Trading
This page will be rebuilt and merged into a new page, over the course of March 2007.
This web page contains my writing on particular subjects within the trading world. For those of you new to this web site, it's helpful to know what I do for a living. At the age of 17, I started as a runner at the Chicago Board of Trade (CBOT), working full time from that point forward. In 1984, I became a member of the CBOT and continued as a member until 2003.
Within that period, from 1984-1995, I traded in the US Treasury Bond pit as a Broker. A broker's job in the trading pit is intuitive in nature. It doesn't require analytical research. After a four-year hiatus from the CBOT, that began in 1995, I returned to the trading world and approached trading from that of an analytical trader. As of 2004 I've been coaching traders, writing trading systems for manual and automated use, and trading for a Broker/Dealer in Chicago. I'm Series 7 licensed.
My research has been ongoing, leaving few stones unturned. This page is a compellation of the papers I've written. Some of the papers contain content written by others, and are noted. More papers will be added as time allows. Research papers and articles written by others can be found on my research page.
Table of Contents for this web page
“I just need to have a follow thru plan and patience on my trades”
I’ve heard the statement above a million times. Here’s my answer to that question.
As you know, this is much easier said than done. However, it can be done. There are two things that will aid you in this process.
Practicing patience is the key, but it’s hard. In the end it will pay off.
The most important thing to understand is “what is the risk” you are taking on a trade? Said another way, ‘understand money management’.
It’s easy to talk about those things, but actually putting them into practice is different. It only comes from practice, practice, practice.
Base your trading on 4 rules and you can achieve the results you want.
why am I getting into this trade?
where am I getting in?
where is my profit target?
where is my stop loss?
Once you implement those 4 rules, a few things will begin to happen.
you’ll begin looking for trades to do.
If you answer question #1, that means you have to find trades. When you begin looking for trades you’ll need to learn how indicators behave and how to combine indicators to find trades.
by answering question #2 you learn the methodology of entering trades.
by answering # 3, you learn what logical profit targets are. Said another way, you’ll start to learn entry techniques.
by answering # 4, you gain
confidence, because you know the risk.
Your confidence will begin to build and something else will happen. You’ll get into the rhythm of the market because you’ll be involved in the market, all day long. You’ll soon be able to scalp trades, in between the times you are waiting for your indicators to give you signals. The scalps will become easier because you’ll begin applying the 4 questions, to a scalp trade.
Example:
why am I getting into this SCALP trade?
Because I’m in the flow. I can see where the market stops and starts
where am I getting in?
as soon as I see it stop or start
where is my profit target?
Usually a tic or two in the TY Futures, depending “Average True Range”.
where is my stop loss?
Same as my profit target. I may look at the “Average True Range” or use a “Detrend Oscillator.
Dissecting the Fed Minutes (how to)
Let’s see if we can pull out some important words from the fed minutes by “Discecting the Fed Minutes”.
* * *
December Meeting Minutes
Show Increasing Worries
Of Inflationary Pressure
By GREG IP
Staff Reporter of THE WALL STREET JOURNAL
January 5, 2005; Page A2
"cost and price pressures were likely to become a clearer intermediate-term risk…absent further interest-rate increases.
The minutes suggest that the Fed is less likely to pause in its interest-rate increases this year than markets may have expected.
"The tone of these minutes is notably more hawkish than in November.”
At their November meeting, officials explicitly said future rate changes were contingent on coming economic data. But in the minutes to December's meeting, officials do not repeat that. Rather, they fret that even at 2.25%, the federal-funds rate "remained below the level" needed to keep inflation stable and the economy at full employment.
…there is no sign the Fed is worried enough about inflation to switch to half-point rate increases.
In the statement released after its meeting, the Fed said it could raise rates at a "measured" pace, and it continued to characterize the risks of inflation going higher or lower as "roughly equal."
That characterization struck some analysts as out of step with the minutes. "They say the [inflation] risks are balanced, but the minutes suggest the risks may be modestly to the upside." He [the analyst] says that means the Fed will likely raise rates at each of its next four meetings.
Mr. Sack noted that the statement released in December was almost identical to November's, but "the tone of the minutes of the two meetings is very different."
While policy makers generally expect underlying inflation to remain near its current low level of about 1.5%, using their preferred measure, the minutes show growing debate over risks. They said that high oil prices and the lower dollar "could get embedded in higher inflation," that slowing productivity growth will make it costlier for companies to boost production, and that it's unclear whether the economy is operating much below full employment.
The Fed…expects inflation, excluding food and energy, to be "stable." In September, it thought inflation would "remain at or below its current level."
The low level of bond yields in the face of the Fed's clear intentions to raise interest rates has also puzzled some Fed officials, who wondered if the prior prolonged period of low interest rates had contributed to "excessive risk taking." Officials pointed to low corporate bond yields relative to risk-free Treasurys, more initial-public-stock offerings, an increase in mergers-and-acquisition…
Write to Greg Ip at greg.ip@wsj.com
* * *
What this tells me is the following:
Watch numbers that have to do with Inflation and Productivity. They will be more volatile than in the past few months. Especially, PPI core, and CPI core.
watch oil
watch the US Dollar
make a note that a .50 hike at any one fed meeting is NOT in the market
Corps raise capital through debt offerings
Sell stock, or
Issue debt
bonds
notes
bills
discounted
1. ABS
a. asset backed Securities
2. Government
a. quick facts
i. biggest debt issuer in the world
ii. larger and larger portion goes debt service
iii. need to sell more and more debt to service debt
iv. the more they issue the better for traders
1. creates supply
v. current deficit is aprox., 6.4 trillion
vi. lower taxes create a bigger deficit
1. taxes finance the deficit
b. Muni’s
i. Municipalities
ii. Tax-free
c. T-bills
i. discounted to yield
a. you’re paying less than par value
ii. no interest payments (no CR)
iii. make the money through the discount on the issue date
iv. receive principal at maturity
v. usually 3 mos., or 6 mos., 1yr duration
d. fixed-income securities
i. quick facts
a. called fixed-income because they literally set a fixed coupon rate payable over the life of the security
ii. notes
a. 2-10 yr duration
b. 2 year note
i. issued last week of month
ii. settles last day of month
iii. 3-year note
a. issued quarterly
iv. 5-year note
a. issued first week of month
b. settles and matures on 15th of month
v. 10-year note
a. issued quarterly, however
i. currently (2003-2004) they are reopening the following month after issue, so
ii. they really issue new notes 4 times a year and they reopen 4 times a year
vi. Bonds
a. anything over 10 year duration is considered a bond
b. treasury stopped issuing bonds in 10/2001 and is reissuing 02/2006.
vii. TIPS
a. 5,10,20yr
b. indexed to CPI (inflation)
c. at maturity you make the CPI adjustment. Example:
d. buy 5yr TIPS with 2.5% coupon
e. CPI average = 4% over 5yr duration
f. at maturity you are awarded a 1.5% adjustment which is added to the original CR of 2.5%.
g. TIPS are complex and getting more popular in 2004
Explanations
“Linear regression [LR] is a statistical tool used to predict the future from past data, and commonly used to determine when prices are overextended.”[1]
“Linear regression is used to explain and/or
predict. The general form is:
Y = a + bX + u
Where Y is the variable that we are trying to predict, X is the variable that we
are using to predict Y, a is the intercept, b is the slope, and u is the
regression residual.
Regression takes a group of random variables, thought to be predicting Y, and tries to find a relationship between them. This relationship is typically in the form of a straight line (linear regression) that best approximates all of the X to Y.”[2]
Reread the last explanation. It looks complicated but it’s not.
Here’s another explanation from Prohet.net.
”A Linear Regression Channel 100% is created by drawing parallel lines above and below the Linear Regression line.
“Parallel and equidistant lines are drawn two standard deviations above and below a Linear Regression trendline. The distance between the channel lines and the regression line is the greatest distance that any one closing price is from the regression line. Regression Channels contain price movement, the bottom channel line provides support and the top channel line provides resistance. Prices may extend outside of the channel for a short period of time but when prices remain outside the channel for a longer period of time, a reversal in trend may be indicated.
“A Linear Regression trendline shows where equilibrium exists but Linear Regression Channels show the range prices can be expected to deviate from a trendline.”[3]
And here’s one last explanation from danielstrading.com
“The Least Squares Linear Regression line indicates the dominant market trend relative to time. In simple terms, is the market trending lower or higher with respect to time? It can inform you when the market is diverging from an established trend, but only when prices fluctuate uniformly around the trendline and within a narrow range. The better the fit of the equation to the data, the more reliable the linear trend. Once the calculations are completed, FutureSource draws the trendline on the screen.
“Do not rely on this study when prices deviate widely about the trendline. The fit of the trend to the data is most likely not very reliable. If the price chart flows uniformly about the regression line, the market should have a tendency to continue in the direction of the statistically fit trendline. Any large deviation from the regression line implies a change in the dominant market trend.
“The least squares methodology can be found in most books on basic statistics. It is a rather intense calculation process.”[4]
There are different types of LR models. One type allows the user to specify a specific start and end point in time. Another model self adapting or automated moving regression lines. I’m interested in the automated LR’s. They are often called Dynamic Regression Channels.
The interpretation of how to trade the LR is important. Paritech.com explains this very well.
“The interpretation of a Linear Regression indicator is similar to a moving average. However, the Linear Regression indicator has two advantages over moving averages.
“Unlike a moving average, a Linear Regression indicator does not exhibit as much delay. Since the indicator is fitting a line to the data points rather than averaging them, the Linear Regression line is more responsive to price changes.
“The indicator is actually a forecast of the next periods (tomorrow’s) price plotted today. The Forecast Oscillator plots the percentage difference between the forecast price and the actual price. Tushar Chande suggests that when prices are persistently above or below the forecast price, prices can be expected to snap back to more realistic levels. In other words the Linear Regression indicator shows where prices should be trading on a statistical basis. Any excessive deviation from the regression line should be short-lived.”[5]
[On a side note, I wanted to point something out. The name that appeared in the paragraph above, Tushar Chande, is important. His book, Beyond Technical Analysis, is nothing short of awesome. I will be getting into his work in a later paper because I’m currently working with his indicators.]
Here’s another way to trade LR, as told by Keystone-web.com.
“There are two ways to use linear regression. The first is to trade in the direction of the linear regression line. The second is to plot the linear regression line and two parallel equidistant lines above and below it. To determine the distance, use a point which is the furthest away from the linear regression line on the price bar. The two lines act as support and resistance. Once the lines are broken for a sustained period of time, this is an indication that the trend has reversed or gained tremendous momentum.
“The space inside the channel is where equilibrium exists, where prices can be expected to deviate from the original linear regression line. As with Bollinger Bands, when prices move outside or to the extreme channel line, price tends to move back to the opposite channel line.”[6]
LR channels are probably the most common way to trade on a short-term basis. To set this up on a chart you’ll have to put in standard deviations to tell the charting software where you want to set the channels.
Standard Deviations Settings:
The web site RT Investor has a tool box that describes this part of the inputs very well.
“The following approximations offer a few rules of thumb for using the standard deviation settings:
Plus or minus one standard deviation takes in 68.3% of all expected outcomes (historical price moves)
Plus or minus two standard deviations takes in 95.4% of all expected outcomes (historical price moves)
Plus or minus three standard deviations takes in 99.7% of all expected outcomes (historical price moves)
“For example, excursions of price more than 2 standard deviations above or below the regression line represent an unlikely event (less than 5% probable). Such excursions usually represent overbought or oversold conditions.”[7]
Start experimenting with Linear Regression on your charts in different time frames and see what you come up with.
