CATCH 22. Comparison of Trading Methods and Management. Presented to the Market Technicians Association at the CME Club 4/23/03.

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1 CATCH 22 Comparison of Trading Methods and Management Presented to the Market Technicians Association at the CME Club 4/23/03 John Jonelis Copyright 2002 John Jonelis Revised 1/6/04

2 CATCH 22 JONELIS PG. 2 SUMMARY The goal of this study is to identify a practical trading system with a reasonable expectation of positive returns year after year, in both up markets and down, executed by the average trader, using end-of-day data. This update is provided in an attempt to answer questions that have cropped up since the report was first released. The same 22 randomly selected stocks were traded over and over, using a number of models, across the same period of time between the summers of 2001 and Many involved in the market will remember this as a dismal period. Most mutual funds showed negative returns. Long-term investors in common stock suffered significant losses. Position traders, medium to short-term swing traders and day traders were cut to pieces by a market that frequently confirmed an entry signal then failed to follow through. Contrary to the general gloom overshadowing the market, twelve of the trading methods tested here proved profitable this, despite the simple, mechanics of the models, all of which were trend following in nature. Each was designed to be systematic rather than intuitive so that results, as far as possible, would depend on the characteristics of the model rather than the skill of the trader, and in the hopes that the experiment could be repeated by others. When comparing results, winning percentage was given greater weight than return on investment, and models were sought that provided a winning percentage of 50% or more and a return on investment of 20% or better. Only four of the twelve successful tests met that standard. Below is an abbreviated table of results. Please refer to Charts A thru D for more detail. MODEL METHOD VARIATION WINNING % AVG. ROI TEST 1A Advanced GET Trailing 54% 46% TEST 1B Advanced GET No trailing 54% 34% TEST 5A Day Trading Trailing 51% 23% TEST 5B Day Trading No trailing 51% 23% TEST 4A Standard Trailing 48% 15% TEST 4B Standard No trailing 48% 14% TEST 2A Standard Trailing 41% 30% TEST 2B Standard No trailing 41% 18% TEST 3A Standard Trailing 35% 17% TEST 3B Standard No trailing 35% 12% TEST 6A Position Trailing 32% 57% TEST 6B Position No trailing 32% 56% TEST 7 Buy-and-Hold n/a (21%) (27%) Each stock was viewed as existing in its own separate world, as if the trader were blind to market, industry conditions, events of the day, and time of year. Better results could no doubt have been achieved by a talented trader, by use of more indicators, by consideration of market swings, or during a less challenging market cycle. However, the goal was to identify a system that would work well in a poor environment, executed by the average trader. To that end, the test year proved an opportune period.

