简介
许多交易商和投资者把止损当作他们日常交易活动的一部分。几乎所有的交易书籍都强调止损的重要性，比如“不设止损的交易就像驾车不系安全带。”这些言论似乎很有依据性，但是证据却表明止损并没有给交易者带来预期的效果。对于中长期的交易系统（覆盖了大部分交易者）而言，止损的弊大于利。
作为交易者，我们会经常设置止损，并在价格快速反转时庆幸止损保护了我们的盈利。尽管止损可以在一些交易中可以让我们减少损失，但它是否能在投资组合中发挥同样的作用仍然值得怀疑。一系列原因可以说明止损不适合投资组合，我们将在后面讲到。
我们不应该紧盯投资组合中每一笔单子的收益，而应该着重于整体回报。我的大量实验表明，止损在个人交易和投资组合中发挥的作用存在不匹配性。我将通过系统的举例引导读者去验证，并通过数据说明该不匹配性。你也可以通过我的方法来检测止损对你本人交易系统的作用，判断自己是否真正收益于止损。
衡量止损的作用
在衡量止损在一个交易系统中所起到的作用之前，需要考虑止损在个人交易和由这些交易组成的投资组合中的作用。为了方便评估止损在个人交易中的作用，我们以交易的日平均收益和持仓天数为基准，并测量它们的变化。
• 交易日平均收益率（$） - 每天平均回报
• 持仓天数
为了方便对交易开仓平仓的基准测试，我们首先假设起始资金没有限制，每笔交易的名义投资资金为 $10,000。我们可以将APR%、Max DD%、Sharpe Ratio设为基准并观察它们的变化，从而得到止损在投资组合中的作用：
• APR％（年回报率） ——投资组合的回报率
• Max DD%（最大亏损率）——投资组合曲线能承受的最大亏损（峰、谷间距离）
• 夏普比率——每单位回报的风险比。忽略无风险利率的调整，夏普比率可以很好的衡量投资组合回报率的不稳定性。（例如，两位不同的交易者在一段时间之后获得的收益都是20%，其中价格波动小的交易者所对应的夏普比率高）
在设置基准投资组合之前，需要考虑到资金量。这种情况下，可以应用相对简单的资金百分比模型：起始资金为o$1,000,000，每笔交易占用2%。
通过检测上述变量，我们可以以一套交易规则所获得的指标为基准。然后，在这套交易规则中加入止损，并观察结果。这会让我们客观的观察到止损对基准指标的影响。
案例分析
大多数的投资者都可以被形容为中长期投资者。本质上来说，他们交易普通股并希望持有时间在数月到数年之间。他们把自己称作趋势投资者，他们的目的就是识别趋势，并试图尽可能长时间的追随趋势交易。通常，一个或多个简单（或指数）移动平均数指标给他们提供交易信号。而且，他们只做多。
因此，我的研究选择了60日指数移动平均数指标：当价格位于指标上方做多，位于指标下方时做空。下图交易示例中粉色的代表EMA（60）的值。
上图数据是 ASX200指数（成立于2000年4月）的成分股走势的一部分。我已经尽可能的根据退市和代码变化调整上述数据，交易结果包括交易成本返佣。考虑到生存偏差，每只成份股的买入指令只在信号生成的当天有效。
请记住，我们的目的不是去判定交易规则是否理想，而是方便我们像大多数交易者一样去识别股票走势特征。
无止损
首先，我们需要测试没有止损情况下交易规则的表现。从而我么可以很好的对比有无止损情况下的不同收益。
个人交易：
每次买卖信号发出时，我们都将买或卖出价值$10,000的股票。交易结果为：
日平均收益 = $ 0.61， 交易的平均天数= 21.44
投资组合：
建立在上述交易中的投资组合表现为：
投资回报率=2.63％，最大亏损率=-34.63％，夏普比率=0.31
现在我们知道潜在的投资回报率，交易原则的风险以及交易结果的整体风险。
止损百分比
许多交易者通过一个固定的百分比来确定他们的止损价位。比如，一位交易者或许会说，“我将会在交易价格下方的5%处设置止损。”这里，我们将会根据EMA（60）产生的买卖信号，测试止损百分比为1%~10%的效果，
上图中数据很清楚的显示，无止损的日均回报是最高的。这符合我们的预期，因为根据定义，止损包括亏损平仓。为了确认止损是否可以减小投资组合中的风险，我们做了下面测试。
投资组合
上图显示，止损不仅没有提高交易的年收益率，而且也没有提高夏普比率。一些较高止损百分百的最大亏损与无止损情况相似。实质上，结合止损之后的交易的回报率都有所降低，并且风险较高。
启示
为了系统的比较投资组合的效果，我们可以使用方差分析法，它可以让我们同时比较所有无止损交易与10种止损百分比交易的盈利情况。这将有助于确定我们统计数据的重要性。结果显示，止损百分比并没有让我们提高盈利。本人中我特意省略了方差分析法，读者可以参阅我的书籍《股票交易系统设计（有无软计算）》并从中找到别的有效分析方法。
总结
我在这篇文章中应用了EMA指标作为基准。下面，该策略的交易收益将与各种止损百分比的投资回报相比较，从而确认止损百分比是否可以降低交易风险。结果表明，所有测试的止损都增加了风险、降低了回报。
Stop-Loss Orders: Help or Hindrance? [Part 1 of 3]
By Dr. Bruce Vanstone
Introduction
Many traders and investors place Stop Loss orders as part of their day-to-day investment activity. Virtually all trading books recommend the use of stops, with many making statements like "Trading without stops is like driving without a seatbelt". The argument for the use of stop-loss rules seems inherently sound, yet there appears to be no real evidence that stops are providing the safety benefits that many traders expect.
