Strategy Part 1,2 & 3

Technical Analysis: “Price Based Quantitative Analysis”

Generating consistent returns of 2-5 percent per month with minimal
draw downs, utilizing quantitative, priced based, automated “black
box” trading models.

Glen M. Demarco - January 10, 2009

Technical vs. Fundamental: Does technical analysis “work” or is it a
“self fulfilling prophesy”?

System Trading Results.

This document is not an offer to sell, lease or otherwise market this Benchmark system or
any system.

* Note: All Sharpe Ratios are monthly figures, to convert to annual ratios multiple by 12.
Monthl Max Sortin Select
Symbol Description y Annual Draw Sharpe o Profit
Rtn % Rtn % Down % Ratio Ratio Factor

SDS ULTRASHORT S&P500 21 262 25 0.53 8.50 1.80
SKF ULTSH FINANCIALS 32 388 0 0.48 2.50 1.70
SRS ULTSH REAL ESTATE 41 499 26 0.66 11.90 1.80
POTASH CORP SASK
POT INC 23 281 55 0.49 7.50 1.40
DIREXION LG CP BEAR
BGZ 3X 45 541 41 0.71 94.70 1.70
GOOG GOOGLE INC 6 78 22 0.15 1.60 -0.90
FSLR FIRST SOLAR INC 11 133 16 0.62 6.90 1.70
SMN ULTSH BASIC MATRLS 6 74 16 0.16 0.70 1.00
CME CME GROUP INC 25 307 38 0.61 2.80 2.10
MZZ ULTRASHORT MDCP400 27 325 46 0.33 3.10 1.10
EEV ULT SHRT MSCI EMRG 94 1130 38 0.57 45.30 2.49

Figure: Monthly and annual return during last 120 trading days for Demo system,
obviously a once in a lifetime performance, or is it?

Objective of it all – Profits, but with minimal draw downs.

Primarily profits, as my focus is trading, fully automated trading of price based quantitative
models comprised of commonly used and other customized “technical analysis indicators”.
Superior returns with “Maximum Intraday Draw Down”. In the process answer the
question: Fundamental vs. Technical - Does technical analysis work?

However incomprehensive these results may look, they are 100 percent accurate and will
be demonstrated. More important then the absolute return the results are the minimal draw
downs. Most importantly results are achieved without any optimization at all,
zero curve fitting. Curve fitting is based on altering system variables to “fit” the data.
This system was not altered on any of the tested runs, as it’s impossible to curve fit a
system to ten different instruments without changing the system in the process.

2

Introduction – “Technical Analysis” or price based quantitative analysis.

Traditional Quantitative Analysis as applied to finance consists of two branches. The first,
Mathematical typically develops models for pricing complex derivative products. The
second, Statistical, or Statistical Arbitrage (StatArb), develops models to decide what
stocks are relatively cheap or expensive based on book value to Price, Price Earnings,
trailing earnings to price, or other individual equity or sector correlation analysis.
Essentially StatArb is a mean reversion - law of large numbers, based strategy.

Technical Analysis can be viewed as third distinct category of traditional quantitative
analysis, Price Based Quantitative Analysis, perhaps we should call ourselves,” Price
Analysts”. Price, in economic terms, along with supply and demand forms the basic
“fundamental” foundation of all economic theory.

Like any powerful tool, technical analysis is only as good as the person utilizing it. For
example, a rifle in the wrong hands can and has caused many tragedies. The record for a
sniper kill was recently made in Afghanistan by an American led sniper team. They killed a
terrorist from a distance of 2,430 meters, over 1 ½ miles away.

So like the combination of a skilled sniper and rifle, with the appropriate skill and
knowledge, price based quantitative technical indicators are enormously profitable, as

3

measured by monthly and annual returns, maximum drawdown and Sharpe and Sortino
Ratios.
The main objective of trading is profits. I am told there are many successful very short term
scalp/day traders who are consistently profitable. Because of the changing market
characteristics, this method presents significant challenges. Even if successful, one is
limited to the number of markets that can be traded on any given day and more importantly
limited in the amount of capital that can be traded, i.e., few institutional managers are
willing to commit capital to an individual traders “streak”, however hot.

Given the fee management and incentive fee structure and the amount of capital raised by
hedge funds, my objective is to duplicate the results of the most successful hedge funds.

Results vary, but given today’s interest rates and recent 33% equity market decline,
consistently returning on average 1-2 percent per month non-leveraged will match or
outperform the vast majority of successful funds and is the minimum level of success.
Is it possible? Any given day on average the S&P will be up or down 1%. There are 240 or
so trading days a year that is a potential profit of over 200 percent. Of course it’s
impossible to predict intra day tops and bottoms. To paraphrase something I heard George
Soros say once in an interview, I’m not looking to catch the high and low, just take a little
out of the middle. My goal is to capture at least 10 percent of that 240 percent middle. It
has taken me 20 years to figure out, but as far as I’m concerned it’s a done deal.

