2026-04-05 17:52:42 +02:00
{
"cells": [
{
"cell_type": "markdown",
"id": "f6ea29f1",
"metadata": {},
"source": [
"# Global Clustering \n",
"\n",
"**Sections:**\n",
"1. Imports & Data Loading\n",
"2. Monthly Panel Construction\n",
"3. Feature Engineering (base + enriched)\n",
"4. Global Clustering (all active accounts)"
]
},
{
"cell_type": "markdown",
"id": "e727f666",
"metadata": {},
"source": [
"## 0. Imports & Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9314f229-0b5d-4a4c-846c-869847d32c73",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import s3fs\n",
"os.environ[\"AWS_ACCESS_KEY_ID\"] = 'UMMV3Z72A70MCCSRV17O'\n",
"os.environ[\"AWS_SECRET_ACCESS_KEY\"] = 'wBFxaez78UPNW3BtchZOf4f238ZNXKnCexeGufaa'\n",
"os.environ[\"AWS_SESSION_TOKEN\"] = 'eyJhbGciOiJIUzUxMiIsInR5cCI6IkpXVCJ9.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.rffoTJijRiGK2DCDhXj5y8R31DRH1LWkTwuH_1lvU9qN_xJSTmBIM4uGR_zp7XpMnq_ePwVhlkoWN15cNUgjMA'\n",
"os.environ[\"AWS_DEFAULT_REGION\"] = 'us-east-1'\n",
"fs = s3fs.S3FileSystem(\n",
" client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'},\n",
" key = os.environ[\"AWS_ACCESS_KEY_ID\"], \n",
" secret = os.environ[\"AWS_SECRET_ACCESS_KEY\"], \n",
" token = os.environ[\"AWS_SESSION_TOKEN\"])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "61e33897",
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from sklearn.preprocessing import RobustScaler\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.metrics import silhouette_score, davies_bouldin_score, pairwise_distances\n",
"from sklearn.linear_model import LinearRegression\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"pd.set_option(\"display.max_columns\", 100)\n",
"\n",
"EPS = 1e-9\n",
"RANDOM_STATE = 42\n",
"\n",
"# Column names\n",
"ID_COL = \"Registrar Account - ID\"\n",
"ISIN_COL = \"Product - Isin\"\n",
"FUND_COL = \"Product - Fund\"\n",
"ASSET_COL = \"Product - Asset Type\"\n",
"FLOW_DATE_COL = \"Centralisation Date\"\n",
"AUM_DATE_COL = \"Centralisation Date\"\n",
"FLOW_QTY_COL = \"Quantity - NetFlows\"\n",
"FLOW_SUB_COL = \"Quantity - Subscription\"\n",
"FLOW_RED_COL = \"Quantity - Redemption\"\n",
"AUM_QTY_COL = \"Quantity - AUM\"\n",
"AUM_VAL_COL = \"Value - AUM €\"\n",
"REGION_COL = \"Registrar Account - Region\"\n",
"COUNTRY_COL = \"RegistrarAccount - Country\"\n",
"NAV_DATE_COL = \"Dat\"\n",
"NAV_ISIN_COL = \"Isin\"\n",
"NAV_PRICE_COL = \"Price (TF PartPrice)\"\n",
"NAV_BENCH_COL = \"PriceBench\"\n",
"RATE_DATE_COL = \"Date\"\n",
"RATE_VAL_COL = \"Yld to Maturity\"\n",
"\n",
"#external data projet-bdc-data /carmignac /Data Modélisation /Nav\n",
"PATH_NAV = \"s3://projet-bdc-data/carmignac/Data Modélisation/Nav/NAV_Bench_data.csv\" #C’
"PATH_RATES = \"s3://projet-bdc-data/carmignac/Data Modélisation/market data/esterRates.csv\"\n",
"\n",
"# optional competitors\n",
"PATH_COMP_FLOWS = \"s3://projet-bdc-data/carmignac/Data Modélisation/competitors/daily_estimated_flows.csv\"\n",
"PATH_COMP_PERF = \"s3://projet-bdc-data/carmignac/Data Modélisation/competitors/weekly_perf_full.csv\"\n",
"PATH_PEERS = \"s3://projet-bdc-carmignac-g3/peers/CAD_peers.csv\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "eb3b2908",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"flows: (2574461, 26)\n",
"aum: (4880297, 19)\n",
"nav: (623914, 6)\n"
]
}
],
"source": [
"df_flows = pd.read_csv(\"flows.csv\", low_memory=False)\n",
"df_aum = df_aum = pd.read_csv(\"s3://projet-bdc-carmignac-g3/paco/AUM_repaired.csv\", low_memory=False)\n",
"df_nav = pd.read_csv(PATH_NAV, sep=\";\")\n",
"df_rates = pd.read_csv(PATH_RATES, sep=\";\")\n",
"\n",
"# Date parsing\n",
"for df, col in [\n",
" (df_flows, FLOW_DATE_COL), (df_aum, AUM_DATE_COL),\n",
" (df_nav, NAV_DATE_COL), (df_rates, RATE_DATE_COL)\n",
"]:\n",
" df[col] = pd.to_datetime(df[col], errors=\"coerce\")\n",
"\n",
"# Month column\n",
"for df, col in [(df_flows, FLOW_DATE_COL), (df_aum, AUM_DATE_COL),\n",
" (df_nav, NAV_DATE_COL), (df_rates, RATE_DATE_COL)]:\n",
" df[\"month\"] = df[col].dt.to_period(\"M\").dt.to_timestamp()\n",
"\n",
"# Numeric coercion\n",
"for col in [FLOW_QTY_COL, FLOW_SUB_COL, FLOW_RED_COL]:\n",
" df_flows[col] = pd.to_numeric(df_flows[col], errors=\"coerce\")\n",
"for col in [AUM_QTY_COL, AUM_VAL_COL]:\n",
" df_aum[col] = pd.to_numeric(df_aum[col], errors=\"coerce\")\n",
"for col in [NAV_PRICE_COL, NAV_BENCH_COL]:\n",
" df_nav[col] = pd.to_numeric(df_nav[col], errors=\"coerce\")\n",
"df_rates[RATE_VAL_COL] = pd.to_numeric(df_rates[RATE_VAL_COL], errors=\"coerce\")\n",
"\n",
"# ISIN as string\n",
"for df in [df_flows, df_aum]:\n",
" df[ISIN_COL] = df[ISIN_COL].astype(str).str.strip()\n",
"df_nav[NAV_ISIN_COL] = df_nav[NAV_ISIN_COL].astype(str).str.strip()\n",
"\n",
"print(\"flows:\", df_flows.shape)\n",
"print(\"aum: \", df_aum.shape)\n",
"print(\"nav: \", df_nav.shape)"
]
},
{
"cell_type": "markdown",
"id": "5929db69",
"metadata": {},
"source": [
"## 1. Monthly Panel Construction\n",
"\n",
"Build a full outer join of AUM and flows at (account, ISIN, month) granularity,\n",
"then enrich with NAV performance and macro rates."
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "d36d0a70",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Panel shape: (4791501, 20)\n"
]
}
],
"source": [
"# --- Flows aggregated to monthly ---\n",
"df_flows_m = (\n",
" df_flows\n",
" .dropna(subset=[ID_COL, ISIN_COL, \"month\"])\n",
" .assign(\n",
" gross_flow_qty = lambda x: x[FLOW_QTY_COL].abs(),\n",
" sub_qty = lambda x: x[FLOW_SUB_COL].fillna(0),\n",
" red_qty = lambda x: x[FLOW_RED_COL].fillna(0)\n",
" )\n",
" .groupby([ID_COL, ISIN_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" net_flow_qty = (FLOW_QTY_COL, \"sum\"),\n",
" gross_flow_qty = (\"gross_flow_qty\", \"sum\"),\n",
" sub_qty = (\"sub_qty\", \"sum\"),\n",
" red_qty = (\"red_qty\", \"sum\"),\n",
" n_tx = (FLOW_QTY_COL, \"size\"),\n",
" region = (REGION_COL, \"last\"),\n",
" country = (COUNTRY_COL, \"last\")\n",
" )\n",
")\n",
"\n",
"# --- AUM aggregated to monthly ---\n",
"df_aum_m = (\n",
" df_aum\n",
" .dropna(subset=[ID_COL, ISIN_COL, \"month\"])\n",
" .groupby([ID_COL, ISIN_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty = (AUM_QTY_COL, \"sum\"),\n",
" aum_val = (AUM_VAL_COL, \"sum\"),\n",
" fund = (FUND_COL, \"last\"),\n",
" asset_type = (ASSET_COL, \"last\"),\n",
" region = (REGION_COL, \"last\"),\n",
" country = (COUNTRY_COL, \"last\")\n",
" )\n",
")\n",
"\n",
"# --- Full outer join ---\n",
"keys = pd.concat([\n",
" df_flows_m[[ID_COL, ISIN_COL, \"month\"]],\n",
" df_aum_m[[ID_COL, ISIN_COL, \"month\"]]\n",
"]).drop_duplicates()\n",
"\n",
"df_rel_m = (\n",
" keys\n",
" .merge(df_aum_m, on=[ID_COL, ISIN_COL, \"month\"], how=\"left\")\n",
" .merge(df_flows_m, on=[ID_COL, ISIN_COL, \"month\"], how=\"left\", suffixes=(\"\", \"_flow\"))\n",
")\n",
"\n",
"for c in [\"aum_qty\",\"aum_val\",\"net_flow_qty\",\"gross_flow_qty\",\"sub_qty\",\"red_qty\",\"n_tx\"]:\n",
" df_rel_m[c] = df_rel_m[c].fillna(0)\n",
"\n",
"df_rel_m[\"region\"] = df_rel_m[\"region\"].fillna(df_rel_m.get(\"region_flow\"))\n",
"df_rel_m[\"country\"] = df_rel_m[\"country\"].fillna(df_rel_m.get(\"country_flow\"))\n",
"\n",
"# --- Active / holding flags ---\n",
"df_rel_m[\"active_rel_month\"] = (df_rel_m[\"gross_flow_qty\"] > 0).astype(int)\n",
"df_rel_m[\"holding_rel_month\"] = (df_rel_m[\"aum_qty\"] > 0).astype(int)\n",
"df_rel_m[\"flow_to_aum_rel\"] = df_rel_m[\"net_flow_qty\"] / (df_rel_m[\"aum_qty\"].abs() + EPS)\n",
"df_rel_m[\"turnover_rel\"] = df_rel_m[\"gross_flow_qty\"] / (df_rel_m[\"aum_qty\"].abs() + EPS)\n",
"\n",
"print(\"Panel shape:\", df_rel_m.shape)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "965d2564",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Enriched panel shape: (4791501, 24)\n"
]
}
],
"source": [
"# --- NAV & benchmark returns ---\n",
"df_nav_m = (\n",
" df_nav\n",
" .dropna(subset=[NAV_ISIN_COL, \"month\", NAV_PRICE_COL])\n",
" .sort_values([NAV_ISIN_COL, \"month\"])\n",
" .groupby([NAV_ISIN_COL, \"month\"], as_index=False)\n",
" .tail(1).copy()\n",
")\n",
"df_nav_m[\"ret_fund_m\"] = df_nav_m.groupby(NAV_ISIN_COL)[NAV_PRICE_COL].pct_change()\n",
"df_nav_m[\"ret_bench_m\"] = df_nav_m.groupby(NAV_ISIN_COL)[NAV_BENCH_COL].pct_change()\n",
"df_nav_m[\"active_return_m\"] = df_nav_m[\"ret_fund_m\"] - df_nav_m[\"ret_bench_m\"]\n",
"df_nav_m = df_nav_m.rename(columns={NAV_ISIN_COL: ISIN_COL})[\n",
" [ISIN_COL, \"month\", \"ret_fund_m\", \"ret_bench_m\", \"active_return_m\"]\n",
"]\n",
"\n",
"# --- Interest rates ---\n",
"df_rates_m = (\n",
" df_rates\n",
" .dropna(subset=[\"month\", RATE_VAL_COL])\n",
" .sort_values(RATE_DATE_COL)\n",
" .groupby(\"month\", as_index=False).tail(1).copy()\n",
")\n",
"df_rates_m[\"delta_rate_m\"] = df_rates_m[RATE_VAL_COL].diff()\n",
"df_rates_m = df_rates_m[[\"month\", RATE_VAL_COL, \"delta_rate_m\"]]\n",
"\n",
"# --- Merge into panel ---\n",
"df_rel_m = df_rel_m.merge(df_nav_m, on=[ISIN_COL, \"month\"], how=\"left\")\n",
"df_rel_m = df_rel_m.merge(df_rates_m[[\"month\",\"delta_rate_m\"]], on=\"month\", how=\"left\")\n",
"\n",
"for c in [\"ret_fund_m\",\"ret_bench_m\",\"active_return_m\",\"delta_rate_m\"]:\n",
" df_rel_m[c] = df_rel_m[c].fillna(0)\n",
"\n",
"print(\"Enriched panel shape:\", df_rel_m.shape)"
]
},
{
"cell_type": "markdown",
"id": "1ef7bba8",
"metadata": {},
"source": [
"## 2. Feature Engineering\n",
"\n",
"### 2a. Monthly account-level aggregation\n",
"### 2b. ISIN-level features (where / when investors put their money)\n",
"### 2c. Asset type & fund composition features\n",
"### 2d. Rolling metrics (1M / 3M / 6M)\n",
"### 2e. Behavioural features (entry/exit, momentum, rate sensitivity)\n",
"### 2f. Trend & beta features"
]
},
{
"cell_type": "code",
"execution_count": 168,
"id": "db5a297c-78ea-4048-98f8-624612fbb60d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"df_month shape: (931333, 21)\n",
"ISIN-level client features: (12584, 12)\n",
"Asset shares: (7475, 6)\n",
"Fund shares: (6562, 11)\n",
"Rolling features: (12584, 3)\n",
"df_client shape: (12584, 46)\n",
"After outlier removal: 7179 accounts\n",
"dfc shape: (7179, 60)\n",
"gross_flow_to_aum: min=0.0000, max=270698.4708, nan=336, inf=0\n",
"flow_direction_balance: min=-1.0000, max=1.0000, nan=1069, inf=0\n",
"sub_share_mean: min=-0.0985, max=77.5673, nan=1069, inf=0\n",
"redemption_bias: min=-154.1345, max=0.0828, nan=1069, inf=0\n",
"entry_rate_per_isin: min=0.0217, max=5.0000, nan=0, inf=0\n",
"aum_final_to_peak: min=-2.4841, max=1.0000, nan=0, inf=0\n",
"flow_roll3m_norm: min=-4935000000000.0000, max=1400000000000.0000, nan=0, inf=0\n",
"flow_roll6m_norm: min=-8699999999999.9990, max=322428000000.0000, nan=0, inf=0\n"
]
}
],
"source": [
"# ============================================================\n",
"# 2a. Monthly account-level panel\n",
"# ============================================================\n",
"tmp = df_rel_m.copy()\n",
"tmp[\"isin_held_flag\"] = (tmp[\"aum_qty\"] > 0).astype(int)\n",
"tmp[\"isin_active_flag\"] = (tmp[\"gross_flow_qty\"] > 0).astype(int)\n",
"\n",
"df_month = (\n",
" tmp.groupby([ID_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty = (\"aum_qty\", \"sum\"),\n",
" aum_val = (\"aum_val\", \"sum\"),\n",
" net_flow_qty = (\"net_flow_qty\", \"sum\"),\n",
" gross_flow_qty = (\"gross_flow_qty\", \"sum\"),\n",
" sub_qty = (\"sub_qty\", \"sum\"),\n",
" red_qty = (\"red_qty\", \"sum\"),\n",
" n_tx = (\"n_tx\", \"sum\"),\n",
" n_isin_held = (\"isin_held_flag\", \"sum\"),\n",
" n_isin_active = (\"isin_active_flag\",\"sum\"),\n",
" delta_rate_m = (\"delta_rate_m\", \"first\"),\n",
" region = (\"region\", \"first\"),\n",
" country = (\"country\", \"first\"),\n",
" )\n",