3 CATCH 22 JONELIS PG. 3 The trials were conducted completely independent of any company or organization. They do not rely on computer back testing, but rather, log the results of one trader attempting to meticulously follow a series of mechanical systems. It is hoped that the human element lends richness to the study. Trades were simulated one at a time, using historical stock charts with all future data masked from the trader. To a surprising degree, common emotions, and the pressures of time and uncertainty cropped up just as they would during actual trading. Before examining the details, a few comments are in order: Advanced GET (Test 1A) was the clear standout among the tests in this study. With a winning percentage of 54% and an average return of 46%, it met the goal of consistency and exceeded the target ROI. This result was achieved after one week of familiarization with the software and a hurried week to complete the simulations, during which an entire year s trades were replicated. Several trades failed due to outright errors by the author, and one may ask what results could have been achieved by a more seasoned hand. This was the first model tested and all others were devised in an attempt to beat it. None beat it. Day Trading (Tests 5A and B) yielded a winning percentage of 51%, and a return of 23%. This model represents a most rudimentary form of day trading, since intra-day data was not used. The assumed advantage of day trading is the turnover-rate-of-capital, which theoretically boosts opportunity and therefore ROI. It is possible for a skilled trader to achieve a return similar to that shown in these trials in a week or a month. However, proof of concept and an exploration into the high failure rate among day traders is outside the scope of this report. Standard Swing Trading (Test 4A) required a reasonable understanding of support and resistance to execute. This test yielded a 48% win ratio, third best, but just short of the goal. The return may have proven better given a more complex model, but as it stands, 15% is positive, but unspectacular. Position Trading (Test 6A) gave the highest average return of all models, but proved to be a home run strategy, with only 3 wins out of every 10 trades. It may be worth examining one s psychological stamina and the depth of one s pockets before setting out to implement such a system. The probability of a major account draw down during the working life of a trader appears uncomfortably high. Buy-and-Hold Investing (Test 7) was included in the report, even though it yielded a negative return for the test period. This is a prevalent strategy and any report would be incomplete without the comparison it provides. The inherent assumption in buy-and-hold is that a net positive return will be achieved if a stock is held for a number of years. Because there is no expectation of a positive return every year, the method does not fulfill the objective of consistency, as defined in this report. Though it is true that many have achieved success over the long haul, particularly with time horizons of five years or greater, recent market conditions have shown the risks to be higher than many investors previously supposed. This report is front-loaded so that the reader can absorb the gist of the study in a few pages, then dig deeper when more information is sought. Following this summary, elements of the study are discussed, definitions presented, models described, and trade data laid out.

4 CATCH 22 JONELIS PG. 4 DISCUSSION Common Invalid Assumptions Because the trials were performed by hand, a number of valuable lessons were learned. Several assumptions, which seemed reasonable in the heat of trading, proved not to be true when the data was tallied. One example is the comparison between the A and B series tests. During trading, much opportunity seemed to be lost when half-positions were closed at the target price. Often the remaining half-position reversed at that point and stopped out at break-even. It seemed obvious that closing the entire position at the target would generate more profit. However, once the final results were summed up, the opposite proved to be true. Another example is Test 3A and B, which used a 2:1 reward/risk ratio. Because many positions fell short of target, a trial was made using the same trades, but with a 1.6:1 r/r. The results proved disappointing and are not shown. One test clearly outperformed the others half way through its trial only to fall behind once complete. The conclusion reached is that it is folly to change proven methodology without first testing the validity of one s assumptions. Yet random change is exactly the behavior common among traders. There exist a few talented traders who rely on intuition. For the rest, there remains experimentation, statistical support, and strict adherence to a pre-defined and tested mechanical system. (For more on this topic, see Trading in the Zone by Mark Douglas.) Objectivity On any given day, a trade was taken whenever the stock chart matched the parameters of the model, regardless of the perceived outcome even in the case of an obviously poor decision. All entry and exit decisions were based exclusively on technical indicators selected for the particular model (see Details.) It is believed that sufficient repetitions exist to achieve statistical significance for each data set (see Charts C and D). Stock Selection The 22 stocks were selected by random call. Various individuals were asked to name companies. Those companies were used without prejudice. This method of selection tended, for the most part, to produce a bias toward well-known names with large market capitalization and good liquidity. The following stocks were selected: BRCD, GE, C, RFMD, DELL, WMT, CYMI, UAL, ZRAN, BRCM, GENZ, BZH, HD, YHOO, GM, BOBJ, INTU, INTC, INFS, XOM, PDLI, QQQ. Money Management With few exceptions, trades were allowed to either cross their target price or stop out. The target price and stop loss price were set at a specific reward/risk ratio for each test. Each position was sized in round lots to maintain approximately $1,000 at risk if stopped out. Trailing, or scaling out of winning trades, was employed in the A series tests while in the B series the entire position was closed at the target price (see Definitions.) Other valid money management methods exist, but the comparison of these two distinct methods proved instructive.

