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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2ef771e3-f905-4c82-97ab-7f879551824c",
"metadata": {},
"outputs": [],
"source": [
"# MEETING 11 MARS\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f645c749-321e-46c4-ae5e-0ba2c1967f81",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: networkx in /opt/python/lib/python3.13/site-packages (3.6.1)\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install networkx"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "909d6019-1d2e-4d5f-9249-86748e5c8f69",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import s3fs\n",
"os.environ[\"AWS_ACCESS_KEY_ID\"] = 'UMMV3Z72A70MCCSRV17O'\n",
"os.environ[\"AWS_SECRET_ACCESS_KEY\"] = 'wBFxaez78UPNW3BtchZOf4f238ZNXKnCexeGufaa'\n",
"os.environ[\"AWS_SESSION_TOKEN\"] = 'eyJhbGciOiJIUzUxMiIsInR5cCI6IkpXVCJ9.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.rffoTJijRiGK2DCDhXj5y8R31DRH1LWkTwuH_1lvU9qN_xJSTmBIM4uGR_zp7XpMnq_ePwVhlkoWN15cNUgjMA'\n",
"os.environ[\"AWS_DEFAULT_REGION\"] = 'us-east-1'\n",
"fs = s3fs.S3FileSystem(\n",
" client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'},\n",
" key = os.environ[\"AWS_ACCESS_KEY_ID\"], \n",
" secret = os.environ[\"AWS_SECRET_ACCESS_KEY\"], \n",
" token = os.environ[\"AWS_SESSION_TOKEN\"])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "38108d9e-a00c-4026-afd8-706b7131566e",
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from sklearn.preprocessing import StandardScaler, RobustScaler\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.mixture import GaussianMixture\n",
"from sklearn.metrics import silhouette_score, davies_bouldin_score, pairwise_distances\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.neighbors import kneighbors_graph\n",
"from sklearn.manifold import MDS\n",
"\n",
"import networkx as nx\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"pd.set_option(\"display.max_columns\", 200)\n",
"pd.set_option(\"display.max_rows\", 200)\n",
"\n",
"EPS = 1e-9\n",
"RANDOM_STATE = 42"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "558c8d6d-9a8d-4c82-9765-620f7ce8d116",
"metadata": {},
"outputs": [
{
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" <th>Value Ccy - Redemption</th>\n",
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" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
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" <td>LUXEMBOURG</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
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" <td>2015-06-12</td>\n",
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" <td>-20.000</td>\n",
" <td>0.00</td>\n",
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" <td>-34294.40</td>\n",
" <td>0.00</td>\n",
" <td>-34294.40</td>\n",
" <td>-34294.40</td>\n",
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" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>LUXEMBOURG</td>\n",
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" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
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" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
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" <td>FIXED INCOME</td>\n",
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" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>FR0010149120</td>\n",
" <td>2015-09-25</td>\n",
" <td>4.443</td>\n",
" <td>0.000</td>\n",
" <td>4.443</td>\n",
" <td>7603.66</td>\n",
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" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
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" <td>LUXEMBOURG</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>FR0010149120</td>\n",
" <td>2015-11-09</td>\n",
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" <td>-440.000</td>\n",
" <td>-440.000</td>\n",
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" <td>-754696.80</td>\n",
" <td>-754696.80</td>\n",
" <td>0.00</td>\n",
" <td>-754696.80</td>\n",
" <td>-754696.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2574460</th>\n",
" <td>2574460</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2016-01-11</td>\n",
" <td>3595.000</td>\n",
" <td>0.000</td>\n",
" <td>3595.000</td>\n",
" <td>358385.55</td>\n",
" <td>0.00</td>\n",
" <td>358385.55</td>\n",
" <td>358385.55</td>\n",
" <td>0.00</td>\n",
" <td>358385.55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2574461 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Agreement - Code Company - Id \\\n",
"0 0 003 166 \n",
"1 1 003 166 \n",
"2 2 003 166 \n",
"3 3 003 166 \n",
"4 4 003 166 \n",
"... ... ... ... \n",
"2574456 2574456 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574457 2574457 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574458 2574458 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574459 2574459 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574460 2574460 PRIVATE CLIENT PRIVATE CLIENT \n",
"\n",
" Company - Ultimate Parent Id Registrar Account - ID \\\n",
"0 166 200127202 \n",
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"3 166 406533 \n",
"4 166 406533 \n",
"... ... ... \n",
"2574456 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574457 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574458 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574459 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574460 PRIVATE CLIENT PRIVATE CLIENT \n",
"\n",
" Registrar Account - Region RegistrarAccount - Country \\\n",
"0 FRANCE FRANCE \n",
"1 FRANCE FRANCE \n",
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"4 FRANCE FRANCE \n",
"... ... ... \n",
"2574456 LUXEMBOURG LUXEMBOURG \n",
"2574457 LUXEMBOURG LUXEMBOURG \n",
"2574458 LUXEMBOURG LUXEMBOURG \n",
"2574459 LUXEMBOURG LUXEMBOURG \n",
"2574460 LUXEMBOURG LUXEMBOURG \n",
"\n",
" Product - Asset Type Product - Strategy Product - Legal Status \\\n",
"0 EQUITY INVESTISSEMENT SICAV \n",
"1 DIVERSIFIED PATRIMOINE FCP \n",
"2 EQUITY INVESTISSEMENT FCP \n",
"3 EQUITY INVESTISSEMENT FCP \n",
"4 EQUITY INVESTISSEMENT FCP \n",
"... ... ... ... \n",
"2574456 FIXED INCOME SÉCURITÉ FCP \n",
"2574457 FIXED INCOME SÉCURITÉ FCP \n",
"2574458 FIXED INCOME SÉCURITÉ FCP \n",
"2574459 FIXED INCOME SÉCURITÉ FCP \n",
"2574460 FIXED INCOME SÉCURITÉ SICAV \n",
"\n",
" Product - Is Dedie ? Product - Fund \\\n",
"0 NO CARMIGNAC PORTFOLIO INVESTISSEMENT \n",
"1 NO CARMIGNAC PATRIMOINE \n",
"2 NO CARMIGNAC INVESTISSEMENT \n",
"3 NO CARMIGNAC INVESTISSEMENT \n",
"4 NO CARMIGNAC INVESTISSEMENT \n",
"... ... ... \n",
"2574456 NO CARMIGNAC SÉCURITÉ \n",
"2574457 NO CARMIGNAC SÉCURITÉ \n",
"2574458 NO CARMIGNAC SÉCURITÉ \n",
"2574459 NO CARMIGNAC SÉCURITÉ \n",
"2574460 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"\n",
" Product - Shareclass Type Product - Shareclass Currency \\\n",
"0 F EUR \n",
"1 A EUR \n",
"2 A EUR \n",
"3 A EUR \n",
"4 A EUR \n",
"... ... ... \n",
"2574456 AW & AW-R EUR \n",
"2574457 AW & AW-R EUR \n",
"2574458 AW & AW-R EUR \n",
"2574459 AW & AW-R EUR \n",
"2574460 AW & AW-R EUR \n",
"\n",
" Product - Isin Centralisation Date Quantity - Subscription \\\n",
"0 LU0992625839 2020-11-05 1636.000 \n",
"1 FR0010135103 2015-03-09 144.690 \n",
"2 FR0010148981 2016-10-26 0.000 \n",
"3 FR0010148981 2018-10-18 0.000 \n",
"4 FR0010148981 2019-04-08 0.000 \n",
"... ... ... ... \n",
"2574456 FR0010149120 2015-06-12 0.000 \n",
"2574457 FR0010149120 2015-09-18 328.726 \n",
"2574458 FR0010149120 2015-09-25 4.443 \n",
"2574459 FR0010149120 2015-11-09 0.000 \n",
"2574460 LU1299306321 2016-01-11 3595.000 \n",
"\n",
" Quantity - Redemption Quantity - NetFlows Value Ccy - Subscription \\\n",
"0 0.000 1636.000 280983.00 \n",
"1 0.000 144.690 99985.13 \n",
"2 -8.321 -8.321 0.00 \n",
"3 -22.083 -22.083 0.00 \n",
"4 -465.992 -465.992 0.00 \n",
"... ... ... ... \n",
"2574456 -20.000 -20.000 0.00 \n",
"2574457 0.000 328.726 564028.07 \n",
"2574458 0.000 4.443 7603.66 \n",
"2574459 -440.000 -440.000 0.00 \n",
"2574460 0.000 3595.000 358385.55 \n",
"\n",
" Value Ccy - Redemption Value Ccy - NetFlows Value € - Subscription \\\n",
"0 0.00 280983.00 280983.00 \n",
"1 0.00 99985.13 99985.13 \n",
"2 -9384.76 -9384.76 0.00 \n",
"3 -25227.40 -25227.40 0.00 \n",
"4 -563775.76 -563775.76 0.00 \n",
"... ... ... ... \n",
"2574456 -34294.40 -34294.40 0.00 \n",
"2574457 0.00 564028.07 564028.07 \n",
"2574458 0.00 7603.66 7603.66 \n",
"2574459 -754696.80 -754696.80 0.00 \n",
"2574460 0.00 358385.55 358385.55 \n",
"\n",
" Value € - Redemption Value € - NetFlows \n",
"0 0.00 280983.00 \n",
"1 0.00 99985.13 \n",
"2 -9384.76 -9384.76 \n",
"3 -25227.40 -25227.40 \n",
"4 -563775.76 -563775.76 \n",
"... ... ... \n",
"2574456 -34294.40 -34294.40 \n",
"2574457 0.00 564028.07 \n",
"2574458 0.00 7603.66 \n",
"2574459 -754696.80 -754696.80 \n",
"2574460 0.00 358385.55 \n",
"\n",
"[2574461 rows x 25 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd \n",
"df_flows = pd.read_csv(\"flows.csv\")\n",
"df_flows"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b1b88d12-7909-435b-b5a8-7814d5ad09af",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Agreement - Code</th>\n",
" <th>Company - Id</th>\n",
" <th>Company - Ultimate Parent Id</th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>Registrar Account - Region</th>\n",
" <th>RegistrarAccount - Country</th>\n",
" <th>Product - Asset Type</th>\n",
" <th>Product - Strategy</th>\n",
" <th>Product - Legal Status</th>\n",
" <th>Product - Is Dedie ?</th>\n",
" <th>Product - Fund</th>\n",
" <th>Product - Shareclass Type</th>\n",
" <th>Product - Shareclass Currency</th>\n",
" <th>Product - Isin</th>\n",
" <th>Centralisation Date</th>\n",
" <th>Quantity - AUM</th>\n",
" <th>Value - AUM CCY</th>\n",
" <th>Value - AUM €</th>\n",
" <th>repair_flag</th>\n",
" <th>n_repairs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2015-03-31</td>\n",
" <td>35.368</td>\n",
" <td>24648.6666</td>\n",
" <td>24648.6666</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2015-11-30</td>\n",
" <td>35.368</td>\n",
" <td>22413.0553</td>\n",
" <td>22413.0553</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2015-12-31</td>\n",
" <td>35.368</td>\n",
" <td>22051.2406</td>\n",
" <td>22051.2406</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2016-03-31</td>\n",
" <td>35.368</td>\n",
" <td>21626.1173</td>\n",
" <td>21626.1173</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2016-11-30</td>\n",
" <td>35.368</td>\n",
" <td>22489.4502</td>\n",
" <td>22489.4502</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5589910</th>\n",
" <td>4880294</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2020-10-31</td>\n",
" <td>3099.000</td>\n",
" <td>318422.2500</td>\n",
" <td>318422.2500</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5589911</th>\n",
" <td>4880294</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2020-10-31</td>\n",
" <td>3099.000</td>\n",
" <td>318422.2500</td>\n",
" <td>318422.2500</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5589912</th>\n",
" <td>4880295</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2021-07-31</td>\n",
" <td>2835.000</td>\n",
" <td>297618.3000</td>\n",
" <td>297618.3000</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5589913</th>\n",
" <td>4880295</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2021-07-31</td>\n",
" <td>2835.000</td>\n",
" <td>297618.3000</td>\n",
" <td>297618.3000</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5589914</th>\n",
" <td>4880296</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>FW &amp; FW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1792391911</td>\n",
" <td>2020-07-31</td>\n",
" <td>2916.394</td>\n",
" <td>287410.6287</td>\n",
" <td>287410.6287</td>\n",
" <td>False</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5589915 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Agreement - Code Company - Id \\\n",
"0 0 3 166.0 \n",
"1 1 3 166.0 \n",
"2 2 3 166.0 \n",
"3 3 3 166.0 \n",
"4 4 3 166.0 \n",
"... ... ... ... \n",
"5589910 4880294 PRIVATE CLIENT PRIVATE CLIENT \n",
"5589911 4880294 PRIVATE CLIENT PRIVATE CLIENT \n",
"5589912 4880295 PRIVATE CLIENT PRIVATE CLIENT \n",
"5589913 4880295 PRIVATE CLIENT PRIVATE CLIENT \n",
"5589914 4880296 PRIVATE CLIENT PRIVATE CLIENT \n",
"\n",
" Company - Ultimate Parent Id Registrar Account - ID \\\n",
"0 166.0 200000647 \n",
"1 166.0 200000647 \n",
"2 166.0 200000647 \n",
"3 166.0 200000647 \n",
"4 166.0 200000647 \n",
"... ... ... \n",
"5589910 PRIVATE CLIENT PRIVATE CLIENT \n",
"5589911 PRIVATE CLIENT PRIVATE CLIENT \n",
"5589912 PRIVATE CLIENT PRIVATE CLIENT \n",
"5589913 PRIVATE CLIENT PRIVATE CLIENT \n",
"5589914 PRIVATE CLIENT PRIVATE CLIENT \n",
"\n",
" Registrar Account - Region RegistrarAccount - Country \\\n",
"0 FRANCE FRANCE \n",
"1 FRANCE FRANCE \n",
"2 FRANCE FRANCE \n",
"3 FRANCE FRANCE \n",
"4 FRANCE FRANCE \n",
"... ... ... \n",
"5589910 SWITZERLAND SWITZERLAND \n",
"5589911 SWITZERLAND SWITZERLAND \n",
"5589912 SWITZERLAND SWITZERLAND \n",
"5589913 SWITZERLAND SWITZERLAND \n",
"5589914 SWITZERLAND SWITZERLAND \n",
"\n",
" Product - Asset Type Product - Strategy Product - Legal Status \\\n",
"0 DIVERSIFIED PATRIMOINE FCP \n",
"1 DIVERSIFIED PATRIMOINE FCP \n",
"2 DIVERSIFIED PATRIMOINE FCP \n",
"3 DIVERSIFIED PATRIMOINE FCP \n",
"4 DIVERSIFIED PATRIMOINE FCP \n",
"... ... ... ... \n",
"5589910 FIXED INCOME SÉCURITÉ SICAV \n",
"5589911 FIXED INCOME SÉCURITÉ SICAV \n",
"5589912 FIXED INCOME SÉCURITÉ SICAV \n",
"5589913 FIXED INCOME SÉCURITÉ SICAV \n",
"5589914 FIXED INCOME SÉCURITÉ SICAV \n",
"\n",
" Product - Is Dedie ? Product - Fund \\\n",
"0 NO CARMIGNAC PATRIMOINE \n",
"1 NO CARMIGNAC PATRIMOINE \n",
"2 NO CARMIGNAC PATRIMOINE \n",
"3 NO CARMIGNAC PATRIMOINE \n",
"4 NO CARMIGNAC PATRIMOINE \n",
"... ... ... \n",
"5589910 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"5589911 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"5589912 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"5589913 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"5589914 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"\n",
" Product - Shareclass Type Product - Shareclass Currency \\\n",
"0 A EUR \n",
"1 A EUR \n",
"2 A EUR \n",
"3 A EUR \n",
"4 A EUR \n",
"... ... ... \n",
"5589910 AW & AW-R EUR \n",
"5589911 AW & AW-R EUR \n",
"5589912 AW & AW-R EUR \n",
"5589913 AW & AW-R EUR \n",
"5589914 FW & FW-R EUR \n",
"\n",
" Product - Isin Centralisation Date Quantity - AUM Value - AUM CCY \\\n",
"0 FR0010135103 2015-03-31 35.368 24648.6666 \n",
"1 FR0010135103 2015-11-30 35.368 22413.0553 \n",
"2 FR0010135103 2015-12-31 35.368 22051.2406 \n",
"3 FR0010135103 2016-03-31 35.368 21626.1173 \n",
"4 FR0010135103 2016-11-30 35.368 22489.4502 \n",
"... ... ... ... ... \n",
"5589910 LU1299306321 2020-10-31 3099.000 318422.2500 \n",
"5589911 LU1299306321 2020-10-31 3099.000 318422.2500 \n",
"5589912 LU1299306321 2021-07-31 2835.000 297618.3000 \n",
"5589913 LU1299306321 2021-07-31 2835.000 297618.3000 \n",
"5589914 LU1792391911 2020-07-31 2916.394 287410.6287 \n",
"\n",
" Value - AUM € repair_flag n_repairs \n",
"0 24648.6666 False 0.0 \n",
"1 22413.0553 False 0.0 \n",
"2 22051.2406 False 0.0 \n",
"3 21626.1173 False 0.0 \n",
"4 22489.4502 False 0.0 \n",
"... ... ... ... \n",
"5589910 318422.2500 False 0.0 \n",
"5589911 318422.2500 False 0.0 \n",
"5589912 297618.3000 False 0.0 \n",
"5589913 297618.3000 False 0.0 \n",
"5589914 287410.6287 False 0.0 \n",
"\n",
"[5589915 rows x 21 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_aum = pd.read_csv(\"stock_repaired.csv\")\n",
"df_aum"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4c799ab2-b16e-4cbe-85ee-818002c758c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['CARMIGNAC PATRIMOINE', 'CARMIGNAC INVESTISSEMENT',\n",
" 'CARMIGNAC PORTFOLIO INVESTISSEMENT',\n",
" 'CARMIGNAC EURO-INVESTISSEMENT', 'CARMIGNAC INNOVATION',\n",
" 'CARMIGNAC ABSOLUTE RETURN EUROPE',\n",
" 'CARMIGNAC PORTFOLIO CLIMATE TRANSITION',\n",
" 'CARMIGNAC EURO-ENTREPRENEURS', 'CARMIGNAC EMERGENTS',\n",
" 'CARMIGNAC PORTFOLIO EMERGING PATRIMOINE',\n",
" 'CARMIGNAC PORTFOLIO GRANDE EUROPE',\n",
" 'CARMIGNAC INVESTISSEMENT LATITUDE', 'CARMIGNAC COURT TERME',\n",
" 'CARMIGNAC SÉCURITÉ', 'CARMIGNAC MULTI EXPERTISE',\n",
" 'CARMIGNAC PORTFOLIO INFOTECH', 'CARMIGNAC PROFIL RÉACTIF 100',\n",
" 'CARMIGNAC PORTFOLIO GLOBAL BOND', 'CARMIGNAC PROFIL RÉACTIF 75',\n",
" 'CARMIGNAC PORTFOLIO PATRIMOINE EUROPE', 'CARMIGNAC CREDIT 2027',\n",
" 'CARMIGNAC PORTFOLIO ASIA DISCOVERY',\n",
" 'CARMIGNAC PORTFOLIO TECH SOLUTIONS',\n",
" 'CARMIGNAC PORTFOLIO FLEXIBLE BOND', 'CARMIGNAC PORTFOLIO CREDIT',\n",
" 'CARMIGNAC PORTFOLIO MARKET NEUTRAL',\n",
" 'CARMIGNAC PORTFOLIO EM DEBT',\n",
" 'CARMIGNAC PORTFOLIO LONG-SHORT EUROPEAN EQUITIES',\n",
" 'CARMIGNAC PORTFOLIO LONG-SHORT GLOBAL EQUITIES',\n",
" 'CARMIGNAC PORTFOLIO PATRIMOINE',\n",
" 'CARMIGNAC PORTFOLIO EURO-ENTREPRENEURS',\n",
" 'CARMIGNAC PORTFOLIO GRANDCHILDREN',\n",
" 'CARMIGNAC PORTFOLIO INVESTISSEMENT LATITUDE',\n",
" 'CARMIGNAC PORTFOLIO EMERGENTS', 'CARMIGNAC PORTFOLIO SÉCURITÉ',\n",
" 'CARMIGNAC PORTFOLIO INFLATION SOLUTION', 'CARMIGNAC CREDIT 2029',\n",
" 'CARMIGNAC CREDIT 2031', 'CARMIGNAC PORTFOLIO CAPITAL CUBE',\n",
" 'CARMIGNAC PORTFOLIO CHINA NEW ECONOMY', 'CARMIGNAC CREDIT 2025',\n",
" 'CARMIGNAC EPARGNE ACTIONS MONDE ISR',\n",
" 'CARMIGNAC PORTFOLIO FAMILY GOVERNED',\n",
" 'CARMIGNAC S.A. SICAV - PART II UCI PRIVATE EVERGREEN',\n",
" 'CARMIGNAC PORTFOLIO HUMAN XPERIENCE',\n",
" 'CARMIGNAC CHINA NEW ECONOMY', 'CARMIGNAC PORTFOLIO CHINA',\n",
" 'CARMIGNAC ALTS ICAV CARMIGNAC CREDIT OPPORTUNITIES',\n",
" 'CARMIGNAC PORTFOLIO MERGER ARBITRAGE PLUS',\n",
" 'CARMIGNAC PORTFOLIO ABSOLUTE RETURN EUROPE',\n",
" 'CARMIGNAC PORTFOLIO FLEXIBLE ALLOCATION 2024',\n",
" 'FP CARMIGNAC EUROPEAN LEADERS',\n",
" 'FP CARMIGNAC GLOBAL EQUITY COMPOUNDERS',\n",
" 'CARMIGNAC PORTFOLIO MERGER ARBITRAGE',\n",
" 'SOLYS - CARMIGNAC EQUITY SELECTION',\n",
" 'FP CARMIGNAC EMERGING MARKETS', 'MAPFRE CARMIGNAC F.P.',\n",
" 'FP CARMIGNAC GLOBAL BOND',\n",
" 'CARMIGNAC ALTS ICAV EUROPEAN LONG SHORT',\n",
" 'CARMIGNAC PORTFOLIO ACTIVE RISK ALLOCATION',\n",
" 'FP CARMIGNAC EMERGING PATRIMOINE', 'FP CARMIGNAC PATRIMOINE',\n",
" 'FP CARMIGNAC EMERGING DISCOVERY',\n",
" 'CREDIT SUISSE CARMIGNAC EMERGING MARKETS MULTI-ASSET FUND',\n",
" 'FONDITALIA CARMIGNAC ACTIVE ALLOCATION',\n",
" 'CARMIGNAC GLOBAL ACTIVE',\n",
" 'LUX IM - CARMIGNAC EMERGING FLEXIBLE BOND',\n",
" 'CARMIGNAC PORTFOLIO EVOLUTION', 'UFF GRANDE EUROPE 0-100',\n",
" 'CFP 1', 'CARMIGNAC PORTFOLIO SUSTAINABLE BOND',\n",
" 'CARMIGNAC PORTFOLIO CROSS ASSET OPPORTUNITIES',\n",
" 'CARMIGNAC PORTFOLIO ALPHA THEMES',\n",
" 'CARMIGNAC PORTFOLIO GLOBAL MARKET NEUTRAL'], dtype=object)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_aum['Product - Fund'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "09068a72-80a4-4239-947a-a73f6de10a57",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>n_days</th>\n",
" <th>n_transactions</th>\n",
" <th>total_netflows</th>\n",
" <th>mean_flow</th>\n",
" <th>std_flow</th>\n",
" <th>total_subscription</th>\n",
" <th>total_redemption</th>\n",
" <th>churn_ratio</th>\n",
" <th>churn_flag</th>\n",
" <th>activity_score</th>\n",
" <th>flow_volatility</th>\n",
" <th>inertia_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100000028</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>-1.092380e+02</td>\n",
" <td>-36.412667</td>\n",
" <td>49.280511</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-1.092380e+02</td>\n",
" <td>-1.092380e+08</td>\n",
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" <th>1</th>\n",
" <td>100000042</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-6.601150e+02</td>\n",
