wehub-resource-sync
2648 lines
588 KiB
Plaintext
2648 lines
588 KiB
Plaintext
{
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"cells": [
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"cell_type": "markdown",
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"id": "5b7c4f74",
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"metadata": {
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"papermill": {
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"duration": 0.015624,
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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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"# Macro-Based Regime Detection\n",
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"\n",
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"**Chapter 1 · §1.4 Market Regimes: Change Is the Constant**\n",
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"\n",
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"**Docker image**: `ml4t`\n",
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"\n",
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"## Purpose\n",
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"\n",
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"Demonstrates unsupervised learning for market regime detection using macroeconomic\n",
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"indicators from FRED, paired with S&P 500 volatility and drawdown for validation.\n",
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"\n",
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"## Learning Objectives\n",
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"\n",
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"- Cluster monthly macro indicators with GMM, K-Means, and hierarchical methods.\n",
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"- Validate macro clusters against realized equity volatility and drawdown.\n",
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"- Compare a four-indicator core view to an extended FRED panel.\n",
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"- Use PCA preprocessing to filter noise before clustering.\n",
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"\n",
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"## Book Reference\n",
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"\n",
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"Section 1.4 of Chapter 1, \"Market Regimes: Change Is the Constant\" — macro-regime\n",
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"follow-up to `factor_regimes.py`. Figure 1.6 in the chapter is generated here.\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"- Familiarity with monthly time-series resampling and standardization.\n",
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"- Conceptual exposure to mixture models, K-Means, and dendrograms.\n",
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"- FRED panel and S&P 500 daily series materialized via `data/macro/` and\n",
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" `data/equities/sp500/` loaders.\n",
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"\n",
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"## Structure\n",
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"\n",
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"1. **Core Analysis (4 indicators)** — UNRATE, DFF, T10Y2Y, CPIAUCSL → CPI YoY.\n",
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"2. **Extended Analysis** — full FRED panel (after coverage filtering) for a richer\n",
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" but noisier view of economic conditions.\n",
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"3. **Comparison** — silhouette scores across core, extended, and PCA-reduced models.\n",
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"\n",
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"## Key Insight\n",
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"\n",
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"Macro regimes line up with distinct *volatility* environments more cleanly than they\n",
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"line up with average returns. This makes macro indicators useful for risk management\n",
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"(anticipating volatility shifts) rather than return prediction."
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]
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},
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{
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"cell_type": "markdown",
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"id": "94596d5b",
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"metadata": {
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"papermill": {
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"duration": 0.004601,
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"end_time": "2026-06-13T02:32:15.022809+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:15.018208+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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"## Imports"
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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": "dd91aade",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-16T02:17:02.614848Z",
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"iopub.status.busy": "2026-06-16T02:17:02.614783Z",
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"iopub.status.idle": "2026-06-16T02:17:04.705995Z",
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"shell.execute_reply": "2026-06-16T02:17:04.705442Z"
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},
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"papermill": {
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"duration": 1.936104,
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||
"end_time": "2026-06-13T02:32:16.961571+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:15.025467+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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"\"\"\"Macro-Based Regime Detection — unsupervised regime detection using FRED macro indicators.\"\"\"\n",
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"\n",
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"from __future__ import annotations\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 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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"import seaborn as sns\n",
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"from matplotlib.gridspec import GridSpec\n",
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"from scipy.cluster.hierarchy import cophenet, dendrogram, linkage\n",
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"from scipy.spatial.distance import pdist\n",
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"from sklearn.cluster import KMeans\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.metrics import silhouette_score\n",
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"from sklearn.mixture import GaussianMixture\n",
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"from sklearn.preprocessing import StandardScaler, scale\n",
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"\n",
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"from data import load_macro, load_sp500_index\n",
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"from utils.paths import get_output_dir\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": "300ec80c",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-16T02:17:04.707676Z",
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"iopub.status.busy": "2026-06-16T02:17:04.707540Z",
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"iopub.status.idle": "2026-06-16T02:17:04.709681Z",
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"shell.execute_reply": "2026-06-16T02:17:04.709107Z"
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},
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"papermill": {
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"duration": 0.005189,
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"end_time": "2026-06-13T02:32:16.969341+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:16.964152+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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"# Production defaults (Papermill overrides for testing)\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": "markdown",
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"id": "b8958229",
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"metadata": {
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"papermill": {
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"duration": 0.004236,
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"end_time": "2026-06-13T02:32:16.977901+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:16.973665+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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"## 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": 3,
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"id": "cecba650",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-16T02:17:04.710781Z",
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"iopub.status.busy": "2026-06-16T02:17:04.710677Z",
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"iopub.status.idle": "2026-06-16T02:17:04.872781Z",
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"shell.execute_reply": "2026-06-16T02:17:04.872272Z"
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},
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"papermill": {
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"duration": 0.164604,
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"end_time": "2026-06-13T02:32:17.146457+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:16.981853+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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||
"OUTPUT_DIR = get_output_dir(1, \"macro_regimes\")\n",
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"OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"set_global_seeds(SEED)\n",
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"\n",
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"DATE_COL = \"timestamp\""
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||
]
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},
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{
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"cell_type": "markdown",
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"id": "e076cec1",
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"metadata": {
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"lines_to_next_cell": 2,
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"papermill": {
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"duration": 0.002504,
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"end_time": "2026-06-13T02:32:17.151603+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:17.149099+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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"## Helper Function\n",
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"\n",
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"Reusable function for regime visualization. Both GMM and K-Means results\n",
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"use the same heatmap format for easy comparison."
