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price-manipulation.ipynb
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{
"source": [
"This notebook explores how easy it is for traders to manipulate different crypto price feeds. We will look at\n",
"\n",
"1. Uniswap\n",
"2. Chainlink\n",
"3. Compound's Open Price Feed\n",
"\n",
"See [this GitHub issue](https://github.com/ConsenSys/defi-score/issues/49) for background and motivation."
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"## Uniswap"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"Uniswap has two main actors:\n",
"\n",
"1. Traders\n",
"2. Liquidity providers\n",
"\n",
"Each role gives rise to a price-manipulation strategy. We explore them in turn."
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Traders"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"A trader wishing to exchange `X` tokens for `Y` tokens may go to a Uniswap pool labeled `X/Y`. Assume the current state of the pool is `x*y = k`, i.e. there are `x` tokens of type `X` and `y` tokens of type `Y` in the pool. Therefore, the marginal price of an `X` token is `y/x`.\n",
"\n",
"Let's say the trader wants to increase or decrease that marginal price by a fixed percentage and is willing to trade as many tokens as needed. Assume without loss of generality that they care about the price of `X` relative to `Y`. This means that\n",
"\n",
"If they want it to increase, they need to add `Y` tokens to the pool. The amount of tokens `dy` needed to reach their target price `p_target` satisfies:\n",
"\n",
"`(y+dy)*(y-(1+f)*dy) = k*p_target`\n",
"\n",
"where `f` is the pool fee awarded to liquidity providers. If they want to decrease it, they need to add `X` tokens to the pool. The amount of tokens `dx` needed to reach their target price `p_target` satisfies:\n",
"\n",
"`(x+dx)*(x-(1+f)*dx) = k/p_target`"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import requests\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['figure.figsize'] = 16, 9\n",
"import seaborn as sns\n",
"import plotly.graph_objects as go\n",
"from ipywidgets import (\n",
" interact,\n",
" interact_manual,\n",
" widgets\n",
")\n",
"\n",
"from utils import (\n",
" tokens, \n",
" get_reserves\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"class EmptyPoolException(Exception):\n",
" pass\n",
"\n",
"class Pool:\n",
"\n",
" def __init__(self, fee=0, **kwds):\n",
" self._kwds = kwds or {'x': 0, 'y': 0}\n",
" assert len(self._kwds) == 2, f\"Pools must contain exactly 2 tokens, but {len(self._kwds)} were provided.\"\n",
" self.balance = self._kwds\n",
" self._ids = list(self.balance.keys())\n",
" # self._qs = list(self._kwds.values())\n",
" self.x_id, self.y_id = self._ids\n",
" self.fee = fee\n",
"\n",
" def trade(self, _id, d):\n",
" \"\"\"Trade `d` tokens of type `_id`.\"\"\"\n",
" if _id == self.x_id:\n",
" dy = (1 - self.fee) * self.y * d / (self.x + d)\n",
" self.x += d\n",
" self.y -= dy\n",
" return dy\n",
" if _id == self.y_id:\n",
" dx = (1 - self.fee) * self.x * d / (self.y + d)\n",
" self.y += d\n",
" self.x -= dx\n",
" return dx\n",
" raise ValueError(f\"({self.x_id},{self.y_id})-pool has no token {_id}\")\n",
"\n",
" def sell(self, _id, d):\n",
" return self.trade(_id, d)\n",
"\n",
" def buy(self, _id, d):\n",
" return self.trade(self.opp(_id), self.price(_id, d))\n",
"\n",
" def provide(self, **kwds):\n",
" \"\"\"Provide liquidity to the pool.\"\"\"\n",
" k0 = self.k\n",
" for k, v in kwds.items():\n",
" self.balance[k] += v\n",