5 CATCH 22 JONELIS PG. 5 Data and Software All daily price and volume data as well as standard indicators were from TC-2000 end-of-day version software (Worden Brothers, Inc.) Tests of Advanced GET end-of-day version software (Trading Techniques, Inc.) used their proprietary indicators. (Note Hourly charts and Futures data are also available for end-of-day GET. Subsequent to these tests, Advanced GET has become available from esignal in both end-of-day and intra-day versions.) Order Entry All information was taken from daily not intra-day stock charts, even for day trades. Entries were made using simulated stop-limit orders, as if placed on the evening prior to market-open, and filled or not filled during the regular trading session. Once a setup occurred, entry would be attempted each day, either until the order was filled, or until the setup no longer matched the model. A conditional stop loss order accompanied each entry order so that a trade could stop out on the day of entry. For the purpose of this report, it was also assumed that trades could be executed market-at-open and market-at-close. Positions were opened from 6/1/01 to 6/28/02, and trailed no later than 7/31/02. Commissions and Slippage Commissions and slippage were not factored into the results. Penny-per-share commissions are available at time of writing, and slippage is variable, depending on order execution and liquidity. Stocks chosen for this test trade in large volume and all positions were taken in round lots. Position size, by-and-large, ranged below 1000 shares. A high quality direct access broker was assumed. Therefore, liquidity was not considered a significant factor. Traders dealing in larger lots or in odd lots should adjust the results as appropriate. Frequency of trades is recorded so that an appropriate number can be calculated for the individual situation. (See Charts C and D) Percent Return In order to calculate ROI, it was necessary to make assumptions regarding the size of the portfolio one would need to trade each model. ROI was presented in two ways Average Return and Minimum Return. Raw data is provided so that the reader can determine this in other ways than shown. Average Return (ROI) is the ratio of dollar gain to average total capital outlay, expressed as a percentage. This is probably the better of the two methods used. Average Return represents an estimate of the potential return, because a trader s activities are not confined to the same 22 stocks traded at the same times as in this report. In tests 1, 2, 3, 4, and 6, it was assumed that only half of the entire portfolio was committed at all times. The day trading tests assumed 35% committed. Buy-and-hold, by definition, is 100% invested. Minimum Return (ROI) is the ratio of dollar gain to the largest total capital outlay (largest single daily outlay of the year) expressed as a percentage (see calendars). This yielded the smallest return that could be calculated for each data set. Minimum Return was shown because market conditions tend to favor entries on certain dates. This number represents a conservative trader who stays in cash much of the time. In determining the capital required to execute each strategy, margin appropriate to the method was used as follows: 4:1 for day trade, 2:1 for all multi-day models, and 1:1 for buy-and-hold.

6 CATCH 22 JONELIS PG. 6 Comments on Advanced GET Since the software is complex and not widely known or understood, extra space is devoted to it. This study began with an opportunity to preview Daily Advanced GET software, leading to one week learning the system and another week running the simulation. The returns were generated by a somewhat overwhelmed trader, attempting for the first time to use a new system, one hand on the instructions, the other on the keyboard. Simple XTL Breakouts comprised the bulk of the trades. Many of the available features, such as MOB and Ellipse, were not explored at all, and outright errors were made using Type 1 and Type 2 setups. However, with only a rudimentary understanding of the system, it outperformed all subsequent tests. It seems reasonable to assume that a trader more familiar with GET would score higher in all categories. Even so, the return and consistency achieved were remarkable (see Charts A and B.) A number of tests were set up in an attempt to beat the Advanced GET results. To that end, the study has failed. That, however, need not be viewed as a negative result. Daily GET provides a substantial array of technical tools, and may be fairly described as a Ferrari of technical analysis. It plots Elliott Waves directly on the stock chart. The package is elegant and visual. A description of all the available tools is beyond the scope of this report. GET, and anagram for Gann- Elliott-Trader is designed to scan for unique opportunities based on Elliott, and other high-level tools, and provides a concise x-ray of individual charts with specific price projections. The program can be applied as a highly sophisticated companion to the easy-to-use TC TC-2000 is a rapid sorting tool, which quickly organizes thousands of stocks, using basic technical indicators and fundamentals, combined with user-written sub-routines (PCFs). Rapid visual screening can be performed in either program, flipping through hundreds of issues at a sitting. Each package has capabilities not found in the other and they compliment each other well. At time of testing, watchlists and scans created in TC-2000 could be accessed seamlessly in GET. Both ran well on an old laptop computer. Limitations The simulations were conducted by hand, and therefore contain errors and inconsistencies, as one might experience during actual trading. It should be noted that price data becomes stale over time due to splits and other events. For example, shortly after testing was complete, the vendor adjusted the data for Citigroup to reflect the spin-off of Travelers. The subsequent chart looked identical, but the numerical data had changed. This, of course, did not alter the final results of the test. Caution is needed when making predictions based on test results since all that can be known with any degree of certainty is what actually happened to a specific security at a specific time in the past. Though one seeks to glean general insight into the behavior of other securities in other time periods, the limitations of such comparisons must always be respected and all traders should be prepared for the unexpected. There are no guarantees. Ultimately, all analysis is based on assumptions. In this case the assumptions are those of the author at time of testing. This material is not intended as investment advice, but rather as an expression of the experiences of the author, and it is anticipated that others will test these materials for themselves. That said, this report is presented in good faith, in the hope of providing meaningful information.

