# MONEY MANAGEMENT STRATEGIES FOR FUTURES TRADERS PDF

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MONEY MANAGEMENT STRATEGIES FOR FUTURES TRADERS 1 Understanding the Money Management Process In a sense, every successful trader. Numerous topics are explored including: why most traders lose at the futures game most of the time; why most mechanical trading systems are apt to fail; the. I had no idea what money manage- Money management in the context .. Balsara, N. , Money Management Strategies for Futures Traders, John Wiley &.

Nauzer J. Thanks are also due to graduate assistants Daniel Snyder and V. Anand for their untiring efforts. Special thanks are due to John Oleson for introducing me to chart-based risk and reward estimation techniques. My debt to these individuals parallels the enormous debt I owe to Dean Olga Engelhardt for encouraging me to write the book and Associate Dean Kathleen Carlson for providing valuable administrative support.

Right-angle triangles are similar to symmetrical triangles but are simpler to trade, in that they do not keep the trader guessing about their intentions as do symmetrical triangles.

Minimum Measuring Objective The distance prices may be expected to move once a breakout occurs from a triangle is a function of the size of the triangle pattern. For a symmetrical triangle, the maximum vertical distance between the two converging boundary lines represents the distance prices should move once they break out of the triangle.

The farther out prices drift into the apex of the triangle without bursting through the boundaries, the less powerful the triangle formation.

## Money Management Strategies for Futures Traders - PDF Free Download

The minimum measuring objective just stated will ensue with the highest probability if prices break out decisively at a point before three-quarters of the horizontal distance from the left-hand corner of the triangle to the apex. The same measuring rule is applicable in the case of a right-angle triangle. However, an alternative method of arriving at measuring objectives is possible, and perhaps more convenient, in the case of rightangle triangles. Assuming we have an ascending right-angle triangle, draw a line sloping upward parallel to the bottom boundary from the top of the first rally that initiated the pattern.

This line slopes upward to the right, forming an upward-sloping parallelogram. At a minimum, prices may be expected to climb until they reach the uppermost corner of the parallelogram.

In the case of a descending right-angle triangle, draw a line parallel to the top boundary from the bottom of the first dip. This line slopes downward to the right, forming a downward-sloping parallelogram.

Prices may be expected to drop until they reach the lowermost corner of the parallelogram. Estimated Risk A logical place to set a protective stop-loss order would be just above the apex of the triangle for a breakout on the downside. Conversely, for a breakout on the upside, a protective stop-loss order may be set just below the apex of the triangle. The dollar value of the difference between the entry price and the stop price represents the permissible risk per contract.

In both cases, the breakout is to the downside, and in both cases the minimum measuring objective is attained and surpassed.

## Money Management Strategies for Futures Traders

WEDGES A wedge is yet another continuation pattern in which price fluctuations are confined within a pair of converging lines. What distinguishes a wedge from a triangle is that both boundary lines of a wedge slope up or down together, without being strictly parallel. In the case of a triangle, it may be recalled that if one boundary line were upward-sloping, the other would necessarily be flat or downward-sloping.

In the case of a rising wedge, both boundary lines slope upward from left to right, but for the two lines to converge the lower line must necessarily be steeper than the upper line.

In the case of a falling wedge, the two boundary lines slant downward from left to right, but the upper boundary line is steeper than the lower line. A wedge normally takes between two and four weeks to form, during which time volume is gradually diminishing.

Typically, a rising wedge is a bearish sign, particularly if it develops in a falling market. Conversely, a falling wedge is bullish, particularly if it develops in a rising market. Minimum Measuring Objective Once prices break out of a wedge, the expectation is that, at a minimum, they will retrace the distance to the point that initiated the wedge.

In a falling wedge, the up move may be expected to take prices back to at least the uppermost point in the wedge. Similarly, in a rising wedge, the down move may be expected to take out the low point that first started the wedge formation.

Care must be taken to ensure that a breakout from a wedge occurs on heavy volume. This is particularly important in the case of a price breakout on the upside out of a falling wedge.

The rationale is that if prices take out this high point, then the breakout is not genuine. Similarly, in the case of a falling wedge, a logical place to set a stop would be just below the lowest point touched prior to the upside breakout. Once again, if prices take out this point, then the wedge is negated. An Example of a Wedge Figure 3.

