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"# AUM–
"\n",
"3 sections: \n",
"1. Build Panel\n",
"2. Rupture Detection (data-driven threshold)\n",
"3. Figures & Diagnostics"
]
},
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"source": [
"## 0. Imports & Data Loading"
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]
},
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"cell_type": "code",
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"execution_count": 1,
"id": "68b07738",
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"metadata": {},
"outputs": [
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"name": "stdout",
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"output_type": "stream",
"text": [
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"Stocks: (4880297, 19)\n",
"Flows: (2574461, 25)\n"
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]
}
],
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"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import plotly.graph_objects as go\n",
"\n",
"stocks = pd.read_csv('stocks.csv', low_memory=False)\n",
"flows = pd.read_csv('flows.csv', low_memory=False)\n",
"\n",
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"])\n",
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"])\n",
"\n",
"print(f\"Stocks: {stocks.shape}\")\n",
"print(f\"Flows: {flows.shape}\")"
]
},
{
"cell_type": "markdown",
"id": "eb07a566",
"metadata": {},
"source": [
"## 1. Build Panel (Account x ISIN x Date)\n",
"\n",
"Merge AUM and flows at the finest granularity. Missing flows are filled with 0. Lagged variables are computed to reconstruct the accounting identity Q(t) = Q(t-1) + F(t-1)."
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]
},
{
"cell_type": "code",
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"execution_count": 2,
"id": "851395c4",
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"metadata": {},
"outputs": [
{
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"name": "stdout",
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"output_type": "stream",
"text": [
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"Panel size: (4880297, 11)\n",
"Accounts: 12,501\n",
"ISINs: 491\n",
"Dates: 130\n"
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]
}
],
"source": [
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"KEY = [\"Registrar Account - ID\", \"Product - Isin\", \"Centralisation Date\"]\n",
"GROUP = [\"Registrar Account - ID\", \"Product - Isin\"]\n",
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"\n",
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"stocks_panel = stocks[KEY + [\"Quantity - AUM\"]].copy()\n",
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"\n",
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"flows_panel = (\n",
" flows[KEY + [\"Quantity - NetFlows\"]]\n",
" .groupby(KEY, as_index=False)[\"Quantity - NetFlows\"]\n",
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" .sum()\n",
")\n",
"\n",
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"df = stocks_panel.merge(flows_panel, on=KEY, how=\"left\")\n",
"df[\"Quantity - NetFlows\"] = df[\"Quantity - NetFlows\"].fillna(0)\n",
"df = df.sort_values(KEY)\n",
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"\n",
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"df[\"prev_stock\"] = df.groupby(GROUP)[\"Quantity - AUM\"].shift(1)\n",
"df[\"prev_flows\"] = df.groupby(GROUP)[\"Quantity - NetFlows\"].shift(1).fillna(0)\n",
"df[\"expected_stock\"] = df[\"prev_stock\"] + df[\"prev_flows\"]\n",
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"\n",
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"df[\"gap\"] = df[\"Quantity - AUM\"] - df[\"expected_stock\"]\n",
"df[\"gap_abs\"] = df[\"gap\"].abs()\n",
"df[\"gap_rel\"] = df[\"gap_abs\"] / df[\"prev_stock\"].abs().replace(0, np.nan)\n",
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"\n",
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"print(f\"Panel size: {df.shape}\")\n",
"print(f\"Accounts: {df['Registrar Account - ID'].nunique():,}\")\n",
"print(f\"ISINs: {df['Product - Isin'].nunique():,}\")\n",
"print(f\"Dates: {df['Centralisation Date'].nunique():,}\")"
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]
},
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"id": "dec85f01",
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"## 2. Rupture Detection (data-driven threshold)\n",
"\n",
"The detection threshold EPSILON is calibrated from the data:\n",
"1. Trim the top 10% of relative gaps to avoid calibrating on resets\n",
"2. Define EPSILON as the 99th percentile of the trimmed distribution\n",
"\n",
"A rupture is flagged when gap_rel > EPSILON, conditional on prev_stock > 0."
