Simple Uni V2 Tree (Part 1)
Assumptions:
Uses Simple Tree
Uses stablecoins (ie, USDC and USDT) to control for impermanent loss
Infinite supply of index tokens
LPs include:
USDC-USDT
USDC-iUSDC
Medium Article: Liquidity Tree Performance using Stablecoins: Part 1
To download notebook to this tutorial, see here
[1]:
import os
import numpy as np
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import scipy.stats as stats
import statsmodels.api as sm
import seaborn as sns
from uniswappy import *
cwd = os.getcwd().replace("notebooks/medium_articles","")
os.chdir(cwd)
Script params
[2]:
init_tkn_lp = 100000
tkn_delta_param = 1000
tkn_invest_amt = 100
tkn_nm = 'USDC'
itkn_nm = 'iUSDC'
usdt_nm = 'USDT'
iusdt_nm = 'iUSDT'
Simulate price data
[3]:
# *************************
# *** Simulation
# *************************
n_sim_runs = 2000
seconds_year = 31536000
shape = 2000
scale = 0.0005
p_arr = np.random.gamma(shape = shape, scale = scale, size = n_sim_runs)
n_runs = len(p_arr)-1
dt = datetime.timedelta(seconds=seconds_year/n_sim_runs)
dates = [datetime.datetime.strptime("2024-09-01", '%Y-%m-%d') + k*dt for k in range(n_sim_runs)]
x_val = np.arange(0,len(p_arr))
fig, (USD_ax) = plt.subplots(nrows=1, sharex=False, sharey=False, figsize=(18, 5))
USD_ax.plot(dates, p_arr, color = 'r',linestyle = 'dashdot', label='initial invest')
USD_ax.set_title(f'Price Chart ({tkn_nm}/{usdt_nm})', fontsize=20)
USD_ax.set_ylabel('Price (USD)', size=20)
USD_ax.set_xlabel('Date', size=20)
[3]:
Text(0.5, 0, 'Date')

Initialization Params
[4]:
user_nm = 'user0'
tkn_amount = init_tkn_lp
usdt_amount = p_arr[0]*tkn_amount
Initialize Simple DEX Tree
[5]:
usdt1 = ERC20(usdt_nm, "0x111")
tkn1 = ERC20(tkn_nm, "0x09")
exchg_data = UniswapExchangeData(tkn0 = tkn1, tkn1 = usdt1, symbol="LP", address="0x011")
TKN_amt = TokenDeltaModel(tkn_delta_param)
iVault1 = IndexVault('iVault1', "0x7")
factory = UniswapFactory(f"{tkn_nm} pool factory", "0x2")
lp = factory.deploy(exchg_data)
Join().apply(lp, user_nm, tkn_amount, usdt_amount)
tkn2 = ERC20(tkn_nm, "0x09")
itkn1 = IndexERC20(itkn_nm, "0x09", tkn1, lp)
exchg_data1 = UniswapExchangeData(tkn0 = tkn2, tkn1 = itkn1, symbol="LP1", address="0x012")
lp1 = factory.deploy(exchg_data1)
JoinTree().apply(lp1, user_nm, iVault1, 10000)
# Re-balance LP price after JoinTree
SwapDeposit().apply(lp, usdt1, user_nm, lp.get_reserve(tkn1) - lp.get_reserve(usdt1))
lp.summary()
lp1.summary()
Exchange USDC-USDT (LP)
Reserves: USDC = 110000.0, USDT = 110000.0
Liquidity: 109982.55020786705
Exchange USDC-iUSDC (LP1)
Reserves: USDC = 9972.071706380655, iUSDC = 4809.500300278058
Liquidity: 6925.36510707068
Take an investment position
[6]:
tkn_invest = 100
invested_user_nm = 'invested_user'
SwapIndexMint(iVault1, opposing = False).apply(lp, tkn1, invested_user_nm, tkn_invest)
