Genetic Algorithms, Genetic Programming, Neural Networks - Quantmod - Computer Science Assignment Help

Assignment Task:

Automatically formulating trading rules
The assignment for this topic is going to look at the problem of automatically formulating trading rules using one or more of the techniques covered in the course: Genetic Algorithms, Genetic Programming, Neural Networks, Particle Swarm Optimisation, or Differential Evolution (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:
Predicting the high price (HP) and/or 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. 
Optimizing the parameters for pre-existing trading rules, such as 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 again check with me first).
With the above problems, you will need to either train a model of some sort (GA, GP, NN...) so we will need some notion of fitness or performance. This is obviously problem-dependent but at some point will need to evaluate profit: for whichever trading rule you are developing 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).
Some Useful Resources
 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 functions 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.

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