own mountain trading company presents · 2012. 9. 19. · energy exploring big hitters such as macd...

© 2009 by Own Mountain Trading Company. All Rights Reserved.

OWN MOUNTAIN TRADING COMPANY PRESENTS

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This report builds upon Issue1. Reading it first is highly recommended.

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Contents

Contents ...................................................................................................................................... 2

Letter from the Editor ................................................................................................................. 3

The Strategy Evaluation Process ................................................................................................. 4

Strategy Under Test .................................................................................................................... 5

Basic Exits Tested .................................................................................................................... 5

Adding Stop Losses ................................................................................................................. 8

Backtesting Results ................................................................................................................... 10

Basic Exits, No Stops ............................................................................................................. 10

Stop Losses ............................................................................................................................ 18

Conclusion ................................................................................................................................. 26

General .................................................................................................................................. 26

Active Investors ..................................................................................................................... 26

Position Traders .................................................................................................................... 26

Swing Traders ........................................................................................................................ 26

Next Steps with MACD .............................................................................................................. 27

Resources .................................................................................................................................. 27

How to Apply This Strategy ................................................................................................... 27

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Bibliography ............................................................................. Error! Bookmark not defined. Software ................................................................................... Error! Bookmark not defined. Web Sites ................................................................................. Error! Bookmark not defined.

Disclaimer ..................................................................................... Error! Bookmark not defined.

HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT BEEN EXECUTED, THE RESULTS MAY HAVE UNDER-OR-OVER COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFIT OR LOSSES SIMILAR TO THOSE SHOWN.

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Letter from the Editor May 2009 Dear Reader- This issue is the second in the series about MACD. The previous report explored the buy signals MACD generates in advance of a moving average crossover. This issue explores the sell signals. It gives not only a comparison of the different exit strategies but also our first glimpse of MACD as a whole system because it reports meaningful expectancy numbers. The tests in this issue help us find out if systems based on MACD can be profitable, and if so, which exit type performed best. In setting up the backtests for this issue, I got mired in the nuances of MACD exit signals. While the isolated events of MACDH ticking up/down and lines crossing up/down are objectively obvious, the actual usage can be quite subjective, especially when using different parameters for entries and exits. For example, when you buy on a 12-26-9 MACDH uptick, the 19-39-9 MACD lines may still be above zero. Do you require them to dip below zero and rise again before accepting an exit signal from them, or not? A human trader may not be consistent in cases like this and wait for a full cycle in strong markets but more likely be quick on the trigger in weaker times. The backtest forces consistency so we choose a rule and live by it in all situations. It’s tempting to try out many little twists on the backtester to eek out more gains. However, any one special usage may be the result of chance and not repeat in the future, especially when the stats come out very close. At this point, a bigger payback probably comes from spending energy exploring big hitters such as MACD indicator/price divergences and different strategies to complement and compete with MACD. Keep in mind while reading this issue that even Gerald Appel, the inventor of the MACD, recommends1 using other strategies along with the MACD. Ultimately our trading strategies will do that too but first we will test each piece independently to find out how it might best contribute to the overall operation. This is the first report with real exit signals and it introduces a new style of graph to compare expectancies. Also new to this issue is an extended Resources section showing how to set up charting tools to display select strategies in this report. This includes links to detailed instructions, products, and services. I hope this will make it easy to experiment on your own and perhaps incorporate MACD strategies into your routine as you see fit. Sincerely, Jackie Ann Patterson

Editor, BackTesting Report Director, Own Mountain Trading Company

http://www.backtestingreport.com/


The Strategy Evaluation Process

You’ll find background information about how to use this report in the grey boxes. Generally it doesn’t change much from report to report. The blue words are usually links to the BackTestingBlog online glossary. If you are already familiar with BackTesting Report, just skip ahead to the next section if you wish.

BackTesting Review Backtesting measures the relative performance of a set of trading strategies on historical price data. Since backtesting relies on past data, it makes no guarantees about future performance and can’t say whether a strategy will do as well in the future as it did in the past. However, if a strategy didn’t perform in the past, there’s no reason to believe it will suddenly turn into a winner. It pays to avoid strategies with a losing track record. Although most traders agree that backtesting is useful, many people don’t do it because of the time, expense, and expertise required. Backtesting Report gives you a leg up on the markets without doing all the work yourself.

How to Compare Entry Strategies Picking entry strategies which have win rates above the baseline is a good start. Also use win rate as a hint on the ease of following a system. For example, consider if you can really 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. How to Compare Exit Strategies Most complete strategies have two types of exit tactics: taking profits and stopping losses. To keep the exploration to a manageable size, first we will compare profit-taking without stops, with the same criteria to sell the stock as to buy the stock. This also gives a fantastic what-if scenario to see what can happen if you run without limiting risk. Next we add a couple different stops losses and compare them both to the stop-less run and to each other. Keep the following criteria in mind while comparing different exit strategies.

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Strategy Under Test The goal is to identify the best MACD exit strategy. To better understand the merits of each exit strategy, it helps to isolate exits from the entry strategies by picking one entry to use consistently throughout the test. Unless otherwise stated, tests in this issue were run with the MACD Histogram Uptick with price above the 200-day moving average. Also unless stated otherwise, all runs used the de-facto standard settings of 12-26-9 (fast-slow-signal). This entry proved to have a decent win rate, although it was not always the best entry signal for all trader types. For more details on how to correctly identify this entry and win rates of several other MACD entries, see the MA Buy Signals BackTesting Report.

