Long Term Secrets To Short-Term Trading

Figure 4.9 Trading on Tuesdays. 

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Figure 4.10 Trading on Wednesdays. 
the S&P 500. The exit is the same as with the bonds shown earlier. Clearly, some are days better than others 
to trade. Figures 4.8 through 4.12 show the buy signals by day of week; Figures 4.13 through 4.17 show 
sell signals by day of week. 
Figure 4.18 shows trading on just the more influential days. The best days to be a buyer were all days 
except Thursday and Friday, while the best sell day was Thursday, with Friday a push, but it is used in the 
following listing. This is not a bad system, it "made" $227,822 with 75 percent accuracy on 
Figure 4.11 Trading on Thursdays. 

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1,333 trades and had a very small drawdown of only $13,737. I would prefer a larger average profit per 
trade than the $170 shown here. 
An astute, thinking trader should be asking questions like, "Could we use a closer volatility expansion 
number to be a buyer on the more bullish days and a farther away entry value on the days that don't work so 
well with the 50 percent value? And how about our exit, would it pay off to hold longer on the more 
bullish/bearish days?" 
 

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These questions can continue indefinitely, but do need to be asked to optimize performance. Proof that 
research pays off is offered by Figure 4.19, which shows the use of the preceding rules, except that the buy 
entry comes at 40 percent of the previous day's range added to the open, the sell entry at 200 percent of the 
range subtracted from the open. There is a big difference here; while it actually makes a little less money 
($14,000), the accuracy goes to 83 percent, the average profit per trade is escalated to $251, and our number 
of trades is reduced by 46 percent! 
Separating Buyers from Sellers to Find Volatility Using Market Swings 
A third way to measure potential volatility expansions comes from looking at price swings over the 
past several days. Mike Chalek deserves credit for this concept with a system he designed and labeled,