diff --git "a/pipeline/bhb.ipynb" "b/pipeline/bhb.ipynb"
--- "a/pipeline/bhb.ipynb"
+++ "b/pipeline/bhb.ipynb"
@@ -1,3 +1,1132 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:34f986af99502cb4a9573cdb83d98c9721baf565d3716d431a1753af1c282985
-size 119843
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load data\n",
+    "portfolio_df = pd.read_pickle('../calculation_result_portfolio.pkl')\n",
+    "stock_df = pd.read_pickle('../stock_result.pkl')\n",
+    "benchmark_df = pd.read_pickle('../dummy_weight_df.pkl')\n",
+    "benchmark_portofolio_df = pd.read_pickle('../benchmark_portfolio.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1458, 20)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stock_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(243, 23)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "portfolio_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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</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>norm_weight</th>\n",
+       "      <th>pct_change</th>\n",
+       "      <th>ptc</th>\n",
+       "      <th>pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-01-01</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00088</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>0.491</td>\n",
+       "      <td>星宇股份</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>601799.XSHG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00491</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>0.118</td>\n",
+       "      <td>捷成股份</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>300182.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00118</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>374</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>0.192</td>\n",
+       "      <td>木林森</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>002745.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00192</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>国轩高科</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>002074.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00710</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          date  weight display_name actual_data       ticker  open  close  \\\n",
+       "0   2021-01-01   0.088         神州高铁  2020-12-31  000008.XSHE   NaN    NaN   \n",
+       "359 2021-01-01   0.491         星宇股份  2020-12-31  601799.XSHG   NaN    NaN   \n",
+       "360 2021-01-01   0.118         捷成股份  2020-12-31  300182.XSHE   NaN    NaN   \n",
+       "374 2021-01-01   0.192          木林森  2020-12-31  002745.XSHE   NaN    NaN   \n",
+       "249 2021-01-01   0.710         国轩高科  2020-12-31  002074.XSHE   NaN    NaN   \n",
+       "\n",
+       "     high  low  volume  money  norm_weight  pct_change  ptc  pct  \n",
+       "0     NaN  NaN     NaN    NaN      0.00088         NaN  NaN  NaN  \n",
+       "359   NaN  NaN     NaN    NaN      0.00491         NaN  NaN  NaN  \n",
+       "360   NaN  NaN     NaN    NaN      0.00118         NaN  NaN  NaN  \n",
+       "374   NaN  NaN     NaN    NaN      0.00192         NaN  NaN  NaN  \n",
+       "249   NaN  NaN     NaN    NaN      0.00710         NaN  NaN  NaN  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "benchmark_portofolio_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected_columns = ['date', 'ticker', 'pct', 'weight_portfolio']\n",
+    "merged_df = pd.merge(stock_df[selected_columns], benchmark_portofolio_df, how='right', on=['date', 'ticker'], suffixes=('_portfolio', '_benchmark'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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",
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+       "        text-align: right;\n",
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+       "<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>pct_portfolio</th>\n",
+       "      <th>weight_portfolio</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>display_name</th>\n",
+       "      <th>actual_data</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>norm_weight</th>\n",
+       "      <th>pct_change</th>\n",
+       "      <th>ptc</th>\n",
+       "      <th>pct_benchmark</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>000008.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>神州高铁</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>0.00088</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>601799.XSHG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>星宇股份</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>300182.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.118</td>\n",
+       "      <td>捷成股份</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>002745.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.192</td>\n",
+       "      <td>木林森</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>002074.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>国轩高科</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00710</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>688002.XSHG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.438</td>\n",
