my journal 2

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Old Nov 11, 2010, 6:50pm   #1741
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page 89, Lunar and Solar Phenomena

Yamato started this thread
Do lunar and solar events influence the markets? Is it possible for an entry model to
capitalize on the price movements induced by such influences? The moon’s role in
the instigation of tides is undisputed. Phases of the moon correlate with rainfall and
with certain biological rhythms, and they influence when farmers plant crops. Solar
phenomena, such as solar flares and sunspots, are also known to impact events on
earth. During periods of high solar activity, magnetic storms occur that can disrupt
power distribution systems, causing serious blackouts. To assume that solar and
lunar phenomena influence the markets is not at all unreasonable; but how might
these influences be used to generate predictive, countertrend entries?
Consider the lunar rhythm: It is not hard to define a model that enters the market
a specified number of days before or after either the full or new moon. The same
applies to solar activity: An entry can be signaled when the sunspot count rises above
some threshold or falls below another threshold. Alternatively, moving averages of
solar activity can be computed and crossovers of these moving averages used to time
market entries. Lunar cycles, sunspots, and other planetary rhythms may have a real,
albeit small, impact on the markets, an impact that might be profitable with a properly
constructed entry model. Whether lunar and solar phenomena actually affect the
markets sufficiently to be taken advantage of by an astute trader is a question for an
empirical investigation, such as that reported in Chapter 9.
I am not likely to read this chapter. To me it sounds like Nostradamus prophecies. These people are astronomers - they are crazy about this stuff. I am not falling for this stuff. It would be as if I created as system based on vito.

page 90, Cycles and Rhythms

Chapter 10 explores cycles and rhythms as a means of timing entries into the market.
The idea behind the use of cycles to time the market is fundamentally simple:
Extrapolate observed cycles into the future, and endeavor to buy the cycle lows
and sell short the cycle highs. If the cycles are sufficiently persistent and accurately
extrapolated, excellent countertrend entries should be the result. If not, the
entries are likely to be poor.
For a very long time, traders have engaged in visual cycle analysis using
charts, drawing tools, and, more recently, charting programs. Although cycles can
be analyzed visually, it is not very difficult to implement cycle recognition and
analysis algorithms in software. Many kinds of algorithms are useful in cycle
analysis-everything from counting the bars between tops or bottoms, to fast
Fourier transforms (FITS) and maximum entropy spectral analyses (MESAS).
Getting such algorithms to work well, however, can be quite a challenge; but having
reliable software for cycle analysis makes it possible to build objective, cyclebased
entry models and to test them on historical data using a trading simulator.
I thought cycles and seasonality were the same thing, but here it seems they are not. What I really did with many of my systems is use what they call "cycles". I'll look at seasonality a bit longer and may drop that chapter, too.

Whether detected visually or by some mathematical algorithm, market
cycles come in many forms. Some cycles are exogenous, i.e., induced by external
phenomena, whether natural or cultural. Seasonal rhythms, anniversary effects,
and cycles tied to periodic events (e.g., presidential elections and earnings reports)
fall into the exogenous category: these cycles are best analyzed with methods that
take the timing of the driving events into account. Other cycles are endogenous;
i.e., their external driving forces are not apparent, and nothing other than price data
is needed to analyze them. The 3-day cycle occasionally observed in the S&P 500
is an example of an endogenous cycle, as is an S-minute cycle observed by the
authors in S&P 500 tick data. Programs based on band-pass filters (Katz and
McCormick, May 1997) and maximum entropy (e.g., MESA96 and TradeCycles)
are good at finding endogenous cycles.
We have already discussed the exogenous seasonal cycles, as well as lunar
and solar rhythms. In Chapter 10, endogenous cycles are explored using a sophisticated
wavelet-based, band-pass filter model.
That's right: most of my systems are based on endogenous cycles. I do not use exogenous cycles at all (unless you consider the time of the day and the month of the year an exogenous thing). Ok, I will read the "seasonality" chapter as well, because it's still not clear whether it is what I need or not. So recap on chapters to read: 6, 8, 10, 11, 12.

page 91, Neural Networks

As discussed in Chapter 11, neural network technology is a form of artificial intelligence
(or AI) that arose from endeavors to emulate the kind of information processing
and decision making that occurs in living organisms. Neural networks (or “nets”)
are components that learn and that are useful for pattern recognition, classification,
and prediction. They can cope with probability estimates in uncertain situations and
with “fuzzy” patterns, i.e., those recognizable by eye but difficult to define using precise
rules. Nets can be used to directly detect turning points or forecast price changes,
in an effort to obtain good, predictive, countertrend entry models. They can also vet
entry signals generated by other models. In addition, neural network technology can
help integrate information from both endogenous sources, such as past prices, and
exogenous sources, such as sentiment da@ seasonal data, and intermarket variables,
in a way that benefits the trader. Neural networks can even be trained to recognize
visually detected patterns in charts, and then serve as pattern-recognition blocks within
traditional rule-based systems (Katz and McCormick, November 1997).
Wow, this really sounds perfect for many chart patterns: "those recognizable by eye but difficult to define using precise rules". So maybe I should read that chapter as well. And I will in fact. It's already on my list. Damn, I wish I could use neural networks correctly. I really need to quit my job to do this stuff properly.

Genetically Evolved Entry Rules

Chapter 12 elaborates a study (Katz and McCormick, December 1996) demonstrating
that genetic evolution can be used to create stable and profitable rule-based
entry models. The process involves putting together a set of model
fragments, or “rule templates” and allowing a genetic algorithm (GA) to combine
and complete these fragments to achieve profitable entries. The way the methodology
can discover surprising combinations of rules that consider both endogenous
and exogenous variables, traditional indicators, and even nontraditional
elements (e.g., neural networks) in making high-performance entry decisions will
be examined. Evolutionary model building is one of the most advanced, cuttingedge,
and unusual techniques available to the trading system developer.
Yeah, I am now familiar with this stuff, thanks to RiskOptimizer. I must try it again before moving on to the rest of this section. For today I am done with reading. Now I will work more on this concept:
The process involves putting together a set of model fragments, or “rule templates” and allowing a genetic algorithm (GA) to combine and complete these fragments to achieve profitable entries.
I will try to get RiskOptimizer to do more than just Portfolio Optimization. It will take hours and days to exploit this beautiful little program.

When I'll come back, I'll have to resume from here:

Everything is falling apart. The only thing I have right now is this book and the systems and the investors. No capital, just my little debt with my own bank. No help from my parents who don't believe in my trading. I have to keep reading. Just in case things turn out for the best. Right now things are sucking badly. Yes, the systems did make some money, but a ridiculous amount for the many months we've been trading them (5 months).

