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"# DATA COLLECTION "
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" 003 166 166.1 200000647 \\\n",
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"\n",
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"... ... ... ... ... ... \n",
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"0 1989-11-07 ... Carmignac Patrimoine A EUR \n",
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"... ... ... ... ... ... \n",
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"4842124 2013-11-25 ... Carmignac Portfolio Sécurité FW & FW-R EUR \n",
"\n",
" FR0010135103 1989-11-07.1 NULL.1 2015-02-28 35.368000000 \\\n",
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},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"chemin_fichier = \"s3://projet-bdc-data/carmignac/AUM ENSAE V1 -20251027.csv\"\n",
"df_aum1 = pd.read_csv(chemin_fichier, sep=';', engine='python')\n",
"df_aum1"
]
},
{
"cell_type": "code",
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"id": "036d6acb-cc41-4dd6-9456-3aaf6fedbda4",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <tr>\n",
" <th>2566305</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>Global Bond</td>\n",
" <td>SICAV</td>\n",
" <td>2007-12-14</td>\n",
" <td>...</td>\n",
" <td>2015-04-07</td>\n",
" <td>0.0</td>\n",
" <td>-1.00000</td>\n",
" <td>-1.00000</td>\n",
" <td>0.00</td>\n",
" <td>-1382.36</td>\n",
" <td>-1382.36</td>\n",
" <td>0.00</td>\n",
" <td>-1382.36</td>\n",
" <td>-1382.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2566306</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>Global Bond</td>\n",
" <td>SICAV</td>\n",
" <td>2007-12-14</td>\n",
" <td>...</td>\n",
" <td>2015-04-10</td>\n",
" <td>0.0</td>\n",
" <td>-31.00000</td>\n",
" <td>-31.00000</td>\n",
" <td>0.00</td>\n",
" <td>-43323.43</td>\n",
" <td>-43323.43</td>\n",
" <td>0.00</td>\n",
" <td>-43323.43</td>\n",
" <td>-43323.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2566307</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>1989-01-26</td>\n",
" <td>...</td>\n",
" <td>2015-10-12</td>\n",
" <td>97.0</td>\n",
" <td>0.00000</td>\n",
" <td>97.00000</td>\n",
" <td>166077.58</td>\n",
" <td>0.00</td>\n",
" <td>166077.58</td>\n",
" <td>166077.58</td>\n",
" <td>0.00</td>\n",
" <td>166077.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2566308</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>1989-01-26</td>\n",
" <td>...</td>\n",
" <td>2015-11-10</td>\n",
" <td>40.0</td>\n",
" <td>-22.00000</td>\n",
" <td>18.00000</td>\n",
" <td>68640.40</td>\n",
" <td>-37752.22</td>\n",
" <td>30888.18</td>\n",
" <td>68640.40</td>\n",
" <td>-37752.22</td>\n",
" <td>30888.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2566309</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>1989-01-26</td>\n",
" <td>...</td>\n",
" <td>2015-12-17</td>\n",
" <td>26.0</td>\n",
" <td>0.00000</td>\n",
" <td>26.00000</td>\n",
" <td>44621.20</td>\n",
" <td>0.00</td>\n",
" <td>44621.20</td>\n",
" <td>44621.20</td>\n",
" <td>0.00</td>\n",
" <td>44621.20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2566310 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" 003 166 166.1 406533 \\\n",
"0 003 166 166 406533 \n",
"1 003 166 166 406533 \n",
"2 003 166 166 406533 \n",
"3 006 968 968 412182 \n",
"4 008 927 927 411986 \n",
"... ... ... ... ... \n",
"2566305 Private Client Private Client Private Client Private Client \n",
"2566306 Private Client Private Client Private Client Private Client \n",
"2566307 Private Client Private Client Private Client Private Client \n",
"2566308 Private Client Private Client Private Client Private Client \n",
"2566309 Private Client Private Client Private Client Private Client \n",
"\n",
" France France.1 Equity Investissement FCP \\\n",
"0 France France Equity Investissement FCP \n",
"1 France France Equity Investissement FCP \n",
"2 France France Equity Investissement FCP \n",
"3 France France Equity Investissement FCP \n",
"4 France France Diversified Multi Expertise FCP \n",
"... ... ... ... ... ... \n",
"2566305 Luxembourg Luxembourg Fixed Income Global Bond SICAV \n",
"2566306 Luxembourg Luxembourg Fixed Income Global Bond SICAV \n",
"2566307 Luxembourg Luxembourg Fixed Income Sécurité FCP \n",
"2566308 Luxembourg Luxembourg Fixed Income Sécurité FCP \n",
"2566309 Luxembourg Luxembourg Fixed Income Sécurité FCP \n",
"\n",
" 1989-01-26 ... 2016-04-04 0.000000000 -25.580000000 \\\n",
"0 1989-01-26 ... 2016-07-12 0.0 -8.87500 \n",
"1 1989-01-26 ... 2016-11-22 0.0 -177.06500 \n",
"2 1989-01-26 ... 2017-12-12 0.0 -72.63700 \n",
"3 1989-01-26 ... 2018-09-18 0.0 -265.02125 \n",
"4 2002-01-02 ... 2022-07-06 0.0 -0.29700 \n",
"... ... ... ... ... ... \n",
"2566305 2007-12-14 ... 2015-04-07 0.0 -1.00000 \n",
"2566306 2007-12-14 ... 2015-04-10 0.0 -31.00000 \n",
"2566307 1989-01-26 ... 2015-10-12 97.0 0.00000 \n",
"2566308 1989-01-26 ... 2015-11-10 40.0 -22.00000 \n",
"2566309 1989-01-26 ... 2015-12-17 26.0 0.00000 \n",
"\n",
" -25.580000000.1 0.0000 -27030.1300 -27030.1300.1 0.0000.1 \\\n",
"0 -8.87500 0.00 -10134.72 -10134.72 0.00 \n",
"1 -177.06500 0.00 -200504.86 -200504.86 0.00 \n",
"2 -72.63700 0.00 -86605.10 -86605.10 0.00 \n",
"3 -265.02125 0.00 -315155.32 -315155.32 0.00 \n",
"4 -0.29700 0.00 -1090.03 -1090.03 0.00 \n",
"... ... ... ... ... ... \n",
"2566305 -1.00000 0.00 -1382.36 -1382.36 0.00 \n",
"2566306 -31.00000 0.00 -43323.43 -43323.43 0.00 \n",
"2566307 97.00000 166077.58 0.00 166077.58 166077.58 \n",
"2566308 18.00000 68640.40 -37752.22 30888.18 68640.40 \n",
"2566309 26.00000 44621.20 0.00 44621.20 44621.20 \n",
"\n",
" -27030.1300.2 -27030.1300.3 \n",
"0 -10134.72 -10134.72 \n",
"1 -200504.86 -200504.86 \n",
"2 -86605.10 -86605.10 \n",
"3 -315155.32 -315155.32 \n",
"4 -1090.03 -1090.03 \n",
"... ... ... \n",
"2566305 -1382.36 -1382.36 \n",
"2566306 -43323.43 -43323.43 \n",
"2566307 0.00 166077.58 \n",
"2566308 -37752.22 30888.18 \n",
"2566309 0.00 44621.20 \n",
"\n",
"[2566310 rows x 27 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chemin_fichier = \"s3://projet-bdc-data/carmignac/Flows ENSAE V1 -20251027.csv\"\n",