+       "      <td>睿创微纳</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00438</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>688099.XSHG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.287</td>\n",
+       "      <td>晶晨股份</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00287</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>688088.XSHG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>虹软科技</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00252</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>002957.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>科瑞技术</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00030</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>601615.XSHG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>明阳智能</td>\n",
+       "      <td>2020-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00252</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>500 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          date       ticker  pct_portfolio  weight_portfolio  weight  \\\n",
+       "0   2021-01-01  000008.XSHE            NaN               NaN   0.088   \n",
+       "1   2021-01-01  601799.XSHG            NaN               NaN   0.491   \n",
+       "2   2021-01-01  300182.XSHE            NaN               NaN   0.118   \n",
+       "3   2021-01-01  002745.XSHE            NaN               NaN   0.192   \n",
+       "4   2021-01-01  002074.XSHE            NaN               NaN   0.710   \n",
+       "..         ...          ...            ...               ...     ...   \n",
+       "495 2021-01-01  688002.XSHG            NaN               NaN   0.438   \n",
+       "496 2021-01-01  688099.XSHG            NaN               NaN   0.287   \n",
+       "497 2021-01-01  688088.XSHG            NaN               NaN   0.252   \n",
+       "498 2021-01-01  002957.XSHE            NaN               NaN   0.030   \n",
+       "499 2021-01-01  601615.XSHG            NaN               NaN   0.252   \n",
+       "\n",
+       "    display_name actual_data  open  close  high  low  volume  money  \\\n",
+       "0           神州高铁  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "1           星宇股份  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "2           捷成股份  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "3            木林森  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "4           国轩高科  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "..           ...         ...   ...    ...   ...  ...     ...    ...   \n",
+       "495         睿创微纳  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "496         晶晨股份  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "497         虹软科技  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "498         科瑞技术  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "499         明阳智能  2020-12-31   NaN    NaN   NaN  NaN     NaN    NaN   \n",
+       "\n",
+       "     norm_weight  pct_change  ptc  pct_benchmark  \n",
+       "0        0.00088         NaN  NaN            NaN  \n",
+       "1        0.00491         NaN  NaN            NaN  \n",
+       "2        0.00118         NaN  NaN            NaN  \n",
+       "3        0.00192         NaN  NaN            NaN  \n",
+       "4        0.00710         NaN  NaN            NaN  \n",
+       "..           ...         ...  ...            ...  \n",
+       "495      0.00438         NaN  NaN            NaN  \n",
+       "496      0.00287         NaN  NaN            NaN  \n",
+       "497      0.00252         NaN  NaN            NaN  \n",
+       "498      0.00030         NaN  NaN            NaN  \n",
+       "499      0.00252         NaN  NaN            NaN  \n",
+       "\n",
+       "[500 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df.loc[merged_df['date']=='2021-01-01']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_df['allocation'] = (merged_df['weight_portfolio'] - merged_df['norm_weight']) * merged_df['pct_benchmark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>pct_portfolio</th>\n",
+       "      <th>weight_portfolio</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>display_name</th>\n",
+       "      <th>actual_data</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>norm_weight</th>\n",
+       "      <th>pct_change</th>\n",
+       "      <th>ptc</th>\n",
+       "      <th>pct_benchmark</th>\n",
+       "      <th>allocation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2148</th>\n",
+       "      <td>2021-01-05</td>\n",
+       "      <td>600409.XSHG</td>\n",
+       "      <td>0.045902</td>\n",
+       "      <td>0.028341</td>\n",
+       "      <td>0.225</td>\n",
+       "      <td>三友化工</td>\n",
+       "      <td>2020-12-31</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>0.002250</td>\n",
+       "      <td>0.045902</td>\n",
+       "      <td>0.045902</td>\n",
+       "      <td>0.045902</td>\n",
+       "      <td>0.001198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2150</th>\n",
+       "      <td>2021-01-05</td>\n",
+       "      <td>600415.XSHG</td>\n",
+       "      <td>0.099251</td>\n",
+       "      <td>0.017384</td>\n",
+       "      <td>0.266</td>\n",
+       "      <td>小商品城</td>\n",
+       "      <td>2020-12-31</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>0.002660</td>\n",
