Avellenda_Stoikov_MM.ipynb

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    "# Backtest of Avellaneda and Stoikov Market Making\n",
    "* **Asset** : `ETHUSDT`\n",
    "* **Start** : `2023-06-01 00:00:00` \n",
    "* **End** :  `2023-06-01 11:59:59`\n",
    "* **Timeframe** : `1 minute`"
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e0f22faf-d991-4908-b90c-bc653777d5bb\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.0.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e0f22faf-d991-4908-b90c-bc653777d5bb\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from typing import Union\n",
    "\n",
    "import time\n",
    "import math\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "random.seed(1)\n",
    "\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.io import output_notebook\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3605f842",
   "metadata": {},
   "outputs": [],
   "source": [
    "#params\n",
    "dt = '1min' # timestep"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb138d0",
   "metadata": {},
   "source": [
    "## Read the Trades data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5b60e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>low</th>\n",
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       "      <th>2023-06-01 00:00:00+00:00</th>\n",
       "      <td>1872.85</td>\n",
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       "      <td>1872.84</td>\n",
       "      <td>1874.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01 00:01:00+00:00</th>\n",
       "      <td>1874.03</td>\n",
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       "      <th>2023-06-01 00:02:00+00:00</th>\n",
       "      <td>1874.71</td>\n",
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       "    </tr>\n",
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       "      <th>2023-06-01 00:03:00+00:00</th>\n",
       "      <td>1875.06</td>\n",
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       "      <td>1874.83</td>\n",
       "      <td>1874.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01 00:04:00+00:00</th>\n",
       "      <td>1874.84</td>\n",
       "      <td>1875.56</td>\n",
       "      <td>1874.67</td>\n",
       "      <td>1874.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                              open     high      low    close\n",
       "datetime                                                     \n",
       "2023-06-01 00:00:00+00:00  1872.85  1875.01  1872.84  1874.02\n",
       "2023-06-01 00:01:00+00:00  1874.03  1874.71  1873.63  1874.70\n",
       "2023-06-01 00:02:00+00:00  1874.71  1875.42  1874.70  1875.06\n",
       "2023-06-01 00:03:00+00:00  1875.06  1876.74  1874.83  1874.83\n",
       "2023-06-01 00:04:00+00:00  1874.84  1875.56  1874.67  1874.99"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = pd.read_csv(\"data/ETHUSDT-trades-2023-06-01.csv\", usecols=['time','price','qty'])\n",
    "trades['datetime'] = pd.to_datetime(trades['time'], utc=True, unit='ms')\n",
    "trades = trades.drop(['time'], axis=1)\n",
    "trades = trades.set_index(\"datetime\")\n",
    "trades = trades.sort_index()\n",
    "#resample it to timestep\n",
    "trades = trades['price'].resample(dt).ohlc()\n",
    "trades.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5de5fd",
   "metadata": {},
   "source": [
