MoltenToffee.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Double bonding curve liquidity pool as automatic swap market maker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chart_studio.plotly as py\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objs as go\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import ssl\n",
    "import time\n",
    "import math\n",
    "import random\n",
    "import decimal\n",
    "import plotly.express as px\n",
    "\n",
    "\n",
    "#disable ssl for cryptory API & virtualenv\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Request Data from Compound.finance API. We request a time step every hour over the last 9 months. The API does not let you get more granular data per request so we do it in 3 loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "itteration 2 start: 1549991968. end: 1557767968. num_buckets: 2160\n",
      "itteration 1 start: 1557767969. end: 1565543969. num_buckets: 2160\n",
      "itteration 0 start: 1565543971. end: 1573319971. num_buckets: 2160\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>block_number</th>\n",
       "      <th>block_timestamp</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7710751</td>\n",
       "      <td>1555427968</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7711711</td>\n",
       "      <td>1555442368</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7715071</td>\n",
       "      <td>1555492768</td>\n",
       "      <td>0.001255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7716031</td>\n",
       "      <td>1555507168</td>\n",
       "      <td>0.002507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7717951</td>\n",
       "      <td>1555535968</td>\n",
       "      <td>0.002499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   block_number  block_timestamp      rate\n",
       "0       7710751       1555427968  0.000000\n",
       "1       7711711       1555442368  0.000000\n",
       "2       7715071       1555492768  0.001255\n",
       "3       7716031       1555507168  0.002507\n",
       "4       7717951       1555535968  0.002499"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "secondsInMonth = 60 * 60 * 24 *30\n",
    "cDaiAddress = '0xf5dce57282a584d2746faf1593d3121fcac444dc'\n",
    "endTimestamp = math.floor(time.time())\n",
    "    \n",
    "startTimeStamp = endTimestamp - 3 * 30 * 24 * 60 * 60 #use the most recent 3 month's worth of data.\n",
    "num_buckets = 3 * 24 * 30 #interest rate every hour\n",
    "\n",
    "interestOverTime = pd.DataFrame()\n",
    "\n",
    "for i in range(2,-1,-1): # we can pull up to 3 months of data at a time if taking data points every hour.\n",
    "    endTimestamp = math.floor(time.time()) - i * secondsInMonth * 3\n",
    "    startTimeStamp = endTimestamp - 3 * 30 * 24 * 60 * 60 #use three months before the start\n",
    "    print(\"itteration %s start: %s. end: %s. num_buckets: %s\" %(i, startTimeStamp, endTimestamp, num_buckets))\n",
    "    requestURL = \"https://api.compound.finance/api/v2/market_history/graph?asset=%s&min_block_timestamp=%s&max_block_timestamp=%s&num_buckets=%s\"% (cDaiAddress, startTimeStamp, endTimestamp, num_buckets)\n",
    "    response = requests.get(requestURL)\n",
    "#     print(response.json())\n",
    "    outputFrame = pd.DataFrame.from_dict(response.json()['supply_rates'])\n",
    "    interestOverTime = interestOverTime.append(outputFrame, ignore_index = True)\n",
    "interestOverTime.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert Data into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "interestOverTime['block_time'] = pd.to_datetime(interestOverTime['block_timestamp'],unit='s')\n",
    "interestOverTime['rate_per_block'] = interestOverTime['rate'] / (4 * 60 * 24 * 365)\n",
    "interestOverTime['rate_per_hour'] = interestOverTime['rate'] / (24 * 365)\n",
    "interestOverTime.tail()\n",
    "interestOverTime = interestOverTime.drop(interestOverTime.head(100).index) # drop last n rows becuase the rate was much lower here\n",
    "interestOverTime = interestOverTime.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4534"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(interestOverTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d1 = pd.DataFrame(0.1/(24*365), index=np.arange(len(interestOverTime)), columns=np.arange(1))\n",
    "# interestOverTime['rate_per_hour'] = d1\n",
    "\n",
    "# d2 = pd.DataFrame(0.1, index=np.arange(len(interestOverTime)), columns=np.arange(1))\n",
    "# interestOverTime['rate'] = d2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Intrest rate over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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       "                        {\"responsive\": true}\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [go.Scatter(\n",
    "        x=interestOverTime['block_time'],\n",
    "        y=interestOverTime['rate']\n",
    "    )]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Compound Interst Rate Over time',\n",
    "    yaxis=dict(title='Lending Rate (%)'),\n",
    "    xaxis=dict(title='Date'),\n",
    "    template='plotly_white')\n",
    "\n",
    "figure = go.Figure(data=data, layout=layout)\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define some helper functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defines the fixed rate offered by the pool as a function of utilization. See Notion for explinaiton\n",
    "def poolFixedRate(poolUtilization, currentFloatingRate, isLong):\n",
    "    alpha = 1.5\n",
    "    beta = 0\n",
    "    if isLong:\n",
    "        return currentFloatingRate * (1 + poolUtilization/alpha + beta)\n",
    "    if not isLong:\n",
    "        return currentFloatingRate * (1 - poolUtilization/alpha - beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.084"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poolFixedRate(0.75,.16,False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Future value NACC(nominal anual, compounding continiously). Assumes t1 and t2 are in seconds\n",
    "def futureValueNACC(nominal, rate, t1, t2):\n",
    "    return nominal * math.exp(rate * (t2 - t1) / (60 * 60 * 24 *365))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Future value NACH(nominal anual, compounding hourly) Assumes t1 and t2 are in seconds\n",
    "def futureValueNACH(nominal, rate, t1, t2):\n",
    "    return nominal * (1 + rate/(24*365)) ** (((t2 - t1) * 24 * 365) / (60 * 60 * 24 * 365))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1556831679"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(interestOverTime.head(1)['block_timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1573312483"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(interestOverTime.tail(1)['block_timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finds the index in the pandas frame that has the closest starting index to the time specified\n",
    "def findClosestIndex(t):\n",
    "    for i in range(0,len(interestOverTime['block_timestamp'])):\n",
    "        if t >= interestOverTime.loc[i,'block_timestamp'] and t <= interestOverTime.loc[i + 1,'block_timestamp']:\n",
    "            return(i)\n",
    "    return(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Floating Value accrued between two time periods in the compound data set.\n",
    "def floatingValue(nominal, t1, t2):\n",
    "    assert t1 < t2, \"invalid input time\"\n",
    "    assert t1 >= int(interestOverTime.head(1)['block_timestamp']), \"t1 before time series start\"\n",
