Split analytics into multiple modules
This commit is contained in:
Greg Pfeil
parent
0a53fd9237
commit
8877d342af
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@ -1,32 +1,19 @@
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#!/usr/bin/env python3
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# Copyright (c) 2022 The Zcash developers
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# Distributed under the MIT software license, see the accompanying
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# file COPYING or https://www.opensource.org/licenses/mit-license.php .
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"""Simple Transaction Analysis
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This contains a class, `Analyzer`, for defining analyses of the blocks and
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transactions on the blockchain. It also exposes a function
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`analyze_blocks`, which handles applying multiple analyses simultaneously over
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some common range of blocks.
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This contains a class, `Analysis`, for defining analyses of the blocks and
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transactions on the blockchain. It also contains a class `Analyzer` with a
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method `analyze_blocks`, which handles applying multiple analyses simultaneously
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over some common range of blocks.
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"""
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import datetime
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import itertools
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import math
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import numpy as np
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import os.path
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from progress.bar import IncrementalBar
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from slickrpc.rpc import Proxy
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from statistics import mean
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import sys
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### TODO: Get host/port from config
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if len(sys.argv) > 1:
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connection_string = sys.argv[1]
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else:
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raise Exception(
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"%s needs to be provided a connection string, like \"http://user:pass@localhost:port\"."
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% sys.argv[0])
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class Analysis:
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"""
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@ -168,727 +155,3 @@ class Analyzer:
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result.append((analysis.name, analysis.aggregate(dict(bucketses)[analysis])))
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return result
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### Helpers
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def identity(x):
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return x
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def get_shielded_spends(tx):
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try:
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shielded_spends = len(tx['vShieldedSpend'])
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except KeyError:
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shielded_spends = 0
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return shielded_spends
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def get_shielded_outputs(tx):
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try:
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shielded_outputs = len(tx['vShieldedOutput'])
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except KeyError:
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shielded_outputs = 0
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return shielded_outputs
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def get_orchard_actions(tx):
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try:
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orchard_actions = len(tx['orchard']['actions'])
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except KeyError:
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orchard_actions = 0
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return orchard_actions
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def count_inputs(tx):
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return len(tx['vin']) + 2 * len(tx['vjoinsplit']) + get_shielded_spends(tx) + get_orchard_actions(tx)
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def count_outputs(tx):
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return len(tx['vout']) + 2 * len(tx['vjoinsplit']) + get_shielded_outputs(tx) + get_orchard_actions(tx)
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def count_ins_and_outs(tx):
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return (len(tx['vin'])
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+ len(tx['vout'])
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+ get_shielded_spends(tx)
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+ get_shielded_outputs(tx)
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+ 2 * len(tx['vjoinsplit'])
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+ 2 * get_orchard_actions(tx))
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def count_actions(tx):
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return (max(len(tx['vin']), len(tx['vout']))
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+ max(get_shielded_spends(tx), get_shielded_outputs(tx))
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+ 2 * len(tx['vjoinsplit'])
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+ get_orchard_actions(tx))
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def expiry_height_delta(block, tx):
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"""
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Returns -1 if there's no expiry, also returns approximately 35,000 (the
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number of blocks in a month) if the expiry is beyond 1 month.
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"""
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month = blocks_per_hour * 24 * 30
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try:
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expiry_height = tx['expiryheight']
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if expiry_height == 0:
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return -1
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elif tx['expiryheight'] - block['height'] > month:
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return month
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else:
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return tx['expiryheight'] - block['height']
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except KeyError:
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# `tx['expiryheight']` is ostensibly an optional field, but it seems
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# like `0` is what tends to be used for "don't expire", so this case
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# generally isn't hit.
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return -1
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def tx_type(tx):
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"""
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Categorizes all tx into one of nine categories: (t)ransparent, (z)shielded,
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or (m)ixed for both inputs and outputs. So some possible results are "t-t",
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"t-z", "m-z", etc.
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"""
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if tx['vjoinsplit'] or get_shielded_spends(tx) != 0 or get_orchard_actions(tx) != 0:
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if tx['vin']:
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ins = "m"
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else:
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ins = "z"
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else:
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ins = "t"
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if tx['vjoinsplit'] or get_shielded_outputs(tx) != 0 or get_orchard_actions(tx) != 0:
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if tx['vout']:
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outs = "m"
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else:
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outs = "z"
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else:
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outs = "t"
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return ins + "-" + outs
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def is_orchard_tx(tx):
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try:
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return tx['orchard']['actions']
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except KeyError:
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return False
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def is_saplingspend_tx(tx):
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try:
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return tx['vShieldedSpend']
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except KeyError:
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return False
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def orchard_anchorage(cache, block, tx):
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"""
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Returns -1 if there is no anchor
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"""
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try:
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return block['height'] - cache[tx['orchard']['anchor']]
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except KeyError:
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return -1
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def sapling_anchorage(cache, block, tx):
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"""
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Returns -1 if there is no anchor
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"""
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try:
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return block['height'] - cache[tx['vShieldedSpend'][0]['anchor']]
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except KeyError:
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return -1
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def is_not_coinbase(tx):
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return 'feePaid' in tx
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# NB: This requires zcashd to be running with `experimentalfeatures=1`,
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# `txindex=1` and `insightexplorer=1`.
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def getFeeDiff(proposedFee, tx):
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try:
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return proposedFee <= tx['feePaid']
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except KeyError:
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return -1
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blocks_per_hour = 48 # half this before NU2?
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# start about a month before sandblasting
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start_range = blocks_per_hour * 24 * 7 * 206
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### Requested Statistics
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def storeAnchor(pool, cache, block):
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"""
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Caches the block height as the value for its anchor hash.
