Learning Python with the Ohmies - Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b0a5f00",
   "metadata": {},
   "source": [
    "# Learning Python with the Ohmies Ω"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cfd24c",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to be a partially guided walkthrough of using python and some Messari tooling to see how we can improve our data gathering abilities!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ab4bd3",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26bf073",
   "metadata": {},
   "source": [
    "First lets do some global imports. If you don't have any of these repositories you should try running this in your terminal `pip install pandas` and `pip install copy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7554781c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import copy\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258e570c",
   "metadata": {},
   "source": [
    "Now lets do some local imports from the messari library. To install these repositories you need to point pip to the github repository we host the code at. Try running this in your terminal `pip install git+https://github.com/messari/messari-python-api-private.git` You may have to restart your jupyter notebook to get this change to take effect, look under Kernel->Restart in the menu bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0cfe99ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from messari.messari import Messari\n",
    "from messari.blockexplorers import Etherscan\n",
    "from messari.defillama import DeFiLlama"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459917f0",
   "metadata": {},
   "source": [
    "And lets set up the clients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e687814",
   "metadata": {},
   "outputs": [],
   "source": [
    "MESSARI_API_KEY = 'api_key_here'\n",
    "MESSARI_API_KEY = '70808c97-9ea3-4ea1-a297-39a8f9037457'\n",
    "messari = Messari(MESSARI_API_KEY)\n",
    "\n",
    "ETHERSCAN_API_KEY = 'api_key_here'\n",
    "ETHERSCAN_API_KEY = 'DWC3QGAEHNFQQM55Z1AYTXUTZ1GPBK51JQ'\n",
    "etherscan = Etherscan(ETHERSCAN_API_KEY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046ab3ee",
   "metadata": {},
   "source": [
    "SPECIAL CASE: Some people have run into some issues installing the messari library. This is the most universal way to brute force the install. In a new cell in this jupyter notebook run this as code."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5dc6cf8",
   "metadata": {},
   "source": [
    "`import sys\n",
    "!{sys.executable} -m pip uninstall messari -y`\n",
    "\n",
    "Then run this:\n",
    "\n",
    "`import sys\n",
    "!{sys.executable} -m pip install git+https://github.com/messari/messari-python-api-private.git`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a02fe8e",
   "metadata": {},
   "source": [
    "TLDR this runs install commands directly from this notebook and installs what you need into this specific enviornment. If you open a new jupyter notebook and want to use the messari library, you'll likely have to do this again. This only needs to be done once per notebook to set up the library, once you've run this code in a notebook you won't have to run it again. This is not how I recomend installing the messari library but in the interest of time I'm confident this will fix most of your problems. If you want to try and wrangle through these issues on your specific computer and install the messari library the right way, hit me up and we can do a screen share sesh at another time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c6c75f",
   "metadata": {},
   "source": [
    "## Initial Research"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f621f87",
   "metadata": {},
   "source": [
    "Before we can get to the fun part and start coding, we need to do some equally run researching! Go out and see if you can find the two ethereum addresses for the Olympus DAO treasuries. Once you have them, assign them below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc97ced7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohm_treasury_v1 = 'address_here'\n",
    "ohm_treasury_v2 = 'address_here'\n",
    "\n",
    "ohm_treasury_v1 = '0x886CE997aa9ee4F8c2282E182aB72A705762399D'\n",
    "ohm_treasury_v2 = '0x31F8Cc382c9898b273eff4e0b7626a6987C846E8'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b182e94b-772a-4451-8bbd-5c7c604cae44",
   "metadata": {},
   "source": [
    "## Extracting data from Ethereum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435ef42d-c649-408c-89eb-f94077382528",
   "metadata": {},
   "source": [
    "Now that you found the Ethereum addresses of the Olympus DAO treasury, we want to get a better understanding of the token transfers that have occured in and out of the treasury. \n",