"df_flows1 = pd.read_csv(chemin_fichier, sep=';', engine='python')\n",
"df_flows1"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f8508d94-74a7-4bb0-8b81-c2e06850c25f",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Agreement - Code</th>\n",
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" <th>Registrar Account - ID</th>\n",
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" <td>FCP</td>\n",
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" <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",
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" <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>2.248945e+04</td>\n",
" <td>2.248945e+04</td>\n",
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" <tr>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4880292</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>2020-02-29</td>\n",
" <td>26801.000</td>\n",
" <td>2.740670e+06</td>\n",
" <td>2.740670e+06</td>\n",
" </tr>\n",
" <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",
" <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>2020-06-30</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",
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" <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>3.184222e+05</td>\n",
" <td>3.184222e+05</td>\n",
" </tr>\n",
" <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",
" </tr>\n",
" <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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4880297 rows × 18 columns</p>\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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" <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",
" <th>Product - Strategy</th>\n",
" <th>Product - Legal Status</th>\n",
" <th>Product - Is Dedie ?</th>\n",
" <th>...</th>\n",
" <th>Centralisation Date</th>\n",
" <th>Quantity - Subscription</th>\n",
" <th>Quantity - Redemption</th>\n",
" <th>Quantity - NetFlows</th>\n",
" <th>Value Ccy - Subscription</th>\n",
" <th>Value Ccy - Redemption</th>\n",
" <th>Value Ccy - NetFlows</th>\n",
" <th>Value € - Subscription</th>\n",
" <th>Value € - Redemption</th>\n",
" <th>Value € - NetFlows</th>\n",
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" <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",
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" <td>2016-10-26</td>\n",
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" <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",
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" <td>Equity</td>\n",
" <td>Investissement</td>\n",
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" <td>Luxembourg</td>\n",
" <td>Fixed Income</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>...</td>\n",
" <td>2015-09-18</td>\n",
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" </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",
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" <td>7603.66</td>\n",
" <td>7603.66</td>\n",
" <td>0.00</td>\n",
" <td>7603.66</td>\n",
" </tr>\n",
" <tr>\n",
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" <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",
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" <td>-754696.80</td>\n",
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" <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": 6,
"id": "5773b911-6b84-448d-962f-8228eeac0250",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4880297, 18)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_aum2.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_aum2.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73c15272-a789-4928-b23e-651dd886efd6",
"metadata": {},
"outputs": [],
"source": [
"'Agreement - Code': Identifie le contrat commercial ou opérationnel entre Carmignac et l’intermédiaire distributeur (banque, assurance, plateforme)., \n",
"\n",
"'Company - Id' : Identifiant interne de l’entité distributrice (banque privée, CGP, assurance…).Chaque \"Company\" = un partenaire B2B, \n",
"\n",
"'Company - Ultimate Parent Id': Identifiant du groupe parent de la société distributrice.\n",
"\n",
"Ex : une banque privée appartient à un grand groupe bancaire //// OU plusieurs filiales → même Ultimate Parent.\n",
"Sert à agréger les flux ou AUM au niveau du groupe, pas seulement d’une filiale.,\n",
"\n",
"'Registrar Account - ID', \n",
"Identifiant du compte investisseur dans les registres.Ce n’est PAS le client final : c’est souvent un compte omnibus, une plateforme, \n",
"une banque dépositaire, etc.s\n",
"→ IMPORTANT car un même investisseur final peut être caché sous plusieurs comptes.,\n",
"\n",
"'Registrar Account - Region',\n",
"\n",
"'RegistrarAccount - Country', \n",
"\n",
"'Product - Asset Type', \n",
"'Product - Strategy', \n",
"'Product - Legal Status': Le cadre réglementaire du fonds, typiquement : UCITS,AIF,SICAV,FCP, \n",
"'Product - Is Dedie ?': Indique si la part est dédiée à un distributeur particulier (shareclass ségrégée). Valeurs : Oui/Non,\n",
"\n",
"'Product - Fund', \n",
"'Product - Shareclass Type': Chaque share classe peut avoir : Devise différente (EUR, USD) /// Frais différents //Distribution des dividendes \n",
"différente .............,\n",
"\n",
"'Product - Shareclass Currency', \n",
"'Product - Isin': c est un code qui identifie un produit financier, pas un client , #REVOIRR \n",
"'Centralisation Date': Cette date n’est pas la date de transaction, mais celle où les positions sont centralisées et publiees pour reporting., ....\n",
"'Quantity - AUM', \n",
"'Value - AUM CCY', Valeur des AUM dans la devise de la share classe,\n",
"'Value - AUM €'\n",
"\n",
"\n",
"AUM = Assets Under Management = Encours sous gestion\n",
"Ce sont les montants totaux investis dans un fonds ou un compte.\n",
"\n",
"Exemple :\n",
"- Quantity : Compte A détient 1 000 parts de Carmignac Patrimoine A EUR\n",
"- Price ou valeurs de la part = 100 EUR\n",
"- AUM Value = 1 000 × 100 = 100 000 EUR\n",