    "    assert t2 <= int(interestOverTime.tail(1)['block_timestamp']), \"t2 after time series end\"\n",
    "    startingIndex = findClosestIndex(t1)\n",
    "    endingIndex = findClosestIndex(t2)\n",
    "    positionValue = nominal\n",
    "    for i in range(startingIndex, endingIndex + 2):\n",
    "        ratePerHour = interestOverTime.loc[i,'rate']/(24 * 365)\n",
    "        positionValue = positionValue * (1 + ratePerHour)\n",
    "    return positionValue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the effective APR for a position. v1 is value at time t1. v2 is value at time t2.\n",
    "#algebraically this comes from taking v2=v1(1+r/(24*365))^((t2-t1)/(60*60)) and solving for r. Then * 100\n",
    "def effectiveAPR(v1,v2, t1, t2):\n",
    "    return -8760 * (v2/v1) ** (-3600/(t1 - t2)) * ((v2/v1) ** (3600/(t1 - t2)) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# when someone goes short how much reserve do we need to keep for them? \n",
    "# this defines the lequidity utilization we needed to be reserved for the position.\n",
    "# not that this equation is not optimal as we over-alocate for the short position by a small amount.\n",
    "# this would be correct if it considered the change in pool utilization and how this inflences the \n",
    "# rate. becuase it doesent consider this it slightly over alocates reserve, which is fine.\n",
    "def reserveRequirementShort(nominal, rate, poolUtilization, poolTotal, t1, t2):\n",
    "    newPoolUtilization = (poolUtilization + nominal) / poolTotal\n",
    "    offeredRate = poolFixedRate(newPoolUtilization, rate, False)\n",
    "    print(\"offeredRate\", offeredRate)\n",
    "    return futureValueNACH(nominal, offeredRate, t1, t2) - nominal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "offeredRate 0.0425\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.341594880072108"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reserveRequirementShort(100, 0.05, 0, 1000, 0, 60*60*24*365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# when someone goes long how much reserve do we need to keep for them? \n",
    "# this defines the lequidity utilization we needed to be reserved for the position\n",
    "# note that with long we dont know what the potentual upside of the position is\n",
    "# becuase there is no defined upper bound on this. For the case of this implementation\n",
    "# we will assume that intrest rates will not exceed 30% and so will reserve future value\n",
    "# grown at 30% for the position to ensure that there is always lequidity. \n",
    "def reserveRequirementLong(nominal, rate, poolUtilization, poolTotal, t1, t2):\n",
    "    return futureValueNACH(nominal, 0.3, t1, t2) - nominal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, simulate the pool value over time with different positions taken out against it. To begin with let's do a simple calculation where we put some arbitary positions against the pools to make sure things make sense. After this we can simulate different positions being taken against the pool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>block_number</th>\n",
       "      <th>block_timestamp</th>\n",
       "      <th>rate</th>\n",
       "      <th>block_time</th>\n",
       "      <th>rate_per_block</th>\n",
       "      <th>rate_per_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7804310</td>\n",
       "      <td>1556831679</td>\n",
       "      <td>0.026341</td>\n",
       "      <td>2019-05-02 21:14:39</td>\n",
       "      <td>1.252912e-08</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7805750</td>\n",
       "      <td>1556853279</td>\n",
       "      <td>0.023087</td>\n",
       "      <td>2019-05-03 03:14:39</td>\n",
       "      <td>1.098135e-08</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7805990</td>\n",
       "      <td>1556856879</td>\n",
       "      <td>0.077711</td>\n",
       "      <td>2019-05-03 04:14:39</td>\n",
       "      <td>3.696321e-08</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7806950</td>\n",
       "      <td>1556871279</td>\n",
       "      <td>0.116131</td>\n",
       "      <td>2019-05-03 08:14:39</td>\n",
       "      <td>5.523736e-08</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7807190</td>\n",
       "      <td>1556874879</td>\n",
       "      <td>0.076435</td>\n",
       "      <td>2019-05-03 09:14:39</td>\n",
       "      <td>3.635592e-08</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7808630</td>\n",
       "      <td>1556896479</td>\n",
       "      <td>0.062027</td>\n",
       "      <td>2019-05-03 15:14:39</td>\n",
       "      <td>2.950298e-08</td>\n",
       "      <td>0.000007</td>\n",
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       "      <th>6</th>\n",
       "      <td>7810550</td>\n",
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       "      <td>2019-05-03 23:14:39</td>\n",
       "      <td>2.753417e-08</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7811270</td>\n",
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       "      <td>0.057745</td>\n",
       "      <td>2019-05-04 02:14:39</td>\n",
       "      <td>2.746621e-08</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7811750</td>\n",
       "      <td>1556943279</td>\n",
       "      <td>0.057565</td>\n",
       "      <td>2019-05-04 04:14:39</td>\n",
       "      <td>2.738065e-08</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7814630</td>\n",
       "      <td>1556986479</td>\n",
       "      <td>0.057496</td>\n",
       "      <td>2019-05-04 16:14:39</td>\n",
       "      <td>2.734801e-08</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7815110</td>\n",
       "      <td>1556993679</td>\n",
       "      <td>0.054793</td>\n",
       "      <td>2019-05-04 18:14:39</td>\n",
       "      <td>2.606221e-08</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7815350</td>\n",
       "      <td>1556997279</td>\n",
       "      <td>0.032670</td>\n",
       "      <td>2019-05-04 19:14:39</td>\n",
       "      <td>1.553938e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7815590</td>\n",
       "      <td>1557000879</td>\n",
       "      <td>0.026358</td>\n",
       "      <td>2019-05-04 20:14:39</td>\n",
       "      <td>1.253727e-08</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7815830</td>\n",
       "      <td>1557004479</td>\n",
       "      <td>0.017461</td>\n",
       "      <td>2019-05-04 21:14:39</td>\n",
       "      <td>8.305365e-09</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7816070</td>\n",
       "      <td>1557008079</td>\n",
       "      <td>0.014372</td>\n",
       "      <td>2019-05-04 22:14:39</td>\n",
       "      <td>6.835858e-09</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7816310</td>\n",
       "      <td>1557011679</td>\n",
       "      <td>0.011278</td>\n",
       "      <td>2019-05-04 23:14:39</td>\n",
       "      <td>5.364565e-09</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7816550</td>\n",
       "      <td>1557015279</td>\n",
       "      <td>0.045943</td>\n",
       "      <td>2019-05-05 00:14:39</td>\n",
       "      <td>2.185266e-08</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7816790</td>\n",
       "      <td>1557018879</td>\n",
       "      <td>0.069831</td>\n",
       "      <td>2019-05-05 01:14:39</td>\n",
       "      <td>3.321491e-08</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7817030</td>\n",
       "      <td>1557022479</td>\n",
       "      <td>0.053355</td>\n",
       "      <td>2019-05-05 02:14:39</td>\n",
