Many ill-informed investors and inexperienced traders believe that it is best to buy into strength and then to sell into weakness. They believe that buying new short term highs is supposed to be a sign of a healthy market that will simply continue. Conversely, selling new short term lows is supposed to be the sign of market weakness, and thus should be sold and avoided. Our analysis, however, shows the opposite.
For example, buying each new 10-day high in the SP500 and then selling one week later would have resulted in losing money between the year 1980 and 2016. This is astonishing considering the SP500 is up over 2300% during that 36-year period.
Flipped the other way, buying each new 10-day low and selling one week later would have allowed a trader to beat the market over that period. Clearly buying into short term market strength is not the best way to invest. Here’s why, the SP500 tends to rise on average about 0.13% per week. However, following a 10-day low, it tends to rise an average of almost 0.5% per week. In other words, following short term lows, the market out performs its average by almost 4x. Clearly there is an advantage to when one buys into the market, and it is not when the market appears to be in a position of short term strength.
This market research shows that the best way to achieve outsized performances is to invest in market weakness and to sell into strength. Put another way, it is best to buy into over sold conditions and sell an overbought market. This is the best way to achieve quick gains, while simultaneously limiting our exposure to the market and thereby reducing our overall risk profile.
CTB algorithmic strategy is currently our introductory strategy that is the easiest to trade. It is a swing trading strategy that generates only a few trades per year while maintaining a relatively low exposure to the market. The CTB algorithm performs roughly 2 - 4 trades per year with back testing suggesting a Pwin (Percent Win) percentage around 90% of the time.
The CTB strategy looks to buy into pullbacks, or short term lows in an otherwise up-trending market. The algorithm first assesses whether the broader market is in a longer term bullish situation, otherwise it remains in cash. During bullish markets, it looks for specific sized pullbacks determined by the relative price declines from day to day. The rate and amplitude of the price decline is checked against historical norms to determine the viability of the trade. If the rate and amplitude of the pullback is not within our predetermined window of acceptance, then no trade is triggered. However, if the pullback falls within the expected range of historical norms, then a buy trigger is provided.
Once a position is taken, the algorithm then checks to see when the market reaches a position of strength as determined by historical norms from the specific level of pullback invested. At this point a sell signal is provided.
The strategy then stays completely in cash until the next trigger is given.
CTB Introduction Trade Setup IssueS Traded Trade Illustration Back Testing Results Real SPXL Data 2008 to 2016 Inclusion of Simulated SPXL Conclusion
Upon executing this strategy, we will be trading SPXL only.
SPXL is the Direxion Daily S&P 500 Bull 3x Shares where it seeks daily investment results, before fees and expenses, of 300%.
You can learn more by going here: http://www.direxioninvestments.com/products/direxion-daily-sp-500-bull-3x-etf
While we will be trading SPXL, others may choose the 2x or 1x versions of the SP500 as a way to reduce equity volatility. But keep mind, the 2x or 1x performance is expected to be greatly diminished over what we will be able to achieve.
Below is an illustration (not real trades) explaining how the trade set-up might look like.
Trades generally occur on average 2 to 4 times per year, where a position may be open for two weeks or potentially two months.
FIGURE 1: Illustration of a CTB trigger. (A) Trigger occurred and all available funds were allocated to the position. (B) 24 days later a sell trigger was provided an all shares were sold
REAL SPXL DATA 2008 TO 2016
Below is the back testing report on this strategy going back to the inception of SPXL in the year 2008.
TABLE 1: CTB results from the inception of SPXL to Dec 2016**
From Table 1, the CTB algorithmic trading strategy is a high performing strategy with a Compound Annual Growth Rate (CAGR) of 35% since the inception of SPXL in 2008, or 8.5 years. This far outperforms the benchmark of holding the SP500, which had a CAGR of 12% during that same time period. To learn more about why using the CAGR metric is so important, please click here.
Along with a high CAGR, this strategy also provided a very high Pwin of 100%, thereby providing a high level of confidence in its outperformance over the benchmark. This strategy also has a high average profit per trade, meaning that it quickly makes money when the conditions are just right, but otherwise stays out of the market for long periods of time. In other words, despite a high CAGR, this strategy also has a low exposure rate.
The Exposure rate measures the percentage of trading days that this strategy has an open trade. Generally, with a given CAGR and Pwin, the lower the exposure rate, the better. This measures the efficiency by which the strategy is able to achieve its results. It also is a measure of risk, the less amount of time that a trader is in the market the less likely he/she will be exposed to potential unexpected market movement that may result in an equity drawdown. So, while one needs to be in the market in order to make money, it is best to be in the market at the most optimal periods to make that money, and to sit in cash otherwise. The results in Table 1 show that HIIT’s exposure is just 20%. That means that this strategy is exposed to the market only 20% of the time, and yet is still able to achieve an outperformance of 35% on the CAGR. Compare that the buy and hold benchmark of the SP500 where it is always exposed and achieves a CAGR of only 12%.
Please click here to learn more about how these performance metrics are calculated.
EXAMPLES OF ALGORITHMIC TRIGGERS Below is a price chart of the SPXL from 2013 to 2014 showing a sample of CTB buy and sell triggers. The green up arrows depict buy triggers, while the red down arrows depict when all shares were sold.
