diff --git a/notes/10 Example - Intraday Events.ipynb b/notes/10 Example - Intraday Events.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intraday Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.chdir(\"..\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating random minute level price data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        requirejs.config({\n",
+       "            paths: {\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
+       "            }\n",
+       "        });\n",
+       "        require(['plotly'], function(Plotly) {\n",
+       "            window._Plotly = Plotly;\n",
+       "        });\n",
+       "        }\n",
+       "        </script>\n",
+       "        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "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>price</th>\n",
+       "      <th>SMA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:00:00</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:01:00</th>\n",
+       "      <td>100.344422</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:02:00</th>\n",
+       "      <td>100.602376</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:03:00</th>\n",
+       "      <td>100.522948</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:04:00</th>\n",
+       "      <td>100.281865</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 15:56:00</th>\n",
+       "      <td>103.313055</td>\n",
+       "      <td>103.418283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 15:57:00</th>\n",
+       "      <td>103.153275</td>\n",
+       "      <td>103.392886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 15:58:00</th>\n",
+       "      <td>103.615842</td>\n",
+       "      <td>103.369473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 15:59:00</th>\n",
+       "      <td>104.014850</td>\n",
+       "      <td>103.360974</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 16:00:00</th>\n",
+       "      <td>104.332968</td>\n",
+       "      <td>103.357182</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>361 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          price         SMA\n",
+       "2020-11-01 10:00:00  100.000000         NaN\n",
+       "2020-11-01 10:01:00  100.344422         NaN\n",
+       "2020-11-01 10:02:00  100.602376         NaN\n",
+       "2020-11-01 10:03:00  100.522948         NaN\n",
+       "2020-11-01 10:04:00  100.281865         NaN\n",
+       "...                         ...         ...\n",
+       "2020-11-01 15:56:00  103.313055  103.418283\n",
+       "2020-11-01 15:57:00  103.153275  103.392886\n",
+       "2020-11-01 15:58:00  103.615842  103.369473\n",
+       "2020-11-01 15:59:00  104.014850  103.360974\n",
+       "2020-11-01 16:00:00  104.332968  103.357182\n",
+       "\n",
+       "[361 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "from datetime import datetime, timedelta\n",
+    "import pandas as pd\n",
+    "import cufflinks as cf\n",
+    "\n",
+    "\n",
+    "random.seed(0)\n",
+    "price = 100\n",
+    "current_time = datetime(2020, 11, 1, 10)\n",
+    "end_time = datetime(2020, 11, 1, 16)\n",
+    "\n",
+    "times, prices = [], []\n",
+    "while current_time <= end_time:\n",
+    "    times.append(current_time)\n",
+    "    prices.append(price)\n",
+    "    current_time += timedelta(minutes=1)\n",
+    "    price += random.random() - 0.5\n",
+    "\n",
+    "df = pd.DataFrame(index=times, data=prices, columns=[\"price\"])\n",
+    "df[\"SMA\"] = df.price.rolling(30).mean()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "linkText": "Export to plot.ly",
+        "plotlyServerURL": "https://plot.ly",
+        "showLink": true
+       },
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+          "shape": "linear",
+          "width": 1.3
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+         "name": "price",
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+         "type": "scatter",
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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Prices\"}, \"xaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\"}, \"zerolinecolor\": \"#E1E5ED\"}, \"yaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\"}, \"zerolinecolor\": \"#E1E5ED\"}},\n",
+       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
+       "                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('68b6d53e-60db-4bd2-99d4-91bc9d202d6a');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })\n",
+       "                };\n",
+       "                });\n",
+       "            </script>\n",
+       "        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.iplot(title=\"Prices\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up our backtest\n",
+    "Initialise our assets and the portfolio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000000.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pytrade import Trade, Strategy, Backtest, History\n",
+    "from pytrade.assets import reset, Cash, Stock, Portfolio\n",