However, on May 29, the pound resumed its journey downwards, meeting and surpassing the objective of the rising wedge. In a downtrend, the formation is turned upside down. Whereas the flag formation is characterized by low volume, the breakout from the flag is characterized by high volume. Seldom does a flag formation last more than five trading sessions; the trend resumes thereafter.

To do this, we must first go back to the beginning of the immediately preceding move, be it a breakout from a previous consolidation or a reversal pattern. Having measured the distance from this breakout to the point at which the flag started to form, we then measure the same distance from the point at which prices penetrate the flag, moving in the direction of the breakout.

This represents the minimum measuring objective for the flag formation. Estimated Risk In the case of a flag in a bull market, a logical place to set a protective stop-loss order would be just below the lowest point of the flag formation. If prices were to retrace to this point, then we have a case of a false breakout. Similarly, in the case of a flag in a bear market, a logical place to set a protective stop-loss order would be just above the highest point of the flag formation. The risk for the trade is measured by the dollar value of the difference between the entry and stop-loss prices.

Each of the flags represents a low-risk opportunity to short the market or to add to existing short positions. As is evident, each of the flags was a reliable indicator of the subsequent move, meeting the minimum measuring objective.

This is especially true when a commodity is charting virgin territory, making new contract highs or lows. In this case, there is no prior support or resistance level to fall back on as a reference point.

Consider, for example, the February crude oil futures chart given in Figure 3. Once prices break through this resistance level and make new contract highs, the trader is left with no means to estimate where prices are headed, primarily because prices are not obeying the dictates of any of the chart patterns discussed above.

One solution is to refer to a longer-term price chart, such as a weekly chart, to study longer-term support or resistance levels. Sometimes even longer-term charts are of little help, as prices touch record highs or record lows. In such a situation, it would be worthwhile to analyze price action in terms of waves and retracements thereof. This information, coupled with Fibonacci ratios, could be used to estimate the magnitude of the subsequent wave.

For example, Fibonacci theory says that a 38 percent retracement of an earlier move projects to a continuation wave 1. Similarly, a 62 percent retracement of an earlier wave projects to a new wave 1.

Prechter3 provides a more detailed discussion on wave theory. A trader who expanded the initial stop to accommodate adverse price action would be under no pressure to pull out of a bad trade.

This could be a very costly lesson in how not to manage risk! Gainesville, GA: New Classics Library, On the contrary, if prices move as anticipated, the original stop-loss price should be moved in the direction of the move, locking in all or a part of the unrealized profits. Let us illustrate this with the help of a hypothetical example. The estimated reward and risk on this trade are graphically displayed in Figure 3.

This is displayed in the adjacent block in Figure 3. Entry price - - - Stop price Stop price Estimated reward: Estimated risk: The higher this ratio, the more attractive the opportunity, disregarding margin considerations. Table 3. What is the probable price? As such, it would be shortsighted to neglect either or both of these estimates before plunging into a trade.

Risk and reward could be viewed as weights resting on adjacent scales of the same weighing machine. If there is an imbalance and the risk outweighs the reward, the trade is not worth pursuing.

Obsession with the expected reward on a trade to the total exclusion of the permissible risk stems from greed. More often than not this is a road to disaster, as instant riches are more of an exception than the rule. The key to success is to survive, to forge ahead slowly but surely, and to look upon each trade as a small step in a long, at times frustrating, journey. This chapter stresses diversification as yet another tool for risk reduction.

Therefore, it is safer to bet on several dissimilar commodities simultaneously than to bet exclusively on a single commodity. The underlying rationale is that a prudent trader is not interested in maximizing returns per se but in maximizing returns for a given level of risk. This insightful fact was originally pointed out by Harry Markowitz. In addition to providing for dips in equity during the life of a trade, a trader also should be able to withstand a string of losses across a series of successive bad trades.

There might be a temptation to shrug this away as a remote possibility. However, a trader who equates a remote possibility with a zero probability is unprepared both financially and emotionally to deal with this contingency should it arise. Eficient Diversijication of Investmenus New York: John Wiley, In the extreme case, the trader might want to give up on trading in general, if the losses suffered have cut deeply into available trading capital.

