Building Your Own Trading System – Part 4

Neural networks don’t generally explain their rules – they simply learn generalised patterns.

Again, you need to be conscious of using a proper backtesting strategy, ensuring that you have plenty of data so that your network doesn’t just learn from a small data set.  There are a number of neural network programs around.  My favourite is Tradecision.  This one hides the complexity of neural networks from you and lets you focus on your trading strategy.  It has a complete set of integrated tools so that you can check out your equity curve, Sharpe ratio and performance of your strategy.

The developers of this package take their work seriously, and actively develop it.  It can do automated trading. In my view, this is the premier neural network tool for traders.

This tool allows you to select some traditional strategies, then the neural network builds rules from a combination of them.  It has its own language built in.

Another way is to use maths tools to explore data. If you want to play with some advanced mathematics, you’ll need some programming expertise.  I recommend the Python language.  It has an add-in called Orange, which is quite useful for generating decision trees from data. My favourite technique in this tool is called Bayesian analysis which I generally find to be superior to neural networks.

You could also use Matlab (a commercial tool) or R, which is a statistical tool for exploring data.

These are some of the tools that you can use to improve the performance of your trading models.  There are no magical solutions – you still need to manage risk and ensure that your models stand up to real life conditions, but they can give you an edge over other traders.  Of course, a simple model will generally be more robust than a very complex, curve fitted model.

The best way is to start experimenting with them on some historical data and understand the capabilities and limitations of these tools.

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