References
http://www.linnsoft.com/tour/techind/linReg.htm
http://www.investopedia.com/terms/r/regression.asp
http://www.paritech.com/paritech-site/education/technical/indicators/trend/linear1.asp
http://www.prophet.net/learn/taglossary.jsp?index=L&entry=LRC
http://www.danielstrading.com/content/reso_inte_lear_pag13.php
I’m always looking for the formula. There’s a formula for everything. When it comes to holding on to losers and taking winners too soon, there’s also a formula.
Kahneman and Tversky (K&T) wrote a paper in 1979 that advanced much of the work done in this field from the late 1930s all the way to the early 1960s. Their theory is called the “prospect theory”. Shefrin and Statman (S&S) took that work a step further, in 1985. Their theory is called the “disposition effect”. Barber and Odean (B&O) took it further and called their theory, simply, “overconfidence”. All these theories can be grouped into a category called ‘Behavioral Finance’.
The one common denominator from these studies, including two studies I haven’t mentioned that were done in the 1930s and 40s, are startling, considering the length of time taken into consideration. All of the studies state that traders hold losers too long and get out of winners too soon. But why? Why do traders do this?
Concerning losers, K&T explain that traders don’t sell losers because of the desire to avoid the regret of a losing trade. It’s that simple; a trader has not come to terms with the loss. This means they didn’t do their homework. This means they didn’t have a plan. A trader must answer four questions before getting into a trade.
why am I getting into the trade?
where am I getting into the trade?
where is my stop?
where is my profit target?
Answering these questions allows you to act analytically and leave the emotion out of it. Emotions will convince you, in the end, to hold on to the losing trade. Emotions will convince you to find a reason to stay in the trade.
K&T write, “…a person who has not made peace with his losses is likely to accept gambles that would be unacceptable to him otherwise.”[1]
The message K&T are trying to get across may be stated as follows: As a trader, you will hold on to losers longer if you don’t have a plan in place before you enter the trade.
The mental tennis that accompanies a losing trade is familiar to all traders. The thoughts that run through a trader’s mind, during a loser are all there to avoid having to cut your losses and get out.
For example the thoughts might range from:
Why does the market always go against me?
Can’t I get into at least one winner?
If I get out then the market will end up going my way, if I don’t get out then the market will keep going against me.
Please please please come back to my price so I can scratch the trade.
I give up. As soon as I get out of this trade I’m going to change careers.
Maybe I should look at the 30 minute chart to see if it’s still bearish? Just because the 5 minute chart went against me doesn’t mean that the 30 minute chart did.
What about the daily chart? It’s still bullish!
Maybe I can find a different indicator and that will support my position? I hate the MACD anyways. I’m going to try the %R.
This trading system stinks, I’m going to kill the guy who gave it to me, it’s all his fault.
How do I know? ‘Cause I’m guilty of all of the above.
If I know what my risk is (what my stop loss is) then it takes all mental tennis (emotion) out of the equation. If I know my profit target, that also takes mental tennis out of the equation. Does that mean I can’t change my profit target when the market gets there? Perhaps moving to a trailing stop? Of course not. I can change my mind about the profit once it’s hit. But, not the stop.
If I know my max loss on a trade then I know all the consequences of that loss
It’s within my daily stop loss limit.
If I get stopped out, then I’m going to have to work hard the rest of the day to dig my way out of the hole.
Number 2, above, is arguably the biggest reason futures traders don’t take losers when they should. Think about it.
The three studies introduced at the beginning of this paper were based on investors in the stock trading world. There are two professors that moved prospect theory, the disposition effect and overconfidence theory to the futures trading world. They are, Locke and Mann.
Part II of this paper will be about their 1999 study of futures traders, Do Professional Traders Exhibit Loss Realization Aversion?
Footnotes
[1] Kahneman, D. and A. Tversky, 1979. Prospect theory: an analysis of decision under risk. Econometrica 47, pp286-287
This paper continues to look into the behavior of traders when it comes to losses. I will be citing two professors that are experts in this field.
Peter J. Locke is from the Finance Department, School of Business and Public Management, The George Washington University, Washington DC.
Steven C. Mann is from the M.J. Neeley School of Business, Texas Christian University. Fort Worth, Texas.
Locke and Mann (L&M) have written some fantastic studies on the behavior of the financial market. Their papers have centered on price and trader behavior. Today, I’ll continue part I of this paper, Losing Trades, and delve into the findings of a 1999 study by Locke and Mann titled, Do Professional Traders Exhibit Loss Realization Aversion?
L&M expand on prospect theory[1] (Kahneman and Tversky 1979)[2] and the disposition effect[3]. L&M call their theory, loss realization aversion.
Allow me to move sideways for a few sentences and explain something about White Papers. White papers are simply scientific studies written on a specific subject. A White Paper begins with an abstract (summary of the paper). The White Papers I’ve been reading about trader’s behavior, concerning losses, all state a very simple concept, in the abstract.
In essence they say traders hold losing trades longer than winning trades and the average position size for losing trades are larger than for winners. The previous sentence is almost verbatim from L&M’s paper, but this is the gist of what all the papers say.
It doesn’t matter what type of trader the papers are covering, professional or not. They all exhibit the same behavior. You are a professional trader, so you are not immune.
L&M write, “Relatively successful traders are less prone to sit on losing trades.”[4]
That statement is simple enough. Yet, it’s appears it’s extremely complicated to the professional trader.
And, the professional trader has some great stories to back up their reasons for holding on to losers. For example, “I’ve had big losses but you’re ignoring my big winners.”
That statement is arguable. Show me the data! Well…the data states something different.
L&M write, “…consider trades with absolute revenues over $100 for the Deutsche mark. While the mean loss is slightly larger than the mean gain ($227 compared to $225), the percentage of large losses (14.5%) exceeds the percentage of large gains (11.8%).”[5]
L&M state that you lose. Large losses are bigger than large gains by just under 3%. Multiply that number by your position limit and probability states you lose. There can be no argument. That’s why casinos have all of your money. They have the probability with them, all the time.
Let’s move back towards the main topic. That topic is, professional traders are generally the same as non-professionals in that they hold losers longer than winners.
L&M write, “Comparing gains to losses, the results are striking: professional traders as a group hold losses significantly longer than gains. Panel A shows that overall, losses are held substantially longer than gains for all four commodities [that they studied]. Median and average holding times for losses range from 35% to 133% longer than counterpart holding times for gains.”[6]
They go on to say, “…we were concerned that gains and losses might be treated differently depending on the size of the absolute revenue. We tested for differences in holding times by revenue magnitude using the revenue categories developed for table 3 [see table(s) at end of paper]. These results are reported in Panel B of table 4, which provides overwhelming evidence that gains are realized more quickly than losses regardless of the magnitude of the absolute gain….Clearly, the professional traders in our sample appear to exhibit the characteristics of loss realization aversion as a group - in that they hold losing trades longer than winning trades.”[7]
Lastly, they write, “Table 5 provides results of tests for differences between prior opportunities to exit trades at gains versus losses by reporting mean and median potential exit minutes for gains compared with losses. The results clearly show that traders, on average, pass up more opportunities to exit losing trades at a loss than they do winning trades at a gain. The first two columns of Table 5 report mean and median potential exit minutes for gains and losses. For all four pits, trades that eventually result in a loss are preceded by significantly more prior opportunities to realize that loss than similar gainful opportunities for their counterpart winning trades.[8]”
If I’ve gotten the point across that traders hold losers longer than winners, then my job is done. Part III will discuss what successful professional traders do to be successful.
[1] “Prospect theory modifies expected utility theory in two areas, and leads to predictions that are consistent with investor loss realization aversion. First, investor utility is assumed to be a function of gains and losses relative to a benchmark, rather than a function of absolute wealth. Second, while standard utility functions are concave on both sides of a wealth point, prospect theory assumes utility functions that are concave for gains and convex for losses (but steeper so that overall risk aversion is attained). The prediction of a disposition effect relies on these two wrinkles to expected utility theory.” -- Do Professional Traders Exhibit Loss Realization Aversion? Locke and Mann, p3.
[2] Prospect theory: an analysis of decision under risk, Kahneman, D. and A. Tversky, 1979.
[3] “…the disposition effect, based on the prospect theory of Kahneman and Tversky (1979), as an explanation for the perceived anecdotal evidence at that time of investor reluctance to realize losses. The disposition effect arises when investors focus on a reference point for their position from which gains and losses are calculated, rather than following a portfolio choice model. Agents are alleged to use a form of “frame reference” - evaluating opportunities to close existing positions as either gains or losses, measured against the reference point.” -- Do Professional Traders Exhibit Loss Realization Aversion? Locke and Mann, p3.
[4] ibid p2.
[5] ibid pp12-13.
[6] ibid p13.
[7] ibid p13-14
[8] ibid p15.
Tables and further reading suggestions follow:
Further Reading
Barber, Brad and Terrance Odean, 2000b. Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, forthcoming.
Barberis, Nicholas, Ming Huang, and Tano Santos, 1999. Prospect theory and asset prices, National Bureau of Economic Research Working Paper.
Benos, Alexandros, 1998. Aggressiveness and survival of overconfident traders. Journal of Financial Markets.
Bernstein, Peter, 1998. Against the Gods: The Remarkable Story of Risk. John Wiley & Sons, New York.
Daniel, Kent, David Hirschleifer, and Avanidhar Subrahmanyam, 1998a.
Investor psychology and security market under-and overreaction. Journal of Finance 53, 1839-1885.
Daniel, Kent, David Hirschleifer and Avanidhar Subrahmanyam, 1998b. Investor overconfidence, covariance risk, and predictors of securities returns. Working paper, University of Michigan.
Fama, Eugene, 1998. Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics 49, 283-306.
Heisler, Jeffrey, 1996. Loss aversion among small speculators in a futures market. Working paper, Boston University
Kahneman, D. and A. Tversky, 1979. Prospect theory: an analysis of decision under risk.
Kuserk, Gregory. and Peter R.. Locke, 1993. Scalper behavior in futures markets: an empirical examination. Journalof Futures Markets 13, 409-431.
Manaster, Steven and Steven C. Mann, 1996. Life in the Pits: competitive market making and inventory control. Review of Financial Studies 9, 953-975.
Manaster, Steven and Steven C. Mann, 1999. Sources of market making profit: man does not live by spread alone. Working paper, Texas Christian University and University of Colorado.
Odean, Terrance, 1998a. Are investors reluctant to realize their losses? Journal of Finance 53, 1775-1798.
Odean, Terrance, 1998b. Volume, volatility, price, and profit when all traders are above average. Journal of Finance 53, 1887-1934.
Odean, Terrance, 1999. Do investors trade too much? American Economic Review 89, 1279-98
Shefrin, Hersh and Meir Statman, 1985. The disposition to sell winners too early and ride losers too long: theory and evidence. Journal of Finance 40, 777-790.
Shiller, Robert J. 1997. Human behavior and the efficiency of the financial system, working paper, Yale University, prepared for Handbook of Macroeconomics, John B. Taylor and Michael Woodford, editors.
Shumway, Tyler, 1997. Explaining returns with loss aversion. Working paper, University of Michigan.
