banteg/fees.ipynb Secret

## fees.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from brownie import project, chain, network\n",
    "from datetime import datetime\n",
    "from google.cloud import bigquery\n",
    "from eth_utils import to_checksum_address\n",
    "from toolz import groupby\n",
    "import pandas as pd\n",
    "from datetime import timedelta\n",
    "from tqdm.notebook import tqdm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from concurrent.futures import ThreadPoolExecutor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.connect('mainnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "interface = project.load('.').interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = bigquery.Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ZERO_ADDRESS = '0x' + '0' * 40\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "vault = interface.yVault('0x5dbcF33D8c2E976c6b560249878e6F1491Bca25c')\n",
    "deploy_block = 10559448  # https://etherscan.io/tx/0x0a01c1bbb7a0769dd39bc0b5f331d65b7998732f7a6218fb82eeaf80ad0ecb33\n",
    "deploy_date = datetime.utcfromtimestamp(chain[deploy_block].timestamp).date().isoformat()\n",
    "controller = interface.ControllerV2(vault.controller())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "select * from `bigquery-public-data.crypto_ethereum.traces`\n",
      "where date(block_timestamp) >= \"2020-07-30\"\n",
      "and to_address in ('0x9e65ad11b299ca0abefc2799ddb6314ef2d91080', '0x31317f9a5e4cc1d231bdf07755c994015a96a37c')\n",
      "and substr(input, 0, 74) = \"0x72cb5d97000000000000000000000000df5e0e81dff6faf3a7e52ba697820c5e32d806a8\"\n",
      "\n",
      "12 results\n"
     ]
    }
   ],
   "source": [
    "set_controller = controller.setStrategy.encode_input(vault.token(), ZERO_ADDRESS)[:-64]\n",
    "controllers = [str(controller).lower(), '0x31317F9A5E4cC1d231bdf07755C994015A96A37c'.lower()]\n",
    "query = f'''\n",
    "select * from `bigquery-public-data.crypto_ethereum.traces`\n",
    "where date(block_timestamp) >= \"{deploy_date}\"\n",
    "and to_address in {tuple(controllers)}\n",
    "and substr(input, 0, {len(set_controller)}) = \"{set_controller}\"\n",
    "'''\n",
    "print(query)\n",
    "job = client.query(query)\n",
    "rows = list(job.result())\n",
    "print(len(rows), 'results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0x07db4b9b3951094b9e278d336adf46a036295de7',\n",
       " '0x2de055fec2b826ed4a7478ceddbeff82c1edfa70',\n",
       " '0x382185f3ea9268e65bb16f81de6b4e725134ed72',\n",
       " '0x594a198048501a304267e63b3bad0f0638da7628',\n",
       " '0x8816b2fb982281c36e6c535b9e56b7a4417e68cf',\n",
       " '0x8c6698dc64f69231e3dc509cd7ad72164d2389f7',\n",
       " '0x8fcb1c3f68ef7abe7b25457f35e88658086dc1ad',\n",
       " '0xa069e33994dcc24928d99f4bbeda83aaef00b5f3',\n",
       " '0xbe197e668d13746bb92e675dea2868ff14da0b73',\n",
       " '0xc999fb87aca383a63d804a575396f65a55aa5ac8'}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategies = {to_checksum_address(row.input[-40:]).lower() for row in rows}\n",
    "strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "select * from `bigquery-public-data.crypto_ethereum.traces`\n",
      "where date(block_timestamp) >= \"2020-07-30\"\n",
      "and to_address in ('0x07db4b9b3951094b9e278d336adf46a036295de7', '0x2de055fec2b826ed4a7478ceddbeff82c1edfa70', '0x8816b2fb982281c36e6c535b9e56b7a4417e68cf', '0xc999fb87aca383a63d804a575396f65a55aa5ac8', '0x8fcb1c3f68ef7abe7b25457f35e88658086dc1ad', '0x382185f3ea9268e65bb16f81de6b4e725134ed72', '0xa069e33994dcc24928d99f4bbeda83aaef00b5f3', '0x594a198048501a304267e63b3bad0f0638da7628', '0x8c6698dc64f69231e3dc509cd7ad72164d2389f7', '0xbe197e668d13746bb92e675dea2868ff14da0b73')\n",
      "and substr(input, 0, 10) in ('0x4641257d', '0x2e1a7d4d')\n",
      "\n",
      "4994 results\n"
     ]
    }
   ],
   "source": [
    "strat = interface.StrategyCurveYVoterProxy(controller)  # we don't call it anyway, so addr is meaningless\n",
    "harvest = strat.harvest.encode_input()\n",