Basic Exits Tested

The first experiment in this report compares several exit signals given by the MACD:

Symmetric to Entry: exit when the 12-26-9 MACDH ticks down and is above zero

Slower Exit: exits when the 19-39-9 MACDH ticks down and is > 0, (with a 12-26-9 entry)

Gerald Appel Power Tools: basic decision structure from Mr. Appel’s book, uses Appel’s histogram but without indicator/price divergences or other chart patterns

Lines Cross: exit when the 19-39-9 MACD lines cross down and are above zero See Figure 1 - Figure 5 below. MACDH_MA_Sym_12-26-9

Figure 1 -TradeStation screenshot of backtesting strategy which buys when MACDH 12-26-9 ticks up (and is below zero) and sells when MACDH 12-26-9 ticks down (and is above zero). MACD lines are ignored. This shot

What is the best exit strategy for the MACD Histogram?

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shows the strategy taking 4 trades, buys denoted by MACDH_buy and sells denoted by MACDH_MA_Sym. Dark green lines connect profitable trades and red lines connect unprofitable trades.

MACDH_MA_Sym_19-39-9

Figure 2 - TradeStation screenshot of backtesting strategy which buys when MACDH 12-26-9 ticks up (and is below zero) and sells when MACDH 19-39-9 ticks down (and is above zero). Each MACD indicator is marked with its parameter settings at the left. 12-26-9 is in the middle of the chart, and 19-39-9 is on the bottom.

Figure 3 -Zoom in on TradeStation screenshot of backtesting strategy which buys when MACDH 12-26-9 ticks up (and is below zero) and sells when MACDH 19-39-9 ticks down (and is above zero).

Comparing Figure 1 and Figure 2 presents a surprise extra trade. Figure 3 zooms in to show that what really happens. Both strategies buy in at the same time, about June 21 – as well they


should, they are using the same entry strategy. The difference is that the slower setting of 19-39-9 shown in Figure 2 actually gives a sell signal early! This is because the slower MACDH has not yet had a chance to get below zero before the faster MACDH triggers the buy. Then the fast MACDH has another buy signal (with a better entry point). In this example, both the faster and the slower histogram tick down at the same time, showing no real difference in the exits. In fact, that happens quite a bit. Visual inspection of several charts gives the impression that the settings impact the zero crossings of the MACDH which are the same as the lines crossing. This change in the settings doesn’t appear to make much difference in the MACDH uptick/downtick timing. But we won’t go by visuals alone, we will backtest to be sure. Appel’s Power Tools

Figure 4 - TradeStation screenshot of backtesting strategy based on Gerald Appel's Power Tools book

The “Power Tools” book by Gerald Appel offers complex instructions for trading with the MACD along with other techniques. In this test, we isolate the basic MACD-related rules. The case where price diverges from the indicator is also not implemented in this run. This strategy uses the “Power Tools” entry method which selects different parameter settings for Appel’s Histogram upticks based upon price being above or below the 50-day Exponential Moving Average (EMA). Looking very closely, you can also see that the parameter settings make a more distinct impact on Appel’s Histogram than MACDH. From Figure 4, we can see the strategy is geared towards later exits by waiting for the second time that the slower lines cross downwards. The second exit signal is defined as the crossing after a pullback which follows the first crossing. A fail-safe exit rule sells if the price closes below the 50-day EMA.


The trade-off for the Power Tools strategy is evidenced by the Oct whipsaw as the strategy entered below the 50-day MA, only to be stopped out when Appel’s histogram ticked down signifying that the MACD lines reached lower than at the entry price - a rule in the book. MACDH_MA_Lines_12-26-9

Figure 5 - TradeStation screenshot of backtesting strategy which buys when MACDH 12-26-9 ticks up (and lines are below zero) and sells when MACD 12-26-9 lines cross down (and are above zero).

The final strategy tested without stops is illustrated by Figure 5. The aim of this strategy was to attempt to let winners run longer by waiting for the MACD lines to cross downward while above zero. To avoid pre-mature exits, that means waiting for the lines to go below zero before entering. The side effects are shown above: no re-entry when the trend resumes in Jul - Sept but a better enter point in the Oct timeframe by waiting for the stock to be oversold as indicated by the lines under zero. Backtesting results will show us the net effect of that trade-off.

Adding Stop Losses

The first set of runs above experimented with different ways to let profits grow. The second section experiments with ways to limit losses. For this series, the MACDH_MA200_Sym1939 served as the basic strategy and three different kinds of stops were pitted against each other. The stop types and parameter variations were:

%: 3% and 9% of price

ATR: 2 and 3 times the average true range

Histo-stop: a downtick of the MACDH from the entry price Rather than taking a fixed number of shares and almost unlimited risk as we do with no stops, setting a stop gives an opportunity to have a fixed risk on every trade by varying the number of shares. If we assume a hypothetical $100,000 account and risk 1% of that account on each

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trade, that amounts to risking $1000 per trade. The risk is considered the dollar amount between the entry price and the stop. So the size = $Risk / (Entry price – Stop Price) The benefit is that trades at different share prices now have equal impacts to risk and reward.

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. May 1994 – April 2004 denoted by darker blue Ten-year period chosen to include major up, down and sideways movements. May 2004 – April 2007 denoted by medium blue Out-of-sample data for the original period. At 3 years, it’s 1/3 as long as original. May 2007 – April 2008 denoted by light blue 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 Entry Strategy: Enter any stock with buy signals as described above, and when volume criterion is met (more than 500,000 shares daily). All entries are next day via Market on Open orders.

