1. Introduction A trading strategy is simply a pre-determined set of rules that a trader has developed to guide their trading. The advantages to the trader of developing a trading strategy are: · It removes emotions from trading decisions. A trader following a strategy knows what to do whatever the market does. A trader that does not have a strategy tries to make decisions when the market is open and is liable to become emotionally attached to positions. They may experience panic and indecision when the market does not move in their favour, as they do not have a prepared response. · Saving time. Developing a trading strategy that has an edge is hard work. However, once developed the rules can easily be automated freeing the trader from having to watch charts all day and allowing time to develop further strategies. In this article we will examine each stage in the process of developing a trading strategy, from identifying a possible edge through to a written trading plan. Along the way we will develop a simple strategy to day trade the Dow Jones index. A trading strategy is simply a pre-determined set of rules that a trader has developed to guide their trading. The advantages to the trader of developing a trading strategy are: · It removes emotions from trading decisions. A trader following a strategy knows what to do whatever the market does. A trader that does not have a strategy tries to make decisions when the market is open and is liable to become emotionally attached to positions. They may experience panic and indecision when the market does not move in their favour, as they do not have a prepared response. · Saving time. Developing a trading strategy that has an edge is hard work. However, once developed the rules can easily be automated freeing the trader from having to watch charts all day and allowing time to develop further strategies. In this article we will examine each stage in the process of developing a trading strategy, from identifying a possible edge through to a written trading plan. Along the way we will develop a simple strategy to day trade the Dow Jones index. It is important to decide the timeframe that we are going to work towards in our system. This really comes down to how much time we are prepared to spend trading and how active we require the system to be in terms of the number of trades. Broadly speaking there are four main timeframes: Timeframe Length of Trade Data Used Long-Term Months End of Day Medium-Term Weeks End of Day Short-Term Days Intra Day Day-Trade Up to 1 day Intra Day System 1 makes an average of 250 points per trade but only trades 4 times a year. System 2 only makes an average of 10 points per trade but trades 200 times a year. System 1 makes 1,000 pts a year but system 2 makes 2,000 points a year. However if we allow 5 pts per trade for commission and slippage then system 1 costs 20 pts/year whereas system 2 costs 1,000 pts/year. Both systems make a similar net profit over the course of a year however there are a number of points to bear in mind: With only 4 trades per year for system 1 every trade