       "      <td>2.537826e-08</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7817270</td>\n",
       "      <td>1557026079</td>\n",
       "      <td>0.058280</td>\n",
       "      <td>2019-05-05 03:14:39</td>\n",
       "      <td>2.772048e-08</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7817510</td>\n",
       "      <td>1557029679</td>\n",
       "      <td>0.091001</td>\n",
       "      <td>2019-05-05 04:14:39</td>\n",
       "      <td>4.328436e-08</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7817750</td>\n",
       "      <td>1557033279</td>\n",
       "      <td>0.103815</td>\n",
       "      <td>2019-05-05 05:14:39</td>\n",
       "      <td>4.937935e-08</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7817990</td>\n",
       "      <td>1557036879</td>\n",
       "      <td>0.092999</td>\n",
       "      <td>2019-05-05 06:14:39</td>\n",
       "      <td>4.423488e-08</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7818230</td>\n",
       "      <td>1557040479</td>\n",
       "      <td>0.083167</td>\n",
       "      <td>2019-05-05 07:14:39</td>\n",
       "      <td>3.955804e-08</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7818470</td>\n",
       "      <td>1557044079</td>\n",
       "      <td>0.102959</td>\n",
       "      <td>2019-05-05 08:14:39</td>\n",
       "      <td>4.897217e-08</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7818710</td>\n",
       "      <td>1557047679</td>\n",
       "      <td>0.100890</td>\n",
       "      <td>2019-05-05 09:14:39</td>\n",
       "      <td>4.798791e-08</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7818950</td>\n",
       "      <td>1557051279</td>\n",
       "      <td>0.097661</td>\n",
       "      <td>2019-05-05 10:14:39</td>\n",
       "      <td>4.645204e-08</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7819190</td>\n",
       "      <td>1557054879</td>\n",
       "      <td>0.094332</td>\n",
       "      <td>2019-05-05 11:14:39</td>\n",
       "      <td>4.486859e-08</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7819430</td>\n",
       "      <td>1557058479</td>\n",
       "      <td>0.094592</td>\n",
       "      <td>2019-05-05 12:14:39</td>\n",
       "      <td>4.499240e-08</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7819670</td>\n",
       "      <td>1557062079</td>\n",
       "      <td>0.089846</td>\n",
       "      <td>2019-05-05 13:14:39</td>\n",
       "      <td>4.273509e-08</td>\n",
       "      <td>0.000010</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4504</th>\n",
       "      <td>8896070</td>\n",
       "      <td>1573208083</td>\n",
       "      <td>0.042348</td>\n",
       "      <td>2019-11-08 10:14:43</td>\n",
       "      <td>2.014290e-08</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4505</th>\n",
       "      <td>8896310</td>\n",
       "      <td>1573211683</td>\n",
       "      <td>0.041075</td>\n",
       "      <td>2019-11-08 11:14:43</td>\n",
       "      <td>1.953712e-08</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4506</th>\n",
       "      <td>8896550</td>\n",
       "      <td>1573215283</td>\n",
       "      <td>0.041387</td>\n",
       "      <td>2019-11-08 12:14:43</td>\n",
       "      <td>1.968556e-08</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4507</th>\n",
       "      <td>8896790</td>\n",
       "      <td>1573218883</td>\n",
       "      <td>0.041363</td>\n",
       "      <td>2019-11-08 13:14:43</td>\n",
       "      <td>1.967421e-08</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4508</th>\n",
       "      <td>8897030</td>\n",
       "      <td>1573222483</td>\n",
       "      <td>0.037338</td>\n",
       "      <td>2019-11-08 14:14:43</td>\n",
       "      <td>1.775979e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4509</th>\n",
       "      <td>8897270</td>\n",
       "      <td>1573226083</td>\n",
       "      <td>0.037483</td>\n",
       "      <td>2019-11-08 15:14:43</td>\n",
       "      <td>1.782860e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4510</th>\n",
       "      <td>8897510</td>\n",
       "      <td>1573229683</td>\n",
       "      <td>0.037522</td>\n",
       "      <td>2019-11-08 16:14:43</td>\n",
       "      <td>1.784735e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4511</th>\n",
       "      <td>8897750</td>\n",
       "      <td>1573233283</td>\n",
       "      <td>0.037653</td>\n",
       "      <td>2019-11-08 17:14:43</td>\n",
       "      <td>1.790960e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4512</th>\n",
       "      <td>8897990</td>\n",
       "      <td>1573236883</td>\n",
       "      <td>0.037703</td>\n",
       "      <td>2019-11-08 18:14:43</td>\n",
       "      <td>1.793323e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4513</th>\n",
       "      <td>8898230</td>\n",
       "      <td>1573240483</td>\n",
       "      <td>0.037713</td>\n",
       "      <td>2019-11-08 19:14:43</td>\n",
       "      <td>1.793788e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4514</th>\n",
       "      <td>8898470</td>\n",
       "      <td>1573244083</td>\n",
       "      <td>0.037745</td>\n",
       "      <td>2019-11-08 20:14:43</td>\n",
       "      <td>1.795337e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4515</th>\n",
       "      <td>8898710</td>\n",
       "      <td>1573247683</td>\n",
       "      <td>0.037837</td>\n",
       "      <td>2019-11-08 21:14:43</td>\n",
       "      <td>1.799715e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4516</th>\n",
       "      <td>8898950</td>\n",
       "      <td>1573251283</td>\n",
       "      <td>0.037874</td>\n",
       "      <td>2019-11-08 22:14:43</td>\n",
       "      <td>1.801443e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4517</th>\n",
       "      <td>8899190</td>\n",
       "      <td>1573254883</td>\n",
       "      <td>0.037923</td>\n",
       "      <td>2019-11-08 23:14:43</td>\n",
       "      <td>1.803810e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4518</th>\n",
       "      <td>8899430</td>\n",
       "      <td>1573258483</td>\n",
       "      <td>0.038046</td>\n",
       "      <td>2019-11-09 00:14:43</td>\n",
       "      <td>1.809627e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4519</th>\n",
       "      <td>8899670</td>\n",
       "      <td>1573262083</td>\n",
       "      <td>0.038069</td>\n",
       "      <td>2019-11-09 01:14:43</td>\n",
       "      <td>1.810736e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4520</th>\n",
       "      <td>8899910</td>\n",
       "      <td>1573265683</td>\n",
       "      <td>0.038090</td>\n",
       "      <td>2019-11-09 02:14:43</td>\n",
       "      <td>1.811724e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4521</th>\n",
       "      <td>8900150</td>\n",
       "      <td>1573269283</td>\n",
       "      <td>0.038097</td>\n",
       "      <td>2019-11-09 03:14:43</td>\n",
       "      <td>1.812069e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4522</th>\n",
       "      <td>8900390</td>\n",
       "      <td>1573272883</td>\n",
       "      <td>0.038098</td>\n",
       "      <td>2019-11-09 04:14:43</td>\n",
       "      <td>1.812134e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4523</th>\n",
       "      <td>8900630</td>\n",
       "      <td>1573276483</td>\n",
       "      <td>0.038108</td>\n",
       "      <td>2019-11-09 05:14:43</td>\n",
       "      <td>1.812591e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4524</th>\n",
       "      <td>8900870</td>\n",