7 CATCH 22 JONELIS PG. 7 DEFINITIONS Signal Candle All tests were performed using daily candle charts. For those not accustomed to Japanese candles, one candle graphically displays the day s opening, high, low, and closing price. The signal candle (or breakout bar) is the most recent candle (today s history) at the point at which the stock has set up for entry on the following day. In this study, entry, initial stop, and target price as well as position size were calculated, by-and-large, from the signal candle. Entry/Exit Entry/Exit (Stop) is described as wide, medium, or tight, and refers to the confirmation required prior to entry and the wiggle room allowed for the stop. For the purposes of this report, Wide is +50% of the range of the signal candle. Medium is generally an entry >$0.13 away from the high or low of the signal candle. Tight is an entry <$0.05 away from the signal candle. The initial stop is typically referenced to the signal candle, but occasionally set at the nearest fractal when appropriate. Specific numbers are noted for each test. (See Details) Reward/Risk and Target Reward/Risk (r/r) is the ratio between the potential dollar reward should the trade prove successful, and the dollar amount at risk should the trade fail. Most tests used a target, of 1.6:1 (160% of risk) corresponding to a common Fibonocci number, 162%, which is significant, probably because of its wide use by futures traders. Exceptions were Tests 3A and B, which used 2:1 r/r, and Tests 6A and B, which used 3:1 r/r. Each position was sized to maintain approximately a $1,000 total risk per trade, using the formula (presented by Sammy Chua): [Q = 1000/(p-s)] where Q is share quantity, p is entry price, s is the stop price. The actual position size was then adjusted to the nearest round lot using standard rounding methods. Trailing at Fractal Trailing is a disciplined method of scaling out of a winning position in defined portions. A fractal, as defined in this report, is meant as a pullback in price to form minor support followed by a breakout through minor resistance. (The opposite applies for a Short.) For example, in a winning Long position, ½ of the position was closed at the target and the stop price moved to break even. When a new minor support level was established and a breakout occurred (a fractal) the stop was moved near the new support level. Please note the stop was moved only after the fractal was fully formed by a new high. Trailing Tight Test 3A used a tighter trailing method trailing by candle. For example, in a winning Long position, ½ of the position was closed at the target and the stop was placed near the low of the prior day s candle. A certain degree of variation was allowed using this method. EMA Trend Long or Short entry points (in Tests 2 thru 6) were defined by exponential moving averages of price (EMA). Entry was allowed only if the EMAs were positioned as follows (from Sammy Chua): [LONG ENTRY] IF [10 EMA > 20 EMA > 50 EMA] ELSE [NO ENTRY] [SHORT ENTRY] IF [50 EMA > 20 EMA > 10 EMA] ELSE [NO ENTRY]

