this so-called crash was in fact nothing more than a pullback in the bull market. This example illustrates the severity and emotionalism that can accompany major corrections against the dominant trend.
Psychological Significance of Indicator-Driven Triggers An indicator-driven trigger can be defined as an occurrence such as a price close above or below a moving average or the crossing of an oscillator above or below a significant level. This is why deriders of technical analysis view it as a self-fulfilling prophecy. Although I agree that indicator-driven triggers often act as self-fulfilling prophecies, I do not believe that this in any way negates their utility. That is, they display a strong tendency toward mean reversion—in other words, prices tend to cluster around the mean.
Why then are such a large portion of technical analysts and mechanical trading systems dedicated to trend identification? The reason is because when prices are not in this mean reversion mode, they tend to trend. In sta- Dispelling Myths and Defining Terms 11 tistical terms, commodity and financial markets are leptokurtic with amplified tails—when they are not in their mean-reverting mode, they tend to display powerful and sustainable trends. The day simple moving average examined earlier provides us with an excellent example of a trend-following indicator.
Another popular variation on this mathematical trend-following indicator is known as the twomoving average crossover system see Figure 1. The two-moving-average crossover system entails the introduction of a second, shorter-term moving average, such as a 9-day simple moving average. Now instead of buying or selling whenever the market closes above or below the day simple moving average, our trend-following trader establishes long positions whenever the 9-day moving average crosses over and closes above the day moving average.
By contrast, whenever the shorterterm moving average crosses over to close below the longer-term moving average, our trader would exit all long positions and initiate short positions. Dispelling Myths and Defining Terms 13 points gained on up days during the 14 days and divide that total by To determine the average down value, we add the total points lost during the down days and divide that total by The general who loses a battle makes but few calculations beforehand.
Thus do many calculations lead to victory, and few calculations to defeat: how much more no calculation at all! It is by attention to this point that I can foresee who is likely to win or lose. This chapter does not attempt to duplicate their work but instead tries to address the essential facets of the most commonly employed indicators, including: an explanation of what the indicators are, why they work, and how they can provide system developers with ideal building blocks for mechanical trading systems.
Although I encourage readers to examine the various mathematical formulas behind these commonly employed indicators, I also freely admit that many traders successfully use these indicators without understanding the formulas on which they are based. Again, I focus on the most widely used indicators because the more market participants focus on a particular indicator, the more likely that it will be useful in system development. Throughout this chapter I provide examples of indicators and trading systems that show profits. I could just as easily illustrate use of each indicator with losses, but I want to show why traders are drawn to a particular tool.
Chapters 3, 4, and 5 discuss which technical indicators can be turned into successful trading systems. For now my goal is merely to explain what the most commonly used indicators are, why they are used, and how they form building blocks for comprehensive trading systems. Although many books on technical analysis treat these various indicators as if they worked exclusively in either trend-following or mean-reverting trading environments, this book will show how indicators can be successfully applied to either realm.
Trend-Following Indicators: Why They Work I have already highlighted some of the psychology behind the success of trend-following indicators in the discussion of reference points in behavioral finance. In Chapter 1, I showed how emphasis on reference price points led traders to take small profits and large losses.
If we assume that the majority of market participants lack the psychological fortitude to allow profits to run and take losses quickly, then successful traders use trend- following indicators that necessarily reinforce their ability to actualize disciplined profit and loss goals. As a result, such trend-following technicians often find themselves on opposite sides of the market from their less successful counterparts. Because successful trend-following traders are both utilizing trend-following indicators and acting contrary to mass psychology, we have shattered another myth of technical analysis, namely, that following the trend and contrarianism are mutually exclusive.
Instead, contrary opinion is often the epitome of trend trading.
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One of the best-known examples of trend-following contrarianism occurred in November when the Dow Jones Industrial Average the Mathematical Technical Analysis 17 Dow traded above 1, Traders buying that level were purchasing all-time new highs, which is in direct opposition to popular market wisdom admonishing us to buy low and sell high.
The ultimate high of the market trend was not achieved until January 14, , at 11, on the Dow. These indicators in turn help them to ignore psychological temptations inherent in fading what appears to be historically high or low prices. The success of trend-following indicators once again illustrates how the market rewards those who train themselves to do that which is unnatural and uncomfortable and punishes those desiring certainty, safety, and security.
Successful trend-following indicators not only force traders to abandon attempts to buy the bottom and sell the top, they reprogram traders away from destructive price reference points by forcing them to buy recent highs and sell recent lows. Mean Reversion Indicators: Why They Work If trend following is such a successful methodology, how can indicators based on the exact opposite philosophy generate consistent profits?