" .sort_values([ID_COL, \"month\"])\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"df_month[\"active_month\"] = (df_month[\"gross_flow_qty\"] > 0).astype(int)\n",
"\n",
"df_month[\"flow_to_aum_m\"] = np.where(\n",
" df_month[\"aum_qty\"].abs() > 0,\n",
" df_month[\"net_flow_qty\"] / df_month[\"aum_qty\"].abs(),\n",
" np.nan\n",
")\n",
"df_month[\"turnover_m\"] = np.where(\n",
" df_month[\"aum_qty\"].abs() > 0,\n",
" df_month[\"gross_flow_qty\"] / df_month[\"aum_qty\"].abs(),\n",
" np.nan\n",
")\n",
"df_month[\"sub_share_m\"] = np.where(\n",
" df_month[\"gross_flow_qty\"] > 0,\n",
" df_month[\"sub_qty\"] / df_month[\"gross_flow_qty\"],\n",
" np.nan\n",
")\n",
"df_month[\"red_share_m\"] = np.where(\n",
" df_month[\"gross_flow_qty\"] > 0,\n",
" df_month[\"red_qty\"] / df_month[\"gross_flow_qty\"],\n",
" np.nan\n",
")\n",
"df_month[\"aum_peak_to_date\"] = df_month.groupby(ID_COL)[\"aum_qty\"].cummax()\n",
"df_month[\"aum_drawdown\"] = np.where(\n",
" df_month[\"aum_peak_to_date\"] > 0,\n",
" 1 - df_month[\"aum_qty\"] / df_month[\"aum_peak_to_date\"],\n",
" np.nan\n",
")\n",
"\n",
"print(\"df_month shape:\", df_month.shape)\n",
"\n",
"# ============================================================\n",
"# 2b. ISIN-level features\n",
"# ============================================================\n",
"tmp = df_rel_m.sort_values([ID_COL, ISIN_COL, \"month\"]).copy()\n",
"tmp[\"prev_aum\"] = tmp.groupby([ID_COL, ISIN_COL])[\"aum_qty\"].shift(1)\n",
"tmp[\"entry_event\"] = ((tmp[\"prev_aum\"].fillna(0) <= 0) & (tmp[\"aum_qty\"] > 0)).astype(int)\n",
"tmp[\"full_exit_event\"] = ((tmp[\"prev_aum\"] > 0) & (tmp[\"aum_qty\"] <= 0)).astype(int)\n",
"\n",
"# Lag de 1 mois pour la réaction à la performance (causalité correcte)\n",
"tmp[\"ret_fund_m_lag1\"] = tmp.groupby([ID_COL, ISIN_COL])[\"ret_fund_m\"].shift(1)\n",
"tmp[\"buy_on_perf\"] = ((tmp[\"net_flow_qty\"] > 0) & (tmp[\"ret_fund_m_lag1\"] > 0)).astype(int)\n",
"tmp[\"sell_on_perf\"] = ((tmp[\"net_flow_qty\"] < 0) & (tmp[\"ret_fund_m_lag1\"] < 0)).astype(int)\n",
"\n",
"df_rel_feat = (\n",
" tmp.groupby([ID_COL, ISIN_COL], as_index=False)\n",
" .agg(\n",
" rel_n_months = (\"month\", \"nunique\"),\n",
" rel_active_months = (\"active_rel_month\", \"sum\"),\n",
" rel_holding_months = (\"holding_rel_month\", \"sum\"),\n",
" rel_aum_mean = (\"aum_qty\", \"mean\"),\n",
" rel_turnover_mean = (\"turnover_rel\", \"mean\"),\n",
" rel_turnover_vol = (\"turnover_rel\", \"std\"),\n",
" rel_flow_to_aum_vol = (\"flow_to_aum_rel\", \"std\"),\n",
" rel_n_tx = (\"n_tx\", \"sum\"),\n",
" rel_full_exit_count = (\"full_exit_event\", \"sum\"),\n",
" rel_entry_count = (\"entry_event\", \"sum\"),\n",
" buy_on_perf_rate = (\"buy_on_perf\", \"mean\"),\n",
" sell_on_perf_rate = (\"sell_on_perf\", \"mean\"),\n",
" )\n",
")\n",
"\n",
"isin_aum = df_rel_feat.groupby(ID_COL)[\"rel_aum_mean\"].transform(\"sum\")\n",
"df_rel_feat[\"isin_weight\"] = np.where(\n",
" isin_aum > 0,\n",
" df_rel_feat[\"rel_aum_mean\"] / isin_aum,\n",
" np.nan\n",
")\n",
"hhi_isin = (\n",
" df_rel_feat.groupby(ID_COL)[\"isin_weight\"]\n",
" .apply(lambda w: np.sum(w**2))\n",
" .reset_index(name=\"hhi_isin\")\n",
")\n",
"\n",
"df_rel_client = (\n",
" df_rel_feat.groupby(ID_COL, as_index=False)\n",
" .agg(\n",
" n_isin_total = (ISIN_COL, \"nunique\"),\n",
" rel_turnover_mean_avg = (\"rel_turnover_mean\", \"mean\"),\n",
" rel_turnover_vol_avg = (\"rel_turnover_vol\", \"mean\"),\n",
" rel_flow_to_aum_vol_avg = (\"rel_flow_to_aum_vol\",\"mean\"),\n",
" full_exit_count = (\"rel_full_exit_count\",\"sum\"),\n",
" entry_count = (\"rel_entry_count\", \"sum\"),\n",
" avg_holding_months_per_isin = (\"rel_holding_months\", \"mean\"),\n",
" max_holding_months_per_isin = (\"rel_holding_months\", \"max\"),\n",
" buy_on_perf_rate_avg = (\"buy_on_perf_rate\", \"mean\"),\n",
" sell_on_perf_rate_avg = (\"sell_on_perf_rate\", \"mean\"),\n",
" )\n",
" .merge(hhi_isin, on=ID_COL, how=\"left\")\n",
")\n",
"\n",
"print(\"ISIN-level client features:\", df_rel_client.shape)\n",
"\n",
"# ============================================================\n",
"# 2c. Asset type & fund composition features\n",
"# ============================================================\n",
"aum_by_asset = (\n",
" df_aum.dropna(subset=[ID_COL, ASSET_COL])\n",
" .groupby([ID_COL, ASSET_COL], as_index=False)[AUM_VAL_COL].sum()\n",
")\n",
"total_aum_acc = aum_by_asset.groupby(ID_COL)[AUM_VAL_COL].sum().rename(\"total_aum\")\n",
"aum_by_asset = aum_by_asset.merge(total_aum_acc, on=ID_COL)\n",
"aum_by_asset[\"share\"] = np.where(\n",
" aum_by_asset[\"total_aum\"] > 0,\n",
" aum_by_asset[AUM_VAL_COL] / aum_by_asset[\"total_aum\"],\n",
" np.nan\n",
")\n",
"asset_shares = (\n",
" aum_by_asset.pivot_table(index=ID_COL, columns=ASSET_COL, values=\"share\", aggfunc=\"mean\")\n",
" .fillna(0).reset_index()\n",
")\n",
"asset_shares.columns = [ID_COL] + [\n",
" f\"share_asset_{c.lower().replace(' ','_')}\" for c in asset_shares.columns[1:]\n",
"]\n",
"\n",
"aum_by_fund = (\n",
" df_aum.dropna(subset=[ID_COL, FUND_COL])\n",
" .groupby([ID_COL, FUND_COL], as_index=False)[AUM_VAL_COL].sum()\n",
")\n",
"aum_by_fund = aum_by_fund.merge(total_aum_acc, on=ID_COL)\n",
"aum_by_fund[\"share\"] = np.where(\n",
" aum_by_fund[\"total_aum\"] > 0,\n",
" aum_by_fund[AUM_VAL_COL] / aum_by_fund[\"total_aum\"],\n",
" np.nan\n",
")\n",
"top_funds = aum_by_fund.groupby(FUND_COL)[AUM_VAL_COL].sum().nlargest(10).index\n",
"fund_shares = (\n",
" aum_by_fund[aum_by_fund[FUND_COL].isin(top_funds)]\n",
" .pivot_table(index=ID_COL, columns=FUND_COL, values=\"share\", aggfunc=\"mean\")\n",
" .fillna(0).reset_index()\n",
")\n",
"fund_shares.columns = [ID_COL] + [\n",
" f\"share_fund_{c.lower().replace(' ','_')[:30]}\" for c in fund_shares.columns[1:]\n",
"]\n",
"\n",
"print(\"Asset shares:\", asset_shares.shape)\n",
"print(\"Fund shares: \", fund_shares.shape)\n",
"\n",
"# ============================================================\n",
"# 2d. Rolling metrics — supprimées car trop sparse (80-90% zéros)\n",
"# On garde uniquement flow_roll3m et flow_roll6m comme signaux\n",
"# de tendance récente, normalisés par l'AUM\n",
"# ============================================================\n",
"df_month_s = df_month.sort_values([ID_COL, \"month\"]).copy()\n",
"\n",
"for w in [3, 6]:\n",
" df_month_s[f\"flow_roll{w}m_norm\"] = (\n",
" df_month_s.groupby(ID_COL)\n",
" .apply(lambda g: (\n",
" g[\"net_flow_qty\"].rolling(w, min_periods=1).sum() /\n",
" (g[\"aum_qty\"].abs().rolling(w, min_periods=1).mean() + EPS)\n",
" ))\n",
" .reset_index(level=0, drop=True)\n",
" )\n",
"\n",
"rolling_feats = (\n",
" df_month_s.groupby(ID_COL, as_index=False)\n",
" .last()[[ID_COL, \"flow_roll3m_norm\", \"flow_roll6m_norm\"]]\n",
")\n",
"\n",
"print(\"Rolling features:\", rolling_feats.shape)\n",
"\n",
"# ============================================================\n",
"# 2e. Static client features\n",
"# ============================================================\n",
"df_client = (\n",
" df_month.groupby(ID_COL, as_index=False)\n",
" .agg(\n",
" n_months = (\"month\", \"nunique\"),\n",
" n_active_months = (\"active_month\", \"sum\"),\n",
" flow_freq = (\"active_month\", \"mean\"),\n",
" aum_qty_mean = (\"aum_qty\", \"mean\"),\n",
" aum_qty_median = (\"aum_qty\", \"median\"),\n",
" aum_qty_max = (\"aum_qty\", \"max\"),\n",
" aum_qty_last = (\"aum_qty\", \"last\"),\n",
" net_flow_qty_sum = (\"net_flow_qty\", \"sum\"),\n",
" gross_flow_qty_sum = (\"gross_flow_qty\", \"sum\"),\n",
" sub_qty_sum = (\"sub_qty\", \"sum\"),\n",
" red_qty_sum = (\"red_qty\", \"sum\"),\n",
" n_tx_total = (\"n_tx\", \"sum\"),\n",
" avg_n_isin_held = (\"n_isin_held\", \"mean\"),\n",
" max_n_isin_held = (\"n_isin_held\", \"max\"),\n",
" aum_drawdown_last = (\"aum_drawdown\", \"last\"),\n",
" region = (\"region\", \"last\"),\n",
" country = (\"country\", \"last\"),\n",
" )\n",
")\n",
"\n",
"df_client = (\n",
" df_client\n",
" .merge(df_rel_client, on=ID_COL, how=\"left\")\n",
" .merge(asset_shares, on=ID_COL, how=\"left\")\n",
" .merge(fund_shares, on=ID_COL, how=\"left\")\n",
" .merge(rolling_feats, on=ID_COL, how=\"left\")\n",
")\n",
"\n",
"print(\"df_client shape:\", df_client.shape)\n",
"\n",
"# ============================================================\n",
"# 2f. Engineered ratios + filtres\n",
"# ============================================================\n",
"dfc = df_client.copy()\n",
"\n",
"dfc[\"log_aum_qty_mean\"] = np.log1p(dfc[\"aum_qty_mean\"].clip(lower=0))\n",
"\n",
"dfc[\"gross_flow_to_aum\"] = np.where(\n",
" dfc[\"aum_qty_mean\"] > 1,\n",
" dfc[\"gross_flow_qty_sum\"] / dfc[\"aum_qty_mean\"],\n",
" np.nan\n",
")\n",
"dfc[\"flow_direction_balance\"] = np.where(\n",
" dfc[\"gross_flow_qty_sum\"] > 0,\n",
" dfc[\"net_flow_qty_sum\"] / dfc[\"gross_flow_qty_sum\"],\n",
" np.nan\n",
")\n",
"dfc[\"sub_share_mean\"] = np.where(\n",
" dfc[\"gross_flow_qty_sum\"] > 0,\n",
" dfc[\"sub_qty_sum\"] / dfc[\"gross_flow_qty_sum\"],\n",
" np.nan\n",
")\n",
"dfc[\"redemption_bias\"] = np.where(\n",
" dfc[\"gross_flow_qty_sum\"] > 0,\n",
" (dfc[\"red_qty_sum\"] - dfc[\"sub_qty_sum\"]) / dfc[\"gross_flow_qty_sum\"],\n",
" np.nan\n",
")\n",
"dfc[\"activity_intensity\"] = np.where(\n",
" dfc[\"n_months\"] > 0,\n",
" dfc[\"n_tx_total\"] / dfc[\"n_months\"],\n",
" np.nan\n",
")\n",
"dfc[\"exit_rate_per_isin\"] = np.where(\n",
" dfc[\"n_isin_total\"] > 0,\n",
" dfc[\"full_exit_count\"] / dfc[\"n_isin_total\"],\n",
" np.nan\n",
")\n",
"dfc[\"entry_rate_per_isin\"] = np.where(\n",
" dfc[\"n_isin_total\"] > 0,\n",
" dfc[\"entry_count\"] / dfc[\"n_isin_total\"],\n",
" np.nan\n",
")\n",
"dfc[\"aum_final_to_peak\"] = np.where(\n",
" dfc[\"aum_qty_max\"] > 0,\n",
" dfc[\"aum_qty_last\"] / dfc[\"aum_qty_max\"],\n",
" np.nan\n",
")\n",
"dfc[\"aum_drawdown_last\"] = dfc[\"aum_drawdown_last\"].clip(0, 1)\n",
"\n",
"# Log-transforms sur variables de taille brute (non utilisées en clustering)\n",
"for col in [\"aum_qty_mean\", \"gross_flow_qty_sum\", \"n_tx_total\"]:\n",
" dfc[f\"log_{col}\"] = np.log1p(dfc[col].clip(lower=0))\n",
"\n",
"# Filtres qualité\n",
"dfc = dfc[(dfc[\"n_months\"] >= 6) & (dfc[\"aum_qty_mean\"] > 0)].copy()\n",
"\n",
"# Retrait des outliers sur variables de taille\n",
"for col in [\"aum_qty_mean\", \"gross_flow_qty_sum\", \"n_tx_total\"]:\n",
" cap = dfc[col].quantile(0.99)\n",
" dfc = dfc[dfc[col] <= cap].copy()\n",
"\n",
"print(f\"After outlier removal: {len(dfc)} accounts\")\n",
"\n",
"# Regroupement géographique\n",
"top_countries = dfc[\"country\"].fillna(\"Unknown\").value_counts().head(10).index\n",
"top_regions = dfc[\"region\"].fillna(\"Unknown\").value_counts().head(10).index\n",
"dfc[\"country_grp\"] = np.where(dfc[\"country\"].isin(top_countries), dfc[\"country\"], \"Other\")\n",
"dfc[\"region_grp\"] = np.where(dfc[\"region\"].isin(top_regions), dfc[\"region\"], \"Other\")\n",
"\n",
"df_last_active = (\n",
" df_month[df_month[\"active_month\"] == 1]\n",
" .groupby(ID_COL)[\"month\"]\n",
" .max()\n",
" .reset_index(name=\"last_active_month\")\n",
")\n",
"reference_date = df_month[\"month\"].max()\n",
"df_last_active[\"months_since_last_tx\"] = (\n",
" (reference_date.to_period(\"M\") - df_last_active[\"last_active_month\"].dt.to_period(\"M\"))\n",
" .apply(lambda x: x.n)\n",
")\n",
"dfc = dfc.merge(df_last_active[[ID_COL, \"months_since_last_tx\"]], on=ID_COL, how=\"left\")\n",
"max_months = dfc[\"months_since_last_tx\"].max()\n",
"dfc[\"months_since_last_tx\"] = dfc[\"months_since_last_tx\"].fillna(max_months + 1)\n",
"\n",
"print(\"dfc shape:\", dfc.shape)"
]
},
{
"cell_type": "markdown",
"id": "d180b613",
"metadata": {},
"source": [
"## 3. Global Clustering (all active accounts)\n",
"\n",
"Baseline clustering on all accounts with sufficient history."
