From Investopedia:
Triangular arbitrage is the result of a discrepancy between three foreign currencies that occurs when the currency’s exchange rates do not exactly match up.
In this guide we are going to see how you can leverage advanced joining capabilities of Quasar to find triangular arbitrage opportunities in cryptocurrencies.
More precisely, in this guide, we will:
Craft a suitable data model for cryptocurrency trade data
Capture and store the data into a Quasar cluster
Query and plot trade data for a single cryptocurrency
Query, align, and plot trade data for multiple single cryptocurrencies
We will be using Python, Pandas, and plotly. You can achieve similar results with any programming language or tool that can query Quasar.
Please install & launch QuasarDB for your environment; the free community edition is sufficient.
For more information and instructions on how to install QuasarDB, please refer to our installation guide.
Please follow the steps below to create and set up your Jupyter notebook:
# Install requirements
$ python3 -m pip install jupyter \
pandas \
numpy \
quasardb
# Launch local notebook
$ jupyter notebook ./qdb-howto.ipynb
[I 17:16:02.496 NotebookApp] Serving notebooks from local directory: /home/user/qdb-howto/
[I 17:16:02.496 NotebookApp] Jupyter Notebook 6.4.6 is running at:
[I 17:16:02.496 NotebookApp] http://localhost:8888/?token=...
[I 17:16:02.496 NotebookApp] or http://127.0.0.1:8888/?token=...
[I 17:16:02.496 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
A new jupyter notebook should automatically open, otherwise please manually navigate and you can navigate your browser to http://localhost:8888/ with the environment prepared.
You are now completed with the preparations.
Triangular arbitrage is done by analyzing the discrepancy between three currencies. We will look at USD, BTC, and ETH in our example.
In theory, converting from ETH to USD, should be equivalent to converting from ETH to BTC, and then BTC to USD. However, in carefully analyzing rates, one can find opportunities that result in small, consistent, and low-risk profit.
To find these opportunities, you need to analyze changes in rates and execute trades as soon as the discrepancy is large enough to profit.
This guide will focus on collecting data and ensuring the values are aligned to plot curves of each rate to find arbitrage opportunities. We will not take into account fees.
Warning
This guide is for educational purposes only. In particular, it doesn’t take into account trading fees and relies on trade data. We offer no guarantee regarding its accuracy or completeness.
Coinbase offers a free, public API to pull realtime trade data from. For this demonstration, we will be retrieving realtime trade data for three different pairs:
BTC-USD
ETH-BTC
ETH-USD
Here is how we define a function to load the data for a single symbol into a Pandas Dataframe:
import numpy as np
import pandas as pd
import requests
import io
def pull_coinbase(product, max_pages=25):
after = None
xs = list()
for i in range(max_pages):
if after is None:
url = 'https://api.pro.coinbase.com/products/{}/trades'.format(product)
else:
url = 'https://api.pro.coinbase.com/products/{}/trades?after={}'.format(product, after)
r = requests.get(url, timeout=30)
xs.extend(r.json())
if not r.headers.get('cb-after'):
break
else:
after = r.headers['cb-after']
dtypes = {'time': np.dtype('datetime64[ns]'),
'price': np.dtype('float64'),
'size': np.dtype('float64'),
'side': np.dtype('U')}
return pd.DataFrame(xs).astype(dtypes).set_index('time').reindex()
We can invoke this function to load BTC-USD symbol data:
btcusd = pull_coinbase('BTC-USD')
btcusd
trade_id | price | size | side | |
---|---|---|---|---|
time | ||||
2023-03-19 05:10:54.615872 | 511106814 | 27226.73 | 0.001913 | sell |
2023-03-19 05:10:54.559560 | 511106813 | 27226.73 | 0.000088 | sell |
2023-03-19 05:10:54.498673 | 511106812 | 27226.72 | 0.001746 | sell |
2023-03-19 05:10:53.091448 | 511106811 | 27225.20 | 0.000871 | buy |
2023-03-19 05:10:50.590936 | 511106810 | 27223.80 | 0.000232 | sell |
... | ... | ... | ... | ... |
2023-03-19 02:39:11.540177 | 511081819 | 27236.93 | 0.022044 | buy |
2023-03-19 02:39:11.042499 | 511081818 | 27237.77 | 0.000927 | buy |
2023-03-19 02:39:09.835830 | 511081817 | 27240.93 | 0.001817 | sell |
2023-03-19 02:39:09.823034 | 511081816 | 27238.21 | 0.000369 | buy |
2023-03-19 02:39:09.032705 | 511081815 | 27240.93 | 0.003495 | sell |
25000 rows × 4 columns
This data looks like this:
import plotly.express as px
fig = px.line(x=btcusd.index, y=btcusd.price)
fig.update_layout(template='plotly_dark',
title='BTC-USD')
fig.show()