" <td>-660.115000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-6.601150e+02</td>\n",
" <td>-6.601150e+08</td>\n",
" <td>0</td>\n",
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" <td>0.000000</td>\n",
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" <td>100000065</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-1.746460e+02</td>\n",
" <td>-174.646000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-1.746460e+02</td>\n",
" <td>-1.746460e+08</td>\n",
" <td>0</td>\n",
" <td>0.693147</td>\n",
" <td>0.000000</td>\n",
" <td>0.999640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>100000069</td>\n",
" <td>65</td>\n",
" <td>73</td>\n",
" <td>-7.479755e+03</td>\n",
" <td>-102.462397</td>\n",
" <td>2168.971331</td>\n",
" <td>3.332040e+04</td>\n",
" <td>-4.080016e+04</td>\n",
" <td>-1.224480e+00</td>\n",
" <td>0</td>\n",
" <td>4.304065</td>\n",
" <td>2168.971331</td>\n",
" <td>0.976619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>100000073</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-1.334020e+02</td>\n",
" <td>-133.402000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-1.334020e+02</td>\n",
" <td>-1.334020e+08</td>\n",
" <td>0</td>\n",
" <td>0.693147</td>\n",
" <td>0.000000</td>\n",
" <td>0.999640</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>6837</th>\n",
" <td>422905</td>\n",
" <td>208</td>\n",
" <td>212</td>\n",
" <td>-4.329218e+03</td>\n",
" <td>-20.420840</td>\n",
" <td>331.677297</td>\n",
" <td>9.699140e+03</td>\n",
" <td>-1.402836e+04</td>\n",
" <td>-1.446351e+00</td>\n",
" <td>0</td>\n",
" <td>5.361292</td>\n",
" <td>331.677297</td>\n",
" <td>0.925180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6838</th>\n",
" <td>422906</td>\n",
" <td>1146</td>\n",
" <td>1556</td>\n",
" <td>4.455099e+03</td>\n",
" <td>2.863174</td>\n",
" <td>201.071555</td>\n",
" <td>6.078686e+04</td>\n",
" <td>-5.633177e+04</td>\n",
" <td>-9.267095e-01</td>\n",
" <td>0</td>\n",
" <td>7.350516</td>\n",
" <td>201.071555</td>\n",
" <td>0.587770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6839</th>\n",
" <td>8307</td>\n",
" <td>204</td>\n",
" <td>221</td>\n",
" <td>2.168303e+04</td>\n",
" <td>98.113249</td>\n",
" <td>2217.940406</td>\n",
" <td>1.204399e+05</td>\n",
" <td>-9.875688e+04</td>\n",
" <td>-8.199681e-01</td>\n",
" <td>0</td>\n",
" <td>5.402677</td>\n",
" <td>2217.940406</td>\n",
" <td>0.926619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6840</th>\n",
" <td>OFF DISTRIBUTION</td>\n",
" <td>2656</td>\n",
" <td>27679</td>\n",
" <td>1.319043e+08</td>\n",
" <td>4765.499704</td>\n",
" <td>391347.475503</td>\n",
" <td>3.388942e+08</td>\n",
" <td>-2.069900e+08</td>\n",
" <td>-6.107804e-01</td>\n",
" <td>0</td>\n",
" <td>10.228465</td>\n",
" <td>391347.475503</td>\n",
" <td>0.044604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6841</th>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>2690</td>\n",
" <td>32363</td>\n",
" <td>-4.181221e+05</td>\n",
" <td>-12.919758</td>\n",
" <td>7572.830139</td>\n",
" <td>1.354277e+07</td>\n",
" <td>-1.396089e+07</td>\n",
" <td>-1.030874e+00</td>\n",
" <td>0</td>\n",
" <td>10.384802</td>\n",
" <td>7572.830139</td>\n",
" <td>0.032374</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6842 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID n_days n_transactions total_netflows \\\n",
"0 100000028 3 3 -1.092380e+02 \n",
"1 100000042 1 1 -6.601150e+02 \n",
"2 100000065 1 1 -1.746460e+02 \n",
"3 100000069 65 73 -7.479755e+03 \n",
"4 100000073 1 1 -1.334020e+02 \n",
"... ... ... ... ... \n",
"6837 422905 208 212 -4.329218e+03 \n",
"6838 422906 1146 1556 4.455099e+03 \n",
"6839 8307 204 221 2.168303e+04 \n",
"6840 OFF DISTRIBUTION 2656 27679 1.319043e+08 \n",
"6841 PRIVATE CLIENT 2690 32363 -4.181221e+05 \n",
"\n",
" mean_flow std_flow total_subscription total_redemption \\\n",
"0 -36.412667 49.280511 0.000000e+00 -1.092380e+02 \n",
"1 -660.115000 NaN 0.000000e+00 -6.601150e+02 \n",
"2 -174.646000 NaN 0.000000e+00 -1.746460e+02 \n",
"3 -102.462397 2168.971331 3.332040e+04 -4.080016e+04 \n",
"4 -133.402000 NaN 0.000000e+00 -1.334020e+02 \n",
"... ... ... ... ... \n",
"6837 -20.420840 331.677297 9.699140e+03 -1.402836e+04 \n",
"6838 2.863174 201.071555 6.078686e+04 -5.633177e+04 \n",
"6839 98.113249 2217.940406 1.204399e+05 -9.875688e+04 \n",
"6840 4765.499704 391347.475503 3.388942e+08 -2.069900e+08 \n",
"6841 -12.919758 7572.830139 1.354277e+07 -1.396089e+07 \n",
"\n",
" churn_ratio churn_flag activity_score flow_volatility inertia_ratio \n",
"0 -1.092380e+08 0 1.386294 49.280511 0.998921 \n",
"1 -6.601150e+08 0 0.693147 0.000000 0.999640 \n",
"2 -1.746460e+08 0 0.693147 0.000000 0.999640 \n",
"3 -1.224480e+00 0 4.304065 2168.971331 0.976619 \n",
"4 -1.334020e+08 0 0.693147 0.000000 0.999640 \n",
"... ... ... ... ... ... \n",
"6837 -1.446351e+00 0 5.361292 331.677297 0.925180 \n",
"6838 -9.267095e-01 0 7.350516 201.071555 0.587770 \n",
"6839 -8.199681e-01 0 5.402677 2217.940406 0.926619 \n",
"6840 -6.107804e-01 0 10.228465 391347.475503 0.044604 \n",
"6841 -1.030874e+00 0 10.384802 7572.830139 0.032374 \n",
"\n",
"[6842 rows x 13 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_client = pd.read_csv(\"client_behavior_clean.csv\")\n",
"df_client"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "791d8a11-fa46-400b-bbf1-55c8c055f524",
"metadata": {},
"outputs": [],
"source": [
"#external data projet-bdc-data /carmignac /Data Modélisation /Nav\n",
"PATH_NAV = \"s3://projet-bdc-data/carmignac/Data Modélisation/Nav/NAV_Bench_data.csv\" #C’est la table de valorisation / performance du produit.\n",
"PATH_RATES = \"s3://projet-bdc-data/carmignac/Data Modélisation/market data/esterRates.csv\"\n",
"\n",
"# optional competitors\n",
"PATH_COMP_FLOWS = \"s3://projet-bdc-data/carmignac/Data Modélisation/competitors/daily_estimated_flows.csv\"\n",
"PATH_COMP_PERF = \"s3://projet-bdc-data/carmignac/Data Modélisation/competitors/weekly_perf_full.csv\"\n",
"PATH_PEERS = \"s3://projet-bdc-carmignac-g3/peers/CAD_peers.csv\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "415015e5-4cdc-4ea9-9c0e-701c58314873",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"flows: (2574461, 25)\n",
"aum: (5589915, 21)\n",
"nav: (623914, 5)\n",
"rates: (2826, 2)\n",
"comp_flows: (963003, 6)\n",
"comp_perf: (2370192, 5)\n",
"peers: (31, 12)\n"
]
}
],
"source": [
"df_nav = pd.read_csv(PATH_NAV, sep=\";\")\n",
"df_rates = pd.read_csv(PATH_RATES,sep=\";\")\n",
"\n",
"df_comp_flows = pd.read_csv(PATH_COMP_FLOWS,sep=\";\")\n",
"df_comp_perf = pd.read_csv(PATH_COMP_PERF,sep=\";\")\n",
"df_peers = pd.read_csv(PATH_PEERS,sep=\"|\")\n",
"\n",
"print(\"flows:\", df_flows.shape)\n",
"print(\"aum:\", df_aum.shape)\n",
"print(\"nav:\", df_nav.shape)\n",
"print(\"rates:\", df_rates.shape)\n",
"print(\"comp_flows:\", df_comp_flows.shape)\n",
"print(\"comp_perf:\", df_comp_perf.shape)\n",
"print(\"peers:\", df_peers.shape)\n",
"\n",
"\n",
"#dbe bel ekhr un dataset avec une ligne pour : un client - un produit Carmignac - un mois "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0a52ab96-1c47-4530-83d0-148e72f70d9b",
"metadata": {},
"outputs": [
{
"data": {
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" <th>Isin</th>\n",
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" <td>148,73</td>\n",
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" <td>148,08</td>\n",
" <td>462,6446111</td>\n",
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" <tr>\n",
" <th>623911</th>\n",
" <td>10/20/2025</td>\n",
" <td>FR001400KIF0</td>\n",
" <td>109597120,24</td>\n",
" <td>150,34</td>\n",
" <td>468,73416853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>623912</th>\n",
" <td>10/21/2025</td>\n",
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" <td>151,19</td>\n",
" <td>470,33788616</td>\n",
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" <tr>\n",
" <th>623913</th>\n",
" <td>10/22/2025</td>\n",
" <td>FR001400KIF0</td>\n",
" <td>109692584,22</td>\n",
" <td>150,47</td>\n",
" <td>468,13202429</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Dat Isin Aum Eur Price (TF PartPrice) \\\n",
"623909 10/16/2025 FR001400KIF0 108424691,27 148,73 \n",
"623910 10/17/2025 FR001400KIF0 107947215,67 148,08 \n",
"623911 10/20/2025 FR001400KIF0 109597120,24 150,34 \n",
"623912 10/21/2025 FR001400KIF0 110216503,12 151,19 \n",
"623913 10/22/2025 FR001400KIF0 109692584,22 150,47 \n",
"\n",
" PriceBench \n",
"623909 462,49002312 \n",
"623910 462,6446111 \n",
"623911 468,73416853 \n",
"623912 470,33788616 \n",
"623913 468,13202429 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_nav.tail()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e15c0a1d-d636-48f5-83ab-ff43b49b0e44",
"metadata": {},
"outputs": [
{
"data": {
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" <th>Yld to Maturity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2821</th>\n",
" <td>16/10/2025</td>\n",
" <td>1.928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2822</th>\n",
" <td>17/10/2025</td>\n",
" <td>1.928</td>\n",
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" <tr>\n",
" <th>2823</th>\n",
" <td>20/10/2025</td>\n",
" <td>1.928</td>\n",
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" <tr>\n",
" <th>2824</th>\n",
" <td>21/10/2025</td>\n",
" <td>1.927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2825</th>\n",
" <td>22/10/2025</td>\n",
" <td>1.928</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Yld to Maturity\n",
"2821 16/10/2025 1.928\n",
"2822 17/10/2025 1.928\n",
"2823 20/10/2025 1.928\n",
"2824 21/10/2025 1.927\n",
"2825 22/10/2025 1.928"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rates.tail() #Table de marché macro, ici probablement un taux de référence."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ede9c196-1d10-48fc-b99f-7369a2fb3d9b",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" 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>shareClass_name</th>\n",
" <th>SecId_MS</th>\n",
" <th>ISIN</th>\n",
" <th>FundId</th>\n",
" <th>Date</th>\n",
" <th>Estimated Fund-level Net Flow (Daily)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>962998</th>\n",
" <td>TM Lansdowne European Spec Sit I Acc GBP</td>\n",
" <td>F00000VH4K</td>\n",
" <td>GB00BTJRQ064</td>\n",
" <td>FS0000BQP3</td>\n",
" <td>2025-06-27</td>\n",
" <td>183110.412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>962999</th>\n",
" <td>Waverton European Dividend Gr B GBP Inc</td>\n",
" <td>F000011TLC</td>\n",
" <td>IE00BF5KV626</td>\n",
" <td>FS0000E90E</td>\n",
" <td>2025-06-27</td>\n",
" <td>16425.603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>963000</th>\n",
" <td>Premier Miton European Opports B Acc</td>\n",
" <td>F00000WMCF</td>\n",
" <td>GB00BZ2K2M84</td>\n",
" <td>FS0000C8WZ</td>\n",
" <td>2025-06-27</td>\n",
" <td>-612606.416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>963001</th>\n",
" <td>Incrementum Active Commodity Fund R EUR</td>\n",
" <td>F00000SVVR</td>\n",
" <td>LI0226274319</td>\n",
" <td>FS0000AMCV</td>\n",
" <td>2025-06-27</td>\n",
" <td>-5123.607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>963002</th>\n",
" <td>AXAWF Inflation Plus A Cap EUR</td>\n",
" <td>F00001CSX2</td>\n",
" <td>LU2257473269</td>\n",
" <td>FS0000H62L</td>\n",
" <td>2025-06-27</td>\n",
" <td>393490.630</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" shareClass_name SecId_MS ISIN \\\n",
"962998 TM Lansdowne European Spec Sit I Acc GBP F00000VH4K GB00BTJRQ064 \n",
"962999 Waverton European Dividend Gr B GBP Inc F000011TLC IE00BF5KV626 \n",
"963000 Premier Miton European Opports B Acc F00000WMCF GB00BZ2K2M84 \n",
"963001 Incrementum Active Commodity Fund R EUR F00000SVVR LI0226274319 \n",
"963002 AXAWF Inflation Plus A Cap EUR F00001CSX2 LU2257473269 \n",
"\n",
" FundId Date Estimated Fund-level Net Flow (Daily) \n",
"962998 FS0000BQP3 2025-06-27 183110.412 \n",
"962999 FS0000E90E 2025-06-27 16425.603 \n",
"963000 FS0000C8WZ 2025-06-27 -612606.416 \n",
"963001 FS0000AMCV 2025-06-27 -5123.607 \n",
"963002 FS0000H62L 2025-06-27 393490.630 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_comp_flows.tail()\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "71407c1d-dda2-4ec6-9df4-499d4502e560",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>perfPeriod</th>\n",
" <th>shareClass_name</th>\n",
" <th>return</th>\n",
" <th>percentile</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2370187</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>BNP Paribas Emerging Eq Cl Eur C</td>\n",
" <td>-1.623478</td>\n",
" <td>83.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370188</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>Capital Group EM Debt (LUX) B</td>\n",
" <td>-0.162338</td>\n",
" <td>88.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370189</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>BGF Global Allocation A2 EUR Hedged</td>\n",
" <td>0.387712</td>\n",
" <td>44.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370190</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>Exane Funds 2 Exane Pleiade B EUR Acc</td>\n",
" <td>0.082896</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370191</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>Invesco Euro Short Term Bond A EUR Acc</td>\n",
" <td>0.034302</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date perfPeriod shareClass_name \\\n",
"2370187 2015-12-30 WeeklyRet BNP Paribas Emerging Eq Cl Eur C \n",
"2370188 2015-12-30 WeeklyRet Capital Group EM Debt (LUX) B \n",
"2370189 2015-12-30 WeeklyRet BGF Global Allocation A2 EUR Hedged \n",
"2370190 2015-12-30 WeeklyRet Exane Funds 2 Exane Pleiade B EUR Acc \n",
"2370191 2015-12-30 WeeklyRet Invesco Euro Short Term Bond A EUR Acc \n",
"\n",
" return percentile \n",
"2370187 -1.623478 83.0 \n",
"2370188 -0.162338 88.0 \n",
"2370189 0.387712 44.0 \n",
"2370190 0.082896 60.0 \n",
"2370191 0.034302 35.0 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_comp_perf.tail() #Performance des concurrents, avec rang relatif.\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "05b87839-dc25-4f0d-8bf8-f43ac8c932ee",
"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",
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" }\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>Name</th>\n",
" <th>ISIN</th>\n",
" <th>SecId_MS</th>\n",
" <th>FundId</th>\n",
" <th>Global Broad Category Group</th>\n",
" <th>Global Category</th>\n",
" <th>Morningstar Category</th>\n",
" <th>Index Fund</th>\n",
" <th>Enhanced Index</th>\n",
" <th>Inception Date</th>\n",
" <th>Inception Date of Fund's Oldest Share Class</th>\n",
" <th>Domicile</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Vontobel mtx EM Ldrs ex Chn S USD Acc</td>\n",
" <td>LU2601939452</td>\n",
" <td>F00001G9PT</td>\n",
" <td>FS0000IAHL</td>\n",
" <td>Equity</td>\n",
" <td>Global Emerging Markets Equity</td>\n",
" <td>EAA Fund Global Emerging Markets ex-China Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2023-09-20</td>\n",
" <td>2023-09-20</td>\n",
" <td>Luxembourg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>PineBridge Asia exJapan SmCap Eq A</td>\n",
" <td>IE00B12V2V27</td>\n",
" <td>FOGBR05LNR</td>\n",
" <td>FSGBR06C4W</td>\n",
" <td>Equity</td>\n",
" <td>Asia ex-Japan Equity</td>\n",
" <td>EAA Fund Asia ex-Japan Small/Mid-Cap Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2006-04-19</td>\n",
" <td>1994-09-01</td>\n",
" <td>Ireland</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>abrdn-Asian Smaller Companies A Acc USD</td>\n",
" <td>LU0231459107</td>\n",
" <td>F0GBR06X7H</td>\n",
" <td>FSGBR05GSY</td>\n",
" <td>Equity</td>\n",
" <td>Asia ex-Japan Equity</td>\n",
" <td>EAA Fund Asia ex-Japan Small/Mid-Cap Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2004-05-14</td>\n",
" <td>2004-05-14</td>\n",
" <td>Luxembourg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Allianz Asian Small Cap Equity A EUR</td>\n",
" <td>LU2420271673</td>\n",
" <td>F00001DBJJ</td>\n",
" <td>FS0000ASMB</td>\n",
" <td>Equity</td>\n",
" <td>Asia ex-Japan Equity</td>\n",
" <td>EAA Fund Asia ex-Japan Small/Mid-Cap Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2022-01-05</td>\n",
" <td>2014-05-13</td>\n",
" <td>Luxembourg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Allianz Asia Ex China Equity A (USD)</td>\n",
" <td>LU0348788117</td>\n",
" <td>F000000F7V</td>\n",
" <td>FSUSA08CND</td>\n",
" <td>Equity</td>\n",
" <td>Asia ex-Japan Equity</td>\n",
" <td>EAA Fund Asia ex-Japan Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2008-10-03</td>\n",
" <td>2008-10-03</td>\n",
" <td>Luxembourg</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name ISIN SecId_MS \\\n",
"26 Vontobel mtx EM Ldrs ex Chn S USD Acc LU2601939452 F00001G9PT \n",
"27 PineBridge Asia exJapan SmCap Eq A IE00B12V2V27 FOGBR05LNR \n",
"28 abrdn-Asian Smaller Companies A Acc USD LU0231459107 F0GBR06X7H \n",
"29 Allianz Asian Small Cap Equity A EUR LU2420271673 F00001DBJJ \n",
"30 Allianz Asia Ex China Equity A (USD) LU0348788117 F000000F7V \n",
"\n",
" FundId Global Broad Category Group Global Category \\\n",
"26 FS0000IAHL Equity Global Emerging Markets Equity \n",
"27 FSGBR06C4W Equity Asia ex-Japan Equity \n",
"28 FSGBR05GSY Equity Asia ex-Japan Equity \n",
"29 FS0000ASMB Equity Asia ex-Japan Equity \n",
"30 FSUSA08CND Equity Asia ex-Japan Equity \n",
"\n",
" Morningstar Category Index Fund \\\n",
"26 EAA Fund Global Emerging Markets ex-China Equity No \n",
"27 EAA Fund Asia ex-Japan Small/Mid-Cap Equity No \n",
"28 EAA Fund Asia ex-Japan Small/Mid-Cap Equity No \n",
"29 EAA Fund Asia ex-Japan Small/Mid-Cap Equity No \n",
"30 EAA Fund Asia ex-Japan Equity No \n",
"\n",
" Enhanced Index Inception Date Inception Date of Fund's Oldest Share Class \\\n",
"26 No 2023-09-20 2023-09-20 \n",
"27 No 2006-04-19 1994-09-01 \n",
"28 No 2004-05-14 2004-05-14 \n",
"29 No 2022-01-05 2014-05-13 \n",
"30 No 2008-10-03 2008-10-03 \n",
"\n",
" Domicile \n",
"26 Luxembourg \n",
"27 Ireland \n",
"28 Luxembourg \n",
"29 Luxembourg \n",
"30 Luxembourg "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_peers.tail() #Ça permet de mesurer la pression concurrentielle."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9128c36b-33fd-44b2-a622-d3059f482c02",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Unnamed: 0', 'Agreement - Code', 'Company - Id',\n",
" 'Company - Ultimate Parent Id', 'Registrar Account - ID',\n",
" 'Registrar Account - Region', 'RegistrarAccount - Country',\n",
" 'Product - Asset Type', 'Product - Strategy', 'Product - Legal Status',\n",
" 'Product - Is Dedie ?', 'Product - Fund', 'Product - Shareclass Type',\n",
" 'Product - Shareclass Currency', 'Product - Isin',\n",
" 'Centralisation Date', 'Quantity - Subscription',\n",
" 'Quantity - Redemption', 'Quantity - NetFlows',\n",
" 'Value Ccy - Subscription', 'Value Ccy - Redemption',\n",
" 'Value Ccy - NetFlows', 'Value € - Subscription',\n",
" 'Value € - Redemption', 'Value € - NetFlows'],\n",
" dtype='object')\n",
"Index(['Unnamed: 0', 'Agreement - Code', 'Company - Id',\n",
" 'Company - Ultimate Parent Id', 'Registrar Account - ID',\n",
" 'Registrar Account - Region', 'RegistrarAccount - Country',\n",
" 'Product - Asset Type', 'Product - Strategy', 'Product - Legal Status',\n",
" 'Product - Is Dedie ?', 'Product - Fund', 'Product - Shareclass Type',\n",
" 'Product - Shareclass Currency', 'Product - Isin',\n",
" 'Centralisation Date', 'Quantity - AUM', 'Value - AUM CCY',\n",
" 'Value - AUM €', 'repair_flag', 'n_repairs'],\n",
" dtype='object')\n",
"Index(['Registrar Account - ID', 'n_days', 'n_transactions', 'total_netflows',\n",
" 'mean_flow', 'std_flow', 'total_subscription', 'total_redemption',\n",
" 'churn_ratio', 'churn_flag', 'activity_score', 'flow_volatility',\n",
" 'inertia_ratio'],\n",
