Predicting Bitcoin prices with AI

It was time for a complete overhaul, and after searching for a long time and not finding any solutions to my problem, my colleague unknowingly gave me my solution. He suggested I should use minute candles instead of daily. This solves the biggest problem of my model: it removes most of the external factors from the price action. Usually, the price of a stock or cryptocurrency is determined by the market sentiment and not the historical price (which I am basing my model on). But if the time period is lower, there will be fewer external events during that time, and any patterns in the historical data will be easier to spot. So using Python with Binance’s API I downloaded 3 million minutes of Bitcoin OHLC data, 2 million of which didn’t fit in Excel. 

This alone didn’t improve the model that much, but then my other colleague told me to normalize the data (dividing the OHLC data by the open price makes the numbers represent % values up or down from the open price (0.123% gain, 0.123% loss etc), instead of having absolute prices like $45 173 or $45 174. This makes it harder for the model, which is a good thing). Lastly, he told me to add some of the previous candles (also normalized) in each row, so I added 5 Close / Close values for n-k minutes, k = {1, ..., 5} and the same for Open / Close. These three factors in combination made my model look like a real AI model. It was learning, and the graph looked like a log(x) graph. It also performed very well. With an almost exactly 50/50 split in the dataset between TRUE and FALSE values for Next day (minute) positive, this model managed to get a staggering 55.6% accuracy. 

By now I had a model that was working well, so naturally I started to think about how many motors I wanted in my new Cybertruck. But first I needed to release this beast into the wild and bring home the bacon. I wrote a simple Python program with the CCXT package that fetches the last fifty-two minutes of Bitcoin prices (Because of how the 50-minute average is calculated, it needs to go back fifty one candles to calculate the average up to the current (52nd) candle). Then I wrote some for-loops that calculated the rest of the data (Close / Previous Close etc). So now the data is ready, but how do you use the Peltarion model on it and get an output more or less instantly, you may ask? The answer is Zapier. Without having to write a single line of code, I can use my almighty oracle AI model on the data in real-time and type out what it thinks the future holds to a Google Sheet. So no messing around with the Peltarion Python API to get the prediction or a Google Drive API to write to a Sheet (I did try the Google Drive API and it was quite a headache). All of this takes about two seconds, so by collecting and evaluating the data just before a minute candle closes, I have a fresh prediction for the next candle, and can act on it. 

The way I did all this was by sending a webhook with the data to Zapier, which was just one line of code in Python. I made my program do this once every minute. Here is a link to the Python code I wrote. The webhook consists of JSON data which conveniently has the same formatting as the Python dictionary I used to store all the data. Then I selected the Peltarion evaluation as the action, fed it everything it had just received, and used that evaluation in another action to write a new row in my Google Sheet. I also wrote the previous Close / Close value, so that I can evaluate if the previous prediction was right or wrong. Now, before throwing in the next month’s rent for this amazing and certainly profitable AI Bitcoin Bot to use, I let it run for about two hours without taking any action on the predictions, to see how it did.

It is also worth noting that you could do this without writing any code at all. There are apps in Zapier that can get you hourly and daily candles (unfortunately no minute candles) of Bitcoin prices, so if you trained your model on that then you could get something half decent in a fraction of the time and without touching any code. 

Obviously 120 candles as data isn’t that much, but what I saw did put a spoke in my wheel. It managed a similar result to the training data, coming in at about 55% correct guesses in total (it was correct almost 60% of the time in the first hour). Which is impressive. But trading isn’t free, so I did a little more analysis on these two hours to see how it would turn out if I had traded on it. When leverage trading on Binance (COIN-M Futures), the fee will be about 0.1% of the traded amount, per completed trade (0.05% to open it, 0.05% to close it). And when looking at all the guesses, the price went up by an average of 0.018% when the model said it would go up, and by an average of -0.014% when the model said it would go down (this data did not fit in the screenshot. The “avg up” and “avg down” in the picture is not related to what the model predicted). So even when being correct >50% of the time on where the price is going, you’re losing money even on the candles you do get right because of the fees. And no, you can’t just use a higher leverage (or none at all) to combat this, since the fee is going to grow by the same amount. So it looks like my white bread and water breakfast isn’t changing anytime soon.  

Since I have limited time and resources, a lot of compromises and shortcuts were made in this project. There are a ton of things that I would do to improve this project if I were to do it again. Both in the modelling itself, and in the way I would use this. 

• In the modelling you could change the target class to be something other than “Next day positive”, which is a pretty bad target class, since a 0.0001% gain will count just as true just as a 0.4% gain would. Instead, you could go for something similar to the fee amount, but then even if you’re right 60% of the time, you will need to make enough money those times to combat the fees + realised loss on the 40% of the time you were wrong. 
• Ideally you would deploy the model on a similar market to the one you trained it on. Maybe making one bull model and one bear model. I did not take this into consideration at all during this project.
• If you don’t want to make a sentiment analysis model (maybe integrate them together?), you might change this one so that it deactivates for an hour if Elon Musk tweets, or something similar to that. The model probably performs terribly at times like that, whether it is a celebrity tweeting about it or a superpower banning it. You could also try to train the model on a time period where there are few external events. 
• One more easy fix is to try to lower the fees, which you can do in several ways. For instance, if you were a taker in the trade instead of being the maker like I was, your fees would be one fifth of what I wrote earlier. And while you’re at it you might as well put in a stop-loss so that your average profit gets a bit higher. And maybe a well-placed take-profit is helpful as well.
• It is entirely possible that I have messed up something in my final calculations about the average gains and losses, or the Python calculations when manipulating the real time data. There are a lot of sources of error in this project.
• And I’m sure you know lots of more ways to improve this that I haven’t thought of!

So as you know by now it is definitely possible and not that hard to predict which way the price of Bitcoin will go. Unfortunately, it looks like the profit margins are lower than the fees you need to pay if you decide to trade on this. But as previously mentioned, I’m sure with some tweaking of the model and optimization of the way you use it could get you closer to actually making a profit. And if you ever manage to get something profitable, it would be interesting to see how that compares to just holding the underlying asset. But nevertheless, it has certainly been a very interesting project, and I would love to revisit it sometime in the future. Maybe with a new take on it, like basing it on sentiment analysis.