Silber, William L., 1984. Marketmaker behavior in an auction market: an analysis of scalpers in futures markets. Journal of Finance 39, 937-953.
Thaler, Richard and Eric Johnson, 1990. Gambling with the house money and trying to break even: the effects of prior outcomes on risky choice. Management Science 36, 643-660.
This paper continues to look into the behavior of traders when it comes to losses. I will be citing two professors that are experts in this field. Locke and Mann (L&M).
Part III will specifically look into the behaviors of successful traders. Most information herein is based on a paper L&M wrote titled, Do Professional Traders Exhibit Loss Realization Aversion?
L&M’s paper states in essence that traders hold losing trades longer than winning trades and the average position size for losing trades are larger than for winners.
So what do successful traders do to avoid this situation? They get out of their losers.
“Defining Success”[1]
“To determine whether success is related to discipline, we first tackle the problem of formulating a working definition of success. Intuitively, trading revenue ought to be directly related to trading success. However, the amount of risk undertaken in order to achieve short-term revenue is certainly vital to long-run survival.
“To accommodate this sampling problem, we utilize two related measures of success. The first measure is total income for the six-month sample period. The second measure, which we label ‘risk-adjusted performance’, or RAP, measures a trader’s daily “return” on a measure related to the economic capital required by traders to cover potential losses undertaken in order to trade the position. The RAP measure gives low rankings to traders who may have been successful in terms of income, but exposed themselves to relatively higher risk in the process of generating the income.
“We estimate a measure related to a trader’s economically required capital by considering the trader’s marked-to-market position for each minute of each day that the trader trades. We define the maximum exposure for each trader each day as the absolute value of the trader’s maximum loss exposure (negative mark-to-market) each day. In some cases this may be the largest loss taken by a trader, but more generally will represent the largest potential loss. We define an ex post value at risk (VaR) measure as the 95th percentile daily maximum exposure for the trader. If a trader trades for one hundred days, we take the trader’s fifth largest potential loss over the hundred days as the ex post VaR.
“Given our VaR estimates related to trading capital requirements, we define the RAP as the average daily income divided by the VaR. Table 7 reports distributional statistics for RAP rankings. From this table, it is clear that traders with similar average trading incomes vary widely in the amount of risk they take in order to earn the income.
“The first two columns report median incomes and median 95th percentile potential losses for the traders within each quartile. The median trader in the highest RAP-ranked quartile for the Deutsche mark earned a daily average of $1,101, and the 95th percentile potential loss for traders in the highest ranked Deutsche mark group was $3,398. The last column of Table 7 provides the RAP for the median trader in the highest-ranked Deutsche mark group has an RAP of 0.359.
“A natural interpretation of the RAP ratio is the relationship of income to potential loss. In this sense, traders with a RAP of 0.20 risk at least 5 times their average daily trading income around once every 20 days. From this table it appears that lower-ranked traders expose themselves to much more risk for a given level of income. For example, the median traders in the second and third ranked Deutsche mark groups have RAPs of 0.142 and 0.058, respectively, which indicates that these traders risk about seven times and seventeen times respectively, their mean daily income every twenty days.”[2]
So, what does all of this mean? Unsuccessful traders expose themselves to more risk to generate income. Overtime this equation (income potential to loss) states that you will either blow out, or never move to the next level.
Said concisely, L&M state, “…median traders in the second- and third-ranked Deutsche mark groups have RAPs of 0.142 and 0.058, respectively, which indicates that these traders risk about seven times and seventeen times respectively, their mean daily income every twenty days.”[3]
Let’s look at this from a different perspective. L&M write, “As Table 8 and the figures show, profitability remains relatively constant across holding times for higher ranked traders, in marked contrast to the lowest ranked traders. For example, the lowest RAP quartile for Dmark traders earns $8.63 per contract on average for trades held less than 1-minute, but lose $11.52 on average for trades held longer than 10 minutes. In contrast, Deutsche mark traders in the highest RAP quartile have comparable revenue per contract of $8.44 and a positive $14.87 respectively.”[4]
“If relative discipline is defined as the relative absence of loss realization aversion, or a relative propensity to quickly take losses, then the evidence in this section is consistent with the notion that relative discipline is related to success. The least successful traders seem particularly prone to the disposition effect.”[5]
“Summary and Conclusion”[6]
“In this paper we provide evidence that professional futures floor traders appear to be subject to the disposition effect. These traders as a group hold losing trades longer on average than gains. As previous research documenting loss realization aversion focuses in small retail customers and experimental subjects, these findings – that professional traders, whose livelihood depends on their success, also exhibit the disposition effect-provide evidence that behavioral attributes are pervasive in the population.”[7]
“Examination of differences in trading activity and subsequent trader success shows that the least successful traders appear to exhibit most strongly the characteristics described as less disciplined. Specifically, while traders at every success level on average hold losses longer than gains, the least successful traders hold losses the longest while the most successful traders hold losses for the shortest time. Thus there is evidence that trading success is negatively related to the degree of loss realization aversion.”[8]
______________________________________
Peter J. Locke is from the Finance Department, School of Business and Public Management, The George Washington University, Washington DC.
Steven C. Mann is from the M.J. Neeley School of Business, Texas Christian University. Fort Worth, Texas.
Do Professional Traders Exhibit Loss Realization Aversion (pdf file of the original paper.)
“…the disposition effect, based on the prospect theory of Kahneman and Tversky (1979), as an explanation for the perceived anecdotal evidence at that time of investor reluctance to realize losses. The disposition effect arises when investors focus on a reference point for their position from which gains and losses are calculated, rather than following a portfolio choice model. Agents are alleged to use a form of “frame reference” - evaluating opportunities to close existing positions as either gains or losses, measured against the reference point.” -- Do Professional Traders Exhibit Loss Realization Aversion? Locke and Mann, p3.
Footnotes
[1] Do Professional Traders Exhibit Loss Realization Aversion? Locke and Mann, p21.
[2] ibid pp21-22
[3] ibid pp21-22
[4] ibid p24
[5] ibid p24
[6] ibid p27
[7] ibid p27
[8] ibid p28
Mandelbrot: Fractals and Scaling in Finance
I'm an avid fan of Benoit Mandelbrot. I’ve been studying him for several years, especially his book “Fractals and Scaling in Finance”.
The book covers, *
classification of different forms of randomness,
their manifestation in terms of distribution theory,
their ability to be represented compactly,
the notion of trading time,
the importance of discontinuities,
the relationship between financial time series and turbulent time series,
the pathologies of commonly abused distributions, particularly the log-normal,
the catalogue of the methods used to derive scaling distributions, both honest and fallacious.
*(http://guava.physics.uiuc.edu/~nigel/articles/mandelbrot.html)
Among some of the things he states:
Kurtosis and Pearson’s measure of skewness is meaningless.
The only criteria of trustworthiness is replicability in time.
ARMA has no intrinsic meaning
ARCH is of use but there are concerns
There is a lot of value in ‘scaling randomness’
Scaling distribution literally cries out for attention
Especially when taking seriously the simplified models based on maximization or linear regression.
The physical distribution of scarce mineral resources and rainfall is important in economics.
The most important feature of the scaling distribution is the length of its tail, not the extreme skewness.
Fourier series is not a model, but a representation.
Hydrology and finance have a lot in common.
Hyperbolic distribution is analytically convenient.
Serial dependence can’t be ignored.
Many refined theorems concerning risk and ruin only holds true in universe which is exactly Gaussian.
On Ralph Elliot (Elliott Wave): “Elliot’s work fails the requirements of objectivity and repeatability”…
What fascinates me about Mandelbrot’s work is said better by this gentleman:
“The implications of these and subsequent findings are profound, yet it is fair to say that the work was practically ignored by economists and practitioners of finance. Even today, the problem of "fat tails" is swept under the rug by the vast majority of financial risk managers, even though the phenomenon is sufficiently widespread and well-recognized…”
--Nigel Goldenfeld
In 1963 Mandelbrot produce the now famous M1963 paper. Nigel Goldenfeld again says it best:
“New York. Mandelbrot’s shocking conclusion, published in 1963, was that the time series was in no way Gaussian: in fact, he argued that the departures from normality could be accounted for by using distribution functions with infinite variance, which are termed L-stable. Mandelbrot examined the convergence in sample number of the variance of the logarithm of the daily price changes and found erratic variation rather than convergence.”
Yet, people ignore his work. Well…not me.
[Disclaimer: Benoit Mandelbrot has not endorsed this paper, nor does he even know it exists. Theses are my opinions/interpretations, about his work.]
This is a paper I wrote that may be helpful to those
starting out and those who are struggling. It's not meant to cause controversy
in any way. It is simply my observations on Mandelbrot’s observations.
_____________________________
Mandelbrot’s Observations from the book, Fractals
and Scaling in Finance
Benoit Mandelbrot, the father of
fractals, has one of the greatest mathematical minds on earth. I’ve been
studying one of his books for…oh…mmmm…well…going on three years now. It’s mind
numbingly complicated; filled with enough mathematical equations to make your
head spin. It’s worth it though, because I knew he was onto something due to his
honesty. He’s completely against the theories that are consistently put out
there to sucker people into believing they are buying the holy-grail of
financial price prediction. (I know the people who sell these theories,
personally, because I’ve been sucked in many times.)
If you relish your money, read what
he has to say, in this paper, about price prediction.
There are several reasons why I
wrote this paper. One reason is to introduce you to his world of thinking.
Although he doesn’t give a specific trading system that you can import into your
analytical trading software, he does write about what you should investigate and
what you shouldn’t investigate. Secondly, I want to warn you that if the word
fractal is tied to an indicator or software program, don’t buy it or even waste
your time with it (if it is free) because there are many bad fractal programs.
More specifically, this paper covers
what Benoit has written concerning certain “indicators” and “trading systems”.
I’ve also included some of his observations about the markets. Meaning, some of
the things he thinks deserve extra attention. However, I’m going to write a
separate paper on that subject because it's deserving.
I’m leaving all math equations out
of the paper… because…well…I don’t understand any of them. (I’ve provide
reference pages from his writing if you’d like to look them up.) What I do know
about his equations is that they are empirical proof that unequivocally discount
many financial price-predicting models and probability-theory models.
One of the greatest things I’ve
realized, from his work, is what not to study. This paper includes a lot of
that. Indeed you may see some things in here that I have personally recommended
and Benoit is indeed discounting the method. It took me a long time to extract
exactly what Benoit was saying in his writings. He writes so technically. And,
as I said before, I am not a math person. Theories and abstractions maybe, but
equations? Fergetit.
What brought me to the writing of
this paper is very important. I was implementing the installation of MATLAB
which is a sophisticated analytical program for financial instruments. The
reason I wanted MATLAB was to run a Mandelbrot theory called the Multifractal
Model of Asset Returns (MMAR) and apply it to our markets.
What I found, while setting up the
software, was an enormous amount of assumptions the software made about
analyzing price data. Assumptions that have been, empirically, proven wrong.
MATLAB is very expensive. And, it’s well regarded in many industries as cutting
edge. It has lots of bells and whistles. But, assumptions have been made from
the writers of the software. These assumptions can skew trading results. MATLAB
even tells you this in the documentation.
However, while reading the
documentation, if you didn’t know what stationary, nonstationary series process,
Fat tails, and Long memory were, you’d of trusted this software to give you
signals to get in and out of the market.
[This doesn't mean that MATLAB is bad. It's awesome.