]
},
{
"cell_type": "markdown",
"id": "55ab41d3-20f6-4559-8e38-68420b4230b1",
"metadata": {},
"source": [
"**Feature set final retenu**\n",
"\n",
"- flow_freq : proportion de mois avec au moins une transaction — mesure la fréquence globale d'activité du compte\n",
"- gross_flow_to_aum : volume brut de flux rapporté à l'AUM moyen — mesure l'intensité des transactions indépendamment de leur direction, après clip p90 et log-transform\n",
"- n_isin_total : nombre total d'ISINs distincts détenus sur toute la période — capte l'étendue du portefeuille exploré\n",
"- avg_holding_months_per_isin : durée moyenne de détention par ISIN — capte la fidélité aux produits\n",
"- exit_rate_per_isin : nombre moyen de sorties complètes par ISIN — mesure la propension à liquider ses positions\n",
"- flow_direction_balance : ratio flux nets sur flux bruts — distingue les acheteurs nets des vendeurs nets sur l'ensemble de la période\n",
"- log_aum_qty_mean : logarithme de l'AUM moyen — seule variable de taille retenue, incluse pour distinguer des comportements identiques sur des niveaux d'engagement très différents\n",
"- months_since_last_tx : nombre de mois écoulés depuis la dernière transaction — variable de récence, la plus discriminante du set (ratio inter/total de 0.89)"
]
},
{
"cell_type": "code",
"execution_count": 197,
"id": "353c7d48-4644-4427-ac4b-02e3f3e31690",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"flow_freq — min=0.0000, max=1.0000, valeurs > 1 : 0\n",
"Accounts: 7179 | Features: 8\n",
"Points > 5 std after scaling: 0 (0.0%)\n",
"\n",
"Features with most extreme values (>5 std):\n",
"Series([], )\n",
"\n",
"K=4 | sil=0.2312 | db=1.5109\n",
"\n",
"=== Tailles des clusters ===\n",
" n_comptes pct\n",
"cluster_k4 \n",
"0 2708 37.7\n",
"1 1174 16.4\n",
"2 1476 20.6\n",
"3 1821 25.4\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== Médianes comportement ===\n",
" gross_flow_to_aum flow_freq flow_direction_balance n_isin_total avg_holding_months_per_isin exit_rate_per_isin log_aum_qty_mean months_since_last_tx\n",
"cluster_k4 \n",
"0 7.884 0.071 0.000 1.0 11.333 1.000 5.280 69.0\n",
"1 5.348 0.617 -0.006 12.0 28.924 0.667 8.768 3.0\n",
"2 1.159 0.043 -1.000 3.0 60.000 0.400 5.167 27.0\n",
"3 1.477 0.012 -1.000 3.0 12.000 0.714 3.407 127.0\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== Médianes allocation ===\n",
" share_asset_fixed_income share_asset_diversified share_asset_equity share_fund_carmignac_patrimoine share_fund_carmignac_investissement share_fund_carmignac_sécurité share_fund_carmignac_emergents\n",
"cluster_k4 \n",
"0 0.767 0.000 0.000 0.000 0.000 0.000 0.000\n",
"1 0.284 0.207 0.155 0.152 0.011 0.018 0.002\n",
"2 0.000 0.372 0.227 0.255 0.000 0.000 0.000\n",
"3 0.000 0.326 0.099 0.169 0.000 0.000 0.000\n"
]
}
],
"source": [
"# ============================================================\n",
"# Feature selection & preprocessing \n",
"# ============================================================\n",
"base_features = [\n",
" \"flow_freq\",\n",
" \"gross_flow_to_aum\",\n",
" \"n_isin_total\",\n",
" \"avg_holding_months_per_isin\",\n",
" \"exit_rate_per_isin\",\n",
" \"flow_direction_balance\",\n",
" \"log_aum_qty_mean\",\n",
" \"months_since_last_tx\",\n",
"]\n",
"all_features = [c for c in base_features if c in dfc.columns]\n",
"\n",
"dfc_clean = dfc.copy()\n",
"\n",
"# --- Diagnostic préalable ---\n",
"vals_ff = dfc_clean[\"flow_freq\"].to_numpy(dtype=float)\n",
"print(f\"flow_freq — min={vals_ff.min():.4f}, max={vals_ff.max():.4f}, \"\n",
" f\"valeurs > 1 : {(vals_ff > 1).sum()}\")\n",
"\n",
"# --- Imputation des NaN par 0 ---\n",
"for col in [\"flow_direction_balance\", \"months_since_last_tx\"]:\n",
" if col in dfc_clean.columns:\n",
" dfc_clean[col] = dfc_clean[col].fillna(0)\n",
"\n",
"# --- Groupe 1 : clip MAD 3 sigma ---\n",
"for col in [\n",
" \"n_isin_total\",\n",
" \"exit_rate_per_isin\",\n",
" \"avg_holding_months_per_isin\",\n",
" \"months_since_last_tx\",\n",
"]:\n",
" if col not in dfc_clean.columns:\n",
" continue\n",
" vals = dfc_clean[col].to_numpy(dtype=float)\n",
" med = np.nanmedian(vals)\n",
" mad = np.nanmedian(np.abs(vals - med)) * 1.4826\n",
" if mad > 0:\n",
" dfc_clean[col] = np.clip(vals, med - 3*mad, med + 3*mad)\n",
" else:\n",
" dfc_clean[col] = np.clip(vals, 0, np.nanpercentile(vals, 95))\n",
"\n",
"# --- Groupe 2 : clip p90 puis log-transform ---\n",
"col = \"gross_flow_to_aum\"\n",
"if col in dfc_clean.columns:\n",
" vals = dfc_clean[col].to_numpy(dtype=float)\n",
" vals = np.clip(vals, 0, np.nanpercentile(vals, 90))\n",
" dfc_clean[col] = np.log1p(vals)\n",
"\n",
"col = \"flow_freq\"\n",
"if col in dfc_clean.columns:\n",
" vals = dfc_clean[col].to_numpy(dtype=float)\n",
" dfc_clean[col] = np.log1p(np.clip(vals, 0, None))\n",
"\n",
"# --- Groupe 3 : log_aum_qty_mean — clip MAD 3 sigma ---\n",
"col = \"log_aum_qty_mean\"\n",
"if col in dfc_clean.columns:\n",
" vals = dfc_clean[col].to_numpy(dtype=float)\n",
" med = np.nanmedian(vals)\n",
" mad = np.nanmedian(np.abs(vals - med)) * 1.4826\n",
" dfc_clean[col] = np.clip(vals, med - 3*mad, med + 3*mad)\n",
"\n",
"# --- Groupe 4 : hhi_isin — clip p90 ---\n",
"col = \"hhi_isin\"\n",
"if col in dfc_clean.columns:\n",
" vals = dfc_clean[col].to_numpy(dtype=float)\n",
" dfc_clean[col] = np.clip(vals, 0, np.nanpercentile(vals, 90))\n",
"\n",
"# --- Construction de X ---\n",
"X_num = dfc_clean[all_features].copy()\n",
"X_num = X_num.loc[:, ~X_num.columns.duplicated()]\n",
"X_num = X_num.fillna(X_num.median())\n",
"\n",
"X_cat = pd.get_dummies(\n",
" dfc_clean[[\"country_grp\", \"region_grp\"]].fillna(\"Unknown\"), drop_first=True\n",
")\n",
"X = X_num.reset_index(drop=True)\n",
"\n",
"scaler = RobustScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"# --- Diagnostic ---\n",
"X_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
"extreme = (X_df.abs() > 5).any(axis=1).sum()\n",
"print(f\"Accounts: {X.shape[0]} | Features: {X.shape[1]}\")\n",
"print(f\"Points > 5 std after scaling: {extreme} ({extreme/len(X_df):.1%})\")\n",
"\n",
"extreme_by_feat = (X_df.abs() > 5).sum().sort_values(ascending=False)\n",
"print(\"\\nFeatures with most extreme values (>5 std):\")\n",
"print(extreme_by_feat[extreme_by_feat > 0].to_string())\n",
"\n",
"# --- Clustering K=4 ---\n",
"RESULTS = {}\n",
"for k in [4]:\n",
" km = KMeans(n_clusters=k, n_init=50, random_state=RANDOM_STATE)\n",
" dfc[f\"cluster_k{k}\"] = km.fit_predict(X_scaled)\n",
" RESULTS[k] = {\n",
" \"model\": km,\n",
" \"silhouette\": silhouette_score(X_scaled, dfc[f\"cluster_k{k}\"]),\n",
" \"davies_bouldin\": davies_bouldin_score(X_scaled, dfc[f\"cluster_k{k}\"]),\n",
" }\n",
" print(f\"\\nK={k} | sil={RESULTS[k]['silhouette']:.4f} | db={RESULTS[k]['davies_bouldin']:.4f}\")\n",
"\n",
"print(\"\\n=== Tailles des clusters ===\")\n",
"counts = dfc[\"cluster_k4\"].value_counts().sort_index()\n",
"props = counts / counts.sum() * 100\n",
"print(pd.DataFrame({\"n_comptes\": counts, \"pct\": props.round(1)}))\n",
"\n",
"# --- Heatmap comportement ---\n",
"profile_vars_behavior = [\n",
" \"gross_flow_to_aum\",\n",
" \"flow_freq\",\n",
" \"flow_direction_balance\",\n",
" \"n_isin_total\",\n",
" \"avg_holding_months_per_isin\",\n",
" \"exit_rate_per_isin\",\n",
" \"log_aum_qty_mean\",\n",
" \"months_since_last_tx\",\n",
"]\n",
"profile_vars_behavior = [c for c in profile_vars_behavior if c in dfc.columns]\n",
"\n",
"prof_behavior = plot_heatmap(\n",
" dfc, profile_vars_behavior, \"cluster_k4\",\n",
" title=\"Cluster signatures — Comportement (K=4, robust z-score)\",\n",
" figsize=(14, 4)\n",
")\n",
"\n",
"print(\"\\n=== Médianes comportement ===\")\n",
"print(prof_behavior.round(3).to_string())\n",
"\n",
"# --- Heatmap allocation (post-clustering, descriptive uniquement) ---\n",
"profile_vars_allocation = [\n",
" c for c in [\n",
" \"share_asset_fixed_income\",\n",
" \"share_asset_diversified\",\n",
" \"share_asset_equity\",\n",
" \"share_fund_carmignac_patrimoine\",\n",
" \"share_fund_carmignac_investissement\",\n",
" \"share_fund_carmignac_sécurité\",\n",
" \"share_fund_carmignac_emergents\",\n",
" ]\n",
" if c in dfc.columns\n",
"]\n",
"\n",
"prof_allocation = plot_heatmap(\n",
" dfc, profile_vars_allocation, \"cluster_k4\",\n",
" title=\"Cluster signatures — Allocation produits (K=4, descriptif post-clustering)\",\n",
" figsize=(12, 4)\n",
")\n",
"\n",
"print(\"\\n=== Médianes allocation ===\")\n",
"print(prof_allocation.round(3).to_string())"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "85747735-d0b4-4aa7-9fc2-adf030f92286",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" n_comptes pct\n",
"cluster_k4 \n",
"0 2708 37.7\n",
"1 1174 16.4\n",
"2 1476 20.6\n",
"3 1821 25.4\n"
]
}
],
"source": [
"counts = dfc[\"cluster_k4\"].value_counts().sort_index()\n",
"props = counts / counts.sum() * 100\n",
"print(pd.DataFrame({\"n_comptes\": counts, \"pct\": props.round(1)}))"
]
},
{
"cell_type": "code",
"execution_count": 204,
"id": "dc171be2-e066-4352-a0ea-32d7b7b046b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" k inertia silhouette davies_bouldin\n",
" 2 20206.150896 0.422448 0.971224\n",
" 3 16684.870723 0.241253 1.542920\n",
" 4 14655.878131 0.231172 1.510868\n",
" 5 13189.616061 0.228827 1.408857\n",
" 6 11997.575028 0.223735 1.416454\n",
" 7 11089.241350 0.229848 1.419999\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x400 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"K=4 | sil=0.2312 | db=1.5109\n",
" n_comptes pct\n",
"cluster_k4 \n",
"0 2708 37.7\n",
"1 1174 16.4\n",
"2 1476 20.6\n",
"3 1821 25.4\n",
"\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABMkAAAGGCAYAAABhZtaKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XdYU9cbwPFvWCJ7ioKiAgIOUHDi1lptHa3aumfduOpqHXXUXeve2rr3qIp1a9Vq69aqdaC1jloXMmXLur8/KNEIaEAg/PT9PE8ezbnnnvsektwkb845V6UoioIQQgghhBBCCCGEEO8xPV0HIIQQQgghhBBCCCGErkmSTAghhBBCCCGEEEK89yRJJoQQQgghhBBCCCHee5IkE0IIIYQQQgghhBDvPUmSCSGEEEIIIYQQQoj3niTJhBBCCCGEEEIIIcR7T5JkQgghhBBCCCGEEOK9J0kyIYQQQgghhBBCCPHekySZEEIIIYQQQgghhHjvSZJMCCF0pH79+owYMULXYeSp7du34+HhwYMHD3QdihBCS3/++SflypXj4cOHug5F59LOYVeuXNF1KOI/GzdupG7duiQkJOg6FCGEEO8ASZIJIUQOu3//PmPHjuWDDz7Ay8sLX19f2rZty+rVq4mPj8+TGOLi4pg/fz5nzpzJk+P9v9q1axerVq3SdRj50vPnz1m1ahWtWrWiYsWKeHl50ahRIyZMmMDdu3d1HV6ue1efG0uWLOGXX37J0j6zZ8+mSZMmODk5qcs6depE06ZN09U9deoU5cuXp0WLFkRERLxtuGr379/Hy8vrvU1QHTt2jPnz5+s6jHypZcuWJCYmsmnTJl2HIoQQ4h0gSTIhhMhBv/76K82aNWPfvn3Uq1ePMWPGMHToUBwdHZk+fTqTJ0/Okzji4uJYsGABZ8+ezZPjaevTTz/lzz//1PiyrUu7d+9mzZo1ug4j3wkLC6Ndu3ZMnToVW1tbBg4cqE78HjlyhGbNmuk6xFz3rj43li5dmqUkWWBgICdPnqRt27ZvrHvq1Cn69OlDyZIlWblyJVZWVm8RqaYpU6ZgYGCQY+39vzl27BgLFizQdRj5UoECBWjevDmrVq1CURRdhyOEEOL/3Pv7aUMIIXLYv//+y+DBg3F0dGT16tUUKlRIva1Dhw78888//Prrr7oLMAfExsZiYmKS7f319fXR19fPwYjyp7i4OAoWLKjrMLJt5MiRBAYGMm/ePBo1aqSxbdCgQcyePVtHkeW+t32Ov2u2bduGo6MjFSpUeG29s2fP4u/vT4kSJXI8Qfbbb7/x+++/06NHDxYvXpxj7QKkpKSQmJhIgQIFcrRdkX3Pnz/H0NAQPT3tf8v/+OOPWbZsGadPn8bPzy8XoxNCCPGuk5FkQgiRQ5YtW0ZsbCyTJ0/WSJClKV68OF26dMl0//nz5+Ph4ZGuPKN1vK5cuUL37t2pWrUq3t7e1K9fn5EjRwLw4MED9ZeEBQsW4OHhgYeHh8ZUndu3bzNw4ECqVKmCl5cXLVu25PDhwxke9+zZs3z77bf4+flRp06d1/4N1q5dS5MmTShfvjyVK1emZcuW7Nq167V9SUlJYf78+dSsWZPy5cvTqVMn/v7773RrtqXte+HCBaZOnUq1atWoUKEC/fr1IywsTCOOX375hV69elGzZk3KlStHgwYNWLhwIcnJyeo6nTp14tdff+Xhw4fqv1H9+vUzjRPgzJkzeHh4aExjTZt2dvXqVTp06ED58uWZNWsWAAkJCcybN48PP/yQcuXKUadOHb7//vt0a+ecOHGCdu3aUalSJXx8fGjUqJG6jbx2+fJlfv31Vz7//PN0CTIAIyMjhg8frlF26tQp2rdvT4UKFahUqRL+/v7cvn1bo07a8/vu3bsMGzaMihUrUq1aNebMmYOiKDx+/Bh/f398fX2pUaMGK1as0Ng/7W+/d+9eZs2aRY0aNahQoQJ9+vTh8ePH6eLct28fLVu2xNvbm6pVqzJs2DCCgoI06owYMQIfHx/u379Pz5498fHxYdiwYa99boD2j6uHhwcTJkxg3759NG7cGG9vb9q0acPNmzcB2LRpEx9++CFeXl506tQpw7X6Ll++TPfu3alYsSLly5enY8eOXLhwIcO/7T///MOIESOoVKkSFStWZOTIkcTFxWnEExsby44dO9T9etO6iIcPH6ZatWqoVKpM65w/f57evXvj7OzMypUrsba2fm2bWZGYmMjkyZPp3Lkzzs7Ob91e2mPy888/06RJE7y8vPjtt98AuH79Oj169MDX1xcfHx+6dOnCpUuXMmwnPj6esWPHUrVqVXx9ffn666959uxZumNlNEXy1XNbYmIiCxYsoGHDhnh5eVG1alXatWvHiRMngNTn6fr169Vtpt0yk/Z8yOimzTqYbzqPAwQFBTFq1Cj1ObZ+/fqMGzdO4zXw77//qt9nypcvT+vWrdP9UJT2ut6zZw+zZ8+mVq1alC9fnujoaEC75z9AuXLlsLKySvc+JoQQQmSVjCQTQogccvToUYoVK4avr2+uHic0NJTu3btjbW1Nr169sLCw4MGDBxw6dAgAGxsbvv32W7799ls+/PBDPvzwQwD1l6pbt27Rrl07HBwc6NmzJyYmJuzbt49+/foxf/58df0048ePx8bGhn79+hEbG5tpXFu2bGHSpEk0atSIzp078/z5c27evMnly5dfOz1v5syZLFu2jHr16lGrVi1u3LhB9+7def78eYb1J02ahIWFBf379+fhw4esXr2aCRMmMGfOHHWdHTt2YGJiwhdffIGJiQmnT59m3rx5REdHqxM8ffr0ISoqiidPnqgTjKampm/462csIiKCnj170qRJEz755BNsbW1JSUnB39+fCxcu0Lp1a1xdXfnrr79YvXo19+7dY9GiRUDq49G7d288PDwYOHAgRkZG/PPPP/zxxx/ZiuVtHTlyBEidGquNkydP0rNnT4oWLUr//v2Jj49n3bp1tGvXju3bt1O0aFGN+oMHD8bV1ZWhQ4dy7NgxFi9ejJWVFZs2baJatWoMGzaMXbt2MW3aNLy8vKhcubLG/osXL0alUtGzZ09CQ0NZvXo1Xbt2ZefOnRgbGwOpSc6RI0fi5eXFkCFDCA0NZc2aNfzxxx8EBARgYWGhbi8pKUn9JXz48OEYGxtjb2+f6XND28c1zfnz5zly5Ajt27cH4IcffqBPnz706NGDDRs20L59e549e8ayZcsYNWqUxhTPU6dO0bNnT8qVK0f//v1RqVRs376dLl26sGHDBry9vTWONWjQIIoWLcqQIUO4fv06W7duxcbGhq+++gqA77//ntGjR+Pt7U3r1q0BXpt4CgoK4tGjR5QpUybTOhcuXFA//qtWrcLGxiZdnaioKBITEzNtI02BAgXSvQZXr15NZGQkffv25eDBg29sQxunT59m3759dOjQAWtra5ycnLh16xYdOnTA1NSUHj16YGBgwObNm+nUqRPr1q2jfPnyGm1MmDBBfR66e/cuGzdu5NGjR6xdu/a1CcWMLFiwgKVLl9KqVSu8vb2Jjo7m6tWrXLt2jRo1atCmTRuePn3KiRMn+P7779/Y3ocffpjucb127RqrV6/O8PF5mTbn8aCgID7//HOioqJo3bo1Li4uBAUFceDAAeLj4zEyMiIkJIS2bdsSFxdHp06dsLa2ZseOHfj7+6sTzC9btGgRhoaGdO/enYSEBAwNDbP8/C9TpozOzptCCCHeIYoQQoi3FhUVpbi7uyv+/v5a71OvXj1l+PDh6vvz5s1T3N3d09Xbtm2b4u7urvz777+KoijKoUOHFHd3d+XPP//MtO3Q0FDF3d1dmTdvXrptXbp0UZo2bao8f/5cXZaSkqK0adNGadiwYbrjtmvXTklKSnpjf/z9/ZUmTZq8ts6rfQkODlbKlCmj9O3bV6Pe/PnzFXd3d42/T9q+Xbt2VVJSUtTlU6ZMUUqXLq1ERkaqy+Li4tIde8yYMUr58uU1+t2rVy+lXr16b4wzzenTpxV3d3fl9OnT6rKOHTsq7u7uysaNGzXqBgQEKJ6ensq5c+c0yjdu3Ki4u7srFy5cUBR
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== Médianes comportement K=4 ===\n",
" gross_flow_to_aum flow_freq flow_direction_balance n_isin_total avg_holding_months_per_isin exit_rate_per_isin log_aum_qty_mean months_since_last_tx\n",
"cluster_k4 \n",