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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": "ef856c8c",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-16T02:17:04.874148Z",
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"iopub.status.busy": "2026-06-16T02:17:04.874075Z",
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"iopub.status.idle": "2026-06-16T02:17:04.877217Z",
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"shell.execute_reply": "2026-06-16T02:17:04.876957Z"
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},
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"papermill": {
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"duration": 0.006495,
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"end_time": "2026-06-13T02:32:17.160471+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:17.153976+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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"def plot_regime_heatmap(\n",
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" assignments: np.ndarray,\n",
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" dates: pd.DatetimeIndex,\n",
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" title: str,\n",
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" n_regimes: int = 4,\n",
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" is_probability: bool = True,\n",
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" figsize: tuple = (14, 4),\n",
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") -> None:\n",
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" \"\"\"\n",
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" Plot regime assignments as a heatmap.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" assignments : np.ndarray\n",
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" Either probability matrix (n_samples, n_regimes) for GMM\n",
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" or hard labels (n_samples,) for K-Means\n",
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" dates : pd.DatetimeIndex\n",
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" Dates corresponding to each sample\n",
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" title : str\n",
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" Plot title\n",
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" n_regimes : int\n",
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" Number of regimes\n",
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" is_probability : bool\n",
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" True if assignments are probabilities, False for hard labels\n",
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" figsize : tuple\n",
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" Figure size\n",
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" \"\"\"\n",
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" fig, ax = plt.subplots(figsize=figsize, layout=\"tight\")\n",
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"\n",
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" # Convert hard labels to one-hot if needed\n",
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" if not is_probability:\n",
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" one_hot = np.zeros((len(assignments), n_regimes))\n",
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" one_hot[np.arange(len(assignments)), assignments] = 1\n",
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" data = one_hot\n",
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" else:\n",
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" data = assignments\n",
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"\n",
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" im = ax.imshow(data.T, aspect=\"auto\", cmap=\"Blues\", vmin=0, vmax=1)\n",
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"\n",
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" # X-axis: years\n",
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" years = [pd.Timestamp(ts).year for ts in dates]\n",
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" year_ticks = [j for j, y in enumerate(years) if j == 0 or years[j - 1] != y]\n",
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" year_labels = [str(years[j]) for j in year_ticks]\n",
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" ax.set_xticks(year_ticks[::2])\n",
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" ax.set_xticklabels(year_labels[::2], fontsize=9)\n",
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"\n",
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" # Y-axis: regimes\n",
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" ax.set_yticks(range(n_regimes))\n",
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" ax.set_yticklabels([f\"Regime {i + 1}\" for i in range(n_regimes)])\n",
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" ax.set_ylabel(\"Regime\")\n",
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" ax.set_xlabel(\"Year\")\n",
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" ax.set_title(title, fontsize=12)\n",
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"\n",
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" cbar = plt.colorbar(im, ax=ax, shrink=0.8)\n",
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" cbar.set_label(\"Probability\" if is_probability else \"Assignment\")\n",
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||
" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5e26a8db",
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"metadata": {
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"papermill": {
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"duration": 0.002336,
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"end_time": "2026-06-13T02:32:17.165222+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:17.162886+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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"---\n",
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"\n",
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"# Load FRED Macro Data\n",
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"\n",
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||
"We load all available FRED macro indicators once, then select subsets for analysis.\n",
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"The dataset includes ~17 series after filtering for data quality."