Whether the trend has matured and is approaching climactic reversal or is still in its infancy and simply correcting a temporarily overbought or oversold condition, the market has an uncanny knack for separating the less experienced from their money by exploiting their greed, lack of patience, and complacency. Imagine speculators who saw the bull move early but allowed fear of losses to prevent them from buying the market.
As the trend matures, their anxiety and regret magnify in lockstep with forfeited profits until they finally capitulate and buy at any price so that they can participate in this once-in-a-lifetime trend. Since the thought process that accompanied their ultimate trading decision was purely emotional and devoid of price risk management considerations, when the inevitable pullback or change in trend occurs, greed and hysteria quickly shift to panic and capitulation.
The variations on moving average indicators are so numerous that a book could be devoted exclusively to their various flavors; however, in the interest of completeness, I address what I believe are some of the most significant alternatives to the simple moving average. As discussed in Chapter 1, simple moving averages are the most widely used and the easiest to calculate because they give equal weighting to each data point within the data set. This issue of equal weighting to each data point leads technicians to seek alternatives to the simple moving average.
The problem with using a moving average that gives equal weight to each data point is that with longer-term moving averages—such as the day moving average—the lagging aspect of indicator means it will be slower to respond to changes in trend. Obviously slower response times to trend changes could mean less reward and greater risk. One solution to the problem of the lagging nature of the simple moving average is to give greater weight to the most recent price action.
Linearly weighted and exponentially smoothed moving averages both attempt to address the equal weighting issue by giving a larger weighting factor to more recent data. The volume-adjusted moving average suggests that directional movement accompanied by strong or weak volume is often a better measure of trend strength than any of the time-driven weighting models. Another problem with moving averages is choosing between shorter and longer time parameters.
But smaller data sets also result in more false trend-following signals during sideways, consolidation environments. As discussed, larger data sets, such as a day moving average, will generate fewer, higher-quality entry signals, but those remaining signals will entail less reward and greater risk. Perry Kaufman, author of many books on Mathematical Technical Analysis 19 technical analysis, with his adaptive moving average, attempts to address the issue of choosing between longer- and shorter-term moving averages by introducing a moving average that is attuned to market volatility, moving slower during periods of low volatility i.
Our trade-off when working with moving averages is choosing between speeds of response to changes in trend and the number of false trend-following signals we are willing to endure. Other solutions to this issue, besides the shorter- and longer-term moving averages, use of weighted moving averages, and the adaptive moving average, include the introduction of a second condition onto the moving average indicator in hopes of confirming valid signals and filtering out false breakouts.
False breakouts are also known as whipsaws because trend-following traders buying or selling on such signals get whipped into loss after loss until the market experiences a sustainable trend. Instead of entry based on fulfillment of the indicator-driven trigger of settling above or below the moving average as illustrated by Figure 2.
When we compare trading systems in later chapters, we will analyze the results on multiple asset classes with low to negative correlations to ensure the robustness of each system. The other major flavor of time-driven patterns is that of modifying the time horizon employed, from day to minute moving averages. The premise behind changing the duration of moving averages is that when markets are trending, longer-term moving averages will be profitable.
Includes data from December 31, , to December 31, These envelopes are constructed by adding and subtracting a percentage of the moving average. Valid trading signals are generated when the market settles beyond the upper or lower envelopes of the moving average see Figure 2.
Although moving average envelopes are traditionally used to filter out false trend-following signals, they also can be used as countertrend FIGURE 2. Although simply fading the moving average envelopes provides a method of generating entry signals, it does so without defining an exit method. Two distinct types of exits must be introduced to transform this indicator into a comprehensive trading system.
First, we need to determine where we will exit the trade if mean reversion does occur as anticipated. Since our intention was to fade the envelopes, the obvious answer is exiting either at the moving average or with a percentage profit i. Two and Three Moving Average Crossovers We have already examined two moving average crossover trading systems in some detail.
Ichimoku also has a whipsaw waiting period built into it, as entry signals require not only that the 9 closes beyond the period moving average, but also that the period moving average starts moving in the direction of the crossover compare Figures 2. By contrast, the three moving average crossover system allows for neutrality see Figure 2. Trade entry requires that the shortest moving average closes beyond the middle moving average and that the middle is beyond the longest.
Ichimoku Kinkou Hyou also has a three-moving-average version that includes the introduction of a period moving average. As with its two-moving-average system, this version contains a whipsaw waiting period that requires that both longer-term moving averages have turned in direction of crossover prior to entry.
Although it is impossible to draw any definitive conclusion from a single case study, it is interesting to note that in both of our examples the Ichimoku versions produced inferior results when compared with the traditional moving average and the three moving average crossovers. In addition, both versions of the three moving average systems generated inferior track records when compared with the simpler, more robust two moving average crossovers compare Figures 2. This concept of simple is better will be revisited throughout the book. Mathematical Technical Analysis 27 oped by Gerald Appel and is commonly used as a trend-following indicator that attempts to minimize trading range whipsaws.
The MACD line is the numerical difference between a shorter-term, period exponential moving average and a longer-term, period exponential moving average. Directional Movement Indicator and Average Directional Movement Index The directional movement indicator DMI is a trend-following indicator developed by Welles Wilder that attempts to measure market strength and direction. Includes data from April 7, , to April 15, Because market direction is determined by whether DMI is above or below the zero line, it is another stop and reverse trend-following system see Figure 2. If the resulting percentage is above 20, the market is viewed as trending, whereas readings below 20 suggest sideways activity see Figure 2.
When comparing Figures 2. Although it is impossible to draw conclusions from a single example, I offer it to readers here as a caution flag. Just because data vendors or indicator developers link two studies together does not necessarily mean their combination will increase profitability. Results include data from December 31, , to December 31, This is another trend-following system that is always in the market and whose stop-and-reverse trigger points take on a parabolic shape as the trend matures.
Chapter 3 examines these issues in more detail; for now, suffice it to say that specific asset classes display a greater propensity to trend. If SAR performs poorly in many markets, it seems logical to fade its stop and reverse signals, as we did in our work with moving average envelopes. To review, we successfully transformed the moving average envelopes from a trend-following system into a mean reversion system by fading all trading signals generated and adding a fail-safe exit to prevent unlimited risk in the event that the market continued trending.
In this instance we made the fail-safe stop loss 2. Trading signals are generated whenever the market price is equal to or greater than the highest high or the lowest low of the past n periods Donchian used 20 days. The most popular oscillators can be categorized as percentage, differential, or statistical oscillators.
In all instances the goal in using oscillators is to fade a temporarily unsustainable level of market emotionalism in hopes of mean reversion. As a market loses momentum, closing prices tend to reverse these trends. Either version produces two lines that are charted on a 0 to scale. Overbought is usually defined as somewhere between 70 and 80, with oversold readings between 30 and Although it is possible to develop a moderately successful trading system using the SK-SD crossover, my slow stochastics extremes trading system offers a simpler alternative.
Mathematical Technical Analysis 33 The day stochastics extremes generate buy signals whenever SD closes below 15 and sell signals when SD closes above As with the fading of trend-following indicators, such as moving average envelopes and parabolics, to transform this mean reversion indicator into a comprehensive trading system, rules for exiting with profits and with losses are needed. For profitable exits, we will use SD closes above 30 and below 70, and our fail-safe exit will be designated as 2. Relative Strength Index Chapter 1 highlighted RSI as a mean reversion indicator because it is among the most popular and well-known of the oscillators.
As with stochastics, the most popular time periods are the 9and day versions. Traditionally, RSI generates entry signals whenever the index extends into overbought or oversold territory then falls below the upper boundary or rises above the lower boundary. Data shows results from December 31, , to December 31, As a result, the 14day RSI extremes trading system see Figure 2.
Exiting with profits occurs whenever RSI closes above 35 or below The system utilizes the same 2. As stated, differential oscillators are based on the difference between two data series. In contrast to percentage oscillators, which range from 0 to , differential oscillators have no numerical limit and so determination of overbought or oversold levels is problematic.
Most technicians view these oscillators as mean reversion indicators, because they lack absolute numerical ceilings or floors. So far I have used differential oscillators only in developing trend-following systems based on the indicator crossing beyond the zero line. Data show results from December 31, , to December 31, Mathematical Technical Analysis 35 monly employed default value for these indicators in relation to the latest closing price.
Momentum subtracts, whereas ROC divides closing price of x periods ago by the latest closing price. If the latest closing price is above the closing price x periods ago, the oscillator is positive, if it is below the closing price x periods ago, the oscillator is negative. Subsequently, these oscillators are tailor made for a stop and reverse trend-following trading system with buy and sell signals triggered by closing beyond the zero level.