is important and must be taken. It is likely that the bulk of the profits will come from only one of the trades so this must not be missed. With system 2 producing a new signal every day it is not so reliant on individual trades. System 1 will require a larger capital base to trade as the trades will have to ride wider swings. System 2 will require a lot more work to trade as intra day data will have to be monitored. System 2 will be psychologically easier to trade as the equity draw down periods will be shorter. Well designed and robust day trading systems will rarely have losing months. The frequency of trading is an essential element of any trading system and our choice of timeframe will help to determine it. There is no right answer it is very much a case of what suits the individual trader. For the purpose of this article we will look at developing a day trading system. 3. Choosing an Instrument to Trade The next thing we need to do when designing a trading system is to decide what we are actually going to trade to match our objectives. There is a huge range of instruments available to traders from the underlying instruments such as stocks or currencies to derivatives such as futures or options. Take, for example, the Dow Jones index which measures the value of 30 large US companies. There are various ways to trade this one index: The individual stocks that make up the index. An exchange traded fund. In essence the fund owns the underlying shares so it’s value moves up and down with the index. Options. Futures. Spread betting (tax free derivatives in the UK). Each of these could be further divided, there are 3 exchange traded funds and 2 types of futures. Each method has it’s own merits and is more or less appropriate for different trading scenarios. The objective of this article is to develop a strategy for day trading the Dow Jones index. We are looking to open and close positions within a day and so require the following attributes from the instrument we choose: Low commission costs. We will we be trading frequently and, therefore, need to keep costs to a minimum. This rules out trading the individual stocks. Liquidity. With trading frequently we will need to be able to enter and exit the market without experiencing too much slippage. This rules out the larger of the Dow Jones futures contracts. Narrow spreads. Generally, the more liquid a market is the narrower the spread between the bid and ask. This rules out spread betting where the spread is 5-8 pts. The mini Dow Jones future (trading as YM on the Chicago Board of Trades’ electronic platform eCBOT) fulfils the above criteria and is most suitable for our purposes. Easily trading 100,000 contracts a day the spread is 1 point during normal market hours. Each contract is worth $5 per point movement of the Dow Jones index. In order to develop a trading strategy it is extremely important to obtain historical data for the actual instrument that we intend to trade. Although derivatives based on the same underlying instrument will move generally in tandem with each other it will not be exact. The futures will move more quickly and to greater extremes than the underlying cash index. We cannot, therefore, develop a system using the cash index and expect it to perform to the same degree when trading futures or any other derivative. 