I'm just warning you about implementing specific systems within MATLAB.]
Unfortunately, 99% of traders trust
the software / system, because they want the holy-grail. If it was the
holy-grail, you know one thing, it wouldn’t be packaged and sold to you.
So, beware of what you don’t know,
concerning trading system/software/indicator claims, etc. Learn from my
mistakes. Understand that there’s a heck of a lot of people selling tents and
shovels.
As you read this paper, don’t freak
out because of the terminology. What’s important is what he’s saying about a
specific term. Terms that are identifiable within software trading systems and
indicators. (Terms that people make unfounded claims about, so you’ll be sold on
their system.) Example of a term in this paper: If you see the term Gaussian,
then there will be an explanation later. It may be written by me or Mandelbrot.
The terms make more sense after you’ve read the whole statement and/or the notes
I’ve made after the statement. [My notes will be encased in brackets.]
The
following is from the book, Fractals and Scaling in Finance, by Benoit
Mandelbrot.
“Gaussianity (i) random walks, and martingales are
attractive hypotheses, but disagree with the evidence concerning price
variation.” (ii)
[In a paper Mandelbrot wrote, called M1963, (iii) he
proved that the price behavior of financial data is clearly not Gaussian. This
conclusion, that the price behavior of financial data is clearly not Gaussian is
a main theme throughout his work. Without going into major background, you need
to understand that price prediction has been based on the theory that prices are
Gaussian, since the Gaussian theory was discovered, in 1900, by the
mathematician, Bachelier. M1963 proved the theory wrong. However, people still
base everything on the theory that prices are Gaussian. Read on, you’ll be a pro
at these terms by the end of this paper. More on Random walk and Martingales
later. They’re important because many software/trading systems contains these
terms. Their sucker terms because they don’t work.]
“Scaling can take two forms, uniscaling and
multiscaling, and the proper model of foreign exchange rates appears to be a
multifractal one.” (iv)
[I note the statement above, because it may come in
handy at a later date. I’ll abbreviate these types of notes as LD, which stands
for Later Date. If for some reason you are studying a system in currency
trading, you’ll know that multifractal scaling is important.]
On Risk & Probability of Ruin theory: “In effect, much
of the statistics places an equal degree of confidence in the Gaussian
distribution. For example, many refined theorems concerning risk and the
probability of ruin only hold in a universe in which the Gaussian holds with
absolute exactitude.” (v)
[In M1963, he proved that the price behavior of
financial data is clearly not Gaussian. So he’s saying that Risk & Probability
of Ruin theory doesn’t hold water because they are based on Gaussian
distribution of prices. This simply blows me away. The street is over run with
probability software and PhDs claiming you must run Risk & Probability of Ruin
statistics! No…No you don’t have to run them. They don’t work.]
“…changes in fast moving interest rates are surely not
independent.”
[This is one of those things that I find extremely
important and have no idea why. So, I toss it in for use at a LD.] (vi)
Concerning ARCH (vii)-like processes and Brownian
motion: “I take the liberty of challenging the proponents of the Brownian motion
and ARCH-like processes to examine how the graphic outputs of their algorithms
compare with the actual data. I have no doubt about the outcome.” (viii)
[There are other examples of these two processes later
in this paper. ARCH stands for “Autoregressive Conditional Heteroscedasticity”.
You’ll see many different bastardizations of this model. You’ll also see the
words Autoregressive and Heteroscedasticity used by software sales-people to
help sell a program. And, you should see the pretty pictures it makes! Of the
terms mentioned above (Brownian motion and ARCH) ARCH is of particular
importance. Many trading theories out there are based on the math of ARCH.]
“Hyperbolic distribution is analytically convenient.”
(ix)
[Another big term thrown around in the trading system
world, hyperbolic distribution. If you see it, run away.]
Hydrology and finance are similar. (x)
[fascinating]
Fourier series is not a model, only a versatile
representation. “A satisfactory statistical fit is of no use in science unless
the fitting parameters are consistent in time and have no intrinsic meaning.”
(xi)
“…ARMA (xii) is not a model, only a versatile
representation. A satisfactory statistical fit is of no use in science unless
the fitting parameters are consistent in time and have no intrinsic
meaning….they have no predictive value whatsoever. (xiii)
[He is saying the same thing about Fourier and ARMA in
the same sentence. I simply separated them into two statements.]
“The ARCH representations and its variants. The
comments that follows are addressed to readers the readers already acquainted
with this common “fix” to Brownian motion. In spirit, the ARCH model is closely
related to models that inject a trading time, such as the 1967 model based on
subordination (M & Taylor 1967 or the M1972 model. The fit of an ARCH-like model
is likely to be good or even excellent, if only because there is no upper bound
on the number of parameters. However, all the reservations concerning ARMA
extend to ARCH….That is, ARCH analysis fails, by its very nature, to be faithful
to the long-term component that the multifratcals involve, and that the eye sees
in the financial data.” (xiv) [Mandelbrot’s emphasis]
“Interpolation and extrapolation. Yet another deep
difference between ARCH and the M 1972 model shows up when models based on daily
data have to be interpolated or extrapolated to higher frequencies. In ARCH,
analytical procedures are not available.” (xv)
Benoit comments that interest in Pareto’s law for
distribution of personal income regarding the scaling distribution related to
the law, should dominate economics. (xvi)
[He’s stating that Pareto’s law should be revisited. He
also goes on to introduce this law into statistical economics as an alternative
to what‘s being used now in statistical economics. He also writes about scaling
distribution, which is very important in his work. More on this below.]
“…I propose to show that the scaling distribution
literally cries out for our attention under many circumstances. Those
circumstances include (1) taking seriously the simplified models based on
maximization or on linear aggression (2) taking a cautious view of the origin of
the economic data or (3) believing that the physical distribution of various
scarce mineral resources and of rainfall is important in economics” (xvii)
“We shall see that the most important feature of the
scaling distribution is the length of its tail, not its extreme skewness.”xviii
“…Pearson’s measures of skewness and kurtosis – are
meaningless.”xix
[I’ve run across Pearson’s and Kurtosis’ measurements
when analyzing trading systems, with backtesting results. Many statisticians
swear by these two measurements. Benoit purports that they are useless.]
“The only criteria of trustworthiness is replicability
in time.” (xx)
[By far, this is his most pertinent statement in the
book. If there is a system that you want to trade, after backtesting it and
running it through the most sophisticated software programs out there, run it
for a while and see if it actually works in real-time, day after day.]
Here are some terms that Mandelbrot
writes about that I’ll list for the particular reason that you should avoid
anything to do with these. In other words, if claims are being made about a
trading system or indicator that include the following terms, avoid them. They
are worthless.
* Random-walk theory
* Chaos theory and the merger of fractals. Mandelbrot
had nothing to do with this and doesn’t endorse any of it.
* Theory of Dynamical Systems
* Disorder Theory
* ARCH, GARCH
* Autoregression
* Elliott Wave (see below)
On probabilists: Mandelbrot calls, Adrei Nikolaievich
Komogorov (1903-1987), the greatest probabilist of the century. (xxi)
On Elliott Wave: [Sorry folks, you won’t like this.
Even I like EW. However…]
“Elliott’s work fails the requirements of objectivity
and repeatability: in his own words [Ralph Elliott’s], “considerable experience
is required to interpret [it] correctly” and “no interpretation [is] valid
unless made by [him or his direct licensees].” (xxii)
On Randomness: Benoit states that randomness must be
broken down into three categories to properly understand it. As he writes,
“…different states of randomness must be distinguished and faced. There is wild
randomness exemplified by distribution with infinite variance. There is also an
intermediate possibility exemplified by the lognormal: it is slow randomness – a
term deliberately selected to imply what it says.” (xxiii)
[What he is saying is that randomness in the market
can’t be taken as a whole. And, that’s exactly what statisticians do when
measuring the market with their ‘tools’. He writes that as physics must
distinguish between different states of matter, so must probability theory. So,
when taking probability theory into account, ask if the software you are using
breaks down the randomness into three categories or does it look at randomness
as a whole. The odds are the software does not take this into account.
Therefore, the software is useless. However, Benoit’s work does take this into
account. I’m looking for the software that includes this feature, concerning
probability theory studies. I’ll cover his theory on Randomness that he thinks
should be implemented, in another paper.]
On Spectral Analysis: “A major but unrecognized “blind
spot” of the spectral analysis; spectral whiteness is insensitive to change of
trading time, therefore misleading.” (xxiv)
[Benoit is referring to analysis that involves some of
the following:
* Wavelets, which include
o Wavelet Packet Transforms
o Wavelet Power Spectrum
o Discrete Wavelet Transform
o Signal processing
What would be considered a good wavelet? One that
simulates Fractal Brownian Motion.]
On Lognormal Distribution: “A hard look at the
lognormal reveals a new phenomenon of delocalized moments. This feature implies
several drawbacks, each of which suffices to make the lognormal dangerous to use
in scientific research….the lognormal’s wondrous properties are irrelevant and
thoroughly misleading; it is not the statisticians best friend, perhaps even
their worst one.” (xxv)
[Again, this is discussing a term. That term is,
Lognormal. This term is thrown around as a price predictor. It isn’t.]
On Martingales: [They don’t work as price prediction
models. (xxvi)]
Well…that’s it for now. Although a
paper like this can be difficult to comprehend due to the unfamiliar
terminology, it’s worth reading through and keeping for later reference.
Especially when you run into the situation where someone is trying to sell you
an indicator or a trading system that has complicated terms attached to it.
Simply search this paper for term. If you don’t find it, search the web and
limit the search to ‘.edu’. See what you come up with. My guess is that the term
can be connected to the terms within this paper; therefore, disproving the snake
oil you are being sold.
________________________
References:
Fractals and Scaling in Finance, by Benoit B.
Mandelbrot.
Statistical analysis of financial time series under
the assumption of local stationarity, by Clémençon Stephan.
A Multifractal Model of Asset Returns, by Benoit
B. Mandelbrot.
________________________
Endnotes"
i) Named after the mathematician, Gauss.
ii) Fractals and Scaling in Finance, p 50
iii) M = Mandelbrot, and 1963 is the year he published
the paper. You could look this paper up in the Internet by typing in “M1963”.
With the quotation marks.
iv) Fractals and Scaling in Finance, p 61
v) ibid, pp 62-63
vi) ibid, p 63
vii) All right, get ready for this term. ARCH stands
for “Autoregressive Conditional Heteroscedasticity”.
viii) Fractals and Scaling in Finance, p 66
ix) ibid, p 69
x) ibid, p 72
xi) ibid, p 72
xii) auto-regressive moving averages
xiii) Fractals and Scaling in Finance, p 72
xiv) ibid, p 73
xv) ibid, p 73
xvi) ibid, p 79
xvii) ibid, p 80
xviii) ibid, p 80
xix) ibid, p 93
xx) ibid, p 95
xxi) ibid, p 115
xxii) ibid, p 115
xxiii) ibid, p 115-16
xxiv) ibid, p 163
xxv) ibid, p 255
xxvi) ibid, p492
What is this document?
This doc includes an outline for very basic things to look for in the market, each and every day.
There are so many things to watch and so much information that losing sight of the basics is easy. I find that returning to the basics is a good way to clear away the bs that we are faced with everyday.
You can break the day down into questions.
3 to be precise:
What’s Greenspan paying attention too?
