簡介
許多交易商和投資者把止損當作他們日常交易活動的一部分。幾乎所有的交易書籍都強調止損的重要性，比如“不設止損的交易就像駕車不系安全帶。”這些言論似乎很有依據性，但是證據卻表明止損並沒有給交易者帶來預期的效果。對於中長期的交易系統（覆蓋了大部分交易者）而言，止損的弊大於利。
作為交易者，我們會經常設置止損，並在價格快速反轉時慶幸止損保護了我們的盈利。盡管止損可以在一些交易中可以讓我們減少損失，但它是否能在投資組合中發揮同樣的作用仍然值得懷疑。一系列原因可以說明止損不適合投資組合，我們將在後面講到。
我們不應該緊盯投資組合中每一筆單子的收益，而應該著重於整體回報。我的大量實驗表明，止損在個人交易和投資組合中發揮的作用存在不匹配性。我將通過系統的舉例引導讀者去驗證，並通過數據說明該不匹配性。你也可以通過我的方法來檢測止損對你本人交易系統的作用，判斷自己是否真正收益於止損。
衡量止損的作用
在衡量止損在一個交易系統中所起到的作用之前，需要考慮止損在個人交易和由這些交易組成的投資組合中的作用。為了方便評估止損在個人交易中的作用，我們以交易的日平均收益和持倉天數為基準，並測量它們的變化。
• 交易日平均收益率（$） - 每天平均回報
• 持倉天數
為了方便對交易開倉平倉的基準測試，我們首先假設起始資金沒有限制，每筆交易的名義投資資金為 $10,000。我們可以將APR%、Max DD%、Sharpe Ratio設為基準並觀察它們的變化，從而得到止損在投資組合中的作用：
• APR％（年回報率） ——投資組合的回報率
• Max DD%（最大虧損率）——投資組合曲線能承受的最大虧損（峰、谷間距離）
• 夏普比率——每單位回報的風險比。忽略無風險利率的調整，夏普比率可以很好的衡量投資組合回報率的不穩定性。（例如，兩位不同的交易者在一段時間之後獲得的收益都是20%，其中價格波動小的交易者所對應的夏普比率高）
在設置基準投資組合之前，需要考慮到資金量。這種情況下，可以應用相對簡單的資金百分比模型：起始資金為o$1,000,000，每筆交易占用2%。
通過檢測上述變量，我們可以以一套交易規則所獲得的指標為基準。然後，在這套交易規則中加入止損，並觀察結果。這會讓我們客觀的觀察到止損對基準指標的影響。
案例分析
大多數的投資者都可以被形容為中長期投資者。本質上來說，他們交易普通股並希望持有時間在數月到數年之間。他們把自己稱作趨勢投資者，他們的目的就是識別趨勢，並試圖盡可能長時間的追隨趨勢交易。通常，一個或多個簡單（或指數）移動平均數指標給他們提供交易信號。而且，他們只做多。
因此，我的研究選擇了60日指數移動平均數指標：當價格位於指標上方做多，位於指標下方時做空。下圖交易示例中粉色的代表EMA（60）的值。
上圖數據是 ASX200指數（成立於2000年4月）的成分股走勢的一部分。我已經盡可能的根據退市和代碼變化調整上述數據，交易結果包括交易成本返佣。考慮到生存偏差，每只成份股的買入指令只在信號生成的當天有效。
請記住，我們的目的不是去判定交易規則是否理想，而是方便我們像大多數交易者一樣去識別股票走勢特徵。
無止損
首先，我們需要測試沒有止損情況下交易規則的表現。從而我麼可以很好的對比有無止損情況下的不同收益。
個人交易：
每次買賣信號發出時，我們都將買或賣出價值$10,000的股票。交易結果為：
日平均收益 = $ 0.61， 交易的平均天數= 21.44
投資組合：
建立在上述交易中的投資組合表現為：
投資回報率=2.63％，最大虧損率=-34.63％，夏普比率=0.31
現在我們知道潛在的投資回報率，交易原則的風險以及交易結果的整體風險。
止損百分比
許多交易者通過一個固定的百分比來確定他們的止損價位。比如，一位交易者或許會說，“我將會在交易價格下方的5%處設置止損。”這裡，我們將會根據EMA（60）產生的買賣信號，測試止損百分比為1%~10%的效果，
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
為了系統的比較投資組合的效果，我們可以使用方差分析法，它可以讓我們同時比較所有無止損交易與10種止損百分比交易的盈利情況。這將有助於確定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。本人中我特意省略了方差分析法，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。
總結
我在這篇文章中應用了EMA指標作為基準。下面，該策略的交易收益將與各種止損百分比的投資回報相比較，從而確認止損百分比是否可以降低交易風險。結果表明，所有測試的止損都增加了風險、降低了回報。
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.
本文翻譯由兄弟財經提供
文章來源：http://www.incrediblecharts.com/trading/stoploss-trading-1.php