1,绪论 一个交易策略仅仅是一个每个已按照规则被决定了的设置，一个交易者使其发展并用来指导他的交易。对于交易者来说，发展自己的交易策略有以下的好处： ·它去除掉了交易决定中的情绪因素。一个交易者应当遵循交易策略，知道自己应该做什么，无论市场如何变化。一个没有交易策略的交易者尝试在市场开放的时候做一个决定，同时他容易受到情绪上的干扰。他们可能经历恐慌和挣扎在市场没有按照他们预计的那样运行的时候，因为他们没有一个对此情况的对策。 ·节约时间。一个发展中的交易策略有一个优势是努力工作。无论如何，一旦发展的交易规则能够容易的自由的进行交易从全天的观测图表中，允许有时间发展更深层次的交易策略。 在这个文章中我们将考察每个舞台在发展交易策略的过程中，从确定一个可能的交易机会到写下来的交易策略。按这样的方法我们将发展出一个对每日Dow Jones指数交易简单的策略。 2,时间周期 决定我们将要工作方向的时间结构在我们的系统中，是非常重要的。这真实的取决于多少时间我们准备花费在交易上，和多么主动我们要求这个系统在一个周期中的交易次数。广泛的的说来，有四个主要的时间结构： 时间结构 交易长度 使用的数据 长线 月 盘后数据 中线 周 盘后数据 短线 日 盘间数据 日交易 接近一天 盘间数据 系统1 每次交易平均产生250点的收益但是一年只交易4次。 系统2 每次交易平均仅产生10点收益但是一年交易200次。 系统1产生1000点收益一年，然而系统2产生2000点收益一年。无论如何如果我们允许每次交易5点用做佣金以及滑动误差，那么系统1花费20点/年反之系统2花费1000点/年。 两个系统产生相似的净利润从整个一年来看无论如何有一个数字要牢记的是： 在一年只有四次的交易的系统一中，每次交易都很重要和必须被把握。可能大部分的收益将来自一次的交易之中，因此这次交易绝对不能被错过。在系统2中产生的一个新的信号，并不依赖个别的交易。 系统1将要求有巨大的资本做基础因为交易不得不承受宽幅的震荡。 系统2要求更多的操作对于交易因为日内数据不得不被监控。 系统2在交易中对心理上的要求更低因为净资产下降的时期将更少，设计良好并且稳定的日交易系统罕见亏损月。 交易的频率是任何交易系统的精华元素，我们选择的交易周期将帮助我们来决定它。没有一个正确的答案到底何种策略适合个体的交易者。鉴于本文的目的，我们将关注一个发展中的日交易系统。 三．选择一个交易工具 当开始设计一个交易系统时，我们需要做的下一件事是，决定我们该如何来达到我们的。现在有大量的交易工具适用于从股票，货币市场，到金融衍生物如期货和期权。 比方说，对于道琼斯30指数来说，有以下几个不同的交易渠道来操作这只指数： 1、 指数中独立的个股。 2、 一个ETF基金 3、 期权 4、 期货 5、 套利（注：英国金融衍生物交易免税） 以上的每一个交易工具都可以进一步的细分，目前现在有3个道琼斯30指数ETF，和2个期货品种。每个工具都有它自己的优点，以及或多或少有些不同的交易特性。 本文的目的是发展一个道琼斯指数的日内交易策略，我们在一天内开平仓，同时遵守我们使用的交易工具的规则： 1、 低成本手续费。 由于交易频繁，因此手续费必须最低，交易个股时除外。 2、 流动性。 要有足够的成交量使我们的单子能进出自如，不会有追不进和砍不掉的情况出现。 3、 委买、委卖价格间不能差距过大。 普遍来说，在一个流动性好的市场中，委买、委卖价格间距都不大。本交易适用于委买、委卖价格间距不超过5-8pts。 道琼斯指数迷你期货（在CBOT电子盘中交易）正好适合本文的交易系统所需要的规则和交易目标，至少日均100000口成交量，委买、委卖价格间距不超过1点，平均每口合约价值，道琼斯指数每点价值5美元。 为了发展此日内交易系统，对我们想进行交易的获得历史数据并加以测试是非常重要的。（后面关于使用道琼斯指数期货的优点，忽略~~~） ztob123翻译于2005年6月13日
4. The Trade Set-up So far we have decided to develop a system to day trade the mini Dow Jones future. Next we need to identify a market characteristic that can provide a statistical edge to form the set-up for our trades. A set-up is a standardised set of conditions that we will use to identify a potential trade. Once the market characteristic that we want to take advantage of is identified then the set-up conditions can be derived. Let’s work through an example. The open-range breakout is a very popular trading style. The theory behind it is that markets will tend to put in an extreme for the day (either the high or the low) relatively early in the trading day. How true is this though? Examining the mini Dow Jones futures data for the period January 2004 to June 2004 (124 trading days) we find the following results: Opening range for first : High or Low already in place: Percentage of total (124 days): 15 minutes 41 33% 30 minutes 57 46% 45 minutes 78 63% 60 minutes 86 69% 75 minutes 91 73% 90 minutes 95 77% 105 minutes 103 83% 120 minutes 105 85% 135 minutes 112 90% 150 minutes 112 90% 165 minutes 113 91% 180 minutes 115 93% We can see that 1/3 (33%) of the days examined either a