止損: 幫助還是阻礙？ [2 / 3]

簡介

在本文中，我將繼續以60日指數移動平均數指標為基準，並與其引入百分比止損、基於ATR（真實波動幅度均值）追蹤止損之後的結果相比較。

百分比跟蹤止損

許多交易者和經紀商使用最初的百分比止損和百分比跟蹤止損來管理自己的倉位。比如，一個交易者或許會說，“我將在開倉價位下方5%處設置止損，並把跟蹤止損設置為前一日收盤價下方的5%處”。這裡，我們將會把1%-10%的止損百分比引入60日指數移動平均數指標來測試這一方法。下圖中綠色的點表示百分比跟蹤止損，粉紅色線為EMA（60）的值。

                                                                                                利用 5%跟蹤控制止損價
1%~10%跟追止損對於單子的獲利和風險的影響如下：
原始交易

 
上面圖表很清楚的顯示，跟蹤止損沒有提高無止損情況下的日均收益。這符合我們的預期，因為根據定義，止損包括虧損平倉。為了確認止損是否可以減小投資組合中的風險，我們做了下面測試。

投資組合

 
百分比跟蹤止損同樣沒有提高無止損情況下的年收益率。同時，無止損可以提高夏普比率。所有加入追蹤止損的60日指數移動平均數指標所得收益都降低，並且有較大風險性。

啟示

為了便於對比投資組合結果，我們使用方差分析法。這樣我們就可以同時比較無止損情況下的交易結果與結合百分比止損之後的交易結果。這將有助於確定我們統計數據的重要性。結果顯示，結合1%~10%止損之後的60日指數移動平均數指標並沒有好的表現。本文我特地省略了使用方差分析法的具體細節。感興趣的讀者可以參閱我的書籍《股票交易系統設計（有無軟計算）》並從中找到別的有效分析方法。

基於ATR指標的止損

許多交易者僅僅根據ATR指標（平均真實波幅）來確定止損價位。比如，一個交易者或許會說，“我會根據5日ATR指標的2倍來設置止損”。為了論證這種方法的實用性，我將其設置為跟蹤止損以鎖定盈利。這也是許多交易者使用的典型方法。下圖中綠色點表示基於ATR指標的跟蹤止損，粉色線顯示了EMA（60）的值。

 
                                                                                              Figure 3: 根據2 x ATR(5)設置止損
原始止損對於交易發收益和風險的影響如下所示：

原始交易

上圖中數據很清楚的顯示，無止損的日均回報是最高的。這符合我們的預期，因為根據定義，止損包括虧損平倉。為了確認止損是否可以減小投資組合中的風險，我們做了下面測試。

投資組合

上圖顯示，止損不僅沒有提高交易的年收益率，而且也沒有提高夏普比率。一些較高止損百分百的最大虧損與無止損情況相似。實質上，結合止損之後的交易的回報率都有所降低，並且風險較高。

啟示

同樣，我們也可以使用方差分析法來確認我們統計數據的重要性。結果顯示，止損百分比並沒有讓我們提高盈利。

結論

本文中，我將60日指數移動平均數指標與不同的止損方法（百分比跟蹤止損和基於ATR跟蹤止損）相結合的結果進行測試。結果顯示，所有測試的止損都增加了原始交易的風險性並減少了投資回報。

Stop-Loss Orders: Help or Hindrance? [Part 2 of 3]
By Dr. Bruce Vanstone
Introduction
This article is part 2 of a 3 part-series on stops. In this article, I continue testing and benchmarking the original EMA crossover strategy by adding in percentage-based and ATR-based trailing stops.
Trailing Percentage Stops
Many traders and brokers use an initial percentage stop and a trailing percentage stop to manage their positions. As an example, a trader might say, "I will set a stop loss 5% below my entry price, and then trail it 5% below the previous days closing price as the trade progresses". Here, we test this method using percentage thresholds from 1% - 10% in steps of 1, for all the trades generated by the ema crossover rules.
An example is shown in Figure 2. The green dots show the position of the percentage-based trailing stop, and the pink line shows the value of the EMA(60).
Figure 2: Percentage trailing stop (5%) used for controlling the stop loss price
The impact that these percentage trailing stops have on both return and risk is presented next.
Raw Trades
From the table presented, it is clear that none of the stop methods tested improved the 'NO STOP LOSS' portfolio's daily mean return. This is as expected, given that, by definition, an initial stop loss rule entails selling at a loss. To determine whether this approach has decreased our risk, we next test within a portfolio setting.
Portfolio
From this table, we can see that none of the stop methods have improved the 'NO STOP LOSS' portfolio's APR. Further, none of the stop loss settings was able to improve the Sharpe Ratio. Again, all combinations of stop loss tested achieved less return, and were riskier.
Implications
To statistically compare the portfolio results, we can use the ANOVA procedure, which allows us to simultaneously compare all the trades generated under the 'NO STOP LOSS' condition, with all the sets of trade possibilities from the 10 stop loss combinations. This allows us to determine whether there is any statistical significance in our findings.
The results indicate that no benefit has been obtained from any of the stop combinations. I have purposefully omitted a detailed explanation of using the ANOVA procedure in this article, to allow us to keep focused on the effects of stop losses. As mentioned earlier, those readers interested in pursuing the benchmarking of trading systems using statistical methods can find all the details in my book, Designing Stockmarket Trading Systems (with and without soft computing).
ATR-based Stops
Many traders simply use a multiple of the ATR (Average True Range) to determine their stop level price. As an example, a trader might say, "I will set a stop loss 2 times the 5-day ATR below my entry price". To demonstrate the versatility of this technique, I have implemented this as both an initial ATR stop, and then allowed it to become a trailing stop as the trade moves into profit. This is typical of the way many retail traders manage their ATR based stops.
An example is shown in Figure 3. The green dots show the position of the ATR-based trailing stop, and the pink line shows the value of the EMA(60).
Figure 3: ATR stop (2 x ATR(5)) used for controlling the stop loss price
The impact that these initial stops have on both return and risk is presented next.
Raw Trades
From the table presented, it is clear that none of the stop methods tested improved the portfolio's return. This is as expected, given that, by definition, an initial stop loss rule entails selling at a loss. To determine whether this approach has decreased our risk, we next test within a portfolio setting.
Portfolio
From this table, we can see that none of the stop methods have improved the 'NO STOP LOSS' portfolio's APR. Further, none of the stop loss settings was able to improve the Sharpe Ratio. Again, all combinations of stop loss tested achieved less return, and were riskier.
Implications
Again we can use the ANOVA procedure to determine the statistical significance of these results. The results indicate that no benefit has been obtained from any of the stop combinations tested.
Summary
In this article, I have continued testing different types of stops to see if they can improve the original EMA crossover strategy. This time I have tested percentage-based trailing stops, and ATR-based trailing stops. It was found that all stops tested increased the risk and reduced the return of the original strategy.
In the next article, I will demonstrate the Monte-Carlo technique and show how it can provide additional insights into the use of stops.

文章翻譯由兄弟財經提供

文章來源：http://www.incrediblecharts.com/trading/stoploss-trading-