I'll just keep working, as I always do, whether I am doing good or not.


To study entries on their own, and to do so in a way that permits valid comparisons
of different strategies, it is essential to implement a srandardized exit that will be
held constant across various tests; this is an aspect of the scientific method that
was discussed earlier. The scientific method involves an effort to hold everything,
except that which is under study, constant in order to obtain reliable information
about the element being manipulated.
Yes, I have that already. I use time exits on every system.

The standardized exit, used for testing entry models in the following chapters,
incorporates the three functions necessary in any exit model: getting out with a profit
when the market moves sufficiently in the trade’s favor, getting out with a limited
loss when the market moves against the trade, and getting out from a languishing
market after a limited time to conserve margin and reduce exposure. The standard
exit is realized using a combination of a stop order, a limit order, and a market order.
Hmm, too much crap for my liking.

Stop and limit orders are placed when a trade is entered. If either order is
filled within a specified interval, the trade is complete, the remaining order is canceled,
and no additional orders are placed. If, after the allotted interval, neither the
stop nor limit orders are filled, they are canceled and a market order is placed to
force an immediate exit from the trade. The stop order, called a money management
stop, serves to close out a losing position with a small, manageable loss. Taking a
profit is accomplished with the limit order, also called a profit target. Positions that
go nowhere are closed out by the market order. More elaborate exit strategies are
discussed in “Part III: The Study of Exits,” where the entries are standardized.
Too complex for me. I don't wanna do it like this. Too many variables.

Let's hope that now my father won't come home and talk to me about his impending death again. Ok, he just showed up, and I didn't let him talk. I told him about the RiskOptimizer, boring him to death as usual. But he didn't die, because he said he has to die in about 10 years. I don't know why I talk to him anymore. I guess I talked to him because I didn't want him to talk to me. Yeah, that's all it was. I was in the kitchen when he showed up and I could not run fast enough to my room and lock myself in. The disgust from talking to him and seeing him bored will vanish in a few hours, but I didn't have to hear his death talk one more time.

Money management stops and profit target limits for the standardized exits
are computed using volatility units, rather than fixed dollar amounts, so that they
will have reasonably consistent meaning across eras and markets. Because, e.g., a
$1,000 stop would be considered tight on today’s S&P 500 (yet loose on wheat),
fixed-dollar-amount stops cannot be used when different eras and markets are
being studied. Volatility units are like standard deviations, providing a uniform
scale of measurement. A stop, placed a certain number of volatility units away
from the current price, will have a consistent probability of being triggered in a
given amount of time, regardless of the market. Use of standardized measures permits
meaningful comparisons across markets and times.
Yes, I use this method for entries, on my volatility measures.

Last edited by Yamato; Nov 11, 2010 at 9:51pm.
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Old Nov 11, 2010, 9:53pm   #1742
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Yamato started this thread
Just as exits must be held constant across entry models, risk and reward potential,
as determined by dollar volatility (different from raw volatility, mentioned above),
must be equalized across markets and eras. This is done by adjusting the number
of contracts traded. Equalization of risk and reward potential is important because
it makes it easier to compare the performance of different entry methods over
commodities and time periods.
Oh... no point in doing that. You could just use market points, and forget about the rest. After all, we're just using the data from the last 10 years or so. At least I am. No need to worry about this. But I'll read this stuff nonetheless.

Equalization is essential for portfolio simulations,
where each market should contribute in roughly equal measure to the performance
of the whole portfolio. The issue of dollar volatility equalization arises because
some markets move significantly more in dollars per unit time than others. Most
traders are aware that markets vary greatly in size, as reflected in differing margin
requirements, as well as in dollar volatility. The S&P 500, for example, is recognized
as a “‘big” contract, wheat as a “small” one; many contracts of wheat would
have to be traded to achieve the same bang as a single S&P 500 contract. Table II-l
shows, broken down by year and market, the dollar volatility of a single contract
and the number of contracts that would have to be traded to equal the dollar
volatility of 10 new S&P 500 contracts at the end of 1998.
Yeah yeah, I know all this. Useless. Get to the point.

For the current studies, the average daily volatility is computed by taking a
200-day moving average of the absolute value of the difference between the current
close and the previous one. The average daily volatility is then multiplied by the dollar
value of a point, yielding the desired average daily dollar volatility. The dollar
value of a point can be obtained by dividing the dollar value of a tick (a market’s
minimum move) by the size of a tick (as a decimal number). For the new S&P 500
contract, this works out to a value of $250 per point (tick value/tick size = $25/0.10).
To obtain the number of contracts of a target market that would have to be traded to
equal the dollar volatility of IO new S&P 500 contracts on 12/31/1998, the dollar
volatility of the new S&P 500 is divided by the dollar volatility of the target market;
the result is multiplied by 10 and rounded to the nearest positive integer.
So let me think... what about our portfolio of systems and futures. Is it affected by this stuff? Nope, because for the most part we're trading stuff that didn't move that much in the last 10 years. The currencies did not, for sure. The stock indexes did not either. I can forget this concern.

All the simulations reported in this book assume that trading always involves
the same amount of dollar volatility. There is no compounding; trade size is not
increased with growth in account equity. Equity curves, therefore, reflect returns
from an almost constant investment in terms of risk exposure. A constant-investment
model avoids the serious problems that arise when a compounded-investment
approach is used in simulations with such margin-based instruments as futures. With
margin-based securities, it is difficult to define return except in absolute dollar
amounts or in relationship to margin requirements or risk, simple ratios cannot be
used. In addition, system equity may occasionally dip below zero, creating problems
with the computation of logarithms and further obscuring the meaning of ratios.
However, given a constant investment (in terms of volatility exposure), monthly
returns measured in dollars will have consistent significance over time, t-tests on
average dollar return values will be valid (the annualized risk-to-reward ratio used to
assess performance in the tests that follow is actually a resealed t-statistic), and it
will be easy to see if a system is getting better or worse over time, even if there are
periods of negative equity. The use of a fixed-investment model, although carried out
more rigorously here by maintaining constant risk, rather than a constant number of
contracts, is in accord with what has appeared in other books concerned with futures
trading. This does not mean that a constant dollar volatility portfolio must always be
traded. Optimal f and other reinvestment strategies can greatly improve overall
returns; they just make simulations much more difficult to interpret. In any case,
such strategies can readily and most appropriately be tested after the fact using
equity and trade-by-trade data generated by a fixed-investment simulation.
Damn, all this huge paragraph to just say that it's better to use a fixed-investment simulation. And it didn't even say it that clearly. That's why I am so prejudiced against books. They're all so filled with bull****. Yeah, because this guy could have said all his stuff in 90 pages, but a book of 90 pages is not a serious book, so he's pressured to write 400 pages and repeat himself over and over again. To make history. Where there's people there's bull****. The bigger the crowd, the more bull**** there is. If I had been his son, he would have written one page with his recommendations. But nope, he has to publish it, so he fills it up with bull**** and reach 400 pages.