    "## Read the Quotes data\n",
    "\n",
    "Compute the mid price as \n",
    "\n",
    "$$\\text{mid_price} = \\frac{\\text{ask_price} + \\text{bid_price}}{2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7bbf503f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>best_bid_price</th>\n",
       "      <th>best_bid_qty</th>\n",
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       "      <th>best_ask_qty</th>\n",
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       "      <th>2023-06-01 00:00:00.027000+00:00</th>\n",
       "      <td>1872.84</td>\n",
       "      <td>55.728</td>\n",
       "      <td>1872.85</td>\n",
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       "      <td>1872.845</td>\n",
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       "      <td>1872.84</td>\n",
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       "      <th>2023-06-01 00:00:00.042000+00:00</th>\n",
       "      <td>1872.84</td>\n",
       "      <td>59.925</td>\n",
       "      <td>1872.85</td>\n",
       "      <td>23.592</td>\n",
       "      <td>1872.845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  best_bid_price  best_bid_qty  \\\n",
       "datetime                                                         \n",
       "2023-06-01 00:00:00.027000+00:00         1872.84        55.728   \n",
       "2023-06-01 00:00:00.027000+00:00         1872.84        43.728   \n",
       "2023-06-01 00:00:00.031000+00:00         1872.84        59.898   \n",
       "2023-06-01 00:00:00.042000+00:00         1872.84        59.898   \n",
       "2023-06-01 00:00:00.042000+00:00         1872.84        59.925   \n",
       "\n",
       "                                  best_ask_price  best_ask_qty  mid_price  \n",
       "datetime                                                                   \n",
       "2023-06-01 00:00:00.027000+00:00         1872.85        24.164   1872.845  \n",
       "2023-06-01 00:00:00.027000+00:00         1872.85        24.164   1872.845  \n",
       "2023-06-01 00:00:00.031000+00:00         1872.85        24.164   1872.845  \n",
       "2023-06-01 00:00:00.042000+00:00         1872.85        23.592   1872.845  \n",
       "2023-06-01 00:00:00.042000+00:00         1872.85        23.592   1872.845  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quotes = pd.read_csv(\"data/ETHUSDT-bookTicker-2023-06-01.csv\", usecols=['transaction_time', 'best_bid_price', 'best_bid_qty', 'best_ask_price', 'best_ask_qty'])\n",
    "quotes['datetime'] = pd.to_datetime(quotes['transaction_time'], utc=True, unit='ms')\n",
    "quotes = quotes.drop(['transaction_time'], axis=1)\n",
    "quotes = quotes.set_index(\"datetime\")\n",
    "\n",
    "quotes['mid_price'] = (quotes['best_ask_price']+quotes['best_bid_price'])/2\n",
    "quotes = quotes.sort_index()\n",
    "quotes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b8925cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime\n",
       "2023-06-01 00:00:00+00:00    1874.025\n",
       "2023-06-01 00:01:00+00:00    1874.705\n",
       "2023-06-01 00:02:00+00:00    1875.055\n",
       "2023-06-01 00:03:00+00:00    1874.835\n",
       "2023-06-01 00:04:00+00:00    1874.985\n",
       "Freq: T, Name: mid_price, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resample the mid price to timestep\n",