+       "      <td>0.099251</td>\n",
+       "      <td>0.099251</td>\n",
+       "      <td>0.099251</td>\n",
+       "      <td>0.001461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2390</th>\n",
+       "      <td>2021-01-05</td>\n",
+       "      <td>002709.XSHE</td>\n",
+       "      <td>0.031973</td>\n",
+       "      <td>0.100364</td>\n",
+       "      <td>0.603</td>\n",
+       "      <td>天赐材料</td>\n",
+       "      <td>2020-12-31</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>0.006030</td>\n",
+       "      <td>0.031973</td>\n",
+       "      <td>0.031973</td>\n",
+       "      <td>0.031973</td>\n",
+       "      <td>0.003016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2401</th>\n",
+       "      <td>2021-01-05</td>\n",
+       "      <td>300274.XSHE</td>\n",
+       "      <td>-0.017478</td>\n",
+       "      <td>0.226404</td>\n",
+       "      <td>1.307</td>\n",
+       "      <td>阳光电源</td>\n",
+       "      <td>2020-12-31</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>0.013069</td>\n",
+       "      <td>-0.017478</td>\n",
+       "      <td>-0.017478</td>\n",
+       "      <td>-0.017478</td>\n",
+       "      <td>-0.003729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2458</th>\n",
+       "      <td>2021-01-05</td>\n",
+       "      <td>002920.XSHE</td>\n",
+       "      <td>0.032667</td>\n",
+       "      <td>0.258388</td>\n",
+       "      <td>0.246</td>\n",
+       "      <td>德赛西威</td>\n",
+       "      <td>2020-12-31</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>0.002460</td>\n",
+       "      <td>0.032667</td>\n",
+       "      <td>0.032667</td>\n",
+       "      <td>0.032667</td>\n",
+       "      <td>0.008360</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           date       ticker  pct_portfolio  weight_portfolio  weight  \\\n",
+       "2148 2021-01-05  600409.XSHG       0.045902          0.028341   0.225   \n",
+       "2150 2021-01-05  600415.XSHG       0.099251          0.017384   0.266   \n",
+       "2390 2021-01-05  002709.XSHE       0.031973          0.100364   0.603   \n",
+       "2401 2021-01-05  300274.XSHE      -0.017478          0.226404   1.307   \n",
+       "2458 2021-01-05  002920.XSHE       0.032667          0.258388   0.246   \n",
+       "\n",
+       "     display_name actual_data   open  close   high    low       volume  \\\n",
+       "2148         三友化工  2020-12-31   9.23   9.57   9.66   9.08   82669289.0   \n",
+       "2150         小商品城  2020-12-31   5.33   5.87   5.87   5.22  180936477.0   \n",
+       "2390         天赐材料  2020-12-31  32.54  33.89  34.22  31.39   59152352.0   \n",
+       "2401         阳光电源  2020-12-31  76.03  76.45  80.20  75.27   51384827.0   \n",
+       "2458         德赛西威  2020-12-31  85.44  87.25  87.95  84.07    3852674.0   \n",
+       "\n",
+       "             money  norm_weight  pct_change       ptc  pct_benchmark  \\\n",
+       "2148  7.803391e+08     0.002250    0.045902  0.045902       0.045902   \n",
+       "2150  1.010225e+09     0.002660    0.099251  0.099251       0.099251   \n",
+       "2390  1.942406e+09     0.006030    0.031973  0.031973       0.031973   \n",
+       "2401  3.961995e+09     0.013069   -0.017478 -0.017478      -0.017478   \n",
+       "2458  3.322598e+08     0.002460    0.032667  0.032667       0.032667   \n",
+       "\n",
+       "      allocation  \n",
+       "2148    0.001198  \n",
+       "2150    0.001461  \n",
+       "2390    0.003016  \n",
+       "2401   -0.003729  \n",
+       "2458    0.008360  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# new df where drop allocation is Nan\n",
+    "new_df = merged_df.dropna(subset=['allocation'])\n",
+    "new_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_allocation(data: pd.DataFrame):\n",
+    "    '''\n",
+    "    calculate portfolio allocation\n",
+    "    Ai = (wi - Wi) / Bi\n",
+    "    Ai: individual asset allocation effective\n",
+    "    wi: individual asset weight\n",
+    "    Wi: benchmark weight\n",
+    "    Bi: benchmark return\n",
+    "    @param data: dataframe with columns ['date', 'ticker', 'display_name', 'pct_benchmark', 'norm_weight']\n",
+    "    '''\n",
+    "    data['allocation'] = (data['weight_portfolio'] - data['norm_weight']) * data['pct_benchmark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_selection(data: pd.DataFrame):\n",
+    "    '''\n",
+    "    calculate portfolio selection\n",
+    "    Si = Wi * (Ri - Bi)\n",
+    "    Si: individual asset selection effective\n",
+    "    Wi: benchmark weight\n",
+    "    Ri: individual asset return\n",
+    "    Bi: benchmark return\n",
+    "    @param data: dataframe with columns ['date', 'ticker', 'display_name', 'pct_benchmark', 'norm_weight']\n",
+    "    '''\n",
+    "    data['selection'] = data['norm_weight'] * (data['pct_portfolio'] - data['pct_benchmark'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_interaction(data: pd.DataFrame):\n",
+    "    '''\n",
+    "    calculate portfolio interaction\n",
+    "    Ii = (wi - Wi) * (Ri - Bi)\n",
+    "    Ii: individual asset interaction effective\n",
+    "    wi: individual asset weight\n",
+    "    Wi: benchmark weight\n",
+    "    Ri: individual asset return\n",
