查看完整版本: 一个不错的采用神经网络的EA的示例

一个不错的采用神经网络的EA的示例

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[b][size=4]去年年底结束的国际大赛的第一名为Better所夺得[/size][/b]
[b][size=4]他采用的就是神经网络原理的EA[/size][/b]
[b][size=4]这使神经网络方法做EA成为不少人关注的焦点[/size][/b]
[b][size=4]这里翻译一篇采用神经网络做EA的不错的示例文章[/size][/b]
[size=4][color=red][b]当然附有源码是吸引人的地方[/b][/color][/size]
[size=4][color=red][b]不过也许作者提出了研究神经网络EA的一些思考更为值得注意[/b][/color][/size]
[b][size=4]作者提出了∶[/size][/b]
[b][size=4]1。“如果有飞机，为什么还要教人类去飞？”
意思是研究是经网络不必从零起步。MT4里已有了不错的“遗传算法”[/size][/b]
[b][size=4]文中介绍了如何利用MT4已有的“遗传算法”[/size][/b]
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2。大家都说做单子最重要的是“顺势而为”，但更需要解决的是∶
“一个基于趋势的交易系统是不能成功交易在盘整(sideways trends)，
也不能识别市场的回调(setbacks)和逆转(reversals.,反向走势)！”[/size][/b]
[b][size=4]这可是抓到不少人心中的“痒处”，有多少人不是到了该逆势时没转向而产生亏损呢？
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[font=宋体][size=10.5pt][font=宋体][size=10.5pt][b][size=4]3。训练神经网络需要用多长的历史数据，提出了并不是用的历史数据越长越好，另外也不是训练的间隔越短越好，文中提出了什么情况下有需再训练它。[/size][/b][/size][/font][/size][/font]
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[font=宋体][size=10.5pt][font=宋体][size=10.5pt][size=4][/size][/size][/font][/size][/font][b][size=4]等等。。[/size][/b]
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[font=宋体][size=10.5pt][font=宋体][size=10.5pt][b][size=4]下面是译文和作者的源码[/size][/b][/size][/font][/size][/font]
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[font=宋体][size=10.5pt][font=宋体][size=10.5pt][font=Times New Roman][b][size=4]Name: Automated Trading System "Сombo" [ ru ]
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[font=Times New Roman][size=4][b]Author:
Reshetov (2008.03.06 10:44) [/b][/size][/font]