Exit Strategy: Exit all stocks according to the signals described above. Profit-taking is done next day via Market on Open orders. Stop losses, where applied, are simulated as stop orders which may be executed intraday. This combination doesn’t exactly model a nimble trader who may grab profits from an exit signal early in a day in which those waiting for end-of-day miss out due to a sell-off before the close. It does model end-of-day trading and investment though. Sizing: Where no stops are involved, size is fixed at 1000 shares. With stops, the risk amount was fixed at $1000 and the size computed as the number of shares that would risk $1000 between the anticipated entry and the stop. Backtesting Engine: TradeStation version 8.6, Build 2525 Data Vendor: CSI Data This data set was specially selected for accuracy after extensive testing. (Click here for background on data preparation.)

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http://backtestingblog.com/backtesting_set_up/selecting-time-periods-for-backtesting/

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http://www.csidata.com/

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Backtesting Results

Basic Exits, No Stops

Table 1

MACDH Basic Exits Productivity Reliability Probability

Name of Strategy Under Test # Trades Avg Hold %Wins Expect StdDev

Baseline 1994 - 2004, 20 days 140473 20 53.92% 0.0188 0.23

MACDH_MA_Sym_1994-2004 49631 12 56.00% 0.0074 0.11

MACDH_MA_Exit1939_1994-2004 51051 12 53.73% 0.0073 0.11

MACD_Power_Tools_1994-2004 87040 13 31.09% 0.0108 0.23

MACDH_MA_Lines_1994-2004 23269 19 43.87% 0.0098 0.17

Baseline 2004-2007, 20 days 41980 20 55.41% 0.0107 0.14

MACDH_MA_Sym_2004-2007 17597 12 56.38% 0.0048 0.07

MACDH_MA_Exit1939_2004-2007 18140 12 54.24% 0.0046 0.08

MACD_Power_Tools_2004-2007 32270 13 31.39% 0.0077 0.89

MACDH_MA_Lines_12269_2004-2007 8415 19 44.07% 0.0091 0.10

Baseline 2007-2008, 20 days 12597 20 43.55% -0.0182 0.13

MACDH_MA_Sym_2007-2008 4419 9 46.18% -0.0061 0.07

MACDH_MA_Exit1939_2007-2008 4550 10 44.74% -0.0056 0.07

MACD_Power_Tools_2007-2008 15743 7 27.61% -0.0136 0.07

MACDH_MA_Lines_2007-2008 2635 15 34.75% -0.0045 0.09

The number of trades was far less than the baseline because the entry strategy is selectively picking times to enter. The MACDH_MA_Lines strategy has far fewer trades than the others,

With exit strategies the key criteria are Expectancy and Maximum Adverse Excursion (MAE), described in detail below. The number of trades measured how often the strategy had an opportunity to trade. The average hold time measures how long each trade went on. Between them you can get an idea of how productively each strategy put its funds to use. Keep in mind that the average hold times are just that – an average. Winning trades tended to go on longer while losing trades were often shorter.

How to Read the Results Table For Exit Testing The table below summarizes the backtesting results for the exit testing. In general, potentially profitable strategies have a positive expectancy, shown in green. Losing strategies have their negative expectancies colored red. Yellow indicates that the expectancy was slightly positive, but rounded down to zero – indicating caution. Note that we compare to 20-day timed exits as the closest to the actual hold times of the strategies with real exits.

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and a longer average hold time which shows that exit strategy stays in winning trades longer. The MACD_Power_Tools strategy probably stays in winning trades the longest but its statistics reveal more trades and short average hold time. This is because it is repeatedly whipsawed while attempting to find the bottom in pullbacks under the 50-day MA.

Win Rates

Graphs of win rates are shown in Figure 6 - Figure 8. It is not entirely surprising that adding exits pushed the win rates under the baseline for two of the strategies. Nor is it particularly cause for alarm. The real acid test of the exit strategies is expectancy and MAE. What we can tell from the win rates is that none are so low as to make the strategy difficult to follow. The lowest is a 30% win rate which would average around two losses for every win, although strings of losses could go on much longer. You decide what is acceptable for you.

Figure 6

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MACDH Symmetric

Exits MACDH Exit 19-39-9

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BaselineStrategy


Figure 7

Figure 8

Expectancy

A large win can make up for several smaller losses and allow a strategy with lower win rate to be more profitable. That’s why expectancy is a very important metric for a complete strategy.

Base

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Exits MACDH

Exit 19-39-9

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MACDH MA 200 Basic Exits 2004-2007

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MACDH MA 200 Basic Exits 2007-2008

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Figure 9

Baseline MACDH

Symmetric Exits MACDH Exit Hist 19-39-9

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MACDH MA 200 with Basic Exits

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How to Read the Expectancy Graph The colorful graph above plots the expectancy for all of the exit strategies under test plus the baseline. Each strategy occupies one space along the horizontal axis and is in the same order as in the data table. All the data for one strategy is aligned in a vertical column. As shown in the legend, each time period has a particular shape to identify its data point. For example, 1994-2004 is denoted with a diamond. If a shape is not visible, it is hiding behind a larger shape which had roughly the same value. The vertical axis displays the expectancy. Negative expectancies (unprofitable) are color-coded red. Notice that the red extends above the zero line. That is not an accident. A strategy that comes out marginally positive in hypothetical backtesting is unlikely to be profitable in real situations. The colors gently fade to yellow to indicate caution. Then they go on to green to signify strategies that were more profitable in backtesting and have a chance at real-world profitability. As the data points spread apart from the zero line, they reveal that increasing risk goes with increasing returns. It’s relatively easy to find rules and leverage that increases the expectancy in good times but it usually comes at the expense of a greater negative expectancy when conditions deteriorate. Look for strategies that go further into the green zone and less into the red zone than others.