________________________________________
What’s Greenspan paying attention too?
There’s no better way to know which economic indicator is the most important, at certain times, than which ones Greenspan is watching. You can find out by searching his testimony at Monetary Policy Report to the Congress.
(Explained later in this document under chapter heading, “Searching Greenspan testimony”)
Here’s what he’s looking at now (early 2004):
Inflation
PPI (Monthly)
If figure is Higher than expected, then, SELL
If figure is Lower than expected, then, BUY
Example
i. PPI expected +.02
ii. PPI actual +.01
iii. Bullish for 10yrs—Buy it
Example
i. PPI expected +.05
ii. PPI actual +.08
iii. Bearish for 10yrs –Sell it
CPI (Monthly)
If figure is Higher than expected, then, SELL
If figure is Lower than expected, then, BUY
GDP Price Deflator (Monthly)
More specifically, the ‘fixed weight deflator'
If either figure is Higher than expected, then, SELL
If either figure is Lower than expected, then, BUY
Jobs
Unemployment (Monthly)
Non-Farm Payrolls (Weekly jobless claims (every Thursday morning @ 7:30))
If figure is Higher than expected, then, SELL
If figure is Lower than expected, then, BUY
Notice the pattern of how the releases impact the fixed income market
If figure is > expected figure SELL
If figure is < expected figure BUY
There’s always something that creeps into the market 3 times, or so, a year.
This is usually tied to a commodity
Oil
Yen
JGB
An index (DOW, SP, ND)
Euro
Us $ etc.
“What’s new” means that something is beginning to have an affect in the fixed income markets. More and more people begin to watch this new ‘indicator’
The news begins to pick up on it and hence the fixed income markets are affected by this ‘indictor’
The question to ask yourself is
How would I trade this?
i. Say we are talking about JGB’s
1. JGB Yields go up, then
2. SELL notes
3. why?
a. who cares
ii. also, this is telling us is that the Japanese bonds market is the new indicator
iii. However, this doesn’t mean that it’s the new LEADING indicator. It’s just a FACTOR affecting us
iv. The leading indicator will always be FED policy, for this day and age. (Of course, this will change at some
point in the future. But not now.)
Read up on what is happening in the specific sector you are trading.
Maybe you trade fixed income. You need to find the sources for information on this specific topic. It may be a newsletter that's emailed to you every morning or you go to the Yahoo! bond center.
The most important factor in collecting this information is to get an understanding what the street is saying. Many times it doesn't matter what you think about the market. It matters what the street thinks.
Economic Releases?
what?
when?
Fed Talk
are there any fed governors or Greenspan spewing rhetoric? To whom are they talking too?
a bank committee?
a senate panel?
Auctions?
what?
when?
Daily and weekly support levels
How will I trade if we get to those levels?
DOW, ND, or SP
Are any of these at significant MONTHLY support or resistance levels, and/or
are the News services or the street talking about them in any significant manner?
______________________________
1. Go to Monetary Policy Report to the Congress
2. open the latest report
3. Search the document by clicking |edit| find|
4. enter the word ‘indicator’ into the box (no quote marks needed)
5. bingo
a. Example (I found this in the last statement)
i. “In the process of assessing risk, we monitor a broad range of economic and financial indicators. Included in this group are a number of measures of liquidity and credit creation in the economy. By most standard measures, aggregate liquidity does not appear excessive. The monetary aggregate M2 expanded only 5-1/4 percent during 2003, somewhat less than nominal GDP, and actually contracted during the fourth quarter. The growth of nonfederal debt, at 7-3/4 percent, was relatively brisk in 2003. However, a significant portion of that growth was associated with the record turnover of existing homes and the high level of cash-out refinancing, which are not expected to continue at their recent pace. A narrower measure (that of credit held by banks) also grew only moderately in 2003. All told, our accommodative monetary policy stance to date does not seem to have generated excessive volumes of liquidity or credit.”
b. Then, search it again
1. Corps raise capital through debt offerings
a. sell stock, or
b. issue debt
i. bonds
ii. notes
iii. bills
1. ABS
a. asset backed Securities
2. Government
a. quick facts
i. biggest debt issuer in the world
ii. larger and larger portion goes debt service
iii. need to sell more and more debt to service debt
iv. the more they issue the better for traders
1. creates supply
v. current federal deficit is 7 trillion
vi. lower taxes create a bigger deficit
1. Taxes finance the deficit
b. Muni’s
i. municipalities
ii. tax-free
c. T-bills
i. discounted to yield
1. you’re paying less than par value
ii. no interest payments (no CR)
iii. make the money through the discount on the issue date
iv. receive principal at maturity
v. usually 3 mos., or 6 mos., 1yr duration
d. fixed-income securities
i. quick facts
1. called fixed-income because they literally set a fixed coupon rate payable over the life of the security
ii. notes
1. 2-10 yr duration
2. Two-year-note
a. issued last week of month
b. settles last day of month
3. Three-year-note
a. issued quarterly
4. Five-year-note
a. issued first week of month
b. settles and matures on 15th of month
5. Ten-year- note
a. issued quarterly, however
i. currently (2003-2004) they are reopening the following month after issue, so
ii. they really issue new notes 4 times a year and they reopen 4 times a year
6. bonds
a. anything over 10 year duration is considered a bond
b. treasury has stopped issuing bonds
c. ceased in fall of 2001 with the cessation of the 30yr
d. will reissue in February 2006
7. TIPS
a. 5,10,20yr
b. indexed to CPI (inflation)
c. at maturity you make the CPI adjustment. Example:
d. buy 5yr TIPS with 2.5% coupon
e. CPI average = 4% over 5yr duration
f. at maturity you are awarded a 1.5% adjustment which is added to the original CR of 2.5%.
g. complex
h. getting more popular in 2004
1. be aware of what is being auctioned that day, week.
2. what’s the spread between the old issue and the new issue?
1. when yield goes up price goes down
2. when price goes up yield goes down
3. government securities are traded in 1/32nds
4. you are actually trading prices, NOT yield
5. bid
a. you are buying
b. you can enter an order on the bid and then you are ‘working’ your bid
i. you haven’t put a position on yet, because you are still working the order. You are not ‘filled’ yet
ii. once your bid is ‘hit’ / ‘executed’, then you have a ‘position in the market’
iii. You want or are thinking the market will go up
c. terms
i. long
1. long the market
2. going long
3. you are long
ii. ‘joining the bid’
iii. lifting the offer
iv. ‘buy at the market’
6. Offer
a. you are selling
b. you can enter an order ‘on the offer’ and then you are ‘working’ your offer
i. you haven’t put a position on yet, because you are still working the order. You are not ‘filled’ yet
ii. once your offer is ‘lifted’ / ‘executed’ , then you have a ‘position in the market’
c. you want or are thinking the market will go down
d. terms
i. short
1. you’re selling short
2. I’m short
3. Short selling
ii. ‘joining the offer’
iii. hitting the bid
iv. ‘selling at market’
1. products
a. if you are currently trading the ‘Cash Market’
i. 2 year ‘cash’ notes
ii. 3 year ‘cash’ notes
iii. 5 year ‘cash’ notes
iv. 10 year ‘cash’ notes
v. 30 year ‘cash’ bonds
vi. all these can be categorized as
1. debt
2. derivatives
3. products
4. these are some of the names
vii. we trade the “On-the-run” issues
viii. we can trade the WI (When, as, if issued, or, “When Issued”)
2. Price
a. the prices in the products we trade ‘move’ in the following manner: (example)
i. 99.000 (even; “it’s trading even”)
ii. 99.002 (quarter; “it’s trading a quarter”)
iii. 100.004 (plus(+) or half; “it’s trading even+” or it’s trading “even and a half”)
iv. 99.006 (three quarters; “it’s trading a THE ORDERS”)
v. then the market would ‘tic’ 99.010
vi. example
1. 99.000, 99.002, 99.00+, 99.006, 99.010, 99.012, 99.01+, 99.016, 99.020, 99.022, 99.02+, 99.026, 99.030
b. Profit / Loss (P&L) and Scratch
i. profit
1. buy 1 @ 99.000
2. sell 1 @ 99.002
a. Profit = “¼ point”
b. ¼ point = $78
3. buy 1 @ 99.000
4. sell 1 @ 99.010
a. Profit = “a full point”
b. Full point = $312.50
ii. Scratch
1. buy 1 @ 99.000
2. sell 1 @ 99.000
a. you’ve ‘Scratched’ the trade because you didn’t profit or lose on the trade, you only pay commissions
iii. Loss
1. there is no such thing as a loss
3. structure of cash market
a. traded OTC (Over The Counter)
b. closed system
i. not an exchange like the NYSE or the CBOT
c. trade with counter parties through a broker (example: Cantor (espeed) or Broker Tec)
d. Cantor (espeed) act as the broker
e. you don’t know who the counter party is
f. Cantor (espeed) guarantee the trades
g. Broker Tec doesn’t guarantee trades
h. Broker/Dealer defined
1. Any individual or firm in the business of buying and selling securities for itself and others. Broker/dealers must register with the SEC. When acting as a broker, a broker/dealer executes orders on behalf of his/her client. When acting as a dealer, a broker/dealer executes trades for his/her firm's own account. Securities bought for the firm's own account may be sold to clients or other firms, or become a part of the firm's holdings.
i. you are trading with other broker/dealers
i. there are about 1,000 broker/dealers
j. you are trading with primary dealers also
i. Primary Dealer (PrD)defined
1. A designation given by the Federal Reserve System to commercial banks or broker/dealers who meet specific criteria, including capital requirements and participation in Treasury auctions.
2. As of January 2004, there were 23 primary dealers.
3. http://www.newyorkfed.org/aboutthefed/fedpoint/fed02.html
4. Only ones who can submit a competitive bid with the Gov for auctions
5. Individuals can submit a bid but it’s NON-competitive
6. PrD’s state that they will
a. Submit bids to the Gov & others during auctions
i. Others can be Funds, Insurance co., small banks, etc.
b. Create and maintain an orderly market
k. What does it take to be a trader in the Cash Market?
i. Pass Series 7 exam
ii. Have the backing of a Broker/Dealer
4. Structure of the Treasury auction system
a. Treasury announces auctions first Wednesday of every month
b. Auction takes place the following week in Tues, Wed, Thursday
c. Auction results are announced a few minutes after 12:00pm (noon)
d. The morning of the auction
i. Primary Dealers find out what their customers are interested in purchasing at the auction
1. There actually asking what yield they’ll pay on a specific issue
2. Their customers are small banks, insurance co., brokerage houses etc.
3. Primary dealers are considered traders in the Primary Market as opposed to the bulk of traders who trade in the Secondary Market
e. auctions are done in 4 digit yields
i. example: 4.000, or 3.996
ii. soon they’ll go to 6 digits
f. No one can own more than 35% of an auction
g. after the announcement (12:02 or so)
i. the issue becomes WI
1. WI = When Issued
2. WI is traded in yields, not price
ii. Auction results are set on the ‘STOP’
1. Stop is the yield the auction went off at
2. So if the Stop was 3.996 the coupon rate would be set at 3 7/8 because the notes are set in 1/8 increments
h. all auctions are “Dutch” auctions
i. if you are in the winning range then you are awarded the Stop price (yield)
i. distribution of the securities
i. pay attention to this aspect
ii. how much do the dealers own?
iii. how do I recognize what the dealers own?
