minipools.py

#!/usr/bin/env nix-shell
#!nix-shell -i python3 -p "python3.withPackages(ps: [ ps.matplotlib ])"

import datetime
import json
import gzip
import os
import shutil
import sys
import time
import urllib.request

# Graphing
import matplotlib
import matplotlib.pyplot as plt

params = {
	 8: { 'step': 1 },
	16: { 'step': 2 },
}

min_collat_percent = 10

dark = "#fa9b02"
light = "#f7b852"

refresh_period_hours = 23

data_file = "./minipools.json.gz"

# Make output SVG deterministic to avoid useless diffs
matplotlib.rcParams['svg.hashsalt'] = "RPL"

def update():
	refresh_data_file()
	stats = load_data()
	generate_page(stats)

def file_age_hours(f):
	age_seconds = time.time() - os.path.getmtime(data_file)
	return age_seconds / 60 / 60

def refresh_data_file():
	if not os.path.exists(data_file) or file_age_hours(data_file) > refresh_period_hours:
		print("Retrieving minipool data", file=sys.stderr)
		headers = { 'Accept-Encoding': 'gzip' }
		req = urllib.request.Request('https://rocketscan.io/api/mainnet/minipools/all', headers=headers)
		with urllib.request.urlopen(req) as response, open(data_file, 'wb') as out_file:
			shutil.copyfileobj(response, out_file)

def load_data():
	with gzip.open(data_file, 'r') as f:
		data = json.load(f)
	
	stats = {
		 8: { 'count': 0, 'total': 0, 'all': [], 'total_effective': 0, 'collateral': [0] * (1 +  50 // params[8]['step']) },
		16: { 'count': 0, 'total': 0, 'all': [], 'total_effective': 0, 'collateral': [0] * (1 + 150 // params[16]['step']), }
	}
	
	for minipool in data:
		#print(minipool, file=sys.stderr)
		leb = int(minipool['nodeDepositBalance']) // 10**18
		node = minipool['node']
		stake = int(node['rplStake'])
		min_stake = int(node['rplMinStake'])
		if min_stake == 0: continue
		collat = stake/min_stake * min_collat_percent / 100
		borrowed = 32 - leb
		stats[leb]['total'] += collat
		stats[leb]['count'] += 1
		stats[leb]['all'].append(collat)
		collat = min(collat, 1.5 * leb / borrowed)
		stats[leb]['total_effective'] += collat
		collat_perc = int(100*collat / params[leb]['step'])
		stats[leb]['collateral'][collat_perc] += borrowed
		
	return stats

def generate_page(stats):
	for kind in [8, 16]:
		s = stats[kind]
		collateralization = s['collateral']
		step = params[kind]['step']
		average = 100 * s['total'] / s['count']
		average_effective = 100 * s['total_effective'] / s['count']
		median = 100 * sorted(s['all'])[len(s['all'])//2]
		
		x=[]
		y=[]
		color=[]
		# Create the figure and axes objects, specify the size and the dots per inches
		fig, ax = plt.subplots(figsize=(15, 4))
		start = min([coll for coll, count in enumerate(collateralization) if count > 0])
		for coll, count in list(enumerate(collateralization))[start:]:
			ratio = step * coll
			x.append(ratio)
			y.append(count)
			color.append([light, dark][ratio >= min_collat_percent])
		
		ax.bar(x, y, color=color, width=step, zorder=2)
		
		ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
		ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
		
		plt.axvline(x=average, label='average', linestyle='dotted')
		matplotlib.pyplot.text(average + max(x) / 100 / 2, max(y) / 2, "average", rotation=90, verticalalignment='center')
		#plt.axvline(x=average_effective, label='average effective', linestyle='dotted')
		#matplotlib.pyplot.text(average_effective + max(x) / 100 / 2, max(y) / 2, "average effective", rotation=90, verticalalignment='center')
		plt.axvline(x=median, label='median', linestyle='dotted')
		matplotlib.pyplot.text(median + max(x) / 100 / 2, max(y) / 2, "median", rotation=90, verticalalignment='center')
		
		plt.xlabel("Staked RPL vs borrowed ETH")
		
		# Add a '+' to the last ticker, e.g. "50%+" as the last ticker for LEB8, since it aggregates all levels above
		@matplotlib.ticker.FuncFormatter
		def major_formatter(tick, pos):
			res = f'{tick:.0f}%'
			if tick == x[-1]:
				res += '+'
			return res
		ax.xaxis.set_major_formatter(major_formatter)
		
		ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(base=5 * step))
		
		plt.ylabel("Borrowed ETH")
		ax.yaxis.set_major_formatter(matplotlib.ticker.StrMethodFormatter('Ξ{x:,.0f}'))
		
		plt.title(f"{kind} ETH Minipools", fontsize=14, weight='bold', alpha=.8)
		plt.savefig(f"../harpocryptes.github.io/minipools-{kind}eth.svg")
	
	data_time = datetime.datetime.fromtimestamp(os.path.getmtime(data_file), datetime.timezone.utc).strftime('%Y-%m-%d %H:%M %Z')
	with open("../harpocryptes.github.io/index.html", "w") as f:
		f.write(f"""\
<html>
<body>
	<div style="text-align: center">
		<img src = "minipools-8eth.svg"  alt="8 ETH Minipools"/  style="padding: 1em">
		<img src = "minipools-16eth.svg" alt="16 ETH Minipools"/ style="padding: 1em">
	</div>
	<div style="text-align: right; color: gray">
		Data retrieved from rocketscan.io/api at {data_time}
	</div>
</body>
</html>
""")

update()