adundestikir.ga Library of Congress Cataloging-in-Publication Data: Kaufman, Perry J. Trading systems and methods / Perry J. Kaufman. to the Chicago Mercantile Exchange to develop fast trading strategies for futures. r Quantitative High-Frequenc New Trading Systems and Methods by Perry. The Psychology of Creative Writing takes a scholarly, psychological look at multiple aspects The Psychology of Creative.
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adundestikir.ga: Trading Systems and Methods + Website (5th edition) Wiley Trading (): Perry J. Kaufman: Books. Trading Systems and Methods, Fifth Edition. Author(s). Perry J. Kaufman. First published:2 January Print ISBN |Online. Trading Systems and Methods, + Website, 5th Edition. Trading Perry J. Kaufman The definitive reference on trading systems, the book explains the tools and.
You like to win. There are excellent books available to both the beginning and advanced trader. The ones that stand out as excellent sources of general information are Jack Schwager's two-volume set, Schwager on Futures Wiley, , which includes one volume on fundamental analysis and one on technical analysis. There are excellent books on more specific topics.
Colby and Thomas A. Meyers Dow Jones Irwin, ; the latter offers an intelligent description of the calculation and trading performance of many market indicators that could be used by traders. Comparing the results of different indicators side by side can give you valuable insight into the practical differences in these techniques.
The basic reference book for general contract information has always been the Commodity Trading Manual Chicago Board of Trade , but each year Futures magazine publishes a Reference Guide which gives the trading hours, contract size, and other specifications of the primary futures and options markets traded around the world.
All of this information is also available on the Internet. The introductory material is not repeated here. A good understanding of the most popular charting method requires reading the classic by Edwards and Magee and now Bassetti , Technical Analysis of Stock Trends, 8th Edition originally published by John Magee , a comprehensive study of bar charting.
A basic understanding of market phenomena and relationships, often requiring some math skill, can be found in the Financial Analysts Journal. There are a number of associations and user groups that can be very helpful to traders at all levels.
For those with higher math skills, the International Association of Financial Engineers IAFE offers excellent resources, and the TradeStation users groups, found in larger cities and on the Internet, can be a means for solving a difficult problem. As for this book, a reader with a good background in high school mathematics can follow everything but the more complex parts.
An elementary course in statistics is ideal, but a knowledge of the type of probability found in Edward Thorp's Beat the Dealer Vintage, is adequate. Fortunately, computer spreadsheet programs, such as Excel and Quattro allow anyone to use statistical techniques immediately, and most of the formulas in this book are presented in such a way that they can be easily adapted to spreadsheets. Even better, if you have a computer with trading software, such as TradeStation Technologies' TradeStation Platform, MetaStock, or any number of other products, you are well equipped to continue.
If you have a live data feed, such as CQG, you will also have access to technical studies that you will also find very helpful. Know what you want to do before you start. Base your trading on a sound premise. It could be an observation of how prices move in response to Government policy, a theory about how prices react to economic reports, or simply a pattern that shows up at the same time each day or each month. This is the underlying premise of your method.
It cannot be discovered by testing everything on a computer. You need to know it in advance. State your idea or question in its simplest form. The more complex it is, the more difficult it will be to evaluate the answer. More complex methods do not usually work as well as simple ones. Do not assume anything. Many projects fail on basic assumptions that were incorrect. It takes practice to avoid making assumptions and to be critical of certain elements that you believe to be true.
Prove everything to your own satisfaction. Try the simplest and most important parts first. Some of the rules in your trading program will be more important than others. Try those first. It's best to understand how each rule or technique contributes to the final system. Then build slowly and carefully to prove the value of each element of the system.
Build one step at a time. Go on to the next step only after the previous ones have been tested successfully. If you start with too many complex steps and fail, you will have to simplify to find out what went wrong. The ability to readily understand the operation of each part of your system is called a transparent solution, rather than a fully integrated or complex one.