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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"id": "39b04cc7",
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"metadata": {
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||
"papermill": {
|
||
"duration": 0.002333,
|
||
"end_time": "2026-06-13T02:32:17.169890+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:17.167557+00:00",
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"status": "completed"
|
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}
|
||
},
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||
"source": [
|
||
"## Load Full Dataset"
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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": "a6f66d98",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-16T02:17:04.878225Z",
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"iopub.status.busy": "2026-06-16T02:17:04.878164Z",
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"iopub.status.idle": "2026-06-16T02:17:04.888382Z",
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"shell.execute_reply": "2026-06-16T02:17:04.888077Z"
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},
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"papermill": {
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"duration": 0.015659,
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"end_time": "2026-06-13T02:32:17.187917+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:32:17.172258+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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||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
|
||
"Loaded FRED macro data: (9497, 26)\n",
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"Date range: 2000-01-01 00:00:00 to 2025-12-31 00:00:00\n",
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"Available series: ['dff', 'dgs1', 'dgs2', 'dgs3', 'dgs5', 'dgs7', 'dgs10', 'dgs20', 'dgs30', 't10y2y', 'vixcls', 'icsa', 'walcl', 'cpiaucsl', 'cpilfesl', 'pcepi', 'unrate', 'payems', 'civpart', 'indpro', 'm2sl', 'gdp', 'gdpc1', 'YIELD_CURVE_SLOPE', 'YIELD_CURVE_5_10']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"macro_raw = load_macro()\n",
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||
"\n",
|
||
"# group_by_dynamic requires Datetime, not Date\n",
|
||
"if macro_raw[\"timestamp\"].dtype == pl.Date:\n",
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||
" macro_raw = macro_raw.with_columns(pl.col(\"timestamp\").cast(pl.Datetime))\n",
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||
"\n",
|
||
"print(f\"Loaded FRED macro data: {macro_raw.shape}\")\n",
|
||
"print(f\"Date range: {macro_raw['timestamp'].min()} to {macro_raw['timestamp'].max()}\")\n",
|
||
"print(f\"Available series: {[c for c in macro_raw.columns if c != 'timestamp']}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8066dec1",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002433,
|
||
"end_time": "2026-06-13T02:32:17.193015+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.190582+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Available Indicators\n",
|
||
"\n",
|
||
"| Category | Series | Description |\n",
|
||
"|----------|--------|-------------|\n",
|
||
"| **Labor** | UNRATE | Unemployment rate (%) |\n",
|
||
"| **Interest Rates** | DFF | Federal Funds effective rate (%) |\n",
|
||
"| | T10Y2Y | 10Y-2Y Treasury spread (yield curve) |\n",
|
||
"| | T10YIE | 10Y breakeven inflation |\n",
|
||
"| **Prices** | CPIAUCSL | Consumer Price Index |\n",
|
||
"| | CPILFESL | Core CPI (ex food & energy) |\n",
|
||
"| **Volatility** | VIXCLS | VIX volatility index |\n",
|
||
"| **Credit** | BAMLHE00EHYIEY | High yield spread |\n",
|
||
"| **Housing** | CSUSHPISA | Case-Shiller home price index |\n",
|
||
"| **Money** | M2SL | M2 money supply |\n",
|
||
"\n",
|
||
"For the **Core Analysis**, we use only 4 fundamental macro indicators."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "13ce6bc7",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002354,
|
||
"end_time": "2026-06-13T02:32:17.197744+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.195390+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"# Core Analysis: 4 Macro Indicators\n",
|
||
"\n",
|
||
"We use four indicators that reflect real economic conditions:\n",
|
||
"\n",
|
||
"- **UNRATE**: Unemployment rate - labor market health\n",
|
||
"- **DFF**: Federal Funds rate - monetary policy stance\n",
|
||
"- **T10Y2Y**: Yield curve slope - recession signal\n",
|
||
"- **CPIAUCSL → cpi_yoy**: CPI year-over-year inflation rate (not the level, which trends upward)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8409a518",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002356,
|
||
"end_time": "2026-06-13T02:32:17.202473+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.200117+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Select Core Indicators"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "f00a300a",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:04.889318Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:04.889250Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:04.891300Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:04.891034Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.005722,