" return (self.k - k0)\n",
"\n",
" def marginal_price(self, _id):\n",
" \"\"\"Marginal price is the hypothetical price of an infinitesimally small trade. It's always equal to the ratio of tokens of each type held in the pool.\"\"\"\n",
" if self.x == self.y == 0:\n",
" raise EmptyPoolException(\"The pool is empty, so marginal prices are ill-defined!\")\n",
" if _id == self.x_id:\n",
" return self.y / self.x if self.x > 0 else inf\n",
" if _id == self.y_id:\n",
" return self.x / self.y if self.y > 0 else inf\n",
" raise ValueError(f\"({self.x_id},{self.y_id})-pool has no token {_id}\")\n",
"\n",
" def price(self, _id, d):\n",
" \"\"\"Actual price paid by trader spending `d` tokens of type `_id` in the pool.\"\"\"\n",
" if d == 0: return 0\n",
" # Create new pool so state in the current one doesn't change\n",
" _pool = self.__class__(fee=self.fee, **self.balance)\n",
" return d / _pool.trade(_id, d) if _pool.trade(_id, d) > 0 else inf\n",
"\n",
" @property\n",
" def x(self):\n",
" return self.balance[self.x_id]\n",
"\n",
" @property\n",
" def y(self):\n",
" return self.balance[self.y_id]\n",
"\n",
" @property\n",
" def k(self):\n",
" return self.x * self.y\n",
"\n",
" def opp(self, _id):\n",
" \"\"\"Returns the name of the opposite token.\"\"\"\n",
" return self.x_id if _id == self.y_id else self.y_id\n",
"\n",
" def __repr__(self):\n",
" return f\"Pool({self.x_id}={self.x:.2f}, {self.y_id}={self.y:.2f}, f={100*self.fee:.2f}%)\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"sqrt = np.sqrt\n",
"inf = np.inf\n",
"\n",
"def check_trading_manipulation(pool, target_marginal_price, _id):\n",
" \"\"\"Checks the trading manipulation works as intended. Final price should be equal to target price.\"\"\"\n",
" current_marginal_price = pool.marginal_price(_id)\n",
" print(f\"Initial price: {current_marginal_price:.3f}\")\n",
"\n",
" spend_id, dz = cost_trading_manipulation(pool, target_marginal_price, _id)\n",
" du = pool.provide(spend_id, dz)\n",
"\n",
" print(f\"Target price: {target_marginal_price:.3f}\")\n",
" print(f\"Final price: {pool.marginal_price(_id):.3f}\")\n",
" print(f\"Cost: {dz:.3f} {spend_id}\")\n",
" print(f\"Received: {du:.3f} {pool.opp(spend_id)}\")\n",
"\n",
"def cost_trading_manipulation(pool, target_marginal_price, _id):\n",
" \"\"\"\n",
" Calculate the cost of bringing a Uniswap liquidity pool to the desired `target_marginal_price` by trading a large amount of tokens.\n",
" @param pool : Pool object.\n",
" @param target_marginal_price : Desired token ratio at the end of the trade.\n",
" @param _id : Token id the price refers to, e.g. if _id is 'x' and the other token in the pool is 'y', the marginal price is y/x.\n",
" @return tuple(id of token added to the pool, amount of said token added to the pool)\n",
" \"\"\"\n",
" f = pool.fee\n",
"\n",
" if target_marginal_price <= pool.marginal_price(_id):\n",
" spend_id = _id\n",
" p = 1/target_marginal_price\n",
" else:\n",
" spend_id = pool.opp(_id)\n",
" p = target_marginal_price\n",
"\n",
" z = pool.balance[spend_id]\n",
" u = pool.balance[pool.opp(spend_id)]\n",
"\n",
" # I hardcoded this formula, but it can be reproduced with the following snippet\n",
" # ```\n",
" # from sympy import symbols, solve\n",
" # z, dz, u, du, f, p = symbols('z dz u du f p')\n",
" # du = solve((z+dz)*(u-du)-u*z, du)[0]\n",
" # print(solve((z+dz)-p*(u-(1-f)*du), dz))\n",
" # ```\n",
" cost = f*p*u/2 - z + sqrt(p*u*(f**2*p*u - 4*f*z + 4*z))/2\n",
"\n",