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"""
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try:
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final_root = block[pool]
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try:
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cache[final_root]
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except KeyError:
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cache[final_root] = block['height']
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except KeyError:
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None
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return cache
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# "how old of anchors are people picking"
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# --- https://zcash.slack.com/archives/CP6SKNCJK/p1660103126252979
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anchor_age_orchard = Analysis(
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"how old of anchors are people picking (for orchard)",
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lambda _c, _b, tx: is_orchard_tx(tx),
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[(orchard_anchorage, sum)],
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lambda *_: 1,
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({}, lambda c, b: storeAnchor('finalorchardroot', c, b)),
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blocks_per_hour * 24
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)
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anchor_age_sapling = Analysis(
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"how old of anchors are people picking (for sapling)",
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lambda _c, _b, tx: is_saplingspend_tx(tx),
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[(sapling_anchorage, sum)],
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lambda *_: 1,
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({}, lambda c, b: storeAnchor('finalsaplingroot', c, b)),
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blocks_per_hour * 24
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)
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# "what's the distribution of expiry height deltas"
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# --- https://zcash.slack.com/archives/CP6SKNCJK/p1660103126252979
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expiry_height_deltas = Analysis(
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"distribution of expiry height deltas",
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lambda *_: True,
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[(lambda _, b, t: expiry_height_delta(b, t), sum)],
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lambda *_: 1
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)
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tx_type_with_long_expiry = Analysis(
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"types of tx with expiries longer than about a month",
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lambda _, b, t: expiry_height_delta(b, t) >= blocks_per_hour * 24 * 30,
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[# (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
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# identity),
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(lambda _c, _b, tx: tx_type(tx), sum)],
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lambda *_: 1
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)
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# "does anyone use locktime"
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# --- https://zcash.slack.com/archives/CP6SKNCJK/p1660103126252979
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locktime_usage = Analysis(
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"proportion of tx using locktime",
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lambda *_: True,
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[(lambda *_: 1,
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lambda d: dict(d)[True] / (dict(d)[False] + dict(d)[True])),
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(lambda _c, _b, tx: tx['locktime'] != 0, sum)],
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lambda *_: 1
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)
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# "I'm seeing a slightly different pattern to the sandblasting transactions,
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# unless I've just missed this before. The transactions I've looked at recently
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# have had > 400 sapling outputs. Has this been the case before and I just
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# missed it? I thought primarily these transactions had slightly over 100
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# outputs in most cases."
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# --- https://zcash.slack.com/archives/CP6SKNCJK/p1660195664187769
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# "Calculate the POFM threshold for historical transactions on-chain and
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# calculate what proportion of those transactions would fall below the POFM
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# threshold"
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# --- https://docs.google.com/document/d/18wtGFCB2N4FO7SoqDPnEgVudAMlCArHMz0EwhE1HNPY/edit
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tx_below_pofm_threshold = Analysis(
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"rate of transactions below POFM threshold",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
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lambda d: dict(d)[False] / (dict(d)[False] + dict(d)[True])),
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(lambda _c, _b, tx: count_ins_and_outs(tx) - 4 > 0, sum)
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],
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lambda *_: 1
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)
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tx_below_pofm_threshold_abs = Analysis(
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"transactions below POFM threshold",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
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lambda d: (dict(d)[False], dict(d)[True])),
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(lambda _c, _b, tx: count_ins_and_outs(tx) - 4 > 0, sum)
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],
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lambda *_: 1
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)
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outs_below_pofm_threshold_abs = Analysis(
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"outputs below POFM threshold",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
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lambda d: (dict(d)[False], dict(d)[True])),
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(lambda _c, _b, tx: count_ins_and_outs(tx) - 4 > 0, sum)
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],
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lambda _c, _b, tx: count_outputs(tx)
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)
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tx_below_pofm_threshold_5 = Analysis(
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"rate of transactions below POFM threshold with a grace window of 5",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
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lambda d: dict(d)[False] / (dict(d)[False] + dict(d)[True])),
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(lambda _c, _b, tx: count_ins_and_outs(tx) - 5 > 0, sum)
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],
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lambda *_: 1
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)
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tx_below_pofm_threshold_max = Analysis(
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"rate of transactions below POFM threshold with max",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
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lambda d: dict(d)[False] / (dict(d)[False] + dict(d)[True])),
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(lambda _c, _b, tx: count_actions(tx) - 4 > 0, sum)
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],
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lambda *_: 1
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)
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tx_below_pofm_threshold_ins = Analysis(
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"rate of transactions below POFM threshold only on inputs",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
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lambda d: dict(d)[False] / (dict(d)[False] + dict(d)[True])),
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(lambda _c, _b, tx: count_inputs(tx) - 4 > 0, sum)
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],
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lambda *_: 1
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)
