remove unused files

api_data_samples.ipynb

@@ -1,15 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example data from jq api call\n",
+    "### This notebook contain samples of jq api call. The purpose of this is to prvide a reference when developing the pipeline. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sqlalchemy import create_engine\n",
-    "import pandas as pd\n",
-    "from datetime import timedelta\n",
-    "import jqdatasdk as jq\n"
+    "import jqdatasdk as jq"
    ]
   },
   {
@@ -829,37 +834,6 @@
    "source": [
     "jq.get_all_securities()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'engine' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[4], line 9\u001b[0m\n\u001b[1;32m      7\u001b[0m     df[\u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(df[\u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m      8\u001b[0m     \u001b[39mreturn\u001b[39;00m df\n\u001b[0;32m----> 9\u001b[0m get_most_recent_profile(\u001b[39m'\u001b[39;49m\u001b[39mportfolio\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
-      "Cell \u001b[0;32mIn[4], line 5\u001b[0m, in \u001b[0;36mget_most_recent_profile\u001b[0;34m(type)\u001b[0m\n\u001b[1;32m      3\u001b[0m table_name \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mbenchmark_profile\u001b[39m\u001b[39m'\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mtype\u001b[39m \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mbenchmark\u001b[39m\u001b[39m'\u001b[39m \u001b[39melse\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mportfolio_profile\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m      4\u001b[0m query \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mSELECT * FROM \u001b[39m\u001b[39m{\u001b[39;00mtable_name\u001b[39m}\u001b[39;00m\u001b[39m WHERE date = (SELECT MAX(date) FROM \u001b[39m\u001b[39m{\u001b[39;00mtable_name\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_sql(query, con\u001b[39m=\u001b[39mengine)\n\u001b[1;32m      6\u001b[0m \u001b[39m# convert date to datetime object\u001b[39;00m\n\u001b[1;32m      7\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(df[\u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m])\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'engine' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "# when update stock price just need the latest portfolio frame\n",
-    "def get_most_recent_profile(type):\n",
-    "    table_name = 'benchmark_profile' if type == 'benchmark' else 'portfolio_profile'\n",
-    "    query = f\"SELECT * FROM {table_name} WHERE date = (SELECT MAX(date) FROM {table_name})\"\n",
-    "    with create_engine(db_url).connect() as conn:\n",
-    "        df = pd.read_sql(query, con=engine)\n",
-    "        # convert date to datetime object\n",
-    "        df['date'] = pd.to_datetime(df['date'])\n",
-    "        return df\n",
-    "get_most_recent_profile('portfolio')"
-   ]
   }
  ],
  "metadata": {

appComponents.py

@@ -8,8 +8,8 @@ import numpy as np
 import hvplot.pandas  # noqa
 from panel.viewable import Viewer
 import param
-from script import styling
-from script import description
+import styling
+import description
 import plotly.graph_objs as go
 # import warnings
 pn.extension('mathjax')

app_ini.ipynb

@@ -1,723 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.1.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\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    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\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    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\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  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\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    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    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-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(> .cell-output-ipywidget-background\n",
-       "    > .lm-Widget\n",
-       "    > *[data-root-id]),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "from datetime import datetime, timedelta\n",
-    "from script import processing\n",
-    "from script import api\n",
-    "from sqlalchemy import create_engine\n",
-    "import pytz\n",
-    "import numpy as np\n",
-    "import hvplot.pandas\n",
-    "db_url = 'sqlite:///instance/local.db'\n",
-    "engine = create_engine(db_url)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: './data/p_profile.pkl'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m## initialize by batchprocess to have initial result \u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m p_profile \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_pickle(\u001b[39m'\u001b[39;49m\u001b[39m./data/p_profile.pkl\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      3\u001b[0m start_date \u001b[39m=\u001b[39m p_profile\u001b[39m.\u001b[39mdate\u001b[39m.\u001b[39mmin()\n\u001b[1;32m      4\u001b[0m end_date \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mto_datetime(datetime\u001b[39m.\u001b[39mnow()\u001b[39m-\u001b[39mtimedelta(days\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m))\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/portfolio_risk_assesment/lib/python3.11/site-packages/pandas/io/pickle.py:179\u001b[0m, in \u001b[0;36mread_pickle\u001b[0;34m(filepath_or_buffer, compression, storage_options)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[39mLoad pickled pandas object (or any object) from file.\u001b[39;00m\n\u001b[1;32m    117\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[39m4    4    9\u001b[39;00m\n\u001b[1;32m    177\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    178\u001b[0m excs_to_catch \u001b[39m=\u001b[39m (\u001b[39mAttributeError\u001b[39;00m, \u001b[39mImportError\u001b[39;00m, \u001b[39mModuleNotFoundError\u001b[39;00m, \u001b[39mTypeError\u001b[39;00m)\n\u001b[0;32m--> 179\u001b[0m \u001b[39mwith\u001b[39;00m get_handle(\n\u001b[1;32m    180\u001b[0m     filepath_or_buffer,\n\u001b[1;32m    181\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    182\u001b[0m     compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[1;32m    183\u001b[0m     is_text\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    184\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m    185\u001b[0m ) \u001b[39mas\u001b[39;00m handles:\n\u001b[1;32m    186\u001b[0m     \u001b[39m# 1) try standard library Pickle\u001b[39;00m\n\u001b[1;32m    187\u001b[0m     \u001b[39m# 2) try pickle_compat (older pandas version) to handle subclass changes\u001b[39;00m\n\u001b[1;32m    188\u001b[0m     \u001b[39m# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError\u001b[39;00m\n\u001b[1;32m    190\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    191\u001b[0m         \u001b[39m# TypeError for Cython complaints about object.__new__ vs Tick.__new__\u001b[39;00m\n\u001b[1;32m    192\u001b[0m         \u001b[39mtry\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/portfolio_risk_assesment/lib/python3.11/site-packages/pandas/io/common.py:868\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    859\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\n\u001b[1;32m    860\u001b[0m             handle,\n\u001b[1;32m    861\u001b[0m             ioargs\u001b[39m.\u001b[39mmode,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    864\u001b[0m             newline\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    865\u001b[0m         )\n\u001b[1;32m    866\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    867\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m--> 868\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(handle, ioargs\u001b[39m.\u001b[39;49mmode)\n\u001b[1;32m    869\u001b[0m     handles\u001b[39m.\u001b[39mappend(handle)\n\u001b[1;32m    871\u001b[0m \u001b[39m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './data/p_profile.pkl'"
-     ]
-    }
-   ],
-   "source": [
-    "## initialize by batchprocess to have initial result \n",
-    "p_profile = pd.read_pickle('./data/p_profile.pkl')\n",
-    "start_date = p_profile.date.min()\n",
-    "end_date = pd.to_datetime(datetime.now()-timedelta(days=7))\n",
-    "# collect data upto 7 days ago  \n",
-    "b_profile, error  = api.update_benchmark_profile(start_date, end_date)\n",
-    "p_stocks, error = api.get_stocks_price(p_profile, start_date, end_date)\n",
-    "b_stocks, error = api.get_stocks_price(b_profile, start_date, end_date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save result \n",
-    "# p_profile.to_pickle('./data/p_profile.pkl')\n",
-    "# b_profile.to_pickle('./data/b_profile.pkl')\n",
-    "p_stocks.to_pickle('./data/p_stocks.pkl')\n",
-    "b_stocks.to_pickle('./data/b_stocks.pkl')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/lamonkey/Desktop/risk monitor/script/processing.py:262: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df.fillna(0, inplace=True)\n",
-      "/Users/lamonkey/Desktop/risk monitor/script/processing.py:263: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['active_return'] = df.pct_p * \\\n",
-      "/Users/lamonkey/Desktop/risk monitor/script/processing.py:266: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['allocation'] = (df.prev_w_in_p_p - df.prev_w_in_p_b) * df.pct_b\n",
-      "/Users/lamonkey/Desktop/risk monitor/script/processing.py:267: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['selection'] = (df.pct_p - df.pct_b) * df.prev_w_in_p_b\n",
-      "/Users/lamonkey/Desktop/risk monitor/script/processing.py:268: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['interaction'] = (df.pct_p - df.pct_b) * \\\n",
-      "/Users/lamonkey/Desktop/risk monitor/script/processing.py:270: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df['notinal_return'] = df.allocation + df.selection + df.interaction\n"
-     ]
-    }
-   ],
-   "source": [
-    "## batch processing \n",
-    "calculated_p_stock = processing.get_processing_result_of_stocks_df(p_stocks, p_profile)\n",
-    "calculated_b_stock = processing.get_processing_result_of_stocks_df(b_stocks, b_profile)\n",
-    "p_eval_df = processing.get_portfolio_evaluation(calculated_p_stock, calculated_b_stock, p_profile)\n",
-    "sector_eval_df = processing.get_portfolio_sector_evaluation(calculated_p_stock, calculated_b_stock)\n",
-    "attribution_result_df = processing.calculate_total_attribution(calculated_p_stock, calculated_b_stock)\n",
-    "s_attribution_result_df = processing.calculate_total_attribution_by_sector(calculated_p_stock, calculated_b_stock)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## save result to db\n",
-    "with engine.connect() as connection:\n",
-    "    # all_stock_info.to_sql('all_stock_info', con=connection, if_exists='replace', index=False)\n",
-    "    calculated_b_stock.to_sql('calculated_b_stock', con=connection, if_exists='replace', index=False)\n",
-    "    calculated_p_stock.to_sql('calculated_p_stock', con=connection, if_exists='replace', index=False)\n",
-    "    p_eval_df.to_sql('p_eval_result', con=connection, if_exists='replace', index=False)\n",
-    "    sector_eval_df.to_sql('sector_eval_result', con=connection, if_exists='replace', index=False)\n",
-    "    attribution_result_df.to_sql('attribution_result', con=connection, if_exists='replace', index=False)\n",
-    "    s_attribution_result_df.to_sql('s_attribution_result', con=connection, if_exists='replace', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load from sql\n",
-    "name_df_map = dict()\n",
-    "with engine.connect() as connection:\n",
-    "    for table in ['calculated_b_stock','calculated_p_stock','p_eval_result','sector_eval_result']:\n",
-    "        try:\n",
-    "            df = pd.read_sql_table(table, con=connection)\n",
-    "            name_df_map[table] = df\n",
-    "        except:\n",
-    "            pass\n",
-    "            # TODO load data from api and calculate result \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load data upto now\n",
-    "# Get the current time in UTC\n",
-    "current_time = datetime.datetime.utcnow()\n",
-    "# Set the timezone to Beijing\n",
-    "beijing_timezone = pytz.timezone('Asia/Shanghai')\n",
-    "# Convert the current time to Beijing time\n",
-    "end_time = pd.to_datetime(current_time.astimezone(beijing_timezone).date())\n",
-    "start_time = name_df_map['p_eval_result'].date.max() + timedelta(days=1)\n",
-    "\n",
-    "# get data up to today\n",
-    "b_profile, error  = api.update_benchmark_profile(start_time, end_time)\n",
-    "p_stocks, error = api.get_stocks_price(p_profile, start_time, end_time)\n",
-    "b_stocks, error = api.get_stocks_price(b_profile, start_time, end_time)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/v5/2108rh5964q9j741wg_s8r1w0000gn/T/ipykernel_35506/2587190678.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmost_recent_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname_df_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'calculated_p_stock'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ticker'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mconcat_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmost_recent_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_stocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'outer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_processing_result_of_stocks_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconcat_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_profile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Desktop/risk monitor/script/processing.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(stock_df, profile_df)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m             \u001b[0mcur_w\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nan'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;31m# if has initial weight, the following row all use this initial weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'initial_weight'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m                 \u001b[0mini_w\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'initial_weight'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m                 \u001b[0mcur_w\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mini_w\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/portfolio_risk_assesment/lib/python3.11/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1464\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1465\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1466\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   1467\u001b[0m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1468\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1469\u001b[0m         )\n",
-      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
-     ]
-    }
-   ],
-   "source": [
-    "most_recent_df = name_df_map['calculated_p_stock'].groupby('ticker').last().reset_index()\n",
-    "concat_df = pd.concat([most_recent_df, p_stocks], axis=0, join='outer')\n",
-    "processing.get_processing_result_of_stocks_df(concat_df, p_profile)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>ticker</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
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-       "      <th>display_name</th>\n",
-       "      <th>name</th>\n",
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-       "      <th>portfolio_return</th>\n",
-       "      <th>cum_pct</th>\n",
-       "      <th>return</th>\n",
-       "      <th>sector_return</th>\n",
-       "      <th>cur_w_in_p</th>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>600409.XSHG</td>\n",
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-       "      <td>2021-01-05</td>\n",
-       "      <td>300274.XSHE</td>\n",
-       "      <td>76.03</td>\n",
-       "      <td>76.45</td>\n",
-       "      <td>80.20</td>\n",
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-       "      <td>2021-01-05</td>\n",
-       "      <td>002920.XSHE</td>\n",
-       "      <td>85.44</td>\n",
-       "      <td>87.25</td>\n",
-       "      <td>87.95</td>\n",
-       "      <td>84.07</td>\n",
-       "      <td>3852674.0</td>\n",
-       "      <td>3.322598e+08</td>\n",
-       "      <td>德赛西威</td>\n",
-       "      <td>DSXW</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>002709.XSHE</td>\n",
-       "      <td>32.54</td>\n",
-       "      <td>33.89</td>\n",
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-       "      <td>125.25</td>\n",
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-       "      <td>121.68</td>\n",
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-       "    <tr>\n",
-       "      <th>3609</th>\n",
-       "      <td>2023-06-27</td>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>8.52</td>\n",
-       "      <td>8.69</td>\n",
-       "      <td>8.78</td>\n",
-       "      <td>8.40</td>\n",
-       "      <td>151396630.0</td>\n",
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-       "      <td>小商品城</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.027187</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.301143</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>3610</th>\n",
-       "      <td>2023-06-28</td>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>8.60</td>\n",
-       "      <td>8.63</td>\n",
-       "      <td>8.68</td>\n",
-       "      <td>8.37</td>\n",
-       "      <td>103167271.0</td>\n",
-       "      <td>8.798186e+08</td>\n",
-       "      <td>小商品城</td>\n",
-       "      <td>XSPC</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.006904</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.299958</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3611</th>\n",
-       "      <td>2023-06-29</td>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>8.60</td>\n",
-       "      <td>8.74</td>\n",
-       "      <td>8.88</td>\n",
-       "      <td>8.58</td>\n",
-       "      <td>128969467.0</td>\n",
-       "      <td>1.125704e+09</td>\n",
-       "      <td>小商品城</td>\n",
-       "      <td>XSPC</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.012746</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.301804</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3612</th>\n",
-       "      <td>2023-06-30</td>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>8.74</td>\n",
-       "      <td>8.53</td>\n",
-       "      <td>8.77</td>\n",
-       "      <td>8.48</td>\n",
-       "      <td>103029932.0</td>\n",
-       "      <td>8.844883e+08</td>\n",
-       "      <td>小商品城</td>\n",
-       "      <td>XSPC</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.024027</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.293612</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3613</th>\n",
-       "      <td>2023-07-03</td>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>8.45</td>\n",
-       "      <td>8.37</td>\n",
-       "      <td>8.46</td>\n",
-       "      <td>8.05</td>\n",
-       "      <td>133732493.0</td>\n",
-       "      <td>1.108033e+09</td>\n",
-       "      <td>小商品城</td>\n",
-       "      <td>XSPC</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.018757</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.286010</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3614 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           date       ticker    open   close    high     low       volume  \\\n",
-       "0    2021-01-05  600409.XSHG    9.23    9.57    9.66    9.08   82669289.0   \n",
-       "1    2021-01-05  300274.XSHE   76.03   76.45   80.20   75.27   51384827.0   \n",
-       "2    2021-01-05  002920.XSHE   85.44   87.25   87.95   84.07    3852674.0   \n",
-       "3    2021-01-05  002709.XSHE   32.54   33.89   34.22   31.39   59152352.0   \n",
-       "4    2021-01-05  603882.XSHG  125.25  124.64  128.31  121.68    6803710.0   \n",
-       "...         ...          ...     ...     ...     ...     ...          ...   \n",
-       "3609 2023-06-27  600415.XSHG    8.52    8.69    8.78    8.40  151396630.0   \n",
-       "3610 2023-06-28  600415.XSHG    8.60    8.63    8.68    8.37  103167271.0   \n",
-       "3611 2023-06-29  600415.XSHG    8.60    8.74    8.88    8.58  128969467.0   \n",
-       "3612 2023-06-30  600415.XSHG    8.74    8.53    8.77    8.48  103029932.0   \n",
-       "3613 2023-07-03  600415.XSHG    8.45    8.37    8.46    8.05  133732493.0   \n",
-       "\n",
-       "             money display_name  name  ... portfolio_pct  prev_w_in_sectore  \\\n",
-       "0     7.803391e+08         三友化工  SYHG  ...           NaN                NaN   \n",
-       "1     3.961995e+09         阳光电源  YGDY  ...           NaN                NaN   \n",
-       "2     3.322598e+08         德赛西威  DSXW  ...           NaN                NaN   \n",
-       "3     1.942406e+09         天赐材料  TCCL  ...           NaN                NaN   \n",
-       "4     8.458543e+08         金域医学  JYYX  ...           NaN                NaN   \n",
-       "...            ...          ...   ...  ...           ...                ...   \n",
-       "3609  1.305075e+09         小商品城  XSPC  ...           NaN                NaN   \n",
-       "3610  8.798186e+08         小商品城  XSPC  ...           NaN                NaN   \n",
-       "3611  1.125704e+09         小商品城  XSPC  ...           NaN                NaN   \n",
-       "3612  8.844883e+08         小商品城  XSPC  ...           NaN                NaN   \n",
-       "3613  1.108033e+09         小商品城  XSPC  ...           NaN                NaN   \n",
-       "\n",
-       "      ini_w_in_sector  sector_pct  portfolio_return  cum_pct  return  \\\n",
-       "0                 1.0         NaN               NaN      NaN     NaN   \n",
-       "1                 0.5         NaN               NaN      NaN     NaN   \n",
-       "2                 1.0         NaN               NaN      NaN     NaN   \n",
-       "3                 0.5         NaN               NaN      NaN     NaN   \n",
-       "4                 1.0         NaN               NaN      NaN     NaN   \n",
-       "...               ...         ...               ...      ...     ...   \n",
-       "3609              NaN    0.027187               NaN      NaN     NaN   \n",
-       "3610              NaN   -0.006904               NaN      NaN     NaN   \n",
-       "3611              NaN    0.012746               NaN      NaN     NaN   \n",
-       "3612              NaN   -0.024027               NaN      NaN     NaN   \n",
-       "3613              NaN   -0.018757               NaN      NaN     NaN   \n",
-       "\n",
-       "      sector_return  cur_w_in_p  pre_w_in_sector  \n",
-       "0               NaN         NaN              NaN  \n",
-       "1               NaN         NaN              NaN  \n",
-       "2               NaN         NaN              NaN  \n",
-       "3               NaN         NaN              NaN  \n",
-       "4               NaN         NaN              NaN  \n",
-       "...             ...         ...              ...  \n",
-       "3609            NaN    0.301143              1.0  \n",
-       "3610            NaN    0.299958              1.0  \n",
-       "3611            NaN    0.301804              1.0  \n",
-       "3612            NaN    0.293612              1.0  \n",
-       "3613            NaN    0.286010              1.0  \n",
-       "\n",
-       "[3614 rows x 27 columns]"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "most_recent_df = name_df_map['calculated_p_stock'].groupby('ticker').last().reset_index()\n",
-    "\n",
-    "def get_last_values(row):\n",
-    "    ticker = row['ticker']\n",
-    "    if ticker in p_profile['ticker'].values:\n",
-    "        return p_profile.loc[p_profile['ticker'] == ticker, ['display_name', 'name', 'aggregate_sector']].iloc[-1]\n",
-    "    else:\n",
-    "        return pd.Series([np.nan, np.nan, np.nan], index=['display_name', 'name', 'aggregate_sector'])\n",
-    "# dispaly_name, name and aggregate_sector\n",
-    "p_stocks[['display_name', 'name', 'aggregate_sector']] = p_stocks.apply(get_last_values, axis=1)\n",
-    "\n",
-    "# use the most recent result to resume calculation\n",
-    "concat_df = pd.concat([most_recent_df, p_stocks], axis=0, join='outer')\n",
-    "\n",
-    "# pct\n",
-    "concat_df['pct'] = concat_df.groupby('ticker')['close'].pct_change()\n",
-    "\n",
-    "# calculate not normalized previous weight and current weight\n",
-    "groups = concat_df.groupby('ticker')\n",
-    "for _, group in groups:\n",
-    "    cur_weight = np.nan\n",
-    "    for index, row in group.iterrows():\n",
-    "        if pd.notna(row['current_weight']):\n",
-    "            cur_weight = row['current_weight']\n",
-    "        else:\n",
-    "            concat_df.loc[index, 'previous_weight'] = cur_weight\n",
-    "            cur_weight = cur_weight * (1 + row['pct'])\n",
-    "            concat_df.loc[index, 'current_weight'] = cur_weight\n",
-    "\n",
-    "# calculate normalized previous and current weight\n",
-    "concat_df['prev_w_in_p'] = concat_df['previous_weight'] / \\\n",
-    "    concat_df.groupby('date')['previous_weight'].transform('sum')\n",
-    "concat_df['cur_w_in_p'] = concat_df['current_weight'] / \\\n",
-    "    concat_df.groupby('date')['current_weight'].transform('sum')\n",
-    "\n",
-    "# calculate previous weight in sector\n",
-    "concat_df['pre_w_in_sector'] = concat_df['prev_w_in_p'] / \\\n",
-    "    concat_df.groupby(['date', 'aggregate_sector'])['prev_w_in_p'].transform('sum')\n",
-    "\n",
-    "# calculate pct in sector\n",
-    "concat_df['sector_pct'] = concat_df['pct'] * concat_df['pre_w_in_sector']\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "# remove group with first date\n",
-    "min_date_group = concat_df.groupby('date')['date'].idxmin()\n",
-    "concat_df = concat_df.drop(min_date_group)\n",
-    "\n",
-    "# merge back to calculated_stock\n",
-    "pd.concat([name_df_map['calculated_p_stock'],concat_df]).reset_index(drop=True)\n",
-    "\n",
-    "# concat_df[concat_df.ticker == '002709.XSHE'][['date','pct','current_weight','previous_weight','prev_w_in_p','cur_w_in_p']]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

script/description.py → description.py

@@ -3,6 +3,7 @@ This file store the tooltip to show in the GUI
 
 '''
 #周期回报
+
 periodic_return_report = '''
 选择周期查看每个周期的回报率,以及每个周期主动回报的归因。
 周期回报的数据点代表周期结束。

index_page.py

@@ -1,57 +1,35 @@
 import panel as pn
-import pandas as pd
-from datetime import datetime, timedelta
-import plotly.express as px
-import holoviews as hv
-import numpy as np
 from sidebar import SideNavBar
-import random
-import scipy.stats as stats
-import hvplot.pandas  # noqa
 from sqlalchemy import create_engine
-import api
-# from backgroundTask import stocks_stream
-from functools import partial
-import plotly.graph_objects as go
-from panel.viewable import Viewer
-import processing
 import appComponents
-import param
 import db_operation as db
-# import warnings
+
 pn.extension('mathjax')
-# warnings.filterwarnings("ignore", category=pd.core.common.SettingWithCopyWarning)
+
 pn.extension('plotly')
 pn.extension('tabulator')
 db_url = 'sqlite:///instance/local.db'
 engine = create_engine(db_url)
 