1. If the yield (after the results are announced) goes down, then it was a good distribution and the cash price will probably continue to go higher
2. Conversely if the yield begins to go down then it was bad distribution and we’d want to sell the cash
Overcoming Information Overload;
How to Quickly Read Through Magazines, Articles, White Papers and More
The best book I ever read on organizing and reading information was, How to Survive the Information Age. It was a step-by-step process that taught me many things, but most importantly how to read periodicals, reports, books, and white papers. This paper will cover some of the things I learned from that book and some of the other books I’ve studied on personal productivity.
Stop.
Forget about all the information that is contained in this document. It’s useless if it overwhelms you, in the least. What we need is a way to survive the onslaught of the information age.
We’ll begin right here, right now, with this document. I promise you that after you’re done reading the next few paragraphs, you’ll never look at information the same way. I’m merely sharing a formula for reading. That’s it. It’s that simple. Lastly, you’ll cut your stress level, when it comes to dealing with documents and information in general.
Get a piece of paper and write down why you are reading this document. What is your purpose?
Maybe you could ask yourself some of these questions to help you find out
i. What do you want to know?
ii. What do you want to get out of it?
iii. Is there a certain question you want answered?
iv. Facts you need to know?
There are six fundamental purposes for reading:
i. to grasp a certain message
ii. to find important details
iii. to answer a specific question
iv. to evaluate what you are reading
v. to apply what you are reading
vi. to be entertained
Answer the question posed and you will cut your reading time a great deal. By answering this simple question. You are automatically filtering out a zillion pieces of information that you do not want to know.
Said a different way
i. Purpose defines reading methods
2. there are 3 basic methods of reading
a. Quick reference (seek specific information on a question posed or concern)
b. Critical (discerning ideas and concepts. Analysis)
c. Pleasure
Let’s get going. Go back to the table of contents (TOC). Read it. Mark the entries that interest you with a pen. If you are viewing this electronically, then use MS Words’ highlighting tool. (It’s the icon right next to the font color icon.) When you are done, come back to this section and go to step 2.
You’ve just cut your reading time by at least 50% or more because you’ve marked what is important to you. This tool is a filter for deleting unwanted data.
The next step is to go to the chapters, you highlighted in the TOC, and do the following:
read the chapter title to reinforce the subject matter
Now read the text and use the highlighter to mark anything that catches your eye.
i. Example: there is a directory location under “Disseminating Information” that shows you where a database is located. Highlight it. You’ll need to use this information later. By highlighting it, you can easily access the information needed at a later time.
save your work if it’s electronic
go to the next topic and repeat steps, a and b
Here’s one more tool you may want to use. Highlight the sections you need to access later in yellow. Highlight the sections that you want to read about later in a different color. That way, you don’t have to keep that information in your head.
That’s all there is to it. You can do this with a magazine, newsletter, white paper etc.
That’s it for now. There are plenty of other tools. But, we need to stick with a few at a time. The idea is to not overwhelm you.
You can move on in this document or come back later.
Ron Fry has a great line of books on personal productivity and more. I quote him often, in this document. Furthermore, many of the methods in this paper are his. Don’t be overwhelmed though.
The tool I provided in the introduction section above called ‘Tools’ is a good tool for reading magazines and white papers. We need a tool for books though. Here’s a list of things that can help you breeze through a book.
get a sheet of paper
Use this paper for the small number of notes you will need to write. Keep the paper in the book at the front after you are done using the book
go to the TOC in the book
Mark the chapters that interest you with a pen. Or, if you don’t want to mark up the book, then get a post it note, write the chapters that interest you on the note and stick the note on the TOC, in the book.
If there’s an ‘Introduction’ or ‘Preface’ in the book, read it. The theme of the book will be in there. Write the theme down on the sheet of paper. If there isn’t a theme, don’t worry, you’ll find it later. When you do come across is or think you have found it, write it down on the top of the paper. (So, you are leaving space at the top of the paper for the theme.)
go to the first chapter you marked
read the title, then, any sub headings in the chapter
read any bullet points
read any diagrams
look at any pictures and read the captions
Look at the end of the chapter and see if there is a chapter summary. Read it.
Now, go back to the beginning of the chapter and read the first line of every paragraph, through the end to the chapter.
You may be done. For some people this is all that is needed to get an understanding what they are trying to say.
If you want to, go back to the beginning of the chapter and read the text.
By following the simple steps above, you’ll understand the theme and concepts of the chapter much better because you’ve already skimmed through it. The ideas will stick much better, also.
The Great Big Book of Personal Productivity:
http://www.careerpress.com/book178.html
The rest of Ron’s books:
http://www.greatjobstore.com/author_search.php?q=Ronald+W.+Fry
Memory techniques can be very helpful when you want to truly commit something to memory.
The three fundamental principles underlying the use of mnemonics are:
Association
Imagination
Location
Association is the method by which you link a thing to be remembered to a method of remembering it. Although we can and will suggest associations to you, your own associations are much better as they reflect the way in which your mind works.
Things can be associated by:
1. being placed on top of the associated object
2. crashing or penetrating into each other
3. merging together
4. wrapping around each other
5. rotating around each other or dancing together
6. being the same color, smell, shape, or feeling
7. etc.
Whatever can be used to link the thing being remembered with the image used to recall it is the association image.
As an example: Linking the number 1 with a goldfish might be done by visualizing a 1-shaped spear being used to spear a goldfish to feed a starving family.
Imagination is used to create the links and associations needed to create effective memory techniques - put simple, imagination is the way in which you use your mind to create the links that have the most meaning for you. Images that I create will have less power and impact for you, because they reflect the way in which we think.
The more strongly you imagine and visualize a situation, the more effectively it will stick in your mind for later recall. Mnemonic imagination can be as violent, vivid, or sensual as you like, as long as it helps you to remember what needs to be remembered.
Location provides you with two things: a coherent context into which information can be placed so that it hangs together, and a way of separating one mnemonic from another: e.g. by setting one mnemonic in one village, I can separate it from a similar mnemonic located in another place.
Location provides context and texture to your mnemonics, and prevents them from being confused with similar mnemonics. For example, by setting one mnemonic with visualizations in the town of Horsham in the UK and another similar mnemonic with images of Manhattan allows us to separate them with no danger of confusion.
So using the three fundamentals of Association, Imagination and Location you can design images that strongly link things with the links between themselves and other things, in a context that allows you to recall those images in a way that does not conflict with other images and associations.
“100 pages of search techniques, tactics and theory by David Novak of the Spire Project. (www.SpireProject.com) This FAQ addresses information literacy; the skills, tools and
theory of information research. Particular attention is paid to the internet as both a reservoir and gateway to information resources.”
Speed-reading is no longer the ‘Evelyn Wood’ courses many of us grew up with. The advent of the computer has helped this skill flourish.
I use Rocket Reader.
What makes this software incredible is its versatility. You can use their suggested reading or you can read your own books. Furthermore you can read web pages with Rocket Reader.
If you’d like an example of how you can use e-books with this program, do the following after you install Rocket Reader
download an e-book from my online library at http://jamesgoulding.com/americanhistory.html
when you get there, click the ‘TOC’ and it will take you to the table of contents
click on any book that interests you and it will come up on your screen
save the file as .txt, on your computer and you are in business
or just click the link below and it will download Thomas Paines, Common Sense
http://jamesgoulding.com/americanhistoryebooks/Revolution/paine/commonsense.txt
Once you have Rocket Reader loaded, it gives you the option to pick the material you want. The only requirement is that it’s in .txt format. Experiment a little bit with it and you’ll be off to the races.
In one month my speed increased from 200 wpm to 450 wpm. (Ok…I’ve been stuck there for a while, but I’ll break that barrier soon.)
You’ve heard about them. They’re everywhere. But what is the best way to use them? What are the short cuts that can be used to get the best results, and which engine is the best?
Here’s a brief synopsis of the major search engines as written in, The Spire Project.
Altavista (http://altavista.com ) includes a very large, fast search
engine. It allows for Basic Boolean AND + NOT - OR | Proximity " " ~
(near - within 10 words of each other.) Several Fields: title:” Spire
Project" domain: gov url:edu link: cn.net.au and Truncation/Wildcard (*)
Of import, Capitals matter with Altavista.
All-the-Web (http://www.alltheweb.com ) is important because it is large
- really large - with a flexible search facility. Allows Partial
Boolean + - Simple Proximity " " and Several Fields a title field
search normal.title:spire url field url.all:.au link text and link url
fields normal.atext:spire link.all:cn.net.au All-the-Web is not case
sensitive. The same database supporting All-the-Web supports Lycos.
Inktomi (via http://hotbot.lycos.com ) provides its substantial web
directory through other companies, in this case, HotBot. also allows
searches by region, by date, and more.
Debriefing (http://www.debriefing.com ) is our meta-search engine of
choice. Use this to find names & named websites. Accepts Partial
Boolean + - Simple Proximity " ". Capitals matter.
Google(http://www.google.com/ ) is a new style of search engine which
ranks sites with more care and concern. This works well for sites you
know a little about in advance. Unfortunately, has no useful field
searches. Allows Partial Boolean + - Simple Proximity " ".
Unfortunately, No Truncation not even for plurals!
When searching for a topic with precise descriptive terms, use a broad
search engines. Always place the Boolean +symbol before each search
word (like this: +word1 +word2) to insist all words appear in the
results. Quotes keep words together ("word1 word2"). These two simple
steps dramatically improve results. Keep adding words and search limits
until the number of hits is reasonable.
For more global search engines, there are numerous lists to consider
like the W3 Search Engines page at the University of Geneva
(http://cui.unige.ch/meta-index.html#INF ) and the Industry Research
Desk (http://www.rbbi.com/links/sengine.htm ).
If you know something of the destination already, like a title or
company name or full name, try using a search tool that excels in
finding named websites. There should be little difficulty in finding
such sites with either Google or a Meta-Search engine, but don't get
excited and use these on other occasions.
When searching for information that lends itself to a particular
category or topic, start with resources which group information in
categories. With few exceptions, these resources index websites, not
webpages. Also, keep your search words simple as these are small
databases.
Yahoo (http://yahoo.com ) is the largest of this type of directory tree;
the definitive site. Accepts Partial Boolean + - Simple Proximity " "
Truncation * and Several Field t: (for titles) u: (for urls) and a
date field through a form.
The Open Directory Project (http://dmoz.org ) is a Netscape effort to,
presumably, mute the strength of Yahoo. It is very good, and very
similar to Yahoo.
Looksmart (http://www.looksmart.com ) is another significant directory.
For an alternative, try the World Wide Web Virtual Library: Subject
Catalogue (http://vlib.org/Overview.html ), a distributed network of
subject lists, not nearly as dominant as Yahoo, but far more
"scholarly" shall we say. This virtual directory has been around many
years, previously famous from www.w3.org.
(Table of Contents for this article)
(Table of Contents for web page)
Summary of Comments on Market Profile Handbook.pdf
Four: Steidlmayer'sTandemTimeFrameConcept
Five: Short-and Long-term Activity is Defined by Behavior
Six: Both the Short-term Trader and the Longer-term Trader Have a Role to Play in Facilitating Trade
Seven: Price Can Only Be Above, Below or Within Value
READING THE MARKET PROFILE ®GRAPHIC
INTRODUCTION 2
THE MARKET'S ORGANIZATIONAL STRUCTURE 5
The Conceptual Framework 5
Six-part Study Guide
Part I discusses how to read the daily Market Profile ®graphic.
