{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e637deae-9168-4fb2-b95f-4e42d8d72d9e",
   "metadata": {},
   "source": [
    "# DATA COLLECTION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8508d94-74a7-4bb0-8b81-c2e06850c25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>003</td>\n",
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       "      <td>France</td>\n",
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       "      <td>2015-03-31</td>\n",
       "      <td>35.368</td>\n",
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       "      <td>2.464867e+04</td>\n",
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       "      <td>FCP</td>\n",
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       "      <td>A</td>\n",
       "      <td>EUR</td>\n",
       "      <td>FR0010135103</td>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>35.368</td>\n",
       "      <td>2.241306e+04</td>\n",
       "      <td>2.241306e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>003</td>\n",
       "      <td>166</td>\n",
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       "      <td>200000647</td>\n",
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       "      <td>Diversified</td>\n",
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       "      <td>2015-12-31</td>\n",
       "      <td>35.368</td>\n",
       "      <td>2.205124e+04</td>\n",
       "      <td>2.205124e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>003</td>\n",
       "      <td>166</td>\n",
       "      <td>166</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>2.162612e+04</td>\n",
       "      <td>2.162612e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>003</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>200000647</td>\n",
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       "      <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>2.248945e+04</td>\n",
       "      <td>2.248945e+04</td>\n",
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       "      <th>...</th>\n",
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       "      <td>Private Client</td>\n",
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       "      <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>2020-02-29</td>\n",
       "      <td>26801.000</td>\n",
       "      <td>2.740670e+06</td>\n",
       "      <td>2.740670e+06</td>\n",
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       "    <tr>\n",
       "      <th>4880293</th>\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",
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       "      <td>EUR</td>\n",
       "      <td>LU1299306321</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>3099.000</td>\n",
       "      <td>3.122862e+05</td>\n",
       "      <td>3.122862e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4880294</th>\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",
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       "      <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>3.184222e+05</td>\n",
       "      <td>3.184222e+05</td>\n",
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       "    <tr>\n",
       "      <th>4880295</th>\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>2.976183e+05</td>\n",
       "      <td>2.976183e+05</td>\n",
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       "    <tr>\n",
       "      <th>4880296</th>\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>2.874106e+05</td>\n",
       "      <td>2.874106e+05</td>\n",
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      ],
      "text/plain": [
       "        Agreement - Code    Company - Id Company - Ultimate Parent Id  \\\n",
       "0                    003             166                          166   \n",
       "1                    003             166                          166   \n",
       "2                    003             166                          166   \n",
       "3                    003             166                          166   \n",
       "4                    003             166                          166   \n",
       "...                  ...             ...                          ...   \n",
       "4880292   Private Client  Private Client               Private Client   \n",
       "4880293   Private Client  Private Client               Private Client   \n",
       "4880294   Private Client  Private Client               Private Client   \n",
       "4880295   Private Client  Private Client               Private Client   \n",
       "4880296   Private Client  Private Client               Private Client   \n",
       "\n",
       "        Registrar Account - ID Registrar Account - Region  \\\n",
       "0                    200000647                     France   \n",
       "1                    200000647                     France   \n",
       "2                    200000647                     France   \n",
       "3                    200000647                     France   \n",
       "4                    200000647                     France   \n",
       "...                        ...                        ...   \n",
       "4880292         Private Client                Switzerland   \n",
       "4880293         Private Client                Switzerland   \n",
       "4880294         Private Client                Switzerland   \n",
       "4880295         Private Client                Switzerland   \n",
       "4880296         Private Client                Switzerland   \n",
       "\n",