+    "    Bi: benchmark return\n",
+    "    @param data: dataframe with columns ['date', 'ticker', 'display_name', 'pct_benchmark', 'norm_weight']\n",
+    "    '''\n",
+    "    data['interaction'] = (data['weight_portfolio'] - data['norm_weight']) * (data['pct_portfolio'] - data['pct_benchmark'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run(portfolio: pd.DataFrame, benchmark: pd.DataFrame):\n",
+    "    '''\n",
+    "    return dataframe with allocation and selection result  \n",
+    "    '''\n",
+    "    selected_portfolio_columns = ['date', 'ticker', 'pct', 'weight_portfolio']\n",
+    "    selected_benchmark_columns = ['date', 'ticker','pct','norm_weight','display_name']\n",
+    "    merged_df = pd.merge(portfolio[selected_portfolio_columns], benchmark[selected_benchmark_columns], how='right', on=['date', 'ticker'], suffixes=('_portfolio', '_benchmark'))\n",
+    "    calculate_allocation(merged_df)\n",
+    "    calculate_selection(merged_df)\n",
+    "    calculate_interaction(merged_df)\n",
+    "    return merged_df\n",
+    "\n",
+    "result = run(stock_df, benchmark_portofolio_df)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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>pct_portfolio</th>\n",
+       "      <th>weight_portfolio</th>\n",
+       "      <th>pct_benchmark</th>\n",
+       "      <th>norm_weight</th>\n",
+       "      <th>display_name</th>\n",
+       "      <th>allocation</th>\n",
+       "      <th>selection</th>\n",
+       "      <th>interaction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>000008.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00088</td>\n",
+       "      <td>神州高铁</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>601799.XSHG</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00491</td>\n",
+       "      <td>星宇股份</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>300182.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00118</td>\n",
+       "      <td>捷成股份</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>002745.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00192</td>\n",
+       "      <td>木林森</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-01-01</td>\n",
+       "      <td>002074.XSHE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.00710</td>\n",
+       "      <td>国轩高科</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date       ticker  pct_portfolio  weight_portfolio  pct_benchmark  \\\n",
+       "0 2021-01-01  000008.XSHE            NaN               NaN            NaN   \n",
+       "1 2021-01-01  601799.XSHG            NaN               NaN            NaN   \n",
+       "2 2021-01-01  300182.XSHE            NaN               NaN            NaN   \n",
+       "3 2021-01-01  002745.XSHE            NaN               NaN            NaN   \n",
+       "4 2021-01-01  002074.XSHE            NaN               NaN            NaN   \n",
+       "\n",
+       "   norm_weight display_name  allocation  selection  interaction  \n",
+       "0      0.00088         神州高铁         NaN        NaN          NaN  \n",
+       "1      0.00491         星宇股份         NaN        NaN          NaN  \n",
+       "2      0.00118         捷成股份         NaN        NaN          NaN  \n",
+       "3      0.00192          木林森         NaN        NaN          NaN  \n",
+       "4      0.00710         国轩高科         NaN        NaN          NaN  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2148      0.0\n",
+       "2150      0.0\n",
+       "2390      0.0\n",
+       "2401      0.0\n",
+       "2458      0.0\n",
+       "         ... \n",
+       "181634    0.0\n",
+       "181636    0.0\n",
+       "181900    0.0\n",
+       "182136    0.0\n",
+       "182137    0.0\n",
+       "Length: 1073, dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pct_difference = result['pct_portfolio'] - result['pct_benchmark']\n",
+    "# count number of null and not null\n",
+    "pct_difference.isnull().sum(), pct_difference.notnull().sum()\n",
+    "# show row that is not null\n",
+    "pct_difference[pct_difference.notnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='date'>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.groupby('date')['selection'].sum().plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='date'>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# drop result with empty allocation\n",
+    "# result = result.dropna(subset=['allocation'])\n",
+    "result.groupby('date')['allocation'].sum().plot()\n",
+    "result.groupby('date')['selection'].sum().plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date\n",
+       "2021-12-23   -0.002253\n",
+       "2021-12-24   -0.004619\n",
+       "2021-12-25    0.000000\n",
+       "2021-12-26    0.000000\n",
+       "2021-12-27   -0.000399\n",
+       "2021-12-28    0.005463\n",
+       "2021-12-29    0.000114\n",
+       "2021-12-30   -0.001395\n",
+       "2021-12-31    0.000068\n",
+       "2022-01-01    0.000000\n",
+       "Name: allocation, dtype: float64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.groupby('date')['allocation'].sum().tail(10)\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"
+  },
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}