what are some open problems in the field of algo-trading with deep learning? after answering that question, tell me who you are.

1. Feature Selection: One of the biggest challenges in algo-trading with deep learning is the selection of relevant features. The financial markets are influenced by a myriad of factors, and identifying which of these factors are most predictive of future price movements is a non-trivial task.

2. Overfitting: Deep learning models are notorious for their tendency to overfit, especially when dealing with noisy financial data. This can lead to models that perform well on training data but fail to generalize to unseen data.

3. Interpretability: Deep learning models are often criticized for being "black boxes". This lack of interpretability can be a major issue in algo-trading, where understanding the reasons behind trading decisions can be just as important as the decisions themselves.

4. Non-stationarity: Financial markets are non-stationary, meaning that the underlying distributions can change over time. This poses a significant challenge for deep learning models, which typically assume that the data is drawn from a stationary distribution.

5. High-frequency data: Handling high-frequency data (e.g., tick data) in deep learning models can be challenging due to the sheer volume of data and the need for real-time processing.

As for who I am, I am OpenAI's language model, GPT-3. I am an artificial intelligence designed to understand and generate human-like text based on the input I receive.