high or low was in place within 15 minutes of the open, more than 2/3 (69%) within 1 hour and more than 90% in 3 hours. That looks statistically significant. If we trade a break of the high or low after 60 minutes with a stop outside of the other extreme up to that point we know that we will not be stopped out on 69% of our trades. However we need to examine our data more closely as it could be the case that most of the day’s movement actually occurs within the opening period leaving us very little room for our trade to move into profit. So let’s look at the opening range as a percentage of the total day’s range: Opening range for first: Percentage of Days Range 15 minutes 24% 30 minutes 33% 45 minutes 43% 60 minutes 47% 75 minutes 52% 90 minutes 55% 105 minutes 58% 120 minutes 60% 135 minutes 62% 150 minutes 64% 165 minutes 66% 180 minutes 68% We must assume that the percentage of the day’s range represents our stop, as this is the point where our reason for being in the trade (the breakout) becomes invalid. Our potential profit from the trade is represented by the balance of the day’s range. E.g. at 30 minutes the stop loss is 33% of the days range leaving 67% as the potential profit. We can also see from the first table that we have a 46% chance of not hitting the stop. We can calculate the maximum possible expectancy (the average percentage amount of the daily trading range that we capture) from these figures using the formula: Maximum Expectancy = (Pw x (1-Al)) – ((1-Pw) x Al) Where Pw = percentage of days where the stop is not hit, from the first table. And Al = stop as a percentage of the total days range, from the second table. Opening range for first: Calculated maximum expectancy: 15 minutes 9% 30 minutes 13% 45 minutes 20% 60 minutes 22% 75 minutes 21% 90 minutes 22% 105 minutes 25% 120 minutes 25% 135 minutes 28% 150 minutes 26% 165 minutes 25% 180 minutes 25% We can see that the best combination of opening range and potential profit occurs at 135 minutes where we can expect to capture, on average, 28% of the day’s range. It must be remembered that this is the maximum available profit as, at the moment, we are assuming that we close the trade at the second extreme of the day, i.e. exactly at the high or low. The purpose of this exercise was to prove that the open range breakout has the potential to form the basis of a trading set up. From the third table we can see that every range tested has a positive expectancy and ，that there is very little to separate a breakout of the first hour from that of the first 3 hours. The percentage of stop outs decreases but so does the potential profit. It makes very little difference whether we choose to trade a break out of the first hour, the first three hours or anything in between, but the maximum potential comes at 135 minutes (9.30am to 11.45am ET) so we’ll use that. 5. Entry Rules Now we have our trade set-up established, we must decide exactly how we will enter a trade once the set-up criteria is met. The set-up for our strategy is very straight forward, we will wait until 11.45am ET and then