Yet Another Bitcoin Arbitrage

We should have won.

Setting

“Hackathons are just about chaining API’s together.” As my project partner, Sam Maynard delivered this truth to me, I felt the magical hope I had towards life diminish slightly. What I thought were congregations to debut new technologies ended up just being competitions to see who could write some wrapper around already existing API’s provided by tech giants. I say some because I was unable to deduce what kind of metric was used to rank projects at the hackathon I went to. Not to mention, HackRice 7 was my very first hackathon, so this was all news to me. In any case, we didn’t let the harsh reality deter us, we set forth to coming up with an interesting project that would solve an interesting problem in an interesting way. After much discussion and staring at the prize page, we decided upon a project.

Predict exchange rates between cryptocurrencies in order to be prepared for when arbitrage occurs in the market.

Conflict

Now, whether you are familiar with financial/cryptocurrency markets or not, I know exactly what you’re thinking. “Eric, ya dingus, there are definitely a million people who monitor arbitrage for cryptocurrencies. They are, for sure, faster than you and have more capital.” “Predicted values? Is this future arbitrage? Isn’t that just trading?” “Are you trying to model something similar to stock prices? People devote entire careers to this. You’re not going to crank anything interesting out.” First of all, reader, chill out. This is a hackathon project. Second of all, don’t worry, we thought of it too; I’ll address these points after some explanation.

There are some current problems people face when attempting to perform arbitrage in cryptocurrency markets. One, certain transactions take an hour to verify, which means it will be an hour before you get your capital back. In this time, exchange rates could easily change in such a way that a plan you had initially would be no longer profitable. Two, transaction fees can render potential profits to be losses. Three, within an exchange, there really shouldn’t exist any opportunities for arbitrage, but across exchanges, there might. However, following my first point, it takes time to move capital between exchanges. Given these three characteristics, it can be difficult for individual, small-scale investors, who don’t have enough liquidity in each currency, to be ready to take advantage of arbitrage were it to become available.

Rising Action

After much deliberation, Sam and I decided on our project pipeline. It would start with data collection. Then we would use the collected data, exchange rates in our case, to generate statistical models that would give us estimates for values in the near future. Finally, we would enter these predicted values into a custom graph search algorithm that would return a suggested sequence of actions to take in order to maximize profit.

To address the points above, we considered this “arbitrage” because it was technically as fast as a small investor could move money around, given the time constraints. Also, we weren’t particularly invested in the accuracy of the models, we simply needed some predicted values, good or bad, to show a proof of concept. Of course, more accurate models would lead to more accurate profits.

So we set to work. Sam worked on writing a program to periodically retrieve data from CoinMarketCap while I wrote the custom graph search algorithm. For simplicity, we decided to just consider the US dollar, Bitcoin, and Ethereum. We modeled the problem as such. Each exchange, Gemini, Bitfinex, etc., consisted of 3 nodes and 3 edges to form a triangle. Each node represented a currency and each edge the exchange rate within the exchange. So within this graph, we would need to find a sequence of actions that would result in the maximum ending capital. The reason we needed our own graph search algorithm is because the edges “changed values” over time, we needed to find the maximum value path, and there are multiple goal states (we considered a goal state to be any node that matched the starting currency type, regardless of the exchange). Finally, to generate predicted values, we used fbprophet for its simplicity, although it really doesn’t seem to be built for stochastic processes, like stock prices.

To verify that our program’s suggestions actually turned a profit, we wrote a script that would feed a prefix of our collected data into the program, perform the suggested actions on real data, and then output the estimated and actual value.

Climax

Our program worked! Given $100, it would, on average, return a sequence of actions with an expected value of $105. Using our verification script, weirdly enough, actual profits were always under the expected values. For an expected value of $105, the actual profits would be around $102. This may have been due to the optimizer’s curse, but I am not sure. We were just happy it would return a profit. Most likely, with better statistical models, the actual value would be closer to the expected.

Falling Action

Well, we weren’t finalists. But we did win a company prize from the gracious HBK. It seemed like the people who were finalists had something to present, whether it be some graphs, slides, or even just a visual demo. I do concede, if we had to present, people would have just heard us drone on while watching nonsensical output scroll away on a terminal.

Resolution

Would I put my own money on this? Maybe. I feel like every part of the program is solid, except the prediction, which is extremely important. Maybe if I were able to provide some confidence intervals, I would have more faith in using this as an investment tool. All in all, it was a fun weekend, and a good first experience for hackathons. Check out Sam’s experience here.

If you would like to check the repo out, go here.