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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-
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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 17.83 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. Greater net profits were observed from long trades (buys), relative to short ones (sells), perhaps due to false breakouts on the short side occasioned by the constant decay in futures prices as contracts neared expiration. Another explanation is that commodity prices are usually more driven by crises and shortages than by excess supply. As with many breakout systems, the percentage of winners was small (43%) with large profits from the occasional trend compensating for frequent small losses. Some may find it hard to accept a system that takes many losing trades while waiting for the big winners that make it all worthwhile. Portfolio equity for the best in-sample look-back rose steadily both in- and out-of-sample; overoptimization was not au issue. The equity curve suggests a gradual increase in market efficiency over time, i.e., these systems worked better in the past. However, the simple channel breakout can still extract good money from the markets. Or can it Remember Test 1 was executed without transaction costs. The next simulation includes slippage and commissions. Test 2: Close-Only Channel Breakout with Entry at Next Open, Transaction Costs Assumed. This test is the same as the previous one except that slippage (three ticks) and commissions ($15 per round turn) are now considered. While this breakout model was profitable without transaction costs, it traded miserably when realistic costs were assumed. Even the best in-sample solution had negative returns (losses); as might be expected, losses continued in the out-of-sample period. Why should relatively small commission and slippage costs so devastate profits when, without such costs, the average trade makes thousands of dollars Because, for many markets, trades involve multiple contracts, and slippage and commissions occur on a per-contract basis. Again, long trades and longer look-backs were associated with higher profits. The model was mildly profitable in the 1980s but lost money thereafter. Considering the profitable results of the previous test, it seems the model became progressively more unable to overcome the costs of trading. When simple computerized breakout systems became the rage in the late 198Os, they possibly caused the markets to become more efficient.
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Table 5-l shows the portfolio performance of the close-only channel breakout system broken down by sample and market (SYM). (For information about the various markets and their symbols, see Table II-1 in the Introduction to Part II.) NETL = the total net profit on long trades, in thousands of dollars; NETS = the total net profit on short trades, in thousands of dollars; ROA% = annualized return-on-account; PROP = associated probability or statistical significance; AVTR = average protlt/loss per trade. Trend-following methods, such as breakouts, supposedly work well on the currencies. This test confirms that supposition: Positive returns were observed both in-sample and out-of-sample for several currencies. Many positive returns were also evidenced in both samples for members of the Oil complex, Coffee, and Lumber. The profitable performance of the stock indices (S&P 500 and NYFE) is probably due to the raging bull of the 1990s. About 10 trades were taken in each market every year, The percentage of wins was similar to that seen in Test I (about 40%). Test 3: Close-Only Channel Breakout with Entry on Limit on Next Bar, Transaction Costs Assumed. To improve model performance by controlling slippage and obtaining entries at more favorable prices, a limit order was used to enter the market the next day at a specified price or better. Believing that the market would retrace at least 50% of the breakout bar (cb) before moving on, the limit price (limprice) was set to the midpoint of that bar. Since most of the code remains unchanged, only significantly altered blocks are presented:
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Trade entry took place inside the bar on a limit. If inside-the-bar profit target and stop-loss orders were used, problems would have arisen. Posting multiple intrabar orders can lead to invalid simulations: The sequence in which such orders are tilled cannot be specified with end-of-day data, but they can still strongly affect the outcome. This is why the standard exit employs orders restricted to the close.
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