It would be much wiser to recognize up front that the best trading systems will generate losing trades from time to time and to provide accordingly for the worst-case scenario. Here is where diversification can help.

Let us, for purposes of illustration, consider the hypothetical trading results for a commodity over a one-year period, shown in Table 4. Here we have a reasonably good trading system, given that the dollar Table 4. The total number of profitable trades exactly equals the total number of losing trades, leading to a 50 percent probability of success.

Nevertheless, there is no denying the fact that the system does suffer from runs of bad trades, and the cumulative effect of these runs is quite substantial. Unless the trader can withstand losses of this magnitude, he is unlikely to survive long enough to reap profits from the system.

A trader might convince himself that the string of losses will be financed by profits already generated by the system. However, this could turn out to be wishful thinking. There is no guarantee that the system will get off to a good start, helping build the requisite profit cushion.

This is why it is essential to trade a diversified portfolio. Assuming that a trader is simultaneously trading a group of unrelated commodities, it is unlikely that all the commodities will go through their lean spells at the same time. On the contrary, it is likely that the losses incurred on one or more of the commodities traded will be offset by profits earned concurrently on the other commodities.

This, in a nutshell, is the rationale behind diversification. In order to understand the concept of diversification, we must understand the risk of trading commodities a individually and b jointly as a portfolio. In Chapter 3, the risk on a trade was defined as the maximum dollar loss that a trader was willing to sustain on the trade. In this chapter, we define statistical risk in terms of the volatility of returns on futures trades.

A logical starting point for the discussion on risk is a clear understanding of how returns are calculated on futures trades. Realized returns are also termed historical returns, just as anticipated returns are commonly referred to as expected returns. In this section, we discuss the derivation of both historical and expected returns. This ratio gives the return over the life of the trade, also known as the holding period return.

If the equity on the trade falls below the maintenance margin level, the trader is required to deposit additional monies to bring the equity back to the initial margin level. This is known as a variation margin call. If the trade registers an unrealized profit, the trader is free to withdraw these profits or to use them for another trade. However, in the interests of simplification, we assume that unrealized profits are inaccessible to the trader until the trade is liquidated.

Therefore, the pertinent cash flows are the following: The initial margin investment 2. Variation margin calls, if any, during the life of the trade 3. The profit or loss realized on the trade, given by the difference between the entry and liquidation prices 4. The release of initial and variation margins on trade liquidation The initial margin represents a cash outflow on inception of the trade.

Whereas cash flows 3 and 4 arise on liquidation of the trade, cash flow 2 can occur at any time during the life of the trade. Since there is a mismatch in the timing of the various cash flows, we need to discount all cash flows back to the trade initiation date.

Discounting future cash flows at a prespecified discount rate, i, gives the present value of these cash flows. Care should be taken to align the rate, i, with the length of the trading interval. If the trading interval is measured in days, then i should be expressed as a rate per day.

If the trading interval is measured in weeks, then i should be expressed as a rate per week. The rate of return, r, for a purchase or a long trade initiated at time t and liquidated at time 1, with an intervening variation margin call at time v, is calculated as follows: Using the foregoing notation, the rate of return, r, for a short sale initiated at time t and liquidated at time I is given as follows: Conversely, for a profitable short trade, the liquidation price, PI, would be lower than the entry price, Pt.

Hence we have a positive sign for the price difference term for a long trade and a negative sign for the same term for a short trade. The variation margin is a cash outflow, hence the negative sign up front. This money reverts back to the trader along with the initial margin when the trade is liquidated, representing a cash inflow.

The rate, Y, represents the holding period return for I - t days. Therefore, the annualized rate of return, R, is Rzzrx l-t This facilitates comparison across trades of unequal duration. Assuming that the annualized interest rate on Treasury bills is 6 percent, we have a daily interest rate, i, of 0. The expected profit represents the difference between the entry price and the anticipated price on liquidation of the trade. Since there is no guarantee that a particular price forecast will prevail, it is customary to work with a set of alternative price forecasts, assigning a probability weight to each forecast.

The weighted sum of the anticipated profits across all price forecasts gives the expected profit on the trade. The anticipated profit resulting from each price forecast, divided by the required investment, gives the anticipated return on investment for that price forecast.

