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FREE Report
Contents of the Baseline Issue of BackTesting Repo= rt
Letter from the Editor. <=
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Back Test Briefing. <=
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Back Test Process. <=
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How to Use Back Test Results<=
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How to Compare Strategies<=
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Strategy Under Test <=
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Backtesting Setup Details<=
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Charts of Examples. <=
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Back Test Results. <=
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Baseline for Active Investors<=
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Baseline for Position Traders<=
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Baseline for Swing Traders<=
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Highlights of Results Distributions<=
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Conclusion. <=
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Next Steps. <=
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How to use BackTesting Report<=
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Understanding Technical Indicators Made Easy with
BackTesting Report<=
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Resources. <=
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Bibliography. <=
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Software. <=
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Web Sites. <=
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Important Disclosure. <=
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Watch the free video at
<= span style=3D'font-size:14.0pt;mso-fareast-font-family:Calibri'>Letter from the = Editor
&nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = &nb= sp; = November 2008
Dear Reader-
BackTestingReport grew out of my own efforts to back t= est strategies in order to become a better trader. I hope that it helps you as well.<= span style=3D'mso-spacerun:yes'> Before we dive in to this report, = I want to share my story with you.
My passion for computers began in 5th grade= and continues to this day. My col= lege degree is in electrical engineering and computer science. I professionally wrote software, m= anaged development teams, and produced worldwide technical seminars. In 2005, I set out on my own to tr= ade the US equity markets. I took various courses, read extensively, and did a rather hurried back test of a trading system. With that sys= tem, I placed second in Dr. Alexander Elder’s Spike trading competitions.
Not content with second place, I painstakingly set up =
the
computers, tools, and processes for more rigorous and comprehensive back te=
sts. I did a detailed exploration of the
market, popular technical indicators, fundamental data, and order types.
Frankly, I was shocked to find that some highly-touted indicators and systems had a historical track record that was no better, and sometimes worse, than pure chance! <= /span>That made identifying the potentially profitable strategies all the more valuabl= e. In some cases, multiple strategies = showed equal P&L. With the back = test data I could see trade-offs between strategies and more confidently pick the choice I liked best. In short= , from the back tests I gained the peace of mind that comes from making an informed decision to select a trading style for me.
As I put the finishing touches on this report, the bro= ad markets are down significantly from last year’s highs. The days of buying and holding for= a sure profit are gone. Now mor= e than ever, investors and traders need solid, well-tested strategies to help navi= gate the ups and downs of the markets. That’s what it takes to prote= ct retirement funds and grow account equity.
My goal as editor of the BackTesting Report is to give readers an outstanding value. This baseline report sets the stage. Subsequent reports test out popular trading strategies to sort the winners from the losers. BackTesting Report offers tremendous insight into market strategies = for a small fraction of the actual time, effort, and cost of backtesting. It costs far less to evaluate a st= rategy with a back test than to pay the price in live trading!
Sincerely,
Jackie
Ann Patterson
Editor, = BackTesting Report
Director, Own Mountain Trading Company
Back Test Briefing
Back t= ests measure the relative performance of a set of t= rading strategies on historical price data. They provide insight into past market behavior as well as the trading strategies. Although most traders agree that i= t’s useful to back test, many traders don’t do it because of the time, ex= pense, and expertise required. This = report gives you a leg up on the markets without having to go through all the work yourself.
![3D"Text](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image008.gif")
A back test is a model of trading. Significant differences with live
trading exist such as: commissions, s=
lippage
or skid, after hours trading, dirty d=
ata,
software bugs, and human error. A
back test doesn’t guarantee future performance but it can help you so=
rt
out whether one strategy did better than another.
Backtesting is a screening tool that is really good at eliminating under-performing trading strategies. If a trading strategy didn’t perform well in the past, it’s unlikely to make money in the future. = Knowing that before risking real mo= ney trading the strategy is incredibly valuable. Backtesting helps a trader to weed= out the likely losers and to understand potentially successful strategies.
One of the toughest challenges in trading is sticking = to a good strategy through a drawdown<= /a>, and back testing helps here too. Armed with BackTestingReport, informed traders can take confidence from choosing a strategy with superior relative performance.