With regard to medium to longer term equity trading systems (which appears to cover the majority of investors and traders), it may well be that stops are causing more harm than good!
As traders, we are used to having an initial stop loss on a trade, and congratulating ourselves when the stop saves us money as the trade goes south very quickly. Although a stop-loss rule may save us from damage on specific trades, it seems doubtful whether this beneficial effect actually holds when we measure it at a portfolio level. There are a number of specific reasons why this may be the case, which I will touch on later in this series.
As traders, we shouldn't really focus on the return of each individual trade; rather we should focus on the overall return of our portfolio. A large amount of my empirical testing appears to show a mismatch between stop performance at an individual trade level, and stop performance at a portfolio level.
In this series of articles, I would like to demonstrate the mismatch that stops appear to introduce, and show you a way to be able to test this for yourself. This article is part 1 of a 3-part series. In this article, I will introduce an example system, and demonstrate how to benchmark the system with and without a variety of stops, and statistically analyse the results.
You can then use this same process to benchmark the effect stops are having on your own individual trading system, to determine if you are actually benefiting from using stops.
Measuring the impact of Stops
To measure the impact of stops on a trading system, it is necessary to consider the effect that stops have on both individual trades, and on specific portfolios constructed from those trades.
To assess the effect that stops have on individual trades, we can benchmark and measure changes in:
? Trade daily mean return ($) – average return per day
? Average number of days trades are open
To benchmark the raw trades signalled by the entry and exit rules, we initially assume unlimited equity, and a nominal investment of $10,000 per trade.
To assess the effect that stops have on specific portfolios, we can benchmark and measure changes in:
? APR% (Annual Percentage Return) – a portfolio's return
? Max DD% (Maximum % Drawdown) – which shows the worst case drawdown (peak to valley) that the portfolio equity curve has suffered.
? Sharpe Ratio - which shows the amount of risk taken per unit of return. Ignoring the risk-free rate adjustment, the Sharpe Ratio is a measure of how volatile portfolio returns have been. (As an example, two different traders may both have achieved a return of 20% over time. The Sharpe Ratio will be highest for the trader who has achieved this result with the least volatility.)
When benchmarking a portfolio, it is important to take account of the amount of equity used. In this case, a relatively simple 'percentage of equity' model is used. We allocate 2% of available equity to each trade, from an initial starting capital of $1,000,000.
By monitoring the variables above, we can benchmark the metrics that are obtained from a set of trading rules. We can then add stops to the trading rules and repeat this process. This will allow us to empirically measure the effects that the stops have on those key metrics. We can then statistically determine whether the portfolio outcome has been improved by the addition of the stop rules.
Case Study
The majority of traders would be best described as medium to longer-term equity investors. In essence, this means that they trade ordinary shares, and aim to hold each share from several months to several years. Typically, this group of investors name themselves 'trend traders', and their aim is to identify and ride a trend for as long as possible. Often one or more simple (or exponential) moving averages provide entry and exit setups. Typically, this group also only trades the long side.
For this reason, I have chosen a 60-day ema crossover system as the example case study system . A 60-day ema crossover system buys when the price crosses above a 60-day ema, and sells when the price crosses below a 60-day ema.
An example trade is shown below in Figure 1. The pink line represents the value of the EMA(60).
Figure 1: Example of a 60-day EMA crossover trade
The data chosen for the case study is the constituents of the ASX200 (since inception April 2000) until the end of 2009. Where possible, I have adjusted this data for delistings and code changes, and trading results include an allowance for transaction costs. To address survivorship bias, buy signals are only issued on stocks which were constituents of the ASX200 on the day the signal was generated.
Remember the objective is not to determine whether these are desirable rules for trading; it is to allow us to select and emulate the basic characteristics of the kind of stocks that the majority of traders and investors in the ASX200 are focused on.
No stops
Initially, we need to benchmark the buy and sell rules without any stops. This gives us a baseline against which to compare the performance of the stops we will introduce.
Raw Trades
The key characteristics of the raw trades generated by buying/selling $10,000 worth of stock every time the buy/sell conditions occur are:
Daily Mean Return = $ 0.61, Average Number of days trades are open = 21.44
Later, when we introduce a variety of stop combinations to the buy/sell rules, we can measure the effects they have using this baseline.
Portfolio
The key characteristics of the portfolio generated by these trades are:
APR = 2.63 %, MAX DD = -34.63 %, Sharpe Ratio = 0.31
Now we know how much potential return there is in the rules (APR%), how risky those rules are (DD%), and a measure of the overall risk for that specific return (Sharpe ratio). Later, when we introduce a variety of stop combinations to the buy/sell rules, we can measure the effects they have using this baseline.
Initial Percentage Stops
Many traders simply use a fixed percentage to determine their stop level price. As an example, a trader might say, "I will set a stop loss 5% below my entry price". Here, we test every initial stop loss percentage threshold from 1% - 10% in steps of 1, for all the trades generated by the ema crossover rules.
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 '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. Some of the higher percentage stops achieve similar Maximum Drawdown%, but none of the stop loss settings was able to improve the Sharpe Ratio. In essence, 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. Those readers that are interested in pursuing the benchmarking of trading systems using statistical methods can find details of this and many other useful procedures in my book, Designing Stockmarket Trading Systems (with and without soft computing).
Summary
In this article, I have benchmarked the results of a simple EMA crossover strategy. Next, the strategy was tested with a variety of initial percentage based stops to see if adding these stops was able to decrease the risk in the strategy. It was found that all stops tested increased the risk and reduced the return of the original strategy.
In the next article, I will test percentage-based trailing stops and ATR-based trailing stops to see whether these types of stops can decrease the strategy risk.
本文翻译由兄弟财经提供
文章来源：http://www.incrediblecharts.com/trading/stoploss-trading-1.php