    "\n",
    "Go into the [blockexplorer documentation](https://github.com/messari/messari-python-api-private/blob/master/examples/notebooks/Etherscan%20API%20Tutorial.ipynb) in the Messari Python library to find the fuction that returns this information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d32536f1",
   "metadata": {},
   "source": [
    "Forgive the hardcoding here but the pagination of Etherscan was throwing me off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "54e90a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code here\n",
    "\n",
    "page1 = etherscan.get_account_token_transfers(ohm_treasury_v2)[ohm_treasury_v2]\n",
    "last_block = int(page1.iloc[-1]['blockNumber']) +1\n",
    "\n",
    "page2 = etherscan.get_account_token_transfers(ohm_treasury_v2, start_block=last_block)[ohm_treasury_v2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38bcb472",
   "metadata": {},
   "outputs": [
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       "12550997     1622582526  0xc2ce59ad653a0984a297331aa97193cbd474b84d746d...   \n",
       "...                 ...                                                ...   \n",
       "14304301     1646179718  0xdf1dfa78b8abca2b4ec8af0c400f8bbdcfe82f17b195...   \n",
       "14304301     1646179718  0xdf1dfa78b8abca2b4ec8af0c400f8bbdcfe82f17b195...   \n",
       "14307166     1646218715  0x18a7577339ea4b31c91d837fd3e7dac46aee6a06b9b0...   \n",
       "14307166     1646218715  0x18a7577339ea4b31c91d837fd3e7dac46aee6a06b9b0...   \n",
       "14307756     1646226118  0x8b9088d9502d29c0386720a67d3912f16fef283c8a2a...   \n",
       "\n",
       "            nonce                                          blockHash  \\\n",
       "blockNumber                                                            \n",
       "12530585      180  0xe95c5b64dfff5a71da13d0cb65482b5c5180b91a7595...   \n",
       "12532291       82  0x82268c3bfa5717bab0d6dae3d20d9a1b5d6044d86d8a...   \n",
       "12532321       83  0x444f3c52ecd962310a2e2688191104300e79d12cc476...   \n",
       "12550991      127  0xf1ca1628e8d4f5df27abfcf8b796f7eb7b4f000a1548...   \n",
       "12550997      128  0xcf6991463b8b36c8d172c5fac7620416b5aff7411ae7...   \n",
       "...           ...                                                ...   \n",
       "14304301      134  0x0355ab12dd4a4f7dcebb20f76ad8ea67adc89d33c46f...   \n",
       "14304301      134  0x0355ab12dd4a4f7dcebb20f76ad8ea67adc89d33c46f...   \n",
       "14307166     1484  0x6f564d64e7c1a54bd7fc45b5e1c256f545261ca2df2d...   \n",
       "14307166     1484  0x6f564d64e7c1a54bd7fc45b5e1c256f545261ca2df2d...   \n",
       "14307756      722  0xbcad9bd2063bf6e048ff28de384686b79a0dd9b1d801...   \n",
       "\n",
       "                                                   from  \\\n",
       "blockNumber                                               \n",
       "12530585     0x763a641383007870ae96067818f1649e5586f6de   \n",
       "12532291     0x245cc372c84b3645bf0ffe6538620b04a217988b   \n",
       "12532321     0x245cc372c84b3645bf0ffe6538620b04a217988b   \n",
       "12550991     0xd03056323b7a63e2095ae97fa1ad92e4820ff045   \n",
       "12550997     0xd03056323b7a63e2095ae97fa1ad92e4820ff045   \n",
       "...                                                 ...   \n",
       "14304301     0x737119790f6e0f85451ab200759f8efa144dcd43   \n",
       "14304301     0x737119790f6e0f85451ab200759f8efa144dcd43   \n",
       "14307166     0xfd7bda47cbeeed93c897273585f666f8d1cc8d45   \n",
       "14307166     0xfd7bda47cbeeed93c897273585f666f8d1cc8d45   \n",
       "14307756     0x34eb308c932fe3bbda8716a1774ef01d302759d9   \n",
       "\n",
       "                                        contractAddress  \\\n",
       "blockNumber                                               \n",
       "12530585     0x6b175474e89094c44da98b954eedeac495271d0f   \n",
       "12532291     0x6b175474e89094c44da98b954eedeac495271d0f   \n",
       "12532321     0x34d7d7aaf50ad4944b70b320acb24c95fa2def7c   \n",
       "12550991     0x6b175474e89094c44da98b954eedeac495271d0f   \n",
       "12550997     0x6b175474e89094c44da98b954eedeac495271d0f   \n",
       "...                                                 ...   \n",
       "14304301     0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2   \n",
       "14304301     0xc0d4ceb216b3ba9c3701b291766fdcba977cec3a   \n",
       "14307166     0x3432b6a60d23ca0dfca7761b7ab56459d9c964d0   \n",
       "14307166     0xc0d4ceb216b3ba9c3701b291766fdcba977cec3a   \n",
       "14307756     0x41d5d79431a913c4ae7d69a668ecdfe5ff9dfb68   \n",
       "\n",
       "                                                     to  \\\n",