How? Well to use an oversimplified example, it could be as simple as: if the S&P during
the first hour of trading is positive the probability that it will continue higher and close out
the day up is greater then a reversal. My systems do not look to predict or project price at
any time in the future. Rather if X (a bullish indication), Y (another bullish indication) and
Z (a third bullish indication) occur, a long trade is taken. Hopefully looking to capture
perhaps .5 to 1 percent profit. This indicator based trade taken 5 times a week and being
correct 60 percent of the time yields the stated objective 2 plus percent a month.

This is overall objective and given the favorable volatility these days it is achievable.
We have all heard of the large number of hedge funds that have failed recently. Unless they
were long only funds, one really has to wonder and it is most unfortunate they stayed long
and watched stocks drop 40% and commodity related stocks, like Steel, Oil Services, etc
loose 70+ percent of their value. I realize fundamentals determine price, but technical
analysis also has value, simply consulting a daily chart would have provided warning
something had changed, dramatically, regardless of one’s opinion. Frankly I gave up
having any opinion regarding long and short bias, way to expensive for me. Frequently
people ask which is superior: technical or fundamental analysis. It’s been a long time but
my economics 101 professor at NYU taught that price in a free market is the direct result of
supply and demand forces and therefore the most “fundamental” of principles. Price charts
form the basis of any technical study, sounds fundamental to me. I also always wondered
where a fundamental trader exits a loosing trade, at zero, after accumulating stock on the
way down? I focus my effort on following what the market is doing rather then trying to
predict what it may do in the future.

4

Simply taking an unbiased look at a daily chart to determine what side of these markets to
be on would have not only have kept many in business, but they would have been able to
capitalize on a one way move down. Potash, US Steel, OIH – Oil Services ETF, in a 6
month period lost 75% of their value. Having a fundamental “opinion” on the market that
contradicts what the price is doing is simply ignoring facts.

Underlying Trading System Principles

Minimize Losses – All the systems are based on trend and volatility. They are designed to
be on the right side of any major move. The worse case scenario is an unusually range
bound or “sideways” market where small losses will occur. During periods of low market
activity trading in that particular instrument, parameters can be adjusted, or trade size can
be reduced until volatility returns to normal, or trading can be curtailed. There is no
requirement for every system to be “always in”. The lower the Maximum Drawdown
(amount of pain) the better, that is my primary selection criteria for “best system”. I prefer
a system that makes less net profit but has a lower drawdown, it’s more reliable.

Market neutrality – Given that nobody can predict future direction, having a market
opinion is dangerous. There is no bullish or bearish bias in any of the systems I have
developed.

Volatility Based – Unlike price, volatility changes slowly over weeks to months. The Dow
did not move from an average range of under 100 points in 2006 to 300 points today
overnight and will not move back to 100 in one or two days. More recently the Dow
volatility went from an average range of 155 points a day on May 30, 2008 and moved
steadily higher to peak at 570 points a day on October 16, 2008, almost 5 months.

Trading system parameters can be adjusted with changing market conditions. This does not
involve “optimization” of system variables, which is prone to “curve fitting” but rather a
minor adjustment of the time intervals used, i.e., the lower the volatility the longer the time
frame used. For example a 5 minute interval on the SP500 ETF is less likely yield the
same positive returns as a 5 minute interval on Google.

5

Trading System Background

For twenty years part time and ten years full time I have been searching not for the “holy
grail” of trading as this doesn’t exist but systems that simply make a consistent profit with
minimal draw downs. Typically my sample market data model will be a broad based index.

I first began using Computrac the first technical analysis software capable of “back
testing” trading strategies. I then started using Tradestation and for the past several years
eSignal. There are advantages and disadvantages to both. eSignal requires proficient
programming skills but can interface with many brokers for automated execution.

Trading system creation using various sources

Trading publication – Technical Analysis of Stocks and Commodities, Trader Magazine,
Futures Magazine, various books published, internet web sites. There are many thousands
of available indicators out there. Converting this vast array of indicators into actual buy sell
signals and back testing to determine the results was a multi-year undertaking and took
thousands of hours.

Trading System Development Methodology:
System Creation begins with the Process of Elimination –

THOUSANDS TO HUNDREDS TO A PRECIOUS FEW.

My current “arsenal” of trading programs consists of over 200 directories and over
5,000 separate indicators and system. Each directory contains from a few to several
hundred programs. Each directory represents an individual trading system project. I
have researched over 200 over the past ten years. Unfortunately or fortunately, the
final outcome of this extensive research was the vast majority of well known
indicators/systems, do not generate profitable trade signals on any instrument in any
time interval.