Transparent solutions are very desirable. Watch for errors of omission. It may seem odd to look for items that are not there, but you must continually review your work, asking yourself if you have included all the necessary costs and accounted for all the risk. Simply because all the questions were answered correctly does not mean that all the right questions were asked. Important questions may be missing. Question the good results. There is a tendency to look for errors when results are extremely bad, but to accept the results that are very good.
Exceptionally good results are just as likely to be caused by errors in rules, formulas, or data. They need to be checked as carefully as extremely bad results. Do not take shortcuts. It is sometimes convenient to use the work of others to speed up the research. Check their work carefully; do not use it if it cannot be verified. Check your spreadsheet calculations manually. One error can ruin all of your hard work. Start at the end. Define your goal and work backwards to find the required input.
In this way, you only work with information relevant to the results; otherwise, you may expend a lot of unnecessary effort.
Execution skill and market psychology are not considered— only the strategies, the methods for testing those strategies, and the means for controlling the risk. This is a goal of significant magnitude. Not everything can be covered in a single book; therefore, some guidelines were needed to control the material included here. Every technique in this book qualifies as systematic; that is, each has clear rules.
Most of them can be automated. We begin with basic concepts, including definitions, how much data to use, how to create an index, some statistics and probability, and other tools that are used throughout the book.
The next several chapters cover the techniques that are most important to trading, such as identifying the trend, followed by momentum.
Other chapters are organized by common grouping so that you can compare the different ways that similar problems have been solved. Although charting is an extremely popular technique, it is included only to the degree that it can be compared with other systematic methods, or when various patterns can be used in a computerized program such as identifying support and resistance or channels. There has been no attempt to provide a comprehensive text on charting; however, various formations may offer very realistic profit objectives or provide reliable entry filters.
Neither stock options nor options on futures are included in this book. Although there are strategies that combine outright trading of stocks or futures with options, the subject is too large and too specialized to be included here. There are already many good books on options strategies.
This book does not attempt to prove that one system is better than another, because it is not possible to know what will happen in the future or how each reader will cleverly apply these techniques. Instead the book evaluates the conditions under which certain methods are likely to do better and situations which will be harmful to specific approaches. By grouping similar systems and techniques together, you should be able to compare the differences and study the results.
Seeing how analysts have modified existing ideas can help you decide how to proceed and give you an understanding of why you might choose one path over another. By seeing a more complete picture, common sense should prevail over computing power. Some of these are simply choices in style, while others are essential to the success of the results. They have been listed here and are discussed briefly as items to bear in mind as you continue the process of creating a trading system.
Changing Markets and System Longevity Markets are not static. They evolve as does everything else. During the past ten years, changes in the markets have continued at an astounding rate.
These changes fall into the categories of technology, participation, globalization, and the cost of doing business. Technology includes communications, trading equipment primarily computers and handheld devices , and electronic exchanges and order entry.
These innovations have accelerated the trading process, provided faster access to quotes, and created instantaneous order entry based on computerized strategies. Electronic markets have changed the nature of the order flow and made information about downloaders and sellers more accessible. It has accelerated the process and changed the way prices react to news. Increased participation is the result of the historic bull market of the s, financial news networks, better communications, computers, and computer software that is user-friendly and readily installed in anyone's home.
More participation has changed the level of noise in individual stocks and futures, but it is most obvious in the index markets. Noise results from a large, constant flow of orders placed for unrelated reasons. Globalization is mostly the result of the reliability of advances in communications.
Not only can we see the same news at the same time everywhere in the world, but we can pass information quickly via the Internet or telephone. Equally important, we do not think about the reliability of this communication.
We expect our televisions, telephones, and Internet connections to work without question. When we trade, we are willing to bet on it. The dramatic reduction in commission cost has been a major influence on trading, opening up opportunities for the fast trader.
Negotiated commissions have served the God of Competition. This not only facilitates fast trading but encourages greater participation. Everyone wins. The challenge for the trader is to find a system that will adapt to future changes, whatever they are. Most changes are not sudden, but are gradually reflected in price patterns. The steady change in the percentage of institutional volume compared to individual trader orders will slowly alter price patterns.
