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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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" require.undef(\"plotly\");\n",
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" paths: {\n",
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"metadata": {},
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],
"source": [
"from conseil import conseil\n",
"import pandas as pd\n",
"import cufflinks as cf\n",
"import numpy as np\n",
"import requests\n",
"import dateutil"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
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"cf.set_config_file(offline=True, world_readable=True, theme='pearl')"
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{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"Operation = conseil.tezos.alphanet.operations\n",
"query = Operation.query(Operation.timestamp) \\\n",
" .filter(Operation.kind == Operation.kind.delegation,\n",
" Operation.source.startswith('tz')) \\\n",
" .order_by(Operation.timestamp)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(query.all())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df.index = df['timestamp'].astype('M8[ms]')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df['total_registrations'] = 1\n",
"df['total_registrations'] = df['total_registrations'].cumsum()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"market_data = requests.get('https://graphs2.coinmarketcap.com/currencies/tezos/').json()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"md = pd.DataFrame(market_data['price_btc'], columns=['ts', 'price_btc'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"md.index = md['ts'].astype('M8[ms]')"
]
},
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"source": [
"athens_ts = int(dateutil.parser.parse('2019-05-30T00:58:55Z').timestamp() * 1000)\n",
"athens_ts"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"md['roll_price_btc'] = np.where(md['ts'] < athens_ts, 10000, 8000) * md['price_btc']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df = pd.merge_asof(df[['total_registrations']], md[['roll_price_btc']], left_index=True, right_index=True)"
]
},
{
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