A comparison of Figures 2. Statistical Oscillators Statistical oscillators are based on a statistical measurement known as the standard deviation a mathematical measure of how widely dispersed a data set is from its mean. The benefit of this approach is that the standard to which current prices are compared changes in response to shifts in market volatility. Bollinger Bands Bollinger bands, which were popularized by John Bollinger, who started as a market technician on CNBC, are constructed by calculating the standard deviation of prices over a specified period of time Bollinger used 20 periods as his default value and then adding and subtracting two standard deviations to a simple period moving average.
By constantly recalculating the standard deviation of recent prices, the indicator remains attuned to changes in market volatility since overbought and oversold levels will be harder to reach in a volatile market and easier to achieve in quiet markets. Because Bollinger bands are based on two standard deviations from the day moving average, they should theoretically encompass around 97 percent of all price action.
When the market closes beyond the upper or lower bands, such price action is traditionally viewed as unsustainable. In fact, this often proves to be the case, and Bollinger bands are a commonly used as a building block in mean reversion trading systems see Figure 2.
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In fact, the breaking of the upper or lower bands can signal the onset of a powerful and sustainable trend, as illustrated by Figure 2. Technicians disagree as to what numbers constitute an unsustainable level for the indicator. Although there are infinite gradations within each of these categories, years of empirical observation have led me to believe that all traders display a natural inclination to gravitate to one of these three basic psychological profiles.
This chapter examines in detail the profitability and robustness of various trend-following systems outlined in Chapter 2. A cursory examination of the trend-following system examples in that chapter clues us in to two traits necessary for successful trend traders: patience and fortitude. Although it is tempting to merely gloss over the other statistics and focus on the total net profit column, we really need to search our innermost selves and ask such questions as: Am I prepared to stick with this trading technique after suffering seven consecutive losing trades?
Few who embark on the path of system trading ever ask themselves these questions beforehand. And yet they are obviously the most important issues for the trend trader to address. Given enough time and the right software, almost anyone can develop a profitable system, but is it the right system for their trading personality?
If not, then they might not have the personality traits needed for successful trend trading. This is not to say that every trend trader will suffer through seven consecutive losses; however, people who adopt trend-following strategies should be psychologically prepared for this occurrence as a distinct possibility. I hope by now that I have shattered any illusion readers might have that using mechanical trading systems will make life as a trader easier. As long as such illusions persist, the discipline and patience required to pursue profitable trading will be sabotaged, for successful trading requires a reprogramming of the trader, a transformation of expectations and an acceptance of the limitations and drawbacks inherent in almost any robust trading methodology.
Now that we are ready to analyze the success or failure of a particular trading system, we need to examine these issues. Considerations with Any Indicator-Driven Triggers Entry and exit levels are self-explanatory for price-driven triggers since the violation of a historical high or low signals entry or exit of a particular pricedriven trading system, such as channel breakout. By contrast, indicatordriven triggers raise a myriad of entry and exit level questions for system developers. The first question is fairly subjective: Are we as traders able to watch the screen and place entry or exit orders as the indicator levels are violated intraday?
If so, we run the risk of trading an intraday violation that could reverse itself and not trigger a signal at end of day. Of course, the advantage in taking an intraday signal is the potential for better prices less risk and greater reward ; however, most system developers prefer knowing that the signal will remain valid at end of day since the results of all intermediate to long-term trading system are necessarily based on end-of-day signals only. Although either of these alternatives is acceptable in most instances, in choosing entry on the close, we run the risk of the indicator trading just beyond the trigger level in Trend-Following Systems 43 the final minute of trading and then settling back to levels that would not generate a signal.
Nevertheless, the only surefire method of avoiding false entry and exit signals is to set the indicator trigger to the close or settlement price and the entry or exit level to the opening price of the following day. Composition of Portfolios In determining the success of a particular trading system, ideally we would like to test our results on as many assets as possible. Unfortunately, many of these assets are highly correlated with each other. Inclusion of too many highly correlated assets e. Next we must make some assumptions regarding slippage and commissions that are both realistic and conservative.
For example, it is unrealistic to assume that our stop price and our fill price will be identical. As a result, ideally our portfolio should contain only those assets that experience minimal slippage, in other words, those that are the most liquid. It is for this reason that low-liquidity instruments such as Nymex coal futures are not included in our portfolio. Note that the liquidity of various assets changes over time. As a result, traders are strongly encouraged to monitor volume and open interest statistics provided by the various exchanges.