       "blockNumber                                               \n",
       "12530585     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "12532291     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "12532321     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "12550991     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "12550997     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "...                                                 ...   \n",
       "14304301     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "14304301     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "14307166     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "14307166     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "14307756     0x31f8cc382c9898b273eff4e0b7626a6987c846e8   \n",
       "\n",
       "                                 value           tokenName tokenSymbol  \\\n",
       "blockNumber                                                              \n",
       "12530585         100902874505316627468      Dai Stablecoin         DAI   \n",
       "12532291     3370301121609949806617922      Dai Stablecoin         DAI   \n",
       "12532321          11750000000000000000  SushiSwap LP Token         SLP   \n",
       "12550991        4500000000000000000000      Dai Stablecoin         DAI   \n",
       "12550997        4500000000000000000000      Dai Stablecoin         DAI   \n",
       "...                                ...                 ...         ...   \n",
       "14304301            185000000000000000       Wrapped Ether        WETH   \n",
       "14304301                    2427293046              BTRFLY      BTRFLY   \n",
       "14307166          16219582006215130916          Frax Share         FXS   \n",
       "14307166                    1620769652              BTRFLY      BTRFLY   \n",
       "14307756           1590940042591064835         Inverse DAO         INV   \n",
       "\n",
       "            tokenDecimal transactionIndex      gas      gasPrice gasUsed  \\\n",
       "blockNumber                                                                \n",
       "12530585              18              118    52000   35000000000   36842   \n",
       "12532291              18                9   104311  150000000000   53880   \n",
       "12532321              18              104   121344  140000000000   85646   \n",
       "12550991              18               39   343458   36800000000  303658   \n",
       "12550997              18              196   218677   36800000000  178877   \n",
       "...                  ...              ...      ...           ...     ...   \n",
       "14304301              18               95   293511   72338077495  264011   \n",
       "14304301               9               95   293511   72338077495  264011   \n",
       "14307166              18               35   420836   36650684700  391336   \n",
       "14307166               9               35   420836   36650684700  391336   \n",
       "14307756              18               23  3000000   41773227675  942226   \n",
       "\n",
       "            cumulativeGasUsed       input confirmations  \n",
       "blockNumber                                              \n",
       "12530585             10284871  deprecated       1778971  \n",
       "12532291              1106645  deprecated       1777265  \n",
       "12532321              2947481  deprecated       1777235  \n",
       "12550991              2854017  deprecated       1758565  \n",
       "12550997             12621888  deprecated       1758559  \n",
       "...                       ...         ...           ...  \n",
       "14304301              7449431  deprecated          5255  \n",
       "14304301              7449431  deprecated          5255  \n",
       "14307166              2995793  deprecated          2390  \n",
       "14307166              2995793  deprecated          2390  \n",
       "14307756              3715639  deprecated          1800  \n",
       "\n",
       "[11925 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohm_tt = pd.concat([page1, page2])\n",
    "ohm_tt.set_index('blockNumber', inplace=True)\n",
    "ohm_tt.sort_index(inplace=True)\n",
    "ohm_tt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "600d2ed4",
   "metadata": {},
   "source": [
    "## Extract list of token names from OHM token transfers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4775e863",
   "metadata": {},
   "source": [
    "Using the DataFrame with all of the Olympus treasury token transfers, can you print a list of the tokens held by the Olympus treasury?\n",
    "\n",