The overall expected return is the summation across all outcomes of the product of a the anticipated return for each outcome and b the associated probability of occurrence of each outcome. The trader reckons that there is a 0. The expected return is calculated in Table 4. Whereas the risk on completed trades is measured in terms of the volatility of historic returns, the projected risk on a trade not yet initiated is measured in terms of the volatility of expected returns.

Measuring the Volatility of Historic Returns The volatility or variance of historic returns is given by the sum of the squared deviations of completed trade returns around the arithmetic mean or average return, divided by the total number of trades in the sample less 1. The historic return on a trade is calculated according to the foregoing formula. The mean return is defined as the sum of the returns across all trades over the sample period, divided by the number of trades, n, considered in the sample.

The lower the volatility of returns, the smaller the dispersion of returns around the arithmetic mean or average return, reducing the degree of risk. To illustrate the concept of risk, Table 4. Whereas the average return for gold is slightly higher than that for silver, there is a much greater dispersion around the mean return in case of gold, leading to a much higher level of variance. Therefore, investing in gold is riskier than investing in silver.

The period over which historical volatility is to be calculated depends upon the number of trades generated by a given trading system. As a general rule, it would be desirable to work with at least 30 returns. The length of the sample period needs to be adjusted accordingly. Measuring the Volatility of Expected Returns This measure of risk is used for calculating the dispersion of anticipated returns on trades not yet initiated.

The variance of expected returns is defined as the summation across all possible outcomes of the product of the following: The squared deviations of individual anticipated returns around the overall expected return The probability of occurrence of each outcome The formula for the variance of expected returns is therefore: The variance of expected returns works out to be 7.

Since assigning probabilities to forecasts of alternative price outcomes is difficult, calculating the variance of expected returns can be cumbersome. In order to simplify computations, the variance of historic returns is often used as a proxy for the variance of expected returns. The assumption is that expected returns will follow a variance pattern identical to that observed over a sample of historic returns. As the name suggests, the covariance between two variables measures their joint variability.

Referring to the example of gold and silver given in Table 4. This leads to a positive covariance term between these two commodities. The covariance between returns on gold and silver is measured as the sum of the product of their joint excess returns over their mean returns divided by the number of trades in the sample less 1. The formula for the covariance between the expected returns on X and Y is similar to that for the covariance across historic returns.

The exception is that each of the i observations is assigned a weight equal to its individual probability of occurrence, Pi. If there are three commodities, X, Y, and Z, under review, there are three covariances to contend with: If there are four commodities under review, there are six distinct covariances between the returns on them.

In the foregoing example, the covariance between the returns on gold and silver works out to be The correlation coefficient between two variables is calculated by dividing the covariance between them by the product of their individual standard deviations.

The standard deviation of returns is the square root of the variance. Two commodities are said to exhibit Perfect negative correlation if a change in the return of one is accompanied by an equal and opposite change in the return of the other.

The concept of correlation is graphically illustrated in Figure 4. In actual practice, examples of perfectly positively or negatively correlated commodities are rarely found. Ideally, the degree of association between two commodities is measured in terms of the correlation between their returns.

For ease of exposition, however, it is assumed that prices parallel returns and that correlations based on prices serve as a good proxy for correlations based on returns.

Given the lower variability of returns of a diversified portfolio, it makes sense to trade a diversified portfolio, especially if the expected return in trading a single commodity is no greater than the expected return from trading a diversified portfolio. We can illustrate this idea by means of a simple example involving two perfectly negatively correlated commodities, X and Y.

The distribution of expected returns is given in Table 4. Consider an investor who wishes to trade a futures contract of one or both of these commodities. If he invests his entire capital in either X or Y, he has a 0.

This results in an expected return of 25 percent and a variance of for both X and Y individually. What will our investor earn, should he decide to split his investment equally between both X and Y? The probability of earning any given return jointly on X and Y is the product of the individual probabilities of achieving this return.

For example, the joint probability that the return on both X and Y will be percent is the product of the probabilities of achieving this return separately for X and Y. This is the product of 0. Similarly, there is a 0. Moreover, there Table 4. Both these conditions are best satisfied when there is perfect negative correlation between the returns on two commodities.