       "      <td>1573280083</td>\n",
       "      <td>0.038143</td>\n",
       "      <td>2019-11-09 06:14:43</td>\n",
       "      <td>1.814242e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>8901110</td>\n",
       "      <td>1573283683</td>\n",
       "      <td>0.038150</td>\n",
       "      <td>2019-11-09 07:14:43</td>\n",
       "      <td>1.814598e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4526</th>\n",
       "      <td>8901350</td>\n",
       "      <td>1573287283</td>\n",
       "      <td>0.038168</td>\n",
       "      <td>2019-11-09 08:14:43</td>\n",
       "      <td>1.815451e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4527</th>\n",
       "      <td>8901590</td>\n",
       "      <td>1573290883</td>\n",
       "      <td>0.038195</td>\n",
       "      <td>2019-11-09 09:14:43</td>\n",
       "      <td>1.816738e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4528</th>\n",
       "      <td>8901830</td>\n",
       "      <td>1573294483</td>\n",
       "      <td>0.038255</td>\n",
       "      <td>2019-11-09 10:14:43</td>\n",
       "      <td>1.819597e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4529</th>\n",
       "      <td>8902070</td>\n",
       "      <td>1573298083</td>\n",
       "      <td>0.038278</td>\n",
       "      <td>2019-11-09 11:14:43</td>\n",
       "      <td>1.820658e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4530</th>\n",
       "      <td>8902310</td>\n",
       "      <td>1573301683</td>\n",
       "      <td>0.038319</td>\n",
       "      <td>2019-11-09 12:14:43</td>\n",
       "      <td>1.822641e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4531</th>\n",
       "      <td>8902550</td>\n",
       "      <td>1573305283</td>\n",
       "      <td>0.039092</td>\n",
       "      <td>2019-11-09 13:14:43</td>\n",
       "      <td>1.859406e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4532</th>\n",
       "      <td>8902790</td>\n",
       "      <td>1573308883</td>\n",
       "      <td>0.039402</td>\n",
       "      <td>2019-11-09 14:14:43</td>\n",
       "      <td>1.874136e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4533</th>\n",
       "      <td>8903030</td>\n",
       "      <td>1573312483</td>\n",
       "      <td>0.039408</td>\n",
       "      <td>2019-11-09 15:14:43</td>\n",
       "      <td>1.874452e-08</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4534 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      block_number  block_timestamp      rate          block_time  \\\n",
       "0          7804310       1556831679  0.026341 2019-05-02 21:14:39   \n",
       "1          7805750       1556853279  0.023087 2019-05-03 03:14:39   \n",
       "2          7805990       1556856879  0.077711 2019-05-03 04:14:39   \n",
       "3          7806950       1556871279  0.116131 2019-05-03 08:14:39   \n",
       "4          7807190       1556874879  0.076435 2019-05-03 09:14:39   \n",
       "5          7808630       1556896479  0.062027 2019-05-03 15:14:39   \n",
       "6          7810550       1556925279  0.057888 2019-05-03 23:14:39   \n",
       "7          7811270       1556936079  0.057745 2019-05-04 02:14:39   \n",
       "8          7811750       1556943279  0.057565 2019-05-04 04:14:39   \n",
       "9          7814630       1556986479  0.057496 2019-05-04 16:14:39   \n",
       "10         7815110       1556993679  0.054793 2019-05-04 18:14:39   \n",
       "11         7815350       1556997279  0.032670 2019-05-04 19:14:39   \n",
       "12         7815590       1557000879  0.026358 2019-05-04 20:14:39   \n",
       "13         7815830       1557004479  0.017461 2019-05-04 21:14:39   \n",
       "14         7816070       1557008079  0.014372 2019-05-04 22:14:39   \n",
       "15         7816310       1557011679  0.011278 2019-05-04 23:14:39   \n",
       "16         7816550       1557015279  0.045943 2019-05-05 00:14:39   \n",
       "17         7816790       1557018879  0.069831 2019-05-05 01:14:39   \n",
       "18         7817030       1557022479  0.053355 2019-05-05 02:14:39   \n",
       "19         7817270       1557026079  0.058280 2019-05-05 03:14:39   \n",
       "20         7817510       1557029679  0.091001 2019-05-05 04:14:39   \n",
       "21         7817750       1557033279  0.103815 2019-05-05 05:14:39   \n",
       "22         7817990       1557036879  0.092999 2019-05-05 06:14:39   \n",
       "23         7818230       1557040479  0.083167 2019-05-05 07:14:39   \n",
       "24         7818470       1557044079  0.102959 2019-05-05 08:14:39   \n",
       "25         7818710       1557047679  0.100890 2019-05-05 09:14:39   \n",
       "26         7818950       1557051279  0.097661 2019-05-05 10:14:39   \n",
       "27         7819190       1557054879  0.094332 2019-05-05 11:14:39   \n",
       "28         7819430       1557058479  0.094592 2019-05-05 12:14:39   \n",
       "29         7819670       1557062079  0.089846 2019-05-05 13:14:39   \n",
       "...            ...              ...       ...                 ...   \n",
       "4504       8896070       1573208083  0.042348 2019-11-08 10:14:43   \n",
       "4505       8896310       1573211683  0.041075 2019-11-08 11:14:43   \n",
       "4506       8896550       1573215283  0.041387 2019-11-08 12:14:43   \n",
       "4507       8896790       1573218883  0.041363 2019-11-08 13:14:43   \n",
       "4508       8897030       1573222483  0.037338 2019-11-08 14:14:43   \n",
       "4509       8897270       1573226083  0.037483 2019-11-08 15:14:43   \n",
       "4510       8897510       1573229683  0.037522 2019-11-08 16:14:43   \n",
       "4511       8897750       1573233283  0.037653 2019-11-08 17:14:43   \n",
       "4512       8897990       1573236883  0.037703 2019-11-08 18:14:43   \n",
       "4513       8898230       1573240483  0.037713 2019-11-08 19:14:43   \n",
       "4514       8898470       1573244083  0.037745 2019-11-08 20:14:43   \n",
       "4515       8898710       1573247683  0.037837 2019-11-08 21:14:43   \n",
       "4516       8898950       1573251283  0.037874 2019-11-08 22:14:43   \n",
       "4517       8899190       1573254883  0.037923 2019-11-08 23:14:43   \n",
       "4518       8899430       1573258483  0.038046 2019-11-09 00:14:43   \n",
       "4519       8899670       1573262083  0.038069 2019-11-09 01:14:43   \n",
       "4520       8899910       1573265683  0.038090 2019-11-09 02:14:43   \n",
       "4521       8900150       1573269283  0.038097 2019-11-09 03:14:43   \n",
       "4522       8900390       1573272883  0.038098 2019-11-09 04:14:43   \n",
       "4523       8900630       1573276483  0.038108 2019-11-09 05:14:43   \n",
       "4524       8900870       1573280083  0.038143 2019-11-09 06:14:43   \n",
       "4525       8901110       1573283683  0.038150 2019-11-09 07:14:43   \n",
       "4526       8901350       1573287283  0.038168 2019-11-09 08:14:43   \n",
       "4527       8901590       1573290883  0.038195 2019-11-09 09:14:43   \n",
       "4528       8901830       1573294483  0.038255 2019-11-09 10:14:43   \n",
       "4529       8902070       1573298083  0.038278 2019-11-09 11:14:43   \n",
       "4530       8902310       1573301683  0.038319 2019-11-09 12:14:43   \n",
       "4531       8902550       1573305283  0.039092 2019-11-09 13:14:43   \n",
       "4532       8902790       1573308883  0.039402 2019-11-09 14:14:43   \n",
       "4533       8903030       1573312483  0.039408 2019-11-09 15:14:43   \n",
       "\n",
       "      rate_per_block  rate_per_hour  \n",
       "0       1.252912e-08       0.000003  \n",
       "1       1.098135e-08       0.000003  \n",