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### Other Examples
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tx_per_day = Analysis(
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"count transactions per day (treating block 0 as midnight ZST)",
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lambda *_: True,
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[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), sum)],
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lambda *_: 1
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)
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mean_tx_per_day = Analysis(
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"mean transactions per day, by block",
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lambda *_: True,
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[(lambda _c, block, _t: int(block['height'] % (blocks_per_hour * 24)), lambda d: mean([x[1] for x in d])),
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(lambda _c, block, _t: int(block['height']/(blocks_per_hour * 24)), sum)
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],
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lambda *_: 1
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)
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mean_inout_per_tx_per_day = Analysis(
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"mean inputs, outputs per transaction per day, by block",
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lambda *_: True,
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[(lambda _c, block, _t: int(block['height'] % (blocks_per_hour * 24)), lambda d: mean(itertools.chain(d.values()))),
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(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity)
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],
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lambda _c, _b, tx: (count_inputs(tx), count_outputs(tx))
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)
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mean_inout_per_tx = Analysis(
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"mean inputs, outputs per transaction, by week",
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lambda *_: True,
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[ ( lambda _c, block, _t: int(block['height']/(blocks_per_hour * 24 * 7)),
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lambda d: (mean([x[0] for x in d]), mean([x[1] for x in d]))
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)
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],
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lambda _c, _b, tx: (count_inputs(tx), count_outputs(tx))
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)
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minimum_pofm_fees_nuttycom = Analysis(
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"distribution of fees in ZAT, by day, using nuttycom's pricing",
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lambda *_: True,
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
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(lambda _c, _b, tx: math.ceil(math.log((1000 + 250 * max(0, count_ins_and_outs(tx) - 4)) / 1000, 2)), sum)
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],
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lambda *_: 1
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)
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minimum_pofm_fees_nuttycom = Analysis(
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"distribution of fees in ZAT, by day, using nuttycom's pricing",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
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(lambda _c, _b, tx: math.ceil(math.log((1000 + 250 * max(0, count_ins_and_outs(tx) - 4)) / 1000, 2)), sum)
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],
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lambda *_: 1
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)
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minimum_pofm_fees_nuttycom2 = Analysis(
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"distribution of fees in ZAT, by day, using nuttycom's changed pricing",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
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(lambda _c, _b, tx: math.ceil(math.log((1000 + 200 * max(0, count_ins_and_outs(tx) - 5)) / 1000, 2)), sum)
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],
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lambda *_: 1
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)
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def meh_fees(tx):
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fee = tx['feePaid']
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if fee == 0:
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return -1
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else:
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result = math.ceil(math.log(tx['feePaid'], 2))
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# if result < 0:
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# print("negative result: %s, %s" % (fee, tx['txid']))
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return result
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actual_fees = Analysis(
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"actual fees",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, _b, tx: meh_fees(tx), sum)
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],
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lambda *_: 1
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)
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proposed_fees = Analysis(
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"",
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lambda _c, _b, tx: is_not_coinbase(tx),
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[ (lambda _c, _b, tx: math.ceil(math.log(5000 * max(2, count_actions(tx)), 2)), sum)
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],
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lambda *_: 1
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)
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arity_heat_map = Analysis(
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"inputs vs outputs",
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lambda *_: True,
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[(lambda _c, _b, tx: min(100, count_outputs(tx)), identity),
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(lambda _c, _b, tx: min(100, count_inputs(tx)), sum)],
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lambda *_: 1
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)
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input_size_dist = Analysis(
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"distribution of input sizes",
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lambda *_: True,
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[(lambda _c, _b, tx: [len(x['scriptSig']['hex']) for x in tx['vin']], identity)],
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lambda *_: 1,
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)
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# very_high_inout_tx = Analysis(
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# "tx with very high in/out counts",
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# lambda _c, _b, tx: count_ins_and_outs(tx) > 100,
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# [(lambda _c, _b, tx: (count_inputs(tx), count_outputs(tx)), identity)],
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# lambda _c, _b, tx: tx['txid']
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# )
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very_high_inout_tx = Analysis(
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"tx with very high in/out counts",
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lambda _c, _b, tx: count_ins_and_outs(tx) > 5000,
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[],
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lambda _c, _b, tx: (tx['txid'], count_ins_and_outs(tx))
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)
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def track_utxos(cache, block):
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for tx in block[tx]:
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for vin in tx['vin']:
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del cache[(vin['txid'], vin['vout'])]
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for vout in tx['vout']:
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cache[(tx['txid'], vout['n'])] = vout['valueZat']
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return cache
|
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utxo_distribution = Analysis(
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"how many UTXOs and how big are they?",
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lambda *_: True,
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
lambda caches: sum([caches[-1][k] for k in caches[-1]]))],
|
||||
lambda cache, _b, _t: cache,
|
||||
({}, track_utxos),
|
||||
1_000_000_000 # back to block 0, TODO: should be able to say this explicitly
|
||||
)
|
||||
|
||||
def is_sandblasting(tx):
|
||||
return get_shielded_outputs(tx) > 300
|