    "The list should not have any repeats and you should try to do this without using any for loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5619cb60",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ANGLE', 'SYN', 'FRAX', 'ALCX', 'FOX', 'CAVE', 'USF', 'SUSHI', 'stkAAVE', 'INV', 'CNV', 'DAI', 'MTA', 'SPELL', 'DIVINE', 'POOL', 'THOR', 'xSDT', 'FLOAT', 'ROOK', 'CRV', 'BPT', 'FDT', 'FXS', 'GRO', 'BOND', 'RUNE', 'LUSD', 'PENDLE', 'ICHI', 'BTRFLY', 'UNI-V2', 'GEL', 'XRUNE', 'xSUSHI', 'SLP', 'CVX', 'KP3R', 'BANK', 'IQ', 'QUARTZ', 'WETH', 'D2D', 'PREMIA', 'BOTTO', 'DISCO']\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "\n",
    "token_names = list(set(ohm_tt['tokenSymbol']))\n",
    "print(token_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6023b65",
   "metadata": {},
   "source": [
    "# Getting running totals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f59f35",
   "metadata": {},
   "source": [
    "In this step you are going to have to bear with me and trust the process. Now that we have a list of all tokens transfers to the ohm treasury we need to turn this into a running list of the treasury balance over time. This list should look a little something like this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682a25e8",
   "metadata": {},
   "source": [
    "`list = [{timeStamp: 10, sushi: 0, dai: 0}, {timeStamp: 11, sushi: 24, dai: 0}, {timeStamp: 12, sushi: 24, dai: 4}]`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbc9d0c",
   "metadata": {},
   "source": [
    "The above list is just the concept, the real list should include all the tokens, and the timestamps will not be sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2731696f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code here\n",
    "\n",
    "token_dict = {}\n",
    "for token_name in token_names:\n",
    "    token_dict[token_name] = 0\n",
    "\n",
    "totals_list = []\n",
    "for idx, row in ohm_tt.iterrows():\n",
    "    symbol = row['tokenSymbol']\n",
    "    decimals = int(row['tokenDecimal'])\n",
    "    amount = int(row['value']) / (10 ** decimals)\n",
    "    \n",
    "    # Big brain check for withdrawals\n",
    "    if row['from'] == ohm_treasury_v2.lower():\n",
    "        amount = amount * -1\n",
    "        \n",
    "    token_dict['blockNumber'] = idx\n",
    "    token_dict['timeStamp'] = row['timeStamp']\n",
    "    token_dict[symbol] += amount\n",
    "    dict_copy = copy.deepcopy(token_dict)\n",
    "    totals_list.append(dict_copy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d33254",
   "metadata": {},
   "source": [
    "Now that we have our list of dictionaries, we can easily turn it into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c14affb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANGLE</th>\n",
       "      <th>SYN</th>\n",
       "      <th>FRAX</th>\n",
       "      <th>ALCX</th>\n",
       "      <th>FOX</th>\n",
       "      <th>CAVE</th>\n",
       "      <th>USF</th>\n",
       "      <th>SUSHI</th>\n",
       "      <th>stkAAVE</th>\n",
       "      <th>INV</th>\n",
       "      <th>...</th>\n",
       "      <th>KP3R</th>\n",
       "      <th>BANK</th>\n",
       "      <th>IQ</th>\n",
       "      <th>QUARTZ</th>\n",
       "      <th>WETH</th>\n",
       "      <th>D2D</th>\n",
       "      <th>PREMIA</th>\n",
       "      <th>BOTTO</th>\n",
       "      <th>DISCO</th>\n",
       "      <th>blockNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2021-06-02</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>2021-06-03</th>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-26</th>\n",
       "      <td>14624.968985</td>\n",
       "      <td>30917.240463</td>\n",
       "      <td>3.725290e-09</td>\n",
       "      <td>361.537452</td>\n",
       "      <td>178354.717543</td>\n",
       "      <td>1.567750e+07</td>\n",
       "      <td>10917.045801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.473244</td>\n",
       "      <td>83.026140</td>\n",
       "      <td>...</td>\n",
       "      <td>5.684342e-14</td>\n",
       "      <td>312919.568939</td>\n",
       "      <td>3.862180e+06</td>\n",
       "      <td>1883.016288</td>\n",
       "      <td>15.877781</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2495.365151</td>\n",
       "      <td>10404.248204</td>\n",
       "      <td>1.978840e+13</td>\n",
       "      <td>14283729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-27</th>\n",
       "      <td>14624.968985</td>\n",
       "      <td>30917.240463</td>\n",
       "      <td>3.725290e-09</td>\n",
       "      <td>361.537452</td>\n",
       "      <td>178354.717543</td>\n",
       "      <td>1.567750e+07</td>\n",
       "      <td>10917.045801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.473244</td>\n",
       "      <td>84.616671</td>\n",
       "      <td>...</td>\n",
       "      <td>5.684342e-14</td>\n",