       "        RegistrarAccount - Country Product - Asset Type Product - Strategy  \\\n",
       "0                           France          Diversified         Patrimoine   \n",
       "1                           France          Diversified         Patrimoine   \n",
       "2                           France          Diversified         Patrimoine   \n",
       "3                           France          Diversified         Patrimoine   \n",
       "4                           France          Diversified         Patrimoine   \n",
       "...                            ...                  ...                ...   \n",
       "4880292                Switzerland         Fixed Income           Sécurité   \n",
       "4880293                Switzerland         Fixed Income           Sécurité   \n",
       "4880294                Switzerland         Fixed Income           Sécurité   \n",
       "4880295                Switzerland         Fixed Income           Sécurité   \n",
       "4880296                Switzerland         Fixed Income           Sécurité   \n",
       "\n",
       "        Product - Legal Status Product - Is Dedie ?  \\\n",
       "0                          FCP                   NO   \n",
       "1                          FCP                   NO   \n",
       "2                          FCP                   NO   \n",
       "3                          FCP                   NO   \n",
       "4                          FCP                   NO   \n",
       "...                        ...                  ...   \n",
       "4880292                  SICAV                   NO   \n",
       "4880293                  SICAV                   NO   \n",
       "4880294                  SICAV                   NO   \n",
       "4880295                  SICAV                   NO   \n",
       "4880296                  SICAV                   NO   \n",
       "\n",
       "                       Product - Fund Product - Shareclass Type  \\\n",
       "0                Carmignac Patrimoine                         A   \n",
       "1                Carmignac Patrimoine                         A   \n",
       "2                Carmignac Patrimoine                         A   \n",
       "3                Carmignac Patrimoine                         A   \n",
       "4                Carmignac Patrimoine                         A   \n",
       "...                               ...                       ...   \n",
       "4880292  Carmignac Portfolio Sécurité                 AW & AW-R   \n",
       "4880293  Carmignac Portfolio Sécurité                 AW & AW-R   \n",
       "4880294  Carmignac Portfolio Sécurité                 AW & AW-R   \n",
       "4880295  Carmignac Portfolio Sécurité                 AW & AW-R   \n",
       "4880296  Carmignac Portfolio Sécurité                 FW & FW-R   \n",
       "\n",
       "        Product - Shareclass Currency Product - Isin Centralisation Date  \\\n",
       "0                                 EUR   FR0010135103          2015-03-31   \n",
       "1                                 EUR   FR0010135103          2015-11-30   \n",
       "2                                 EUR   FR0010135103          2015-12-31   \n",
       "3                                 EUR   FR0010135103          2016-03-31   \n",
       "4                                 EUR   FR0010135103          2016-11-30   \n",
       "...                               ...            ...                 ...   \n",
       "4880292                           EUR   LU1299306321          2020-02-29   \n",
       "4880293                           EUR   LU1299306321          2020-06-30   \n",
       "4880294                           EUR   LU1299306321          2020-10-31   \n",
       "4880295                           EUR   LU1299306321          2021-07-31   \n",
       "4880296                           EUR   LU1792391911          2020-07-31   \n",
       "\n",
       "         Quantity - AUM  Value - AUM CCY  Value - AUM €  \n",
       "0                35.368     2.464867e+04   2.464867e+04  \n",
       "1                35.368     2.241306e+04   2.241306e+04  \n",
       "2                35.368     2.205124e+04   2.205124e+04  \n",
       "3                35.368     2.162612e+04   2.162612e+04  \n",
       "4                35.368     2.248945e+04   2.248945e+04  \n",
       "...                 ...              ...            ...  \n",
       "4880292       26801.000     2.740670e+06   2.740670e+06  \n",
       "4880293        3099.000     3.122862e+05   3.122862e+05  \n",
       "4880294        3099.000     3.184222e+05   3.184222e+05  \n",
       "4880295        2835.000     2.976183e+05   2.976183e+05  \n",
       "4880296        2916.394     2.874106e+05   2.874106e+05  \n",
       "\n",
       "[4880297 rows x 18 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "chemin_fichier = \"s3://projet-bdc-data/carmignac/AUM ENSAE V2 -20251105.csv\"\n",
    "df_aum2 = pd.read_csv(chemin_fichier, sep=';', engine='python')\n",
    "df_aum2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4644da13-5aea-4ca0-9fcf-947324766292",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Agreement - Code</th>\n",
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       "  </thead>\n",
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       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>Equity</td>\n",
       "      <td>Investissement</td>\n",
       "      <td>SICAV</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>1636.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1636.000</td>\n",