||||
|
||||
sandblasters_per_day = Analysis(
|
||||
"how many transactions have >300 Sapling outputs each day?",
|
||||
lambda _c, _b, tx: is_sandblasting(tx),
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
sandblasters_and_more_per_day = Analysis(
|
||||
"how many transactions have >300 outputs each day?",
|
||||
lambda _c, _b, tx: count_outputs(tx) > 300,
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
sandblaster_average_outputs_per_day = Analysis(
|
||||
"how many outputs do sandblasters have?",
|
||||
lambda _c, _b, tx: is_sandblasting(tx),
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), (lambda txs: sum(txs) / len(txs)))],
|
||||
lambda _c, _b, tx: count_outputs(tx)
|
||||
)
|
||||
|
||||
nuttycom_fees_vs_actual = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using nuttycom's pricing",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(250 * max(4, count_ins_and_outs(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
action_fees_vs_actual = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(500 * max(3, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
nuttycom_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that wouldn't pay more under the new model, by day, using nuttycom's pricing",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(250 * max(4, count_ins_and_outs(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
action_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(500 * max(3, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
greg_fees_vs_actual = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(250 * max(4, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
greg_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(250 * max(4, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
latest_fees_vs_actual = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(1000 * max(2, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
latest_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(1000 * max(2, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
flat_fees_vs_actual = Analysis(
|
||||
"transactions that would pass the original 10k ZAT fee, by day",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(10_000, tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
shielding_tx_heat_map = Analysis(
|
||||
"shielding tx",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and (tx_type(tx) == 't-z' or tx_type(tx) == 'm-z'),
|
||||
[(lambda _c, _b, tx: min(100, count_outputs(tx)), identity),
|
||||
(lambda _c, _b, tx: min(100, count_inputs(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
shielding_tx_actions = Analysis(
|
||||
"shielding tx",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and (tx_type(tx) == 't-z' or tx_type(tx) == 'm-z'),
|
||||
[(lambda _c, _b, tx: min(100, count_actions(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
fees_from_sandblasting = Analysis(
|
||||
"fees collected from sandblasting",
|
||||
lambda _c, _b, tx: is_sandblasting(tx),
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), sum)],
|
||||
lambda _c, _b, tx: 500 * max(2, count_actions(tx))
|
||||
)
|
||||
|
||||
flat_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that would pass the original 10k ZAT fee, by day",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(10_000, tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
transparent_tx_that_would_fail_heat_map = Analysis(
|
||||
"heat map of transparent tx that would fail under `500 * max(3, |actions|)`",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t' and getFeeDiff(500 * max(3, count_actions(tx)), tx) == False,
|
||||
[(lambda _c, _b, tx: min(100, count_outputs(tx)), identity),
|
||||
(lambda _c, _b, tx: min(100, count_inputs(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
historical_fees = Analysis(
|
||||
"histogram of actual fees paid",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[(lambda _c, _b, tx: check_fee_paid(tx), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
arity_heat_map = Analysis(
|
||||
"inputs vs outputs",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[(lambda _c, _b, tx: min(100, count_outputs(tx)), identity),
|
||||
(lambda _c, _b, tx: min(100, count_inputs(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
transparent_input_histogram = Analysis(
|
||||
"how many transparent inputs do txs have?",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: min(100, len(tx['vin'])), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
nuttycom_fees_vs_10k = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using nuttycom's pricing",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 250 * max(4, count_ins_and_outs(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
action_fees_vs_10k = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 500 * max(3, count_actions(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
latest_fees_vs_10k = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 1000 * max(2, count_actions(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
|
||||
a = Analyzer(connection_string)
|
||||
|
||||
def make_weekly_range(starting_week, number_of_weeks):
|
||||
start_of_range = blocks_per_hour * 24 * 7 * starting_week
|
||||
end_of_range = start_of_range + (blocks_per_hour * 24 * 7 * number_of_weeks)
|
||||
return range(start_of_range, end_of_range)
|
||||
|
||||
|
||||
# start about a month before sandblasting, overlapping with it
|
||||
|
||||
pre_sandblasting_range = make_weekly_range(206, 12)
|
||||
recent_range = make_weekly_range(220, 1)
|
||||
|
||||
# start = datetime.datetime.now()
|
||||
# for analysis in a.analyze_blocks(some_range,
|
||||
# [ # sandblaster_average_outputs_per_day,
|
||||
# # flat_fees_vs_actual,
|
||||
# # flat_fees_vs_actual_trans,
|
||||
# # transparent_tx_that_would_fail_heat_map
|
||||
# nuttycom_fees_vs_actual,
|
||||
# action_fees_vs_actual,
|
||||
# nuttycom_fees_vs_actual_trans,
|
||||
# action_fees_vs_actual_trans,
|
||||
# greg_fees_vs_actual,
|
||||
# greg_fees_vs_actual_trans,
|
||||
# # historical_fees,
|
||||
# # transparent_input_histogram,
|
||||
# ]):
|
||||
# print(analysis)
|
||||
# print(datetime.datetime.now() - start)
|
||||
|
||||
# rerunning old data …
|
||||
# start = datetime.datetime.now()
|
||||
# for analysis in a.analyze_blocks(make_weekly_range(206, 1),
|
||||
# [ actual_fees,
|
||||
# proposed_fees,
|
||||
# ]):
|
||||
# print(analysis)
|
||||
# print(datetime.datetime.now() - start)
|
||||
|
||||
nuttycom_fees_vs_10k2 = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using nuttycom's pricing",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and not is_sandblasting(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 250 * max(4, count_ins_and_outs(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
action_fees_vs_10k2 = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and not is_sandblasting(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 500 * max(3, count_actions(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
latest_fees_vs_10k2 = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and not is_sandblasting(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 1000 * max(2, count_actions(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
def vin_value(vin):
|
||||
if 'valueSat' in vin:
|
||||
return vin['valueSat']
|
||||
else:
|
||||
return 0
|
||||
|
||||
def tx_pool_movement(tx):
|
||||
transparent = sum(vout['valueZat'] for vout in tx['vout']) - sum([vin_value(vin) for vin in tx['vin']])
|
||||
sprout = sum([vjoinsplit['vpub_newZat'] - vjoinsplit['vpub_oldZat'] for vjoinsplit in tx['vjoinsplit']])
|
||||
sapling = - tx['valueBalanceZat']
|
||||
if 'orchard' in tx:
|
||||
orchard = - tx['orchard']['valueBalanceZat']
|
||||
else:
|
||||
orchard = 0
|
||||
# print("(%d, %d, %d, %d) – %d -> %d" % (transparent, sprout, sapling, orchard, count_inputs(tx), count_outputs(tx)))
|
||||
return (transparent, sprout, sapling, orchard)
|
||||
|
||||
pool_movement = Analysis(
|
||||
"how are funds moving between pools?",
|
||||
lambda *_: True,
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), lambda vals: np.sum(np.array(vals), 0))],
|
||||
lambda _c, _b, tx: tx_pool_movement(tx)
|
||||
)
|
||||
|
||||
|
||||
start = datetime.datetime.now()
|
||||
for analysis in a.analyze_blocks(recent_range,
|
||||
[ pool_movement
|
||||
]):
|
||||
print(analysis)
|
||||
print(datetime.datetime.now() - start)
|
||||
|
||||
# start = datetime.datetime.now()
|
||||
# for analysis in a.analyze_blocks(pre_sandblasting_range,
|
||||
# [ tx_below_pofm_threshold,
|
||||
# tx_below_pofm_threshold_5,
|
||||
# tx_below_pofm_threshold_max,
|
||||
# tx_below_pofm_threshold_ins,
|
||||
# tx_below_pofm_threshold_abs,
|
||||
# outs_below_pofm_threshold_abs,
|
||||
# arity_heat_map,
|
||||
# minimum_pofm_fees_nuttycom,
|
||||
# minimum_pofm_fees_nuttycom2,
|
||||
# ]):
|
||||
# print(analysis)
|
||||
# print(datetime.datetime.now() - start)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,600 @@
|
|||
#!/usr/bin/env python3
|
||||
# Copyright (c) 2022 The Zcash developers
|
||||
# Distributed under the MIT software license, see the accompanying
|
||||
# file COPYING or https://www.opensource.org/licenses/mit-license.php .
|
||||
"""Simple Transaction Analysis
|
||||
|
||||
This contains a class, `Analyzer`, for defining analyses of the blocks and
|
||||
transactions on the blockchain. It also exposes a function
|
||||
`analyze_blocks`, which handles applying multiple analyses simultaneously over
|
||||
some common range of blocks.