"0 7.884 0.071 0.000 1.0 11.333 1.000 5.280 69.0\n",
"1 5.348 0.617 -0.006 12.0 28.924 0.667 8.768 3.0\n",
"2 1.159 0.043 -1.000 3.0 60.000 0.400 5.167 27.0\n",
"3 1.477 0.012 -1.000 3.0 12.000 0.714 3.407 127.0\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABB8AAAGGCAYAAAAzaSmEAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XdUFFcbwOHf0sQCSFdRUEHAAoq9K2rs3dh7793YNXaNGiu22HuLPbGbGBNrNLYo9o6KNCkC0ub7g4/VFRZRgZXwPufMgZ25c/edvTuzu3duUSmKoiCEEEIIIYQQQgiRRvR0HYAQQgghhBBCCCH+26TyQQghhBBCCCGEEGlKKh+EEEIIIYQQQgiRpqTyQQghhBBCCCGEEGlKKh+EEEIIIYQQQgiRpqTyQQghhBBCCCGEEGlKKh+EEEIIIYQQQgiRpqTyQQghhBBCCCGEEGlKKh+EEEIIIYQQQgiRpqTyQYivVI0aNRg9erSuw0hXu3fvxsXFhWfPnuk6FJFKRo8eTY0aNTTWubi4sHjxYh1F9HGLFy/GxcVF12HozJeUT3pdt+Li4mjYsCHLli1L8+fSlaTOna9JWpzH2j4DVq1aRc2aNSlcuDBNmjRJ1ef8WnXs2JGOHTvqOozPcv78eVxcXDh//nyaPs/QoUMZPHhwmj6HECJ1SeWDEOnsyZMnTJw4kZo1a+Lm5kbJkiVp06YN69evJzIyMl1iiIiIYPHixWn+xSCjO3DgAOvWrdN1GF+tkJAQ3NzccHFx4f79+7oO55PIOZA+7t27x+LFi1O9QvGXX37hxYsXdOjQQb0u4Yfr9evXNdKGhoby7bff4ubmxqlTp1I1jq5du+Li4sKUKVNSNd//uuXLl3P8+PEUpf3rr7+YM2cOJUuWZObMmQwbNiyNo/s0afUeT2ufUgZfq549e3L06FFu3bql61CEECkklQ9CpKOTJ0/SqFEjDh06hKenJxMmTGD48OHkyZOHOXPmMH369HSJIyIiAi8vLy5cuJAuz5dSTZo04dq1a9jZ2ek6FCD+B86GDRt0HcZX6/Dhw6hUKqytrdm/f7+uw/kkyZ0Dffv25dq1azqIKuM7fPgwU6dOVT++d+8eXl5e+Pj4pOrzrF69mgYNGmBiYpJsurCwMLp168bt27fx8vKiatWqqRbD0aNHuXLlSqrll9Fcu3aNvn37fta+K1asSPKHb1KfAefOnUNPT4/p06fTtGlTqlWr9tkxp4W0eo+nNW1lkBrKlCnDtWvXKFOmTJrkn6BIkSIUK1aMNWvWpOnzCCFSj4GuAxAis3j69ClDhw4lT548rF+/HhsbG/W29u3b8/jxY06ePKm7AFNBeHg42bJl++z99fX10dfXT8WIvk4RERFkzZpV12F8sf3791OtWjXy5MnDL7/8wtChQ3UdUqowMDDAwCDjfTx+6fmXGoyMjNL8OW7evMmtW7c+2r0jLCyM7t274+3tjZeXV6r+aH379i2zZs2iR48eLFq0KNXy/drFxcURHR1NlixZyJIlS6rnn9RnQEBAAMbGxuny3hJf5u3btxgaGqKnp5cm74+k1KtXj8WLF/PmzRuyZ8+eLs8phPh80vJBiHSyatUqwsPDmT59ukbFQwIHBwc6d+6sdX9t/dCT6iN7/fp1unfvTrly5XB3d6dGjRqMGTMGgGfPnlGhQgUAvLy8cHFxSdR39/79+wwaNIiyZcvi5uZG8+bNOXHiRJLPe+HCBSZNmkSFChU++uV+48aNNGjQgOLFi1OmTBmaN2/OgQMHkj2WuLg4Fi9eTOXKlSlevDgdO3bk3r17ifqWJ+x76dIlZs6cSfny5SlRogT9+/cnMDBQI47jx4/Tq1cvKleuTLFixahVqxZLliwhNjZWnaZjx46cPHkSHx8f9WuU0P9aW7/kpPq5duzYkYYNG/Lvv//Svn17ihcvzrx58wCIiopi0aJFfPPNNxQrVoxq1aoxe/ZsoqKiNPI9ffo0bdu2pXTp0nh4eFCnTh11Hrry/PlzLl68SP369WnQoAHPnj3jn3/++ez8bt68SY8ePShZsiQeHh507tw5ybvKISEhzJgxgxo1alCsWDGqVq3KyJEj1WUcFRXFwoULad68OaVKlaJEiRK0a9eOc+fOqfP42DmQ1LkWExPDkiVLqFWrFsWKFaNGjRrMmzcvUVnVqFGD3r17c/HiRXVT/5o1a7J3796PvgbPnj3DxcWF1atXs27dOjw9PXF3d6dDhw7cuXNHI+3o0aPx8PDgyZMn9OzZEw8PD0aMGAHEV0LMmjWLatWqUaxYMerUqcPq1atRFEUjj6ioKGbMmEH58uXx8PCgT58+vHz5MlFc2sYeSOp1ev+83L17t7o/dqdOndSvc8L5kdx1KjnHjx/H0NCQ0qVLa03z5s0bevTowY0bN1i8eDHVq1f/aL6fYuXKlSiKQvfu3VMlv+PHj9OwYUPc3Nxo2LAhx44dSzJdXFwc69ato0GDBri5uVGxYkUmTpxIcHCwRrqUvLZxcXGsX7+eRo0a4ebmRvny5enevbtGt5WELiX79+9XP+eff/6p3vb+50bC++H+/fsMHjyYkiVLUq5cOaZNm8bbt2818gwPD2fPnj3q98T775n3r60uLi7s3r2b8PBwddrdu3drfR3fv962adNGfexbt25NlDYgIICxY8dSsWJF3NzcaNy4MXv27EmU7tdff6V58+Z4eHhQsmRJGjVqxPr169XxJvceT86+ffv49ttv1Z+H7du356+//tKa/lM+dx49esTAgQOpVKkSbm5uVK1alaFDhxIaGgokXwYAvr6+jBkzhooVK1KsWDEaNGjAzz//nOTz/vrrr8yfP58qVapQvHhxwsLCkv0svHfvHh07dqR48eJUqVKFlStXJjpWHx8f+vTpQ4kSJahQoQIzZszgzz//TPK1rVixIuHh4Zw5c+ajr7kQQvcy3q0dITKo33//nXz58lGyZMk0fZ6AgAC6d++Oubk5vXr1wtTUlGfPnqm/zFpYWDBp0iQmTZrEN998wzfffAOg/hFx9+5d2rZti62tLT179iRbtmwcOnSI/v37s3jxYnX6BJMnT8bCwoL+/fsTHh6uNa4dO3Ywbdo06tSpQ6dOnXj79i23b9/m6tWrNGrUSOt+P/74I6tWrcLT05MqVapw69YtunfvrvFl9n3Tpk3D1NSUAQMG4OPjw/r165kyZQoLFixQp9mzZw/ZsmWja9euZMuWjXPnzrFo0SLCwsIYNWoUAH369CE0NJSXL1+qv7R/7l2V169f07NnTxo0aEDjxo2xtLQkLi6Ovn37cunSJVq1aoWjoyN37txh/fr1PHr0iKVLlwLx5dG7d29cXFwYNGgQRkZGPH78+It+6KeGX375haxZs+Lp6YmxsTH29vYcOHDgs97fd+/epX379mTPnp0ePXpgYGDA9u3b6dixI5s2baJ48eJA/A/K9u3bc//+fVq0aEGRIkUICgrit99+w9fXFwsLC8LCwti5cycNGzakZcuWvHnzhp9//pkePXqwc+dOChcu/NFzICnjx49nz5491KlTh65du3Lt2jVWrFjB/fv3WbJkiUbax48fM3jwYL799luaNWvGrl27GD16NEWLFqVQoUIffT327t3LmzdvaNeuHW/fvmXjxo107tyZAwcOYGVlpU4XExND9+7dKVWqFKNGjcLY2BhFUejbty/nz5/n22+/pXDhwvz555/Mnj0bX19fxo4dq95/3Lhx7N+/n4YNG1KyZEnOnTtHr169PqnsklOmTBk6duzIxo0b6dOnDwULFgTA0dHxo9ep5Fy+fBlnZ2cMDQ2T3B4REUHPnj35999/WbhwIZ6enonSREVFERYWlqLjsLCw0Hj8/PlzVq5cyYwZMzA2Nk5RHsn566+/GDhwIE5OTgwfPpygoCDGjBlDrly5EqWdOHEie/bsoXnz5nTs2JFnz56xefNmbt68ydatWzE0NEzxaztu3Dh2795N1apV+fbbb4mNjeXixYtcvXoVNzc3dbpz585x6NAh2rdvj7m5+Ue7xQ0ZMgQ7OzuGDx/OlStX2LhxIyEhIcyePRuA2bNnM378eNz
"text/plain": [
"<Figure size 1200x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== Médianes allocation K=4 ===\n",
" share_asset_fixed_income share_asset_diversified share_asset_equity share_fund_carmignac_patrimoine share_fund_carmignac_investissement share_fund_carmignac_sécurité share_fund_carmignac_emergents\n",
"cluster_k4 \n",
"0 0.767 0.000 0.000 0.000 0.000 0.000 0.000\n",
"1 0.284 0.207 0.155 0.152 0.011 0.018 0.002\n",
"2 0.000 0.372 0.227 0.255 0.000 0.000 0.000\n",
"3 0.000 0.326 0.099 0.169 0.000 0.000 0.000\n",
"\n",
"=== Distribution par pays (top 10) ===\n",
"country_grp Belgium FRANCE France Italy Latam Luxembourg Other Spain Switzerland United Kingdom Us Offshore\n",
"cluster_k4 \n",
"0 1.5 0.1 17.1 5.0 12.5 6.1 3.0 5.0 3.6 6.6 39.4\n",
"1 3.9 8.3 32.1 12.7 0.8 4.6 17.7 6.7 4.5 8.3 0.3\n",
"2 0.9 0.5 69.6 2.8 10.0 1.4 3.9 1.6 2.2 6.7 0.5\n",
"3 2.0 0.0 53.0 4.2 2.9 5.1 7.2 2.1 7.3 10.8 5.3\n",
"\n",
"=== Distribution par région ===\n",
"region_grp Belgium FRANCE France Germany Italy LATAM Luxembourg Other Spain Switzerland United Kingdom\n",
"cluster_k4 \n",
"0 1.5 0.1 17.2 0.6 5.0 52.0 6.1 1.9 5.2 3.6 6.7\n",
"1 3.9 8.4 32.5 1.2 12.7 1.2 4.6 15.5 7.2 4.5 8.3\n",
"2 0.9 0.5 69.8 0.9 2.8 10.6 1.4 2.5 1.7 2.2 6.8\n",
"3 2.0 0.0 53.0 4.3 4.2 8.2 5.1 2.6 2.3 7.3 10.9\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1800x400 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ============================================================\n",
"# K-selection diagnostics\n",
"# ============================================================\n",
"rows = []\n",
"for k in range(2, 8):\n",
" km = KMeans(n_clusters=k, n_init=50, random_state=RANDOM_STATE)\n",
" labels = km.fit_predict(X_scaled)\n",
" rows.append({\n",
" \"k\": k,\n",
" \"inertia\": km.inertia_,\n",
" \"silhouette\": silhouette_score(X_scaled, labels),\n",
" \"davies_bouldin\": davies_bouldin_score(X_scaled, labels),\n",
" })\n",
"df_kdiag = pd.DataFrame(rows)\n",
"print(df_kdiag.to_string(index=False))\n",
"\n",
"fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
"for ax, col, title in zip(axes,\n",
" [\"inertia\", \"silhouette\", \"davies_bouldin\"],\n",
" [\"Elbow / Inertia\", \"Silhouette (higher=better)\", \"Davies-Bouldin (lower=better)\"]):\n",
" ax.plot(df_kdiag[\"k\"], df_kdiag[col], marker=\"o\")\n",
" ax.set_title(title)\n",
" ax.set_xlabel(\"K\")\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# ============================================================\n",
"# Clustering K=4\n",
"# ============================================================\n",
"RESULTS = {}\n",
"for k in [4]:\n",
" km = KMeans(n_clusters=k, n_init=50, random_state=RANDOM_STATE)\n",
" dfc[f\"cluster_k{k}\"] = km.fit_predict(X_scaled)\n",
" RESULTS[k] = {\n",
" \"model\": km,\n",
" \"silhouette\": silhouette_score(X_scaled, dfc[f\"cluster_k{k}\"]),\n",
" \"davies_bouldin\": davies_bouldin_score(X_scaled, dfc[f\"cluster_k{k}\"]),\n",
" }\n",
" print(f\"K={k} | sil={RESULTS[k]['silhouette']:.4f} | db={RESULTS[k]['davies_bouldin']:.4f}\")\n",
" counts = dfc[f\"cluster_k{k}\"].value_counts().sort_index()\n",
" props = counts / counts.sum() * 100\n",
" print(pd.DataFrame({\"n_comptes\": counts, \"pct\": props.round(1)}))\n",
" print()\n",
"\n",
"# ============================================================\n",
"# Heatmap comportement\n",
"# ============================================================\n",
"prof_behavior = plot_heatmap(\n",
" dfc, profile_vars_behavior, \"cluster_k4\",\n",
" title=\"Cluster signatures — Comportement (K=4, robust z-score)\",\n",
" figsize=(14, 4)\n",
")\n",
"print(\"\\n=== Médianes comportement K=4 ===\")\n",
"print(prof_behavior.round(3).to_string())\n",
"\n",
"# ============================================================\n",
"# Heatmap allocation (descriptif post-clustering)\n",
"# ============================================================\n",
"prof_allocation = plot_heatmap(\n",
" dfc, profile_vars_allocation, \"cluster_k4\",\n",
" title=\"Cluster signatures — Allocation produits (K=4, descriptif post-clustering)\",\n",
" figsize=(12, 4)\n",
")\n",
"print(\"\\n=== Médianes allocation K=4 ===\")\n",
"print(prof_allocation.round(3).to_string())\n",
"\n",
"# ============================================================\n",
"# Description géographique post-clustering\n",
"# ============================================================\n",
"print(\"\\n=== Distribution par pays (top 10) ===\")\n",
"geo_country = pd.crosstab(\n",
" dfc[\"cluster_k4\"],\n",
" dfc[\"country_grp\"].fillna(\"Unknown\"),\n",
" normalize=\"index\"\n",
").round(3) * 100\n",
"print(geo_country.to_string())\n",
"\n",
"print(\"\\n=== Distribution par région ===\")\n",
"geo_region = pd.crosstab(\n",
" dfc[\"cluster_k4\"],\n",
" dfc[\"region_grp\"].fillna(\"Unknown\"),\n",
" normalize=\"index\"\n",
").round(3) * 100\n",
"print(geo_region.to_string())\n",
"\n",
"# Visualisation heatmap géographique\n",
"fig, axes = plt.subplots(1, 2, figsize=(18, 4))\n",
"\n",
"sns.heatmap(\n",
" geo_country,\n",
" cmap=\"Blues\", annot=True, fmt=\".1f\",\n",
" ax=axes[0],\n",
" cbar_kws={\"label\": \"%\"}\n",
")\n",
"axes[0].set_title(\"Distribution par pays (% par cluster)\")\n",
"axes[0].set_xlabel(\"Pays\")\n",
"axes[0].set_ylabel(\"Cluster\")\n",
"\n",
"sns.heatmap(\n",
" geo_region,\n",
" cmap=\"Blues\", annot=True, fmt=\".1f\",\n",
" ax=axes[1],\n",
" cbar_kws={\"label\": \"%\"}\n",
")\n",
"axes[1].set_title(\"Distribution par région (% par cluster)\")\n",
"axes[1].set_xlabel(\"Région\")\n",
"axes[1].set_ylabel(\"Cluster\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 201,
"id": "50ecf35e-de7e-44ae-afee-404186c4d42c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" flow_freq gross_flow_to_aum n_isin_total \\\n",
"flow_freq 1.000 0.043 0.484 \n",
"gross_flow_to_aum 0.043 1.000 0.087 \n",
"n_isin_total 0.484 0.087 1.000 \n",
"avg_holding_months_per_isin 0.074 -0.019 0.027 \n",
"exit_rate_per_isin -0.066 -0.029 -0.103 \n",
"flow_direction_balance 0.182 0.007 0.038 \n",
"log_aum_qty_mean 0.522 -0.047 0.381 \n",
"months_since_last_tx -0.513 -0.012 -0.229 \n",
"\n",
" avg_holding_months_per_isin exit_rate_per_isin \\\n",
"flow_freq 0.074 -0.066 \n",
"gross_flow_to_aum -0.019 -0.029 \n",
"n_isin_total 0.027 -0.103 \n",
"avg_holding_months_per_isin 1.000 -0.257 \n",
"exit_rate_per_isin -0.257 1.000 \n",
"flow_direction_balance -0.163 0.093 \n",
"log_aum_qty_mean 0.140 0.024 \n",
"months_since_last_tx -0.306 0.159 \n",
"\n",
" flow_direction_balance log_aum_qty_mean \\\n",
"flow_freq 0.182 0.522 \n",
"gross_flow_to_aum 0.007 -0.047 \n",
"n_isin_total 0.038 0.381 \n",
"avg_holding_months_per_isin -0.163 0.140 \n",
"exit_rate_per_isin 0.093 0.024 \n",
"flow_direction_balance 1.000 0.298 \n",
"log_aum_qty_mean 0.298 1.000 \n",
"months_since_last_tx -0.119 -0.389 \n",
"\n",
" months_since_last_tx \n",
"flow_freq -0.513 \n",
"gross_flow_to_aum -0.012 \n",
"n_isin_total -0.229 \n",
"avg_holding_months_per_isin -0.306 \n",
"exit_rate_per_isin 0.159 \n",
"flow_direction_balance -0.119 \n",
"log_aum_qty_mean -0.389 \n",
"months_since_last_tx 1.000 \n"
]
}
],
"source": [
"# Test : corrélations entre features\n",
"corr_matrix = dfc[base_features].corr().round(3)\n",
"print(corr_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 202,
"id": "273392b8-c60c-4b19-ab4e-760616d3c246",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"base_features: 8\n",
"share_asset: []\n",
"share_fund: []\n",
"X columns: 8\n",
"X_num columns: 8\n",
"X_cat columns: 20\n"
]
}
],
"source": [
"print(\"base_features:\", len(base_features))\n",
"print(\"share_asset:\", [c for c in all_features if c.startswith(\"share_asset_\")])\n",
"print(\"share_fund:\", [c for c in all_features if c.startswith(\"share_fund_\")])\n",
"print(\"X columns:\", X.shape[1])\n",
"print(\"X_num columns:\", X_num.shape[1])\n",
"print(\"X_cat columns:\", X_cat.shape[1])"
]
},
{
"cell_type": "code",
"execution_count": 203,
"id": "d42b5319-c66c-4a7f-aeac-d3044d07f499",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== share_fund_ disponibles ===\n",
"['share_fund_carmignac_court_terme', 'share_fund_carmignac_emergents', 'share_fund_carmignac_investissement', 'share_fund_carmignac_patrimoine', 'share_fund_carmignac_portfolio_credit', 'share_fund_carmignac_portfolio_flexible_b', 'share_fund_carmignac_portfolio_global_bon', 'share_fund_carmignac_portfolio_patrimoine', 'share_fund_carmignac_portfolio_sécurité', 'share_fund_carmignac_sécurité']\n",
"\n",
"=== share_asset_ disponibles ===\n",
"['share_asset_alternative', 'share_asset_diversified', 'share_asset_equity', 'share_asset_fixed_income', 'share_asset_private_assets']\n",
"share_fund_carmignac_court_terme: 0.000000\n",
"share_fund_carmignac_emergents: 0.000001\n",
"share_fund_carmignac_investissement: 0.000032\n",
"share_fund_carmignac_patrimoine: 0.011248\n",
"share_fund_carmignac_portfolio_credit: 0.000000\n",
"share_fund_carmignac_portfolio_flexible_b: 0.000000\n",
"share_fund_carmignac_portfolio_global_bon: 0.000000\n",
"share_fund_carmignac_portfolio_patrimoine: 0.000000\n",
"share_fund_carmignac_portfolio_sécurité: 0.000000\n",
"share_fund_carmignac_sécurité: 0.000080\n",
"share_asset_alternative: 0.000000\n",
"share_asset_diversified: 0.027594\n",
"share_asset_equity: 0.009158\n",
"share_asset_fixed_income: 0.130769\n",
"share_asset_private_assets: 0.000000\n"
]
}
],
"source": [
"# Identifier les share_fund_ et share_asset_ les plus discriminantes\n",
"top_share_funds = [c for c in dfc.columns if c.startswith(\"share_fund_\")]\n",
"top_share_assets = [c for c in dfc.columns if c.startswith(\"share_asset_\")]\n",
