573 lines
17 KiB
Plaintext
573 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e637deae-9168-4fb2-b95f-4e42d8d72d9e",
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"metadata": {},
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"source": [
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"# DATA COLLECTION "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9f99615b-5a9d-434a-baa0-dca55edf7699",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f8508d94-74a7-4bb0-8b81-c2e06850c25f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd \n",
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"chemin_fichier = \"s3://projet-bdc-data/carmignac/AUM ENSAE V2 -20251105.csv\"\n",
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"df_aum2 = pd.read_csv(chemin_fichier, sep=';', engine='python')\n",
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"df_aum2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4644da13-5aea-4ca0-9fcf-947324766292",
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"metadata": {},
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"outputs": [],
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"source": [
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"chemin_fichier = \"s3://projet-bdc-data/carmignac/Flows ENSAE V2 -20251105.csv\"\n",
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"df_flows2 = pd.read_csv(chemin_fichier, sep=';', engine='python')\n",
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"df_flows2"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59d31eaf-c06c-4ebe-9f8c-cb9158a50976",
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"metadata": {},
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"source": [
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"## DATA ANALYSIS"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5773b911-6b84-448d-962f-8228eeac0250",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_aum2.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6f571810-c373-4d30-8ca5-c3a074b95b08",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_aum2.columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "af25fd07-a613-4adc-b88b-93a8d300379c",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_flows2.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c6d0fe83-2957-430b-89cf-cd30833b7cab",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_flows2.columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5fac74b0-662f-48d0-a234-7edc3c3e86ad",
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"metadata": {},
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"outputs": [],
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"source": [
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"#dict avec valeurs unique de chaque col \n",
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"rows = []\n",
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"\n",
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"for col in df_aum2.columns:\n",
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" uniques = df_aum2[col].unique()\n",
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" rows.append({\n",
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" \"Colonne\": col,\n",
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" \"Nbr Lignes\": df_aum2.shape[0], #4.8millions\n",
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" \"Nb valeurs uniques\": len(uniques),\n",
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" \"Exemples de valeurs\": uniques,\n",
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" \"Nan Values\" : df_aum2[col].isna().sum()\n",
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" })\n",
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"\n",
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"df_uniques = pd.DataFrame(rows)\n",
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"df_uniques"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5de53ba3-b3db-4935-acac-435b05b909e2",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"#dict avec valeurs unique de chaque col \n",
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"rowsf = []\n",
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"\n",
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"for col in df_flows2.columns:\n",
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" uniques = df_flows2[col].unique()\n",
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" rowsf.append({\n",
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" \"Colonne\": col,\n",
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" \"Nbr Lignes\": df_flows2.shape[0], #4.8millions\n",
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" \"Nb valeurs uniques\": len(uniques),\n",
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" \"Exemples de valeurs\": uniques,\n",
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" \"Nan Values\" : df_flows2[col].isna().sum()\n",
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" })\n",
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"\n",
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"df_unique_flows = pd.DataFrame(rowsf)\n",
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"df_unique_flows"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e07a2b28-13f7-49f6-a55b-7d09a506407b",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_aum = df_uniques[['Colonne', 'Nbr Lignes', 'Nb valeurs uniques']]\n",
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"df2_flows = df_unique_flows[['Colonne', 'Nbr Lignes', 'Nb valeurs uniques']]\n",
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"\n",
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"df_merged = df1_aum.merge(df2_flows, on='Colonne', suffixes=('_aum', '_flows'))\n",
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"df_merged"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4ce2ad22-08e6-4e63-96b2-c2301172516e",
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"metadata": {},
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"source": [
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"# ETUDE ET ANALYSE DES ANOMALIES"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c883dfc2-b9b9-4d3e-80d3-0140cd222492",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_aum2['Centralisation Date'] = pd.to_datetime(df_aum2['Centralisation Date'])\n",
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"df_flows2['Centralisation Date'] = pd.to_datetime(df_flows2['Centralisation Date'])\n",
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"key_cols = ['Registrar Account - ID', 'Product - Isin']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f47b276d-cce6-433c-87c5-860810d71d34",
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"metadata": {},
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"outputs": [],
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"source": [
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"cols= ['Company - Id', 'Company - Ultimate Parent Id',\n",
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" 'Registrar Account - ID', 'Registrar Account - Region','Product - Isin']\n",
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"\n",
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"doublons_aum2 = df_aum2[df_aum2.duplicated(subset=cols + ['Centralisation Date'], keep=False)]\n",
