Assignment – Automatically formulating trading rules
神经网络代写 The assignment for this topic is going to look at the problem of automatically formulating trading rules using one of the techniques
The assignment for this topic is going to look at the problem of automatically formulating trading rules using one of the techniques introduced in the lecture: GA, PSO, DE or ANN (or others of your own choice but check with me first). The choice of technique is up to you, as is the exact form of the problem you choose.
The approaches covered in the lecture looked at:神经网络代写
- Predicting the high price (HP) and low price (LP) for the coming day, based upon various key factors from previous days such as opening price, closing price, high price, low price, volume, Relative Strength Index (RSI), and Exponential Moving Average (EMA). These likely values for HP and LP can then be built into trading rules which will automatically buy or sell assets at what should be the optimum point.
- Optimising the parameters for pre-existing trading rules, in particular the long, short and signal values for MACD.
You can investigate either of these or some other aspect of algorithmic trading of your own choice (but best check with me first).神经网络代写
With the above problems you will need to either train a neural network or evaluate a GA (or similar algorithm) so will need some notion of fitness or performance. This will be based upon profit: for whichever trading rule you are considering you will need to iterate through the training data and calculate your earnings (through returns or sales) on some notional amount of investment if your trading rule was to be invoked.
You will also need to keep some data in reserve for back-testing: i.e. keep some unseen historical data in reserve on which to evaluate the profitability of your final rule.
Also, as with forecasting and prediction, you will be working with time series data, so the size of the window needs to be considered (this will depend upon the characteristics of the data you are using).神经网络代写
Some Useful Resources 神经网络代写
A useful package to assist in doing this is quantmod – designed to support the rapid development and evaluation of trading models. Amongst other things it makes getting hold of data very straightforward and also provides functions for the opening and closing prices, high and low values, volume etc. – just take a look at some of the examples.
Another value package is TTR, which again provides a myriad of function for building trading rules, but in particular ones for RSI and EMA. If you use quantmod then TTR will be installed by default as quantmod depends on it.
NOTE: A couple of hints… 神经网络代写
When using the quantmod package the data is in a time series structure called “xts”. You can check this as follows (for example):
> getSymbols(“GOOG”,src=”yahoo”)
> str(GOOG) # this will give you the structure
> head(GOOG) #will give you the first few lines of the data
This means it has to be extracted for use within the fitness function. There are several ways to doing this but one fairly simple one is to use the coredata() function.
So if we wanted to extract the opening prices:
> OP <- coredata(GOOG$GOOG.Open)神经网络代写
If you check this using
> str(OP)
> head(OP)
You will see that this is a still a two dimensional vector with several rows and one column (check this using dim(), nrow() and ncol()) so we need to extract this further using [,1] . Or in one step:
> OP <- coredata(GOOG$GOOG.Open)[,1]
Which gives a simple dimensionless vector. Hope that helps (and you probably worked this out already). I suspect there may be a simpler way of doing this so don’t hesitate to let me know!神经网络代写
Also, one of the other things you might find is that the volume values are too high for the GP to handle without losing accuracy so you could try scaling these down by several orders of magnitude.
Assignment Requirements 神经网络代写
You write also need to submit a report (around 6-10 pages) covering:
- Background to the problem – a short review of the new approach you are going to employ and an overview of related work in the area drawn from key published papers
- Your choice of data – what did you choose to work with. As usual, summary plots would be welcome.
- Details of the approach taken and any specific decisions about the representation, fitness function etc. along with details about any key parameters.
- Presentation of and comments on the solutions achieved: How well do they fit the training data. What fitness values were achieved?
- The performance of the model. How does this perform over unseen data and what level of profit would it yield?神经网络代写
- Comparison against other approaches. The choice of what you compare with is yours. At least try a random solution or the performance of the mean value (if appropriate) and look at the performance obtained. If you wish you may also consider other approaches.
- Please also include the R code, either as an appendix or in a separate file.