"==> AUM représente la valeur financière totale détenue par le compte pour ce fonds.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b179431d-0d07-4eff-8bb7-976ebbe3f987",
"metadata": {},
"outputs": [],
"source": [
"#FLOWS VS AUM\n",
"Flows = mouvements entrants (subscriptions) ou sortants (redemptions) pendant une période\n",
"AUM = stock à un instant donné\n",
"\n",
"\n",
"\n",
"REMARQUE: CLIENT IDENTIFICATION : le client final n’est pas directement identifiable, car intermediaire.\n",
"\n",
"1- Registrar Account ID → identifie le compte dans le registre → peut représenter un ou plusieurs clients finaux.\n",
"2- Company - Id Ultimate Parent → identifie le distributeur, pas le client final.\n",
"\n",
"En pratique pour le clustering client : Il faudra utiliser Registrar Account - ID + Company - Id comme proxy client.\n",
"Mais attention : même investisseur peut avoir plusieurs comptes / plusieurs banques."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "af25fd07-a613-4adc-b88b-93a8d300379c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2574461, 24)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_flows2.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_flows2.columns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5fac74b0-662f-48d0-a234-7edc3c3e86ad",
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Colonne</th>\n",
" <th>Nbr Lignes</th>\n",
" <th>Nb valeurs uniques</th>\n",
" <th>Exemples de valeurs</th>\n",
" <th>Nan Values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Agreement - Code</td>\n",
" <td>4880297</td>\n",
" <td>2427</td>\n",
" <td>[003, 005, 006, 008, 009, 011, 016, 020, 021, ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Company - Id</td>\n",
" <td>4880297</td>\n",
" <td>1562</td>\n",
" <td>[166, nan, 968, 927, 42, 15181, 13536, 231, 48...</td>\n",
" <td>113750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Company - Ultimate Parent Id</td>\n",
" <td>4880297</td>\n",
" <td>1068</td>\n",
" <td>[166, nan, 968, 927, 42, 877, 13536, 14977, 48...</td>\n",
" <td>113750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Registrar Account - ID</td>\n",
" <td>4880297</td>\n",
" <td>12501</td>\n",
" <td>[200000647, 200000654, 200127202, 404391, 4054...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Registrar Account - Region</td>\n",
" <td>4880297</td>\n",
" <td>15</td>\n",
" <td>[France, Switzerland, Luxembourg, Belgium, Int...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>RegistrarAccount - Country</td>\n",
" <td>4880297</td>\n",
" <td>39</td>\n",
" <td>[France, Switzerland, Luxembourg, Belgium, Mon...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Product - Asset Type</td>\n",
" <td>4880297</td>\n",
" <td>6</td>\n",
" <td>[Diversified, Equity, nan, Alternative, Fixed ...</td>\n",
" <td>315831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Product - Strategy</td>\n",
" <td>4880297</td>\n",
" <td>53</td>\n",
" <td>[Patrimoine, Investissement, Euro-Investisseme...</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Product - Legal Status</td>\n",
" <td>4880297</td>\n",
" <td>6</td>\n",
" <td>[FCP, SICAV, FCPE, ICAV, OEIC, Others]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Product - Is Dedie ?</td>\n",
" <td>4880297</td>\n",
" <td>2</td>\n",
" <td>[NO, YES]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Product - Fund</td>\n",
" <td>4880297</td>\n",
" <td>74</td>\n",
" <td>[Carmignac Patrimoine, Carmignac Investissemen...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Product - Shareclass Type</td>\n",
" <td>4880297</td>\n",
" <td>12</td>\n",
" <td>[A, F, AW &amp; AW-R, FW &amp; FW-R, Not Applicable, E...</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Product - Shareclass Currency</td>\n",
" <td>4880297</td>\n",
" <td>6</td>\n",
" <td>[EUR, CHF, USD, GBP, JPY, CAD]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Product - Isin</td>\n",
" <td>4880297</td>\n",
" <td>491</td>\n",
" <td>[FR0010135103, FR0010148981, LU0992625839, FR0...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Centralisation Date</td>\n",
" <td>4880297</td>\n",
" <td>130</td>\n",
" <td>[2015-03-31, 2015-11-30, 2015-12-31, 2016-03-3...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Quantity - AUM</td>\n",
" <td>4880297</td>\n",
" <td>554404</td>\n",
" <td>[35.368, 0.0, 193.97, 170.2, 285.2, 277.8, 189...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Value - AUM CCY</td>\n",
" <td>4880297</td>\n",
" <td>1689620</td>\n",
" <td>[24648.6666, 22413.0553, 22051.2406, 21626.117...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Value - AUM €</td>\n",
" <td>4880297</td>\n",
" <td>1697923</td>\n",
" <td>[24648.6666, 22413.0553, 22051.2406, 21626.117...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Colonne Nbr Lignes Nb valeurs uniques \\\n",
"0 Agreement - Code 4880297 2427 \n",
"1 Company - Id 4880297 1562 \n",
"2 Company - Ultimate Parent Id 4880297 1068 \n",
"3 Registrar Account - ID 4880297 12501 \n",
"4 Registrar Account - Region 4880297 15 \n",
"5 RegistrarAccount - Country 4880297 39 \n",
"6 Product - Asset Type 4880297 6 \n",
"7 Product - Strategy 4880297 53 \n",
"8 Product - Legal Status 4880297 6 \n",
"9 Product - Is Dedie ? 4880297 2 \n",
"10 Product - Fund 4880297 74 \n",
"11 Product - Shareclass Type 4880297 12 \n",
"12 Product - Shareclass Currency 4880297 6 \n",
"13 Product - Isin 4880297 491 \n",
"14 Centralisation Date 4880297 130 \n",
"15 Quantity - AUM 4880297 554404 \n",
"16 Value - AUM CCY 4880297 1689620 \n",
"17 Value - AUM € 4880297 1697923 \n",
"\n",
" Exemples de valeurs Nan Values \n",
"0 [003, 005, 006, 008, 009, 011, 016, 020, 021, ... 0 \n",
"1 [166, nan, 968, 927, 42, 15181, 13536, 231, 48... 113750 \n",
"2 [166, nan, 968, 927, 42, 877, 13536, 14977, 48... 113750 \n",
"3 [200000647, 200000654, 200127202, 404391, 4054... 0 \n",
"4 [France, Switzerland, Luxembourg, Belgium, Int... 0 \n",
"5 [France, Switzerland, Luxembourg, Belgium, Mon... 0 \n",
"6 [Diversified, Equity, nan, Alternative, Fixed ... 315831 \n",
"7 [Patrimoine, Investissement, Euro-Investisseme... 75 \n",