The increase in overall participation affects the level of market noise and may also affect volatility and risk. Index arbitrage and the trading of indices force the component stocks to move in the same direction regardless of their individual fundamentals. The creation of your own successful trading program may require the utmost simplicity or the inclusion of features that adapt to an uncertain future.
It is both challenging and rewarding to create a program with longevity. The Choice of Data System decisions are limited by the data used in the analysis. Although price and volume for the specific stock or futures market may be the definitive criteria, there is a multitude of other valid statistical information that might also be used.
Some of this data is easily included, such as price data from companies in the same sector or industrial group, or the current yield curve relationship. Other statistical data, including the wide range of U.
Diversification Not all traders are interested in diversification, which tends to reduce returns at the same time it limits risk. Concentrating all of your resources on a single market that you understand may produce a specialized approach and much better results than using a more generalized technique over more markets. Diversification may be gained by trading two or more unique strategies applied to the same market, instead of one strategy used on a broad set of markets.
Time Frame The time frame of the data impacts both the type of system and the characteristics of the results. Using five-minute bars introduces considerable noise into your program, making it difficult to find the trend, while using only weekly data puts the greatest emphasis on the trend to the exclusion of other techniques.
A shorter time may guarantee faster response to price changes, but does not assure better results. Using more frequent data usually results in a shorter holding period for the trade and greater sensitivity to execution. There is no universal trading system that works in all time frames. You will need to learn whether you are best trading fast or slow, then concentrate in that area. Choosing a Method of Analysis Some methods of analyzing the market are more complex than others. This has no bearing on the final success.
All good trading methods begin with a sound premise. You must first know what you are trying to extract from the market before you select a technique. If you want to capitalize on long interest rate trends or the result of government policy, then a weekly moving average or trend system is the place to start.
If you see false breakouts whenever the price penetrates the high of the day in the second half of the trading session, you should look at a momentum indicator based on 5-, , or minute data. First isolate the idea, then choose the tool. Trade Selection Although a trading system produces signals regularly, it is not necessary to enter all of them.
Selecting one over another can be done by a method of filtering. This can be a confirmation of another technique or system, a limitation on the amount of risk that can be accepted on any one trade, the use of outside information, or the current volume. Many of these additional rules add a touch of reality to an automated process. You may find, however, that too many filters result in no trading. Testing There is a lot of emphasis in this book on testing and the way to evaluate test results.
A mistake in testing may cause you to trade a losing strategy or discard a profitable one. Back-testing is the only option available to confirm or validate your ideas. Testing is misguided when it is used to "discover" a successful trading method by massive scanning of combinations of techniques. The purpose of testing is to validate an idea and show robustness—that the method works over a wide range of situations in a similar manner.
It can also provide a good indication of expectations, both returns and risk. A robust solution, one that works on many stocks or across similar markets, is not as good as the optimized results of a single stock.
But using the same system for all stocks in the same sector will give you a more realistic assessment of expectations and a much better chance of success. Risk Control Trading survival is based on risk control.
Most analysts believe that nearly any system can be profitable with proper risk management. This also means that any system can lead to ruin without risk controls. Risk must be addressed at the individual trade level by using a strategy with entry and exit signals that minimize losses, such as a simple but fast trend method. Trade risk can also be controlled using a stop-loss. Risk must also be managed at the portfolio level by diversification and correct allocation of size to each asset. Futures traders must also pay attention to leverage.
Risk management does not need to be complex, but it has many tiers. Order Entry A system that performs well on paper may be dismal when actually traded. Part of a trading program is knowing how to enter and exit the market, as well as having realistic expectations about the transaction costs, both commissions and slippage. Short-term, fast trading systems are most sensitive to transaction costs because the expected profit on each trade is small. Directional trading strategies, those that download as prices are rising and sell when they are falling, have larger slippage than mean reversion techniques.
There is equal damage in overestimating costs as there is in underestimating them. By burdening a system with unrealistic fees, tests may show a loss instead of a profit, causing you to reject a successful trading method. Performance Monitoring and Feedback A system is not done when you begin trading; it is only entering into a new phase. Actual trading results must be carefully monitored and compared with expectations in order to know if it is performing properly.