    "mid_price = quotes[\"mid_price\"].resample(dt).last()\n",
    "mid_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b77e8ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1874.70</td>\n",
       "      <td>1875.06</td>\n",
       "      <td>1875.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01 00:03:00+00:00</th>\n",
       "      <td>1875.06</td>\n",
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       "      <td>1874.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-01 00:04:00+00:00</th>\n",
       "      <td>1874.84</td>\n",
       "      <td>1875.56</td>\n",
       "      <td>1874.67</td>\n",
       "      <td>1874.99</td>\n",
       "      <td>1874.985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              open     high      low    close  mid_price\n",
       "datetime                                                                \n",
       "2023-06-01 00:00:00+00:00  1872.85  1875.01  1872.84  1874.02   1874.025\n",
       "2023-06-01 00:01:00+00:00  1874.03  1874.71  1873.63  1874.70   1874.705\n",
       "2023-06-01 00:02:00+00:00  1874.71  1875.42  1874.70  1875.06   1875.055\n",
       "2023-06-01 00:03:00+00:00  1875.06  1876.74  1874.83  1874.83   1874.835\n",
       "2023-06-01 00:04:00+00:00  1874.84  1875.56  1874.67  1874.99   1874.985"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades['mid_price'] = mid_price\n",
    "trades.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "02905ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MarketMaking:\n",
    "    \"\"\"\n",
    "    Create an instance of Avellaneda and Stoikov's \n",
    "    High Frequency Trading in Limit order book.\n",
    "    \"\"\"\n",
    "    def __init__(self, X:pd.DataFrame, risk_aversion:float=0.1, \n",
    "                 liquidity:float=1.5, sigma:float=1, \n",
    "                 percent_fee:float=0.00009): \n",
    "        \"\"\"\n",
    "        :param X: (pd.DataFrame) a dataframe containing \n",
    "                  open, high, low, close and mid_price.\n",
    "        :param risk_aversion: (float) \"gamma\"risk aversion \n",
    "                                against holding inventory \n",
    "                                for the dealer. Default 0.1 \n",
    "                                low risk aversion.\n",
    "        :param liquidity: (float) \"kappa\" order book \n",
    "                             liquidity. Default 1.5 according \n",
    "                             to Gopalakrishnan et. al. [2000]\n",
    "        :param sigma: (float) constant volatility. Default 1.\n",
    "        :param percent_fee: (float) the percentage commission \n",
    "                             fee for market makers. Default is \n",
    "                             0.09% for binance.\n",
    "        \n",
    "        \"\"\"\n",
    "        self.gamma = risk_aversion\n",
    "        self.kappa = liquidity\n",
    "        self.X = X\n",
    "        self.sigma = sigma\n",
    "        self.fees = percent_fee\n",
    "        \n",
    "        self.T = 1.0\n",
    "        self.M = X.shape[0]\n",
    "        self.dt = 1.0/self.M\n",
    "    \n",
    "        self.q = 0\n",
    "        self.w = 0\n",
    "        self.fee = 0\n",
    "        self.total_fee = 0\n",
    "        \n",
    "        self.equity = [0]\n",
    "        self.bid = [np.nan]\n",
    "        self.ask = [np.nan]\n",