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"doublons_flows2 = df_flows2[df_flows2.duplicated(subset=cols + ['Centralisation Date'], keep=False)]\n",
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"\n",
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"print(\" Cols: \", cols)\n",
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"print(\"Doublons AUM:\", doublons_aum2.shape[0])\n",
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"print(\"Doublons Flows:\", doublons_flows2.shape[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d2f355e6-30c5-420e-a3db-9095dd5e0147",
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Comptes avec same flux et same product ISIN mais IDs différents ---> candidats pseudo-client (FAUX)\n",
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"# #plusieurs comptes diff réalisent EXACTEMENT le même flux sur le même produit le même jour.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4b9173e1-1c01-4ef3-adcd-9c587a97dd5d",
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"metadata": {},
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"source": [
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"## Stat Descrp FLOWS "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8df98c34-b1f7-4fb9-bbc7-c9bbe762022a",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = df_flows2.copy()\n",
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"df[\"Date\"] = pd.to_datetime(df[\"Centralisation Date\"])\n",
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"\n",
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"# Groupby par ISIN et Date\n",
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"grouped = df.groupby([\"Product - Isin\", \"Date\"])\n",
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"\n",
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"transfers = []\n",
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"\n",
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"for (isin, date), group in grouped:\n",
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" # Sépare flux positifs et négatifs\n",
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" entrants = group[group[\"Value € - NetFlows\"] > 0][[\"Registrar Account - ID\", \"Value € - NetFlows\"]]\n",
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" sortants = group[group[\"Value € - NetFlows\"] < 0][[\"Registrar Account - ID\", \"Value € - NetFlows\"]]\n",
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"\n",
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" # On cherche des paires +M / -M\n",
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" for _, row_sortie in sortants.iterrows():\n",
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" montant_sortie = row_sortie[\"Value € - NetFlows\"]\n",
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" compte_sortant = row_sortie[\"Registrar Account - ID\"]\n",
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"\n",
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" # Chercher un +M qui matche exactement le -M\n",
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" match = entrants[entrants[\"Value € - NetFlows\"] == -montant_sortie]\n",
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"\n",
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" if len(match) > 0:\n",
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" for _, row_entree in match.iterrows():\n",
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" transfers.append({\n",
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" \"ISIN\": isin,\n",
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" \"Date\": date,\n",
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" \"Compte sortant\": compte_sortant,\n",
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" \"Montant sortie\": montant_sortie,\n",
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" \"Compte entrant\": row_entree[\"Registrar Account - ID\"],\n",
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" \"Montant entrée\": row_entree[\"Value € - NetFlows\"]\n",
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" })\n",
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"\n",
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"\n",
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"transf_compte = pd.DataFrame(transfers)\n",
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"transf_compte\n",
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"\n",
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"#df initiale : 2 574 461 \n",
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"# 27 880 rows "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "df0c0bbb-4cff-4205-86e0-0393f83a4cc7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Extraire tous les comptes sortants et entrants\n",
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"all_accounts = pd.concat([\n",
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" transf_compte[\"Compte sortant\"],\n",
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" transf_compte[\"Compte entrant\"]\n",
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"])\n",
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"\n",
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"# Comptes uniques impliqués dans au moins un transfert\n",
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"unique_accounts = all_accounts.unique()\n",
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"print(f\"Nombre de comptes uniques impliqués dans les transferts : {len(unique_accounts)}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6dacddcb-74f1-441f-adbe-83275c8f9216",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"transf_compte[\"Date\"] = pd.to_datetime(transf_compte[\"Date\"])\n",
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"\n",
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"# Nombre de transferts par jour\n",
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"transfers_per_day = transf_compte.groupby(\"Date\").size()\n",
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"\n",
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"plt.figure(figsize=(14,5))\n",
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"transfers_per_day.plot(kind=\"line\")\n",
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"plt.title(\"Nombre de transferts détectés par jour\")\n",
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"plt.xlabel(\"Date\")\n",
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"plt.ylabel(\"Nombre de transferts\")\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "be0708c4-95df-4915-82f9-a6347187bd70",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Détection de jours anormaux (ex: > 95e percentile)\n",
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"threshold = transfers_per_day.quantile(0.95)\n",
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"anomalous_days = transfers_per_day[transfers_per_day > threshold]\n",
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"print(\"Jours anormaux (avec beaucoup de transferts) :\")\n",
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"display(anomalous_days)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9416dd81-8f73-4e87-a5e8-b640882fbba4",
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"metadata": {},