However, diversification will work even if there is less than perfect negative correlation between two commodities.

The returns associated with the strategy of concentrating all resources in a single commodity could be higher than the returns associated with diversification, especially if prices unfold as anticipated. Returns Return Probabilitv Prob. In both these cases, the expected return works out to be 25 percent, as. Using this information, we come up with the probability distribution of returns for a portfolio which includes X and Y in equal proportions.

The results are outlined in Table 4. Notice that the expected return of the portfolio of X and Y at 25 percent is the same as the expected return on either X or Y separately.

However, the variance of the portfolio at The creation of the portfolio reduces the variability or dispersion of joint returns, primarily by reducing the probability of large losses and large gains. Assuming that our investor is risk-averse, he is happier as the variance of returns is reduced for a given level of expected return. In the foregoing example, we have shown how diversification can help an investor when the returns on two commodities are perfectly negatively correlated.

In practice, it is difficult to find perfectly negatively correlated returns. However, as long as the return distributions on two commodities are even mildly negatively correlated, the trader could stand to gain from the risk reduction properties of diversification. For Joint Returns on a Table 4. Treasury bonds is less risky than a long position in two contracts of either crude oil or Treasury bonds. Trading the same side of two or more positively correlated commodities concurrently is known as aggregation.

Just as diversification helps reduce portfolio risk, aggregation increases it. An example would help to clarify this. Given the high positive correlation between Deutsche marks and Swiss francs, a portfolio comprising a long position in both the Deutsche mark and the Swiss franc is more risky than investing in either the Deutsche mark or the Swiss franc exclusively.

The first step to limiting the risk associated with concurrent exposure to positively correlated commodities is to categorize commodities according to the degree of correlation between them. This is done in Appendix C. Next, the trader must devise a set of rules which will prevent him or her from trading the same side of two or more positively correlated commodities simultaneously. The correlations are arranged commodity by commodity in descending order, beginning with the highest number and working down to the lowest number.

Checking the Statistical Significance of Correlations The most common test of significance checks whether a sample correlation coefficient could have come from a population with a correlation coefficient of 0. The null hypothesis, Ho, posits that the correlation coefficient, C, is 0. The alternative hypothesis, Ht , says that the population correlation coefficient is significantly different from 0.

Since Hr simply says that the correlation is significantly different from 0 without saying anything about the direction of the correlation, we use a two-tailed test of rejection of the null hypothesis. The null hypothesis is tested as a t-test with n - 2 degrees of freedom, where y1 is the number of paired observations in the sample. Ideally, we would like to see at least 32 paired observations in our sample to ensure validity of the results.

The value of t is defined as follows: The value of t thus calculated is compared with the theoretical or tabulated value of t at a prespecified level of significance, typically 1 percent or 5 percent. A 1 percent level of significance implies that the theoretical t value encompasses 99 percent of the distribution under the bell-shaped curve. The theoretical or tabulated t value at a 1 percent level of significance for a two-tailed test with degrees of freedom is Similarly, a 5 percent level of significance implies that the theoretical t value encompasses 95 percent of the distribution under the bell-shaped curve.

The corresponding tabulated t value at a 5 percent level of significance for a two-tailed test with degrees of freedom is k1. If the calculated t value lies beyond the theoretical or tabulated value, there is reason to believe that the correlation is nonzero. Is this statistically significant at a 1 percent level of significance? Since the calculated t value is well in excess of 3. In some cases the correlation numbers are meaningful and can be justified.

Similarly, the Deutsche mark and the Swiss franc are likely to be evenly affected by any news influencing the foreign exchange markets.

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However, some of the correlations are not meaningful, and too much weight should not be attached to them, notwithstanding the fact that they have a correlation in excess of 0. If two seemingly unrelated commodities have been trending in the same direction over any length of time, we would have a case of positively correlated commodities. Similarly, if two unrelated commodities have been trending in opposite directions for a long time, we would have a case of negative correlation. This is where statistics could be misleading.

In the following section, we outline a procedure to guard against spurious correlations. For example, the period may be broken down into subperiods, such as , , and , and correlations obtained for each of these subperiods, to check for consistency of the results. Appendix C presents correlations over each of the three subperiods. If the numbers are fairly consistent over each of the subperiods, we can conclude that the correlations are genuine.