"\n",
"# Regarder lesquelles varient le plus entre clusters\n",
"print(\"=== share_fund_ disponibles ===\")\n",
"print(top_share_funds)\n",
"print(\"\\n=== share_asset_ disponibles ===\")\n",
"print(top_share_assets)\n",
"\n",
"# Variance inter-cluster pour chaque share variable\n",
"for col in top_share_funds + top_share_assets:\n",
" var_inter = dfc.groupby(\"cluster_k4\")[col].median().var()\n",
" print(f\"{col}: {var_inter:.6f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7de1a58e-37ec-4d13-9807-5b047ec6ff42",
"metadata": {},
"outputs": [],
"source": [
"import subprocess\n",
"subprocess.run([\"jupyter\", \"nbconvert\", \"--to\", \"html\", \"clustering_clean.ipynb\"])"
]
},
{
"cell_type": "code",
"execution_count": 206,
"id": "b56a53c8-c1eb-4117-a028-ba9b6c1c45af",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== Types d'assets disponibles ===\n",
"Product - Asset Type\n",
"Equity 2023860\n",
"Diversified 1377424\n",
"Fixed Income 948728\n",
"Alternative 214235\n",
"Private Assets 219\n",
"Name: count, dtype: int64\n",
"\n",
"df_month_asset shape: (1745996, 15)\n",
"df_client_asset shape: (13328, 20)\n",
"\n",
"Comptes par asset type:\n",
"Product - Asset Type\n",
"Diversified 4161\n",
"Fixed Income 3934\n",
"Equity 3901\n",
"Alternative 1319\n",
"Private Assets 13\n",
"Name: Registrar Account - ID, dtype: int64\n",
"\n",
"Asset types retenus (>= 50 comptes) : ['Alternative', 'Diversified', 'Equity', 'Fixed Income']\n",
"\n",
"============================================================\n",
"ASSET TYPE : Alternative\n",
"============================================================\n",
" k silhouette davies_bouldin\n",
" 2 0.4568 0.9959\n",
" 3 0.3416 1.1374\n",
" 4 0.2564 1.3750\n",
" 5 0.2815 1.2393\n",
" 6 0.2640 1.3549\n",
"→ K retenu : 2 (silhouette=0.4568)\n",
" n_comptes pct\n",
"cluster_alternative \n",
"0 311 23.6\n",
"1 1008 76.4\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Alternative:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_asset aum_final_to_peak aum_drawdown_last\n",
"cluster_alternative \n",
"0 0.085 1.037 1.000 0.104 5.777 12.0 0.914 0.086\n",
"1 0.069 4.729 0.512 -0.072 5.067 66.0 0.000 1.000\n",
"\n",
"============================================================\n",
"ASSET TYPE : Diversified\n",
"============================================================\n",
" k silhouette davies_bouldin\n",
" 2 0.6029 0.6519\n",
" 3 0.5108 0.8195\n",
" 4 0.4847 0.9830\n",
" 5 0.4697 0.8759\n",
" 6 0.3327 1.1264\n",
"→ K retenu : 2 (silhouette=0.6029)\n",
" n_comptes pct\n",
"cluster_diversified \n",
"0 3368 80.9\n",
"1 793 19.1\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Diversified:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_asset aum_final_to_peak aum_drawdown_last\n",
"cluster_diversified \n",
"0 0.044 3.045 0.625 -0.577 5.064 80.0 0.000 1.000\n",
"1 0.085 0.218 1.000 -0.682 5.156 12.0 0.907 0.093\n",
"\n",
"============================================================\n",
"ASSET TYPE : Equity\n",
"============================================================\n",
" k silhouette davies_bouldin\n",
" 2 0.3701 1.3831\n",
" 3 0.4248 0.9505\n",
" 4 0.2858 1.3678\n",
" 5 0.2587 1.4438\n",
" 6 0.2779 1.3114\n",
"→ K retenu : 3 (silhouette=0.4248)\n",
" n_comptes pct\n",
"cluster_equity \n",
"0 2384 61.1\n",
"1 769 19.7\n",
"2 748 19.2\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Equity:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_asset aum_final_to_peak aum_drawdown_last\n",
"cluster_equity \n",
"0 0.025 3.296 0.576 -0.835 3.976 90.0 0.000 1.000\n",
"1 0.071 0.064 1.056 -0.935 4.554 12.0 0.975 0.025\n",
"2 0.646 3.610 3.588 -0.099 8.474 0.0 0.154 0.846\n",
"\n",
"============================================================\n",
"ASSET TYPE : Fixed Income\n",
"============================================================\n",
" k silhouette davies_bouldin\n",
" 2 0.6758 0.5130\n",
" 3 0.4226 0.8457\n",
" 4 0.4348 0.9977\n",
" 5 0.4601 0.9193\n",
" 6 0.4386 0.9478\n",
"→ K retenu : 2 (silhouette=0.6758)\n",
" n_comptes pct\n",
"cluster_fixed_income \n",
"0 3142 79.9\n",
"1 792 20.1\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Fixed Income:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_asset aum_final_to_peak aum_drawdown_last\n",
"cluster_fixed_income \n",
"0 0.060 6.231 0.48 0.000 5.142 69.0 0.000 1.000\n",
"1 0.182 2.224 1.50 0.472 7.279 2.0 0.998 0.002\n",
"\n",
"============================================================\n",
"RÉSUMÉ — Clustering par asset type\n",
"============================================================\n",
" Alternative : K=2, sil=0.4568, n=1319\n",
" Diversified : K=2, sil=0.6029, n=4161\n",
" Equity : K=3, sil=0.4248, n=3901\n",
" Fixed Income : K=2, sil=0.6758, n=3934\n"
]
}
],
"source": [
"# ============================================================\n",
"# CLUSTERING PAR TYPE D'ASSET\n",
"# ============================================================\n",
"\n",
"# ── 0. Vérification des types d'assets disponibles ────────────────────────\n",
"print(\"=== Types d'assets disponibles ===\")\n",
"print(df_aum[ASSET_COL].value_counts())\n",
"\n",
"# ── 1. Construction du panel par asset type ───────────────────────────────\n",
"# Pour chaque compte x asset type, on agrège les features comportementales\n",
"\n",
"# Merge AUM avec flows pour avoir les flux par asset type\n",
"df_rel_m_asset = df_rel_m.copy()\n",
"df_rel_m_asset = df_rel_m_asset.merge(\n",
" df_aum[[ID_COL, ISIN_COL, \"month\", ASSET_COL]].drop_duplicates(),\n",
" on=[ID_COL, ISIN_COL, \"month\"],\n",
" how=\"left\"\n",
")\n",
"\n",
"# Panel mensuel par compte x asset type\n",
"tmp_asset = df_rel_m_asset.copy()\n",
"tmp_asset[\"isin_held_flag\"] = (tmp_asset[\"aum_qty\"] > 0).astype(int)\n",
"tmp_asset[\"isin_active_flag\"] = (tmp_asset[\"gross_flow_qty\"] > 0).astype(int)\n",
"\n",
"df_month_asset = (\n",
" tmp_asset.dropna(subset=[ASSET_COL])\n",
" .groupby([ID_COL, ASSET_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty = (\"aum_qty\", \"sum\"),\n",
" net_flow_qty = (\"net_flow_qty\", \"sum\"),\n",
" gross_flow_qty = (\"gross_flow_qty\", \"sum\"),\n",
" sub_qty = (\"sub_qty\", \"sum\"),\n",
" red_qty = (\"red_qty\", \"sum\"),\n",
" n_tx = (\"n_tx\", \"sum\"),\n",
" n_isin_held = (\"isin_held_flag\", \"sum\"),\n",
" )\n",
" .sort_values([ID_COL, ASSET_COL, \"month\"])\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"df_month_asset[\"active_month\"] = (df_month_asset[\"gross_flow_qty\"] > 0).astype(int)\n",
"df_month_asset[\"flow_direction\"] = np.where(\n",
" df_month_asset[\"gross_flow_qty\"] > 0,\n",
" df_month_asset[\"net_flow_qty\"] / df_month_asset[\"gross_flow_qty\"],\n",
" np.nan\n",
")\n",
"df_month_asset[\"sub_share\"] = np.where(\n",
" df_month_asset[\"gross_flow_qty\"] > 0,\n",
" df_month_asset[\"sub_qty\"] / df_month_asset[\"gross_flow_qty\"],\n",
" np.nan\n",
")\n",
"df_month_asset[\"aum_peak\"] = df_month_asset.groupby(\n",
" [ID_COL, ASSET_COL]\n",
")[\"aum_qty\"].cummax()\n",
"df_month_asset[\"aum_drawdown\"] = np.where(\n",
" df_month_asset[\"aum_peak\"] > 0,\n",
" 1 - df_month_asset[\"aum_qty\"] / df_month_asset[\"aum_peak\"],\n",
" np.nan\n",
")\n",
"\n",
"print(\"\\ndf_month_asset shape:\", df_month_asset.shape)\n",
"\n",
"# ── 2. Feature engineering par compte x asset type ────────────────────────\n",
"# months_since_last_tx par asset\n",
"reference_date = df_month_asset[\"month\"].max()\n",
"\n",
"last_active_asset = (\n",
" df_month_asset[df_month_asset[\"active_month\"] == 1]\n",
" .groupby([ID_COL, ASSET_COL])[\"month\"]\n",
" .max()\n",
" .reset_index(name=\"last_active_month\")\n",
")\n",
"last_active_asset[\"months_since_last_tx_asset\"] = (\n",
" (reference_date.to_period(\"M\") - last_active_asset[\"last_active_month\"].dt.to_period(\"M\"))\n",
" .apply(lambda x: x.n)\n",
")\n",
"\n",
"df_client_asset = (\n",
" df_month_asset.groupby([ID_COL, ASSET_COL], as_index=False)\n",
" .agg(\n",
" n_months = (\"month\", \"nunique\"),\n",
" n_active_months = (\"active_month\", \"sum\"),\n",
" flow_freq = (\"active_month\", \"mean\"),\n",
" aum_qty_mean = (\"aum_qty\", \"mean\"),\n",
" aum_qty_max = (\"aum_qty\", \"max\"),\n",
" aum_qty_last = (\"aum_qty\", \"last\"),\n",
" gross_flow_qty_sum = (\"gross_flow_qty\", \"sum\"),\n",
" net_flow_qty_sum = (\"net_flow_qty\", \"sum\"),\n",
" n_tx_total = (\"n_tx\", \"sum\"),\n",
" avg_n_isin_held = (\"n_isin_held\", \"mean\"),\n",
" flow_direction_mean = (\"flow_direction\", \"mean\"),\n",
" sub_share_mean = (\"sub_share\", \"mean\"),\n",
" aum_drawdown_last = (\"aum_drawdown\", \"last\"),\n",
" )\n",
")\n",
"\n",
"# Merge months_since_last_tx\n",
"df_client_asset = df_client_asset.merge(\n",
" last_active_asset[[ID_COL, ASSET_COL, \"months_since_last_tx_asset\"]],\n",
" on=[ID_COL, ASSET_COL], how=\"left\"\n",
")\n",
"max_months = df_client_asset[\"months_since_last_tx_asset\"].max()\n",
"df_client_asset[\"months_since_last_tx_asset\"] = (\n",
" df_client_asset[\"months_since_last_tx_asset\"].fillna(max_months + 1)\n",
")\n",
"\n",
"# Ratios protégés\n",
"df_client_asset[\"gross_flow_to_aum\"] = np.where(\n",
" df_client_asset[\"aum_qty_mean\"] > 1,\n",
" df_client_asset[\"gross_flow_qty_sum\"] / df_client_asset[\"aum_qty_mean\"],\n",
" np.nan\n",
")\n",
"df_client_asset[\"flow_direction_balance\"] = np.where(\n",
" df_client_asset[\"gross_flow_qty_sum\"] > 0,\n",
" df_client_asset[\"net_flow_qty_sum\"] / df_client_asset[\"gross_flow_qty_sum\"],\n",
" np.nan\n",
")\n",
"df_client_asset[\"aum_final_to_peak\"] = np.where(\n",
" df_client_asset[\"aum_qty_max\"] > 0,\n",
" np.clip(df_client_asset[\"aum_qty_last\"] / df_client_asset[\"aum_qty_max\"], 0, 1),\n",
" np.nan\n",
")\n",
"df_client_asset[\"log_aum_qty_mean\"] = np.log1p(\n",
" df_client_asset[\"aum_qty_mean\"].clip(lower=0)\n",
")\n",
"\n",
"# Filtre qualité\n",
"df_client_asset = df_client_asset[\n",
" (df_client_asset[\"n_months\"] >= 6) &\n",
" (df_client_asset[\"aum_qty_mean\"] > 0)\n",
"].copy()\n",
"\n",
"print(\"df_client_asset shape:\", df_client_asset.shape)\n",
"print(\"\\nComptes par asset type:\")\n",
"print(df_client_asset.groupby(ASSET_COL)[ID_COL].nunique().sort_values(ascending=False))\n",
"\n",
"# ── 3. Sélection des asset types avec suffisamment de comptes ─────────────\n",
"min_accounts = 50\n",
"asset_counts = df_client_asset.groupby(ASSET_COL)[ID_COL].nunique()\n",
"valid_assets = asset_counts[asset_counts >= min_accounts].index.tolist()\n",
"print(f\"\\nAsset types retenus (>= {min_accounts} comptes) : {valid_assets}\")\n",
"\n",
"# ── 4. Features pour le clustering par asset ──────────────────────────────\n",
"asset_features = [\n",
" \"flow_freq\",\n",
" \"gross_flow_to_aum\",\n",
" \"avg_n_isin_held\",\n",
" \"flow_direction_balance\",\n",
" \"log_aum_qty_mean\",\n",
" \"months_since_last_tx_asset\",\n",
" \"aum_final_to_peak\",\n",
" \"aum_drawdown_last\",\n",
"]\n",
"\n",
"# ── 5. Clustering par asset type ──────────────────────────────────────────\n",
"ASSET_RESULTS = {}\n",
"\n",
"for asset in valid_assets:\n",
" print(f\"\\n{'='*60}\")\n",
" print(f\"ASSET TYPE : {asset}\")\n",
" print(f\"{'='*60}\")\n",
"\n",
" df_a = df_client_asset[df_client_asset[ASSET_COL] == asset].copy()\n",
" feats = [c for c in asset_features if c in df_a.columns]\n",
"\n",
" # Preprocessing\n",
" d = df_a.copy()\n",
" d[\"flow_direction_balance\"] = d[\"flow_direction_balance\"].fillna(0)\n",
"\n",
" for col in [\"avg_n_isin_held\", \"months_since_last_tx_asset\",\n",
" \"aum_drawdown_last\", \"aum_final_to_peak\"]:\n",
" if col not in d.columns:\n",
" continue\n",
" vals = d[col].to_numpy(dtype=float)\n",
" med = np.nanmedian(vals)\n",
" mad = np.nanmedian(np.abs(vals - med)) * 1.4826\n",
" if mad > 0:\n",
" d[col] = np.clip(vals, med - 3*mad, med + 3*mad)\n",
" else:\n",
" d[col] = np.clip(vals, 0, np.nanpercentile(vals, 95))\n",
"\n",
" for col in [\"gross_flow_to_aum\"]:\n",
" if col not in d.columns:\n",
" continue\n",
" vals = d[col].to_numpy(dtype=float)\n",
" d[col] = np.log1p(np.clip(vals, 0, np.nanpercentile(vals, 90)))\n",
"\n",
" for col in [\"flow_freq\"]:\n",
" if col not in d.columns:\n",
" continue\n",
" vals = d[col].to_numpy(dtype=float)\n",
" d[col] = np.log1p(np.clip(vals, 0, None))\n",
"\n",
" X_a = d[feats].fillna(d[feats].median()).to_numpy()\n",
" X_a_scaled = RobustScaler().fit_transform(X_a)\n",
"\n",
" # K-selection\n",
" best_k = 2\n",
" best_sil = -1\n",
" rows_k = []\n",
" max_k = min(6, len(df_a) // 50) # K max raisonnable selon taille\n",
"\n",
" for k in range(2, max_k + 1):\n",
" km = KMeans(n_clusters=k, n_init=30, random_state=RANDOM_STATE)\n",
" labels = km.fit_predict(X_a_scaled)\n",
" sil = silhouette_score(X_a_scaled, labels)\n",
" db = davies_bouldin_score(X_a_scaled, labels)\n",
" rows_k.append({\"k\": k, \"silhouette\": round(sil, 4), \"davies_bouldin\": round(db, 4)})\n",
" if sil > best_sil:\n",
" best_sil = sil\n",
" best_k = k\n",
"\n",
" df_k = pd.DataFrame(rows_k)\n",
" print(df_k.to_string(index=False))\n",
" print(f\"→ K retenu : {best_k} (silhouette={best_sil:.4f})\")\n",
"\n",
" # Clustering final\n",
" km_final = KMeans(n_clusters=best_k, n_init=50, random_state=RANDOM_STATE)\n",
" df_a[f\"cluster_{asset.lower().replace(' ','_')}\"] = km_final.fit_predict(X_a_scaled)\n",
" cluster_col = f\"cluster_{asset.lower().replace(' ','_')}\"\n",
"\n",
" # Tailles\n",
" counts = df_a[cluster_col].value_counts().sort_index()\n",
" props = counts / counts.sum() * 100\n",
" print(pd.DataFrame({\"n_comptes\": counts, \"pct\": props.round(1)}))\n",
"\n",
" # Heatmap comportement\n",
" profile_vars_asset = [c for c in asset_features if c in df_a.columns]\n",
" prof = plot_heatmap(\n",
" df_a, profile_vars_asset, cluster_col,\n",
" title=f\"Cluster signatures — {asset} (K={best_k}, robust z-score)\",\n",
" figsize=(14, 4)\n",