       "      <td>312919.568939</td>\n",
       "      <td>3.862180e+06</td>\n",
       "      <td>1883.016288</td>\n",
       "      <td>16.054514</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2495.365151</td>\n",
       "      <td>10404.248204</td>\n",
       "      <td>1.978840e+13</td>\n",
       "      <td>14287674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>14624.968985</td>\n",
       "      <td>30917.240463</td>\n",
       "      <td>3.725290e-09</td>\n",
       "      <td>361.537452</td>\n",
       "      <td>178354.717543</td>\n",
       "      <td>1.567750e+07</td>\n",
       "      <td>10917.045801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.473244</td>\n",
       "      <td>86.207411</td>\n",
       "      <td>...</td>\n",
       "      <td>5.684342e-14</td>\n",
       "      <td>312919.568939</td>\n",
       "      <td>3.862180e+06</td>\n",
       "      <td>1883.016288</td>\n",
       "      <td>16.342141</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2495.365151</td>\n",
       "      <td>10404.248204</td>\n",
       "      <td>1.978840e+13</td>\n",
       "      <td>14297724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>14624.968985</td>\n",
       "      <td>30917.240463</td>\n",
       "      <td>3.725290e-09</td>\n",
       "      <td>361.537452</td>\n",
       "      <td>178354.717543</td>\n",
       "      <td>1.567750e+07</td>\n",
       "      <td>10917.045801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.473244</td>\n",
       "      <td>87.797935</td>\n",
       "      <td>...</td>\n",
       "      <td>5.684342e-14</td>\n",
       "      <td>312919.568939</td>\n",
       "      <td>3.862180e+06</td>\n",
       "      <td>1883.016288</td>\n",
       "      <td>16.342141</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2495.365151</td>\n",
       "      <td>10404.248204</td>\n",
       "      <td>1.978840e+13</td>\n",
       "      <td>14300415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-02</th>\n",
       "      <td>14624.968985</td>\n",
       "      <td>30917.240463</td>\n",
       "      <td>3.725290e-09</td>\n",
       "      <td>361.537452</td>\n",
       "      <td>178354.717543</td>\n",
       "      <td>1.567750e+07</td>\n",
       "      <td>10917.045801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.473244</td>\n",
       "      <td>89.388875</td>\n",
       "      <td>...</td>\n",
       "      <td>5.684342e-14</td>\n",
       "      <td>312919.568939</td>\n",
       "      <td>3.862180e+06</td>\n",
       "      <td>1883.016288</td>\n",
       "      <td>16.527141</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2495.365151</td>\n",
       "      <td>10404.248204</td>\n",
       "      <td>1.978840e+13</td>\n",
       "      <td>14307756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>275 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ANGLE           SYN          FRAX        ALCX  \\\n",
       "2021-05-29      0.000000      0.000000  0.000000e+00    0.000000   \n",
       "2021-06-01      0.000000      0.000000  0.000000e+00    0.000000   \n",
       "2021-06-02      0.000000      0.000000  0.000000e+00    0.000000   \n",
       "2021-06-03      0.000000      0.000000  0.000000e+00    0.000000   \n",
       "2021-06-04      0.000000      0.000000  0.000000e+00    0.000000   \n",
       "...                  ...           ...           ...         ...   \n",
       "2022-02-26  14624.968985  30917.240463  3.725290e-09  361.537452   \n",
       "2022-02-27  14624.968985  30917.240463  3.725290e-09  361.537452   \n",
       "2022-02-28  14624.968985  30917.240463  3.725290e-09  361.537452   \n",
       "2022-03-01  14624.968985  30917.240463  3.725290e-09  361.537452   \n",
       "2022-03-02  14624.968985  30917.240463  3.725290e-09  361.537452   \n",
       "\n",
       "                      FOX          CAVE           USF  SUSHI    stkAAVE  \\\n",
       "2021-05-29       0.000000  0.000000e+00      0.000000    0.0   0.000000   \n",
       "2021-06-01       0.000000  0.000000e+00      0.000000    0.0   0.000000   \n",
       "2021-06-02       0.000000  0.000000e+00      0.000000    0.0   0.000000   \n",
       "2021-06-03       0.000000  0.000000e+00      0.000000    0.0   0.000000   \n",
       "2021-06-04       0.000000  0.000000e+00      0.000000    0.0   0.000000   \n",
       "...                   ...           ...           ...    ...        ...   \n",
       "2022-02-26  178354.717543  1.567750e+07  10917.045801    0.0  37.473244   \n",
       "2022-02-27  178354.717543  1.567750e+07  10917.045801    0.0  37.473244   \n",
       "2022-02-28  178354.717543  1.567750e+07  10917.045801    0.0  37.473244   \n",
       "2022-03-01  178354.717543  1.567750e+07  10917.045801    0.0  37.473244   \n",