Alternatively, if the numbers differ substantially over time, we have reason to doubt the results. This process is likely to filter away any chance relationships, because there is little likelihood of a chance relationship persisting with a high correlation score across time. Table 4. For example, the correlation between soybean oil and Kansas wheat is 0. Similarly, the correlation between corn and crude oil ranges from a low of Obviously it would not make sense to attach too much significance to high positive or negative correlation numbers in any one period, unless the strength of the correlations persists across time.

If the high correlations do not persist over time, these commodities ought not to be thought of as being interrelated for purposes of diversification. Therefore, a trader should not have any qualms about buying or selling corn and crude oil simultaneously.

Only those commodities that display a consistently high degree of positive correlation should be treated as being alike and ought not to be bought or sold simultaneously. If two commodities are positively correlated and a trader were to hold similar positions either long or short in each of them concurrently, the resulting aggregation would result in the creation of a high-risk portfolio.

For example, an uptrend in soybeans is likely to be accompanied by an uptrend in soymeal and soybean oil. A trendfollowing system would recommend the simultaneous. This simultaneous purchase ignores the overall riskiness of the portfolio should some bearish news hit the soybean market. It is here that the diversification skills of a trader are tested. He or she must select the most promising commodity out of two or more positively correlated commodities, ignoring all others in the group.

Often the positions are held in direct violation of diversification theory. Alternatively, opposing positions could be assumed in two or more negatively correlated commodities.

If the scenario were to materialize as anticipated, each of the trades could result in a profit. However, if the scenario were not to materialize, the domino effect could be devastating, underscoring the inherent danger of this strategy. For example, believing that lower inflation is likely to lead to lower interest rates and lower silver prices, a trader might want to buy a contract of Eurodollar futures and sell a contract of silver futures. This portfolio could result in profits on both positions if the scenario were to materialize.

However, if inflation were to pick up instead of abating, leading to higher silver prices and lower Eurodollar prices, losses would be incurred on both positions, because of the strong negative correlation between silver and Eurodollars. This is commonly termed spread trading.

The objective of spread trading is to profit from differences in the relative speeds of adjustment of two positively correlated commodities. For example, a trader who is convinced of an impending upward move in the currencies and who believes that the yen will move up faster than the Deutsche mark, might want to buy one contract of the yen and simultaneously short-sell one contract of the Deutsche mark for the same contract period.

In technical parlance, this is called an intercommodity spread. A spread trade such as this helps to reduce risk inasmuch as it reduces the impact of a forecast error. To continue our example, if our trader is wrong about the strength of the yen relative to the mark, he or she could incur a loss on the long yen position. However, assuming that the mark falls, a portion of the loss on the yen will be cushioned by the profits earned on the short Deutsche mark position.

The net profit or loss picture will be determined by the relative speeds of adjustment of the yen against the Deutsche mark. To continue with our example, if the yen were to fall as the mark rallied, the trader would be left with a loss on both the long yen and the short mark positions. In this exceptional case, a spread trade could actually turn out to be riskier than an outright position trade, negating the premise that spread trades are theoretically less risky than outright positions.

After all, it is this theoretical premise that is responsible for lower margins on spread trades as compared to outright position trades. Even if a trader were to increase the number of commodities in the portfolio indefinitely, he or she would still have to contend with some risk. This is illustrated graphically in Figure 4. Notice that the gains from diversification in terms of reduced portfolio risk are very apparent as the number of commodities increases from 1 to 5.

However, the gains quickly taper off, as portfolio risk can no longer be diversified away. This is represented by the risk line becoming parallel Portfolio risk 0 5 10 20 40 60 80 Number of commodities Figure 4.

There is a certain level of risk inherent in trading commodities, and this minimum level of risk cannot be eliminated even if the number of commodities were to be increased indefinitely. Traders who tend to get carried away by the prospects of large gains sometimes deliberately overlook the fact that leverage is a double-edged sword.

This leads to unhealthy trading habits. Typically, diversification is one of the first casualties, as traders tend to place all their eggs in one basket, hoping to maximize leverage for their investment dollars. If there were such a thing as perfect foresight, it would make sense to bet everything on a given trade. Diversification helps reduce risk, as measured by the variability of overall trading returns. Ideally, this is accomplished by assuming similar positions across two unrelated or negatively correlated commodities.