A standardportfolio of futures markets is employed for all tests of entry methods
reported in this section. The reason for a standard portfolio is the same as that for
a fixed-exit strategy or dollar volatility equalization: to ensure that test results will
be valid, comparable, and consistent in meaning. All price series were obtained
from Pinnacle Data in the form of continuous contracts, linked and back-adjusted
as suggested by Schwager (1992). The standard portfolio is composed of the following
markets (also see Table II-l): the stock indices (S&P 500, NYFE), interest
rate markets (T-Bonds, 90-day T-Bills, lo-Year Notes), currencies (British Pound,
Deutschemark, Swiss Franc, Japanese Yen, Canadian Dollar, Eurodollars), energy
or oil markets (Light Crude, #2 Heating Oil, Unleaded Gasoline), metals (Gold,
Silver, Platinum, Palladium), livestock (Feeder Cattle, Live Cattle, Live Hogs,
Pork Bellies), traditional agriculturals (Soybeans, Soybean Meal Soybean Oil,
Corn, Oats, Wheat), and other miscellaneous commodities (Coffee, Cocoa, Sugar,
Orange Juice, #2 Cotton, Random Lumber). Selection of markets was aimed at
creating a high level of diversity and a good balance of market types. While the
stock index bond, currency, metal, energy, livestock, and grain markets all have
representation, several markets (e.g., the Nikkei Index and Natural Gas) would
have improved the balance of the portfolio, but were not included due to the lack
of a sufficient history. In the chapters that follow, entry models are tested both on
the complete standard portfolio and on the individual markets that compose it.
Since a good system should be able to trade a variety of markets with the same
parameters, the systems were not optimized for individual markets, only for the
entire portfolio. Given the number of data points available, optimizing on specific
markets could lead to undesirable curve-fitting.
Hmm, this is a bold statement right here: "a good system should be able to trade a variety of markets with the same parameters". I do have some systems with the same exact parameters and identical code which I use on different markets, but it is not the rule. I usually need some customization.

Unless otherwise noted, quotes from August 1, 1985, through December 31,
1994, are treated as in-sample or optimization data, while those from January 1,
1995, through February 1,1999, are used for out-of-sample verification. The number
of contracts traded is adjusted to achieve a constant effective dollar volatility
across all markets and time periods; in this way, each market and time period is
more comparable with other markets and periods, and contributes about equally to
the complete portfolio in terms of potential risk and reward. All tests use the same
standardized exit technique to allow meaningful performance comparisons
between entry methods.
Good. My ratio for out-of-sample periods is about the same, about one third of the whole sample. I also found out when they wrote this book, during 1999. But some chapters are from 1996 maybe, because of the processor they mention.
I don't adjust the number of contracts, ever. I always use one. On this I don't agree.
I also standardize things and keep things very orderly so I know what I am doing and can make comparisons and all that.

Ok, according to plans I am now skipping chapter 5.

Page 123, Moving Average Models


A moving average is used to reduce unwanted noise in a time series so that the
underlying behavior, unmasked by interference, can be more clearly perceived; it
serves as a data smoother...

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Old Nov 12, 2010, 5:09pm   #1743
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page 124, THE ISSUE OF LAG

Yamato started this thread
Also, lag may not be a serious problem in models that enter when prices cross a
moving average line: In fact, the price must lead the moving average for such models
to work.
Yeah, this is what my CL_ID_3 does.

Furthermore, what they don't mention is that one doesn't necessarily need to use moving averages for timing only. I use moving averages in most of my systems, but except for the CL_ID_3, I don't use them for timing of entries. As I said, my timing is given by time. Moving averages are important but only tell me where we are and have been going. Have we been rising, falling? My counter-trend systems will enter at a given time, depending on where we've been going (against where we've been going). My trendfollowing systems will enter at another time in the same direction as we've been going, as indicated by the moving average.


Consistently entering trends, even if late in their course, may be a more reliable way to make money than anticipating reversals that only sometimes occur when expected.

Because of the need for a standard exit, and because no serious trader would trade without the protection of money management stops...
What? I am not a serious trader? I don't use any stops. 8 years of back-tests on 61 trading systems proved to me that it is ok to not use stops. On top of it, it is a good thing in that it keeps things simple in terms of both back-testing and automation.

Skipping some useless parts...


When designing an entry model, try to effectively combine a countertrend
element with a trend-following one. This may be done in any number of
ways, e.g., buy on a short-term countertrend move when a longer-term
trend is in progress; look for a breakout when a countertrend move is in
progress; or apply a trend-following filter to a countertrend model.
Yes, I am doing this stuff already. Combining elements of different edges into a single system: overstretchedness, trend, cycles...

Even though traditional indicators, used in standard ways, usually fail (as
do such time-honored systems as volatility breakouts), classical concepts
like suppoa/resistance may not fail; they may actually be quite useful. In
breakouts, models based on the notion of support/resistance held up better
than did, e.g., volatility breakouts. Likewise, moving average models
using the concept of support/resistance did better than others...
This is one thing I am not using: support and resistance. I built all my systems without even trying to use it. The concept escapes me. Same applies to volume. I built all my 61 systems totally disregarding the support/resistance and volume "edges", which obviously I don't consider edges since I can't find a way to exploit them. The same applies to pivots, which belong to the category of support/resistance.