       "2022-03-02  178354.717543  1.567750e+07  10917.045801    0.0  37.473244   \n",
       "\n",
       "                  INV  ...          KP3R           BANK            IQ  \\\n",
       "2021-05-29   0.000000  ...  0.000000e+00       0.000000  0.000000e+00   \n",
       "2021-06-01   0.000000  ...  0.000000e+00       0.000000  0.000000e+00   \n",
       "2021-06-02   0.000000  ...  0.000000e+00       0.000000  0.000000e+00   \n",
       "2021-06-03   0.000000  ...  0.000000e+00       0.000000  0.000000e+00   \n",
       "2021-06-04   0.000000  ...  0.000000e+00       0.000000  0.000000e+00   \n",
       "...               ...  ...           ...            ...           ...   \n",
       "2022-02-26  83.026140  ...  5.684342e-14  312919.568939  3.862180e+06   \n",
       "2022-02-27  84.616671  ...  5.684342e-14  312919.568939  3.862180e+06   \n",
       "2022-02-28  86.207411  ...  5.684342e-14  312919.568939  3.862180e+06   \n",
       "2022-03-01  87.797935  ...  5.684342e-14  312919.568939  3.862180e+06   \n",
       "2022-03-02  89.388875  ...  5.684342e-14  312919.568939  3.862180e+06   \n",
       "\n",
       "                 QUARTZ       WETH       D2D       PREMIA         BOTTO  \\\n",
       "2021-05-29     0.000000   0.000000       0.0     0.000000      0.000000   \n",
       "2021-06-01     0.000000   0.000000       0.0     0.000000      0.000000   \n",
       "2021-06-02     0.000000   0.000000       0.0     0.000000      0.000000   \n",
       "2021-06-03     0.000000   0.000000       0.0     0.000000      0.000000   \n",
       "2021-06-04     0.000000   0.000000       0.0     0.000000      0.000000   \n",
       "...                 ...        ...       ...          ...           ...   \n",
       "2022-02-26  1883.016288  15.877781  500000.0  2495.365151  10404.248204   \n",
       "2022-02-27  1883.016288  16.054514  500000.0  2495.365151  10404.248204   \n",
       "2022-02-28  1883.016288  16.342141  500000.0  2495.365151  10404.248204   \n",
       "2022-03-01  1883.016288  16.342141  500000.0  2495.365151  10404.248204   \n",
       "2022-03-02  1883.016288  16.527141  500000.0  2495.365151  10404.248204   \n",
       "\n",
       "                   DISCO  blockNumber  \n",
       "2021-05-29  0.000000e+00     12532321  \n",
       "2021-06-01  0.000000e+00     12551691  \n",
       "2021-06-02  0.000000e+00     12558162  \n",
       "2021-06-03  0.000000e+00     12564447  \n",
       "2021-06-04  0.000000e+00     12570102  \n",
       "...                  ...          ...  \n",
       "2022-02-26  1.978840e+13     14283729  \n",
       "2022-02-27  1.978840e+13     14287674  \n",
       "2022-02-28  1.978840e+13     14297724  \n",
       "2022-03-01  1.978840e+13     14300415  \n",
       "2022-03-02  1.978840e+13     14307756  \n",
       "\n",
       "[275 rows x 47 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here\n",
    "\n",
    "# Create DataFrame with list\n",
    "ohm_treasury = pd.DataFrame(totals_list)\n",
    "\n",
    "# Set the index to 'timeStamp'\n",
    "ohm_treasury.set_index('timeStamp', inplace=True)\n",
    "\n",
    "# Convert the index to a datetime\n",
    "ohm_treasury.index = pd.to_datetime(ohm_treasury.index, unit='s')\n",
    "\n",
    "# Replace the index with just the date of the index\n",
    "ohm_treasury.index = ohm_treasury.index.date\n",
    "\n",
    "# Filter the DataFrame to just have the last entry of each date\n",
    "ohm_treasury = ohm_treasury[~ohm_treasury.index.duplicated(keep='last')]\n",
    "ohm_treasury"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ebb7f7-fb04-4535-9f91-a833d486fb5c",
   "metadata": {},
   "source": [
    "## Pull Price data from Messari's API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc0740e9",
   "metadata": {},
   "source": [
    "Now we need to get the most recent price for each asset in the OHM treasury. However you chose to do this, the output should look like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9919ded1",
   "metadata": {},
   "source": [
    "`{DAI: 1, SUSHI: 3.4, FXS: 23.2, ......}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5579a045",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing timeseries data for FOX\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOX price not supported by messari\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing timeseries data for CAVE\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CAVE price not supported by messari\n",
      "stkAAVE price not supported by messari\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing timeseries data for INV\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INV price not supported by messari\n",
      "CNV price not supported by messari\n",
      "xSDT price not supported by messari\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing timeseries data for BPT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BPT price not supported by messari\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing timeseries data for RUNE\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNE price not supported by messari\n",
      "UNI-V2 price not supported by messari\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing timeseries data for BANK\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BANK price not supported by messari\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Missing timeseries data for QUARTZ\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QUARTZ price not supported by messari\n",
      "DISCO price not supported by messari\n"
     ]
    }
   ],
   "source": [
    "# Write your code here to pull the most recent price for the list of assets\n",
    "# you created in the previous step\n",
    "messari_supported = []\n",
    "for token_name in token_names:\n",
    "    time.sleep(1)\n",
    "    try:\n",
    "        messari.get_metric_timeseries(token_name, 'price')\n",
    "        messari_supported.append(token_name)\n",
    "    except:\n",
    "        print(f'{token_name} price not supported by messari')\n",
    "        \n",
    "# Get prices from the messari api, turn into dictionary\n",
    "prices = messari.get_metric_timeseries(messari_supported, 'price')\n",
    "prices = prices.xs('close', axis=1, level=1)\n",
    "prices_dict = prices.iloc[-1].to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37effa7b",
   "metadata": {},
   "source": [
    "Now, can you use the prices in the dictionary you made to create a new dataframe where the tokens are scaled by the most recent price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95698041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANGLE</th>\n",
       "      <th>SYN</th>\n",
       "      <th>FRAX</th>\n",
       "      <th>ALCX</th>\n",
       "      <th>USF</th>\n",
       "      <th>SUSHI</th>\n",
       "      <th>DAI</th>\n",
       "      <th>MTA</th>\n",
       "      <th>SPELL</th>\n",
       "      <th>DIVINE</th>\n",
       "      <th>...</th>\n",
       "      <th>XRUNE</th>\n",
       "      <th>xSUSHI</th>\n",
       "      <th>SLP</th>\n",
       "      <th>CVX</th>\n",
       "      <th>KP3R</th>\n",
       "      <th>IQ</th>\n",
       "      <th>WETH</th>\n",
       "      <th>D2D</th>\n",
       "      <th>PREMIA</th>\n",
       "      <th>BOTTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-26</th>\n",
       "      <td>3146.960194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.714442e-09</td>\n",
       "      <td>50936.138584</td>\n",
       "      <td>2394.516236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17919.622692</td>\n",
       "      <td>9707.215451</td>\n",
       "      <td>31308.002957</td>\n",
       "      <td>3230.42862</td>\n",
       "      <td>...</td>\n",
       "      <td>11671.785447</td>\n",
       "      <td>-1.247360e-10</td>\n",
       "      <td>0.628159</td>\n",
       "      <td>22187.747048</td>\n",
       "      <td>3.865703e-11</td>\n",
       "      <td>35379.448835</td>\n",
       "      <td>47239.095648</td>\n",
       "      <td>135588.528393</td>\n",
       "      <td>3920.771645</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-27</th>\n",
       "      <td>3146.960194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.714442e-09</td>\n",
       "      <td>50936.138584</td>\n",
       "      <td>2394.516236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17919.622692</td>\n",
       "      <td>9707.215451</td>\n",
       "      <td>31308.002957</td>\n",
       "      <td>3230.42862</td>\n",
       "      <td>...</td>\n",
       "      <td>11671.785447</td>\n",
       "      <td>-1.247360e-10</td>\n",
       "      <td>0.628159</td>\n",
       "      <td>22187.747048</td>\n",
       "      <td>3.865703e-11</td>\n",
       "      <td>35379.448835</td>\n",
       "      <td>47764.907556</td>\n",
       "      <td>135588.528393</td>\n",
       "      <td>3920.771645</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>3146.960194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.714442e-09</td>\n",
       "      <td>50936.138584</td>\n",
       "      <td>2394.516236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17919.622692</td>\n",
       "      <td>9707.215451</td>\n",
       "      <td>31308.002957</td>\n",
       "      <td>3230.42862</td>\n",
       "      <td>...</td>\n",
       "      <td>11671.785447</td>\n",
       "      <td>-1.247360e-10</td>\n",
       "      <td>0.628159</td>\n",
       "      <td>22187.747048</td>\n",
