cadcad_UNI_BAL_Swap_Model_the_graph.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "#!pip install cadCAD==0.4.23 &> /dev/null\n",
        "#!pip uninstall numpy\n",
        "#!pip uninstall pandas\n",
        "\n",
        "#!pip install --user numpy\n",
        "#!pip install --user  pandas"
      ],
      "metadata": {
        "id": "vWwnXhrp56AJ"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLd9Jhs2yTW_"
      },
      "source": [
        "# Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:13.211181Z",
          "iopub.status.busy": "2021-06-15T20:38:13.210416Z",
          "iopub.status.idle": "2021-06-15T20:38:13.466270Z",
          "shell.execute_reply": "2021-06-15T20:38:13.466663Z"
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        "id": "1ywh4SU6yTW_"
      },
      "outputs": [],
      "source": [
        "# cadCAD standard dependencies\n",
        "\n",
        "# cadCAD configuration modules\n",
        "from cadCAD.configuration.utils import config_sim\n",
        "from cadCAD.configuration import Experiment\n",
        "\n",
        "# cadCAD simulation engine modules\n",
        "from cadCAD.engine import ExecutionMode, ExecutionContext\n",
        "from cadCAD.engine import Executor\n",
        "\n",
        "# cadCAD global simulation configuration list\n",
        "from cadCAD import configs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:13.470224Z",
          "iopub.status.busy": "2021-06-15T20:38:13.469645Z",
          "iopub.status.idle": "2021-06-15T20:38:13.679440Z",
          "shell.execute_reply": "2021-06-15T20:38:13.679832Z"
        },
        "id": "Djj4HnOjyTXA"
      },
      "outputs": [],
      "source": [
        "# Additional dependencies\n",
        "\n",
        "# For parsing the data from the API\n",
        "import json\n",
        "# For downloading data from API\n",
        "import requests as req\n",
        "# For generating random numbers\n",
        "import math\n",
        "# For analytics\n",
        "import pandas as pd\n",
        "# For visualization\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R7kIURZeyTXB"
      },
      "source": [
        "## Query the Balancer subgraph for a UNI-BAL pool"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:13.684567Z",
          "iopub.status.busy": "2021-06-15T20:38:13.683962Z",
          "iopub.status.idle": "2021-06-15T20:38:14.236280Z",
          "shell.execute_reply": "2021-06-15T20:38:14.237019Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wI2FZQacyTXC",
        "outputId": "5b4373f8-e959-45bd-b209-c79c9ba3e8f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Print first 500 characters of the response\n",
            "{\"data\":{\"swaps\":[{\"timestamp\":1606030017,\"tokenAmountIn\":\"348\",\"tokenAmountOut\":\"78.737271727059410272\",\"tokenInSym\":\"UNI\",\"tokenOutSym\":\"BAL\"},{\"timestamp\":1610476730,\"tokenAmountIn\":\"87.907993216103989248\",\"tokenAmountOut\":\"254.406534991955029855\",\"tokenInSym\":\"BAL\",\"tokenOutSym\":\"UNI\"},{\"timestamp\":1611736783,\"tokenAmountIn\":\"40.890844784118336934\",\"tokenAmountOut\":\"61.294208877262298814\",\"tokenInSym\":\"BAL\",\"tokenOutSym\":\"UNI\"},{\"timestamp\":1618859828,\"tokenAmountIn\":\"112.259419481903871425\"\n"
          ]
        }
      ],
      "source": [
        "# You can explore the subgraph at https://thegraph.com/explorer/subgraph/balancer-labs/balancer\n",
        "# Pool address can be found on Etherscan\n",
        "API_URI = 'https://api.thegraph.com/subgraphs/name/balancer-labs/balancer'\n",
        "\n",
        "# Query for retrieving the history of swaps on a BAL <> UNI 50-50 pool\n",
        "GRAPH_QUERY = '''\n",
        "{\n",
        "  swaps (where: {poolAddress: \"0xf3168b50751173e150af543997abe0fec5d58b7f\"}) { \n",
        "    tokenAmountIn,\n",
        "    tokenInSym,\n",
        "    tokenAmountOut,\n",
        "    tokenOutSym,\n",
        "    timestamp\n",
        "  }\n",
        "}\n",
        "'''\n",
        "\n",
        "# Retrieve data from query\n",
        "JSON = {'query': GRAPH_QUERY}\n",
        "r = req.post(API_URI, json=JSON)\n",
        "graph_data = json.loads(r.content)['data']\n",
        "\n",
        "print(\"Print first 500 characters of the response\")\n",
        "print(r.text[:500])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwbafqnPyTXD"
      },
      "source": [