At a glance we can see from Figure 9 that none of the basic exit strategies tested demonstrated great profitability and in fact all results are clustered around the breakeven point. The next two sub-sections will explore underlying reasons for the lackluster results and then will take steps to see if we can improve the results.

Risk as Measured by MAE

The MAEs in Table 2 give clues as to why the results weren’t better. Huge adverse excursions make these strategies untenable. Note, however, that MACD strategies still did much better than the baseline MAE. The section on stops will show them doing even better. The Power Tools strategy consistently controlled risk the best as evidenced by the smallest MAEs in each time period. Table 2

MACDH Basic Exits Viability Per Share

Name of Strategy Under Test $ MAE Avg $ MAE Max MAE Avg MAE Max

Baseline 1994 - 2004, 20 days $ 1,953.28 $ 518,000.00 $ 1.95 $ 518.00

MACDH_MA_Sym_1994-2004 $ 1,577.46 $ 640,620.00 $ 1.58 $ 640.62

MACDH_MA_Exit1939_1994-2004 $ 1,525.52 $ 458,000.00 $ 1.53 $ 458.00

MACD_Power_Tools_1994-2004 $ 844.83 $ 182,500.00 $ 0.84 $ 182.50

MACDH_MA_Lines_1994-2004 $ 1,687.21 $ 331,000.00 $ 1.69 $ 331.00


Baseline 2004-2007, 20 days $ 1,643.57 $ 207,600.00 $ 1.64 $ 207.60

MACDH_MA_Sym_2004-2007 $ 1,290.12 $ 52,930.00 $ 1.29 $ 52.93

MACDH_MA_Exit1939_2004-2007 $ 1,272.55 $ 71,940.00 $ 1.27 $ 71.94

MACD_Power_Tools_2004-2007 $ 644.43 $ 27,070.00 $ 0.64 $ 27.07

MACDH_MA_Lines_12269_2004-2007 $ 1,366.30 $ 28,240.00 $ 1.37 $ 28.24

How to Assess Risk with Maximum Adverse Excursion (MAE) Equally important to understanding the potential for gain is assessing the risk of loss. Drawdown is frequently quoted in the industry but, because most of us are not managing a portfolio of 7147 stocks, it’s not very useful here. Instead we can gain knowledge of the 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. MAE is not the same as the biggest losing trade because a stock may wander down for a huge open loss and come back before the exit. To stay in the game, the MAE needs to stay under the size of your account. To be successful, the MAE needs to be limited to a fraction of your account. In the table below, the baseline “no-strategy” strategy shows how far awry trades can go. The 20-day timed exit functions as the baseline because that most closely matches the average number of days that the other strategies held their stocks.















Baseline 2007-2008, 20 days $ 3,121.15 $ 75,660.00 $ 3.12 $ 75.66

MACDH_MA_Sym_2007-2008 $ 2,184.19 $ 83,760.00 $ 2.18 $ 83.76

MACDH_MA_Exit1939_2007-2008 $ 2,122.20 $ 82,160.00 $ 2.12 $ 82.16

MACD_Power_Tools_2007-2008 $ 947.67 $ 51,220.00 $ 0.95 $ 51.22

MACDH_MA_Lines_2007-2008 $ 2,232.86 $ 51,440.00 $ 2.23 $ 51.44

See Figure 10 for an example of how MAEs can get so large. First off, the stock is high priced. Secondly, the strategy always buys a fixed number of shares, in this case 1000. So it makes a huge position in just one stock. Then the price goes against the position. While this gigantic open drawdown is taking place, a triple divergence forms on both the MACD lines and the MACDH. Still the drawdown grows to roughly 30% before the stock bounces off the 200-day MA and reverses upwards. That thrust carries price upwards to the breakeven point for this position and an exit signal occurs slightly above. This example happens to have a happy ending with a small profit but it could well have been devastating. It illustrates the need to somehow get out of the position early with minimal loss and be ready to re-enter at a more favorable time.

Figure 10 - TradeStation screenshot of the worst MAE for the MACDH Lines Strategy

Basic MACD Strategies without stops didn’t deliver a winning edge because huge losses matched the gains and open losses caused blow outs.







Select R-Multiple Distribution

The R-Multiple distributions of all the basic strategies look similar so only one is presented here for reference. In Figure 11 we see the two tall spikes containing trades that produced results in the range of -1R to 1R. Note that only 14 times in 14 years did this strategy more than double the money invested (these are the tiny numbers above the positive bins in Figure 11 and Figure

13). This is characteristic of strategies without stops, where all the money invested is considered at risk, and so 1R represents a 100% return on the investment.

Figure 11

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Long MACDH MA No Stop 19-39-9 1994-2004

Distribution of Results Tables of results give a good summary but are necessarily leave out detail. For deeper insight, check out the results distribution graphs. They indicate whether to expect big losses often or a multitude of little hits. Likewise, some strategies will have many small gains and others earn their keep in a few large paydays. Click here for more about distribution graphs. Next page too.

Remember that R is the amount risked per trade, in our test $1000. R-Multiple is the number of times that risk is returned, so +5R is $5000 profit while -1R is a $1000 loss. The trades are sorted into “bins” which correspond to a bar on the graph. The label of the bin is the mid-point of its contents so the 0.5 bar represents all trades that returned between 0 and 1 R. Anything more negative than -1 R means those trades gapped past their stops or jumped the risk limits due to opening gaps.

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Figure 12

Figure 13

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Figure 14

Summary of Basic MACD Exit Strategy Results

Overall, roughly breakeven, with slight positive expectancy in favorable markets and slight negative expectancy in down years.

