Hi @austineaero,
Sorry for my late reply!
First of all, the problem you are trying to tackle is really hard, as FOREX quotes don’t move just based on previous prices. In addition to that, the signal to noise ration is very low, so I’m not sure how successful you or anybody else may be with this approach.
If you want to use the tsai library, I’d recommend you to take a look at the documentation. In particular at the data preparation section and the tutorial notebooks.
To use any of the models in tsai you will need to create an array of shape [samples x variables x timesteps]. To do this you can use the SlidingWindow function provided with the library. I don’t know if you are planning to use Close prices only, or OHLC, or what. Those would be your variables. As to the timesteps that will be determined by your window_len parameter. You will need to decide if you want to run a classification or regression task.
Once you have the data ready you can create a TSDataLoaders object. And then use ts_learner and an architecture of your choice to test it. And you can start to run it.
There are MANY decision you need to make:
- which input data to use? All currencies? Just one? A few?
- How many variables? Close? OHLC?
- Window length? Stride? Overlapping windows?
- Classification or regression?
- Architecture?
etc.
As I said this will be an extremely difficult task, and don’t know how much success you will have, but you can certainly use tsai to test your ideas.
PS. There’s a very interesting book called " Advances in Financial Machine Learning" by Marcos Lopez de Prado that might give you some ideas on how you can approach this task.