Good, yet another chapter read, without being able to learn anything useful to create new systems. Damn. And to think that this book is entitled "encyclopedia of trading strategies".

page 167, chapter 8, Seasonality

Last edited by Yamato; Nov 12, 2010 at 8:03pm.
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Old Nov 12, 2010, 5:55pm   #1744
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page 167, Chapter 8, Seasonality

Yamato started this thread
Imagine that tomorrow is June 7, 1997. You need to decide whether or not to
trade. If you do trade, you will enter at the open and exit at the close. You also need
to decide how to enter the market: Should you go long or short? As part of the
decision process, you examine the behavior of the market on all June 7s that
occurred within a look-back window of some number of years (e.g., 10). You tabulate
the number of June 7s on which trading occurred, the average open-to-close
change in price, and the percentage of time the market rallied or collapsed.
Perhaps, in the past 10 years, there were eight instances when the market was open
and June 7 was a trading day: of those instances. perhaps the market closed higher
than it opened six times (75%) and the average change in price was 2.50 (a reasonable
figure for the S&P 500). On the basis of this information, you place a
trading order to enter long tomorrow at the open and to exit at the close. Tomorrow
evening you repeat the procedure for June 8, the evening after that for June 9, and
so on. This is one form of seasonal trading. Will you make a profit trading this
way? Will your trading at least be better than chance? These are the questions that
arise when discussing seasonal trading and that this chapter attempts to answer.
Yes, this is great and exactly what I expected. I do this type of stuff in my systems, a lot. And yes, to the answer if it's better than chance, using the system above. It is better than chance, but there's more profitable and simpler methods.


There has been some discussion of the so-called January Effect, in which stocks tend to rise in January...
Good system right there. I'll be testing this.

For our current purposes, seasonaliry is defined as cyclic or recurrent phenomena that are consistently linked to the calendar. The term is being used in a broad sense to mean market behavior related to the time of the year or to particular dates, including anniversaries of critical events (e.g., the October 16, 1987, crash). In short, seasonality is being construed as calendar-related cyclic phenomena. It should be made clear, however, that while all seasonality is cyclic, not all cycles are seasonal.
Major points being made here. What the **** does "cycles" mean then? Katz, always making efforts to be unclear and verbose as possible. Oh I see: maybe he's trying to get most people to drop the book, so he can keep his secrets.

page 169:
There are many ways to time entries using seasonal rhythms. Two basic approaches will be examined: momentum and crossover...
Yeah, this is the part I didn't understand. If I enter on a date why do I need momentum or crossover? Isn't this contradictory? Seasonal entries are time-related, he just said this. So how do time-related entries get triggered? Time? Nope, something else: momentum and crossover. Very clear, Katz.

Skipping the next page of bull****.


Consider trading a simple moving average crossover system. Such a system is
usually good at capturing trends, but it lags the market and experiences frequent
whipsaws. If slower moving averages are used, the whipsaws can be avoided,
but the lag is made worse. Now add seasonality to the equation, The trend-following
moving average system is applied, not to a series of prices, but to a
series that captures the seasonal ebb and flow of the market. Then compute the
seasonal series so it represents that ebb and flow, as it will be several days from
now-just far enough ahead to cancel out the annoying lag! The result: A system
without lag (despite the use of slow, smooth, moving averages) that follows
seasonal trends. The ability to remove lag in this way stems from one of the
characteristics of seasonality: Seasonal patterns can be estimated far in
advance. In other words, seasonality-based models are predictive, as opposed
to merely responsive
Yeah, that's right. All my systems are predictive, because they use seasonality in one form or another: time of the day, day of the week, month of the year. They are all predictive in that, and not responsive. I am not late in this sense.

Since seasonality-based models are predictive, and allow turning points to be
identified before their occurrence, seasonal-based trading lends itself to countertrend
trading styles. Moreover, because predictions can be made far in advance,
very high quality smoothing can be applied. Therefore, the kind of whipsaw trading
encountered in responsive models is reduced or eliminated.
That's right. I am doing that, too.

Another nice characteristic of seasonality is the ability to know days, weeks, months, or even years
in advance when trades will occur--certainly a convenience.
I have that, too, yes.

I am a seasonal trader, basically.

Seasonality also has a downside. The degree to which any given market may
be predicted using a seasonal model may be poor. Although there may be few
whipsaws, the typical trade may not be very profitable or likely to win. If inversions
do occur, but the trading model being used was not designed to take them
into account, sharp losses could be experienced because the trader could end up
going short at an exact bottom, or long at an exact top.
Mmh, that is why I do use some other filters. I don't just go long because it's the right season. I first make sure, with a moving average, that we've been going the other way until then. But I maintain that my entry is triggered by time. The moving average is used as a filter.


This is a good one:

Last edited by Yamato; Nov 13, 2010 at 8:16am.
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Old Nov 13, 2010, 10:26am   #1745
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190, conclusion

Yamato started this thread Skipping some bull**** in between...

These explorations into seasonality have demonstrated that there are significant
seasonal effects to be found in the markets. Decisions about how to trade can be
made based on an examination of the behavior of the market at nearby dates for a
number of years in the past. The information contained on the same date (or a date
before or a date after) for a number of years in the past is useful in making a determination about what the market will do in the near future. Although the seasonal
effect is not sufficient to be really tradable on the whole portfolio, it is sufficient
to overcome transaction costs leading to some profits. For specific markets, however,
even the simple models tested might be worth trading. In other words, seasonal
phenomena appear to be real and able to provide useful information. There
are times of the year when a market rises and times of the year when a market falls,
and models like those tested in the chapter can capture such seasonal ebbs and
flows in a potentially profitable manner.
Seasonality, as defined herein, has been demonstrated to be worthy of serious
consideration. If the kinds of simple entry models illustrated above are elaborated
by adding confirmations and by using an exit better than the standard one,
some impressive trading results are likely to result.
Yeah, a lot of words with little content, as often is the case with this book and all books. All I've got out of this chapter is that I have to test a system on whether markets rise in January. And maybe another one on whether they rise on the first days of each month.

1) Recurrent seasonal patterns appear to have real predictive validity and are
definitely worthy of further study.
2) The usefulness of seasonal patterns for trading varies from market to
market, with certain markets being particularly amenable to seasonal trading.
Trading a basket of seasonally reactive markets could be a highly
lucrative endeavor.
3) To obtain the best results, raw seasonal information should be combined
with some form of confirmation or trend detection. Making use of additional
information can improve the performance of an unadorned seasonal model.
Yes, this is all true, but they haven't shown it during the chapter. It would have been better to write a 1-page or half a page long chapter with these 3 statements.

A good search on google, with "seasonal patterns" will tell me more on this stuff:

The good thing is that they brought it up as a category.

Good links:

Recapitulation on chapters left to read: 10, 11, 12.