"8 [FCP, SICAV, FCPE, ICAV, OEIC, Others] 0 \n",
"9 [NO, YES] 0 \n",
"10 [Carmignac Patrimoine, Carmignac Investissemen... 0 \n",
"11 [A, F, AW & AW-R, FW & FW-R, Not Applicable, E... 35 \n",
"12 [EUR, CHF, USD, GBP, JPY, CAD] 0 \n",
"13 [FR0010135103, FR0010148981, LU0992625839, FR0... 0 \n",
"14 [2015-03-31, 2015-11-30, 2015-12-31, 2016-03-3... 0 \n",
"15 [35.368, 0.0, 193.97, 170.2, 285.2, 277.8, 189... 0 \n",
"16 [24648.6666, 22413.0553, 22051.2406, 21626.117... 0 \n",
"17 [24648.6666, 22413.0553, 22051.2406, 21626.117... 0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#dict avec valeurs unique de chaque col \n",
"rows = []\n",
"\n",
"for col in df_aum2.columns:\n",
" uniques = df_aum2[col].unique()\n",
" rows.append({\n",
" \"Colonne\": col,\n",
" \"Nbr Lignes\": df_aum2.shape[0], #4.8millions\n",
" \"Nb valeurs uniques\": len(uniques),\n",
" \"Exemples de valeurs\": uniques,\n",
" \"Nan Values\" : df_aum2[col].isna().sum()\n",
" })\n",
"\n",
"df_uniques = pd.DataFrame(rows)\n",
"df_uniques"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5de53ba3-b3db-4935-acac-435b05b909e2",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Colonne</th>\n",
" <th>Nbr Lignes</th>\n",
" <th>Nb valeurs uniques</th>\n",
" <th>Exemples de valeurs</th>\n",
" <th>Nan Values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Agreement - Code</td>\n",
" <td>2574461</td>\n",
" <td>1611</td>\n",
" <td>[003, 008, 006, 009, 016, 020, 021, 022, 030, ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Company - Id</td>\n",
" <td>2574461</td>\n",
" <td>1231</td>\n",
" <td>[166, 927, 968, 42, 13536, 231, 481, 91, 1038,...</td>\n",
" <td>1540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Company - Ultimate Parent Id</td>\n",
" <td>2574461</td>\n",
" <td>815</td>\n",
" <td>[166, 927, 968, 42, 13536, 14977, 481, 91, 103...</td>\n",
" <td>1540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Registrar Account - ID</td>\n",
" <td>2574461</td>\n",
" <td>6842</td>\n",
" <td>[200127202, 406533, 411986, 200000647, 412028,...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Registrar Account - Region</td>\n",
" <td>2574461</td>\n",
" <td>15</td>\n",
" <td>[France, International, Belgium, Spain, Luxemb...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>RegistrarAccount - Country</td>\n",
" <td>2574461</td>\n",
" <td>34</td>\n",
" <td>[France, Monaco, Israel, Belgium, Spain, Luxem...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Product - Asset Type</td>\n",
" <td>2574461</td>\n",
" <td>6</td>\n",
" <td>[Equity, Diversified, Fixed Income, Alternativ...</td>\n",
" <td>2049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Product - Strategy</td>\n",
" <td>2574461</td>\n",
" <td>50</td>\n",
" <td>[Investissement, Patrimoine, Emerging Patrimoi...</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Product - Legal Status</td>\n",
" <td>2574461</td>\n",
" <td>6</td>\n",
" <td>[SICAV, FCP, FCPE, ICAV, OEIC, Others]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Product - Is Dedie ?</td>\n",
" <td>2574461</td>\n",
" <td>2</td>\n",
" <td>[NO, YES]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Product - Fund</td>\n",
" <td>2574461</td>\n",
" <td>70</td>\n",
" <td>[Carmignac Portfolio Investissement, Carmignac...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Product - Shareclass Type</td>\n",
" <td>2574461</td>\n",
" <td>11</td>\n",
" <td>[F, A, AW &amp; AW-R, FW &amp; FW-R, E, IW, Dedicated,...</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Product - Shareclass Currency</td>\n",
" <td>2574461</td>\n",
" <td>6</td>\n",
" <td>[EUR, USD, CHF, GBP, JPY, CAD]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Product - Isin</td>\n",
" <td>2574461</td>\n",
" <td>474</td>\n",
" <td>[LU0992625839, FR0010135103, FR0010148981, LU0...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Centralisation Date</td>\n",
" <td>2574461</td>\n",
" <td>2780</td>\n",
" <td>[2020-11-05, 2015-03-09, 2016-10-26, 2018-10-1...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Quantity - Subscription</td>\n",
" <td>2574461</td>\n",
" <td>359661</td>\n",
" <td>[1636.0, 144.69, 0.0, 160.698, 17.285, 115.0, ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Quantity - Redemption</td>\n",
" <td>2574461</td>\n",
" <td>374378</td>\n",
" <td>[0.0, -8.321, -22.083, -465.992, -0.397, -36.0...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Quantity - NetFlows</td>\n",
" <td>2574461</td>\n",
" <td>667586</td>\n",
" <td>[1636.0, 144.69, -8.321, -22.083, -465.992, 16...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Value Ccy - Subscription</td>\n",
" <td>2574461</td>\n",
" <td>926633</td>\n",
" <td>[280983.0, 99985.13, 0.0, 21851.77, 19795.99, ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Value Ccy - Redemption</td>\n",
" <td>2574461</td>\n",
" <td>1296468</td>\n",
" <td>[0.0, -9384.76, -25227.4, -563775.76, -1536.53...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Value Ccy - NetFlows</td>\n",
" <td>2574461</td>\n",
" <td>1972319</td>\n",
" <td>[280983.0, 99985.13, -9384.76, -25227.4, -5637...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Value € - Subscription</td>\n",
" <td>2574461</td>\n",
" <td>955890</td>\n",
" <td>[280983.0, 99985.13, 0.0, 21851.77, 19795.99, ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Value € - Redemption</td>\n",
" <td>2574461</td>\n",
" <td>1323531</td>\n",
" <td>[0.0, -9384.76, -25227.4, -563775.76, -1536.53...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Value € - NetFlows</td>\n",
" <td>2574461</td>\n",
" <td>2018916</td>\n",
" <td>[280983.0, 99985.13, -9384.76, -25227.4, -5637...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Colonne Nbr Lignes Nb valeurs uniques \\\n",
"0 Agreement - Code 2574461 1611 \n",
"1 Company - Id 2574461 1231 \n",
"2 Company - Ultimate Parent Id 2574461 815 \n",
"3 Registrar Account - ID 2574461 6842 \n",
"4 Registrar Account - Region 2574461 15 \n",
"5 RegistrarAccount - Country 2574461 34 \n",
"6 Product - Asset Type 2574461 6 \n",
"7 Product - Strategy 2574461 50 \n",