 
-
 analytic_p = db.get_portfolio_analytic_df()
 analytic_b = db.get_benchmark_analytic_df()
-# load benchmark stock
-# with engine.connect() as connection:
-#     analytics_df = pd.read
-#     calculated_b_stock = pd.read_sql('calculated_b_stock', con=connection)
-#     calculated_p_stock = pd.read_sql('calculated_p_stock', con=connection)
-#     p_eval_df = pd.read_sql('p_eval_result', con=connection)
 
 stock_overview = appComponents.BestAndWorstStocks(
     analytic_df=analytic_p)
 composation_card = appComponents.PortfolioComposationCard(
     analytic_p)
 monthly_return_card = appComponents.HistReturnCard(
-     calculated_p_stock=analytic_p, calculated_b_stock=analytic_b)
-total_return_card = appComponents.TotalReturnCard(name='Range', 
+    calculated_p_stock=analytic_p, calculated_b_stock=analytic_b)
+total_return_card = appComponents.TotalReturnCard(name='Range',
                                                   b_stock_df=analytic_b,
                                                   p_stock_df=analytic_p,
                                                   value=(0, 20))
 drawdown_card = appComponents.DrawDownCard(
-   calculated_p_stock=analytic_p)
+    calculated_p_stock=analytic_p)
 
 top_header = appComponents.TopHeader(
-    eval_df = analytic_p,
+    eval_df=analytic_p,
 )
 
 template = pn.template.FastListTemplate(

instance/local.db

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cbb513c601ed039cdf11c093c2192afc7383eaa8f9db8f7dead5fba68a94ff0d
-size 164798464
+oid sha256:77911978f609eea6d1dc9285228549b79a1f05a040207d28af2abb4fe7a37063
+size 261677056

pipeline.py

@@ -12,7 +12,7 @@ import processing
 # import settings
 # fetch new stock price
 stock_price_stream = Stream()
-
+event = Stream()
 
 # log
 log = Log('instance/log.json')
@@ -187,7 +187,9 @@ def save_stock_price_to_db(df: pd.DataFrame):
 
 
 def update_portfolio_profile_to_db(portfolio_df):
-    '''overwrite the portfolio profile table in db'''
+    '''overwrite the portfolio profile table in db, and trigger a left fill on benchmark, stock price
+    and recomputation of analysis
+    '''
 
     if (_validate_schema(portfolio_df, ts.PORTFOLIO_TABLE_SCHEMA)):
         raise ValueError(
@@ -198,10 +200,14 @@ def update_portfolio_profile_to_db(portfolio_df):
         try:
             portfolio_df[ts.PORTFOLIO_TABLE_SCHEMA.keys()].to_sql(
                 ts.PORTFOLIO_TABLE, con=conn, if_exists='append', index=False)
+            
+            event.emit('update_portfolio')
+            # handle_portfolio_update()
+           
             return True
         except:
             return False
-    # TODO trigger recomputation of analysis
+    
 
 
 def right_fill_stock_price():
@@ -413,6 +419,26 @@ def batch_processing():
     db.save_benchmark_analytic_df(analytic_b)
 
 
+def left_fill():
+    left_fill_benchmark_profile()
+    left_fill_stocks_price()
+
+
+def handle_portfolio_update():
+    '''
+    execute when portfolio is updated, 
+    left fill benchmark and stock price
+
+    update method is idempotent, so it is safe to call multiple times
+    '''
+    left_fill_benchmark_profile()
+    print("left fill benchmark profile")
+    left_fill_stocks_price()
+    print('left fill stock price db')
+    batch_processing()
+    print('done processing')
+
+
 async def daily_update():
     '''
     left and right fill stock price and benchmark weight based on portfolio
@@ -423,7 +449,7 @@ async def daily_update():
 
     '''
     last_update = log.get_time('daily_update')
-    # check if need to update
+    # less than today 9am, since it need to force to update at 9
     if last_update is None or utils.time_in_beijing() - last_update >= dt.timedelta(days=1):
         print("running daily update")
 
@@ -475,16 +501,11 @@ async def run():
             # print(stock_df)
             stock_price_stream.emit(stock_df)
 
-        # # latest stock price
-        # stock_df = update_stocks_price()
-        # # add display name and sector to stock_df
-        # stock_df = add_details_to_stock_df(stock_df)
-        # save_stock_price_to_db(stock_df)
-        # stock_price_stream.emit(stock_df)
-    # update sotck_price
-
-    # send fetched data
 
-    # run processing
+def handle_event(e):
+    if e == "update_portfolio":
+        print("handling portfolio update")
+        handle_portfolio_update()
+        print("done handling portfolio update")
 
-    # send fetched data
+event.sink(handle_event)

pipeline/create_dumpy_data.ipynb

@@ -1,36 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from test_db_peration import db_operator\n",
-    "from datetime import datetime\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

pipeline/db_operation.py

@@ -1,35 +0,0 @@
-from model import Stock
-from sqlalchemy import create_engine
-from sqlalchemy.orm import Session
-
-
-class db_operator():
-    def __init__(self, db_url):
-        self.engine = create_engine(db_url)
-
-    def get_stocks_between(self, d1, d2):
-        with Session(self.engine) as session:
-            return session.query(Stock).filter(
-                Stock.date.between(d1, d2)
-                ).all()
-    
-    def add_stock(self, stock_data: dict):
-        with Session(self.engine) as session:
-            new_stock = Stock(**stock_data)
-            session.add(new_stock)
-            session.commit()
-    
-    def delete_stocks_between(self, d1, d2):
-        with Session(self.engine) as session:
-            session.query(Stock).filter(
-                Stock.date.between(d1, d2)
-                ).delete()
-            session.commit()
-    
-    def delete_all_stocks(self):
-        with Session(self.engine) as session:
-            session.query(Stock).delete()
-            session.commit()
-    
-
-    

pipeline/model.py

@@ -1,41 +0,0 @@
-from sqlalchemy import Column, Integer, String, Float, create_engine, DateTime, PickleType
-from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy.orm import sessionmaker
-
-Base = declarative_base()
-
-class Stock(Base):
-    __tablename__ = 'stock'
-
-    id = Column(Integer, primary_key=True)
-    ticker = Column(String(50))
-    weight = Column(Float, nullable=True)
-    display_name = Column(String(50), nullable=False)
-    date = Column(DateTime, nullable=False)
-    order = Column(Integer, autoincrement=True, nullable=True)
-
-    def __repr__(self):
-        return f"<{self.ticker}\
-                    {self.date}\
-                        {self.display_name}\
-                            {round(self.weight * 100)}%>"
-
-
-class Portfolio(Base):
-    __tablename__ = 'portfolio'
-    
-    id = Column(Integer, primary_key=True)
-    stocks = Column(PickleType, nullable=False)
-    cached_result = Column(PickleType, nullable=True)
-    # data = Column(PickleType, nullable=False)
-    date = Column(DateTime, nullable=False)
-    order = Column(Integer, autoincrement=True, nullable=True)
-    
-
-# db_url = 'sqlite:///local_db.db'  # Replace 'stocks.db' with the desired database name and location
-
-# engine = create_engine(db_url)
-# Base.metadata.create_all(engine)
-
-# Session = sessionmaker(bind=engine)
-# session = Session()

pipeline/test_db_peration.py

@@ -1,107 +0,0 @@
-from unittest import TestCase, main
-from db_operation import db_operator
-from datetime import datetime
-class test_db_operation(TestCase):
-    def setUp(self) -> None:
-        self.db_operator = db_operator('sqlite:///local_db.db')
-        self.db_operator.delete_all_stocks()
-        self.stock1 = {
-            "ticker": 'AAPL',
-            "weight": 1.0,
-            "display_name": 'Apple Inc.',
-            "date": datetime(2021, 1, 1)
-        }
-        self.stock2 = {
-            "ticker": 'MSFT',
-            "weight": 1.0,
-            "display_name": 'Microsoft Corporation',
-            "date": datetime(2021, 1, 1)
-        }
-        
-    def test_insert(self):
-        stock1 = {
-            "ticker": 'AAPL',
-            "weight": 1.0,
-            "display_name": 'Apple Inc.',
-            "date": datetime(2021, 1, 1)
-        }
-        self.db_operator.add_stock(stock1)
-        retrieved_stock = self.db_operator.get_stocks_between(datetime(2021, 1, 1), datetime(2021, 1, 1))[0]
-        self.assertEqual(retrieved_stock.ticker, 'AAPL')
-        self.assertEqual(retrieved_stock.weight, 1.0)
-        self.assertEqual(retrieved_stock.display_name, 'Apple Inc.')
-        self.assertEqual(retrieved_stock.date, datetime(2021, 1, 1))
-
-    def test_delete(self):
-        self.db_operator.add_stock(self.stock1)
-        self.db_operator.add_stock(self.stock2)
-        self.db_operator.delete_stocks_between(
-            datetime(2021, 1, 1),
-            datetime(2021, 1, 1))
-        retrieved_stocks = self.db_operator.get_stocks_between(
-            datetime(2021, 1, 1),
-            datetime(2021, 1, 1))
-        self.assertEqual(len(retrieved_stocks), 0)
-
-    def test_query_window_1d(self):
-        # insert 2 stocks between 2021-01-01 and 2021-01-01 every hour
-        for i in range(24):
-            self.stock1['date'] = datetime(2021, 1, 1, i)
-            self.stock2['date'] = datetime(2021, 1, 1, i)
-            self.db_operator.add_stock(self.stock1)
-            self.db_operator.add_stock(self.stock2)
-        # insert two on 2021-01-02
-        self.stock1['date'] = datetime(2021, 1, 2)
-        self.stock2['date'] = datetime(2021, 1, 2)
-        self.db_operator.add_stock(self.stock1)
-        self.db_operator.add_stock(self.stock2)
-        # query 1d
-        retrieved_stocks = self.db_operator.get_stocks_between(
-            datetime(2021, 1, 1),
-            datetime(2021, 1, 2))
-        self.assertEqual(len(retrieved_stocks), 50)
-    
-    def test_query_window_12h(self):
-        # insert 2 stocks every hour between 2021-01-01 and 2021-01-01
-        for i in range(24):
-            self.stock1['date'] = datetime(2021, 1, 1, i)
-            self.stock2['date'] = datetime(2021, 1, 1, i)
-            self.db_operator.add_stock(self.stock1)
-            self.db_operator.add_stock(self.stock2)
-        # query 12h
-        retrieved_stocks = self.db_operator.get_stocks_between(
-            datetime(2021, 1, 1, 0),
-            datetime(2021, 1, 1, 12))
-        self.assertEqual(len(retrieved_stocks), 26)
-        # self.assertTrue(False)
-    
-    def test_query_window_1h(self):
-       # insert 2 stocks every mins between 2021-01-01 and 2021-01-01
-        for i in range(60):
-            self.stock1['date'] = datetime(2021, 1, 1, 0, i)
-            self.stock2['date'] = datetime(2021, 1, 1, 0, i)
-            self.db_operator.add_stock(self.stock1)
-            self.db_operator.add_stock(self.stock2)
-        # query 1h
-        retrieved_stocks = self.db_operator.get_stocks_between(
-            datetime(2021, 1, 1, 0),
-            datetime(2021, 1, 1, 1))
-        self.assertEqual(len(retrieved_stocks), 120)
-    
-    def test_query_window_30m(self):
-        # insert 2 stocks every 1 between 2021-01-01-00:00 and 2021-01-01-00:20
-        for i in range(20):
-            self.stock1['date'] = datetime(2021, 1, 1, 0, i)
-            self.stock2['date'] = datetime(2021, 1, 1, 0, i)
-            self.db_operator.add_stock(self.stock1)
-            self.db_operator.add_stock(self.stock2)
-        # query 30m
-        retrieved_stocks = self.db_operator.get_stocks_between(
-            datetime(2021, 1, 1, 0),
-            datetime(2021, 1, 1, 0, 30))
-        self.assertEqual(len(retrieved_stocks), 40)
-    
-    
-
-if __name__ == '__main__':
-    main()

portfolioEditingPage.py

@@ -160,9 +160,9 @@ def app():
             # patch all synced_to_db to true
             indices = history_tabulator.value[
                 ~history_tabulator.value['change_saved']].index.to_list()
-            
+
             # add an offset to address the issue when df is empty index start from 1
-            
+
             history_tabulator.patch({
                 'change_saved': [(index, True) for index in indices]
             }, as_index=True)
@@ -237,11 +237,11 @@ def app():
                           shares=row[2].value,
                           ave_price=row[3].value,
                           date=datetime_picker.value) for row in stock_column]
-        
+
         if len(new_entry) == 0:
             print("no entry added")
-            return 
-        
+            return
+
         new_profile = pipeline.create_portfolio_profile_df(new_entry)
         # calculate share changes
         tmp_profile = pd.concat([p_profile, new_profile], ignore_index=True)
@@ -294,12 +294,12 @@ def app():
         # update history tabulator and portfolio tabulator
         if successed:
             # mark changes as saved
-            indices = selected_portfolio[~selected_portfolio['change_saved']].index.to_list()
+            indices = selected_portfolio[~selected_portfolio['change_saved']].index.to_list(
+            )
             portfolio_tabulator.patch({
                 'change_saved': [(index, True) for index in indices]
             }, as_index=True)
 
-        
             _update_history_tabulator('sync')
 
     def handle_edit_portfolio_tabulator(e):

script/bnb_sector_result.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dc104daae7afcd56a05f8aff9fa4d80d139a7db33d271fd69fbacb29db22283a
-size 152021