       "      <td>280983.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>280983.00</td>\n",
       "      <td>280983.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>280983.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>003</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>406533</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>...</td>\n",
       "      <td>2015-03-09</td>\n",
       "      <td>144.690</td>\n",
       "      <td>0.000</td>\n",
       "      <td>144.690</td>\n",
       "      <td>99985.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99985.13</td>\n",
       "      <td>99985.13</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Equity</td>\n",
       "      <td>Investissement</td>\n",
       "      <td>FCP</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-8.321</td>\n",
       "      <td>-8.321</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-9384.76</td>\n",
       "      <td>-9384.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-9384.76</td>\n",
       "      <td>-9384.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>003</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>406533</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>Equity</td>\n",
       "      <td>Investissement</td>\n",
       "      <td>FCP</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-22.083</td>\n",
       "      <td>-22.083</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-25227.40</td>\n",
       "      <td>-25227.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-25227.40</td>\n",
       "      <td>-25227.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>003</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>406533</td>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>Equity</td>\n",
       "      <td>Investissement</td>\n",
       "      <td>FCP</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-04-08</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-465.992</td>\n",
       "      <td>-465.992</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-563775.76</td>\n",
       "      <td>-563775.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-563775.76</td>\n",
       "      <td>-563775.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574456</th>\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>FCP</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-06-12</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-20.000</td>\n",
       "      <td>-20.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-34294.40</td>\n",
       "      <td>-34294.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-34294.40</td>\n",
       "      <td>-34294.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574457</th>\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>FCP</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-09-18</td>\n",
       "      <td>328.726</td>\n",
       "      <td>0.000</td>\n",
       "      <td>328.726</td>\n",
       "      <td>564028.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>564028.07</td>\n",
       "      <td>564028.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>564028.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574458</th>\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>FCP</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</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",
       "      <td>0.00</td>\n",
       "      <td>7603.66</td>\n",
       "      <td>7603.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7603.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574459</th>\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>FCP</td>\n",
       "      <td>NO</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-11-09</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-440.000</td>\n",
       "      <td>-440.000</td>\n",
       "      <td>0.00</td>\n",
       "      <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>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>...</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 × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Agreement - Code    Company - Id Company - Ultimate Parent Id  \\\n",
       "0                    003             166                          166   \n",
       "1                    003             166                          166   \n",
       "2                    003             166                          166   \n",
       "3                    003             166                          166   \n",
       "4                    003             166                          166   \n",
       "...                  ...             ...                          ...   \n",
       "2574456   Private Client  Private Client               Private Client   \n",
       "2574457   Private Client  Private Client               Private Client   \n",
       "2574458   Private Client  Private Client               Private Client   \n",
       "2574459   Private Client  Private Client               Private Client   \n",
       "2574460   Private Client  Private Client               Private Client   \n",
       "\n",
       "        Registrar Account - ID Registrar Account - Region  \\\n",
       "0                    200127202                     France   \n",
       "1                       406533                     France   \n",
       "2                       406533                     France   \n",
       "3                       406533                     France   \n",
       "4                       406533                     France   \n",
