wehub-resource-sync
2719 lines
606 KiB
Plaintext
2719 lines
606 KiB
Plaintext
{
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"cells": [
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},
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"source": [
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"# Wasserstein Regime Clustering\n",
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"\n",
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"**Docker image**: `ml4t`\n",
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"\n",
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"This notebook implements the methodology from **\"Clustering Market Regimes Using the\n",
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"Wasserstein Distance\"** (Horvath et al., 2021). Instead of clustering\n",
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"on moment features (mean, variance, skewness), each time window is treated as an\n",
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"empirical distribution and clustered using optimal transport.\n",
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"\n",
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"**Learning Objectives**:\n",
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"- Understand Wasserstein distance as a distributional similarity metric\n",
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"- Implement stream-lift partitioning and empirical measure construction\n",
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"- Apply Wasserstein k-means with barycenter centroids\n",
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"- Extract regime features: `wasserstein_cluster`, `cluster_distance`, `tail_divergence`\n",
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"\n",
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"**Book Reference**: Chapter 9, Section 9.5 (Regime Features)\n",
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"\n",
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"**Prerequisites**: `11_hmm_regimes` for parametric regime detection (HMM);\n",
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"this notebook provides a non-parametric alternative."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ec663413",
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"metadata": {
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},
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"outputs": [],
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"source": [
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"\"\"\"Wasserstein Regime Clustering — detect market regimes using optimal transport distance.\"\"\"\n",
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"\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"import math\n",
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"import time\n",
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"from collections.abc import Mapping, Sequence\n",
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"from dataclasses import dataclass\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import polars as pl\n",
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"from IPython.display import display\n",
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"from matplotlib.colors import ListedColormap\n",
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"from matplotlib.patches import Patch\n",
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"from ml4t.diagnostic.evaluation.drift import compute_psi, compute_wasserstein_distance\n",
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"from numpy.typing import NDArray\n",
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"from sklearn.cluster import KMeans\n",
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"from sklearn.metrics import adjusted_rand_score, silhouette_score\n",
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"from sklearn.mixture import GaussianMixture\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"\n",
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"from data import load_sp500_index\n",
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"from utils.reproducibility import set_global_seeds"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "880b1424",
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"metadata": {
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"execution": {
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"papermill": {
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"duration": 0.011126,
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"end_time": "2026-06-14T17:00:38.930358+00:00",
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"exception": false,
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"start_time": "2026-06-14T17:00:38.919232+00:00",
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"status": "completed"
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},
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"WINDOW_LEN = 21\n",
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"OVERLAP = 5\n",
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"N_STEPS = 2000\n",
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"MMD_BOOTSTRAPS = 200\n",
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"SEED = 42"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "e8c005fc",
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"metadata": {
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"execution": {
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"status": "completed"
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},
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"outputs": [],
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"source": [
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"set_global_seeds(SEED)\n",
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"RANDOM_STATE = SEED"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0a14a3d7",
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"metadata": {
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"duration": 0.003863,
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"end_time": "2026-06-14T17:00:39.186065+00:00",
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"exception": false,
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"status": "completed"
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}
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},
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"source": [
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"## Configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "ee1da61c",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-14T17:00:39.194696Z",
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"iopub.status.busy": "2026-06-14T17:00:39.194556Z",
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"shell.execute_reply": "2026-06-14T17:00:39.196002Z"
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"papermill": {
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"duration": 0.006751,
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"end_time": "2026-06-14T17:00:39.196700+00:00",
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"exception": false,
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"start_time": "2026-06-14T17:00:39.189949+00:00",
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"status": "completed"
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}
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},
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"outputs": [],
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"source": [
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"# Clustering\n",
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"N_CLUSTERS = 2 # Risk-on vs Risk-off\n",
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"\n",
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"# Wasserstein exponent (p=1 for L1, p=2 for L2)\n",
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"WASSERSTEIN_P = 1.0\n",
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"\n",
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"# MMD validation\n",
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"MMD_SIGMA = 0.1"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2236b73",
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"metadata": {
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"papermill": {
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"duration": 0.003791,
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"end_time": "2026-06-14T17:00:39.204351+00:00",
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"exception": false,
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"start_time": "2026-06-14T17:00:39.200560+00:00",
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"status": "completed"
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}
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},
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"source": [
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"## Part 1: Core Implementation\n",
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"\n",
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"### Stream Lift Function\n",
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"\n",
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"The stream lift transforms a return series into overlapping windows. Each window\n",
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"becomes an empirical distribution that we can cluster."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3f516985",
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"metadata": {
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"execution": {
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"lines_to_next_cell": 2,
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"papermill": {
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"duration": 0.056059,
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"status": "completed"
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}
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},
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"outputs": [],
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"source": [
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"FloatArray = NDArray[np.float64]\n",
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"IntArray = NDArray[np.int64]\n",
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"\n",
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"\n",
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"@dataclass(frozen=True)\n",
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"class LiftedStream:\n",
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" \"\"\"Representation of a lifted return stream.\"\"\"\n",
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"\n",
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" segments: FloatArray # Raw windowed returns (n_segments, window_len)\n",
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" sorted_segments: FloatArray # Sorted returns per window\n",
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" starts: IntArray # Start indices in original array\n",
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" window_len: int\n",
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" step: int"
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]
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},
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{
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"cell_type": "markdown",
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"id": "93cea820",
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"metadata": {
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"lines_to_next_cell": 2,
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"papermill": {
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"duration": 0.004079,
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"end_time": "2026-06-14T17:00:39.272534+00:00",
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"exception": false,
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"start_time": "2026-06-14T17:00:39.268455+00:00",
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"status": "completed"
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}
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},
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"source": [
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"Transform a return series into overlapping windows for distributional clustering."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "d1cc81ef",
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"metadata": {
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"execution": {
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},
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"lines_to_next_cell": 2,
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"papermill": {
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"duration": 0.008089,
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"end_time": "2026-06-14T17:00:39.284477+00:00",
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"exception": false,
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"status": "completed"
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}
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},
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"outputs": [],
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"source": [
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"def lift_stream(\n",
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" returns: FloatArray,\n",
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" window_len: int,\n",
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" overlap: int,\n",
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") -> LiftedStream:\n",
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" \"\"\"\n",
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" Lift a 1D return stream into overlapping windows.\n",
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"\n",
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" This is the computational analogue of the paper's stream lift: windows of\n",
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" length h1 with offset h2, where step = h1 - h2.\n",
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" \"\"\"\n",
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" if returns.ndim != 1:\n",
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" raise ValueError(\"returns must be a 1D array.\")\n",
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" if window_len < 2:\n",
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" raise ValueError(\"window_len must be at least 2.\")\n",
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" if overlap < 0 or overlap >= window_len:\n",
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" raise ValueError(\"overlap must satisfy 0 <= overlap < window_len.\")\n",
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"\n",
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" step = window_len - overlap\n",
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" if returns.shape[0] < window_len:\n",
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" raise ValueError(\"returns length must be >= window_len.\")\n",
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"\n",
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" # Sliding window view (zero-copy), then stride by step\n",
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" windows_view = np.lib.stride_tricks.sliding_window_view(returns, window_shape=window_len)\n",
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" windows_view = windows_view[::step]\n",
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" segments = np.ascontiguousarray(windows_view, dtype=np.float64)\n",
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"\n",
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" sorted_segments = np.sort(segments, axis=1)\n",
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" starts = np.arange(0, (segments.shape[0] * step), step, dtype=np.int64)\n",
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"\n",
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" return LiftedStream(\n",
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" segments=segments,\n",
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" sorted_segments=sorted_segments,\n",
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" starts=starts,\n",
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" window_len=window_len,\n",
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" step=step,\n",
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" )"
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]
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},
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"cell_type": "markdown",
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"id": "2b2c466c",
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}
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},
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"source": [
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"### Wasserstein Distance and Barycenter\n",
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"\n",
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"For 1D empirical measures with equal weights, the p-Wasserstein distance reduces to\n",
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"comparing sorted quantiles. The barycenter (centroid) is the component-wise median\n",
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"(p=1) or mean (p=2) of the sorted atoms."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "a854c058",
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"status": "completed"
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}
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},
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"outputs": [],
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"source": [
|
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"def wasserstein_distance_1d(\n",
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" sorted_a: FloatArray,\n",
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" sorted_b: FloatArray,\n",
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" p: float = 1.0,\n",
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") -> float:\n",
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" \"\"\"\n",
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" Compute the 1D p-Wasserstein distance between two empirical measures.\n",
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"\n",
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" For equal-weight measures: W_p^p = mean(|a_i - b_i|^p) over aligned quantiles.\n",
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" \"\"\"\n",
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" if sorted_a.shape != sorted_b.shape:\n",
|
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" raise ValueError(\"sorted_a and sorted_b must have the same shape.\")\n",
|
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" diff_p = np.abs(sorted_a - sorted_b) ** p\n",
|
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" return float(diff_p.mean() ** (1.0 / p))"
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]
|
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},
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{
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"cell_type": "markdown",
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"id": "10a87521",
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"status": "completed"
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}
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},
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"source": [
|
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"Batch distance computation from segments to a single centroid."
|
|
]
|
|
},
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|
{
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"cell_type": "code",
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|
"execution_count": 8,
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"id": "8872154d",
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}
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},
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"outputs": [],
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"source": [
|
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"def _wasserstein_distance_to_centroid(\n",
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" sorted_segments: FloatArray,\n",
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" centroid: FloatArray,\n",
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" p: float,\n",
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") -> FloatArray:\n",
|
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" \"\"\"Compute Wasserstein distance from each segment to a centroid.\"\"\"\n",
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" diff_p = np.abs(sorted_segments - centroid[None, :]) ** p\n",
|
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" return (diff_p.mean(axis=1)) ** (1.0 / p)"
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]
|
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},
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{
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"cell_type": "markdown",
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"status": "completed"
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}
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},
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"source": [
|
|
"Compute the Wasserstein barycenter (centroid) for a cluster of sorted distributions."