       "2       3.696321e-08       0.000009  \n",
       "3       5.523736e-08       0.000013  \n",
       "4       3.635592e-08       0.000009  \n",
       "5       2.950298e-08       0.000007  \n",
       "6       2.753417e-08       0.000007  \n",
       "7       2.746621e-08       0.000007  \n",
       "8       2.738065e-08       0.000007  \n",
       "9       2.734801e-08       0.000007  \n",
       "10      2.606221e-08       0.000006  \n",
       "11      1.553938e-08       0.000004  \n",
       "12      1.253727e-08       0.000003  \n",
       "13      8.305365e-09       0.000002  \n",
       "14      6.835858e-09       0.000002  \n",
       "15      5.364565e-09       0.000001  \n",
       "16      2.185266e-08       0.000005  \n",
       "17      3.321491e-08       0.000008  \n",
       "18      2.537826e-08       0.000006  \n",
       "19      2.772048e-08       0.000007  \n",
       "20      4.328436e-08       0.000010  \n",
       "21      4.937935e-08       0.000012  \n",
       "22      4.423488e-08       0.000011  \n",
       "23      3.955804e-08       0.000009  \n",
       "24      4.897217e-08       0.000012  \n",
       "25      4.798791e-08       0.000012  \n",
       "26      4.645204e-08       0.000011  \n",
       "27      4.486859e-08       0.000011  \n",
       "28      4.499240e-08       0.000011  \n",
       "29      4.273509e-08       0.000010  \n",
       "...              ...            ...  \n",
       "4504    2.014290e-08       0.000005  \n",
       "4505    1.953712e-08       0.000005  \n",
       "4506    1.968556e-08       0.000005  \n",
       "4507    1.967421e-08       0.000005  \n",
       "4508    1.775979e-08       0.000004  \n",
       "4509    1.782860e-08       0.000004  \n",
       "4510    1.784735e-08       0.000004  \n",
       "4511    1.790960e-08       0.000004  \n",
       "4512    1.793323e-08       0.000004  \n",
       "4513    1.793788e-08       0.000004  \n",
       "4514    1.795337e-08       0.000004  \n",
       "4515    1.799715e-08       0.000004  \n",
       "4516    1.801443e-08       0.000004  \n",
       "4517    1.803810e-08       0.000004  \n",
       "4518    1.809627e-08       0.000004  \n",
       "4519    1.810736e-08       0.000004  \n",
       "4520    1.811724e-08       0.000004  \n",
       "4521    1.812069e-08       0.000004  \n",
       "4522    1.812134e-08       0.000004  \n",
       "4523    1.812591e-08       0.000004  \n",
       "4524    1.814242e-08       0.000004  \n",
       "4525    1.814598e-08       0.000004  \n",
       "4526    1.815451e-08       0.000004  \n",
       "4527    1.816738e-08       0.000004  \n",
       "4528    1.819597e-08       0.000004  \n",
       "4529    1.820658e-08       0.000004  \n",
       "4530    1.822641e-08       0.000004  \n",
       "4531    1.859406e-08       0.000004  \n",
       "4532    1.874136e-08       0.000004  \n",
       "4533    1.874452e-08       0.000004  \n",
       "\n",
       "[4534 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interestOverTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If the pool had just invested into the money market it would be worth: 1049.977582712235 with APR: 9.331956831555233\n"
     ]
    }
   ],
   "source": [
    "# Say the pool has 1000 dai in it\n",
    "N = 1000\n",
    "t1 = int(interestOverTime.head(1)['block_timestamp'])\n",
    "t2 = int(interestOverTime.tail(1)['block_timestamp'])\n",
    "startingRate = float(interestOverTime.head(1)['rate'])\n",
    "\n",
    "#find the future value of the market position\n",
    "moneyMarketFutureValue = floatingValue(N,t1,t2)\n",
    "\n",
    "#find the effective marketAPR\n",
    "effectiveMarketApr = effectiveAPR(N,moneyMarketFutureValue,t1,t2)\n",
    "\n",
    "print(\"If the pool had just invested into the money market it would be worth: %s with APR: %s\" % \n",
    "      (moneyMarketFutureValue, effectiveMarketApr))\n",
    "p_long = p_short = N/2 #long and short pools are half of the nominal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Floating Rate: 0.02634122012667002\n",
      "Long offer(trader long)   -> pool recives fixed rate: 0.03556064717100453 & pays float\n",
      "Short offer(trader short) -> pool pays fixed rate:    0.017121793082335513 & recives floating rate\n"
     ]
    }
   ],
   "source": [
    "# say someone goes long with 500 dai and someone goes short with 500 dai. \n",
    "# both positions are opened at the same time at the start of the data set\n",
    "# This will result in half pool utilization for both trades\n",
    "# for this part of the simulation assume that they hold the full value to maturity.\n",
    "\n",
    "swap_N_short = 500\n",
    "swap_N_long = 500\n",
    "\n",
    "longFixedRate = poolFixedRate(swap_N_long/N,startingRate,True) #Fixed rate counterparty will pay\n",
    "shortFixedRate = poolFixedRate(swap_N_short/N,startingRate,False) #Fixed rate counterparty will recive\n",
    "print(\"Floating Rate: %s\\nLong offer(trader long)   -> pool recives fixed rate: %s & pays float\\nShort offer(trader short) -> pool pays fixed rate:    %s & recives floating rate\" %\n",
    "      (startingRate, longFixedRate, shortFixedRate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-15.609882980749035\n",
      "20.494770639702608\n",
      "Final value of pool after offering swaps is 1054.8624703711887 with an APR of 10.220133407976958. Was pool profitable?: True\n"
     ]
    }
   ],
   "source": [
    "# now find the future value of the position, from the perspective of the lequidity pool.\n",
    "# As a result we are paying the lower fixed rate and reciving the higher fixed rate.\n",
    "\n",
    "# in the long offer swap the lequidity pool is short: reciving a fixed rate and paying a floating rate\n",
    "SwapNPV_long = futureValueNACH(swap_N_long, longFixedRate, t1, t2) - floatingValue(swap_N_long, t1,t2)\n",
    "print(SwapNPV_long)\n",
    "\n",
    "# in the short offer swap the lequidity pool is long: reciving floating rate, paying fixed rate\n",
    "SwapNPV_short = floatingValue(swap_N_short, t1,t2) - futureValueNACH(swap_N_short, shortFixedRate, t1, t2)\n",
    "print(SwapNPV_short)\n",
    "\n",
    "FinalPoolValue = SwapNPV_short + SwapNPV_long + moneyMarketFutureValue\n",
    "print(\"Final value of pool after offering swaps is %s with an APR of %s. Was pool profitable?: %s\" %\n",
    "      (FinalPoolValue, effectiveAPR(N,FinalPoolValue,t1,t2), FinalPoolValue >= moneyMarketFutureValue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate hyper parameters over provided functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will sweep over long and short pool sizes against different positions in the data set for different swap maturities. Then run this over a ranger of different alpha values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulate swap over duration\n",
    "def swapSimulate(swap_N_long, swap_N_short,startingRate, t1, t2, totalPool):\n",
    "    # find long and short offered rates\n",
    "    longFixedRate = poolFixedRate(swap_N_long/N,startingRate,True) # Fixed rate counterparty will pay\n",
    "    shortFixedRate = poolFixedRate(swap_N_short/N,startingRate,False) # Fixed rate counterparty will recive\n",
    "\n",
    "    # find value of swaps from lequidity pools perspective\n",
    "    SwapNPV_long = futureValueNACH(swap_N_long, longFixedRate, t1, t2) - floatingValue(swap_N_long, t1,t2)\n",
    "    SwapNPV_short = floatingValue(swap_N_short, t1,t2) - futureValueNACH(swap_N_short, shortFixedRate, t1, t2)\n",
    "\n",