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]
},
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"cell_type": "code",
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"execution_count": 3,
"id": "bd7cfce8",
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Detection threshold EPSILON: 32.4591%\n",
"Total ruptures detected: 189,537\n",
"Share of observations flagged: 3.88%\n"
]
}
],
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"source": [
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"# Calibration sample: valid observations with positive previous stock\n",
"valid_gaps = df.loc[\n",
" df[\"gap_rel\"].notna() & (df[\"prev_stock\"] > 0),\n",
" \"gap_rel\"\n",
"]\n",
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"\n",
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"# Trim top 10% to avoid calibrating on structural resets\n",
"gap_rel_trimmed = valid_gaps[valid_gaps <= valid_gaps.quantile(0.90)]\n",
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"\n",
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"# EPSILON = 99th percentile of the trimmed distribution\n",
"EPSILON = gap_rel_trimmed.quantile(0.99)\n",
"print(f\"Detection threshold EPSILON: {EPSILON:.4%}\")\n",
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"\n",
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"# Flag ruptures\n",
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"df[\"rupture_flag\"] = (\n",
" df[\"prev_stock\"].notna()\n",
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" & (df[\"prev_stock\"] > 0)\n",
" & (df[\"gap_rel\"] > EPSILON)\n",
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")\n",
"\n",
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"# Remove edge effect at last observation date\n",
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"last_date = df[\"Centralisation Date\"].max()\n",
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"df.loc[(df[\"rupture_flag\"]) & (df[\"Centralisation Date\"] == last_date), \"rupture_flag\"] = False\n",
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"\n",
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"# ISIN-level summary\n",
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"rupture_isin_summary = (\n",
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" df.groupby(GROUP)\n",
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" .agg(\n",
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" n_ruptures =(\"rupture_flag\", \"sum\"),\n",
" total_obs =(\"rupture_flag\", \"count\"),\n",
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" rupture_ratio=(\"rupture_flag\", \"mean\"),\n",
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" max_gap_abs =(\"gap_abs\", \"max\"),\n",
" max_gap_rel =(\"gap_rel\", \"max\")\n",
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" )\n",
" .reset_index()\n",
")\n",
"\n",
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"# Account-level summary\n",
"rupture_account_summary = (\n",
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" df.groupby(\"Registrar Account - ID\")\n",
" .agg(\n",
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" n_ruptures =(\"rupture_flag\", \"sum\"),\n",
" total_obs =(\"rupture_flag\", \"count\"),\n",
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" rupture_ratio=(\"rupture_flag\", \"mean\"),\n",
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" max_gap_abs =(\"gap_abs\", \"max\"),\n",
" max_gap_rel =(\"gap_rel\", \"max\")\n",
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" )\n",
" .reset_index()\n",
")\n",
"\n",
"df.to_csv(\"aum_flow_gaps.csv\", index=False)\n",
"rupture_isin_summary.to_csv(\"rupture_isin_summary.csv\", index=False)\n",
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"rupture_account_summary.to_csv(\"rupture_summary.csv\", index=False)\n",
"\n",
"print(f\"Total ruptures detected: {df['rupture_flag'].sum():,}\")\n",
"print(f\"Share of observations flagged: {df['rupture_flag'].mean():.2%}\")"
]
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"source": [
"## 3. Figures"
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"geo": {
"bgcolor": "white",
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"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
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"polar": {
"angularaxis": {
"gridcolor": "white",
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"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
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}
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"scene": {
"xaxis": {
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"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
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"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
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"zerolinecolor": "white",
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"yaxis": {
"automargin": true,
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"title": {
"standoff": 15
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},
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"image/png": "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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"# ---- Figure A: Distribution of accounts by rupture intensity ----\n",
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"\n",
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"rs = rupture_account_summary.copy()\n",
"bins = [0, 0.01, 0.10, 0.30, 1.01]\n",