       "...                        ...                        ...   \n",
       "2574456         Private Client                 Luxembourg   \n",
       "2574457         Private Client                 Luxembourg   \n",
       "2574458         Private Client                 Luxembourg   \n",
       "2574459         Private Client                 Luxembourg   \n",
       "2574460         Private Client                 Luxembourg   \n",
       "\n",
       "        RegistrarAccount - Country Product - Asset Type Product - Strategy  \\\n",
       "0                           France               Equity     Investissement   \n",
       "1                           France          Diversified         Patrimoine   \n",
       "2                           France               Equity     Investissement   \n",
       "3                           France               Equity     Investissement   \n",
       "4                           France               Equity     Investissement   \n",
       "...                            ...                  ...                ...   \n",
       "2574456                 Luxembourg         Fixed Income           Sécurité   \n",
       "2574457                 Luxembourg         Fixed Income           Sécurité   \n",
       "2574458                 Luxembourg         Fixed Income           Sécurité   \n",
       "2574459                 Luxembourg         Fixed Income           Sécurité   \n",
       "2574460                 Luxembourg         Fixed Income           Sécurité   \n",
       "\n",
       "        Product - Legal Status Product - Is Dedie ?  ... Centralisation Date  \\\n",
       "0                        SICAV                   NO  ...          2020-11-05   \n",
       "1                          FCP                   NO  ...          2015-03-09   \n",
       "2                          FCP                   NO  ...          2016-10-26   \n",
       "3                          FCP                   NO  ...          2018-10-18   \n",
       "4                          FCP                   NO  ...          2019-04-08   \n",
       "...                        ...                  ...  ...                 ...   \n",
       "2574456                    FCP                   NO  ...          2015-06-12   \n",
       "2574457                    FCP                   NO  ...          2015-09-18   \n",
       "2574458                    FCP                   NO  ...          2015-09-25   \n",
       "2574459                    FCP                   NO  ...          2015-11-09   \n",
       "2574460                  SICAV                   NO  ...          2016-01-11   \n",
       "\n",
       "        Quantity - Subscription Quantity - Redemption Quantity - NetFlows  \\\n",
       "0                      1636.000                 0.000            1636.000   \n",
       "1                       144.690                 0.000             144.690   \n",
       "2                         0.000                -8.321              -8.321   \n",
       "3                         0.000               -22.083             -22.083   \n",
       "4                         0.000              -465.992            -465.992   \n",
       "...                         ...                   ...                 ...   \n",
       "2574456                   0.000               -20.000             -20.000   \n",
       "2574457                 328.726                 0.000             328.726   \n",
       "2574458                   4.443                 0.000               4.443   \n",
       "2574459                   0.000              -440.000            -440.000   \n",
       "2574460                3595.000                 0.000            3595.000   \n",
       "\n",
       "        Value Ccy - Subscription  Value Ccy - Redemption  \\\n",
       "0                      280983.00                    0.00   \n",
       "1                       99985.13                    0.00   \n",
       "2                           0.00                -9384.76   \n",
       "3                           0.00               -25227.40   \n",
       "4                           0.00              -563775.76   \n",
       "...                          ...                     ...   \n",
       "2574456                     0.00               -34294.40   \n",
       "2574457                564028.07                    0.00   \n",
       "2574458                  7603.66                    0.00   \n",
       "2574459                     0.00              -754696.80   \n",
       "2574460                358385.55                    0.00   \n",
       "\n",
       "         Value Ccy - NetFlows  Value € - Subscription  Value € - Redemption  \\\n",
       "0                   280983.00               280983.00                  0.00   \n",
       "1                    99985.13                99985.13                  0.00   \n",
       "2                    -9384.76                    0.00              -9384.76   \n",
       "3                   -25227.40                    0.00             -25227.40   \n",
       "4                  -563775.76                    0.00            -563775.76   \n",
       "...                       ...                     ...                   ...   \n",
       "2574456             -34294.40                    0.00             -34294.40   \n",
       "2574457             564028.07               564028.07                  0.00   \n",
       "2574458               7603.66                 7603.66                  0.00   \n",