Of the few useful indicators that I incorporate in system development in and of
themselves are still not profitable. MACD, Stochastics, Moving Averages, Directional
Movement, Commodity Channel Index, Price Oscillators, Elliott Wave, Fibonacci,
Gann, Bollinger Bands, RSI, Donchian, and many more, even if used “properly” are
not profitable.

6

However as “tools” to facilitate trading I have found Directional Movement, Stochastics
and Fibonacci retrenchments useful and do incorporate them into several systems, although
in unconventional ways. using them as divergence indicators, or as final trade triggers
based other favorable custom and proprietary indicator trade setup.

For example, one of the most popular indicators is stochastics, created by George Lane.
Essentially stochastics is similar to other oscillators such as MACD and RSI, as it
oscillates usually on a scale between 0 and 100. Stochastics measures the current close in
relation to the high and low range for a given period, typically 10-20 days.

Like all technical indicators much is open to interoperation, however most will agree the
traditional signal is the crossing of the two stochastic lines; a buy occurs when fast K
crosses from below the slower smoothed slow D. My research of stochastics clearly
demonstrated that taking a trade signal for every cross over will insure massive losses,
especially when used intraday. However, taking the trade on a cross over using a modified
version of stochastics and incorporating other proprietary techniques yields a “system”
much better then the standard stochastics oscillator.

Here is a screen image of an automated trading strategy developed in eSignal’s JavaScript
programming language. eSignals’s JavaScript is used to back test trading systems on
historical data, build customized indicators, and interface with a brokerage firm for
automated trading strategies. In this example the strategy is implemented on symbol SPY,
the S&P 500 Equity ETF, hardly the most profitable instrument to trade but familiar to
most and the benchmark symbol I use for strategy development. This and any JavaScript
strategy can be extensively tested and customized. During development, and in this case for
illustration purposes, this strategy “paints” the screen background to easily indicate the buy
and sell signals. These signals can be just as easily modified to alert the trader to manually
enter a trade; or if a brokerage account is active, automatically send the trade for immediate
execution. The green back ground on the chart indicates a long signals and the red back
ground indicates a short signal.

7

Figure: Basic Stochastics Cross Over Trading System: Red (darker shaded
background) is Short, Green (lighter shaded background) is Long trade signal, and
sounds good on CNBC or in a text book, but never worked for me.

The system generated 771 trades and lost $2,228, or over 18% on the initial investment of
$13,000 non-leveraged cost for trading 100 shares of ETF (Symbol SPY) for the last 120
days approximately 5 1/2 months ending January 10, 2009.

Stochastics Formula
A momentum or price velocity indicator that was developed by George C. Lane. It represents a
fixed period-to-period moving calculation that is very susceptible to instability and false signals
since it is possible for the indicator to fluctuate wildly by simply removing data from the oldest
period.

K = Lane's Stochastics
C = the latest closing price of the stock or contract
L = n-period low price of the stock or contract
H = n-period high price of the stock or contract
n = any number (Lane recommends a range of 5 to 21)
SK or %K = three-period simple moving average of K
SD or %D = three-period simple moving average of SK

Source: The Encyclopedia of Technical Market Indicators, Colby and Meyers

8

Figure: Equity Curve of Standard Stochastics Crossover System including slippage
of $5.00 a trade and $1.00 per trade on 100 shares. All trading results are based on a
100 share position non leveraged position.

9

Demo System, a modified version of the stochastics system using a
custom formula and additional proprietary techniques uncovered
during many years of testing.

Figure: Screen image with trade signals of a Demo system based on a modified
version of the stochastics formula and incorporating other techniques and indicators.

10

Figure: This is the equity curve of the same Demo trading system.

This system is possibly worth trading after some additional enhancements. Right now it’s
a rather “dumb” system, minus many of the features usually incorporated into a production
system. What was meant as an illustrative example to explain the overall development
surprisingly performed much better then I expected as it is less complex then most. The
performance was the result of the recent historic level in volatility. The data is for the last
120 trading days, ending January 10, 2009. In 6 months it returned over 57 percent non
leveraged trading 100 shares of the SPY ETF minus slippage of 5 cents a share and 1 cent a
share commission. My guess is the volatility index will continue at relatively high levels
but not at the historic level, so it will make money, but not nearly as much.

However, the numbers, I have gone through a painstaking process of guaranteeing the
results are real. I am absolutely confident that 2 percent per month is a very conservative
estimate. Fortunately, trading volatility based, market neutral strategies allow adjustments
to be made gradually to the system parameters, as the market volatility changes.

What is the objective? Match or exceed the returns of the largest, most successful hedge
funds, which is approximately 2 percent per month.

11

Figure: This is the graph of the monthly return and maximum draw down
percentages for the Demo system.

