In [1]:

import os, sys
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme()
%matplotlib inline

import marketdata as md

import os, sys import matplotlib.pyplot as plt import seaborn as sns sns.set_theme() %matplotlib inline import marketdata as md

In [2]:

# Fixed manually
x = ['DAI','USDC','USDT','ETH','BTC','SQETH', 'SOL']
y = ["dai","usd-coin","tether","ethereum","bitcoin","opyn-squeeth","solana"]

stablecoins = dict(zip(x,y))

# Fixed manually x = ['DAI','USDC','USDT','ETH','BTC','SQETH', 'SOL'] y = ["dai","usd-coin","tether","ethereum","bitcoin","opyn-squeeth","solana"] stablecoins = dict(zip(x,y))

In [3]:

hmd         = md.HistoricalMarketData( y, 'usd', '2021-01-01','2022-08-15').price()
hmd.columns = [i.lower() for i in x]
hmd         = hmd.fillna(method='bfill')
hmd.tail()

hmd = md.HistoricalMarketData( y, 'usd', '2021-01-01','2022-08-15').price() hmd.columns = [i.lower() for i in x] hmd = hmd.fillna(method='bfill') hmd.tail()

Out[3]:

	dai	usdc	usdt	eth	btc	sqeth	sol
2022-08-11	1.000538	1.000510	1.000595	1852.878555	23948.829553	184.590822	42.295780
2022-08-12	1.001991	0.999959	0.999916	1881.427405	23948.751704	187.335758	42.752870
2022-08-13	1.001456	1.000885	1.001999	1959.330925	24411.379872	202.250714	45.340525
2022-08-14	1.000894	1.001678	1.000588	1982.411828	24433.653747	207.888264	46.627628
2022-08-15	0.999468	0.999402	0.999533	1936.701164	24312.541425	198.498785	44.977699

In [4]:

# Send to csv
hmd.to_csv('market_data.csv')

# Send to csv hmd.to_csv('market_data.csv')