rewriting the trading algorithm
I have the script now running at a realistic 1.14% per day return, taking into account the average number of times that I’ve had to pay a trading fee, the average offset of what price a trade happens vs what price the algorithm wanted it to happen, and other unpredictable things.
The script as it is, is very hard to train in any way other than almost a brute force fashion. Because most of the variables (length of EMA long tail, number of bars to measure for ATR, etc) are integers, I can’t hone into the right values using a momentum-based trainer, so I need to do it using long, laborious loops.
There is one improvement that I could do, which would probably double or more the return, without drastically changing the script. At the moment, there is a lot of “down time”, where the script has some cash sitting in the wallet, and is waiting for a good opportunity to jump on a deal. If the script were to consider other currencies at the same time, then it would be able to use that down time to buy in those currencies.
On second thought, I think the returns would be much higher, because when the script currently makes a BUY trade, its usually sold again within about 30 minutes. That means that it could potentially make 48 BUY trades in a day, each with an average of maybe a .5% return. That’s about a 27% return (
(1+(.5/100))48 = 1.27).
That would be nice, but it’s impossible with the GDAX market, because the GDAX market only caters to four cryptocurrencies at the moment. Also, while the money aspect is nice, I’m actually doing this more for the puzzle-solving aspect. I get a real thrill out of adding in the real-life toils and troubles (fees, unpredictable trades, etc) and coming up with solutions that return interest despite those. So, I’m not going to refine the hell out of the script. Instead, I’m going to start working on another version.
Before I started on the current version, I had made a naive attempt at market prediction using neural networks. While some of the results looked very realistic, none of them stood up to scrutiny. I failed. But, I also learned a lot.
I’m going to make another neural-net-based attempt, using a different approach.
The idea I have is that instead of trying to predict the upcoming market values, I’ll simply try to predict whether I should buy or sell. This reduces the complexity a lot, because instead of trying to predict an arbitrary number, I’m only outputting a 1 (buy), 0 (nothing), or -1 (sell). This can then be checked by running the market from day 1 of the test data using each iteration of the network, and then adjusting the weights of the network based on whether the end result was higher or lower than the last time it was run.
I noticed that the best tunings from my current script only made a very few trades, on the very few days where the market basically dropped like a stone and then rebounded. But I can’t assume those will happen very often, so I like to see a few trades happen every day. With the neural network, I can increase the number of trades by simply adjusting the output function that decides whether a result is -1, 0, or 1 (this is usually done by converting the inputs into a value between -1 and 1, then rounding to an integer based on whether the value is above/below 0.7/-0.7. That value can be adjusted easily to provide more 1/-1 hits).
The current approach also involves a lot of math to measure ATR (average true range), running EMA (exponential moving average), etc. With the neural network approach, I will just need to squish the numbers down to form a pattern between -1 and 1 (where 0 is the current market price) and run directly against those numbers.
Because neural networks are a lot more “touchy-feely” than the current EMA/stop-gain/stop-loss approach I’m using, I will be able to “hone in” on good values – something I cannot do at the moment because of the step-like nature of integers.
I won’t be using any off-the-shell networks, as I can’t imagine how to write a good trainer for them. Instead, I’ll write my own, using a cascade-correlation approach.
Cascade-correlation is an approach to neural networks which allows a network to “grow” and gradually learn features of the input data. You train a network layer against every single input, until the output stops improving. You then “freeze” that network layer so is not adjusted anymore. You then create a second layer which trains against every single input and the previously trained network. You continue adding layers until there is no more noticeable improvement.
The point of this is that the first training layer will notice a feature in the data that produces a good result, and will train itself to recognise that feature very well. The second layer will then be able to ignore that feature (because it’s already being checked for), and find another one that improves the results. It’s like how you decide what animal is in a picture – does it have 6 legs (level one), is it red (level two), does it have a stinger (level three) – it’s a scorpion! Instead of trying to learn all the features of a successful sale at once, the algorithm picks up a new one at each level.
Around Christmas, I was playing with the FANN version of cascade correlation, and I think it’s very limited. It appears to create its new levels based on all inputs, but only the last feature-detection layer. Using the above example, this would make it difficult to recognise a black scorpion, as it would not be red. I believe that ideally, each feature layer should be treated as a separate new input, letting the end output make decisions based on multiple parallel features, not a single linear feature decision.