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[font=Times New Roman][size=4][b]The problem is stated for this automated trading system (ATS) as follows:[/b][/size][/font]
[b][size=4][font=Times New Roman](ATS)[/font][font=宋体]自动的[/font][font=Times New Roman]([/font][font=宋体]智能的，采用神经网络的[/font][font=Times New Roman])[/font][font=宋体]交易系统的问题表述如下[/font][font=Times New Roman] [/font][/size][/b]
[font=Times New Roman][size=4][b]Let's consider we have a basic trading system - BTS. It is necessary to create and teach a neural network in order it to do things that cannot be done with the BTS. This must result in creation of a trading system consisting of two combined and mutually complementary BTS and NN (neural network). [/b][/size][/font]
[b][size=4][font=宋体]如果我们有一个[/font][font=Times New Roman](BTS, basic trading system)[/font][font=宋体]，同时需要用创建一个神经网络系统并教会它做[/font][font=Times New Roman]BTS[/font][font=宋体]所不能做的事，按这个思路就是要创建这样一个交易系统∶它由互相补充[/font][font=Times New Roman]([/font][font=宋体]配合[/font][font=Times New Roman])[/font][font=宋体]的两部分组成，[/font][font=Times New Roman]BTS[/font][font=宋体]和[/font][font=Times New Roman]NN([/font][font=宋体]神经网络[/font][font=Times New Roman])[/font][font=宋体]。[/font][/size][/b]
[font=Times New Roman][size=4][b]Or, the English of this is: There is no need to discover the continents again, they were all discovered. Why to teach somebody to run fast, if we have a car, or to fly, if we have a plane? [/b][/size][/font]
[font=宋体][b][size=4]呃，英语说，我们不需要再去发现“新大陆”，它们是已经存在的东西！进一步说，如果我们已经有了汽车，那为什么还要教人如何跑得快？如果有飞机，为什么还要教人类去飞？[/size][/b][/font]
[font=Times New Roman][size=4][b]Once we have a trend-following ATS, we just have to teach the neural network in countertrend strategy. This is necessary, because a system intended for trend-based trading cannot trade on sideways trends or recognize market setbacks or reversals. You can, of course, take two ATSes - a trend-following one and a countertrend one - and attach them to the same chart. On the other hand, you can teach a neural network to complement your existing trading system. [/b][/size][/font]
[b][size=4][font=宋体]一旦有一个趋势交易系统的[/font][font=Times New Roman]ATS[/font][font=宋体]，我们仅需要教会这个神经网络如何逆势[/font][font=Times New Roman]([/font][font=宋体]反趋势[/font][font=Times New Roman])[/font][font=宋体]交易的策略。这一点是非常必要的，因为一个基于趋势的交易系统是不能成功交易在盘整[/font][font=Times New Roman](sideways trends)[/font][font=宋体]，也不能识别市场的回调[/font][font=Times New Roman](setbacks)[/font][font=宋体]和逆转[/font][font=Times New Roman](reversals.,[/font][font=宋体]反向走势[/font][font=Times New Roman])[/font][font=宋体]！当然，你可以采用两个[/font][font=Times New Roman]ATS[/font][font=宋体]，一个基于“趋势”，一个基于“反趋势”[/font][font=Times New Roman]([/font][font=宋体]逆向[/font][font=Times New Roman])[/font][font=宋体]，然后把它们挂到同一图表上。另一个办法是，你能教会神经网络如何与你现有的系统“互补地”协调工作！[/font][/size][/b]
[font=Times New Roman][size=4][b]For this purpose, we designed a two-layer neural network consisting of two perceptrons in the lower layer and one perceptron in the upper layer.[/b][/size][/font]
[b][size=4][font=宋体]为实现这个目标，我们设计了一个两层的神经网络，下层有两个感知机[/font][font=Times New Roman](perceptrons)[/font][font=宋体]上层有一个感知机。[/font][font=Times New Roman] [/font][/size][/b]
[font=Times New Roman][size=4][b]The output of the neural network can be in one of these three states:[/b][/size][/font]
[font=宋体][b][size=4]这个神经网络的能输出下列三种状态之一[/size][/b][/font]
[font=Times New Roman][size=4][b]Entering the market with a long position[/b][/size][/font]
[b][size=4][font=Times New Roman](Entering)[/font][font=宋体]市场是处在多向仓[/font][/size][/b]
[font=Times New Roman][size=4][b]Entering the market with a short position[/b][/size][/font]
[b][size=4][font=Times New Roman](Entering)[/font][font=宋体]市场是处在空向仓[/font][/size][/b]
[font=Times New Roman][size=4][b]Indeterminate state[/b][/size][/font]
[b][size=4][font=宋体]不确定的[/font][font=Times New Roman], ([/font][font=宋体]不明确的[/font][font=Times New Roman], [/font][font=宋体]模糊的[/font][font=Times New Roman])[/font][font=宋体]状态[/font][/size][/b]
[font=Times New Roman][size=4][b]Actually, the third state is the state of passing control over to the BTS, whereas in the first two states the trade signals are given by the neural network. [/b][/size][/font]
[b][size=4][font=宋体]实际上，第三种状态是就把控制权交给[/font][font=Times New Roman]BTS[/font][font=宋体]，反之前两种状态是交易信号由神经网络给出。[/font][/size][/b]
[font=Times New Roman][size=4][b]The teaching of the neural network is divided into three stages, each stage for teaching one perceptron. At any stage, the optimized BTS must be present for perceptrons to know what it can do. [/b][/size][/font]
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[b][font=Times New Roman][size=4]待续。。。[/size][/font][/b]



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[[i] 本帖最后由 careearn 于 2008-6-27 14:15 编辑 [/i]]

careearn同学的口味很重 走得路线也很高端 :lol

期待更完整的资料

[quote]原帖由 [i]redstart[/i] 于 2008-6-27 13:17 发表 [url=http://www.forex-town.com/redirect.php?goto=findpost&pid=5518&ptid=977][img]http://www.forex-town.com/images/common/back.gif[/img][/url]
careearn同学的口味很重 走得路线也很高端 :lol

期待更完整的资料 [/quote]
还是到下面去看原汁原味的东西吧。
[url=http://codebase.mql4.com/2792]http://codebase.mql4.com/2792[/url]

看不太明白呀...有没有模板呀...