It is very likely that slippage will result in some changes to the system rules or to the size of the position traded. Performance monitoring provides the essential feedback needed to be successful. Even a well-designed and well-tested program may start out badly, but proper monitoring can put it on track. There are also more complex systems and indicators that appear in both Excel and EasyLanguage, but mostly in the latter. Although these programs have been entered and tested on TradeStation, there are occasional errors introduced during final editing and in transferring the code into this book.
Recent market activity may also produce combinations of price movement that did not occur during testing. Readers are advised to check over the code and test it thoroughly before using it. Computer software used to develop trading strategies may vary in the notation they use to express the simplest statistical functions.
For the standard deviation, Excel uses stdev while Easy Language uses stddev.
One program expects the mean to be avg while another requires average. Please check each formula and solution for notation consistent with your needs. The most prudent investor, therefore, is one who pursues only a general course of action which is "normally" right and who avoids acts and policies which are "normally" wrong. While we are still waiting for those advancements, we do not notice that we depend on computers, calculators, cell phones with automatic dialing and infrared reception, and countless other devices to perform tasks that once were done manually.
There will come a time when we will no longer know how to do the calculation for long division because miniature, voice-activated computers will be everywhere. We might not even need to be able to add; it will all be done for us.
We will just assume that the answer is correct, because computers don't make mistakes. In a small way this is happening now. Not everyone checks their spreadsheet calculations by hand to be certain they are correct before going further. Nor does everyone print the intermediate results of computer calculations to verify their accuracy.
Computers don't make mistakes, but people do. With computer software making technical analysis easier and more sophisticated, we no longer think of the steps involved in a moving average or linear regression. A few years ago, we looked at the correlation between investments only when absolutely necessary because they were too complicated and time-consuming to calculate. It would even be difficult to know if you had made a mistake without having someone else repeat the same calculations.
Now we face a different problem: If the computer does it all, we lose our understanding of why a moving average trendline differs from a linear regression. Without looking at the data, we don't see an erroneous outlier or that the stock wasn't adjusted for splits. By not reviewing each hypothetical trade, we miss seeing that the slippage can turn a profit into a loss.
To avoid losing the edge needed to create a profitable trading strategy, the basic tools of the trade are explained in this chapter. Those of you already familiar with these methods may skip over it; others need to be confident that they can perform these calculations manually. In trading, the law of averages is most often referred to when an abnormally long series of losses is expected to be offset by an equal and opposite run of profits. It is equally wrong to expect a market that is currently overvalued or overbought to next become undervalued or oversold.
That is not what is meant by the law of averages. Over a large sample, the bulk of events will be scattered close to the average in such a way that the typical values overwhelm the abnormal events and cause them to be insignificant. This principle is illustrated in Figure 2.
It's the same as being the only passenger on a Boeing Your weight is insignificant to the operation of the airplane. A long run of profits, losses, or an unusually sustained price movement is simply a rare, abnormal event that will be offset over time by the overwhelming large number of normal events.
Further discussion of this and how it affects trading can be found in "Gambling Technique—the Theory of Runs," Chapter Figure 2. The normal cases over-whelm the unusual ones. It is not necessary for the extreme cases to alternate—one higher, the next lower—to create a balance.
Bias in Data When sampling is used to obtain data, it is common to divide entire subsets of data into discrete parts and attempt a representative sampling of each portion. These samples are then weighted to reflect the perceived impact of each part on the whole. Such a weighting will magnify or reduce the errors in each of the discrete sections.
The result of such weighting may cause an error in bias. Even large numbers within a sample cannot overcome intentional bias introduced by weighting one or more of the parts.
Price analysis and trading techniques often introduce bias in both implicit and explicit ways. A weighted average is an overt way of adding a positive bias positive because it is intentional. The use of two analytic methods acting together may unknowingly rely doubly on one statistical aspect of the data; at the same time, other data may be used only once or may be negated by offsetting use. The daily high and low used in one part of a program and the daily range high to low in another section would introduce bias.
