As of 11/20/2024
Indus: 43,408 +139.53 +0.3%
Trans: 17,002 -26.31 -0.2%
Utils: 1,055 +1.25 +0.1%
Nasdaq: 18,966 -21.33 -0.1%
S&P 500: 5,917 +0.13 +0.0%
|
YTD
+15.2%
+6.9%
+19.7%
+26.3%
+24.1%
|
 46,000 or 43,000 by 12/01/202418,000 or 16,600 by 12/01/20241,075 or 1,000 by 12/01/202420,000 or 18,400 by 12/01/20246,100 or 5,800 by 12/01/2024 |
As of 11/20/2024
Indus: 43,408 +139.53 +0.3%
Trans: 17,002 -26.31 -0.2%
Utils: 1,055 +1.25 +0.1%
Nasdaq: 18,966 -21.33 -0.1%
S&P 500: 5,917 +0.13 +0.0%
|
YTD
+15.2%
+6.9%
+19.7%
+26.3%
+24.1%
| |
|
46,000 or 43,000 by 12/01/202418,000 or 16,600 by 12/01/20241,075 or 1,000 by 12/01/202420,000 or 18,400 by 12/01/20246,100 or 5,800 by 12/01/2024 | ||
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Encyclopedia of Chart Patterns Second Edition
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This article discusses a trading setup that uses a dead-cat bounce to time the entry and a fixed time to exit.
Based on a test using 43 trades, this worked 77% of the time and created profits per trade of $2,162.90 on a $10,000 investment per trade, including commissions of $20 per round trip and 11 cent SEC fee on the sale. How this setup will work in the future is anyone's guess. The out-of-sample example is the only trade that qualifies since this study was released. It had profits of $2,589, but the draw down was substantial.
Note: You may find the graphic on the right helpful in the following discussion, but I will explain it later in the trading setup.
I conducted research into declines to find if there was a buying opportunity to be had and found one, but that was for longer term declines. The event decline of a dead-cat bounce happens in a day (for these tests, I used the close before the event to the event day's low price as the measure of the event decline). Can we profit from riding the bounce higher or from the recovery after the dead-cat bounce pattern completes?
For answers, I did more research. I used $10,000 per position with $10 commissions per trade ($20 round trip) with no allowance for slippage but included an 11 cent SEC fee (which is the fee on $10,000 as of early 2009 when I conducted this study). Then I bought shares using the price of the low on the day of (and the next 9 days) the large event decline as the buy price (assuming it would be hit sometime in the future) and exited from 1 to 44 trading days later (up to 2 calendar months later). The table shows the results.
| Entry Day | Best Exit | Per Trade | Profitable | Samples |
| Event Day | 43 | $170.18 | 50% | 1,041 |
| 2 | 44 | $313.25 | 51% | 1,056 |
| 3 | 44 | $322.48 | 54% | 1,062 |
| 4 | 44 | $326.42 | 54% | 1,065 |
| 5 | 44 | $242.99 | 52% | 1,050 |
| 6 | 43 | $329.22 | 52% | 1,072 |
| 7 | 42 | $364.46 | 53% | 1,074 |
| 8 | 41 | $324.39 | 54% | 1,078 |
| 9 | 42 | $240.14 | 52% | 1,067 |
| 10 | 42 | $312.28 | 54% | 1,063 |
For example, the best performance comes when you use the low price 6 days (entry day 7) after the event decline begins as the entry price. You do not enter the trade on that day, but if price drops to the entry price sometime in the future, then you buy. If you exit 42 trading days later (not calendar days), you will make an average of $364.46 per trade, winning 53% of the time on the 1,074 trades counted. The number of samples varies because price may not decline to the buy price but climb instead, so entry is never consummated.
Notice that many of the exit days are near 44. Since I only tested to 44 days, it suggests that a buy and hold approach is best.
Do not be confused with the results and the bell-shaped curve of the dead-cat bounce pattern. I did not track how far into the future each trade occurred, so it is conceivable that entry was near the bottom of the event decline and exited at the top of the bounce or that entry/exit shifted well into the future. Also, these tests ignored the size of the event decline.
The next test determines which event decline sizes are the best to trade. See the following table.
| Entry Day | DCB Range | Winners | Per Trade | Samples |
| Event Day | 39% - 44% | 53% | $1,103.44 | 45 |
| 2 | 39% - 44% | 43% | $877.04 | 46 |
| 3 | 39% - 44% | 40% | $923.92 | 47 |
| 4 | 39% - 44% | 38% | $1,066.07 | 47 |
| 5 | 41% - 46% | 52% | $1,041.89 | 42 |
| 6 | 41% - 46% | 56% | $1,090.91 | 41 |
| 7 | 44% - 49% | 61% | $1,156.34 | 28 |
| 8 | 41% - 46% | 39% | $1,066.04 | 41 |
| 9 | 41% - 46% | 60% | $1,038.52 | 42 |
| 10 | 40% - 45% | 55% | $1,136.27 | 42 |
As an example, let's take entry day 1 (labeled Event Day in the table). This sets a buy price using the low price the day of the large event decline. For all trades that had event declines between 39% and 44%, the profits averaged $1,103.44 and 45 trades qualified. To get $1,103.44, I added all of the profits and losses on trades for a particular stock with exit days from 1 to 44 and divided that total by the number of samples used.
For each entry period, the table lists the optimum event decline regardless of when the shares were sold (meaning I used an average selling price). The row highlighted in green is the most profitable, but has few samples (28). The numbers suggest that event declines between 39% and 49% give the best results (the most profits). Samples are few, so keep that in mind.
Using the best of both tables, namely setting a buy-in price using the low on day 7, exiting 42 price bars (trading days) after entry, and using event declines in the range of 39% to 49%, I conducted additional tests and found the optimum parameters. The 43 trades worked 77% of the time with profits per trade of $2,162.90. Event declines in the range of 41% to 46% worked best with those larger than 46% had profits dropping off.
Here are the rules for this trading setup.
For example, if the price gaps open lower on July 10, you would measure the drop from the close on July 9 to the low on July 10, even if price continued lower on succeeding days.
The chart shows the only out-of-sample trade so far using this setup. Let's go through it.
Step 1 is to find a one-day drop between 41% and 46%. On January 9, 2009, Rambus dropped from a prior close of 18.50 (point A) to a low of 10.25 (point B), for a decline of 45.2%, which is in the range we are looking for.
Step 2 is to wait 7 trading days before setting a buy price. Day 7 was on January 20. The buy price is that day's low of 8.10. The next day, point C, the stock made a lower low and a limit order would have executed. Assume we spent $10,000 to buy the stock at 8.10 with a $10 commission, giving us 1,233 shares.
Step 3 is to close our eyes and hold our noses and pray that the stock climbs. In this example, it does...eventually. 42 price bars after point C, we sell the stock at point D and receive the closing price of 10.20. Our gain is $2,589.19, including $10 commissions and 11 cent SEC fee on the sale. That is over 25% in about 2 months.
Before you get too excited about this setup, the two in-sample trades (meaning they were included in testing and optimizing this method) in 2008 resulted in losses: $5,312 and $1,150. The draw down from this setup can be severe, so trade this one with care.
In case you want to explore this setup more, I have two Excel spreadsheets of data for you to look at. Both use 7 days as the entry but the exit varies from 1 to 44 days. The large one shows all dead-cat bounces. The smaller sheet shows just those in the optimized range of 41% to 46%. Both are included in a compressed file.
DCBSetup.zip (645k)-- Thomas Bulkowski
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