How Do You Retrieve Cryptocurrency Data for One Day in the Past Using Python?

Problem scenario
You want to find the historic data for a given cryptocurrency using Python. You want to print out the low, high, open and close prices on a specific day. How do you get data for one specific date?


This assumes you have installed pandas: pip install pandas

Run this program:

Usage instructions: Change the "2016-12-25" the value (assigned to "date_to_see") to the day you want to view.
Change "BTC" to the symbol of the cryptocurrency you want to examine.
You may or may note want to change "USD" or "Coinbase"

Written in March of 2021 by

It was adapted from these postings:


import pandas as pd
import requests
from datetime import datetime

from_symbol = 'BTC'
to_symbol = 'USD'
exchange = 'Coinbase'      # "Bitstamp" and "Coinbase" are valid among others.
datetime_interval = 'day'

def get_filename(from_symbol, to_symbol, exchange, datetime_interval, download_date):
    return '%s_%s_%s_%s_%s.csv' % (from_symbol, to_symbol, exchange, datetime_interval, download_date)

def download_data(from_symbol, to_symbol, exchange, datetime_interval):
    supported_intervals = {'minute', 'hour', 'day'}
    print('Downloading %s trading data for %s %s from %s' %
          (datetime_interval, from_symbol, to_symbol, exchange))
    base_url = ''
    url = '%s%s' % (base_url, datetime_interval)    
    params = {'fsym': from_symbol, 'tsym': to_symbol,
              'limit': 2000, 'aggregate': 1,
              'e': exchange}
    request = requests.get(url, params=params)
    data = request.json()
    return data

def convert_to_dataframe(data):
    df = pd.json_normalize(data, ['Data'])
    df['datetime'] = pd.to_datetime(df.time, unit='s')
    df = df[['datetime', 'low', 'high', 'open',
             'close', 'volumefrom', 'volumeto']]
    return df

def filter_empty_datapoints(df):
    indices = df[df.sum(axis=1) == 0].index
    print('Filtering %d empty datapoints' % indices.shape[0])
    df = df.drop(indices)
    return df

def file_creator(from_symbol, to_symbol, exchange, datetime_interval):
    data = download_data(from_symbol, to_symbol, exchange, datetime_interval)
    df = convert_to_dataframe(data)
    df = filter_empty_datapoints(df)
    latest_date = 
    filename = get_filename(from_symbol, to_symbol, exchange, datetime_interval, latest_date)
    print('The oldest day in the range obtained for ' + from_symbol + ' was ' + str(df['datetime'].min()))
    df.to_csv(filename, index=False)

def get_filename(from_symbol, to_symbol, exchange, datetime_interval, download_date):
    return '%s_%s_%s_%s_%s.csv' % (from_symbol, to_symbol, exchange, datetime_interval, download_date)

def read_dataset(filename):
    df = pd.read_csv(filename)
    df.datetime = pd.to_datetime(df.datetime) # change to datetime
    df = df.set_index('datetime') 
    df = df.sort_index() # sort by datetime
    return df

file_creator(from_symbol, to_symbol, exchange, datetime_interval)

latest_date = 
df_btc = read_dataset(get_filename(from_symbol, to_symbol, exchange, datetime_interval, latest_date))
btc_date = open((get_filename(from_symbol, to_symbol, exchange, datetime_interval, latest_date)), 'r')
get_top =[0:-1]

print("This prints out data for one cryptocurrency on one day.  It downloads a file first.")
print(" ")
print("Here is data for " + from_symbol + " for " + date_to_see + ":")

for line in get_top.split():
    if line.split(",")[0] == date_to_see:

For aggregate data over a date range, see this posting.

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