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Old Nov 13, 2010, 11:19am   #1746
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chapter 10, Cycle-Based Entries, page 216

Yamato started this thread
A cycle is a rhythmic oscillation that has an identifiable frequency (e.g., 0.1 cycle per day) or, equivalently, periodicity (e.g., 10 days per cycle). In the previous two chapters, phenomena that are cyclic in nature were discussed. Those cycles were exogenous in origin and of a known, if not fixed, periodicity.
Seasonality, one such form of cyclic phenomena, is induced by the periodicity and recurrence of the seasons and, therefore, is tied into an external driving force. However, while all seasonality is cyclic, not all cycles are seasonal.
Oh, this reminds me of when, in chapter 8, they said:
For our current purposes, seasonaliry is defined as cyclic or recurrent phenomena that are consistently linked to the calendar. The term is being used in a broad sense to mean market behavior related to the time of the year or to particular dates, including anniversaries of critical events (e.g., the October 16, 1987, crash). In short, seasonality is being construed as calendar-related cyclic phenomena. It should be made clear, however, that while all seasonality is cyclic, not all cycles are seasonal.
Now listen to yourself: "A cycle is a rhythmic oscillation that has an identifiable frequency or, equivalently, periodicity". Cycles have "identifiable frequency or periodicity".
Then you say that seasonal patterns have a "known, if not fixed, periodicity", but that "not all cycles are seasonal". Not clear at all, damn it.
What are you saying then? That the difference between seasonal cycles and other cycles is a "known periodicity"? Nope, because you said all cycles have "an identifiable frequency or, equivalently, periodicity". So all you could be saying is that seasonal patterns have a "fixed" periodicity and the other cycles do not. Why did you have to use one whole page to make your point so unclearly? Why didn't you write it in Greek while you were at it? Goddamn ****er. Is the purpose of writing books for you to confuse people?

In this chapter, cycles that can be detected in price data alone, and that do not
necessarily have any external driving source, are considered. Some of these cycles
may be due to as yet unidentified influences, Others may result only from resonances
in the markets. Whatever their source, these are the kinds of cycles that
almost every trader has seen when examining charts. In the old days, a trader
would take a comb-like instrument, place it on a chart, and look for bottoms and
tops occurring with regular intervals between them. The older techniques have
now been made part of modem, computerized charting programs, making it easy
to visually analyze cycles. When it comes to the mechanical detection and analysis
of cycles, ma*imum entropy spectral analysis (MESA) has become the preeminent
Ok, I guess I understand a little more now, in my process of translating your incomprehensible language. So basically the difference, in simple words, is that seasonal cycles are seasonal, time-related cycles. "Cycles" in general are not necessarily time-related. You should go to jail for writing it so unclearly, with such a waste of words and time for the reader. All it took was one sentence, to differentiate between calendar-related cycles and all other cycles. But hell no, you had to write two pages about this, and with the result of wasting my time and being even less clear. On top of it, this truth was self-evident, so you didn't even need to mention it. The concept is implicit in the term "seasonal cycles". I guess you're too good with numbers to be good with words, too. Your whole book could have been written in 50 pages.

Ok, i've read the Amazon reviews again, and even though the title of this book is clearly a lie, this review convinced me to keep on reading:

Currently, there are at least three major software products for traders that employ the
maximum entropy method for the analysis of market cycles: Cycle Trader (Bressert),
MESA (Ehlers, 800.633.6372), and TradeCycles (Scientific Consultant Services,
516-696-3333, and Ruggiero Associates, 800-21 l-9785). This kind of analysis has
204 been found useful by many market technicians. For example, Ruggiero (October
1996) contends that adaptive breakout systems that make use of the maximum
entropy method (MEM) of cycle analysis perform better than those that do not.
Maximum entropy is an elegant and efficient way to determine cyclic activity
in a time series, Its particular strength is its ability to detect sharp spectral features
with small amounts of data, a desirable characteristic when it comes to analyzing
market cycles. The technique has been extensively studied, and implementations
using maximum entropy have become polished relative to appropriate preprocessing
and postprocessing of the data, as required when using that algorithm.
A number of problems, however, exist with the maximum entropy method,
as well as with many other mathematical methods for determining cycles. MEM,
for example, is somewhat finicky. It can be extremely sensitive to small changes
in the data or in such parameters as the number of poles and the look-back period.
In addition, the price data must not only be de-trended or differenced, but also be
passed through a low-pass filter for smoothing before the data can be handed to
the maximum entropy algorithm; the algorithm does not work very well on noisy,
raw data. The problem with passing the data through a filter, prior to the maximum
entropy cycle extraction, is that lag and phase shifts are induced. Consequently,
extrapolations of the cycles detected can be incorrect in terms of phase and timing
unless additional analyses are employed.
Hmm, "maximum entropy method". Sounds like I can't get mixed with this stuff right now.
Hmm, "sharp spectral features"... does it really have to get so complicated? Hmm, I don't think so. Right, besides, whenever there's a special moving average or special indicator, they always mention there's extra problems, so why not deal with simple things and not have the extra problems? What is the point of adding so many variables to the picture if they add nothing except complexity? It's all bull****.

Watching this:

This Jesse Eisenberg is good at choosing the movies he's in.

Now watching this:

For a long time, we have been seeking a method other than maximum entropy to
detect and extract useful information about cycles. Besides avoiding some of the
problems associated with maximum entropy, the use of a novel approach was also
a goal: When dealing with the markets, techniques that are novel sometimes work
better simply because they are different from methods used by other traders. One
such approach to detecting cycles uses banks of specially designed band-pass filters.
This is a method encountered in electronics engineering, where filter banks
are often used for spectral analysis. The use of a filter bank approach allows the
bandwidth, and other filter characteristics, to be tailored, along with the overlap
between successive filters in the bank. This technique helps yield an effective,
adaptive response to the markets.
I know nothing about this either. More bull**** coming my way from this book. I am going to skip the whole section.

page 219, Butterworth Filters
Butterworth filters are not difftcult to understand. A low-pass Bunenvorthfilter is
like a moving average: It both attenuates higher-frequency (shorter-periodicity)
signals (or noise) and passes lower-frequency (higher-periodicity) signals unimpeded;
in other words, it smooths data. While an exponential moving average has
a stop-band cutoff of 6 decibels (db) per octave (halving of the output for every
halving of the signal’s period below the cutoff period), a 4-pole Butterworth filter
(the kind used in our May 1997 study) has a stop-band attenuation of 18 decibels
per octave (output drops by a factor of 8 for every halving of a signal’s period
below the cut-off period). The sharper attenuation of unwanted higher-frequency
(lower-periodicity) activity with tire Butterworth filter comes at a price: greater lag
and distorting phase shifts.
Not clear at all. They are either talking about detecting the trend with a moving average, in which case the whole chapter is useless. Or they're talking about something else, but they didn't explain clearly what it is, in which case the chapter is once again useless. Let's try to get done with this chapter fast.

page 220, Wavelet-Based Filters
The Morelet wavelet behaves very much like a localized Fourier transform.
Oh yeah? So much for wavelets. These guys just want to show off their knowledge and don't give a **** about explaining useful things to me.

more bull****.

