Historical Price Per Minute Between Two Timestamps For A Cryptocurrency

I am successfully using the following code to output the price per minute for historical price data ranging back a couple of hours:

import requests
import datetime
import pandas as pd
import matplotlib.pyplot as plt

def minute_price_historical(symbol, comparison_symbol, limit, aggregate, exchange=''):
    url = '{}&tsym={}&limit={}&aggregate={}'\
        .format(symbol.upper(), comparison_symbol.upper(), limit, aggregate)
    if exchange:
        url += '&e={}'.format(exchange)
    page = requests.get(url)
    data = page.json()['Data']
    df = pd.DataFrame(data)
    df['timestamp'] = [datetime.datetime.fromtimestamp(d) for d in df.time]
    return df

time_delta = 1 # Bar width in minutes
df = minute_price_historical('BTC', 'USD', 9999, time_delta)
print('Max length = %s' % len(df))
print('Max time = %s' % (df.timestamp.max() - df.timestamp.min()))

plt.plot(df.timestamp, df.close)

Thumbs up to the Cryptocompare API guys.

Ultimately, I would like to achieve the following:

1) Output the price per minute between two timestamps, e.g. 3/12/18 3.00pm (15.00) and 3/12/18 3.30pm (15.30)

2) I would like to save this data into 3 columns "Token"(in the case above BTC), "Timestamp", "Price" as a csv or json ideally

Anyone with an idea or two on how to do that with the given code or an alternative approach?



so to answer the first part, you can create two timestamp min and max for the interval you want:

time_min = pd.Timestamp('2018-05-26 15:00')
time_max = pd.Timestamp('2018-05-26 15:30')

and then create a mask to select only the rows of your df between this two times:

mask = (df.timestamp >= time_min) & (df.timestamp <= time_max)

Now if you do df[mask] you will get only the rows where the timestamp is within this 30 min window.

For the second question:

# you can first create the column for the token do:
df['token'] = 'BTC' # should fill all the rows of your df with this word
# rename the column close to price
df = df.rename(columns={'close':'price'})
# finally save as csv only the columns you want:

and if you want to add the mask, then do


EDIT for the json, it depends how you want it, so I would suggest to read the documentation about to_json to find what is possible