Preamble

Let’s set up the environment first. We need the coingecko API to fetch prices, and pandas to work with timeseries (and tabular data in general).

from pycoingecko import CoinGeckoAPI
gecko = CoinGeckoAPI()

import pandas as pd

import matplotlib.pyplot as plt
%matplotlib notebook

import datetime as dt

Basic

Ping

Is coingecko up?

if gecko.ping()['gecko_says'].find('To the Moon!') != -1:
    print ("CoinGecko is up!")
else:
    print ("CoinGecko is down...")

CoinGecko is up!

Congrats?!

List of coins

Let’s see what assets we have to work with.

coins_list = pd.DataFrame(gecko.get_coins_list())

Is BTC present in this list? Is FLOW? Just a sanity check.

coins_list.loc[coins_list.applymap(lambda x: x == "btc").any(axis=1)]

	id	symbol	name
809	bitcoin	btc	Bitcoin

coins_list.loc[coins_list.applymap(lambda x: x == "flow").any(axis=1)]

	id	symbol	name
2457	flow	flow	Flow

Seems so.

Price

What about their current market price? (And other data?)

p = gecko.get_price('bitcoin,ethereum,flow', 'usd', include_market_cap='true', include_24hr_vol='true', include_24hr_change='true', include_last_updated_at='true')

for key in p:
    print ("{}:".format(key.capitalize()))
    print ("\tPrice: ${}, Market Cap: ${}".format(p[key]['usd'], p[key]['usd_market_cap']))
    print ("\t24Hr Change: {:.2f}%, 24Hr Volume: {:.2f}".format(p[key]['usd_24h_change'], p[key]['usd_24h_vol']))
    print ("\t(Last updated at {} UTC)".format(dt.datetime.utcfromtimestamp(p[key]['last_updated_at'])))
    print ()

Bitcoin:
	Price: $56366, Market Cap: $1048775494095.7839
	24Hr Change: 3.20%, 24Hr Volume: 47613266908.91
	(Last updated at 2021-03-27 21:53:37 UTC)

Ethereum:
	Price: $1728.32, Market Cap: $199025398969.79926
	24Hr Change: 2.31%, 24Hr Volume: 21377093874.10
	(Last updated at 2021-03-27 21:52:54 UTC)

Flow:
	Price: $27.93, Market Cap: $915053170.8916276
	24Hr Change: -2.35%, 24Hr Volume: 59084374.80
	(Last updated at 2021-03-27 21:53:04 UTC)

Historical price charts

Finally, let us chart the price for the past 2 years.

p = gecko.get_coin_market_chart_by_id('bitcoin', 'usd', days=730)

df = pd.DataFrame(p['prices'], columns=['Date (Raw)', 'Price'])
df.index = pd.to_datetime(df['Date (Raw)'].apply(lambda x: dt.datetime.utcfromtimestamp(x/1000).date()))
df.index.name = 'Date'

fig = plt.figure(); df['Price'].plot(figure=fig);

<IPython.core.display.Javascript object>

That’s the chart of BTCUSD over the past two years. Really should’ve bought some, shouldn’t we?

Next, the chart of the total volume per day. Notice the cyclical nature.

df = pd.DataFrame(p['total_volumes'], columns=['Date (Raw)', 'Volume'])
df.index = pd.to_datetime(df['Date (Raw)'].apply(lambda x: dt.datetime.utcfromtimestamp(x/1000).date()))
df.index.name = 'Date'

fig = plt.figure(); df['Volume'].plot(figure=fig);

<IPython.core.display.Javascript object>