|
|
]
|
|
},
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{
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|
"cell_type": "code",
|
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"execution_count": 9,
|
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"id": "33c04603",
|
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"metadata": {
|
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"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.346400Z",
|
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"iopub.status.busy": "2026-06-14T17:00:39.346258Z",
|
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"iopub.status.idle": "2026-06-14T17:00:39.349359Z",
|
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"shell.execute_reply": "2026-06-14T17:00:39.348850Z"
|
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},
|
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"lines_to_next_cell": 2,
|
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"papermill": {
|
|
"duration": 0.008265,
|
|
"end_time": "2026-06-14T17:00:39.349995+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.341730+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def wasserstein_barycenter_1d(\n",
|
|
" sorted_cluster_members: FloatArray,\n",
|
|
" p: float = 1.0,\n",
|
|
") -> FloatArray:\n",
|
|
" \"\"\"\n",
|
|
" Compute 1D Wasserstein barycenter for cluster members.\n",
|
|
"\n",
|
|
" For p=1: component-wise median (closed-form)\n",
|
|
" For p=2: component-wise mean (closed-form)\n",
|
|
" \"\"\"\n",
|
|
" if sorted_cluster_members.ndim != 2:\n",
|
|
" raise ValueError(\"sorted_cluster_members must be a 2D array.\")\n",
|
|
" if sorted_cluster_members.shape[0] == 0:\n",
|
|
" raise ValueError(\"Cannot compute barycenter of an empty cluster.\")\n",
|
|
"\n",
|
|
" if p == 1.0:\n",
|
|
" return np.median(sorted_cluster_members, axis=0).astype(np.float64)\n",
|
|
" if p == 2.0:\n",
|
|
" return sorted_cluster_members.mean(axis=0).astype(np.float64)\n",
|
|
"\n",
|
|
" # General p>1: bisection solver (rarely needed)\n",
|
|
" low = sorted_cluster_members.min(axis=0)\n",
|
|
" high = sorted_cluster_members.max(axis=0)\n",
|
|
" bary = 0.5 * (low + high)\n",
|
|
"\n",
|
|
" for _ in range(40):\n",
|
|
" mid = 0.5 * (low + high)\n",
|
|
" diff = mid[None, :] - sorted_cluster_members\n",
|
|
" g = np.sum(diff * (np.abs(diff) ** (p - 2.0)), axis=0)\n",
|
|
" high = np.where(g > 0.0, mid, high)\n",
|
|
" low = np.where(g <= 0.0, mid, low)\n",
|
|
" bary = mid\n",
|
|
"\n",
|
|
" return bary.astype(np.float64)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7ae298ed",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.003806,
|
|
"end_time": "2026-06-14T17:00:39.357757+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.353951+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### Wasserstein K-Means Algorithm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "d3b4c1c7",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.366567Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.366427Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.368704Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.368349Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.007386,
|
|
"end_time": "2026-06-14T17:00:39.369018+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.361632+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"@dataclass\n",
|
|
"class WKMeansResult:\n",
|
|
" \"\"\"Result container for WassersteinKMeans1D.\"\"\"\n",
|
|
"\n",
|
|
" labels: IntArray\n",
|
|
" centroids: FloatArray\n",
|
|
" inertia: float\n",
|
|
" loss_history: list[float]\n",
|
|
" n_iter: int\n",
|
|
" converged: bool"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "fc46d82f",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.377714Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.377602Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.384863Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.384473Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.01229,
|
|
"end_time": "2026-06-14T17:00:39.385318+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.373028+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"class WassersteinKMeans1D:\n",
|
|
" \"\"\"\n",
|
|
" Wasserstein k-means for 1D empirical measures.\n",
|
|
"\n",
|
|
" The algorithm mirrors standard k-means:\n",
|
|
" - Assignment step uses W_p distance\n",
|
|
" - Update step uses Wasserstein barycenter\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" def __init__(\n",
|
|
" self,\n",
|
|
" n_clusters: int,\n",
|
|
" p: float = 1.0,\n",
|
|
" max_iter: int = 100,\n",
|
|
" tol: float = 1e-4,\n",
|
|
" n_init: int = 10,\n",
|
|
" init: str = \"kmeans++\",\n",
|
|
" random_state: int | None = None,\n",
|
|
" ) -> None:\n",
|
|
" self.n_clusters = int(n_clusters)\n",
|
|
" self.p = float(p)\n",
|
|
" self.max_iter = int(max_iter)\n",
|
|
" self.tol = float(tol)\n",
|
|
" self.n_init = int(n_init)\n",
|
|
" self.init = init\n",
|
|
" self.random_state = random_state\n",
|
|
"\n",
|
|
" def fit(self, sorted_segments: FloatArray) -> WKMeansResult:\n",
|
|
" \"\"\"Fit WK-means.\"\"\"\n",
|
|
" n_samples, window_len = sorted_segments.shape\n",
|
|
" if n_samples < self.n_clusters:\n",
|
|
" raise ValueError(\"n_samples must be >= n_clusters.\")\n",
|
|
"\n",
|
|
" rng = np.random.default_rng(self.random_state)\n",
|
|
"\n",
|
|
" best: WKMeansResult | None = None\n",
|
|
" best_inertia = float(\"inf\")\n",
|
|
"\n",
|
|
" for _ in range(self.n_init):\n",
|
|
" centroids = self._init_centroids(sorted_segments, rng)\n",
|
|
" result = self._run_lloyd(sorted_segments, centroids, rng)\n",
|
|
"\n",
|
|
" if result.inertia < best_inertia:\n",
|
|
" best_inertia = result.inertia\n",
|
|
" best = result\n",
|
|
"\n",
|
|
" if best is None:\n",
|
|
" raise RuntimeError(\"WK-means failed to produce a result.\")\n",
|
|
" return best\n",
|
|
"\n",
|
|
" def _init_centroids(self, sorted_segments: FloatArray, rng: np.random.Generator) -> FloatArray:\n",
|
|
" n_samples = sorted_segments.shape[0]\n",
|
|
" k = self.n_clusters\n",
|
|
"\n",
|
|
" if self.init == \"random\":\n",
|
|
" idx = rng.choice(n_samples, size=k, replace=False)\n",
|
|
" return np.ascontiguousarray(sorted_segments[idx], dtype=np.float64)\n",
|
|
"\n",
|
|
" # kmeans++ adapted to Wasserstein distance\n",
|
|
" centroids = np.empty((k, sorted_segments.shape[1]), dtype=np.float64)\n",
|
|
" first_idx = int(rng.integers(0, n_samples))\n",
|
|
" centroids[0] = sorted_segments[first_idx]\n",
|
|
"\n",
|
|
" closest_dist_sq = (\n",
|
|
" _wasserstein_distance_to_centroid(sorted_segments, centroids[0], self.p) ** 2\n",
|
|
" )\n",
|
|
"\n",
|
|
" for c in range(1, k):\n",
|
|
" probs = closest_dist_sq / float(closest_dist_sq.sum())\n",
|
|
" next_idx = int(rng.choice(n_samples, p=probs))\n",
|
|
" centroids[c] = sorted_segments[next_idx]\n",
|
|
"\n",
|
|
" dist_sq = _wasserstein_distance_to_centroid(sorted_segments, centroids[c], self.p) ** 2\n",
|
|
" closest_dist_sq = np.minimum(closest_dist_sq, dist_sq)\n",
|
|
"\n",
|
|
" return centroids\n",
|
|
"\n",
|
|
" def _run_lloyd(\n",
|
|
" self,\n",
|
|
" sorted_segments: FloatArray,\n",
|
|
" centroids_init: FloatArray,\n",
|
|
" rng: np.random.Generator,\n",
|
|
" ) -> WKMeansResult:\n",
|
|
" centroids = centroids_init.copy()\n",
|
|
" k = self.n_clusters\n",
|
|
" loss_history: list[float] = []\n",
|
|
"\n",
|
|
" labels = np.zeros(sorted_segments.shape[0], dtype=np.int64)\n",
|
|
" converged = False\n",
|
|
"\n",
|
|
" for it in range(self.max_iter):\n",
|
|
" # Assignment step\n",
|
|
" distances = np.empty((sorted_segments.shape[0], k), dtype=np.float64)\n",
|
|
" for j in range(k):\n",
|
|
" distances[:, j] = _wasserstein_distance_to_centroid(\n",
|
|
" sorted_segments, centroids[j], self.p\n",
|
|
" )\n",
|
|
"\n",
|
|
" labels = distances.argmin(axis=1)\n",
|
|
"\n",
|
|
" # Update step\n",
|
|
" old_centroids = centroids.copy()\n",
|
|
" for j in range(k):\n",
|
|
" members = sorted_segments[labels == j]\n",
|
|
" if members.shape[0] == 0:\n",
|
|
" # Re-seed empty cluster\n",
|
|
" min_d = distances.min(axis=1)\n",
|
|
" farthest = int(np.argmax(min_d))\n",
|
|
" centroids[j] = sorted_segments[farthest]\n",
|
|
" else:\n",
|
|
" centroids[j] = wasserstein_barycenter_1d(members, p=self.p)\n",
|
|
"\n",
|
|
" # Loss: sum of centroid movements\n",
|
|
" loss = sum(\n",
|
|
" wasserstein_distance_1d(old_centroids[j], centroids[j], p=self.p) for j in range(k)\n",
|
|
" )\n",
|
|
" loss_history.append(loss)\n",
|
|
"\n",
|
|
" if loss < self.tol:\n",
|
|
" converged = True\n",
|
|
" break\n",
|
|
"\n",
|
|
" # Inertia: sum of within-cluster distances\n",
|
|
" final_distances = np.empty((sorted_segments.shape[0], k), dtype=np.float64)\n",
|
|
" for j in range(k):\n",
|
|
" final_distances[:, j] = _wasserstein_distance_to_centroid(\n",
|
|
" sorted_segments, centroids[j], self.p\n",
|
|
" )\n",
|
|
" min_d = final_distances.min(axis=1)\n",
|
|
" inertia = float(min_d.sum())\n",
|
|
"\n",
|
|
" return WKMeansResult(\n",
|
|
" labels=labels,\n",
|
|
" centroids=centroids,\n",
|
|
" inertia=inertia,\n",
|
|
" loss_history=loss_history,\n",
|
|
" n_iter=(it + 1),\n",
|
|
" converged=converged,\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "17e4f782",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.003874,
|
|
"end_time": "2026-06-14T17:00:39.393278+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.389404+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Part 2: Baseline Methods for Comparison\n",
|
|
"\n",
|
|
"The paper benchmarks WK-means against traditional approaches that use moment features."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "7900eada",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.401825Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.401692Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.403745Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.403475Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.007195,
