Trading Costs & Execution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np\n",
    "import matplotlib as mpl, matplotlib.pyplot as plt, seaborn as sns\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "# from help_functions import *\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style('whitegrid')\n",
    "mpl.rcParams['savefig.dpi'] = 300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_tickround(df, tick_size=1):\n",
    "    return np.round(df.astype(float).divide(tick_size)).multiply(tick_size).astype(int)\n",
    "\n",
    "def tickround(x, tick_size=5):\n",
    "    return int(tick_size * round(float(x)/tick_size))\n",
    "\n",
    "def calc_n_ticks(side, inverse, target_price, tick_size, maker_fee, taker_fee):\n",
    "    if side == 'long':\n",
    "        if inverse:\n",
    "            n_ticks = -target_price * (taker_fee - maker_fee) / (tick_size * (taker_fee - maker_fee - 1)) + 1\n",
    "        else:\n",
    "            n_ticks = target_price * (taker_fee - maker_fee) / tick_size + 1\n",
    "    elif side == 'short':\n",
    "        if inverse:\n",
    "            n_ticks = (target_price/tick_size) * (1 / (maker_fee - taker_fee -1) + 1) + 1\n",
    "        else:\n",
    "            n_ticks = target_price * (taker_fee - maker_fee) / tick_size + 1\n",
    "    return abs(n_ticks)\n",
    "\n",
    "def calc_exec_price(side, inverse, target_price, tick_size, maker_fee, taker_fee, thresh=5):\n",
    "    n_ticks = calc_n_ticks(side, inverse, target_price, tick_size, maker_fee, taker_fee)\n",
    "    n_ticks = np.max([0, n_ticks-thresh])\n",
    "    if side == 'long':\n",
    "        exec_price = target_price + n_ticks * tick_size\n",
    "    elif side == 'short':\n",
    "        exec_price = target_price - n_ticks * tick_size\n",
    "    return tickround(exec_price, tick_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>contract</th>\n",
       "      <th>target_price</th>\n",
       "      <th>multiplier</th>\n",
       "      <th>tick_size</th>\n",
       "      <th>inverse</th>\n",
       "      <th>initial_margin</th>\n",
       "      <th>maint_margin</th>\n",
       "      <th>maker_fee</th>\n",
       "      <th>taker_fee</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XBTUSD</th>\n",
       "      <td>XBTUSD</td>\n",
       "      <td>1029050</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.00025</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XBT1</th>\n",
       "      <td>XBTU19</td>\n",
       "      <td>1035500</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.00025</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHUSD</th>\n",
       "      <td>ETHUSD</td>\n",
       "      <td>18810</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-0.00025</td>\n",
       "      <td>0.00075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADAXBT</th>\n",
       "      <td>ADAU19</td>\n",
       "      <td>457</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>0.00250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCHXBT</th>\n",
       "      <td>BCHU19</td>\n",
       "      <td>3034000</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>0.00250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EOSXBT</th>\n",
       "      <td>EOSU19</td>\n",
       "      <td>34960</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>0.00250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHXBT</th>\n",
       "      <td>ETHU19</td>\n",
       "      <td>1814000</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>0.00250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LTCXBT</th>\n",
       "      <td>LTCU19</td>\n",
       "      <td>732500</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>500</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>0.00250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRXXBT</th>\n",