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import itertools
|
||||
import math
|
||||
import numpy as np
|
||||
from statistics import mean
|
||||
import sys
|
||||
|
||||
from analyze import Analysis, Analyzer
|
||||
from helpers import *
|
||||
|
||||
### TODO: Get host/port from config
|
||||
if len(sys.argv) > 1:
|
||||
connection_string = sys.argv[1]
|
||||
else:
|
||||
raise Exception(
|
||||
"%s needs to be provided a connection string, like \"http://user:pass@localhost:port\"."
|
||||
% (sys.argv[0],))
|
||||
|
||||
blocks_per_hour = 48 # half this before NU2?
|
||||
|
||||
# start about a month before sandblasting
|
||||
start_range = blocks_per_hour * 24 * 7 * 206
|
||||
|
||||
### Requested Statistics
|
||||
|
||||
def storeAnchor(pool, cache, block):
|
||||
"""
|
||||
Caches the block height as the value for its anchor hash.
|
||||
"""
|
||||
try:
|
||||
final_root = block[pool]
|
||||
try:
|
||||
cache[final_root]
|
||||
except KeyError:
|
||||
cache[final_root] = block['height']
|
||||
except KeyError:
|
||||
None
|
||||
|
||||
return cache
|
||||
|
||||
# "how old of anchors are people picking"
|
||||
# --- https://zcash.slack.com/archives/CP6SKNCJK/p1660103126252979
|
||||
anchor_age_orchard = Analysis(
|
||||
"how old of anchors are people picking (for orchard)",
|
||||
lambda _c, _b, tx: is_orchard_tx(tx),
|
||||
[(orchard_anchorage, sum)],
|
||||
lambda *_: 1,
|
||||
({}, lambda c, b: storeAnchor('finalorchardroot', c, b)),
|
||||
blocks_per_hour * 24
|
||||
)
|
||||
|
||||
anchor_age_sapling = Analysis(
|
||||
"how old of anchors are people picking (for sapling)",
|
||||
lambda _c, _b, tx: is_saplingspend_tx(tx),
|
||||
[(sapling_anchorage, sum)],
|
||||
lambda *_: 1,
|
||||
({}, lambda c, b: storeAnchor('finalsaplingroot', c, b)),
|
||||
blocks_per_hour * 24
|
||||
)
|
||||
|
||||
# "what's the distribution of expiry height deltas"
|
||||
# --- https://zcash.slack.com/archives/CP6SKNCJK/p1660103126252979
|
||||
expiry_height_deltas = Analysis(
|
||||
"distribution of expiry height deltas",
|
||||
lambda *_: True,
|
||||
[(lambda _, b, t: expiry_height_delta(b, t), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
tx_type_with_long_expiry = Analysis(
|
||||
"types of tx with expiries longer than about a month",
|
||||
lambda _, b, t: expiry_height_delta(b, t) >= blocks_per_hour * 24 * 30,
|
||||
[# (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
# identity),
|
||||
(lambda _c, _b, tx: tx_type(tx), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
# "does anyone use locktime"
|
||||
# --- https://zcash.slack.com/archives/CP6SKNCJK/p1660103126252979
|
||||
locktime_usage = Analysis(
|
||||
"proportion of tx using locktime",
|
||||
lambda *_: True,
|
||||
[(lambda *_: 1,
|
||||
lambda d: dict(d)[True] / (dict(d)[False] + dict(d)[True])),
|
||||
(lambda _c, _b, tx: tx['locktime'] != 0, sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
# "I'm seeing a slightly different pattern to the sandblasting transactions,
|
||||
# unless I've just missed this before. The transactions I've looked at recently
|
||||
# have had > 400 sapling outputs. Has this been the case before and I just
|
||||
# missed it? I thought primarily these transactions had slightly over 100
|
||||
# outputs in most cases."
|
||||
# --- https://zcash.slack.com/archives/CP6SKNCJK/p1660195664187769
|
||||
|
||||
|
||||
# "Calculate the POFM threshold for historical transactions on-chain and
|
||||
# calculate what proportion of those transactions would fall below the POFM
|
||||
# threshold"
|
||||
# --- https://docs.google.com/document/d/18wtGFCB2N4FO7SoqDPnEgVudAMlCArHMz0EwhE1HNPY/edit
|
||||
tx_below_pofm_threshold = Analysis(
|
||||
"rate of transactions below POFM threshold",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
lambda d: dict(d)[False] / (dict(d)[False] + dict(d)[True])),
|
||||
(lambda _c, _b, tx: count_ins_and_outs(tx) - 4 > 0, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
tx_below_pofm_threshold_abs = Analysis(
|
||||
"transactions below POFM threshold",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
lambda d: (dict(d)[False], dict(d)[True])),
|
||||
(lambda _c, _b, tx: count_ins_and_outs(tx) - 4 > 0, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
outs_below_pofm_threshold_abs = Analysis(
|
||||
"outputs below POFM threshold",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
lambda d: (dict(d)[False], dict(d)[True])),
|
||||
(lambda _c, _b, tx: count_ins_and_outs(tx) - 4 > 0, sum)
|
||||
],
|
||||
lambda _c, _b, tx: count_outputs(tx)
|
||||
)
|
||||
|
||||
tx_below_pofm_threshold_5 = Analysis(
|
||||
"rate of transactions below POFM threshold with a grace window of 5",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
lambda d: dict(d)[False] / (dict(d)[False] + dict(d)[True])),
|
||||
(lambda _c, _b, tx: count_ins_and_outs(tx) - 5 > 0, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
|
||||
tx_below_pofm_threshold_max = Analysis(
|
||||
"rate of transactions below POFM threshold with max",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
lambda d: dict(d)[False] / (dict(d)[False] + dict(d)[True])),
|
||||
(lambda _c, _b, tx: count_actions(tx) - 4 > 0, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
tx_below_pofm_threshold_ins = Analysis(
|
||||
"rate of transactions below POFM threshold only on inputs",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
lambda d: dict(d)[False] / (dict(d)[False] + dict(d)[True])),
|
||||
(lambda _c, _b, tx: count_inputs(tx) - 4 > 0, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
### Other Examples
|
||||
|
||||
tx_per_day = Analysis(
|
||||
"count transactions per day (treating block 0 as midnight ZST)",
|
||||
lambda *_: True,
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
mean_tx_per_day = Analysis(
|
||||
"mean transactions per day, by block",
|
||||
lambda *_: True,
|
||||
[(lambda _c, block, _t: int(block['height'] % (blocks_per_hour * 24)), lambda d: mean([x[1] for x in d])),
|
||||
(lambda _c, block, _t: int(block['height']/(blocks_per_hour * 24)), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
mean_inout_per_tx_per_day = Analysis(
|
||||
"mean inputs, outputs per transaction per day, by block",
|
||||
lambda *_: True,
|
||||
[(lambda _c, block, _t: int(block['height'] % (blocks_per_hour * 24)), lambda d: mean(itertools.chain(d.values()))),