" dtype='object')\n",
"Index(['Dat', 'Isin', 'Aum Eur', 'Price (TF PartPrice)', 'PriceBench'], dtype='object')\n",
"Index(['Date', 'Yld to Maturity'], dtype='object')\n",
"Index(['shareClass_name', 'SecId_MS', 'ISIN', 'FundId', 'Date',\n",
" 'Estimated Fund-level Net Flow (Daily)'],\n",
" dtype='object')\n",
"Index(['Date', 'perfPeriod', 'shareClass_name', 'return', 'percentile'], dtype='object')\n",
"Index(['Name', 'ISIN', 'SecId_MS', 'FundId', 'Global Broad Category Group',\n",
" 'Global Category', 'Morningstar Category', 'Index Fund',\n",
" 'Enhanced Index', 'Inception Date',\n",
" 'Inception Date of Fund's Oldest Share Class', 'Domicile'],\n",
" dtype='object')\n"
]
}
],
"source": [
"for d in [df_flows,df_aum,df_client,df_nav,df_rates,df_comp_flows,df_comp_perf,df_peers]:\n",
" print (d.columns)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "7e82983d-aaf3-40c1-9186-2cb13c6274d7",
"metadata": {},
"outputs": [],
"source": [
"ID_COL = \"Registrar Account - ID\"\n",
"ISIN_COL = \"Product - Isin\"\n",
"\n",
"FLOW_DATE_COL = \"Centralisation Date\"\n",
"AUM_DATE_COL = \"Centralisation Date\"\n",
"\n",
"FLOW_QTY_COL = \"Quantity - NetFlows\"\n",
"FLOW_SUB_COL = \"Quantity - Subscription\"\n",
"FLOW_RED_COL = \"Quantity - Redemption\"\n",
"\n",
"AUM_QTY_COL = \"Quantity - AUM\"\n",
"AUM_VAL_COL = \"Value - AUM €\"\n",
"\n",
"REGION_COL = \"Registrar Account - Region\"\n",
"COUNTRY_COL = \"RegistrarAccount - Country\"\n",
"\n",
"NAV_DATE_COL = \"Dat\"\n",
"NAV_ISIN_COL = \"Isin\"\n",
"NAV_PRICE_COL = \"Price (TF PartPrice)\"\n",
"NAV_BENCH_COL = \"PriceBench\"\n",
"\n",
"RATE_DATE_COL = \"Date\"\n",
"RATE_VAL_COL = \"Yld to Maturity\""
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "6dd153e1-7e6f-47b7-81b1-c77fa763a087",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' Pourquoi le mois ?\\nParce que :\\nles flux journaliers sont trop bruités\\nle churn et les comportements de portefeuille se lisent mieux au mois #le churn signifie qu’un client quitte le fonds\\nla plupart des comportements d’allocation sont plus lisibles à cette fréquence'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for df, date_col in [\n",
" (df_flows, FLOW_DATE_COL),\n",
" (df_aum, AUM_DATE_COL),\n",
" (df_nav, NAV_DATE_COL),\n",
" (df_rates, RATE_DATE_COL),\n",
"]:\n",
" df[date_col] = pd.to_datetime(df[date_col], errors=\"coerce\")\n",
"\n",
"df_flows[\"month\"] = df_flows[FLOW_DATE_COL].dt.to_period(\"M\").dt.to_timestamp()\n",
"df_aum[\"month\"] = df_aum[AUM_DATE_COL].dt.to_period(\"M\").dt.to_timestamp()\n",
"df_nav[\"month\"] = df_nav[NAV_DATE_COL].dt.to_period(\"M\").dt.to_timestamp()\n",
"df_rates[\"month\"] = df_rates[RATE_DATE_COL].dt.to_period(\"M\").dt.to_timestamp()\n",
"\n",
"for col in [FLOW_QTY_COL, FLOW_SUB_COL, FLOW_RED_COL]:\n",
" df_flows[col] = pd.to_numeric(df_flows[col], errors=\"coerce\")\n",
"\n",
"for col in [AUM_QTY_COL, AUM_VAL_COL]:\n",
" df_aum[col] = pd.to_numeric(df_aum[col], errors=\"coerce\")\n",
"\n",
"for col in [NAV_PRICE_COL, NAV_BENCH_COL]:\n",
" df_nav[col] = pd.to_numeric(df_nav[col], errors=\"coerce\")\n",
"\n",
"df_rates[RATE_VAL_COL] = pd.to_numeric(df_rates[RATE_VAL_COL], errors=\"coerce\")\n",
"\n",
"for df, col in [(df_flows, ISIN_COL), (df_aum, ISIN_COL)]:\n",
" df[col] = df[col].astype(str).str.strip()\n",
"\n",
"df_nav[NAV_ISIN_COL] = df_nav[NAV_ISIN_COL].astype(str).str.strip()\n",
"\n",
"''' Pourquoi le mois ?\n",
"Parce que :\n",
"les flux journaliers sont trop bruités\n",
"le churn et les comportements de portefeuille se lisent mieux au mois #le churn signifie qu’un client quitte le fonds\n",
"la plupart des comportements d’allocation sont plus lisibles à cette fréquence'''"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "ac43bb83-5800-4000-af9f-8cd4cae9e9d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4906626, 18)\n"
]
},
{
"data": {
"text/plain": [
"'\\nOn agrège séparément :\\nles flows au niveau client-produit-mois\\nles AUM au niveau client-produit-mois\\n\\nPuis on les fusionne.\\n\\nDatasets créés : df_flows_rel_m\\nTable des transactions mensuelles au niveau : Registrar Account - ID × Product - Isin × month'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''On veut s’assurer que l’univers des produits Carmignac détenus par les clients est bien cohérent avec l’univers des NAV \n",
"utilisés pour calculer les performances.'''\n",
"\n",
"#pour merge flux et aum c est un full\n",
"'''\n",
"Si un mois existe :\n",
"dans les flux mais pas dans les encours → la clé est gardée\n",
"dans les encours mais pas dans les flux → la clé est gardée\n",
"dans les deux → une seule ligne de clé\n",
"👉 C’est donc une logique proche d’un full outer join, mais construite manuellement.\n",
"'''\n",
"df_flows_rel_m = (\n",
" df_flows\n",
" .dropna(subset=[ID_COL, ISIN_COL, \"month\"])\n",
" .assign(\n",
" gross_flow_qty=lambda x: x[FLOW_QTY_COL].abs(),\n",
" sub_qty=lambda x: x[FLOW_SUB_COL].fillna(0),\n",
" red_qty=lambda x: x[FLOW_RED_COL].fillna(0)\n",
" )\n",
" .groupby([ID_COL, ISIN_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" net_flow_qty=(FLOW_QTY_COL, \"sum\"),\n",
" gross_flow_qty=(\"gross_flow_qty\", \"sum\"),\n",
" sub_qty=(\"sub_qty\", \"sum\"),\n",
" red_qty=(\"red_qty\", \"sum\"),\n",
" n_tx=(FLOW_QTY_COL, \"size\"),\n",
" region=(REGION_COL, \"last\"),\n",
" country=(COUNTRY_COL, \"last\")\n",
" )\n",
")\n",
"\n",
"df_aum_rel_m = (\n",
" df_aum\n",
" .dropna(subset=[ID_COL, ISIN_COL, \"month\"])\n",
" .groupby([ID_COL, ISIN_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty=(AUM_QTY_COL, \"sum\"),\n",
" aum_val=(AUM_VAL_COL, \"sum\"),\n",
" region=(REGION_COL, \"last\"),\n",
" country=(COUNTRY_COL, \"last\")\n",
" )\n",
")\n",
"\n",
"keys = pd.concat([\n",
" df_flows_rel_m[[ID_COL, ISIN_COL, \"month\"]],\n",
" df_aum_rel_m[[ID_COL, ISIN_COL, \"month\"]]\n",
"]).drop_duplicates()\n",
"\n",
"df_rel_m = (\n",
" keys\n",
" .merge(df_aum_rel_m, on=[ID_COL, ISIN_COL, \"month\"], how=\"left\", suffixes=(\"\", \"_aum\"))\n",
" .merge(df_flows_rel_m, on=[ID_COL, ISIN_COL, \"month\"], how=\"left\", suffixes=(\"\", \"_flow\"))\n",
")\n",
"\n",
"for c in [\"aum_qty\", \"aum_val\", \"net_flow_qty\", \"gross_flow_qty\", \"sub_qty\", \"red_qty\", \"n_tx\"]:\n",
" df_rel_m[c] = df_rel_m[c].fillna(0)\n",
"\n",
"df_rel_m[\"region\"] = df_rel_m[\"region\"].fillna(df_rel_m.get(\"region_flow\"))\n",
"df_rel_m[\"country\"] = df_rel_m[\"country\"].fillna(df_rel_m.get(\"country_flow\"))\n",
"\n",
"df_rel_m[\"active_rel_month\"] = (df_rel_m[\"gross_flow_qty\"] > 0).astype(int)\n",
"df_rel_m[\"holding_rel_month\"] = (df_rel_m[\"aum_qty\"] > 0).astype(int)\n",
"df_rel_m[\"flow_to_aum_rel\"] = df_rel_m[\"net_flow_qty\"] / (df_rel_m[\"aum_qty\"].abs() + EPS)\n",
"df_rel_m[\"turnover_rel\"] = df_rel_m[\"gross_flow_qty\"] / (df_rel_m[\"aum_qty\"].abs() + EPS)\n",
"\n",
"print(df_rel_m.shape)\n",
"df_rel_m.head()\n",
"\n",
"\n",
"'''\n",
"On agrège séparément :\n",
"les flows au niveau client-produit-mois\n",
"les AUM au niveau client-produit-mois\n",
"\n",
"Puis on les fusionne.\n",
"\n",
"Datasets créés : df_flows_rel_m\n",
"Table des transactions mensuelles au niveau : Registrar Account - ID × Product - Isin × month'''"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "2597a326-88f7-493f-830b-31826112eefa",
"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>Product - Isin</th>\n",
" <th>month</th>\n",
" <th>ret_fund_m</th>\n",
" <th>ret_bench_m</th>\n",
" <th>active_return_m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>617756</th>\n",
" <td>FR0007486709</td>\n",
" <td>2012-11-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>613737</th>\n",
" <td>FR0010135103</td>\n",
" <td>2010-03-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>613810</th>\n",
" <td>FR0010135103</td>\n",
" <td>2010-06-01</td>\n",
" <td>0.070565</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>614192</th>\n",
" <td>FR0010135103</td>\n",
" <td>2011-12-01</td>\n",
" <td>-0.024482</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>614258</th>\n",
" <td>FR0010135103</td>\n",
" <td>2012-03-01</td>\n",
" <td>0.028958</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Product - Isin month ret_fund_m ret_bench_m active_return_m\n",
"617756 FR0007486709 2012-11-01 NaN NaN NaN\n",
"613737 FR0010135103 2010-03-01 NaN NaN NaN\n",
"613810 FR0010135103 2010-06-01 0.070565 NaN NaN\n",
"614192 FR0010135103 2011-12-01 -0.024482 NaN NaN\n",
"614258 FR0010135103 2012-03-01 0.028958 NaN NaN"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_nav_m = (\n",
" df_nav\n",
" .dropna(subset=[NAV_ISIN_COL, \"month\", NAV_PRICE_COL])\n",
" .sort_values([NAV_ISIN_COL, \"month\"])\n",
" .groupby([NAV_ISIN_COL, \"month\"], as_index=False)\n",
" .tail(1)\n",
" .copy()\n",
")\n",
"\n",
"df_nav_m[\"ret_fund_m\"] = df_nav_m.groupby(NAV_ISIN_COL)[NAV_PRICE_COL].pct_change()\n",
"df_nav_m[\"ret_bench_m\"] = df_nav_m.groupby(NAV_ISIN_COL)[NAV_BENCH_COL].pct_change()\n",
"df_nav_m[\"active_return_m\"] = df_nav_m[\"ret_fund_m\"] - df_nav_m[\"ret_bench_m\"]\n",
"\n",
"df_nav_m = df_nav_m.rename(columns={NAV_ISIN_COL: ISIN_COL})\n",
"df_nav_m = df_nav_m[[ISIN_COL, \"month\", \"ret_fund_m\", \"ret_bench_m\", \"active_return_m\"]]\n",
"\n",
"df_nav_m.head()\n",
"#on agrège au niveau mensuel en prenant la dernière observation disponible du mois. pour nav et rates "
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "73cb228a-4c21-4407-a3f5-526c4b88639f",
"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>month</th>\n",
" <th>Yld to Maturity</th>\n",
" <th>delta_rate_m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-12-01</td>\n",
" <td>0.144</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2015-01-01</td>\n",
" <td>0.086</td>\n",
" <td>-0.058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>2015-02-01</td>\n",
" <td>0.064</td>\n",
" <td>-0.022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>2015-03-01</td>\n",
" <td>0.050</td>\n",
" <td>-0.014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2015-04-01</td>\n",
" <td>-0.027</td>\n",
" <td>-0.077</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" month Yld to Maturity delta_rate_m\n",
"0 2014-12-01 0.144 NaN\n",
"22 2015-01-01 0.086 -0.058\n",
"43 2015-02-01 0.064 -0.022\n",
"65 2015-03-01 0.050 -0.014\n",
"86 2015-04-01 -0.027 -0.077"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rates_m = (\n",
" df_rates\n",
" .dropna(subset=[\"month\", RATE_VAL_COL])\n",
" .sort_values(RATE_DATE_COL)\n",
" .groupby(\"month\", as_index=False)\n",
" .tail(1)\n",
" .copy()\n",
")\n",
"\n",
"df_rates_m[\"delta_rate_m\"] = df_rates_m[RATE_VAL_COL].diff()\n",
"df_rates_m = df_rates_m[[\"month\", RATE_VAL_COL, \"delta_rate_m\"]]\n",
"\n",
"df_rates_m.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1e1f0fa9-9c62-4a5e-8ee1-2605c76fb80a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nCréer un dataset complet contenant : fsionner ala left uaane hasab rel m shufyo bkhdu inter\\nclient + produit + performance + environnement macro.'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rel_m = df_rel_m.merge(\n",
" df_nav_m,\n",
" on=[ISIN_COL, \"month\"],\n",
" how=\"left\"\n",
")\n",
"\n",
"df_rel_m = df_rel_m.merge(\n",
" df_rates_m[[\"month\", \"delta_rate_m\"]],\n",
" on=\"month\",\n",
" how=\"left\"\n",
")\n",
"\n",
"for c in [\"ret_fund_m\", \"ret_bench_m\", \"active_return_m\", \"delta_rate_m\"]:\n",
" df_rel_m[c] = df_rel_m[c].fillna(0)\n",
"\n",
"df_rel_m.head()\n",
"\n",
"'''\n",
"Créer un dataset complet contenant : fsionner ala left uaane hasab rel m shufyo bkhdu inter\n",
"client + produit + performance + environnement macro.'''"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "41a4ac43-9035-46f3-8cba-5ba0d88ec98a",
"metadata": {},
"outputs": [],
"source": [
"def weighted_mean(x, w):\n",
" x = np.asarray(x, dtype=float)\n",
" w = np.asarray(w, dtype=float)\n",
" mask = np.isfinite(x) & np.isfinite(w) & (w >= 0)\n",
" if mask.sum() == 0 or w[mask].sum() == 0:\n",
" return np.nan\n",
" return np.average(x[mask], weights=w[mask])\n",
"\n",
"def hhi_from_weights(w):\n",
" w = np.asarray(w, dtype=float)\n",
" w = np.clip(w, 0, None)\n",
" s = w.sum()\n",
" if s <= 0:\n",
" return np.nan\n",
" p = w / s\n",
" return np.sum(p**2)\n",
"\n",
"def compute_trend(y):\n",
" y = np.asarray(y, dtype=float)\n",
" if len(y) < 4:\n",
" return np.nan\n",
" x = np.arange(len(y)).reshape(-1, 1)\n",
" mask = np.isfinite(y)\n",
" if mask.sum() < 4:\n",
" return np.nan\n",
" reg = LinearRegression().fit(x[mask], y[mask])\n",
" return reg.coef_[0]\n",
"\n",
"def compute_beta(y, x):\n",
" y = np.asarray(y, dtype=float)\n",
" x = np.asarray(x, dtype=float)\n",
" mask = np.isfinite(y) & np.isfinite(x)\n",
" if mask.sum() < 6:\n",
" return np.nan\n",
" reg = LinearRegression().fit(x[mask].reshape(-1, 1), y[mask])\n",
" return reg.coef_[0]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "79facbea-b77c-4cf9-95f7-526e1dedf9b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1002117, 21)\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>Registrar Account - ID</th>\n",
" <th>month</th>\n",
" <th>aum_qty</th>\n",
" <th>aum_val</th>\n",
" <th>net_flow_qty</th>\n",
" <th>gross_flow_qty</th>\n",
" <th>sub_qty</th>\n",
" <th>red_qty</th>\n",
" <th>n_tx</th>\n",
" <th>n_isin_held</th>\n",
" <th>n_isin_active</th>\n",
" <th>delta_rate_m</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>active_month</th>\n",
" <th>flow_to_aum_m</th>\n",
" <th>turnover_m</th>\n",
" <th>sub_share_m</th>\n",
" <th>red_share_m</th>\n",
" <th>aum_peak_to_date</th>\n",
" <th>aum_drawdown</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7905</td>\n",
" <td>2015-01-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.058</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7905</td>\n",
" <td>2015-02-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.022</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7905</td>\n",
" <td>2015-03-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.014</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7905</td>\n",
" <td>2015-04-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.077</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7905</td>\n",
" <td>2015-05-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.053</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID month aum_qty aum_val net_flow_qty \\\n",
"0 7905 2015-01-01 0.0 0.0 0.0 \n",
"1 7905 2015-02-01 0.0 0.0 0.0 \n",
"2 7905 2015-03-01 0.0 0.0 0.0 \n",
"3 7905 2015-04-01 0.0 0.0 0.0 \n",
"4 7905 2015-05-01 0.0 0.0 0.0 \n",
"\n",
" gross_flow_qty sub_qty red_qty n_tx n_isin_held n_isin_active \\\n",
"0 0.0 0.0 0.0 0.0 0 0 \n",
"1 0.0 0.0 0.0 0.0 0 0 \n",
"2 0.0 0.0 0.0 0.0 0 0 \n",
"3 0.0 0.0 0.0 0.0 0 0 \n",
"4 0.0 0.0 0.0 0.0 0 0 \n",
"\n",
" delta_rate_m region country active_month flow_to_aum_m \\\n",
"0 -0.058 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"1 -0.022 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"2 -0.014 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"3 -0.077 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"4 -0.053 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"\n",
" turnover_m sub_share_m red_share_m aum_peak_to_date aum_drawdown \n",
"0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 1.0 \n",
"2 0.0 0.0 0.0 0.0 1.0 \n",
"3 0.0 0.0 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 0.0 1.0 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# =========================\n",
"# ULTRA LIGHT VERSION\n",
"# =========================\n",
"\n",
"tmp = df_rel_m.copy()\n",
"tmp[\"isin_held_flag\"] = (tmp[\"aum_qty\"] > 0).astype(int)\n",
"tmp[\"isin_active_flag\"] = (tmp[\"gross_flow_qty\"] > 0).astype(int)\n",
"\n",
"df_month = (\n",
" tmp.groupby([ID_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty=(\"aum_qty\", \"sum\"),\n",
" aum_val=(\"aum_val\", \"sum\"),\n",
" net_flow_qty=(\"net_flow_qty\", \"sum\"),\n",
" gross_flow_qty=(\"gross_flow_qty\", \"sum\"),\n",
" sub_qty=(\"sub_qty\", \"sum\"),\n",
" red_qty=(\"red_qty\", \"sum\"),\n",
" n_tx=(\"n_tx\", \"sum\"),\n",
" n_isin_held=(\"isin_held_flag\", \"sum\"),\n",
" n_isin_active=(\"isin_active_flag\", \"sum\"),\n",
" delta_rate_m=(\"delta_rate_m\", \"first\"),\n",
" region=(\"region\", \"first\"),\n",
" country=(\"country\", \"first\"),\n",
" )\n",
" .sort_values([ID_COL, \"month\"])\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"df_month[\"active_month\"] = (df_month[\"gross_flow_qty\"] > 0).astype(int)\n",
"df_month[\"flow_to_aum_m\"] = df_month[\"net_flow_qty\"] / (df_month[\"aum_qty\"].abs() + EPS)\n",
"df_month[\"turnover_m\"] = df_month[\"gross_flow_qty\"] / (df_month[\"aum_qty\"].abs() + EPS)\n",
"df_month[\"sub_share_m\"] = df_month[\"sub_qty\"] / (df_month[\"gross_flow_qty\"] + EPS)\n",
"df_month[\"red_share_m\"] = df_month[\"red_qty\"] / (df_month[\"gross_flow_qty\"] + EPS)\n",
"\n",
"df_month[\"aum_peak_to_date\"] = df_month.groupby(ID_COL)[\"aum_qty\"].cummax()\n",
"df_month[\"aum_drawdown\"] = 1 - (df_month[\"aum_qty\"] / (df_month[\"aum_peak_to_date\"] + EPS))\n",
"\n",
"print(df_month.shape)\n",
"df_month.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "1a2d234f-42f8-4cd1-b50e-7fc3f8c829d2",
"metadata": {},
"outputs": [
{
"data": {
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" 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>Registrar Account - ID</th>\n",
" <th>Product - Isin</th>\n",
" <th>rel_n_months</th>\n",
" <th>rel_active_months</th>\n",
" <th>rel_holding_months</th>\n",
" <th>rel_aum_mean</th>\n",
" <th>rel_turnover_mean</th>\n",
" <th>rel_turnover_vol</th>\n",
" <th>rel_flow_to_aum_vol</th>\n",
" <th>rel_n_tx</th>\n",
" <th>rel_full_exit_count</th>\n",
" <th>rel_entry_count</th>\n",
" </tr>\n",
" </thead>\n",
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"text/plain": [
" Registrar Account - ID Product - Isin rel_n_months rel_active_months \\\n",
"0 7905 FR0010135103 80 0 \n",
"1 7905 FR0010147603 80 0 \n",
"2 7905 FR0010148981 80 0 \n",
"3 7905 FR0010148999 80 0 \n",
"4 7905 FR0010149096 80 0 \n",
"\n",
" rel_holding_months rel_aum_mean rel_turnover_mean rel_turnover_vol \\\n",
"0 0 0.0 0.0 0.0 \n",
"1 0 0.0 0.0 0.0 \n",
"2 0 0.0 0.0 0.0 \n",
"3 0 0.0 0.0 0.0 \n",
"4 0 0.0 0.0 0.0 \n",
"\n",
" rel_flow_to_aum_vol rel_n_tx rel_full_exit_count rel_entry_count \n",
"0 0.0 0.0 0 0 \n",
"1 0.0 0.0 0 0 \n",
"2 0.0 0.0 0 0 \n",
"3 0.0 0.0 0 0 \n",
"4 0.0 0.0 0 0 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmp = df_rel_m.sort_values([ID_COL, ISIN_COL, \"month\"]).copy()\n",
"tmp[\"prev_aum\"] = tmp.groupby([ID_COL, ISIN_COL])[\"aum_qty\"].shift(1)\n",
"tmp[\"full_exit_event\"] = ((tmp[\"prev_aum\"] > 0) & (tmp[\"aum_qty\"] <= 0)).astype(int)\n",
"tmp[\"entry_event\"] = ((tmp[\"prev_aum\"].fillna(0) <= 0) & (tmp[\"aum_qty\"] > 0)).astype(int)\n",
"\n",
"df_rel_feat = (\n",
" tmp.groupby([ID_COL, ISIN_COL], as_index=False)\n",
" .agg(\n",
" rel_n_months=(\"month\", \"nunique\"),\n",
" rel_active_months=(\"active_rel_month\", \"sum\"),\n",
" rel_holding_months=(\"holding_rel_month\", \"sum\"),\n",
" rel_aum_mean=(\"aum_qty\", \"mean\"),\n",
" rel_turnover_mean=(\"turnover_rel\", \"mean\"),\n",
" rel_turnover_vol=(\"turnover_rel\", \"std\"),\n",
" rel_flow_to_aum_vol=(\"flow_to_aum_rel\", \"std\"),\n",
" rel_n_tx=(\"n_tx\", \"sum\"),\n",
" rel_full_exit_count=(\"full_exit_event\", \"sum\"),\n",
" rel_entry_count=(\"entry_event\", \"sum\")\n",
" )\n",
")\n",
"\n",
"df_rel_feat.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "9b61ce6a-1f54-423a-8a54-9897fe1bce20",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(17236, 34)\n"
]
},
{
"data": {
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>n_months</th>\n",
" <th>n_active_months</th>\n",
" <th>flow_freq</th>\n",