|
|
"end_time": "2026-06-14T17:00:39.404298+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.397103+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def moment_features(segments: FloatArray, n_moments: int) -> FloatArray:\n",
|
|
" \"\"\"\n",
|
|
" Compute truncated raw-moment features for each segment.\n",
|
|
"\n",
|
|
" The n-th raw moment is E[x^n] = mean(x^n), scaled by 1/n! per the paper.\n",
|
|
" \"\"\"\n",
|
|
" feats = np.empty((segments.shape[0], n_moments), dtype=np.float64)\n",
|
|
" for m in range(1, n_moments + 1):\n",
|
|
" feats[:, m - 1] = (segments**m).mean(axis=1) / math.factorial(m)\n",
|
|
" return feats"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "01788644",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.003788,
|
|
"end_time": "2026-06-14T17:00:39.411917+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.408129+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"Result container and fitting function for K-means on moment features."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "54a5810f",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.420572Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.420452Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.422539Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.422147Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.007204,
|
|
"end_time": "2026-06-14T17:00:39.422939+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.415735+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"@dataclass\n",
|
|
"class MomentKMeansResult:\n",
|
|
" labels: IntArray\n",
|
|
" centroids: FloatArray\n",
|
|
" inertia: float"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "564ca93f",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.003802,
|
|
"end_time": "2026-06-14T17:00:39.430600+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.426798+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"K-means clustering on standardized moment features (paper baseline)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "38d24e3b",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.439122Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.438993Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.441180Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.440899Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.007418,
|
|
"end_time": "2026-06-14T17:00:39.441791+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.434373+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def fit_moment_kmeans(\n",
|
|
" segments: FloatArray,\n",
|
|
" n_clusters: int,\n",
|
|
" n_moments: int = 4,\n",
|
|
" random_state: int | None = None,\n",
|
|
") -> MomentKMeansResult:\n",
|
|
" \"\"\"K-means on standardized moment features (paper baseline).\"\"\"\n",
|
|
" feats = moment_features(segments, n_moments=n_moments)\n",
|
|
" scaler = StandardScaler()\n",
|
|
" feats_z = scaler.fit_transform(feats)\n",
|
|
"\n",
|
|
" model = KMeans(n_clusters=n_clusters, n_init=\"auto\", random_state=random_state)\n",
|
|
" labels = model.fit_predict(feats_z).astype(np.int64)\n",
|
|
" return MomentKMeansResult(\n",
|
|
" labels=labels,\n",
|
|
" centroids=model.cluster_centers_.astype(np.float64),\n",
|
|
" inertia=float(model.inertia_),\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
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"id": "006dd57d",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
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"papermill": {
|
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"duration": 0.003901,
|
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"end_time": "2026-06-14T17:00:39.449631+00:00",
|
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"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.445730+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"Result container and fitting function for GMM on moment features."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "1b0e5dc3",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.458380Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.458250Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.460475Z",
|
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"shell.execute_reply": "2026-06-14T17:00:39.460104Z"
|
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},
|
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"lines_to_next_cell": 2,
|
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"papermill": {
|
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"duration": 0.007188,
|
|
"end_time": "2026-06-14T17:00:39.460812+00:00",
|
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"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.453624+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"@dataclass\n",
|
|
"class GMMResult:\n",
|
|
" labels: IntArray\n",
|
|
" responsibilities: FloatArray\n",
|
|
" means: FloatArray\n",
|
|
" lower_bound: float"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9a3cfba0",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.003788,
|
|
"end_time": "2026-06-14T17:00:39.468460+00:00",
|
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"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.464672+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"GMM clustering on standardized moment features (paper baseline)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "6704715c",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.477294Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.477099Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.481032Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.480278Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.009593,
|
|
"end_time": "2026-06-14T17:00:39.482125+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.472532+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def fit_gmm_on_moments(\n",
|
|
" segments: FloatArray,\n",
|
|
" n_components: int,\n",
|
|
" n_moments: int = 4,\n",
|
|
" random_state: int | None = None,\n",
|
|
") -> GMMResult:\n",
|
|
" \"\"\"GMM on standardized moment features (paper baseline).\"\"\"\n",
|
|
" feats = moment_features(segments, n_moments=n_moments)\n",
|
|
" scaler = StandardScaler()\n",
|
|
" feats_z = scaler.fit_transform(feats)\n",
|
|
"\n",
|
|
" model = GaussianMixture(n_components=n_components, random_state=random_state, reg_covar=1e-6)\n",
|
|
" model.fit(feats_z)\n",
|
|
" responsibilities = model.predict_proba(feats_z).astype(np.float64)\n",
|
|
" labels = responsibilities.argmax(axis=1).astype(np.int64)\n",
|
|
"\n",
|
|
" return GMMResult(\n",
|
|
" labels=labels,\n",
|
|
" responsibilities=responsibilities,\n",
|
|
" means=model.means_.astype(np.float64),\n",
|
|
" lower_bound=float(model.lower_bound_),\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e96d1518",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.008994,
|
|
"end_time": "2026-06-14T17:00:39.500219+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.491225+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Part 3: MMD-Based Validation\n",
|
|
"\n",
|
|
"The paper uses Maximum Mean Discrepancy (MMD) to validate clustering quality.\n",
|
|
"Good clusters should have low within-cluster MMD and high between-cluster MMD."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "fe2dd85e",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.511882Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.511722Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.514075Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.513703Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.007912,
|
|
"end_time": "2026-06-14T17:00:39.514476+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.506564+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def gaussian_rbf_kernel(x: FloatArray, y: FloatArray, sigma: float) -> FloatArray:\n",
|
|
" \"\"\"Gaussian RBF kernel matrix k(x_i, y_j) = exp(-||x_i - y_j||^2 / (2*sigma^2)).\"\"\"\n",
|
|
" x_norm = np.sum(x * x, axis=1, keepdims=True)\n",
|
|
" y_norm = np.sum(y * y, axis=1, keepdims=True).T\n",
|
|
" sq_dists = x_norm + y_norm - 2.0 * (x @ y.T)\n",
|
|
" return np.exp(-sq_dists / (2.0 * sigma * sigma))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "df4179cd",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.524252Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.524067Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.526715Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.526177Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.008075,
|
|
"end_time": "2026-06-14T17:00:39.527147+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.519072+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def mmd_biased(x: FloatArray, y: FloatArray, sigma: float) -> float:\n",
|
|
" \"\"\"Biased empirical MMD estimator.\"\"\"\n",
|
|
" k_xx = gaussian_rbf_kernel(x, x, sigma=sigma)\n",
|
|
" k_yy = gaussian_rbf_kernel(y, y, sigma=sigma)\n",
|
|
" k_xy = gaussian_rbf_kernel(x, y, sigma=sigma)\n",
|
|
"\n",
|
|
" mmd2 = float(k_xx.mean() - 2.0 * k_xy.mean() + k_yy.mean())\n",
|
|
" return math.sqrt(max(mmd2, 0.0))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5d887815",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.003906,
|
|
"end_time": "2026-06-14T17:00:39.535196+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.531290+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"Bootstrap MMD similarity score for validating cluster quality."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "949e1f2f",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.544465Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.544235Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.548229Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.547675Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.009424,