       "      <td>3.865703e-11</td>\n",
       "      <td>35379.448835</td>\n",
       "      <td>48620.643944</td>\n",
       "      <td>135588.528393</td>\n",
       "      <td>3920.771645</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>3146.960194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.714442e-09</td>\n",
       "      <td>50936.138584</td>\n",
       "      <td>2394.516236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17919.622692</td>\n",
       "      <td>9707.215451</td>\n",
       "      <td>31308.002957</td>\n",
       "      <td>3230.42862</td>\n",
       "      <td>...</td>\n",
       "      <td>11671.785447</td>\n",
       "      <td>-1.247360e-10</td>\n",
       "      <td>0.628159</td>\n",
       "      <td>22187.747048</td>\n",
       "      <td>3.865703e-11</td>\n",
       "      <td>35379.448835</td>\n",
       "      <td>48620.643944</td>\n",
       "      <td>135588.528393</td>\n",
       "      <td>3920.771645</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-02</th>\n",
       "      <td>3146.960194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.714442e-09</td>\n",
       "      <td>50936.138584</td>\n",
       "      <td>2394.516236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17919.622692</td>\n",
       "      <td>9707.215451</td>\n",
       "      <td>31308.002957</td>\n",
       "      <td>3230.42862</td>\n",
       "      <td>...</td>\n",
       "      <td>11671.785447</td>\n",
       "      <td>-1.247360e-10</td>\n",
       "      <td>0.628159</td>\n",
       "      <td>22187.747048</td>\n",
       "      <td>3.865703e-11</td>\n",
       "      <td>35379.448835</td>\n",
       "      <td>49171.050367</td>\n",
       "      <td>135588.528393</td>\n",
       "      <td>3920.771645</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ANGLE  SYN          FRAX          ALCX          USF  SUSHI  \\\n",
       "2022-02-26  3146.960194  NaN  3.714442e-09  50936.138584  2394.516236    0.0   \n",
       "2022-02-27  3146.960194  NaN  3.714442e-09  50936.138584  2394.516236    0.0   \n",
       "2022-02-28  3146.960194  NaN  3.714442e-09  50936.138584  2394.516236    0.0   \n",
       "2022-03-01  3146.960194  NaN  3.714442e-09  50936.138584  2394.516236    0.0   \n",
       "2022-03-02  3146.960194  NaN  3.714442e-09  50936.138584  2394.516236    0.0   \n",
       "\n",
       "                     DAI          MTA         SPELL      DIVINE  ...  \\\n",
       "2022-02-26  17919.622692  9707.215451  31308.002957  3230.42862  ...   \n",
       "2022-02-27  17919.622692  9707.215451  31308.002957  3230.42862  ...   \n",
       "2022-02-28  17919.622692  9707.215451  31308.002957  3230.42862  ...   \n",
       "2022-03-01  17919.622692  9707.215451  31308.002957  3230.42862  ...   \n",
       "2022-03-02  17919.622692  9707.215451  31308.002957  3230.42862  ...   \n",
       "\n",
       "                   XRUNE        xSUSHI       SLP           CVX          KP3R  \\\n",
       "2022-02-26  11671.785447 -1.247360e-10  0.628159  22187.747048  3.865703e-11   \n",
       "2022-02-27  11671.785447 -1.247360e-10  0.628159  22187.747048  3.865703e-11   \n",
       "2022-02-28  11671.785447 -1.247360e-10  0.628159  22187.747048  3.865703e-11   \n",
       "2022-03-01  11671.785447 -1.247360e-10  0.628159  22187.747048  3.865703e-11   \n",
       "2022-03-02  11671.785447 -1.247360e-10  0.628159  22187.747048  3.865703e-11   \n",
       "\n",
       "                      IQ          WETH            D2D       PREMIA  BOTTO  \n",
       "2022-02-26  35379.448835  47239.095648  135588.528393  3920.771645    NaN  \n",
       "2022-02-27  35379.448835  47764.907556  135588.528393  3920.771645    NaN  \n",
       "2022-02-28  35379.448835  48620.643944  135588.528393  3920.771645    NaN  \n",
       "2022-03-01  35379.448835  48620.643944  135588.528393  3920.771645    NaN  \n",
       "2022-03-02  35379.448835  49171.050367  135588.528393  3920.771645    NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_df = pd.DataFrame()\n",
    "for price in prices_dict:\n",
    "    prices_df[f'{price}'] = ohm_treasury[price] * prices_dict[price]\n",
    "prices_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a71d14",
   "metadata": {},
   "source": [
    "# Charting ♥\n",
    "\n",
    "Lastly plot a line chart of the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "def2588f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices_df.plot(figsize=(15,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea64d51",
   "metadata": {},
   "source": [
    "Once you have the chart you should do some exploring. Can you find some other sources to confirm the accuracry of what you've plotted? "
   ]
  }
 ],
 "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": 5
}