I like reading Dr. Van Tharp’s weekly newsletters. In a previous issue, there was an article that caught my eye. Ali Moin-Afshari wrote a 4-part article on Band trading which was particularly interesting to me as I’ve done a lot of work with the MACD BB indicator which is basically Bollinger Bands around the distance between two Moving Averages. In Part 3 of this series, Ali discusses the Standard Error (SE) Bands and how they are superior due to being built upon Regression theory in Statistics. He also discusses a possible entry strategy that I thought I would test and see how well it worked.
Standard Error bands are constructed by first plotting a Regression Line which can act like a more reactive Moving Average. The Standard Error is then calculated, multiplied by the band multiplier and then plotted above and below the Regression Line.
Strategy Setup #1
- Regression Period = 21
- SE Multiplier = 2
Entry Conditions: If the current bar closes outside of one of bands, enter a trade in the direction of the breakout.
Exit Conditions: Standard entry testing exit. Exit after 5, 10, 15 and 20 bars and look only at Win % to see how often the entry is correct. Ideally, we are looking for a win % greater than 55%. Also observe how many trade opportunities are presented.
Here’s the win % results:
ES (S&P500 E-mini contract)
Daily Bars
Jan 2010 to Sept 24 2013
Both Short and Long Trades
| Instrument | Num Bars to Exit | Total # of Trades | Percent Profitable |
| ES ##-## | 5 | 44 | 30% |
| ES ##-## | 10 | 38 | 34% |
| ES ##-## | 15 | 32 | 50% |
| ES ##-## | 20 | 28 | 39% |
The results were definitely not that great on the ES so I decided to do an optimization on it to see if changing the parameter values helped any. Of course, I found a few better results, but less than 10% of the optimizations were above 55%, so this doesn’t seem to be a good entry technique for the ES. This isn’t too surprising because the ES is more of a mean reversion instrument while this particular entry technique is more of a trend following strategy. However, I also wanted to see if there was a difference between long and short trades. Here’s the results:
Long Only Trades
| Instrument | Parameters | Total # of Trades | Percent Profitable |
| ES ##-## | 5 | 17 | 47% |
| ES ##-## | 10 | 17 | 47% |
| ES ##-## | 15 | 17 | 71% |
| ES ##-## | 20 | 16 | 56% |
Short Only Trades
| Instrument | Num Bars To Exit | Total # of Trades | Percent Profitable |
| ES ##-## | 5 | 32 | 25% |
| ES ##-## | 10 | 29 | 38% |
| ES ##-## | 15 | 27 | 33% |
| ES ##-## | 20 | 22 | 32% |
As you can see, Long trades definitely fared better than Short trades. It’s possible it’s due to the market type being mostly Bullish over 2010 to now. Further study over a period that spams multiple market types would help.
Next I wanted to try it on a basket of currencies as currencies tend to trend more than the ES. Here are the results:
Currencies
Daily Bars
Jan 2010 to Sept 24, 2013
| Instrument | Num Bars To Exit | Total # of Trades | Percent Profitable |
| $AUDUSD | 5 | 44 | 66% |
| $AUDUSD | 10 | 42 | 57% |
| $AUDUSD | 15 | 35 | 54% |
| $AUDUSD | 20 | 29 | 48% |
| $EURCHF | 5 | 43 | 49% |
| $EURCHF | 15 | 31 | 42% |
| $EURCHF | 20 | 26 | 65% |
| $EURCHF | 10 | 35 | 43% |
| $EURGBP | 5 | 46 | 43% |
| $EURGBP | 10 | 38 | 50% |
| $EURGBP | 15 | 32 | 56% |
| $EURGBP | 20 | 28 | 64% |
| $EURJPY | 5 | 44 | 52% |
| $EURJPY | 10 | 36 | 36% |
| $EURJPY | 15 | 29 | 59% |
| $EURJPY | 20 | 28 | 68% |
| $EURUSD | 5 | 48 | 50% |
| $EURUSD | 10 | 42 | 55% |
| $EURUSD | 15 | 35 | 49% |
| $EURUSD | 20 | 32 | 44% |
| $GBPUSD | 5 | 49 | 61% |
| $GBPUSD | 10 | 39 | 59% |
| $GBPUSD | 15 | 35 | 54% |
| $GBPUSD | 20 | 27 | 59% |
| $USDCAD | 5 | 53 | 51% |
| $USDCAD | 10 | 44 | 45% |
| $USDCAD | 15 | 37 | 54% |
| $USDCAD | 20 | 30 | 57% |
| $USDCHF | 5 | 54 | 54% |
| $USDCHF | 10 | 44 | 61% |
| $USDCHF | 15 | 37 | 51% |
| $USDCHF | 20 | 32 | 53% |
| $USDJPY | 5 | 51 | 31% |
| $USDJPY | 10 | 40 | 43% |
| $USDJPY | 15 | 37 | 43% |
| $USDJPY | 20 | 32 | 47% |
These results definitely look better. Certain currencies show promise; $GBPUSD is almost above 55% on all 4 parameters, $EURGBP improves as the number of bars increases, and $USDCHF also shows promise. Additionally, the Long trades performed slightly better than Short trades, making the edge even greater.