mint_itkn1_deposit = lp1.convert_to_human(iVault1.index_tokens[itkn_nm]['last_lp_deposit'])
SwapDeposit().apply(lp1, itkn1, invested_user_nm, mint_itkn1_deposit)
lp.summary()
lp1.summary()
lp_invest_track = lp.get_liquidity_from_provider(invested_user_nm)
lp1_invest_track = lp1.get_liquidity_from_provider(invested_user_nm)
# Redeem from parent
tkn_redeem_parent = LPQuote(False).get_amount_from_lp(lp, tkn1, lp_invest_track)
# Redeem from tree (child + parent)
itkn_redeem_child = LPQuote(False).get_amount_from_lp(lp1, itkn1, lp1_invest_track)
tkn_redeem_tree = LPQuote(False).get_amount_from_lp(lp, tkn1, itkn_redeem_child)
print(f'{tkn_redeem_parent:.3f} USDC redeemed from {lp_invest_track:.3f} LP tokens if {tkn_invest:.1f} invested USDC immediately pulled from parent')
print(f'{tkn_redeem_tree:.3f} USDC redeemed from {lp1_invest_track:.3f} LP1 tokens if {tkn_invest:.1f} invested USDC immediately pulled from tree')
Exchange USDC-USDT (LP)
Reserves: USDC = 110100.0, USDT = 110000.0
Liquidity: 110032.45583580383
Exchange USDC-iUSDC (LP1)
Reserves: USDC = 9972.071706380655, iUSDC = 4859.405928214833
Liquidity: 6961.1489588849345
99.700 USDC redeemed from 49.906 LP tokens if 100.0 invested USDC immediately pulled from parent
99.403 USDC redeemed from 35.784 LP1 tokens if 100.0 invested USDC immediately pulled from tree
Simulate trading
[7]:
arb = CorrectReserves(lp, x0 = 1)
arb1 = CorrectReserves(lp1, x0 = lp1.reserve1/lp1.reserve0)
TKN_amt = TokenDeltaModel(n_sim_runs)
lp_direct_invest_arr = []; lp1_direct_invest_arr = []; lp1_tree_invest_arr = [];
pTKN_USDT_arr = []; pTKN_iTKN_arr = []
fee_lp_arr = []; fee_lp1_arr = [];
for k in range(n_sim_runs):
# *****************************
# ***** Parent Arbitrage ******
# *****************************
arb.apply(p_arr[k])
# *****************************
# ***** Child Arbitrage ******
# *****************************
#p_lp1 = SettlementLPToken().apply(lp, tkn1, lp1.reserve0)/lp1.reserve0
p_lp1 = LPQuote().get_lp_from_amount(lp, tkn1, lp1.get_reserve(tkn2))/lp1.get_reserve(tkn2)
arb1.apply(p_lp1)
# *****************************
# ***** Random Swapping ******
# *****************************
Swap().apply(lp, tkn1, user_nm, TKN_amt.delta())
Swap().apply(lp, usdt1, user_nm, TKN_amt.delta())
# conservatively assume 20% of parent trading by volume
Swap().apply(lp1, tkn2, user_nm, 0.2*TKN_amt.delta())
#Swap().apply(lp1, itkn1, user_nm, SettlementLPToken().apply(lp, tkn1, 0.2*TKN_amt.delta()))
Swap().apply(lp1, itkn1, user_nm, LPQuote().get_lp_from_amount(lp, tkn1, 0.2*TKN_amt.delta()))
# *****************************
# ******* Data Capture ********
# *****************************
# price
pTKN_USDT_arr.append(LPQuote().get_price(lp, tkn1))
pTKN_iTKN_arr.append(LPQuote().get_price(lp1, tkn1))