script/downloadData.ipynb

@@ -1,751 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from api import create_portfolio, get_portfile_data, get_benchmark_portfolio\n",
-    "from datetime import datetime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df = pd.read_pickle('../dummy_portfolio.pkl')\n",
-    "# df.head(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ## create a portfolio\n",
-    "\n",
-    "# # weight can use mony\n",
-    "# portfolio_profile_data = [\n",
-    "# {'ticker': '002709.XSHE', 'date': datetime(2021, 1, 5), 'weight': 100},\n",
-    "# {'ticker': '002920.XSHE', 'date': datetime(2021, 1, 5), 'weight': 100},\n",
-    "# {'ticker': '300274.XSHE', 'date': datetime(2021, 1, 5), 'weight': 100},\n",
-    "# {'ticker': '600409.XSHG', 'date': datetime(2021, 1, 5), 'weight': 100},\n",
-    "# {'ticker': '600415.XSHG', 'date': datetime(2021, 1, 5), 'weight': 100},\n",
-    "# {'ticker': '603882.XSHG', 'date': datetime(2021, 1, 5), 'weight': 100},\n",
-    "# ]\n",
-    "# # ten thousand\n",
-    "# mkt_cap = 100000\n",
-    "# portfolio_profile, error = create_portfolio(portfolio_profile_data, mkt_cap)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print(error)\n",
-    "# # save \n",
-    "# portfolio_profile.to_pickle('../data/portfolio_portfile.pkl')\n",
-    "# portfolio_profile.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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>ticker</th>\n",
-       "      <th>date</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>display_name</th>\n",
-       "      <th>name</th>\n",
-       "      <th>sector</th>\n",
-       "      <th>aggregate_sector</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>002709.XSHE</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>天赐材料</td>\n",
-       "      <td>TCCL</td>\n",
-       "      <td>电气设备I 电池II 电池化学品III 化学原料和化学制品制造业 电池部件及材料 工业</td>\n",
-       "      <td>工业</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>002920.XSHE</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>德赛西威</td>\n",
-       "      <td>DSXW</td>\n",
-       "      <td>计算机I 软件开发II 垂直应用软件III 汽车制造业 汽车电子 可选消费</td>\n",
-       "      <td>信息与通信</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>300274.XSHE</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>阳光电源</td>\n",
-       "      <td>YGDY</td>\n",
-       "      <td>电气设备I 光伏设备II 逆变器III 电气机械和器材制造业 光伏设备 工业</td>\n",
-       "      <td>工业</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>600409.XSHG</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>三友化工</td>\n",
-       "      <td>SYHG</td>\n",
-       "      <td>化工I 化学原料II 纯碱III 化学原料和化学制品制造业 粘胶 原材料</td>\n",
-       "      <td>原料与能源</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>小商品城</td>\n",
-       "      <td>XSPC</td>\n",
-       "      <td>商业贸易I 一般零售II 商业物业经营III 商务服务业 市场服务 工业</td>\n",
-       "      <td>消费</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>603882.XSHG</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>100</td>\n",
-       "      <td>金域医学</td>\n",
-       "      <td>JYYX</td>\n",
-       "      <td>医药生物I 医疗服务II 诊断服务III 卫生 体外诊断 医药卫生</td>\n",
-       "      <td>医药卫生</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        ticker       date  weight display_name  name  \\\n",
-       "0  002709.XSHE 2021-01-05     100         天赐材料  TCCL   \n",
-       "1  002920.XSHE 2021-01-05     100         德赛西威  DSXW   \n",
-       "2  300274.XSHE 2021-01-05     100         阳光电源  YGDY   \n",
-       "3  600409.XSHG 2021-01-05     100         三友化工  SYHG   \n",
-       "4  600415.XSHG 2021-01-05     100         小商品城  XSPC   \n",
-       "5  603882.XSHG 2021-01-05     100         金域医学  JYYX   \n",
-       "\n",
-       "                                        sector aggregate_sector  \n",
-       "0  电气设备I 电池II 电池化学品III 化学原料和化学制品制造业 电池部件及材料 工业               工业  \n",
-       "1        计算机I 软件开发II 垂直应用软件III 汽车制造业 汽车电子 可选消费            信息与通信  \n",
-       "2       电气设备I 光伏设备II 逆变器III 电气机械和器材制造业 光伏设备 工业               工业  \n",
-       "3         化工I 化学原料II 纯碱III 化学原料和化学制品制造业 粘胶 原材料            原料与能源  \n",
-       "4         商业贸易I 一般零售II 商业物业经营III 商务服务业 市场服务 工业               消费  \n",
-       "5            医药生物I 医疗服务II 诊断服务III 卫生 体外诊断 医药卫生             医药卫生  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## load portfolio_profile\n",
-    "portfolio_profile = pd.read_pickle('../data/portfolio_portfile.pkl')\n",
-    "portfolio_profile.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_date = datetime(2021, 1, 5)\n",
-    "end_date = datetime(2022, 1, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "auth success \n"
-     ]
-    }
-   ],
-   "source": [
-    "# get portfolio data\n",
-    "portfile_data, error = get_portfile_data(portfolio_profile, start_date, end_date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[]\n",
-      "(1482, 8)\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>ticker</th>\n",
-       "      <th>date</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>money</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2022-01-07</th>\n",
-       "      <td>603882.XSHG</td>\n",
-       "      <td>2022-01-07</td>\n",
-       "      <td>91.13</td>\n",
-       "      <td>87.99</td>\n",
-       "      <td>91.17</td>\n",
-       "      <td>87.72</td>\n",
-       "      <td>6971998.0</td>\n",
-       "      <td>6.176535e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-07</th>\n",
-       "      <td>002709.XSHE</td>\n",
-       "      <td>2022-01-07</td>\n",
-       "      <td>51.28</td>\n",
-       "      <td>51.72</td>\n",
-       "      <td>52.62</td>\n",
-       "      <td>50.47</td>\n",
-       "      <td>32210458.0</td>\n",
-       "      <td>1.661823e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-07</th>\n",
-       "      <td>600409.XSHG</td>\n",
-       "      <td>2022-01-07</td>\n",
-       "      <td>8.23</td>\n",
-       "      <td>8.22</td>\n",
-       "      <td>8.29</td>\n",
-       "      <td>8.19</td>\n",
-       "      <td>35003739.0</td>\n",
-       "      <td>2.884990e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-07</th>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>2022-01-07</td>\n",
-       "      <td>4.74</td>\n",
-       "      <td>4.70</td>\n",
-       "      <td>4.79</td>\n",
-       "      <td>4.68</td>\n",
-       "      <td>24902567.0</td>\n",
-       "      <td>1.178837e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-10</th>\n",
-       "      <td>300274.XSHE</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>127.49</td>\n",
-       "      <td>124.09</td>\n",
-       "      <td>127.49</td>\n",
-       "      <td>123.29</td>\n",
-       "      <td>17238708.0</td>\n",
-       "      <td>2.148032e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-10</th>\n",
-       "      <td>600409.XSHG</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>8.24</td>\n",
-       "      <td>8.35</td>\n",
-       "      <td>8.39</td>\n",
-       "      <td>8.21</td>\n",
-       "      <td>32516017.0</td>\n",
-       "      <td>2.699300e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-10</th>\n",
-       "      <td>002920.XSHE</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>130.36</td>\n",
-       "      <td>138.43</td>\n",
-       "      <td>141.96</td>\n",
-       "      <td>130.11</td>\n",
-       "      <td>5005400.0</td>\n",
-       "      <td>6.901614e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-10</th>\n",
-       "      <td>002709.XSHE</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>51.63</td>\n",
-       "      <td>50.73</td>\n",
-       "      <td>51.93</td>\n",
-       "      <td>50.03</td>\n",
-       "      <td>29821246.0</td>\n",
-       "      <td>1.518902e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-10</th>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>4.70</td>\n",
-       "      <td>4.75</td>\n",
-       "      <td>4.85</td>\n",
-       "      <td>4.67</td>\n",
-       "      <td>39278041.0</td>\n",
-       "      <td>1.859827e+08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2022-01-10</th>\n",
-       "      <td>603882.XSHG</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>88.45</td>\n",
-       "      <td>95.53</td>\n",
-       "      <td>95.59</td>\n",
-       "      <td>88.39</td>\n",
-       "      <td>6991445.0</td>\n",
-       "      <td>6.468392e+08</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 ticker       date    open   close    high     low  \\\n",
-       "2022-01-07  603882.XSHG 2022-01-07   91.13   87.99   91.17   87.72   \n",
-       "2022-01-07  002709.XSHE 2022-01-07   51.28   51.72   52.62   50.47   \n",
-       "2022-01-07  600409.XSHG 2022-01-07    8.23    8.22    8.29    8.19   \n",
-       "2022-01-07  600415.XSHG 2022-01-07    4.74    4.70    4.79    4.68   \n",
-       "2022-01-10  300274.XSHE 2022-01-10  127.49  124.09  127.49  123.29   \n",
-       "2022-01-10  600409.XSHG 2022-01-10    8.24    8.35    8.39    8.21   \n",
-       "2022-01-10  002920.XSHE 2022-01-10  130.36  138.43  141.96  130.11   \n",
-       "2022-01-10  002709.XSHE 2022-01-10   51.63   50.73   51.93   50.03   \n",
-       "2022-01-10  600415.XSHG 2022-01-10    4.70    4.75    4.85    4.67   \n",
-       "2022-01-10  603882.XSHG 2022-01-10   88.45   95.53   95.59   88.39   \n",
-       "\n",
-       "                volume         money  \n",
-       "2022-01-07   6971998.0  6.176535e+08  \n",
-       "2022-01-07  32210458.0  1.661823e+09  \n",
-       "2022-01-07  35003739.0  2.884990e+08  \n",
-       "2022-01-07  24902567.0  1.178837e+08  \n",
-       "2022-01-10  17238708.0  2.148032e+09  \n",
-       "2022-01-10  32516017.0  2.699300e+08  \n",
-       "2022-01-10   5005400.0  6.901614e+08  \n",
-       "2022-01-10  29821246.0  1.518902e+09  \n",
-       "2022-01-10  39278041.0  1.859827e+08  \n",
-       "2022-01-10   6991445.0  6.468392e+08  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(error)\n",
-    "print(portfile_data.shape)\n",
-    "portfile_data.sort_values(by=['date'], inplace=True)\n",
-    "portfile_data.tail(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save\n",
-    "portfile_data.to_pickle('../data/portfolio_data.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load benchmark portfolio\n",
-    "benchmark_portfolio, error = get_benchmark_portfolio(start_date, end_date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[]\n",
-      "(185500, 15)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>display_name_x</th>\n",
-       "      <th>actual_data</th>\n",
-       "      <th>ticker</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>money</th>\n",
-       "      <th>display_name_y</th>\n",
-       "      <th>name</th>\n",
-       "      <th>sector</th>\n",
-       "      <th>aggregate_sector</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>185163</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.274</td>\n",
-       "      <td>厦门钨业</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600549.XSHG</td>\n",
-       "      <td>21.50</td>\n",
-       "      <td>21.59</td>\n",
-       "      <td>21.75</td>\n",
-       "      <td>21.43</td>\n",
-       "      <td>10451128.0</td>\n",
-       "      <td>2.254199e+08</td>\n",
-       "      <td>厦门钨业</td>\n",
-       "      <td>XMWY</td>\n",
-       "      <td>有色金属I 稀有金属II 钨III 有色金属冶炼和压延加工业 钨钼 原材料</td>\n",
-       "      <td>原料与能源</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185162</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.116</td>\n",
-       "      <td>山煤国际</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600546.XSHG</td>\n",
-       "      <td>6.74</td>\n",
-       "      <td>6.86</td>\n",
-       "      <td>6.86</td>\n",
-       "      <td>6.71</td>\n",
-       "      <td>66929559.0</td>\n",
-       "      <td>4.539690e+08</td>\n",
-       "      <td>山煤国际</td>\n",
-       "      <td>SMGJ</td>\n",
-       "      <td>煤炭I 煤炭开采II 动力煤III 批发业 煤炭 能源</td>\n",
-       "      <td>原料与能源</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185161</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.211</td>\n",
-       "      <td>中国软件</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600536.XSHG</td>\n",
-       "      <td>29.01</td>\n",
-       "      <td>30.90</td>\n",
-       "      <td>31.45</td>\n",
-       "      <td>28.98</td>\n",
-       "      <td>35489167.0</td>\n",
-       "      <td>1.087404e+09</td>\n",
-       "      <td>中国软件</td>\n",
-       "      <td>ZGRJ</td>\n",
-       "      <td>计算机I IT服务II IT服务III 软件和信息技术服务业 行业应用软件 信息技术</td>\n",
-       "      <td>信息与通信</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185160</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.205</td>\n",
-       "      <td>天士力</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600535.XSHG</td>\n",
-       "      <td>15.04</td>\n",
-       "      <td>15.52</td>\n",
-       "      <td>15.75</td>\n",
-       "      <td>14.95</td>\n",
-       "      <td>46823950.0</td>\n",
-       "      <td>7.247465e+08</td>\n",
-       "      <td>天士力</td>\n",
-       "      <td>TSL</td>\n",
-       "      <td>医药生物I 中药II 中药III 医药制造业 中成药 医药卫生</td>\n",
-       "      <td>医药卫生</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185159</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.297</td>\n",
-       "      <td>山东药玻</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600529.XSHG</td>\n",
-       "      <td>42.39</td>\n",
-       "      <td>40.14</td>\n",
-       "      <td>42.39</td>\n",
-       "      <td>39.47</td>\n",
-       "      <td>12543641.0</td>\n",
-       "      <td>5.049473e+08</td>\n",
-       "      <td>山东药玻</td>\n",
-       "      <td>SDYB</td>\n",
-       "      <td>医药生物I 医疗器械II 医疗耗材III 非金属矿物制品业 医疗耗材 医药卫生</td>\n",
-       "      <td>医药卫生</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185158</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.156</td>\n",
-       "      <td>中铁工业</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600528.XSHG</td>\n",
-       "      <td>8.69</td>\n",
-       "      <td>9.08</td>\n",
-       "      <td>9.26</td>\n",
-       "      <td>8.68</td>\n",
-       "      <td>98213530.0</td>\n",
-       "      <td>8.961637e+08</td>\n",
-       "      <td>中铁工业</td>\n",
-       "      <td>ZTGY</td>\n",
-       "      <td>机械设备I 运输设备II 铁路设备III 专用设备制造业 城轨铁路 工业</td>\n",
-       "      <td>工业</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185157</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.642</td>\n",
-       "      <td>中天科技</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600522.XSHG</td>\n",
-       "      <td>15.21</td>\n",
-       "      <td>14.76</td>\n",
-       "      <td>15.26</td>\n",
-       "      <td>14.53</td>\n",
-       "      <td>112744173.0</td>\n",
-       "      <td>1.670952e+09</td>\n",
-       "      <td>中天科技</td>\n",
-       "      <td>ZTKJ</td>\n",
-       "      <td>通信I 通信设备II 通信线缆及配套III 电气机械和器材制造业 通信系统设备及组件 通信服务</td>\n",
-       "      <td>信息与通信</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185156</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.276</td>\n",
-       "      <td>华海药业</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600521.XSHG</td>\n",
-       "      <td>22.32</td>\n",
-       "      <td>23.32</td>\n",
-       "      <td>23.43</td>\n",
-       "      <td>21.90</td>\n",
-       "      <td>29810065.0</td>\n",
-       "      <td>6.827298e+08</td>\n",
-       "      <td>华海药业</td>\n",
-       "      <td>HHYY</td>\n",
-       "      <td>医药生物I 化学制药II 化学制剂III 医药制造业 药品制剂 医药卫生</td>\n",
-       "      <td>医药卫生</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185169</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.240</td>\n",
-       "      <td>卧龙电驱</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>600580.XSHG</td>\n",
-       "      <td>16.66</td>\n",
-       "      <td>16.51</td>\n",
-       "      <td>16.68</td>\n",
-       "      <td>16.23</td>\n",
-       "      <td>19294606.0</td>\n",
-       "      <td>3.175398e+08</td>\n",
-       "      <td>卧龙电驱</td>\n",
-       "      <td>WLDQ</td>\n",
-       "      <td>电气设备I 电机II 电机III 电气机械和器材制造业 电动机与工控自动化 工业</td>\n",
-       "      <td>工业</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185499</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.350</td>\n",
-       "      <td>思瑞浦</td>\n",
-       "      <td>2021-12-31</td>\n",
-       "      <td>688536.XSHG</td>\n",
-       "      <td>476.70</td>\n",
-       "      <td>463.10</td>\n",
-       "      <td>476.70</td>\n",
-       "      <td>446.03</td>\n",
-       "      <td>924992.0</td>\n",
-       "      <td>4.254053e+08</td>\n",
-       "      <td>思瑞浦</td>\n",
-       "      <td>SRP</td>\n",
-       "      <td>电子I 半导体II 模拟芯片设计III 软件和信息技术服务业 集成电路设计 信息技术</td>\n",
-       "      <td>信息与通信</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             date  weight display_name_x actual_data       ticker    open  \\\n",
-       "185163 2022-01-10   0.274           厦门钨业  2021-12-31  600549.XSHG   21.50   \n",
-       "185162 2022-01-10   0.116           山煤国际  2021-12-31  600546.XSHG    6.74   \n",
-       "185161 2022-01-10   0.211           中国软件  2021-12-31  600536.XSHG   29.01   \n",
-       "185160 2022-01-10   0.205            天士力  2021-12-31  600535.XSHG   15.04   \n",
-       "185159 2022-01-10   0.297           山东药玻  2021-12-31  600529.XSHG   42.39   \n",
-       "185158 2022-01-10   0.156           中铁工业  2021-12-31  600528.XSHG    8.69   \n",
-       "185157 2022-01-10   0.642           中天科技  2021-12-31  600522.XSHG   15.21   \n",
-       "185156 2022-01-10   0.276           华海药业  2021-12-31  600521.XSHG   22.32   \n",
-       "185169 2022-01-10   0.240           卧龙电驱  2021-12-31  600580.XSHG   16.66   \n",
-       "185499 2022-01-10   0.350            思瑞浦  2021-12-31  688536.XSHG  476.70   \n",
-       "\n",
-       "         close    high     low       volume         money display_name_y  \\\n",
-       "185163   21.59   21.75   21.43   10451128.0  2.254199e+08           厦门钨业   \n",
-       "185162    6.86    6.86    6.71   66929559.0  4.539690e+08           山煤国际   \n",
-       "185161   30.90   31.45   28.98   35489167.0  1.087404e+09           中国软件   \n",
-       "185160   15.52   15.75   14.95   46823950.0  7.247465e+08            天士力   \n",
-       "185159   40.14   42.39   39.47   12543641.0  5.049473e+08           山东药玻   \n",
-       "185158    9.08    9.26    8.68   98213530.0  8.961637e+08           中铁工业   \n",
-       "185157   14.76   15.26   14.53  112744173.0  1.670952e+09           中天科技   \n",
-       "185156   23.32   23.43   21.90   29810065.0  6.827298e+08           华海药业   \n",
-       "185169   16.51   16.68   16.23   19294606.0  3.175398e+08           卧龙电驱   \n",
-       "185499  463.10  476.70  446.03     924992.0  4.254053e+08            思瑞浦   \n",
-       "\n",
-       "        name                                           sector aggregate_sector  \n",
-       "185163  XMWY            有色金属I 稀有金属II 钨III 有色金属冶炼和压延加工业 钨钼 原材料            原料与能源  \n",
-       "185162  SMGJ                      煤炭I 煤炭开采II 动力煤III 批发业 煤炭 能源            原料与能源  \n",
-       "185161  ZGRJ       计算机I IT服务II IT服务III 软件和信息技术服务业 行业应用软件 信息技术            信息与通信  \n",
-       "185160   TSL                  医药生物I 中药II 中药III 医药制造业 中成药 医药卫生             医药卫生  \n",
-       "185159  SDYB          医药生物I 医疗器械II 医疗耗材III 非金属矿物制品业 医疗耗材 医药卫生             医药卫生  \n",
-       "185158  ZTGY             机械设备I 运输设备II 铁路设备III 专用设备制造业 城轨铁路 工业               工业  \n",
-       "185157  ZTKJ  通信I 通信设备II 通信线缆及配套III 电气机械和器材制造业 通信系统设备及组件 通信服务            信息与通信  \n",
-       "185156  HHYY             医药生物I 化学制药II 化学制剂III 医药制造业 药品制剂 医药卫生             医药卫生  \n",
-       "185169  WLDQ         电气设备I 电机II 电机III 电气机械和器材制造业 电动机与工控自动化 工业               工业  \n",
-       "185499   SRP       电子I 半导体II 模拟芯片设计III 软件和信息技术服务业 集成电路设计 信息技术            信息与通信  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(error)\n",
-    "print(benchmark_portfolio.shape)\n",
-    "benchmark_portfolio.sort_values(by=['date'], inplace=True)\n",
-    "benchmark_portfolio.tail(10)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save\n",
-    "benchmark_portfolio.to_pickle('../data/benchmark_portfolio.pkl')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

script/downloadData.py

@@ -1,10 +0,0 @@
-import pandas as pd
-from api import update_portfolio_profile, get_stocks_price, get_benchmark_portfolio
-
-portfolio_profile_data = [
-    
-]
-
-
-def main():
-    # create a portfolio

script/pipeline.ipynb

@@ -1,521 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import math\n",
-    "from datetime import datetime\n",
-    "import hvplot.pandas\n",
-    "import math\n",
-    "import numpy as np\n",
-    "# load data\n",
-    "profile_df = pd.read_pickle('../data/portfolio_portfile.pkl')\n",
-    "benchmark_df = pd.read_pickle('../data/benchmark_portfolio.pkl')\n",
-    "portfolio_df = pd.read_pickle('../data/portfolio_data.pkl')"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## This section transfrom benchmark_df and creat an benchmark profile to accomadate current pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# drop weight\n",
-    "# benchmark_df = benchmark_df.drop(columns=['weight'])\n",
-    "\n",
-    "## simulate update potfolio weigth at 2021-01-10\n",
-    "update_profile_df = profile_df.copy()\n",
-    "update_profile_df['date'] = datetime(2021,1,10)\n",
-    "update_profile_df['weight'] = [50,100,200,300,400,500]\n",
-    "profile_df = pd.concat([profile_df, update_profile_df])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## create a profile for benchmark\n",
-    "b_profile = benchmark_df.drop_duplicates(subset=['ticker', 'actual_data'])\n",
-    "# df_unique[df_unique.ticker == \"000008.XSHE\"]\n",
-    "# only keep ticker\tdate\tweight\tdisplay_name\tname\tsector\taggregate_sector column\n",
-    "b_profile = b_profile[['ticker','date','weight','name','sector','aggregate_sector','display_name_x']]\n",
-    "b_profile.rename(columns={'display_name_x': 'display_name'}, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "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>date</th>\n",
-       "      <th>ticker</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>money</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>000008.XSHE</td>\n",
-       "      <td>2.52</td>\n",
-       "      <td>2.57</td>\n",
-       "      <td>2.67</td>\n",
-       "      <td>2.49</td>\n",
-       "      <td>33215803.0</td>\n",
-       "      <td>85358605.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date       ticker  open  close  high   low      volume        money\n",
-       "0 2021-01-05  000008.XSHE  2.52   2.57  2.67  2.49  33215803.0  85358605.99"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# drop weight in benchmark\n",
-    "benchmark_df = benchmark_df.drop(columns=['weight'])\n",
-    "benchmark_df = benchmark_df.drop(columns=['display_name_x'])\n",
-    "# drop display_name_y\n",
-    "benchmark_df = benchmark_df.drop(columns=['display_name_y'])\n",
-    "# drop actual_data\n",
-    "benchmark_df = benchmark_df.drop(columns=['actual_data'])\n",
-    "# drop name\n",
-    "benchmark_df = benchmark_df.drop(columns=['name'])\n",
-    "# drop aggregate_sector\n",
-    "benchmark_df = benchmark_df.drop(columns=['aggregate_sector'])\n",
-    "# drop sector\n",
-    "benchmark_df = benchmark_df.drop(columns=['sector'])\n",
-    "benchmark_df.head(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for col_name in benchmark_df.columns:\n",
-    "    if (col_name not in portfolio_df.columns):\n",
-    "        print(f'portfolio does not have {col_name}')\n",
-    "    \n",
-    "\n",
-    "for col_name in portfolio_df.columns:\n",
-    "    if (col_name not in benchmark_df.columns):\n",
-    "        print(f'benchmark does not have {col_name}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "True\n"
-     ]
-    }
-   ],
-   "source": [
-    "for col_name in b_profile.columns:\n",
-    "    print(col_name in profile_df.columns)\n",
-    "\n",
-    "for col_name in profile_df.columns:\n",
-    "    print(col_name in b_profile.columns)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## calculate result for each individual stock this part should return a table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_processing_result_of_stocks_df(stock_df, profile_df):\n",
-    "    ## add sector_name display_name name\n",
-    "    ticker_sector_map = dict(zip(profile_df['ticker'], profile_df['aggregate_sector']))\n",
-    "    ticker_display_name_map = dict(zip(profile_df['ticker'], profile_df['display_name']))\n",
-    "    ticker_name_map = dict(zip(profile_df['ticker'], profile_df['name']))\n",
-    "\n",
-    "    stock_df['display_name'] = stock_df['ticker'].map(ticker_display_name_map)\n",
-    "    stock_df['name'] = stock_df['ticker'].map(ticker_name_map)\n",
-    "    stock_df['aggregate_sector'] = stock_df['ticker'].map(ticker_sector_map)\n",
-    "\n",
-    "    ## calculate pct using closing price\n",
-    "    stock_df.sort_values(by=['date'], inplace=True)\n",
-    "    stock_df['pct'] = stock_df.groupby('ticker')['close'].pct_change()\n",
-    "\n",
-    "    ## calculate weight TODO: think about how to optimize this\n",
-    "    stock_df = stock_df.merge(profile_df[['weight', 'date', 'ticker']], on=['ticker', 'date'], how='outer')\n",
-    "    stock_df.rename(columns={'weight': 'initial_weight'}, inplace=True)\n",
-    "    stock_df['current_weight'] = float('nan')\n",
-    "    stock_df['previous_weight'] = float('nan')\n",
-    "    df_grouped = stock_df.groupby('ticker')\n",
-    "    for _, group in df_grouped:\n",
-    "        pre_w = float('nan')\n",
-    "        ini_w = float('nan')\n",
-    "        for index, row in group.iterrows():\n",
-    "            cur_w = float('nan')\n",
-    "\n",
-    "            # if has initial weight, the following row all use this initial weight\n",
-    "            if not pd.isna(row['initial_weight']):\n",
-    "                ini_w = row['initial_weight']\n",
-    "                cur_w = ini_w\n",
-    "\n",
-    "            # just calculate current weight based on previous weight\n",
-    "            else:\n",
-    "                cur_w = pre_w * (1 + row['pct'])\n",
-    "\n",
-    "            stock_df.loc[index, 'current_weight'] = cur_w    \n",
-    "            stock_df.loc[index, 'previous_weight'] = pre_w\n",
-    "            stock_df.loc[index, 'initial_weight'] = ini_w\n",
-    "            pre_w = cur_w\n",
-    "\n",
-    "    stock_df.rename(columns={'weight': 'initial_weight'}, inplace=True)\n",
-    "    stock_df.dropna(subset=['close'], inplace=True)\n",
-    "\n",
-    "    ## normalize weight\n",
-    "    stock_df['prev_w_in_p'] = stock_df['previous_weight'] / \\\n",
-    "                                    stock_df.groupby('date')['previous_weight'].transform('sum')\n",
-    "\n",
-    "    stock_df['ini_w_in_p'] = stock_df['initial_weight'] / \\\n",
-    "                                    stock_df.groupby('date')['initial_weight'].transform('sum')\n",
-    "\n",
-    "    ## calculate weighted pct in portfolio\n",
-    "    stock_df['portfolio_pct'] = stock_df['pct'] * stock_df['prev_w_in_p']\n",
-    "\n",
-    "    ## calculate weight in sector TODO: remove\n",
-    "    stock_df['prev_w_in_sectore'] = stock_df['previous_weight'] / \\\n",
-    "                                                stock_df.groupby(['date', 'aggregate_sector'])['previous_weight'].transform('sum')\n",
-    "    stock_df['ini_w_in_sector'] = stock_df['initial_weight'] / \\\n",
-    "                                                stock_df.groupby(['date', 'aggregate_sector'])['initial_weight'].transform('sum')\n",
-    "    ## weighted pct in sector TODO: remove\n",
-    "    stock_df['sector_pct'] = stock_df['pct'] * stock_df['prev_w_in_sectore']\n",
-    "\n",
-    "    ## portfolio return\n",
-    "    stock_df['cum_p_pct'] = stock_df.groupby('ticker')['portfolio_pct'].cumsum()\n",
-    "    stock_df['portfolio_return'] = np.exp(stock_df['cum_p_pct']) -1 \n",
-    "    # drop intermediate columns\n",
-    "    stock_df = stock_df.drop(columns=['cum_p_pct'])\n",
-    "\n",
-    "\n",
-    "    ## sector return TODO:remove \n",
-    "    # stock_df['sector_return'] = stock_df['ini_w_in_sector'] * stock_df['return']\n",
-    "\n",
-    "    return stock_df\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# portfolio_stock = get_processing_result_of_stocks_df(portfolio_df, profile_df)\n",
-    "# portfolio_stock[portfolio_stock.ticker == '002709.XSHE'][['date','portfolio_pct','prev_w_in_p','portfolio_return']]\n",
-    "# # benchmark_stock = get_processing_result_of_stocks_df(benchmark_df, b_profile)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# profile_df.groupby('date')['weight'].sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## total return by date\n",
-    "def get_portfolio_evaluation(portfolio_stock, benchmark_stock, profile_df):\n",
-    "    # add pct of benchmark \n",
-    "    merged_df = portfolio_stock.merge(benchmark_stock[['ticker','date','portfolio_pct','portfolio_return']], \n",
-    "                                    on=['ticker','date'],how='left',suffixes=('_p','_b'))\n",
-    "\n",
-    "    # sum up pct and return from portfolio and benchmark\n",
-    "    merged_df = merged_df.groupby('date',as_index=False).agg({'portfolio_return_p':'sum',\n",
-    "                                                            'portfolio_return_b':'sum',\n",
-    "                                                            'portfolio_pct_p':'sum',\n",
-    "                                                            'portfolio_pct_b':'sum'})\n",
-    "\n",
-    "    # portfolio mkt cap\n",
-    "    mkt_adjustment = pd.DataFrame(profile_df.groupby('date')['weight'].sum())\n",
-    "    mkt_adjustment.rename(columns={'weight':'mkt_cap'}, inplace=True)\n",
-    "    merged_df = merged_df.merge(mkt_adjustment, on=['date'], how='outer')\n",
-    "\n",
-    "    for i in range(len(merged_df)):\n",
-    "        if pd.isna(merged_df.loc[i, 'mkt_cap']):\n",
-    "            merged_df.loc[i, 'mkt_cap'] = merged_df.loc[i-1, 'mkt_cap'] * (1 + merged_df.loc[i, 'portfolio_pct_p'])\n",
-    "    # drop where portfolio_return_p is nan\n",
-    "    merged_df.dropna(subset=['portfolio_return_p'], inplace=True)\n",
-    "    # portfolio pnl TODO seem I can just use current wegith to do this\n",
-    "    merged_df['prev_mkt_cap'] = merged_df['mkt_cap'].shift(1)\n",
-    "    merged_df['pnl'] = merged_df['prev_mkt_cap'] * merged_df['portfolio_pct_p']\n",
-    "\n",
-    "    # risk std(pct)\n",
-    "    merged_df['risk'] = merged_df['portfolio_pct_p'].rolling(len(merged_df), min_periods=1).std() * math.sqrt(252)\n",
-    "\n",
-    "    # active return\n",
-    "    merged_df['active_return'] = merged_df['portfolio_pct_p'] - merged_df['portfolio_pct_b']\n",
-    "\n",
-    "    # tracking errro std(active return)\n",
-    "    merged_df['tracking_error'] = merged_df['active_return'].rolling(len(merged_df), min_periods=1).std() * math.sqrt(252)\n",
-    "\n",
-    "    # cum pnl\n",
-    "    merged_df['cum_pnl'] = merged_df['pnl'].cumsum()\n",
-    "\n",
-    "    return merged_df\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# portfolio_eval_df = get_portfolio_evaluation(portfolio_stock, benchmark_stock, profile_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# portfolio_stock.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## TODO convert below to funciton\n",
-    "\n",
-    "def get_portfolio_sector_evaluation(portfolio_stock,benchmark_df):\n",
-    "# aggregate on sector and day\n",
-    "    p_sector_df = portfolio_stock.groupby(['date','aggregate_sector'], as_index=False)\\\n",
-    "        .agg({'prev_w_in_p': 'sum','ini_w_in_p':\"sum\",\"current_weight\":'sum',\\\n",
-    "            \"portfolio_pct\":\"sum\", \"portfolio_return\":\"sum\"})\n",
-    "    # TODO shrink it down before aggregate\n",
-    "    b_sector_df = benchmark_df.groupby(['date','aggregate_sector'], as_index=False)\\\n",
-    "        .agg({'prev_w_in_p': 'sum','ini_w_in_p':\"sum\",\"current_weight\":'sum',\\\n",
-    "            \"portfolio_pct\":\"sum\", \"portfolio_return\":\"sum\"})\n",
-    "    \n",
-    "    # merge portfolio and benchmark\n",
-    "    merge_df = p_sector_df.merge(b_sector_df, on=['date','aggregate_sector'], how='left', suffixes=('_p','_b'))\n",
-    "\n",
-    "    return merge_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sector_eval_df = get_portfolio_sector_evaluation(portfolio_stock, benchmark_stock)\n",
-    "# sector_eval_df[sector_eval_df.date == datetime(2021, 10,13)].hvplot.bar(x='aggregate_sector', y=['portfolio_pct_p','portfolio_pct_b'], stacked=True, rot=90, title='sector pct')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_on_date(portfolio_stock, benchmark_df):\n",
-    "    p_selected = portfolio_stock.reset_index()[['ini_w_in_p', 'portfolio_return', 'date', 'ticker', 'display_name']]\n",
-    "    b_selected = benchmark_df.reset_index()[['ini_w_in_p', 'portfolio_return', 'date', 'ticker']]\n",
-    "    merged_stock_df = pd.merge(p_selected, b_selected, on=['date', 'ticker'], how='outer', suffixes=('_p', '_b'))\n",
-    "    return merged_stock_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# merged_df = merge_on_date(portfolio_stock, benchmark_stock)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_bhb_result(merged_stock_df):\n",
-    "    # merged_stock_df['ini_w_in_p_p'].fillna(0, inplace=True)\n",
-    "    # merged_stock_df['ini_w_in_p_b'].fillna(0, inplace=True)\n",
-    "    # merged_stock_df['portfolio_return_b'].fillna(0, inplace=True)\n",
-    "    # merged_stock_df['portfolio_return_p'].fillna(0, inplace=True)\n",
-    "    # allocation\n",
-    "    merged_stock_df['allocation'] = (merged_stock_df['ini_w_in_p_p'] - merged_stock_df['ini_w_in_p_b']) \\\n",
-    "        * merged_stock_df['portfolio_return_b']\n",
-    "\n",
-    "    # selection\n",
-    "    merged_stock_df['selection'] = merged_stock_df['ini_w_in_p_b'] * \\\n",
-    "        (merged_stock_df['portfolio_return_p'] - merged_stock_df['portfolio_return_b'])\n",
-    "\n",
-    "    # interaction\n",
-    "    merged_stock_df['interaction'] = (merged_stock_df['ini_w_in_p_p'] - merged_stock_df['ini_w_in_p_b']) * \\\n",
-    "        (merged_stock_df['portfolio_return_p'] - merged_stock_df['portfolio_return_b'])\n",
-    "\n",
-    "    # excess\n",
-    "    merged_stock_df['excess'] = merged_stock_df['portfolio_return_p'] - merged_stock_df['portfolio_return_b']\n",
-    "\n",
-    "    # replace inf with nan\n",
-    "    merged_stock_df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
-    "    return merged_stock_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# test ing pipeline here "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "portfolio_stock = get_processing_result_of_stocks_df(portfolio_df, profile_df)\n",
-    "benchmark_stock = get_processing_result_of_stocks_df(benchmark_df, b_profile)\n",
-    "\n",
-    "portfolio_eval_df = get_portfolio_evaluation(portfolio_stock, benchmark_stock, profile_df)\n",
-    "sector_eval_df = get_portfolio_sector_evaluation(portfolio_stock, benchmark_stock)\n",
-    "merged_df = merge_on_date(portfolio_stock, benchmark_stock)\n",
-    "bnb_sector_result = get_bhb_result(sector_eval_df)\n",
-    "bnb_stock_result = get_bhb_result(merged_df)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save result \n",
-    "portfolio_eval_df.to_pickle('../data/portfolio_eval_df.pkl')\n",
-    "sector_eval_df.to_pickle('../data/sector_eval_df.pkl')\n",
-    "# merged_df.to_csv('merged_df.csv')\n",
-    "bnb_sector_result.to_pickle('../data/bnb_sector_result.pkl')\n",
-    "bnb_stock_result.to_pickle('../data/bnb_stock_result.pkl')\n",
-    "profile_df.to_pickle('../data/protfolio_profile.pkl')\n",
-    "b_profile.to_pickle('../data/benchmark_profile.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bnb_sector_result[bnb_sector_result.date == datetime(2021, 10,13)].hvplot.bar(x='aggregate_sector', y=['allocation','selection','interaction'], stacked=True, rot=90, title='sector allocation')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

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-size 25105

script/processing.ipynb

@@ -1,501 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
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i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\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    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = 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else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    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-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(> .cell-output-ipywidget-background\n",
-       "    > .lm-Widget\n",
-       "    > *[data-root-id]),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import math\n",
-    "from datetime import datetime\n",
-    "import hvplot.pandas\n",
-    "# load data\n",
-    "profile_df = pd.read_pickle('../data/portfolio_portfile.pkl')\n",
-    "benchmark_df = pd.read_pickle('../data/benchmark_portfolio.pkl')\n",
-    "portfolio_df = pd.read_pickle('../data/portfolio_data.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## calculate pct using closing price for each date\n",
-    "portfolio_df.sort_values(by=['date'], inplace=True)\n",
-    "portfolio_df['pct'] = portfolio_df.groupby(['ticker'])['close'].pct_change()\n",
-    "\n",
-    "benchmark_df.sort_values(by=['date'], inplace=True)\n",
-    "benchmark_df['pct'] = benchmark_df.groupby(['ticker'])['close'].pct_change()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## return and weight\n",
-    "merge_df = portfolio_df.merge(profile_df[['ticker','weight','date']], on=['ticker','date'], how='left')\n",
-    "merge_df.sort_values(by=['date'], inplace=True)\n",
-    "benchmark_df.sort_values(by=['date'], inplace=True)\n",
-    "\n",
-    "# return for each stock\n",
-    "# portfolio_df['return'] = portfolio_df['close'] / portfolio_df.groupby(['ticker'])['close'].transform('first') - 1\n",
-    "# benchmark_df['return'] = benchmark_df['close'] / benchmark_df.groupby(['ticker'])['close'].transform('first') - 1\n",
-    "\n",
-    "# stock return in portfolio\n",
-    "first_close_per_ticker = merge_df.groupby('ticker')['close'].transform('first')\n",
-    "merge_df['return'] = merge_df['close'] / first_close_per_ticker - 1\n",
-    "\n",
-    "# stock return in benchmark\n",
-    "first_close_per_ticker = benchmark_df.groupby('ticker')['close'].transform('first')\n",
-    "benchmark_df['return'] = benchmark_df['close'] / first_close_per_ticker - 1\n",
-    "\n",
-    "# weight\n",
-    "initial_weight = merge_df.groupby('ticker')['weight'].transform('first') \n",
-    "merge_df['weight'] =  initial_weight + (merge_df['return'] * initial_weight)\n",
-    "\n",
-    "# initial_weight\n",
-    "# merge_df[merge_df.ticker==\"002709.XSHE\"]\n",
-    "# merge_df\n",
-    "# benchmark_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "portfolio_df = merge_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## normalize weight\n",
-    "benchmark_df['norm_weight'] = benchmark_df['weight'] / benchmark_df.groupby(['date'])['weight'].transform('sum')\n",
-    "portfolio_df['norm_weight'] = portfolio_df['weight'] / portfolio_df.groupby(['date'])['weight'].transform('sum')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## calculate weighted return\n",
-    "portfolio_df['weighted_pct'] = portfolio_df['pct'] * portfolio_df['norm_weight']\n",
-    "benchmark_df['weighted_pct'] = benchmark_df['pct'] * benchmark_df['norm_weight']\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Calculate portfolio pct of return by summing the 'weighted_return' for each date\n",
-    "\n",
-    "# reset index becaue its a new table\n",
-    "portfolio_pct = portfolio_df.groupby('date')['weighted_pct'].sum().reset_index()\n",
-    "portfolio_pct.columns = ['date', 'pct']\n",
-    "\n",
-    "# benchmark pct summing the 'weighted_return' for each date\n",
-    "#TODO chagne to use new weight in order to perform simulation\n",
-    "benchmark_pct = benchmark_df.groupby('date')['weighted_pct'].sum().reset_index()\n",
-    "benchmark_pct.columns = ['date', 'pct']\n",
-    "\n",
-    "# 'date', 'portfolio_return', and 'benchmark_return' columns\n",
-    "return_result = pd.merge(portfolio_pct, benchmark_pct, on='date', suffixes=('_portfolio', '_benchmark'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## add total return\n",
-    "portfolio_df['weighted_return'] = portfolio_df['return'] * portfolio_df['norm_weight']\n",
-    "portfolio_return = portfolio_df.groupby('date')['weighted_return'].sum().reset_index()\n",
-    "portfolio_return.columns = ['date', 'return']\n",
-    "return_result = return_result.merge(portfolio_return, on='date')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#  daily_active_return\n",
-    "return_result['daily_active_return'] = return_result['pct_portfolio'] - return_result['pct_benchmark']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# daily pnl\n",
-    "return_result['mkt_cap'] = None\n",
-    "\n",
-    "# assign initial market cap\n",
-    "# sum weight because it is the money invested in the stock\n",
-    "# TODO chagne here to add simulation functionality\n",
-    "return_result.loc[return_result['date'] == profile_df['date'].values[0],'mkt_cap'] = profile_df['weight'].sum()\n",
-    "return_result.sort_values(by=['date'], inplace=True)\n",
-    "\n",
-    "# calculate daily mkt_cap\n",
-    "for i in range(1, len(return_result)):\n",
-    "    return_result.loc[i, 'mkt_cap'] = return_result.loc[i-1, 'mkt_cap'] * (1 + return_result.loc[i, 'pct_portfolio'])\n",
-    "\n",
-    "# calculate daily pnl\n",
-    "return_result['daily_pnl'] = return_result['mkt_cap'].diff()\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## return volatilty mean return tracking erro cumulative pnl\n",
-    "# volatitly\n",
-    "return_result['pct_volitity_p'] = return_result['pct_portfolio'].expanding().std() * math.sqrt(252)\n",
-    "return_result['pct_volitity_b'] = return_result['pct_benchmark'].expanding().std() * math.sqrt(252)\n",
-    "# mean return\n",
-    "return_result['mean_return'] = return_result['return'].mean()\n",
-    "# tracking error \n",
-    "return_result['tracking_error'] = return_result['daily_active_return'].expanding().std() * math.sqrt(252)\n",
-    "# cumulative pnl\n",
-    "return_result['cum_pnl'] = return_result['daily_pnl'].cumsum()\n",
-    "# drawdown \n",
-    "return_result['drawdown'] = (return_result['cum_pnl'] - return_result['cum_pnl'].cummax()) / return_result['cum_pnl'].cummax()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## save\n",
-    "return_result.to_pickle('../data/risk_return_result.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# add sector info to portfolio_df\n",
-    "portfolio_df = pd.merge(portfolio_df, profile_df[['ticker','aggregate_sector']], on='ticker', how='left')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/v5/2108rh5964q9j741wg_s8r1w0000gn/T/ipykernel_1263/1040563745.py:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  benchmark_df[benchmark_df.aggregate_sector.isna()].aggregate_sector = '其他'\n"
-     ]
-    }
-   ],
-   "source": [
-    "# hanlde sector is nan\n",
-    "benchmark_df[benchmark_df.aggregate_sector.isna()].aggregate_sector = '其他'\n",
-    "portfolio_df[portfolio_df.aggregate_sector.isna()].aggregate_sector = '其他'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "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>date</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>display_name_x</th>\n",
-       "      <th>actual_data</th>\n",
-       "      <th>ticker</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>money</th>\n",
-       "      <th>display_name_y</th>\n",
-       "      <th>name</th>\n",
-       "      <th>sector</th>\n",
-       "      <th>aggregate_sector</th>\n",
-       "      <th>pct</th>\n",
-       "      <th>return</th>\n",
-       "      <th>norm_weight</th>\n",
-       "      <th>weighted_pct</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>0.088</td>\n",
-       "      <td>神州高铁</td>\n",
-       "      <td>2020-12-31</td>\n",
-       "      <td>000008.XSHE</td>\n",
-       "      <td>2.52</td>\n",
-       "      <td>2.57</td>\n",
-       "      <td>2.67</td>\n",
-       "      <td>2.49</td>\n",
-       "      <td>33215803.0</td>\n",
-       "      <td>85358605.99</td>\n",
-       "      <td>神州高铁</td>\n",
-       "      <td>SZGT</td>\n",
-       "      <td>机械设备I 运输设备II 铁路设备III 铁路、船舶、航空航天和其他运输设备制造业 城轨铁路 工业</td>\n",
-       "      <td>工业</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00088</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date  weight display_name_x actual_data       ticker  open  close  \\\n",
-       "0 2021-01-05   0.088           神州高铁  2020-12-31  000008.XSHE  2.52   2.57   \n",
-       "\n",
-       "   high   low      volume        money display_name_y  name  \\\n",
-       "0  2.67  2.49  33215803.0  85358605.99           神州高铁  SZGT   \n",
-       "\n",
-       "                                              sector aggregate_sector  pct  \\\n",
-       "0  机械设备I 运输设备II 铁路设备III 铁路、船舶、航空航天和其他运输设备制造业 城轨铁路 工业               工业  NaN   \n",
-       "\n",
-       "   return  norm_weight  weighted_pct  \n",
-       "0     0.0      0.00088           NaN  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "benchmark_df.head(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## aggregate as sector \n",
-    "\n",
-    "# weighted return in a sector, weight in a sector\n",
-    "portfolio_df['weight_in_sector'] = portfolio_df['norm_weight']/ portfolio_df.groupby(['aggregate_sector','date'])['weight'].transform('sum')\n",
-    "portfolio_df['weighted_s_return'] = portfolio_df['return'] * portfolio_df['weight_in_sector']\n",
-    "\n",
-    "# return and weight by sector\n",
-    "return_by_sector = portfolio_df.groupby(['aggregate_sector','date'])['weighted_s_return'].sum().reset_index()\n",
-    "normed_sector_weight = portfolio_df.groupby(['aggregate_sector','date'])['norm_weight'].sum().reset_index()\n",
-    "p_sector_result = pd.merge(return_by_sector, normed_sector_weight, on=['aggregate_sector','date'], how='left')\n",
-    "p_sector_result.rename(columns={'weighted_s_return':'return'}, inplace=True)\n",
-    "\n",
-    "# same thing for benchmark\n",
-    "benchmark_df['weight_in_sector'] = benchmark_df['norm_weight']/ benchmark_df.groupby(['aggregate_sector','date'])['norm_weight'].transform('sum')\n",
-    "benchmark_df['weighted_s_return'] = benchmark_df['return'] * benchmark_df['weight_in_sector']\n",
-    "return_by_sector = benchmark_df.groupby(['aggregate_sector','date'])['weighted_s_return'].sum().reset_index()\n",
-    "normed_sector_weight = benchmark_df.groupby(['aggregate_sector','date'])['norm_weight'].sum().reset_index()\n",
-    "b_sector_result = pd.merge(return_by_sector, normed_sector_weight, on=['aggregate_sector','date'], how='left')\n",
-    "b_sector_result.rename(columns={'weighted_s_return':'return'}, inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## selection, allocation, interaction for sector\n",
-    "\n",
-    "\n",
-    "bnb_result_sector = pd.merge(\n",
-    "    p_sector_result, b_sector_result,\n",
-    "    on=['date','aggregate_sector',], how='outer', suffixes=('_p', '_b'))\n",
-    "\n",
-    "# replace nan norm_weight_b with 0\n",
-    "bnb_result_sector['norm_weight_b'] = bnb_result_sector['norm_weight_b'].fillna(0)\n",
-    "bnb_result_sector['norm_weight_p'] = bnb_result_sector['norm_weight_p'].fillna(0)\n",
-    "\n",
-    "# active weight\n",
-    "bnb_result_sector['active_weight'] = bnb_result_sector['norm_weight_p'] - bnb_result_sector['norm_weight_b']\n",
-    "\n",
-    "# allocation \n",
-    "bnb_result_sector['allocation'] = bnb_result_sector['active_weight'] * bnb_result_sector['return_b']\n",
-    "\n",
-    "#selection\n",
-    "bnb_result_sector['selection'] = bnb_result_sector['norm_weight_p'] * (bnb_result_sector['return_p'] - bnb_result_sector['return_b'])\n",
-    "\n",
-    "#inetration\n",
-    "bnb_result_sector['interaction'] = (bnb_result_sector['norm_weight_p'] - bnb_result_sector['norm_weight_b']) *\\\n",
-    "                            (bnb_result_sector['return_p'] - bnb_result_sector['return_b'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## save result \n",
-    "bnb_result_sector.to_pickle('../data/bnb_result_sector.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## selection, allocation, interaction for stocks\n",
-    "\n",
-    "# I keep weight to exame the stock not in benchmark but in portfolio and vice versa\n",
-    "bnb_result = pd.merge(\n",
-    "    portfolio_df[['date','ticker','norm_weight','weight','return','aggregate_sector']],\n",
-    "    benchmark_df[['date','ticker','norm_weight','weight','return']], on=['date','ticker',], how='outer', suffixes=('_p', '_b'))\n",
-    "\n",
-    "# replace nan norm_weight_b with 0\n",
-    "bnb_result['norm_weight_b'] = bnb_result['norm_weight_b'].fillna(0)\n",
-    "bnb_result['norm_weight_p'] = bnb_result['norm_weight_p'].fillna(0)\n",
-    "\n",
-    "# active weight\n",
-    "bnb_result['active_weight'] = bnb_result['norm_weight_p'] - bnb_result['norm_weight_b']\n",
-    "\n",
-    "# allocation \n",
-    "bnb_result['allocation'] = bnb_result['active_weight'] * bnb_result['return_b']\n",
-    "\n",
-    "#selection\n",
-    "bnb_result['selection'] = bnb_result['norm_weight_p'] * (bnb_result['return_p'] - bnb_result['return_b'])\n",
-    "\n",
-    "#inetration\n",
-    "bnb_result['interaction'] = (bnb_result['norm_weight_p'] - bnb_result['norm_weight_b']) *\\\n",
-    "                            (bnb_result['return_p'] - bnb_result['return_b'])\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## svae\n",
-    "bnb_result.to_pickle('../data/bnb_result.pkl')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

script/processing2.ipynb

@@ -1,1836 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import math\n",
-    "from datetime import datetime\n",
-    "import hvplot.pandas\n",
-    "import math\n",
-    "# load data\n",
-    "profile_df = pd.read_pickle('../data/portfolio_portfile.pkl')\n",
-    "benchmark_df = pd.read_pickle('../data/benchmark_portfolio.pkl')\n",
-    "portfolio_df = pd.read_pickle('../data/portfolio_data.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# to acoomodate the current pipe line\n",
-    "min_dates = benchmark_df.groupby('ticker')['date'].min()\n",
-    "\n",
-    "for ticker, min_date in min_dates.items():\n",
-    "   benchmark_df.loc[(benchmark_df['ticker'] == ticker) & (benchmark_df['date'] != min_date), 'weight'] = float('nan')\n",
-    "\n",
-    "benchmark_df['initial_weight'] = benchmark_df['weight']\n",
-    "# drop weight\n",
-    "benchmark_df = benchmark_df.drop(columns=['weight'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Series([], Name: initial_weight, dtype: int64)"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# check if all unique ticker has an weight\n",
-    "count_list = benchmark_df.groupby('ticker')['initial_weight'].count().sort_values(ascending=False)\n",
-    "count_list[count_list != 1]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_profile_df = profile_df.copy()\n",
-    "update_profile_df['date'] = datetime(2021,1,10)\n",
-    "update_profile_df['weight'] = [50,100,200,300,400,500]\n",
-    "profile_df = pd.concat([profile_df, update_profile_df])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calculate_pct(stock_df):\n",
-    "    stock_df['pct'] = stock_df.groupby(['ticker'])['close'].pct_change()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# step 1 pct\n",
-    "calculate_pct(portfolio_df)\n",
-    "calculate_pct(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def return_weighted_stock_df(stock_price_df, profile_df=None):\n",
-    "    # TODO change later this a temporary solution\n",
-    "    # initialize weight if profile_df is not none\n",
-    "    merged_df = pd.DataFrame()\n",
-    "    if profile_df is not None:\n",
-    "        merged_df = stock_price_df.merge(profile_df[['weight', 'date', 'ticker']], on=['ticker', 'date'], how='outer')\n",
-    "        merged_df.sort_values(by=['date'], inplace=True)\n",
-    "        merged_df.rename(columns={'weight': 'initial_weight'}, inplace=True)\n",
-    "    else:\n",
-    "        merged_df = stock_price_df.copy()\n",
-    "    merged_df['current_weight'] = float('nan')\n",
-    "    merged_df['previous_weight'] = float('nan')\n",
-    "    df_grouped = merged_df.groupby('ticker')\n",
-    "    for _, group in df_grouped:\n",
-    "        pre_w = float('nan')\n",
-    "        ini_w = float('nan')\n",
-    "        for index, row in group.iterrows():\n",
-    "            cur_w = float('nan')\n",
-    "            # if has initial weight, the following row all use this initial weight\n",
-    "            if not pd.isna(row['initial_weight']):\n",
-    "                ini_w = row['initial_weight']\n",
-    "                cur_w = ini_w\n",
-    "            # just calculate current weight based on previous weight\n",
-    "            else:\n",
-    "                cur_w = pre_w * (1 + row['pct'])\n",
-    "\n",
-    "            merged_df.loc[index, 'current_weight'] = cur_w    \n",
-    "            merged_df.loc[index, 'previous_weight'] = pre_w\n",
-    "            merged_df.loc[index, 'initial_weight'] = ini_w\n",
-    "            pre_w = cur_w\n",
-    "            \n",
-    "    # drop row where closing price is none\n",
-    "    merged_df = merged_df[~pd.isna(merged_df['close'])]\n",
-    "    # drop index\n",
-    "    return merged_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO consider save the weight calculation\n",
-    "portfolio_df = return_weighted_stock_df(portfolio_df, profile_df)\n",
-    "benchmark_df = return_weighted_stock_df(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# benchmark_df[benchmark_df.ticker =='000008.XSHE']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## normalize all weight\n",
-    "def normalize_weight(stock_df):\n",
-    "    stock_df['current_weight'] = stock_df['current_weight'] / \\\n",
-    "                                    stock_df.groupby('date')['current_weight'].transform('sum')\n",
-    "\n",
-    "    stock_df['previous_weight'] = stock_df['previous_weight'] / \\\n",
-    "                                    stock_df.groupby('date')['previous_weight'].transform('sum')\n",
-    "\n",
-    "    stock_df['initial_weight'] = stock_df['initial_weight'] / \\\n",
-    "                                    stock_df.groupby('date')['initial_weight'].transform('sum')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "normalize_weight(portfolio_df)\n",
-    "normalize_weight(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "260    0.032258\n",
-       "258    0.064516\n",
-       "262    0.129032\n",
-       "263    0.258065\n",
-       "259    0.322581\n",
-       "261    0.193548\n",
-       "Name: initial_weight, dtype: float64"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "portfolio_df[portfolio_df.date == datetime(2021, 3, 12)]['initial_weight']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n",
-      "1.0\n",
-      "1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(benchmark_df[benchmark_df.date == datetime(2021, 3, 12)]['initial_weight'].sum())\n",
-    "print(benchmark_df[benchmark_df.date == datetime(2021, 3, 12)]['current_weight'].sum())\n",
-    "print(benchmark_df[benchmark_df.date == datetime(2021, 3, 12)]['previous_weight'].sum())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# step 3 sector wegiht\n",
-    "\n",
-    "# add sector information first\n",
-    "def create_sector_weight(stock_df, profile_df=None):\n",
-    "    # if profile_df is none assume the aggregate_sector stock info already in stock_df\n",
-    "    merged_df = None\n",
-    "    if profile_df is not None:\n",
-    "        merged_df = stock_df.merge(profile_df[['ticker', 'aggregate_sector']], on='ticker', how='left')\n",
-    "    else:\n",
-    "        merged_df = stock_df.copy()\n",
-    "    # set null to others\n",
-    "    merged_df['aggregate_sector'] = merged_df['aggregate_sector'].fillna('其他')\n",
-    "    # calculate previous_sector_weight\n",
-    "    merged_df['previous_sector_weight'] = merged_df['previous_weight'] / \\\n",
-    "                                            merged_df.groupby(['date', 'aggregate_sector'])['previous_weight'].transform('sum')\n",
-    "    # calculate initial sectore weight\n",
-    "    merged_df['initial_sector_weight'] = merged_df['initial_weight'] / \\\n",
-    "                                            merged_df.groupby(['date', 'aggregate_sector'])['initial_weight'].transform('sum')\n",
-    "    \n",
-    "    return merged_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "portfolio_df = create_sector_weight(stock_df = portfolio_df, profile_df = profile_df)\n",
-    "benchmark_df = create_sector_weight(benchmark_df)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aggregate_sector\n",
-      "信息与通信    1.0\n",
-      "公用事业     1.0\n",
-      "其他       1.0\n",
-      "医药卫生     1.0\n",
-      "原料与能源    1.0\n",
-      "工业       1.0\n",
-      "消费       1.0\n",
-      "金融与地产    1.0\n",
-      "Name: previous_sector_weight, dtype: float64\n",
-      "aggregate_sector\n",
-      "信息与通信    1.0\n",
-      "公用事业     1.0\n",
-      "其他       1.0\n",
-      "医药卫生     1.0\n",
-      "原料与能源    1.0\n",
-      "工业       1.0\n",
-      "消费       1.0\n",
-      "金融与地产    1.0\n",
-      "Name: initial_sector_weight, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# check result \n",
-    "print(benchmark_df[benchmark_df.date == datetime(2021, 3, 12)].groupby('aggregate_sector')['previous_sector_weight'].sum())\n",
-    "print(benchmark_df[benchmark_df.date == datetime(2021, 3, 12)].groupby('aggregate_sector')['initial_sector_weight'].sum())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aggregate_sector\n",
-      "信息与通信    1.0\n",
-      "医药卫生     1.0\n",
-      "原料与能源    1.0\n",
-      "工业       1.0\n",
-      "消费       1.0\n",
-      "Name: previous_sector_weight, dtype: float64\n",
-      "aggregate_sector\n",
-      "信息与通信    1.0\n",
-      "医药卫生     1.0\n",
-      "原料与能源    1.0\n",
-      "工业       1.0\n",
-      "消费       1.0\n",
-      "Name: initial_sector_weight, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# check result \n",
-    "print(portfolio_df[portfolio_df.date == datetime(2021, 3, 12)].groupby('aggregate_sector')['previous_sector_weight'].sum())\n",
-    "print(portfolio_df[portfolio_df.date == datetime(2021, 3, 12)].groupby('aggregate_sector')['initial_sector_weight'].sum())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## return define as the total return since the portfolio created\n",
-    "def calcualte_return(stock_df):\n",
-    "    stock_df['return'] = stock_df['close'] / stock_df.groupby(['ticker'])['close'].transform('first') - 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "calcualte_return(portfolio_df)\n",
-    "calcualte_return(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calculate_weighted_sector_return(stock_df):\n",
-    "    stock_df['weighted_sectore_return'] = stock_df['return'] * stock_df['initial_sector_weight']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "calculate_weighted_sector_return(portfolio_df)\n",
-    "calculate_weighted_sector_return(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## weighted return and sector weighred return \n",
-    "def calculate_weighted_return(stock_df):\n",
-    "    stock_df['weighted_return'] = stock_df['return'] * stock_df['initial_weight']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# step\n",
-    "calculate_weighted_return(portfolio_df)\n",
-    "calculate_weighted_return(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calculate_weighted_sector_return(stock_df):\n",
-    "    stock_df['weighted_sector_return'] = stock_df['return'] * stock_df['initial_sector_weight']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "calculate_weighted_sector_return(portfolio_df)\n",
-    "calculate_weighted_sector_return(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## calcualte weighted pc\n",
-    "def calculate_weighted_pct(stock_df):\n",
-    "    stock_df['weighted_pct'] = stock_df['pct'] * stock_df['previous_weight']\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calculate_weighted_sector_pct(stock_df):\n",
-    "    stock_df['weighted_sector_pct'] = stock_df['pct'] * stock_df['previous_sector_weight']\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "calculate_weighted_sector_pct(portfolio_df)\n",
-    "calculate_weighted_sector_pct(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "calculate_weighted_pct(portfolio_df)\n",
-    "calculate_weighted_pct(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "calculate_weighted_sector_return(portfolio_df)\n",
-    "calculate_weighted_sector_return(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## aggregate by date\n",
-    "\n",
-    "# pct and weighted_return\n",
-    "# def agg_by_date(stock_df)\n",
-    "def agg_by_date(stock_df):\n",
-    "    agg_on_date_df = pd.DataFrame(stock_df.groupby('date')[['weighted_return','weighted_pct']].sum())\n",
-    "    agg_on_date_df.rename(columns={'weighted_return': 'return', 'weighted_pct': 'pct'}, inplace=True)\n",
-    "    return agg_on_date_df\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_total_view = agg_by_date(portfolio_df)\n",
-    "b_total_view = agg_by_date(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## aggregate by sector\n",
-    "def agg_by_sector(stock_df):\n",
-    "    agg_on_sector_df = pd.DataFrame(stock_df.groupby(['aggregate_sector','date'])[['weighted_sector_return','weighted_sector_pct']].sum())\n",
-    "    agg_on_sector_df.rename(columns={'weighted_sector_return': 'return', 'weighted_sector_pct': 'pct'}, inplace=True)\n",
-    "    return agg_on_sector_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_sector_view = agg_by_sector(portfolio_df)\n",
-    "b_sector_view = agg_by_sector(benchmark_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_risk_table(portfolio_summary, benchmark_summary):\n",
-    "    # total risk tracking error  \n",
-    "    merged_df = pd.merge(portfolio_summary, benchmark_summary, on='date', how='outer', suffixes=('_p', '_b'))\n",
-    "    merged_df['risk_p'] = merged_df['return_p'].expanding().std() * math.sqrt(252)\n",
-    "    merged_df['risk_b'] = merged_df['return_b'].expanding().std() * math.sqrt(252)\n",
-    "    merged_df['active_return'] = merged_df['return_p'] - merged_df['return_b']\n",
-    "    merged_df['tracking_error'] = merged_df['active_return'].expanding().std() * math.sqrt(252)\n",
-    "    merged_df['date'] = merged_df.index\n",
-    "    # drop index\n",
-    "    merged_df.reset_index(drop=True, inplace=True)\n",
-    "    return merged_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "portfolio_risk_by_date_df = create_risk_table(p_total_view, b_total_view)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "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>return_p</th>\n",
-       "      <th>pct_p</th>\n",
-       "      <th>return_b</th>\n",
-       "      <th>pct_b</th>\n",
-       "      <th>risk_p</th>\n",
-       "      <th>risk_b</th>\n",
-       "      <th>active_return</th>\n",
-       "      <th>tracking_error</th>\n",
-       "      <th>date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.012146</td>\n",
-       "      <td>0.012146</td>\n",
-       "      <td>-0.001934</td>\n",
-       "      <td>-0.001934</td>\n",
-       "      <td>0.136341</td>\n",
-       "      <td>0.021705</td>\n",
-       "      <td>0.014080</td>\n",
-       "      <td>0.158046</td>\n",
-       "      <td>2021-01-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.086830</td>\n",
-       "      <td>0.074233</td>\n",
-       "      <td>-0.000811</td>\n",
-       "      <td>0.001125</td>\n",
-       "      <td>0.746402</td>\n",
-       "      <td>0.015414</td>\n",
-       "      <td>0.087641</td>\n",
-       "      <td>0.747127</td>\n",
-       "      <td>2021-01-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.089435</td>\n",
-       "      <td>0.002496</td>\n",
-       "      <td>0.002535</td>\n",
-       "      <td>0.003349</td>\n",
-       "      <td>0.756382</td>\n",
-       "      <td>0.030137</td>\n",
-       "      <td>0.086900</td>\n",
-       "      <td>0.740979</td>\n",
-       "      <td>2021-01-08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.148063</td>\n",
-       "      <td>0.029363</td>\n",
-       "      <td>-0.013015</td>\n",
-       "      <td>-0.015511</td>\n",
-       "      <td>0.970984</td>\n",
-       "      <td>0.095654</td>\n",
-       "      <td>0.161078</td>\n",
-       "      <td>1.032423</td>\n",
-       "      <td>2021-01-11</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>0.028005</td>\n",
-       "      <td>-0.071081</td>\n",
-       "      <td>0.086827</td>\n",
-       "      <td>0.000156</td>\n",
-       "      <td>2.097631</td>\n",
-       "      <td>0.886298</td>\n",
-       "      <td>-0.058822</td>\n",
-       "      <td>1.856213</td>\n",
-       "      <td>2022-01-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>-0.033053</td>\n",
-       "      <td>-0.059582</td>\n",
-       "      <td>0.067931</td>\n",
-       "      <td>-0.017386</td>\n",
-       "      <td>2.099052</td>\n",
-       "      <td>0.884891</td>\n",
-       "      <td>-0.100984</td>\n",
-       "      <td>1.861347</td>\n",
-       "      <td>2022-01-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244</th>\n",
-       "      <td>-0.042238</td>\n",
-       "      <td>-0.008112</td>\n",
-       "      <td>0.069522</td>\n",
-       "      <td>0.001490</td>\n",
-       "      <td>2.101118</td>\n",
-       "      <td>0.883542</td>\n",
-       "      <td>-0.111761</td>\n",
-       "      <td>1.867445</td>\n",
-       "      <td>2022-01-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>245</th>\n",
-       "      <td>-0.073118</td>\n",
-       "      <td>-0.031015</td>\n",
-       "      <td>0.062056</td>\n",
-       "      <td>-0.006981</td>\n",
-       "      <td>2.105760</td>\n",
-       "      <td>0.881986</td>\n",
-       "      <td>-0.135174</td>\n",
-       "      <td>1.875959</td>\n",
-       "      <td>2022-01-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>246</th>\n",
-       "      <td>-0.029944</td>\n",
-       "      <td>0.044300</td>\n",
-       "      <td>0.064588</td>\n",
-       "      <td>0.002384</td>\n",
-       "      <td>2.106749</td>\n",
-       "      <td>0.880502</td>\n",
-       "      <td>-0.094532</td>\n",
-       "      <td>1.880060</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>247 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     return_p     pct_p  return_b     pct_b    risk_p    risk_b  \\\n",
-       "0    0.000000  0.000000  0.000000  0.000000       NaN       NaN   \n",
-       "1    0.012146  0.012146 -0.001934 -0.001934  0.136341  0.021705   \n",
-       "2    0.086830  0.074233 -0.000811  0.001125  0.746402  0.015414   \n",
-       "3    0.089435  0.002496  0.002535  0.003349  0.756382  0.030137   \n",
-       "4    0.148063  0.029363 -0.013015 -0.015511  0.970984  0.095654   \n",
-       "..        ...       ...       ...       ...       ...       ...   \n",
-       "242  0.028005 -0.071081  0.086827  0.000156  2.097631  0.886298   \n",
-       "243 -0.033053 -0.059582  0.067931 -0.017386  2.099052  0.884891   \n",
-       "244 -0.042238 -0.008112  0.069522  0.001490  2.101118  0.883542   \n",
-       "245 -0.073118 -0.031015  0.062056 -0.006981  2.105760  0.881986   \n",
-       "246 -0.029944  0.044300  0.064588  0.002384  2.106749  0.880502   \n",
-       "\n",
-       "     active_return  tracking_error       date  \n",
-       "0         0.000000             NaN 2021-01-05  \n",
-       "1         0.014080        0.158046 2021-01-06  \n",
-       "2         0.087641        0.747127 2021-01-07  \n",
-       "3         0.086900        0.740979 2021-01-08  \n",
-       "4         0.161078        1.032423 2021-01-11  \n",
-       "..             ...             ...        ...  \n",
-       "242      -0.058822        1.856213 2022-01-04  \n",
-       "243      -0.100984        1.861347 2022-01-05  \n",
-       "244      -0.111761        1.867445 2022-01-06  \n",
-       "245      -0.135174        1.875959 2022-01-07  \n",
-       "246      -0.094532        1.880060 2022-01-10  \n",
-       "\n",
-       "[247 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# add mkt cap\n",
-    "portfolio_risk_by_date_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "date\n",
-       "2021-01-05     600\n",
-       "2021-01-10    1550\n",
-       "Name: weight, dtype: int64"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "profile_df.groupby('date')['weight'].sum()\n",
-    "\n",
-    "# for i in range(1, len(portfolio_risk_by_date_df)):\n",
-    "#     cur_mkt = portfolio_risk_by_date_df.loc[i, 'mkt_cap']\n",
-    "#     if pd.isna(cur_mkt):\n",
-    "#         portfolio_risk_by_date_df.loc[i, 'mkt_cap'] = portfolio_risk_by_date_df.loc[i-1, 'mkt_cap'] * (1 + portfolio_risk_by_date_df.loc[i, 'pct_p'])\n",
-    "  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "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>return_p</th>\n",
-       "      <th>pct_p</th>\n",
-       "      <th>return_b</th>\n",
-       "      <th>pct_b</th>\n",
-       "      <th>risk_p</th>\n",
-       "      <th>risk_b</th>\n",
-       "      <th>active_return</th>\n",
-       "      <th>tracking_error</th>\n",
-       "      <th>date</th>\n",
-       "      <th>mkt_cap</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>600.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   return_p  pct_p  return_b  pct_b  risk_p  risk_b  active_return  \\\n",
-       "0       0.0    0.0       0.0    0.0     NaN     NaN            0.0   \n",
-       "\n",
-       "   tracking_error       date  mkt_cap  \n",
-       "0             NaN 2021-01-05    600.0  "
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# display row where mkt_cap is not nana\n",
-    "portfolio_risk_by_date_df[portfolio_risk_by_date_df['mkt_cap'].notna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/v5/2108rh5964q9j741wg_s8r1w0000gn/T/ipykernel_23255/2871737262.py:10: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  pct['weighted_pct'] = pct['pct'] * pct['norm_weight']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>return</th>\n",
-       "      <th>pct</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2021-01-06</td>\n",
-       "      <td>0.007011</td>\n",
-       "      <td>0.036439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2021-01-07</td>\n",
-       "      <td>0.047531</td>\n",
-       "      <td>0.218707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2021-01-08</td>\n",
-       "      <td>0.047111</td>\n",
-       "      <td>0.013639</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2021-01-11</td>\n",
-       "      <td>0.052768</td>\n",
-       "      <td>0.014559</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>2022-01-04</td>\n",
-       "      <td>0.363845</td>\n",
-       "      <td>-0.199827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>2022-01-05</td>\n",
-       "      <td>0.306697</td>\n",
-       "      <td>-0.193598</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244</th>\n",
-       "      <td>2022-01-06</td>\n",
-       "      <td>0.331291</td>\n",
-       "      <td>0.023418</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>245</th>\n",
-       "      <td>2022-01-07</td>\n",
-       "      <td>0.313726</td>\n",
-       "      <td>-0.080728</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>246</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.313262</td>\n",
-       "      <td>0.110254</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>247 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          date    return       pct\n",
-       "0   2021-01-05  0.000000  0.000000\n",
-       "1   2021-01-06  0.007011  0.036439\n",
-       "2   2021-01-07  0.047531  0.218707\n",
-       "3   2021-01-08  0.047111  0.013639\n",
-       "4   2021-01-11  0.052768  0.014559\n",
-       "..         ...       ...       ...\n",
-       "242 2022-01-04  0.363845 -0.199827\n",
-       "243 2022-01-05  0.306697 -0.193598\n",
-       "244 2022-01-06  0.331291  0.023418\n",
-       "245 2022-01-07  0.313726 -0.080728\n",
-       "246 2022-01-10  0.313262  0.110254\n",
-       "\n",
-       "[247 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## aggregate by date\n",
-    "# step 7 aggregate (get portfolio return and pct(change of daily return))by date\n",
-    "def create_agg_by_date(stock_df):\n",
-    "    # sum up weighted return to get return \n",
-    "    agg_return = stock_df.groupby(['date'])['weighted_return'].sum().reset_index()\n",
-    "    agg_return.rename(columns={'weighted_return':'return'}, inplace=True)\n",
-    "\n",
-    "    # sum up weighted pct to get pct\n",
-    "    pct = stock_df[['date','pct','norm_weight','ticker']]\n",
-    "    pct['weighted_pct'] = pct['pct'] * pct['norm_weight']\n",
-    "    agg_pct = pct.groupby(['date'])['pct'].sum().reset_index()\n",
-    "\n",
-    "    agg_df = pd.merge(agg_return, agg_pct, on='date', how='outer')\n",
-    "    return agg_df\n",
-    "\n",
-    "\n",
-    "\n",
-    "p_perform_result = create_agg_by_date(p_stock_df)\n",
-    "p_perform_result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ticker</th>\n",
-       "      <th>date</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>money</th>\n",
-       "      <th>pct</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>return</th>\n",
-       "      <th>norm_weight</th>\n",
-       "      <th>weighted_return</th>\n",
-       "      <th>aggregate_sector</th>\n",
-       "      <th>display_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1452</th>\n",
-       "      <td>603882.XSHG</td>\n",
-       "      <td>2022-01-04</td>\n",
-       "      <td>106.89</td>\n",
-       "      <td>98.84</td>\n",
-       "      <td>106.89</td>\n",
-       "      <td>98.67</td>\n",
-       "      <td>5140406.0</td>\n",
-       "      <td>5.181929e+08</td>\n",
-       "      <td>-0.076262</td>\n",
-       "      <td>79.300385</td>\n",
-       "      <td>-0.206996</td>\n",
-       "      <td>0.107586</td>\n",
-       "      <td>-0.022270</td>\n",
-       "      <td>医药卫生</td>\n",
-       "      <td>金域医学</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1453</th>\n",
-       "      <td>002709.XSHE</td>\n",
-       "      <td>2022-01-04</td>\n",
-       "      <td>57.64</td>\n",
-       "      <td>54.64</td>\n",
-       "      <td>57.87</td>\n",
-       "      <td>54.29</td>\n",
-       "      <td>42150916.0</td>\n",
-       "      <td>2.333429e+09</td>\n",
-       "      <td>-0.028277</td>\n",
-       "      <td>161.227501</td>\n",
-       "      <td>0.612275</td>\n",
-       "      <td>0.218735</td>\n",
-       "      <td>0.133926</td>\n",
-       "      <td>工业</td>\n",
-       "      <td>天赐材料</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1454</th>\n",
-       "      <td>600409.XSHG</td>\n",
-       "      <td>2022-01-04</td>\n",
-       "      <td>8.16</td>\n",
-       "      <td>8.21</td>\n",
-       "      <td>8.25</td>\n",
-       "      <td>8.15</td>\n",
-       "      <td>27288613.0</td>\n",
-       "      <td>2.237925e+08</td>\n",
-       "      <td>0.007362</td>\n",
-       "      <td>85.788924</td>\n",
-       "      <td>-0.142111</td>\n",
-       "      <td>0.116389</td>\n",
-       "      <td>-0.016540</td>\n",
-       "      <td>原料与能源</td>\n",
-       "      <td>三友化工</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1455</th>\n",
-       "      <td>002920.XSHE</td>\n",
-       "      <td>2022-01-04</td>\n",
-       "      <td>139.71</td>\n",
-       "      <td>131.69</td>\n",
-       "      <td>140.91</td>\n",
-       "      <td>131.45</td>\n",
-       "      <td>5410083.0</td>\n",
-       "      <td>7.233361e+08</td>\n",
-       "      <td>-0.060833</td>\n",
-       "      <td>150.934097</td>\n",
-       "      <td>0.509341</td>\n",
-       "      <td>0.204770</td>\n",
-       "      <td>0.104298</td>\n",
-       "      <td>信息与通信</td>\n",
-       "      <td>德赛西威</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1456</th>\n",
-       "      <td>300274.XSHE</td>\n",
-       "      <td>2022-01-04</td>\n",
-       "      <td>146.52</td>\n",
-       "      <td>134.96</td>\n",
-       "      <td>148.46</td>\n",
-       "      <td>134.61</td>\n",
-       "      <td>24205007.0</td>\n",
-       "      <td>3.333125e+09</td>\n",
-       "      <td>-0.071291</td>\n",
-       "      <td>176.533682</td>\n",
-       "      <td>0.765337</td>\n",
-       "      <td>0.239501</td>\n",
-       "      <td>0.183299</td>\n",
-       "      <td>工业</td>\n",
-       "      <td>阳光电源</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1457</th>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>2022-01-04</td>\n",
-       "      <td>4.80</td>\n",
-       "      <td>4.89</td>\n",
-       "      <td>4.90</td>\n",
-       "      <td>4.78</td>\n",
-       "      <td>58291943.0</td>\n",
-       "      <td>2.832956e+08</td>\n",
-       "      <td>0.029474</td>\n",
-       "      <td>83.304940</td>\n",
-       "      <td>-0.166951</td>\n",
-       "      <td>0.113019</td>\n",
-       "      <td>-0.018869</td>\n",
-       "      <td>消费</td>\n",
-       "      <td>小商品城</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           ticker       date    open   close    high     low      volume  \\\n",
-       "1452  603882.XSHG 2022-01-04  106.89   98.84  106.89   98.67   5140406.0   \n",
-       "1453  002709.XSHE 2022-01-04   57.64   54.64   57.87   54.29  42150916.0   \n",
-       "1454  600409.XSHG 2022-01-04    8.16    8.21    8.25    8.15  27288613.0   \n",
-       "1455  002920.XSHE 2022-01-04  139.71  131.69  140.91  131.45   5410083.0   \n",
-       "1456  300274.XSHE 2022-01-04  146.52  134.96  148.46  134.61  24205007.0   \n",
-       "1457  600415.XSHG 2022-01-04    4.80    4.89    4.90    4.78  58291943.0   \n",
-       "\n",
-       "             money       pct      weight    return  norm_weight  \\\n",
-       "1452  5.181929e+08 -0.076262   79.300385 -0.206996     0.107586   \n",
-       "1453  2.333429e+09 -0.028277  161.227501  0.612275     0.218735   \n",
-       "1454  2.237925e+08  0.007362   85.788924 -0.142111     0.116389   \n",
-       "1455  7.233361e+08 -0.060833  150.934097  0.509341     0.204770   \n",
-       "1456  3.333125e+09 -0.071291  176.533682  0.765337     0.239501   \n",
-       "1457  2.832956e+08  0.029474   83.304940 -0.166951     0.113019   \n",
-       "\n",
-       "      weighted_return aggregate_sector display_name  \n",
-       "1452        -0.022270             医药卫生         金域医学  \n",
-       "1453         0.133926               工业         天赐材料  \n",
-       "1454        -0.016540            原料与能源         三友化工  \n",
-       "1455         0.104298            信息与通信         德赛西威  \n",
-       "1456         0.183299               工业         阳光电源  \n",
-       "1457        -0.018869               消费         小商品城  "
-      ]
-     },
-     "execution_count": 194,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p_stock_df[p_stock_df.date==datetime(2022,1,4)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>mkt_cap</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2021-01-10</td>\n",
-       "      <td>1550</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date  mkt_cap\n",
-       "0 2021-01-05      600\n",
-       "1 2021-01-10     1550"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mkt_cap_df =  pd.DataFrame(profile_df.groupby(['date'])['weight'].sum()).reset_index()\n",
-    "mkt_cap_df.rename(columns={'weight':'mkt_cap'}, inplace=True)\n",
-    "mkt_cap_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>return</th>\n",
-       "      <th>pct</th>\n",
-       "      <th>mkt_cap</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>600.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2021-01-06</td>\n",
-       "      <td>0.007011</td>\n",
-       "      <td>0.036439</td>\n",
-       "      <td>621.863161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2021-01-07</td>\n",
-       "      <td>0.047531</td>\n",
-       "      <td>0.218707</td>\n",
-       "      <td>757.869005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2021-01-08</td>\n",
-       "      <td>0.047111</td>\n",
-       "      <td>0.013639</td>\n",
-       "      <td>768.205269</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date    return       pct     mkt_cap\n",
-       "0 2021-01-05  0.000000  0.000000  600.000000\n",
-       "1 2021-01-06  0.007011  0.036439  621.863161\n",
-       "2 2021-01-07  0.047531  0.218707  757.869005\n",
-       "3 2021-01-08  0.047111  0.013639  768.205269"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# get mkt adjustment (weight is the fund in a stock)\n",
-    "mkt_adjustment =  pd.DataFrame(profile_df.groupby(['date'])['weight'].sum()).reset_index()\n",
-    "mkt_adjustment.rename(columns={'weight':'mkt_cap'}, inplace=True)\n",
-    "merge_df = p_perform_result.merge(mkt_adjustment, on='date', how='outer')\n",
-    "\n",
-    "\n",
-    "for i in range(1, len(merge_df)):\n",
-    "    merge_df.loc[i, 'mkt_cap'] = merge_df.loc[i-1, 'mkt_cap'] * (1 + merge_df.loc[i, 'pct'])\n",
-    "\n",
-    "# # calculate daily mkt_cap\n",
-    "# # initial_mkt_cap = merge_df.loc[0, 'mkt_cap']\n",
-    "# for i in range(1, len(merge_df)):\n",
-    "#     row = merge_df.loc[i]\n",
-    "#     if pd.isna(row['mkt_cap']):\n",
-    "#         merge_df.loc[i, 'mkt_cap'] = merge_df.loc[i-1, 'mkt_cap'] * (1 + merge_df.loc[i, 'pct_portfolio'])\n",
-    "    \n",
-    "# # step 8 calculate daily mkt cap\n",
-    "\n",
-    "merge_df[merge_df.date < datetime(2021,1,10)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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>ticker</th>\n",
-       "      <th>date</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>money</th>\n",
-       "      <th>pct</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>return</th>\n",
-       "      <th>norm_weight</th>\n",
-       "      <th>weighted_return</th>\n",
-       "      <th>aggregate_sector</th>\n",
-       "      <th>display_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>002709.XSHE</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>32.54</td>\n",
-       "      <td>33.89</td>\n",
-       "      <td>34.22</td>\n",
-       "      <td>31.39</td>\n",
-       "      <td>59152352.0</td>\n",
-       "      <td>1.942406e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>工业</td>\n",
-       "      <td>天赐材料</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>5.33</td>\n",
-       "      <td>5.87</td>\n",
-       "      <td>5.87</td>\n",
-       "      <td>5.22</td>\n",
-       "      <td>180936477.0</td>\n",
-       "      <td>1.010225e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>消费</td>\n",
-       "      <td>小商品城</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>600409.XSHG</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>9.23</td>\n",
-       "      <td>9.57</td>\n",
-       "      <td>9.66</td>\n",
-       "      <td>9.08</td>\n",
-       "      <td>82669289.0</td>\n",
-       "      <td>7.803391e+08</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>原料与能源</td>\n",
-       "      <td>三友化工</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>300274.XSHE</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>76.03</td>\n",
-       "      <td>76.45</td>\n",
-       "      <td>80.20</td>\n",
-       "      <td>75.27</td>\n",
-       "      <td>51384827.0</td>\n",
-       "      <td>3.961995e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>工业</td>\n",
-       "      <td>阳光电源</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>002920.XSHE</td>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>85.44</td>\n",
-       "      <td>87.25</td>\n",
-       "      <td>87.95</td>\n",
-       "      <td>84.07</td>\n",
-       "      <td>3852674.0</td>\n",
-       "      <td>3.322598e+08</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>信息与通信</td>\n",
-       "      <td>德赛西威</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1477</th>\n",
-       "      <td>600409.XSHG</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>8.24</td>\n",
-       "      <td>8.35</td>\n",
-       "      <td>8.39</td>\n",
-       "      <td>8.21</td>\n",
-       "      <td>32516017.0</td>\n",
-       "      <td>2.699300e+08</td>\n",
-       "      <td>0.015815</td>\n",
-       "      <td>87.251829</td>\n",
-       "      <td>-0.127482</td>\n",
-       "      <td>0.121949</td>\n",
-       "      <td>-0.015546</td>\n",
-       "      <td>原料与能源</td>\n",
-       "      <td>三友化工</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1478</th>\n",
-       "      <td>002920.XSHE</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>130.36</td>\n",
-       "      <td>138.43</td>\n",
-       "      <td>141.96</td>\n",
-       "      <td>130.11</td>\n",
-       "      <td>5005400.0</td>\n",
-       "      <td>6.901614e+08</td>\n",
-       "      <td>0.046888</td>\n",
-       "      <td>158.659026</td>\n",
-       "      <td>0.586590</td>\n",
-       "      <td>0.221752</td>\n",
-       "      <td>0.130077</td>\n",
-       "      <td>信息与通信</td>\n",
-       "      <td>德赛西威</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1479</th>\n",
-       "      <td>002709.XSHE</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>51.63</td>\n",
-       "      <td>50.73</td>\n",
-       "      <td>51.93</td>\n",
-       "      <td>50.03</td>\n",
-       "      <td>29821246.0</td>\n",
-       "      <td>1.518902e+09</td>\n",
-       "      <td>-0.019142</td>\n",
-       "      <td>149.690174</td>\n",
-       "      <td>0.496902</td>\n",
-       "      <td>0.209216</td>\n",
-       "      <td>0.103960</td>\n",
-       "      <td>工业</td>\n",
-       "      <td>天赐材料</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1480</th>\n",
-       "      <td>600415.XSHG</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>4.70</td>\n",
-       "      <td>4.75</td>\n",
-       "      <td>4.85</td>\n",
-       "      <td>4.67</td>\n",
-       "      <td>39278041.0</td>\n",
-       "      <td>1.859827e+08</td>\n",
-       "      <td>0.010638</td>\n",
-       "      <td>80.919932</td>\n",
-       "      <td>-0.190801</td>\n",
-       "      <td>0.113099</td>\n",
-       "      <td>-0.021579</td>\n",
-       "      <td>消费</td>\n",
-       "      <td>小商品城</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1481</th>\n",
-       "      <td>603882.XSHG</td>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>88.45</td>\n",
-       "      <td>95.53</td>\n",
-       "      <td>95.59</td>\n",
-       "      <td>88.39</td>\n",
-       "      <td>6991445.0</td>\n",
-       "      <td>6.468392e+08</td>\n",
-       "      <td>0.085692</td>\n",
-       "      <td>76.644737</td>\n",
-       "      <td>-0.233553</td>\n",
-       "      <td>0.107123</td>\n",
-       "      <td>-0.025019</td>\n",
-       "      <td>医药卫生</td>\n",
-       "      <td>金域医学</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1482 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           ticker       date    open   close    high     low       volume  \\\n",
-       "0     002709.XSHE 2021-01-05   32.54   33.89   34.22   31.39   59152352.0   \n",
-       "1     600415.XSHG 2021-01-05    5.33    5.87    5.87    5.22  180936477.0   \n",
-       "2     600409.XSHG 2021-01-05    9.23    9.57    9.66    9.08   82669289.0   \n",
-       "3     300274.XSHE 2021-01-05   76.03   76.45   80.20   75.27   51384827.0   \n",
-       "4     002920.XSHE 2021-01-05   85.44   87.25   87.95   84.07    3852674.0   \n",
-       "...           ...        ...     ...     ...     ...     ...          ...   \n",
-       "1477  600409.XSHG 2022-01-10    8.24    8.35    8.39    8.21   32516017.0   \n",
-       "1478  002920.XSHE 2022-01-10  130.36  138.43  141.96  130.11    5005400.0   \n",
-       "1479  002709.XSHE 2022-01-10   51.63   50.73   51.93   50.03   29821246.0   \n",
-       "1480  600415.XSHG 2022-01-10    4.70    4.75    4.85    4.67   39278041.0   \n",
-       "1481  603882.XSHG 2022-01-10   88.45   95.53   95.59   88.39    6991445.0   \n",
-       "\n",
-       "             money       pct      weight    return  norm_weight  \\\n",
-       "0     1.942406e+09       NaN  100.000000  0.000000     0.166667   \n",
-       "1     1.010225e+09       NaN  100.000000  0.000000     0.166667   \n",
-       "2     7.803391e+08       NaN  100.000000  0.000000     0.166667   \n",
-       "3     3.961995e+09       NaN  100.000000  0.000000     0.166667   \n",
-       "4     3.322598e+08       NaN  100.000000  0.000000     0.166667   \n",
-       "...            ...       ...         ...       ...          ...   \n",
-       "1477  2.699300e+08  0.015815   87.251829 -0.127482     0.121949   \n",
-       "1478  6.901614e+08  0.046888  158.659026  0.586590     0.221752   \n",
-       "1479  1.518902e+09 -0.019142  149.690174  0.496902     0.209216   \n",
-       "1480  1.859827e+08  0.010638   80.919932 -0.190801     0.113099   \n",
-       "1481  6.468392e+08  0.085692   76.644737 -0.233553     0.107123   \n",
-       "\n",
-       "      weighted_return aggregate_sector display_name  \n",
-       "0            0.000000               工业         天赐材料  \n",
-       "1            0.000000               消费         小商品城  \n",
-       "2            0.000000            原料与能源         三友化工  \n",
-       "3            0.000000               工业         阳光电源  \n",
-       "4            0.000000            信息与通信         德赛西威  \n",
-       "...               ...              ...          ...  \n",
-       "1477        -0.015546            原料与能源         三友化工  \n",
-       "1478         0.130077            信息与通信         德赛西威  \n",
-       "1479         0.103960               工业         天赐材料  \n",
-       "1480        -0.021579               消费         小商品城  \n",
-       "1481        -0.025019             医药卫生         金域医学  \n",
-       "\n",
-       "[1482 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## agg by sector and day\n",
-    "p_stock_df['weight_in_sector'] = p_stock_df.groupby"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def creaet_portfolio_return(stock_df):\n",
-    "    portfolio_df = stock_df.groupby(['date'])['weighted_return'].sum().reset_index()\n",
-    "    portfolio_df.rename(columns={'weighted_return':'portfolio_return'}, inplace=True)\n",
-    "    return portfolio_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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>date</th>\n",
-       "      <th>portfolio_return</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2021-01-05</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2021-01-06</td>\n",
-       "      <td>0.007011</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2021-01-07</td>\n",
-       "      <td>0.047531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2021-01-08</td>\n",
-       "      <td>0.047111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2021-01-11</td>\n",
-       "      <td>0.052768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>2022-01-04</td>\n",
-       "      <td>0.363845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>2022-01-05</td>\n",
-       "      <td>0.306697</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244</th>\n",
-       "      <td>2022-01-06</td>\n",
-       "      <td>0.331291</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>245</th>\n",
-       "      <td>2022-01-07</td>\n",
-       "      <td>0.313726</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>246</th>\n",
-       "      <td>2022-01-10</td>\n",
-       "      <td>0.313262</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>247 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          date  portfolio_return\n",
-       "0   2021-01-05          0.000000\n",
-       "1   2021-01-06          0.007011\n",
-       "2   2021-01-07          0.047531\n",
-       "3   2021-01-08          0.047111\n",
-       "4   2021-01-11          0.052768\n",
-       "..         ...               ...\n",
-       "242 2022-01-04          0.363845\n",
-       "243 2022-01-05          0.306697\n",
-       "244 2022-01-06          0.331291\n",
-       "245 2022-01-07          0.313726\n",
-       "246 2022-01-10          0.313262\n",
-       "\n",
-       "[247 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "portfolio_df = creaet_portfolio_return(p_stock_df)\n",
-    "portfolio_df"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