        "## Data Wrangle the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.255279Z",
          "iopub.status.busy": "2021-06-15T20:38:14.247623Z",
          "iopub.status.idle": "2021-06-15T20:38:14.258132Z",
          "shell.execute_reply": "2021-06-15T20:38:14.258620Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LCmBsteKyTXD",
        "outputId": "8fd23708-a51e-4cd3-8dea-3db93e0c989a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>tokenAmountIn</th>\n",
              "      <th>tokenAmountOut</th>\n",
              "      <th>tokenInSym</th>\n",
              "      <th>tokenOutSym</th>\n",
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              "      <th>0</th>\n",
              "      <td>1606030017</td>\n",
              "      <td>348</td>\n",
              "      <td>78.737271727059410272</td>\n",
              "      <td>UNI</td>\n",
              "      <td>BAL</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1610476730</td>\n",
              "      <td>87.907993216103989248</td>\n",
              "      <td>254.406534991955029855</td>\n",
              "      <td>BAL</td>\n",
              "      <td>UNI</td>\n",
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              "      <th>2</th>\n",
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              "      <td>40.890844784118336934</td>\n",
              "      <td>61.294208877262298814</td>\n",
              "      <td>BAL</td>\n",
              "      <td>UNI</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1618859828</td>\n",
              "      <td>112.259419481903871425</td>\n",
              "      <td>195.629827478447515933</td>\n",
              "      <td>BAL</td>\n",
              "      <td>UNI</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1601954527</td>\n",
              "      <td>138.245503251295453184</td>\n",
              "      <td>27.894370921499086237</td>\n",
              "      <td>UNI</td>\n",
              "      <td>BAL</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "    timestamp           tokenAmountIn  ... tokenInSym tokenOutSym\n",
              "0  1606030017                     348  ...        UNI         BAL\n",
              "1  1610476730   87.907993216103989248  ...        BAL         UNI\n",
              "2  1611736783   40.890844784118336934  ...        BAL         UNI\n",
              "3  1618859828  112.259419481903871425  ...        BAL         UNI\n",
              "4  1601954527  138.245503251295453184  ...        UNI         BAL\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "raw_df = pd.DataFrame(graph_data['swaps'])\n",
        "\n",
        "raw_df.head(5)"
      ]
    },
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      "execution_count": 6,
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          "iopub.status.busy": "2021-06-15T20:38:14.272443Z",
          "iopub.status.idle": "2021-06-15T20:38:14.275081Z",
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        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>tokenAmountIn</th>\n",
              "      <th>tokenAmountOut</th>\n",
              "      <th>tokenInSym</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>76</td>\n",
              "      <td>2020-09-19 15:11:26</td>\n",
              "      <td>213.163101</td>\n",
              "      <td>78.928218</td>\n",
              "      <td>UNI</td>\n",
              "      <td>BAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>73</td>\n",
              "      <td>2020-09-21 18:58:57</td>\n",
              "      <td>53.681512</td>\n",
              "      <td>185.873296</td>\n",
              "      <td>BAL</td>\n",
              "      <td>UNI</td>\n",
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              "      <th>2</th>\n",
              "      <td>44</td>\n",
              "      <td>2020-09-24 00:26:18</td>\n",
              "      <td>52.072207</td>\n",
              "      <td>156.679612</td>\n",
              "      <td>BAL</td>\n",
              "      <td>UNI</td>\n",
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              "      <th>3</th>\n",
              "      <td>57</td>\n",
              "      <td>2020-09-24 12:54:53</td>\n",
              "      <td>203.769109</td>\n",
              "      <td>69.935233</td>\n",
              "      <td>UNI</td>\n",
              "      <td>BAL</td>\n",
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              "      <th>4</th>\n",