" return spend_id, cost"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def check_lp_manipulation(pool, target_marginal_price, _id):\n",
" \"\"\"Checks LP manipulation works as intended. Final price should be equal to target price.\"\"\"\n",
" current_marginal_price = pool.marginal_price(_id)\n",
" print(f\"Initial price: {current_marginal_price:.3f}\")\n",
"\n",
" spend_id, dz = cost_lp_manipulation(pool, target_marginal_price, _id)\n",
" dk = pool.provide(**{spend_id: dz})\n",
"\n",
" print(f\"Target price: {target_marginal_price:.3f}\")\n",
" print(f\"Final price: {pool.marginal_price(_id):.3f}\")\n",
" print(f\"Cost: {dz:.3f} {spend_id}\")\n",
" print(f\"Received: {dk:.3f} {pool.x_id}/{pool.y_id}\")\n",
"\n",
"def cost_lp_manipulation(pool, target_marginal_price, _id):\n",
" \"\"\"\n",
" Calculate the cost of bringing a Uniswap liquidity pool to the desired `target_marginal_price` by providing liquidity.\n",
" @param pool : Pool object.\n",
" @param target_marginal_price : Desired token ratio at the end of the trade.\n",
" @param _id : Token id the price refers to, e.g. if _id is 'x' and the other token in the pool is 'y', the marginal price is y/x.\n",
" @return tuple(id of token added to the pool, amount of said token added to the pool)\n",
" \"\"\"\n",
" if target_marginal_price <= pool.marginal_price(_id):\n",
" spend_id = _id\n",
" p = 1/target_marginal_price\n",
" else:\n",
" spend_id = pool.opp(_id)\n",
" p = target_marginal_price\n",
"\n",
" z = pool.balance[spend_id]\n",
" u = pool.balance[pool.opp(spend_id)]\n",
" \n",
" return spend_id, p*u-z"
]
},
{
"source": [
"### Using real data"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def get_pool(token0, token1, fee=.3/100):\n",
" \"\"\"Get pool reserves on Uniswap and create a Pool object.\"\"\"\n",
" try:\n",
" with open('reserves.json', 'r') as f:\n",
" reserves = json.load(f)\n",
" if f'{token0}/{token1}' in reserves:\n",
" reserve = reserves[f'{token0}/{token1}']\n",
" elif f'{token1}/{token0}' in reserves:\n",
" reserve = reserves[f'{token1}/{token0}']\n",
" else:\n",
" raise KeyError(\"Pool not in file\")\n",
" except:\n",
" reserve = get_reserves(token0, token1)\n",
" \n",
" pool = Pool(**reserve, fee=fee)\n",
" return pool\n",
"\n",
"def get_parametric_manipulation(token0, token1, manipulation, fee=.3/100, dps=np.logspace(-1, 1, 101)):\n",
" \"\"\"Calculate cost of manipulation for different % changes in price.\"\"\"\n",
" pool = get_pool(token0, token1, fee)\n",
" p0 = pool.marginal_price(token0)\n",
" manipulation_fn = {'trading': cost_trading_manipulation, 'lp': cost_lp_manipulation}[manipulation]\n",
" costs = np.array([manipulation_fn(pool, dp*p0, token0) for dp in dps])\n",
" d = pd.DataFrame([100*(dps-1), costs[:,0], costs[:,1]]).T\n",
" # cols = f'{token0}/{token1} [% change]', 'Spend token', 'Cost'\n",
" cols = f'Price change [%]', 'Spend token', 'Cost'\n",
" d.columns = cols\n",
" d[f'Cost / {token1}'] = d.apply(lambda row: row['Cost'].astype(float) * (1 if row['Spend token']==token1 else p0), axis=1)\n",
" d['Manipulation'] = manipulation\n",
" d['Pool'] = f'{token0}/{token1}'\n",
" return d\n",
"\n",
"def plot_manipulation(token0, token1, manipulation, fee=.3/100, dps=np.logspace(-1, 1, 101), ax=None):\n",
" d = get_parametric_manipulation(token0, token1, manipulation, fee, dps)\n",
" cols = d.columns\n",
" ax = sns.lineplot(data=d, x=cols[0], y=cols[-1], ax=ax)\n",
" return ax\n",
"\n",
"def compare_pools(token0s, token1, ax=None, manipulations=['Trading', 'LP']):\n",
