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"<style>*[data-root-id],\n",
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" box-sizing: border-box;\n",
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" 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": {
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"<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_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",
" 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_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",
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},
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}
|