              "      <td>64</td>\n",
              "      <td>2020-09-24 22:49:34</td>\n",
              "      <td>17.135790</td>\n",
              "      <td>50.760343</td>\n",
              "      <td>BAL</td>\n",
              "      <td>UNI</td>\n",
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              "                                                     [key], {});\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "   index           timestamp  ...  tokenInSym  tokenOutSym\n",
              "0     76 2020-09-19 15:11:26  ...         UNI          BAL\n",
              "1     73 2020-09-21 18:58:57  ...         BAL          UNI\n",
              "2     44 2020-09-24 00:26:18  ...         BAL          UNI\n",
              "3     57 2020-09-24 12:54:53  ...         UNI          BAL\n",
              "4     64 2020-09-24 22:49:34  ...         BAL          UNI\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Clean the data:\n",
        "# 1. convert the raw timestamps to Python DateTime objects\n",
        "# 2. make the token flow values numerical\n",
        "# 3. order by time\n",
        "df = (raw_df.assign(timestamp=lambda df: pd.to_datetime(df.timestamp, unit='s'))\n",
        "            .assign(tokenAmountIn=lambda df: pd.to_numeric(df.tokenAmountIn))\n",
        "            .assign(tokenAmountOut=lambda df: pd.to_numeric(df.tokenAmountOut))\n",
        "            .sort_values('timestamp')\n",
        "            .reset_index()\n",
        "      )\n",
        "\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1Xa2OO6yTXE"
      },
      "source": [
        "# Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrQiRK2nyTXF"
      },
      "source": [
        "## 1. State Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.280368Z",
          "iopub.status.busy": "2021-06-15T20:38:14.279597Z",
          "iopub.status.idle": "2021-06-15T20:38:14.282120Z",
          "shell.execute_reply": "2021-06-15T20:38:14.282568Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "do2alcwvyTXF",
        "outputId": "89b9149c-27aa-40dd-c797-7ed5e9e1289f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cumulative_fee_BAL': 0.0,\n",
              " 'cumulative_fee_UNI': 0.0,\n",
              " 'cumulative_swap_BAL': 0.0,\n",
              " 'cumulative_swap_UNI': 0.0}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "initial_state = {\n",
        "    # Cumulative transaction fees paid for each token\n",
        "    'cumulative_fee_BAL': 0.0,\n",
        "    'cumulative_fee_UNI': 0.0,\n",
        "    \n",
        "    # Cumulative swap volume\n",
        "    # (positive means more tokens locked)\n",
        "    # (negative means less tokens locked)\n",
        "    'cumulative_swap_BAL': 0.0,\n",
        "    'cumulative_swap_UNI': 0.0   \n",
        "}\n",
        "initial_state"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ObAKKN_yTXF"
      },
      "source": [
        "## 2. System Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.287415Z",
          "iopub.status.busy": "2021-06-15T20:38:14.286746Z",
          "iopub.status.idle": "2021-06-15T20:38:14.288593Z",
          "shell.execute_reply": "2021-06-15T20:38:14.289037Z"
        },
        "id": "tEx0qetXyTXF"
      },
      "outputs": [],
      "source": [
        "# Transform the swap history data frame into a {timestep: data} dictionary\n",
        "swap_dict = df.to_dict(orient='index')\n",
        "\n",
        "system_params = {\n",
        "    'swap_sequence': [swap_dict],\n",
        "    \n",
        "    # Transaction fees being applied to the input token\n",
        "    'transaction_fee': [0.01, 0.05]\n",
        "}\n",
        "\n",
        "# Element for timestep = 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CaGIqhOhyTXG"
      },
      "source": [
        "## 3. Policy Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.294864Z",
          "iopub.status.busy": "2021-06-15T20:38:14.294191Z",
          "iopub.status.idle": "2021-06-15T20:38:14.296012Z",
          "shell.execute_reply": "2021-06-15T20:38:14.296446Z"
        },
        "id": "I5ksptfSyTXG"
      },
      "outputs": [],
      "source": [
        "def p_swap(params, substep, state_history, previous_state):\n",
        "    \"\"\"\n",
        "    Calculate cumulative transaction fees & swaps\n",
        "    from a swap event\n",
        "    \"\"\"\n",