Huge losses due to putting the entire investment at risk at 1000 shares per stock

Despite trying to slow the exits, still not staying with winners for the full ride. The average hold time is closest to position traders

Stop Losses

The next set of runs explores the addition of stops to the strategy. For five of the seven runs below, the basic strategy entered on MACDH (12-26-9) uptick while price was above the 200-day MA and exited if the MACDH (19-39-9) ticked down while above zero or the price dropped below the 200-day MA. Various stops were added and the results compared. The sixth strategy used the “Power Tools” entries and exits, with a 2ATR stop added. The seventh strategy used the basic entry with an exit when MACD lines crossed down and a 2ATR stop. (2 ATR was chosen based on the results of the previous runs with stops.)

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How to Read the Results Table For Exit Testing The table below summarizes the backtesting results for the exit testing. In general, potentially profitable strategies have a positive expectancy, shown in green. Losing strategies have their negative expectancies colored red. Yellow indicates that the expectancy was slightly positive, but rounded down to zero – indicating caution. Note that we compare to 20-day timed exits as the closest to the actual hold times of the strategies with real exits.


Table 3

MACDH Stops and Exits Productivity Reliability Probability

Name of Strategy Under Test # Trades Avg Hold %Wins Expect StdDev

Baseline 1994 - 2004, 20 days 140473 20 53.92% 0.0188 0.23

MACDHMA_3%_Stp_1994-2004 84113 5 32.31% 0.0889 2.33

MACDHMA_9%_Stp_1994-2004 60433 9 47.80% 0.0396 1.08

MACDHMA_3ATR_Stp_1994-2004 57170 10 50.75% 0.0639 0.82

MACDHMA_2ATR_Stp_1994-2004 63053 9 46.36% 0.0807 1.13

MACDHMA_Histo_Stp_1994-2004 104960 3 23.60% 0.2236 4.59

Power_Tools_2ATR_Stp_1994-2004 87644 13 30.82% 0.1464 1.85

MACDH_Lines_2ATR_Stp_1994-2004 61640 13 41.50% 0.1214 1.47

Baseline 2004-2007, 20 days 41980 20 55.41% 0.0107 0.14

MACDHMA_3%_Stp_2004-2007 26748 7 38.40% 0.0527 1.85

MACDHMA_9%_Stp_2004-2007 19947 11 51.66% 0.0366 0.80

MACDHMA_3ATR_Stp_2004-2007 20356 10 51.55% 0.0616 0.83

MACDHMA_2ATR_Stp_2004-2007 22450 9 47.33% 0.0764 1.15

MACDHMA_Histo_Stp_2004-2007 41226 3 21.85% 0.0995 4.34

Power_Tools_2ATR_Stp_2004-2007 32549 12 31.12% 0.0986 1.98

MACDH_Lines_2ATR_Stp_2004-2007 21655 13 42.17% 0.1152 1.41

Baseline 2007-2008, 20 days 12597 20 43.55% -0.0182 0.13

MACDHMA_3%_Stp_2007-2008 6995 5 30.50% -0.1558 1.63

MACDHMA_9%_Stp_2007-2008 5117 8 42.13% -0.0830 0.77

MACDHMA_3ATR_Stp_2007-2008 5074 8 42.96% -0.0860 0.70

MACDHMA_2ATR_Stp_2007-2008 5537 7 40.25% -0.1104 0.96

MACDHMA_Histo_Stp_2007-2008 9622 2 21.02% -0.1674 3.06

Power_Tools_2ATR_Stp_2007-2008 16015 7 27.47% -0.1744 0.66

MACDH_Lines_2ATR_Stp_2007-2008 9553 4 15.89% -0.3086 3.16

Expectancy

Let’s cut to the chase and examine expectancy first. In Figure 15 below, we can immediately

Effects on Expectancy of Limiting Risk with Stops and Sizing The baseline strategy as well as the symmetric strategies without stops produced results clustered around the breakeven point. With stops, the difference in expectancies between the time periods is accentuated. Clearly the profits in good times make a nice positive jump. The negative expectancies (losses) in 2007-2008 also become more extreme as stops are added. The extreme jump is largely an effect of the change in sizing and risk calculations. It illustrates how reducing your risk can improve profits when the right strategy is employed for the market conditions. However adding stops is not a panacea for a mismatch between strategy and market conditions as shown by the losses in 2007-2008

http://backtestingblog.com/glossary/productivity-definition/

http://backtestingblog.com/glossary/reliability-definition/

http://backtestingblog.com/glossary/probability-definition/


http://backtestingblog.com/glossary/number-of-trades-definition/

http://backtestingblog.com/glossary/avg-hold-definition/

http://backtestingblog.com/glossary/win-rate-definition/


http://backtestingblog.com/glossary/stddev-or-standard-deviation-definition/


see that adding stops markedly changed the expectancy. The general pattern seems to be that during time periods where the market rose (1994-2004 and 2004-2007) the expectancy crept into positive territory. However, in the time period where the market headed down, the expectancy becomes clearly negative. Observe that as the stops get wider, 3% vs 9% and 2ATR vs 3ATR, the range of expectancy actually gets narrower. The most dramatic case is the Histo Stop. In most trades, it is the closest stop, coming just at the price where the MACDH would tick down again from the first uptick entry signal. In this case, we see that it got better expectancy in the positive situations while the negative went from bad to worse. Two interesting points about the MACD Power Tools Strategy: First, despite have a lower win rate than the other strategies, it produced a higher positive expectancy in rising market conditions. Secondly, the Power Tools strategy compares favorably to the MACD Lines exits, as demonstrated by similar expectancies in the relatively favorable conditions of 1994 - 2007 but not as negative expectancy in the tough conditions of 2007 – 2008.