The total return for the 6 month period was over 57 percent, over 100
percent annualized. Obviously the market will not continue to yield such
returns, i.e., 5+ percent per month, but 2 percent return per month, our
stated objective is a “slam dunk.

12

Again, this example system was not meant to be pitch for this particular system but rather
used for illustration purposes although I have verified that the results are 100 percent
accurate.

Based on extensive testing what is profitable and worth trading are customized indicators
incorporating separate risk and money management algorithms in combination with
proprietary indicators I have developed over the past 20 years of research.

Figure: Total Trade Analysis illustrating that the Demo system generate a total of 80
trades, 55 percent profitable and the average trade of +$88 on a 100 share SPY
position. Average winning trade of $291 and average loosing trade of ($160).

13

Figure: Strategy Performance Report, 6 month return of 57 % on $13,000 of capital,
the cost of purchasing 100 shares of the SPY ETF at the maximum price of $130.

14

Choosing the “best” system to trade.

What is the best system? The system that makes the most money? Being risk averse I
favor systems with the lowest maximum intraday drawdown rather then those that net the
largest profit. I addition, one has to be very careful to eliminate all “outlining” or
exceptional trades. If most of the profit was made on a relatively few number of trades then
it’s eliminated from consideration. There are many industry standard measures of “best”
system, using Sharpe or Sortino Ratios calculations and several more. I look at the Sharpe
and Sortino ratios but usually rely on up trending equity curve (with minimal draw downs)
for possible system selection.

This forms the basic foundation of a trading system or the “how” to trade. No system
makes money on all instruments in all time periods.

What other markets yield good results with this “system”? One measure of a system’s
value or robustness is how it performs when applied to other instruments. This system,
unchanged in any way run on the British Pound for the same period returned 35 percent
generating 43 trades with an average trade on a $100K position of $4000 or 400 pips on
average.

Same Demo System Applied to British Pound Sterling, performs well on
Cash FX, Equities, and Financial Commodity Futures.

Figure: Trade signals when applied to the British Pound using current data (we’re
short!).

15

Figure: Equity Curve of Demo system run on British Pound Sterling, cash FX

16

Figure: Strategy Performance Report showing return on capital of over 30% and
Sharpe ratio of 1.02.

17

Figure: Trade Analysis report for Proprietary Trading System applied to the British
Pound, average trade over 400 pips, 43 trades in the last 120 days.

If I applied the same system to symbols like SKF, the symbol for the US Dow Jones
Financial Index ETF or other volatile symbols the returns are significantly better. In
general if a system makes money on the S&P Futures or SPY ETF it invariably will make
much more on highly liquid, volatile names.

What to trade….scanning the symbol universe?

The next problem to solve once a possible viable trading system algorithm is identified is
what to trade, in what interval, and what time of day to trade it.

Equity instrument selection is done using scanners of the entire symbol universe to select
possible candidates. The scanner allows filtering based on hundreds of different selection
criteria. Ideally to minimize the risk of adverse “news” events the preferred instruments
are: FX, Stock Index Futures, Broad Based ETF Indices, and individual equity symbols.

Although at times small and mid cap names turn up in the final results of the scanner runs.
Generally when individual equity names are chosen, only the most liquid are selected, the
average daily volume for the prior ninety days should be at least 1-5 million shares a day,

18

and typically much higher. The objective is to eventually “size up” and not have our orders
impacting price movement to any significant degree. For example a stock that trades 5
million shares a day, a 5,000 share trade represents one tenth of one percent of the average
volume and will have no impact on the stock price and positions of 50,000-100,000
minimal impact during periods of high market activity.

Curve fitting, curve fitting and more curve fitting, hypothetical results are bogus.

Any teenager with a computer these days and Tradestation or eSignal using charting
software can eventually come up with some combination of variables and indicators that
were customized or curve fitted to the data and make a fortune on the past data.
Tradestation has an “optimizer” that will actually do the work for you, i.e., go through
millions of variables, i.e., all moving average crossovers, to find the winning combination
and produce a wonderful report of all the money you will make. Just don’t order the BMW
before the money hits your account as it will disappear fast when you start trading it real
time.

Will that teenager’s golden goose system make money unchanged, on even one symbol
other then the one where all the winning number combinations were tested upon? Highly
doubtful.

Will the system, unmodified make money on the 10-15 of most highly liquid broad based
stock index and sector ETFS’s with a few of the most active household NASDAQ names
thrown in for good measure, using the same intraday time interval for each?

If it does, offer him a job, better yet offer me one, because that’s exactly what this demo
system does. The list of symbols I use in this demo was not filtered from a large list of
varied symbols, selecting the most profitable for inclusion in this report.