Back Test Process
In a back test, a trading strategy is written as an objective set of rules in a computer language. The back test engine applies the strategy to the historical data for each selected stock symbol. It simulates trades whenever entry= and exit signals are given by the strategy.&nb= sp; The results are recorded for each trade. Ultimately, results are compiled fo= r all trades on all symbols. For BackTesting Report, this means hundreds of thousands of trades with 7147 st= ock symbols over a 14 year time period. These results are then statistically analyzed and presented in a user-friendly form.
How to Use Back Test Results
To get the most out of backtesting, you need to know w= hat to look at and when to look at it. To get a handle on the problem of designing a trading system, break the problem in= to pieces such as entry, stops, exits, trade size and tackle things one at a t= ime. First pick an ent= ry strategy by backtesting several different candidates, all using the same simple exit strategy. Decide on the e= ntry by comparing the win rate<= /a> and opportunity (number of trades).
After choosing the best entry, you need to pick the ex= it strategy. Compare the strateg= ies using expectancy, standard deviation, average holding time and pick the one with = the best relative performance.
Some strategies will show better than the baseline pur= ely by luck or randomness. Advanced statistical methods which estimate the probabilities of that happening are beyond the scope of this document.
How to Compare Strategies
Picking entry strategies with win rates above the base= line is a good start. Also use win= rate as a hint on the ease of following a system. For example, consider if you can r= eally stick to a trading plan that only wins 10% of the time. Most importantly, remember that win= rate alone doesn’t determine if a strategy is profitable – the size = of the wins and losses matters, too.
One measure of excellence in a trading strategy is a l=
arge
positive expectancy and small standard deviation.![3D"Text](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image009.gif")
=
The
standard deviation tells you how much the trade results vary. Smaller standard deviations indica=
te
smoother equity growth whereas large deviations mean a wild ride.
Expectancy measures a trading strategy’s
profit potential. It considers the win rate as well as the am=
ount
gained by each win. Expectancy can compare trading strategies th=
at give
small gains often versus strategies that rarely win but win big when t=
hey
do. Over a large number of trades, the expectancy is the expected gai=
n of
the trading strategy. Higher expectancy is better, and negative
expectancy is the kiss of death for a strategy. Backtesting Repo=
rt
tells you the expectancy of all strategies tested so to help you avoid losi=
ng
money on negative expectancy strategies.
Trading coach Van Tharp defines expectancy in terms of risk, as the avera=
ge
of the R-multiples returned by tradi=
ng
or backtesting the system. Ve=
ry
briefly, an R-Multiple of a trade is the profit/loss divided by the am=
ount
risked.
To calculate the expectancy via R-multiples, it’s necessary to estimate the risk. In this report, the total investment is assumed to be at risk because it doesn’t include a stop loss. <= /span>See the Exit Strategies series of BackTesting Report for back tests with stop losses and trailing stops.
Assessing Risk with Maximum Adverse Excursion (MAE)
![3D"Folded](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image010.gif")
Equally important to understanding the
potential for gain is assessing the risk of loss. Drawdown is frequently quote=
d in
the industry but it’s not very useful here because most of us are not
managing a portfolio of the 7147 stocks back tested. Instead we can gain knowledge of t=
he
risks by tracking the =
Maximum
Adverse Excursion (MAE). =
Don’t
let that technical term put you off, it really just means knowing how much =
the
position went against you. MA=
E is
not the same as the biggest losing trade because a stock may wander down fo=
r a
huge open loss and come back before the exit. (See JDSU example in BTR5-MACD Sell
Signals).
![3D"Text](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image011.gif")
Average Bars in Trade
Another factor in your overall profitability is how long your
money is tied up in each trade.
Average bars per trade can give you a hint. Keep in mind that we’re tryi=
ng to
find strategies that fit the maxim “let winners run and cut losses=
221;
so the winning trades may go on far longer than the average shown.
<= span style=3D'font-size:14.0pt;mso-fareast-font-family:Calibri'>Strategy Under T= est
This report establishes the baseline for comparison wi= th all other trading strategies. Thi= s is a crucial step because you need a way to tell if a strategy did well when the market just happened to pull it along, or if the strategy really might sign= al timely entries and exits. A good bas= eline shows what happens in the absence of a strategy. Rather than exercise judgment on w= hen to enter, the baseline strategy always enters when it doesn’t have a position and likewise, always exits after a pre-defined number of days.