I also ran the strategy on a basket of ETFs and found that about 25% of runs were above a 55% win rate. The instruments with the greatest potential were: BSV, ERO, FXE, FXF, PBE, PGJ, SLV, and TIP.
The next entry I wanted to test was the entry strategy discussed in Ali’s article. It’s pretty much the same except we wait for 2 consecutive closes outside the band before we enter.
Strategy Setup #2
- Regression Period = 21
- SE Multiplier = 2
Entry Conditions: If we have 2 consecutive bars close outside of one of bands, enter a trade in the direction of the breakout.
Exit Conditions: Standard entry testing exit. Exit after 5, 10, 15 and 20 bars and look only at Win % to see how often the entry is correct. Ideally, we are looking for a win % greater than 55%. Also observe how many trade opportunities are presented.
Here’s the win % results:
ES (S&P500 E-mini contract)
Daily Bars
Jan 2010 to Sept 30 2013
| Instrument | Num Bars to Exit | Total # of Trades | Percent Profitable | Long Trades | Short Trades |
| ES ##-## | 5 | 15 | 47% | 67% | 33% |
| ES ##-## | 10 | 15 | 53% | 50% | 56% |
| ES ##-## | 15 | 13 | 54% | 83% | 33% |
| ES ##-## | 20 | 12 | 50% | 80% | 44% |
Here we see that the results are better than with just a single close outside the bands. There are fewer trades, as we would expect with tighter entry criteria.
Currencies
Daily Bars
Jan 2010 to Sept 30, 2013
| Instrument | Num Bars To Exit | Total # of Trades | Percent Profitable | Long Trades | Short Trades |
| $AUDUSD | 5 | 24 | 50% | 50% | 50% |
| $AUDUSD | 10 | 23 | 52% | 50% | 53% |
| $AUDUSD | 15 | 22 | 50% | 63% | 47% |
| $AUDUSD | 20 | 19 | 42% | 43% | 47% |
| $EURCHF | 5 | 18 | 50% | 36% | 71% |
| $EURCHF | 10 | 16 | 50% | 45% | 57% |
| $EURCHF | 15 | 16 | 63% | 45% | 86% |
| $EURCHF | 20 | 15 | 67% | 55% | 83% |
| $EURGBP | 5 | 23 | 39% | 27% | 50% |
| $EURGBP | 10 | 22 | 50% | 50% | 50% |
| $EURGBP | 15 | 20 | 50% | 50% | 50% |
| $EURGBP | 20 | 18 | 61% | 70% | 55% |
| $EURJPY | 5 | 18 | 44% | 50% | 38% |
| $EURJPY | 10 | 17 | 59% | 60% | 63% |
| $EURJPY | 15 | 17 | 71% | 60% | 88% |
| $EURJPY | 20 | 17 | 71% | 60% | 88% |
| $EURUSD | 5 | 24 | 58% | 50% | 63% |
| $EURUSD | 10 | 22 | 59% | 50% | 63% |
| $EURUSD | 15 | 20 | 55% | 63% | 50% |
| $EURUSD | 20 | 19 | 68% | 63% | 69% |
| $GBPUSD | 5 | 17 | 76% | 86% | 70% |
| $GBPUSD | 10 | 16 | 75% | 71% | 78% |
| $GBPUSD | 15 | 14 | 64% | 71% | 67% |
| $GBPUSD | 20 | 14 | 79% | 57% | 78% |
| $USDCAD | 5 | 24 | 54% | 43% | 70% |
| $USDCAD | 10 | 22 | 45% | 50% | 44% |
| $USDCAD | 15 | 18 | 44% | 58% | 44% |
| $USDCAD | 20 | 17 | 41% | 42% | 44% |
| $USDCHF | 5 | 22 | 50% | 36% | 75% |
| $USDCHF | 10 | 22 | 55% | 43% | 75% |
| $USDCHF | 15 | 19 | 53% | 36% | 75% |
| $USDCHF | 20 | 19 | 58% | 43% | 75% |
| $USDJPY | 5 | 29 | 38% | 35% | 38% |
| $USDJPY | 10 | 28 | 46% | 41% | 54% |
| $USDJPY | 15 | 26 | 35% | 25% | 58% |
| $USDJPY | 20 | 21 | 52% | 40% | 64% |
What’s interesting is that some currencies did better and some did worse when comparing them with the 1 bar close outside the bands, while others did worse. What is also interesting is that for some of the currencies, short trades worked better than the long trades ($USDCHF, EURCHF). Again that could be due to the market type over the period, but additional study is required to determine if there is a correlation.
I also tested the strategy against the same basket of ETFs. Instruments that did well include ERO, EU, FXB, EWW, GBB, IXJ, KOL, PBE, RSX, TAO, and UDN.
So it appear that for some instruments, this entry strategy certainly would be exploring in detail more. Additional study is needed to determine if there is a correlation between market type and the discrepancy between long and short trades. However, with a good exit strategy, this entry has the potential of offering an edge for your trading.
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