FIGURE 2: Buy and Sell trigger examples from the CTB strategy between 2013 and 2016
EQUITY GROWTH OF BACKTESTED RESULTS Below is the equity growth over time from the inception of SPXL in 2008. This is a linear plot illustrating what would have occurred by reinvesting all profits right back into the strategy.
FIGURE 3: Linear equity plot of the CTB strategy from 2008 to 2016
Looking at figure 3 above, it can be seen that the CTB strategy far outperforms the buy and hold of the SP500. It does so, so much that the red line depicting the buy and hold on the Sp500 is barely visible.
This back testing result assumes total reinvestment of all profits and takes $10,000 to many millions in only 8.5 years. This is the power of a high consistent CAGR.
Please keep in mind that while the results are impressive, this is for illustration purposes only. The back testing results here are meant to illustrate the potential for this strategy to boost our existing investment portfolio. To learn more about how our trading strategies are not ‘get rich quick schemes’, please click here.
Below is a log plot of what is seen in Figure 3. This is a useful plot because it allows the reviewer to see variability throughout the entire period of testing. It basically linearizes the exponential effects of the compounded growth rate, thereby allowing the reviewer to observe the consistency of the strategy over time
FIGURE 4: Log equity plot of the CTB strategy from 2008 to 2016
Figure 4 above shows that the strategy is very consistent over the 8 year back test period. The dotted line is an exponential regression through the data based on a compounded growth of 35% per year. The R2 value is 0.98, meaning that the line does a very good job of describing the data. A perfect score would be 1.00. This means that the strategy performs consistently well year after year.
INCLUSION OF SIMULATED SPXL
The results of this back testing are very encouraging. It generated a CAGR of over 28% over that period with a win rate over 90%. It was able to recover quickly from a losing trade when they did occur, and had a relatively low exposure rate in conjunction with a high growth rate.
The results below provide further evidence that this is a high performing trading strategy.
EQUITY GROWTH OF BACKTESTED RESULTS Below in Figure 5 is the 25-year linear equity results of what occurs with a starting value of $10,000 invested into the CTB strategy. It also includes the SP500, provided as a reference. Please note that the SP500 profile is plotted using the secondary right hand axis. This is done so that the reviewer can easily compare the shape and volatility of both to see how the CTB strategy performed in various market conditions.
FIGURE 5: Linear equity plot of the CTB strategy from 1991 to 2016. Also provides the SP500 on the right hand axis as a reference
When looking at the SP500 profile, notice the amount of volatility that exist in the broader market as well as the inconsistent direction of the market over the 25-year period. During this time period there were two severe bear markets that resulted in a 45% drawdown from 2000 to 2003 and a 55% drawdown that occurred between 2008 and early 2009.
One area that we were especially interested in assessing was how the strategy would perform as the broader market transitioned from a long term bull market to a severe bear market, and then back into a bull market. The design of the algorithm was meant to keep the trader out of the market during severe longer term bear markets. As evidenced from Figure 5 above, it did exactly that. So, while no money was made during that time period, traders that used the CTB algorithm at least could relax knowing they were not losing money, while others who were invested in mutual funds etc., had to suffer through as much as 45% losses.
The log plot in figure 6 linearizes the exponential effects of the compounded growth rate, thereby allowing the reviewer to observe the consistency of the strategy over time. This can be seen by looking at the regression line through the data below in figure 9. Notice how consistently the data follows this linear regression straight. This means that the strategy was consistent in maintaining a CAGR of 28% over the entire 25 year period.
FIGURE 6: Log equity plot of the CTB strategy from 1991 to 2016. Also provides the buy and hold SP500 results on the right hand scale.
CONTINUOUS TWO-YEAR PERFORMANCE
Lastly, we want to provide further evidence of the consistency that the CTB algorithm has been able to perform over a long period of time.
Figure 7 represents the absolute 2-year return that we could have expected when beginning the strategy on any given trading day over the past 25 years. It takes the 2-year return beginning on the first day 25 years ago and then rolls it forward to attain thousands of 2-year samples. The results show that regardless of the starting point over that 25-year period, the return 2-years later would have been very good.
FIGURE 7: Histogram on the continuous two-year performance
Each CTB trigger is made by assessing short term weakness based on the magnitude of its pullback and the rate in which it falls in an otherwise longer term upward trending market. The algorithm assesses the magnitude and rate of the market drop to determine if a buy trigger will be made. This strategy identifies when the market is oversold and then takes advantage in an attempt to make a relatively quick gain that far outpaces the buy and hold benchmark of the SP500.
We believe the CTB strategy is a high performing strategy that can be used to amplify our existing investment strategies. It has a Pwin of over 90% and a CAGR of over 35% through 8 years of testing on SPXL.
The addition of simulated data going back 25 years suggests that the trading algorithm is a high performing strategy able to benefit from a large diverse range of market conditions.
Disclaimer: Market Chronologix, Inc. makes a good-faith effort to accurately convey the performance metrics of our strategies, but we assume no liability for incorrect information, or for any losses that may be incurred as a result of using these strategies. Past performance of our strategies does not guarantee or imply that they will continue to perform at the same level in the future. All investing involves a degree of risk. You may have a profit or a loss when you sell shares of an investment, and you should carefully consider what level of risk you can accept before investing any money. We are not registered financial advisers and do not offer investment advice, nor should any of our written materials or services be construed as such.
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