" )\n",
" print(f\"\\nMédianes — {asset}:\")\n",
" print(prof.round(3).to_string())\n",
"\n",
" ASSET_RESULTS[asset] = {\n",
" \"df\": df_a,\n",
" \"cluster_col\": cluster_col,\n",
" \"k\": best_k,\n",
" \"silhouette\": best_sil,\n",
" \"profile\": prof,\n",
" }\n",
"\n",
"# ── 6. Résumé global ──────────────────────────────────────────────────────\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"RÉSUMÉ — Clustering par asset type\")\n",
"print(\"=\"*60)\n",
"for asset, res in ASSET_RESULTS.items():\n",
" print(f\" {asset:20s} : K={res['k']}, sil={res['silhouette']:.4f}, \"\n",
" f\"n={len(res['df'])}\")"
]
},
{
"cell_type": "code",
"execution_count": 207,
"id": "78b9b46c-577c-4e00-80ef-07e4aeb807be",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Colonnes disponibles : ['Registrar Account - ID', 'cluster_k4', 'cluster_alternative', 'cluster_diversified', 'cluster_equity', 'cluster_fixed_income']\n",
"Shape : (7179, 6)\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1800x1200 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"============================================================\n",
"Global x Alternative\n",
"============================================================\n",
"\n",
"% par cluster global (chaque ligne somme à 100%) :\n",
" Non exposé Asset C0 Asset C1\n",
"Global C0 91.1 1.1 7.8\n",
"Global C1 48.7 10.7 40.5\n",
"Global C2 86.5 6.2 7.3\n",
"Global C3 93.2 0.9 5.9\n",
"\n",
"% par cluster asset (chaque colonne somme à 100%) :\n",
" Non exposé Asset C0 Asset C1\n",
"Global C0 41.0 11.0 23.5\n",
"Global C1 9.5 47.9 52.7\n",
"Global C2 21.2 34.6 12.0\n",
"Global C3 28.2 6.5 11.8\n",
"\n",
"============================================================\n",
"Global x Diversified\n",
"============================================================\n",
"\n",
"% par cluster global (chaque ligne somme à 100%) :\n",
" Non exposé Asset C0 Asset C1\n",
"Global C0 64.2 33.9 1.8\n",
"Global C1 21.1 61.7 17.2\n",
"Global C2 31.6 40.4 27.9\n",
"Global C3 40.9 54.0 5.1\n",
"\n",
"% par cluster asset (chaque colonne somme à 100%) :\n",
" Non exposé Asset C0 Asset C1\n",
"Global C0 54.4 28.5 6.6\n",
"Global C1 7.8 22.5 26.7\n",
"Global C2 14.6 18.5 54.4\n",
"Global C3 23.3 30.5 12.3\n",
"\n",
"============================================================\n",
"Global x Equity\n",
"============================================================\n",
"\n",
"% par cluster global (chaque ligne somme à 100%) :\n",
" Non exposé Asset C0 Asset C1 Asset C2\n",
"Global C0 70.6 25.9 3.2 0.2\n",
"Global C1 18.7 23.1 9.7 48.6\n",
"Global C2 37.4 32.8 29.5 0.3\n",
"Global C3 44.2 49.1 6.8 0.0\n",
"\n",
"% par cluster asset (chaque colonne somme à 100%) :\n",
" Non exposé Asset C0 Asset C1 Asset C2\n",
"Global C0 54.8 29.9 11.6 0.9\n",
"Global C1 6.3 11.5 15.0 98.3\n",
"Global C2 15.8 20.6 57.2 0.9\n",
"Global C3 23.1 38.0 16.2 0.0\n",
"\n",
"============================================================\n",
"Global x Fixed Income\n",
"============================================================\n",
"\n",
"% par cluster global (chaque ligne somme à 100%) :\n",
" Non exposé Asset C0 Asset C1\n",
"Global C0 34.5 61.5 4.0\n",
"Global C1 19.3 52.9 27.9\n",
"Global C2 65.4 21.2 13.3\n",
"Global C3 72.0 24.1 3.9\n",
"\n",
"% par cluster asset (chaque colonne somme à 100%) :\n",
" Non exposé Asset C0 Asset C1\n",
"Global C0 27.2 54.8 15.5\n",
"Global C1 6.6 20.4 46.4\n",
"Global C2 28.1 10.3 28.0\n",
"Global C3 38.2 14.4 10.1\n",
"\n",
"============================================================\n",
"Adjusted Rand Index — cohérence global x asset\n",
"============================================================\n",
"(1 = identiques, 0 = aléatoire, <0 = pire qu'aléatoire)\n",
"\n",
" Alternative : ARI=0.0278 (n=1166 comptes communs)\n",
" Diversified : ARI=0.0347 (n=3980 comptes communs)\n",
" Equity : ARI=0.1587 (n=3691 comptes communs)\n",
" Fixed Income : ARI=0.1106 (n=3743 comptes communs)\n",
"\n",
"============================================================\n",
"Exposition multi-asset par cluster global\n",
"============================================================\n",
"\n",
"Nombre moyen d'asset types par cluster global :\n",
"cluster_k4\n",
"0 1.40\n",
"1 2.92\n",
"2 1.79\n",
"3 1.50\n",
"Name: n_asset_types, dtype: float64\n",
"\n",
"Distribution du nombre d'asset types par cluster global :\n",
" 0 asset(s) 1 asset(s) 2 asset(s) 3 asset(s) 4 asset(s)\n",
"Global C0 0.4 73.9 14.8 7.3 3.5\n",
"Global C1 0.8 17.5 13.6 25.0 43.1\n",
"Global C2 0.0 49.2 29.3 14.8 6.7\n",
"Global C3 0.0 64.7 23.3 9.6 2.4\n"
]
}
],
"source": [
"# ============================================================\n",
"# CROISEMENT — clustering global x clustering par asset type\n",
"# ============================================================\n",
"\n",
"# ── 1. Merge des labels asset dans dfc ────────────────────────────────────\n",
"dfc_cross = dfc[[ID_COL, \"cluster_k4\"]].copy()\n",
"\n",
"for asset, res in ASSET_RESULTS.items():\n",
" cluster_col = res[\"cluster_col\"]\n",
" df_a = res[\"df\"][[ID_COL, cluster_col]].copy()\n",
" dfc_cross = dfc_cross.merge(df_a, on=ID_COL, how=\"left\")\n",
"\n",
"print(\"Colonnes disponibles :\", dfc_cross.columns.tolist())\n",
"print(\"Shape :\", dfc_cross.shape)\n",
"\n",
"# ── 2. Tables de contingence global x asset ───────────────────────────────\n",
"fig, axes = plt.subplots(2, 2, figsize=(18, 12))\n",
"axes = axes.flatten()\n",
"\n",
"for i, (asset, res) in enumerate(ASSET_RESULTS.items()):\n",
" cluster_col = res[\"cluster_col\"]\n",
"\n",
" if cluster_col not in dfc_cross.columns:\n",
" continue\n",
"\n",
" # Table de contingence normalisée par ligne (% par cluster global)\n",
" ct = pd.crosstab(\n",
" dfc_cross[\"cluster_k4\"],\n",
" dfc_cross[cluster_col].fillna(-1).astype(int),\n",
" normalize=\"index\"\n",
" ).round(3) * 100\n",
"\n",
" # Renommer les colonnes\n",
" col_names = {\n",
" c: f\"Asset C{c}\" if c >= 0 else \"Non exposé\"\n",
" for c in ct.columns\n",
" }\n",
" ct = ct.rename(columns=col_names)\n",
" ct.index = [f\"Global C{i}\" for i in ct.index]\n",
"\n",
" sns.heatmap(\n",
" ct,\n",
" cmap=\"Blues\",\n",
" annot=True,\n",
" fmt=\".1f\",\n",
" ax=axes[i],\n",
" cbar_kws={\"label\": \"%\"},\n",
" vmin=0,\n",
" vmax=100,\n",
" )\n",
" axes[i].set_title(f\"Global x {asset} (% par cluster global)\")\n",
" axes[i].set_xlabel(f\"Cluster {asset}\")\n",
" axes[i].set_ylabel(\"Cluster Global\")\n",
"\n",
"plt.suptitle(\"Croisement Clustering Global x Clustering par Asset Type\",\n",
" fontsize=14, y=1.02)\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# ── 3. Tables de contingence détaillées ───────────────────────────────────\n",
"for asset, res in ASSET_RESULTS.items():\n",
" cluster_col = res[\"cluster_col\"]\n",
" if cluster_col not in dfc_cross.columns:\n",
" continue\n",
"\n",
" print(f\"\\n{'='*60}\")\n",
" print(f\"Global x {asset}\")\n",
" print(f\"{'='*60}\")\n",
"\n",
" # % par cluster global\n",
" ct_row = pd.crosstab(\n",
" dfc_cross[\"cluster_k4\"],\n",
" dfc_cross[cluster_col].fillna(-1).astype(int),\n",
" normalize=\"index\"\n",
" ).round(3) * 100\n",
" ct_row.index = [f\"Global C{i}\" for i in ct_row.index]\n",
" ct_row.columns = [f\"Asset C{c}\" if c >= 0 else \"Non exposé\"\n",
" for c in ct_row.columns]\n",
" print(\"\\n% par cluster global (chaque ligne somme à 100%) :\")\n",
" print(ct_row.to_string())\n",
"\n",
" # % par cluster asset\n",
" ct_col = pd.crosstab(\n",
" dfc_cross[\"cluster_k4\"],\n",
" dfc_cross[cluster_col].fillna(-1).astype(int),\n",
" normalize=\"columns\"\n",
" ).round(3) * 100\n",
" ct_col.index = [f\"Global C{i}\" for i in ct_col.index]\n",
" ct_col.columns = [f\"Asset C{c}\" if c >= 0 else \"Non exposé\"\n",
" for c in ct_col.columns]\n",
" print(\"\\n% par cluster asset (chaque colonne somme à 100%) :\")\n",
" print(ct_col.to_string())\n",
"\n",
"# ── 4. Indice de Rand Ajusté — mesure de cohérence ────────────────────────\n",
"from sklearn.metrics import adjusted_rand_score\n",
"\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"Adjusted Rand Index — cohérence global x asset\")\n",
"print(\"=\"*60)\n",
"print(\"(1 = identiques, 0 = aléatoire, <0 = pire qu'aléatoire)\\n\")\n",
"\n",
"for asset, res in ASSET_RESULTS.items():\n",
" cluster_col = res[\"cluster_col\"]\n",
" if cluster_col not in dfc_cross.columns:\n",
" continue\n",
"\n",
" # Garder seulement les comptes présents dans les deux clusterings\n",
" mask = dfc_cross[cluster_col].notna()\n",
" labels_global = dfc_cross.loc[mask, \"cluster_k4\"].values\n",
" labels_asset = dfc_cross.loc[mask, cluster_col].values\n",
"\n",
" ari = adjusted_rand_score(labels_global, labels_asset)\n",
" n_common = mask.sum()\n",
" print(f\" {asset:20s} : ARI={ari:.4f} (n={n_common} comptes communs)\")\n",
"\n",
"# ── 5. Profil des comptes présents dans plusieurs asset types ─────────────\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"Exposition multi-asset par cluster global\")\n",
"print(\"=\"*60)\n",
"\n",
"# Compter le nombre d'asset types par compte\n",
"asset_cols = [res[\"cluster_col\"] for res in ASSET_RESULTS.values()\n",
" if res[\"cluster_col\"] in dfc_cross.columns]\n",
"dfc_cross[\"n_asset_types\"] = dfc_cross[asset_cols].notna().sum(axis=1)\n",
"\n",
"print(\"\\nNombre moyen d'asset types par cluster global :\")\n",
"print(dfc_cross.groupby(\"cluster_k4\")[\"n_asset_types\"].mean().round(2))\n",
"\n",
"print(\"\\nDistribution du nombre d'asset types par cluster global :\")\n",
"ct_multi = pd.crosstab(\n",
" dfc_cross[\"cluster_k4\"],\n",
" dfc_cross[\"n_asset_types\"],\n",
" normalize=\"index\"\n",
").round(3) * 100\n",
"ct_multi.index = [f\"Global C{i}\" for i in ct_multi.index]\n",
"ct_multi.columns = [f\"{c} asset(s)\" for c in ct_multi.columns]\n",
"print(ct_multi.to_string())"
]
},
{
"cell_type": "code",
2026-04-07 20:26:19 +02:00
"execution_count": 208,
2026-04-05 17:52:42 +02:00
"id": "b78c0c1c-86ff-4b65-b592-57caf37c15a5",
"metadata": {},
2026-04-07 20:26:19 +02:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== Fonds disponibles (top 20 par AUM) ===\n",
"Product - Fund\n",
"Carmignac Patrimoine 1.838372e+12\n",
"Carmignac Sécurité 1.053010e+12\n",
"Carmignac Investissement 5.300035e+11\n",
"Carmignac Portfolio Sécurité 2.562135e+11\n",
"Carmignac Portfolio Flexible Bond 2.223185e+11\n",
"Carmignac Portfolio Patrimoine 2.035761e+11\n",
"Carmignac Emergents 1.218833e+11\n",
"Carmignac Portfolio Global Bond 1.119945e+11\n",
"Carmignac Portfolio Credit 8.413277e+10\n",
"Carmignac Court Terme 8.224046e+10\n",
"Carmignac Portfolio Emerging Patrimoine 7.216283e+10\n",
"Carmignac Portfolio Grande Europe 6.404670e+10\n",
"Carmignac Portfolio Long-Short European Equities 6.162365e+10\n",
"Carmignac Portfolio Climate Transition 5.125412e+10\n",
"Carmignac Absolute Return Europe 4.100414e+10\n",
"Carmignac Credit 2027 3.937522e+10\n",
"Carmignac Portfolio Patrimoine Europe 3.880186e+10\n",
"Carmignac Portfolio Investissement 3.643094e+10\n",
"Carmignac Investissement Latitude 3.474053e+10\n",
"Carmignac Portfolio Emergents 2.942745e+10\n",
"\n",
"Fonds retenus (15) :\n",
" Carmignac Patrimoine : 6185 clients, AUM=1838371784989\n",
" Carmignac Sécurité : 2827 clients, AUM=1053009711661\n",
" Carmignac Investissement : 4984 clients, AUM=530003536916\n",
" Carmignac Portfolio Sécurité : 1266 clients, AUM=256213510896\n",
" Carmignac Portfolio Flexible Bond : 1433 clients, AUM=222318524868\n",
" Carmignac Portfolio Patrimoine : 1236 clients, AUM=203576125394\n",
" Carmignac Emergents : 4233 clients, AUM=121883253216\n",
" Carmignac Portfolio Global Bond : 2116 clients, AUM=111994458004\n",
" Carmignac Portfolio Credit : 1137 clients, AUM=84132771053\n",
" Carmignac Court Terme : 1333 clients, AUM=82240456223\n",
" Carmignac Portfolio Emerging Patrimoine : 1675 clients, AUM=72162829525\n",
" Carmignac Portfolio Grande Europe : 2952 clients, AUM=64046699189\n",
" Carmignac Portfolio Long-Short European Equities : 691 clients, AUM=61623651990\n",
" Carmignac Portfolio Climate Transition : 3099 clients, AUM=51254115016\n",
" Carmignac Absolute Return Europe : 1990 clients, AUM=41004139345\n",
"\n",
"df_month_fund shape: (3873987, 19)\n",
"df_client_fund shape: (20740, 23)\n",
"\n",
"Comptes par fund :\n",
"Product - Fund\n",
"Carmignac Patrimoine 3155\n",
"Carmignac Investissement 2194\n",
"Carmignac Emergents 1781\n",
"Carmignac Portfolio Global Bond 1718\n",
"Carmignac Sécurité 1624\n",
"Carmignac Portfolio Grande Europe 1388\n",
"Carmignac Portfolio Climate Transition 1280\n",
"Carmignac Portfolio Sécurité 1163\n",
"Carmignac Portfolio Patrimoine 1145\n",
"Carmignac Portfolio Emerging Patrimoine 1137\n",
"Carmignac Portfolio Flexible Bond 1089\n",
"Carmignac Portfolio Credit 1018\n",
"Carmignac Absolute Return Europe 914\n",
"Carmignac Portfolio Long-Short European Equities 607\n",
"Carmignac Court Terme 527\n",
"Name: Registrar Account - ID, dtype: int64\n",
"\n",
"============================================================\n",
"FUND : Carmignac Patrimoine\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.5061 0.8297 656\n",
" 3 0.4952 0.8445 571\n",
" 4 0.3581 1.2279 337\n",
" 5 0.3858 1.1136 276\n",
" 6 0.3209 1.1835 271\n",
"→ K retenu : 2 (silhouette=0.5061)\n",
" n_comptes pct\n",
"cluster_carmignac_patrimoine \n",
"0 2499 79.2\n",
"1 656 20.8\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Patrimoine:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_patrimoine \n",
"0 0.038 2.322 0.65 -0.879 4.746 83.0 0.000 1.000 0.0\n",
"1 0.085 0.127 1.00 -1.000 4.749 12.0 0.907 0.093 0.0\n",
"\n",
"============================================================\n",
"FUND : Carmignac Sécurité\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.5716 0.8488 309\n",
" 3 0.5540 0.8263 192\n",
" 4 0.4157 0.9818 96\n",
" 5 0.3493 1.1866 84\n",