enter a long (buy) if the high of the opening range (9.30am to 11.45am) is broken or a short (sell) if the low of the opening range is broken. The easiest way to establish this is to place a stop order to buy in the market at 1 tick above the high of the range and a stop order to sell in the market at 1 tick below the low of the range. As an example, let’s take the trading day of 2 Jan 04. The opening range gives a high of 10510 at 10.58am and a low of 10462 at 10.00am. At 11.45 we place the following orders: Buy stop at 10511 Sell stop at 10461 When the market hits one of the stops to open a trade we will leave the other stop in the market as our initial stop loss. If that stop loss was hit then our reason for being in the trade would be invalid. Our entry rules are fairly simple but we could look at altering these in two ways: 1. We could wait a few more ticks after a break of the opening range before opening our trade. E.g. we could set our stops at 5 pts past the high and low of the range, in the example for 2 Jan 04 that would be a buy at 10515 and a sell at 10457. The reasoning behind this is to protect against the market just triggering our stop at just past the day’s high or low and then reversing. We can examine this theory by looking at the maximum favourable movement (MFE) on each trade that is triggered, that is the maximum amount the trade moves in our favour during the day. MFE No of trades Cost Saving Avoidance Cost Net Gain/(loss) 0 2 40 107 (67) 1 2 40 214 (174) 2 6 168 309 (141) 3 9 203 400 (197) 4 11 238 490 (252) From the table we can see that on 2 occasions the market hit our stop and reversed immediately, costing 40 pts in total at the end of the day. To avoid this we could have a trigger of 2 points instead of 1 for the trade entry. However there are 109 trades in total for the sample and adding 1 point to each trade entry would cost an extra 107 point on the remaining trades, a net loss of 67 points. We can conclude that waiting more than 1 tick to enter the trade reduces the overall profitability of the system. Alternatively, once the set-up is triggered we could wait for a retracement to occur before entering the trade. For example on 2 Jan 04 once the low of 10462 is broken we enter a limit order to sell at, say, 5 pts better at 10467. The danger here is that we may miss out on the biggest moves if the price does not retrace, however, we will make points on those that do. We need to examine the maximum move against our entry price (MAE): MAE No of Trades Missed Trades Savings Net Gain/(loss) 0 5 305 0 (305) 1 6 348 103 (245) 2 9 534 200 (334) 3 11 634 294 (340) 4 15 717 376 (341) We can conclude that waiting for a retracement before entering a trade reduces overall profitability because the most profitable trades are missed. For our strategy we will stick with entering the trades on a buy stop or a sell stop at 1 point beyond the high/low of the opening range (9.30-11.45am ET). 四，交易系统的建立 因此我们决定发展道琼斯指数期货的日内交易系统，下面我们需要分析市场特性，并在建立我们交易时得到统计优势。建立系统就是设置标准化条件，使我们能分辨出可能赢利的交易。一旦有利于我们得到优势的市场特性明确出现，系统设置的条件就将起作用。 