       "2574459            -754696.80                    0.00            -754696.80   \n",
       "2574460             358385.55               358385.55                  0.00   \n",
       "\n",
       "         Value € - NetFlows  \n",
       "0                 280983.00  \n",
       "1                  99985.13  \n",
       "2                  -9384.76  \n",
       "3                 -25227.40  \n",
       "4                -563775.76  \n",
       "...                     ...  \n",
       "2574456           -34294.40  \n",
       "2574457           564028.07  \n",
       "2574458             7603.66  \n",
       "2574459          -754696.80  \n",
       "2574460           358385.55  \n",
       "\n",
       "[2574461 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chemin_fichier = \"s3://projet-bdc-data/carmignac/Flows ENSAE V2 -20251105.csv\"\n",
    "df_flows2 = pd.read_csv(chemin_fichier, sep=';', engine='python')\n",
    "df_flows2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d31eaf-c06c-4ebe-9f8c-cb9158a50976",
   "metadata": {},
   "source": [
    "## DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5773b911-6b84-448d-962f-8228eeac0250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4880297, 18)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aum2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f571810-c373-4d30-8ca5-c3a074b95b08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Agreement - Code', 'Company - Id', 'Company - Ultimate Parent Id',\n",
       "       'Registrar Account - ID', 'Registrar Account - Region',\n",
       "       'RegistrarAccount - Country', 'Product - Asset Type',\n",
       "       'Product - Strategy', 'Product - Legal Status', 'Product - Is Dedie ?',\n",
       "       'Product - Fund', 'Product - Shareclass Type',\n",
       "       'Product - Shareclass Currency', 'Product - Isin',\n",
       "       'Centralisation Date', 'Quantity - AUM', 'Value - AUM CCY',\n",
       "       'Value - AUM €'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aum2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af25fd07-a613-4adc-b88b-93a8d300379c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2574461, 24)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flows2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6d0fe83-2957-430b-89cf-cd30833b7cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Agreement - Code', 'Company - Id', 'Company - Ultimate Parent Id',\n",
       "       'Registrar Account - ID', 'Registrar Account - Region',\n",
       "       'RegistrarAccount - Country', 'Product - Asset Type',\n",
       "       'Product - Strategy', 'Product - Legal Status', 'Product - Is Dedie ?',\n",
       "       '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')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_flows2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce2ad22-08e6-4e63-96b2-c2301172516e",
   "metadata": {},
   "source": [
    "# ETUDE ET ANALYSE DES ANOMALIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c883dfc2-b9b9-4d3e-80d3-0140cd222492",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aum2['Centralisation Date'] = pd.to_datetime(df_aum2['Centralisation Date'])\n",
    "df_flows2['Centralisation Date'] = pd.to_datetime(df_flows2['Centralisation Date'])\n",
    "\n",
    "\n",
    "key_cols = ['Registrar Account - ID', 'Product - Isin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64ca9883-372b-4a5e-8fc1-10e5d835c411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doublons AUM: 250232\n",
      "Doublons Flows: 4849\n"
     ]
    }
   ],
   "source": [
    "# Vérifier doublons exacts\n",
    "doublons_aum = df_aum2[df_aum2.duplicated(subset=key_cols + ['Centralisation Date'], keep=False)]\n",
    "doublons_flows = df_flows2[df_flows2.duplicated(subset=key_cols + ['Centralisation Date'], keep=False)]\n",
    "\n",
    "print(\"Doublons AUM:\", doublons_aum.shape[0])\n",
    "print(\"Doublons Flows:\", doublons_flows.shape[0])\n",
    "\n",
    "#same date, code isin du produit, et account ==> revoir les autres caracteristiques "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f47b276d-cce6-433c-87c5-860810d71d34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Cols:  ['Company - Id', 'Company - Ultimate Parent Id', 'Registrar Account - ID', 'Registrar Account - Region', 'Product - Isin']\n",
      "Doublons AUM: 26452\n",
      "Doublons Flows: 0\n"
     ]
    }
   ],
   "source": [
    "cols= ['Company - Id', 'Company - Ultimate Parent Id',\n",
    "       'Registrar Account - ID', 'Registrar Account - Region','Product - Isin']\n",
    "\n",
    "doublons_aum2 = df_aum2[df_aum2.duplicated(subset=cols + ['Centralisation Date'], keep=False)]\n",
    "doublons_flows2 = df_flows2[df_flows2.duplicated(subset=cols + ['Centralisation Date'], keep=False)]\n",
    "\n",
    "print(\" Cols: \", cols)\n",
    "print(\"Doublons AUM:\", doublons_aum2.shape[0])\n",
    "print(\"Doublons Flows:\", doublons_flows2.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2f355e6-30c5-420e-a3db-9095dd5e0147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Comptes avec same flux et same product ISIN mais IDs différents ---> candidats pseudo-client\n",
    "# #plusieurs comptes diff réalisent EXACTEMENT le même flux sur le même produit le même jour.\n",
    "# #date?\n",
    "\n",
    "# pseudo_client_candidates = df_flows2.groupby(['Product - Isin','Centralisation Date', 'Quantity - NetFlows', 'Value € - NetFlows'])['Registrar Account - ID'].nunique()\n",
    "# pseudo_client_candidates = pseudo_client_candidates[pseudo_client_candidates > 1]\n",