        "    t = previous_state['timestep']\n",
        "    \n",
        "    # Data for this timestep\n",
        "    ts_data = params['swap_sequence'][t]    \n",
        "    \n",
        "    timestamp = ts_data['timestamp']\n",
        "    # Swap amounts\n",
        "    # swap_in: Swap being locked to the pool\n",
        "    swap_in = ts_data['tokenAmountIn']\n",
        "    # swap_out: Swap being unlocked from the pool\n",
        "    swap_out = -1.0 * ts_data['tokenAmountOut']\n",
        "    \n",
        "    # Fee to be paid\n",
        "    swap_fee = swap_in * params['transaction_fee']\n",
        "    \n",
        "    # Logic for mapping the input and output into tokens\n",
        "    if ts_data['tokenInSym'] == 'UNI':\n",
        "        fee_UNI = swap_fee\n",
        "        fee_BAL = 0.0\n",
        "        swap_UNI = swap_in\n",
        "        swap_BAL = swap_out\n",
        "    elif ts_data['tokenInSym'] == 'BAL':\n",
        "        fee_UNI = 0.0\n",
        "        fee_BAL = swap_fee\n",
        "        swap_UNI = swap_out\n",
        "        swap_BAL = swap_in\n",
        "    else:\n",
        "        raise Exception\n",
        "\n",
        "    return {'fee_UNI': fee_UNI,\n",
        "            'fee_BAL': fee_BAL,\n",
        "            'swap_UNI': swap_UNI,\n",
        "            'swap_BAL': swap_BAL,\n",
        "            'timestamp': timestamp}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oi3DNG2nyTXG"
      },
      "source": [
        "## 4. State Update Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.302586Z",
          "iopub.status.busy": "2021-06-15T20:38:14.301790Z",
          "iopub.status.idle": "2021-06-15T20:38:14.303592Z",
          "shell.execute_reply": "2021-06-15T20:38:14.303971Z"
        },
        "id": "NVFwrvkmyTXH"
      },
      "outputs": [],
      "source": [
        "def s_timestamp(params,\n",
        "                      substep,\n",
        "                      state_history,\n",
        "                      previous_state,\n",
        "                      policy_input):\n",
        "    value = policy_input['timestamp']\n",
        "    return ('timestamp', value)\n",
        "\n",
        "def s_cumulative_fee_UNI(params,\n",
        "                              substep,\n",
        "                              state_history,\n",
        "                              previous_state,\n",
        "                              policy_input):\n",
        "    variable = 'cumulative_fee_UNI'\n",
        "    fee = policy_input['fee_UNI']\n",
        "    value = previous_state['cumulative_fee_UNI'] + fee \n",
        "    return (variable, value)\n",
        "\n",
        "\n",
        "def s_cumulative_fee_BAL(params,\n",
        "                              substep,\n",
        "                              state_history,\n",
        "                              previous_state,\n",
        "                              policy_input):\n",
        "    variable = 'cumulative_fee_BAL'\n",
        "    fee = policy_input['fee_BAL']\n",
        "    value = previous_state['cumulative_fee_BAL'] + fee \n",
        "    return (variable, value)\n",
        "\n",
        "\n",
        "def s_cumulative_swap_UNI(params,\n",
        "                              substep,\n",
        "                              state_history,\n",
        "                              previous_state,\n",
        "                              policy_input):\n",
        "    variable = 'cumulative_swap_UNI'\n",
        "    swap = policy_input['swap_UNI']\n",
        "    value = previous_state['cumulative_swap_UNI'] + swap\n",
        "    return (variable, value)\n",
        "\n",
        "\n",
        "def s_cumulative_swap_BAL(params,\n",
        "                              substep,\n",
        "                              state_history,\n",
        "                              previous_state,\n",
        "                              policy_input):\n",
        "    variable = 'cumulative_swap_BAL'\n",
        "    swap = policy_input['swap_BAL']\n",
        "    value = previous_state['cumulative_swap_BAL'] + swap\n",
        "    return (variable, value)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQzdpoCJyTXH"
      },
      "source": [
        "## 5. Partial State Update Blocks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.307339Z",