"8 Product - Legal Status 2574461 6 \n",
"9 Product - Is Dedie ? 2574461 2 \n",
"10 Product - Fund 2574461 70 \n",
"11 Product - Shareclass Type 2574461 11 \n",
"12 Product - Shareclass Currency 2574461 6 \n",
"13 Product - Isin 2574461 474 \n",
"14 Centralisation Date 2574461 2780 \n",
"15 Quantity - Subscription 2574461 359661 \n",
"16 Quantity - Redemption 2574461 374378 \n",
"17 Quantity - NetFlows 2574461 667586 \n",
"18 Value Ccy - Subscription 2574461 926633 \n",
"19 Value Ccy - Redemption 2574461 1296468 \n",
"20 Value Ccy - NetFlows 2574461 1972319 \n",
"21 Value € - Subscription 2574461 955890 \n",
"22 Value € - Redemption 2574461 1323531 \n",
"23 Value € - NetFlows 2574461 2018916 \n",
"\n",
" Exemples de valeurs Nan Values \n",
"0 [003, 008, 006, 009, 016, 020, 021, 022, 030, ... 0 \n",
"1 [166, 927, 968, 42, 13536, 231, 481, 91, 1038,... 1540 \n",
"2 [166, 927, 968, 42, 13536, 14977, 481, 91, 103... 1540 \n",
"3 [200127202, 406533, 411986, 200000647, 412028,... 0 \n",
"4 [France, International, Belgium, Spain, Luxemb... 0 \n",
"5 [France, Monaco, Israel, Belgium, Spain, Luxem... 0 \n",
"6 [Equity, Diversified, Fixed Income, Alternativ... 2049 \n",
"7 [Investissement, Patrimoine, Emerging Patrimoi... 6 \n",
"8 [SICAV, FCP, FCPE, ICAV, OEIC, Others] 0 \n",
"9 [NO, YES] 0 \n",
"10 [Carmignac Portfolio Investissement, Carmignac... 0 \n",
"11 [F, A, AW & AW-R, FW & FW-R, E, IW, Dedicated,... 2 \n",
"12 [EUR, USD, CHF, GBP, JPY, CAD] 0 \n",
"13 [LU0992625839, FR0010135103, FR0010148981, LU0... 0 \n",
"14 [2020-11-05, 2015-03-09, 2016-10-26, 2018-10-1... 0 \n",
"15 [1636.0, 144.69, 0.0, 160.698, 17.285, 115.0, ... 0 \n",
"16 [0.0, -8.321, -22.083, -465.992, -0.397, -36.0... 0 \n",
"17 [1636.0, 144.69, -8.321, -22.083, -465.992, 16... 0 \n",
"18 [280983.0, 99985.13, 0.0, 21851.77, 19795.99, ... 0 \n",
"19 [0.0, -9384.76, -25227.4, -563775.76, -1536.53... 0 \n",
"20 [280983.0, 99985.13, -9384.76, -25227.4, -5637... 0 \n",
"21 [280983.0, 99985.13, 0.0, 21851.77, 19795.99, ... 0 \n",
"22 [0.0, -9384.76, -25227.4, -563775.76, -1536.53... 0 \n",
"23 [280983.0, 99985.13, -9384.76, -25227.4, -5637... 0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#dict avec valeurs unique de chaque col \n",
"rowsf = []\n",
"\n",
"for col in df_flows2.columns:\n",
" uniques = df_flows2[col].unique()\n",
" rowsf.append({\n",
" \"Colonne\": col,\n",
" \"Nbr Lignes\": df_flows2.shape[0], #4.8millions\n",
" \"Nb valeurs uniques\": len(uniques),\n",
" \"Exemples de valeurs\": uniques,\n",
" \"Nan Values\" : df_flows2[col].isna().sum()\n",
" })\n",
"\n",
"df_unique_flows = pd.DataFrame(rowsf)\n",
"df_unique_flows"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "520a5211-3735-44d8-9fe5-b2468c950606",
"metadata": {},
"outputs": [],
"source": [
"#rev pour df_flows2 et df_aum2 pas meme valeurs uniques "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e07a2b28-13f7-49f6-a55b-7d09a506407b",
"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>Colonne</th>\n",
" <th>Nbr Lignes_aum</th>\n",
" <th>Nb valeurs uniques_aum</th>\n",
" <th>Nbr Lignes_flows</th>\n",
" <th>Nb valeurs uniques_flows</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Agreement - Code</td>\n",
" <td>4880297</td>\n",
" <td>2427</td>\n",
" <td>2574461</td>\n",
" <td>1611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Company - Id</td>\n",
" <td>4880297</td>\n",
" <td>1562</td>\n",
" <td>2574461</td>\n",
" <td>1231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Company - Ultimate Parent Id</td>\n",
" <td>4880297</td>\n",
" <td>1068</td>\n",
" <td>2574461</td>\n",
" <td>815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Registrar Account - ID</td>\n",
" <td>4880297</td>\n",
" <td>12501</td>\n",
" <td>2574461</td>\n",
" <td>6842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Registrar Account - Region</td>\n",
" <td>4880297</td>\n",
" <td>15</td>\n",
" <td>2574461</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>RegistrarAccount - Country</td>\n",
" <td>4880297</td>\n",
" <td>39</td>\n",
" <td>2574461</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Product - Asset Type</td>\n",
" <td>4880297</td>\n",
" <td>6</td>\n",
" <td>2574461</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Product - Strategy</td>\n",
" <td>4880297</td>\n",
" <td>53</td>\n",
" <td>2574461</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Product - Legal Status</td>\n",
" <td>4880297</td>\n",
" <td>6</td>\n",
" <td>2574461</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Product - Is Dedie ?</td>\n",
" <td>4880297</td>\n",
" <td>2</td>\n",
" <td>2574461</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Product - Fund</td>\n",
" <td>4880297</td>\n",
" <td>74</td>\n",
" <td>2574461</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Product - Shareclass Type</td>\n",
" <td>4880297</td>\n",
" <td>12</td>\n",
" <td>2574461</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Product - Shareclass Currency</td>\n",
" <td>4880297</td>\n",
" <td>6</td>\n",
" <td>2574461</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Product - Isin</td>\n",
" <td>4880297</td>\n",
" <td>491</td>\n",
" <td>2574461</td>\n",
" <td>474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Centralisation Date</td>\n",
" <td>4880297</td>\n",
" <td>130</td>\n",
" <td>2574461</td>\n",
" <td>2780</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Colonne Nbr Lignes_aum Nb valeurs uniques_aum \\\n",
"0 Agreement - Code 4880297 2427 \n",
"1 Company - Id 4880297 1562 \n",
"2 Company - Ultimate Parent Id 4880297 1068 \n",
"3 Registrar Account - ID 4880297 12501 \n",
"4 Registrar Account - Region 4880297 15 \n",
"5 RegistrarAccount - Country 4880297 39 \n",
"6 Product - Asset Type 4880297 6 \n",
"7 Product - Strategy 4880297 53 \n",
"8 Product - Legal Status 4880297 6 \n",
"9 Product - Is Dedie ? 4880297 2 \n",
"10 Product - Fund 4880297 74 \n",
"11 Product - Shareclass Type 4880297 12 \n",
"12 Product - Shareclass Currency 4880297 6 \n",
"13 Product - Isin 4880297 491 \n",
"14 Centralisation Date 4880297 130 \n",