" <th>aum_qty_mean</th>\n",
" <th>aum_qty_median</th>\n",
" <th>aum_qty_max</th>\n",
" <th>aum_qty_last</th>\n",
" <th>net_flow_qty_sum</th>\n",
" <th>gross_flow_qty_sum</th>\n",
" <th>gross_flow_qty_mean</th>\n",
" <th>n_tx_total</th>\n",
" <th>net_flow_vol</th>\n",
" <th>turnover_mean</th>\n",
" <th>turnover_vol</th>\n",
" <th>flow_to_aum_mean</th>\n",
" <th>flow_to_aum_vol</th>\n",
" <th>avg_n_isin_held</th>\n",
" <th>max_n_isin_held</th>\n",
" <th>sub_share_mean</th>\n",
" <th>red_share_mean</th>\n",
" <th>delta_rate_mean</th>\n",
" <th>aum_drawdown_last</th>\n",
" <th>aum_drawdown_max</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>n_isin_total</th>\n",
" <th>rel_turnover_mean_avg</th>\n",
" <th>rel_turnover_vol_avg</th>\n",
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" <th>avg_holding_months_per_isin</th>\n",
" <th>max_holding_months_per_isin</th>\n",
" </tr>\n",
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" <td>LUXEMBOURG</td>\n",
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" <td>0.00000</td>\n",
" <td>-0.008925</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>5</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
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" </tr>\n",
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" <th>2</th>\n",
" <td>7962</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
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" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>4</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8307</td>\n",
" <td>130</td>\n",
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" <td>0.108358</td>\n",
" <td>0.00648</td>\n",
" <td>0.103427</td>\n",
" <td>11.784615</td>\n",
" <td>16</td>\n",
" <td>0.260828</td>\n",
" <td>-0.36224</td>\n",
" <td>0.013723</td>\n",
" <td>0.991766</td>\n",
" <td>0.991766</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>29</td>\n",
" <td>4.749062e+09</td>\n",
" <td>3.095092e+10</td>\n",
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" <td>31</td>\n",
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" <th>4</th>\n",
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" <td>-0.010063</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID n_months n_active_months flow_freq aum_qty_mean \\\n",
"0 7905 80 0 0.000000 0.000000 \n",
"1 7912 80 0 0.000000 0.000000 \n",
"2 7962 80 0 0.000000 0.000000 \n",
"3 8307 130 77 0.592308 22613.710831 \n",
"4 8354 64 0 0.000000 0.000000 \n",
"\n",
" aum_qty_median aum_qty_max aum_qty_last net_flow_qty_sum \\\n",
"0 0.00 0.00 0.0 0.000 \n",
"1 0.00 0.00 0.0 0.000 \n",
"2 0.00 0.00 0.0 0.000 \n",
"3 22712.98 59145.06 487.0 27252.124 \n",
"4 0.00 0.00 0.0 0.000 \n",
"\n",
" gross_flow_qty_sum gross_flow_qty_mean n_tx_total net_flow_vol \\\n",
"0 0.00 0.000000 0.0 0.000000 \n",
"1 0.00 0.000000 0.0 0.000000 \n",
"2 0.00 0.000000 0.0 0.000000 \n",
"3 177077.31 1362.133154 161.0 3508.455222 \n",
"4 0.00 0.000000 0.0 0.000000 \n",
"\n",
" turnover_mean turnover_vol flow_to_aum_mean flow_to_aum_vol \\\n",
"0 0.00000 0.000000 0.00000 0.000000 \n",
"1 0.00000 0.000000 0.00000 0.000000 \n",
"2 0.00000 0.000000 0.00000 0.000000 \n",
"3 0.04922 0.108358 0.00648 0.103427 \n",
"4 0.00000 0.000000 0.00000 0.000000 \n",
"\n",
" avg_n_isin_held max_n_isin_held sub_share_mean red_share_mean \\\n",
"0 0.000000 0 0.000000 0.00000 \n",
"1 0.000000 0 0.000000 0.00000 \n",
"2 0.000000 0 0.000000 0.00000 \n",
"3 11.784615 16 0.260828 -0.36224 \n",
"4 0.000000 0 0.000000 0.00000 \n",
"\n",
" delta_rate_mean aum_drawdown_last aum_drawdown_max region \\\n",
"0 -0.008925 1.000000 1.000000 LUXEMBOURG \n",
"1 -0.008925 1.000000 1.000000 LUXEMBOURG \n",
"2 -0.008925 1.000000 1.000000 LUXEMBOURG \n",
"3 0.013723 0.991766 0.991766 SWITZERLAND \n",
"4 -0.010063 1.000000 1.000000 SWITZERLAND \n",
"\n",
" country n_isin_total rel_turnover_mean_avg rel_turnover_vol_avg \\\n",
"0 LUXEMBOURG 12 0.000000e+00 0.000000e+00 \n",
"1 LUXEMBOURG 5 0.000000e+00 0.000000e+00 \n",
"2 LUXEMBOURG 4 0.000000e+00 0.000000e+00 \n",
"3 SWITZERLAND 29 4.749062e+09 3.095092e+10 \n",
"4 SWITZERLAND 1 0.000000e+00 0.000000e+00 \n",
"\n",
" rel_flow_to_aum_vol_avg full_exit_count entry_count \\\n",
"0 0.000000e+00 0 0 \n",
"1 0.000000e+00 0 0 \n",
"2 0.000000e+00 0 0 \n",
"3 3.095092e+10 27 31 \n",
"4 0.000000e+00 0 0 \n",
"\n",
" avg_holding_months_per_isin max_holding_months_per_isin \n",
"0 0.000000 0 \n",
"1 0.000000 0 \n",
"2 0.000000 0 \n",
"3 52.827586 130 \n",
"4 0.000000 0 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rel_client = (\n",
" df_rel_feat\n",
" .groupby(ID_COL, as_index=False)\n",
" .agg(\n",
" n_isin_total=(ISIN_COL, \"nunique\"),\n",
" rel_turnover_mean_avg=(\"rel_turnover_mean\", \"mean\"),\n",
" rel_turnover_vol_avg=(\"rel_turnover_vol\", \"mean\"),\n",
" rel_flow_to_aum_vol_avg=(\"rel_flow_to_aum_vol\", \"mean\"),\n",
" full_exit_count=(\"rel_full_exit_count\", \"sum\"),\n",
" entry_count=(\"rel_entry_count\", \"sum\"),\n",
" avg_holding_months_per_isin=(\"rel_holding_months\", \"mean\"),\n",
" max_holding_months_per_isin=(\"rel_holding_months\", \"max\")\n",
" )\n",
")\n",
"\n",
"df_client = (\n",
" df_month\n",
" .groupby(ID_COL, as_index=False)\n",
" .agg(\n",
" n_months=(\"month\", \"nunique\"),\n",
" n_active_months=(\"active_month\", \"sum\"),\n",
" flow_freq=(\"active_month\", \"mean\"),\n",
"\n",
" aum_qty_mean=(\"aum_qty\", \"mean\"),\n",
" aum_qty_median=(\"aum_qty\", \"median\"),\n",
" aum_qty_max=(\"aum_qty\", \"max\"),\n",
" aum_qty_last=(\"aum_qty\", \"last\"),\n",
"\n",
" net_flow_qty_sum=(\"net_flow_qty\", \"sum\"),\n",
" gross_flow_qty_sum=(\"gross_flow_qty\", \"sum\"),\n",
" gross_flow_qty_mean=(\"gross_flow_qty\", \"mean\"),\n",
" n_tx_total=(\"n_tx\", \"sum\"),\n",
"\n",
" net_flow_vol=(\"net_flow_qty\", \"std\"),\n",
" turnover_mean=(\"turnover_m\", \"mean\"),\n",
" turnover_vol=(\"turnover_m\", \"std\"),\n",
" flow_to_aum_mean=(\"flow_to_aum_m\", \"mean\"),\n",
" flow_to_aum_vol=(\"flow_to_aum_m\", \"std\"),\n",
"\n",
" avg_n_isin_held=(\"n_isin_held\", \"mean\"),\n",
" max_n_isin_held=(\"n_isin_held\", \"max\"),\n",
"\n",
" sub_share_mean=(\"sub_share_m\", \"mean\"),\n",
" red_share_mean=(\"red_share_m\", \"mean\"),\n",
"\n",
" delta_rate_mean=(\"delta_rate_m\", \"mean\"),\n",
" aum_drawdown_last=(\"aum_drawdown\", \"last\"),\n",
" aum_drawdown_max=(\"aum_drawdown\", \"max\"),\n",
"\n",
" region=(\"region\", \"last\"),\n",
" country=(\"country\", \"last\")\n",
" )\n",
")\n",
"\n",
"df_client = df_client.merge(df_rel_client, on=ID_COL, how=\"left\")\n",
"\n",
"print(df_client.shape)\n",
"df_client.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "48d1b22b-f0ca-448e-a330-6b7d64fe48b3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Registrar Account - ID', 'n_months', 'n_active_months', 'flow_freq',\n",
" 'aum_qty_mean', 'aum_qty_median', 'aum_qty_max', 'aum_qty_last',\n",
" 'net_flow_qty_sum', 'gross_flow_qty_sum', 'gross_flow_qty_mean',\n",
" 'n_tx_total', 'net_flow_vol', 'turnover_mean', 'turnover_vol',\n",
" 'flow_to_aum_mean', 'flow_to_aum_vol', 'avg_n_isin_held',\n",
" 'max_n_isin_held', 'sub_share_mean', 'red_share_mean',\n",
" 'delta_rate_mean', 'aum_drawdown_last', 'aum_drawdown_max', 'region',\n",
" 'country', 'n_isin_total', 'rel_turnover_mean_avg',\n",
" 'rel_turnover_vol_avg', 'rel_flow_to_aum_vol_avg', 'full_exit_count',\n",
" 'entry_count', 'avg_holding_months_per_isin',\n",
" 'max_holding_months_per_isin'],\n",
" dtype='object')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_client.columns"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "a9b34e2b-503f-41a1-9629-670ceb7615ba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(17236, 34)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_client.shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "934507d6-8aaf-43e2-8a2d-d6cfea0b6af1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(17236, 38)\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>Registrar Account - ID</th>\n",
" <th>n_months</th>\n",
" <th>n_active_months</th>\n",
" <th>flow_freq</th>\n",
" <th>aum_qty_mean</th>\n",
" <th>aum_qty_median</th>\n",
" <th>aum_qty_max</th>\n",
" <th>aum_qty_last</th>\n",
" <th>net_flow_qty_sum</th>\n",
" <th>gross_flow_qty_sum</th>\n",
" <th>gross_flow_qty_mean</th>\n",
" <th>n_tx_total</th>\n",
" <th>net_flow_vol</th>\n",
" <th>turnover_mean</th>\n",
" <th>turnover_vol</th>\n",
" <th>flow_to_aum_mean</th>\n",
" <th>flow_to_aum_vol</th>\n",
" <th>avg_n_isin_held</th>\n",
" <th>max_n_isin_held</th>\n",
" <th>sub_share_mean</th>\n",
" <th>red_share_mean</th>\n",
" <th>delta_rate_mean</th>\n",
" <th>aum_drawdown_last</th>\n",
" <th>aum_drawdown_max</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>n_isin_total</th>\n",
" <th>rel_turnover_mean_avg</th>\n",
" <th>rel_turnover_vol_avg</th>\n",
" <th>rel_flow_to_aum_vol_avg</th>\n",
" <th>full_exit_count</th>\n",
" <th>entry_count</th>\n",
" <th>avg_holding_months_per_isin</th>\n",
" <th>max_holding_months_per_isin</th>\n",
" <th>flow_trend_12m</th>\n",
" <th>aum_trend_12m</th>\n",
" <th>drawdown_trend_12m</th>\n",
" <th>beta_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7905</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
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" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>12</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
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" <th>1</th>\n",
" <td>7912</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.008925</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>5</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <th>2</th>\n",
" <td>7962</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.008925</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>4</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
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" <th>3</th>\n",
" <td>8307</td>\n",
" <td>130</td>\n",
" <td>77</td>\n",
" <td>0.592308</td>\n",
" <td>22613.710831</td>\n",
" <td>22712.98</td>\n",
" <td>59145.06</td>\n",
" <td>487.0</td>\n",
" <td>27252.124</td>\n",
" <td>177077.31</td>\n",
" <td>1362.133154</td>\n",
" <td>161.0</td>\n",
" <td>3508.455222</td>\n",
" <td>0.04922</td>\n",
" <td>0.108358</td>\n",
" <td>0.00648</td>\n",
" <td>0.103427</td>\n",
" <td>11.784615</td>\n",
" <td>16</td>\n",
" <td>0.260828</td>\n",
" <td>-0.36224</td>\n",
" <td>0.013723</td>\n",
" <td>0.991766</td>\n",
" <td>0.991766</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>29</td>\n",
" <td>4.749062e+09</td>\n",
" <td>3.095092e+10</td>\n",
" <td>3.095092e+10</td>\n",
" <td>27</td>\n",
" <td>31</td>\n",
" <td>52.827586</td>\n",
" <td>130</td>\n",
" <td>-0.003463</td>\n",
" <td>-1142.587413</td>\n",
" <td>0.019318</td>\n",
" <td>-0.069433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8354</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.010063</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID n_months n_active_months flow_freq aum_qty_mean \\\n",
"0 7905 80 0 0.000000 0.000000 \n",
"1 7912 80 0 0.000000 0.000000 \n",
"2 7962 80 0 0.000000 0.000000 \n",
"3 8307 130 77 0.592308 22613.710831 \n",
"4 8354 64 0 0.000000 0.000000 \n",
"\n",
" aum_qty_median aum_qty_max aum_qty_last net_flow_qty_sum \\\n",
"0 0.00 0.00 0.0 0.000 \n",
"1 0.00 0.00 0.0 0.000 \n",
"2 0.00 0.00 0.0 0.000 \n",
"3 22712.98 59145.06 487.0 27252.124 \n",
"4 0.00 0.00 0.0 0.000 \n",
"\n",
" gross_flow_qty_sum gross_flow_qty_mean n_tx_total net_flow_vol \\\n",
"0 0.00 0.000000 0.0 0.000000 \n",
"1 0.00 0.000000 0.0 0.000000 \n",
"2 0.00 0.000000 0.0 0.000000 \n",
"3 177077.31 1362.133154 161.0 3508.455222 \n",
"4 0.00 0.000000 0.0 0.000000 \n",
"\n",
" turnover_mean turnover_vol flow_to_aum_mean flow_to_aum_vol \\\n",
"0 0.00000 0.000000 0.00000 0.000000 \n",
"1 0.00000 0.000000 0.00000 0.000000 \n",
"2 0.00000 0.000000 0.00000 0.000000 \n",
"3 0.04922 0.108358 0.00648 0.103427 \n",
"4 0.00000 0.000000 0.00000 0.000000 \n",
"\n",
" avg_n_isin_held max_n_isin_held sub_share_mean red_share_mean \\\n",
"0 0.000000 0 0.000000 0.00000 \n",
"1 0.000000 0 0.000000 0.00000 \n",
"2 0.000000 0 0.000000 0.00000 \n",
"3 11.784615 16 0.260828 -0.36224 \n",
"4 0.000000 0 0.000000 0.00000 \n",
"\n",
" delta_rate_mean aum_drawdown_last aum_drawdown_max region \\\n",
"0 -0.008925 1.000000 1.000000 LUXEMBOURG \n",
"1 -0.008925 1.000000 1.000000 LUXEMBOURG \n",
"2 -0.008925 1.000000 1.000000 LUXEMBOURG \n",
"3 0.013723 0.991766 0.991766 SWITZERLAND \n",
"4 -0.010063 1.000000 1.000000 SWITZERLAND \n",
"\n",
" country n_isin_total rel_turnover_mean_avg rel_turnover_vol_avg \\\n",
"0 LUXEMBOURG 12 0.000000e+00 0.000000e+00 \n",
"1 LUXEMBOURG 5 0.000000e+00 0.000000e+00 \n",
"2 LUXEMBOURG 4 0.000000e+00 0.000000e+00 \n",
"3 SWITZERLAND 29 4.749062e+09 3.095092e+10 \n",
"4 SWITZERLAND 1 0.000000e+00 0.000000e+00 \n",
"\n",
" rel_flow_to_aum_vol_avg full_exit_count entry_count \\\n",
"0 0.000000e+00 0 0 \n",
"1 0.000000e+00 0 0 \n",
"2 0.000000e+00 0 0 \n",
"3 3.095092e+10 27 31 \n",
"4 0.000000e+00 0 0 \n",
"\n",
" avg_holding_months_per_isin max_holding_months_per_isin flow_trend_12m \\\n",
"0 0.000000 0 0.000000 \n",
"1 0.000000 0 0.000000 \n",
"2 0.000000 0 0.000000 \n",
"3 52.827586 130 -0.003463 \n",
"4 0.000000 0 0.000000 \n",
"\n",
" aum_trend_12m drawdown_trend_12m beta_rate \n",
"0 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 \n",
"3 -1142.587413 0.019318 -0.069433 \n",
"4 0.000000 0.000000 0.000000 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def compute_trend(y):\n",
" y = np.asarray(y, dtype=float)\n",
" if len(y) < 4:\n",
" return np.nan\n",
" x = np.arange(len(y)).reshape(-1, 1)\n",
" mask = np.isfinite(y)\n",
" if mask.sum() < 4:\n",
" return np.nan\n",
" reg = LinearRegression().fit(x[mask], y[mask])\n",
" return reg.coef_[0]\n",
"\n",
"def compute_beta(y, x):\n",
" y = np.asarray(y, dtype=float)\n",
" x = np.asarray(x, dtype=float)\n",
" mask = np.isfinite(y) & np.isfinite(x)\n",
" if mask.sum() < 6:\n",
" return np.nan\n",
" reg = LinearRegression().fit(x[mask].reshape(-1, 1), y[mask])\n",
" return reg.coef_[0]\n",
"\n",
"rows = []\n",
"\n",
"for acc, g in df_month.groupby(ID_COL):\n",
" g = g.sort_values(\"month\")\n",
"\n",
" flow = g[\"flow_to_aum_m\"].values\n",
" aum = g[\"aum_qty\"].values\n",
" delta_rate = g[\"delta_rate_m\"].values\n",
" drawdown = g[\"aum_drawdown\"].values\n",
"\n",
" rows.append({\n",
" ID_COL: acc,\n",
" \"flow_trend_12m\": compute_trend(flow[-12:]),\n",
" \"aum_trend_12m\": compute_trend(aum[-12:]),\n",
" \"drawdown_trend_12m\": compute_trend(drawdown[-12:]),\n",
" \"beta_rate\": compute_beta(flow, delta_rate),\n",
" })\n",
"\n",
"df_beta = pd.DataFrame(rows)\n",
"\n",
"df_client = df_client.merge(df_beta, on=ID_COL, how=\"left\")\n",
"\n",
"print(df_client.shape)\n",
"df_client.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "3f78e685-b3e7-4c02-81d2-fc480d524814",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nb clients = 9656\n",
"Nb features = 34\n",
"['log_aum_qty_mean', 'flow_freq', 'gross_flow_to_aum', 'turnover_vol', 'flow_to_aum_vol', 'activity_intensity', 'log_n_tx_total', 'avg_n_isin_held', 'n_isin_total', 'avg_holding_months_per_isin', 'exit_rate_per_isin', 'flow_direction_balance', 'redemption_bias', 'aum_drawdown_last', 'country_grp_FRANCE', 'country_grp_GERMANY', 'country_grp_ITALY', 'country_grp_LATAM', 'country_grp_LUXEMBOURG', 'country_grp_Other', 'country_grp_SPAIN', 'country_grp_SWITZERLAND', 'country_grp_UNITED KINGDOM', 'country_grp_UNITED STATES', 'region_grp_FRANCE', 'region_grp_GERMANY', 'region_grp_ITALY', 'region_grp_LATAM', 'region_grp_LUXEMBOURG', 'region_grp_NORDICS', 'region_grp_Other', 'region_grp_SPAIN', 'region_grp_SWITZERLAND', 'region_grp_UNITED KINGDOM']\n"
]
}
],
"source": [
"dfc = df_client.copy()\n",
"\n",
"dfc[\"gross_flow_to_aum\"] = dfc[\"gross_flow_qty_sum\"] / (dfc[\"aum_qty_mean\"].abs() + EPS)\n",
"dfc[\"avg_ticket\"] = dfc[\"gross_flow_qty_sum\"] / (dfc[\"n_tx_total\"] + EPS)\n",
"dfc[\"flow_direction_balance\"] = dfc[\"net_flow_qty_sum\"] / (dfc[\"gross_flow_qty_sum\"] + EPS)\n",
"dfc[\"redemption_bias\"] = dfc[\"red_share_mean\"] - dfc[\"sub_share_mean\"]\n",
"dfc[\"activity_intensity\"] = dfc[\"n_tx_total\"] / (dfc[\"n_months\"] + EPS)\n",
"dfc[\"exit_rate_per_isin\"] = dfc[\"full_exit_count\"] / (dfc[\"n_isin_total\"] + EPS)\n",
"dfc[\"entry_rate_per_isin\"] = dfc[\"entry_count\"] / (dfc[\"n_isin_total\"] + EPS)\n",
"dfc[\"aum_final_to_peak\"] = dfc[\"aum_qty_last\"] / (dfc[\"aum_qty_max\"] + EPS)\n",
"\n",
"for col in [\"aum_qty_mean\", \"gross_flow_qty_sum\", \"n_tx_total\", \"avg_ticket\"]:\n",
" dfc[f\"log_{col}\"] = np.log1p(dfc[col].clip(lower=0))\n",
"\n",
"dfc = dfc[(dfc[\"n_months\"] >= 6) & (dfc[\"aum_qty_mean\"] > 0)].copy()\n",
"\n",
"top_countries = dfc[\"country\"].fillna(\"Unknown\").value_counts().head(10).index\n",
"top_regions = dfc[\"region\"].fillna(\"Unknown\").value_counts().head(10).index\n",
"\n",
"dfc[\"country_grp\"] = np.where(dfc[\"country\"].isin(top_countries), dfc[\"country\"], \"Other\")\n",
"dfc[\"region_grp\"] = np.where(dfc[\"region\"].isin(top_regions), dfc[\"region\"], \"Other\")\n",
"\n",
"base_features = [\n",
" \"log_aum_qty_mean\",\n",
" \"flow_freq\",\n",
" \"gross_flow_to_aum\",\n",
" \"turnover_vol\",\n",
" \"flow_to_aum_vol\",\n",
" \"activity_intensity\",\n",
" \"log_n_tx_total\",\n",
" \"avg_n_isin_held\",\n",
" \"n_isin_total\",\n",
" \"avg_holding_months_per_isin\",\n",
" \"exit_rate_per_isin\",\n",
" \"flow_direction_balance\",\n",
" \"redemption_bias\",\n",
" \"aum_drawdown_last\",\n",
"]\n",
"\n",
"base_features = [c for c in base_features if c in dfc.columns]\n",
"\n",
"X_num = dfc[base_features].replace([np.inf, -np.inf], np.nan).fillna(dfc[base_features].median())\n",
"X_cat = pd.get_dummies(dfc[[\"country_grp\", \"region_grp\"]].fillna(\"Unknown\"), drop_first=True)\n",
"\n",
"X = pd.concat([X_num.reset_index(drop=True), X_cat.reset_index(drop=True)], axis=1)\n",
"\n",
"scaler = RobustScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"print(\"Nb clients =\", X.shape[0])\n",
"print(\"Nb features =\", X.shape[1])\n",
"print(X.columns.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "7e865952-a1d2-4f39-bbbc-306c8ed7857c",
"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>k</th>\n",
" <th>inertia</th>\n",
" <th>silhouette</th>\n",
" <th>davies_bouldin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>4.524683e+27</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>5.112656e+26</td>\n",
" <td>0.999550</td>\n",
" <td>0.261256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2.261592e+26</td>\n",
" <td>0.999409</td>\n",
" <td>0.421666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1.116792e+26</td>\n",
" <td>0.999430</td>\n",
" <td>0.246382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>4.183978e+25</td>\n",
" <td>0.999075</td>\n",
" <td>0.293760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>2.520739e+25</td>\n",
" <td>0.999065</td>\n",
" <td>0.338481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>1.141968e+25</td>\n",
" <td>0.998960</td>\n",
" <td>0.257299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>5.503246e+24</td>\n",
" <td>0.998934</td>\n",
" <td>0.138057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1.305120e+24</td>\n",
" <td>0.998801</td>\n",
" <td>0.064579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>5.648568e+23</td>\n",
" <td>0.998694</td>\n",
" <td>0.126169</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" k inertia silhouette davies_bouldin\n",
"0 1 4.524683e+27 NaN NaN\n",
"1 2 5.112656e+26 0.999550 0.261256\n",
"2 3 2.261592e+26 0.999409 0.421666\n",
"3 4 1.116792e+26 0.999430 0.246382\n",
"4 5 4.183978e+25 0.999075 0.293760\n",