Watching this:
Not good.


Last edited by Yamato; Nov 13, 2010 at 11:52pm.
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Old Nov 14, 2010, 11:49am   #1747
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Old Nov 14, 2010, 12:19pm   #1748
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page 237, CONCLUSION

Yamato started this thread useless.

Nothing. This whole section and chapter is like their own notes on their own stuff, and they don't make much efforts to explain to you what's going on, nor to summarize the subject.

Page 240, Neural Networks
...In the early 199Os, interest peaked, more development tools appeared, but the fervor then waned for reasons discussed later...
Oh great, yet another chapter on something that doesn't work. Instead of calling it "encyclopedia of trading strategies" you should have called this book "list of non-profitable trading strategies".

Neural networks had their heyday in the late 1980 and early 1990s. Then the honeymoon
ended. What happened? Basically, disillusionment set in among traders
who believed that this new technology could, with little or no effort on the trader’s
part, magically provide the needed edge. System developers would “train” their
nets on raw or mildly preprocessed data, hoping the neural networks themselves
would discover something useful. This approach was naive; nothing is ever so simple,
especially when trading the markets. Not only was this “neural newbie”
approach an ineffective way to use neural networks, but so many people were
attempting to use nets that whatever edge was originally gained was nullified by
the response of the markets, which was to become more efficient with regard to
the technology. The technology itself was blamed and discarded with little consideration
to the thought that it was being inappropriately applied. A more sophisticated,
reasoned approach was needed if success was going to be achieved.
Good, finally some useful information.

Two neural networks will be trained.
One will predict whether tomorrow’s open represents a bottom turning point, i.e.,
has a price that is lower than the prices on earlier and later bars. The other will predict
whether tomorrow’s open represents a top turning point, i.e., has a price that is
higher than the prices on earlier or later bars. Being able to predict whether a bottom
or a top will occur at tomorrow’s open is also useful for the trader trying to
decide when to enter the market and whether to go long or short. The goal in this
study is to achieve such predictions in any market to which the model is applied.
Do we need neural networks to test this? I don't think so.

more useless stuff.

this part could be useful, because it tells me what I have to do with RiskOptimizer as well, if I want to use its genetica algorithms to find things other than portfolio optimization.
The first step in developing a neural forecasting model is to prepare a trainingfact
set, which is the sample of data consisting of examples from which the net learns;
i.e., it is the data used to train the network and to estimate certain statistics.
Yes, this is the tough part, because I have to work on excel functions so to convert excel into tradestation, and allow it back-test a system. Then I can pretty much do all I want with RiskOptimizer. No need for anything else. But I first have to be able to back-test a system on excel, so that the variables of the systems can be considered "adjustable" cells by RiskOptimizer.

Let's get on google and start searching for backtesting and backtesting on excel, so that maybe I don't have to build this thing all by myself from scratch but can use someone else's work.

Links, in order of visit: (good attitude but too basic) (this is it, but it's not free) (even better, but it uses macros and I need functions-only)

This video shows what can be done on excel and basically shows that tradestation is light years ahead of excel.

Let alone what I could do on excel. I'd better buy an genetic algorithm add-in for tradestation:

This is it:
Second, we’ve added a new method for optimizing back-tests based on genetic algorithms. Genetic optimizations will allow you to run normally lengthy and process-intensive back-test optimizations much faster, which also allows you to select a wider range of parameters in your strategy optimizations.

This explains clearly what it is all about (very refreshing after reading katz):
New Optimization Method Using Genetic Algorithm
We have great news for strategy developers who are sometimes faced with time-consuming and processor-intensive optimizations: TradeStation 8.5 includes a new optimization method based on a genetic algorithm.

Before 8.5, the only way to optimize parameters in strategies was the classic “exhaustive” method. While the exhaustive method calculates all possible combinations of all strategy parameters in the range specified, and guarantees that the algorithm finds the best set of parameters for the optimization criterion, it can also be very time consuming because it goes through all possible combinations of the values in the parameters. Genetic optimizations do not calculate all possible strategy parameter combinations but will use an algorithm based on natural selection that will arrive at an answer that is statistically significant.

But guess what. Reading this made me realize that things are even simpler for me. I can do brute-force optimization because so far all my optimizations ever required was 2 minutes. So if I am willing to let them run for an hour, I can make it do anything I want. I can also do this selectively, and let my discretion be my own genetic algorithm.

Anyway, let's get back to katz book and skip all the bull****, going directly to the last 2 pages.

yeah, whatever.

Yeah, they say that the more data the better. I knew this. They say, even in other parts of the chapter, that these optimization techniques have to be used by informed users and don't do everything by themselves. Furthermore, I would add that no one without great experience would actually trade a system coming out of this software. Basically, my conclusion is that what counts in this business is experience, knowledge of the market, and reasoning. The tools are less important. I can do my stuff fine without neural networks and without genetic algorithms. And someone else, inexperienced, won't be able to do anything despite using these tools. Maybe I am saying this to make myself feel better about the fact that this stuff is beyond my intellectual reach, but maybe I am appraising things correctly. In fact, I say the same about the RSI and the MACD, which are not beyond my reach, but which I discard, to keep things simple. So I think I have a point, because I coherently apply this principle in all areas of my life, even in writing and speaking. I speak and write as simple as possible. I always look for the fastest and clearest solution. The same applies to dressing, eating, furniture, work. Bottom line is screw neural networks, at least at the moment. I can make money without them.

Partly I agree, partly i don't know what they are talking about. Obviously, since I've never had such software.

page 270, Genetic Algorithms, chapter 12
Finally I am at the end of this bull**** book and only with one chapter left to read. I will take a break. Maybe I will even resume tomorrow.

Last edited by Yamato; Nov 14, 2010 at 5:42pm.
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Old Nov 14, 2010, 4:42pm   #1749
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page 270, Genetic Algorithms, chapter 12

Yamato started this thread
...The trading community first took notice around 1993, when a few articles (Burke, 1993; Katz and McCormick, 1994; Oliver, 1994) and software products appeared. Since then, a few vendors have added a genetic training option to their neural network development shells and a few have “industrial strength” genetic optimization toolkits.
In the trading community, GAS never really had the kind of heyday experienced by neural networks. The popularity of this technology probably never grew due to its nature. Genetic algorithms are a bit difficult for the average person to understand and more than a bit difficult to use properly. Regardless of their image, from our experience, GAS can be extremely beneficial for system developers.