"\n",
" Nbr Lignes_flows Nb valeurs uniques_flows \n",
"0 2574461 1611 \n",
"1 2574461 1231 \n",
"2 2574461 815 \n",
"3 2574461 6842 \n",
"4 2574461 15 \n",
"5 2574461 34 \n",
"6 2574461 6 \n",
"7 2574461 50 \n",
"8 2574461 6 \n",
"9 2574461 2 \n",
"10 2574461 70 \n",
"11 2574461 11 \n",
"12 2574461 6 \n",
"13 2574461 474 \n",
"14 2574461 2780 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_aum = df_uniques[['Colonne', 'Nbr Lignes', 'Nb valeurs uniques']]\n",
"df2_flows = df_unique_flows[['Colonne', 'Nbr Lignes', 'Nb valeurs uniques']]\n",
"\n",
"df_merged = df1_aum.merge(df2_flows, on='Colonne', suffixes=('_aum', '_flows'))\n",
"df_merged"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a673272a-d109-470e-9da9-02f61ca2550b",
"metadata": {},
"outputs": [],
"source": [
"#descriptio colonnes de df_flows2\n",
"Colonne\tSignification pour df_flows2\n",
"\n",
"1- Agreement - Code\t: Code interne de l’accord/fonds (pour identifier les fonds ou stratégies)\n",
"\n",
"2- Company - Id : \tID de l’intermédiaire (banque, conseiller)\n",
"3- Company - Ultimate Parent Id\t: ID de la société mère de l’intermédiaire\n",
"\n",
"4- Registrar Account - ID\t: Identifiant unique du compte (le client chez le dépositaire) #hyda code client !!!\n",
"5- Registrar Account - Region / Country: \tRégion ou pays du compte\n",
"\n",
"6- Product - Asset Type / Strategy / Legal Status / Is Dedie? : \n",
"Type de produit (Equity, Diversified…), stratégie d’investissement, type légal du fonds (SICAV, FCP), dédié ou non\n",
"\n",
"8-Product - Fund / Shareclass Type / Currency / ISIN\t:\n",
"Nom du fonds, type de shareclass, devise, ISIN unique du produit\n",
"\n",
"9- Centralisation Date : \tDate de reporting du flux\n",
"10- Quantity - Subscription / Redemption / NetFlows : \t\n",
"Nombre de parts souscrites, rachetées et flux net (Subscription - Redemption)\n",
"\n",
"11- Value Ccy / Value € - Subscription / Redemption / NetFlows\t: \n",
"Valeurs en devise du fonds et en euro pour les souscriptions, rachats et flux net \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2152a444-4a84-426e-ac7a-4806a8d0a35e",
"metadata": {},
"outputs": [],
"source": [
"Pour faire de la segmentation client et du clustering, il faut que le dataset soit cohérent et \n",
"représentatif des clients finaux, pas juste des comptes !! JE DOIS CLEAN ET IDENTIFIER LES MISMATCH \n",
"\n",
"ANOMALIES :EXEMPLE CI DESSOUS, Y EN A D AUTRES? ??????\n",
"1- Un client change de compte / dépositaire :\n",
"\n",
"-- Compte A → flux sortants (retrait)\n",
"-- Compte B → flux entrants (nouveau compte)\n",
"Montants identiques et même ISIN / produit → même client peut etre\n",
"\n",
"2- Un compte disparaît et un autre se crée avec mêmes montants → flux continu"
]
},
{
"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": "ff0284ad-2c28-4bb4-b6a5-92f52181e433",
"metadata": {},
"outputs": [],
"source": [
"# # Groupby pour détecter les flows identiques le même jour, sur le même ISIN\n",
"\n",
"# grouped = df_flows2.groupby(\n",
"# ['Product - Isin', 'Centralisation Date', 'Quantity - NetFlows', 'Value € - NetFlows']\n",
"# )['Registrar Account - ID'] \\\n",
"# .agg(['nunique', list]) \\\n",
"# .reset_index()\n",
"\n",
"# #plusieurs comptes différents apparaissent\n",
"# pseudo_client_candidates = grouped[grouped['nunique'] > 1]\n",
"\n",
"# print(\"Comptes potentiels à fusionner :\")\n",
"# display(pseudo_client_candidates)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3ff99523-0a79-465b-b257-ea15d6c42d2e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2574461, 24)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_flows2.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8df98c34-b1f7-4fb9-bbc7-c9bbe762022a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>ISIN</th>\n",
" <th>Date</th>\n",
" <th>Compte sortant</th>\n",
" <th>Montant sortie</th>\n",
" <th>Compte entrant</th>\n",
" <th>Montant entrée</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>FR0010135103</td>\n",
" <td>2015-01-02</td>\n",
" <td>365247</td>\n",
" <td>-3126.05</td>\n",
" <td>200002169</td>\n",
" <td>3126.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>FR0010135103</td>\n",
" <td>2015-01-02</td>\n",
" <td>365888</td>\n",
" <td>-3751.26</td>\n",
" <td>365241</td>\n",
" <td>3751.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>FR0010135103</td>\n",
" <td>2015-01-05</td>\n",
" <td>419848</td>\n",
" <td>-3135.85</td>\n",
" <td>200001747</td>\n",
" <td>3135.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>FR0010135103</td>\n",
" <td>2015-01-05</td>\n",
" <td>417952</td>\n",
" <td>-627.17</td>\n",
" <td>200002169</td>\n",
" <td>627.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>FR0010135103</td>\n",
" <td>2015-01-05</td>\n",
" <td>417937</td>\n",
" <td>-5644.53</td>\n",
" <td>406074</td>\n",
" <td>5644.53</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",
" </tr>\n",
" <tr>\n",
" <th>27875</th>\n",
" <td>LU2809794220</td>\n",
" <td>2025-10-16</td>\n",
" <td>200058108</td>\n",
" <td>-1315.00</td>\n",
" <td>200127345</td>\n",
" <td>1315.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27876</th>\n",
" <td>LU2809794220</td>\n",
" <td>2025-10-31</td>\n",
" <td>420350</td>\n",
" <td>-141.03</td>\n",
" <td>200127644</td>\n",
" <td>141.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27877</th>\n",
" <td>LU2809794220</td>\n",
" <td>2025-11-03</td>\n",
" <td>200127356</td>\n",
" <td>-2996.91</td>\n",
" <td>420350</td>\n",
" <td>2996.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27878</th>\n",
" <td>LU2809794493</td>\n",
" <td>2024-12-09</td>\n",
" <td>200058108</td>\n",
" <td>-97.27</td>\n",
" <td>420350</td>\n",
" <td>97.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27879</th>\n",
" <td>LU2970252958</td>\n",
" <td>2025-04-09</td>\n",
" <td>200002082</td>\n",
" <td>-2613.34</td>\n",
" <td>200002271</td>\n",
" <td>2613.34</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>27880 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" ISIN Date Compte sortant Montant sortie Compte entrant \\\n",