script/sector_eval_df.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dc104daae7afcd56a05f8aff9fa4d80d139a7db33d271fd69fbacb29db22283a
-size 152021

script/stream_pricessing.ipynb

@@ -1,617 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.1.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\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    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\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    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\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  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\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    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = 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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    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-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(> .cell-output-ipywidget-background\n",
-       "    > .lm-Widget\n",
-       "    > *[data-root-id]),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import math\n",
-    "from datetime import datetime\n",
-    "import hvplot.pandas\n",
-    "import math\n",
-    "import numpy as np\n",
-    "from streamz import Stream"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b_stocks = pd.read_pickle('../data/b_stocks.pkl')\n",
-    "p_stocks = pd.read_pickle('../data/p_stocks.pkl')\n",
-    "p_profile = pd.read_pickle('../data/p_profile.pkl')\n",
-    "b_profile = pd.read_pickle('../data/b_profile.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>ticker</th>\n",
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-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>volume</th>\n",
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-       "      <th>1</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>002233.XSHE</td>\n",
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-       "      <td>2021-01-04</td>\n",
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-       "      <td>2021-01-04</td>\n",
-       "      <td>001914.XSHE</td>\n",
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-       "      <td>6619778.0</td>\n",
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-       "    <tr>\n",
-       "      <th>985</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>002384.XSHE</td>\n",
-       "      <td>25.66</td>\n",
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-       "      <td>26.00</td>\n",
-       "      <td>25.17</td>\n",
-       "      <td>50695885.0</td>\n",
-       "      <td>1.296706e+09</td>\n",
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-       "      <th>...</th>\n",
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-       "      <th>147355</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>600511.XSHG</td>\n",
-       "      <td>46.39</td>\n",
-       "      <td>45.10</td>\n",
-       "      <td>46.55</td>\n",
-       "      <td>44.32</td>\n",
-       "      <td>45375375.0</td>\n",
-       "      <td>2.043297e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>147601</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>600236.XSHG</td>\n",
-       "      <td>4.05</td>\n",
-       "      <td>4.05</td>\n",
-       "      <td>4.05</td>\n",
-       "      <td>4.02</td>\n",
-       "      <td>5788783.0</td>\n",
-       "      <td>2.339606e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>147847</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>000807.XSHE</td>\n",
-       "      <td>7.32</td>\n",
-       "      <td>7.58</td>\n",
-       "      <td>7.71</td>\n",
-       "      <td>7.13</td>\n",
-       "      <td>136647514.0</td>\n",
-       "      <td>1.027073e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148093</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>002815.XSHE</td>\n",
-       "      <td>13.41</td>\n",
-       "      <td>13.55</td>\n",
-       "      <td>13.67</td>\n",
-       "      <td>13.29</td>\n",
-       "      <td>5410989.0</td>\n",
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-       "    <tr>\n",
-       "      <th>148339</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>002690.XSHE</td>\n",
-       "      <td>31.68</td>\n",
-       "      <td>31.68</td>\n",
-       "      <td>32.12</td>\n",
-       "      <td>31.26</td>\n",
-       "      <td>3641409.0</td>\n",
-       "      <td>1.153387e+08</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>604 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             date       ticker   open  close   high    low       volume  \\\n",
-       "1      2021-01-04  002233.XSHE  11.27  11.28  11.34  11.17   16262377.0   \n",
-       "247    2021-01-04  601778.XSHG   7.27   7.65   7.77   7.25   59723781.0   \n",
-       "493    2021-01-04  002368.XSHE  25.41  28.18  28.18  25.27   17448308.0   \n",
-       "739    2021-01-04  001914.XSHE  21.21  20.39  21.33  20.26    6619778.0   \n",
-       "985    2021-01-04  002384.XSHE  25.66  25.98  26.00  25.17   50695885.0   \n",
-       "...           ...          ...    ...    ...    ...    ...          ...   \n",
-       "147355 2021-01-04  600511.XSHG  46.39  45.10  46.55  44.32   45375375.0   \n",
-       "147601 2021-01-04  600236.XSHG   4.05   4.05   4.05   4.02    5788783.0   \n",
-       "147847 2021-01-04  000807.XSHE   7.32   7.58   7.71   7.13  136647514.0   \n",
-       "148093 2021-01-04  002815.XSHE  13.41  13.55  13.67  13.29    5410989.0   \n",
-       "148339 2021-01-04  002690.XSHE  31.68  31.68  32.12  31.26    3641409.0   \n",
-       "\n",
-       "               money  \n",
-       "1       1.829668e+08  \n",
-       "247     4.523540e+08  \n",
-       "493     4.729692e+08  \n",
-       "739     1.366024e+08  \n",
-       "985     1.296706e+09  \n",
-       "...              ...  \n",
-       "147355  2.043297e+09  \n",
-       "147601  2.339606e+07  \n",
-       "147847  1.027073e+09  \n",
-       "148093  7.290842e+07  \n",
-       "148339  1.153387e+08  \n",
-       "\n",
-       "[604 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# start stream here\n",
-    "dates = b_stocks.date.unique()\n",
-    "b_stocks[b_stocks.date == dates[1]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data1 = b_stocks[b_stocks.date == dates[0]]\n",
-    "data2 = b_stocks[b_stocks.date == dates[1]]\n",
-    "data3 = b_stocks[b_stocks.date == dates[2]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th>147847</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>000807.XSHE</td>\n",
-       "      <td>7.32</td>\n",
-       "      <td>7.58</td>\n",
-       "      <td>7.71</td>\n",
-       "      <td>7.13</td>\n",
-       "      <td>136647514.0</td>\n",
-       "      <td>1.027073e+09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148093</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>002815.XSHE</td>\n",
-       "      <td>13.41</td>\n",
-       "      <td>13.55</td>\n",
-       "      <td>13.67</td>\n",
-       "      <td>13.29</td>\n",
-       "      <td>5410989.0</td>\n",
-       "      <td>7.290842e+07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148339</th>\n",
-       "      <td>2021-01-04</td>\n",
-       "      <td>002690.XSHE</td>\n",
-       "      <td>31.68</td>\n",
-       "      <td>31.68</td>\n",
-       "      <td>32.12</td>\n",
-       "      <td>31.26</td>\n",
-       "      <td>3641409.0</td>\n",
-       "      <td>1.153387e+08</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1208 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             date       ticker   open  close   high    low       volume  \\\n",
-       "0      2020-12-31  002233.XSHE  11.23  11.30  11.38  11.19    9712496.0   \n",
-       "246    2020-12-31  601778.XSHG   7.28   7.23   7.39   7.20   29971398.0   \n",
-       "492    2020-12-31  002368.XSHE  24.96  25.62  25.92  24.96    7090839.0   \n",
-       "738    2020-12-31  001914.XSHE  20.52  21.26  21.36  20.52    5598757.0   \n",
-       "984    2020-12-31  002384.XSHE  25.67  25.65  25.90  25.15   49307624.0   \n",
-       "...           ...          ...    ...    ...    ...    ...          ...   \n",
-       "147355 2021-01-04  600511.XSHG  46.39  45.10  46.55  44.32   45375375.0   \n",
-       "147601 2021-01-04  600236.XSHG   4.05   4.05   4.05   4.02    5788783.0   \n",
-       "147847 2021-01-04  000807.XSHE   7.32   7.58   7.71   7.13  136647514.0   \n",
-       "148093 2021-01-04  002815.XSHE  13.41  13.55  13.67  13.29    5410989.0   \n",
-       "148339 2021-01-04  002690.XSHE  31.68  31.68  32.12  31.26    3641409.0   \n",
-       "\n",
-       "               money  \n",
-       "0       1.096390e+08  \n",
-       "246     2.181267e+08  \n",
-       "492     1.811902e+08  \n",
-       "738     1.171876e+08  \n",
-       "984     1.256593e+09  \n",
-       "...              ...  \n",
-       "147355  2.043297e+09  \n",
-       "147601  2.339606e+07  \n",
-       "147847  1.027073e+09  \n",
-       "148093  7.290842e+07  \n",
-       "148339  1.153387e+08  \n",
-       "\n",
-       "[1208 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_df = pd.concat([data1, data2])\n",
-    "merged_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             date       ticker   open  close   high    low      volume  \\\n",
-      "0      2020-12-31  002233.XSHE  11.23  11.30  11.38  11.19   9712496.0   \n",
-      "246    2020-12-31  601778.XSHG   7.28   7.23   7.39   7.20  29971398.0   \n",
-      "492    2020-12-31  002368.XSHE  24.96  25.62  25.92  24.96   7090839.0   \n",
-      "738    2020-12-31  001914.XSHE  20.52  21.26  21.36  20.52   5598757.0   \n",
-      "984    2020-12-31  002384.XSHE  25.67  25.65  25.90  25.15  49307624.0   \n",
-      "...           ...          ...    ...    ...    ...    ...         ...   \n",
-      "147354 2020-12-31  600511.XSHG  44.43  46.33  48.45  44.16  69779041.0   \n",
-      "147600 2020-12-31  600236.XSHG   4.07   4.05   4.10   4.02   6542015.0   \n",
-      "147846 2020-12-31  000807.XSHE   7.34   7.35   7.48   7.20  72260375.0   \n",
-      "148092 2020-12-31  002815.XSHE  13.22  13.41  13.51  13.19   4198958.0   \n",
-      "148338 2020-12-31  002690.XSHE  31.14  31.48  31.60  30.79   4010199.0   \n",
-      "\n",
-      "               money  \n",
-      "0       1.096390e+08  \n",
-      "246     2.181267e+08  \n",
-      "492     1.811902e+08  \n",
-      "738     1.171876e+08  \n",
-      "984     1.256593e+09  \n",
-      "...              ...  \n",
-      "147354  3.258608e+09  \n",
-      "147600  2.649244e+07  \n",
-      "147846  5.305392e+08  \n",
-      "148092  5.621386e+07  \n",
-      "148338  1.257796e+08  \n",
-      "\n",
-      "[604 rows x 8 columns]\n",
-      "             date       ticker   open  close   high    low       volume  \\\n",
-      "0      2020-12-31  002233.XSHE  11.23  11.30  11.38  11.19    9712496.0   \n",
-      "246    2020-12-31  601778.XSHG   7.28   7.23   7.39   7.20   29971398.0   \n",
-      "492    2020-12-31  002368.XSHE  24.96  25.62  25.92  24.96    7090839.0   \n",
-      "738    2020-12-31  001914.XSHE  20.52  21.26  21.36  20.52    5598757.0   \n",
-      "984    2020-12-31  002384.XSHE  25.67  25.65  25.90  25.15   49307624.0   \n",
-      "...           ...          ...    ...    ...    ...    ...          ...   \n",
-      "147355 2021-01-04  600511.XSHG  46.39  45.10  46.55  44.32   45375375.0   \n",
-      "147601 2021-01-04  600236.XSHG   4.05   4.05   4.05   4.02    5788783.0   \n",
-      "147847 2021-01-04  000807.XSHE   7.32   7.58   7.71   7.13  136647514.0   \n",
-      "148093 2021-01-04  002815.XSHE  13.41  13.55  13.67  13.29    5410989.0   \n",
-      "148339 2021-01-04  002690.XSHE  31.68  31.68  32.12  31.26    3641409.0   \n",
-      "\n",
-      "               money  \n",
-      "0       1.096390e+08  \n",
-      "246     2.181267e+08  \n",
-      "492     1.811902e+08  \n",
-      "738     1.171876e+08  \n",
-      "984     1.256593e+09  \n",
-      "...              ...  \n",
-      "147355  2.043297e+09  \n",
-      "147601  2.339606e+07  \n",
-      "147847  1.027073e+09  \n",
-      "148093  7.290842e+07  \n",
-      "148339  1.153387e+08  \n",
-      "\n",
-      "[1208 rows x 8 columns]\n",
-      "             date       ticker   open  close   high    low       volume  \\\n",
-      "0      2020-12-31  002233.XSHE  11.23  11.30  11.38  11.19    9712496.0   \n",
-      "246    2020-12-31  601778.XSHG   7.28   7.23   7.39   7.20   29971398.0   \n",
-      "492    2020-12-31  002368.XSHE  24.96  25.62  25.92  24.96    7090839.0   \n",
-      "738    2020-12-31  001914.XSHE  20.52  21.26  21.36  20.52    5598757.0   \n",
-      "984    2020-12-31  002384.XSHE  25.67  25.65  25.90  25.15   49307624.0   \n",
-      "...           ...          ...    ...    ...    ...    ...          ...   \n",
-      "147356 2021-01-05  600511.XSHG  45.00  45.58  46.79  43.58   45296155.0   \n",
-      "147602 2021-01-05  600236.XSHG   4.04   4.01   4.05   4.00    4302703.0   \n",
-      "147848 2021-01-05  000807.XSHE   7.61   7.99   8.21   7.48  177268867.0   \n",
-      "148094 2021-01-05  002815.XSHE  13.52  13.63  13.65  13.44    5800866.0   \n",
-      "148340 2021-01-05  002690.XSHE  31.68  31.85  32.14  31.42    5082815.0   \n",
-      "\n",
-      "               money  \n",
-      "0       1.096390e+08  \n",
-      "246     2.181267e+08  \n",
-      "492     1.811902e+08  \n",
-      "738     1.171876e+08  \n",
-      "984     1.256593e+09  \n",
-      "...              ...  \n",
-      "147356  2.035299e+09  \n",
-      "147602  1.726972e+07  \n",
-      "147848  1.383377e+09  \n",
-      "148094  7.856429e+07  \n",
-      "148340  1.612986e+08  \n",
-      "\n",
-      "[1812 rows x 8 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def add(prev_df, new_df):\n",
-    "    merged_df = pd.concat([prev_df, new_df])\n",
-    "    return merged_df\n",
-    "\n",
-    "source = Stream()\n",
-    "source.accumulate(add).sink(print)\n",
-    "source.emit(b_stocks[b_stocks.date == dates[0]])\n",
-    "source.emit(b_stocks[b_stocks.date == dates[1]])\n",
-    "source.emit(b_stocks[b_stocks.date == dates[2]])\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

script/stream_processing.py

@@ -1,32 +0,0 @@
-import pandas as pd
-import math
-from datetime import datetime
-import hvplot.pandas
-import math
-import numpy as np
-import time
-from streamz import Stream
-
-b_stocks = pd.read_pickle('../data/b_stocks.pkl')
-p_stocks = pd.read_pickle('../data/p_stocks.pkl')
-p_profile = pd.read_pickle('../data/p_profile.pkl')
-b_profile = pd.read_pickle('../data/b_profile.pkl')
-
-# start stream here
-dates = b_stocks.date.unique()
-b_stocks[b_stocks.date == dates[1]]
-
-def add(prev_df, new_df):
-    merged_df = pd.concat([prev_df, new_df])
-    merged_df.sort_values(by=['date'], inplace=True)
-    merged_df['pct'] = merged_df.groupby('ticker')['close'].pct_change()
-
-    # remove prev_df
-    merged_df = merged_df[~merged_df.isin(prev_df)].dropna()
-    return merged_df
-
-source = Stream()
-source.accumulate(add).sink(print)
-source.emit(b_stocks[b_stocks.date == dates[0]])
-source.emit(b_stocks[b_stocks.date == dates[1]])
-source.emit(b_stocks[b_stocks.date == dates[2]])

script/styling.py

@@ -1,15 +0,0 @@
-plot_layout = dict(
-    legend=dict(
-        orientation="h",
-    ),
-    yaxis_title=None,
-    xaxis_title=None,
-    margin=dict(l=0, r=0, t=30, b=0),
-    uniformtext_mode='hide',
-    
-)
-
-barplot_trace = dict(
-    marker_line_width=0,
-    selector=dict(type="bar"),
-)

test.ipynb

@@ -1,47 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import panel as pn\n",
-    "\n",
-    "pn.extension('perspective', template='fast', sizing_mode='stretch_width')\n",
-    "df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD')).cumsum()\n",
-    "\n",
-    "rollover = pn.widgets.IntInput(name='Rollover', value=15)\n",
-    "\n",
-    "perspective = pn.pane.Perspective(df, height=400)\n",
-    "\n",
-    "def stream():\n",
-    "    data = df.iloc[-1] + np.random.randn(4)\n",
-    "    perspective.stream(data, rollover=rollover.value)\n",
-    "\\\n",
-    "cb = pn.state.add_periodic_callback(stream, 50)\n",
-    "\n",
-    "pn.Column(\n",
-    "    pn.Row(cb.param.period, rollover, perspective.param.theme),\n",
-    "    perspective\n",
-    ").servable()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

test_background_task.py

@@ -1,26 +0,0 @@
-import pipeline
-import settings
-
-def test_need_to_update():
-    table_list = settings.TABLE_NAME_AND_FREQ
-    for table, freq in table_list:
-        result = pipeline.need_to_update(table, freq)
-        print(result)
-
-# test_need_to_update()
-
-def test_fetch_stock_price():
-    df = pipeline.fetch_stock_price()
-    return df
-
-def test_need_to_update_stocks_price():
-    print(pipeline.need_to_update_stocks_price())
-
-
-def test_add_details_to_stock_df():
-    stock_df = pipeline.update_stocks_price()
-    stock_df = pipeline.add_details_to_stock_df(stock_df)
-
-    return stock_df
-
-print(test_add_details_to_stock_df())

test_responsive.py

@@ -1,14 +0,0 @@
-import random
-import panel.widgets as pnw
-import panel as pn
-pn.extension()
-
-
-def rcolor(): return "#%06x" % random.randint(0, 0xFFFFFF)
-
-
-box = pn.FlexBox(*[pn.pane.HTML(str(i), styles=dict(background=rcolor()),
-                 width=370, height=100, sizing_mode='fixed') for i in range(24)],
-                 align_conten='start', styles={'background': 'black'}
-                 )
-box.servable()