As with the process of biological selection (where less-fit members of the population leave fewer progenies), the less-fit solutions are weeded out so the more-fit solutions can proliferate, yielding another generution that may contain some better solutions than the previous one. The process of recombination, random mutation, and selection has been shown to be an extremely powerful problem-solving mechanism.

What would happen if a GA were allowed to search, not merely for the best parameters (the more common way a GA is applied by traders), but also for the best rules? In this chapter, the consequences of using a GA to evolve a complete entry model, by discovering both the rules and the optimal parameters for those rules, will be explored. Although somewhat complex, this methodology proved to be effective in our first investigation (Katz and McCormick, February 1997).
This is what I want. I wonder how I can do this, particularly in tradestation. Not just the optimization of parameters but that of rules. Can the new tradestation do that? I checked it and couldn't find out. Besides the costs are very high, like 200 dollars in fees every month. I am not ready for that yet, and on top of everything, I'd have to depend on them for life. They'd hold me by the balls.

How can a GA be used to discover great trading rules? The garden variety
GA just juggles numbers. It is necessary to find a way to map sets of numbers in
a one-to-one fashion to sets of rules. There are many ways this can be accomplished.
A simple and effective method involves the construction of a set of rule
templates. A rule template is a partial specification for a rule, one that contains
blanks that need to be filled in. For example, if some of the rules in previous chapters
were regarded as rule templates, the blanks to be filled in would be the values
for the look-backs, thresholds, and other parameters. Using rule templates, as
defined in this manner, a one-to-one mapping of sets of numbers to fully specified
rules can easily be achieved. The first number (properly scaled) of any set is used
as an index into a table of rule templates. The remaining numbers of the set are
then used to fill in the blanks, with the result being a fully specified rule.
Yes, this is the way to go about it. It is very complex but it can be done on excel as well. If I do this, I can do this optimization via RiskOptimizer. RiskOptimizer can practically do anything it wants. But as i said in the previous post, back-testing a system on excel and customizing it for RiskOptimizer is going to take a lot of effort. I'd have to postpone it until I quit my job.

...Although C+ + was used in the current study, this method can also be implemented in TradeStation using the TS-EVOLVE software from Scientific Consultant Services (516-696-3333).
Ok. I'll go on emule and look for this software once again:

I am not ready to pay 159 dollars for it yet. But this is the most exciting part of the book to me, worth reading:

In short, we are going to attempt to engage in the selective breeding of rule-based entry methods!
This is why I will have to buy their program, ts evolve. I couldn't find it on emule.

Instead of beginning with a particular principle on which to base a model (e.g., seasonal@, breakouts, etc.), the starting point is an assortment of ideas that might contribute to the development of a profitable entry. Instead of testing these ideas one by one or in combinations to determine what works, something very unusual will be done: The genetic process of evolution will be allowed to breed the best possible entry model from the raw ideas.
This is exactly what I had in mind.

The GA will search an extremely broad space of possibilities to find the best
rule-based entry model that can be achieved given the constraints imposed by the
rule templates, the data, and the limitation of restricting the models to a specified
number of rules (to prevent curve-fitting). To accomplish this, it is necessary to
find the best sets of numbers (those that map to the best sets of rule-based entry
models) from an exceedingly large universe of possibilities. The kind of massive
search for solutions would be almost impossible-certainly impractical, in any
realistic sense-to accomplish without the use of genetic algorithms. There are
alternatives to GAS, e.g., brute force searching may be used, but we do not have
thousands of years to wait for the results. Another alternative might be through the
process of rule induction, i.e., where an attempt is made to infer rules from a set
of observations; however, this approach would not necessarily allow a complex
function, such as that of the risk-to-reward ratio of a trading model, to be maximized.
Genetic algorithms provide an efticient way to accomplish very large
searches, especially when there are no simple problem-solving heuristics or techniques
that may otherwise be used.
Yes, I totally agree. Hopefully their software will be up to the task. I think so. By how they're talking, it's exactly what I am looking for.
TS-Evolve, originally distributed by Ruggiero Associates for $495, is an industrial strength genetic optimizer for TradeStation. With TS-Evolve, TradeStation users can perform the same kinds of genetic-algorthm-based system development as described in "The Encyclopedia of Trading Strategies" (Jeffrey Katz & Donna McCormick, McGraw Hill, 2000) and in several articles by the same authors in Stocks & Commodities. Works with all 32-bit versions of TradeStation (2000i, Version 6, etc.) providing seamless genetic algorithm capability. As far as we know, this is the only GA currently available for TradeStation. The product consists of a DLL and a computational Server that provides "Global Variables" as well as GAs, and comes with a detailed example to help you get up and running. Purchase includes telephone support to get you started. There are no shipping costs! The product is normally sent by email (as a zip file) and includes full installation instructions.
watching this:

They're making a bunch of comparisons to genes and chromosomes which only confuse the reader. I am going to skip to page 282, TEST RESULTS

Most of the early solutions were close to random and not very good, but the quality of the solutions improved as generations progressed; this is normal for a GA.
Good. Only six pages to go. Then I will just have to worry about getting ts-evolve, and I'll be done with this endeavour.

page 287, Market-by-Market Test Results
No efforts made by authors to be understood.

page 290, The Rules for the Solutions Tested
No efforts made by authors to be understood.

page 292, CONCLUSION
As was the case in our earlier study, the use of a GA to select and instantiate rule
templates continued to work well as a means of developing a trading system, or,
at least, an entry model. Results were still impressive, despite such problems as
inadequate numbers of trades in many of the solutions generated. The approach is
certainly one that can serve as the basis for further development efforts. In this
exercise, only a small base of rule templates, involving such fairly simple elements
as price comparisons, moving averages, and indicators, were used. Undoubtedly,
much better results could be obtained by using a more sophisticated and complete
set of role templates as grist for the genetic mill.
Finally something comprehensible.

Long positions tend to perform better than short positions for the markets
in our standard portfolio and with the models that were tested. Therefore,
it is probably more worthwhile to place development efforts on a system
that emphasizes the long rather than short side.
I agree. That's how most of my systems trade.

Genetic algorithms appear to be an effective means of discovering small
inefficiencies that are buried in a mountain of efficient market behaviors.
Mmh... I don't like this concept of efficient vs inefficient. The markets move and change. There is nothing efficient about that. The only efficient line would be a straight line. Anything else is not. So stop telling me this bull**** about efficient markets.

