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WIP attack modeling
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@poliwop
poliwop committed Jul 7, 2023
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<H1>Relationship between IL and price change in a stableswap pool</H1>\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": [],
"ExecuteTime": {
"end_time": "2023-07-06T20:50:06.289107809Z",
"start_time": "2023-07-06T20:50:06.245262315Z"
}
},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import glob\n",
"import random\n",
"import copy\n",
"sys.path.append('../..')\n",
"\n",
"from model import processing\n",
"from matplotlib import pyplot as plt\n",
"\n",
"from model import run\n",
"from model import plot_utils as pu\n",
"from model.amm.omnipool_amm import OmnipoolState, cash_out_omnipool, value_assets, usd_price, lrna_price\n",
"from model.amm.stableswap_amm import StableSwapPoolState\n",
"from model.amm.agents import Agent\n",
"from model.amm.trade_strategies import omnipool_arbitrage, invest_all, price_sensitive_trading\n",
"from model.amm.global_state import GlobalState, fluctuate_prices, historical_prices\n",
"\n",
"# same seed, same parameters = same simulation result\n",
"random.seed(42)\n",
"# price_list = processing.import_binance_prices(['BTC', 'ETH', 'DOT'], start_date='Jan 1 2023', days = 120)\n",
"\n",
"assets = {\n",
" 'USDA': {'usd price': 1, 'weight': 0.5},\n",
" 'USDB': {'usd price': 1, 'weight': 0.5}\n",
"}\n",
"\n",
"assert sum([t['weight'] for t in assets.values()]) == 1\n",
"\n",
"initial_tvl = 1000000\n",
"\n",
"initial_state = GlobalState(\n",
" pools={\n",
" 'stableswap': StableSwapPoolState(\n",
" tokens={\n",
" tkn: initial_tvl * assets[tkn]['weight'] for tkn in assets\n",
" },\n",
" amplification=1000,\n",
" trade_fee=0\n",
" )\n",
" },\n",
" agents = {\n",
" 'LP': Agent(\n",
" holdings={'USDA': 5100, 'USDB': 0},\n",
" trade_strategy=invest_all('stableswap')\n",
" ),\n",
" 'Trader': Agent(\n",
" holdings={tkn: 10000000000000 for tkn in assets}\n",
" )\n",
" },\n",
" external_market={tkn: assets[tkn]['usd price'] for tkn in assets},\n",
" # archive_all=False\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": [],
"ExecuteTime": {
"end_time": "2023-07-06T20:50:06.289443943Z",
"start_time": "2023-07-06T20:50:06.288961439Z"
}
},
"outputs": [
{
"data": {
"text/plain": "global state \npools: \n\n Stable Swap Pool: stableswap\n ********************************\n trade fee: 0\n shares: 1000000.0\n amplification constant: 1000\n tokens: (\n \n USDA\n quantity: 500000.0\n weight: 0.5\n \n USDB\n quantity: 500000.0\n weight: 0.5\n \n )\n error message:Agent has insufficient funds.\n\nagents: \n\n Agent: LP\n ********************************\n trade strategy: invest all (stableswap)\n holdings: (\n \n *USDA*: 5100.0\n \n *USDB*: 0\n \n *USD*: 0\n \n *stableswap*: 0.0\n price: 1.0000012679\n )\n \n Agent: Trader\n ********************************\n trade strategy: None\n holdings: (\n \n *USDA*: 10000000000000\n \n *USDB*: 10000000000000\n \n *USD*: 0\n )\n \nmarket prices: \n\n USDA: $1\n USDB: $1\n USD: $1\n\nevolution function: None"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# steps = 201\n",
"# # price_min = 0.2\n",
"# # price_max = 5\n",
"# trade_size_min, trade_size_max = -1000000, 1000000\n",
"# step_factor = (trade_size_max / trade_size_min) ** (1 / (steps - 1))\n",
"# trade_sizes = [trade_size_min + i * (trade_size_max - trade_size_min) / steps for i in range(steps)]\n",
"# # [-trade_size_min * step_factor ** i for i in range(steps)][::-1] + [trade_size_min * step_factor ** i for i in range(steps)]\n",
"\n",
"trade_size = 1000000\n",
"\n",
"events = []\n",
"\n",
"initial_holdings = copy.deepcopy(initial_state.agents['LP'].holdings)\n",
"initial_trader_holdings = copy.deepcopy(initial_state.agents['Trader'].holdings)\n",
"first_state = initial_state.copy()\n",
"\n",
"events.append(\n",
" run.run(initial_state, time_steps=1, silent=True)[-1].execute_swap(\n",
" pool_id='stableswap',\n",
" agent_id='LP',\n",
" tkn_sell='USDA' if trade_size > 0 else 'USDB',\n",
" tkn_buy='USDB' if trade_size > 0 else 'USDA',\n",
" sell_quantity=max(abs(trade_size), 0),\n",
" )\n",
")\n",
"\n",
"new_state = events[-1].copy()\n",
"\n",
"# StableSwapPoolState.execute_remove_uniform(\n",
"# state=event.pools['stableswap'],\n",
"# agent=event.agents['LP'],\n",
"# shares_removed=event.agents['LP'].holdings['stableswap']\n",
"# )\n",
"\n",
"StableSwapPoolState.execute_remove_liquidity(\n",
" state=new_state.pools['stableswap'],\n",
" agent=new_state.agents['LP'],\n",
" shares_removed=new_state.agents['LP'].holdings['stableswap'],\n",
" tkn_remove='USDA'\n",
")\n",
"\n",
"final_state = new_state.copy()\n",
"\n",
"trade_size2 = -trade_size\n",
"\n",
"final_state.execute_swap(\n",
" pool_id='stableswap',\n",
" agent_id='LP',\n",
" tkn_sell='USDA' if trade_size2 > 0 else 'USDB',\n",
" tkn_buy='USDB' if trade_size2 > 0 else 'USDA',\n",
" sell_quantity=abs(trade_size2),\n",
" )\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 1.0]\n"
]
}
],
"source": [
"final_prices = [1, final_state.pools['stableswap'].spot_price]\n",
"print(final_prices)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-07-06T20:50:06.289617582Z",
"start_time": "2023-07-06T20:50:06.289242369Z"
}
}
},
{
"cell_type": "code",
"execution_count": 7,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'USDA': 5100, 'USDB': 0}\n"
]
}
],
"source": [
"print(initial_holdings)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-07-06T20:50:27.177343274Z",
"start_time": "2023-07-06T20:50:27.171229893Z"
}
}
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'USDA': 5100.0, 'USDB': 0, 'USD': 0, 'stableswap': 0.0}\n"
]
}
],
"source": [
"print(final_state.agents['LP'].holdings)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-07-06T20:50:32.313529007Z",
"start_time": "2023-07-06T20:50:32.290166910Z"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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