" \"\"\"Compare the cost of manipulating different pools with the same token.\"\"\"\n",
" data = pd.DataFrame([], columns=['Price change [%]', f'Cost / {token1}', 'Pool', 'Manipulation'])\n",
" if ax is None:\n",
" fig, ax = plt.subplots()\n",
" for token0 in token0s:\n",
" for manipulation in manipulations:\n",
" d = get_parametric_manipulation(token0, token1, manipulation.lower())\n",
" data = pd.concat([data, d])\n",
" ax = sns.lineplot(data=data, x='Price change [%]', y=f'Cost / {token1}', hue='Pool', style='Manipulation', ax=ax)\n",
" return ax, data"
]
},
{
"source": [
"Example: If we have a lot of DAI lying around, which of the following pools would be cheaper to manipulate by some fixed % amount?\n",
"\n",
"- WETH/DAI\n",
"- WBTC/DAI\n",
"- USDC/DAI"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"# TODO check that the trading curve corresponds to the following equation on page 22 of https://arxiv.org/pdf/1911.03380.pdf for the case with no fees:\n",
"# C(eps) = y * (sqrt(1+eps) + 1 / sqrt(1+eps) - 2)\n",
"# with p_target = (1+eps)*p_marginal\n",
"\n",
"ax, _ = compare_pools(('WBTC', 'WETH', 'USDC'), 'DAI')\n",
"ax.get_figure().show()\n",
"ax.set_yscale('log')"
]
},
{
"source": [
"Answer: USDC/DAI since it's the one with the least liquidity. Driving the price of USDC wrt DAI 90% down by trading DAI against the pool would cost ~200k DAI. A similar price change for WETH or WBTC requires ~10M-100M DAI.\n",
"\n",
"Also note that adding liquidity is more capital intensive, so it's a less attractive strategy from this point of view, although this doesn't take into account the returns from fees -- if the pool has high trade volume, this strategy becomes more attractive."
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Interactive viz"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "interactive(children=(Combobox(value='', description='token0', options=('WETH', 'WBTC', 'DAI', 'USDC'), placeh…",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "da954f227aaa4fd98d7409227922507f"
}
},
"metadata": {}
}
],
"source": [
"# TODO use plotly express - it is ~ seaborn, high-level\n",
"token_choices = lambda n: widgets.Combobox(\n",
" placeholder=f'Token {n}',\n",
" options=tokens,\n",
" description='',\n",
" ensure_option=False,\n",
" disabled=False,\n",
")\n",
"\n",
"# TODO checkboxes should appear grouped - strategy in one col, x/y-log scale in other\n",
"# TODO after plotting a pair, keep it as a checked checkbox to allow comparison across pairs\n",
"@interact_manual(token0=token_choices(1), token1=token_choices(2), trading=True, lp=False, xlog=False, ylog=False)\n",
"def interactive_compare_pools(token0, token1, trading=True, lp=False, xlog=False, ylog=False):\n",
" # Read manipulations to plot from checkboxes\n",
" manipulations = []\n",
" if trading: manipulations.append('Trading')\n",
" if lp: manipulations.append('LP')\n",
" \n",
" # Create figure\n",
" fig = go.FigureWidget()\n",
" fig.update_xaxes(title_text='Price change [%]')\n",
" fig.update_yaxes(title_text='Cost / DAI')\n",
" \n",
" if not (token0 and token1): # don't do anything if no data\n",
" return fig\n",
"\n",
" # Main calculation\n",
" # TODO cache\n",
" d = pd.concat([get_parametric_manipulation(token0, token1, manipulation.lower()) for manipulation in manipulations])\n",
" \n",
" # Update plot\n",
" with fig.batch_update():\n",
" fig.update_xaxes(type=\"log\" if xlog else \"linear\")\n",
" fig.update_yaxes(type=\"log\" if ylog else \"linear\")\n",
" k = 0\n",
" # TODO legend\n",
