介紹
本文我將引進蒙特•卡羅方法，並展示我們將如何利用它加強我們關於不同類型止損的認識。
蒙特•卡羅分析法
根據定義，投資組合是根據信號下單的交易集合。投資組合中交易數量通常小於最大可交易數量，這通常和交易者如何管理資金有關。因此，一個投資組合具有特定的資金量對於檢測投資組合效益很有必要。資金量和資金管理方式細微差異所對應的投資組合都有所不同。
假設一位交易者的資金為$10,000，有時兩只股票同時給出買賣信號，這中情況下將會如何呢？很明顯，只有一只股票的信號會被成功執行。但會是哪一只呢？隨著時間的推移，這兩只股票可能有兩種不同的結果：比如，一只上升，另一只下降。
當運行投資組合的時候，同時出現多個交易信號的情況很常見，尤其是在趨勢交易中。同樣，當交易信號發出而交易者沒有資金去支持該交易，結果將會怎樣？當然是跳過該信號。為了便於充分認識交易信號被跳過和成功執行的含義，定量分析師會借助蒙特•卡羅分析法。它可以得出所有互相獨立的投資組合的結果概率。
比如上文中所舉的例子，我們想知道在資金只支持一次交易的情況下，要如何模擬兩種交易信號下投資組合的可能結果？根據數學計算方法，理論上我們這時面對著兩種投資組合，每種投資組合只有一只股票。從這一邏輯來看，經過一段時間之後，實際上已經產生了大量的投資組合（基於交易者的不同選擇），所有的這些組合都有可能是最終的結果。
為了充分的評估止損的作用，我們不僅要模擬一種投資組合的結果（正如我們之前做的那樣），而是要把所有的理論結果都展現出來。這裡，我們可以利用蒙特•卡羅方法反映不同投資組合的回報概率。
下面圖片分別顯示原始交易收益的概率分佈和1000種無止損情況下投資組合的最大虧損。1000種投資組合的最小、平均和最大收益顯示在相應的圖表下方。
最小收益 =-17.32%, 平均收益 = 11.66%, 最大收益 = 43.82%
最小虧損率 = -30.00%, 平均虧損率 = -44.44%, 最大虧損率 = -53.21%
結果
下圖是1000種可能的投資組合結合25種止損後的平均收益和最大虧損率。
啟示
上面的圖表顯示，1000種可能的投資組合中，無止損的收益是最大的。同時，基於平均真實波幅(ATR)止損的投資組合收益低於所有百分比止損的投資組合收益。因此，無止損情況下的投資組合可以提高收益。
結論
一些交易者似乎在利用止損為自己提供心理安慰。如果在你測試過自己的交易系統之後仍然覺得自己無法離開止損，那麼你必須繼續使用止損。也許，你可以考慮一下將止損的範圍設置得大一些。然而，許多交易者認為止損是萬能藥。事實證明，止損會加速投資者交易的失敗，同時是導致交易組合表現不理想的原因所在。
止損不利於交易的原因之一可能是許多止損都是同時被觸發。這通常是由於整體市場的影響，而不是股票所在公司的變化。
Stop-Loss Orders: Help or Hindrance? [Part 3 of 3]
Monte-Carlo Analysis
By definition a portfolio is a subset of the raw trades signalled by the entry and exit rules. The most common reason that a portfolio usually has less trades than the total possible relates to the way the trader/investor manages money, and that is why it is important to test a portfolio with a specific amount of capital. Different amounts of capital (and money management approaches) can give rise to different possible portfolios.
Consider a trader/investor who invests his/her money in multiples of $10,000 according to the above buy/sell rules. What will the trader/investor do if 2 stocks are signalled on the same day, yet the trader/investor only has $10,000 left? Clearly, only 1 trade can be taken, but which one?
It may well be that over time the two trades have very different outcomes. For example, one goes up, and one goes down!
When running a portfolio, the issue of having more trading opportunities than money can occur reasonably frequently, particularly in a trend-trading approach. Again, in the above example, what will the trader/investor do on the next day, when yet another trade is signalled, and there is no money left to take it. Of course, it must be skipped from the portfolio.
To fully understand the implications of taking and skipping certain trades, quantitative analysts may resort to Monte-Carlo modeling, which allows us to build a probability outcome of all the possible portfolios which could have been built dependent on the decisions the trader/investor took.
As an example, in an earlier paragraph we wondered how to model the portfolio outcome when there were two possible trading candidates but only enough money to take 1 trade. The solution using computational mathematical methods is that from this point forward, there are now two theoretical portfolios � one with each possible stock in it. Following on from this logic, you can see that over a period of time, there could actually be a great number of possible portfolios, all dependent on the decisions taken by the individual trader/investor. All of these portfolios would be real possible outcomes, totally dependent on the choices made by the trader/investor on a day to day basis!
To assess the impact of stops completely, we need to consider not just one simulated portfolio outcome (as we did earlier), but a large number of the possible theoretical outcomes. We can approach this using the Monte-Carlo methodology, and determine the probability of various return and drawdown outcomes.
The following figures show the probability distributions for the Raw Return (aka Net Profit) and the Maximum Drawdown metrics for 1000 of the possible 'NO STOP LOSS' portfolios. These provide the benchmark for this final piece of analysis. Under each figure, I have also included the smallest, average and largest values obtained from the 1000 simulations.
Smallest =-17.32%, Average = 11.66%, Largest = 43.82%
Smallest = -30.00%, Average = -44.44%, Largest = -53.21%
Outcomes
The following table shows the average values for both the Net Profit % (not the APR%), and the Maximum Drawdown % for 1000 possible portfolios for each of the 25 stop combinations tested.
Implications
From inspection of this table, we can see that there was no set of 1000 possible portfolios more profitable than the 'NO STOP LOSS' combinations. We can also see the ATR based stop methods have performed quite poorly compared to nearly all of the simple percentage based stop methods. In summary, no combination of stops was able to improve on the basic strategy without stops.
Conclusion
Some traders appear to use stops to provide a level of comfort about the risk they take with their trading. If you feel you absolutely cannot live without stops, even after performing similar tests to these on your own system, you must, of course, continue using them. Perhaps you could even consider simply making them wider.
However, many traders and investors appear to view the stop loss order as a panacea. These empirical results show that the stop loss order may actually be contributing to the poor performance of some traders, and may even be the cause of their lower than expected returns.
One of the reasons that this behavior may be occurring is that many stops are being hit at the same time. This is more likely due to changes in the overall market rather than having any specific relationship to changes in some particular company share price.
I have seen similar results in the past when testing stop orders against long-only, equity based, trend-following types of systems.
If your trading style is best described by phrases like "long-only", "equity based", and "trend-following", and you use stop-loss orders, then you may wish to consider testing your trading rules to see if the stops are actually helping or hindering your performance. You can follow the procedure outlined in this series of articles, and in my book, Designing Stockmarket Trading Systems (with and without soft computing), to help you do this.
Forum Discussion
We hope you find this series of articles interesting. There is a thread on the forum where readers can contribute their views: Do Stop Losses Really Work?
Regards,
Colin Twiggs
本文翻譯由兄弟財經提供
文章來源：http://www.incrediblecharts.com/trading/stoploss-trading-3.php