In Part II explains how longer-term trends can be monitored with daily data.
Part III discusses why the fundamental perception of value is at the heart of every market decision.
Part IV shows how daily and long-term market activity distributes over time.
Part V tells you how to combine market activity and the perception of value in order to analyze a developing trading session.
Part VI analyzes Liquidity Data Bank ®volume in order to gauge strength or weakness in the market.
Traders and investors are still basically either short- or long-term market participants.
And their behavior is still determined by their view of value. And value is still at the heart of market activity.
It is the interaction between short- and long-term market participants that distributes trading volume in a bell-shaped curve. The back-and-forth movement reflects the continual tug-of war between long-term traders and short-term market participants.
The volume of everything distributes around a mean over time. Why should trading volume be different?
The market's ultimate common denominator is a balanced distribution-in other words, the bell shaped curve. (see next page)
When market activity forms a bell shaped profile, a segment of market action is complete. Stated another way, a balanced distribution is the long-term framework to which you relate short-term moves in individual sessions.
Probably the most important change since Steidlmayer first introduced the Market Profile concept is that the day, as a definitive market segment with a definite beginning and end, is outdated. In 24-hour markets, you're working with a timeless continuum. This means that a new beginning can occur at any time.
Despite this change, however, the market's basic imbalance-balance
behavior pattern is still the same.
As you go through Parts I and II, however, keep in mind that you're going to expand what happens in a single session to 24-hour markets. This means that certain ideas-the initial balance and the time/price opportunity (TPO) count, for example-are going to become less important.
The market has only a finite number of behavior patterns and that the finite number is universal from market to market.
[MP] knowledge can be applied in all markets.
Market Profile data involves grasping principles-not just memorizing rules.
Market Profile is a decision tool not a trading system.
What Market Profile can do is help you to understand the present.
The purpose of the marketplace is to facilitate trade. As the price moves up, it brings in more buying or, as the price moves down, it brings in more selling.
The marketplace facilitates trade with the dual auction process.
Basically, the market auctions up until there are no more buyers. Then it reverses and moves down until there are no more sellers.
The end of an up auction is the beginning of a down auction, etc.
All market activity occurs within this broad framework-with the market moving up to shut off buying and down to shut off selling.
Getting a little more specific, we can say that the market begins, moves directionally, and advertises for an opposite response to shut off the directional move
Say the market moves up directionally and the up move brings in selling. The selling is an opposite response which one stops the up move-in other words, shuts off the buying-and two causes the market to reverse and move down. The result: the up auction ends and a down auction begins.
Now let's say the market moves up and advertises for selling but doesn't get any. Instead, it brings in more buying. Therefore, the market has to move higher to bring in an opposite response. The result: the up auction continues.
At bottom, that's what you're always looking for : continuation or change.
Now if we get even more specific, we can say that a directional move establishes parameters that contain the auction's price range, an unfair low at the low end and an unfair high at the high end.
THREE RELATED PRICES
The unfair low and the unfair high are excesses.
Once the market defines a range with excesses at each end, it negotiates within that range to establish value. The market trades between the established excesses until it either trades above the high excess or below the low one.
Stop the market at any point in time and you'll see these three reference points:
unfair low
unfair high
value somewhere in between
These three price areas define the negotiating process-the method the marketplace uses to facilitate trade.
Let's look at a bar chart of the Dow Jones from April 1987 to the end of October 1989
The unfair high on this chart (point A) was established in August 1987, the unfair low (point B) in October 1987. You can see that these parameters were containing the market's long-term range at that time. (A new unfair high at the 2900 level was established in June 1990.)
Once the parameters at A and B were established, the market negotiated between the two excesses to develop value. The negotiating process moved value up gradually from the unfair low to the unfair high. Value reached the unfair high (point C) on October 13, 1989.
Because of the perception of value at that time, the market couldn't trade above the high parameter and it reversed. The result: the excess established in 1987 continued to contain the range on the upside until June 1990. In other words, at the end of October 1989, the market attempted to take out the unfair high.
However, when the United Airlines deal collapsed and seemed to indicate a possible end to leveraged buyouts, market participants lost confidence and the market reversed.
To facilitate trade in order to distribute goods and services, the market moves from imbalance to balance to imbalance and back again. It uses this behavior pattern in a single session and in longer term trends or auctions.
The market is rotating because it has found a fair price around which it can distribute.
If the market is imbalanced, either buying or selling is predominant. The market is moving higher or lower in order to find an opposite fair price around which it can distribute.
In brief, a balanced market has found a fair price. An imbalanced market is seeking a fair price.
The market is moving directionally because it is seeking Buying below.
This is simply another demand. Buyers demand and sellers supply. The market is either in equilibrium between buyers and sellers or it is working toward that
equilibrium.
First let's define Steidlmayer's use of the term "time frame'.' Time frames are forcing points-in other words, points in time that force a decision. These points can be imposed by the market (i.e., the close) or by something in your personal situation (i.e., you have the right to an option that expires in two months).
To explain, say the market has been trading for three hours and the close is coming up in 45 minutes. If you don't want to carry the position overnight, your time is running out. The close is forcing you to make a decision within a relatively short-term time frame. You're a short-term trader in this situation because the forcing point is only 45 minutes away. You're a longer-term trader in the second situation because your option doesn't expire for two months.
The forcing point is two months off. Consequently, you have a longer-term time frame in which you can operate without having to make a decision.
With this insight, Steidlmayer was able to divide all market activity into two categories:
short-term
longer-term
He calls short-term activity day time frame activity.
He calls longer-term activity other time frame activity.
His tandem time frame concept visualizes short-term or day time frame activity on one side of the tandem and all longer-term activity on the other side (hence the name "other time frame".
Since long- and short-term activity exist simultaneously in the marketplace, you have to be able to separate one kind of activity from the other. We're going to separate one from the other with behavior.
The short-term trader's behavior characteristic is his desire for a fair price. The best he can do is a fair price because he has to trade today. Since a fair price is acceptable to both buyers and sellers, short-term buyers and sellers do trade with each other at the same price at the same time.
The longer-term trader's behavior characteristic is his desire for an advantageous price. He can wait for an advantageous price because he doesn't have to trade today.
Since longer-term buyers' and sellers' objectives are different, they do not trade with each other at the same price at the same time. (In a strict technical sense, a longer-term buyer with a 10 to 15-day time frame may trade with a longer-term seller with a two- to three-day time frame. But these traders are a small part of the total longer-term group. Steidlmayer treats longer-term traders as a single entity because he is concentrating on the active longer-term trader who drives the market and affects range development.)
Longer-term buyers want to buy low; longer-term sellers want to sell high. Therefore, the same price can't be advantageous for both at the same time.
That's why you can know exactly who (buyer or seller) is doing what at any time in the day's range.
How do we know it's the longer-term trader who is active at advantageous prices?
Only traders with a longer-term time frame-in other words, those who don't have to trade today-can take a chance on making their trade in an area where the market doesn't spend much time. If you have to trade today, you can't count on being able to enter your trade in a low volume, basically unfair area. The high volume area where the market spends most of its time provides the liquidity you need.
This role grows out of their behavior. Since the short-term trader is seeking a fair price, his role is to find a price area where two-sided trade can occur.
Steidlmayer calls this an
initial balance area.
(Currently, it seems to take one hour in CBOT grain futures to find an area where two sided trade can occur. Before CBOT financial futures sessions were lengthened, it also seemed to take an hour in those contracts to find an initial balance area. Now that the sessions are longer, however, finding the initial balance in CBOT financials seems to take one hour and 40 minutes.)
As the financial contracts underscore, initial balance parameters can change. Therefore, the important thing is to understand the initial balance concept-the amount of time it takes the shorter-term trader to find an area where two-sided trade can occur. Then you can determine these parameters in any market.
Earlier, we said the role of the short-term trader was to find an area that market participants would consider fair. We also said that the market opens and moves directionally in order to establish parameters to contain the range. If the unfair high and the unfair low established in the initial balance period hold throughout the session, the shorter-term trader is in control.
If, on the other hand, the longer-term trader enters the market with enough volume, he can disturb the initial balance and extend the range-establishing a new high or low parameter.
This takes us to the longer-term trader's role in facilitating trade: his role is to move the market directionally-in other words, to extend the range up or down.
We're going to monitor the activity level of the longer-term trader as he responds to prices above, below or within value in order to anticipate whether the market will move up, down or sideways. Our focus is always on what the longer-term trader is doing because, in pursuing his interests, he is responsible for the way the day's range develops and for the length of time a longer-term trend
lasts.
We'll discuss the longer-term trader's influence on trend development in Part II. In this section, we're going to consider his influence on the way a single session develops.
We're going to examine the principles we've just discussed in relatively uncomplicated sessions so that you can see how they work. We believe that once you understand how these concepts work in a single session, you'll be able to apply them to longer-term trends and then to 24-hour markets.
Keep in mind, though, that certain ideas such as the initial balance, the TPO count and the kinds of range development are going to become less important. These ideas will continue to contribute to your overall understanding, but they're going to become part of your background knowledge.
(Table of Contents for Market Profile)
(Table of Contents for web page)
There’s a great book on Technical Analysis, by Martin J. Pring, Technical Analysis Explained. I have the 4th Edition, which was printed in 2002. This paper will cover some of the basics that Pring writes about in his book.
I want to know what the formula is. I don’t care what I’m working on. It could be writing a book, writing a paper, a business plan, building a spreadsheet, photography, music, or what-have-you. There’s a basic formula for everything and Technical Analysis (TA) is no exception.
Once TA is broken down into categories it’s much easier to manage.
There are 3 categories to TA[i]
Sentiment
Flow-of-Funds
Market Structure indicators
This indicator tracks opinion. That opinion is coming from insiders, fund managers (advisory services), individual investors, and floor traders (pros).
Assumption:
Different groups of investors are consistent in their actions at major market turning points.[ii]
Examples
This is a branch of TA used on the stock market. However, I think of this Technical Indicator (TI) as the Commitment of Traders Report (COT) released by the CFTC weekly.
“The COT reports provide a breakdown of each Tuesday's open interest for markets in which 20 or more traders hold positions equal to or above the reporting levels established by the CFTC. The weekly reports for Futures-Only Commitments of Traders and for Futures-and-Options-Combined Commitments of Traders are released every Friday at 3:30 p.m. Eastern time.”[v]
The report is broken down into type of trader. There are 2 categories by the CFTC.
“Commercial and Non-commercial Traders – When an individual reportable trader is identified to the Commission, the trader is classified either as "commercial" or "non-commercial." All of a trader's reported futures positions in a commodity are classified as commercial if the trader uses futures contracts in that particular commodity for hedging as defined in the Commission's regulations (1.3(z)). A trading entity generally gets classified as a "commercial" by filing a statement with the Commission (on CFTC Form 40) that it is commercially "...engaged in business activities hedged by the use of the futures or option markets." In order to ensure that traders are classified with accuracy and consistency, the Commission staff may exercise judgment in re-classifying a trader if it has additional information about the trader’s use of the markets.” [vi]
“A trader may be classified as a commercial in some commodities and as a non-commercial in other commodities. A single trading entity cannot be classified as both a commercial and non-commercial in the same commodity. Nonetheless, a multi-functional organization that has more than one trading entity may have each trading entity classified separately in a commodity. For example, a financial organization trading in financial futures may have a banking entity whose positions are classified as commercial and have a separate money-management entity whose positions are classified as non-commercial.” [vii]
However, there’s a better way to categorize the traders. Breaking them down into 3 categories, not 2.