Backtesting Setup Details
Markets: = US Stocks and international stocks represented by ADRs on NYSE, AMEX, NASDAQ including delisted tickers. (Click here for stock lists.)
Time Periods: May 1994 - April 2008, divided into three samples to prevent = over= -optimization.
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image012.gif")
May 1994 – April 2004
denoted by darker blue
&nbs= p; Ten-year period chosen to include major up, down and sideways movements.
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image013.gif")
May 2004 – April 2007
denoted by medium blue
&nbs= p; Out-of-= sample data for the original period. At 3 years, it’s 1/3 as long as origina= l.
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image014.gif")
May 2007 – April 2008
denoted by light blue
&nbs= p; Current data. It’s 1/3 of= the previous sample and is more out-of-sample data.
(Click here for background on time period selection.)
Direction: Long Only= and Short On= ly
Entry Strategy: Enter all stock= s all the time, as long as volume criterion is met (more than 500,000 shares dail= y). This deliberately simple strategy s= ets the baseline to compare to supposedly smarter strategies tested in later BackTesting Reports. It enter= s via Market on Open orders.
Exit Strategy: Timed Exits of 2 days, 20 days, 200 days, chosen as the simplest way to make a baseline for = popular Trader Types. It exits via Ma= rket on Close orders.
Sizing: 1000 shares for= every trade.
Backtesting Engine: TradeStation® version 8= .3, Build 1631
Data Vendor: CSI Data This data set was specially selected for accuracy after extensive testing. (Click here for background on data preparation.)
Charts of Examples
These charts are screenshots of the TradeStation back = test engine running Backtesting Report custom code for the baseline strategies t= ested in this issue.
![3D"ScrZ_AAPL_Long.jpg"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image016.jpg")
Figure SEQ Figure \* ARABIC 1: Example Long Trades
The solid aqua lines are profitable trades and the hot= pink lines are losses. In Figure 1= , if the market goes up during the trade, a profit results. Figure 2 shows the short strategy,= which profits when the market goes down (in aqua), but it loses money when the ma= rket goes up.
![3D"ScrZ_AAPL_Short.jpg"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image018.jpg")
=
Figure SEQ Figure \* ARABIC 2: Example Short Selling
Back Test Results
This report focuses on Win Rate<= /a>, which is the metric to beat for other ent= ry strategies in similar exit conditions.= BackTesting Report= research reports test popular t= rading strategies so you can see how they compare. Results are separated by Trader Types = for readability.
Baseline for Active Investors
An active investor manages his/her positions and holds= stocks for about a year. Typically, = this investor trades the long side of the market and puts effort into entry stra= tegies to decide when to buy. Besides knowing the benefits of Long Term Capital Gains tax treatment, not much more effort goes into a formal exit strategy. Few active investors practice selli= ng short. A successful active in= vestor catches a trend, rides it even through rough patches, and exits rather than give all profits back on an opposing trend.
Figure 3
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image020.gif")
Figure 4
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image022.gif")
Looking at the baseline data in the figures above, the super-simple strategy of ALWAYS buying any stock with decent volume had a w= in rate well over 50% until 2007. We can see how the market carried the active investor on a rising tide through the first two test periods.
Things aren’t always so easy. Even though the third test period = ends in May 2008 (before the intense carnage of the credit crisis), it clearly s= hows that the tide turned and long-term, long-only trading styles found a diffic= ult environment. The simple basel= ine shows that always buying and holding only won 24% of the time in 2007-2008.
Keep in mind that there were no stops in this experime= nt so the price could have dropped dramatically over the longer holding period. The open loss may have exceeded a trader’s ability to stomach it, even on a trade that ultimately ended= in profit.
Obviously, you need more robust strategies than just b= uying and holding on. Ideally, you = want entry strategies that start off in the right direction and exit strategies = that get out with a profit. Read BackTesting Report to help y= ou screen candidate strategies looking for the ones with the best relative performance.
To be a real winner, a strategy for active investors w= ill need to beat the baseline in Table 1-Table 2. Potentially profitable strategies = have a positive expectancy, shown in green. Losing strategies have their negati= ve expectancies colored red. Sma= ller standard deviations are better.