|
||||
(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity)
|
||||
],
|
||||
lambda _c, _b, tx: (count_inputs(tx), count_outputs(tx))
|
||||
)
|
||||
|
||||
mean_inout_per_tx = Analysis(
|
||||
"mean inputs, outputs per transaction, by week",
|
||||
lambda *_: True,
|
||||
[ ( lambda _c, block, _t: int(block['height']/(blocks_per_hour * 24 * 7)),
|
||||
lambda d: (mean([x[0] for x in d]), mean([x[1] for x in d]))
|
||||
)
|
||||
],
|
||||
lambda _c, _b, tx: (count_inputs(tx), count_outputs(tx))
|
||||
)
|
||||
|
||||
minimum_pofm_fees_nuttycom = Analysis(
|
||||
"distribution of fees in ZAT, by day, using nuttycom's pricing",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: math.ceil(math.log((1000 + 250 * max(0, count_ins_and_outs(tx) - 4)) / 1000, 2)), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
minimum_pofm_fees_nuttycom = Analysis(
|
||||
"distribution of fees in ZAT, by day, using nuttycom's pricing",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: math.ceil(math.log((1000 + 250 * max(0, count_ins_and_outs(tx) - 4)) / 1000, 2)), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
minimum_pofm_fees_nuttycom2 = Analysis(
|
||||
"distribution of fees in ZAT, by day, using nuttycom's changed pricing",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: math.ceil(math.log((1000 + 200 * max(0, count_ins_and_outs(tx) - 5)) / 1000, 2)), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
def meh_fees(tx):
|
||||
fee = tx['feePaid']
|
||||
if fee == 0:
|
||||
return -1
|
||||
else:
|
||||
result = math.ceil(math.log(tx['feePaid'], 2))
|
||||
return result
|
||||
|
||||
actual_fees = Analysis(
|
||||
"actual fees",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, _b, tx: meh_fees(tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
proposed_fees = Analysis(
|
||||
"",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, _b, tx: math.ceil(math.log(5000 * max(2, count_actions(tx)), 2)), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
arity_heat_map = Analysis(
|
||||
"inputs vs outputs",
|
||||
lambda *_: True,
|
||||
[(lambda _c, _b, tx: min(100, count_outputs(tx)), identity),
|
||||
(lambda _c, _b, tx: min(100, count_inputs(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
input_size_dist = Analysis(
|
||||
"distribution of input sizes",
|
||||
lambda *_: True,
|
||||
[(lambda _c, _b, tx: [len(x['scriptSig']['hex']) for x in tx['vin']], identity)],
|
||||
lambda *_: 1,
|
||||
)
|
||||
|
||||
# very_high_inout_tx = Analysis(
|
||||
# "tx with very high in/out counts",
|
||||
# lambda _c, _b, tx: count_ins_and_outs(tx) > 100,
|
||||
# [(lambda _c, _b, tx: (count_inputs(tx), count_outputs(tx)), identity)],
|
||||
# lambda _c, _b, tx: tx['txid']
|
||||
# )
|
||||
|
||||
very_high_inout_tx = Analysis(
|
||||
"tx with very high in/out counts",
|
||||
lambda _c, _b, tx: count_ins_and_outs(tx) > 5000,
|
||||
[],
|
||||
lambda _c, _b, tx: (tx['txid'], count_ins_and_outs(tx))
|
||||
)
|
||||
|
||||
def track_utxos(cache, block):
|
||||
for tx in block[tx]:
|
||||
for vin in tx['vin']:
|
||||
del cache[(vin['txid'], vin['vout'])]
|
||||
for vout in tx['vout']:
|
||||
cache[(tx['txid'], vout['n'])] = vout['valueZat']
|
||||
return cache
|
||||
|
||||
utxo_distribution = Analysis(
|
||||
"how many UTXOs and how big are they?",
|
||||
lambda *_: True,
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)),
|
||||
lambda caches: sum([caches[-1][k] for k in caches[-1]]))],
|
||||
lambda cache, _b, _t: cache,
|
||||
({}, track_utxos),
|
||||
1_000_000_000 # back to block 0, TODO: should be able to say this explicitly
|
||||
)
|
||||
|
||||
def is_sandblasting(tx):
|
||||
return get_shielded_outputs(tx) > 300
|
||||
|
||||
sandblasters_per_day = Analysis(
|
||||
"how many transactions have >300 Sapling outputs each day?",
|
||||
lambda _c, _b, tx: is_sandblasting(tx),
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
sandblasters_and_more_per_day = Analysis(
|
||||
"how many transactions have >300 outputs each day?",
|
||||
lambda _c, _b, tx: count_outputs(tx) > 300,
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
sandblaster_average_outputs_per_day = Analysis(
|
||||
"how many outputs do sandblasters have?",
|
||||
lambda _c, _b, tx: is_sandblasting(tx),
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), (lambda txs: sum(txs) / len(txs)))],
|
||||
lambda _c, _b, tx: count_outputs(tx)
|
||||
)
|
||||
|
||||
nuttycom_fees_vs_actual = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using nuttycom's pricing",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(250 * max(4, count_ins_and_outs(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
action_fees_vs_actual = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(500 * max(3, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
nuttycom_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that wouldn't pay more under the new model, by day, using nuttycom's pricing",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(250 * max(4, count_ins_and_outs(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
action_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(500 * max(3, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
greg_fees_vs_actual = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(250 * max(4, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
greg_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(250 * max(4, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
latest_fees_vs_actual = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(1000 * max(2, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
latest_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(1000 * max(2, count_actions(tx)), tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
flat_fees_vs_actual = Analysis(
|
||||
"transactions that would pass the original 10k ZAT fee, by day",
|
||||
lambda *_: True,
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(10_000, tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
shielding_tx_heat_map = Analysis(
|
||||
"shielding tx",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and (tx_type(tx) == 't-z' or tx_type(tx) == 'm-z'),
|
||||
[(lambda _c, _b, tx: min(100, count_outputs(tx)), identity),
|
||||
(lambda _c, _b, tx: min(100, count_inputs(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
shielding_tx_actions = Analysis(
|
||||
"shielding tx",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and (tx_type(tx) == 't-z' or tx_type(tx) == 'm-z'),
|
||||
[(lambda _c, _b, tx: min(100, count_actions(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
fees_from_sandblasting = Analysis(
|
||||
"fees collected from sandblasting",
|
||||
lambda _c, _b, tx: is_sandblasting(tx),
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), sum)],
|
||||
lambda _c, _b, tx: 500 * max(2, count_actions(tx))
|
||||
)
|
||||
|
||||
flat_fees_vs_actual_trans = Analysis(
|
||||
"transparent transactions that would pass the original 10k ZAT fee, by day",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t',
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: getFeeDiff(10_000, tx), sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
transparent_tx_that_would_fail_heat_map = Analysis(
|
||||
"heat map of transparent tx that would fail under `500 * max(3, |actions|)`",
|
||||
lambda _c, _b, tx: tx_type(tx) == 't-t' and getFeeDiff(500 * max(3, count_actions(tx)), tx) == False,
|
||||
[(lambda _c, _b, tx: min(100, count_outputs(tx)), identity),
|
||||
(lambda _c, _b, tx: min(100, count_inputs(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
historical_fees = Analysis(
|
||||
"histogram of actual fees paid",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[(lambda _c, _b, tx: check_fee_paid(tx), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
arity_heat_map = Analysis(
|
||||
"inputs vs outputs",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[(lambda _c, _b, tx: min(100, count_outputs(tx)), identity),
|
||||
(lambda _c, _b, tx: min(100, count_inputs(tx)), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
transparent_input_histogram = Analysis(
|
||||
"how many transparent inputs do txs have?",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: min(100, len(tx['vin'])), sum)],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
nuttycom_fees_vs_10k = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using nuttycom's pricing",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 250 * max(4, count_ins_and_outs(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
action_fees_vs_10k = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 500 * max(3, count_actions(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
latest_fees_vs_10k = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 1000 * max(2, count_actions(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
|
||||
a = Analyzer(connection_string)
|
||||
|
||||
def make_weekly_range(starting_week, number_of_weeks):
|
||||
start_of_range = blocks_per_hour * 24 * 7 * starting_week
|
||||
end_of_range = start_of_range + (blocks_per_hour * 24 * 7 * number_of_weeks)
|
||||
return range(start_of_range, end_of_range)
|
||||
|
||||
|
||||
# start about a month before sandblasting, overlapping with it
|
||||
pre_sandblasting_range = make_weekly_range(206, 12)
|
||||
|
||||
# well into sandblasting
|
||||
recent_range = make_weekly_range(220, 1)
|
||||
|
||||
start = datetime.datetime.now()
|
||||
for analysis in a.analyze_blocks(pre_sandblasting_range,
|
||||
[ # sandblaster_average_outputs_per_day,
|
||||
# flat_fees_vs_actual,
|
||||
# flat_fees_vs_actual_trans,
|
||||
# transparent_tx_that_would_fail_heat_map
|
||||
nuttycom_fees_vs_actual,
|
||||
action_fees_vs_actual,
|
||||
nuttycom_fees_vs_actual_trans,
|
||||
action_fees_vs_actual_trans,
|
||||
greg_fees_vs_actual,
|
||||
greg_fees_vs_actual_trans,
|
||||
# historical_fees,
|
||||
# transparent_input_histogram,
|
||||
]):
|
||||
print(analysis)
|
||||
print(datetime.datetime.now() - start)
|
||||
|
||||
# rerunning old data …
|
||||
start = datetime.datetime.now()
|
||||
for analysis in a.analyze_blocks(make_weekly_range(206, 1),
|
||||
[ actual_fees,
|
||||
proposed_fees,
|
||||
]):
|
||||
print(analysis)
|
||||
print(datetime.datetime.now() - start)
|
||||
|
||||
nuttycom_fees_vs_10k2 = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using nuttycom's pricing",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and not is_sandblasting(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 250 * max(4, count_ins_and_outs(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
action_fees_vs_10k2 = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and not is_sandblasting(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 500 * max(3, count_actions(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
latest_fees_vs_10k2 = Analysis(
|
||||
"transactions that wouldn't pay more under the new model, by day, using actions",
|
||||
lambda _c, _b, tx: is_not_coinbase(tx) and not is_sandblasting(tx),
|
||||
[ (lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), identity),
|
||||
(lambda _c, _b, tx: 1000 * max(2, count_actions(tx)) <= 10_000, sum)
|
||||
],
|
||||
lambda *_: 1
|
||||
)
|
||||
|
||||
pool_movement = Analysis(
|
||||
"how are funds moving between pools?",
|
||||
lambda *_: True,
|
||||
[(lambda _c, block, _t: int(block['height'] / (blocks_per_hour * 24)), lambda vals: np.sum(np.array(vals), 0))],
|
||||
lambda _c, _b, tx: tx_pool_movement(tx)
|
||||
)
|
||||
|
||||
start = datetime.datetime.now()
|
||||
for analysis in a.analyze_blocks(recent_range,
|
||||
[ pool_movement
|
||||
]):
|
||||
print(analysis)
|
||||
print(datetime.datetime.now() - start)
|
||||
|
||||
start = datetime.datetime.now()
|
||||
for analysis in a.analyze_blocks(pre_sandblasting_range,
|
||||
[ tx_below_pofm_threshold,
|
||||
tx_below_pofm_threshold_5,
|
||||
tx_below_pofm_threshold_max,
|
||||
tx_below_pofm_threshold_ins,
|
||||
tx_below_pofm_threshold_abs,
|
||||
outs_below_pofm_threshold_abs,
|
||||
arity_heat_map,
|
||||
minimum_pofm_fees_nuttycom,
|
||||
minimum_pofm_fees_nuttycom2,
|
||||
]):
|
||||
print(analysis)
|
||||
print(datetime.datetime.now() - start)
|
||||
|
|
@ -0,0 +1,161 @@
|
|||
# Copyright (c) 2022 The Zcash developers
|
||||
# Distributed under the MIT software license, see the accompanying
|
||||
# file COPYING or https://www.opensource.org/licenses/mit-license.php .
|
||||
"""Useful function for transaction analyses
|
||||
|
||||
This is a collection of functions that make it easier to write new transaction
|
||||
analyses.
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import itertools
|
||||
import math
|
||||
|
||||
def identity(x):
|
||||
return x
|
||||
|
||||
def get_shielded_spends(tx):
|
||||
try:
|
||||
shielded_spends = len(tx['vShieldedSpend'])
|
||||
except KeyError:
|
||||
shielded_spends = 0
|
||||
|
||||
return shielded_spends
|
||||
|
||||
def get_shielded_outputs(tx):
|
||||
try:
|
||||
shielded_outputs = len(tx['vShieldedOutput'])
|
||||
except KeyError:
|
||||
shielded_outputs = 0
|
||||
|
||||
return shielded_outputs
|
||||
|
||||
def get_orchard_actions(tx):
|
||||
try:
|
||||
orchard_actions = len(tx['orchard']['actions'])
|
||||
except KeyError:
|
||||
orchard_actions = 0
|
||||
|
||||
return orchard_actions
|
||||
|
||||
def count_inputs(tx):
|
||||
return len(tx['vin']) + 2 * len(tx['vjoinsplit']) + get_shielded_spends(tx) + get_orchard_actions(tx)
|
||||
|
||||
def count_outputs(tx):
|
||||
return len(tx['vout']) + 2 * len(tx['vjoinsplit']) + get_shielded_outputs(tx) + get_orchard_actions(tx)
|
||||
|
||||
def count_ins_and_outs(tx):
|
||||
return (len(tx['vin'])
|
||||
+ len(tx['vout'])
|
||||
+ get_shielded_spends(tx)
|
||||
+ get_shielded_outputs(tx)
|
||||
+ 2 * len(tx['vjoinsplit'])
|
||||
+ 2 * get_orchard_actions(tx))
|
||||
|
||||
def count_actions(tx):
|
||||
return (max(len(tx['vin']), len(tx['vout']))
|
||||
+ max(get_shielded_spends(tx), get_shielded_outputs(tx))
|
||||
+ 2 * len(tx['vjoinsplit'])
|
||||
+ get_orchard_actions(tx))
|
||||
|
||||
def expiry_height_delta(block, tx):
|
||||
"""
|
||||
Returns -1 if there's no expiry, also returns approximately 35,000 (the
|
||||
number of blocks in a month) if the expiry is beyond 1 month.
|
||||
"""
|
||||
month = blocks_per_hour * 24 * 30
|
||||
try:
|
||||
expiry_height = tx['expiryheight']
|
||||
if expiry_height == 0:
|
||||
return -1
|
||||
elif tx['expiryheight'] - block['height'] > month:
|
||||
return month
|
||||
else:
|
||||
return tx['expiryheight'] - block['height']
|
||||
except KeyError:
|
||||
# `tx['expiryheight']` is ostensibly an optional field, but it seems
|
||||
# like `0` is what tends to be used for "don't expire", so this case
|
||||
# generally isn't hit.
|
||||
return -1
|
||||
|
||||
def tx_type(tx):
|
||||
"""
|
||||
Categorizes all tx into one of nine categories: (t)ransparent, (z)shielded,
|
||||
or (m)ixed for both inputs and outputs. So some possible results are "t-t",
|
||||
"t-z", "m-z", etc.
|
||||
"""
|
||||
if tx['vjoinsplit'] or get_shielded_spends(tx) != 0 or get_orchard_actions(tx) != 0:
|
||||
if tx['vin']:
|
||||
ins = "m"
|
||||
else:
|
||||
ins = "z"
|
||||
else:
|
||||
ins = "t"
|
||||
|
||||
if tx['vjoinsplit'] or get_shielded_outputs(tx) != 0 or get_orchard_actions(tx) != 0:
|
||||
if tx['vout']:
|
||||
outs = "m"
|
||||
else:
|
||||
outs = "z"
|
||||
else:
|
||||
outs = "t"
|
||||
|
||||
return ins + "-" + outs
|
||||
|
||||
def is_orchard_tx(tx):
|
||||
try:
|
||||
return tx['orchard']['actions']
|
||||
except KeyError:
|
||||
return False
|
||||
|
||||
def is_saplingspend_tx(tx):
|
||||
try:
|
||||
return tx['vShieldedSpend']
|
||||
except KeyError:
|
||||
return False
|
||||
|
||||
def orchard_anchorage(cache, block, tx):
|
||||
"""
|
||||
Returns -1 if there is no anchor
|
||||
"""
|
||||
try:
|
||||
return block['height'] - cache[tx['orchard']['anchor']]
|
||||
except KeyError:
|
||||
return -1
|
||||
|
||||
def sapling_anchorage(cache, block, tx):
|
||||
"""
|
||||
Returns -1 if there is no anchor
|
||||
"""
|
||||
try:
|
||||
return block['height'] - cache[tx['vShieldedSpend'][0]['anchor']]
|
||||
except KeyError:
|
||||
return -1
|
||||
|
||||
def is_not_coinbase(tx):
|
||||
return 'feePaid' in tx
|
||||
|
||||
# NB: This requires zcashd to be running with `experimentalfeatures=1`,
|
||||
# `txindex=1` and `insightexplorer=1`.
|
||||
def getFeeDiff(proposedFee, tx):
|
||||
try:
|
||||
return proposedFee <= tx['feePaid']
|
||||
except KeyError:
|
||||
return -1
|
||||
|
||||
def vin_value(vin):
|
||||
if 'valueSat' in vin:
|
||||
return vin['valueSat']
|
||||
else:
|
||||
return 0
|
||||
|
||||
def tx_pool_movement(tx):
|
||||
transparent = sum(vout['valueZat'] for vout in tx['vout']) - sum([vin_value(vin) for vin in tx['vin']])
|
||||
sprout = sum([vjoinsplit['vpub_newZat'] - vjoinsplit['vpub_oldZat'] for vjoinsplit in tx['vjoinsplit']])
|
||||
sapling = - tx['valueBalanceZat']
|
||||
if 'orchard' in tx:
|
||||
orchard = - tx['orchard']['valueBalanceZat']
|
||||
else:
|
||||
orchard = 0
|
||||
# print("(%d, %d, %d, %d) – %d -> %d" % (transparent, sprout, sapling, orchard, count_inputs(tx), count_outputs(tx)))
|
||||
return (transparent, sprout, sapling, orchard)
|
||||
Loading…
Reference in New Issue