8 CATCH 22 JONELIS PG. 8 Pullback or Bounce Entry (for Tests 2 thru 6) was by trend pullback. This is an orderly price reversal, setting up a continuation-of-trend. Pullback is defined as lower highs following a bull run. Bounce is defined as higher lows following a bear run. The following price action was required: [LONG ENTRY] IF [MINIMUM 3 DAYS OF PULLBACK] [SHORT ENTRY] IF [MINIMUM 2 DAYS OF BOUNCE] Stochastic Oscillator A widely used oscillator to determine an overbought or oversold condition. Normally, price reversal occurs when the oscillator crosses the 20% or 80% line, then changes direction, or when the %K line crosses the %D line (fast crossing slow). But these were trend-following models and an oscillator, while useful in defining pullbacks, is best applied when trading a range. For the purposes of these trials, anticipatory entries were taken opposite the direction of the oscillator if and only if both lines had pulled back 50% (reversal imminent). By this method, the oscillator was used, not to confirm the merit of an entry, but rather, to restrict entry and create objectivity for each test. This was important because the same stocks were traded over and over. Parameters were set to industry norms [8-3-3]. False Bar Stochastic (In Advanced GET) A Stochastic oscillator with the addition of a Bar which alerts the trader to ignore the overbought/oversold indication. This tool allows entry into strong trends which otherwise would have been disallowed by the common use of the oscillator. FB Stochastics can be used in an XTL continuation trade to confirm entry when a False Bar appears in the direction of major trend with subsequent pullback of the oscillator to the opposite extreme. The algorithm is proprietary. OB/OS lines were 75/25. Parameters were set to Advanced GET recommendations [12-3-3]. Elliott Wave Theory (in Advanced GET) An alternate way of viewing the price cycle. The cycle is seen in 5 saw-tooth price runs (waves) after which the major trend reverses. Elliott waves are automatically plotted directly on the stock chart, once a Wave 3 is detected. Advanced GET wave counts may differ from theoretical Elliott methods. Elliot Oscillator (In Advanced GET) The oscillator is the difference between two moving averages, displayed as a histogram referenced by a zero point (much like MACD), and is used to confirm Type 1 and Type 2 entries. Parameters are set to [5,17], [5,35], and [10,70] to identify the action of short, medium and long term players. [5,35] is considered standard. Regression Channel (In Advanced GET) A linear regression line with an Envelope Channel set at +2 Standard Deviations. The regression automatically calculates off the lows in an uptrend and the highs in a downtrend. The channel is drawn through a Wave 4 or 5, and price closing outside the channel is used as confirmation for Type 1 and Type 2 entries. PTI (In Advanced GET) The Profit Taking Index is a number displayed when a Wave 4 is identified. A number of 35 or greater indicates the likelihood that the subsequent Wave 5 will culminate in a higher high or lower low.