          "iopub.status.busy": "2021-06-15T20:38:14.306726Z",
          "iopub.status.idle": "2021-06-15T20:38:14.308144Z",
          "shell.execute_reply": "2021-06-15T20:38:14.308678Z"
        },
        "id": "3s7Y8qt0yTXH"
      },
      "outputs": [],
      "source": [
        "partial_state_update_blocks = [\n",
        "    {\n",
        "        'policies': {\n",
        "            'policy_swap': p_swap\n",
        "        },\n",
        "        'variables': {\n",
        "            'timestamp': s_timestamp,\n",
        "            'cumulative_fee_UNI': s_cumulative_fee_UNI,\n",
        "            'cumulative_fee_BAL': s_cumulative_fee_BAL,\n",
        "            'cumulative_swap_UNI': s_cumulative_swap_UNI,\n",
        "            'cumulative_swap_BAL': s_cumulative_swap_BAL\n",
        "        }\n",
        "    }\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAFJv5H-yTXH"
      },
      "source": [
        "# Simulation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjWz898JyTXI"
      },
      "source": [
        "## 6. Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.311711Z",
          "iopub.status.busy": "2021-06-15T20:38:14.311173Z",
          "iopub.status.idle": "2021-06-15T20:38:14.312812Z",
          "shell.execute_reply": "2021-06-15T20:38:14.313196Z"
        },
        "id": "CiN1bCSgyTXI"
      },
      "outputs": [],
      "source": [
        "sim_config = config_sim({\n",
        "    \"N\": 1, # the number of times we'll run the simulation (\"Monte Carlo runs\")\n",
        "    \"T\": range(len(df)), # the number of timesteps the simulation will run for\n",
        "    \"M\": system_params # the parameters of the system\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.316105Z",
          "iopub.status.busy": "2021-06-15T20:38:14.315555Z",
          "iopub.status.idle": "2021-06-15T20:38:14.317352Z",
          "shell.execute_reply": "2021-06-15T20:38:14.317731Z"
        },
        "id": "1dp6Cdz7yTXI"
      },
      "outputs": [],
      "source": [
        "del configs[:] # Clear any prior configs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.323624Z",
          "iopub.status.busy": "2021-06-15T20:38:14.323082Z",
          "iopub.status.idle": "2021-06-15T20:38:14.324773Z",
          "shell.execute_reply": "2021-06-15T20:38:14.325155Z"
        },
        "id": "5wPtLHzoyTXI"
      },
      "outputs": [],
      "source": [
        "experiment = Experiment()\n",
        "experiment.append_configs(\n",
        "    initial_state = initial_state,\n",
        "    partial_state_update_blocks = partial_state_update_blocks,\n",
        "    sim_configs = sim_config\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEDLoAbQyTXI"
      },
      "source": [
        "## 7. Execution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.334207Z",
          "iopub.status.busy": "2021-06-15T20:38:14.333591Z",
          "iopub.status.idle": "2021-06-15T20:38:14.356629Z",
          "shell.execute_reply": "2021-06-15T20:38:14.357023Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Uuu49kLyTXJ",
        "outputId": "a61a58a2-d80c-46ef-9d25-e112a2e52e28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "                  ___________    ____\n",
            "  ________ __ ___/ / ____/   |  / __ \\\n",
            " / ___/ __` / __  / /   / /| | / / / /\n",
            "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
            "\\___/\\__,_/\\__,_/\\____/_/  |_/_____/\n",
            "by cadCAD\n",
            "\n",
            "Execution Mode: local_proc\n",
            "Configuration Count: 1\n",
            "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (100, 2, 2, 4)\n",
            "Execution Method: local_simulations\n",
            "SimIDs   : [0, 0]\n",
            "SubsetIDs: [0, 1]\n",
            "Ns       : [0, 1]\n",
            "ExpIDs   : [0, 0]\n",
            "Execution Mode: parallelized\n",
            "Total execution time: 0.04s\n"
          ]
        }
      ],
      "source": [
        "exec_context = ExecutionContext()\n",
        "simulation = Executor(exec_context=exec_context, configs=configs)\n",
        "raw_result, tensor_field, sessions = simulation.execute()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmgMKOYjyTXJ"
      },
      "source": [
        "## 8. Output Preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.367309Z",
          "iopub.status.busy": "2021-06-15T20:38:14.366679Z",
