5.4.1. Preparation¶

# 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).

5.4.2. Triangular arbitrage¶

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.

5.4.3. Getting started¶

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

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()