For my ETH experiment I’ve decided to concentrate on working with the MLP on a fixed set of data, rather than trying to vary both the data and the MLP settings, which is going to get a little too complicated (for me). Keep it simple.
So, I’ll include some more lags, also the Fear and Greed Index from alternative.me. They have an API that allowed me to download all their data, which goes back further than the ETH/USDT data I got from Binance. I think I’ll include a 1 day lag of the BTC return, as altcoins tend to move in concert with BTC. I think I can still call ETH an altcoin. Another input will be day of the week, which has been discussed in various courses I’ve done as a potentially useful factor in crypto trading. That will probably require one hot encoding, which is easy to do in pandas.
That’s not a very comprehensive set of input factors, but it should do for exploring various settings of the MLP, such as number of layers, number of nodes per layer, activation functions, optimizer, learning rate, etc.
I’ve considered other inputs such as the S&P500 from the equities market, but that has the disadvantage that it only trades about 250 days per year, Monday to Friday excluding public holidays. Dealing with issues like that is important in data science, but at this stage it’s just easier to avoid the problem altogether.
Other possible inputs include various technical indicators. I have the TA library installed in one of my Docker containers, but I’m not actually too concerned at this point about getting the best set of data. I need to concentrate on exploring MLP architecture and not get too sidetracked by other issues.
Anyway, I have another pressing issue (tax) to deal with, so I probably won’t be getting back to this for a couple of weeks.