I scan the symbol universe using eSignal for intraday scans or Stockfetcher for evening
preparation. The filters used took a year or so to come up with and proprietary but nothing
overly sophisticated. I’ve learned some of the price characteristics that yield the most
promising trading results and run this filter periodically. Here is a screen shot of the list
from today and the trading results using the exact same demo system, unmodified, on
exactly the same time interval for each of these symbols.

Anyone who thinks that is an easy task to pull off, so did I, ten years ago when I left
Goldman Sachs. As I was already “so close”, its taken ten years and a depleted saving’s
account to pull off. Better late then never I always say. It’s infinitely more difficult then I
ever imagined, or frankly I’d still be at Goldman. This system alone, unchanged will match
or beat the vast majority of hedge funds out there today, as is and only gets better with
further refining. Those results I will save for an in person demo.

19

Figure: Customized Stockfetcher execution report of all equity symbols, basically
scanning for the most liquid, highest volume and most importantly highest volatile
securities. The higher the volatility the better.

The “optimization” process, there is none!

Optimization, or searching for the best variables which is actually “or curve fitting” is
absolutely out of the question. The Demo system used in this report is identical
throughout. No variables have been changed for any of the historical tests.

Once the system and the candidate list of symbols are determined the “optimization”
process begins. In trading system development terminology optimization is synonymous
with curve fitting, i.e., checking all the combinations of system variables with the actual
price data until a profitable combination is found. Test enough combinations and
eventually a profitable combination will be found to have been profitable on past historical
data. Rarely if ever will this system show profits in the future. Some “optimization” makes
sense.

My optimization consists of choosing the best performing symbol/time period
combination. There is no guarantee we will be here tomorrow let alone how a given stock
will behave. But given the systems are market-neutral and volatility based, the probability
that a stock with an average daily range of 5 points will suddenly range 1 point is less then
continuing closed to the 90 day average of 5 points, and if that happens we make money.
Higher volatility is even better.

Because characteristics of markets change adjustments are made not to the underlying
principles of the system once determined, but rather the money management component.
For example, as volatility decreases, profit targets may be adjusted downward to close out a
trade earlier or volatility based trailing stops tightened, etc.

20

So how did it do on the symbols produced by the Stockfetcher scan?

Fortunately or unfortunately the results were stratospheric, off the charts, ridiculous. I
considered putting in $1.00 in slippage to bring the results in line with historical values for
the sake of credibility, but wanted to be accurate. Again my goal is 2-5% a month with
minimal draw downs and cannot over emphasize that there is very little chance recent
numbers will ever be reproduced. The markets volatility was “off the charts” literally,
historic levels.

Monthl Max Sortin Select
Symbol Description y Annual Draw Sharpe o Profit
Rtn % Rtn % Down % Ratio Ratio Factor

SDS ULTRASHORT S&P500 21 262 25 0.53 8.50 1.80
SKF ULTSH FINANCIALS 32 388 0 0.48 2.50 1.70
SRS ULTSH REAL ESTATE 41 499 26 0.66 11.90 1.80
POTASH CORP SASK
POT INC 23 281 55 0.49 7.50 1.40
DIREXION LG CP BEAR
BGZ 3X 45 541 41 0.71 94.70 1.70
GOOG GOOGLE INC 6 78 22 0.15 1.60 -0.90
FSLR FIRST SOLAR INC 11 133 16 0.62 6.90 1.70
SMN ULTSH BASIC MATRLS 6 74 16 0.16 0.70 1.00
CME CME GROUP INC 25 307 38 0.61 2.80 2.10
MZZ ULTRASHORT MDCP400 27 325 46 0.33 3.10 1.10
EEV ULT SHRT MSCI EMRG 94 1130 38 0.57 45.30 2.49

Figure: Monthly and annual return during last 120 trading days for Demo system,
obviously a once in a lifetime performance, or is it?

Of course the system made money, the market is in a massive bear market and your system
is trading the ultra shorts? The only reason I trade the ultra shorts is for the volatility.
Using the SDS for example, the ultra short S&P 500 ETF, in mid July the start of the 120
day trading period, it was trading at $75, yesterday is was also trading at $75, the range
during the time period was a low of $61 in September to a high of $133 in mid November.
Here are the last 10 trades for the SDS:

21

Symbol # of % Avg Avg Avg Net Long Short
Trades Profit Trade Win Loss Profit Profit Profit

SDS 79 51 $125 $503 $262 $9,915 6,068.00 $4,825
SKF 75 52 $344 $1,271 $661 $25,772 14,127.00 $12,558
SRS 77 53 $368 $1,130 $499 $28,370 12,996.00 $15,768
POT 76 49 $120 $765 $491 $9,169 (1,497.00) $11,586
BGZ 29 55 $247 $754 $373 $7,188 3,736.00 $3,777
GOOG 95 40 $115 $1,320 $687 $10,991 (3,977.00) $14,090
FSLR 78 46 $238 $1,169 $559 $18,592 3,423.00 $15,942
SMN 77 45 $136 $763 $386 $10,522 6,062.00 $5,205
CME 78 42 $326 $1,949 $863 $25,468 2,397.00 $21,368
MZZ 83 43 $120 $669 $299 $10,024 5,551.00 $4,883
EEV 71 50 $452 $1,298 $417 $32,125 15,168.00 $17,437