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"source": [
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"## Etude de la saisonalite"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f7da5d09-7c97-4fa2-921d-886928fdf80f",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"transf_compte[\"Date\"] = pd.to_datetime(transf_compte[\"Date\"])\n",
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"transf_comptee = transf_compte[transf_compte[\"Date\"].dt.year >= 2021]\n",
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"# Nombre de transferts par jour\n",
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"transfers_per_day = transf_comptee.groupby(\"Date\").size().rename(\"n_transfers\")\n",
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"\n",
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"# Détection des jours anormaux (au-dessus du 95e percentile)\n",
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"threshold = transfers_per_day.quantile(0.95)\n",
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"anomalous_days = transfers_per_day[transfers_per_day > threshold]\n",
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"\n",
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"# Ajouter weekday et month\n",
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"anomalous_table = anomalous_days.reset_index()\n",
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"anomalous_table[\"weekday\"] = anomalous_table[\"Date\"].dt.day_name()\n",
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"anomalous_table[\"month\"] = anomalous_table[\"Date\"].dt.month_name()\n",
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"\n",
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"pd.set_option('display.max_rows', None)\n",
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"print(\"Jours anormaux (weekday + month) :\")\n",
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"display(anomalous_table.sort_values(\"n_transfers\").tail(20))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1fa564f5-5844-4a1b-bdca-b392b829d734",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Nombre total de comptes impliqués \n",
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"\n",
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"all_accounts = pd.concat([\n",
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" transf_compte[\"Compte sortant\"],\n",
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" transf_compte[\"Compte entrant\"]\n",
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"]).unique()\n",
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"\n",
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"print(f\"Nombre total de comptes impliqués dans au moins un transfert : {len(all_accounts)}\")\n",
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"\n",
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"# Nombre de comptes impliqués par jour \n",
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"\n",
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"accounts_per_day = transf_compte.groupby(\"Date\").agg(\n",
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" comptes_uniques=(\"Compte sortant\", lambda x: set(x)) # temp\n",
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")\n",
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"\n",
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"# On ajoute aussi les comptes entrants\n",
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"accounts_per_day[\"comptes_uniques\"] = accounts_per_day.index.map(\n",
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" lambda d: set(transf_compte[transf_compte[\"Date\"] == d][\"Compte sortant\"]) |\n",
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" set(transf_compte[transf_compte[\"Date\"] == d][\"Compte entrant\"])\n",
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")\n",
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"\n",
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"accounts_per_day[\"n_comptes\"] = accounts_per_day[\"comptes_uniques\"].apply(len)\n",
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"\n",
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"# Plot\n",
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"plt.figure(figsize=(14,5))\n",
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"plt.plot(accounts_per_day.index, accounts_per_day[\"n_comptes\"], marker=\"o\")\n",
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"plt.title(\"Nombre de comptes impliqués dans des transferts par jour\")\n",
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"plt.xlabel(\"Date\")\n",
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"plt.ylabel(\"Nombre de comptes uniques\")\n",
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"plt.grid(True)\n",
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"plt.show()\n",
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"\n",
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"print(\"Aperçu :\")\n",
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"accounts_per_day.head()\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c898b0c5-0a8e-4640-bc52-9490ee80e53d",
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"metadata": {},
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"source": [
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"# MERGE AUM & FLOWS "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ce33dbf8-1c59-416a-adc4-6eb7c1ea9d8e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"df_aum2 = df_aum2.rename(columns={\n",
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" \"Registrar Account - ID\": \"Account_ID\",\n",
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" \"Product - Isin\": \"ISIN\",\n",
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" \"Centralisation Date\": \"Date\",\n",
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" \"Value - AUM €\": \"AUM_EUR\"\n",
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"})\n",
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"\n",
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"df_flows2 = df_flows2.rename(columns={\n",
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" \"Registrar Account - ID\": \"Account_ID\",\n",
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" \"Product - Isin\": \"ISIN\",\n",
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" \"Centralisation Date\": \"Date\",\n",
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" \"Value € - NetFlows\": \"Flow_EUR\"\n",
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"})\n",
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"\n",
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"\n",
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"df_aum2[\"Date\"] = pd.to_datetime(df_aum2[\"Date\"])\n",
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"df_flows2[\"Date\"] = pd.to_datetime(df_flows2[\"Date\"])\n",
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"\n",
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"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"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ded9b4f6-df92-479e-bc7b-aaa489dad228",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.11"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|