|
|
"end_time": "2026-06-14T17:00:39.548567+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.539143+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def similarity_score_mmd(\n",
|
|
" data_a: FloatArray,\n",
|
|
" data_b: FloatArray | None,\n",
|
|
" sigma: float,\n",
|
|
" n_bootstrap: int = 500,\n",
|
|
" sample_size: int = 200,\n",
|
|
" random_state: int | None = None,\n",
|
|
") -> float:\n",
|
|
" \"\"\"\n",
|
|
" Median-of-bootstraps MMD similarity score.\n",
|
|
"\n",
|
|
" If data_b is None, computes within-set similarity by sampling two batches from data_a.\n",
|
|
" \"\"\"\n",
|
|
" rng = np.random.default_rng(random_state)\n",
|
|
" n_a = data_a.shape[0]\n",
|
|
"\n",
|
|
" if data_b is None:\n",
|
|
" n_b = n_a\n",
|
|
" data_b = data_a\n",
|
|
" else:\n",
|
|
" n_b = data_b.shape[0]\n",
|
|
"\n",
|
|
" mmd_vals = np.empty(n_bootstrap, dtype=np.float64)\n",
|
|
"\n",
|
|
" for i in range(n_bootstrap):\n",
|
|
" idx_a = rng.choice(n_a, size=sample_size, replace=(n_a < sample_size))\n",
|
|
" idx_b = rng.choice(n_b, size=sample_size, replace=(n_b < sample_size))\n",
|
|
" x = data_a[idx_a]\n",
|
|
" y = data_b[idx_b]\n",
|
|
" mmd_vals[i] = mmd_biased(x, y, sigma=sigma)\n",
|
|
"\n",
|
|
" return float(np.median(mmd_vals))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "64bbb6c2",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.004042,
|
|
"end_time": "2026-06-14T17:00:39.558249+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.554207+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Part 4: Synthetic Data Generation\n",
|
|
"\n",
|
|
"We create a controlled experiment with known regime switches to benchmark methods."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "b6e8b278",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.567773Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.567609Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.570098Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.569643Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.007991,
|
|
"end_time": "2026-06-14T17:00:39.570366+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.562375+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def simulate_gbm_log_returns(\n",
|
|
" n_steps: int,\n",
|
|
" mu: float,\n",
|
|
" sigma: float,\n",
|
|
" dt: float = 1.0,\n",
|
|
" random_state: int | None = None,\n",
|
|
") -> FloatArray:\n",
|
|
" \"\"\"Simulate log returns from geometric Brownian motion.\"\"\"\n",
|
|
" rng = np.random.default_rng(random_state)\n",
|
|
" z = rng.standard_normal(n_steps).astype(np.float64)\n",
|
|
" return (mu - 0.5 * sigma * sigma) * dt + sigma * math.sqrt(dt) * z"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2561d719",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.004039,
|
|
"end_time": "2026-06-14T17:00:39.578652+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.574613+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"Merton jump-diffusion log returns for testing regime detection."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "ea58b4ac",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.587764Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.587631Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.590631Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.590083Z"
|
|
},
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.008348,
|
|
"end_time": "2026-06-14T17:00:39.591016+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.582668+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def simulate_merton_jump_log_returns(\n",
|
|
" n_steps: int,\n",
|
|
" mu: float,\n",
|
|
" sigma: float,\n",
|
|
" dt: float = 1.0,\n",
|
|
" jump_intensity: float = 0.0,\n",
|
|
" jump_mean: float = 0.0,\n",
|
|
" jump_std: float = 0.0,\n",
|
|
" random_state: int | None = None,\n",
|
|
") -> FloatArray:\n",
|
|
" \"\"\"Simulate log returns from Merton jump diffusion.\"\"\"\n",
|
|
" rng = np.random.default_rng(random_state)\n",
|
|
"\n",
|
|
" # Diffusion part\n",
|
|
" z = rng.standard_normal(n_steps).astype(np.float64)\n",
|
|
" diffusion = (mu - 0.5 * sigma * sigma) * dt + sigma * math.sqrt(dt) * z\n",
|
|
"\n",
|
|
" # Jump part\n",
|
|
" n_jumps = rng.poisson(lam=jump_intensity * dt, size=n_steps).astype(np.int64)\n",
|
|
" jumps = np.zeros(n_steps, dtype=np.float64)\n",
|
|
" for t in range(n_steps):\n",
|
|
" if n_jumps[t] > 0:\n",
|
|
" jumps[t] = rng.normal(loc=jump_mean, scale=jump_std, size=n_jumps[t]).sum()\n",
|
|
"\n",
|
|
" return diffusion + jumps"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d9638b2d",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.004006,
|
|
"end_time": "2026-06-14T17:00:39.599367+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.595361+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"Piecewise two-regime return stream with known switch points for benchmarking."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "5120295f",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.608551Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.608415Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.612688Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.611996Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.009753,
|
|
"end_time": "2026-06-14T17:00:39.613148+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.603395+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def simulate_two_regime_stream(\n",
|
|
" n_steps: int,\n",
|
|
" regime0_params: Mapping[str, float],\n",
|
|
" regime1_params: Mapping[str, float],\n",
|
|
" switch_points: Sequence[int],\n",
|
|
" model: str = \"gbm\",\n",
|
|
" random_state: int | None = None,\n",
|
|
") -> tuple[FloatArray, IntArray]:\n",
|
|
" \"\"\"\n",
|
|
" Create a piecewise two-regime return stream.\n",
|
|
"\n",
|
|
" Returns (returns, true_regime) arrays.\n",
|
|
" \"\"\"\n",
|
|
" rng = np.random.default_rng(random_state)\n",
|
|
" switch_points = list(sorted(set(int(x) for x in switch_points if 0 < x < n_steps)))\n",
|
|
" boundaries = [0] + switch_points + [n_steps]\n",
|
|
"\n",
|
|
" returns = np.empty(n_steps, dtype=np.float64)\n",
|
|
" true_regime = np.empty(n_steps, dtype=np.int64)\n",
|
|
"\n",
|
|
" regime = 0\n",
|
|
" for i in range(len(boundaries) - 1):\n",
|
|
" start, end = boundaries[i], boundaries[i + 1]\n",
|
|
" params = regime0_params if regime == 0 else regime1_params\n",
|
|
"\n",
|
|
" if model == \"gbm\":\n",
|
|
" seg = simulate_gbm_log_returns(\n",
|
|
" n_steps=end - start,\n",
|
|
" mu=float(params[\"mu\"]),\n",
|
|
" sigma=float(params[\"sigma\"]),\n",
|
|
" random_state=int(rng.integers(0, 2**32 - 1)),\n",
|
|
" )\n",
|
|
" elif model == \"merton\":\n",
|
|
" seg = simulate_merton_jump_log_returns(\n",
|
|
" n_steps=end - start,\n",
|
|
" mu=float(params[\"mu\"]),\n",
|
|
" sigma=float(params[\"sigma\"]),\n",
|
|
" jump_intensity=float(params.get(\"jump_intensity\", 0)),\n",
|
|
" jump_mean=float(params.get(\"jump_mean\", 0)),\n",
|
|
" jump_std=float(params.get(\"jump_std\", 0)),\n",
|
|
" random_state=int(rng.integers(0, 2**32 - 1)),\n",
|
|
" )\n",
|
|
" else:\n",
|
|
" raise ValueError(\"model must be 'gbm' or 'merton'.\")\n",
|
|
"\n",
|
|
" returns[start:end] = seg\n",
|
|
" true_regime[start:end] = regime\n",
|
|
" regime = 1 - regime\n",
|
|
"\n",
|
|
" return returns, true_regime"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d30960b0",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.00426,
|
|
"end_time": "2026-06-14T17:00:39.621741+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.617481+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Part 5: Benchmark on Synthetic Data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "c816d798",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.631890Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.631713Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.635841Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.635372Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.010092,
|
|
"end_time": "2026-06-14T17:00:39.636181+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.626089+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Simulating 2,000 steps with 6 regime switches...\n",
|
|
"Regime distribution: 0=1,143, 1=857\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Regime parameters (low-vol bull vs high-vol bear)\n",
|
|
"regime0_params = {\"mu\": 0.0005, \"sigma\": 0.01} # Bull market\n",
|
|
"regime1_params = {\"mu\": -0.0003, \"sigma\": 0.025} # Bear market\n",
|
|
"\n",
|
|
"# Create regime switches\n",
|
|
"n_regimes = 6\n",
|
|
"switch_points = [int(N_STEPS * (i + 1) / (n_regimes + 1)) for i in range(n_regimes)]\n",
|
|
"\n",
|
|
"print(f\"Simulating {N_STEPS:,} steps with {len(switch_points)} regime switches...\")\n",
|
|
"returns, true_regime = simulate_two_regime_stream(\n",
|
|
" n_steps=N_STEPS,\n",
|
|
" regime0_params=regime0_params,\n",
|
|
" regime1_params=regime1_params,\n",
|
|
" switch_points=switch_points,\n",
|
|
" model=\"gbm\",\n",
|
|
" random_state=RANDOM_STATE,\n",
|
|
")\n",
|
|
"\n",
|
|
"print(f\"Regime distribution: 0={np.sum(true_regime == 0):,}, 1={np.sum(true_regime == 1):,}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "a90da2c5",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.646192Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.645998Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.649060Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.648617Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.00865,
|
|