# investment performance
tkn_redeem_parent = LPQuote(False).get_amount_from_lp(lp, tkn1, lp_invest_track)
itkn_redeem_child = LPQuote(False).get_amount_from_lp(lp1, itkn1, lp1_invest_track)
tkn_redeem_tree = LPQuote(False).get_amount_from_lp(lp, tkn1, itkn_redeem_child)
lp_direct_invest_arr.append(tkn_redeem_parent)
lp1_direct_invest_arr.append(RebaseIndexToken().apply(lp1, tkn2, lp1_invest_track))
lp1_tree_invest_arr.append(tkn_redeem_tree)
# DEX Fees
fee_lp_arr.append(TreeAmountQuote().get_tot_y(lp, lp.get_fee(tkn1), lp.get_fee(usdt1)))
fee_lp1_arr.append(TreeAmountQuote().get_tot_y(lp1, lp1.get_fee(tkn2), lp1.get_fee(itkn1)))
lp.summary()
lp1.summary()
# Redeem from parent
tkn_redeem_parent = LPQuote(False).get_amount_from_lp(lp, tkn1, lp_invest_track)
# Redeem from tree (child + parent)
itkn_redeem_child = LPQuote(False).get_amount_from_lp(lp1, itkn1, lp1_invest_track)
tkn_redeem_tree = LPQuote(False).get_amount_from_lp(lp, tkn1, itkn_redeem_child)
print(f'{tkn_redeem_parent:.3f} USDC redeemed from {lp_invest_track:.3f} LP tokens if {tkn_invest:.1f} invested USDC pulled from parent (lp)')
print(f'{tkn_redeem_tree:.3f} USDC redeemed from {lp1_invest_track:.3f} LP1 tokens if {tkn_invest:.1f} invested USDC pulled from tree (lp + lp1)')
Exchange USDC-USDT (LP)
Reserves: USDC = 149152.43424879533, USDT = 145484.9365428984
Liquidity: 139003.15033667785
Exchange USDC-iUSDC (LP1)
Reserves: USDC = 12226.673882800053, iUSDC = 5686.2825516738585
Liquidity: 7560.236409074802
106.919 USDC redeemed from 49.906 LP tokens if 100.0 invested USDC pulled from parent (lp)
114.877 USDC redeemed from 35.784 LP1 tokens if 100.0 invested USDC pulled from tree (lp + lp1)
[8]:
fig, (TKN_ax, USDT_ax) = plt.subplots(nrows=2, sharex=False, sharey=False, figsize=(15, 8))
strt_pt = 5
TKN_ax.plot(dates[strt_pt:], p_arr[strt_pt:], color = 'g',linestyle = 'dashed', linewidth=1, label=f'{tkn_nm} Price (Market)')
TKN_ax.plot(dates[strt_pt:], pTKN_USDT_arr[strt_pt:], color = 'b',linestyle = '-', linewidth=0.7, label=f'{tkn_nm}/{usdt_nm} (LP)')
TKN_ax.set_title('Price comparison: parent vs child LPs', fontsize=20)
TKN_ax.set_ylabel('Price (USD)', size=20)
TKN_ax.legend(fontsize=12)
TKN_ax.grid()
USDT_ax.plot(dates[strt_pt:], pTKN_iTKN_arr[strt_pt:], color = 'b',linestyle = 'dashed', label=f'{tkn_nm}/{itkn_nm} (LP1)')
USDT_ax.set_ylabel('prices', size=20)
USDT_ax.set_ylabel('Price (USD)', size=20)
USDT_ax.legend(fontsize=12)
USDT_ax.grid()

[9]:
fig, ax = plt.subplots(1, 2, figsize=(15,5))
sns.distplot(pTKN_USDT_arr, hist=True, kde=True, bins=int(30), color = 'darkblue',
hist_kws={'edgecolor':'black'}, kde_kws={'linewidth': 2}, ax=ax[0])
sns.distplot(pTKN_iTKN_arr, hist=True, kde=True, bins=int(30), color = 'darkblue',
hist_kws={'edgecolor':'black'}, kde_kws={'linewidth': 2}, ax=ax[1])