    "withdraw = strat.withdraw['uint256'].encode_input(0)[:-64]\n",
    "\n",
    "query = f'''\n",
    "select * from `bigquery-public-data.crypto_ethereum.traces`\n",
    "where date(block_timestamp) >= \"{deploy_date}\"\n",
    "and to_address in {tuple(strategies)}\n",
    "and substr(input, 0, 10) in {(harvest, withdraw)}\n",
    "'''\n",
    "print(query)\n",
    "job = client.query(query)\n",
    "rows_harvest_withdraw = list(job.result())\n",
    "print(len(rows_harvest_withdraw), 'results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "withdrawn 488177924.24655455\n",
      "fees 2440889.6212327727\n"
     ]
    }
   ],
   "source": [
    "total_withdrawals = 0\n",
    "for row in rows_harvest_withdraw:\n",
    "    if row.input.startswith(withdraw):\n",
    "        amount = strat.withdraw['uint256'].decode_input(row.input)[0] / 1e18\n",
    "#         print(amount)\n",
    "        total_withdrawals += amount\n",
    "        \n",
    "print('withdrawn', total_withdrawals)\n",
    "print('fees', total_withdrawals * 0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([x for x in rows_harvest_withdraw if x.input.startswith(harvest)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "select * from `bigquery-public-data.crypto_ethereum.traces`\n",
      "where date(block_timestamp) >= \"2020-07-30\"\n",
      "and to_address in ('0x9e65ad11b299ca0abefc2799ddb6314ef2d91080', '0x31317f9a5e4cc1d231bdf07755c994015a96a37c')\n",
      "and substr(input, 0, 10) = \"0xec38a862\"\n",
      "\n",
      "2 results\n"
     ]
    }
   ],
   "source": [
    "set_rewards = controller.setRewards.encode_input(ZERO_ADDRESS)[:-64]\n",
    "query = f'''\n",
    "select * from `bigquery-public-data.crypto_ethereum.traces`\n",
    "where date(block_timestamp) >= \"{deploy_date}\"\n",
    "and to_address in {tuple(controllers)}\n",
    "and substr(input, 0, {len(set_rewards)}) = \"{set_rewards}\"\n",
    "'''\n",
    "print(query)\n",
    "job = client.query(query)\n",
    "rows_set_rewards = list(job.result())\n",
    "print(len(rows_set_rewards), 'results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0xfeb4acf3df3cdea7399794d0869ef76a6efaff52',\n",
       " '0x93a62da5a14c80f265dabc077fcee437b1a0efde',\n",
       " '0xb99a40fce04cb740eb79fc04976ca15af69aaaae']"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rewards = [controller.setRewards.decode_input(row.input)[0].lower() for row in rows_set_rewards]\n",
    "rewards.append(controller.rewards(block_identifier=10635930).lower())  # rewards from old controller\n",
    "rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# withdrawals\n",
    "# IERC20(want).safeTransfer(Controller(controller).rewards(), _fee);\n",
    "\n",
    "# harvest\n",
    "# IERC20(want).safeTransfer(Controller(controller).rewards(), _fee);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "select * from `bigquery-public-data.crypto_ethereum.token_transfers`\n",
      "where date(block_timestamp) >= \"2020-07-30\"\n",
      "and token_address = \"0xdf5e0e81dff6faf3a7e52ba697820c5e32d806a8\"\n",
      "and from_address in ('0x07db4b9b3951094b9e278d336adf46a036295de7', '0x2de055fec2b826ed4a7478ceddbeff82c1edfa70', '0x8816b2fb982281c36e6c535b9e56b7a4417e68cf', '0xc999fb87aca383a63d804a575396f65a55aa5ac8', '0x8fcb1c3f68ef7abe7b25457f35e88658086dc1ad', '0x382185f3ea9268e65bb16f81de6b4e725134ed72', '0xa069e33994dcc24928d99f4bbeda83aaef00b5f3', '0x594a198048501a304267e63b3bad0f0638da7628', '0x8c6698dc64f69231e3dc509cd7ad72164d2389f7', '0xbe197e668d13746bb92e675dea2868ff14da0b73')\n",
      "and to_address in ('0xfeb4acf3df3cdea7399794d0869ef76a6efaff52', '0x93a62da5a14c80f265dabc077fcee437b1a0efde', '0xb99a40fce04cb740eb79fc04976ca15af69aaaae')\n",
      "\n",
      "4600 results\n"
     ]
    }
   ],
   "source": [
    "query = f'''\n",
    "select * from `bigquery-public-data.crypto_ethereum.token_transfers`\n",
    "where date(block_timestamp) >= \"{deploy_date}\"\n",
    "and token_address = \"{vault.token().lower()}\"\n",