Visit this web site, commitmentsoftraders.com and you will see that George Slezak has broken the traders down into
Small Specs
Large Specs
Commercials (Hedgers)
There’s an example of his work below
The idea is to fade small specs and go with large specs. This is arguable. There’s been much written about this formula. However, the COT is a powerful tool. I’ll be writing a separate paper on this specific subject, shortly.
This is the main theme in Pring’s book and the main concern for us, commodity traders.
“These indicators monitor the trend of various price indexes [anything you can chart],…cycles, volume, and so on in order to evaluate the health of the prevailing trend.”[viii]
Indicators include
Moving averages
Peak and trough analysis
Price patterns
Trendlines etc…[ix]
“Most of the time, price and internal measures, such as market breadth, momentum, and volume, rise and fall together, but towards the end of the market movements, the paths of many of these indicators diverge from the price. Such divergences offer signs of technical deterioration during advances and technical strength following declines.”[x]
“Since the technical approach is based on the theory that the price is a reflection of mass psychology, or crowd in action, it attempts to forecast future price movements on the assumption that crowd psychology moves between panic, fear, and pessimism on one hand and confidence, excessive optimism, and greed on the other.[xi]
“…the art of technical analysis is concerned with identifying these changes at an early phase…”[xii]
[i] Technical Analysis Explained, Pring. p3
[ii] ibid, p3
[iii] ibid, p4
[iv] ibid, p4
[vi] ibid
[vii] ibid
[viii] Technical Analysis Explained, Pring. p5
[ix] ibid p5
[x] ibid p5
[xi] Technical Analysis Explained, Pring. p5
[xii] Technical Analysis Explained, Pring. p5
Trade Day of Month (TDOM)
When a golfer loses his swing, he looks at video tapes of when he was winning. It’s crucial to have a record of that swing when he was striping the ball. What would he do if he didn’t have that video?
What would a trader do if he/she didn’t have a record of their trades when they were trading badly? They’d be out of luck. Traders should have a record of all their trades in excel so they can be analyzed.
TDOM is a very important concept because it allows you to track when you trade well and when you don’t. TDOM is a video tape of your trading.
TDOM eliminates weekends and counts each business day as 1.
Example below: TDOM represented by (1)
|
Sun |
Mon |
Tues |
Wed |
Thur |
Fri |
Sat |
|
1 |
2(1) |
3(2) |
4(3) |
5(4) |
6(5) |
7 |
|
8 |
9(6) |
10(7) |
11(8) |
12(9) |
13(10) |
14 |
|
15 |
16(11) |
17(12) |
18(13) |
19(14) |
20(15) |
21 |
|
22 |
23(16) |
24(17) |
25(18) |
26(19) |
27(20) |
28 |
|
29 |
30(21) |
31(22) |
|
|
|
|
23 is possible. It only happens twice a year.
By recording how you trade on all TDOMs you can spot certain times of the month where you always trade badly. Then, this can be changed by figuring out why it happens.
In most cases it’s tied to an economic release.
Example of cumulative TDOMs over the course of a 1 ½ years, follows:
|
|
|
|
|
|
Rank Average worst to best for TDOM |
||
|
TDOM |
P&L |
Total TDOMs traded |
Average per TDOM |
|
Ranking |
Average per TDOM |
TDOM |
|
1 |
$919 |
17 |
$54.04 |
|
1 |
($54.02) |
20 |
|
2 |
$1,356 |
17 |
$79.79 |
|
2 |
$54.04 |
1 |
|
3 |
$5,990 |
17 |
$352.34 |
|
3 |
$63.26 |
18 |
|
4 |
$5,502 |
17 |
$323.62 |
|
4 |
$75.28 |
16 |
|
5 |
$3,363 |
18 |
$186.85 |
|
5 |
$79.79 |
2 |
|
6 |
$2,433 |
16 |
$152.07 |
|
6 |
$88.20 |
17 |
|
7 |
$4,370 |
18 |
$242.77 |
|
7 |
$104.69 |
12 |
|
8 |
$2,837 |
19 |
$149.30 |
|
8 |
$112.91 |
11 |
|
9 |
$1,903 |
16 |
$118.92 |
|
9 |
$118.92 |
9 |
|
10 |
$6,293 |
18 |
$349.59 |
|
10 |
$149.30 |
8 |
|
11 |
$1,807 |
16 |
$112.91 |
|
11 |
$152.07 |
6 |
|
12 |
$1,885 |
18 |
$104.69 |
|
12 |
$171.62 |
14 |
|
13 |
$5,459 |
19 |
$287.32 |
|
13 |
$186.85 |
5 |
|
14 |
$3,261 |
19 |
$171.62 |
|
14 |
$242.77 |
7 |
|
15 |
$6,612 |
19 |
$347.97 |
|
15 |
$244.14 |
21 |
|
16 |
$1,355 |
18 |
$75.28 |
|
16 |
$262.95 |
19 |
|
17 |
$1,588 |
18 |
$88.20 |
|
17 |
$287.32 |
13 |
|
18 |
$1,139 |
18 |
$63.26 |
|
18 |
$323.62 |
4 |
|
19 |
$3,944 |
15 |
$262.95 |
|
19 |
$347.97 |
15 |
|
20 |
($864) |
16 |
($54.02) |
|
20 |
$349.59 |
10 |
|
21 |
$3,418 |
14 |
$244.14 |
|
21 |
$352.34 |
3 |
The first set of four columns collect data from within the spreadsheet. As you put your P&L in the spreadsheet on a daily basis, there's one page that collects the data and adds it up. The example above is a snapshot from a spreadsheet that collected three years worth of an actual traders daily P&L.
The first column, titled TDOM is the Trading day of the Month. The second column is the total P&L for every TDOM this trader has traded in the last three years. The third column is the number of times he's traded that TDOM and the fourth column is the average profit for that particular TDOM.
The three columns on the right are a ranking of the TDOMs, 1-21. The first column is the ranking from worst (1) to best (21). The second column is the average profit for that particular TDOM, taken from the fourth column from the right. The last column is the actual TDOM.
The red highlights the poorest trading days of the month. The blue highlights the best. Using the example, and pretending that it's your P&L, go back to those TDOMs and find out why you are losing money. The last column on the right, titled TDOM, shows the 20th, 1st, 18th ,and 16th TDOM to be the worst. Correlate those to economic releases. Or, look at the pattern for TDOM 1, on the daily chart, for your commodity. Many commodities have a tendency to do the same thing on TDOM. 1. TDOM 20 usually correlates with certain economic releases. Maybe it's Michigan's releases? Maybe it's some other release? The point is that you have a way to nail it down.
You can download an example spreadsheet here: TDOM. (click to download xls file)
Numbers_2003-2005.xls (Click to download)
What about tracking economic numbers / releases? Download the excel file above to see how it's done.
Here’s a detailed account of how I plan for any important economic release.
Establish the same thing that I establish for all my trades, which is
Why am I getting into the market?
Where am I getting into the market?
Where is my profit target?
Where is my stop loss?
Answer the questions above (I’ll use an example of a Retail Sales release that I actually traded.)
“Why am I getting into the market?”
Retail sales may offer an opportunity to trade
Then, evaluate Retail Sales (RS) release
Evaluating the RS release:
What is the overall bias in the market?
Daily chart is bullish
30 min chart is bullish
What’s the consensus on the street for RS?
Total = -0.1
Ex = +0.2
Is there a bias to the number? Meaning, is there a whisper number or does this number have a tendency to do something different?
Whisper number? = No
Tendency to do something different? = Yes it does
That tendency is that the ex-auto component number and the total number are usually underestimated by the street (consensus)
That tendency tells me something. It sets up a trade.
If traders see the number come out better than expected then they will sell it off expecting it to go lower. Since the overall market is set up in a bullish pattern on the daily and weekly charts, and the tendency is to already underestimate the number then a higher number is not really bearish, is it. Therefore I need to be a buyer, below the market.
I have established a reason to trade answering the first question “Why am I getting into the market?”
“Where am I getting into the market?”
Does the last release or prior release of RS tell me anything about where I can get in? Yes
The August release showed that the headline number came out bearish (above consensus) and the 10 yr note futures (TY) broke 5 tics, stopped and ground higher.
Where are Tys trading this morning?
Answer these questions
Range: 112’16 to 112’22+
Open: 112’17+
Yesterdays close: 112’15
Take last months action off of the RS number and subtract it from where we are trading a few minutes before the current RS release.
Current price = 112’21
Last months action sold off 5 tics
112’21 – 5 tics = 112’16
I want to buy it somewhere in here
Lastly, look at yesterdays close and today’s open
Open: 112’17+
Yesterdays close: 112’15+
Now look at where I might want to be a buyer
112’16
The TY has a tendency to make a new low by one tic and reverse. Since we are trading a number, and one that I expect to not be very volatile, then I’ll look for the T-Notes to make a new low by 2 , 2 ½ tics.
Taking all this into account,
Range: 112’16 to 112’22+
Open: 112’17+
Yesterdays close: 112’15+
Where I want to buy: 112’16
Tendency to make a new low by 2 tics
I have decided that my best entry point is 14+
Therefore I’ve answered the question, “Where am I getting into the market.”
“Where’s my profit target?”
There’s no resistance above 112’22+ until 113’03+ (last weeks high)
Develop a ‘what if’ scenario:
If traders come in and sell the number off, thinking its bearish, then they’ll all get short in the hole and have to buy it back. We’ll rally through 22+ easily, and test the old high of 113’03+.
However, I know from experience that there probably isn’t a reason to take out the old high today, because
there’s other numbers to be released this week
Oil is topping out @45 bbl
the stocks are near their highs.
Therefore, I’ll take a profit well under the old highs. TY’s have a tendency to stop at 28+ and 29 on rallies and pull back to the old resistance, (112’22+).
I’m out at 112’28.
“Where’s my stop?”
The TYs show major support all the way down to 112’04.
This is evident from the daily and weekly charts
Since I have a potential upside of 14 or so tics, then I’ll risk the 112’04 level on the downside and sell 112’03s on a stop.
Conclusion: I’ve built a trade that defines 4 things.
Why am I getting into the market? Retail Sales has offered an opportunity to trade.
Where am I getting into the market? 112’14+
Where is my profit target? 112’28
Where is my stop loss? 112’03
So what did we do this morning?
We decided to enter two orders
Buy 1 @ 112’15+
Buy 1 @ 112’13+
This is an average of 14+ entry
The numbers came out different than we expected, but we decided to stay with the 15+ bid, and pull the 13+ bid.
We did this because the release seemed to tell us that there was a lot for sale after an apparently bullish release and 15+ looked like a good buy after all the early buyers got out of their longs.
We moved the sale down to 112’21 because that’s where we were trading on the release and TY’s looked like they’d test that level.
We moved are stop to 112’12+ because if we got there, we’d likely see 112-08+.
We stayed in the trade because we had the 30 min MACD bullish.
When that MACD crossed to negative territory we got out @ 112’16
We did a lot of work and made only a ½ tic.
But, that work goes a long way into the future when other numbers are released. We get better and better at building a plan and we will catch a great trade 2 to 3 times a month by doing this.
DOW 35k (Under Construction)
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