Figure 15

Even with stops, the basic MACD strategies lost money in down markets and didn’t push solidly into the profit zone during favorable times. Therefore, it’s probably not going to be profitable to blindly follow the MACD signals. Since they did have some positive expectancies though, they might fit in as part of a larger strategy.

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MACDH Exit 19-39-9

3% Stop MACDH Exit 19-39-9

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Graphs of win rates are shown in Figure 16 - Figure 18. Adding stops pushed the win rate under the baseline in every case. That is natural because a trade that gets stopped out becomes a loss and has no chance to recover and become a winner. One concern is that the win rates are consistently below 25% for the histo-stops, which may make this strategy too demoralizing for most people to book so many losses before finding a winner.

Figure 16

Figure 17

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Figure 18

Risk as Measured by MAE

This section shows what we got in return for the lower win rates with stops. Adding stops trimmed the MAE down considerably. Certainly, the per share MAE shown decreased by adding stops. The MAE in total dollars of the positions – Viability -- is still surprisingly high. Table 4

MACDH Stops and Exits Viability Per Share


Baseline 1994 - 2004, 20 days $ 1,953.28 $ 518,000.00 $ 1.95 $ 518.00

MACDHMA_3%_Stp_1994-2004 $ 420.05 $ 6,666.66 $ 0.33 $ 47.82

MACDHMA_9%_Stp_1994-2004 $ 416.20 $ 2,845.25 $ 0.95 $ 95.00

MACDHMA_3ATR_Stp_1994-2004 $ 392.82 $ 1,982.02 $ 1.17 $ 331.00

MACDHMA_2ATR_Stp_1994-2004 $ 465.47 $ 2,973.03 $ 0.94 $ 191.00

MACDHMA_Histo_Stp_1994-2004 $ 308.59 $ 32,543.22 $ 0.21 $ 90.00

Power_Tools_2ATR_Stp_1994-2004 $ 391.30 $ 9,999.99 $ 0.75 $ 95.00

MACDH_Lines_2ATR_Stp_1994-2004 $ 500.82 $ 3,946.50 $ 1.01 $ 251.25

Baseline 2004-2007, 20 days $ 1,643.57 $ 207,600.00 $ 1.64 $ 207.60

MACDHMA_3%_Stp_2004-2007 $ 474.52 $ 8,333.33 $ 0.48 $ 7.75

MACDHMA_9%_Stp_2004-2007 $ 349.52 $ 3,036.00 $ 1.00 $ 18.70

MACDHMA_3ATR_Stp_2004-2007 $ 390.83 $ 1,592.64 $ 0.97 $ 27.17

MACDHMA_2ATR_Stp_2004-2007 $ 467.79 $ 2,120.58 $ 0.77 $ 12.70




Base

MACDH Exit 19-39-9

3% Stop

MACDH Exit 19-39-9

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Stop


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BaselineStrategy




Baseline 2007-2008, 20 days $ 3,121.15 $ 75,660.00 $ 3.12 $ 75.66

MACDHMA_3%_Stp_2007-2008 $ 456.19 $ 3,333.33 $ 0.61 $ 9.70

MACDHMA_9%_Stp_2007-2008 $ 393.64 $ 1,581.66 $ 1.56 $ 21.54

MACDHMA_3ATR_Stp_2007-2008 $ 392.23 $ 1,353.40 $ 1.60 $ 24.85

MACDHMA_2ATR_Stp_2007-2008 $ 467.41 $ 1,791.30 $ 1.30 $ 20.75




Remember, these runs held to a $1000 maximum risk by sizing the position according to the distance from the stop. No doubt a few trades will gap past the stop for a slightly larger loss, but an open loss of over $32,000 for the histo-stop is outrageous. Probing deeper revealed two difficulties which actually applied to all stop strategies tested:

1. Entry Price Below the Stop. The decision to enter a trade is based on today’s end-of-day data but the entry takes place the next day at the open. So far, so good. The trouble is that sometimes the stock price gaps down below the stop overnight, before we even had a chance to get in. The automated strategy goes awry here because it doesn’t detect that situation, enters anyway, and is immediately stopped out. While this didn’t account for the biggest MAEs, it’s still not realistic because a human would thank their lucky stars and either scratch this stock or recalculate a new stop before entering. An automated strategy operating in real time would also need to be able to detect and correct this situation. Having an entry below the stop is an artifact of historical backtesting.

2. Close Stop Makes Oversized Position. Simply sizing to the stop means that a close stop on a high priced stock can make for a wildly large position. Then a gap past the stop can be devastating.

Special post-processing was done to address these difficulties. The premise is an account size of $100,000 with no margin. Risk is limited to roughly $1000 per position, and the overall cost of the position can’t be over the account size of $100,000. Trades from the original backtest that don’t meet these criteria are thrown out. The results are not shown here but the upshot is this: Removing the oversized and scratched trades didn’t make much difference to the expectancy or the win rate but it did bring the maximum MAE down under $1200.

Select R-Multiple Distributions

Figure 19 - Figure 22 display the R-Multiple distributions for the basic MACDH uptick/downtick strategy with 2ATR stops. With stops, 1 R-Multiple is $1000, the nominal dollar amount risked between entry and stop. Then 2 R-Multiples is $2000 gained, 3-R is $3000, and so on. Compared to the distributions with no stops, the distribution graphs with stops are quite spread out (Figure 19, Figure 21, Figure 22). Also notice how the red bins down to -2R are larger than the green bins up to 2R. However, at the higher R-multiples, the profitable green bins win out. That says the money is made by a relatively few large gains.