Finally, if the market chosen for our backtesting produces consistent profits, but those profits are so small—due to either lack of volatility or value of contract—that commissions and slippage turn those paper profits into net losers, then those markets should be omitted. The other issue to consider regarding contract size is that just as we avoided inclusion of highly correlated assets in our portfolio to ensure the robustness of the system, as much as possible we should ensure that no single market within our portfolio has a contract size that dwarfs or enlarges the weighting of other portfolio components.
Finally, many system developers include weighting matrices to address these issues. With these considerations in mind, I have chosen to include one asset from the asset classes shown in Table 3. This was fine for showcasing how specific technical indicators can be transformed into trading systems, but to generate 10 years of backtested results for a particular trading system on a portfolio, we need to address the issue of expiration of futures contracts. Nearest Futures Charts The traditional method of dealing with expiration of futures contracts is known as linked nearest contract or nearest futures charting.
The nearest futures chart is constructed by including the data history of the futures contract closest to expiration. Data source: CQG, Inc. This divergence between the two data sets could result in huge price gaps and, more important, for our purposes, false trading signals. For example, by comparing Figures 3. Equalized Continuation Price Series Charts Most high-functionality data providers enable their subscribers to overcome this problem of false trading signals on long-term nearest futures charts by providing equalized continuation or point-based back-adjusted data series charting.
Returning to the lean hogs contract rollover problem, if in March we were to backtest a particular trading system for lean hogs using a equalized continuation price series chart with a designated rollover date of January 19, , as of that date our chart would begin to reflect February FIGURE 3.
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The first and most obvious problem is that the numbers displayed on these charts are derived through an artificial adjustment of prices, and so the price levels shown are worthless in terms of determining horizontal and trend-line support and resistance and retracement level. Another problem with equalized continuation charting is that the process of deriving equivalent historical prices often leads to data within the series containing prices of zero or negative numbers.
Although we could always refer back to the actual historical prices at the time of entry to derive a percentage-based stop-loss level, there is no need to bother as there are a plethora of equally robust mechanisms for stop-loss placement that can be employed instead. Point Value versus Percentage Changes in Data History A final issue applies not only to equalized continuation charts, but also to all of historical data.
This is the problem of point value changes as opposed to percentage value changes. I will use equalized continuation charts to exemplify the issue. In many instances, if the asset in question has experienced a longterm bull market trend, then the price differences between entry and exit will be dramatically different from the percentage differences. Readers who feel that their backtested results will be affected by such limitations are encouraged to adopt his solutions.
In other words, if data are based solely on trading a market with a historical trend similar to the natural gas example, then use percentage instead of price changes. Blindly applying a percentage change without consideration of this fact and of how the software vendor handles such changes can skew results as dramatically as sticking with the originally flawed price change calculations. Because foreign exchange price increases or decreases are totally dependent on the base currency chosen for valuation, the application of percentage changes are subjective and misleading.
Based on these price comparisons, we might erroneously assume that greater weighting should be given to trades executed in since equal price moves would represent a greater percentage change. Applying percentage as opposed to price changes to the fixed income market implies a less severe but equally flawed assumption regarding the data.
This is due to the inverse relationship between price and yield. Despite the flaws just detailed, in light of the nature and historical trends of the assets contained with my model portfolio, I remain reasonably comfortable with using equalized continuation charts and have chosen to set the rollover date to 20 days prior to expiration of the contract.
Backtested Portfolio Results Another practical limitation in the presentation of historically backtested results on any significant sampling for intermediate to long-term systems, 10 to 30 years of historical data are considered a statistically significant data sampling is the problem of estimating worst peak-to-valley equity drawdowns.
To accurately calculate the worst peak-to-valley drawdown on a daily basis, we would need to track daily mark to markets on all assets within the portfolio for the entire data history in question. At the time of this writing, most data vendors with system development and backtesting capabilities do not offer backtested results for a portfolio of assets. Total net profit examines profitability irrespective of risk taken to 2. Because of this limitation, other measures included in our backtested results are superior analytical tools.
However, this number is useful because it allows us to quickly add and compare various portfolio component results for numerous systems without additional calculations. Number of trades Trades shows the total number of trades taken during the backtested period. For trend-following systems, we want this number to be as low as possible without sacrificing profitability. Number of days Days shows the average duration of a trade.
As with number of trades, all else being equal, the lower the number of days in a trade while still generating superior results the better. Pairs Trading Using Futures. CrossMarket Trading and the Stress.http://outer-edge-design.com/components/facebook/1989-mobile-phone.php
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