"5 6 2.520739e+25 0.999065 0.338481\n",
"6 7 1.141968e+25 0.998960 0.257299\n",
"7 8 5.503246e+24 0.998934 0.138057\n",
"8 9 1.305120e+24 0.998801 0.064579\n",
"9 10 5.648568e+23 0.998694 0.126169"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rows = []\n",
"\n",
"for k in range(1, 11):\n",
" km = KMeans(n_clusters=k, n_init=50, random_state=42)\n",
" labels = km.fit_predict(X_scaled)\n",
"\n",
" row = {\n",
" \"k\": k,\n",
" \"inertia\": km.inertia_\n",
" }\n",
"\n",
" if k >= 2:\n",
" row[\"silhouette\"] = silhouette_score(X_scaled, labels)\n",
" row[\"davies_bouldin\"] = davies_bouldin_score(X_scaled, labels)\n",
" else:\n",
" row[\"silhouette\"] = np.nan\n",
" row[\"davies_bouldin\"] = np.nan\n",
"\n",
" rows.append(row)\n",
"\n",
"df_kdiag = pd.DataFrame(rows)\n",
"df_kdiag"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "04c350be-d871-4de8-93d6-3775d78e4725",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1600x400 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n",
"\n",
"axes[0].plot(df_kdiag[\"k\"], df_kdiag[\"inertia\"], marker=\"o\")\n",
"axes[0].set_title(\"Elbow / Inertia\")\n",
"axes[0].set_xlabel(\"K\")\n",
"\n",
"axes[1].plot(df_kdiag[\"k\"], df_kdiag[\"silhouette\"], marker=\"o\")\n",
"axes[1].set_title(\"Silhouette\")\n",
"axes[1].set_xlabel(\"K\")\n",
"\n",
"axes[2].plot(df_kdiag[\"k\"], df_kdiag[\"davies_bouldin\"], marker=\"o\")\n",
"axes[2].set_title(\"Davies-Bouldin\")\n",
"axes[2].set_xlabel(\"K\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "a7249fea-2ad6-4d32-af6f-c9a234cbfe1c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K=2 | silhouette=0.9996 | davies_bouldin=0.2613\n",
"K=5 | silhouette=0.9991 | davies_bouldin=0.2938\n",
"K=10 | silhouette=0.9987 | davies_bouldin=0.1262\n"
]
}
],
"source": [
"RESULTS = {}\n",
"\n",
"for k in [2, 5, 10]:\n",
" km = KMeans(n_clusters=k, n_init=50, random_state=42)\n",
" labels = km.fit_predict(X_scaled)\n",
" dfc[f\"cluster_k{k}\"] = labels\n",
"\n",
" RESULTS[k] = {\n",
" \"model\": km,\n",
" \"labels\": labels,\n",
" \"silhouette\": silhouette_score(X_scaled, labels),\n",
" \"davies_bouldin\": davies_bouldin_score(X_scaled, labels)\n",
" }\n",
"\n",
"for k in [2, 5, 10]:\n",
" print(f\"K={k} | silhouette={RESULTS[k]['silhouette']:.4f} | davies_bouldin={RESULTS[k]['davies_bouldin']:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "0dd70e26-f831-4140-8815-eebb40c9aba7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== K=2 =====\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>n_clients</th>\n",
" <th>aum_qty_mean_med</th>\n",
" <th>gross_flow_to_aum_med</th>\n",
" <th>flow_freq_med</th>\n",
" <th>n_tx_total_med</th>\n",
" <th>avg_n_isin_held_med</th>\n",
" <th>n_isin_total_med</th>\n",
" <th>avg_holding_months_per_isin_med</th>\n",
" <th>exit_rate_per_isin_med</th>\n",
" <th>flow_direction_balance_med</th>\n",
" <th>redemption_bias_med</th>\n",
" <th>aum_drawdown_last_med</th>\n",
" <th>aum_final_to_peak_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k2</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></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>0</th>\n",
" <td>9651</td>\n",
" <td>200.835000</td>\n",
" <td>2.524501</td>\n",
" <td>0.058824</td>\n",
" <td>2.0</td>\n",
" <td>0.683333</td>\n",
" <td>2.0</td>\n",
" <td>14.0</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-5.714286e-02</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>75884.615385</td>\n",
" <td>4.651799</td>\n",
" <td>0.094595</td>\n",
" <td>11.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>36.0</td>\n",
" <td>0.666667</td>\n",
" <td>0.208726</td>\n",
" <td>-4.423077e+12</td>\n",
" <td>0.964708</td>\n",
" <td>0.035292</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients aum_qty_mean_med gross_flow_to_aum_med flow_freq_med \\\n",
"cluster_k2 \n",
"0 9651 200.835000 2.524501 0.058824 \n",
"1 5 75884.615385 4.651799 0.094595 \n",
"\n",
" n_tx_total_med avg_n_isin_held_med n_isin_total_med \\\n",
"cluster_k2 \n",
"0 2.0 0.683333 2.0 \n",
"1 11.0 1.000000 1.0 \n",
"\n",
" avg_holding_months_per_isin_med exit_rate_per_isin_med \\\n",
"cluster_k2 \n",
"0 14.0 1.000000 \n",
"1 36.0 0.666667 \n",
"\n",
" flow_direction_balance_med redemption_bias_med \\\n",
"cluster_k2 \n",
"0 0.000000 -5.714286e-02 \n",
"1 0.208726 -4.423077e+12 \n",
"\n",
" aum_drawdown_last_med aum_final_to_peak_med \n",
"cluster_k2 \n",
"0 1.000000 0.000000 \n",
"1 0.964708 0.035292 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== K=5 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" 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>n_clients</th>\n",
" <th>aum_qty_mean_med</th>\n",
" <th>gross_flow_to_aum_med</th>\n",
" <th>flow_freq_med</th>\n",
" <th>n_tx_total_med</th>\n",
" <th>avg_n_isin_held_med</th>\n",
" <th>n_isin_total_med</th>\n",
" <th>avg_holding_months_per_isin_med</th>\n",
" <th>exit_rate_per_isin_med</th>\n",
" <th>flow_direction_balance_med</th>\n",
" <th>redemption_bias_med</th>\n",
" <th>aum_drawdown_last_med</th>\n",
" <th>aum_final_to_peak_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k5</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></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>0</th>\n",
" <td>9649</td>\n",
" <td>200.729972</td>\n",
" <td>2.524501</td>\n",
" <td>0.058824</td>\n",
" <td>2.0</td>\n",
" <td>0.683333</td>\n",
" <td>2.0</td>\n",
" <td>14.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-5.714286e-02</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>94722.757683</td>\n",
" <td>5.784578</td>\n",
" <td>0.070374</td>\n",
" <td>10.0</td>\n",
" <td>0.586798</td>\n",
" <td>2.0</td>\n",
" <td>35.166667</td>\n",
" <td>1.500000</td>\n",
" <td>0.001110</td>\n",
" <td>-4.646436e+12</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>116019.862250</td>\n",
" <td>4.326850</td>\n",
" <td>0.082639</td>\n",
" <td>7.5</td>\n",
" <td>1.168750</td>\n",
" <td>2.0</td>\n",
" <td>35.833333</td>\n",
" <td>0.333333</td>\n",
" <td>0.682622</td>\n",
" <td>-2.943535e+12</td>\n",
" <td>0.482354</td>\n",
" <td>0.517646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>152151.798643</td>\n",
" <td>3.417115</td>\n",
" <td>0.052149</td>\n",
" <td>7.5</td>\n",
" <td>1.381561</td>\n",
" <td>3.0</td>\n",
" <td>45.750000</td>\n",
" <td>0.875000</td>\n",
" <td>0.199417</td>\n",
" <td>-9.113122e+11</td>\n",
" <td>0.940068</td>\n",
" <td>0.059932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>75884.615385</td>\n",
" <td>4.651799</td>\n",
" <td>0.282051</td>\n",
" <td>15.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>39.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.201133</td>\n",
" <td>-6.666667e+12</td>\n",
" <td>0.685315</td>\n",
" <td>0.314685</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients aum_qty_mean_med gross_flow_to_aum_med flow_freq_med \\\n",
"cluster_k5 \n",
"0 9649 200.729972 2.524501 0.058824 \n",
"1 2 94722.757683 5.784578 0.070374 \n",
"2 2 116019.862250 4.326850 0.082639 \n",
"4 2 152151.798643 3.417115 0.052149 \n",
"3 1 75884.615385 4.651799 0.282051 \n",
"\n",
" n_tx_total_med avg_n_isin_held_med n_isin_total_med \\\n",
"cluster_k5 \n",
"0 2.0 0.683333 2.0 \n",
"1 10.0 0.586798 2.0 \n",
"2 7.5 1.168750 2.0 \n",
"4 7.5 1.381561 3.0 \n",
"3 15.0 1.000000 1.0 \n",
"\n",
" avg_holding_months_per_isin_med exit_rate_per_isin_med \\\n",
"cluster_k5 \n",
"0 14.000000 1.000000 \n",
"1 35.166667 1.500000 \n",
"2 35.833333 0.333333 \n",
"4 45.750000 0.875000 \n",
"3 39.000000 0.000000 \n",
"\n",
" flow_direction_balance_med redemption_bias_med \\\n",
"cluster_k5 \n",
"0 0.000000 -5.714286e-02 \n",
"1 0.001110 -4.646436e+12 \n",
"2 0.682622 -2.943535e+12 \n",
"4 0.199417 -9.113122e+11 \n",
"3 0.201133 -6.666667e+12 \n",
"\n",
" aum_drawdown_last_med aum_final_to_peak_med \n",
"cluster_k5 \n",
"0 1.000000 0.000000 \n",
"1 1.000000 0.000000 \n",
"2 0.482354 0.517646 \n",
"4 0.940068 0.059932 \n",
"3 0.685315 0.314685 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== K=10 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\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>n_clients</th>\n",
" <th>aum_qty_mean_med</th>\n",
" <th>gross_flow_to_aum_med</th>\n",
" <th>flow_freq_med</th>\n",
" <th>n_tx_total_med</th>\n",
" <th>avg_n_isin_held_med</th>\n",
" <th>n_isin_total_med</th>\n",
" <th>avg_holding_months_per_isin_med</th>\n",
" <th>exit_rate_per_isin_med</th>\n",
" <th>flow_direction_balance_med</th>\n",
" <th>redemption_bias_med</th>\n",
" <th>aum_drawdown_last_med</th>\n",
" <th>aum_final_to_peak_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k10</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></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>0</th>\n",
" <td>9639</td>\n",
" <td>200.644385</td>\n",
" <td>2.528102</td>\n",
" <td>0.058824</td>\n",
" <td>2.0</td>\n",
" <td>0.683333</td>\n",
" <td>2.0</td>\n",
" <td>14.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-5.667766e-02</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>11281.487500</td>\n",
" <td>1.646680</td>\n",
" <td>0.056000</td>\n",
" <td>7.0</td>\n",
" <td>0.637500</td>\n",
" <td>3.0</td>\n",
" <td>43.222222</td>\n",
" <td>1.000000</td>\n",
" <td>-0.260413</td>\n",
" <td>-2.225352e+11</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3</td>\n",
" <td>16597.286822</td>\n",
" <td>2.348949</td>\n",
" <td>0.015504</td>\n",
" <td>3.0</td>\n",
" <td>0.751938</td>\n",
" <td>1.0</td>\n",
" <td>46.000000</td>\n",
" <td>0.333333</td>\n",
" <td>-0.547161</td>\n",
" <td>-6.525000e+10</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>40190.138462</td>\n",
" <td>7.329808</td>\n",
" <td>0.046154</td>\n",
" <td>11.0</td>\n",
" <td>0.538462</td>\n",
" <td>3.0</td>\n",
" <td>23.333333</td>\n",
" <td>1.000000</td>\n",
" <td>-0.206507</td>\n",
" <td>-4.423077e+12</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>75884.615385</td>\n",
" <td>4.651799</td>\n",
" <td>0.282051</td>\n",
" <td>15.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>39.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.201133</td>\n",
" <td>-6.666667e+12</td>\n",
" <td>6.853147e-01</td>\n",
" <td>0.314685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>20646.937000</td>\n",
" <td>1.000000</td>\n",
" <td>0.027778</td>\n",
" <td>2.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>36.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-3.347944e+12</td>\n",
" <td>4.840572e-14</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>211392.787500</td>\n",
" <td>7.653700</td>\n",
" <td>0.137500</td>\n",
" <td>13.0</td>\n",
" <td>1.337500</td>\n",
" <td>3.0</td>\n",
" <td>35.666667</td>\n",
" <td>0.666667</td>\n",
" <td>0.365245</td>\n",
" <td>-2.539125e+12</td>\n",
" <td>9.647075e-01</td>\n",
" <td>0.035292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>152961.538462</td>\n",
" <td>4.491325</td>\n",
" <td>0.030769</td>\n",
" <td>6.0</td>\n",
" <td>1.630769</td>\n",
" <td>4.0</td>\n",
" <td>53.000000</td>\n",
" <td>0.750000</td>\n",
" <td>-0.149927</td>\n",
" <td>-6.461538e+11</td>\n",
" <td>8.801370e-01</td>\n",
" <td>0.119863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>151342.058824</td>\n",
" <td>2.342905</td>\n",
" <td>0.073529</td>\n",
" <td>9.0</td>\n",
" <td>1.132353</td>\n",
" <td>2.0</td>\n",
" <td>38.500000</td>\n",
" <td>1.000000</td>\n",
" <td>0.548762</td>\n",
" <td>-1.176471e+12</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>149255.376905</td>\n",
" <td>4.239348</td>\n",
" <td>0.094595</td>\n",
" <td>9.0</td>\n",
" <td>0.635135</td>\n",
" <td>1.0</td>\n",
" <td>47.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.208726</td>\n",
" <td>-4.869795e+12</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients aum_qty_mean_med gross_flow_to_aum_med \\\n",
"cluster_k10 \n",
"0 9639 200.644385 2.528102 \n",
"6 7 11281.487500 1.646680 \n",
"9 3 16597.286822 2.348949 \n",
"1 1 40190.138462 7.329808 \n",
"2 1 75884.615385 4.651799 \n",
"3 1 20646.937000 1.000000 \n",
"5 1 211392.787500 7.653700 \n",
"4 1 152961.538462 4.491325 \n",
"7 1 151342.058824 2.342905 \n",
"8 1 149255.376905 4.239348 \n",
"\n",
" flow_freq_med n_tx_total_med avg_n_isin_held_med \\\n",
"cluster_k10 \n",
"0 0.058824 2.0 0.683333 \n",
"6 0.056000 7.0 0.637500 \n",
"9 0.015504 3.0 0.751938 \n",
"1 0.046154 11.0 0.538462 \n",
"2 0.282051 15.0 1.000000 \n",
"3 0.027778 2.0 1.000000 \n",
"5 0.137500 13.0 1.337500 \n",
"4 0.030769 6.0 1.630769 \n",
"7 0.073529 9.0 1.132353 \n",
"8 0.094595 9.0 0.635135 \n",
"\n",
" n_isin_total_med avg_holding_months_per_isin_med \\\n",
"cluster_k10 \n",
"0 2.0 14.000000 \n",
"6 3.0 43.222222 \n",
"9 1.0 46.000000 \n",
"1 3.0 23.333333 \n",
"2 1.0 39.000000 \n",
"3 1.0 36.000000 \n",
"5 3.0 35.666667 \n",
"4 4.0 53.000000 \n",
"7 2.0 38.500000 \n",
"8 1.0 47.000000 \n",
"\n",
" exit_rate_per_isin_med flow_direction_balance_med \\\n",
"cluster_k10 \n",
"0 1.000000 0.000000 \n",
"6 1.000000 -0.260413 \n",
"9 0.333333 -0.547161 \n",
"1 1.000000 -0.206507 \n",
"2 0.000000 0.201133 \n",
"3 0.000000 1.000000 \n",
"5 0.666667 0.365245 \n",
"4 0.750000 -0.149927 \n",
"7 1.000000 0.548762 \n",
"8 2.000000 0.208726 \n",
"\n",
" redemption_bias_med aum_drawdown_last_med aum_final_to_peak_med \n",
"cluster_k10 \n",
"0 -5.667766e-02 1.000000e+00 0.000000 \n",
"6 -2.225352e+11 1.000000e+00 0.000000 \n",
"9 -6.525000e+10 1.000000e+00 0.000000 \n",
"1 -4.423077e+12 1.000000e+00 0.000000 \n",
"2 -6.666667e+12 6.853147e-01 0.314685 \n",
"3 -3.347944e+12 4.840572e-14 1.000000 \n",
"5 -2.539125e+12 9.647075e-01 0.035292 \n",
"4 -6.461538e+11 8.801370e-01 0.119863 \n",
"7 -1.176471e+12 1.000000e+00 0.000000 \n",
"8 -4.869795e+12 1.000000e+00 0.000000 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_vars = [\n",
" \"aum_qty_mean\",\n",
" \"gross_flow_to_aum\",\n",
" \"flow_freq\",\n",
" \"n_tx_total\",\n",
" \"avg_n_isin_held\",\n",
" \"n_isin_total\",\n",
" \"avg_holding_months_per_isin\",\n",
" \"exit_rate_per_isin\",\n",
" \"flow_direction_balance\",\n",
" \"redemption_bias\",\n",
" \"aum_drawdown_last\",\n",
" \"aum_final_to_peak\",\n",
"]\n",
"\n",
"profile_vars = [c for c in profile_vars if c in dfc.columns]\n",
"\n",
"for k in [2, 5, 10]:\n",
" print(f\"\\n===== K={k} =====\")\n",
" prof = (\n",
" dfc.groupby(f\"cluster_k{k}\")\n",
" .agg(\n",
" n_clients=(ID_COL, \"count\"),\n",
" **{f\"{c}_med\": (c, \"median\") for c in profile_vars}\n",
" )\n",
" .sort_values(\"n_clients\", ascending=False)\n",
" )\n",
" display(prof)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "9368ab73-fd28-4b0e-9a8a-17b860cbf6d4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def robust_zscore_col(s):\n",
" med = np.nanmedian(s)\n",
" mad = np.nanmedian(np.abs(s - med))\n",
" if mad == 0 or np.isnan(mad):\n",
" return np.zeros(len(s))\n",
" return (s - med) / (1.4826 * mad)\n",
"\n",
"for k in [2, 5, 10]:\n",
" prof = dfc.groupby(f\"cluster_k{k}\")[profile_vars].median()\n",
" prof_z = prof.copy()\n",
"\n",
" for c in prof.columns:\n",
" prof_z[c] = robust_zscore_col(prof[c].values)\n",
"\n",
" plt.figure(figsize=(14, 4))\n",
" sns.heatmap(prof_z, cmap=\"RdBu_r\", center=0)\n",
" plt.title(f\"Cluster signatures — K={k}\")\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "6e7fa1e2-cec7-41be-943f-d06b25e1e175",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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98cYbWrdundmPckv88ssvOnDggAYNGmTVWAsbiTnwAPz73//W8ePHlZ2drYsXL2rPnj367rvv5OXlpU8//TTPqQqzZs3S/v37FRoaqsqVKys1NVVLly6Vp6enaf5t1apVVaZMGS1btkylSpWSi4uL6tevrxo1aqhq1ar6+OOPdf78eZUuXVqbN28uUBLYtGlTtWvXTosWLdKpU6f0xBNPKCcnRz/88IOCgoL06quvqmrVqho0aJAmT56ss2fP6umnn1apUqV05swZbdu2TS+//LLCw8PzHcO9PPnkk9qyZYv69eunJ598UmfOnNGyZctUs2ZNZWRkmMY5OzurZs2a2rhxo6pVq6ayZcuqVq1aql27tkX3++9//2vaN/3P/c1jYmLUvn17RUVF6ZNPPsnz/Hr16mnXrl2aP3++KlasqCpVqqhBgwYWxXC/37mjo6PGjRunN998Uy+++KLCwsL06KOP6vz589qzZ49Kly6tOXPmWHTvwhIQEKAZM2aob9++6t27t5YsWaJy5coVaI75n/NSjx07Jul2sv3n/u0RERGmcYMHD1bXrl3Vo0cPvfzyy/r99981f/58hYSEqEWLFgV8MgB/OnfunFavXq1vvvnG9OM7PDxc//nPf7R69WoNGTLEouu1aNFCly5dUnZ2tvr372+Vl5M9SCTmwAPw5xSKEiVKqGzZsqpdu7bef/99hYWFmb3A5E5atWqls2fPatWqVfrjjz9Urlw5Pf744xowYIBcXV1N142JidGUKVM0evRo3bp1S9HR0QoLC9OcOXM0btw4xcbGqmTJkmrdurW6d++udu3a5ft5oqOjZTAYtHLlSk2YMEGurq7y8/NTQECAacxbb72latWq6fPPP9esWbMkSZ6enmrevLnZy3kKQ1hYmC5evKjly5dr586dqlmzpiZOnKhNmzZp7969ZmPHjRunsWPHKjo6Wjdv3lT//v0tSsyzs7MVGRmpcuXK6f333ze1V6tWTUOGDNE//vEPffXVV2ZTIv5qxIgRGjVqlKZNm6bMzEx16NDB4sRcuv/vPCgoSMuXL9fs2bO1ePFiZWRkyMPDQ/Xr17e5P/mGhIRowoQJGjp0qN588019/vnn9/zfTF7++iNp1apVpn/+38S8Xr16mj9/viZNmqTo6GiVKlVKnTp1sjhJAJC3I0eOKDs7O9dforKysu7rhWB/tWTJEmVkZOjgwYOaPHmyfHx89OKLL1op2sLnYDTm8bYKAAAAwEoMBoPZrixfffWVhg0bpvXr1+daqO3i4pJrOtqIESN09erVXLuy3Mns2bP1xRdf3HXBqC2iYg4AAIAiUbduXWVnZ+vSpUtq3LixVa+dk5Njtl2uPSAxBwAAQKFJT09XcnKy6fjMmTNKTEyUm5ubqlevrrZt22r48OEaMWKE6tatqz/++EO7du2SwWDQk08+Ken2upCbN2/q8uXLSk9PV2JioqT/2wxhyZIlqlSpkmrUqCHp9t7p8+bNU48ePR7swxZQkU5lWbJkieLi4pSSkqI6depo5MiRql+/flGFAwAAACvbs2fPHbc47dChg2JiYnTz5k19+umnWrt2rS5cuKCyZcuqYcOGGjBggAwGg6T/W2/1V3++H2HRokVavny5zpw5o2LFiqlq1arq3LmzunbtWmRvMs6PIkvMv/rqKw0fPlxRUVFq0KCBFixYoE2bNmnTpk15vgURAAAAeBgVWWLeuXNn+fv7a9SoUZJuzwMKDQ1Vjx499NZbbxVFSAAAAECRKZLaflZWlg4fPmy2D62jo6OaNWumAwcOFEVIAAAAQJEqksT8jz/+UHZ2dq4pK+XLl9fFixeLIiQAAACgSNntriytHe3rTU4AAACFbfO5g5KkFxo9q41nZxRxNLnl/G7Zm5WtydHzSJHd+34VScW8XLlyKlasmFJTU83aU1NTVaFChaIICQAAAChSRZKYOzk5qV69etq1a5epLScnR7t27TJ7pTcAAADwd1FkU1l69+6tyMhI+fn5qX79+lqwYIGuX7+usLCwogoJAAAAhShHOUV2b3vYzbzIEvPnn39ely5d0vTp05WSkqK6devqn//8J1NZAAAA8LdUpIs/X331Vb366qtFGQIAAAAekGxj0VXM7WHHE3uo6gMAAAAPPXv48QAAAICHQI6K5IXzdoOKOQAAAGADSMwBAAAAG8BUFgAAADwQRbldoj2gYg4AAADYAKsn5vv27VPfvn0VEhIig8Ggbdu2mfVv2bJFr7/+uoKCgmQwGJSYmGjtEAAAAGCDso3GIvvYA6sn5hkZGTIYDProo4/u2h8YGKhhw4ZZ+9YAAACA3bL6HPPQ0FCFhobetb99+/aSpDNnzlj71gAAAIDdYvEnAAAAHgj2Mc8biz8BAAAAG0DFHAAAAA9ENhXzPFExBwAAAGwAFXMAAAA8EMwxz5vVE/P09HQlJyebjs+cOaPExES5ubnJy8tLly9f1m+//aYLFy5Ikk6cOCFJqlChgjw8PKwdDgAAAGAXrJ6YHzp0SD179jQdR0dHS5I6dOigmJgYff3113rvvfdM/YMHD5Yk9e/fXwMGDLB2OAAAAIBdsHpiHhQUpKSkpLv2h4WFKSwszNq3BQAAgI2zlzdwFhUWfwIAAAA2gMWfAAAAeCByijoAG0fFHAAAALABJOYAAACADWAqCwAAAB4I3vyZNyrmAAAAgA2wesU8NjZWW7Zs0fHjx+Xs7KyAgAANGzZMNWrUkCRdvnxZM2bM0M6dO/Xbb7/J3d1dTz/9tN555x25urpaOxwAAADYiGwK5nmyemK+d+9ede/eXf7+/srOztaUKVMUHh6uDRs2yMXFRRcuXNCFCxcUGRmpmjVr6uzZsxo9erQuXLig6dOnWzscAAAAwC44GI2Fu9P7pUuXFBwcrMWLF6tJkyZ3HLNx40a9++67SkhIUPHi9/dbobVjZ2uGCQAAYPc2nzsoSXqh0bPaeHZGEUeT2/EzlYrs3jWq/FZk975fhT7HPC0tTZLk5uZ21zHXrl1T6dKl7zspBwAAAB42hZqY5+TkaPz48QoMDFTt2rXvOObSpUuaPXu2unTpUpihAAAAADatUEvUUVFROnr0qJYuXXrH/mvXrqlPnz7y9fVV//79CzMUAAAAFLFsORR1CDat0BLzMWPGaMeOHVq8eLE8PT1z9V+7dk1vvPGGSpUqpVmzZqlEiRKFFQoAAABg86yemBuNRo0dO1Zbt27VokWL5O3tnWvMtWvXFB4eLicnJ3366acqWbKktcMAAACAjclhu8Q8WT0xj4qK0vr16zV79myVKlVKKSkpkiRXV1c5Ozvr2rVrev3113X9+nVNnDhR165d07Vr1yRJ7u7uKlasmLVDAgAAAGye1RPz+Ph4SVKPHj3M2qOjoxUWFqbDhw/r4MHbW/m0bt3abMz27dtVpUoVa4cEAAAA2DyrJ+ZJSUl59gcFBd1zDAAAAB4+LP7MW6HvYw4AAADg3nijDwAAAB4IKuZ5o2IOAAAA2AAq5gAAAHggcoxUzPNCxRwAAACwASTmAAAAgA2w+lSWpUuXKj4+XmfPnpUk1apVSxEREQoNDZUkjRo1St9//70uXLggFxcXBQQEaNiwYfL19bV2KAAAALAhLP7Mm9UTc09PTw0bNkw+Pj4yGo1au3at+vXrpzVr1qhWrVqqV6+e2rZtq0qVKunKlSuaMWOGwsPDtX37dt76CQAAgL8tqyfmrVq1MjsePHiw4uPjlZCQoFq1aqlLly6mvipVqmjQoEFq166dzp49q6pVq1o7HAAAANiIbGZR56lQd2XJzs7Wpk2blJGRoYCAgFz9GRkZWr16tapUqSJPT8/CDAUAAACwaYWSmCclJalr1666ceOGXFxcNGvWLNWsWdPUv2TJEk2aNEkZGRmqXr265s+fLycnp8IIBQAAADaC7RLzVih/T6hevbrWrl2rFStWqFu3boqMjNSxY8dM/S+99JLWrFmjxYsXq1q1aho0aJBu3LhRGKEAAAAAdqFQEnMnJyf5+PjIz89PQ4cOVZ06dbRw4UJTv6urq6pVq6YmTZpo+vTpOn78uLZu3VoYoQAAAAB24YG8+TMnJ0dZWVl37TcajXn2AwAAwP6xXWLerJ6YT548WS1atFClSpWUnp6u9evXa+/evYqLi9Pp06f11VdfqXnz5nJ3d9fvv/+uuXPnytnZ2bTPOQAAAPB3ZPXEPDU1VZGRkbpw4YJcXV1lMBgUFxen5s2b6/z589q/f78WLFigq1evqnz58mrcuLHi4+NVvnx5a4cCAAAAG5JtZLvEvFg9MR8/fvxd+x599FF99tln1r4lAAAAYPf42QIAAADYgAey+BMAAADIoSacJ74dAAAAwAZQMQcAAMADwXaJeaNiDgAAANiAQk/M586dK4PBoH/84