    "and from_address in {tuple(strategies)}\n",
    "and to_address in {tuple(rewards)}\n",
    "'''\n",
    "print(query)\n",
    "job = client.query(query)\n",
    "rows_fees = list(job.result())\n",
    "print(len(rows_fees), 'results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1300 harvests\n",
      "3693 withdraws\n"
     ]
    }
   ],
   "source": [
    "temp = groupby(lambda row: row.input.startswith(withdraw), rows_harvest_withdraw)\n",
    "harvests = set(row.transaction_hash for row in temp[False])\n",
    "withdraws = set(row.transaction_hash for row in temp[True])\n",
    "print(len(harvests), 'harvests')\n",
    "print(len(withdraws), 'withdraws')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1299 harvests\n",
      "3299 withdraws\n"
     ]
    }
   ],
   "source": [
    "print(len([row for row in rows_fees if row.transaction_hash in harvests]), 'harvests')\n",
    "print(len([row for row in rows_fees if row.transaction_hash in withdraws]), 'withdraws')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "4e84df403a3e472c937c1fcc485dd571",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=4600.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "for row in tqdm(rows_fees):\n",
    "    action = 'harvest' if row.transaction_hash in harvests else 'withdraw'\n",
    "    data.append({\n",
    "        'block': row.block_number,\n",
    "        'timestamp': row.block_timestamp,\n",
    "        'action': action,\n",
    "        'fee': int(row.value) / 1e18,\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a44b97ae49f6445c900be5b265d474fd",
       "version_major": 2,
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       "HBox(children=(FloatProgress(value=0.0, max=4544.0), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "blocks = list({x['block'] for x in data})\n",
    "func = lambda block: vault.balance(block_identifier=block) / 1e18\n",
    "aums = {block: aum for block, aum in tqdm(zip(blocks, ThreadPoolExecutor(10).map(func, blocks)), total=len(blocks))}\n",
    "for row in data:\n",
    "    row['aum'] = aums[row['block']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data).sort_values('block').set_index('block')\n",
    "df.to_csv('yusd_fees.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.804997508249793\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fee</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>harvest</th>\n",
       "      <td>4.668221e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>withdraw</th>\n",
       "      <td>2.243079e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   fee\n",
       "action                \n",
       "harvest   4.668221e+05\n",
       "withdraw  2.243079e+06"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_fees = df.groupby('action').sum()\n",
    "print(total_fees.fee.withdraw / total_fees.fee.harvest)\n",
    "total_fees[['fee']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "harvest fee: 466822.08094960777 17.226536248789873\n",
      "withdraw fee: 2243078.9357588505 82.77346375121014\n",
      "target fee: 2709901.016708458\n",
      "result fee: 2566525.728972749 94.71% of target\n",
      "perf fee: 1867288.323798431 72.76% of result\n",
      "mgmt fee: 699237.4051743175 27.24% of result\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>action</th>\n",
       "      <th>fee</th>\n",
       "      <th>aum</th>\n",
       "      <th>timediff</th>\n",
       "      <th>yeardiff</th>\n",
       "      <th>perf</th>\n",
       "      <th>mgmt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>block</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10559528</th>\n",
       "      <td>2020-07-30 07:06:45+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>2.790010</td>\n",
       "      <td>1.003882e+05</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.160042</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10560197</th>\n",
       "      <td>2020-07-30 09:40:09+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>155.514046</td>\n",
       "      <td>3.070419e+05</td>\n",
       "      <td>02:33:24</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>622.056184</td>\n",
       "      <td>1.791019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10560731</th>\n",
       "      <td>2020-07-30 11:43:53+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>205.889770</td>\n",
       "      <td>3.072477e+05</td>\n",
       "      <td>02:03:44</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>823.559078</td>\n",
       "      <td>1.445615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10561225</th>\n",
       "      <td>2020-07-30 13:34:16+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>231.214960</td>\n",
       "      <td>4.467248e+05</td>\n",
       "      <td>01:50:23</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>924.859839</td>\n",
       "      <td>1.875085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10562196</th>\n",
       "      <td>2020-07-30 17:19:58+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>495.551263</td>\n",
       "      <td>4.472204e+05</td>\n",
       "      <td>03:45:42</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>1982.205053</td>\n",
       "      <td>3.838225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11163804</th>\n",
       "      <td>2020-10-31 09:29:02+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>253.783663</td>\n",
       "      <td>6.821779e+07</td>\n",
       "      <td>05:45:56</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>1015.134652</td>\n",
       "      <td>897.361350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11165662</th>\n",
       "      <td>2020-10-31 16:04:03+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>300.176434</td>\n",
       "      <td>6.934888e+07</td>\n",
       "      <td>06:35:01</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>1200.705736</td>\n",
       "      <td>1041.674791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11169970</th>\n",
       "      <td>2020-11-01 08:08:35+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>659.585454</td>\n",
       "      <td>6.866700e+07</td>\n",
       "      <td>16:04:32</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>2638.341816</td>\n",
       "      <td>2518.503659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11171718</th>\n",
       "      <td>2020-11-01 14:25:59+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>222.235992</td>\n",
       "      <td>6.890185e+07</td>\n",
       "      <td>06:17:24</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>888.943966</td>\n",
       "      <td>988.803689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11173643</th>\n",
       "      <td>2020-11-01 21:40:19+00:00</td>\n",
       "      <td>harvest</td>\n",
       "      <td>252.451896</td>\n",
       "      <td>6.884899e+07</td>\n",
       "      <td>07:14:20</td>\n",
       "      <td>0.000826</td>\n",
       "      <td>1009.807584</td>\n",
       "      <td>1137.098232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1299 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         timestamp   action         fee           aum  \\\n",
       "block                                                                   \n",
       "10559528 2020-07-30 07:06:45+00:00  harvest    2.790010  1.003882e+05   \n",
       "10560197 2020-07-30 09:40:09+00:00  harvest  155.514046  3.070419e+05   \n",
       "10560731 2020-07-30 11:43:53+00:00  harvest  205.889770  3.072477e+05   \n",
       "10561225 2020-07-30 13:34:16+00:00  harvest  231.214960  4.467248e+05   \n",
       "10562196 2020-07-30 17:19:58+00:00  harvest  495.551263  4.472204e+05   \n",
       "...                            ...      ...         ...           ...   \n",
       "11163804 2020-10-31 09:29:02+00:00  harvest  253.783663  6.821779e+07   \n",
       "11165662 2020-10-31 16:04:03+00:00  harvest  300.176434  6.934888e+07   \n",
       "11169970 2020-11-01 08:08:35+00:00  harvest  659.585454  6.866700e+07   \n",