" 6 0.2712 1.1710 81\n",
"→ K retenu : 2 (silhouette=0.5716)\n",
" n_comptes pct\n",
"cluster_carmignac_sécurité \n",
"0 309 19.0\n",
"1 1315 81.0\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Sécurité:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_sécurité \n",
"0 0.111 1.312 1.000 0.067 5.183 12.0 0.916 0.084 0.0\n",
"1 0.071 4.444 0.525 -0.128 4.721 81.0 0.000 1.000 0.0\n",
"\n",
"============================================================\n",
"FUND : Carmignac Investissement\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.4310 1.0214 381\n",
" 3 0.4221 0.9168 363\n",
" 4 0.2891 1.2951 314\n",
" 5 0.3039 1.2235 216\n",
" 6 0.2898 1.2247 209\n",
"→ K retenu : 2 (silhouette=0.4310)\n",
" n_comptes pct\n",
"cluster_carmignac_investissement \n",
"0 381 17.4\n",
"1 1813 82.6\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Investissement:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_investissement \n",
"0 0.531 1.860 1.352 -0.468 7.592 0.0 0.123 0.877 0.029\n",
"1 0.025 1.067 0.738 -1.000 3.244 92.0 0.000 1.000 0.000\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Sécurité\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.7984 0.3113 180\n",
" 3 0.6484 0.4722 180\n",
" 4 0.6613 0.5310 61\n",
" 5 0.7217 0.4865 61\n",
" 6 0.7004 0.6374 44\n",
"→ K retenu : 2 (silhouette=0.7984)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_sécurité \n",
"0 983 84.5\n",
"1 180 15.5\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Sécurité:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_sécurité \n",
"0 0.061 7.229 0.419 0.000 6.053 72.0 0.0 1.0 0.0\n",
"1 0.221 1.468 1.000 0.333 8.488 0.0 1.0 0.0 0.0\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Flexible Bond\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.6092 0.7548 121\n",
" 3 0.3969 1.1736 75\n",
" 4 0.4146 0.9109 34\n",
" 5 0.3019 1.1830 33\n",
" 6 0.2697 1.1367 31\n",
"→ K retenu : 2 (silhouette=0.6092)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_flexible_b \n",
"0 968 88.9\n",
"1 121 11.1\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Flexible Bond:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_flexible_b \n",
"0 0.060 3.924 0.568 -0.155 3.991 81.0 0.000 1.000 0.000\n",
"1 0.738 6.069 1.662 0.121 7.714 0.0 0.683 0.317 0.085\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Patrimoine\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.5967 0.5513 251\n",
" 3 0.6520 0.5031 170\n",
" 4 0.6269 0.5990 99\n",
" 5 0.6057 0.7167 74\n",
" 6 0.5876 0.7718 66\n",
"→ K retenu : 3 (silhouette=0.6520)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_patrimoine \n",
"0 238 20.8\n",
"1 737 64.4\n",
"2 170 14.8\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Patrimoine:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_patrimoine \n",
"0 0.040 3.795 0.377 -1.0 5.872 48.0 0.0 1.0 0.0\n",
"1 0.057 5.614 0.460 0.0 5.617 80.0 0.0 1.0 0.0\n",
"2 0.041 4.016 0.463 1.0 5.188 90.0 0.0 1.0 0.0\n",
"\n",
"============================================================\n",
"FUND : Carmignac Emergents\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.4352 1.1739 509\n",
" 3 0.4730 0.8737 290\n",
" 4 0.4057 1.0207 140\n",
" 5 0.2767 1.3023 136\n",
" 6 0.2953 1.2352 136\n",
"→ K retenu : 3 (silhouette=0.4730)\n",
" n_comptes pct\n",
"cluster_carmignac_emergents \n",
"0 347 19.5\n",
"1 1144 64.2\n",
"2 290 16.3\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Emergents:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_emergents \n",
"0 0.070 0.027 1.000 -0.926 3.372 12.0 0.907 0.093 0.000\n",
"1 0.018 2.101 0.425 -1.000 2.718 95.0 0.000 1.000 0.000\n",
"2 0.546 2.578 1.000 -0.168 7.015 0.0 0.181 0.819 0.028\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Global Bond\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.7574 0.3756 294\n",
" 3 0.8455 0.2185 202\n",
" 4 0.8210 0.3162 97\n",
" 5 0.8302 0.3349 94\n",
" 6 0.8284 0.3586 60\n",
"→ K retenu : 3 (silhouette=0.8455)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_global_bon \n",
"0 1246 72.5\n",
"1 270 15.7\n",
"2 202 11.8\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Global Bond:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_global_bon \n",
"0 0.073 7.429 0.375 0.000 4.430 69.0 0.0 1.0 0.0\n",
"1 0.042 3.957 0.432 -1.000 4.338 60.0 0.0 1.0 0.0\n",
"2 0.066 3.125 0.570 0.955 4.353 71.0 0.0 1.0 0.0\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Credit\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.7006 0.5926 107\n",
" 3 0.5971 0.6643 36\n",
" 4 0.4844 0.7914 17\n",
" 5 0.2672 1.1071 17\n",
" 6 0.2958 1.0764 17\n",
"→ K retenu : 2 (silhouette=0.7006)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_credit \n",
"0 107 10.5\n",
"1 911 89.5\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Credit:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_credit \n",
"0 0.821 4.720 1.113 0.586 9.596 0.0 1.0 0.0 0.123\n",
"1 0.069 2.483 0.684 0.098 5.963 25.0 0.0 1.0 0.000\n",
"\n",
"============================================================\n",
"FUND : Carmignac Court Terme\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.4719 0.9125 113\n",
" 3 0.4108 0.8594 109\n",
" 4 0.3615 1.1596 108\n",
" 5 0.3464 1.2502 57\n",
" 6 0.3987 1.0217 35\n",
"→ K retenu : 2 (silhouette=0.4719)\n",
" n_comptes pct\n",
"cluster_carmignac_court_terme \n",
"0 414 78.6\n",
"1 113 21.4\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABNwAAAGGCAYAAACg4ZwmAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA901JREFUeJzs3Xdcjf3/B/DXOS3SUpGEkJJRZITidovb3pvbKpvsmZERsneRFbJJbiPjtld2drnJXmloKRrn+v3h1/l2VJyOcup4PR+P83jU57quz/W+uvqc8T6fIRIEQQARERERERERERHlCrGyAyAiIiIiIiIiIlIlTLgRERERERERERHlIibciIiIiIiIiIiIchETbkRERERERERERLmICTciIiIiIiIiIqJcxIQbERERERERERFRLmLCjYiIiIiIiIiIKBcx4UZERERERERERJSLmHAjIiIiIiIiIiLKRUy4ERH9hpycnDB58mRlh/FL7d+/HxUrVsTr16+VHQqpkFWrVqFixYrKDoN+QwMHDsS0adOUHUaWUlJS0LBhQ2zfvl3ZoRARESmNurIDICKi3PPy5Uts2LABly5dwocPH6ChoQErKyu0aNEC3bp1Q6FChfI8hqSkJGzYsAH29vaoU6dOnp+voDp06BCioqLQr18/ZYeS73z58gU7d+7EkSNH8PTpUyQnJ6NkyZJwdHRE7969Ua5cOWWHqLLyw3PIt27duoVLly6hb9++0NPTy3a/q1evok+fPnLV+ejRo9wKTylu3ryJS5cu4ejRo9Ky9OtfsWIFmjdvLi1PTk7GiBEjcO7cOcyZMwedO3dW+LxBQUE4ePAgbt26hffv38PY2Bh169bFqFGjULx4cel+GhoacHZ2xtq1a9G5c2doaWkpfE4iIqKCigk3IiIVcfbsWYwaNQqamppo164drKyskJKSgps3b2LRokV48uQJPDw88jyOpKQkrF69Gq6urvkq4dauXTu0atUKmpqayg4FAHD48GE8fvyYCbdvREdHY8CAAXjw4AEaNWqE1q1bQ1tbG8+ePUNgYCD27NmD+/fvKztMqaFDh2LQoEHKDiNX5JfnkG8FBwdj9erV6NChw3cTbhYWFli4cKFM2dKlS6GtrY0hQ4bkdZi/1MaNG1GvXj2Ym5t/d7+UlBSMHDkS586dg4eHx08l2wBg0aJFiI2NRfPmzVG2bFm8evUK27Ztw9mzZ3HgwAEUK1ZMum/Hjh2xePFiHDp06KfPS0REVBAx4UZEpAJevXqFMWPGoGTJktiyZYtMT4O///4bL168wNmzZ5UXYC5ITEyEtra2wserqalBTU0tFyPKn5KSklC4cGFlh6EwNzc3hISEYOXKlWjWrJnMttGjR2PZsmW5cp6f/X9Kp66uDnX1gv92Kj8+h+T0HhkbG6Ndu3YyZevXr0fRokUzlSsiNTUVEolE6Un7qKgonDt3DjNnzvzufikpKRg9ejTOnj2L2bNno0uXLj99bjc3N9SsWRNi8f9mpWnQoAF69eqFbdu2YcyYMdJyPT091K9fHwEBAUy4ERHRb4lzuBERqYANGzYgMTERc+fOlfmgnM7c3Bx9+/bN9vjs5qHKat6ze/fuoX///qhTpw5sbW3h5OQENzc3AMDr169Rr149AMDq1atRsWJFVKxYEatWrZIeHxYWhpEjR8Le3h42Njbo2LEjTp06leV5r127hpkzZ6JevXpo2LDhd/8Gfn5+aNWqFapVq4batWujY8eOOHTo0HevRSKRYNWqVahfvz6qVauG3r1748mTJ5nmuEs/9ubNm/D09ETdunVRvXp1DB8+HNHR0TJxnDx5EoMGDUL9+vVRtWpVNGnSBF5eXkhLS5Pu07t3b5w9exZv3ryR/o2cnJyyjRP4OlysYsWKuHr1qkw9rVu3xv379/H333+jWrVqWLp0KYCvw8hWrlyJv/76C1WrVkXDhg2xcOFCJCcny9R76dIl9OjRA7Vq1YKdnR2aNWsmreNXu3PnDs6ePYvOnTtnSrYBgKamJiZNmiT9PTQ0FJMnT0bjxo1hY2MDR0dHuLm54ePHjzLHpf9/P3nyBOPGjUPt2rXRs2dPAF/nMxw8eDCuXr2Kjh07wtbWFm3atJH+nU+cOIE2bdpI/1cfPnyYZd0Zff78GXPmzEGdOnVgZ2eHIUOGIDw8PFNbSD/2xYsXmDx5MmrVqoWaNWvCzc0NSUlJMnX6+/ujT58+qFevHqpWrYqWLVtix44dWf4dz507h169esHOzg41atRAp06dZNpCVnL6HJKamgovLy80adIEVatWhZOTE5YuXZrp/+vba06XXRv7ts2vWrVK2mutcePG0vbyM3MxxsXFYe7cuWjYsCGqVq2Kv/76C+vWrYNEIpHu8/r1a1SsWBEbN27E5s2b0aRJE9jY2CAsLEx63549e4bx48ejZs2aqFu3LpYvXw5BEPDu3TsMHToUNWrUgKOjIzZt2pQpBnnbZ1bOnj2L1NRUODg4ZLtPamoqxo4di1OnTmHmzJno2rWrYn+sb9SuXVsm2ZZeZmBggKdPn2ba38HBATdv3kRMTEyunJ+IiKggKfhfyRIREc6cOYPSpUujRo0aeXqeqKgo9O/fH0WLFsWgQYOgp6eH169f499//wUAGBoaYubMmZg5cyb++usv/PXXXwAgTUg8fvwYPXr0gImJCQYOHAhtbW0cPXoUw4cPx6pVq6T7p5s1axYMDQ0xfPhwJCYmZhvXnj17MGfOHDRr1gx9+vTBly9f8OjRI9y5cwdt2rTJ9rglS5Zgw4YNaNSoERo0aIDQ0FD0798fX758yXL/OXPmQE9PD66urnjz5g22bNmC2bNnY/ny5dJ9AgICoK2tDWdnZ2hra+PKlStYuXIlEhISpMmiIUOGID4+Hu/fv5cmK4sUKfKDv37WYmJiMHDgQLRq1Qpt27aFkZERJBIJhg4dips3b6Jr166wsLDAf//9hy1btuD58+fw9vYG8PV+DB48GBUrVsTIkSOhqamJFy9e4NatWwrF8rNOnz4NAHL3Rrp8+TJevXqFjh07olixYnj8+DH27NmDJ0+eYM+ePRCJRDL7jxo1Cubm5hgzZgwEQZCWv3jxAuPGjUP37t3Rtm1bbNq0CUOGDMGsWbOwbNky9OjRAwCwbt06jB49GseOHcuUdMho8uTJOHr0KNq1a4dq1arh+vXr3x12Onr0aJQqVQpjx47Fw4cPsXfvXhgaGmLChAnSfXbu3AlLS0s4OTlBXV0dZ86cwaxZsyAIAv7++2/pfvv378eUKVNgaWmJwYMHQ1dXFyEhIbhw4cJ320JOn0OmTZuGgIAANGvWDM7Ozrh79y58fHwQFhYGLy8vuerIyrdt/o8//sDz589x+PBhuLm5oWjRogC+PtcoIikpCb169UJ4eDi6d+8OU1NTBAcHY+nSpYiIiMDUqVNl9t+/fz++fPmCrl27QlNTE/r6+tJtY8aMgYWFBcaNG4dz585hzZo1MDAwwK5du1C3bl2MHz8ehw4dwoIFC2BjY4PatWsDgNztMzvBwcEwMDCAmZlZltvT0tIwduxY/Pvvv3B3d0f37t0z7ZOSkoL4+Hi5/mYGBgbf/X//9OkTPn36JL03GVWpUgWCICA4OBiNGjWS63xERESqggk3IqICLiEhAeHh4WjcuHGenys4OBixsbHYuHEjbGxspOXpw4i0tbXRrFkzzJw5ExUrVsyUOJk7dy5MTU3h7+8vHZbVs2dP9OjRA4sXL86UcNPX18fmzZt/OBT07NmzsLS0xMqVK+W+lsjISGnPlYwJgtWrV2fZIwf4+sFz06ZN0kSORCKBn58f4uPjoaurC+BrEi/jxPI9evSAu7s7du7ciTFjxkBTUxOOjo7YunUr4uLifnqoW0REBGbNmiXzofqff/7B5cuX4efnh1q1aknLLS0tMWPGDNy6dQs1atTApUuXkJKSgvXr1yucwMhNYWFhAAArKyu59u/ZsydcXFxkyqpXr46xY8fi5s2bMtcOANbW1liyZEmmep49e4Zdu3bBzs4OAFChQgX0798f06dPx9GjR1GyZEkAX/8f3d3dcf369WznJ3z
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Court Terme:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_court_terme \n",
"0 0.053 7.457 0.335 -0.023 1.945 87.0 0.00 1.00 0.000\n",
"1 0.254 5.379 1.000 0.286 4.609 0.0 0.89 0.11 0.008\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Emerging Patrimoine\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.5594 1.0477 252\n",
" 3 0.6004 0.7581 133\n",
" 4 0.4796 0.9787 67\n",
" 5 0.4237 1.1134 30\n",
" 6 0.4481 1.0397 35\n",
"→ K retenu : 3 (silhouette=0.6004)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_emerging_p \n",
"0 852 74.9\n",
"1 152 13.4\n",
"2 133 11.7\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Emerging Patrimoine:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_emerging_p \n",
"0 0.038 3.156 0.500 -0.613 5.213 83.0 0.000 1.000 0.000\n",
"1 0.037 0.283 1.000 -0.142 5.668 29.0 0.926 0.074 0.000\n",
"2 0.714 4.279 2.212 -0.115 9.002 0.0 0.218 0.782 0.063\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Grande Europe\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.3724 1.1306 232\n",
" 3 0.3474 1.1547 218\n",
" 4 0.2732 1.3479 165\n",
" 5 0.2529 1.3471 147\n",
" 6 0.2597 1.3180 118\n",
"→ K retenu : 2 (silhouette=0.3724)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_grande_eur \n",
"0 232 16.7\n",
"1 1156 83.3\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Grande Europe:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_grande_eur \n",
"0 0.519 4.254 1.000 -0.034 7.587 0.0 0.398 0.602 0.034\n",
"1 0.029 1.860 0.593 -0.524 4.094 81.0 0.000 1.000 0.000\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Long-Short European Equities\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.3490 1.3508 188\n",
" 3 0.3819 1.0101 118\n",
" 4 0.3969 0.9225 54\n",
" 5 0.3524 1.0438 54\n",
" 6 0.3727 1.0432 46\n",
"→ K retenu : 4 (silhouette=0.3969)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_long-short \n",
"0 111 18.3\n",
"1 335 55.2\n",
"2 107 17.6\n",
"3 54 8.9\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Long-Short European Equities:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_long-short \n",
"0 0.056 3.823 0.412 -1.000 4.991 29.0 0.0 1.0 0.0\n",
"1 0.114 6.651 0.635 0.000 6.755 32.0 0.0 1.0 0.0\n",
"2 0.125 1.688 1.000 0.537 7.833 3.0 1.0 0.0 0.0\n",
"3 0.041 4.727 0.348 1.000 5.110 56.0 0.0 1.0 0.0\n",
"\n",
"============================================================\n",
"FUND : Carmignac Portfolio Climate Transition\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.6659 0.6413 156\n",
" 3 0.5623 0.7455 57\n",
" 4 0.3956 1.0145 37\n",
" 5 0.2261 1.2444 37\n",
" 6 0.2237 1.2368 18\n",
"→ K retenu : 2 (silhouette=0.6659)\n",
" n_comptes pct\n",
"cluster_carmignac_portfolio_climate_tr \n",
"0 156 12.2\n",
"1 1124 87.8\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Portfolio Climate Transition:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_portfolio_climate_tr \n",
"0 0.679 5.287 0.962 -0.049 7.379 7.0 0.0 1.0 0.044\n",
"1 0.025 1.496 0.584 -0.792 4.221 92.0 0.0 1.0 0.000\n",
"\n",
"============================================================\n",
"FUND : Carmignac Absolute Return Europe\n",
"============================================================\n",
" k silhouette davies_bouldin min_cluster_size\n",
" 2 0.4593 0.9283 146\n",
" 3 0.5112 0.7321 122\n",
" 4 0.4436 0.9496 72\n",
" 5 0.4150 1.0153 42\n",
" 6 0.3870 1.0866 28\n",
"→ K retenu : 3 (silhouette=0.5112)\n",
" n_comptes pct\n",
"cluster_carmignac_absolute_return_euro \n",
"0 180 19.7\n",
"1 122 13.3\n",
"2 612 67.0\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Médianes — Carmignac Absolute Return Europe:\n",
" flow_freq gross_flow_to_aum avg_n_isin_held flow_direction_balance log_aum_qty_mean months_since_last_tx_fund aum_final_to_peak aum_drawdown_last buy_on_perf_rate\n",
"cluster_carmignac_absolute_return_euro \n",
"0 0.052 0.298 1.000 -0.882 4.301 16.5 0.907 0.093 0.000\n",
"1 0.532 5.635 0.973 -0.020 7.317 35.5 0.000 1.000 0.047\n",
"2 0.038 2.433 0.488 -0.539 4.221 81.0 0.000 1.000 0.000\n",
"\n",
"============================================================\n",
"RÉSUMÉ — Clustering par fund\n",
"============================================================\n",
" Carmignac Patrimoine : K=2, sil=0.5061, n=3155\n",
" Carmignac Sécurité : K=2, sil=0.5716, n=1624\n",
" Carmignac Investissement : K=2, sil=0.4310, n=2194\n",