9 CATCH 22 JONELIS PG. 9 MOB (In Advanced GET) The Make or Break predicts support/resistance based on the momentum of a previous swing in the Elliott cycle, and is useful to project the culmination of a Wave 5. As such, MOB can be used to set an initial target in a Type 1 trade, or as confirmation for entry into a Type 2 trade. The indicator is proprietary. At time of testing, the author had not yet explored this tool. Ellipse (In Advanced GET) The Ellipse projects Fibonacci price and time in one indicator, and is useful in confirming the end of a Wave 4 (for Type 1 entries). At time of testing, the author had not yet explored this tool. Type 1 Trade (In Advanced GET) A continuation-of-major-trend play. Type 1 is an entry into the beginning of Wave 5. Pullback of the Elliott Oscillator to zero, a PTI >35, and Ellipse resistance are used as confirmation. Clean setups are sought and the trade is skipped if price has pulled back beyond a level defined by automatically optimized moving averages (commonly called railroad tracks ). The trigger is price closing outside the regression channel drawn through the Wave 4 run, and the trade is taken when the high/low of that bar is taken out. MOB is used to project the initial target. Type 1 is a complex and powerful setup, yet visually obvious once mastered. Due to the short time for familiarization of the software, few Type 1 trades were taken during these tests. Type 2 Trade (In Advanced GET) A reversal play, entering opposite the trend at the end of the Elliott cycle (end of Wave 5). Divergence between the Elliott Oscillator and price, as well as MOB resistance, is used to confirm entry. The trigger is price closing outside the regression channel drawn through the Wave 5 run. A Type 2 trade is an entry after a major bottom or top, which is, by definition, high risk and high reward. Due to the short time for familiarization of the software, few Type 2 trades were taken during these tests. XTL (In Advanced Get) Wave 3 is typically the longest price run in the Elliott cycle. XTL is an entry method, which catches the early stages of a Wave 3 using a statistical algorithm to detect a potentially strong trend. The indicator also helps one stay in the trade over a long run, by preventing shakeouts during pullbacks. The color of the candle (red, blue, black) is used as a simple flag for potential entry and to define trend. XTL Breakout entries are taken when price exceeds 50% of the range of the signal candle (or breakout bar). Continuation entries are taken at the break of a regression channel, and confirmed by False Bar Stochastic. The algorithm is proprietary. Most of the trades in Test 1A and 1B were taken as XTL Breakouts.

10 CATCH 22 JONELIS PG. 10 DETAILS TEST 1, Advanced GET Three basic entries were used: XTL (with False Bar Stochastic optional), Elliott Type 1, and Elliott Type 2. Of these, XTL provided the majority of profitable trades. Since each security was traded from beginning date to end date, most XTL entries were treated as breakouts, not continuation trades. Because of time constraints, many of the tools and studies available in the software were not explored at all. At time of testing, the author had not explored the MOB and Ellipse studies, and Type 1 and Type 2 setups were not performed as taught, yielding mixed results. Few such trades were taken and further testing is warranted. For the purposes of this report, if XTL had been used exclusively, ROI would have been higher. Entries and Stops were 50% away from the signal candle. Margin was 2:1. The GET trials were the first to be run, and at that time, a run-off was not anticipated. To paraphrase Joseph Heller in Catch 22, You don t know what you are trying to create until you have created it. (The Catch 22 of Creativity, 10/31/02, author unknown, Each GET trade was recorded by trade number, not date. Average Return was calculated in the usual manner, but to determine Minimum Return, the largest capital outlay of the year was borrowed from the Test 2 calendar (which used identical money management). Also, it was unnecessary to record signal candle data when testing GET, because target and stop price could be plotted directly on the chart, using the Fibonacci expansion tool. Since recording of these numbers was necessary to execute Tests 2 thru 6, the GET data sheets show a calculation of what the high and low or each signal candle might have been. Due to slight variations in the drawing of Fibonacci lines, these are not hard numbers and are shown merely for the sake of consistency. Test 1A 1.) Entry after Signal Candle if any one of the following conditions: a. XTL [blue = long; red = short] (most trades) and False Bar Stochastic with pullback to OB/OS 75/25 (optional), or b. Type 1 - price closing outside regression channel, or c. Type 2 - price closing outside regression channel 2.) Position size adjusted to maintain $1,000 risk per trade. 3.) Entry 50% ahead of signal candle. 4.) Stop 50% behind signal candle. 5.) Target at 162% Fib level (1.6:1 r/r) a. ½ position closed at Target b. Stop moved to break even. c. Trailing stops at Fractal. Test 1B Test 1B was exactly the same as 1A, but without trailing of partial positions. The entire position was closed at 1.6:1 r/r.

