Calling all quants, traders, fintech enthusiasts, and all others who wish to use Deep Learning to transform the financial industry here!
I’m gonna pose some questions here, particularly for graduates of the Fastai course, but feel free to interject your own questions or thoughts here as long as they’re related to DL in Finance.
I’ve built a few predictive models using Keras/Tensorflow, Scikit-learn (mostly SVMs) and Q-learning in the past with limited success (frequently running into problems with overfitting to various degrees.) I’m hoping to add Fast.ai to my toolkit to revisit these past models and modernize them with the latest DL techniques. Right now, I’m curious if what’s taught in this course will be of use here.
Financial data tends to come in Time Series form and this requires the use of RNNs (which I see is a topic later in the course.) As of right now, I’m only on lesson 2 of part 1, so i’m wondering if any concepts or theories will be covered later in the course which will hopefully answer my following questions:
Financial data is often updated every single weekday. Ideally, I’d like to do some update training on my model every single day after the market closes and prints out a new datapoint for every stock I’m watching. This would be analogous to the Dogs & Cats model receiving one new picture of a dog or cat everyday and having to update the model to reflect having trained on that new picture everyday. Is it efficient and practical (not just possible) to train an existing model on a single new datapoint every single day without retraining it from scratch each time and have it continue to work reliably? Or would it be more effective to gather together several new datapoints (say every week or month) and add them to the model in batches?
I can easily formulate a trading or investing model as a binary classification problem, just like CATS or DOGS:
- Is the market going UP or DOWN tomorrow (or in a week, month, year, etc.)?
- Are we in a HIGH or LOW risk market environment?
However, I’d also like to build a model that’ll estimate how much the investment will return, say, after a week or a month (give outputs like 5.2% or -1.3% rather than a simple UP or DOWN.) Is this also possible using the Fast.ai stack?