+    "from pytrade.compliance import Compliance, UnitLimit\n",
+    "\n",
+    "reset()\n",
+    "stock = Stock(\"XXX US\", currency_code=\"USD\")\n",
+    "usd = Cash(\"USD\")\n",
+    "portfolio = Portfolio(\"USD\")\n",
+    "portfolio.transfer(usd, int(1e6))\n",
+    "\n",
+    "portfolio.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Impose a holding limit of 1000 shares for stock 'XXX US'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "portfolio.compliance = Compliance().add_rule(\n",
+    "    UnitLimit(stock, 1000)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a trading strategy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class IntradayStrategy(Strategy):\n",
+    "    \"\"\" Keep buying +100 shares where price > sma.\n",
+    "        Start selling your existing position, 100 shares at a time, when price < sma.\n",
+    "    \"\"\"\n",
+    "    def generate_trades(self):\n",
+    "        sma = backtest.get_indicator(\"SMA\")\n",
+    "        if sma is None:  # don't trade if we don't yet have sma data\n",
+    "            return\n",
+    "\n",
+    "        price = stock.price\n",
+    "        position = portfolio.get_holding_units(\"XXX US\")\n",
+    "        if price > sma:  # establish a position\n",
+    "            return Trade(portfolio, stock, +100)\n",
+    "\n",
+    "        if price < sma and position > 0:  # then start selling your position\n",
+    "            return Trade(portfolio, stock, -100)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a backtest object and load our random data as events."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backtest = Backtest(IntradayStrategy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "361"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pytrade.events import load_frame_events, AssetPriceEvent, IndicatorEvent\n",
+    "\n",
+    "load_frame_events(\n",
+    "    stock,                       # load events for stock 'XXX US'\n",
+    "    df, \"price\",                 # from dataframe 'df', column 'price'\n",
+    "    backtest=backtest,           # for this backtest\n",
+    "    event_class=AssetPriceEvent, # and of Event type AssetPriceEvent\n",
+    ")\n",
+    "\n",
+    "load_frame_events(\n",
+    "    \"SMA\",                       # load indicator events into the backtest under the code 'SMA'\n",
+    "    df, \"SMA\",                   # from dataframe 'df', column 'SMA'\n",
+    "    backtest=backtest,           # for this backtest\n",
+    "    event_class=IndicatorEvent,  # and of Event type IndicatorEvent\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the backtest and view results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Portfolio</th>\n",
+       "      <th>Portfolio_USD</th>\n",
+       "      <th>Portfolio_XXX US</th>\n",
+       "      <th>SMA</th>\n",
+       "      <th>USD</th>\n",
+       "      <th>XXX US</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:00:00</th>\n",
+       "      <td>1.000000e+06</td>\n",
+       "      <td>1000000.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:01:00</th>\n",
+       "      <td>1.000000e+06</td>\n",
+       "      <td>1000000.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.344422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:02:00</th>\n",
+       "      <td>1.000000e+06</td>\n",
+       "      <td>1000000.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.602376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:03:00</th>\n",
+       "      <td>1.000000e+06</td>\n",
+       "      <td>1000000.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.522948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 10:04:00</th>\n",
+       "      <td>1.000000e+06</td>\n",
+       "      <td>1000000.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.281865</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>2020-11-01 15:56:00</th>\n",
+       "      <td>1.000896e+06</td>\n",
+       "      <td>938908.070131</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>103.418283</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>103.313055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 15:57:00</th>\n",
+       "      <td>1.000800e+06</td>\n",
+       "      <td>949223.397659</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>103.392886</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>103.153275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 15:58:00</th>\n",
+       "      <td>1.001031e+06</td>\n",
+       "      <td>938861.813485</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>103.369473</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>103.615842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 15:59:00</th>\n",
+       "      <td>1.001271e+06</td>\n",
+       "      <td>928460.328507</td>\n",
+       "      <td>700.0</td>\n",
+       "      <td>103.360974</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>104.014850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-11-01 16:00:00</th>\n",
+       "      <td>1.001493e+06</td>\n",