"end_time": "2026-06-14T17:00:39.649337+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.640687+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Created 124 segments of length 21\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Lift the stream\n",
|
|
"lifted = lift_stream(returns, window_len=WINDOW_LEN, overlap=OVERLAP)\n",
|
|
"print(f\"Created {lifted.segments.shape[0]} segments of length {lifted.window_len}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1575f89d",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.004211,
|
|
"end_time": "2026-06-14T17:00:39.658221+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.654010+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### Run All Methods"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "cc6c5d3f",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.667925Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.667769Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.675009Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.674593Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.012978,
|
|
"end_time": "2026-06-14T17:00:39.675606+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.662628+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Fitting Wasserstein K-Means...\n",
|
|
" Converged: True in 3 iterations\n",
|
|
" Inertia: 0.4662\n",
|
|
" Cluster sizes: [72 52]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"results = {}\n",
|
|
"\n",
|
|
"# Wasserstein K-Means\n",
|
|
"print(\"\\nFitting Wasserstein K-Means...\")\n",
|
|
"start = time.perf_counter()\n",
|
|
"wk = WassersteinKMeans1D(\n",
|
|
" n_clusters=N_CLUSTERS,\n",
|
|
" p=WASSERSTEIN_P,\n",
|
|
" n_init=10,\n",
|
|
" max_iter=100,\n",
|
|
" random_state=RANDOM_STATE,\n",
|
|
")\n",
|
|
"wk_result = wk.fit(lifted.sorted_segments)\n",
|
|
"wk_time = time.perf_counter() - start\n",
|
|
"results[\"WK-means\"] = {\n",
|
|
" \"labels\": wk_result.labels,\n",
|
|
" \"time\": wk_time,\n",
|
|
" \"converged\": wk_result.converged,\n",
|
|
" \"n_iter\": wk_result.n_iter,\n",
|
|
" \"inertia\": wk_result.inertia,\n",
|
|
"}\n",
|
|
"print(f\" Converged: {wk_result.converged} in {wk_result.n_iter} iterations\")\n",
|
|
"print(f\" Inertia: {wk_result.inertia:.4f}\")\n",
|
|
"print(f\" Cluster sizes: {np.bincount(wk_result.labels)}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "8693302f",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.685724Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.685571Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.721449Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.720678Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.041725,
|
|
"end_time": "2026-06-14T17:00:39.721862+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.680137+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Fitting Moment K-Means...\n",
|
|
" Cluster sizes: [95 29]\n",
|
|
"\n",
|
|
"Fitting GMM on Moments...\n",
|
|
" Cluster sizes: [71 53]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Moment K-Means\n",
|
|
"print(\"\\nFitting Moment K-Means...\")\n",
|
|
"start = time.perf_counter()\n",
|
|
"mk_result = fit_moment_kmeans(\n",
|
|
" lifted.segments, n_clusters=N_CLUSTERS, n_moments=4, random_state=RANDOM_STATE\n",
|
|
")\n",
|
|
"mk_time = time.perf_counter() - start\n",
|
|
"results[\"Moment K-means\"] = {\n",
|
|
" \"labels\": mk_result.labels,\n",
|
|
" \"time\": mk_time,\n",
|
|
" \"inertia\": mk_result.inertia,\n",
|
|
"}\n",
|
|
"print(f\" Cluster sizes: {np.bincount(mk_result.labels)}\")\n",
|
|
"\n",
|
|
"# GMM on Moments\n",
|
|
"print(\"\\nFitting GMM on Moments...\")\n",
|
|
"start = time.perf_counter()\n",
|
|
"gmm_result = fit_gmm_on_moments(\n",
|
|
" lifted.segments, n_components=N_CLUSTERS, n_moments=4, random_state=RANDOM_STATE\n",
|
|
")\n",
|
|
"gmm_time = time.perf_counter() - start\n",
|
|
"results[\"GMM Moments\"] = {\n",
|
|
" \"labels\": gmm_result.labels,\n",
|
|
" \"time\": gmm_time,\n",
|
|
" \"lower_bound\": gmm_result.lower_bound,\n",
|
|
"}\n",
|
|
"print(f\" Cluster sizes: {np.bincount(gmm_result.labels)}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "404e6821",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.004544,
|
|
"end_time": "2026-06-14T17:00:39.732120+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.727576+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### Evaluate Against Ground Truth\n",
|
|
"\n",
|
|
"We compute Adjusted Rand Index (ARI) against the true regimes, accounting for\n",
|
|
"the fact that segment labels must be aggregated from overlapping windows."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "c1cce618",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.742137Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.741931Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:39.770555Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:39.769804Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.034547,
|
|
"end_time": "2026-06-14T17:00:39.771090+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.736543+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>method</th>\n",
|
|
" <th>ari_vs_true</th>\n",
|
|
" <th>silhouette</th>\n",
|
|
" <th>time_s</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>WK-means</td>\n",
|
|
" <td>0.874095</td>\n",
|
|
" <td>0.665292</td>\n",
|
|
" <td>0.004146</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Moment K-means</td>\n",
|
|
" <td>0.311486</td>\n",
|
|
" <td>0.449713</td>\n",
|
|
" <td>0.027496</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>GMM Moments</td>\n",
|
|
" <td>0.843937</td>\n",
|
|
" <td>0.664218</td>\n",
|
|
" <td>0.004612</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" method ari_vs_true silhouette time_s\n",
|
|
"0 WK-means 0.874095 0.665292 0.004146\n",
|
|
"1 Moment K-means 0.311486 0.449713 0.027496\n",
|
|
"2 GMM Moments 0.843937 0.664218 0.004612"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def aggregate_segment_labels(\n",
|
|
" starts: IntArray, window_len: int, labels: IntArray, n_timestamps: int\n",
|
|
") -> IntArray:\n",
|
|
" \"\"\"Convert segment labels to timestamp labels via voting.\"\"\"\n",
|
|
" n_clusters = int(labels.max() + 1)\n",
|
|
" counts = np.zeros((n_timestamps, n_clusters), dtype=np.float64)\n",
|
|
"\n",
|
|
" for start, lab in zip(starts.tolist(), labels.tolist(), strict=False):\n",
|
|
" end = min(start + window_len, n_timestamps)\n",
|
|
" counts[start:end, lab] += 1.0\n",
|
|
"\n",
|
|
" return counts.argmax(axis=1).astype(np.int64)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Get ground truth at segment level (mode of true_regime in each window)\n",
|
|
"segment_true_labels = np.array(\n",
|
|
" [true_regime[s : s + WINDOW_LEN].mean() > 0.5 for s in lifted.starts], dtype=np.int64\n",
|
|
")\n",
|
|
"\n",
|
|
"benchmark_rows = []\n",
|
|
"for name, data in results.items():\n",
|
|
" labels = data[\"labels\"]\n",
|
|
" ari = adjusted_rand_score(segment_true_labels, labels)\n",
|
|
" mv_proj = np.column_stack([lifted.segments.mean(axis=1), lifted.segments.std(axis=1)])\n",
|
|
" sil = silhouette_score(mv_proj, labels) if len(np.unique(labels)) > 1 else 0.0\n",
|
|
" benchmark_rows.append(\n",
|
|
" {\"method\": name, \"ari_vs_true\": ari, \"silhouette\": sil, \"time_s\": data[\"time\"]}\n",
|
|
" )\n",
|
|
"\n",
|
|
"display(pd.DataFrame(benchmark_rows))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e5beec4d",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.004389,
|
|
"end_time": "2026-06-14T17:00:39.779900+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.775511+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Interpretation**: ARI measures agreement with the known ground truth (1.0 = perfect).\n",
|
|
"WK-means typically matches or exceeds moment-based methods on this synthetic dataset\n",
|
|
"because the regime switch involves a variance change that affects the full distribution\n",
|
|
"shape — exactly what Wasserstein distance captures. For regime changes involving only\n",
|
|
"the mean (no shape change), moment methods may perform comparably."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9522edcc",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.004143,
|
|
"end_time": "2026-06-14T17:00:39.788315+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.784172+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### MMD Validation\n",
|
|
"\n",
|
|
"The paper recommends MMD as a model-free validation metric."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "324f0506",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:39.798018Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:39.797766Z",
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"iopub.status.idle": "2026-06-14T17:00:41.894553Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:41.894106Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 2.102542,
|
|
"end_time": "2026-06-14T17:00:41.895045+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:39.792503+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>method</th>\n",
|
|
" <th>within_0</th>\n",
|
|
" <th>within_1</th>\n",
|
|
" <th>between</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>WK-means</td>\n",
|
|
" <td>0.013924</td>\n",
|
|
" <td>0.030340</td>\n",
|
|
" <td>0.548131</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>Moment K-means</td>\n",
|
|
" <td>0.021933</td>\n",
|
|
" <td>0.029845</td>\n",
|
|
" <td>0.497526</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>GMM Moments</td>\n",
|
|
" <td>0.013383</td>\n",
|
|
" <td>0.029674</td>\n",
|
|
" <td>0.545718</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" method within_0 within_1 between\n",
|
|
"0 WK-means 0.013924 0.030340 0.548131\n",
|
|
"1 Moment K-means 0.021933 0.029845 0.497526\n",
|
|