x+5+oxGo9544w0ZDAZt27atsEMBAABAEco2OhbZxx4UapQ//fSTli1bJoPBcMf+BQsWyMGBP2kAAAAAhZaYp6en691339W4cePk5uaWqz8xMVHz5s3Lc99zAAAA4O+i0BLzMWPGKDQ0VM2aNcvVd/36dQ0dOlSjRo2Sh4dHYYUAAAAAG5IjhyL72INC2ZVlw4YN+uWXX7Ry5co79kdHRysgIEBPP/10YdweAAAAsDtWT8x/++03/eMf/9C8efNUsmTJXP3bt2/X7t27tWbNGmvfGgAAADYsmw0B82T1xPzw4cNKTU1VWFiYqS07O1v79u3TkiVL1K1bNyUnJ6tJkyZm5w0YMECNGzfWokWLrB0SAAAAYPOsnpg3bdpU69atM2t77733VKNGDb355psqV66cunTpYtbftm1bvffee2rZsqW1wwEAAADsgtUT89KlS6t27dpmbS4uLipbtqyp/U4LPr28vOTt7W3tcAAAAGAj7GU/8aLCtwMAAADYgELZleWv7jVvPCkp6UGEAQAAgCKUQ004T3w7AAAAgA14IBVzAAAAINto+y/6iY2N1ZYtW3T8+HE5OzsrICBAw4YNU40aNe56zurVq/Xee++ZtTk5Oennn3+26N4k5gAAAMD/t3fvXnXv3l3+/v7Kzs7WlClTFB4erg0bNsjFxeWu55UuXVqbNm0yHTs4WP4jhMQcAAAA+P/i4uLMjmNiYhQcHKzDhw/neg/P/3JwcLjjzoOWIDEHAADAA2GPb/5MS0uTJLm5ueU5LiMjQy1btlROTo4ee+wxDRkyRLVq1bLoXiTmAAAAeOhlZWUpKyvLrM3JyUlOTk53PScnJ0fjx49XYGBgrvf0/K/q1atr/PjxMhgMSktL07x589S1a1dt2LBBnp6e9x2j1RPzGTNmaObMmbmC/XPOTY8ePbR3716z/i5dumjMmDHWDgUAAAA2JKcIXzAUGxubK0ft37+/BgwYcNdzoqKidPToUS1dujTPawcEBCggIMDs+Pnnn9eyZcs0aNCg+46xUCrmtWrV0vz5803HxYoVM+t/+eWXNXDgQNPxI488UhhhAAAAAJKkPn36qHfv3mZteVXLx4wZox07dmjx4sUWVb0lqUSJEqpbt66Sk5MtOq9QEvNixYrlOfnd2dm5wJPjAQAAgPt1r2krfzIajRo7dqy2bt2qRYsWydvb2+J7ZWdn68iRIwoNDbXovEJJzE+dOqWQkBCVLFlSDRs21NChQ+Xl5WXqX7dunb788kt5eHioZcuWioiIoGoOAADwkLOHxZ9RUVFav369Zs+erVKlSiklJUWS5OrqKmdnZ0nS8OHD9eijj2ro0KGSpJkzZ6phw4by8fHR1atXFRcXp3Pnzqlz584W3dvqiXn9+vUVHR2t6tWrKyUlRbNmzVL37t21bt06lS5dWi+++KK8vLxUsWJFJSUladKkSTpx4kSuOT8AAADAgxYfHy/p9rrI/xUdHa2wsDBJ0m+//SZHx//7kXH16lWNHDlSKSkpcnNzU7169bRs2TLVrFnTons7GI1GYwHjz9PVq1fVsmVLjRgx4o6/Gnbt2qVevXpp69atqlq16n1ft7WjZb9AAAAAHnabzx2UJL3Q6FltPDujiKPJbdHRpkV27x61dhfZve9Xof89oUyZMqpWrdpdJ783aNBA0u3pLwAAAMDfVaHvY56enq7Tp0/fdbFnYmKiJLEYFAAA4CGXYwdzzIuS1RPzjz/+WC1btpSXl5cuXLigGTNmyNHRUS+++KKSk5O1bt06hYaGqmzZskpKSlJ0dLSaNGmiOnXqWDsUAAAAwG5YPTH//fffNWTIEF2+fFnu7u5q1KiRVqxYIXd3d924cUO7du3SwoULlZGRoUqVKumZZ55RRESEtcMAAAAA7IrVE/OpU6feta9SpUpavHixtW8JAAAAO5BdhG/+tAd8OwAAAIANKPTFnwAAAIAk5cihqEOwaVTMAQAAABtAxRwAAAAPBHPM88a3AwAAANgAEnMAAADABhRKYn7+/HkNGzZMQUFBql+/vtq2bauff/7ZbMx///tf9e3bV40aNVLDhg3VsWNHnTt3rjDCAQAAgA3IlmORfeyB1eeYX7lyRd26dVNQUJA+++wzlStXTqdOnZKbm5tpTHJysl555RV17NhRAwcOVOnSpXX06FGVLFnS2uEAAAAAdsHqiflnn30mT09PRUdHm9q8vb3NxkydOlUtWrTQ8OHDTW1Vq1a1digAAACwITlGtkvMi9Xr+l9//bX8/Pw0cOBABQcHq3379lqxYoWpPycnRzt27FC1atUUHh6u4OBgde7cWdu2bbN2KAAAAIDdsHpifvr0acXHx6tatWqKi4tTt27dNG7cOK1Zs0aSlJqaqoyMDH322Wd64oknNG/ePLVu3Vr9+/fX3r17rR0OAAAAYBesPpXFaDTKz89PQ4YMkSQ99thjOnr0qJYtW6YOHTooJydHkvTUU0+pV69ekqS6devqxx9/1LJly/T4449bOyQAAADYAHtZhFlUrP7teHh4yNfX16ytRo0aph1XypUrp+LFi+ca4+vry64sAAAA+NuyesU8MDBQJ06cMGs7efKkKleuLElycnKSv79/nmMAAADw8MnhzZ95svq389prr+ngwYOaM2eOTp06pXXr1mnFihV65ZVXTGPCw8O1ceNGrVixQqdOndLixYv1zTffqFu3btYOBwAAALALVq+Y169fXzNnztSUKVM0a9YsValSRe+//75eeukl05jWrVtr9OjRmjt3rsaNG6fq1atr+vTpaty4sbXDAQAAgI3IFtsl5sXqibkktWzZUi1btsxzTKdOndSpU6fCuD0AAABgd5joAwAAANiAQqmYAwAAAH/F4s+88e0AAAAANoCKOQAAAB4IFn/mjYo5AAAAYANIzAEAAAAbYPWpLK1atdLZs2dztb/yyisKDw/XU089dcfzpk2bpjZt2lg7HAAAANgIFn/mzeqJ+cqVK5WdnW06Pnr0qHr37q3nnntOlSpV0s6dO83GL1++XHFxcWrRooW1QwEAAADshtUTc3d3d7PjuXPnqmrVqnr88cfl4OAgDw8Ps/5t27apTZs2KlWqlLVDAQAAgA3JpmKep0L9drKysvTll1+qY8eOcnDIvQr30KFDSkxM5A2gAAAA+Nsr1O0St23bprS0NHXo0OGO/StXrpSvr68CAwMLMwwAAADYgBy2S8xToVbMV61apRYtWujRRx/N1ZeZman169dTLQcAAABUiIn52bNn9f3339818d60aZMyMzPVvn37wgoBAAAAsBuFNpVl9erVKl++vJ588sk79q9atUqtWrXKtVgUAAAADycWf+atUL6dnJwcrV69Wu3bt1fx4rlz/1OnTmnfvn1MYwEAAAD+v0KpmH///fc6d+6cOnbseMf+VatWydPTUyEhIYVxewAAANigHCOLP/NSKBXzkJAQJSUlqXr16nfsHzJkiHbs2CFHR/6cAQAAAEiFvCsLAAAAgPtTqPuYAwAAAH/KpiacJ74dAAAAwAZQMQcAAMADweLPvFExBwAAAGwAFXMAAAA8EDnUhPNk9W8nOztb06ZNU6tWrVS/fn09/fTTmjVrloxGo2lMenq6xowZoxYtWqh+/fp6/vnnFR8fb+1QAAAAALth9Yr5Z599pvj4eH388ceqWbOmDh06pPfee0+urq7q2bOnJCkmJka7d+/WxIkTVblyZX333XeKiopSxYoV9dRTT1k7JAAAAMDmWT0xP3DggJ566ik9+eSTkqQqVapow4YN+umnn8zGtG/fXkFBQZKkLl26aPny5frpp59IzAEAAB5S2Sz+zJPVp7IEBARo9+7dOnHihCTp119/1Q8//KAWLVqYjfn66691/vx5GY1G0/iQkBBrhwMAAADYBatXzN966y1du3ZNbdq0UbFixZSdna3BgwfrpZdeMo0ZOXKkRo4cqRYtWqh48eJycHDQuHHj1KRJE2uHAwAAABvBdol5s3pivnHjRq1bt06TJ09WzZo1lZiYqOjoaFWsWFEdOnSQJC1atEgJCQn69NNP5eXlpf3795vmmDdr1szaIQEAAAA2z+qJ+YQJE/TWW2/phRdekCQZDAadO3dOsbGx6tChgzIzMzV16lTNnDnTNA+9Tp06SkxMVFxcHIk5AADAQyrHyHaJebH6t5OZmSkHB/M/UxQrVsy0XeKtW7d08+bNPMcAAAAAfzdWr5i3bNlSc+bMkZeXl2kqy/z589WxY0dJUunSpfX4449r4sSJcnZ2lpeXl/bt26e1a9dqxIgR1g4HAAAAsAtWT8w//PBDffLJJ4qKilJqaqoqVqyoLl26qF+/fqYxU6ZM0ZQpUzRs2DBduXJFXl5eGjx4sLp162btcAAAAGAjssXiz7xYPTEvXbq0PvjgA33wwQd3HePh4aHo6Ghr3xoAAACwW1ZPzAEAAIA7YbvEvLE0FgAAALABJOYAAACADWAqCwAAAB4I9jHPG98OAAAAYAMKJTG/du2a/vGPf6hly5aqX7++unbtqp9++snUf/HiRY0YMUIhISFq0KCBwsPDdfLkycIIBQAAADYiRw5F9rEHhZKYf/jhh/r+++81YcIErVu3Ts2bN1fv3r11/vx5GY1G9evXT6dPn9bs2bO1Zs0aVa5cWb1791ZGRkZhhAMAAADYPKsn5pmZmdqyZYveffddNWnSRD4+PhowYIB8fHy0dOlSnTx5UgkJCRo9erTq16+vGjVqaPTo0crMzNSGDRusHQ4AAABsRLbRocg+9sDqifmtW7eUnZ2tkiVLmrWXLFlSP/74o7KyskzHpiAcHeXk5KQffvjB2uEAAAAAdsHqiXnp0qUVEBCg2bNn6/z588rOztYXX3yhhIQEXbhwQTVq1JCXl5cmT56sK1euKCsrS3PnztXvv/+ulJQUa4cDAAAA2IVCmWM+YcIEGY1GtWjRQv7+/lq0aJFeeOEFOTo6qkSJEpoxY4ZOnjypxx9/XA0bNtSePXvUokULOTjYx58ZAAAAYLkco2ORfexBoexjXrVqVS1evFgZGRm6du2aKlasqEGDBsnb21uS5Ofnpy+++EJpaWm6efOm3N3d1blzZ/n5+RVGOAAAAIDNK9SfDy4uLqpYsaKuXLminTt36qmnnjLrd3V1lbu7u06ePKlDhw7l6gcAAMDDI8foUGQfe1AoFfP//Oc/MhqNql69upKTkzVhwgTVqFFDYWFhkqSNGzfK3d1dXl5eSkpK0vjx4/X0008rJCSkMMIBAAAAbF6hJOZpaWmaMmWKfv/9d5UtW1bPPPOMBg8erBIlSkiSUlJSFBMTo9TUVHl4eKhdu3aKiIgojFAAAAAAu1Aoifnzzz+v559//q79PXv2VM+ePQvj1gAAALBR9vIGzqJiH0tUAQAAgIdcoVTMAQAAgL+yl0WYRYWKOQAAAGADqJgDAADggbCXF/0UFb4dAAAAwAaQmAMAAAA2wOLEfN++ferbt69CQkJkMBi0bds2s/4tW7bo9ddfV1BQkAwGgxITE3Nd48aNG4qKilJQUJACAgI0YMAAXbx4Mf9PAQAAAJvHmz/zZnFinpGRIYPBoI8++uiu/YGBgRo2bNhdrzF+/Hh98803mjZtmhYtWqQLFy6of//+loYCAAAAPDQsXvwZGhqq0NDQu/a3b99eknTmzJk79qelpWnVqlWaNGmSgoODJd1O1J9//nklJCSoYcOGloYEAAAAO8ALhvL2wOeYHzp0SDdv3lSzZs1Mbb6+vvLy8lJCQsKDDgcAAACwCQ98u8SLFy+qRIkSKlOmjFl7+fLllZKS8qDDAQAAwANiL3O9iwq7sgAAAAA24IEn5hUqVNDNmzd19epVs/bU1FR5eHg86HAAAAAAm/DAE3M/Pz+VKFFCu3btMrUdP35c586dY+EnAADAQ4ztEvNm8Rzz9PR0JScnm47PnDmjxMREubm5ycvLS5cvX9Zvv/2mCxcuSJJOnDgh6Xal3MPDQ66ururYsaNiYmLk5uam0qVLa9y4cQoICCAxBwAAwN+WxYn5oUOH1LNnT9NxdHS0JKlDhw6KiYnR119/rffee8/UP3jwYElS//79NWDAAEnS+++/L0dHRw0cOFBZWVkKCQm5677oAAAAeDjYS+W6qDgYjUZjUQeRH60dOxd1CAAAADZl87mDkqQXGj2rjWdnFHE0ubX9z4Aiu/e6J2zv+/grdmUBAAAAbMAD38ccAAAAf09MZckbFXMAAADABlAxBwAAwAORIyrmeaFiDgAAANgAKuYAAAB4IJhjnjeLK+b79u1T3759FRISIoPBoG3btpn6bt68qYkTJ6pt27Zq2LChQkJCNHz4cJ0/f97sGp9++qm6du2qBg0aqHHjxgV/CgAAAMDOWZyYZ2RkyGAw3PGFQJmZmfrll1/09ttva/Xq1Zo5c6ZOnDiht99+22zczZs39dxzz6lbt275jxwAAACwstjYWHXs2FEBAQEKDg5WRESEjh8/fs/zNm7cqOeee07+/v5q27atvv32W4vvbfFUltDQUIWGht6xz9XVVfPnzzdrGzlypDp37qxz587Jy8tLkjRw4EBJ0urVqy29PQAAAOyUPUxl2bt3r7p37y5/f39lZ2drypQpCg8P14YNG+Ti4nLHc3788UcNHTpUQ4YMUcuWLbVu3Tr169dPq1evVu3ate/73oW++PPatWtycHBQmTJlCvtWAAAAQIHExcUpLCxMtWrVUp06dRQTE6Nz587p8OHDdz1n4cKFeuKJJ/TGG2/I19dXgwYN0mOPPabFixdbdO9CTcxv3LihSZMm6YUXXlDp0qUL81YAAACwcTlGhyL75FdaWpokyc3N7a5jEhISFBwcbNYWEhKihIQEi+5VaLuy3Lx5U++8846MRqOioqIK6zYAAADAPWVlZSkrK8uszcnJSU5OTnc9JycnR+PHj1dgYGCeU1IuXryoChUqmLWVL19eFy9etCjGQknMb968qUGDBuncuXNasGAB1XIAAAAUqdjYWM2cOdOsrX///howYMBdz4mKitLRo0e1dOnSwg5PUiEk5n8m5adOndLChQtVrlw5a98CAAAAdqgoF3/26dNHvXv3NmvLq1o+ZswY7dixQ4sXL5anp2ee165QoUKu6nhqamquKvq9WJyYp6enKzk52XR85swZJSYmys3NTR4eHho4cKB++eUXxcbGKjs7WykpKZJuz8v58+HPnTunK1eu6Ny5c8rOzlZiYqIkqWrVqipVqpSlIQEAAAB5ute0lT8ZjUaNHTtWW7du1aJFi+Tt7X3Pcxo2bKjdu3erV69eprbvv/9eDRs2tChGixPzQ4cOqWfPnqbj6OhoSVKHDh3Uv39/ff3115Kkdu3amZ23cOFCBQUFSZKmT5+uNWvWmPrat2+fawwAAAAeLkY72C4xKipK69ev1+zZs1WqVClTkdnV1VXOzs6SpOHDh+vRRx/V0KFDJUk9e/ZUjx49NG/ePIWGhuqrr77SoUOHNGbMGIvu7WA0Go3WfZwHo7Vj56IOAQAAwKZsPndQkvRCo2e18eyMIo4mtye3Dyuye+94atJ9jTMYDHdsj46OVlhYmCSpR48eqly5smJiYkz9Gzdu1LRp03T27FlVq1ZN77777l3f/XM3hbYrCwAAAPC/cmT7FfOkpKR7jlm0aFGutjZt2qhNmzYFunehv2AIAAAAwL2RmAMAAAA2gKksAAAAeCCKcrtEe0DFHAAAALABVMwBAADwQNjDdolFyeKK+b59+9S3b1+FhITIYDBo27ZtZv0zZszQc889p4YNG6pJkybq1auXDh48aOo/c+aM3n//fbVq1Ur169fX008/renTpysrK6vgTwMAAADYKYsr5hkZGTIYDOrYsaP69++fq79atWoaNWqUvL29lZmZqc8//1yvv/66tm7dKnd3dx0/flxGo1FjxoyRj4+Pjhw5opEjR+r69euKjIy0ykMBAAAA9sbixDw0NDTPzdLbtm1rdvzee+9p5cqVSkpKUnBwsFq0aKEWLVqY+r29vXXixAnFx8eTmAMAADzEWPyZt0Jd/JmVlaXly5fL1dX1rm9RkqS0tDS5ubkVZigAAACATSuUxZ/ffPONhgwZouvXr8vDw0Pz5s2Tu7v7HceeOnVKixcvploOAADwkGPxZ94KpWIeFBSktWvXatmyZXriiSc0aNAgpaam5hp3/vx5vfHGG3ruuef08ssvF0YoAAAAgF0olMTcxcVFPj4+atiwocaPH6/ixYtr5cqVZmPOnz+vnj17KiAgQGPHji2MMAAAAGBDcowORfaxBw/kBUM5OTlm2yH+mZTXq1dP0dHRcnTkPUcAAAD4e7N4jnl6erqSk5NNx2fOnFFiYqLc3NxUtmxZzZkzR61atZKHh4f++OMPLVmyROfPn9dzzz0n6XZS3qNHD3l5eSkyMlKXLl0yXcvDw8MKjwQAAADYH4sT80OHDqlnz56m4+joaElShw4dFBUVpePHj2vNmjX6448/VLZsWfn7+2vJkiWqVauWJOm7777TqVOndOrUKbNtEyUpKSmpIM8CAAAAG2Y0FnUEts3ixDwoKCjPBHrmzJl5nh8WFqawsDBLbwsAAAA81Aplu0QAAADgr3JkH4swiwqrLgEAAAAbQMUcAAAADwQvGMobFXMAAADABpCYAwAAADaAqSwAAAB4IOzlDZxFxeKK+b59+9S3b1+FhITIYDBo27Ztdx07atQoGQwGff7552btffv21ZNPPil/f3+FhITo3Xff1fnz5y0OHgAAAHhYWJyYZ2RkyGAw6KOPPspz3NatW3Xw4EFVrFgxV1/Tpk01bdo0bdq0SdOnT9fp06f1zjvvWBoKAAAA7IjRWHQfe2DxVJbQ0FCFhobmOeb8+fMaO3as4uLi1KdPn1z9vXr1Mv1z5cqV9eabb6pfv366efOmSpQoYWlIAAAAgN2z+uLPnJwcvfvuuwoPD1etWrXuOf7y5ctat26dAgICSMoBAADwt2X1xZ+fffaZihcvrp49e+Y5buLEiVqyZImuX7+uhg0bas6cOdYOBQAAADaEfczzZtWK+aFDh7Rw4UJFR0fLwSHvLz48PFxr1qzRvHnz5OjoqMjISBntZQIQAAAAYGVWrZjv379fqampatmypaktOztbH3/8sRYuXKivv/7a1O7u7i53d3dVr15dvr6+Cg0NVUJCggICAqwZEgAAAGwEFfO8WTUxb9eunZo1a2bWFh4ernbt2iksLOyu5+Xk5EiSsrKyrBkOAAAAYDcsTszT09OVnJxsOj5z5owSExPl5uYmLy8vlStXzmx8iRIlVKFCBdWoUUOSdPDgQf38889q1KiRypQpo+TkZH3yySeqWrUq1XIAAICHGC8YypvFifmhQ4fMFnZGR0dLkjp06KCYmJh7nu/s7KwtW7ZoxowZysjIkIeHh5544glFRETIycnJ0nAAAACAh4LFiXlQUJCSkpLue/z/ziuXJIPBoIULF1p6WwAAAOChZvXtEgEAAIA7YQO+vFn9BUMAAAAALEfFHAAAAA8E2yXmjYo5AAAAYANIzAEAAAAbYHFivm/fPvXt21chISEyGAzatm2bWf+IESNkMBjMPuHh4Xe8VlZWltq1ayeDwaDExMT8PQEAAADsgtHoUGQfe2DxHPOMjAwZDAZ17NhR/fv3v+OYJ554wrS/uaS77k8+YcIEVaxYUb/++qulYQAAAAAPFYsT89DQUIWGhuY5xsnJSR4eHnmO+fbbb/Xdd99pxowZ+ve//21pGAAAALAz7JaYt0LZlWXv3r0KDg5WmTJl1LRpUw0aNEjlypUz9V+8eFEjR47UrFmz5OzsXBghAAAAAHbF6on5E088odatW6tKlSo6ffq0pkyZojfffFPLly9XsWLFZDQaNWLECHXt2lX+/v46c+aMtUMAAACADbKXud5FxeqJ+QsvvGD65z8Xfz799NOmKvqiRYuUnp6uPn36WPvWAAAAgN0q9O0Svb29Va5cOZ06dUqStHv3biUkJMjf31+PPfaYnnnmGUlSx44dFRkZWdjhAAAAADap0N/8+fvvv+vy5cumxaAffvihBg0aZOq/cOGCwsPDNXXqVDVo0KCwwwEAAEBRYfVnnixOzNPT05WcnGw6PnPmjBITE+Xm5iY3NzfNnDlTzz77rCpUqKDTp09r4sSJ8vHx0RNPPCFJ8vLyMruei4uLJKlq1ary9PQsyLMAAAAAdsvixPzQoUPq2bOn6fjP/co7dOig0aNH68iRI1q7dq3S0tJUsWJFNW/eXO+8885d9zIHAADA3wOLP/NmcWIeFBSkpKSku/bHxcVZdL0qVarkeT0AAADg76DQF38CAAAAuLdCX/wJAAAASJKRxZ95omIOAAAA2AAq5gAAAHggWPyZNyrmAAAAgA2gYg4AAIAHg4p5niyumO/bt099+/ZVSEiIDAaDtm3blmvMf//7X/Xt21eNGjVSw4YN1bFjR507d87U36NHDxkMBrPPqFGjCvYkAAAAgB2zuGKekZEhg8Ggjh07qn///rn6k5OT9corr6hjx44aOHCgSpcuraNHj6pkyZJm415++WUNHDjQdPzII4/kI3wAAADg4WBxYh4aGqrQ0NC79k+dOlUtWrTQ8OHDTW1Vq1bNNc7Z2VkeHh6W3h4AAAB2iu0S82bVxZ85OTnasWOHqlWrpvDwcAUHB6tz5853nO6ybt06BQUF6cUXX9TkyZN1/fp1a4YCAAAA2BWrLv5MTU1VRkaGPvvsMw0aNEjDhg3Tf/7zH/Xv318LFy7U448/Lkl68cUX5eXlpYoVKyopKUmTJk3SiRMnNHPmTGuGAwAAAFtCxTxPVk3Mc3JyJElPPfWUevXqJUmqW7eufvzxRy1btsyUmHfp0sV0jsFgkIeHh3r16qXk5OQ7TnsBAAAAHnZWncpSrlw5FS9eXL6+vmbtvr6+Zruy/FWDBg0kSadOnbJmOAAAALAhRqNDkX3sgVUTcycnJ/n7++vEiRNm7SdPnlTlypXvel5iYqIksRgUAAAAf1sWT2VJT09XcnKy6fjMmTNKTEyUm5ubvLy8FB4ersGDB6tJkyYKCgrSf/7zH33zzTdauHChpNvbKa5bt06hoaEqW7askpKSFB0drSZNmqhOnTrWezIAAADAjlicmB86dEg9e/Y0HUdHR0uSOnTooJiYGLVu3VqjR4/W3LlzNW7cOFWvXl3Tp09X48aNJUklSpTQrl27tHDhQmVkZKhSpUp65plnFBERYaVHAgAAgE1i8WeeLE7Mg4KClJSUlOeYTp06qVOnTnfsq1SpkhYvXmzpbQEAAICHmlV3ZQEAAADuxl4WYRYVqy7+BAAAAJA/JOYAAACADWAqCwAAAB4MFn/miYo5AAAAYAOomAMAAOABYfFnXiyumO/bt099+/ZVSEiIDAaDtm3bZtZvMBju+PnnP/9pNm7Hjh3q3Lmz6tevryZNmrCPOQAAAP7WLK6YZ2RkyGAwqGPHjurfv3+u/p07d5od//vf/9YHH3ygZ5991tS2efNmjRw5UoMHD1bTpk2VnZ2tI0eO5CN8AAAA2A3mmOfJ4sQ8NDRUoaGhd+338PAwO96+fbuCgoLk7e0tSbp165b+8Y9/6N1331Xnzp1N42rWrGlpKAAAAMBDo1AXf168eFHffvut2VtAf/nlF50/f16Ojo5q3769QkJC9MYbb1AxBwAAwN9aoSbma9asUalSpfTMM8+Y2k6fPi1Jmjlzpt5++23NmTNHbm5u6tGjhy5fvlyY4QAAAKAoGYvwYwcKNTFftWqV2rZtq5IlS5racnJyJEl9+/bVs88+Kz8/P0VHR8vBwUGbNm0qzHAAAAAAm1Voifn+/ft14sQJs3nk0v/NQff19TW1OTk5ydvbW7/99lthhQMAAICiZnQouo8dKLTEfOXKlapXr57q1Klj1u7n5ycnJyedOHHC1Hbz5k2dPXtWXl5ehRUOAAAAYNMs3pUlPT1dycnJpuMzZ84oMTFRbm5upsT62rVr2rRpkyIjI3OdX7p0aXXt2lUzZsxQpUqV5OXlpbi4OEnSc889l9/nAAAAAOyaxYn5oUOH1LNnT9NxdHS0JKlDhw6KiYmRJG3YsEFGo1EvvvjiHa8xfPhwFS9eXMOHD1dmZqYaNGigBQsWyM3NLT/PAAAAADtgtJNFmEXFwWi0z6+otWPnew8CAAD4G9l87qAk6YVGz2rj2RlFHE1uPvMmFNm9T70+vMjufb8srpgDAAAA+WKX5eAHp1C3SwQAAABwf6iYAwAA4MGwk20LiwoVcwAAAMAGkJgDAAAANoCpLAAAAHggHFj8mSeLK+b79u1T3759FRISIoPBoG3btpn1p6ena8yYMWrRooXq16+v559/XvHx8ab+M2fOyGAw3PGzcePGgj8RAAAAYIcsrphnZGTIYDCoY8eO6t+/f67+mJgY7d69WxMnTlTlypX13XffKSoqShUrVtRTTz2lSpUqaefOnWbnLF++XHFxcWrRokX+nwQAAAC2jYp5nixOzENDQxUaGnrX/gMHDqh9+/YKCgqSJHXp0kXLly/XTz/9pKeeekrFihWTh4eH2Tnbtm1TmzZtVKpUKUvDAQAAAB4KVl/8GRAQoK+//lrnz5+X0WjU7t27deLECYWEhNxx/KFDh5SYmKhOnTpZOxQAAADAblh98efIkSM1cuRItWjRQsWLF5eDg4PGjRunJk2a3HH8ypUr5evrq8DAQGuHAgAAAFvCPuZ5snpivmjRIiUkJOjTTz+Vl5eX9u/fb5pj3qxZM7OxmZmZWr9+vSIiIqwdBgAAAGBXrJqYZ2ZmaurUqZo5c6aefPJJSVKdOnWUmJiouLi4XIn5pk2blJmZqfbt21szDAAAANgiFn/myapzzG/duqWbN2/KwcH8zxTFihWT0Zj738SqVavUqlUrubu7WzMMAAAAIN/utT34X+3Zs+eOW4GnpKRYdF+LK+bp6elKTk42HZ85c0aJiYlyc3OTl5eXHn/8cU2cOFHOzs7y8vLSvn37tHbtWo0YMcLsOqdOndK+ffs0d+5cS0MAAACAPbKTivm9tge/m02bNql06dKm4/Lly1t0X4sT80OHDqlnz56m4+joaElShw4dFBMToylTpmjKlCkaNmyYrly5Ii8vLw0ePFjdunUzu86qVavk6el5191aAAAAgKJwr+3B76Z8+fIqU6ZMvu9rcWIeFBSkpKSku/Z7eHiYkvW8DBkyREOGDLH09gAAAIDFsrKylJWVZdbm5OQkJycnq92jffv2ysrKUq1atdS/f381atTIovOtvisLAAAAcEdFOJUlNjZWM2fONGvr37+/BgwYUOBre3h4KCoqSn5+fsrKytK//vUv9ezZUytWrFC9evXu+zok5gAAAHjo9enTR7179zZrs1a1vEaNGqpRo4bpODAwUKdPn9bnn3+uiRMn3vd1SMwBAADwYBThC4asPW3lXvz9/fXjjz9adI5Vt0sEAAAAIP3666/y8PCw6Bwq5gAAAHggHOxku8R7bQ8+efJknT9/XhMmTJAkff7556pSpYpq1aqlGzdu6F//+pd2796tefPmWXRfEnMAAADgf9xre/CUlBT99ttvpv6bN2/q448/1vnz5/XII4+odu3amj9/vpo2bWrRfR2Md3olZx727dunuLg4HTp0SCkpKZo1a5aefvppU//Fixc1adIk7dy5U2lpaWrcuLFGjhypatWqmcakpKRowoQJ+v7775Wenq7q1aurb9++evbZZ+87jtaOnS0JGwAA4KG3+dxBSdILjZ7VxrMzijia3KrPnFxk9z7Rf2iR3ft+WTzH/M83IX300Ue5+oxGo/r166fTp09r9uzZWrNmjSpXrqzevXsrIyPDNC4yMlInTpzQp59+qnXr1ql169YaNGiQfvnll4I9DQAAAGyXsQg/dsDixDw0NFSDBw9W69atc/WdPHlSCQkJGj16tOrXr68aNWpo9OjRyszM1IYNG0zjDhw4oFdffVX169eXt7e3IiIiVKZMGR0+fLhgTwMAAADYKavuyvLn25RKliz5fzdwdJSTk5N++OEHU1tAQIA2btyoy5cvKycnRxs2bNCNGzf0+OOPWzMcAAAAwG5YNTGvUaOGaaXqlStXlJWVpblz5+r3339XSkqKady0adN069YtBQUFyd/fX6NGjdLMmTPl4+NjzXAAAAAAu2HVxLxEiRKaMWOGTp48qccff1wNGzbUnj171KJFCzk4/N+G8p988omuXr2qzz//XKtWrVLv3r01aNAgJSUlWTMcAAAAwG5YfbtEPz8/ffHFF0pLS9PNmzfl7u6uzp07y8/PT5KUnJysxYsXa/369apVq5YkqU6dOtq/f7+WLFmiMWPGWDskAAAA2AB72ce8qBTamz9dXV3l7u6ukydP6tChQ3rqqackSdevX799Y0fzWxcrVkwW7twIAAAAPDQsrpjf601IGzdulLu7u7y8vJSUlKTx48fr6aefVkhIiKTb89B9fHw0atQoRUZGqmzZstq2bZu+++47xcbGWu/JAAAAYFuMDvce8zdmcWJ+P29CiomJUWpqqjw8PNSuXTtFRESYxpcoUUJz587V5MmT1bdvX2VkZKhq1aqKiYlRaGioFR4JAAAAsD8Wv/nTVvDmTwAAAHO2/ubPGtOmFNm9jw8aUmT3vl+FNsccAAAAwP0jMQcAAABsgNW3SwQAAADuyC4nUD84VMwBAAAAG0DFHAAAAA8ELxjKGxVzAAAAwAZYlJjHxsaqY8eOCggIUHBwsCIiInT8+HGzMTdu3FBUVJSCgoIUEBCgAQMG6OLFi2Zjdu3apa5duyogIEDNmzfXxIkTdevWrYI/DQAAAGCnLErM9+7dq+7du2vFihWaP3++bt26pfDwcGVkZJjGjB8/Xt98842mTZumRYsW6cKFC+rfv7+p/9dff9Wbb76pkJAQrV27VlOnTtXXX3+tyZMnW++pAAAAYHuMRfixAxYl5nFxcQoLC1OtWrVUp04dxcTE6Ny5czp8+LAkKS0tTatWrdKIESMUHBwsPz8/jR8/XgcOHFBCQoIk6auvvpLBYFD//v3l4+Ojxx9/XO+++66WLFmia9euWf0BAQAAAHtQoDnmaWlpkiQ3NzdJ0qFDh3Tz5k01a9bMNMbX11deXl6mxDwrK0slS5Y0u46zs7Nu3LhhSvABAADwEKJinqd8J+Y5OTkaP368AgMDVbt2bUnSxYsXVaJECZUpU8ZsbPny5ZWSkiJJCgkJ0YEDB7R+/XplZ2fr/PnzmjVrliSZxgAAAAB/N/lOzKOionT06FFNnTrVovNCQkI0fPhwffTRR/L399ezzz6r0NDQ28E4skkMAADAw8rBWHQfe5CvTHjMmDHasWOHFixYIE9PT1N7hQoVdPPmTV29etVsfGpqqjw8PEzHvXv31v79+/XNN99o9+7deuqppyRJVapUyU84AAAAgN2zKDE3Go0aM2aMtm7dqgULFsjb29us38/PTyVKlNCuXbtMbcePH9e5c+fUsGFDs7EODg569NFH5ezsrPXr16tSpUqqV69e/p8EAAAAsGMWvfkzKipK69ev1+zZs1WqVCnTnHBXV1c5OzvL1dVVHTt2VExMjNzc3FS6dGmNGzdOAQEBZon5P//5Tz3xxBNydHTUli1b9Nlnn2natGkqVqyYVR8OAAAANsToUNQR2DSLEvP4+HhJUo8ePczao6OjFRYWJkl6//335ejoqIEDByorK0shISH66KOPzMb/+9//1pw5c5SVlaU6depo1qxZpnnmAAAAwN+RRYl5UlLSPceULFlSH330Ua5k/H8tXLjQktsCAADgYWAnizCLCtugAAAAADbAooo5AAAAkF/2sm1hUaFiDgAAANgAEnMAAADABjCVBQAAAA8GU1nyZFHFPDY2Vh07dlRAQICCg4MVERGh48ePm/ovX76ssWPH6tlnn1X9+vX15JNPaty4cUpLSzO7zrlz5/TWW2+pQYMGCg4O1scff6xbt25Z54kAAAAAO2RRxXzv3r3q3r27/P39lZ2drSlTpig8PFwbNmyQi4uLLly4oAsXLigyMlI1a9bU2bNnNXr0aF24cEHTp0+XJGVnZ6tPnz6qUKGCli1bZhpfokQJDRkypFAeEgAAAEWPxZ95sygxj4uLMzuOiYlRcHCwDh8+rCZNmqh27dqaMWOGqb9q1aoaNGiQ3n33Xd26dUvFixfXzp07dezYMc2fP18VKlRQ3bp19c4772jSpEnq37+/nJycrPNkAAAAgB0p0OLPP6eouLm53XXMtWvXVLp0aRUvfvs3QEJCgmrXrq0KFSqYxoSEhOjatWs6duxYQcIBAAAA7Fa+F3/m5ORo/PjxCgwMVO3ate845tKlS5o9e7a6dOliart48aJZUi7JdJySkpLfcAAAAGDrmMqSp3wn5lFRUTp69KiWLl16x/5r166pT58+8vX1Vf/+/fMdIAAAAPB3kK+pLGPGjNGOHTu0YMECeXp65uq/du2a3njjDZUqVUqzZs1SiRIlTH0VKlTQxYsXzcb/eezh4ZGfcAAAAGAPjEX4sQMWJeZGo1FjxozR1q1btWDBAnl7e+cac+3aNYWHh6tEiRL69NNPVbJkSbP+hg0b6siRI0pNTTW1ff/99ypdurRq1qyZz8cAAAAA7JtFU1mioqK0fv16zZ49W6VKlTLNCXd1dZWzs7OuXbum119/XdevX9fEiRN17do1Xbt2TZLk7u6uYsWKKSQkRDVr1tTw4cP17rvvKiUlRdOmTVP37t3ZkQUAAOAhxnaJebMoMY+Pj5ck9ejRw6w9OjpaYWFhOnz4sA4ePChJat26tdmY7du3q0qVKipWrJjmzJmj0aNHq0uXLnrkkUfUoUMHDRw4sCDPAQAAANg1ixLzpKSkPPuDgoLuOUaSKleurM8++8ySWwMAAAAPtQLtYw4AAADAOkjMAQAAABuQ733MAQAAAIuw+DNPVMwBAAAAG0BiDgAAANgAprIAAADggWAf87xZlJjHxsZqy5YtOn78uJydnRUQEKBhw4apRo0apjGjRo3S999/rwsXLsjFxcU0xtfX1zRm3Lhx+vHHH3XkyBH5+vrqiy++sN4TAQAAAHbIoqkse/fuVffu3bVixQrNnz9ft27dUnh4uDIyMkxj6tWrp+joaH311VeKi4uT0WhUeHi4srOzza7VsWNHPf/889Z5CgAAANg+YxF+7IBFFfO4uDiz45iYGAUHB+vw4cNq0qSJJKlLly6m/ipVqmjQoEFq166dzp49q6pVq0qSPvzwQ0nSpUuX7uuFRAAAAMDDrkBzzNPS0iRJbm5ud+zPyMjQ6tWrVaVKFXl6ehbkVgAAALB3dlK5Lir5TsxzcnI0fvx4BQYGqnbt2mZ9S5Ys0aRJk5SRkaHq1atr/vz5cnJyKnCwAAAAwMMq39slRkVF6ejRo5o6dWquvpdeeklr1qzR4sWLVa1aNQ0aNEg3btwoUKAAAADAwyxfFfMxY8Zox44dWrx48R2nqLi6usrV1VXVqlVTgwYN9Pjjj2vr1q168cUXCxwwAAAA7BPbJebNosTcaDRq7Nix2rp1qxYtWiRvb+/7Pi8rKytfAQIAAAB/BxYl5lFRUVq/fr1mz56tUqVKKSUlRdLtCrmzs7NOnz6tr776Ss2bN5e7u7t+//13zZ07V87OzgoNDTVd59SpU8rIyFBKSooyMzOVmJgoSfL19WUuOgAAwMOKinmeLErM4+PjJUk9evQwa4+OjlZYWJicnJy0f/9+LViwQFevXlX58uXVuHFjxcfHq3z58qbxH374ofbu3Ws6bt++vSRp+/btqlKlSn6fBQAAALBbFiXm99pz/NFHH9Vnn312z+ssWrTIktsCAAAAD70C7WMOAAAA3C8Wf+Yt39slAgAAALAeKuYAAAB4MKiY54mKOQAAAGADqJgDAADgwaBinicq5gAAAIANsCgxj42NVceOHRUQEKDg4GBFRETo+PHjdxxrNBr1xhtvyGAwaNu2bab2X3/9VUOGDFFoaKjq16+vNm3aaMGCBQV7CgAAAMDOWTSVZe/everevbv8/f2VnZ2tKVOmKDw8XBs2bJCLi4vZ2AULFsjBwSHXNQ4dOiR3d3dNnDhRlSpV0o8//qhRo0apWLFievXVVwv2NAAAALBZbJeYN4sS87i4OLPjmJgYBQcH6/Dhw2rSpImpPTExUfPmzdOqVasUEhJidk6nTp3Mjr29vZWQkKAtW7aQmAMAAOBvq0CLP9PS0iRJbm5uprbr169r6NChGjVqlDw8PO77OmXLli1IKAAAALB1VMzzlO/Fnzk5ORo/frwCAwNVu3ZtU3t0dLQCAgL09NNP39d1fvzxR23cuFEvv/xyfkMBAAAA7F6+K+ZRUVE6evSoli5damrbvn27du/erTVr1tzXNY4cOaKIiAj169cv15QXAAAAPGSomOcpX4n5mDFjtGPHDi1evFienp6m9t27dys5OdlsvrkkDRgwQI0bN9aiRYtMbceOHVOvXr3UpUsXRURE5DN8AAAA4OFgUWJuNBo1duxYbd26VYsWLZK3t7dZ/1tvvaXOnTubtbVt21bvvfeeWrZsaWo7evSoXnvtNbVv316DBw8uQPgAAADAw8GixDwqKkrr16/X7NmzVapUKaWkpEiSXF1d5ezsLA8Pjzsu+PTy8jIl8UeOHNFrr72mkJAQ9e7d23SNYsWKyd3dvaDPAwAAABvFdol5sygxj4+PlyT16NHDrD06OlphYWH3dY3Nmzfr0qVL+vLLL/Xll1+a2itXrqyvv/7aknAAAACAh4ZFiXlSUpLFN/jrOQMGDNCAAQMsvg4AAADsHBXzPOV7u0QAAAAA1kNiDgAAANiAAr35EwAAALhfLP7MGxVzAAAAwAZQMQcAAMCDQcU8T1TMAQAAABtgUcU8NjZWW7Zs0fHjx+Xs7KyAgAANGzZMNWrUMI3p0aOH9u7da3Zely5dNGbMGEnSH3/8oWHDhikpKUmXL19W+fLl9dRTT2nIkCEqXbq0FR4JAAAANomKeZ4sSsz37t2r7t27y9/fX9nZ2ZoyZYrCw8O1YcMGubi4mMa9/PLLGjhwoOn4kUceMf2zo6OjnnrqKQ0aNEju7u5KTk5WVFSUrly5osmTJ1vhkQAAAAD7Y1FiHhcXZ3YcExOj4OBgHT58WE2aNDG1Ozs7y8PD447XcHNz0yuvvGI6rly5sl555ZVc1wYAAAD+Tgo0xzwtLU3S7WT7f61bt05BQUF68cUXNXnyZF2/fv2u1zh//ry2bt1qltgDAADg4eNQhB97kO9dWXJycjR+/HgFBgaqdu3apvYXX3xRXl5eqlixopKSkjRp0iSdOHFCM2fONDt/yJAh2r59uzIzM9WyZUv94x//yP9TAAAAAHYu34l5VFSUjh49qqVLl5q1d+nSxfTPBoNBHh4e6tWrl5KTk1W1alVT33vvvad+/frp5MmTmjJliqKjozV69Oj8hgMAAABbx+LPPOUrMR8zZox27NihxYsXy9PTM8+xDRo0kCSdOnXKLDH38PCQh4eHfH195ebmpu7duysiIkIVK1bMT0gAAACAXbMoMTcajRo7dqy2bt2qRYsWydvb+57nJCYmStJdF4P+eV1JysrKsiQcAAAA4KFhUWIeFRWl9evXa/bs2SpVqpRSUlIkSa6urnJ2dlZycrLWrVun0NBQlS1bVklJSYqOjlaTJk1Up04dSdK3336rixcvyt/fXy4uLjp27JgmTJigwMBAValSxfpPCAAAAJvgwFSWPFmUmMfHx0u6/RKh/xUdHa2wsDCVKFFCu3bt0sKFC5WRkaFKlSrpmWeeUUREhGlsyZIl9