分配-自动制定交易规则 神经网络代写
该主题的任务将研究使用讲座中介绍的一种技术自动制定交易规则的问题：GA，PSO，DE或ANN（或您自己选择的其他技术，但请先与我联系）。技术的选择取决于您，问题的确切形式也取决于您。讲座涉及的方法包括：
根据前几天的各种关键因素，例如开盘价，收盘价，高价，低价，数量，相对强度指数（RSI），预测未来一天的高价（HP）和低价（LP），和指数移动平均线（EMA）。然后，可以将HP和LP的这些可能值构建到交易规则中，该规则将在最佳点自动购买或出售资产。神经网络代写
优化现有交易规则的参数，尤其是MACD的多头，空头和信号值。
您可以研究这些或自己选择的算法交易的其他方面（但最好先与我联系）。
遇到上述问题，您将需要训练神经网络或评估GA（或类似算法），因此需要适应性或性能的一些概念。这将基于利润：无论您考虑使用哪种交易规则，如果要调用交易规则，则都需要遍历训练数据并计算某些名义金额的收益（通过收益或销售）。
您还需要保留一些数据以进行回测：即保留一些看不见的历史数据以评估最终规则的获利能力。
另外，与预测和预测一样，您将使用时间序列数据，因此需要考虑窗口的大小（这取决于您使用的数据的特征）。
一些有用的资源
Quantmod是一个有助于完成此任务的有用软件包，旨在支持交易模型的快速开发和评估。除其他事项外，它使获取数据变得非常简单，并且还提供了开盘价和收盘价，高低价位，交易量等功能-仅看一些示例。
另一个价值包是TTR，它再次提供了无数的功能来建立交易规则，尤其是RSI和EMA。如果您使用quantmod，则默认情况下将安装TTR，因为quantmod取决于它。
注意：一些提示…
使用Quantmod软件包时，数据处于称为“ xts”的时间序列结构中。您可以按以下方式进行检查（例如）：
> getSymbols（“ GOOG”，src =“ yahoo”）神经网络代写
> str（GOOG）＃这将为您提供结构
> head（GOOG）＃将为您提供数据的前几行
这意味着必须将其提取出来以用于适应度函数。有多种方法可以执行此操作，但一种相当简单的方法是使用coredata（）函数。
因此，如果我们要提取开盘价：
> OP <-coredata（GOOG $ GOOG.Open）
如果您使用
> str（OP）
>头（OP）
您将看到这仍然是一个二维矢量，具有多行和一列（请使用dim（），nrow（）和ncol（）进行检查），因此我们需要使用[，1]进一步提取该矢量。或一步：
> OP <-coredata（GOOG $ GOOG.Open）[，1]
给出一个简单的无量纲向量。希望能对您有所帮助（您可能已经解决了这个问题）。我怀疑这可能是一种更简单的方法，所以请不要犹豫让我知道！
另外，您可能会发现的另一件事是音量值太大，GP无法处理而又不会损失精度，因此您可以尝试将其缩小几个数量级。神经网络代写
作业要求
您写的还需要提交一份报告（约6-10页），内容包括：
问题的背景-简要回顾您将采用的新方法，并从主要发表的论文中得出该领域相关工作的概述
您选择的数据-您选择使用什么数据。像往常一样，欢迎使用摘要图。神经网络代写
所采用方法的详细信息以及有关表示形式，适用性函数等的任何特定决策，以及有关任何关键参数的详细信息。
对已实现的解决方案的介绍和评论：它们与培训数据的拟合程度如何。达到了哪些健身价值？神经网络代写
模型的性能。在看不见的数据上该如何处理？它将产生多少利润？
与其他方法的比较。选择与您进行比较的是您的选择。至少尝试随机解决方案或平均值的性能（如果适用），然后查看获得的性能。如果您愿意，也可以考虑其他方法。
还请以附录或单独的文件形式包含R代码。
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