Table 1
|
Table of Results: Long |
||||||
|
=
Active Investor<=
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Long Baseline
1994-2004 200 day |
26049 |
192 |
57% |
0.15 |
1.11 |
|
=
Active Investor<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Long Baseline
2004-2007 200 day |
5497 |
196 |
62% |
0.11 |
1.16 |
|
=
Active Investor<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Long Baseline
2007-2008 200 day |
1378 |
199 |
24% |
-0.16 |
0.32 |
Table 2
|
Table of Results: Short |
||||||
|
=
Active Investor<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Short Baseline
1994-2004 200 day |
26049 |
192 |
42% |
-0.16 |
1.11 |
|
=
Active Investor<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Short Baseline
2004-2007 200 day |
5497 |
196 |
38% |
-0.12 |
1.16 |
|
=
Active Investor<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Short Baseline
2007-2008 200 day |
1378 |
199 |
76% |
0.15 |
0.32 |
As we can see from Figure 4 and Table 2, short selling had a much higher win rate in the last period. The key is to know when to apply t= hat style of trading because shorting was not generally rewarded in the earlier periods.
<= span style=3D'font-size:13.0pt;mso-fareast-font-family:Calibri'>Baseline for Pos= ition Traders
A position trader seeks intermediate-term opportunities. To be successf= ul, a position trader gets in on a burst of action -- perhaps one leg of a longer-running trend -- and gets out before the action fades.
The results in this report set the baseline: to be considered useful, an entry strategy needs to show fewer trades and higher = win rate with a comparable average holding time. Potentially profitable strategies = have a positive expectancy, shown in green. Losing strategies have their negative expectancies colored red. Smaller standard deviations are generally better.
The results to beat for position traders are shown bel= ow in Table 3 - Table 4.
Table 3
|
Table of Results: Long |
||||||
|
=
Position Trader<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Long Baseline
1994-2004 20 day |
140473 |
20 |
54% |
0.02 |
0.23 |
|
=
Position Trader<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Long Baseline
2004-2007 20 day |
41980 |
20 |
55% |
0.01 |
0.14 |
|
=
Position Trader<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Long Baseline
2007-2008 20 day |
12597 |
20 |
44% |
-0.02 |
0.13 |
Not surprisingly, the standard deviation increases wit= h the holding time. The longer you = own a stock the more the price can move around.&= nbsp; The other sections show that prices didn’t change much in 2 da= ys but spread out quite a bit over 200 days.&= nbsp; Here at 20 days, the variance was moderate for the position trader.<= /p>
Table 4
|
Table of Results: Short |
||||||
|
=
Position Trader<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Short Baseline
1994-2004 20 day |
140473 |
20 |
44% |
-0.02 |
0.23 |
|
=
Position Trader<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Short Baseline
2004-2007 20 day |
41980 |
20 |
44% |
-0.01 |
0.14 |
|
=
Position Trader<=
span
style=3D'font-size:11.0pt;mso-fareast-font-family:"Times New Roman";color=
:blue;
mso-bidi-language:AR-SA'> |
Short Baseline
2007-2008 20 day |
12597 |
20 |
56% |
0.02 |
0.13 |
The effects of the market trend on the position trader= are muted compared to the active investor: neither the gains nor losses were as great. However, a directional= bias is still clear from the back test data. The simple baseline strategy for po= sition traders still won more often when it went with the major trend of the marke= ts.
Ultimately, you need a strategy that performs well acr=
oss
changing market conditions, not just during a bull or bear market. Note that this may mean that a str=
ategy
has rules to avoid trading under certain conditions. That’s okay as long as it wo=
rks at
the right edge of the chart, not just in hindsight. For example, a rule that says stand
aside while the price is below the 200 day moving average can be useful.
Figure 5
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image024.gif")
Figure 6
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image026.gif")
Close inspection of the two graphs in Figure 5 and Figure 6 shows that a slight gap opened between the win rates for the long and short trades. Because they share th= e same entry points and are the same trades in opposite directions one might expect their win rates to add up to 100%. <= /span>Since they don’t, it means some trades must lose in both directions. How? Commissions are the culprit. A stock must gain enough to cover = the cost of commissions or be counted as a loss. In the first period, the long win r= ate was 54% and the short win rate was 44%, implying that roughly 2% of the tra= des couldn’t make up for the commission after 20 days. This effect gets worse as the aver= age hold time decreases.