Figure: Trade breakdown for all symbols

Potash (POT) and Google (GOOG) shows a negative -$1497 and -$3997 trading from the
long side during the time period what happened? The market happened, POT was cut 75
percent went from a high of $215 to a low of $47, and GOOG was cut in half from $500 to
a low of $250, tough to make money on the long side so loosing $1497 on 37 long trades or
40 cents a trade in POT and 84 cents on 47 trades in GOOG is understandable.

Most of the other symbols are fairly balanced with profit from the long and short side
except for FSLR, which went from $300 to $85 and CME a high of $440 to a low of $155.

22

Figure: Last 10 trades for the SDS, during last 120 days SDS and most of the others
symbols made about the same profit on both the long and short side. Except for POT
and GOOG, given POT was down 75% and GOOG 50% loosing from the long side is
t be expected.

When to trade?

Once we have the How (the system), the What (the symbol/time period combination) the
next problem is the When?

One valuable lesson that was derived by sitting in front of the screen for years trying to
make money was the importance of time of day.
For day trading, 2-3 trades a day, the most profitable time of day is the trade signal that
occurs between 10:00 and 11:00am. If I only took one trade a day it would be at this time.
The next most profitable time is 3:00-4:00pm. Trading between 12:00 and 1:30 is a lower
probability trade and usually only yields positive results when the morning trading session
was unusually narrow which does occur perhaps 20 percent of the time.

23

For longer term swing systems, one trade every few days, the time of day is less important
although usually trades are generated during the high activity time periods describes above.
After years of testing I have found the most profitable systems trade between 1-3 times per
week. This seems to reflect the fact that markets have a tendency to trade in one direction
for 2-3 days at a time before retracing a portion of the prior move, typically 25 to 68
percent of the prior move before continuing.

Run Prototype and production tuning

The final stop in the development process is to run the systems real time during market
hours firing the automated trades to the simulated broker account. The purpose is to most
importantly validate the profit results from the back test which is run after market hours on
historical data. Frequently back testing results which yield excellent profit results are found
to be far less profitable when run real time. This discrepancy can be caused by software
flaws in the charting package, programmer error, underestimating slippage costs etc.
Validating the back testing results is essential and can only be done in real time.

In addition, watching a system trade real time often uncovers observations that were missed
using historical data, resulting in some stop or profit management replacement.

“Market Time Data”, tick charts, constant volume charts, range charts.

I also research combining various time intervals, using several correlated symbols to
increase the winning trade percentage and using customized time intervals, tick, range,
volume etc. All of which can and frequently improve the systems performance and will be
covered in another section.

24

Part 2. Out of sample testing and live trading January 29, 2009

Figure: Screen Image of Demo strategy modified to include a Risk Management
algorithm run on out of sample data. Symbol SDS (SP&500 ultra short) trades during
the three week time period after Part 1 of this paper was distributed.

Today’s date is January 30th, 2009 part one was written on January 10th 2009, part two
describes the process of adding risk management algorithms, testing on out of sample data,
real time testing and actual trade execution. The purpose of part two is to examine how the
systems performed during this 3 week time period. In this case the first “real trade” was
profitable; it could have just easily resulted in a loss. The objective was describing the
methodology, not touting this system.

25

However Costly, Risk Management is essential.

Surprisingly adding money management algorithms such as stops will often result in a less
profitable strategy. Apparently an “always in” strategy enables an earlier entry and this can
out weight the advantage of limiting losses via stops placement.

Stops in a sense are insurance. Insuring against a potential disaster is essential. Surprisingly
stops frequently erode strategy performance but can be viewed as life insurance. Like any
insurance policy there is a “premium” cost associated. In the case of this strategy, stops
improve the strategy performance.

There are a large number of stop algorithms to choose from: fixed percentage, moving
average base, dynamic volatility stops, trailing, parabolic, etc. After extensive testing I rely
on a method that serves two purposes: limiting risk and protecting profits.

Screen image of strategy modified to incorporate money management algorithm, which
encompasses the time period since Part 1 was written. On the following screen image for
the S&P 500 Ultra short ETF the red labels were added indicating Long and Short signals,
LX and SX, labels indicate exiting short and long positions, and the red dots represent a
proprietary “trailing stop” algorithm.