Figure 19

In Figure 20, we zoom in on the action between -1R and +1R for a close-up of many more small losses than small wins. The taller spike to 4088 losses at label -0.95R corresponds to trades that hit their stops. The trades that lost less than that either had a better-than-expected entry price or got an exit signal that took them out below the breakeven point.

Figure 20

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Figure 22

Summary of MACD Exit Strategy with Stops Results

Stop losses reduced risk and accentuated expectancy o Expectancy improved in rising markets versus not using stops o Expectancy decreased in tough market even with stops

MACD needs to be combined with another strategy that identifies market direction

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Conclusion

General

Simply applying the MACD indicator “as-is, out-of-the-box” didn’t consistently make money, even when combined with the 200-day MA. These tests can give no reason to expect it will in the future either. Without stops, in favorable markets, the MACD strategies tested delivered positive expectancy but underwhelming profits and overwhelming MAEs. In difficult markets, the expectancy ranged from bad to worse. Stop losses improved profitability as measured by expectancy and helped to control MAE or open drawdowns. Given the huge MAEs of the basic exits, some means of limiting losses appears essential to survival in the markets. With stops and in favorable markets, MACD did somewhat better than the baseline. Given some means to identify favorable markets, MACD and MACDH could ultimately prove useful in timing entries and exits a little better than chance. No single MACD exit strategy did the best in all market conditions. In general, tighter stops and slower exits produced more extremes in expectancy – more profits and more losses. Histo-stops had the highest expectancy at the expense of the highest MAE and lowest win rate. Power Tools moderated the MAEs and still had nearly as high expectancy.

Active Investors

MACD is often used with a longer term outlook, typically associated with active investors who look to stay in a stock for a year or more. However, from these tests we see that basic MACD signals didn’t keep you in positions all that long when applied to a daily chart. Even when we specifically tried to hang in there longer by using the MACD lines or skipping a signal per Power Tools, the average trade length was under 20 days. Perhaps applying MACD to weekly charts would yield positions that lasted over a year.

Position Traders

Position trading – grabbing one leg of a longer-running trend - appeared to be the sweet spot of MACD Histogram on a daily chart. Most winning trades had a duration measured in weeks.

Swing Traders

Waiting for MACD Histogram to tick down may try the patience of a swing trader, even as it cuts out before the full extent of the price move. Skipping a signal or waiting for the lines to cross takes even longer (and still might not grab the full move). The tighter stops – 3%, 2ATR, histo-stop – all had shorter average hold times and might be a better match for the swing trader’s temperament. Perhaps faster settings would speed a swing trader in and out a little faster.


Next Steps with MACD

MACD and histogram divergences between the indicator and price are said to be among the strongest signals in technical analysis. That warrants further exploration and will be the topic of an upcoming report.

Checking MACD and MACDH on different timescales such as weekly charts would resolve the conflicting opinions that MACD does better on weekly charts versus the notion that stock charts are fractal-like in nature and the behavior on one timescale is similar to all timescales. In any event, Gerald Appel’s advice1 is that most private investors should make weekly charts the primary info source and it would be interesting to test. The MACD Buy Signals BackTesting Report showed that swing trades got better win rates with faster parameter settings. A backtesting run to see if that is the case could be useful. It might also be worthwhile to compare MACD Histogram to other indicators to find out if these results are better or worse than other strategies and which strategies might go well together. Alert readers may notice that the MACD lines crossing zero were not tested. That’s because the MACD zero crossing is actually a moving average crossover and are tested as such in BackTesting Report #7 – The Missing Link Between MAs and MACD. Any value in the MACD itself comes from producing earlier signals that those given by moving averages alone.

Resources

How to Apply This Strategy

First consider that the backtesting data in this report shows that you probably won’t rack up profits by simply following the basic signals of MACD or MACD Histogram – even with the 200-day MA as a guide. However, you may still want to bring MACD up on a chart. If you have other reasons for believing the stock is of interest, MACDH and the 200-day MA may give you better entry and exit timing than pure chance.

Free Charts

You can simply plot the MACD lines and histogram on a chart. Most tools can do this with built-in functions. For detailed instructions on how to use a free tool, see http://www.backtestingreport.com/MACD_on_BestFreeCharts.pdf . FreeStockCharts.com - free interactive charts made with BATS real-time data

Yahoo.com – free, online stock charts made with CSI Data for historical charts

http://www.backtestingreport.com/MACD_on_BestFreeCharts.pdf

http://www.freestockcharts.com/

http://www.yahoo.com/


Automated Scans for MACD Divergences

To save yourself hundreds of hours searching for MACD Divergences, check out the BackTesting Report custom scans for MACD Divergences. Check out BackTesting Report’s package of TradeStation (TS) strategies and functions which highlight MACD Divergences on a chart. The TS strategies generate MACD Divergence buy and sell signals that can be used (at your own risk), with either the TS automated trade execution, the backtesting engine, the scanner, the RadarScreen®, or simply to see the strategy trades highlighted on the chart. For more information visit: http://backtestingblog.com/code/macd-divergences/

Figure 23 - TradeStation screenshot of the MACD Divergence strategy and RadarScreen

http://backtestingblog.com/code/macd-divergences/


Understanding Technical Indicators Made Easy with BackTesting Report

BTR1: Baseline (Free Bonus Report) Establishes a standard for comparison for the US Stock market from 1994 - 2008. Brief reference filled with background info on backtesting and evaluating strategies.