    "# print(\"Comptes potentiels à fusionner:\")\n",
    "# print(pseudo_client_candidates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8df98c34-b1f7-4fb9-bbc7-c9bbe762022a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_flows2.copy()\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Centralisation Date\"])\n",
    "\n",
    "# Groupby par ISIN et Date\n",
    "grouped = df.groupby([\"Product - Isin\", \"Date\"])\n",
    "\n",
    "transfers = []\n",
    "\n",
    "for (isin, date), group in grouped:\n",
    "    # Sépare flux positifs et négatifs\n",
    "    entrants = group[group[\"Value € - NetFlows\"] > 0][[\"Registrar Account - ID\", \"Value € - NetFlows\"]]\n",
    "    sortants = group[group[\"Value € - NetFlows\"] < 0][[\"Registrar Account - ID\", \"Value € - NetFlows\"]]\n",
    "\n",
    "    # On cherche des paires +M / -M\n",
    "    for _, row_sortie in sortants.iterrows():\n",
    "        montant_sortie = row_sortie[\"Value € - NetFlows\"]\n",
    "        compte_sortant = row_sortie[\"Registrar Account - ID\"]\n",
    "\n",
    "        # Chercher un +M qui matche exactement le -M\n",
    "        match = entrants[entrants[\"Value € - NetFlows\"] == -montant_sortie]\n",
    "\n",
    "        if len(match) > 0:\n",
    "            for _, row_entree in match.iterrows():\n",
    "                transfers.append({\n",
    "                    \"ISIN\": isin,\n",
    "                    \"Date\": date,\n",
    "                    \"Compte sortant\": compte_sortant,\n",
    "                    \"Montant sortie\": montant_sortie,\n",
    "                    \"Compte entrant\": row_entree[\"Registrar Account - ID\"],\n",
    "                    \"Montant entrée\": row_entree[\"Value € - NetFlows\"]\n",
    "                })\n",
    "\n",
    "\n",
    "transf_compte = pd.DataFrame(transfers)\n",
    "transf_compte"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c898b0c5-0a8e-4640-bc52-9490ee80e53d",
   "metadata": {},
   "source": [
    "# MERGE AND ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce33dbf8-1c59-416a-adc4-6eb7c1ea9d8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merged dataset:\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "df_aum2 = df_aum2.rename(columns={\n",
    "    \"Registrar Account - ID\": \"Account_ID\",\n",
    "    \"Product - Isin\": \"ISIN\",\n",
    "    \"Centralisation Date\": \"Date\",\n",
    "    \"Value - AUM €\": \"AUM_EUR\"\n",
    "})\n",
    "\n",
    "df_flows2 = df_flows2.rename(columns={\n",
    "    \"Registrar Account - ID\": \"Account_ID\",\n",
    "    \"Product - Isin\": \"ISIN\",\n",
    "    \"Centralisation Date\": \"Date\",\n",
    "    \"Value € - NetFlows\": \"Flow_EUR\"\n",
    "})\n",
    "\n",
    "\n",
    "df_aum2[\"Date\"] = pd.to_datetime(df_aum2[\"Date\"])\n",
    "df_flows2[\"Date\"] = pd.to_datetime(df_flows2[\"Date\"])\n",
    "\n",
    "df_aum2[\"Account_ID\"] = df_aum2[\"Account_ID\"].astype(str)\n",
    "df_flows2[\"Account_ID\"] = df_flows2[\"Account_ID\"].astype(str)\n",
    "\n",
    "df_aum2[\"ISIN\"] = df_aum2[\"ISIN\"].str.upper()\n",
    "df_flows2[\"ISIN\"] = df_flows2[\"ISIN\"].str.upper()\n",
    "\n",
    "\n",
    "df_merged = pd.merge(\n",
    "    df_aum2[[\"Account_ID\", \"ISIN\", \"Date\", \"AUM_EUR\"]],\n",
    "    df_flows2[[\"Account_ID\", \"ISIN\", \"Date\", \"Flow_EUR\"]],\n",
    "    on=[\"Account_ID\", \"ISIN\", \"Date\"],\n",
    "    how=\"outer\"\n",
    ").sort_values([\"Account_ID\", \"ISIN\", \"Date\"])\n",
    "\n",
    "print(\"Merged dataset:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e5d642e-5c16-4c78-8d83-075094902670",
   "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>Account_ID</th>\n",
       "      <th>ISIN</th>\n",
       "      <th>Date</th>\n",
       "      <th>AUM_EUR</th>\n",
       "      <th>Flow_EUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000014</td>\n",
       "      <td>LU0553415323</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100000014</td>\n",
       "      <td>LU0553415323</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100000014</td>\n",
       "      <td>LU0553415323</td>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100000014</td>\n",
       "      <td>LU0553415323</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000014</td>\n",
       "      <td>LU0553415323</td>\n",
       "      <td>2015-05-31</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7369446</th>\n",
       "      <td>Private Client</td>\n",
       "      <td>LU2809794220</td>\n",
       "      <td>2025-10-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1623.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7369447</th>\n",
       "      <td>Private Client</td>\n",
       "      <td>LU2809794220</td>\n",
       "      <td>2025-10-31</td>\n",
       "      <td>4.438147e+06</td>\n",
       "      <td>4946.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7369448</th>\n",
       "      <td>Private Client</td>\n",
       "      <td>LU2809794576</td>\n",
       "      <td>2025-09-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71660.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7369449</th>\n",
       "      <td>Private Client</td>\n",
       "      <td>LU2809794576</td>\n",
       "      <td>2025-09-30</td>\n",
       "      <td>7.094499e+04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7369450</th>\n",
       "      <td>Private Client</td>\n",
       "      <td>LU2809794576</td>\n",
       "      <td>2025-10-31</td>\n",
       "      <td>7.871629e+04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7369451 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Account_ID          ISIN       Date       AUM_EUR  Flow_EUR\n",
       "0             100000014  LU0553415323 2015-01-31  0.000000e+00       NaN\n",
       "1             100000014  LU0553415323 2015-02-28  0.000000e+00       NaN\n",
       "2             100000014  LU0553415323 2015-03-31  0.000000e+00       NaN\n",
       "3             100000014  LU0553415323 2015-04-30  0.000000e+00       NaN\n",
       "4             100000014  LU0553415323 2015-05-31  0.000000e+00       NaN\n",
       "...                 ...           ...        ...           ...       ...\n",
       "7369446  Private Client  LU2809794220 2025-10-30           NaN  -1623.71\n",
       "7369447  Private Client  LU2809794220 2025-10-31  4.438147e+06   4946.23\n",
       "7369448  Private Client  LU2809794576 2025-09-23           NaN  71660.14\n",
       "7369449  Private Client  LU2809794576 2025-09-30  7.094499e+04       NaN\n",
       "7369450  Private Client  LU2809794576 2025-10-31  7.871629e+04       NaN\n",
       "\n",
       "[7369451 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e484a3df-260e-4532-850b-3917592fcfdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date   2025-11-28\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged[['Date']].max() #2015-01-02 jusuqua 2025-11-28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ea14866a-1ce6-4b19-9225-d725304af8ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. HISTOGRAMME DES AUM (SANS ISIN)\n",
    "\n",
    "# We keep the mean AUM per Account \n",
    "aum_by_account = (\n",
    "    df_merged.groupby(\"Account_ID\")[\"AUM_EUR\"]\n",
    "    .mean()\n",
    "    .dropna()\n",
    ")\n",
    "\n",
    "# plt.figure(figsize=(10,6))\n",
    "# plt.hist(aum_by_account, bins=50)\n",
    "# plt.xlabel(\"Mean AUM value (€)\")\n",
    "# plt.ylabel(\"Number of client accounts\")\n",
    "# plt.title(\"Distribution of client average AUM (one value per Account_ID)\")\n",
    "# plt.grid(True)\n",
    "# plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(aum_by_account, bins=100)\n",
    "\n",
    "# ZOOM sur les petits AUM (0 à 0.5M €)\n",
    "plt.xlim(0, 0.5e7)\n",
    "\n",
    "plt.xlabel(\"Mean AUM value (€)\")\n",
    "plt.ylabel(\"Number of client accounts\")\n",
    "plt.title(\"Distribution of client average AUM \")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8d0dec63-9b9a-47ea-90bd-ffe059bb232a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. HISTOGRAMME DES AUM (SANS ISIN) POUR 2022+\n",
    "df_filtered = df_merged[df_merged[\"Date\"].dt.year >= 2022]\n",
    "\n",
    "aum_by_account = (\n",
    "    df_filtered\n",
    "    .groupby(\"Account_ID\")[\"AUM_EUR\"]\n",
    "    .mean()\n",
    "    .dropna()\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(aum_by_account, bins=100)\n",
    "\n",
    "# ZOOM sur les petits AUM (0 à 5M €)\n",
    "plt.xlim(0, 0.5e7)\n",
    "\n",
    "plt.xlabel(\"Mean AUM value (€)\")\n",
    "plt.ylabel(\"Number of client accounts\")\n",
    "plt.title(\"Distribution of client average AUM per account (2022–2025)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c79ae705-833e-420f-ab3e-51ccb2ae52c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 1) Créer une colonne Année-Mois\n",
    "\n",
    "# df_filtered = df_merged[df_merged[\"Date\"].dt.year >= 2022]\n",
    "# df_filtered[\"YearMonth\"] = df_merged[\"Date\"].dt.to_period(\"M\")\n",
    "\n",
    "# # 2) AUM moyen par compte ET par mois\n",
    "# aum_monthly = (\n",
    "#     df_merged.groupby([\"Account_ID\", \"YearMonth\"])[\"AUM_EUR\"]\n",
    "#     .mean()\n",
    "#     .reset_index()\n",
    "# )\n",
    "\n",
    "# # 3) Pour chaque compte : moyenne des AUM mensuels sur toute la période\n",
    "# aum_avg_over_time = (\n",
    "#     aum_monthly.groupby(\"Account_ID\")[\"AUM_EUR\"]\n",
    "#     .mean()\n",
    "#     .dropna()\n",
    "# )\n",
    "\n",
    "# # 4) Histogramme des AUM mensuels moyens par compte (sur toute l’historique)\n",
    "# plt.figure(figsize=(10,6))\n",
    "# plt.hist(aum_avg_over_time, bins=100)\n",
    "\n",
    "# # Option : zoom sur les petits AUM (ex. 0 à 5M €)\n",
    "# plt.xlim(0, 0.5e7)\n",
    "\n",
    "# plt.xlabel(\"Average monthly AUM per account (€)\")\n",
    "# plt.ylabel(\"Number of client accounts\")\n",
    "# plt.title(\"Distribution of average monthly AUM per account (full history)\")\n",
    "# plt.grid(True)\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f13c213e-7f72-494a-bcf0-b4cd9feee55d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monthly flow summary:\n",
      "           n_positive_flows  n_negative_flows\n",
      "YearMonth                                    \n",
      "2015-01               10688             11779\n",
      "2015-02               12905             10822\n",
      "2015-03               15497             13207\n",
      "2015-04               13838             11641\n",
      "2015-05               11143              9065\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###############################################################################\n",
    "# 4. ANALYSE MENSUELLE DES FLOWS (ENTRANTS / SORTANTS)\n",
    "\n",
    "df_merged[\"YearMonth\"] = df_merged[\"Date\"].dt.to_period(\"M\")\n",
    "\n",
    "flows_monthly = df_merged.groupby(\"YearMonth\").agg(\n",
    "    n_positive_flows=(\"Flow_EUR\", lambda x: (x > 0).sum()),\n",
    "    n_negative_flows=(\"Flow_EUR\", lambda x: (x < 0).sum()),\n",