    "        self.spread = [np.nan]\n",
    "        self.inventory = [0]\n",
    "        self.reserve_price =[np.nan]\n",
    "        \n",
    "    def run(self):\n",
    "        \"\"\"\n",
    "        Run the backtest. \n",
    "        \"\"\"\n",
    "        for t in range(1, self.M): \n",
    "            S = self.X['mid_price'].iloc[t-1]\n",
    "            high, low, close = self.X['high'].iloc[t], self.X['low'].iloc[t], self.X['close'].iloc[t] \n",
    "            #reservation price    \n",
    "            r =  S - self.q * self.gamma * (self.sigma ** 2) * (self.T - t/self.M)   \n",
    "            #optimal spread\n",
    "            spread = self.gamma * (self.sigma ** 2) * (self.T - t/self.M) + (2/self.sigma) * math.log(1 + (self.gamma/self.kappa))\n",
    "            #optimal bid ask placement\n",
    "            bid = r - spread/2.# bid\n",
    "            ask = r + spread/2.# ask\n",
    "            if high >= ask:\n",
    "                #sell limit order is filled by market order\n",
    "                self.q = self.q - 1# position\n",
    "                self.w = self.w + ask# wealth\n",
    "                self.fee = ask*self.fees\n",
    "            if low <= bid:\n",
    "                #buy limit order if filled by market order\n",
    "                self.q = self.q + 1# position\n",
    "                self.w = self.w - bid# wealth\n",
    "                self.fee = ask*self.fees\n",
    "            self.equity.append((self.w + self.q * close) - self.fee)\n",
    "            self.bid.append(bid)\n",
    "            self.ask.append(ask)\n",
    "            self.reserve_price.append(r)\n",
    "            self.spread.append(spread)\n",
    "            self.inventory.append(self.q)\n",
    "            self.total_fee += self.fee\n",
    "            self.fee = 0\n",
    "            \n",
    "    def results(self):\n",
    "        \"\"\"\n",
    "        Display results of backtest.\n",
    "        \"\"\"\n",
    "        print(\"                   Results              \")\n",
    "        print(\"----------------------------------------\")\n",
    "        print(\"%14s %21s\" % ('statistic', 'value'))\n",
    "        print(40 * \"-\")\n",
    "        print(\"%14s %20.2f\" % (\"Average spread :\", np.nanmean(np.array(self.spread))))\n",
    "        print(\"%16s %20.2f\" % (\"Profit :\", self.equity[-1]))\n",
    "        print(\"%16s %20.2f\" % (\"Std(Profit) :\", np.array(self.equity).std()))\n",
    "        print(\"%16s %20.0f\" % (\"End Inventory :\", int(self.inventory[-1])))\n",
    "        print(\"%16s %20.2f\" % (\"Total Fees :\", self.total_fee))\n",
    "        \n",
    "        x = self.X.index\n",
    "\n",
    "        # Create Bokeh figure objects\n",
    "        fig1 = figure(title='Prices', width=800, height=400, x_axis_type='datetime')\n",
    "        fig1.line(x, self.X['mid_price'], line_width=1., legend_label='Mid-market price', color='navy')\n",
    "        fig1.line(x, self.reserve_price, line_width=1., legend_label='Indifference price', color='orange')\n",
    "        fig1.circle(x, self.ask, size=1, line_color=\"red\", fill_color=\"red\", fill_alpha=0.8, legend_label='Priced ask')\n",
    "        fig1.circle(x, self.bid, size=1, line_color=\"green\", fill_color=\"green\", fill_alpha=0.8, legend_label='Priced bid')\n",