"2 GMM Moments 0.013383 0.029674 0.545718"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Lower within-cluster MMD = more homogeneous clusters; higher between-cluster MMD = better separation.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"mmd_rows = []\n",
|
|
"for name, data in results.items():\n",
|
|
" labels = data[\"labels\"]\n",
|
|
" c0_data = lifted.sorted_segments[labels == 0]\n",
|
|
" c1_data = lifted.sorted_segments[labels == 1]\n",
|
|
"\n",
|
|
" within_0 = similarity_score_mmd(\n",
|
|
" c0_data, None, sigma=MMD_SIGMA, n_bootstrap=MMD_BOOTSTRAPS, random_state=RANDOM_STATE\n",
|
|
" )\n",
|
|
" within_1 = similarity_score_mmd(\n",
|
|
" c1_data, None, sigma=MMD_SIGMA, n_bootstrap=MMD_BOOTSTRAPS, random_state=RANDOM_STATE\n",
|
|
" )\n",
|
|
" between = similarity_score_mmd(\n",
|
|
" c0_data, c1_data, sigma=MMD_SIGMA, n_bootstrap=MMD_BOOTSTRAPS, random_state=RANDOM_STATE\n",
|
|
" )\n",
|
|
" mmd_rows.append(\n",
|
|
" {\"method\": name, \"within_0\": within_0, \"within_1\": within_1, \"between\": between}\n",
|
|
" )\n",
|
|
"\n",
|
|
"display(pd.DataFrame(mmd_rows))\n",
|
|
"print(\n",
|
|
" \"Lower within-cluster MMD = more homogeneous clusters; higher between-cluster MMD = better separation.\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a5c11507",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.006777,
|
|
"end_time": "2026-06-14T17:00:41.908594+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:41.901817+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Interpretation**: Low within-cluster MMD indicates that members of each cluster are\n",
|
|
"distributionally similar to each other. High between-cluster MMD indicates the two\n",
|
|
"regimes are distributionally distinct. The ratio between/within serves as a\n",
|
|
"distributional analogue of the F-statistic in ANOVA — higher ratios indicate cleaner\n",
|
|
"separation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "26189752",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.006553,
|
|
"end_time": "2026-06-14T17:00:41.922071+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:41.915518+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Part 6: Visualization (Synthetic Data)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "dc7879b2",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:41.936041Z",
|
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|
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|
|
"shell.execute_reply": "2026-06-14T17:00:42.247661Z"
|
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},
|
|
"papermill": {
|
|
"duration": 0.320187,
|
|
"end_time": "2026-06-14T17:00:42.248726+00:00",
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"exception": false,
|
|
"start_time": "2026-06-14T17:00:41.928539+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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",
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"text/plain": [
|
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"<Figure size 1200x1000 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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}
|
|
],
|
|
"source": [
|
|
"fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
|
|
"\n",
|
|
"# Plot 1: Returns with true regime coloring\n",
|
|
"ax = axes[0, 0]\n",
|
|
"colors = np.where(true_regime == 0, \"#2ecc71\", \"#e74c3c\")\n",
|
|
"ax.scatter(range(len(returns)), returns, c=colors, s=1, alpha=0.5)\n",
|
|
"ax.axhline(0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n",
|
|
"ax.set_xlabel(\"Time\")\n",
|
|
"ax.set_ylabel(\"Log Return\")\n",
|
|
"ax.set_title(\"Simulated Returns (True Regimes)\")\n",
|
|
"ax.legend(\n",
|
|
" handles=[\n",
|
|
" plt.Line2D(\n",
|
|
" [0], [0], marker=\"o\", color=\"w\", markerfacecolor=\"#2ecc71\", label=\"Regime 0 (Bull)\"\n",
|
|
" ),\n",
|
|
" plt.Line2D(\n",
|
|
" [0], [0], marker=\"o\", color=\"w\", markerfacecolor=\"#e74c3c\", label=\"Regime 1 (Bear)\"\n",
|
|
" ),\n",
|
|
" ],\n",
|
|
" loc=\"upper right\",\n",
|
|
")\n",
|
|
"\n",
|
|
"# Plot 2: Mean-Variance projection with WK-means labels\n",
|
|
"ax = axes[0, 1]\n",
|
|
"means = lifted.segments.mean(axis=1)\n",
|
|
"stds = lifted.segments.std(axis=1)\n",
|
|
"wk_labels = results[\"WK-means\"][\"labels\"]\n",
|
|
"colors = np.where(wk_labels == 0, \"#3498db\", \"#9b59b6\")\n",
|
|
"ax.scatter(stds, means, c=colors, s=10, alpha=0.6)\n",
|
|
"ax.set_xlabel(\"Volatility (std)\")\n",
|
|
"ax.set_ylabel(\"Mean Return\")\n",
|
|
"ax.set_title(\"WK-means Clusters (Mean-Vol Space)\")\n",
|
|
"\n",
|
|
"# Plot 3: Regime timeline comparison using imshow (swimlane style)\n",
|
|
"ax = axes[1, 0]\n",
|
|
"ts_labels = aggregate_segment_labels(lifted.starts, WINDOW_LEN, wk_labels, len(returns))\n",
|
|
"\n",
|
|
"# Stack true vs predicted for swimlane comparison\n",
|
|
"regime_stack = np.vstack([true_regime.reshape(1, -1), ts_labels.reshape(1, -1)])\n",
|
|
"cmap_regime = ListedColormap([\"#d0d0d0\", \"#404040\"]) # Light gray = 0, dark gray = 1\n",
|
|
"ax.imshow(regime_stack, aspect=\"auto\", cmap=cmap_regime, vmin=0, vmax=1)\n",
|
|
"ax.set_yticks([0, 1])\n",
|
|
"ax.set_yticklabels([\"True\", \"WK-means\"])\n",
|
|
"ax.set_xlabel(\"Time\")\n",
|
|
"ax.set_title(\"Regime Timeline: True vs WK-means\")\n",
|
|
"\n",
|
|
"# Plot 4: Centroid comparison (quantile functions)\n",
|
|
"ax = axes[1, 1]\n",
|
|
"quantiles = np.linspace(0, 1, WINDOW_LEN)\n",
|
|
"ax.plot(quantiles, wk_result.centroids[0], \"b-\", linewidth=2, label=\"Cluster 0 centroid\")\n",
|
|
"ax.plot(quantiles, wk_result.centroids[1], \"r-\", linewidth=2, label=\"Cluster 1 centroid\")\n",
|
|
"ax.set_xlabel(\"Quantile\")\n",
|
|
"ax.set_ylabel(\"Return\")\n",
|
|
"ax.set_title(\"Wasserstein Barycenters (Cluster Centroids)\")\n",
|
|
"ax.legend()\n",
|
|
"ax.axhline(0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "369c7f2c",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005004,
|
|
"end_time": "2026-06-14T17:00:42.259111+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:42.254107+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Part 7: Application to Real Data\n",
|
|
"\n",
|
|
"Apply WK-means to S&P 500 returns (1980-2025)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "52dc2fce",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:42.269602Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:42.269465Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:42.308261Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:42.307801Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.044639,
|
|
"end_time": "2026-06-14T17:00:42.308544+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:42.263905+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Loaded 11,588 daily S&P 500 returns\n",
|
|
"Date range: 1980-01-03 to 2025-12-31\n",
|
|
"Created 723 segments of length 21\n",
|
|
"Cluster sizes: [522 201]\n",
|
|
"High-volatility cluster: 1 (vol=0.0179)\n",
|
|
"Low-volatility cluster: 0 (vol=0.0074)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Load S&P 500 index data (bundled with repository)\n",
|
|
"sp500_df = load_sp500_index().sort(\"timestamp\")\n",
|
|
"\n",
|
|
"# Compute log returns\n",
|
|
"prices = sp500_df[\"close\"].to_numpy()\n",
|
|
"sp500_returns = np.log(prices[1:] / prices[:-1])\n",
|
|
"sp500_dates = sp500_df[\"timestamp\"].to_numpy()[1:]\n",
|
|
"\n",
|
|
"print(f\"Loaded {len(sp500_returns):,} daily S&P 500 returns\")\n",
|
|
"print(f\"Date range: {sp500_dates[0]} to {sp500_dates[-1]}\")\n",
|
|
"\n",
|
|
"# Lift and cluster\n",
|
|
"real_lifted = lift_stream(sp500_returns, window_len=WINDOW_LEN, overlap=OVERLAP)\n",
|
|
"print(f\"Created {real_lifted.segments.shape[0]} segments of length {WINDOW_LEN}\")\n",
|
|
"\n",
|
|
"real_wk = WassersteinKMeans1D(n_clusters=2, p=1.0, n_init=10, random_state=RANDOM_STATE)\n",
|
|
"real_result = real_wk.fit(real_lifted.sorted_segments)\n",
|
|
"\n",
|
|
"print(f\"Cluster sizes: {np.bincount(real_result.labels)}\")\n",
|
|
"\n",
|
|
"# Identify which cluster is high-vol\n",
|
|
"cluster_vols = [real_lifted.segments[real_result.labels == i].std() for i in range(2)]\n",
|
|
"high_vol_cluster = int(np.argmax(cluster_vols))\n",
|
|
"low_vol_cluster = 1 - high_vol_cluster\n",
|
|
"print(f\"High-volatility cluster: {high_vol_cluster} (vol={cluster_vols[high_vol_cluster]:.4f})\")\n",
|
|
"print(f\"Low-volatility cluster: {low_vol_cluster} (vol={cluster_vols[low_vol_cluster]:.4f})\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "96e1ee09",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.004889,
|
|
"end_time": "2026-06-14T17:00:42.318662+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:42.313773+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### Regime Statistics\n",
|
|
"\n",
|
|
"Show that the clusters capture meaningfully different market conditions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "27134415",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:42.329757Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:42.329597Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:42.335212Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:42.334884Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.011783,
|
|
"end_time": "2026-06-14T17:00:42.335481+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:42.323698+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div><style>\n",
|
|
".dataframe > thead > tr,\n",
|
|
".dataframe > tbody > tr {\n",
|
|
" text-align: right;\n",
|
|
" white-space: pre-wrap;\n",
|
|
"}\n",
|
|
"</style>\n",
|
|
"<small>shape: (2, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Regime</th><th>Days</th><th>% Time</th><th>Ann. Return</th><th>Ann. Vol</th><th>Sharpe</th><th>Max DD</th></tr><tr><td>str</td><td>i64</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>"Low-Vol"</td><td>8667</td><td>"74.8%"</td><td>"+18.9%"</td><td>"12.0%"</td><td>"1.57"</td><td>"-31.0%"</td></tr><tr><td>"High-Vol"</td><td>2921</td><td>"25.2%"</td><td>"-20.1%"</td><td>"29.0%"</td><td>"-0.69"</td><td>"-95.1%"</td></tr></tbody></table></div>"
