I’m expanding the input features to my ADA neural network. Have added day of the week (one hot encoded), a measure of the range of each period (low/high), and an RSI indicator courtesy of the TA-lib. Plus I updated the data from Binance and now have over 8000 six-hour periods. The spreadsheet with the data looks quite impressive. Below is a screenshot showing the first 80 periods, or 20 days, one percent of the total.
Some of the column headings are not quite accurate. 30dayret is actually 30periodret, where the period is 6 hours, not 1 day. In the past I’ve mostly worked with daily data so it’s a habit to refer to everything as 30day, 60day, etc.
Running my training script on this data gave me an average return per trade of 0.5% (before transaction costs), and maybe 100 such trades per year. I guess if I cleared 0.3% per trade on 100 trades that would be about 30% per year, which is not too shabby. Still, rosy test results have cost me quite a bit in the past. Those trading gods are fickle, if not downright malicious.
I guess I’ll have to redo my hyperparameter tuning now that I’m using an altered data set, and some validation of course. And maybe explore different network topologies, more nodes, more layers, potentially different kinds of layer such as RNN or CNN.
So far I’m only looking at a long-only strategy. I could expand this to a long-short strategy, but that’s harder to actually trade now that Binance doesn’t allow margin trading (in Australia). Perhaps I should check out 1inch or similar. Binance was so convenient. Not going to get too excited. If all goes well I might put $100 into trading the strategy, just to maintain some interest.
An interesting possibility is that the model trained on ADA could be used on other coins. Seems to be a common practice, using pre-trained models for similar problems. I doesn’t take that long to train a model though. Currently about an hour for 1,000,000 episodes (each episode is one period of data)