When used correctly, and in such a manner as discussed above, overoptimization
(curve-fitting) does not seem to be a serious problem, despite
the optimization power of genetic algorithms. Restrictions on the number
and complexity of the rules in any solution seems to be the key element
in controlling the curve-fitting demon.
Mmh. I don't know about this.

Evolution, as used herein, has the great benefit of producing explicit rules
that can be translated into plain language and understood. Unlike neural
network systems, the trading rules produced by GAS are not hidden in an
inscrutable black box.
Ok, so if the conclusion was that GA is better than Net why did you waste and make me waste a whole chapter on what does not work?

Using genetics in the manner described above has the benefit of producing
a large number of distinct, yet profitable, solutions. It would be easy
evolve and then put together a portfolio of systems.
Gee, that's great. Did you have to wait 12 chapters to tell me so? Probably by now a lot of readers have dropped the book.

Last edited by Yamato; Nov 15, 2010 at 2:57pm.
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Old Nov 15, 2010, 7:02pm   #1750
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third week of quiet vito

Yamato started this thread Vito's silence is holding up very well (he doesn't bother me any more, nor messes with my stuff - we just say "hello" and "goodbye"), but I have a feeling he's turning the whole office against me. He's always busy making friends. He goes to lunch with 10 people while I stay in the office and keep working.

Now my reputation will really be put to a test. We will see if this idiot, with his focus on superficial relationships, will be able to overcome my 5 years of serious work at the office and turn everyone against me. I really wonder. I won't fight and won't say anything bad about him (except if asked). I don't have much of a choice because I can't change that much: I am only capable of working quietly and non-stop. I can't act like a fool like he does. I can't spend the whole day saying "hi, how are you" to everyone who walks by in the hallway.

He's probably saying to all his new friends that I am unbearable with my silence and seriousness. He's probably saying he feels like he's in jail.

I will keep doing just like I've been doing. Not talking at all to him is much much better than interacting with him. He's the one who's unbearable. Besides, changing anything now would drive him crazy, because he would not understand what exactly I want. I want silence, so let's keep going just like this, and make sure he keeps being quiet.

Last edited by Yamato; Nov 15, 2010 at 8:44pm.
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Old Nov 16, 2010, 12:01am   #1751
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next steps

Yamato started this thread 1) resting for a week or two
2) buying ts evolve
3) building systems with it
4) building systems on seasonal cycles (all kinds)
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Old Nov 16, 2010, 12:07am   #1752
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Yamato started this thread I will let some songs express my present state of mind. I am too tired to write.

Too tired for song with words.

Last edited by Yamato; Nov 16, 2010 at 7:48am.
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Old Nov 17, 2010, 5:34pm   #1753
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Educating Vito

Yamato started this thread The young dick-head at work did it again. He asked me for help and again started getting familiar with me, as he should never do. I was distracted and I gave him a high five or whatever you call it. My mistake. Tomorrow I won't fall for it again.

He asked me for help, which I never deny to him. I solved his problem and he showed me his hand in the "gimme five" gesture, and I gave him a five. It's not a small thing: he starts like this and ends by flipping by scanner and messing with my stuff. The young dick-head must be stopped from early on. He was totally quiet for about 20 days and now he's starting to show signs of hyperactivity again.

As long as I stay serious and act boring and do not give him fives or let him touch me, he will keep behaving. Not easy because this guy is hyperactive and it's as if I were keeping him in a cage by forcing him to act like an adult.

I wish he could just die and not show up at work one day. Oh well, this isn't likely to happen any time soon. With the drawdown my systems are in, I won't be able to hire someone to kill him. Let's just all pray for his death.

Last edited by Yamato; Nov 17, 2010 at 9:46pm.
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Old Nov 17, 2010, 8:58pm   #1754
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re: my journal 2

Yamato started this thread

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Old Nov 17, 2010, 10:26pm   #1755
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some conclusions i can draw

Yamato started this thread I remember how I was boasting about all my systems throughout this journal, and I have to admit I was wrong.

Some investors listened to me and they tried my systems, but it turned out that it's not, as i thought, that I was losing money merely because of my compulsive gambling and my systems were profitable.

My systems so far have sucked. After keeping the same systems for 5 months, we can say that we've either been totally unlucky or my systems are not what I made them out to be.

After 5 months of non-tampering non-interfered trading of my best systems we're still at zero profit. We've gone down to minus 2700 first, then up to plus 3200 and now we're back at zero.

My dreams of.. not just grandeur but even my modest dreams of quitting my job are vanishing. It's going to be Vito forever.

But I am not resigned to that idea yet. I will definitely come up with something, within trading, before i let that happen. I am going to die trying.

On the other hand, right now I don't even know if I should get busy creating even more systems when my most profitable systems have failed to produce any profit in the past 5 months.

Maybe it was just an illusion. With all these systems, there's ALWAYS some systems making money, but they're never the same maybe. Maybe the systems that turned out to be profitable this year will be unprofitable next year, and this way I'll just have a combination of break-even systems no matter what.

If a person, me in particular, could just be quiet... The total opposite of vito. One could be so much more productive. Why do I write journals, emails... all this stuff and then I probably have nothing in my hands, and I have wasted all this time writing about what I have, which is nothing, partly because I have spent so much time writing about it. I thought I could afford to rest, and write, but maybe I can't. Maybe I just need to be working all the time.

Working at work, and working at home.

When is it going to be my turn to have sex with the lady in the video above and to have vito killed? When are my efforts going to pay off? How much does an intelligent person like me have to work if he wants to get on top of all the scumbags idiots surrounding me? I am better than them in every single way, I work harder than them, and yet I can neither get away from them nor get on top of them. I should be their boss. Not the other way around. When am I going to get what I deserve? Where do I have to look if not in trading? Trading is supposed to reward efforts and intelligence like no other field. This is all meritocracy. Yet it's not rewarding me. And it never rewarded me.

My discretionary trading is awful. But my automated trading is healthy: orderly, hard-working... technically everything has constantly been getting better during the years. Yet, after all these years of work, when you really get down to it, and trade for 5 straight months, the 10 best systems you can find... then, no profit. How can I be so unlucky or what am I doing wrong?

I cannot afford to stay at my job another year and tell everyone that in 3 months I'll quit my job thanks to my trading. And even if I don't say it, it's become a joke.

Even if my dad tomorrow were to give me 100k to trade it at my discretion, I would not know if I can rely on my systems. Yeah, I can rely on them to break even. Or... I can rely on three of them to make money, but it would be unwise to invest all the money on those three, because of a lack of diversification.

I am tired. On top of it, there's the young dick-head at work adding to my fatigue. And the neighbours slamming their door, just now. What does it take to have them all killed?

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