       "11171718 2020-11-01 14:25:59+00:00  harvest  222.235992  6.890185e+07   \n",
       "11173643 2020-11-01 21:40:19+00:00  harvest  252.451896  6.884899e+07   \n",
       "\n",
       "         timediff  yeardiff         perf         mgmt  \n",
       "block                                                  \n",
       "10559528      NaT       NaN    11.160042          NaN  \n",
       "10560197 02:33:24  0.000292   622.056184     1.791019  \n",
       "10560731 02:03:44  0.000235   823.559078     1.445615  \n",
       "10561225 01:50:23  0.000210   924.859839     1.875085  \n",
       "10562196 03:45:42  0.000429  1982.205053     3.838225  \n",
       "...           ...       ...          ...          ...  \n",
       "11163804 05:45:56  0.000658  1015.134652   897.361350  \n",
       "11165662 06:35:01  0.000751  1200.705736  1041.674791  \n",
       "11169970 16:04:32  0.001834  2638.341816  2518.503659  \n",
       "11171718 06:17:24  0.000718   888.943966   988.803689  \n",
       "11173643 07:14:20  0.000826  1009.807584  1137.098232  \n",
       "\n",
       "[1299 rows x 8 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data).sort_values('block').set_index('block')\n",
    "target = df.fee.sum()\n",
    "print('harvest fee:', df[df.action == 'harvest'].fee.sum(), df[df.action == 'harvest'].fee.sum() / target * 100)\n",
    "print('withdraw fee:', df[df.action == 'withdraw'].fee.sum(), df[df.action == 'withdraw'].fee.sum() / target * 100)\n",
    "print('target fee:', target)\n",
    "df = df[df.action == 'harvest']\n",
    "df['timediff'] = df.timestamp.diff()\n",
    "df['yeardiff'] = df.timediff / timedelta(days=365.25)\n",
    "new_performance = 0.20\n",
    "new_management = 0.02\n",
    "df['perf'] = df['fee'] / .05 * new_performance\n",
    "df['mgmt'] = df.aum * df.yeardiff * new_management\n",
    "result = df.perf.sum() + df.mgmt.sum()\n",
    "print('result fee:', result, f'{result / target:.2%} of target')\n",
    "print('perf fee:', df.perf.sum(), f'{df.perf.sum() / result:.2%} of result')\n",
    "print('mgmt fee:', df.mgmt.sum(), f'{df.mgmt.sum() / result:.2%} of result')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 426
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(data).sort_values('block').set_index('timestamp')\n",
    "df2[df2.action == 'harvest'].fee.cumsum().plot(label='harvest')\n",
    "ax = df2[df2.action == 'withdraw'].fee.cumsum().plot(label='withdraw')\n",
    "plt.rcParams['font.sans-serif'] = 'Menlo'\n",
    "plt.legend()\n",
    "plt.title('old model')\n",
    "plt.xlabel('')\n",
    "total_harvest = df2[df2.action == 'harvest'].fee.sum()\n",
    "total_withdraw = df2[df2.action == 'withdraw'].fee.sum()\n",
    "ax.annotate(f'{total_harvest:,.0f}', xy=(df2.index[-1], total_harvest), textcoords='offset points', xytext=(20, 0))\n",
    "ax.annotate(f'{total_withdraw:,.0f}', xy=(df2.index[-1], total_withdraw), textcoords='offset points', xytext=(20, 0))\n",
    "plt.tight_layout()\n",
    "plt.savefig('fees-old.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 426
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = df.set_index('timestamp')\n",
    "df3.perf.cumsum().plot()\n",
    "ax = df3.mgmt.cumsum().plot()\n",
    "total_perf = df3.perf.sum()\n",
    "total_mgmt = df3.mgmt.sum()\n",
    "ax.annotate(f'{total_perf:,.0f}', xy=(df3.index[-1], total_perf), textcoords='offset points', xytext=(20, 0))\n",
    "ax.annotate(f'{total_mgmt:,.0f}', xy=(df3.index[-1], total_mgmt), textcoords='offset points', xytext=(20, 0))\n",
    "\n",
    "plt.legend()\n",
    "plt.title('suggested model')\n",
    "plt.xlabel('')\n",
    "plt.tight_layout()\n",
    "plt.savefig('fees-new.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 422
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.aum.plot()\n",
    "plt.legend()\n",
    "plt.xlabel('')\n",
    "plt.title('yusd')\n",
    "plt.tight_layout()\n",
    "plt.savefig('fees-aum.png', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}