    ")\n",
    "\n",
    "print(\"Monthly flow summary:\")\n",
    "print(flows_monthly.head())\n",
    "\n",
    "# ---- Plot bar chart ----\n",
    "flows_monthly.index = flows_monthly.index.astype(str)\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.bar(flows_monthly.index, flows_monthly[\"n_positive_flows\"], label=\"Positive flows (inflows)\", alpha=0.7)\n",
    "plt.bar(flows_monthly.index, flows_monthly[\"n_negative_flows\"], label=\"Negative flows (outflows)\", alpha=0.7)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Year-Month\")\n",
    "plt.ylabel(\"Number of accounts with flows\")\n",
    "plt.title(\"Monthly number of accounts with inflows vs outflows\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4af0e205-a304-4b10-be1d-de69904aa0da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged[\"YearMonth\"] = df_merged[\"Date\"].dt.to_period(\"M\")\n",
    "\n",
    "monthly_flow_amounts = df_merged.groupby(\"YearMonth\").agg(\n",
    "    avg_positive_flow=(\"Flow_EUR\", lambda x: x[x > 0].mean()),\n",
    "    avg_negative_flow=(\"Flow_EUR\", lambda x: x[x < 0].mean())\n",
    ")\n",
    "\n",
    "monthly_flow_amounts.index = monthly_flow_amounts.index.astype(str)\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.plot(monthly_flow_amounts.index, monthly_flow_amounts[\"avg_positive_flow\"], label=\"Average inflow (positive)\", marker=\"o\")\n",
    "plt.plot(monthly_flow_amounts.index, monthly_flow_amounts[\"avg_negative_flow\"], label=\"Average outflow (negative)\", marker=\"o\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Year-Month\")\n",
    "plt.ylabel(\"Average flow (€)\")\n",
    "plt.title(\"Monthly average inflows and outflows\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d548152-196f-456a-b21e-4d05e39d03c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df_merged.copy()\n",
    "df[\"YearMonth\"] = df[\"Date\"].dt.to_period(\"M\")\n",
    "\n",
    "# 1) Flow mensuel total par client\n",
    "monthly_client_flow = (\n",
    "    df.groupby([\"Account_ID\", \"YearMonth\"])[\"Flow_EUR\"]\n",
    "    .sum()\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# 2) Calcul des seuils de quantiles (tu peux ajuster)\n",
    "q1 = monthly_client_flow[\"Flow_EUR\"].quantile(0.33)\n",
    "q2 = monthly_client_flow[\"Flow_EUR\"].quantile(0.66)\n",
    "\n",
    "# 3) Catégorisation des clients\n",
    "def categorize(flow):\n",
    "    if flow <= q1:\n",
    "        return \"Petits flows\"\n",
    "    elif flow <= q2:\n",
    "        return \"Flows moyens\"\n",
    "    else:\n",
    "        return \"Grands flows\"\n",
    "\n",
    "monthly_client_flow[\"Flow_category\"] = monthly_client_flow[\"Flow_EUR\"].apply(categorize)\n",
    "\n",
    "# 4) Nombre de clients dans chaque catégorie par mois\n",
    "category_counts = (\n",
    "    monthly_client_flow.groupby([\"YearMonth\", \"Flow_category\"])\n",
    "    .size()\n",
    "    .unstack(fill_value=0)\n",
    ")\n",
    "\n",
    "category_counts.index = category_counts.index.astype(str)\n",
    "\n",
    "# 5) Plot\n",
    "category_counts.plot(kind=\"bar\", stacked=True, figsize=(14,6))\n",
    "plt.title(\"Répartition des clients par niveau de flux mensuel\")\n",
    "plt.xlabel(\"Mois\")\n",
    "plt.ylabel(\"Nombre de clients\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c5a83e0-1faf-4745-88b3-c0fb363317fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Répartition des comptes par catégorie :\n",
      "Grands flows : YearMonth\n",
      "2015-01    432\n",
      "2015-02    499\n",
      "2015-03    536\n",
      "2015-04    530\n",
      "2015-05    472\n",
      "          ... \n",
      "2025-07    412\n",
      "2025-08    388\n",
      "2025-09    407\n",
      "2025-10    418\n",
      "2025-11    231\n",
      "Name: Grands flows, Length: 131, dtype: int64 comptes\n",
      "Petits flows : YearMonth\n",
      "2015-01    7429\n",
      "2015-02    7380\n",
      "2015-03    7390\n",
      "2015-04    7416\n",
      "2015-05    7493\n",
      "           ... \n",
      "2025-07    3474\n",
      "2025-08    3507\n",
      "2025-09    3509\n",
      "2025-10    3529\n",
      "2025-11     174\n",
      "Name: Petits flows, Length: 131, dtype: int64 comptes\n"
     ]
    }
   ],
   "source": [
    "print(\"Répartition des comptes par catégorie :\")\n",
    "for cat, count in category_counts.items():\n",
    "    print(f\"{cat} : {count} comptes\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "943b359e-b1c0-435e-943b-94167f8c566f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moyenne du nombre de comptes par catégorie (sur toute la période) :\n",
      "Grands flows : 334 comptes en moyenne par mois\n",
      "Petits flows : 6736 comptes en moyenne par mois\n"
     ]
    }
   ],
   "source": [
    "# Nombre de comptes dans chaque catégorie par mois\n",
    "category_counts_by_month = (\n",
    "    monthly_client_flow.groupby([\"YearMonth\", \"Flow_category\"])\n",
    "    .size()\n",
    "    .unstack(fill_value=0)\n",
    ")\n",
    "\n",
    "# Moyenne sur tous les mois\n",
    "category_counts_mean = category_counts_by_month.mean()\n",
    "\n",
    "print(\"Moyenne du nombre de comptes par catégorie (sur toute la période) :\")\n",
    "for cat, mean_val in category_counts_mean.items():\n",
    "    print(f\"{cat} : {mean_val:.0f} comptes en moyenne par mois\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6bb455c-91f9-4169-a179-273e98ef3ebd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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