testing_pipeline.ipynb

@@ -1,561 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.1.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\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    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\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    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\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  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\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    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(> .cell-output-ipywidget-background\n",
-       "    > .lm-Widget\n",
-       "    > *[data-root-id]),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pipeline\n",
-    "import settings\n",
-    "from sqlalchemy import create_engine\n",
-    "import pandas as pd\n",
-    "import processing\n",
-    "import datetime as dt\n",
-    "import api\n",
-    "import table_schema as ts\n",
-    "import db_operation as db\n",
-    "import numpy as np\n",
-    "import utils\n",
-    "import hvplot.pandas  # noqa\n",
-    "pd.options.plotting.backend = 'holoviews'\n",
-    "db_url = 'sqlite:///instance/local.db'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# unique ticker list\n",
-    "def get_stocks_in_profile(profile_df):\n",
-    "    ticker_list = profile_df['ticker'].unique().tolist()\n",
-    "    stocks_df = db.get_stocks_price(ticker_list)\n",
-    "    return stocks_df\n",
-    "portfolio_p = db.get_all_portfolio_profile()\n",
-    "benchmark_p = db.get_all_benchmark_profile()\n",
-    "p_stocks_df = get_stocks_in_profile(portfolio_p)\n",
-    "b_stocks_df = get_stocks_in_profile(benchmark_p)\n",
-    "\n",
-    "## temperaraly handle rename date to time\n",
-    "portfolio_p.rename(columns={'date': 'time','weight':'ini_w'}, inplace=True)\n",
-    "benchmark_p.rename(columns={'date': 'time'}, inplace=True)\n",
-    "# normalize weight in benchmark\n",
-    "grouped = benchmark_p.groupby('time')\n",
-    "benchmark_p['ini_w'] = grouped['weight'].transform(lambda x: x / x.sum())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "analytic_b = processing.create_analytic_df(b_stocks_df, benchmark_p)\n",
-    "analytic_p = processing.create_analytic_df(p_stocks_df, portfolio_p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# p stock weight\n",
-    "processing.calculate_cash(analytic_p)\n",
-    "processing.calculate_weight_using_cash(analytic_p)\n",
-    "processing.calculate_pct(analytic_p)\n",
-    "# b stock weight\n",
-    "analytic_b.sort_values(by=['time'], inplace=True)\n",
-    "grouped = analytic_b.groupby('ticker')\n",
-    "analytic_b['pct'] = grouped['close'].pct_change()\n",
-    "processing.calculate_weight_using_pct(analytic_b)\n",
-    "\n",
-    "# analytic_df.hvplot.line(x='time', y='cur_w', by='ticker', width=500, height=400)\n",
-    "# analytic_d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# crop before processing\n",
-    "analytic_b = analytic_b[analytic_b.time >= analytic_p.time.min()].copy()\n",
-    "processing.calculate_log_return(analytic_p)\n",
-    "processing.calculate_log_return(analytic_b)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['time', 'ticker', 'open', 'close', 'high', 'low', 'volume', 'money',\n",
-       "       'shares', 'sector', 'aggregate_sector', 'display_name', 'name', 'cash',\n",
-       "       'ini_w', 'ave_price', 'weight', 'pct', 'log_return',\n",
-       "       'weighted_log_return'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "analytic_p.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {},
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
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-       "  var render_items = [{\"docid\":\"9024d76f-8d10-4a0a-8327-1e46e4bcf29d\",\"roots\":{\"p1149\":\"ad7e920f-cf41-4c46-8ae3-2357eee49c67\"},\"root_ids\":[\"p1149\"]}];\n",
-       "  var docs = Object.values(docs_json)\n",
-       "  if (!docs) {\n",
-       "    return\n",
-       "  }\n",
-       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
-       "  const is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1\n",
-       "  function embed_document(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
-       "\tconst id_el = document.getElementById(root_id)\n",
-       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t}\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  function get_bokeh(root) {\n",
-       "    if (root.Bokeh === undefined) {\n",
-       "      return null\n",
-       "    } else if (root.Bokeh.version !== py_version && !is_dev) {\n",
-       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
-       "\treturn null\n",
-       "      }\n",
-       "      return root.Bokeh.versions.get(py_version);\n",
-       "    } else if (root.Bokeh.version === py_version) {\n",
-       "      return root.Bokeh\n",
-       "    }\n",
-       "    return null\n",
-       "  }\n",
-       "  function is_loaded(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
-       "  }\n",
-       "  if (is_loaded(root)) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (is_loaded(root)) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 200) {\n",
-       "          clearInterval(timer);\n",
-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ],
-      "text/plain": [
-       ":Bars   [period,Variable]   (value)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "p1149"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agg_b = processing.aggregate_analytic_df_by_period(analytic_b, 'D')\n",
-    "agg_p = processing.aggregate_analytic_df_by_period(analytic_p, 'D')\n",
-    "bhb_result = processing.calculate_periodic_BHB(agg_b, agg_p)\n",
-    "agg_bhb_result = processing.aggregate_bhb_df(bhb_result)\n",
-    "agg_bhb_result.hvplot.bar(\n",
-    "                          y=['active_return','allocation',\n",
-    "                             'interaction','selection',\n",
-    "                             'notional_active_return'],rot=45,legend='top')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agg_bhb_result['period_str'] = agg_bhb_result.index.map(lambda x: str(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\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>active_return</th>\n",
-       "      <th>allocation</th>\n",
-       "      <th>interaction</th>\n",
-       "      <th>selection</th>\n",
-       "      <th>notional_active_return</th>\n",
-       "      <th>period_str</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>period</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>2023-08-08/2023-08-14</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2023-08-08/2023-08-14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-08-15/2023-08-21</th>\n",
-       "      <td>0.043550</td>\n",
-       "      <td>0.043253</td>\n",
-       "      <td>-0.028373</td>\n",
-       "      <td>0.02867</td>\n",
-       "      <td>0.043550</td>\n",
-       "      <td>2023-08-15/2023-08-21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-08-22/2023-08-28</th>\n",
-       "      <td>0.014808</td>\n",
-       "      <td>0.015379</td>\n",
-       "      <td>-0.017291</td>\n",
-       "      <td>0.01672</td>\n",
-       "      <td>0.014808</td>\n",
-       "      <td>2023-08-22/2023-08-28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2023-08-29/2023-09-04</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2023-08-29/2023-09-04</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       active_return  allocation  interaction  selection  \\\n",
-       "period                                                                     \n",
-       "2023-08-08/2023-08-14       0.000000    0.000000     0.000000    0.00000   \n",
-       "2023-08-15/2023-08-21       0.043550    0.043253    -0.028373    0.02867   \n",
-       "2023-08-22/2023-08-28       0.014808    0.015379    -0.017291    0.01672   \n",
-       "2023-08-29/2023-09-04       0.000000    0.000000     0.000000    0.00000   \n",
-       "\n",
-       "                       notional_active_return             period_str  \n",
-       "period                                                                \n",
-       "2023-08-08/2023-08-14                0.000000  2023-08-08/2023-08-14  \n",
-       "2023-08-15/2023-08-21                0.043550  2023-08-15/2023-08-21  \n",
-       "2023-08-22/2023-08-28                0.014808  2023-08-22/2023-08-28  \n",
-       "2023-08-29/2023-09-04                0.000000  2023-08-29/2023-09-04  "
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agg_bhb_result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.1.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = true;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\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    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'plotly': 'https://cdn.plot.ly/plotly-2.18.0.min', 'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"plotly\"], function(Plotly) {\n\twindow.Plotly = Plotly\n\ton_load()\n      })\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 10;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    }    if (((window['Plotly'] !== undefined) && (!(window['Plotly'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/plotlyplot/plotly-2.18.0.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; 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i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\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    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\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  var js_urls = [\"https://cdn.holoviz.org/panel/1.1.1/dist/bundled/jquery/jquery.slim.min.js\", \"https://cdn.holoviz.org/panel/1.1.1/dist/bundled/plotlyplot/plotly-2.18.0.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; 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-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || 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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(> .cell-output-ipywidget-background\n",
-       "    > .lm-Widget\n",
-       "    > *[data-root-id]),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "49cd388784a7420f8243cbcf76d1cd8b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "BokehModel(combine_events=True, render_bundle={'docs_json': {'fba37937-2449-4574-bb28-cb6a96c8cbc3': {'version…"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import plotly.express as px\n",
-    "import panel as pn\n",
-    "pn.extension('plotly')\n",
-    "fig = px.bar(agg_bhb_result,x='period_str',y=[\n",
-    "                     'allocation', 'selection', 'interaction', 'notional_active_return', 'active_return'])\n",
-    "pn.pane.Plotly(fig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bhb_result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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>time</th>\n",
-       "      <th>ticker</th>\n",
-       "      <th>open</th>\n",
-       "      <th>close</th>\n",
-       "      <th>high</th>\n",
-       "      <th>low</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>money</th>\n",
-       "      <th>shares</th>\n",
-       "      <th>sector</th>\n",
-       "      <th>aggregate_sector</th>\n",
-       "      <th>display_name</th>\n",
-       "      <th>name</th>\n",
-       "      <th>cash</th>\n",
-       "      <th>ini_w</th>\n",
-       "      <th>ave_price</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>pct</th>\n",
-       "      <th>norm_pct</th>\n",
-       "      <th>return</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [time, ticker, open, close, high, low, volume, money, shares, sector, aggregate_sector, display_name, name, cash, ini_w, ave_price, weight, pct, norm_pct, return]\n",
-       "Index: []"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# check if there a row with same ticker and time\n",
-    "analytic_b[analytic_b.duplicated(['ticker', 'time'])]\n",
-    "analytic_p[analytic_p.duplicated(['ticker', 'time'])]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "risk-dashboard",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

total_return.ipynb

@@ -1,713 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.1.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\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    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; 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i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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    }    if (((window['MathJax'] !== undefined) && (!(window['MathJax'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-MML-AM_CHTML'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\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    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\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  var js_urls = [\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-MML-AM_CHTML\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\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    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
-      "application/vnd.holoviews_load.v0+json": ""
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-     "output_type": "display_data"
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google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || 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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(> .cell-output-ipywidget-background\n",
-       "    > .lm-Widget\n",
-       "    > *[data-root-id]),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import panel as pn\n",
-    "import pandas as pd\n",
-    "import scipy.stats as stats\n",
-    "from sqlalchemy import create_engine\n",
-    "import hvplot.pandas\n",
-    "from datetime import datetime, timedelta\n",
-    "from script import processing\n",
-    "import numpy as np\n",
-    "pn.extension()\n",
-    "db_url = 'sqlite:///instance/local.db'\n",
-    "engine = create_engine(db_url)\n",
-    "import plotly.express as px\n",
-    "pn.extension('mathjax')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p_eval_df = None\n",
-    "calculated_b_stock = None\n",
-    "calculated_p_stock = None\n",
-    "# load benchmark stock\n",
-    "with engine.connect() as connection:\n",
-    "    calculated_b_stock = pd.read_sql('calculated_b_stock', con=connection)\n",
-    "    calculated_p_stock = pd.read_sql('calculated_p_stock', con=connection)\n",
-    "    p_eval_df = pd.read_sql('p_eval_result', con=connection)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-06-29 00:00:00\n",
-      "interaction        0.012116\n",
-      "allocation         0.002211\n",
-      "selection         -0.012116\n",
-      "active_return      0.002211\n",
-      "notional_return    0.002211\n",
-      "dtype: float64\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>portfolio_return_p</th>\n",
-       "      <th>portfolio_pct_p</th>\n",
-       "      <th>portfolio_return_b</th>\n",
-       "      <th>portfolio_pct_b</th>\n",
-       "      <th>mkt_cap</th>\n",
-       "      <th>prev_mkt_cap</th>\n",
-       "      <th>pnl</th>\n",
-       "      <th>risk</th>\n",
-       "      <th>active_return</th>\n",
-       "      <th>tracking_error</th>\n",
-       "      <th>cum_pnl</th>\n",
-       "      <th>return_p</th>\n",
-       "      <th>return_b</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>600</th>\n",
-       "      <td>2023-06-29</td>\n",
-       "      <td>0.343213</td>\n",
-       "      <td>0.006552</td>\n",
-       "      <td>-0.017097</td>\n",
-       "      <td>-0.000125</td>\n",
-       "      <td>1219.334595</td>\n",
-       "      <td>1211.396987</td>\n",
-       "      <td>7.937608</td>\n",
-       "      <td>0.309501</td>\n",
-       "      <td>0.002785</td>\n",
-       "      <td>0.218361</td>\n",
-       "      <td>140.338486</td>\n",
-       "      <td>0.014219</td>\n",
-       "      <td>0.011434</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          date  portfolio_return_p  portfolio_pct_p  portfolio_return_b  \\\n",
-       "600 2023-06-29            0.343213         0.006552           -0.017097   \n",
-       "\n",
-       "     portfolio_pct_b      mkt_cap  prev_mkt_cap       pnl      risk  \\\n",
-       "600        -0.000125  1219.334595   1211.396987  7.937608  0.309501   \n",
-       "\n",
-       "     active_return  tracking_error     cum_pnl  return_p  return_b  \n",
-       "600       0.002785        0.218361  140.338486  0.014219  0.011434  "
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## check why activate return and nominal return is not same \n",
-    "# start_date = p_eval_df['date'].min()\n",
-    "# start_date = datetime(2022, 12, 14)\n",
-    "start_date = datetime(2023,6,25)\n",
-    "end_date = p_eval_df['date'].max()\n",
-    "print(end_date)\n",
-    "ticker = \"601117.XSHG\"\n",
-    "attributes_df = processing.calculate_attributes_between_dates(\n",
-    "    start_date, end_date, calculated_b_stock=calculated_b_stock, calculated_p_stock=calculated_p_stock)\n",
-    "total_attributes = attributes_df.aggregate({\n",
-    "        'interaction': 'sum',\n",
-    "        'allocation': 'sum',\n",
-    "        'selection': 'sum',\n",
-    "        'active_return': 'sum',\n",
-    "        'notional_return': 'sum'\n",
-    "    }) \n",
-    "# print(attributes_df.columns)\n",
-    "print(total_attributes)\n",
-    "return_df = processing.calculate_return(p_eval_df, start_date, end_date)\n",
-    "return_df\n",
-    "most_recent_row = return_df.tail(1)\n",
-    "# print(return_df.head(1))\n",
-    "most_recent_row\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1219.334714348576"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1038.573137 * (1 + 0.174048)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.01448137201285984"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# portfolio return\n",
-    "attributes_df['w_return_p'] = attributes_df.pct_p * attributes_df.prev_w_in_p_p\n",
-    "attributes_df.w_return_p.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.09551009084622147"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "0.17190200433908703 - 0.07639191349286556"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.011250737770502067"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# benchmark return\n",
-    "attributes_df['w_return_b'] = attributes_df.pct_b * attributes_df.prev_w_in_p_b\n",
-    "attributes_df.w_return_b.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/v5/2108rh5964q9j741wg_s8r1w0000gn/T/ipykernel_87460/2230555833.py:4: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  selected_df['return_p'] = (1 + selected_df.portfolio_pct_p).cumprod()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>portfolio_return_p</th>\n",
-       "      <th>portfolio_pct_p</th>\n",
-       "      <th>portfolio_return_b</th>\n",
-       "      <th>portfolio_pct_b</th>\n",
-       "      <th>mkt_cap</th>\n",
-       "      <th>prev_mkt_cap</th>\n",
-       "      <th>pnl</th>\n",
-       "      <th>risk</th>\n",
-       "      <th>active_return</th>\n",
-       "      <th>tracking_error</th>\n",
-       "      <th>cum_pnl</th>\n",
-       "      <th>return_p</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>2022-12-14</td>\n",
-       "      <td>0.151775</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.002796</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1038.573137</td>\n",
-       "      <td>1043.453255</td>\n",
-       "      <td>-4.880117</td>\n",
-       "      <td>0.327324</td>\n",
-       "      <td>-0.000508</td>\n",
-       "      <td>0.225463</td>\n",
-       "      <td>-40.422972</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>472</th>\n",
-       "      <td>2022-12-15</td>\n",
-       "      <td>0.166816</td>\n",
-       "      <td>0.013638</td>\n",
-       "      <td>0.004505</td>\n",
-       "      <td>0.001707</td>\n",
-       "      <td>1052.737107</td>\n",
-       "      <td>1038.573137</td>\n",
-       "      <td>14.163970</td>\n",
-       "      <td>0.327122</td>\n",
-       "      <td>0.011931</td>\n",
-       "      <td>0.225384</td>\n",
-       "      <td>-26.259002</td>\n",
-       "      <td>1.013638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>473</th>\n",
-       "      <td>2022-12-16</td>\n",
-       "      <td>0.156618</td>\n",
-       "      <td>-0.009792</td>\n",
-       "      <td>-0.000671</td>\n",
-       "      <td>-0.005172</td>\n",
-       "      <td>1042.429190</td>\n",
-       "      <td>1052.737107</td>\n",
-       "      <td>-10.307917</td>\n",
-       "      <td>0.326859</td>\n",
-       "      <td>-0.004619</td>\n",
-       "      <td>0.225175</td>\n",
-       "      <td>-36.566919</td>\n",
-       "      <td>1.003713</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>474</th>\n",
-       "      <td>2022-12-19</td>\n",
-       "      <td>0.144766</td>\n",
-       "      <td>-0.012217</td>\n",
-       "      <td>-0.022720</td>\n",
-       "      <td>-0.022046</td>\n",
-       "      <td>1029.693832</td>\n",
-       "      <td>1042.429190</td>\n",
-       "      <td>-12.735359</td>\n",
-       "      <td>0.326642</td>\n",
-       "      <td>0.009829</td>\n",
-       "      <td>0.225043</td>\n",
-       "      <td>-49.302277</td>\n",
-       "      <td>0.991450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>475</th>\n",
-       "      <td>2022-12-20</td>\n",
-       "      <td>0.140936</td>\n",
-       "      <td>-0.003925</td>\n",
-       "      <td>-0.032668</td>\n",
-       "      <td>-0.009947</td>\n",
-       "      <td>1025.652205</td>\n",
-       "      <td>1029.693832</td>\n",
-       "      <td>-4.041626</td>\n",
-       "      <td>0.326312</td>\n",
-       "      <td>0.006022</td>\n",
-       "      <td>0.224844</td>\n",
-       "      <td>-53.343904</td>\n",
-       "      <td>0.987559</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>596</th>\n",
-       "      <td>2023-06-21</td>\n",
-       "      <td>0.338226</td>\n",
-       "      <td>-0.012860</td>\n",
-       "      <td>-0.012265</td>\n",
-       "      <td>-0.020746</td>\n",
-       "      <td>1213.343064</td>\n",
-       "      <td>1229.149762</td>\n",
-       "      <td>-15.806698</td>\n",
-       "      <td>0.310372</td>\n",
-       "      <td>0.007886</td>\n",
-       "      <td>0.219003</td>\n",
-       "      <td>134.346955</td>\n",
-       "      <td>1.168279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>597</th>\n",
-       "      <td>2023-06-26</td>\n",
-       "      <td>0.327466</td>\n",
-       "      <td>-0.009151</td>\n",
-       "      <td>-0.028546</td>\n",
-       "      <td>-0.016269</td>\n",
-       "      <td>1202.239760</td>\n",
-       "      <td>1213.343064</td>\n",
-       "      <td>-11.103304</td>\n",
-       "      <td>0.310175</td>\n",
-       "      <td>0.007118</td>\n",
-       "      <td>0.218860</td>\n",
-       "      <td>123.243651</td>\n",
-       "      <td>1.157588</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>598</th>\n",
-       "      <td>2023-06-27</td>\n",
-       "      <td>0.339179</td>\n",
-       "      <td>0.010630</td>\n",
-       "      <td>-0.016308</td>\n",
-       "      <td>0.012234</td>\n",
-       "      <td>1215.019735</td>\n",
-       "      <td>1202.239760</td>\n",
-       "      <td>12.779975</td>\n",
-       "      <td>0.309985</td>\n",
-       "      <td>-0.001604</td>\n",
-       "      <td>0.218682</td>\n",
-       "      <td>136.023626</td>\n",
-       "      <td>1.169893</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>599</th>\n",
-       "      <td>2023-06-28</td>\n",
-       "      <td>0.335389</td>\n",
-       "      <td>-0.002982</td>\n",
-       "      <td>-0.016974</td>\n",
-       "      <td>-0.000665</td>\n",
-       "      <td>1211.396987</td>\n",
-       "      <td>1215.019735</td>\n",
-       "      <td>-3.622748</td>\n",
-       "      <td>0.309735</td>\n",
-       "      <td>-0.002316</td>\n",
-       "      <td>0.218507</td>\n",
-       "      <td>132.400878</td>\n",
-       "      <td>1.166405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>600</th>\n",
-       "      <td>2023-06-29</td>\n",
-       "      <td>0.343213</td>\n",
-       "      <td>0.006552</td>\n",
-       "      <td>-0.017097</td>\n",
-       "      <td>-0.000125</td>\n",
-       "      <td>1219.334595</td>\n",
-       "      <td>1211.396987</td>\n",
-       "      <td>7.937608</td>\n",
-       "      <td>0.309501</td>\n",
-       "      <td>0.006678</td>\n",
-       "      <td>0.218361</td>\n",
-       "      <td>140.338486</td>\n",
-       "      <td>1.174048</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>130 rows × 13 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          date  portfolio_return_p  portfolio_pct_p  portfolio_return_b  \\\n",
-       "471 2022-12-14            0.151775         0.000000            0.002796   \n",
-       "472 2022-12-15            0.166816         0.013638            0.004505   \n",
-       "473 2022-12-16            0.156618        -0.009792           -0.000671   \n",
-       "474 2022-12-19            0.144766        -0.012217           -0.022720   \n",
-       "475 2022-12-20            0.140936        -0.003925           -0.032668   \n",
-       "..         ...                 ...              ...                 ...   \n",
-       "596 2023-06-21            0.338226        -0.012860           -0.012265   \n",
-       "597 2023-06-26            0.327466        -0.009151           -0.028546   \n",
-       "598 2023-06-27            0.339179         0.010630           -0.016308   \n",
-       "599 2023-06-28            0.335389        -0.002982           -0.016974   \n",
-       "600 2023-06-29            0.343213         0.006552           -0.017097   \n",
-       "\n",
-       "     portfolio_pct_b      mkt_cap  prev_mkt_cap        pnl      risk  \\\n",
-       "471         0.000000  1038.573137   1043.453255  -4.880117  0.327324   \n",
-       "472         0.001707  1052.737107   1038.573137  14.163970  0.327122   \n",
-       "473        -0.005172  1042.429190   1052.737107 -10.307917  0.326859   \n",
-       "474        -0.022046  1029.693832   1042.429190 -12.735359  0.326642   \n",
-       "475        -0.009947  1025.652205   1029.693832  -4.041626  0.326312   \n",
-       "..               ...          ...           ...        ...       ...   \n",
-       "596        -0.020746  1213.343064   1229.149762 -15.806698  0.310372   \n",
-       "597        -0.016269  1202.239760   1213.343064 -11.103304  0.310175   \n",
-       "598         0.012234  1215.019735   1202.239760  12.779975  0.309985   \n",
-       "599        -0.000665  1211.396987   1215.019735  -3.622748  0.309735   \n",
-       "600        -0.000125  1219.334595   1211.396987   7.937608  0.309501   \n",
-       "\n",
-       "     active_return  tracking_error     cum_pnl  return_p  \n",
-       "471      -0.000508        0.225463  -40.422972  1.000000  \n",
-       "472       0.011931        0.225384  -26.259002  1.013638  \n",
-       "473      -0.004619        0.225175  -36.566919  1.003713  \n",
-       "474       0.009829        0.225043  -49.302277  0.991450  \n",
-       "475       0.006022        0.224844  -53.343904  0.987559  \n",
-       "..             ...             ...         ...       ...  \n",
-       "596       0.007886        0.219003  134.346955  1.168279  \n",
-       "597       0.007118        0.218860  123.243651  1.157588  \n",
-       "598      -0.001604        0.218682  136.023626  1.169893  \n",
-       "599      -0.002316        0.218507  132.400878  1.166405  \n",
-       "600       0.006678        0.218361  140.338486  1.174048  \n",
-       "\n",
-       "[130 rows x 13 columns]"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "selected_df = p_eval_df[p_eval_df.date.between(start_date, end_date)]\n",
-    "selected_df.iloc[0, selected_df.columns.get_indexer(\n",
-    "    ['portfolio_pct_p', 'portfolio_pct_b'])] = 0\n",
-    "selected_df['return_p'] = (1 + selected_df.portfolio_pct_p).cumprod()\n",
-    "selected_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['date', 'portfolio_return_p', 'portfolio_pct_p', 'portfolio_return_b',\n",
-       "       'portfolio_pct_b', 'mkt_cap', 'prev_mkt_cap', 'pnl', 'risk',\n",
-       "       'active_return', 'tracking_error', 'cum_pnl'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p_eval_df.columns"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "portfolio_risk_assesment",
-   "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": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}