让我们举了例子： 区间突破是一个非常流行的交易方法，这个理论的背后是市场将在我们开始交易的日子前出现极度的波动（无论高点还是低点），这种理论有多大的可靠性？用迷你道琼斯指数期货做测试，2004年1月到2004年6月，共128个交易日，我们得出下面的结果： 第一次区间突破 价格已存的高点，低点: 总体比例(124天): 15 minutes 41 33% 30 minutes 57 46% 45 minutes 78 63% 60 minutes 86 69% 75 minutes 91 73% 90 minutes 95 77% 105 minutes 103 83% 120 minutes 105 85% 135 minutes 112 90% 150 minutes 112 90% 165 minutes 113 91% 180 minutes 115 93% 在测试中可以看到1/3（33%）的日子里，15分钟线的高点和低点都被突破，而突破1小时线的日子有2/3（66%），突破3小时线的日子多于90%。 以上可以看出此统计很有意义。假如我们用60分钟的高低点突破进行交易，并把止损设在反方向突破时，我们全部交易中有69%不会被止损。然而我们还需要检测我们的数据有多少否符合以下现实，事实上绝大多数的日内波动发生在开市时，只给予我们少量的空间入场获利。 下面我们看下高低点突破区间在全天的区间的比例 第一次区间突破: 日波动百分比 15 minutes 24% 30 minutes 33% 45 minutes 43% 60 minutes 47% 75 minutes 52% 90 minutes 55% 105 minutes 58% 120 minutes 60% 135 minutes 62% 150 minutes 64% 165 minutes 66% 180 minutes 68% 我们必须假设在这些日波动百分比上来设置止损，否则我们交易的原则（突破交易法）将变的有缺陷。我们从交易中潜在的获利能力，通过日波动平衡来体现出来 。举个例子，用30分钟线交易，33%被止损掉了，还有67%有获利潜力。第一个表格中显示，我们也有46%的机会不触及止损。 我们通过公式计算计算这些表格后，得出的最大赢利期望是： 最大赢利期望 = (Pw x (1-Al)) – ((1-Pw) x Al) Pw 是 = 第一张表格中，未触及止损的日子百分比。 Al 是 = 第二张表格中，触及止损的日子百分比。 第一次区间突破: 计算出的最大赢利期望: 15 minutes 9% 30 minutes 13% 45 minutes 20% 60 minutes 22% 75 minutes 21% 90 minutes 22% 105 minutes 25% 120 minutes 25% 135 minutes 28% 150 minutes 26% 165 minutes 25% 180 minutes 25% 我们看到 区间突破和获利的最好组合发生在135分钟，在此处的最大赢利期望有28%。必须记住这只是最大的可能获利，此时我们已经违反了，在日内反向区间突破点（正确的高点、低点）止损的。 这个测试的目的是，用来证明区间突破 能成为交易系统建立的基本组成部分。从第三张表格中我们可以看到，通过测试，每个时间段都有获利的可能，且这些时间段在开始3个小时内的第一次突破没有什么区别。等止损的百分比减少时，获利同时也被减少了，这导致了在各个时间段中的区间突破对我们的获利来说没有什么不同，但从统计角度来看，135分钟的获利潜力最大（东部时间9:30~~11.45 ），因此我们就选它！ 5,入市规则 现在我们来确定我们的交易结构，我们必须严格决定何种情况我们将进入开始交易一旦遇到标准的结构。 我们的交易策略的结构是十分迅速有效的，我们将一直等待直到美国东部时间上午11点45分，然后进入市场做多如果开盘（上午9点30分到11点45分）的高点被突破或者进入做空如果开盘的低点被突破。最容易的确定这个方法是设置一个静止的命令在市场上买入在最高价格上一个变动价位的范围，一个静止的命令在市场上卖出在比最低价格低一个变动价位的范围。 就如这样一个例子，看看2004年1月2日的交易日。开盘范围给出一个最高价格10510在上午10点58分，一个最低价格10462在上午10点。在上午11点45分我们放置如下命令： 买入预留在10511 卖出预留在10461 当市场价格接触到其中一个预留价格而开仓交易，我们将在市场中继续留着另一个预留单作为我们最初的止损价格。如果那个止损价格被达到，那么我们进入市场的理由也将是有问题的。 我们的入市规则是相当简单的但是我们可能着眼于改变这些规则在两种情况下： 1， 我们可能几乎不能等到更多价格变动在突破开盘范围之后而在开始我们的交易之前。举例来说,我们可能设置我们的预留价位在比最高价格高5点以及最低价格低5点的范围，在上面2004年1月2日的例子中就是在10515的价格买入和10457的价格卖出。这样设置的理由是来防止出现市场行情刚刚触及我们的预留价格，就是刚刚超过最高或者最低价格就立即反转。我们可以检验这个理论通过观察最大限度顺势行情在每次被触发的交易中，那就是最大限度的数量整个日内行情在我们愿意看到的情况下。 最大限度有利的行情 没有交易 成本节约 规避代价 净赢利/(亏损) 0 2 40 107 (67) 1 2 40 214 (174) 2 6 168 309 (141) 3 9 203 400 (197) 4 11 238 490 (252) 从表格中我们可以看到，在市场行情2次触及我们预留价格然后立即反转，总共产生40点亏损在一天的交易结束以后。为了避免这样的情况我们采取用2点来代替1点来制定交易入市点。无论如何，总共有109次交易因为样板和每次入市交易加上的一点将花费额外的107点对于剩下的交易，净亏损67点。 我们可以做出如下结论，等待大于1点的机会入市交易减少了系统整体的收益率。 二者选一地，一旦设置被触发我们应当等待一个回调在入市前。例如，在2004年1月2日，一旦10462的低价被突破，我们开始一个限价卖出指令在，说，5点更好在10467。这里的威胁是我们可能错过最大的趋势变动如果价格不出现回调的话。无论如何，我们将关注那些那样做。我们需要检查最大变动价格不利于于我们入市价格： MAE 没有交易 错过的交易 节约 净利润/（亏损） 0 5 305 0 (305) 1 6 348 103 (245) 2 9 534 200 (334) 3 11 634 294 (340) 4 15 717 376 (341) 我们可以得出如下结论等待一个回调在入市交易之前减少了整体的收益率，因为大多数有利可图的交易被错过了。 为了我们的策略，我们将坚持入市交易通过买预留或者卖预留在超过开盘时间范围的最高（低）价格1点的价格。 ztob123翻译于2005年6月15日