"0 FR0010135103 2015-01-02 365247 -3126.05 200002169 \n",
"1 FR0010135103 2015-01-02 365888 -3751.26 365241 \n",
"2 FR0010135103 2015-01-05 419848 -3135.85 200001747 \n",
"3 FR0010135103 2015-01-05 417952 -627.17 200002169 \n",
"4 FR0010135103 2015-01-05 417937 -5644.53 406074 \n",
"... ... ... ... ... ... \n",
"27875 LU2809794220 2025-10-16 200058108 -1315.00 200127345 \n",
"27876 LU2809794220 2025-10-31 420350 -141.03 200127644 \n",
"27877 LU2809794220 2025-11-03 200127356 -2996.91 420350 \n",
"27878 LU2809794493 2024-12-09 200058108 -97.27 420350 \n",
"27879 LU2970252958 2025-04-09 200002082 -2613.34 200002271 \n",
"\n",
" Montant entrée \n",
"0 3126.05 \n",
"1 3751.26 \n",
"2 3135.85 \n",
"3 627.17 \n",
"4 5644.53 \n",
"... ... \n",
"27875 1315.00 \n",
"27876 141.03 \n",
"27877 2996.91 \n",
"27878 97.27 \n",
"27879 2613.34 \n",
"\n",
"[27880 rows x 6 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"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\n",
"\n",
"#df initiale : 2 574 461 \n",
"# 27 880 rows "
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "df0c0bbb-4cff-4205-86e0-0393f83a4cc7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre de comptes uniques impliqués dans les transferts : 1346\n"
]
}
],
"source": [
"# Extraire tous les comptes sortants et entrants\n",
"all_accounts = pd.concat([\n",
" transf_compte[\"Compte sortant\"],\n",
" transf_compte[\"Compte entrant\"]\n",
"])\n",
"\n",
"# Comptes uniques impliqués dans au moins un transfert\n",
"unique_accounts = all_accounts.unique()\n",
"\n",
"print(f\"Nombre de comptes uniques impliqués dans les transferts : {len(unique_accounts)}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6dacddcb-74f1-441f-adbe-83275c8f9216",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"transf_compte[\"Date\"] = pd.to_datetime(transf_compte[\"Date\"])\n",
"\n",
"# Nombre de transferts par jour\n",
"transfers_per_day = transf_compte.groupby(\"Date\").size()\n",
"\n",
"plt.figure(figsize=(14,5))\n",
"transfers_per_day.plot(kind=\"line\")\n",
"plt.title(\"Nombre de transferts détectés par jour\")\n",
"plt.xlabel(\"Date\")\n",
"plt.ylabel(\"Nombre de transferts\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "be0708c4-95df-4915-82f9-a6347187bd70",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Jours anormaux (avec beaucoup de transferts) :\n"
]
},
{
"data": {
"text/plain": [
"Date\n",
"2015-01-15 26\n",
"2015-01-16 29\n",
"2015-01-26 33\n",
"2015-01-27 26\n",
"2015-01-29 32\n",
" ..\n",
"2019-04-01 28\n",
"2021-01-04 26\n",
"2021-01-18 26\n",
"2021-02-08 27\n",
"2021-04-06 27\n",
"Length: 121, dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Détection de jours anormaux (ex: > 95e percentile)\n",
"threshold = transfers_per_day.quantile(0.95)\n",
"anomalous_days = transfers_per_day[transfers_per_day > threshold]\n",
"print(\"Jours anormaux (avec beaucoup de transferts) :\")\n",
"display(anomalous_days)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "f7da5d09-7c97-4fa2-921d-886928fdf80f",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Jours anormaux (weekday + month) :\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>n_transfers</th>\n",
" <th>weekday</th>\n",
" <th>month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2021-07-15</td>\n",
" <td>20</td>\n",
" <td>Thursday</td>\n",
" <td>July</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2021-10-01</td>\n",
" <td>20</td>\n",
" <td>Friday</td>\n",
" <td>October</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2021-06-15</td>\n",
" <td>20</td>\n",
" <td>Tuesday</td>\n",
" <td>June</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2021-07-08</td>\n",
" <td>21</td>\n",
" <td>Thursday</td>\n",
" <td>July</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2021-09-01</td>\n",
" <td>21</td>\n",
" <td>Wednesday</td>\n",
" <td>September</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2021-10-13</td>\n",
" <td>21</td>\n",
" <td>Wednesday</td>\n",
" <td>October</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>2024-02-05</td>\n",
" <td>22</td>\n",
" <td>Monday</td>\n",
" <td>February</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2021-08-11</td>\n",
" <td>22</td>\n",
" <td>Wednesday</td>\n",
" <td>August</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>2023-05-15</td>\n",
" <td>22</td>\n",
" <td>Monday</td>\n",
" <td>May</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>2025-10-13</td>\n",
" <td>22</td>\n",
" <td>Monday</td>\n",
" <td>October</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2021-05-03</td>\n",
" <td>23</td>\n",
" <td>Monday</td>\n",
" <td>May</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-01-08</td>\n",
" <td>24</td>\n",
" <td>Friday</td>\n",
" <td>January</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2021-02-04</td>\n",
" <td>25</td>\n",
" <td>Thursday</td>\n",
" <td>February</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2021-08-05</td>\n",
" <td>25</td>\n",
" <td>Thursday</td>\n",
" <td>August</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2021-06-03</td>\n",
" <td>25</td>\n",
" <td>Thursday</td>\n",
" <td>June</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-01-25</td>\n",
" <td>25</td>\n",
" <td>Monday</td>\n",
" <td>January</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-01-18</td>\n",
" <td>26</td>\n",
" <td>Monday</td>\n",
" <td>January</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-01-04</td>\n",
" <td>26</td>\n",
" <td>Monday</td>\n",
" <td>January</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2021-04-06</td>\n",
" <td>27</td>\n",
" <td>Tuesday</td>\n",
" <td>April</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2021-02-08</td>\n",
" <td>27</td>\n",
" <td>Monday</td>\n",
" <td>February</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date n_transfers weekday month\n",
"22 2021-07-15 20 Thursday July\n",
"29 2021-10-01 20 Friday October\n",
"19 2021-06-15 20 Tuesday June\n",
"21 2021-07-08 21 Thursday July\n",
"26 2021-09-01 21 Wednesday September\n",
"30 2021-10-13 21 Wednesday October\n",