11 CATCH 22 JONELIS PG. 11 TEST 2, Standard (Wide) This test was set up to isolate the Advanced GET money management system from the GET indicators. Money management was exactly the same as Test 1, but GET indicators were removed. Margin was 2:1. Again, everything was the same as in Test 1, except the indicators, and then only the listed indicators were used. Test 2A 1.) Entry after Signal Candle if all of the following conditions: a. EMA Trend [10>20>50 or 50>20>10] and b. Minimum 3 day pullback or 2 day bounce, and c. Stochastic pullback past 50% (anticipating overbought/oversold) 2.) Position size adjusted to maintain $1,000 risk per trade. 3.) Entry 50% ahead of signal candle. [ Wide ] 4.) Stop 50% behind signal candle. [ Wide ] 5.) Target price at 1.6:1 r/r. a. ½ position closed at Target b. Stop moved to break even. c. Trailing stops at fractal. Test 2B Test 2B was exactly the same as 2A, but without trailing of partial positions. The entire position was closed at 1.6:1 r/r TEST 3, Standard (Medium) This was the first test performed after the GET trials, and took place several weeks after their completion. As a result, the individual price swings had faded from memory and no artificial method was needed to force objectivity. Discretion was permitted in use of Stochastics and other indicators could be referenced at will. Entry and Stop were tightened in this test. The reward to risk ratio was expanded to 2:1. The trailing method was very tight, defined by the previous candle, and discretionary. Margin was 2:1. Test 3A 1.) Entry after Signal Candle if all of the following conditions: a. EMA Trend [10>20>50 or 50>20>10] and b. Minimum 3 day pullback or 2 day bounce c. (Stochastics and other indicators at trader s discretion.) 2.) Position size adjusted to maintain $1,000 risk per trade. 3.) Entry $0.13 ahead of signal candle. [ Medium ] 4.) Stop $0.13 behind signal candle. [ Medium ] 5.) Target price at 2:1 r/r. a. ½ position closed at Target. b. Stop moved to break even. c. Trailing stops by candle at trader s discretion. [Trailing Tight] Test 3B Test 3B was exactly the same as 3A, but without trailing of partial positions. The entire position was closed at 2:1 r/r.

12 CATCH 22 JONELIS PG. 12 TEST 4, Standard (Tight) This test represents classic swing trading as understood by the author. Most significantly, the initial target was set just short of minor support/resistance and pre-screened for a minimum 1.6:1 r/r or the trade was skipped. The stop was set at the nearest fractal. Entries and Stops were tight. The entire position was closed at market-open on day five. Margin was 2:1. Test 4A 1.) Entry after Signal Candle if all of the following conditions: a. EMA Trend [10>20>50 or 50>20>10] and b. Minimum 3 day pullback or 2 day bounce, and c. Stochastic pullback past 50% (anticipating overbought/oversold) 2.) Position size adjusted to maintain $1,000 risk per trade. 3.) Entry $0.01 ahead of signal candle. [ Tight ] 4.) Stop $0.05 behind signal candle or fractal [ Tight ] 5.) Target $0.13 short of the first support/resistance (minimum 1.6:1 r/r) a. ½ position closed at Target b. Stop moved to break even. c. Trailing stops at fractal. d. Position closed at the opening bell on day 5. Test 4B Test 4B was exactly the same as 4A, but without trailing of partial positions. The entire position was closed at the target or at the opening bell on day 5. TEST 5, Day Trading This is a rudimentary form of Day Trading, using only end-of-day data. The return was probably depressed by the bare-bones nature of the model and the small number of trade entries relative to day trading norms (206 entries borrowed from Test 4). Stop price and position size were calculated strictly using the daily signal candle. Entries were tight. Reward to risk ratio was pre-screened to a minimum of 1.6:1, just prior to minor support/resistance. Half positions were closed at 1.6:1 r/r then trailed (rare) or closed at end-of-day (common.) No more than six trades per day. Margin 4:1. (After trials, it was noted that the six-trade per day limit was exceeded once. Intra-day charts were referenced to determine which trades had fired first and the remainder executed the following day.) Test 5A 1.) Entry after Signal Candle if all of the following (same entries as Test 4): a. EMA Trend [10>20>50 or 50>20>10] and b. Minimum 3 day pullback or 2 day bounce, and c. Stochastics past 50% (anticipating overbought/oversold) 2.) Position size adjusted to maintain $1,000 risk per trade. 3.) Entry $0.01 ahead of signal candle. [ Tight ] 4.) Stop $0.05 behind signal candle. [ Tight ] 5.) Target $0.13 short of the first support/resistance (minimum 1.6:1 r/r) a. ½ position closed at Target b. Stop moved to break even. c. Remainder of position closed at end-of-day 6.) Six trades per day limit. Test 5B Test 5B was exactly the same as 5A, but without trailing of partial positions. The entire position was closed at 1.6:1 r/r, or at end-of-day (typical.)