       "      <td>TRXU19</td>\n",
       "      <td>167</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>0.00250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XRPXBT</th>\n",
       "      <td>XRPU19</td>\n",
       "      <td>2541</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>0.00250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       contract  target_price   multiplier  tick_size  inverse  \\\n",
       "symbol                                                           \n",
       "XBTUSD   XBTUSD       1029050        100.0         50     True   \n",
       "XBT1     XBTU19       1035500        100.0         50     True   \n",
       "ETHUSD   ETHUSD         18810        100.0          5    False   \n",
       "ADAXBT   ADAU19           457  100000000.0          1    False   \n",
       "BCHXBT   BCHU19       3034000  100000000.0       1000    False   \n",
       "EOSXBT   EOSU19         34960  100000000.0         10    False   \n",
       "ETHXBT   ETHU19       1814000  100000000.0       1000    False   \n",
       "LTCXBT   LTCU19        732500  100000000.0        500    False   \n",
       "TRXXBT   TRXU19           167  100000000.0          1    False   \n",
       "XRPXBT   XRPU19          2541  100000000.0          1    False   \n",
       "\n",
       "        initial_margin  maint_margin  maker_fee  taker_fee  \n",
       "symbol                                                      \n",
       "XBTUSD            0.01         0.005   -0.00025    0.00075  \n",
       "XBT1              0.01         0.005   -0.00025    0.00075  \n",
       "ETHUSD            0.02         0.010   -0.00025    0.00075  \n",
       "ADAXBT            0.05         0.025   -0.00050    0.00250  \n",
       "BCHXBT            0.05         0.025   -0.00050    0.00250  \n",
       "EOSXBT            0.05         0.025   -0.00050    0.00250  \n",
       "ETHXBT            0.05         0.025   -0.00050    0.00250  \n",
       "LTCXBT            0.05         0.025   -0.00050    0.00250  \n",
       "TRXXBT            0.05         0.025   -0.00050    0.00250  \n",
       "XRPXBT            0.05         0.025   -0.00050    0.00250  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "settings_path = 'data/symbol_settings.csv'\n",
    "symbol_settings = pd.read_csv(settings_path, index_col=0)\n",
    "symbol_settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bitmex-Specific Idiosyncracies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Break-Evens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code in this section isn't the cleanest; you're gonna have to forgive me on that one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "symbol = 'XBTUSD'\n",
    "\n",
    "# Load symbol-specific parameters\n",
    "ind = symbol_settings.loc[symbol]\n",
    "side = 'long'\n",
    "inverse = ind['inverse']\n",
    "tick_size = ind['tick_size']\n",
    "maker_fee = ind['maker_fee']\n",
    "taker_fee = ind['taker_fee']\n",
    "target_price = ind['target_price']\n",
    "\n",
    "lo_ticks = 0\n",
    "hi_ticks = 30\n",
    "ticks = np.arange(-lo_ticks, hi_ticks)\n",
    "prices = pd_tickround(pd.Series(target_price + ticks*tick_size, index=ticks))\n",
    "\n",
    "# Calculate passive costs\n",
    "if side == 'long':\n",
    "    if inverse:\n",
    "        limit_fees = 1 - target_price/(prices-tick_size) + maker_fee\n",
    "    else:\n",
    "        limit_fees = (prices-tick_size)/target_price - 1 + maker_fee\n",
    "elif side == 'short':\n",
    "    if inverse:\n",
    "        limit_fees = maker_fee - (1 - target_price/(prices+tick_size))\n",
    "    else:\n",
    "        limit_fees = maker_fee - (prices+tick_size)/target_price + 1\n",
    "\n",
    "n_ticks = calc_n_ticks(side=side, inverse=inverse, maker_fee=maker_fee, taker_fee=taker_fee, target_price=target_price, tick_size=tick_size)\n",