26

Out of Sample Testing - Verify historical back testing results

Every system needs to have the historical results verified. The first step is to insure the
back testing trade signals and actual report results are the same as those occurring during
real time market hours. One has to be completely certain the reported back testing profit
and loss figures are accurate. Most trading strategy development programs are accurate if
the strategy is coded correctly. However, in order to gauge accuracy and properly factor in
slippage, the strategy is tested in real time on our of sample data.

Once it is determined that the reports are accurate the out of sample results will determine
if the overall strategy results are in line with the back test on historical data. As long as the
strategy is not optimized, results should be similar. The more variable permutations, or
optimization done the greater the chance that the strategy is “curve fitted”. Optimized
strategies rarely if ever produce results on out of sample data. The exact same strategy is
being run on each symbol, eliminating any possibility of “optimization”.

Figure: Trades generated on out of sample symbol SDS (SP500 Ultra short) during
three week period since Part 1 of this paper was distributed. The out of sample results
validate the profitability of the strategy.

27

Figure: Strategy Performance Report for out of sample time period system returned
15 percent during the time period, very much in line with prior 6 months in sample
performance results.

28

Figure: Total trade analysis report for out of sample time period validates in sample
historical testing period.

29

Figure: Equity curve for out of sample time period

30

Live Trading Begins

Initially minimally sized trades will be executed while continuing to further validate
trading results prior to increasing position size. The symbols being traded should scale well
as the objective is to maximize strategy profitability. The symbols being trades are highly
liquid, broad based index and sector ETFs.

Compare actual trade execution and eSignal reported execution.

Figure: This was the eSignal trade report signaling a long trade at approximately
10:00am at a price of 75.93 am on 1/29/09 and closed at 1/30/09 for a profit of $350 or
approximately 5 percent. The execution report produced by my firms front end
execution program, HydraTrade, reports the trade, order # 402578 on the INET.
INET is the ECN I routed the order to for symbol SDS and the other symbols
mentioned in this report. The .02 difference is accounted for by slippage which I
specify as .05 for each trade.

31

1/29/2009
EEV Ultra Short MSCI Emerging Mrkts ProShares ETF
Order Gross Ecn Net
Time Id Exchg Liq B/S Qty Price Realized Comm Fee SEC TAF Realized
-0.0
10:01:42 402527 INET A B 10 56.54 -565.4 1 2 0 0 -566.38
Overnight Adjustment 565.4 0 565.4
-0.0
Fills: 1 10 0 1 2 0 0 -0.98
MZZ Ultra Short MidCap400 ProShares ETF
Order Gross Ecn Net
-0.1
10:01:58 402536 INET A B 10 59.61 -5961 1 9 0 0 -5961.8
-0.3
Fills: 2 190 -2.7 2 6 0.08 0.01 -4.43
SDS Ultra Short S&P500 ProShares ETF
Order Gross Ecn Net
-0.0
10:04:18 402578 INET A B 10 75.91 -759.1 1 2 0 0 -760.08
-0.0
Fills: 1 10 0 1 2 0 0 -0.98
SRS Ultra Short Real Estate ProShares ETF
Order Gross Ecn Net
-0.0
10:06:09 402635 INET A B 10 51.88 -518.8 1 2 0 0 -519.78
-0.0
Fills: 1 10 0 1 2 0 0 -0.98
TZA Direxion Small Cap Bear 3X Shares ETF
Order Gross Ecn Net
-0.0
10:05:16 402600 INET A B 10 53.04 -530.4 1 2 0 0 -531.38
-0.0
Fills: 1 10 0 1 2 0 0 -0.98

Figure: Clearing broker execution report listing the prior days opening trades.

32

Figure: Because these trades were executed today, the clearing broker reports will not
be available until tomorrow (actually Monday). This is the trade report produced by
the HydaTrade front end trade execution. Initially only 10 shares are traded as the
system is being validated, even 100 shares in these symbols could result in losses of
thousands of dollars. The instruments traded are highly liquid and position sizes of
many thousands of shares can be held.

Net Net
Symbol Open Close Points Pct

EEV 56.54 58.24 1.7 3.01%
MZZ 59.61 62.48 2.87 4.81%
SDS 75.91 79.57 3.66 4.82%
SRS 51.88 56.66 4.78 9.21%
TZA 53.04 57.66 4.62 8.71%

Figure: Actual Profit and Loss calculation taking data from the earlier execution
report copied into an excel spreadsheet as reported by execution broker report.

Combining Multiple Trading Models – Trend and Counter Trend

33

Markets typically go through cycles of trending and non-trending periods. Ideally at least
two separate models traded simultaneously will smooth out the Equity Curve by limiting
draw downs.