    "        fig1.grid.grid_line_alpha = 0.3\n",
    "        fig1.legend.location = 'top_right'\n",
    "        fig1.xaxis.axis_label = 'Time'\n",
    "        fig1.yaxis.axis_label = 'Stock Price'\n",
    "        \n",
    "        pnl = np.diff(np.array(self.equity))\n",
    "        \n",
    "\n",
    "        fig2 = figure(title='Position', width=800, height=400, x_axis_type='datetime')\n",
    "        fig2.line(x, self.inventory, line_color='green', line_width=1., legend_label='q')\n",
    "        fig2.grid.grid_line_alpha = 0.3\n",
    "        fig2.legend.location = 'top_right'\n",
    "        fig2.xaxis.axis_label = 'Time'\n",
    "        fig2.yaxis.axis_label = 'Position'\n",
    "        \n",
    "        \n",
    "        fig3 = figure(title='Histogram', width=400, height=400)\n",
    "        fig3.quad(top=np.histogram(pnl, bins=100)[0], bottom=0, left=np.histogram(pnl, bins=100)[1][:-1], right=np.histogram(pnl, bins=100)[1][1:], fill_color='red', line_color='black', legend_label='Inventory Strategy') \n",
    "        fig3.grid.grid_line_alpha = 0.3\n",
    "        fig3.legend.location = 'top_right'\n",
    "        fig3.xaxis.axis_label = 'pnl'\n",
    "        fig3.yaxis.axis_label = 'N'\n",
    "\n",
    "        fig4 = figure(title='Equity', width=400, height=400, x_axis_type='datetime')\n",
    "        fig4.line(x, self.equity, line_width=1., legend_label='equity', color='navy')\n",
    "        fig4.grid.grid_line_alpha = 0.3\n",
    "        fig4.legend.location = 'top_right'\n",
    "        fig4.xaxis.axis_label = 'Time'\n",
    "        fig4.yaxis.axis_label = 'Price'\n",
    "\n",
    "        # Create a grid layout of figures\n",
    "        grid = gridplot([[fig3, fig4]])\n",
    "        show(fig1)\n",
    "        show(fig2)\n",
    "        # Show the plot\n",
    "        show(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f62dd79f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Results              \n",
      "----------------------------------------\n",
      "     statistic                 value\n",
      "----------------------------------------\n",
      "Average spread :                 2.50\n",
      "        Profit :               279.86\n",
      "   Std(Profit) :                55.10\n",
      " End Inventory :                   -4\n",
      "    Total Fees :                42.52\n"
     ]
    },
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AAAACABJ1AkML1KNwCnUDiehSuxwKdQPYoXI9CAZ1AcD0K1yMCnUD2KFyPQgKdQOxRuB4FA51ANDMzM7P/nEBI4XoULgCdQOJ6FK7HAJ1AZmZmZub+nECQwvUo3P6cQHA9Ctcj/pxA9ihcj0IAnUDiehSuxwCdQOJ6FK7HAJ1AZmZmZuYAnUBwPQrXIwOdQIbrUbieA51ArkfhepT/nEBwPQrXI/6cQAAAAACA/pxAFK5H4fr/nEBwPQrXIwCdQNijcD2KAZ1AuB6F69H/nEC4HoXr0f6cQArXo3C9/JxACtejcL38nEA+Ctej8PqcQOxRuB4F/JxACtejcL38nEAAAAAAgPycQOJ6FK7H/JxANDMzM7P8nEB6FK5HYfqcQChcj8J1+5xA7FG4HgX7nECQwvUo3PqcQEjhehQu+5xApHA9Clf8nEA+Ctej8PmcQFK4HoVr+pxApHA9Clf7nECuR+F6lPucQOxRuB4F/JxAuB6F69H7nEA+Ctej8P6cQOxRuB4F/pxANDMzM7P8nEDiehSux/ycQLgehevR+pxAHoXrUTj6nEDC9ShcD/mcQArXo3C99ZxAFK5H4fr3nEA+Ctej8PecQB6F61E495xAUrgehWv3nECkcD0KV/icQIbrUbie9JxApHA9Clf1nEBSuB6Fa/WcQKRwPQpX9pxAehSuR2H2nEA+Ctej8PScQHA9Ctcj9pxAkML1KNz3nEAUrkfh+vucQHA9Ctcj+5xAHoXrUTj4nEBI4XoULvmcQJDC9Sjc+5xASOF6FC7+nECuR+F6lP6cQAAAAACA/pxAFK5H4fr/nECQwvUo3P6cQBSuR+H6/pxAFK5H4fr+nEAAAAAAgACdQML1KFwPAZ1AmpmZmRkBnUCamZmZGQGdQGZmZmbm/5xAZmZmZuYAnUAUrkfh+v+cQIbrUbieAJ1AAAAAAID/nED2KFyPQv6cQGZmZmbm/5xArkfhepT+nEAehetROPucQHoUrkdh+pxANDMzM7P6nEAoXI/CdfmcQML1KFwP+ZxApHA9Clf5nEB6FK5HYfWcQGZmZmbm85xAAAAAAID3nECkcD0KV/mcQArXo3C99pxAFK5H4fr0nEAUrkfh+vKcQGZmZmbm75xArkfhepTsnED2KFyPQu+cQJDC9Sjc65xA2KNwPYrtnEBwPQrXI/CcQEjhehQu8JxAKFyPwnXtnED2KFyPQvGcQJDC9Sjc9ZxAPgrXo/DynEA+Ctej8PKcQMzMzMxM95xAzMzMzEz3nEA+Ctej8PacQML1KFwP95xANDMzM7P+nEDMzMzMTAadQPYoXI9CBZ1AcD0K1yMGnUDiehSuxwidQAAAAACACJ1AXI/C9agEnUC4HoXr0QSdQGZmZmbmA51A9ihcj0ICnUBSuB6FawKdQNijcD2KAJ1AkML1KNz8nECG61G4ngCdQPYoXI9CAp1A9ihcj0ICnUCamZmZGQWdQArXo3C9Bp1AAAAAAID+nEAAAAAAgP2cQEjhehQu+5xA9ihcj0L+nECG61G4nvucQArXo3C9+pxAUrgehWv6nEC4HoXr0ficQOxRuB4F+JxA7FG4HgX4nEA0MzMzs/mcQAAAAACA95xAuB6F69HynED2KFyPQvOcQB6F61E49ZxAuB6F69H3nEC4HoXr0fecQFK4HoVr9pxAcD0K1yP1nEDMzMzMTPecQDQzMzOz9pxAPgrXo/DunEAoXI/Cde6cQMzMzMxM8ZxApHA9ClfznECQwvUo3PKcQDQzMzOz9ZxA4noUrsf2nEAUrkfh+vecQMzMzMxM+ZxAmpmZmRn6nEDMzMzMTPmcQOxRuB4F+5xA4noUrsf4nEBcj8L1qPqcQMzMzMxM/ZxArkfhepT9nEBmZmZm5v+cQMzMzMxM/ZxAPgrXo/D8nEBmZmZm5vqcQHoUrkdh/ZxAPgrXo/D7nEAUrkfh+vucQPYoXI9C/pxA2KNwPYoBnUB6FK5HYQOdQOxRuB4FAp1AXI/C9agDnUDsUbgeBQSdQML1KFwPA51ApHA9ClcAnUAUrkfh+v+cQOxRuB4FAJ1AZmZmZub/nEAUrkfh+v+cQIbrUbie/5xAehSuR2EBnUB6FK5HYQGdQMzMzMxMAp1A4noUrscBnUDiehSuxwSdQOxRuB4FBp1AFK5H4foHnUCkcD0KVwidQOxRuB4FCJ1AFK5H4foGnUC4HoXr0QidQFyPwvWoCZ1A4noUrscInUC4HoXr0QidQMzMzMxMCp1AcD0K1yMLnUAUrkfh+gmdQBSuR+H6D51AHoXrUTgPnUCQwvUo3A+dQPYoXI9CD51AXI/C9agRnUBcj8L1qBKdQFyPwvWoFJ1A2KNwPYoTnUAoXI/CdRadQGZmZmbmFJ1AhutRuJ4VnUD2KFyPQhOdQFyPwvWoDp1AHoXrUTgSnUA0MzMzsxGdQHoUrkdhE51AehSuR2ESnUDMzMzMTBSdQK5H4XqUE51AFK5H4foQnUCQwvUo3BKdQHoUrkdhFJ1APgrXo/AXnUAehetROBidQB6F61E4Fp1A7FG4HgUVnUBmZmZm5hedQPYoXI9CFJ1A9ihcj0IUnUBmZmZm5hmdQJqZmZkZF51AAAAAAIAbnUDiehSuxxidQArXo3C9HZ1A2KNwPYoanUCuR+F6lBudQB6F61E4HZ1AUrgehWsbnUDiehSuxxydQGZmZmbmHp1AHoXrUTgenUDC9ShcDx2dQJDC9SjcGp1ASOF6FC4ZnUAUrkfh+hedQFyPwvWoFp1AFK5H4foZnUAUrkfh+hmdQDQzMzOzGZ1AcD0K1yMXnUA0MzMzsxedQOxRuB4FGZ1ApHA9ClcXnUAUrkfh+hedQIbrUbieGJ1AhutRuJ4YnUB6FK5HYRmdQOxRuB4FGZ1ASOF6FC4XnUCkcD0KVxidQFyPwvWoFp1A7FG4HgUUnUD2KFyPQhadQJqZmZkZFZ1AHoXrUTgWnUDMzMzMTBedQK5H4XqUFJ1ApHA9ClcTnUAK16NwvRSdQK5H4XqUFZ1AHoXrUTgRnUCQwvUo3BKdQAAAAACAEZ1AzMzMzEwQnUA0MzMzsxCdQD4K16PwDZ1AzMzMzEwNnUA0MzMzsw6dQEjhehQuD51AKFyPwnUOnUBmZmZm5gydQB6F61E4C51AAAAAAIALnUDsUbgeBQydQFyPwvWoEJ1AXI/C9agQnUA0MzMzsw+dQChcj8J1FJ1ASOF6FC4VnUDsUbgeBRmdQKRwPQpXFJ1ASOF6FC4UnUAK16NwvRKdQOxRuB4FE51A7FG4HgUWnUAUrkfh+hWdQHA9CtcjF51A7FG4HgUWnUBmZmZm5hWdQOxRuB4FFJ1AFK5H4foTnUD2KFyPQhOdQJDC9SjcEZ1AXI/C9agSnUCuR+F6lBOdQJDC9SjcEJ1AwvUoXA8SnUAUrkfh+hGdQLgehevRFp1A4noUrscUnUA+Ctej8BOdQMzMzMxMFJ1A7FG4HgUUnUDsUbgeBRSdQChcj8J1Fp1AHoXrUTgTnUDYo3A9ihGdQBSuR+H6EZ1AwvUoXA8TnUAoXI/CdRGdQPYoXI9CDJ1AmpmZmRkOnUCQwvUo3A2dQOJ6FK7HCp1A9ihcj0IPnUDMzMzMTA+dQEjhehQuD51A7FG4HgUPnUDsUbgeBQ+dQOJ6FK7HDp1AKFyPwnUNnUCkcD0KVw+dQKRwPQpXD51AAAAAAIAPnUBcj8L1qA+dQLgehevRC51AFK5H4foPnUCQwvUo3A+dQBSuR+H6D51ANDMzM7MQnUA0MzMzsxCdQFK4HoVrEZ1AhutRuJ4RnUAehetROBGdQIbrUbieEZ1ASOF6FC4SnUCkcD0KVxKdQJqZmZkZD51ArkfhepQLnUAK16NwvQmdQHA9CtcjB51A4noUrscInUC4HoXr0QedQOxRuB4FCJ1A2KNwPYoInUDMzMzMTAudQArXo3C9C51ASOF6FC4OnUA0MzMzswydQArXo3C9DZ1AKFyPwnUNnUAehetROAydQHA9CtcjDp1AehSuR2ESnUCG61G4ng+dQChcj8J1EJ1AehSuR2ENnUC4HoXr0QydQHA9CtcjDp1A2KNwPYoPnUDC9ShcDxGdQOJ6FK7HEp1AzMzMzEwUnUCQwvUo3BGdQAAAAACADp1AwvUoXA8QnUDC9ShcDxKdQEjhehQuDp1ASOF6FC4OnUAoXI/CdQ+dQChcj8J1D51AUrgehWsRnUC4HoXr0RGdQB6F61E4E51A4noUrscSnUBSuB6FaxCdQFK4HoVrEJ1AUrgehWsQnUDsUbgeBRCdQBSuR+H6D51AuB6F69ENnUDsUbgeBQydQBSuR+H6C51AXI/C9agKnUDYo3A9