|
|
],
|
|
"text/plain": [
|
|
"shape: (2, 7)\n",
|
|
"┌──────────┬──────┬────────┬─────────────┬──────────┬────────┬────────┐\n",
|
|
"│ Regime ┆ Days ┆ % Time ┆ Ann. Return ┆ Ann. Vol ┆ Sharpe ┆ Max DD │\n",
|
|
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
|
|
"│ str ┆ i64 ┆ str ┆ str ┆ str ┆ str ┆ str │\n",
|
|
"╞══════════╪══════╪════════╪═════════════╪══════════╪════════╪════════╡\n",
|
|
"│ Low-Vol ┆ 8667 ┆ 74.8% ┆ +18.9% ┆ 12.0% ┆ 1.57 ┆ -31.0% │\n",
|
|
"│ High-Vol ┆ 2921 ┆ 25.2% ┆ -20.1% ┆ 29.0% ┆ -0.69 ┆ -95.1% │\n",
|
|
"└──────────┴──────┴────────┴─────────────┴──────────┴────────┴────────┘"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Compute statistics per regime\n",
|
|
"ts_labels = aggregate_segment_labels(\n",
|
|
" real_lifted.starts, WINDOW_LEN, real_result.labels, len(sp500_returns)\n",
|
|
")\n",
|
|
"\n",
|
|
"regime_stats = []\n",
|
|
"for regime in [low_vol_cluster, high_vol_cluster]:\n",
|
|
" mask = ts_labels == regime\n",
|
|
" regime_returns = sp500_returns[mask]\n",
|
|
" n_days = mask.sum()\n",
|
|
" pct_time = 100 * n_days / len(sp500_returns)\n",
|
|
"\n",
|
|
" # Annualized metrics (252 trading days)\n",
|
|
" ann_return = 252 * regime_returns.mean() * 100\n",
|
|
" ann_vol = np.sqrt(252) * regime_returns.std() * 100\n",
|
|
" sharpe = ann_return / ann_vol if ann_vol > 0 else 0\n",
|
|
"\n",
|
|
" # Drawdown of compounded regime-only returns (bounded by -100%).\n",
|
|
" cum = np.cumprod(1 + regime_returns)\n",
|
|
" peak = np.maximum.accumulate(cum)\n",
|
|
" dd = ((cum - peak) / peak).min() * 100\n",
|
|
"\n",
|
|
" regime_name = \"Low-Vol\" if regime == low_vol_cluster else \"High-Vol\"\n",
|
|
" regime_stats.append(\n",
|
|
" {\n",
|
|
" \"Regime\": regime_name,\n",
|
|
" \"Days\": n_days,\n",
|
|
" \"% Time\": f\"{pct_time:.1f}%\",\n",
|
|
" \"Ann. Return\": f\"{ann_return:+.1f}%\",\n",
|
|
" \"Ann. Vol\": f\"{ann_vol:.1f}%\",\n",
|
|
" \"Sharpe\": f\"{sharpe:.2f}\",\n",
|
|
" \"Max DD\": f\"{dd:.1f}%\",\n",
|
|
" }\n",
|
|
" )\n",
|
|
"\n",
|
|
"stats_df = pl.DataFrame(regime_stats)\n",
|
|
"stats_df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2664cae4",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.004875,
|
|
"end_time": "2026-06-14T17:00:42.345838+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:42.340963+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Interpretation**: The low-vol regime shows higher annualized returns with\n",
|
|
"substantially lower volatility, producing a superior Sharpe ratio — the classic\n",
|
|
"\"volatility drag\" effect. High-vol periods have deeper drawdowns and may\n",
|
|
"exhibit negative mean returns. This regime asymmetry is why regime-conditional\n",
|
|
"position sizing (reduce exposure in high-vol) captures most of the portfolio\n",
|
|
"benefit from regime detection."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c32b9ed2",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005101,
|
|
"end_time": "2026-06-14T17:00:42.355826+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:42.350725+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### ml4t-diagnostic: Distributional Distance Comparison\n",
|
|
"\n",
|
|
"Our custom Wasserstein implementation above operates on sorted quantiles of\n",
|
|
"windowed return distributions. `ml4t-diagnostic` provides `compute_wasserstein_distance()`\n",
|
|
"and `compute_psi()` (Population Stability Index) for comparing two samples.\n",
|
|
"We use them to measure inter-cluster divergence as a validation metric."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "9f2d6fa6",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:42.366618Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:42.366474Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:45.170871Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:45.170469Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 2.810992,
|
|
"end_time": "2026-06-14T17:00:45.171754+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:42.360762+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"=== ml4t-diagnostic: Inter-Cluster Divergence ===\n",
|
|
"Wasserstein distance: 0.007385\n",
|
|
"Drifted: True\n",
|
|
"PSI: 0.5549 (alert level: red)\n",
|
|
"\n",
|
|
"PSI interpretation: <0.1 = stable, 0.1-0.2 = moderate shift, >0.2 = significant shift\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Sample returns from each cluster\n",
|
|
"c0_returns = sp500_returns[ts_labels == low_vol_cluster]\n",
|
|
"c1_returns = sp500_returns[ts_labels == high_vol_cluster]\n",
|
|
"\n",
|
|
"w_result = compute_wasserstein_distance(c0_returns, c1_returns)\n",
|
|
"psi_result = compute_psi(c0_returns, c1_returns)\n",
|
|
"\n",
|
|
"print(\"=== ml4t-diagnostic: Inter-Cluster Divergence ===\")\n",
|
|
"print(f\"Wasserstein distance: {w_result.distance:.6f}\")\n",
|
|
"print(f\"Drifted: {w_result.drifted}\")\n",
|
|
"print(f\"PSI: {psi_result.psi:.4f} (alert level: {psi_result.alert_level})\")\n",
|
|
"print()\n",
|
|
"print(\"PSI interpretation: <0.1 = stable, 0.1-0.2 = moderate shift, >0.2 = significant shift\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b25622de",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.020643,
|
|
"end_time": "2026-06-14T17:00:45.212797+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:45.192154+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Interpretation**: The Wasserstein distance between cluster return distributions\n",
|
|
"quantifies how different the two regimes are in terms of optimal transport cost.\n",
|
|
"PSI provides a complementary bin-based measure commonly used in production\n",
|
|
"model monitoring. Both metrics confirm that the WK-means clusters capture\n",
|
|
"meaningfully different market conditions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b2e20a45",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.018761,
|
|
"end_time": "2026-06-14T17:00:45.256799+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:45.238038+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### Swimlane Visualization\n",
|
|
"\n",
|
|
"Four-panel view: regime timeline, cumulative returns, rolling volatility, and centroids."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "c8e91a8d",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:45.279650Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:45.279130Z",
|
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"iopub.status.idle": "2026-06-14T17:00:45.630553Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:45.630048Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.362461,
|
|
"end_time": "2026-06-14T17:00:45.631025+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:45.268564+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"text/plain": [
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"<Figure size 1400x1000 with 4 Axes>"
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|
]
|
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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}
|
|
],
|
|
"source": [
|
|
"import matplotlib.dates as mdates\n",
|
|
"\n",
|
|
"# Convert dates for matplotlib\n",
|
|
"dates_num = mdates.date2num(pd.to_datetime(sp500_dates))\n",
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"\n",
|
|
"fig, axes = plt.subplots(4, 1, figsize=(14, 10), height_ratios=[1, 3, 2, 2])\n",
|
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"fig.subplots_adjust(hspace=0.05)\n",
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"\n",
|
|
"# Panel 1: Regime swimlane (imshow)\n",
|
|
"ax1 = axes[0]\n",
|
|
"labels_2d = ts_labels.reshape(1, -1)\n",
|
|
"# Map: low-vol=light gray, high-vol=dark gray\n",
|
|
"label_colors = np.where(labels_2d == high_vol_cluster, 1, 0)\n",
|
|
"cmap_regime = ListedColormap([\"#d0d0d0\", \"#404040\"])\n",
|
|
"ax1.imshow(\n",
|
|
" label_colors, aspect=\"auto\", cmap=cmap_regime, extent=[dates_num[0], dates_num[-1], 0, 1]\n",
|
|
")\n",
|
|
"ax1.set_yticks([0.5])\n",
|
|
"ax1.set_yticklabels([\"Regime\"])\n",
|
|
"ax1.set_xlim(dates_num[0], dates_num[-1])\n",
|
|
"ax1.xaxis.set_visible(False)\n",
|
|
"ax1.set_title(\"S&P 500 Regime Detection via Wasserstein K-Means (1980-2025)\")\n",
|
|
"\n",
|
|
"# Add legend\n",
|
|
"legend_elements = [\n",
|
|
" Patch(facecolor=\"#d0d0d0\", edgecolor=\"black\", label=\"Low Volatility\"),\n",
|
|
" Patch(facecolor=\"#404040\", edgecolor=\"black\", label=\"High Volatility\"),\n",
|
|
"]\n",
|
|
"ax1.legend(handles=legend_elements, loc=\"upper right\", ncol=2, fontsize=8)\n",
|
|
"\n",
|
|
"# Panel 2: Cumulative returns (log scale)\n",
|
|
"ax2 = axes[1]\n",
|
|
"cum_returns = np.exp(np.cumsum(sp500_returns))\n",
|
|
"ax2.semilogy(dates_num, cum_returns, \"k-\", linewidth=0.5)\n",
|
|
"ax2.set_ylabel(\"Cumulative Return\\n(log scale)\")\n",
|
|
"ax2.set_xlim(dates_num[0], dates_num[-1])\n",
|
|
"ax2.xaxis.set_visible(False)\n",
|
|
"ax2.grid(True, alpha=0.3)\n",
|
|
"\n",
|
|
"# Shade high-vol periods\n",
|
|
"high_vol_mask = ts_labels == high_vol_cluster\n",
|
|
"ax2.fill_between(\n",
|
|
" dates_num,\n",
|
|
" cum_returns.min(),\n",
|
|
" cum_returns.max(),\n",
|
|
" where=high_vol_mask,\n",
|
|
" alpha=0.2,\n",
|
|
" color=\"#404040\",\n",
|
|
")\n",
|
|
"\n",
|
|
"# Panel 3: Rolling volatility (21-day)\n",
|
|
"ax3 = axes[2]\n",
|
|
"roll_vol = pd.Series(sp500_returns).rolling(21).std() * np.sqrt(252) * 100\n",
|
|
"ax3.plot(dates_num, roll_vol.values, \"b-\", linewidth=0.5, alpha=0.7)\n",
|
|
"ax3.axhline(\n",
|
|
" roll_vol.median(),\n",
|
|
" color=\"gray\",\n",
|
|
" linestyle=\"--\",\n",
|
|
" alpha=0.5,\n",
|
|
" label=f\"Median: {roll_vol.median():.1f}%\",\n",
|
|