          "iopub.status.idle": "2021-06-15T20:38:14.369312Z",
          "shell.execute_reply": "2021-06-15T20:38:14.368894Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9a7hnePtyTXJ",
        "outputId": "43d262c6-69bd-4f45-c7ce-c6508353802e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3bf73ceb-4d7c-47ef-a1f6-9e797cb1b669\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>cumulative_fee_BAL</th>\n",
              "      <th>cumulative_fee_UNI</th>\n",
              "      <th>cumulative_swap_BAL</th>\n",
              "      <th>cumulative_swap_UNI</th>\n",
              "      <th>simulation</th>\n",
              "      <th>subset</th>\n",
              "      <th>run</th>\n",
              "      <th>substep</th>\n",
              "      <th>timestep</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.131631</td>\n",
              "      <td>-78.928218</td>\n",
              "      <td>213.163101</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-09-19 15:11:26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.536815</td>\n",
              "      <td>2.131631</td>\n",
              "      <td>-25.246706</td>\n",
              "      <td>27.289805</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-09-21 18:58:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.057537</td>\n",
              "      <td>2.131631</td>\n",
              "      <td>26.825501</td>\n",
              "      <td>-129.389807</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2020-09-24 00:26:18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.057537</td>\n",
              "      <td>4.169322</td>\n",
              "      <td>-43.109731</td>\n",
              "      <td>74.379302</td>\n",
              "      <td>0</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3bf73ceb-4d7c-47ef-a1f6-9e797cb1b669 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3bf73ceb-4d7c-47ef-a1f6-9e797cb1b669');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   cumulative_fee_BAL  cumulative_fee_UNI  ...  timestep           timestamp\n",
              "0            0.000000            0.000000  ...         0                 NaT\n",
              "1            0.000000            2.131631  ...         1 2020-09-19 15:11:26\n",
              "2            0.536815            2.131631  ...         2 2020-09-21 18:58:57\n",
              "3            1.057537            2.131631  ...         3 2020-09-24 00:26:18\n",
              "4            1.057537            4.169322  ...         4 2020-09-24 12:54:53\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "simulation_result = pd.DataFrame(raw_result)\n",
        "simulation_result.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GsI4v_mMyTXJ"
      },
      "source": [
        "## 9. Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.372481Z",
          "iopub.status.busy": "2021-06-15T20:38:14.371908Z",
          "iopub.status.idle": "2021-06-15T20:38:14.777964Z",
          "shell.execute_reply": "2021-06-15T20:38:14.777491Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Hj3BZTITyTXJ",
        "outputId": "bf3bff8b-d492-4e6d-ed69-e3998193db7f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          "metadata": {}
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      ],
      "source": [
        "# Visualize how much transaction fees were paid over time on each token\n",
        "px.line(simulation_result,\n",
        "           x='timestamp',\n",
        "           y=['cumulative_fee_BAL', 'cumulative_fee_UNI'],\n",
        "           facet_row='subset')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-15T20:38:14.789816Z",
          "iopub.status.busy": "2021-06-15T20:38:14.789288Z",
          "iopub.status.idle": "2021-06-15T20:38:14.859875Z",
          "shell.execute_reply": "2021-06-15T20:38:14.860250Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "8r55u3kFyTXJ",
        "outputId": "02b31603-b018-4906-abbc-6266a10b3aa9"
      },
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "# Visualize the cumulative swaps:\n",
        "# * Positive means that the pool has more tokens than initially\n",
        "# * Negative means the opposite\n",
        "px.line(simulation_result,\n",
        "           x='timestamp',\n",
        "           y=['cumulative_swap_BAL', 'cumulative_swap_UNI'],\n",
        "           facet_row='subset')"
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