    "break_even = target_price + n_ticks * tick_size\n",
    "\n",
    "# Plot Break-Evens\n",
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax.plot(prices, limit_fees)\n",
    "ax.axvline(target_price, c='k', lw=0.5)\n",
    "ax.axhline(0, c='k', lw=0.5)\n",
    "ax.axhline(taker_fee, c='r', lw=0.5, linestyle=':')\n",
    "ax.scatter(break_even, taker_fee, marker='o', color='r')\n",
    "ax.annotate('# ticks: {:.3}'.format(n_ticks), xy=(break_even, taker_fee), \n",
    "            xytext=(break_even*0.9996, taker_fee*1.25),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "\n",
    "ax.set_xticks(ax.get_xticks()[::1])\n",
    "xvals = ax.get_xticks()\n",
    "ax.set_xticklabels([int(x/100) for x in xvals])\n",
    "yvals = ax.get_yticks()\n",
    "ax.set_yticklabels(['{:,.2%}'.format(x) for x in yvals])\n",
    "ax.set_xlabel('Price (USD)')\n",
    "ax.set_ylabel('Total Costs (% of Order Value)')\n",
    "ax.set_title('XBTUSD Break-Evens', fontsize=13)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig('XBTUSD Break-Evens')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "symbol = 'TRXXBT'\n",
    "\n",
    "# Load symbol-specific parameters\n",
    "ind = symbol_settings.loc[symbol]\n",
    "side = 'long'\n",
    "inverse = ind['inverse']\n",
    "tick_size = ind['tick_size']\n",
    "maker_fee = ind['maker_fee']\n",
    "taker_fee = ind['taker_fee']\n",
    "target_price = ind['target_price']\n",
    "\n",
    "lo_ticks = 0\n",
    "hi_ticks = 5\n",
    "ticks = np.arange(-lo_ticks, hi_ticks)\n",
    "prices = pd_tickround(pd.Series(target_price + ticks*tick_size, index=ticks))\n",
    "\n",
    "# Calculate passive costs\n",
    "if side == 'long':\n",
    "    if inverse:\n",
    "        limit_fees = 1 - target_price/(prices-tick_size) + maker_fee\n",
    "    else:\n",
    "        limit_fees = (prices-tick_size)/target_price - 1 + maker_fee\n",
    "elif side == 'short':\n",
    "    if inverse:\n",
    "        limit_fees = maker_fee - (1 - target_price/(prices+tick_size))\n",
    "    else:\n",
    "        limit_fees = maker_fee - (prices+tick_size)/target_price + 1\n",
    "\n",
    "n_ticks = calc_n_ticks(side=side, inverse=inverse, maker_fee=maker_fee, taker_fee=taker_fee, target_price=target_price, tick_size=tick_size)\n",
    "break_even = target_price + n_ticks * tick_size\n",
    "\n",
    "# Plot Break-Evens\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.plot(prices, limit_fees)\n",
    "ax.axvline(target_price, c='k', lw=0.5)\n",
    "ax.axhline(0, c='k', lw=0.5)\n",
    "ax.axhline(taker_fee, c='r', lw=0.5, linestyle=':')\n",
    "ax.scatter(break_even, taker_fee, marker='o', color='r')\n",
    "# ax.annotate('# ticks: {}'.format(n_ticks), xy=(2,1))\n",
    "\n",
    "ax.annotate('# ticks: {:,.2}'.format(n_ticks), xy=(break_even, taker_fee), \n",
    "            xytext=(break_even*.993, taker_fee*1.55),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "\n",
    "\n",
    "ax.set_xlim(166, 172)\n",
    "ax.set_xticks(ax.get_xticks()[::1])\n",
    "yvals = ax.get_yticks()\n",
    "ax.set_yticklabels(['{:,.2%}'.format(x) for x in yvals])\n",
    "ax.set_xlabel('Price (satoshis)')\n",
    "ax.set_ylabel('Total Costs (% of Order Value)')\n",
    "ax.set_title('TRXU19 Cost Break-Evens', fontsize=13)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig('TRXU19 Break-Evens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Break-Evens for all symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "200913106c214d13be5a4cf26ecccf4a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "long\n",
      "XBTUSD 21 1029050 1029900\n",
      "XBT1 21 1035500 1036350\n",
      "ETHUSD 4 18810 18810\n",
      "ADAXBT 2 457 457\n",
      "BCHXBT 10 3034000 3039000\n",
      "EOSXBT 11 34960 35020\n",
      "ETHXBT 6 1814000 1815000\n",