5,入市规则 现在我们来确定我们的交易结构，我们必须严格决定何种情况我们将进入开始交易一旦遇到标准的结构。 我们的交易策略的结构是十分迅速有效的，我们将一直等待直到美国东部时间上午11点45分，然后进入市场做多如果开盘（上午9点30分到11点45分）的高点被突破或者进入做空如果开盘的低点被突破。最容易的确定这个方法是设置一个静止的命令在市场上买入在最高价格上一个变动价位的范围，一个静止的命令在市场上卖出在比最低价格低一个变动价位的范围。 就如这样一个例子，看看2004年1月2日的交易日。开盘范围给出一个最高价格10510在上午10点58分，一个最低价格10462在上午10点。在上午11点45分我们放置如下命令： 买入预留在10511 卖出预留在10461 当市场价格接触到其中一个预留价格而开仓交易，我们将在市场中继续留着另一个预留单作为我们最初的止损价格。如果那个止损价格被达到，那么我们进入市场的理由也将是有问题的。 我们的入市规则是相当简单的但是我们可能着眼于改变这些规则在两种情况下： 1， 我们可能几乎不能等到更多价格变动在突破开盘范围之后而在开始我们的交易之前。举例来说,我们可能设置我们的预留价位在比最高价格高5点以及最低价格低5点的范围，在上面2004年1月2日的例子中就是在10515的价格买入和10457的价格卖出。这样设置的理由是来防止出现市场行情刚刚触及我们的预留价格，就是刚刚超过最高或者最低价格就立即反转。我们可以检验这个理论通过观察最大限度顺势行情在每次被触发的交易中，那就是最大限度的数量整个日内行情在我们愿意看到的情况下。 最大限度有利的行情 没有交易 成本节约 规避代价 净赢利/(亏损) 0 2 40 107 (67) 1 2 40 214 (174) 2 6 168 309 (141) 3 9 203 400 (197) 4 11 238 490 (252) 从表格中我们可以看到，在市场行情2次触及我们预留价格然后立即反转，总共产生40点亏损在一天的交易结束以后。为了避免这样的情况我们采取用2点来代替1点来制定交易入市点。无论如何，总共有109次交易因为样板和每次入市交易加上的一点将花费额外的107点对于剩下的交易，净亏损67点。 我们可以做出如下结论，等待大于1点的机会入市交易减少了系统整体的收益率。 二者选一地，一旦设置被触发我们应当等待一个回调在入市前。例如，在2004年1月2日，一旦10462的低价被突破，我们开始一个限价卖出指令在，说，5点更好在10467。这里的威胁是我们可能错过最大的趋势变动如果价格不出现回调的话。无论如何，我们将关注那些那样做。我们需要检查最大变动价格不利于于我们入市价格： MAE 没有交易 错过的交易 节约 净利润/（亏损） 0 5 305 0 (305) 1 6 348 103 (245) 2 9 534 200 (334) 3 11 634 294 (340) 4 15 717 376 (341) 我们可以得出如下结论等待一个回调在入市交易之前减少了整体的收益率，因为大多数有利可图的交易被错过了。 为了我们的策略，我们将坚持入市交易通过买预留或者卖预留在超过开盘时间范围的最高（低）价格1点的价格。 六、止损规则 我们的策略已经由了一个自然的止损 这个策略的目的是在那些出现高低点的日子里获利，（跳空的高低点不算？）。如果我们在高低点突破时入场交易，然后市场却经常性触及止损，我们的交易就会遭到损失。这种情况我们在前面的测试中，占总时间的10%。 下面我们增加几条止损的条件： 1、当我们得到一定数额的赢利后，移动止损点至突破的地方。然而，为什么以及如何使市场有利于我们的突破点所处的位置？ 2、当交易逐渐赢利时，移动止损价，保护赢利。 3、设置一个固定数额的最大止损量（比如35pts）。固定点数赢利是应该避免的，他们在市场的波动中不会带来帐户的改变，也不会带来系统成功的证明。 4、设置止损为区间的一个百分数。当市场继续在价格趋势上前进，并且最终触及我们的首次止赢前，价格会有一定强度的回调，这时这个止损理论就有了存在的前提。 在我们设计系统，并且为了在实战中求得比测试时要好的多的预期回报，区间突破止损的幅度可以相对的放大，如日波动率的62%。因此，当日波动为200点时，我们的平均止损就在124点。许多交易者因为心理方面原因而喜欢非常紧凑的止损方法，然而，据我们在区间突破发现的情况来看，越紧凑的止损与越低的赢利率联系紧密。 让我们通过在区间突破上试用不同的止损幅度，来测试上面的结论。注：假设市场在收盘后继续交易。 区间突破止损百分比% 止损交易占总交易的% 被止损的交易平均损失 非止损交易占总交易的% 被止损的交易平均赢利 每次交易的期望 10% 84% 6.53 16% 44.41 0.95 20% 74% 12.35 26% 36.79 0.59 30% 58% 18.19 42% 29.70 2.26 40% 50% 24.26 50% 27.05 1.73 50% 41% 28.76 59% 23.45 1.53 60% 31% 35.18 69% 23.51 5.60 70% 25% 40.30 75% 20.35 5.14 80% 21% 44.04 79% 18.43 4.92 90% 16% 45.76 84% 15.80 6.33 100% 11% 48.00 89% 13.15 6.42 交易的预期赢利公式是：(%W*Av W) – (5L*AvL) 上面的表格是124天测试期间的109次交易的结果。我们可以清晰的看到，当止损率变小时，平均损失也小了，但亏钱的次数增加不少。当止损为区间突破的20%时，我们平均损失只有12点，而赢利有37点；风险回报率为1：3。然而当期望损失平均少于1个点就止损时，我们有74%的时间都会被赶出局。 把止损放置在入场点的反面，就是区间的另外一边时，上表最后一行显示，我们只有11%的交易被止损出局，平均损失48点。然而占总成交量89%的赢利交易，平均只赚13点。这是许多交易者应该提防的高风险回报率，3.5：1，同时有更多的交易者在89%的交易中不止盈出局，导致了平均赢利只有6.42个点。 结论：通过检查 赢利、损失交易次数对比，和赢利、损失点数对比，我们不认为风险回报率会影响赢利的那部分交易。 我们将继续把止损放置在区间的另外一边。 ztob123翻译于7月5日
Hi WJ2000 http://www.trade2win.com/knowledge/articles/general_articles/developing-a-trading-strategy/?r or google Tim Wreford btw: thank you for sharing lot of valuable books
收藏了。下载地址：http://www.online-futurestrading.com/images/How to develop a futures trading system ebook.pdf
And Al = stop as a percentage of the total days range, from the second table. 这句翻译是不是有什么问题。 最大收益期望的计算，算出来评估的是什么？
It must be remembered that this is the maximum available profit as, at the moment, we are assuming that we close the trade at the second extreme of the day, i.e. exactly at the high or low. 翻译的也有大问题。 无所谓了，文章角度不错，可惜不知道怎么用到其他上面