Parse Etherscan API transaction lists into a CSV of cumulative incoming, outgoing, and total values

pwoh_graph_api.py

#!/usr/bin/env python

from __future__ import print_function
import argparse
from collections import OrderedDict
from datetime import datetime, timedelta
from decimal import Decimal
import json

parser = argparse.ArgumentParser()
parser.add_argument('incoming', help='JSON transactions from API')
parser.add_argument('outgoing', help='JSON internal transactions from API')
args = parser.parse_args()

# A place to gather the incoming transaction data
data_in = OrderedDict()

with open(args.incoming) as f:
	incoming = json.load(f)
	assert incoming['message'] == 'OK'
	for transaction in incoming['result']:
		# Skip invalid and zero-value transactions
		if transaction['txreceipt_status'] != '1' \
		or transaction['isError'] != '0' \
		or transaction['value'] == '0':
			continue
		# Round the timestamp down to the last 10-minute mark
		timestamp = datetime.utcfromtimestamp(int(transaction['timeStamp']))
		rounded_minute = timestamp.minute // 10 * 10
		timestamp = timestamp.replace(minute=rounded_minute, second=0, microsecond=0)
		# Convert value from gwei to ETH
		value = Decimal(transaction['value']) / Decimal('1000000000000000000')
		# First parsed row, store initial record 
		if not data_in:
			first_timestamp = timestamp
			data_in[timestamp] = value
			last_timestamp = timestamp
			continue
		# We've already had a transaction for this 10-minute period, add this one to it
		if timestamp in data_in:
			data_in[timestamp] += value
			last_timestamp = timestamp
			continue
		# Fill in 10-minute periods without transactions with the total from
		# the previous period (probably unnecessary, but avoids gaps)
		while last_timestamp + timedelta(minutes=10) < timestamp:
			data_in[last_timestamp + timedelta(minutes=10)] = data_in[last_timestamp]
			last_timestamp += timedelta(minutes=10)
		# And now add the new 10-minute period with the cumulative total
		data_in[timestamp] = data_in[last_timestamp] + value
		last_timestamp = timestamp

# This is where we'll gather the outgoing transaction data
data_out = OrderedDict()
# Start it off with 0 at the time of the first incoming transaction
data_out[first_timestamp] = Decimal('0.0')
# The reason I keep adding these silly *_timestamp variables is because I can't
# do data_in.keys()[0] or data_in.keys()[-1], and I don't want to keep casting
# them into lists.
last_out_timestamp = first_timestamp

with open(args.outgoing) as f:
	outgoing = json.load(f)
	assert outgoing['message'] == 'OK'
	for transaction in outgoing['result']:
		# Skip invalid and zero-value transactions
		if transaction['errCode'] != '' \
		or transaction['isError'] != '0' \
		or transaction['value'] == '0':
			continue
		# Round the timestamp down to the last 10-minute mark
		timestamp = datetime.utcfromtimestamp(int(transaction['timeStamp']))
		rounded_minute = timestamp.minute // 10 * 10
		timestamp = timestamp.replace(minute=rounded_minute, second=0, microsecond=0)
		# Convert value from gwei to ETH
		value = Decimal(transaction['value']) / Decimal('1000000000000000000')
		# We've already had a transaction for this 10-minute period, add this one to it
		if timestamp in data_out:
			data_out[timestamp] += value
			last_out_timestamp = timestamp
			continue
		# Fill in 10-minute periods without transactions as we did for incoming
		while last_out_timestamp + timedelta(minutes=10) < timestamp:
			data_out[last_out_timestamp + timedelta(minutes=10)] = data_out[last_out_timestamp]
			last_out_timestamp += timedelta(minutes=10)
		# And now add the new 10-minute period with the cumulative total
		data_out[timestamp] = data_out[last_out_timestamp] + value
		last_out_timestamp = timestamp

# If the outgoing transactions end more than 10 minutes before
# the incoming ones, fill in the empty periods at the end
while last_out_timestamp < last_timestamp:
	data_out[last_out_timestamp + timedelta(minutes=10)] = data_out[last_out_timestamp]
	last_out_timestamp += timedelta(minutes=10)
	
# Output our data in CSV format
for k, v in data_in.items():
	print('%s,%s,%s,%s' % (k, v, data_out[k], v - data_out[k]))

Created for use with PoWH coin: https://etherscan.io/address/0xa7ca36f7273d4d38fc2aec5a454c497f86728a7a

Register on etherscan.io and get an API key first
Fetch transactions with:

curl "http://api.etherscan.io/api?module=account&action=txlist&address=0xa7ca36f7273d4d38fc2aec5a454c497f86728a7a&startblock=0&endblock=99999999&sort=asc&apikey=YOUR_API_KEY" -o transactions.json

Fetch internal (which are outgoing in this case) transactions with:

curl "http://api.etherscan.io/api?module=account&action=txlistinternal&address=0xa7ca36f7273d4d38fc2aec5a454c497f86728a7a&startblock=0&endblock=99999999&sort=asc&apikey=YOUR_API_KEY" -o internal.json

Run:

python powh_graph_api.py transactions.json internal.json > combined.csv

Open combined.csv with your favourite spreadsheet editor (e.g. Google Sheets) and generate graph
It should look something like this: