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When calculating the number of contracts, no correction is explicitly made for the S&P 500 split The new contract is.

Do your homework and nd out how much you are spending to acquire customers. Keep track of who is referring whom. Find out how much more valuable referrals are than regular customers. Then set up a program to share that pro t and to reward your customers for referring other people who become customers. Table 13-5 shows how regular customers might look on a LTV chart. Your regular customers cost you $120 to acquire and $20 to maintain once they are acquired. Your retention rate begins at 60 percent. These customers are worth $90 in the third year. Now let s look at referred customers (see Table 13-6). Let s say you can generate 10,000 referrals. As you can see, these referred customers have a much higher retention rate. As a result, even after we reward our regular customers with $50 per referred customer, these customers have a third-year LTV of $206 instead of $90. They are really pro table customers. You will discover the same thing. Figure it out, and design a referral program.

treated as identical to the old one, both by the simulator and by the code. All simulations are, nevertheless, correct under the assumption that the trader (not the simulator) trades two new contracts for every old contract: The simulator is instructed to sell half as many new contracts as it should, but treats these contracts as twice their current size. Limit-locked days are detected by range checking: A zero range (high equal to the low) suggests poor liquidity, and a possibly limitlocked market. Although this detection scheme is not ideal, simulations using it resemble results obtained in actual trading. Compiling the information from the exchanges needed to identify limit-locked days would have been almost impossible; therefore, the zero-range method is used. The code allows re-entry into persistent trends as long as new highs or lows are made.

Summary

// declare local scratch variables static int cb, n, ncontracts, maxhold; static float mlnstp, ptlim, atr;

From the Key Equipment case, we learned how to Create relevant and highly targeted prospecting lists that the lead managers would believe to be credible.

The code was compiled and linked with the development shell and associated libraries; in TradeStationTM, this is called verifying a system. Using development shell commands, the look-back parameter was brute-force optimized. The best solution (in terms of the risk-to-reward ratio) was then verified on out-of-sample data. Optimization involved stepping the entry model look-back (n) from 5 to 100, in increments of 5. The stop-loss parameter (mmsrp) was fixed at 1 (representing 1 volatility, or average true range, unit), the profit target doflim) at 4 (4 units), and the maximum holding period (mardays) at 10 days. These values are used for the standard exit parameters in all tests of entry methods, unless other-

wise specified To provide a sense of scale when considering the stop-loss and profit target used in the standard exit, the S&P 500 at the end of 1998 had an average true range of 1783 points, or about $4,457 for one new contract For the fist test, slippage and commissions were set to zero For such a simple system, the results are surprisingly good: an annual return of 76% against maximum drawdown All look-back parameter values were profitable; the best in terms of risk-to-reward ratio was 80 days A t-test for daily returns (calculated using the risk-to-reward ratio) reveals the probability is far less than one in one-thousand that chance explains the performance; when corrected for the number of tests in the optimization, this probability is still under one in one-hundred As expected given these statistics, profits continued out-of-sample.

Year 1 Customers Retention rate Spending rate Revenue Cost rate Costs Referral fee/retention fee ($50/$20) Total costs Pro t Discount rate NPV of pro t Cumulative of NPV pro t Lifetime value 10,000 80% $275 $2,750,000 60% $1,650,000 $ 500,000 $2,150,000 $600,000 1.00 $600,000 $600,000 $60 Year 2 8,000 82% $285 $2,280,000 55% $1,254,000 $ 160,000 $1,414,000 $866,000 1.07 $807,836 $1,407,836 $140.78 Year 3 6,560 85% $295 $1,935,200 54% $1,045,008 $ 131,200 $1,176,208 $758,992 1.15 $660,462 $2,068,298 $206.83

Wheat is not a big part of the recipes in The Detox Revolution. The gluten found in wheat is one of the most common causes of allergic responses in people. Its use is minimized here so that you can benefit from the detoxifying properties of food without also having to deal with the distraction of food allergies.