a9//UvR0dHKyspSpUqV1Lp1a7311ltWeBwAAADAPlmUmCclJeXZX6lSJS1evDjPMU2bNtWyZcssuS0AAAAeBlTM81SgfcwBAAAAWAeJOQAAAB4MYxF+LLBv3z717dtXISEhMhgM2rZt2z3P2bNnjzp06CA/Pz+1bt1aq1evtuymIjEHAAAAzGRkZMhgMOijjz66r/GnT59Wnz59FBQUpC+++EKvvfaaPvzwQ/3nP/+x6L75fsEQAAAA8DAKDQ1VaGjofY9ftmyZqlSpohEjRkiSfH199cMPP+jzzz/XE088cd/XoWIOAACAB8LBWHSfwpSQkKDg4GCztpCQECUkJFh0HSrmAAAAeOhlZWXlepmlk5OTnJycCnztixcvqkKFCmZtFSpU0LVr15SZmSlnZ+f7uo5FFfPY2Fh17NhRAQEBCg4OVkREhI4fP55r3IEDB9SzZ081bNhQgYGB6t69uzIzM3ONy8rKUrt27WQwGExvCAUAAMBDqggXf8bGxqpRo0Zmn9jY2Afx1PfNoor53r171b17d/n7+ys7O1tTpkxReHi4NmzYIBcXF0m3k/I33nhDffr00ciRI1WsWDH9+uuvcnTM/RtgwoQJqlixon799VfrPA0AAABwB3369FHv3r3N2qxRLZduV8cvXrxo1nbx4kWVLl36vqvlkoWJeVxcnNlxTEyMgoODdfjwYTVp0kTS7beA9ujRw+xNnjVq1Mh1rW+//VbfffedZsyYoX//+9+WhAEAAABYxFrTVu6kYcOGufLZ77//Xg0bNrToOgVa/JmWliZJcnNzkySlpqbq4MGDKl++vLp27apmzZrp1Vdf1f79+83Ou3jxokaOHKkJEyZY9CsCAAAA9steFn+mp6crMTHRNNX6zJkzSkxM1Llz5yRJkydP1vDhw03ju3btqtOnT2vChAn673//qyVLlmjjxo3q1auXRffNd2Kek5Oj8ePHKzAwULVr15Z0ew9HSZo5c6Y6d+6sf/7zn3rsscfUq1cvnTx5UpJkNBo1YsQIde3aVf7+/vm9PQAAAFAoDh06pPbt26t9+/aSbs8Iad++vaZPny5JSklJ0W+//WYa7+3trdjYWH3//fdq166d5s+fr3Hjxlm0VaJUgF1ZoqKidPToUS1dutTUlpOTI0nq0qWLOnbsKEl67LHHtGvXLq1atUpDhw7VokWLlJ6erj59+uT31gAAALBHhbxtobUEBQUpKSnprv0xMTF3PGft2rUFum++EvMxY8Zox44dWrx4sTw9PU3tHh4ekm5vqv6/fH19TaX/3bt3KyEhIVe1vGPHjmrbtq0+/vjj/IQEAAAA2DWLEnOj0aixY8dq69atWrRokby9vc36q1SpoooVK+rEiRNm7SdPnlSLFi0kSR9++KEGDRpk6rtw4YLCw8M1depUNWjQIJ+PAQAAAFtX2C/6sXcWJeZRUVFav369Zs+erVKlSiklJUWS5OrqKmdnZzk4OCg8PFwzZsxQnTp1VLduXa1Zs0bHjx83zcnx8vIyu+af2yxWrVrVrPoOAAAA/J1YlJjHx8dLknr06GHWHh0drbCwMElSr169lJWVpejoaF25ckV16tTRvHnzVLVqVSuFDAAAADx8LErM85oE/7/eeusts33M81KlSpX7vi4AAADsGFNZ8lSgfcwBAAAAWEe+t0sEAAAALELFPE9UzAEAAAAbQMUcAAAADwTbJeaNijkAAABgAyyqmMfGxmrLli06fvy4nJ2dFRAQoGHDhqlGjRqSpDNnzuipp56647nTpk1TmzZtJEkGgyFX/5QpU/TCCy9YGj8AAADwULAoMd+7d6+6d+8uf39/ZWdna8qUKQoPD9eGDRvk4uKiSpUqaefOnWbnLF++XHFxcaY3f/4pOjpaTzzxhOm4TJkyBXgMAAAA2DymsuTJosQ8Li7O7DgmJkbBwcE6fPiwmjRpomLFisnDw8NszLZt29SmTRuVKlXKrL1MmTK5xgIAAAB/VwWaY56WliZJcnNzu2P/oUOHlJiYqE6dOuXqi4qKUlBQkDp16qSVK1fKaOQnFAAAwMPMwWgsso89yPeuLDk5ORo/frwCAwNVu3btO45ZuXKlfH19FRgYaNY+cOBANW3aVI888oh27typqKgoZWRkqGfPnvkNBwAAALBr+U7Mo6KidPToUS1duvSO/ZmZmVq/fr0iIiJy9fXr18/0z4899piuX7+uuLg4EnMAAAD8beVrKsuYMWO0Y8cOLViwQJ6ennccs2nTJmVmZqp9+/b3vF6DBg30+++/KysrKz/hAAAAwB4Yi/BjByyqmBuNRo0dO1Zbt27VokWL5O3tfdexq1atUqtWreTu7n7P6yYmJsrNzU1OTk6WhAMAAAA8NCxKzKOiorR+/XrNnj1bpUqVUkpKiiTJ1dVVzs7OpnGnTp3Svn37NHfu3FzX+Prrr5WamqoGDRqoZMmS+u677xQbG6vXX3+9gI8CAAAAW8abP/NmUWIeHx8vSerRo4dZe3R0tMLCwkzHq1atkqenp0JCQnLfsHhxLVmyROPHj5ckVa1aVSNGjNDLL79scfAAAADAw8KixDwpKem+xg0ZMkRDhgy5Y1+LFi1yvWwIAAAAfwNUzPNUoH3MAQAAAFgHiTkAAABgA/K9jzkAAABgCRZ/5o2KOQAAAGADqJgDAADgwaBinicq5gAAAIANsCgxj42NVceOHRUQEKDg4GBFRETo+PHjZmNSUlL07rvvqnnz5mrYsKE6dOigzZs357rWjh071LlzZ9WvX19NmjRRREREwZ4EAAAAsGMWTWXZu3evunfvLn9/f2VnZ2vKlCkKDw/Xhg0b5OLiIkmKjIzU1atX9emnn6pcuXJat26dBg0apFWrVumxxx6TJG3evFkjR47U4MGD1bRpU2VnZ+vIkSPWfzoAAADYDBZ/5s2ixDwuLs7sOCYmRsHBwTp8+LCaNGkiSTpw4IA++ugj1a9fX5IUERGhBQsW6PDhw3rsscd069Yt/eMf/9C7776rzp07m65Vs2bNgj4LAAAAYLcKNMc8LS1NkuTm5mZqCwgI0MaNG3X58mXl5ORow4YNunHjhh5//HFJ0i+//KLz58/L0dFR7du3V0hIiN544w0q5gAAAA87YxF+7EC+E/OcnByNHz9egYGBql27tql92rRpunXrloKCguTv769Ro0Zp5syZ8vHxkSSdPn1akjRz5ky9/fbbmjNnjtzc3NSjRw9dvny5YE8DAAAA2Kl8J+ZRUVE6evSopk6datb+ySef6OrVq/r888+1atUq9e7dW4MGDVJSUpKk2wm9JPXt21fPPvus/Pz8FB0dLQcHB23atKkAjwIAAABb5mAsuo89yNc+5mPGjNGOHTu0ePFieXp6mtqTk5O1ePFirV+/XrVq1ZIk1alTR/v379eSJUs0ZswYeXh4SJJ8fX1N5zk5Ocnb21u//fZbQZ4FAAAAsFsWVcyNRqPGjBmjrVu3asGCBfL29jbrv379+u2LOppftlixYjIab/9U8fPzk5OTk06cOGHqv3nzps6ePSsvL698PQQAAABg7yyqmEdFRWn9+vWaPXu2SpUqpZSUFEmSq6urnJ2dVaNGDfn4+GjUqFGKjIxU2bJltW3bNn333XeKjY2VJJUuXVpdu3bVjBkzVKlSJXl5eZl2e3nuuees/HgAAACwGUY7mVNSRCxKzOPj4yVJPXr0MGuPjo5WWFiYSpQooblz52ry5Mnq27evMjIyVLVqVcXExCg0NNQ0fvjw4SpevLiGDx+uzMxMNWjQQAsWLDDb3QUAAAD4O3EwGu3zp0trx873HgQAAPA3svncQUnSC42e1cazM4o4mtyCX5lcZPfetXRokd37fhVoH3MAAAAA1pGvXVkAAAAAi9nlPI0Hh4o5AAAAYANIzAEAAAAbwFQWAAAAPBAOOUUdgW2jYg4AAADYAIsq5rGxsdqyZYuOHz8uZ2dnBQQEaNiwYapRo4ZpTHJysj7++GP98MMPysrK0hNPPKGRI0eqQoUKkqQ9e/aoZ8+ed7z+v/71L9WvX78AjwMAAACbxeLPPFlUMd+7d6+6d++uFStWaP78+bp165bCw8OVkZEhScrIyNDrr78uBwcHLViwQPHx8bp586b69u2rnJzbf7sICAjQzp07zT6dO3dWlSpV5O/vb/0nBAAAAOyARRXzuLg4s+OYmBgFBwfr8OHDatKkiX788UedPXtWa9euVenSpSVJH3/8sZo0aaLdu3erWbNmcnJykoeHh+kaN2/e1Pbt2/Xqq6/KwcHBCo8EAAAA2J8CzTFPS0uTJLm5uUmSsrKy5ODgICcnJ9OYkiVLytHRUT/88MMdr/H111/r8uXL6tixY0FCAQAAgI1zMBbdxx7kOzHPycnR+PHjFRgYqNq1a0uSGjZsqEceeUQTJ07U9evXlZGRoY8//ljZ2dlKSUm543VWrlypkJAQeXp65jcUAAAAwO7lOzGPiorS0aNHNXXqVFObu7u7PvnkE33zzTcKCAhQ48aNdfXqVdWrV++O01R+//137dy5U506dcpvGAAAALAXRmPRfexAvvYxHzNmjHbs2KHFixfnqnSHhIRo27ZtunTpkooXL64yZcqoefPmev7553NdZ9WqVSpbtqxatWqVv+gBAACAh4RFibnRaNTYsWO1detWLVq0SN7e3ncd6+7uLknatWuXUlNTcyXfRqNRq1evVvv27VWiRIl8hA4AAAB7Yi9zvYuKRYl5VFSU1q9fr9mzZ6tUqVKmeeOurq5ydnaWdLsK7uvrK3d3dx04cEDjx49Xr169zPY6l6Tdu3frzJkzTGMBAAAAZGFiHh8fL0nq0aOHWXt0dLTCwsIkSSdOnNCUKVN05coVVa5cWX379lWvXr1yXWvlypUKCAiQr69vPkMHAAAAHh4WJeZJSUn3HDNs2DANGzbsnuMmT55sya0BAABg75jKkqcC7WMOAAAAwDrytSsLAAAAYCkWf+aNijkAAABgA0jMAQAAABvAVBYAAAA8GHbyBs6iQsUcAAAAsAEWJeZLly5V27ZtFRgYqMDAQHXp0kXffvutqf/GjRuKiopSUFCQAgICNGDAAF28eNHsGj/99JNee+01NW7cWE2aNFF4eLh+/fVX6zwNAAAAbJaDseg+9sCixNzT01PDhg3T6tWrtWrVKjVt2lT9+vXT0aNHJUnjx4/XN998o2nTpmnRokW6cOGC+vfvbzo/PT1db775pry8vLRixQotXbpUpUqVUnh4uG7evGndJwMAAADsiEWJeatWrRQaGqpq1aqpevXqGjx4sFxcXJSQkKC0tDStWrVKI0aMUHBwsPz8/DR+/HgdOHBACQkJkqTjx4/r8uXLGjhwoGrUqKFatWqpX79+unjxos6dO1cYzwcAAABbYSzCjx3I9xzz7OxsbdiwQRkZGQoICNChQ4d08+ZNNWvWzDTG19dXXl5epsS8evXqKlu2rFauXKmsrCxlZmZq5cqV8vX1VeXKlQv8MAAAAIC9snhXlqSkJHXt2lU3btyQi4uLZs2apZo1ayoxMVElSpRQmTJlzMaXL19eKSkpkqTSpUtr0aJF6tevn2bPni1J8vHxUVxcnIoXZ4MYAAAA/H1ZXDGvXr261q5dqxUrVqhbt26KjIzUsWPH7uvczMxMffDBBwoMDNTy5csVHx+v2rVrq0+fPsrMzLQ4eAAAANgPFn/mzeIytZOTk3x8fCRJfn5++vnnn7Vw4UK1adNGN2/e1NWrV82q5qmpqfLw8JAkrVu3TmfPntXy5cvl6Hj7N8GkSZP0+OOPa/v27XrhhRes8UwAAACA3SnwPuY5OTnKysqSn5+fSpQooV27dpn6jh8/rnPnzqlhw4aSblfMHR0d5eDg8H8B/P/jnJycgoYCAAAAW5ZjLLqPHbCoYj558mS1aNFClSpVUnp6utavX6+9e/cqLi5Orq6u6tixo2JiYuTm5qbSpUtr3LhxCggIMCXmzZo104QJExQVFaUePXooJydHc+fOVbFixRQUFFQYzwcAAADYBYsS89TUVEVGRurChQtydXWVwWBQXFycmjdvLkl6//335ejoqIEDByorK0shISH66KOPTOf7+vpqzpw5mjlzprp06SJHR0fVrVtX//znP1WxYkXrPhkAAABgRxyMRqN91Pb/orVj56IOAQAAwKZsPndQkvRCo2e18eyMIo4mt9DnJxTZvb/9aniR3ft+FXiOOQAAAICCY/NwAAAAPBD2sm1hUaFiDgAAANgAKuYAAAB4MOxzaeMDQ8UcAAAAsAEk5gAAAIANsCgxX7p0qdq2bavAwEAFBgaqS5cu+vbbb039y5cvV48ePRQYGCiDwaCrV6/musbhw4fVu3dvNW7cWEFBQRo5cqTS09ML/iQAAACwaQ7GovvYA4sSc09PTw0bNkyrV6/WqlWr1LRpU/Xr109Hjx6VJF2/fl1PPPGE+vbte8fzz58/r969e6tq1apasWKFPvvsMx09elTvvfdewZ8EAAAAsGMWLf5s1aqV2fHgwYMVHx+vhIQE1apVS7169ZIk7dmz547n79ixQ8WLF9dHH30kR8fbvwmioqL00ksv6dSpU/Lx8cnHIwAAAMAu2Enluqjke455dna2NmzYoIyMDAUEBNzXOVlZWSpRooQpKZckZ2dnSdIPP/yQ31AAAAAAu2dxYp6UlKSAgAD5+/vro48+0qxZs1SzZs37Ordp06a6ePGi/vnPfyorK0tXrlzR5MmTJUkpKSmWhgIAAAA74mA0FtnHHlicmFevXl1r167VihUr1K1bN0VGRurYsWP3dW6tWrUUExOj+fPnq2HDhmrevLkqV66sChUqyMHBweLgAQAAgIeFxS8YcnJyMs0F9/Pz088//6yFCxdqzJgx93V+27Zt1bZtW128eFGPPPKIHBwc9Pnnn8vb29vSUAAAAICHRoHf/JmTk6OsrCyLz6tQoYIkaeXKlSpZsqSaN29e0FAAAABgy3KKOgDbZlFiPnnyZLVo0UKVKlVSenq61q9fr7179youLk7S7XniFy9eVHJysiTpyJEjKlWqlCpVqqSyZctKkhYvXqyAgAC5uLjo+++/14QJEzR06FCVKVPGuk8GAAAA2BGLEvPU1FRFRkbqwoULcnV1lcFgUFxcnKnavWzZMs2cOdM0vnv37pKk6OhohYWFSZJ++uknzZgxQ+np6apRo4aioqLUvn17Kz0OAAAAbJW9LMIsKg5Go31+Q60dOxd1CAAAADZl87mDkqQXGj2rjWdnFHE0uT3VKrrI7r39a9t/oWW+9zEHAAAAYD0FXvwJAAAA3Be7nKfx4FAxBwAAAGwAFXMAAAA8GPa5tPGBoWIOAAAA2ACLEvOlS5eqbdu2CgwMVGBgoLp06aJvv/3W1D9q1Cg9/fTTql+/vpo2baq3335b//3vf82uce7cOb311ltq0KCBgoOD9fHHH+vWrVvWeRoAAADYLAdj0X3sgUVTWTw9PTVs2DD5+PjIaDRq7dq16tevn9asWaNatWqpXr16atu2rSpVqqQrV65oxowZCg8P1/bt21WsWDFlZ2erT58+qlChgpYtW6YLFy4oMjJSJUqU0JAhQwrrGQEAAACbZ1HFvFWrVgoNDVW1atVUvXp1DR48WC4uLkpISJAkdenSRU2aNFGVKlVUr149DRo0SL/99pvOnj0rSdq5c6eOHTumiRMnqm7dugoNDdU777yjJUuWKCsry+oPBwAAAOTHkiVL1KpVK/n7+6tz58766aef7jp29erVMhgMZh9/f3+L75nvOebZ2dnasGGDMjIyFBAQkKs/IyNDq1evVpUqVeTp6SlJSkhIUO3atVWhQgXTuJCQEF27dk3Hjh3LbygAAACwB0Zj0X0s8NVXXyk6Oto0M6ROnToKDw9XamrqXc8pXbq0du7cafp88803Fn89Fu/KkpSUpK5du+rGjRtycXHRrFmzVLNmTVP/kiVLNGnSJGVkZKh69eqaP3++nJycJEkXL140S8olmY5TUlIsDh4AAACwtvnz5+vll19Wx44dJUlRUVHasWOHVq1apbfeeuuO5zg4OMjDw6NA97W4Yl69enWtXbtWK1asULdu3RQZGWlW7X7ppZe0Zs0aLV68WNWqVdOgQYN048aNAgUJAAAA++eQU3SfrKwsXbt2zexzp6nUWVlZOnz4sJo1a2Zqc3R0VLNmzXTgwIG7PltGRoZatmyp0NBQvf322zp69KjF34/FibmTk5N8fHzk5+enoUOHqk6dOlq4cKGp39XVVdWqVVOTJk00ffp0HT9+XFu3bpV0uzp+8eJFs+v9eVzQXxgAAADA3cTGxqpRo0Zmn9jY2Fzj/vjjD2VnZ6t8+fJm7eXLl8+Vx/6pevXqGj9+vGbPnq2JEyfKaDSqa9eu+v333y2KscAvGMrJyclz4abRaDT1N2zYUHPmzFFqaqrpYb///nuVLl3abDoMAAAAYE19+vRR7969zdr+nG5dUAEBAWZrLgMCAvT8889r2bJlGjRo0H1fx6LEfPLkyWrRooUqVaqk9PR0rV+/Xnv37lVcXJxOnz6tr776Ss2bN5e7u7t+//13zZ07V87OzgoNDZV0e6FnzZo1NXz4cL377rtKSUnRtGnT1L17d6t9MQAAALBRRfjmTycnp/vKN8uVK6dixYrlWuiZmpqaa63k3ZQoUUJ169ZVcnKyRTFalJinpqYqMjJSFy5ckKurqwwGg+Li4tS8eXOdP39e+/fv14IFC3T16lWVL19ejRs3Vnx8vKk6XqxYMc2ZM0ejR49Wly5d9Mgjj6hDhw4aOHCgRUEDAAAAhcHJyUn16tXTrl279PTTT0u6PUNk165devXVV+/rGtnZ2Tpy5IipOH2/LErMx48ff9e+Rx99VJ999tk9r1G5cuX7GgcAAICHjJ28gbN3796KjIyUn5+f6tevrwULFuj69esKCwuTJA0fPlyPPvqohg4dKkmaOXOmGjZsKB8fH129elVxcXE6d+6cOnfubNF9CzzHHAAAAHiYPP/887p06ZKmT5+ulJQU1a1bV//85z9NU1l+++03OTr+3x4qV69e1ciRI5WSkiI3NzfVq1dPy5Yts3gNpYPRWISTfQqgtaNlv0AAAAAedpvPHZQkvdDoWW08O6OIo8ntmeCxRXbvLbtGFtm971e+3/wJAAAAwHpIzAEAAAAbwBxzAAAAPBj2OYP6gaFiDgAAANgAixLzpUuXqm3btgoMDFRgYKC6dOmib7/9Ntc4o9GoN954QwaDQdu2bTPrGzdunMLCwuTn56d27doVLHoAAADYj5wi/NgBi6ayeHp6atiwYfLx8ZHRaNTatWvVr18/rVmzRrVq1TKNW7BggRwcHO56nY4dO+rgwYNKSkrKf+QAAADAQ8SixLxVq1Zmx4MHD1Z8fLwSEhJMiXliYqLmzZunVatWKSQkJNc1PvzwQ0nSpUuXSMwBAACA/y/fiz+zs7O1adMmZWRkKCAgQJJ0/fp1DR06VKNGjZKHh4fVggQAAID9c2DxZ54sTsyTkpLUtWtX3bhxQy4uLpo1a5bprUbR0dEKCAjQ008/bfVAAQAAgIeZxYl59erVtXbtWqWlpWnz5s2KjIzU4sWLderUKe3evVtr1qwpjDgBAABg76iY58nixNzJyUk+Pj6SJD8/P/38889auHChSpYsqeTkZDVp0sRs/IABA9S4cWMtWrTIOhEDAAAAD6ECv2AoJydHWVlZGjBggDp37mzW17ZtW7333ntq2bJlQW8DAAAAe0fFPE8WJeaTJ09WixYtVKlSJaWnp2v9+vXau3ev4uLi5OHhcccFn15eXvL29jYdnzp1ShkZGUpJSVFmZqYSExMlSb6+vnJycirg4wAAAAD2yaLEPDU1VZGRkbpw4YJcXV1lMBgUFxen5s2b3/c1PvzwQ+3du9d03L59e0nS9u3bVaVKFUvCAQAAAB4aFiXm48ePt+jid9qnnLnmAAAAf1N28gbOouJY1AEAAAAAsMLiTwAAAOB+8IKhvFExBwAAAGwAFXMAAAA8GFTM80TFHAAAALABJOYAAACADbBoKsvSpUsVHx+vs2fPSpJq1aqliIgIhYaGSpJ69Ohhtke5JHXp0kVjxoyRJP3666+aO3eufvjhB/3xxx+qXLmyunbtqtdee80azwIAAABbxlSWPFmUmHt6emrYsGHy8fGR0WjU2rVr1a9fP61Zs0a1atWSJL388ssaOHCg6ZxHHnnE9M+HDh2Su7u7Jk6cqEqVKunHH3/UqFGjVKxYMb366qtWeiQAAADA/liUmLdq1crsePDgwYqPj1dCQoIpMXd2dpaHh8cdz+/UqZPZsbe3txISErRlyxYScwAAgIcdFfM85XuOeXZ2tjZs2KCMjAwFBASY2tetW6egoCC9+OKLmjx5sq5fv57nddLS0lS2bNn8hgEAAAA8FCzeLjEpKUldu3bVjRs35OLiolmzZqlmzZqSpBdffFFeXl6qWLGikpKSNGnSJJ04cUIzZ86847V+/PFHbdy4UbGxsQV7CgAAAMDOWZyYV69eXWvXrlVaWpo2b96syMhILV68WDVr1lSXLl1M4wwGgzw8PNSrVy8lJyeratWqZtc5cuSIIiIi1K9fP4WEhBT8SQAAAGDbcoo6ANtm8VQWJycn+fj4yM/PT0OHDlWdOnW0cOHCO45t0KCBJOnUqVNm7ceOHVOvXr3UpUsXRURE5CNsAAAA4OFS4Dd/5uTkKCsr6459iYmJkmS2GPTo0aN67bXX1L59ew0ePLigtwcAAICdcGDxZ54sSswnT56sFi1aqFKlSkpPT9f69eu1d+9excXFKTk5WevWrVNoaKjKli2rpKQkRUdHq0mTJqpTp46k29NXXnvtNYWEhKh3795KSUmRJBUrVkzu7u7WfzoAAADATliUmKempioyMlIXLlyQq6urDAaD4uLi1Lx5c/3222/atWuXFi5cqIyMDFWqVEnPPPOM2VSVzZs369KlS/ryyy/15ZdfmtorV66sr7/+2npPBQAAANtDxTxPDkajfX5DrR07F3UIAAAANmXzuYOSpBcaPauNZ2cUcTS5tan7XpHde2NidJHd+37lex9zAAAAANZT4MWfAAAAwH3JscuJGg8MFXMAAADABlAxBwAAwINhn0sbHxgq5gAAAIANIDEHAAAAbIBFU1mWLl2q+Ph4nT17VpJUq1YtRUREKDQ01DTmwIEDmjp1qn766Sc5Ojqqbt26iouLk7OzsySpb9+++vXXX5Wamio3NzcFBwdr2LBhevTRR634WAAAALA5TGXJk0WJuaenp4YNGyYfHx8ZjUatXbtW/fr105o1a1SrVi0dOHBAb7zxhvr06aORI0eqWLFi+vXXX+Xo+H+F+aZNm6pv377y8PDQ+fPnNWHCBL3zzjtatmyZ1R8OAAAAsBcWJeatWrUyOx48eLDi4+OVkJCgWrVqKTo6Wj169NBbb71lGlOjRg2zc3r16mX658qVK+vNN99Uv379dPPmTZUoUSIfjwAAAAC7QMU8T/meY56dna0NGzYoIyNDAQEBSk1N1cGDB1W+fHl17dpVzZo106uvvqr9+/ff9RqXL1/WunXrFBAQQFIOAACAvzWLt0tMSkpS165ddePGDbm4uGjWrFmqWbOmEhISJEkzZ87U8OHDVbduXa1du1a9evXS+vXrVa1aNdM1Jk6cqCVLluj69etq2LCh5syZY63nAQAAgK3iBUN5srhiXr16da1du1YrVqxQt27dFBkZqWPHjiknJ0eS1KVLF3Xs2FGPPfaY3n//fVWvXl2rVq0yu0Z4eLjWrFmjefPmydHRUZGRkTLypw0AAAD8jVlcMXdycpKPj48kyc/PTz///LMWLlyoN998U5Lk6+trNt7X11fnzp0za3N3d5e7u7uqV68uX19fhYaGKiEhQQEBAfl9DgAAAMCuFXgf85ycHGVlZalKlSqqWLGiTpw4YdZ/8uRJVa5cOc/zJSkrK6ugoQAAAMCWGXOK7mMHLKqYT548WS1atFClSpWUnp6u9evXa+/evYqLi5ODg4PCw8M1Y8YM1alTR3Xr1tWaNWt0/PhxTZ8+XZJ08OBB/fzzz2rUqJHKlCmj5ORkffLJJ6patSrVcgAAAPytWZSYp6amKjIyUhcuXJCrq6sMBoPi4uLUvHlzSbe3QszKylJ0dLSuXLmiOnXqaN68eapataokydnZWVu2bNGMGTOUkZEhDw8PPfHEE4qIiJCTk5P1nw4AAAC2gzWFeXIw2umqy9aOnYs6BAAAAJuy+dxBSdILjZ7VxrMzijia3NpUG1xk9954cmqR3ft+FXiOOQAAAICCs3hXFgAAACBf2Mc8T1TMAQAAABtAxRwAAAAPhn0ubXxgqJgDAAAANoCKOQAAAB4MKuZ5sigxX7p0qeLj43X27FlJUq1atRQREaHQ0FCdOXNGTz311B3PmzZtmtq0aWPW9scff6hdu3Y6f/689u3bpzJlyuTzEQAAAAD7Z1Fi7unpqWHDhsnHx0dGo1Fr165Vv379tGbNGtWoUUM7d+40G798+XLFxcWpRYsWua71wQcfyGAw6Pz58wV7AgAAAOAhYFFi3qpVK7PjwYMHKz4+XgkJCapVq5Y8PDzM+rdt26Y2bdqoVKlSZu1Lly5VWlqaIiIi9O9//zufoQMAAMCuMJUlT/le/Jmdna0NGzYoIyNDAQEBufoPHTqkxMREderUyaz92LFjmj17tj7++GM5OrL2FAAAAJDysfgzKSlJXbt21Y0bN+Ti4qJZs2apZs2aucatXLlSvr6+CgwMNLVlZWVpyJAhevfdd+Xl5aXTp08XLHoAAADYj5ycoo7Apllcsq5evbrWrl2rFStWqFu3boqMjNSxY8fMxmRmZmr9+vW5quWTJ0+Wr6+v2rVrV7CoAQAAgIeMxRVzJycn+fj4SJL8/Pz0888/a+HChRozZoxpzKZNm5SZman27dubnbt7924dOXJEmzdvliQZ//88o6ZNm6pv374aOHBgfp8DAAAAto455nkq8D7mOTk5ysrKMmtbtWqVWrVqJXd3d7P2GTNmKDMz03T8888/6/3339eSJUtUtWrVgoYCAAAA2C2LEvPJkyerRYsWqlSpktLT07V+/Xrt3btXcXFxpjGnTp3Svn37NHfu3Fzn/zX5/uOPPyRJvr6+7GMOAACAvzWLEvPU1FRFRkbqwoULcnV1lcFgUFxcnJo3b24as2rVKnl6eiokJMTqwQIAAMCOMZUlTw5Go31+Q60dOxd1CAAAADZl87mDkqQXGj2rjWdnFHE0ubV59O0iu/fG858W2b3vV4HnmAMAAAD3Jccu68EPDG/4AQAAAGwAiTkAAABgA5jKAgAAgAfCaOTNn3mhYg4AAADYAIsq5kuXLlV8fLzOnj0rSapVq5YiIiIUGhoqSUpJSdGECRP0/fffKz09XdWrV1ffvn317LPPmq7RqlUr0/l/Gjp0qN56662CPgsAAABsGYs/82RRYu7p6alhw4bJx8dHRqNRa9euVb9+/bRmzRrVqlVLkZGRunr1qj799FOVK1dO69at06BBg7Rq1So99thjpusMHDhQL7/8sum4VKlS1nsiAAAAwA5ZNJWlVatWCg0NVbVq1VS9enUNHjxYLi4uSkhIkCQdOHBAr776qurXry9vb29FRESoTJkyOnz4sNl1SpUqJQ8PD9PHxcXFag8EAAAAG2U0Ft3HDuR7jnl2drY2bNigjIwMBQQESJICAgK0ceNGXb58WTk5OdqwYYNu3Lihxx9/3Ozczz77TEH/r737Dovq2v4G/h1Uohd71ESNmqhxhl5UQMGCCjY0YCBY0CAgIlHEWFC5Yu+CqBgbBms0JAT0F7uJJVEk9hZFBZUmSLEhZYBZ7x+8nMsITJExjmZ9nocnmXNmlvucmdmz9ylrWVnByckJERERKCkpqdlWMMYYY4wx9o5TOytLQkIChg8fjqKiIvznP//B+vXr0bFjRwBAWFgYpkyZAisrK9SuXRt169ZFeHg42rVrJ7x+9OjRMDAwQKNGjXD58mWEhoYiKysLs2bN0txWMcYYY4wx9o5Re2D+2WefITY2Fi9evMCRI0cQGBiIXbt2oWPHjlizZg2eP3+Obdu2oUmTJjh+/DgCAgKwe/duiMViAMDYsWOFWBKJBHXq1MHcuXMxdepU6Orqam7LGGOMMcaYdpFxukRF1B6Y6+rqCkfAjYyMcP36dezYsQPe3t7YtWsXfv31V3z++ecAygbeFy5cwO7du7FgwYIq45mamqKkpASpqalo3759DTaFMcYYY4yxd1eNCwzJZDJIpVIUFBQAAHR05C9br1WrFkjBBfe3bt2Cjo4OPvzww5o2hTHGGGOMabN35CbMt0WtgXlISAh69uyJli1b4uXLl/j111/x119/YevWrWjfvj3atWuH4OBgBAYGonHjxjh+/DjOnDmDTZs2ASjL2nL16lVYW1tDT08Ply9fxtKlSzF06FA0atTojWwgY4wxxhhj7wK1BuY5OTkIDAzE48eP0aBBA4jFYmzduhU2NjYAgM2bNyMkJAS+vr7Iz89H27ZtsWzZMqEAka6uLg4ePIjw8HBIpVJ88skn8PDwkLvunDHGGGOMsX8jESm6zkSL2eu4vu0mMMYYY4xplSPpVwEAgzv3x6G0dW+5NZX1r//1W/u3j+Rtf2v/tqpeO485Y4wxxhhjTHNqfPMnY4wxxhhjKnk3L9T4x/ARc8YYY4wxxrQAHzFnjDHGGGP/DBkfMVeEj5gzxhhjjDGmBXhgzhhjjDHGmBZQa2D+ww8/YMiQIbCwsICFhQXc3Nxw6tQpYX1ycjK++eYbWFtbw8LCApMnT0Z2dnalOCdPnoSrqytMTEzQtWtX+Pn51XxLGGOMMcaYdiPZ2/t7B6h1jfnHH3+MadOmoV27diAixMbG4ptvvkFMTAxat24NT09PSCQSbN9elidyzZo18PX1RVRUFHR0yuYAR44cwZw5czBlyhRYW1ujtLQUd+7c0fyWMcYYY4wx9g5Ra2Dep08fucdTpkzBnj17cOXKFWRmZiItLQ2xsbGoX78+AGD58uXo2rUrzp07h+7du6OkpASLFy/G9OnT4er6vwJBHTt21MCmMMYYY4wxbUZ886dCr32NeWlpKQ4cOID8/HyYm5tDKpVCJBJBV1dXeM4HH3wAHR0dXLx4EQDw999/IzMzEzo6OnBycoKtrS28vb35iDljjDHGGPvXU3tgnpCQAHNzcxgbG2Pu3LlYv349OnbsCDMzM9SrVw8rV65EQUEB8vPzsXz5cpSWliIrKwsAkJKSAgAIDw/HhAkTsHHjRjRq1AijR4/G06dPNbphjDHGGGNMy/A15gqpPTD/7LPPEBsbi6ioKIwYMQKBgYG4d+8emjZtijVr1uDEiRMwNzdHly5d8Pz5cxgaGkIkEgEAZLKyneLr64v+/fvDyMgIS5cuhUgkwuHDhzW7ZYwxxhhjjL1D1C4wpKuri3bt2gEAjIyMcP36dezYsQMLFiyAra0tjh8/jtzcXNSuXRsNGzaEjY0NBg0aBABo3rw5AKBDhw5y8dq0aYNHjx5pYnsYY4wxxhh7J9U4j7lMJoNUKpVb1rRpUzRs2BBxcXHIyckRbho1MjKCrq4u7t+/Lzy3uLgYaWlpaNWqVU2bwhhjjDHGtBjJ6K39qWv37t3o06cPjI2N4erqimvXril8/qFDhzBgwAAYGxtjyJAhcinFVaXWwDwkJATnz59HamoqEhISEBISgr/++gtDhgwBAERHR+PKlStITk7Gvn37EBAQAA8PD7Rv3x4AUL9+fQwfPhzr1q3Dn3/+iaSkJMybNw8AMGDAALUbzxhjjDHGmKYdPHgQS5cuFdKCSyQSeHl5IScnp8rnX7p0CVOnToWLiwtiY2PRt29ffPPNN2onOBERkcpTiNmzZ+PcuXN4/PgxGjRoALFYjHHjxsHGxgYAsGrVKsTExODZs2do3bo1hg8fDg8PD+Eac6DsCHloaCj27duHwsJCmJqaYvbs2fj888/Vargso5Naz2eMMcYYe9/1b2UKAAh9GAfjNqlvuTWV2eu4Kn/SG3JM9pPKz3V1dYWxsTGCg4MBlF0h0qtXL4wePRo+Pj6Vnh8QEICCggJs2rRJWPbVV19BIpFgwYIFKv+7ag3MtcnA1pPedhMYY4wxxrTKinMxAIBv23VTayD6T3kXBuZSqRRmZmZYu3Yt+vXrJywPDAzE8+fPsWHDhkqv6d27Nzw8PODh4SEsW7t2LY4fP479+/er3Ea1b/5kjDHGGGPsXSOVSivdF6mrqytXgwcAnjx5gtLSUnz44Ydyyz/88EMkJSVVGTs7OxvNmjWr9Pzs7Gy12vjODswPpa17201gjDHGGNMyZeOjY1qatvttHsVft24dwsPD5ZZNnDgRkyZpz1UY7+zAnDHGGGOMMVWNHz8eY8eOlVv26tFyAGjSpAlq1apV6UbPnJycSkfFyzVr1qzS0XFFz69OjdMlMsYYY4wxpu10dXVRv359ub+qBua6urowNDREXFycsEwmkyEuLg7m5uZVxjYzM8O5c+fklp09exZmZmZqtZEH5owxxhhjjFUwduxYREVFISYmBomJiZg3bx4KCgowbNgwAMCMGTMQEhIiPH/MmDH4448/8P333yMxMRHr1q3DjRs34O7urta/y5eyMMYYY4wxVsGgQYOQm5uLtWvXIisrC/r6+oiIiBAuTXn06BF0dP53fNvCwgKrVq1CWFgYQkND8emnn2L9+vXo1Em99N7vbLpExhhjjDHG3id8KQtjjDHGGGNagAfmjDHGGGOMaQEemDPGGGOMMaYFeGDOGGOMMcaYFuCBOWOMMcYYY1rgnUmXmJubi+joaFy5ckWorNSsWTOYm5tj2LBhaNq06VtuIWOMMcYYY6/vnUiXeO3aNXh7e6Nu3bro3r07PvzwQwBlpU7j4uJQWFiIiIgIGBsbv7U2jhkzBkuXLkXr1q3/0X/39u3buHHjBqysrNCmTRvcvXsXu3fvhkwmg729PXr06KFSHKlUiuPHj1c58enbt2+VlbH+SX379sXWrVvx6aefqvT8jIwM6OrqChO2CxcuYM+ePXj06BFatWqFUaNGVVu9izFNCw8Px8iRI1/7AMLz589x+PBhpKeno3Xr1hgwYAAaNGig0mtLS0uF1+no6AjfdSKClZWVyuWiS0pKcO/ePWRlZQEAmjdvjg4dOqBOnTqvtU2aNGvWLAQEBOCjjz56rdeXlJQgPj5e2E9WVlaoVauWSq/NycnBnTt3YGRkhAYNGiA7OxsxMTEgIvTq1Qtisfi12sQY+3d6JwbmX331FSQSCebPnw+RSCS3jogwd+5cJCQk4Mcff1QpXkZGBho0aAA9PT255cXFxbhy5Qq6du1a7Wt/++23KpdPmjQJQUFB+PjjjwGUDSQVkUqlEIlEwo9acnIyoqOjkZ6ejlatWsHFxQVt2rRRGOPo0aMICAhAgwYNIJVKsX79ekyePBlGRkbQ0dFBXFwcli9fjiFDhiiM8/DhQ3h5eeHx48cwNTWVm/hcvXoVH3/8MbZs2YJ27dopjFNOJpPJJd2vuDwjIwOtWrWq9rU7duyocvmyZcvg7e0tDCLGjBmjsA2urq7w8/ODnZ0djh8/jkmTJqF3797o0KEDHjx4gJMnT2LdunWws7NTuj2a/OHNysrC1atX5SY/pqamaN68ucox3pSaDm5ed/CoqcklUDaJv3z5cqXJpYmJicoxavL5zcvLq7SMiNCtWzf88MMPaN++PQCgfv36CtswceJEODo6YsCAAbh79y5Gjx4NkUiENm3aIC0tDSKRCNu3b0eHDh0Uxrl9+za8vb2Rk5ODjh07YvPmzfDx8UFqaioAoE6dOoiIiFC4f2QyGdasWYMffvgBL168kFvXoEEDjBo1Cv7+/lXus1ft3r0bx44dQ6NGjTB8+HB069ZNWJebmwtXV9dq+9jy7amKi4sLwsLC8MknnwAAJBKJwnYsXLgQtra2sLOzQ0ZGBsaOHYuHDx+iSZMmePLkCTp06ICIiAil34X4+Hj4+vqioKAAzZo1Q0REBMaPH4+6detCJBIhLS0NGzZsgK2trcI4gHZPfICaTS5rMvHR1MRSm/teoGb9b00m7kz7vBMDcxMTE8TExFT7I5SYmAhnZ2dcu3ZNYZzHjx/Dz88PN2/ehEgkgqOjI+bOnSsM0LOzs9GjRw/cunWr2hgSiQQikQiKdptIJFIYAwBGjx6NUaNGYcCAAbh48SI8PDzw2WefCQPH+/fvIzIyUuFR3WHDhsHe3h4TJkzAgQMHMG/ePHh4eOCbb74BAHz//ffYv38/YmNjFbZl7NixqFevHlasWFFpwJCXl4cZM2agqKgIW7duVRgnLy8PQUFBOHHiBOrXrw83NzdMnDhR6IBV3b8fffRRpU47PT0dLVq0QO3atSESiRT+eAOAubk59u/fjzZt2uCrr75Cv3794OPjI6zftWsXoqOjERMTozCOpn548/PzERwcjIMHD0IkEqFRo0YAgGfPnoGIMHjwYCxYsAD16tWrNkZxcTFWr14tN7BxcXER1quyfwHNDW40MXjU1OQyJycHkyZNwqVLl9CqVSu5yWV6ejosLCywbt06YXlVNPH51dfXr3I5EQn9hir9g6WlJfbs2YMOHTpg3LhxaNiwIZYuXQpdXV0UFxdj3rx5yMjIUPqd9PLygp6eHiZOnIiffvoJZ86cweeff45Vq1ZBJBJh1qxZyM7ORmRkZLUxVqxYgZiYGEyePBm2trbCQCg7OxtnzpzBmjVr4OzsjOnTpytsy44dOxAaGophw4bhxYsXOHToECZNmoTx48cL8WrS/6qzf21sbBAZGYlOnTohICAAz549Q0hICJo2bYqnT59i5syZ0NXVxdq1axXGGTlyJCQSCaZOnYq9e/di+/bt6NevH4KDgwEAy5cvx+XLl7F3795qY2hq4qOp/kETk0tNTXw0MbHURN9brqYTy/Jtqoo6/a+mJu5MS9E7wM7OjmJiYqpdHxMTQ3Z2dkrjzJgxg1xdXenatWt05swZcnZ2pmHDhtHTp0+JiCgrK4vEYrHCGF5eXuTj40PZ2dlyyw0MDOju3bvKN+b/s7CwoPv37xMRkbu7Oy1ZskRu/erVq2n48OEKY5iZmVFKSgoREclkMjI0NKTbt28L65OTk8nMzExpW0xMTCghIaHa9bdv3yYTExOlcRYuXEgODg506NAhioqKIjs7O/Lx8aGioiIiUm3/zpkzh7744gu6d++e3HJ192/nzp3p1q1bRETUrVs34f/LPXz4kExNTZXGGTFiBM2fP5/y8vIoIiKCevToQfPnzxfWL1u2jNzc3JTGmT17Njk4ONDp06eppKREWF5SUkJ//PEHOTg4UFBQkMIYa9eupe7du1NERASFhoZS586dac6cOcJ6VfYvEZFYLCaJREJisbjSX/lyiUSiNE7Xrl2F98nb25u+/fZb4b2WSqU0e/Zs8vT0VBjD2dmZvvvuOyIi+vXXX6lLly4UHh4urN+6dSt98cUXStsyadIkcnNzo8TExErrEhMTyc3NjSZNmqQwhiY+vz169CAfHx+Ki4uj+Ph4io+Pp3PnzpG+vj5FR0cLy5QxMTGhhw8fEhGRjY0N3bx5U259UlISde7cWWmciu9RQUEB6evr09WrV4X1d+7cIUtLS4UxunfvTqdPn652/enTp6lbt25K2zJo0CDav3+/8PjixYtkbW1NYWFhRFS2f5V97oYOHUo+Pj507949Sk1NpdTUVEpJSSEDAwM6c+aMsEwZY2NjSk5OJiKinj17yu0TIqKEhASysrJSGsfCwkJ4n4qLi8nAwID+/vtvYf39+/eVvk/Lly8na2tr2rNnD6WkpFBBQQEVFBRQSkoK7d27l7p160YrVqxQ2hZN9Q8SiaTKP3X6h+7duwu/KZMnTyYPDw/KyckhIqInT57Q+PHjlX4fiYg8PT1p0qRJlJCQQIsWLaKBAweSv78/SaVSKi4upmnTppGHh4fCGJroe4mItm/fTqampjR//nyaNm0aGRoa0saNG4X1qnx+iTTT/2qi72Xa650YmO/atYuMjIxo4cKFdPz4cbpy5QpduXKFjh8/TgsXLiQTExPatWuX0ji2trZyHXBRURGNHz+evvjiC3ry5InKX6zIyEjq1asX/f7778IydQeOZmZmwhere/fuVQ4clQ2qbWxs6Pr160RE9PTpUxKLxXTu3Dlh/dWrV8nGxkZpW2xsbOS25VW//fabSnF69+4t9+/n5OSQi4sLeXp6UlFRkcr79+jRo9SrVy/auXOnsEzd/evr60urVq0iorLOffv27XLro6KiyMHBQWkcTfzwEhF16dKFLl68WO36CxcuUJcuXRTGsLe3l3ufHjx4QPb29jRz5kySyWQq719NDW40MXjU1OTSzMys0r9f0fXr15XG0cTn98mTJ+Tn50ejR4+mjIwMYbm6n19XV1f68ccfiYjIycmJjh07Jrf+zz//VOk72aVLF+EAgFQqJX19fbpx44aw/t69e9S1a1eFMUxNTeXek1fdunVL5QMA5e91uYSEBOrevTutWrVKpf1bVFREixYtokGDBsm93+ru3yFDhtCBAweIiGjgwIF05swZufWXLl1SOmEhIrKysqI7d+4QEVF+fj5JJBK6fPmysP7WrVtKB/iamvhoqn/QxORSUxMfTUwsNdH3EmlmYkmkmf5XUxN3pp3eiXSJo0aNwvLly3Ht2jX4+/vDzc0Nbm5u8Pf3x7Vr17B06VKMGjVKaZy8vDw0bNhQeKyrq4vw8HC0bt0aY8aMQU5Ojkrt8fDwwIYNG7Bq1SoEBwejoKBA7W0yMTHBiRMnAABt2rSpdHrr1q1baNy4scIY3bp1w4IFC7B//34EBgbCxsYGoaGhSExMRFJSElauXAkLCwulbXF1dUVgYCC2bduG27dvIzs7G9nZ2bh9+za2bduGWbNmwc3NTWmc3NxcuetvmzZtisjISLx8+RLjxo1TeT/Z29vjxx9/xLFjx+Dt7S1cc6mOadOmISoqCoGBgejcuTNWr16N6dOnY+PGjQgMDMSCBQuEU+iK1KlTB0VFRQDKThXLZDLhMQAUFhaidm3lyY1kMpnCa0Xr1KkDmUymMEZmZiY+//xz4XG7du2wc+dOXLp0CdOnT0dpaanSdgDATz/9hLZt28Lf3x/Pnj1D69athdOnLVq0QOvWrVW6iVksFuPcuXMAyq6HTU9Pl1ufnp6OunXrKoyhp6eHp0+fAii7TrKkpER4DABPnjypdC9IVXR1das8BV/u5cuXSm9g1sTnt3Hjxli/fj0GDBgAFxcX/Prrr0pfUxU/Pz+EhITgl19+wejRo7FkyRL89NNPuHTpEqKjoxEUFIShQ4cqjWNoaIgtW7YgMzMTmzZtwieffIJdu3YJ63ft2iX3maqKpaUlVqxYgdzc3ErrcnNzsWrVKlhaWiptS5MmTZCRkSG3rFOnTti+fTt++eUXrFy5UmkMXV1dBAUFYcaMGZgwYQI2bdqk9HtTFQ8PDyxfvhzx8fHw8fHBokWLEBcXh8zMTJw7dw7BwcGwt7dXGsfCwgIhISG4ePEili5dCgMDA2zYsAH5+fkoKCjAd999ByMjI4UxXr58iRYtWlS7vnnz5ip99jTVP+zfvx+1a9fGd999h3bt2sHS0hJWVlYQiUQwMTGBpaWl0vf7008/xfXr1wGUfcdf/W6+fPlS4eWg5YhIuJTs1f8CgI6OjtI4muh7ASA1NVXu0lILCwts374dUVFRCAkJUfr6cprofzXR9zIt9pYnBmqTSqWUmZlJmZmZJJVK1Xqto6MjHT58uNLy4uJi8vPzo969e6s04y1XUFBAc+bMIQcHB9LX11friM2lS5eoc+fOtHbtWtq5cydZWVnR6tWraf/+/bRmzRrq0qULbd68WWGMrKwsGjt2LJmZmZGnpyc9f/6cFixYIJwKc3BwEGbVymzatIlsbGyE15afUrOxsVHajnL9+/enkydPVlqel5dHbm5uNHToULX2r0wmo40bN5KNjY3a+5eo7KxDQEAAmZubC6cKDQ0Nyc3NrdIRyOpMmDCBxo8fTxcuXKA5c+bQsGHDyMfHh16+fEn5+fk0adIk8vLyUhrn22+/JScnpyqP6t68eZOcnZ1p6tSpCmP06dOHzp49W2l5RkYGOTg40NixY9XavydPnqSePXvSxo0bqbS0VO2jjidOnCBLS0uKjo6m6OhosrOzo6ioKLp48SL9/PPP1KtXL1q+fLnCGNOmTSNXV1fat28fjR8/njw9Pemrr76ie/fuUWJiIrm7u6t0ynvevHlkZ2dHR48epRcvXgjLX7x4QUePHiU7OztasGCBwhia/vzevXuXhg4dSt9++63a+5aI6PDhw9SzZ89Kp72NjY1p8eLFcqflq3P16lWytLQkiURC1tbWdOfOHXJ1dSUbGxuytbUlExOTKj9TFaWnp5OjoyMZGBiQk5MTeXl5kZeXFzk5OZGBgQENGTKE0tPTlbbl22+/pcWLF1e57s6dO2Rtba3W/s3KyiJvb28aOXLka+3f77//nkxNTcnExIQMDQ3lLtvw8/OjvLw8pTHu379PDg4OJBaLaeDAgZSRkUG+vr5kYGBABgYGZG1tLXeGoirjxo0jT09P4VKPinJycoTLJ5XRdP+we/dusrW1pf/7v/8jIvXOSkRHR1PPnj3p3LlzFBMTQwMHDqSzZ89SRkYGxcXFkaOjo0qXj3z99dc0e/ZsysjIoHXr1glnAMrNmzePRo4cqTCGJvpeIqJevXrR+fPnKy2/e/cude/enWbMmPGP9b+a6HuZ9nonbv7UlJUrV+L27dtV3jBVUlKCSZMm4cSJE9XenFGd3377DfHx8Rg/frzCm8tedfnyZSxbtgxXr16VW96iRQt4eXnh66+/Vqsd5VJSUlBQUID27durdDT31ddWvGtdWWaYihYuXIisrKwqb5jKy8uDp6cnrl+/rvTmo1fduHEDFy9ehJOTk3DjjjqICDk5OZDJZGjSpIlaWQ4ePHiA8ePH4+HDh2jfvj0iIyMxb948nD59GgDQsGFDREREwNDQUGGcZ8+eYerUqfjzzz/RqFEjIbNBbm4unj9/DltbW4SEhMid0XlVUFAQiAhLliyptC4zMxOjR49GSkqKWvs3Ozsbs2bNQn5+Pq5cuYJ9+/ahY8eOKr/+yJEjWLJkCR4/fix35EpXVxfDhw9HYGCgwuwL2dnZmDFjBi5fvgwLCwuEhYUhLCwMu3fvhkgkQtu2bbFlyxa0bdtWYTukUikWL16M6OholJaWCu9xcXExatWqBRcXF8yePVvhUfNFixbh8ePHGv38SqVShISEID4+HuvWrVPr+wSUZaS4efMmUlNTIZPJ0Lx5cxgaGirN6lJRfn4+kpKS8Nlnn0FPTw9FRUXYv38/ioqK0L17d+FmPkVkMhn++OOPSlktzMzMYGtrq1JGltu3b+PmzZv48ssvq1x/584dHD16FBMnTlR524Cym0rj4+MxZ84cISuWqp4/f44zZ84gJSUFRITmzZvDwsJC5bSs5Z48eYImTZoIj8vT+JqZmcktr8qjR4/g4+ODpKQkdOrUSe7G5Tt37qBDhw7YtGkTWrZsqTDOm+gf7t27h6lTp6Jjx444fPiwWv1DZGQk1qxZAyJCaWmp3BH7Pn36YMWKFUrPhl27dg3jxo3D8+fP0bhxY+zYsQNBQUFIT0+HSCTC8+fPsXHjRrmbMF+lib4XAKZOnYoPP/wQs2fPrrTu7t27GDNmDJ4+ffqP9b817XuZ9vpXDcxLSkpQWFhY7Y9aSUkJMjMz//Fc5Lm5uUhJSRF+eMtPa70uIyMj7Nu37x+/I/vZs2d4/PhxtafG8/Ly8Pfff6t02lsTHj9+jD179uDixYvIysqCjo4O2rRpg759+2LYsGFqdVo1+eGtKDExsVKueDMzM5Xeq7S0NCQlJVWbPjAzMxNnz56Fs7Ozyu0pV5PBjSYGj69KTk5GYWGh2pPLvLw83LhxQ27/GhkZqdQWbfv8sn8PTUx83lT/UJPJpSYmPpqYWAI163uBNzexBF6//30TfS97+/5VA3NlHj16hLVr12Lp0qUKn1dYWIgbN26gcePGlWa3RUVFOHToEJycnJT+e+Udhbm5Odq3b4/ExETs2LEDUqkUQ4cOVXgUAEC17dyxYweGDh0qXKM+a9YshXFu3ryJhg0bCh1ubGws9u7dKxTjcXd3x+DBg5Vuz8KFCzFw4EB06dJF6XMV2bVrF65du4ZevXph8ODBiI2NxebNmyGTyeDg4AB/f3+lg7Xr169j7NixaNu2LerWrYsrV67A0dERxcXF+PPPP4V0XdyBsTdBE/nUFXn27BlOnDihUj8D1Cw3e3U0UVQtJSUFycnJaN68OTp16qT0+Zqo/wCUHW3s2bOnSinylNFkHn7GGOOBeQW3b9+Gs7OzwlNR9+/fh5eXl3AqrXPnzggNDRVu3lE1V+zp06fh5+cHPT09FBQUIDw8HIGBgZBIJJDJZDh//jy2bt2qcHAukUggkUgqFRI4f/48jIyMUK9ePYhEomqL9pQbOnQoZs6cie7du+Onn37CokWL4Orqig4dOuD+/fv46aefEBQUJJcTt7r2lF9+8OWXX8LZ2Vnt4g3fffcdIiIiYGtri0uXLmHMmDHYunUrPDw8oKOjg23btmHEiBHw9/dXGGfEiBGwsbERjl7s27cPu3fvRlRUFJ49e4avv/4aXbp0wX//+1+lbdJkVdSaFLcCyo7cJyQkQCKRoHHjxsjNzcXPP/8MqVSKgQMH1ugsibrVVV9FRIiPjxcGWra2tkovG9JkhdYTJ07g2rVrsLW1RefOnREXF4fvv/9emNCpcgNzYWEhfv3110pnWfr166d0ogxoJp+6KlTpqwDN5GbXVFG1efPmYfr06dDT00NhYSFmzJiBY8eOCbnHu3btig0bNii8vEET9R+Asr5KT08PgwYNgouLC0xNTRU+vzqaysMPvH+TOSJCamoqWrZsidq1awv9qFQqRc+ePV+7Ci6g3qTwfZ7MxcXFVeqr+vTp89p9ONMS//A17W/V8ePHFf5FRkYqvXnDz8+PfHx8KCcnhx48eEA+Pj7Up08fSktLIyLVUya5ublRaGgoEZXlb+7atavwmIho1apVNHbsWIUxNm3aVOUNP+reCGViYiKkZ3JychLStJXbv38/DRo0SGkcsVhMZ8+epUWLFpGVlRUZGhqSr68v/f7771RaWqpSW/r160dHjhwhorJUY/r6+rRv3z5h/dGjR8ne3l6lbSpP10VEVFpaSoaGhpSVlUVEZenmbG1tlcZ58OAB9e3bl4yNjcnd3Z0mT55MkydPJnd3dzI2NiZ7e3t68OCB0jiZmZn05ZdfkkQiIX19fZo+fbrczWWqfG6uXr1KnTt3JrFYTF27dqXr169Tnz59yMHBgfr160cmJiZKbzQjKsvHW9Wfvr4+hYSECI+V8fb2pufPnxNRWZpAV1dXEovFwk18AwYMqPKGtopcXFyEFG/Hjh0jiURCvr6+tHLlSvrmm2/I0NBQYSrPcnv27CEDAwNydnYmCwsLio2NJXNzcwoKCqI5c+aQiYkJbdu2TWGMBw8ekJ2dHXXr1o169epFYrGYfHx8yNXVlfT19cnf35+Ki4sVxtBEPnWisptWFf2dP39epX5GE7nZFeVdrph/WRmJRCLUfwgJCaGePXtSXFwc5efn04ULF6hfv35CitPqaKL+Q/k2hYeHk5OTE4nFYho8eDBFRkZSbm6u0tdWpIk8/NnZ2TRixAgSi8VkZ2dHLi4u5OLiQnZ2diQWi2nEiBGV6ma8jlu3bqn0Pr148YL8/f3J2NiYunXrRmFhYXI3GqvSVyUmJpKdnR1JJBKyt7en5ORkcnZ2JjMzMzI1NSUrKyvhfVSkut9qfX192rVrl/BYEXd3dzp06BARlaVGNDIyoiFDhlBAQAA5OTmRqakpXbp0SWlbxGIxWVhY0H//+1+6cuWK0udX58iRI6Svr0+WlpZkZmZGZ86coS5dupCHhwd5enqSvr6+XFrGqmRnZ5OLiwtJJBIyMDAgiURCzs7OQqIEvvHz3favGphr4gemW7ducjl9ZTIZBQcHU+/evSk5OVnlgbmFhYUwoCu/I7viXePl+X2VuXr1Kjk4ONCyZcuELDXqDswtLS2FfOjVFeNRpcCQWCwWfkCkUikdOHBA6GhsbW0pNDRU6SDWxMREmOQQERkaGgp5gomIUlNTVSoMZGdnRxcuXBAeZ2ZmklgspoKCAiIiSklJIWNjY6VxPDw8aMKECXKZPsq9ePGCJkyYoFIhB00Ut/Lw8KCgoCB68eIFRUREUM+ePeUyG8ycOZP8/PyUtkUsFlPPnj3Jzs5O7k8sFlOPHj3Izs6O+vTpo1Kc8vd77ty5NGjQIGEy9OjRI3J2dqbg4GCFMczMzITXuLq60qZNm+TW79y5k5ycnJS2ZdCgQcKEMi4ujoyNjeVqG0RHR9PAgQMVxvD29qY5c+aQTCYjorKJr7e3NxGVZd+ws7OjtWvXKt2emuZTJ/pfX6Ws2IsymsjNrqmiahU/L46OjkK2j3LHjx9XWltAE/UfXm3L9evXae7cudSlSxcyMjIif39/+vPPP1XaJk3k4X8fJ3MTJkwgX19fun37Ni1evJgGDhxIEyZMIKlUKtQPmTZtmtK2aOI3+32czAUEBJCfnx+9ePGCioqKaMGCBTRjxgwiIjp79ixZWloqPRDBtNe/amBua2urME3e33//rfRLbm5uXqkqJRHR/PnzqWfPnip3fhUL1xDJD1CIygagqgwcicrSuc2YMYOGDBlCCQkJZGhoqNYP5rRp02j27NlEROTv70+rV6+WW79x40ZydHRUGqfij11FaWlptHbtWuEIiiJ9+vShU6dOEVHZYEgikdDBgweF9SdPnlSpyuuiRYvI0dGRTp06RXFxcTR69Ghyd3cX1p8+fZr69eunNI6mqqJqorhVxWIbUqmUJBKJXMwbN25Qjx49lLZFU9VVK77f/fv3r3Tk6uzZs0oH+Jqq0FrVhK7i+5aSkqI0jqmpqdxRvKKiIjI0NBR+eI8dO6b0s2dpaamw+Mq5c+dUKlxjYWFBmzdvFoq5vPoXFRWlUj/z6pkjorIBnJubG40ZM4aSk5P/saJqYrFYOINSsTBPudTUVKXfpTFjxtCWLVuIqOys46sVoQ8fPky9e/dWqS2v9lWFhYUUExND7u7uJJFIVOpnNFHk7X2czFlbWwuF2F6+fElisVgu1eDFixdVep80MSl8HydzFhYWct+fly9fkqGhoXAAKTY2lvr3769Se5j2US+X3jvO0NAQN2/eRL9+/apcLxKJlBYraN++Pa5fv17pWt7g4GAAwIQJE1RqS+vWrfHgwQMhDdyPP/4olw7r0aNHKl+fraenh+XLl+PAgQMYO3asyoUkyk2bNg0jRoyAu7s7jIyMEBkZib/++ku4xvzKlStYv369WjEratWqFSZNmoSJEyfi7NmzCp87ZMgQBAYGom/fvoiLi4O3tzdWrFiBp0+fQiQSYePGjejfv7/SfzMgIABBQUGYMGECSktLYWZmJlfARCQS4dtvv1Uap0GDBkhLS6v2xrS0tLRK1/hXpbriVpMnT8aYMWNUKq5SXFyMDz74AEBZUYy6devKZYRp0qSJXHGe6ixYsADHjh2Dl5cXvL294e7urvQ11RGJRADKsi+8mtKwbdu2ePz4scLXd+3aFQcOHIBEIoG+vj7++usvSCQSYX18fDw++ugjpe1o3LixcM1oZmYmSkpK8OjRI+F9S09PV5pqs0GDBnj58qXwuLCwECUlJcK1qWKxWGmxq0GDBmHmzJmYNWsWunXrJtxcnJeXh7i4OCxduhSOjo5Kt8fAwAAAqs0A07BhQ5UKtLRs2RJJSUly19DWr18fW7duhZeXl8oZJDw8PGBlZYVp06bhxIkTSm8or05YWBjq1asHHR2dShlwnj59qvT63YCAAKHYk6OjI5YtW4YHDx4IfdXOnTvh4+OjtB3ln9uKPvjgAzg5OcHJyQkPHz7EL7/8ojROeZE3d3d3HDx4UCjytmTJEohEIpWKvGmiOBZQ9jvg6+tb7fXyDx8+FH6nFKmu0Ja3tzfGjRuHRYsWKY2Rn58vfN/+85//oF69enJFlFq2bClcS69IREQEtm3bhi+//BJz586FnZ2d0te8qryYX4cOHYRifhX7GFWK+b3KyMgIRkZGmDVrFg4dOoTo6Gh4e3ujZcuW+P333xW+tryo2ieffPLaRdV0dXXlPsM6OjooLS1FSUkJgLLiR2lpaWptE9Me/6qBube3N/Lz86td37ZtW6U3Strb2+PAgQNV3kATHBwMmUyGvXv3Km3LiBEj5KqNvTrwO336NKytrZXGqWjw4MHo3Lkzbty4oVaWhY8++kjIfHLixAkQEa5du4aMjAyYm5tjz549MDY2VhqnVatWCtN6iUQi2NjYKIzh7+8vZFH56quv4OPjA4lEgpUrV6KgoAB9+vTB5MmTlbZFT08PYWFhKCoqQklJSaWOztbWVmkM4H9VUf38/GBtbY1mzZoBKLtp7ty5c9iwYYNKA9tPPvkECQkJcjfl1K5dG2vWrMHkyZPh6+urNMbHH3+MlJQUIZ3m6tWr5SZvWVlZKqdutLe3h4mJCWbMmIGTJ08qzURUnZkzZ0JXVxclJSVITU2VG2hlZ2crzQ08bdo0jBw5Eo8fPxYqtJZPfO/fv4+DBw9i/vz5StvRt29fBAUFwcnJCb///jucnJywbNkyiEQiiEQirFixQulnz8bGBsuWLcO8efOgq6uL0NBQ6OvrC4PrR48eKb1pc9asWZDJZJgyZUq1+dQDAwOVbs+QIUNQWFhY7fpmzZqpNKi2tbVFdHQ0evXqJbdcT08PERER8PT0VBqjnL6+PqKjo7FkyRI4OTmpNDGoqGvXrrh//z4AoEOHDpWqFZ46dUppFVJzc3Ns2bJFrv7Dxo0bAZTVf5g4caJK9R+Utb1du3aYMmWK0jiBgYGYMWMG5s6dK5eHf/DgwcKN8IsXL1YY432czLVo0UKYKAPA9OnT5W72zM3NVbkmRU0nhe/jZK5z585Yu3Ytli1bhjp16iA0NBRt2rQRJhjq7F+mfTgrC2NKbN68GTt27EB2drbQORMRmjVrhq+//hrjxo1TGkMTxa3Cw8Px2WefVZu6cvXq1UhKSsK6detU3LKy7di8eTN27tyJ3Nxc7N+/X+UCF6/+QPbo0QODBg0SHq9YsQIJCQlVbnNFycnJCAsLw8mTJ4WJc+3atWFkZARvb+9qz3BVlJ+fj6VLlwrpR//73/9i586dWL16NUpKStC1a1eEhYUpHFjn5OTAz88PV69ehUgkQsuWLREeHi4MeA4fPoysrCyMHj1aaXtqkk9dk95UbvbXLaqmSEpKCurUqaNyHuea1H9IS0tDq1atqhxsaYI6Rd40URwLAKKiolBQUFDtxCQ7Oxt79+5VOrDWRKGt4OBgGBsbw9XVtcr1mzdvxoULF7B582aFbamosLAQS5YsQXx8PFJSUtTqqzRRzE8ikeDMmTM1/rwrKqoGlE0KlRVVS0lJgaenp5Adrl69elizZg26d+8OAPjll19w//59TJ06tUZtZW8HD8wZU1FNqqL+E8WtCgoKUKtWLbXSN5araXXVquTn56NWrVrC5TfKUA0qtFanqKgIxcXFag2IHzx4AKlU+lqVcxl7Xe/7ZK6ilJQUfPDBB3KXt6iqJpPC92UyB5T195cuXYJUKoWpqWmN0k8y7aK8nBhjDADQpk0bmJubw9zcXBiUP3r0SKVTq7Vr11b4A5uVlYXw8PAate/p06eYO3fua73WyMgIX3/9NRo1aqTyNinz7NkzzJs3T+Xni0QiNGvWDC1atBAG5TVtywcffID69eurFefTTz9Fp06dKv04qhqjsLAQFy5cwL179yqtKyoqQmxsrErt0KY43JY3GycxMRFHjhxB8+bN4ejoCH19fRw6dAiLFy9GXFycSu0ojxMdHY3ExETh8dy5czFr1iyV4zRq1Ag6OjrVxrl+/bpKg3JFbUlNTVV5UP5qnE8//RSFhYVYtWqVyttUHuPp06cwNTVFw4YNsWXLFrX2S+vWrZGUlFTj/VuxPUlJScLjiIgIREZG4vz58yrFSE9PR0ZGBtq2bYumTZu+dluYFnort5wy9p5QNTfwPxFHm9qiqTjvWluSkpKE1JMSiYRGjRpFGRkZwnpV06lWFSczM/OtxOG2vNk4p06dIkNDQ7K0tCRjY2M6deoUWVtbk4eHB40ZM4b09fUr1arQ9jjclndjm5h24vO0jClQXdXDcikpKf9YHG1qi6bivG9tWbVqFT7//HP8/PPPePHiBZYsWYKRI0di586dat2QXVWcESNGvJU43JY3G+e7776Dl5cXpkyZggMHDghZsspvPg0JCcGWLVuUVp7Vpjjclndjm5iWetszA8a0maaqHmoijja15X3cJm0qQKZNcbgtbzaOporNaVMcbsu7sU1MO/E15owp0Lx5c6xbtw63b9+u8i8mJuYfi6NNbXkft0kTMQoLC+WuTReJRJg/fz7s7Ozg7u6OBw8eqLQ92hSH2/Lm45TfUKijowNdXV252gh6enp48eLFOxeH2/Jm42iqLUz78MCcMQXKi1JVR5WiVJqKo01t0VSc960t5QXIXhUcHIy+ffuqXIBMm+JwW95snPJic+Vet9icNsXhtrzZOJpqC9NOPDBnTAFvb2+Ym5tXu16VolSaiqNNbdFUnPetLeUFyKoSHByMwYMHqzTR0KY43JY3G6eqYnMVj8KrWmxOm+JwW95sHE21hWknzmPOGGOMMcaYFuAj5owxxhhjjGkBHpgzxhhjjDGmBXhgzhhjjDHGmBbggTljjDHGGGNagAfmjDHGGGOMaQEemDPGGGOMMaYFeGDOGGOMMcaYFuCBOWOMMcYYY1rg/wG/NOqSZ7TfOQAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 800x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_distance_matrix_sorted(X_scaled, labels, max_points=400, title=\"Distance matrix\"):\n",
" n = X_scaled.shape[0]\n",
" idx = np.arange(n)\n",
"\n",
" if n > max_points:\n",
" rng = np.random.default_rng(42)\n",
" idx = rng.choice(idx, size=max_points, replace=False)\n",
"\n",
" X_sub = X_scaled[idx]\n",
" labels_sub = np.asarray(labels)[idx]\n",
"\n",
" order = np.lexsort((np.arange(len(labels_sub)), labels_sub))\n",
" X_sub = X_sub[order]\n",
"\n",
" D = pairwise_distances(X_sub)\n",
"\n",
" plt.figure(figsize=(8, 7))\n",
" sns.heatmap(D, cmap=\"viridis\")\n",
" plt.title(title)\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"for k in [2, 5, 10]:\n",
" plot_distance_matrix_sorted(\n",
" X_scaled,\n",
" dfc[f\"cluster_k{k}\"].values,\n",
" title=f\"Distance matrix triée — K={k}\"\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "3c052322-6566-4567-b084-81148fa65538",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def intra_inter_distances(X_scaled, labels):\n",
" D = pairwise_distances(X_scaled)\n",
" labels = np.asarray(labels)\n",
"\n",
" intra, inter = [], []\n",
" n = len(labels)\n",
"\n",
" for i in range(n):\n",
" for j in range(i+1, n):\n",
" if labels[i] == labels[j]:\n",
" intra.append(D[i, j])\n",
" else:\n",
" inter.append(D[i, j])\n",
"\n",
" return np.array(intra), np.array(inter)\n",
"\n",
"for k in [2, 5]:\n",
" intra, inter = intra_inter_distances(X_scaled, dfc[f\"cluster_k{k}\"].values)\n",
"\n",
" plt.figure(figsize=(8, 4))\n",
" sns.kdeplot(intra, label=\"Intra-cluster\", fill=True)\n",
" sns.kdeplot(inter, label=\"Inter-cluster\", fill=True)\n",
" plt.title(f\"Distances dans l'espace original — K={k}\")\n",
" plt.xlabel(\"Distance\")\n",
" plt.legend()\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "b4a85ccf-34e1-4788-adfc-35da95ba774f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"churn_hard 0.801471\n",
"churn_soft 0.840928\n",
"churn_warning 0.403065\n",
"dtype: float64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfc[\"churn_hard\"] = (dfc[\"aum_final_to_peak\"] < 0.10).astype(int)\n",
"\n",
"dfc[\"churn_soft\"] = (\n",
" (dfc[\"aum_final_to_peak\"] < 0.40) &\n",
" (dfc[\"aum_drawdown_last\"] > 0.40)\n",
").astype(int)\n",
"\n",
"dfc[\"churn_warning\"] = (\n",
" (dfc[\"flow_direction_balance\"] < 0) &\n",
" (dfc[\"aum_drawdown_last\"] > 0.20)\n",
").astype(int)\n",
"\n",
"dfc[[\"churn_hard\", \"churn_soft\", \"churn_warning\"]].mean()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "e990eea6-b569-4fe9-9196-42ec4ccc0e17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== CHURN PAR CLUSTER K=2 =====\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>n_clients</th>\n",
" <th>churn_hard_rate</th>\n",
" <th>churn_soft_rate</th>\n",
" <th>churn_warning_rate</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k2</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9651</td>\n",
" <td>0.801575</td>\n",
" <td>0.840949</td>\n",
" <td>0.403171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>0.600000</td>\n",
" <td>0.800000</td>\n",
" <td>0.200000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients churn_hard_rate churn_soft_rate churn_warning_rate\n",
"cluster_k2 \n",
"0 9651 0.801575 0.840949 0.403171\n",
"1 5 0.600000 0.800000 0.200000"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== CHURN PAR CLUSTER K=5 =====\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>n_clients</th>\n",
" <th>churn_hard_rate</th>\n",
" <th>churn_soft_rate</th>\n",
" <th>churn_warning_rate</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k5</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9649</td>\n",
" <td>0.801637</td>\n",
" <td>0.840916</td>\n",
" <td>0.403151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients churn_hard_rate churn_soft_rate churn_warning_rate\n",
"cluster_k5 \n",
"0 9649 0.801637 0.840916 0.403151\n",
"1 2 1.000000 1.000000 0.500000\n",
"2 2 0.500000 0.500000 0.000000\n",
"4 2 0.500000 1.000000 0.500000\n",
"3 1 0.000000 1.000000 0.000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for k in [2, 5]:\n",
" out = (\n",
" dfc.groupby(f\"cluster_k{k}\")\n",
" .agg(\n",
" n_clients=(ID_COL, \"count\"),\n",
" churn_hard_rate=(\"churn_hard\", \"mean\"),\n",
" churn_soft_rate=(\"churn_soft\", \"mean\"),\n",
" churn_warning_rate=(\"churn_warning\", \"mean\")\n",
" )\n",
" .sort_values(\"n_clients\", ascending=False)\n",
" )\n",
" print(f\"\\n===== CHURN PAR CLUSTER K={k} =====\")\n",
" display(out)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "4ad61286-89b0-473f-811d-f3affee994f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== NIVEAU 2 / EXPLICATION — K=2 =====\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_clients</th>\n",
" <th>beta_rate_med</th>\n",
" <th>delta_rate_mean_med</th>\n",
" <th>aum_trend_12m_med</th>\n",
" <th>flow_trend_12m_med</th>\n",
" <th>drawdown_trend_12m_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k2</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>0</th>\n",
" <td>9651</td>\n",
" <td>0.000000</td>\n",
" <td>-0.004333</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>0.355197</td>\n",
" <td>0.022432</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients beta_rate_med delta_rate_mean_med aum_trend_12m_med \\\n",
"cluster_k2 \n",
"0 9651 0.000000 -0.004333 0.0 \n",
"1 5 0.355197 0.022432 0.0 \n",
"\n",
" flow_trend_12m_med drawdown_trend_12m_med \n",
"cluster_k2 \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== NIVEAU 2 / EXPLICATION — K=5 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\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>n_clients</th>\n",
" <th>beta_rate_med</th>\n",
" <th>delta_rate_mean_med</th>\n",
" <th>aum_trend_12m_med</th>\n",
" <th>flow_trend_12m_med</th>\n",
" <th>drawdown_trend_12m_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k5</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>0</th>\n",
" <td>9649</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-0.004333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>-5.372138e+11</td>\n",
" <td>0.018078</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>4.439995e-01</td>\n",
" <td>0.013204</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-1.546122e-33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>1.972259e-02</td>\n",
" <td>0.024369</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.546122e-33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>-7.467253e-02</td>\n",
" <td>0.051667</td>\n",
" <td>-769.230769</td>\n",
" <td>-0.017094</td>\n",
" <td>5.379236e-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients beta_rate_med delta_rate_mean_med aum_trend_12m_med \\\n",
"cluster_k5 \n",
"0 9649 0.000000e+00 -0.004333 0.000000 \n",
"1 2 -5.372138e+11 0.018078 0.000000 \n",
"2 2 4.439995e-01 0.013204 0.000000 \n",
"4 2 1.972259e-02 0.024369 0.000000 \n",
"3 1 -7.467253e-02 0.051667 -769.230769 \n",
"\n",
" flow_trend_12m_med drawdown_trend_12m_med \n",
"cluster_k5 \n",
"0 0.000000 0.000000e+00 \n",
"1 0.000000 0.000000e+00 \n",
"2 0.000000 -1.546122e-33 \n",
"4 0.000000 1.546122e-33 \n",
"3 -0.017094 5.379236e-03 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#niv 2 explciation eocno \n",
"explain_vars = [\n",
" \"beta_rate\",\n",
" \"delta_rate_mean\",\n",
" \"aum_trend_12m\",\n",
" \"flow_trend_12m\",\n",
" \"drawdown_trend_12m\",\n",
"]\n",
"\n",
"explain_vars = [c for c in explain_vars if c in dfc.columns]\n",
"\n",
"for k in [2, 5]:\n",
" print(f\"\\n===== NIVEAU 2 / EXPLICATION — K={k} =====\")\n",
" out = (\n",
" dfc.groupby(f\"cluster_k{k}\")\n",
" .agg(\n",
" n_clients=(ID_COL, \"count\"),\n",
" **{f\"{c}_med\": (c, \"median\") for c in explain_vars}\n",
" )\n",
" .sort_values(\"n_clients\", ascending=False)\n",
" )\n",
" display(out)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "0ae8a553-1ec2-4789-8b6c-c70909495b18",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for k in [2, 5]:\n",
" tmp = (\n",
" dfc.groupby(f\"cluster_k{k}\")\n",
" .agg(\n",
" churn_hard=(\"churn_hard\", \"mean\"),\n",
" churn_soft=(\"churn_soft\", \"mean\"),\n",
" churn_warning=(\"churn_warning\", \"mean\")\n",
" )\n",
" )\n",
"\n",
" tmp.plot(kind=\"bar\", figsize=(8, 4))\n",
" plt.title(f\"Churn par cluster — K={k}\")\n",
" plt.ylabel(\"Rate\")\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5fc62ca1-a145-4669-8798-33d72352d4d5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.13.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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