      "LTCXBT 5 732500 732500\n",
      "TRXXBT 1 167 167\n",
      "XRPXBT 8 2541 2545\n",
      "short\n",
      "XBTUSD 21 1029050 1028200\n",
      "XBT1 21 1035500 1034650\n",
      "ETHUSD 4 18810 18810\n",
      "ADAXBT 2 457 457\n",
      "BCHXBT 10 3034000 3029000\n",
      "EOSXBT 11 34960 34900\n",
      "ETHXBT 6 1814000 1813000\n",
      "LTCXBT 5 732500 732500\n",
      "TRXXBT 1 167 167\n",
      "XRPXBT 8 2541 2537\n"
     ]
    }
   ],
   "source": [
    "symbols = symbol_settings.index\n",
    "t = tqdm(symbols, leave=True)\n",
    "\n",
    "for side in ['long', 'short']:\n",
    "    print(side)\n",
    "    for symbol in t:\n",
    "        t.set_description(symbol)\n",
    "        t.refresh()\n",
    "\n",
    "        ind = symbol_settings.loc[symbol] # individual settings\n",
    "        inverse = ind['inverse']\n",
    "        tick_size = ind['tick_size']\n",
    "        maker_fee = ind['maker_fee']\n",
    "        taker_fee = ind['taker_fee']\n",
    "        target_price = ind['target_price']\n",
    "\n",
    "        n_ticks = calc_n_ticks(side=side, inverse=inverse, maker_fee=maker_fee, taker_fee=taker_fee, target_price=target_price, tick_size=tick_size)\n",
    "        exec_price = calc_exec_price(side=side, inverse=inverse, maker_fee=maker_fee, taker_fee=taker_fee, target_price=target_price, tick_size=tick_size)\n",
    "        print(symbol, int(n_ticks), target_price, exec_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimating Timing Risk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is going to take a while.. go grab a cup of coffee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_trials = 50000\n",
    "auto_correlations = [0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65]\n",
    "results = pd.DataFrame(columns=auto_correlations, index=np.arange(n_trials))\n",
    "\n",
    "for auto_correlation in tqdm(auto_correlations):\n",
    "    \n",
    "    for trial in range(n_trials):\n",
    "    #     print('\\n Trial #{}'.format(trial+1))\n",
    "\n",
    "        # Reset the count\n",
    "        down_ticks = 0\n",
    "        up_ticks = 0\n",
    "        total_excursion = 0 # tracks the total drift away from the start\n",
    "\n",
    "        # Choose the first tick randomly\n",
    "        tick = np.random.randint(0,2)\n",
    "\n",
    "        while down_ticks < 2 and total_excursion < 25:\n",
    "            # Set probability of next tick based on prev tick\n",
    "            prob_up = auto_correlation if tick > 0 else (1 - auto_correlation)\n",
    "            prob_down = 1 - prob_up\n",
    "            tick = np.random.choice([0,1], p=[prob_down, prob_up])\n",
    "\n",
    "    #         print('tick: {}1'.format('+' if tick>0 else '-'))\n",
    "            # downtick\n",
    "            if tick == 0:\n",
    "                down_ticks += 1\n",
    "                total_excursion -= 1\n",
    "            elif tick == 1:\n",
    "    #             up_ticks += 1\n",
    "                down_ticks = 0 # reset the count; looking for 2 consecutive\n",
    "                total_excursion += 1\n",
    "            fill = total_excursion + 1\n",
    "    #         print('Down Ticks: {}, Total Excursion: {}'.format(down_ticks, total_excursion))\n",
    "\n",
    "    #     print('Fill: {}'.format(fill))\n",
    "        results.loc[trial, auto_correlation] = fill\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2, 5)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,7))\n",
    "\n",
    "for col in results.columns:\n",
    "    (results[col].value_counts() / len(results)).sort_index().cumsum().plot(label=col, ax=ax)\n",
    "plt.legend(title='Auto-Cor')\n",
    "plt.axhline(0.9, c='k', lw=0.55)\n",
    "# plt.xticks(np)\n",
    "plt.xlim(-2, 5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}