")\n",
|
|
"ax3.set_ylabel(\"21-day Vol\\n(ann. %)\")\n",
|
|
"ax3.set_xlim(dates_num[0], dates_num[-1])\n",
|
|
"ax3.xaxis.set_visible(False)\n",
|
|
"ax3.legend(loc=\"upper right\", fontsize=8)\n",
|
|
"ax3.grid(True, alpha=0.3)\n",
|
|
"\n",
|
|
"# Shade high-vol periods\n",
|
|
"ax3.fill_between(dates_num, 0, roll_vol.max(), where=high_vol_mask, alpha=0.2, color=\"#404040\")\n",
|
|
"\n",
|
|
"# Panel 4: Wasserstein centroids (quantile functions)\n",
|
|
"ax4 = axes[3]\n",
|
|
"quantiles = np.linspace(0, 1, WINDOW_LEN)\n",
|
|
"ax4.plot(\n",
|
|
" quantiles,\n",
|
|
" real_result.centroids[low_vol_cluster] * 100,\n",
|
|
" \"#3498db\",\n",
|
|
" linewidth=2,\n",
|
|
" label=\"Low-Vol Centroid\",\n",
|
|
")\n",
|
|
"ax4.plot(\n",
|
|
" quantiles,\n",
|
|
" real_result.centroids[high_vol_cluster] * 100,\n",
|
|
" \"#e74c3c\",\n",
|
|
" linewidth=2,\n",
|
|
" label=\"High-Vol Centroid\",\n",
|
|
")\n",
|
|
"ax4.axhline(0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n",
|
|
"ax4.set_xlabel(\"Quantile\")\n",
|
|
"ax4.set_ylabel(\"Return (%)\")\n",
|
|
"ax4.legend(loc=\"upper left\", fontsize=8)\n",
|
|
"ax4.set_title(\"Wasserstein Barycenters (Cluster Centroids)\", fontsize=10)\n",
|
|
"ax4.grid(True, alpha=0.3)\n",
|
|
"\n",
|
|
"# Format x-axis for date panels\n",
|
|
"for ax in [ax1, ax2, ax3]:\n",
|
|
" ax.xaxis_date()\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
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"plt.show()"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "markdown",
|
|
"id": "fe35d8d4",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005652,
|
|
"end_time": "2026-06-14T17:00:45.642947+00:00",
|
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"exception": false,
|
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"start_time": "2026-06-14T17:00:45.637295+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Feature Extraction\n",
|
|
"\n",
|
|
"Construct the three features listed in the chapter's feature catalog:\n",
|
|
"`wasserstein_cluster`, `cluster_distance`, and `tail_divergence`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "9bffc509",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:45.654831Z",
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"iopub.status.busy": "2026-06-14T17:00:45.654695Z",
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},
|
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"papermill": {
|
|
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|
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"end_time": "2026-06-14T17:00:45.662429+00:00",
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"exception": false,
|
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"start_time": "2026-06-14T17:00:45.648441+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 1. wasserstein_cluster: cluster assignment at each timestamp\n",
|
|
"wasserstein_cluster = ts_labels.copy()\n",
|
|
"\n",
|
|
"# 2. cluster_distance: Wasserstein distance from each window to its assigned centroid\n",
|
|
"segment_distances = np.zeros(real_lifted.sorted_segments.shape[0])\n",
|
|
"for i, (seg, lab) in enumerate(zip(real_lifted.sorted_segments, real_result.labels, strict=False)):\n",
|
|
" segment_distances[i] = wasserstein_distance_1d(seg, real_result.centroids[lab], p=1.0)\n",
|
|
"\n",
|
|
"# Aggregate segment-level distances to timestamp-level (equal-weighted average)\n",
|
|
"# Each timestamp may be covered by multiple overlapping windows; accumulate\n",
|
|
"# sum and count, then divide for a proper average\n",
|
|
"ts_distance_sum = np.zeros(len(sp500_returns))\n",
|
|
"ts_distance_count = np.zeros(len(sp500_returns))\n",
|
|
"for start, dist in zip(real_lifted.starts, segment_distances, strict=False):\n",
|
|
" end = min(start + WINDOW_LEN, len(sp500_returns))\n",
|
|
" ts_distance_sum[start:end] += dist\n",
|
|
" ts_distance_count[start:end] += 1\n",
|
|
"\n",
|
|
"ts_cluster_distance = np.where(ts_distance_count > 0, ts_distance_sum / ts_distance_count, np.nan)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"id": "06150faf",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-14T17:00:45.674356Z",
|
|
"iopub.status.busy": "2026-06-14T17:00:45.674248Z",
|
|
"iopub.status.idle": "2026-06-14T17:00:45.684440Z",
|
|
"shell.execute_reply": "2026-06-14T17:00:45.683925Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.016577,
|
|
"end_time": "2026-06-14T17:00:45.684802+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-14T17:00:45.668225+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Cluster distribution: [8667 2921]\n",
|
|
"Mean cluster distance: 0.002839\n",
|
|
"Tail divergence rate: 18.6% of days\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div><style>\n",
|
|
".dataframe > thead > tr,\n",
|
|
".dataframe > tbody > tr {\n",
|
|
" text-align: right;\n",
|
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" white-space: pre-wrap;\n",
|
|
"}\n",
|
|
"</style>\n",
|
|
"<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>wasserstein_cluster</th><th>cluster_distance</th><th>tail_divergence</th></tr><tr><td>date</td><td>i64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-12-24</td><td>0</td><td>NaN</td><td>0.0</td></tr><tr><td>2025-12-26</td><td>0</td><td>NaN</td><td>0.0</td></tr><tr><td>2025-12-29</td><td>0</td><td>NaN</td><td>0.0</td></tr><tr><td>2025-12-30</td><td>0</td><td>NaN</td><td>0.0</td></tr><tr><td>2025-12-31</td><td>0</td><td>NaN</td><td>0.0</td></tr></tbody></table></div>"
|
|
],
|
|
"text/plain": [
|
|
"shape: (5, 4)\n",
|
|
"┌────────────┬─────────────────────┬──────────────────┬─────────────────┐\n",
|
|
"│ timestamp ┆ wasserstein_cluster ┆ cluster_distance ┆ tail_divergence │\n",
|
|
"│ --- ┆ --- ┆ --- ┆ --- │\n",
|
|
"│ date ┆ i64 ┆ f64 ┆ f64 │\n",
|
|
"╞════════════╪═════════════════════╪══════════════════╪═════════════════╡\n",
|
|
"│ 2025-12-24 ┆ 0 ┆ NaN ┆ 0.0 │\n",
|
|
"│ 2025-12-26 ┆ 0 ┆ NaN ┆ 0.0 │\n",
|
|
"│ 2025-12-29 ┆ 0 ┆ NaN ┆ 0.0 │\n",
|
|
"│ 2025-12-30 ┆ 0 ┆ NaN ┆ 0.0 │\n",
|
|
"│ 2025-12-31 ┆ 0 ┆ NaN ┆ 0.0 │\n",
|
|
"└────────────┴─────────────────────┴──────────────────┴─────────────────┘"
|
|
]
|
|
},
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 3. tail_divergence: difference between Wasserstein and moment-based cluster assignment\n",
|
|
"# Moment-based clustering uses mean and variance only\n",
|
|
"mk_real = fit_moment_kmeans(\n",
|
|
" real_lifted.segments, n_clusters=2, n_moments=2, random_state=RANDOM_STATE\n",
|
|
")\n",
|
|
"moment_ts_labels = aggregate_segment_labels(\n",
|
|
" real_lifted.starts, WINDOW_LEN, mk_real.labels, len(sp500_returns)\n",
|
|
")\n",
|
|
"# Align labels: ensure both methods use the same convention (high-vol = 1)\n",
|
|
"moment_vols = [sp500_returns[moment_ts_labels == i].std() for i in range(2)]\n",
|
|
"moment_high_vol = int(np.argmax(moment_vols))\n",
|
|
"if moment_high_vol != high_vol_cluster:\n",
|
|
" moment_ts_labels = 1 - moment_ts_labels\n",
|
|
"\n",
|
|
"# tail_divergence = 1 where methods disagree (distributional shape matters)\n",
|
|
"tail_divergence = (wasserstein_cluster != moment_ts_labels).astype(np.float64)\n",
|
|
"\n",
|
|
"# Combine into feature DataFrame\n",
|
|
"feature_df = pl.DataFrame(\n",
|
|
" {\n",
|
|
" \"timestamp\": sp500_dates,\n",
|
|
" \"wasserstein_cluster\": wasserstein_cluster,\n",
|
|
" \"cluster_distance\": ts_cluster_distance,\n",
|
|
" \"tail_divergence\": tail_divergence,\n",
|
|
" }\n",
|
|
")\n",
|
|
"\n",
|
|
"print(f\"Cluster distribution: {np.bincount(wasserstein_cluster)}\")\n",
|
|
"print(f\"Mean cluster distance: {np.nanmean(ts_cluster_distance):.6f}\")\n",
|
|
"print(f\"Tail divergence rate: {tail_divergence.mean():.1%} of days\")\n",
|
|
"feature_df.tail(5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4eece935",
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"metadata": {
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|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"`tail_divergence` flags periods where Wasserstein and moment-based methods\n",
|
|
"disagree — indicating that distributional shape (tails, skewness) rather than\n",
|
|
"just mean/variance is driving the regime classification. High tail divergence\n",
|
|
"signals potential hedging demand."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "42dd3ecc",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005755,
|
|
"end_time": "2026-06-14T17:00:45.711175+00:00",
|
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"exception": false,
|
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"start_time": "2026-06-14T17:00:45.705420+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Summary\n",
|
|
"\n",
|
|
"**Key findings from this notebook:**\n",
|
|
"\n",
|
|
"1. **Wasserstein k-means** captures distributional shape, not just moments\n",
|
|
"2. The **stream lift** converts time series into a clustering problem on distributions\n",
|
|
"3. **MMD validation** provides a model-free assessment of cluster quality\n",
|
|
"4. WK-means often achieves **better ARI** when distributions differ in shape (tails, skewness)\n",
|
|
"5. For simple mean-variance regime changes, moment methods may perform comparably\n",
|
|
"\n",
|
|
"**When to use WK-means over traditional methods:**\n",
|
|
"- Regime changes involve tail behavior or higher moments\n",
|
|
"- You want a distribution-aware distance metric\n",
|
|
"- Interpretable centroids as return quantile functions\n",
|
|
"\n",
|
|
"**Reference**: Horvath et al. (2021) \"Clustering Market Regimes Using the Wasserstein Distance\"\n",
|
|
"\n",
|
|
"**Previous**: `11_hmm_regimes` for parametric regime detection (HMM, MS-AR).\n",
|
|
"**Next**: `13_regime_as_feature` for integrating regime features into ML pipelines."
|
|
]
|
|
}
|
|
],
|
|
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