How Much Data Is Enough? Statisticians will say, "More is better. Technical analysis is fortunate to be based on a perfect set of data. Each price that is recorded by the exchange is exact and reflects the netting out of all information at that moment. Economic Data Most other statistical data are not as timely, not as precise, and not as reliable. A monthly average represents a broad range of numbers.
The lack of a range of values or a standard deviation of the component values reduces the usefulness of the information. Statistical data is often revised in the following month; sometimes those revisions can be quite large. Sample Error When an average is used, it is necessary to have enough data to make that average accurate. Because much statistical data is gathered by sampling, particular care is given to accumulating a sufficient amount of representative data. This holds true with prices as well.
Averaging a few prices, or analyzing small market moves, will show more erratic results. It is difficult to draw an accurate picture from a very small sample. When using small, incomplete, or representative sets of data, the approximate error, or accuracy, of the sample can be found by using the standard deviation as discussed in the previous section.
A large standard deviation indicates an extremely scattered set of points, which in turn makes the average less representative of the data. This process is called the testing of significance. The most basic of these tests is the error resulting from a small amount of data.
The size of the error is important to the reliability of any trading system. If a system has had only 4 trades, whether profits or losses, it is very difficult to draw any reliable conclusions about performance expectations. There must be sufficient trades to assure a comfortably small error factor.
This presents a dilemma for a very slow trend-following method that may only generate 2 or 3 trades each year. To compensate for this, the identical method can be applied across many markets and the number of trades used collectively.
Quality of Data Used The amount of data is a good estimate of its usefulness; however, the data should represent at least one bull market, one bear market, and some sideways periods. More than one of each is even better. A trading strategy would be profitable whenever it was a downloader, if you held the position long enough.
Your test results would show that downloading was good and selling was bad. Unless you included a variety of other price patterns, you would not be able to create a strategy that would survive a downturn in the market. Hardcover , 4th Edition , pages. More Details Original Title. Other Editions 2.
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Community Reviews. Showing Rating details. Sort order. Jan 01, Nick rated it it was amazing Shelves: This review has been hidden because it contains spoilers.
To view it, click here. Great book on system testing and system design. Chapter System Testing -Expectations are an essential part of system development. They force you to define a plan in advance that can achieve certain goals. Must be based on a sound premise fundamental reason for existence of the pattern , it must adapt to changing market conditions, it must be tested properly with proper expectations, it should be modestly sensitive to changing parameter values, parameters should be derived from market environment whenever possible.
The size of the shift in optimal parameter values should be small when new data is added. Not all have to work equally well, but their results should share statistical similarity or the approach is not robust.
A market that performs well using a daily breakout, but fails with a dual trend crossover is not a good candidate for trending profits in the future.
Nov 05, Keith Lansford rated it liked it. Dry, dry, dry Use it for reference May 22, Tadas Talaikis rated it it was amazing Shelves: If you're not acquainted with math, this book is very good starting point, a lot of classic concepts presented in very simple manner.
You'll not find actually working systems a lot of them are dead long ago , but for me it was good source of further ideas. I've programmed more than 10 indicators based on those ideas, so it was a long, but worthwhile read. May 09, Bernd rated it really liked it Shelves: Very thick volume, a treasure trove of trading systems, methods, supporting math and concepts, all the while very readable to the motivated trader.
It can be read non-sequentially and in piece meal, without losing context. I use it frequently as a reference, or for occasional inspirations of different methods. Jun 28, Matt rated it it was amazing.
Holy crap is this a lot of math! If you're a programmer and are researching automated trading systems, this book delves into translating statistical theorems to computer algorithms. Oct 22, Ed Ball rated it really liked it Shelves: Felt like I needed a college math course to brush up on before reading this. But lots of good systems, the trick is finding the system that works in today's market. Bog rated it it was ok Dec 14, Dan rated it liked it Apr 23, Kumar rated it liked it Sep 11, Andy Hou rated it liked it Dec 28,