During time periods of consolidation or “side ways” market movements, characterized by
less directionality and volatility. A counter trend system will typically profit during the
time period where the trend following system does not. Conversely the counter trending
strategy will under perform the trend following system during higher directional and
volatility time periods. In addition the counter trending model’s performance can be further
enhanced by knowing when to “turn off” or ignore the counter trending time periods. There
are a variety of very effective filters for markets that are strongly trending.

One technique is through regression analysis using linear regression or least square analysis
by calculating the Pearson product-moment correlation coefficient, or Pearson’s R. There
are other more reliable techniques available that are incorporated within the quantitative
trading models.

Figure: Pearson’s R “Trend Strength” Calculation. Determining when a market is
trending and when a market is consolidating. This determines the selection of
available trading models.

34

Figure: JavaScript Implementation of Pearson’s R calculation

35

Figure: Screen Image with trading signals of “Trend Following” model.

36

Figure: Screen Image of trade signals of “Counter Trend” model.

37

Figure: Side by side comparison of Trend (left) and Counter Trend (right) daily P&L
for last two week periods.

38

Notice the largest loosing days on the left trend following column dated 1/22/09 a loss of $
-1386 was offset by a gain of $ +2243 in the trend following system.

Conversely the largest loosing day for the counter trend strategy was today, 2/04/09 for a
loss of $ - 673 which was offset by a gain of $1181 in the trend following system.

The over all effect is that the loosing day percentage goes from approximately 50 percent
to under 25 percent.

39

Figure: February 4, 2009 live trading results combining trend and counter trend
following systems.

40

Strategy Parallel Testing Phase Prior to Automating Trading.

There is only one way to insure with absolute certainty that a trading model’s results are
accurate. Equally important is insuring that there are not and error in sending orders to an
automated brokerage account. Sending automated trades for immediate execution to any
exchange can entail massive financial liability in the event of programming errors.

The system must be traded in real time during market hours in an actual brokerage account.
It is critical as an interim step to manually execute the trades before turning on automatic
execution.

Due to the time lag between recognizing a trade signal and manually entering the order, the
execution prices reported in the back test reports will not completely synchronized. Once
automated execution is implemented execution price should be 100% synchronized minus
slippage and commission costs. The foundation of a trading systems implementation is the
simulated back tested and forward tested results. These results must be meticulously
verified.

The main objective of this process is to manually insure when a trade signal occurs that the
prices reported in the back tester are aligned, to insure no erroneous trade orders are
generated and to insure that the rules designed into the model are being followed.
Programming in any language requires absolute precision and care in the design and
programming. Programming errors can occur this phase will eliminate the possibility that
any errors are present.

The actual results of today’s trading a profit of $527 matches the $515 reported by the back
testing report ($1188 profit in the trend following system minus a loss of $673 in the
counter trend trading system). Part of the trading model that is not yet implemented is a
scaling in and out of a position, e.g. when a significant profit occurs in a position a portion
of the position is closed. The back testing module is using a fixed share size of 100 shares,
the actual trading size for this phase of testing is between 25 and 200 shares, with 100
being the average to synchronize actual with back testing results.

41

Glen M. Demarco – Professional Background

I have worked for over 20 years in the industry starting with Merrill Lynch in the mid
eighties, then Nikko Securities, Deutsche Bank, and finally Goldman Sachs where I left as
a Vice President to pursue full time my interest in money management.

While at Merrill Lynch IT’s department I became extremely interested in technical analysis
and trading. There I began an extensive education process taking many courses at the NYIF
and NYU relating to trading, technical analysis, options, futures, currencies, etc. During
that time I received the Certified Market Technicians (CMT) designation by the Market
Technicians Association. This required quite a few classes, passing a series of exams,
being active in the MTA, and finally writing a thesis on an original technical analysis
concept. Fred Schutzman and Ralph Acompora were my mentors in the program and Phil
Roth reviewed and approved the final thesis requirement.

Since leaving Goldman Sachs I have dedicated myself exclusively to researching,
developing and trading systems. For several years full time I worked as a trader, and traded
options, futures and FX with mixed results. Very short term trading was never my
objective; rather I felt it important to immerse myself in trading as a way of enhancing my
theoretical knowledge base. It was well worth the effort as there are things one learns glued
to the screen for years watching markets that can never be learned any other way.

Please feel free to contact me anytime as I am always available to discuss system trading or
to discuss any mutual beneficial business arrangement. Thanks for listening.

Glen Demarco
20 River Terrace #20A
New York, NY 10282
demarcog@aol.com
home phone 212 786-0909
cell phone 212 433-0872

.

.

42

Strategy Part 1,2 & 3

Recommandé

Recommandé

Contenu connexe

En vedette

En vedette (20)

Similaire à Strategy Part 1,2 & 3

Similaire à Strategy Part 1,2 & 3 (20)

Dernier

Dernier (20)

Strategy Part 1,2 & 3