" for m, g in d.groupby('Manipulation'):\n",
" fig.add_scatter()\n",
" scatt = fig.data[k]\n",
" # TODO user should be able to set cost unit\n",
" scatt.x, scatt.y = g['Price change [%]'], g['Cost / DAI']\n",
" k += 1\n",
" return fig\n",
"\n",
"fig = interactive_compare_pools(token0='WETH', token1='DAI')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# TODO TWAPs\n",
"# - they work by accumulation, see https://uniswap.org/docs/v2/core-concepts/oracles/\n",
"# - this means the TWAP over a period (t0, t1) is (acc[t1] - acc[t0]) / (t1 - t0)\n",
"# - motivating example (why are TWAPs needed) https://samczsun.com/taking-undercollateralized-loans-for-fun-and-for-profit/\n",
"# TODO front-running & arbitrage ?"
]
},
{
"source": [
"## ChainLink"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# TODO\n",
"# There are only 21 oracles for the ETH/USD price (!!!) - source: https://docs.chain.link/docs/architecture-decentralized-model\n",
"# See also: https://docs.chain.link/docs/get-the-latest-price\n",
"# whitepaper: https://uploads-ssl.webflow.com/5f6b7190899f41fb70882d08/603651a1101106649eef6a53_chainlink-ocr-protocol-paper-02-24-20.pdf"
]
}
]
}
Raw
utils.py
from collections import OrderedDict
import json
import requests
from web3 import Web3
from gql import gql, Client
from gql.transport.requests import RequestsHTTPTransport
from keys import API_KEYS
## CONSTANTS ##
tokens = (
"WETH", # calling ETH directly gives error, probably because it's not ERC20 compliant
"WBTC",
"DAI",
"USDC",
)
# Unit conversion
wei = 18
# WBTC & USDC not expressed in weis; i calculated these factors empirically, don't know where they come from
units = {
'WBTC': 8,
'USDC': 6,
}
# see on etherscan https://etherscan.io/address/0x5C69bEe701ef814a2B6a3EDD4B1652CB9cc5aA6f
uniswap_factory_address = "0x5C69bEe701ef814a2B6a3EDD4B1652CB9cc5aA6f"
## h/t https://towardsdatascience.com/exploring-decentraland-marketplace-sales-with-thegraph-and-graphql-2f5e8e7199b5
# Select your transport with a defined url endpoint
transport = RequestsHTTPTransport(
url="https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v2")
# Create a GraphQL client using the defined transport
client = Client(transport=transport, fetch_schema_from_transport=True)
# Use infura to fetch and call smart contracts via HTTP
web3 = Web3(Web3.HTTPProvider(f'https://mainnet.infura.io/v3/{API_KEYS["infura"]}'))
def get_reserves(token0, token1, cache='reserves.json'):
_token0, _token1, pool = get_uniswap_pool(token0, token1)
reserve0, reserve1, blockTimeStampLast = pool.functions.getReserves().call()
decimals0, decimals1 = map(get_decimals, (_token0, _token1))
ret = OrderedDict({
_token0: reserve0 / 10**decimals0,
_token1: reserve1 / 10**decimals1,
})
# TODO switch to csv cache so you don't have to parse the whole file every time you want to append
if cache:
with open(cache, 'r') as f:
data = json.load(f)
data[f'{_token0}/{_token1}'] = ret
with open(cache, 'w') as f:
json.dump(data, f)
return ret
def get_marginal_price(token0, token1):
"""Marginal price of `token0` in units of `token1`"""
_token0, _token1, pool = get_uniswap_pool(token0, token1)
reserve0, reserve1, blockTimeStampLast = pool.functions.getReserves().call()
decimals0, decimals1 = map(get_decimals, (_token0, _token1))
reserve0 /= 10**decimals0
reserve1 /= 10**decimals1
return reserve0 / reserve1, f"{_token0}/{_token1}"
def get_uniswap_pool(token0, token1):
"""
Get contract object representing the Uniswap pool between `token0` and `token1`.