    "    # find growth of underlying during duration\n",
    "    moneyMarketFutureValue = floatingValue(N,t1,t2)\n",
    "\n",
    "    # balance the sides and the underlying at maturity\n",
    "    FinalPoolValue = SwapNPV_short + SwapNPV_long + moneyMarketFutureValue\n",
    "    effectiveAPRValue = effectiveAPR(N,FinalPoolValue,t1,t2)\n",
    "    return(effectiveAPRValue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i: 0, p_short: 0, p_long: 0, r: 0.10818, apr: 0.09352, effectiveApr: 0.09352, profit: 0.0\n",
      "i: 0, p_short: 0, p_long: 100, r: 0.10818, apr: 0.09572, effectiveApr: 0.09352, profit: 0.02297\n",
      "i: 0, p_short: 0, p_long: 200, r: 0.10818, apr: 0.09937, effectiveApr: 0.09352, profit: 0.05889\n",
      "i: 0, p_short: 0, p_long: 300, r: 0.10818, apr: 0.10448, effectiveApr: 0.09352, profit: 0.1049\n",
      "i: 0, p_short: 0, p_long: 400, r: 0.10818, apr: 0.11104, effectiveApr: 0.09352, profit: 0.15778\n",
      "i: 0, p_short: 0, p_long: 500, r: 0.10818, apr: 0.11904, effectiveApr: 0.09352, profit: 0.21441\n",
      "i: 0, p_short: 100, p_long: 0, r: 0.10818, apr: 0.09277, effectiveApr: 0.09352, profit: -0.00805\n",
      "i: 0, p_short: 100, p_long: 100, r: 0.10818, apr: 0.09497, effectiveApr: 0.09352, profit: 0.01529\n",
      "i: 0, p_short: 100, p_long: 200, r: 0.10818, apr: 0.09863, effectiveApr: 0.09352, profit: 0.05178\n",
      "i: 0, p_short: 100, p_long: 300, r: 0.10818, apr: 0.10374, effectiveApr: 0.09352, profit: 0.09849\n",
      "i: 0, p_short: 100, p_long: 400, r: 0.10818, apr: 0.1103, effectiveApr: 0.09352, profit: 0.15212\n",
      "i: 0, p_short: 100, p_long: 500, r: 0.10818, apr: 0.1183, effectiveApr: 0.09352, profit: 0.20951\n",
      "i: 0, p_short: 200, p_long: 0, r: 0.10818, apr: 0.09347, effectiveApr: 0.09352, profit: -0.00051\n",
      "i: 0, p_short: 200, p_long: 100, r: 0.10818, apr: 0.09567, effectiveApr: 0.09352, profit: 0.02248\n",
      "i: 0, p_short: 200, p_long: 200, r: 0.10818, apr: 0.09932, effectiveApr: 0.09352, profit: 0.05843\n",
      "i: 0, p_short: 200, p_long: 300, r: 0.10818, apr: 0.10443, effectiveApr: 0.09352, profit: 0.10449\n",
      "i: 0, p_short: 200, p_long: 400, r: 0.10818, apr: 0.11099, effectiveApr: 0.09352, profit: 0.15742\n",
      "i: 0, p_short: 200, p_long: 500, r: 0.10818, apr: 0.11899, effectiveApr: 0.09352, profit: 0.2141\n",
      "i: 0, p_short: 300, p_long: 0, r: 0.10818, apr: 0.09561, effectiveApr: 0.09352, profit: 0.02183\n",
      "i: 0, p_short: 300, p_long: 100, r: 0.10818, apr: 0.0978, effectiveApr: 0.09352, profit: 0.04379\n",
      "i: 0, p_short: 300, p_long: 200, r: 0.10818, apr: 0.10145, effectiveApr: 0.09352, profit: 0.07819\n",
      "i: 0, p_short: 300, p_long: 300, r: 0.10818, apr: 0.10655, effectiveApr: 0.09352, profit: 0.12233\n",
      "i: 0, p_short: 300, p_long: 400, r: 0.10818, apr: 0.11311, effectiveApr: 0.09352, profit: 0.17318\n",
      "i: 0, p_short: 300, p_long: 500, r: 0.10818, apr: 0.1211, effectiveApr: 0.09352, profit: 0.22777\n",
      "i: 0, p_short: 400, p_long: 0, r: 0.10818, apr: 0.09916, effectiveApr: 0.09352, profit: 0.05693\n",
      "i: 0, p_short: 400, p_long: 100, r: 0.10818, apr: 0.10136, effectiveApr: 0.09352, profit: 0.07733\n",
      "i: 0, p_short: 400, p_long: 200, r: 0.10818, apr: 0.105, effectiveApr: 0.09352, profit: 0.10934\n",
      "i: 0, p_short: 400, p_long: 300, r: 0.10818, apr: 0.11009, effectiveApr: 0.09352, profit: 0.15055\n",
      "i: 0, p_short: 400, p_long: 400, r: 0.10818, apr: 0.11663, effectiveApr: 0.09352, profit: 0.19819\n",
      "i: 0, p_short: 400, p_long: 500, r: 0.10818, apr: 0.12462, effectiveApr: 0.09352, profit: 0.24955\n",
      "i: 0, p_short: 500, p_long: 0, r: 0.10818, apr: 0.10413, effectiveApr: 0.09352, profit: 0.10192\n",
      "i: 0, p_short: 500, p_long: 100, r: 0.10818, apr: 0.10632, effectiveApr: 0.09352, profit: 0.12039\n",
      "i: 0, p_short: 500, p_long: 200, r: 0.10818, apr: 0.10995, effectiveApr: 0.09352, profit: 0.14946\n",
      "i: 0, p_short: 500, p_long: 300, r: 0.10818, apr: 0.11503, effectiveApr: 0.09352, profit: 0.18703\n",
      "i: 0, p_short: 500, p_long: 400, r: 0.10818, apr: 0.12156, effectiveApr: 0.09352, profit: 0.23067\n",
      "i: 0, p_short: 500, p_long: 500, r: 0.10818, apr: 0.12952, effectiveApr: 0.09352, profit: 0.27796\n",
      "i: 1, p_short: 0, p_long: 0, r: 0.06834, apr: 0.09512, effectiveApr: 0.09512, profit: 0.0\n",
      "i: 1, p_short: 0, p_long: 100, r: 0.06834, apr: 0.09291, effectiveApr: 0.09512, profit: -0.02383\n",
      "i: 1, p_short: 0, p_long: 200, r: 0.06834, apr: 0.0916, effectiveApr: 0.09512, profit: -0.03847\n",
      "i: 1, p_short: 0, p_long: 300, r: 0.06834, apr: 0.09119, effectiveApr: 0.09512, profit: -0.04306\n",
      "i: 1, p_short: 0, p_long: 400, r: 0.06834, apr: 0.0917, effectiveApr: 0.09512, profit: -0.03729\n",
      "i: 1, p_short: 0, p_long: 500, r: 0.06834, apr: 0.09312, effectiveApr: 0.09512, profit: -0.02148\n",
      "i: 1, p_short: 100, p_long: 0, r: 0.06834, apr: 0.09823, effectiveApr: 0.09512, profit: 0.03168\n",
      "i: 1, p_short: 100, p_long: 100, r: 0.06834, apr: 0.09602, effectiveApr: 0.09512, profit: 0.00939\n",
      "i: 1, p_short: 100, p_long: 200, r: 0.06834, apr: 0.09471, effectiveApr: 0.09512, profit: -0.0043\n",
      "i: 1, p_short: 100, p_long: 300, r: 0.06834, apr: 0.09431, effectiveApr: 0.09512, profit: -0.00859\n",
      "i: 1, p_short: 100, p_long: 400, r: 0.06834, apr: 0.09482, effectiveApr: 0.09512, profit: -0.0032\n",
      "i: 1, p_short: 100, p_long: 500, r: 0.06834, apr: 0.09624, effectiveApr: 0.09512, profit: 0.01158\n",
      "i: 1, p_short: 200, p_long: 0, r: 0.06834, apr: 0.10224, effectiveApr: 0.09512, profit: 0.06961\n",
      "i: 1, p_short: 200, p_long: 100, r: 0.06834, apr: 0.10003, effectiveApr: 0.09512, profit: 0.04908\n",
      "i: 1, p_short: 200, p_long: 200, r: 0.06834, apr: 0.09872, effectiveApr: 0.09512, profit: 0.0365\n",
      "i: 1, p_short: 200, p_long: 300, r: 0.06834, apr: 0.09832, effectiveApr: 0.09512, profit: 0.03256\n",
      "i: 1, p_short: 200, p_long: 400, r: 0.06834, apr: 0.09883, effectiveApr: 0.09512, profit: 0.03751\n",
      "i: 1, p_short: 200, p_long: 500, r: 0.06834, apr: 0.10024, effectiveApr: 0.09512, profit: 0.0511\n",
      "i: 1, p_short: 300, p_long: 0, r: 0.06834, apr: 0.10713, effectiveApr: 0.09512, profit: 0.11209\n",
      "i: 1, p_short: 300, p_long: 100, r: 0.06834, apr: 0.10493, effectiveApr: 0.09512, profit: 0.09345\n",
      "i: 1, p_short: 300, p_long: 200, r: 0.06834, apr: 0.10362, effectiveApr: 0.09512, profit: 0.08204\n",
      "i: 1, p_short: 300, p_long: 300, r: 0.06834, apr: 0.10322, effectiveApr: 0.09512, profit: 0.07847\n",
      "i: 1, p_short: 300, p_long: 400, r: 0.06834, apr: 0.10373, effectiveApr: 0.09512, profit: 0.08296\n",
      "i: 1, p_short: 300, p_long: 500, r: 0.06834, apr: 0.10514, effectiveApr: 0.09512, profit: 0.09528\n",
      "i: 1, p_short: 400, p_long: 0, r: 0.06834, apr: 0.1129, effectiveApr: 0.09512, profit: 0.15746\n",
      "i: 1, p_short: 400, p_long: 100, r: 0.06834, apr: 0.1107, effectiveApr: 0.09512, profit: 0.14073\n",
      "i: 1, p_short: 400, p_long: 200, r: 0.06834, apr: 0.1094, effectiveApr: 0.09512, profit: 0.13052\n",
      "i: 1, p_short: 400, p_long: 300, r: 0.06834, apr: 0.109, effectiveApr: 0.09512, profit: 0.12732\n",
      "i: 1, p_short: 400, p_long: 400, r: 0.06834, apr: 0.1095, effectiveApr: 0.09512, profit: 0.13134\n",
      "i: 1, p_short: 400, p_long: 500, r: 0.06834, apr: 0.11091, effectiveApr: 0.09512, profit: 0.14238\n",
      "i: 1, p_short: 500, p_long: 0, r: 0.06834, apr: 0.11954, effectiveApr: 0.09512, profit: 0.20426\n",