BackTest= ing Report investigates several popular trading strategies, looking for the ones that = enable the position trader to make more profits and limit losses.
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image028.jpg")
SneakPeek 1 - Example Expectancy Graph. Each vertical gridline denotes one strategy. The strategy is bac= ktested in the three timeframes and resulting expectancy denoted by the triangle, diamond, and square markers. = Color-coded zones represent profitable strategies (green), strategies with a very thin = edge (yellow), and unprofitable strategies (red). This example is typical of many strategies that produce profits in favorable conditions but lose even more = in unfavorable market conditions.
![3D"Folded](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image029.gif")
Baseline for Swing Traders
A Swing Trad= er capitalizes on short-term price movements. A swing trader will h= old overnight, possibly for several days, which distinguishes swing trading from day-trading. However, a swing trader rarely rides the full long-term trend. Instead a swing trader is ready wi= th a quick exit the moment the trade goes the other way. Given the shorter time span, swing= traders need entry and exit strategies that pounce on the opportunities to get in, capture profits, and get out all the while minimizing the inevitable losses= .
Figure 7
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image031.gif")
Figure 8
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image033.gif")
Swing trades lasting two days were not much affected b= y the market trend. All the win rat= es clustered close to 50% for both long and short trades.
Looking closely at the two graphs in Figure 7-Figure 8, the gap widens between the win rates for the long and short trades. Again, because they share the same= entry points and are the same trades in opposite directions one might expect their win rates to add up to 100%. = Since they don’t, it means some trades must lose in both directions due to commissions. A stock must gain enough to cover the cost of commissions or be counted as a loss. In the first period, the long win r= ate was 49% and the short win rate was 47%, implying that roughly 4% of the tra= des couldn’t make up for the commission after 2 days. This is double the rate of the lon= ger position trades, which shows how transaction costs such as commissions take their toll on a short term strategy.
Neither long trades nor short trades had greater than = 50% win rate in the first period. This illustrates the need for swing trading strategies to signal when a stock is moving with surgical precision.
The short-term trading styles get far more trade oppor= tunities than the intermediate or longer-term styles, according to the “# Trades” column in Table 5 - Table 6.
In general, potentially profitable strategies have a positive expectancy, shown in green. Losing strategies have their negative expectancies colored red. In this case, all the expectancies= for the swing trades were roughly zero (breakeven). The values in Table 5 - Table 6 are colored differently because they are not exactly zero. Yellow indicates that the expectan= cy was slightly positive, but rounded down to zero – indicating caution. Conversely, red indicates rounding= up to zero – warning against a slightly unprofitable strategy.
Table 5
|
Table of Results: Long Trades |
||||||
|
Long Baseline
1994-2004 2 day |
514487 |
2 |
49% |
0.0012 |
0.08 |
|
|
Long Baseline
2004-2007 2 day |
177925 |
2 |
51% |
0.0003 |
0.05 |
|
|
Long Baseline
2007-2008 2 day |
58654 |
2 |
48% |
-0.0024 |
0.05 |
Table 6
|
Table of Results: Short Trades |
||||||
|
Short Baseline
1994-2004 2 day |
514487 |
2 |
47% |
-0.0038 |
0.08 |
|
|
Short Baseline
2004-2007 2 day |
177925 |
2 |
47% |
-0.0027 |
0.05 |
|
|
Short Baseline
2007-2008 2 day |
58654 |
2 |
52% |
0.0003 |
0.06 |
This issue of BackTesting Report provides the baseline using a stunningly simple strategy. This is the baseline for compariso= n for in-depth research reports which back test general entry and exit signals, l= ooking to uncover the means to finesse a swing trade.
Highlights of Results Distributions
(Click here for instructions on how to read a results distribution.)
All the long results distribution graphs in this issue= show the effects of not using a stop loss. By setting the risk as the total amount of the investment, it is not possible to lose more than 1R in a trade.&= nbsp; Hence all the losing long R-multiples are in the -0.5R bin as shown = in Figure 9 below.
Assuming the total cost is risked makes it impossible =
to
lose more than 1R-multiple for long trades in this report. Losing 1R means the stock dropped =
to
zero and the whole investment is lost.&nbs=
p;
The stock price can’t go lower than zero, so 1R is the maximum=
loss
from buying without stops.