+       "      <td>918027.031690</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>103.357182</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>104.332968</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>361 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Portfolio   Portfolio_USD  Portfolio_XXX US  \\\n",
+       "2020-11-01 10:00:00  1.000000e+06  1000000.000000               0.0   \n",
+       "2020-11-01 10:01:00  1.000000e+06  1000000.000000               0.0   \n",
+       "2020-11-01 10:02:00  1.000000e+06  1000000.000000               0.0   \n",
+       "2020-11-01 10:03:00  1.000000e+06  1000000.000000               0.0   \n",
+       "2020-11-01 10:04:00  1.000000e+06  1000000.000000               0.0   \n",
+       "...                           ...             ...               ...   \n",
+       "2020-11-01 15:56:00  1.000896e+06   938908.070131             600.0   \n",
+       "2020-11-01 15:57:00  1.000800e+06   949223.397659             500.0   \n",
+       "2020-11-01 15:58:00  1.001031e+06   938861.813485             600.0   \n",
+       "2020-11-01 15:59:00  1.001271e+06   928460.328507             700.0   \n",
+       "2020-11-01 16:00:00  1.001493e+06   918027.031690             800.0   \n",
+       "\n",
+       "                            SMA  USD      XXX US  \n",
+       "2020-11-01 10:00:00         NaN  1.0  100.000000  \n",
+       "2020-11-01 10:01:00         NaN  1.0  100.344422  \n",
+       "2020-11-01 10:02:00         NaN  1.0  100.602376  \n",
+       "2020-11-01 10:03:00         NaN  1.0  100.522948  \n",
+       "2020-11-01 10:04:00         NaN  1.0  100.281865  \n",
+       "...                         ...  ...         ...  \n",
+       "2020-11-01 15:56:00  103.418283  1.0  103.313055  \n",
+       "2020-11-01 15:57:00  103.392886  1.0  103.153275  \n",
+       "2020-11-01 15:58:00  103.369473  1.0  103.615842  \n",
+       "2020-11-01 15:59:00  103.360974  1.0  104.014850  \n",
+       "2020-11-01 16:00:00  103.357182  1.0  104.332968  \n",
+       "\n",
+       "[361 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "history = History(\n",
+    "    portfolios=portfolio,\n",
+    "    backtest=backtest,\n",
+    ")\n",
+    "\n",
+    "backtest.run()\n",
+    "df = history.get()\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "linkText": "Export to plot.ly",
+        "plotlyServerURL": "https://plot.ly",
+        "showLink": true
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+       "data": [
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+         "name": "Portfolio_XXX US",
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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Intraday Holdings\"}, \"xaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\"}, \"zerolinecolor\": \"#E1E5ED\"}, \"yaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\"}, 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+       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
+       "                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('9f75c97a-7f63-441e-aff8-64e97752483f');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })\n",
+       "                };\n",
+       "                });\n",
+       "            </script>\n",
+       "        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cufflinks as cf\n",
+    "\n",
+    "columns = [\"Portfolio_XXX US\", \"XXX US\", \"SMA\"]\n",
+    "\n",
+    "df[columns].iplot(\n",
+    "    secondary_y=[\"XXX US\", \"SMA\"],\n",
+    "    title=\"Intraday Holdings\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "linkText": "Export to plot.ly",
+        "plotlyServerURL": "https://plot.ly",
+        "showLink": true
+       },
+       "data": [
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+         "name": "Portfolio",
+         "text": "",
+         "type": "scatter",
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+       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
+       "                    ).then(function(){\n",
+       "                            \n",
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+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
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+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
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+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
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+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
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+       "\n",
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+       "            </script>\n",
+       "        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "columns = [\"Portfolio\", \"XXX US\", \"SMA\"]\n",
+    "\n",
+    "df[columns].iplot(\n",
+    "    secondary_y=[\"XXX US\", \"SMA\"],\n",
+    "    title=\"Portfolio Value (USD)\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***"
+   ]
+  }
+ ],
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+   "language": "python",
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+}