"labels = [\"Clean / quasi-clean (<=1%)\", \"Moderate (1-10%)\", \"High (10-30%)\", \"Severe (>30%)\"]\n",
"rs[\"rupture_class\"] = pd.cut(rs[\"rupture_ratio\"], bins=bins, labels=labels, include_lowest=True)\n",
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"\n",
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"dist = (rs[\"rupture_class\"].value_counts(normalize=True).sort_index() * 100).round(1)\n",
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"\n",
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"fig_a = go.Figure(data=[go.Pie(\n",
" labels=dist.index, values=dist.values,\n",
" hole=0.45, textinfo=\"percent\", hoverinfo=\"label+percent\"\n",
")])\n",
"fig_a.update_layout(\n",
" legend=dict(orientation=\"h\", yanchor=\"top\", y=-0.15, xanchor=\"center\", x=0.5),\n",
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" legend_title_text=\"Rupture ratio\"\n",
")\n",
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"fig_a.show()"
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]
},
{
"cell_type": "code",
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"execution_count": 6,
"id": "3a065d8f",
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"metadata": {},
"outputs": [
{
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"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
"source": [
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"# ---- Figure B: Rupture rate over time (active flows only) ----\n",
"# Denominator restricted to observations with active flows\n",
"# to avoid dilution by dormant positions.\n",
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"\n",
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"active_obs = df[df[\"Quantity - NetFlows\"] != 0]\n",
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"\n",
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"time_stats = (\n",
" active_obs\n",
" .groupby(\"Centralisation Date\")\n",
" .agg(total_obs=(\"rupture_flag\", \"count\"), n_ruptures=(\"rupture_flag\", \"sum\"))\n",
" .reset_index()\n",
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")\n",
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"time_stats[\"rupture_rate\"] = time_stats[\"n_ruptures\"] / time_stats[\"total_obs\"]\n",
"time_stats[\"rupture_rate_ma\"] = time_stats[\"rupture_rate\"].rolling(window=6, center=True).mean()\n",
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"\n",
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"plt.figure(figsize=(12, 5))\n",
"plt.plot(time_stats[\"Centralisation Date\"], time_stats[\"rupture_rate\"] * 100,\n",
" color=\"lightgray\", linewidth=1, alpha=0.6, label=\"Monthly rupture rate (active flows only)\")\n",
"plt.plot(time_stats[\"Centralisation Date\"], time_stats[\"rupture_rate_ma\"] * 100,\n",
" color=\"#1f77b4\", linewidth=2.5, label=\"6-month moving average\")\n",
"plt.ylabel(\"Rupture rate (%)\")\n",
"plt.xlabel(\"Date\")\n",
"plt.grid(True, linestyle=\"--\", alpha=0.4)\n",
"plt.legend(frameon=False)\n",
"plt.tight_layout()\n",
"plt.show()"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
"id": "85efa034",
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"metadata": {},
"outputs": [
{
"data": {
2026-04-05 17:49:37 +02:00
"image/png": "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
"text/plain": [
"<Figure size 1000x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1000x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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"<Figure size 1000x400 with 1 Axes>"
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]
},
"metadata": {},
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"output_type": "display_data"
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}
],
"source": [
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"# ---- Figure C: Example trajectories (top 5 worst) ----\n",
"\n",
"def plot_isin_dynamics(df, account_id, isin, title_suffix=\"\"):\n",
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" sub = df[\n",
" (df[\"Registrar Account - ID\"] == account_id) &\n",
" (df[\"Product - Isin\"] == isin)\n",
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" ].sort_values(\"Centralisation Date\")\n",
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"\n",
" if sub.empty:\n",
" print(\"No data for this (account, ISIN).\")\n",
" return\n",
"\n",
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" fig, ax = plt.subplots(figsize=(10, 4))\n",
" ax.plot(sub[\"Centralisation Date\"], sub[\"Quantity - AUM\"],\n",
" label=\"Observed AUM\", linewidth=2, color=\"black\")\n",
" ax.plot(sub[\"Centralisation Date\"], sub[\"expected_stock\"],\n",
" label=\"Flow-implied AUM\", linestyle=\"--\", linewidth=2, color=\"grey\")\n",
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" rupt = sub[sub[\"rupture_flag\"]]\n",
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" ax.scatter(rupt[\"Centralisation Date\"], rupt[\"Quantity - AUM\"],\n",
" color=\"red\", s=25, zorder=5, label=\"Discontinuity\")\n",
" ax.set_title(f\"Account {account_id} — ISIN {isin} {title_suffix}\", fontsize=11)\n",
" ax.set_ylabel(\"AUM (shares)\")\n",
" ax.legend(loc=\"best\")\n",
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" plt.tight_layout()\n",
" plt.show()\n",
"\n",
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"worst = rupture_isin_summary.sort_values(\"rupture_ratio\", ascending=False).head(5)\n",
"for _, row in worst.iterrows():\n",
" plot_isin_dynamics(df, row[\"Registrar Account - ID\"], row[\"Product - Isin\"])"
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]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