Diversification could also be accomplished by assuming opposing positions in two positively correlated commodities, a practice known as spread trading. Finally, a trader might assume that the unfolding of a certain scenario will affect related commodities in a certain fashion.

Accordingly, he or she would hold similar positions in two positively correlated commodities and opposing positions in two or more negatively correlated commodities. This is known as synergistic trading. Synergistic trading is a risky strategy, because nonrealization of the forecast scenario could lead to losses on all positions. Welles Wilder, Jr. If the trader follows the same commodities or stocks all of the time, then his system has to be good enough to make more money 30 percent of the time than it will give back 70 percent of the time.

This is the underlying concept.. The premise behind the selection process is that not all 50 contracts offer trading opportunities that are equally attractive.

The goal is to enable the trader to identify the most promising opportunities, allowing him or her to concentrate on these trades instead of chasing every opportunity that presents itself. By ranking commodities on a desirability scale, commodity selection creates a short list of opportunities, thereby helping to allocate limited resources more effectively.

Trend Research, How to Ensure Profit and Avoid the Risk of Ruin Wiley Trading McDowell encourages his students to use Balsara's risk-of-ruin tables when designing their own personal money management system.

It improves your bottom line when you calculate your current payoff ratio and win ratio and accurately determine the risk you should be taking on each trade by referring to the risk-of-ruin tables. Balsara also covers Optimal F in detail, which is another way to determine the amount to risk on any one trade based on your current payoff ratio and win ratio.

Of course, another great author on this topic is Ralph Vince and his latest book is probably the most thorough account of using Optimal F effectivly: The Handbook of Portfolio Mathematics: The key is to do the calculations and know where you stand at any given moment.

Nazer J. Balsara's Money Management Strategies For Futures Traders provides a wealth of materials for futures and stocks traders alike. The book is a must-read and a relatively easy-read for those who wish to enhance their risk management sophistication with complex tools and who believe that the best way to survive and prosper in the markets it to contain your losses, play defensively and let profits ride.

All trading opportunities are not created equally and part of a trader's job is ferreting out the best markets to trade. The chapter on commodity selection presents four approaches to market selection, based largely on the work of J.

Here, the book is a good review of Wilder's ADX but focuses on the less-known aspect of his work: Wilder's approach uses ADX to identify futures yielding the greatest dollar-value price-moves for a given margin investment, in short, getting you in on the most appealing trades.

Balsara also shows the utility of Wilder's price movement index when it is it is not possible to determine or estimate reward, thereby enhancing the analysis and return in mechanical trading systems. Sharpe ratios are also considered as a way of measuring risk-adjusted returns.

The text gives useful approaches to managing risk through stop-loss orders by laying out the usage of time stops, dollar-value stops and volatility stops. There is also a presentation on how to survive locked-limit markets by creating synthetic options positions, spreads or offsetting positions in the cash markets.

A studied read of this finance professor's work will help traders develop both the skill and the art of disciplined risk-taking. By John E Moore I only wish I had utilized the statistical tools the author provides earlier in my trading career. However, I did find the book in time. Don't let the reference to statistics scare you. The author uses basic alegebra to aid you in trade selection and risk control.

This book may not guarantee you success in trading, but I do believe that if one does not apply the basic money management principles presented by Prof. Balsara, sooner or later, failure in the futures market is almost certain.

If you can't name the 5 basic steps of money management, I suggest you stop trading immediately, get this book with a couple of ticks worth of money you'll not be losing while your not trading. Read it a few times, set up your money management spreadsheet and may you trade with clarity previously unknown in your endeavors in the futures market. Balsara Kindle. Een reactie posten. Sales Rank: Money Management Strategies for Futures Traders.

Description About the Author Table of contents Series. Selected type: Added to Your Shopping Cart. Balsara ISBN: Distills complex theories for the benefit of the average trader with little or no background in finance or mathematics by offering a wide range of valuable, practical strategies for limiting risk, avoiding catastrophic losses and managing the futures portfolio to maximize profits.

Numerous topics are explored including: About the Author Nauzer J.

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