Buying New Trends Series

BTR2: Trading Above the Moving Averages: Shows you when it made sense to wait for a market ripe for buying by highlighting which MAs

worked and which didn’t. BTR3: Price Crossing the MA: Learn simple ways to trigger an objective buy signal on a rising trend BTR4: Moving Average Crossovers Tests out the buy signals from this classic strategy. Plus a free bonus! Best of Moving Average Buy Signals, comparing the best signals from previous reports plus introducing a new strategy with promising results, especially for swing traders. This bonus is exclusively for BackTesting Report package customers. All four moving average issues are zipped into one download.

Custom Strategies and Scans

EasyLanguage® for TradeStation enables you to scan the markets for opportunities to use the strategies tested by BackTesting Report. Mark charts with the buy and sell signals taken by the most promising strategies. TradeStation strategies also support RadarScreen to scan a symbol list in real time. For example, you can save hours each day in identifying the elusive and powerful MACD divergences on US stocks.


WHAT WORKED, WHAT DIDN’T WORK AND HOW TO AVOID THE MISTAKES EVEN EXPERTS MAKE

BTR5: Anticipating the Cross with MACD Buy Signals Get-started guide explains the moving parts of the MACD, clearing up the mysteries of the multiple histograms. Pits MACD lines versus histograms to choose an entry signal. Popular parameter settings covered as well.

BTR6: Catching the Wiggles with MACD Sell Signals Backtests basic MACD signals - buys and sells - seeking the ways to capture profits from usual end-of-day action in the stock market.

BTR7: Missing Link Between MAs and MACD See how the 12/26 moving average crossover compares. This moving average combination is singled out because it forms the basis of the MACD.

BTR8: Finding Big Bottoms with MACD Divergences Get a handle on divergences between indicator and price. Explore the combination of MACD bullish divergences as buy signals and MACD bearish divergences as sell signals.

Copyright 200-2012. Own Mountain Trading Company. All rights reserved. www.backtestingreport.com

Related Reading The author’s current reading list is posted at http://backtestingblog.com/order/books/

Bibliography

Appel, Gerald. Master Class with Gerald Appel, Financial Trading Seminars, 2003. 2 Appel, Gerald. Technical Analysis: Power Tools for Active Investors, FT Press, 2005. Aronson, David. Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals, Wiley, 2007. 1Elder, Alexander. Trading for a Living, Wiley, 1993. Chuck LeBeau and David Lucas. Technical Traders Guide To Computer Analysis of the Futures Market, The Book Press, 1992. D.R Barton, Chuck LeBeau. Class notes of The Systems Development Workshop. Offered by Van Tharps Institute, 2007. Tharp, Van. Trade Your Way to Financial Freedom, 2nd edition, McGraw Hill, 2007. Wiessman, Richard L. Mechanical Trading Systems: Pairing Trader Psychology with Technical Analysis, Wiley Trading, 2005.

Videos

http://truthaboutmacd.com – free video on MACD technical indicator and in-depth video course

Software

MACD Divergence Detectors – scanners to automatically find several kinds of MACD divergence, see backtestingblog.com/code/macd-divergences/

TradeStation® – the backtesting engine used in this report, see tradestation.com

Web Sites

BackTestingBlog.com – background information on backtesting, including glossary Divergence-Alerts.com – daily alerts on MACD Divergences in stocks, ETFs and futures. Also tracks an ETF rotation investment strategy.

http://backtestingblog.com/order/books/

http://truthaboutmacd.com/

http://backtestingblog.com/code/macd-divergences/

http://www.backtestingblog.com/

http://backtestingblog.com/category/glossary/

http://divergence-alerts.com/


Disclaimer By purchasing this report, you are agreeing to the following disclaimer:

Own Mountain Trading Company, its owners, directors, managers and officers, (“Own Mountain”), are not responsible for the success or failure of your decisions relating to any information presented in this report. The information presented in this report should be carefully considered and evaluated, before reaching a decision, on whether to use them.

This report contains comparisons, assertions, and conclusions regarding performance based on backtesting. Backtesting is the process of testing a trading strategy on prior time periods. When you backtest, the results achieved are highly dependent on the movements of the tested period. One should not assume that what happens in the past will happen in the future, and this assumption can cause potential risks for the strategy. Back-testing is not identical to live trading. As such the backtesting performance may differ from the actual performance. Markets are always changing and evolving. The market today can be very different from the market last year. The past performance does not equal future results.

There can be no assurance that any prior successes, or past results can be used as an indication of your future success or results. Results are based on many factors. Own Mountain has no way of knowing how well you will do, as we do not know you, your background, your work ethic, or your skills or practices. Therefore Own Mountain does not guarantee or imply that you will get rich, that you will do as well, or make any money at all. There is no assurance you will do as well. If you rely upon the information presented in this report; you must accept the risk of not doing as well. Any earnings or income statements, or earnings or income examples, are only estimates of what we think you could earn.

You are advised to do your own due diligence when it comes to making business decisions and all information should be independently verified by your own qualified professionals.

All businesses have unknown risks involved, and are not suitable for everyone. You could experience significant losses, or make no money at all. Use caution and seek the advice of qualified professionals. Check with your accountant, lawyer or professional advisor, before acting on this or any information

Own Mountain makes no express or implied claims that you will make money as a result of purchasing this report and using the information presented in this report.

You agree that Own Mountain is not responsible for any success or failure that you or your business may experience as a result of using the information presented in this report. You freely and of your own will risk any and all capital you may choose to spend in using the information. You will do so with skill and common sense. You will not hold Own Mountain Trading Company accountable in any way for any failure of the information to live up to your expectations.

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