      "i: 1, p_short: 500, p_long: 100, r: 0.06834, apr: 0.11735, effectiveApr: 0.09512, profit: 0.18939\n",
      "i: 1, p_short: 500, p_long: 200, r: 0.06834, apr: 0.11605, effectiveApr: 0.09512, profit: 0.18033\n",
      "i: 1, p_short: 500, p_long: 300, r: 0.06834, apr: 0.11565, effectiveApr: 0.09512, profit: 0.1775\n",
      "i: 1, p_short: 500, p_long: 400, r: 0.06834, apr: 0.11615, effectiveApr: 0.09512, profit: 0.18106\n",
      "i: 1, p_short: 500, p_long: 500, r: 0.06834, apr: 0.11756, effectiveApr: 0.09512, profit: 0.19085\n",
      "i: 2, p_short: 0, p_long: 0, r: 0.12578, apr: 0.08819, effectiveApr: 0.08819, profit: 0.0\n",
      "i: 2, p_short: 0, p_long: 100, r: 0.12578, apr: 0.09281, effectiveApr: 0.08819, profit: 0.04985\n",
      "i: 2, p_short: 0, p_long: 200, r: 0.12578, apr: 0.09913, effectiveApr: 0.08819, profit: 0.1104\n",
      "i: 2, p_short: 0, p_long: 300, r: 0.12578, apr: 0.10714, effectiveApr: 0.08819, profit: 0.17688\n",
      "i: 2, p_short: 0, p_long: 400, r: 0.12578, apr: 0.11682, effectiveApr: 0.08819, profit: 0.24513\n",
      "i: 2, p_short: 0, p_long: 500, r: 0.12578, apr: 0.12819, effectiveApr: 0.08819, profit: 0.31204\n",
      "i: 2, p_short: 100, p_long: 0, r: 0.12578, apr: 0.08525, effectiveApr: 0.08819, profit: -0.03443\n",
      "i: 2, p_short: 100, p_long: 100, r: 0.12578, apr: 0.08988, effectiveApr: 0.08819, profit: 0.01887\n",
      "i: 2, p_short: 100, p_long: 200, r: 0.12578, apr: 0.09621, effectiveApr: 0.08819, profit: 0.08335\n",
      "i: 2, p_short: 100, p_long: 300, r: 0.12578, apr: 0.10422, effectiveApr: 0.08819, profit: 0.15383\n",
      "i: 2, p_short: 100, p_long: 400, r: 0.12578, apr: 0.11391, effectiveApr: 0.08819, profit: 0.22585\n",
      "i: 2, p_short: 100, p_long: 500, r: 0.12578, apr: 0.12529, effectiveApr: 0.08819, profit: 0.29611\n",
      "i: 2, p_short: 200, p_long: 0, r: 0.12578, apr: 0.08401, effectiveApr: 0.08819, profit: -0.04977\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i: 2, p_short: 200, p_long: 100, r: 0.12578, apr: 0.08864, effectiveApr: 0.08819, profit: 0.0051\n",
      "i: 2, p_short: 200, p_long: 200, r: 0.12578, apr: 0.09496, effectiveApr: 0.08819, profit: 0.07137\n",
      "i: 2, p_short: 200, p_long: 300, r: 0.12578, apr: 0.10298, effectiveApr: 0.08819, profit: 0.14365\n",
      "i: 2, p_short: 200, p_long: 400, r: 0.12578, apr: 0.11268, effectiveApr: 0.08819, profit: 0.21737\n",
      "i: 2, p_short: 200, p_long: 500, r: 0.12578, apr: 0.12405, effectiveApr: 0.08819, profit: 0.28913\n",
      "i: 2, p_short: 300, p_long: 0, r: 0.12578, apr: 0.08445, effectiveApr: 0.08819, profit: -0.0443\n",
      "i: 2, p_short: 300, p_long: 100, r: 0.12578, apr: 0.08908, effectiveApr: 0.08819, profit: 0.01\n",
      "i: 2, p_short: 300, p_long: 200, r: 0.12578, apr: 0.0954, effectiveApr: 0.08819, profit: 0.07564\n",
      "i: 2, p_short: 300, p_long: 300, r: 0.12578, apr: 0.10342, effectiveApr: 0.08819, profit: 0.14727\n",
      "i: 2, p_short: 300, p_long: 400, r: 0.12578, apr: 0.11312, effectiveApr: 0.08819, profit: 0.22038\n",
      "i: 2, p_short: 300, p_long: 500, r: 0.12578, apr: 0.12449, effectiveApr: 0.08819, profit: 0.29161\n",
      "i: 2, p_short: 400, p_long: 0, r: 0.12578, apr: 0.08656, effectiveApr: 0.08819, profit: -0.01875\n",
      "i: 2, p_short: 400, p_long: 100, r: 0.12578, apr: 0.09119, effectiveApr: 0.08819, profit: 0.03297\n",
      "i: 2, p_short: 400, p_long: 200, r: 0.12578, apr: 0.09751, effectiveApr: 0.08819, profit: 0.09565\n",
      "i: 2, p_short: 400, p_long: 300, r: 0.12578, apr: 0.10552, effectiveApr: 0.08819, profit: 0.16429\n",
      "i: 2, p_short: 400, p_long: 400, r: 0.12578, apr: 0.11522, effectiveApr: 0.08819, profit: 0.23459\n",
      "i: 2, p_short: 400, p_long: 500, r: 0.12578, apr: 0.12658, effectiveApr: 0.08819, profit: 0.30332\n",
      "i: 2, p_short: 500, p_long: 0, r: 0.12578, apr: 0.09035, effectiveApr: 0.08819, profit: 0.02395\n",
      "i: 2, p_short: 500, p_long: 100, r: 0.12578, apr: 0.09497, effectiveApr: 0.08819, profit: 0.07147\n",
      "i: 2, p_short: 500, p_long: 200, r: 0.12578, apr: 0.10129, effectiveApr: 0.08819, profit: 0.12935\n",
      "i: 2, p_short: 500, p_long: 300, r: 0.12578, apr: 0.10929, effectiveApr: 0.08819, profit: 0.19308\n",
      "i: 2, p_short: 500, p_long: 400, r: 0.12578, apr: 0.11897, effectiveApr: 0.08819, profit: 0.25875\n",
      "i: 2, p_short: 500, p_long: 500, r: 0.12578, apr: 0.13032, effectiveApr: 0.08819, profit: 0.32333\n",
      "i: 3, p_short: 0, p_long: 0, r: 0.09832, apr: 0.07564, effectiveApr: 0.07564, profit: 0.0\n",
      "i: 3, p_short: 0, p_long: 100, r: 0.09832, apr: 0.07857, effectiveApr: 0.07564, profit: 0.0373\n",
      "i: 3, p_short: 0, p_long: 200, r: 0.09832, apr: 0.08282, effectiveApr: 0.07564, profit: 0.08666\n",
      "i: 3, p_short: 0, p_long: 300, r: 0.09832, apr: 0.08838, effectiveApr: 0.07564, profit: 0.14413\n",
      "i: 3, p_short: 0, p_long: 400, r: 0.09832, apr: 0.09526, effectiveApr: 0.07564, profit: 0.2059\n",
      "i: 3, p_short: 0, p_long: 500, r: 0.09832, apr: 0.10344, effectiveApr: 0.07564, profit: 0.26874\n",
      "i: 3, p_short: 100, p_long: 0, r: 0.09832, apr: 0.07403, effectiveApr: 0.07564, profit: -0.02181\n",
      "i: 3, p_short: 100, p_long: 100, r: 0.09832, apr: 0.07696, effectiveApr: 0.07564, profit: 0.01711\n",
      "i: 3, p_short: 100, p_long: 200, r: 0.09832, apr: 0.08121, effectiveApr: 0.07564, profit: 0.06852\n",
      "i: 3, p_short: 100, p_long: 300, r: 0.09832, apr: 0.08677, effectiveApr: 0.07564, profit: 0.12825\n",
      "i: 3, p_short: 100, p_long: 400, r: 0.09832, apr: 0.09365, effectiveApr: 0.07564, profit: 0.19227\n",
      "i: 3, p_short: 100, p_long: 500, r: 0.09832, apr: 0.10184, effectiveApr: 0.07564, profit: 0.25721\n",
      "i: 3, p_short: 200, p_long: 0, r: 0.09832, apr: 0.07373, effectiveApr: 0.07564, profit: -0.02597\n",
      "i: 3, p_short: 200, p_long: 100, r: 0.09832, apr: 0.07666, effectiveApr: 0.07564, profit: 0.01326\n",
      "i: 3, p_short: 200, p_long: 200, r: 0.09832, apr: 0.08091, effectiveApr: 0.07564, profit: 0.06507\n",
      "i: 3, p_short: 200, p_long: 300, r: 0.09832, apr: 0.08647, effectiveApr: 0.07564, profit: 0.12523\n",
      "i: 3, p_short: 200, p_long: 400, r: 0.09832, apr: 0.09335, effectiveApr: 0.07564, profit: 0.18968\n",
      "i: 3, p_short: 200, p_long: 500, r: 0.09832, apr: 0.10154, effectiveApr: 0.07564, profit: 0.25503\n",
      "i: 3, p_short: 300, p_long: 0, r: 0.09832, apr: 0.07474, effectiveApr: 0.07564, profit: -0.01209\n",
      "i: 3, p_short: 300, p_long: 100, r: 0.09832, apr: 0.07767, effectiveApr: 0.07564, profit: 0.02611\n",
      "i: 3, p_short: 300, p_long: 200, r: 0.09832, apr: 0.08192, effectiveApr: 0.07564, profit: 0.0766\n",
      "i: 3, p_short: 300, p_long: 300, r: 0.09832, apr: 0.08748, effectiveApr: 0.07564, profit: 0.13532\n",
      "i: 3, p_short: 300, p_long: 400, r: 0.09832, apr: 0.09436, effectiveApr: 0.07564, profit: 0.19833\n",
      "i: 3, p_short: 300, p_long: 500, r: 0.09832, apr: 0.10254, effectiveApr: 0.07564, profit: 0.26234\n",
      "i: 3, p_short: 400, p_long: 0, r: 0.09832, apr: 0.07706, effectiveApr: 0.07564, profit: 0.01839\n",
      "i: 3, p_short: 400, p_long: 100, r: 0.09832, apr: 0.07999, effectiveApr: 0.07564, profit: 0.05434\n",
      "i: 3, p_short: 400, p_long: 200, r: 0.09832, apr: 0.08423, effectiveApr: 0.07564, profit: 0.102\n",
      "i: 3, p_short: 400, p_long: 300, r: 0.09832, apr: 0.08979, effectiveApr: 0.07564, profit: 0.1576\n",
      "i: 3, p_short: 400, p_long: 400, r: 0.09832, apr: 0.09667, effectiveApr: 0.07564, profit: 0.2175\n",