igmdQML1KFwPCp1ApHA9ClcJnUD2KFyPQgidQDQzMzOzC51AkML1KNwInUDsUbgeBQedQBSuR+H6A51AhutRuJ4MnUBSuB6FawmdQOJ6FK7HCZ1AuB6F69EJnUAAAAAAgA2dQOJ6FK7HEJ1AzMzMzEwRnUB6FK5HYROdQAAAAACAEZ1AkML1KNwQnUCuR+F6lBGdQFyPwvWoDJ1AXI/C9agSnUAUrkfh+hudQIbrUbieG51ArkfhepQcnUCkcD0KVxydQOxRuB4FGJ1AmpmZmRkTnUDsUbgeBRSdQML1KFwPEp1AFK5H4foRnUAK16NwvRGdQKRwPQpXEZ1ANDMzM7MUnUDC9ShcDxKdQGZmZmbmFZ1AehSuR2ETnUA0MzMzsxadQOxRuB4FGp1AuB6F69EYnUAUrkfh+hadQArXo3C9GJ1AmpmZmRkdnUA0MzMzsyGdQLgehevRJJ1AFK5H4fognUDMzMzMTB2dQJDC9SjcHZ1AhutRuJ4gnUCkcD0KVx+dQOxRuB4FH51AhutRuJ4jnUAUrkfh+iWdQDQzMzOzI51AUrgehWsmnUA+Ctej8CadQMzMzMxMJZ1AFK5H4fojnUA0MzMzsyedQOxRuB4FJZ1AUrgehWsjnUAUrkfh+iOdQOxRuB4FI51AwvUoXA8inUCamZmZGSOdQOxRuB4FIZ1AZmZmZuYenUDiehSuxxmdQJqZmZkZGp1A9ihcj0IanUAK16NwvRidQPYoXI9CFp1AwvUoXA8anUDsUbgeBRqdQFyPwvWoGp1ArkfhepQWnUCamZmZGRudQOJ6FK7HGJ1AkML1KNwYnUD2KFyPQhqdQJDC9SjcFp1ANDMzM7MVnUBI4XoULhOdQHoUrkdhFJ1AKFyPwnUWnUA+Ctej8BWdQK5H4XqUEJ1AHoXrUTgRnUCkcD0KVxadQOJ6FK7HFZ1A9ihcj0IanUDsUbgeBRidQJqZmZkZGJ1AUrgehWsTnUAoXI/CdRGdQLgehevRD51APgrXo/ALnUA+Ctej8A6dQAAAAACAD51AHoXrUTgOnUA0MzMzsxKdQHoUrkdhDp1AAAAAAIAOnUC4HoXr0Q6dQArXo3C9Ep1AzMzMzEwRnUDsUbgeBRWdQOxRuB4FGJ1A4noUrscYnUBmZmZm5hidQArXo3C9EZ1ANDMzM7MdnUBwPQrXIxadQBSuR+H6F51AehSuR2EWnUDiehSuxxmdQEjhehQuHZ1A9ihcj0IenUDiehSuxx6dQHA9CtcjHJ1AAAAAAIAVnUDsUbgeBRidQAAAAACAGJ1A2KNwPYocnUBmZmZm5hidQIbrUbieG51AzMzMzEwYnUDiehSuxxadQArXo3C9FJ1AuB6F69EGnUAoXI/CdQadQDQzMzOzC51AHoXrUTgFnUCkcD0KVwmdQDQzMzOzBZ1A4noUrscBnUB6FK5HYQWdQOxRuB4FB51AFK5H4foHnUCQwvUo3AadQIbrUbieCJ1ACtejcL0FnUAehetROAKdQFyPwvWoBZ1AehSuR2EInUB6FK5HYQidQJDC9SjcBZ1ACtejcL0EnUAoXI/CdQWdQLgehevRBp1AkML1KNwInUDC9ShcDwedQNijcD2KB51AhutRuJ4FnUCuR+F6lAmdQHA9CtcjDp1ApHA9ClcTnUBcj8L1qBKdQHA9CtcjD51AcD0K1yMKnUAUrkfh+g2dQEjhehQuDJ1AehSuR2EJnUAK16NwvQqdQOxRuB4FDp1ApHA9ClcRnUBI4XoULhKdQD4K16PwEJ1AmpmZmRkPnUBI4XoULg6dQJqZmZkZD51AFK5H4foNnUCuR+F6lAidQML1KFwPDZ1AHoXrUTgPnUDC9ShcDwydQFK4HoVrCp1ACtejcL0GnUBSuB6FawadQB6F61E4DJ1AUrgehWsSnUBcj8L1qBCdQOJ6FK7HFJ1AwvUoXA8XnUDsUbgeBRSdQHoUrkdhGZ1AUrgehWsbnUA+Ctej8BqdQPYoXI9CHJ1ArkfhepQdnUDMzMzMTBidQGZmZmbmFZ1AhutRuJ4YnUA0MzMzsxedQPYoXI9CF51AkML1KNwYnUD2KFyPQhydQHA9CtcjGZ1A9ihcj0IYnUDYo3A9ihidQFyPwvWoFp1A9ihcj0IWnUAUrkfh+hedQJDC9SjcG51AAAAAAIAcnUDiehSuxxydQAAAAACAHJ1AUrgehWscnUCuR+F6lBedQBSuR+H6F51AFK5H4foVnUDYo3A9ihedQHoUrkdhFp1AXI/C9agXnUAAAAAAgBidQB6F61E4F51AXI/C9agVnUCG61G4nhWdQGZmZmbmFJ1ANDMzM7MTnUA0MzMzsxOdQFK4HoVrEp1A7FG4HgUVnUAehetROBWdQAAAAACAFJ1A2KNwPYoUnUBI4XoULhOdQKRwPQpXFJ1A2KNwPYoUnUCamZmZGRWdQEjhehQuG51ArkfhepQZnUCamZmZGRydQBSuR+H6Hp1AXI/C9aghnUC4HoXr0SCdQBSuR+H6H51AehSuR2EgnUDiehSuxyCdQIbrUbieIZ1AehSuR2EhnUA0MzMzsyOdQBSuR+H6I51AAAAAAIAhnUAUrkfh+h2dQHoUrkdhIJ1A2KNwPYodnUDsUbgeBR6dQHA9CtcjIp1AAAAAAIAinUBSuB6FayadQEjhehQuJ51AFK5H4foknUDiehSuxySdQNijcD2KJp1A7FG4HgUlnUDYo3A9iiWdQK5H4XqUH51ArkfhepQhnUBSuB6Fax+dQBSuR+H6H51AkML1KNwhnUBwPQrXIxmdQD4K16PwGJ1A7FG4HgUYnUDiehSuxxOdQAAAAACAFp1AehSuR2EYnUCG61G4nhWdQHoUrkdhEJ1ASOF6FC4UnUDsUbgeBRWdQMzMzMxMFJ1AZmZmZuYRnUBmZmZm5hGdQAAAAACAEZ1A2KNwPYoRnUBSuB6FaxWdQEjhehQuFZ1AhutRuJ4VnUAUrkfh+hidQOxRuB4FHJ1AmpmZmRk6nUA+Ctej8D2dQOJ6FK7HS51AXI/C9ahDnUAoXI/CdUmdQNijcD2KWZ1A4noUrsdUnUBI4XoULlOdQOJ6FK7HTp1A4noUrsdZnUAUrkfh+kedQD4K16PwR51AFK5H4fpLnUCkcD0KV0adQOJ6FK7HSJ1ApHA9CldJnUBcj8L1qEmdQBSuR+H6Q51AzMzMzExBnUDiehSuxzmdQHA9CtcjMZ1AexSuR+E+nUDC9ShcDzudQEjhehQuQ51A9ihcj0I/nUBSuB6Fa06dQIbrUbieS51AUrgehWtNnUBI4XoULk2dQAAAAACAT51AFK5H4fpTnUAUrkfh+ledQPYoXI9CUp1A7FG4HgVMnUA0MzMzs0mdQLgehevRTJ1AHoXrUThMnUC4HoXr0UydQFyPwvWoSJ1AAAAAAIBEnUBcj8L1qD+dQHA9CtcjO51AHoXrUTg4nUAoXI/CdTydQChcj8J1PZ1ANDMzM7M/nUDC9ShcD0CdQOxRuB4FPp1AkML1KNw9nUDMzMzMTDydQMzMzMxMPp1A9ihcj0I9nUCuR+F6lESdQK5H4XqUQJ1AKFyPwnVDnUCuR+F6lECdQFK4HoVrQp1AehSuR2FCnUC4HoXr0UWdQHA9CtcjQZ1AHoXrUThBnUBcj8L1qDudQGZmZmbmPp1AKFyPwnU8nUB6FK5HYTqdQK5H4XqUOZ1AcD0K1yM5nUDsUbgeBTedQOJ6FK7HOJ1A9ihcj0I7nUDiehSuxzidQML1KFwPN51A2KNwPYo3nUBwPQrXIzqdQIbrUbieOZ1AzMzMzEw/nUCG61G4nkGdQPYoXI9CQ51ANDMzM7NAnUDC9ShcD0KdQOJ6FK7HRJ1AUrgehWtGnUB6FK5HYUidQOJ6FK7HSJ1AAAAAAIBNnUCuR+F6lEudQKRwPQpXTp1AuB6F69FNnUB6FK5HYU6dQBSuR+H6UZ1AmpmZmRlOnUCG61G4nk6dQML1KFwPUZ1A7FG4HgVKnUBSuB6Fa0idQEjhehQuRZ1APgrXo/BEnUA+Ctej8ESdQB6F61E4RJ1A7FG4HgVCnUAK16NwvUCdQIbrUbiePZ1AwvUoXA9BnUAoXI/CdUGdQAAAAACAQ51ACtejcL1CnUDC9ShcD0CdQAAAAACAPp1A7FG4HgU8nUDsUbgeBTydQFyPwvWoOp1ArkfhepQ4nUBcj8L1qDmdQFyPwvWoOp1AFK5H4fo5nUC4HoXr0TidQJDC9SjcPZ1AZmZmZuY5nUBmZmZm5jmdQB6F61E4OZ1A4noUrsc6nUCQwvUo3DmdQOxRuB4FOZ1AzMzMzEw4nUAAAAAAgDmdQFyPwvWoO51A7FG4HgU/nUA+Ctej8D6dQIbrUbieQ51A9ihcj0JGnUAAAAAAgEOdQEjhehQuQ51A2KNwPYpHnUCkcD0KV0edQChcj8J1Rp1ASOF6FC5GnUDMzMzMTEadQKRwPQpXR51AHoXrUThGnUDiehSux0SdQB6F61E4Qp1AwvUoXA9FnUDYo3A9ikWdQMzMzMxMRp1AkML1KNxEnUCamZmZGUKdQJDC9SjcQJ1ANDMzM7NBnUCQwvUo3D+dQLgehevRQp1AKFyPwnVCnUCG61G4nkGdQLgehevRP51AwvUoXA89nUDiehSuxzydQD4K16PwPp1AzMzMzEw9nUAehetRODydQOxRuB4FNJ1AcD0K1yM5nUDYo3A9iiedQJDC9SjcEJ1AHoXrUTgNnUB6FK5HYQ+dQArXo3C9FZ1ASOF6FC4anUCuR+F6lB2dQOJ6FK7HHp1AcD0K1yMhnUCuR+F6lCGdQHoUrkdhI51AXI/C9agnnUBmZmZm5iCdQKRwPQpXJJ1AFK5H4foonUBSuB6FaymdQGZmZmbmKp1AcD0K1yMsnUBwPQrXIyudQLgehevRJZ1AkML1KNwnnUBI4XoULiudQB6F61E4L51A4noUrscwnUCuR+F6lDKdQLgehevRMJ1AkML1KNwvnUAAAAAAgDGdQChcj8J1Mp1ArkfhepQ1nUDsUbgeBTidQOxRuB4FO51ASOF6FC48nUDC9ShcDzqdQMzMzMxMPJ1AHoXrUTg+nUAehetROD2dQFyPwvWoPJ1A2KNwPYo5nUBcj8L1qDmdQOxRuB4FOZ1A9ihcj0I2nUCuR+F6lDidQOJ6FK7HOJ1AKFyPwnU1nUBmZmZm5jWdQB6F61E4Op1A9ihcj0I5nUAUrkfh+jqdQEjhehQuOZ1AwvUoXA85nUBcj8L1qDWdQIbrUbieM51AHoXrUTg1nUA0MzMzszadQHoUrkdhM51AZmZmZuYxnUBwPQrXIzCdQK5H4XqULZ1APgrXo/AtnUCG61G4ni+dQIbrUbieL51AhutRuJ4ynUBSuB6FazSdQB6F61E4NJ1AwvUoXA80nUCG61G4njSdQK5H4XqUNJ1A4noUrsc3nUDC9ShcDzSdQB6F61E4MJ1ArkfhepQtnUAUrkfh+i+dQD4K16PwLJ1AzMzMzEwtnUCuR+F6lC2dQK5H4XqULZ1ArkfhepQsnUC4HoXr0SudQFK4HoVrLJ1A4noUrscsnUA+Ctej8DCdQML1KFwPMZ1A4noUrscwnUAoXI/CdTCdQChcj8J1MJ1A2KNwPYovnUBI4XoULjGdQB6F61E4MZ1Arkfhep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       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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       "      if (root.Bokeh !== undefined) {\n",
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       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "  const render_items = [{\"docid\":\"e6a1a8ec-663d-44b4-a3c5-51849eb01542\",\"roots\":{\"p1234\":\"da211afc-7290-4d4c-9430-39d143175eb4\"},\"root_ids\":[\"p1234\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
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       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
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       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
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      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1234"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = MarketMaking(trades, risk_aversion=0.2, liquidity=1.5, sigma=0.1)\n",
    "mm.run()\n",
    "mm.results()"
   ]
  }
 ],
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