"42 2024-02-05 22 Monday February\n",
"25 2021-08-11 22 Wednesday August\n",
"39 2023-05-15 22 Monday May\n",
"53 2025-10-13 22 Monday October\n",
"13 2021-05-03 23 Monday May\n",
"1 2021-01-08 24 Friday January\n",
"6 2021-02-04 25 Thursday February\n",
"24 2021-08-05 25 Thursday August\n",
"17 2021-06-03 25 Thursday June\n",
"4 2021-01-25 25 Monday January\n",
"3 2021-01-18 26 Monday January\n",
"0 2021-01-04 26 Monday January\n",
"11 2021-04-06 27 Tuesday April\n",
"7 2021-02-08 27 Monday February"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"transf_compte[\"Date\"] = pd.to_datetime(transf_compte[\"Date\"])\n",
"transf_comptee = transf_compte[transf_compte[\"Date\"].dt.year >= 2021]\n",
"# Nombre de transferts par jour\n",
"transfers_per_day = transf_comptee.groupby(\"Date\").size().rename(\"n_transfers\")\n",
"\n",
"# Détection des jours anormaux (au-dessus du 95e percentile)\n",
"threshold = transfers_per_day.quantile(0.95)\n",
"anomalous_days = transfers_per_day[transfers_per_day > threshold]\n",
"\n",
"# Ajouter weekday et month\n",
"anomalous_table = anomalous_days.reset_index()\n",
"anomalous_table[\"weekday\"] = anomalous_table[\"Date\"].dt.day_name()\n",
"anomalous_table[\"month\"] = anomalous_table[\"Date\"].dt.month_name()\n",
"\n",
"pd.set_option('display.max_rows', None)\n",
"print(\"Jours anormaux (weekday + month) :\")\n",
"display(anomalous_table.sort_values(\"n_transfers\").tail(20))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "1fa564f5-5844-4a1b-bdca-b392b829d734",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre total de comptes impliqués dans au moins un transfert : 1346\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Aperçu :\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>comptes_uniques</th>\n",
" <th>n_comptes</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-01-02</th>\n",
" <td>{365261, 200002169, 406197, 365888, 365247, 41...</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-05</th>\n",
" <td>{312478, 364999, 364765, 419848, 417937, 41928...</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-06</th>\n",
" <td>{422250, 364999, 417937, 417622, 411426, 41933...</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-07</th>\n",
" <td>{365364, 365377, 364999, 200001895, 200000201,...</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-08</th>\n",
" <td>{405798, 364999, 417937, 348493, 365852, 24969...</td>\n",
" <td>21</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" comptes_uniques n_comptes\n",
"Date \n",
"2015-01-02 {365261, 200002169, 406197, 365888, 365247, 41... 16\n",
"2015-01-05 {312478, 364999, 364765, 419848, 417937, 41928... 31\n",
"2015-01-06 {422250, 364999, 417937, 417622, 411426, 41933... 25\n",
"2015-01-07 {365364, 365377, 364999, 200001895, 200000201,... 27\n",
"2015-01-08 {405798, 364999, 417937, 348493, 365852, 24969... 21"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#- 2) Nombre total de comptes impliqués ---------\n",
"\n",
"all_accounts = pd.concat([\n",
" transf_compte[\"Compte sortant\"],\n",
" transf_compte[\"Compte entrant\"]\n",
"]).unique()\n",
"\n",
"print(f\"Nombre total de comptes impliqués dans au moins un transfert : {len(all_accounts)}\")\n",
"\n",
"# --------- Nombre de comptes impliqués par jour ----------\n",
"\n",
"accounts_per_day = transf_compte.groupby(\"Date\").agg(\n",
" comptes_uniques=(\"Compte sortant\", lambda x: set(x)) # temp\n",
")\n",
"\n",
"# On ajoute aussi les comptes entrants\n",
"accounts_per_day[\"comptes_uniques\"] = accounts_per_day.index.map(\n",
" lambda d: set(transf_compte[transf_compte[\"Date\"] == d][\"Compte sortant\"]) |\n",
" set(transf_compte[transf_compte[\"Date\"] == d][\"Compte entrant\"])\n",
")\n",
"\n",
"accounts_per_day[\"n_comptes\"] = accounts_per_day[\"comptes_uniques\"].apply(len)\n",
"\n",
"# Plot\n",
"plt.figure(figsize=(14,5))\n",
"plt.plot(accounts_per_day.index, accounts_per_day[\"n_comptes\"], marker=\"o\")\n",
"plt.title(\"Nombre de comptes impliqués dans des transferts par jour\")\n",
"plt.xlabel(\"Date\")\n",
"plt.ylabel(\"Nombre de comptes uniques\")\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"print(\"Aperçu :\")\n",
"accounts_per_day.head()\n"
]
},
{
"cell_type": "markdown",
"id": "c898b0c5-0a8e-4640-bc52-9490ee80e53d",
"metadata": {},
"source": [
"# MERGE AND ANALYSIS"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce33dbf8-1c59-416a-adc4-6eb7c1ea9d8e",
"metadata": {},
"outputs": [],
"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": null,
"id": "7e5d642e-5c16-4c78-8d83-075094902670",
"metadata": {},
"outputs": [],
"source": [
"df_merged"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea14866a-1ce6-4b19-9225-d725304af8ec",
"metadata": {},
"outputs": [],
"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()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f13c213e-7f72-494a-bcf0-b4cd9feee55d",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# 3. ANALYSE DES FLOWS POUR UN COMPTE DANS UN FONDS\n",
"\n",
"account_to_plot = \"YOUR_ACCOUNT_ID_HERE\"\n",
"isin_to_plot = \"YOUR_ISIN_HERE\"\n",
"\n",
"client_flows = df_merged[\n",
" (df_merged[\"Account_ID\"] == account_to_plot) &\n",
" (df_merged[\"ISIN\"] == isin_to_plot)\n",
"].sort_values(\"Date\")\n",
"\n",
"plt.figure(figsize=(12,5))\n",
"plt.plot(client_flows[\"Date\"], client_flows[\"Flow_EUR\"], marker=\"o\")\n",
"plt.axhline(0, color=\"black\", linewidth=1)\n",
"plt.xlabel(\"Date\")\n",
"plt.ylabel(\"Flow (€)\")\n",
"plt.title(f\"Flow movements for Account {account_to_plot}, ISIN {isin_to_plot}\")\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"###############################################################################\n",
"# 4. ANALYSE MENSUELLE DES FLOWS (ENTRANTS / SORTANTS)\n",
"###############################################################################\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"
]
}
],
"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": "3.13.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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