      "i: 3, p_short: 400, p_long: 500, r: 0.09832, apr: 0.10485, effectiveApr: 0.07564, profit: 0.27857\n",
      "i: 3, p_short: 500, p_long: 0, r: 0.09832, apr: 0.08068, effectiveApr: 0.07564, profit: 0.06249\n",
      "i: 3, p_short: 500, p_long: 100, r: 0.09832, apr: 0.08361, effectiveApr: 0.07564, profit: 0.09531\n",
      "i: 3, p_short: 500, p_long: 200, r: 0.09832, apr: 0.08785, effectiveApr: 0.07564, profit: 0.139\n",
      "i: 3, p_short: 500, p_long: 300, r: 0.09832, apr: 0.09341, effectiveApr: 0.07564, profit: 0.19021\n",
      "i: 3, p_short: 500, p_long: 400, r: 0.09832, apr: 0.10028, effectiveApr: 0.07564, profit: 0.24567\n",
      "i: 3, p_short: 500, p_long: 500, r: 0.09832, apr: 0.10846, effectiveApr: 0.07564, profit: 0.30255\n",
      "i: 4, p_short: 0, p_long: 0, r: 0.08176, apr: 0.06653, effectiveApr: 0.06653, profit: 0.0\n",
      "i: 4, p_short: 0, p_long: 100, r: 0.08176, apr: 0.0686, effectiveApr: 0.06653, profit: 0.03016\n",
      "i: 4, p_short: 0, p_long: 200, r: 0.08176, apr: 0.07176, effectiveApr: 0.06653, profit: 0.07288\n",
      "i: 4, p_short: 0, p_long: 300, r: 0.08176, apr: 0.07602, effectiveApr: 0.06653, profit: 0.12474\n",
      "i: 4, p_short: 0, p_long: 400, r: 0.08176, apr: 0.08136, effectiveApr: 0.06653, profit: 0.18222\n",
      "i: 4, p_short: 0, p_long: 500, r: 0.08176, apr: 0.08779, effectiveApr: 0.06653, profit: 0.24215\n",
      "i: 4, p_short: 100, p_long: 0, r: 0.08176, apr: 0.06556, effectiveApr: 0.06653, profit: -0.01491\n",
      "i: 4, p_short: 100, p_long: 100, r: 0.08176, apr: 0.06763, effectiveApr: 0.06653, profit: 0.01614\n",
      "i: 4, p_short: 100, p_long: 200, r: 0.08176, apr: 0.07079, effectiveApr: 0.06653, profit: 0.06008\n",
      "i: 4, p_short: 100, p_long: 300, r: 0.08176, apr: 0.07504, effectiveApr: 0.06653, profit: 0.11335\n",
      "i: 4, p_short: 100, p_long: 400, r: 0.08176, apr: 0.08038, effectiveApr: 0.06653, profit: 0.17229\n",
      "i: 4, p_short: 100, p_long: 500, r: 0.08176, apr: 0.08682, effectiveApr: 0.06653, profit: 0.23364\n",
      "i: 4, p_short: 200, p_long: 0, r: 0.08176, apr: 0.06567, effectiveApr: 0.06653, profit: -0.01316\n",
      "i: 4, p_short: 200, p_long: 100, r: 0.08176, apr: 0.06774, effectiveApr: 0.06653, profit: 0.01779\n",
      "i: 4, p_short: 200, p_long: 200, r: 0.08176, apr: 0.0709, effectiveApr: 0.06653, profit: 0.06158\n",
      "i: 4, p_short: 200, p_long: 300, r: 0.08176, apr: 0.07515, effectiveApr: 0.06653, profit: 0.11468\n",
      "i: 4, p_short: 200, p_long: 400, r: 0.08176, apr: 0.0805, effectiveApr: 0.06653, profit: 0.17346\n",
      "i: 4, p_short: 200, p_long: 500, r: 0.08176, apr: 0.08693, effectiveApr: 0.06653, profit: 0.23463\n",
      "i: 4, p_short: 300, p_long: 0, r: 0.08176, apr: 0.06687, effectiveApr: 0.06653, profit: 0.00507\n",
      "i: 4, p_short: 300, p_long: 100, r: 0.08176, apr: 0.06894, effectiveApr: 0.06653, profit: 0.03493\n",
      "i: 4, p_short: 300, p_long: 200, r: 0.08176, apr: 0.0721, effectiveApr: 0.06653, profit: 0.07723\n",
      "i: 4, p_short: 300, p_long: 300, r: 0.08176, apr: 0.07636, effectiveApr: 0.06653, profit: 0.12862\n",
      "i: 4, p_short: 300, p_long: 400, r: 0.08176, apr: 0.0817, effectiveApr: 0.06653, profit: 0.18561\n",
      "i: 4, p_short: 300, p_long: 500, r: 0.08176, apr: 0.08813, effectiveApr: 0.06653, profit: 0.24506\n",
      "i: 4, p_short: 400, p_long: 0, r: 0.08176, apr: 0.06917, effectiveApr: 0.06653, profit: 0.03804\n",
      "i: 4, p_short: 400, p_long: 100, r: 0.08176, apr: 0.07123, effectiveApr: 0.06653, profit: 0.06597\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i: 4, p_short: 400, p_long: 200, r: 0.08176, apr: 0.07439, effectiveApr: 0.06653, profit: 0.10565\n",
      "i: 4, p_short: 400, p_long: 300, r: 0.08176, apr: 0.07864, effectiveApr: 0.06653, profit: 0.15399\n",
      "i: 4, p_short: 400, p_long: 400, r: 0.08176, apr: 0.08399, effectiveApr: 0.06653, profit: 0.2078\n",
      "i: 4, p_short: 400, p_long: 500, r: 0.08176, apr: 0.09042, effectiveApr: 0.06653, profit: 0.26416\n",
      "i: 4, p_short: 500, p_long: 0, r: 0.08176, apr: 0.07254, effectiveApr: 0.06653, profit: 0.08284\n",
      "i: 4, p_short: 500, p_long: 100, r: 0.08176, apr: 0.07461, effectiveApr: 0.06653, profit: 0.10826\n",
      "i: 4, p_short: 500, p_long: 200, r: 0.08176, apr: 0.07777, effectiveApr: 0.06653, profit: 0.14448\n",
      "i: 4, p_short: 500, p_long: 300, r: 0.08176, apr: 0.08202, effectiveApr: 0.06653, profit: 0.18881\n",
      "i: 4, p_short: 500, p_long: 400, r: 0.08176, apr: 0.08736, effectiveApr: 0.06653, profit: 0.2384\n",
      "i: 4, p_short: 500, p_long: 500, r: 0.08176, apr: 0.09379, effectiveApr: 0.06653, profit: 0.29061\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "poolReserve = N/2\n",
    "\n",
    "for i in range(0,5):\n",
    "    # Add the new dataframe to store new results\n",
    "    results.append(pd.DataFrame())\n",
    "    for short_pool in range(0,600,100):\n",
    "        for long_pool in range(0,600,100):\n",
    "            # re-calculate t1 and t2 based on the new data set.\n",
    "            # t1 is found by shifting the initial starting time stamp forward 1/5 a fraction of the\n",
    "            # total time every loop. This gives us a equal slice over the whole time series data\n",
    "            t1 = int(int(interestOverTime.head(1)['block_timestamp']) + (int(interestOverTime.tail(1)['block_timestamp']) - int(interestOverTime.head(1)['block_timestamp']))/5 * i)\n",
    "            t2 = int(interestOverTime.tail(1)['block_timestamp'])\n",
    "            \n",
    "            # find the starting rate at this spesific point in time\n",
    "            startingRate = float(interestOverTime.loc[int(len(interestOverTime)/5 * i),'rate'])\n",
    "            apr = swapSimulate(long_pool, short_pool ,startingRate ,t1 ,t2, poolReserve)\n",
    "            \n",
    "            # find the effective market APR to compare to the pool\n",
    "            moneyMarketFutureValue = floatingValue(N,t1,t2)\n",
    "            effectiveMarketApr = effectiveAPR(N,moneyMarketFutureValue,t1,t2)\n",
    "\n",
    "            profit = (apr - effectiveMarketApr) / apr\n",
    "            print(\"i: %s, p_short: %s, p_long: %s, r: %s, apr: %s, effectiveApr: %s, profit: %s\" \n",
    "                  %(i, short_pool, long_pool, round(startingRate,5), round(apr,5), round(effectiveMarketApr,5), round(profit,5)))\n",
    "            newResult = pd.DataFrame({\"longPool\": long_pool/poolReserve,\n",
    "                                      \"shortPool\": short_pool/poolReserve,\n",
    "                                      \"apr\": apr,\n",
    "                                      \"profit\": profit,\n",
    "                                      \"profitable\": int(profit>0)}, index=[0])\n",
    "            results[i] = results[i].append(newResult, ignore_index = True)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
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   ],
   "source": [
    "for result in results:\n",
    "    fig = px.scatter_3d(result, x='longPool', y='shortPool', z='apr',\n",
    "                  color='profitable')\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall mean profit is: 11.460312886189403 percent\n"
     ]
    }
   ],
   "source": [
    "meanProfits = []\n",
    "for result in results:\n",
    "    meanProfits.append(result[\"profit\"].mean())\n",
    "print(\"Overall mean profit is: %s percent\" % (np.mean(meanProfits)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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