13 CATCH 22 JONELIS PG. 13 TEST 6, Position Entry was loosened to $0.25 past the signal candle. The range of the signal candle was ignored. The stop loss was set at 8% behind the entry price (stops as described by William O Neil.) The target was 25% beyond the entry price (common practice), resulting in a 3:1 reward to risk ratio. Margin was 2:1. The result was that positions were held longer than in other tests and that return and the risk were both higher. Test 6A 1.) Entry after Signal Candle off ALL of the following: a. EMA Trend [10>20>50 or 50>20>10] and b. Minimum 3 day pullback or 2 day bounce, and c. Stochastic pullback past 50% (anticipating overbought/oversold) 2.) Position size adjusted to maintain $1,000 risk per trade. 3.) Entry $0.25 ahead of signal candle. [ Medium ] 4.) Stop 8% behind entry price. [ Wide ] 5.) Target price 25% beyond entry price (3:1 r/r) a. ½ position closed at Target b. Stop moved to break even. c. Trailing stops at fractal. Test 6B Test 6B was exactly the same as 6A, but without trailing of partial positions. The entire position was closed 25% beyond entry price (3:1 r/r) TEST 7, Buy-and-Hold All 22 stocks were entered at the start of the test period and closed for reference at the end of the test period. (The last price was for comparison purposes, since during actual investing, the positions would not necessarily have been closed at that time.) All positions were Long. No entry parameters, money management techniques or stop losses were used. No margin was used. The result was a 21% loss. Though negative, this outperformed the Nasdaq Market for that period, as referenced by the QQQ position, which lost a net 42%. 1.) Entry at the mean of the daily price range on 6/1/02. 2.) All positions Long. 3.) No stop loss. 4.) Closing reference price at the mean of the daily price range on 6/28/02. The various models are compared and contrasted in the attached Charts.

14 CATCH 22 JONELIS PG. 14 ATTACHMENTS Standard Version CHART A & B, TOTALS (2 pgs.) CHART C & D, POSITION SIZES (2 pg.) Sample Trading Sheet (1 pg.) Sample Calendar (1 pg.) Expanded Version CHART A & B, TOTALS (2 pgs.) CHART C & D, POSITION SIZES (2 pgs.) TRADING SHEETS, TEST 1A 6B; 2B 6B; 7 (265 pgs.) CALENDARS, TEST 2A 6A; 2B 6B (63 pgs.) Web Version CHART A & B, TOTALS (2 pgs.) CHART C & D, POSITION SIZES (2 pgs.) Sample Trading Sheet (1 pg.) Sample Calendar (1 pg.) STOCK CHART ILLUSTRATIONS (5 pgs.) Advanced GET XTL Example Advanced GET Type 1 Example Advanced GET Type 2 Example Test 2A Swing Trade Example Standard Swing Trade Example

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20 CATCH 22 JONELIS PG. 20

21 CATCH 22 JONELIS PG. 21

22 CATCH 22 JONELIS PG. 22

23 CATCH 22 JONELIS PG. 23

24 CATCH 22 JONELIS PG. 24

25 CATCH 22 JONELIS PG. 25