Genetic Algorithms, Genetic Programming and Neural Networks - IT 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. 

• Optimising 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 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 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.

Assignment Requirements

You write also need to submit a short report in either notebook or markdown format covering the following points:

• Background to the problem - a short review of the new approach you are going to employ and a brief overview of related work in the area drawn from either published papers or blog postings (4 marks)

• An overview of the of data you chose 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 and 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?

• 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

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