@param token0, token1: String e.g. "DAI" or "WETH". Token symbols are searched on Uniswap's subgraph. The contract with that symbol and highest number of transactions is returned.
@return (tokenA, tokenB, pool): `(tokenA, tokenB)` are the pool tokens _in the order they are defined by the contract_. `pool` is the contract object.
"""
address0, address1 = map(get_token_address, (token0, token1))
uniswap_factory = get_contract_from_address(uniswap_factory_address)
pool_address = uniswap_factory.functions.getPair(address0, address1).call()
pool = get_contract_from_address(pool_address)
_address0, _address1 = pool.functions.token0().call(), pool.functions.token1().call()
if _address0 == address0 and \
_address1 == address1:
return (token0, token1, pool)
elif _address0 == address1 and \
_address1 == address0:
print(f"WARNING: You requested {token0}/{token1} but this pool is {token1}/{token0}")
return (token1, token0, pool)
else:
raise Exception("Pool symbols don't match")
def get_decimals(token, graphql_client=client, web3_provider=web3):
contract = get_contract_from_address(get_token_address(token, graphql_client=graphql_client), web3_provider=web3_provider)
try:
return contract.functions.decimals().call()
except ABIFunctionNotFound:
print(f"{token} ERC20 contract has no `decimals` function. Defaulting to {units.get(token, wei)}.")
return units.get(token, wei)
def get_token_address(token, graphql_client=client):
# graphQL magic
# TODO use graphene, it's safer
query = gql(f"""
{{
tokens(where: {{symbol:"{token}"}}, orderBy:txCount, orderDirection: desc, first: 1){{
id,
txCount,
symbol,
}}
}}""")
result = client.execute(query)
assert len(result['tokens']) == 1, f"Found more than one token with name {token}"
# TODO addresses are not checksum
# I don't know if this is TheGraph's or Uniswap's fault
# I'm fixing it myself but this is a hack and it's not safe
return Web3.toChecksumAddress(result['tokens'][0]['id'])
def get_contract_from_address(address, web3_provider=web3):
"""Get web3 contract object from address. Calls etherscan's API to retrieve ABI."""
abi = get_abi(address)
contract = web3.eth.contract(address=address, abi=abi)
return contract
def get_abi(address):
# TODO work around API limit of 5 calls / sec
response = requests.get(
f'https://api.etherscan.io/api?module=contract&action=getabi&apikey={API_KEYS["etherscan"]}&address={address}')
rjson = response.json()
if rjson['status']=='1' and rjson['message']=='OK':
return rjson['result']
print(rjson)
raise Exception(rjson['message'])
def get_twap(pool, t0, t1):
"""
Get time-weighted average price of a token pair between times `t0` and `t1`.
@param t0
@param t1
"""
# TODO see https://uniswap.org/docs/v2/smart-contract-integration/building-an-oracle/
pass
if __name__=='__main__':
reserves = {}
for i, t0 in enumerate(tokens):
for j, t1 in enumerate(tokens):
if j<=i: continue
reserves[f'{t0}/{t1}'] = get_reserves(t0, t1)
with open('reserves.json', 'w') as f:
json.dump(reserves, f)
@jclancy93
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jclancy93 commented Mar 15, 2021

This looks like a great start!

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