Figure 9
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image035.gif")
Figure 10
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image037.gif")
Zooming in with Figure 10 shows that 41% of the losses were over 15% of the total equity and 7% lost = more than half their value. And the period 2004 – 2007 was a strong market for buying stocks! This shows the importance of havin= g a selective entry strategy and a robust exit strategy to limit losses and grow profits.
Another
interesting aspect of the long results distribution is the “fat
tail” to the right. Not=
ice
the few outliers in the range of six to eight R-Multiples (bins marked 6.5 =
and
7.5). The distribution is lopsided with some trades gaining more than 1R. <=
span
style=3D'mso-spacerun:yes'> We could design a trading strategy =
to go
after just those trades, but it would be unlikely that those specific
parameters would continue to work since the market is constantly changing.<=
span
style=3D'mso-spacerun:yes'> A better approach is t=
o come
up with a broad strategy that tends to capture more winners and fewer losse=
s.
It is possible for short trades to lose more than the total investment if the pri= ce runs up more than 100%. However, a short trade can never earn more than that total amount invested, and then only if the price falls all the way to zero. The results distributi= ons from the short backtesting runs confirms it.&nb= sp; Figure 11 illustrates this fact.<= o:p>
Figure 11
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image039.gif")
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image041.jpg")
SneakPeek 2 - Results Distribution from BTR #6: M= ACD Sell Signals
Conclusion
This report establishes a baseline for comparison with= real trading strategies. To = get insight into the market, hundreds of thousands of trades by a very simple strategy were simulated over 14 years of data for 7147 stocks.
To validate the usefulness of indicators and strategie= s you need a backtesting report that tests them over the same time period and sto= ck list. A long strategy t= hat performs worse than this baseline gets a no-confidence vote that says don’t tr= ade it – or if it’s bad enough consider testing it for short selling. A strategy that tests out better than the baseline might help live trading and is worthy of further investigation.
![3D"Text](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image042.gif")
Next Steps
The next step is to back test strategies over the same= time periods with the same stock tickers to compare to the baseline. In this way, BackTestingReport can weed o= ut the weak trading strategies, identify better trading strategies, and help you b= uild confidence in the trading strategies you ultimately choose to use.
Taking positive action is a necessary part of getting = better results. While the market may= go up or down regardless of what any one of us does, we can each prepare ourselve= s to make the most of any given situation by honing our tools and skills.
Take the next step by visiting http://www.backtestingreport.com= and see how various strategies compare to the baseline.
How to use BackTesting Report
The purpose of this issue is different from other BackTesting Reports. This is = the baseline for comparison with strategies tested in later issues. After reading other BackTesting Re= ports, you may want to return here for reference.
In the other BackTesting Reports, this section instruc= ts you on how to apply the strategies that emerged as the winners from the back te= st.
For a detailed example of adding an indicator to a cha= rt using free tools, see http:/= /www.backtestingreport.com/MACD_on_BestFreeCharts.pdf
Also available from BackTesting Report are advanced cu=
stom
software modules to scan for opportunities and mark signals on the chart. SneakPeek 3 - StockFinder screenshot of layout and
scans for MACD Histogram strategy To make use of these scans, you’ll need StockFin=
der
from Worden Brothers, Inc or TradeStation.=
If you have a choice, we recommend StockFinder –=
which
comes with a 30-day free trial at the time of this writing. ( Own Mountain Trading Company is
affiliate #948 of Worden Brothers) See http://www.stockfinder.com/?AFCODE=
=3D948![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image044.jpg")
Understanding Technical Indicators Made Easy =
with BackTesting
Report
![3D"base_cover_soft_sm"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image046.gif")
![3D"btr_ma_cover_for_now_lump_sm"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image048.gif")
![3D"stockfinder_box_sm"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image050.gif")
![](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image051.jpg")
![3D"macd_histos_soft_sm"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image053.gif")
![3D"macd_sell_soft_sm"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image055.gif")
![3D"macd_link_soft_sm"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image057.gif")
![3D"macd_div_soft_sm1"](3D"file:///C:\2C87B225\BackTestingReportBaseline_files\image059.gif")