ax[0].set_title(f'Distribution: {tkn_nm}/{usdt_nm} LP price (parent)')
ax[0].set_xlabel('Price')
ax[0].set_ylabel('Frequency')
ax[1].set_title(f'Distribution: {tkn_nm}/{itkn_nm} LP1 price (child)')
ax[1].set_xlabel('Price')
ax[1].set_ylabel('Frequency')
[9]:
Text(0, 0.5, 'Frequency')

[10]:
lowess = sm.nonparametric.lowess
x = range(0,n_sim_runs)
res = lowess(lp_direct_invest_arr, x, frac=1/15); sm_lp_direct = res[:,1]
res = lowess(lp1_direct_invest_arr, x, frac=1/15); sm_lp1_direct = res[:,1]
res = lowess(lp1_tree_invest_arr, x, frac=1/15); sm_lp1_tree= res[:,1]
strt_ind = 3
fig, (p_ax) = plt.subplots(nrows=1, sharex=True, sharey=False, figsize=(15, 8))
fig.suptitle('Simple Tree (USDC / USDT) performance ', fontsize=20)
p_ax.plot(dates[strt_ind:], lp_direct_invest_arr[strt_ind:], linestyle='dashed', linewidth=0.5, color = 'g')
p_ax.plot(dates[strt_ind:], sm_lp_direct[strt_ind:], color = 'g', label = 'Expected return from parent (LP)')
p_ax.plot(dates[strt_ind:], lp1_direct_invest_arr[strt_ind:], linestyle='dashed', linewidth=0.5, color = 'b')
p_ax.plot(dates[strt_ind:], sm_lp1_direct[strt_ind:], color = 'b', label = 'Expected return from child (LP1)')
p_ax.plot(dates[strt_ind:], lp1_tree_invest_arr[strt_ind:], linestyle='dashed', linewidth=0.5, color = 'r')
p_ax.plot(dates[strt_ind:], sm_lp1_tree[strt_ind:], color = 'r', label = 'Expected return from tree (LP+LP1)')
p_ax.legend( fontsize=12)
p_ax.set_ylim(80, 140)
p_ax.set_ylabel("$100 USD Investment", fontsize=14)
[10]:
Text(0, 0.5, '$100 USD Investment')

[11]:
print(f'{tkn_invest:.3f} TKN before is worth {sm_lp_direct[-1]:.3f} TKN after direct investment into parent (lp)')
print(f'{tkn_invest:.3f} TKN before is worth {sm_lp1_direct[-1]:.3f} TKN after direct investment into child (lp1)')
print(f'{tkn_invest:.3f} TKN before is worth {sm_lp1_tree[-1]:.3f} TKN after investment into simple tree (lp + lp1)')
100.000 TKN before is worth 105.966 TKN after direct investment into parent (lp)
100.000 TKN before is worth 113112280374347202560.000 TKN after direct investment into child (lp1)
100.000 TKN before is worth 116.191 TKN after investment into simple tree (lp + lp1)
[12]:
t = np.arange(0,len(fee_lp_arr))
fee_lpB = np.array(fee_lp1_arr)
fee_lpA = fee_lpB+np.array(fee_lp_arr)
fig = plt.figure(figsize=(15, 5))
plt.plot(dates, fee_lpA, color = 'r', label = f'Parent LP ({tkn_nm}/{usdt_nm})')
plt.fill_between(dates, fee_lpB, fee_lpA, alpha=0.3, color='r')
plt.plot(dates, fee_lpB, color = 'b', label = f'Child LP1 ({tkn_nm}/{itkn_nm})')
plt.fill_between(dates, np.repeat(0,len(fee_lp_arr)), fee_lpB, alpha=0.3, color='b')
plt.title('Cumulative Arbitrage Fees (Stablecoin Simple Tree, Uni V2)', fontsize = 20)
plt.ylabel("Collected Fees (USD)", fontsize=14)
plt.legend(fontsize=12)
[12]:
<matplotlib.legend.Legend at 0x16a8017e0>