" Carmignac Portfolio Sécurité : K=2, sil=0.7984, n=1163\n",
" Carmignac Portfolio Flexible Bond : K=2, sil=0.6092, n=1089\n",
" Carmignac Portfolio Patrimoine : K=3, sil=0.6520, n=1145\n",
" Carmignac Emergents : K=3, sil=0.4730, n=1781\n",
" Carmignac Portfolio Global Bond : K=3, sil=0.8455, n=1718\n",
" Carmignac Portfolio Credit : K=2, sil=0.7006, n=1018\n",
" Carmignac Court Terme : K=2, sil=0.4719, n=527\n",
" Carmignac Portfolio Emerging Patrimoine : K=3, sil=0.6004, n=1137\n",
" Carmignac Portfolio Grande Europe : K=2, sil=0.3724, n=1388\n",
" Carmignac Portfolio Long-Short European : K=4, sil=0.3969, n=607\n",
" Carmignac Portfolio Climate Transition : K=2, sil=0.6659, n=1280\n",
" Carmignac Absolute Return Europe : K=3, sil=0.5112, n=914\n",
"\n",
"============================================================\n",
"ARI — cohérence clustering global x fund\n",
"============================================================\n",
" Carmignac Patrimoine : ARI=0.0241 (n=3002)\n",
" Carmignac Sécurité : ARI=0.0121 (n=1478)\n",
" Carmignac Investissement : ARI=0.0427 (n=2055)\n",
" Carmignac Portfolio Sécurité : ARI=0.0813 (n=1047)\n",
" Carmignac Portfolio Flexible Bond : ARI=-0.0456 (n=945)\n",
" Carmignac Portfolio Patrimoine : ARI=0.0116 (n=1030)\n",
" Carmignac Emergents : ARI=0.0158 (n=1641)\n",
" Carmignac Portfolio Global Bond : ARI=0.0797 (n=1585)\n",
" Carmignac Portfolio Credit : ARI=0.0087 (n=902)\n",
" Carmignac Court Terme : ARI=-0.0346 (n=424)\n",
" Carmignac Portfolio Emerging Patrimoine : ARI=-0.0337 (n=998)\n",
" Carmignac Portfolio Grande Europe : ARI=-0.0266 (n=1248)\n",
" Carmignac Portfolio Long-Short European : ARI=0.0524 (n=496)\n",
" Carmignac Portfolio Climate Transition : ARI=-0.0461 (n=1143)\n",
" Carmignac Absolute Return Europe : ARI=-0.0319 (n=788)\n"
]
}
],
"source": [
"# ============================================================\n",
"# CLUSTERING PAR FUND FAMILY (top fonds)\n",
"# ============================================================\n",
"\n",
"# ── 0. Vérification des fonds disponibles ─────────────────────────────────\n",
"print(\"=== Fonds disponibles (top 20 par AUM) ===\")\n",
"top_funds_aum = (\n",
" df_aum.groupby(FUND_COL)[AUM_VAL_COL].sum()\n",
" .sort_values(ascending=False)\n",
" .head(20)\n",
")\n",
"print(top_funds_aum.to_string())\n",
"\n",
"# ── 1. Sélection des top fonds ────────────────────────────────────────────\n",
"# Critères : top 15 par AUM total + au moins 20 clients\n",
"min_clients_per_fund = 20\n",
"n_top_funds = 15\n",
"\n",
"aum_by_fund_total = df_aum.groupby(FUND_COL)[AUM_VAL_COL].sum()\n",
"clients_by_fund = df_aum.groupby(FUND_COL)[ID_COL].nunique()\n",
"\n",
"valid_funds = (\n",
" aum_by_fund_total[\n",
" clients_by_fund >= min_clients_per_fund\n",
" ]\n",
" .sort_values(ascending=False)\n",
" .head(n_top_funds)\n",
" .index.tolist()\n",
")\n",
"print(f\"\\nFonds retenus ({len(valid_funds)}) :\")\n",
"for f in valid_funds:\n",
" print(f\" {f} : {clients_by_fund[f]} clients, AUM={aum_by_fund_total[f]:.0f}\")\n",
"\n",
"# ── 2. Construction du panel par compte x fund ────────────────────────────\n",
"df_rel_m_fund = df_rel_m.copy()\n",
"df_rel_m_fund = df_rel_m_fund.merge(\n",
" df_aum[[ID_COL, ISIN_COL, \"month\", FUND_COL]].drop_duplicates(),\n",
" on=[ID_COL, ISIN_COL, \"month\"],\n",
" how=\"left\"\n",
")\n",
"\n",
"# Panel mensuel par compte x fund\n",
"tmp_fund = df_rel_m_fund.copy()\n",
"tmp_fund[\"isin_held_flag\"] = (tmp_fund[\"aum_qty\"] > 0).astype(int)\n",
"tmp_fund[\"isin_active_flag\"] = (tmp_fund[\"gross_flow_qty\"] > 0).astype(int)\n",
"\n",
"df_month_fund = (\n",
" tmp_fund.dropna(subset=[FUND_COL])\n",
" .groupby([ID_COL, FUND_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty = (\"aum_qty\", \"sum\"),\n",
" net_flow_qty = (\"net_flow_qty\", \"sum\"),\n",
" gross_flow_qty = (\"gross_flow_qty\", \"sum\"),\n",
" sub_qty = (\"sub_qty\", \"sum\"),\n",
" red_qty = (\"red_qty\", \"sum\"),\n",
" n_tx = (\"n_tx\", \"sum\"),\n",
" n_isin_held = (\"isin_held_flag\", \"sum\"),\n",
" ret_fund_m = (\"ret_fund_m\", \"mean\"),\n",
" )\n",
" .sort_values([ID_COL, FUND_COL, \"month\"])\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"df_month_fund[\"active_month\"] = (df_month_fund[\"gross_flow_qty\"] > 0).astype(int)\n",
"df_month_fund[\"flow_direction\"] = np.where(\n",
" df_month_fund[\"gross_flow_qty\"] > 0,\n",
" df_month_fund[\"net_flow_qty\"] / df_month_fund[\"gross_flow_qty\"],\n",
" np.nan\n",
")\n",
"df_month_fund[\"sub_share\"] = np.where(\n",
" df_month_fund[\"gross_flow_qty\"] > 0,\n",
" df_month_fund[\"sub_qty\"] / df_month_fund[\"gross_flow_qty\"],\n",
" np.nan\n",
")\n",
"df_month_fund[\"aum_peak\"] = df_month_fund.groupby(\n",
" [ID_COL, FUND_COL]\n",
")[\"aum_qty\"].cummax()\n",
"df_month_fund[\"aum_drawdown\"] = np.where(\n",
" df_month_fund[\"aum_peak\"] > 0,\n",
" 1 - df_month_fund[\"aum_qty\"] / df_month_fund[\"aum_peak\"],\n",
" np.nan\n",
")\n",
"\n",
"# Lag performance pour réactivité\n",
"df_month_fund[\"ret_fund_lag1\"] = df_month_fund.groupby(\n",
" [ID_COL, FUND_COL]\n",
")[\"ret_fund_m\"].shift(1)\n",
"df_month_fund[\"buy_on_perf\"] = (\n",
" (df_month_fund[\"net_flow_qty\"] > 0) &\n",
" (df_month_fund[\"ret_fund_lag1\"] > 0)\n",
").astype(int)\n",
"df_month_fund[\"has_perf\"] = df_month_fund[\"ret_fund_lag1\"].notna().astype(int)\n",
"\n",
"print(\"\\ndf_month_fund shape:\", df_month_fund.shape)\n",
"\n",
"# ── 3. Feature engineering par compte x fund ──────────────────────────────\n",
"reference_date = df_month_fund[\"month\"].max()\n",
"\n",
"# months_since_last_tx par fund\n",
"last_active_fund = (\n",
" df_month_fund[df_month_fund[\"active_month\"] == 1]\n",
" .groupby([ID_COL, FUND_COL])[\"month\"]\n",
" .max()\n",
" .reset_index(name=\"last_active_month\")\n",
")\n",
"last_active_fund[\"months_since_last_tx_fund\"] = (\n",
" (reference_date.to_period(\"M\") -\n",
" last_active_fund[\"last_active_month\"].dt.to_period(\"M\"))\n",
" .apply(lambda x: x.n)\n",
")\n",
"\n",
"df_client_fund = (\n",
" df_month_fund.groupby([ID_COL, FUND_COL], as_index=False)\n",
" .agg(\n",
" n_months = (\"month\", \"nunique\"),\n",
" n_active_months = (\"active_month\", \"sum\"),\n",
" flow_freq = (\"active_month\", \"mean\"),\n",
" aum_qty_mean = (\"aum_qty\", \"mean\"),\n",
" aum_qty_max = (\"aum_qty\", \"max\"),\n",
" aum_qty_last = (\"aum_qty\", \"last\"),\n",
" gross_flow_qty_sum = (\"gross_flow_qty\", \"sum\"),\n",
" net_flow_qty_sum = (\"net_flow_qty\", \"sum\"),\n",
" n_tx_total = (\"n_tx\", \"sum\"),\n",
" avg_n_isin_held = (\"n_isin_held\", \"mean\"),\n",
" flow_direction_mean = (\"flow_direction\", \"mean\"),\n",
" sub_share_mean = (\"sub_share\", \"mean\"),\n",
" aum_drawdown_last = (\"aum_drawdown\", \"last\"),\n",
" buy_on_perf_months = (\"buy_on_perf\", \"sum\"),\n",
" has_perf_months = (\"has_perf\", \"sum\"),\n",
" )\n",
")\n",
"\n",
"# Merge months_since_last_tx\n",
"df_client_fund = df_client_fund.merge(\n",
" last_active_fund[[ID_COL, FUND_COL, \"months_since_last_tx_fund\"]],\n",
" on=[ID_COL, FUND_COL], how=\"left\"\n",
")\n",
"max_months = df_client_fund[\"months_since_last_tx_fund\"].max()\n",
"df_client_fund[\"months_since_last_tx_fund\"] = (\n",
" df_client_fund[\"months_since_last_tx_fund\"].fillna(max_months + 1)\n",
")\n",
"\n",
"# Ratios protégés\n",
"df_client_fund[\"gross_flow_to_aum\"] = np.where(\n",
" df_client_fund[\"aum_qty_mean\"] > 1,\n",
" df_client_fund[\"gross_flow_qty_sum\"] / df_client_fund[\"aum_qty_mean\"],\n",
" np.nan\n",
")\n",
"df_client_fund[\"flow_direction_balance\"] = np.where(\n",
" df_client_fund[\"gross_flow_qty_sum\"] > 0,\n",
" df_client_fund[\"net_flow_qty_sum\"] / df_client_fund[\"gross_flow_qty_sum\"],\n",
" np.nan\n",
")\n",
"df_client_fund[\"aum_final_to_peak\"] = np.where(\n",
" df_client_fund[\"aum_qty_max\"] > 0,\n",
" np.clip(df_client_fund[\"aum_qty_last\"] / df_client_fund[\"aum_qty_max\"], 0, 1),\n",
" np.nan\n",
")\n",
"df_client_fund[\"log_aum_qty_mean\"] = np.log1p(\n",
" df_client_fund[\"aum_qty_mean\"].clip(lower=0)\n",
")\n",
"df_client_fund[\"buy_on_perf_rate\"] = np.where(\n",
" df_client_fund[\"has_perf_months\"] >= 6,\n",
" df_client_fund[\"buy_on_perf_months\"] / df_client_fund[\"has_perf_months\"],\n",
" np.nan\n",
")\n",
"\n",
"# Filtre qualité\n",
"df_client_fund = df_client_fund[\n",
" (df_client_fund[\"n_months\"] >= 6) &\n",
" (df_client_fund[\"aum_qty_mean\"] > 0) &\n",
" (df_client_fund[FUND_COL].isin(valid_funds))\n",
"].copy()\n",
"\n",
"print(\"df_client_fund shape:\", df_client_fund.shape)\n",
"print(\"\\nComptes par fund :\")\n",
"print(df_client_fund.groupby(FUND_COL)[ID_COL].nunique().sort_values(ascending=False))\n",
"\n",
"# ── 4. Features pour le clustering par fund ───────────────────────────────\n",
"fund_features = [\n",
" \"flow_freq\",\n",
" \"gross_flow_to_aum\",\n",
" \"avg_n_isin_held\",\n",
" \"flow_direction_balance\",\n",
" \"log_aum_qty_mean\",\n",
" \"months_since_last_tx_fund\",\n",
" \"aum_final_to_peak\",\n",
" \"aum_drawdown_last\",\n",
" \"buy_on_perf_rate\",\n",
"]\n",
"\n",
"# ── 5. Clustering par fund ────────────────────────────────────────────────\n",
"FUND_RESULTS = {}\n",
"\n",
"for fund in valid_funds:\n",
" print(f\"\\n{'='*60}\")\n",
" print(f\"FUND : {fund}\")\n",
" print(f\"{'='*60}\")\n",
"\n",
" df_f = df_client_fund[df_client_fund[FUND_COL] == fund].copy()\n",
" feats = [c for c in fund_features if c in df_f.columns]\n",
"\n",
" # Preprocessing\n",
" d = df_f.copy()\n",
" d[\"flow_direction_balance\"] = d[\"flow_direction_balance\"].fillna(0)\n",
" d[\"buy_on_perf_rate\"] = d[\"buy_on_perf_rate\"].fillna(0)\n",
"\n",
" for col in [\"avg_n_isin_held\", \"months_since_last_tx_fund\",\n",
" \"aum_drawdown_last\", \"aum_final_to_peak\"]:\n",
" if col not in d.columns:\n",
" continue\n",
" vals = d[col].to_numpy(dtype=float)\n",
" med = np.nanmedian(vals)\n",
" mad = np.nanmedian(np.abs(vals - med)) * 1.4826\n",
" if mad > 0:\n",
" d[col] = np.clip(vals, med - 3*mad, med + 3*mad)\n",
" else:\n",
" d[col] = np.clip(vals, 0, np.nanpercentile(vals, 95))\n",
"\n",
" for col in [\"gross_flow_to_aum\"]:\n",
" if col not in d.columns:\n",
" continue\n",
" vals = d[col].to_numpy(dtype=float)\n",
" d[col] = np.log1p(np.clip(vals, 0, np.nanpercentile(vals, 90)))\n",
"\n",
" for col in [\"flow_freq\"]:\n",
" if col not in d.columns:\n",
" continue\n",
" vals = d[col].to_numpy(dtype=float)\n",
" d[col] = np.log1p(np.clip(vals, 0, None))\n",
"\n",
" X_f = d[feats].fillna(d[feats].median()).to_numpy()\n",
" X_f_scaled = RobustScaler().fit_transform(X_f)\n",
"\n",
" # K-selection\n",
" best_k = 2\n",
" best_sil = -1\n",
" rows_k = []\n",
" max_k = min(6, len(df_f) // 20)\n",
"\n",
" for k in range(2, max_k + 1):\n",
" km = KMeans(n_clusters=k, n_init=30, random_state=RANDOM_STATE)\n",
" labels = km.fit_predict(X_f_scaled)\n",
" # Vérifier que les clusters ne sont pas trop déséquilibrés\n",
" sizes = pd.Series(labels).value_counts()\n",
" if sizes.min() < 10:\n",
" continue\n",
" sil = silhouette_score(X_f_scaled, labels)\n",
" db = davies_bouldin_score(X_f_scaled, labels)\n",
" rows_k.append({\n",
" \"k\": k,\n",
" \"silhouette\": round(sil, 4),\n",
" \"davies_bouldin\": round(db, 4),\n",
" \"min_cluster_size\": sizes.min(),\n",
" })\n",
" if sil > best_sil:\n",
" best_sil = sil\n",
" best_k = k\n",
"\n",
" df_k = pd.DataFrame(rows_k)\n",
" print(df_k.to_string(index=False))\n",
" print(f\"→ K retenu : {best_k} (silhouette={best_sil:.4f})\")\n",
"\n",
" # Clustering final\n",
" km_final = KMeans(n_clusters=best_k, n_init=50, random_state=RANDOM_STATE)\n",
" cluster_col = f\"cluster_{fund.lower().replace(' ','_')[:30]}\"\n",
" df_f[cluster_col] = km_final.fit_predict(X_f_scaled)\n",
"\n",
" # Tailles\n",
" counts = df_f[cluster_col].value_counts().sort_index()\n",
" props = counts / counts.sum() * 100\n",
" print(pd.DataFrame({\"n_comptes\": counts, \"pct\": props.round(1)}))\n",
"\n",
" # Heatmap\n",
" profile_vars_fund = [c for c in fund_features if c in df_f.columns]\n",
" prof = plot_heatmap(\n",
" df_f, profile_vars_fund, cluster_col,\n",
" title=f\"Cluster signatures — {fund[:40]} (K={best_k})\",\n",
" figsize=(14, 4)\n",
" )\n",
" print(f\"\\nMédianes — {fund}:\")\n",
" print(prof.round(3).to_string())\n",
"\n",
" FUND_RESULTS[fund] = {\n",
" \"df\": df_f,\n",
" \"cluster_col\": cluster_col,\n",
" \"k\": best_k,\n",
" \"silhouette\": best_sil,\n",
" \"profile\": prof,\n",
" \"n\": len(df_f),\n",
" }\n",
"\n",
"# ── 6. Résumé global ──────────────────────────────────────────────────────\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"RÉSUMÉ — Clustering par fund\")\n",
"print(\"=\"*60)\n",
"for fund, res in FUND_RESULTS.items():\n",
" print(f\" {fund[:40]:40s} : K={res['k']}, \"\n",
" f\"sil={res['silhouette']:.4f}, n={res['n']}\")\n",
"\n",
"# ── 7. Croisement avec le clustering global ───────────────────────────────\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"ARI — cohérence clustering global x fund\")\n",
"print(\"=\"*60)\n",
"\n",
"dfc_fund_cross = dfc[[ID_COL, \"cluster_k4\"]].copy()\n",
"\n",
"for fund, res in FUND_RESULTS.items():\n",
" cluster_col = res[\"cluster_col\"]\n",
" df_f = res[\"df\"][[ID_COL, cluster_col]].copy()\n",
" dfc_fund_cross = dfc_fund_cross.merge(df_f, on=ID_COL, how=\"left\")\n",
"\n",
" mask = dfc_fund_cross[cluster_col].notna()\n",
" if mask.sum() < 10:\n",
" continue\n",
" labels_global = dfc_fund_cross.loc[mask, \"cluster_k4\"].values\n",
" labels_fund = dfc_fund_cross.loc[mask, cluster_col].values\n",
" ari = adjusted_rand_score(labels_global, labels_fund)\n",
" print(f\" {fund[:40]:40s} : ARI={ari:.4f} (n={mask.sum()})\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7bc432da-3be4-4e39-93f9-9131a3548a14",
"metadata": {},
2026-04-05 17:52:42 +02:00
"outputs": [],
"source": []
}
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