|
||
"end_time": "2026-06-13T02:32:17.210554+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.204832+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Core indicators selected: 4/4\n",
|
||
" unrate -> unrate\n",
|
||
" dff -> dff\n",
|
||
" t10y2y -> t10y2y\n",
|
||
" cpiaucsl -> cpiaucsl\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Core 4 indicators (case-insensitive matching)\n",
|
||
"CORE_INDICATORS = [\"unrate\", \"dff\", \"t10y2y\", \"cpiaucsl\"]\n",
|
||
"\n",
|
||
"macro_columns = macro_raw.columns\n",
|
||
"core_cols = []\n",
|
||
"col_name_map = {}\n",
|
||
"\n",
|
||
"for indicator in CORE_INDICATORS:\n",
|
||
" for col in macro_columns:\n",
|
||
" if col.lower() == indicator:\n",
|
||
" core_cols.append(col)\n",
|
||
" col_name_map[col] = indicator\n",
|
||
" break\n",
|
||
"\n",
|
||
"print(f\"Core indicators selected: {len(core_cols)}/{len(CORE_INDICATORS)}\")\n",
|
||
"for col in core_cols:\n",
|
||
" print(f\" {col} -> {col_name_map[col]}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8ab33211",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002391,
|
||
"end_time": "2026-06-13T02:32:17.215365+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.212974+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Resample to Monthly"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "d7c67986",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:04.892197Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:04.892129Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:04.896470Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:04.896202Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.007748,
|
||
"end_time": "2026-06-13T02:32:17.225533+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.217785+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Monthly data: 289 months\n",
|
||
"Date range: 2002-01-01 00:00:00 to 2026-01-01 00:00:00\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"macro_monthly = (\n",
|
||
" macro_raw.select([DATE_COL] + core_cols)\n",
|
||
" .sort(DATE_COL)\n",
|
||
" .group_by_dynamic(DATE_COL, every=\"1mo\", label=\"right\")\n",
|
||
" .agg([pl.col(c).last() for c in core_cols])\n",
|
||
" # Forward-fill carries last observation through release lags; the small\n",
|
||
" # backward-fill catches any leading nulls in early months. The latter\n",
|
||
" # introduces a one-period look-ahead at the panel boundary — acceptable\n",
|
||
" # for a regime-detection demo, not for a backtested strategy.\n",
|
||
" .fill_null(strategy=\"forward\")\n",
|
||
" .fill_null(strategy=\"backward\")\n",
|
||
" .drop_nulls(subset=core_cols)\n",
|
||
")\n",
|
||
"\n",
|
||
"# Filter to 2002+ where most FRED series have good coverage\n",
|
||
"if macro_monthly.height > 0:\n",
|
||
" min_date = macro_monthly[DATE_COL].min()\n",
|
||
" if min_date is not None and str(min_date) < \"2002-01-01\":\n",
|
||
" macro_monthly = macro_monthly.filter(pl.col(DATE_COL) >= pl.datetime(2002, 1, 1))\n",
|
||
"\n",
|
||
"print(f\"Monthly data: {macro_monthly.height} months\")\n",
|
||
"if macro_monthly.height > 0:\n",
|
||
" print(f\"Date range: {macro_monthly[DATE_COL].min()} to {macro_monthly[DATE_COL].max()}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a254f909",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002454,
|
||
"end_time": "2026-06-13T02:32:17.230449+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.227995+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Standardize for Clustering"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "8a57dedc",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:04.897377Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:04.897308Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:04.908210Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:04.907897Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.014435,
|
||
"end_time": "2026-06-13T02:32:17.247331+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.232896+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Core Indicator Statistics (raw):\n",
|
||
" unrate dff t10y2y cpi_yoy\n",
|
||
"count 277.00 277.00 277.00 277.00\n",
|
||
"mean 5.75 1.76 1.07 2.58\n",
|
||
"std 2.02 1.91 0.97 1.81\n",
|
||
"min 3.40 0.04 -1.06 -1.96\n",
|
||
"25% 4.20 0.10 0.24 1.64\n",
|
||
"50% 5.00 1.04 1.01 2.33\n",
|
||
"75% 6.70 3.09 1.90 3.24\n",
|
||
"max 14.80 5.41 2.84 8.98\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if macro_monthly.height > 0:\n",
|
||
" macro_df = macro_monthly.select(core_cols).to_pandas()\n",
|
||
" macro_df.columns = [col_name_map.get(c, c) for c in macro_df.columns]\n",
|
||
" macro_df.index = macro_monthly[DATE_COL].to_pandas()\n",
|
||
"\n",
|
||
" # Convert CPI level to YoY percentage change — the level is non-stationary\n",
|
||
" # and would make the clustering capture \"early vs recent\" rather than\n",
|
||
" # \"inflationary vs non-inflationary\"\n",
|
||
" macro_df[\"cpi_yoy\"] = macro_df[\"cpiaucsl\"].pct_change(12) * 100\n",
|
||
" macro_df = macro_df.drop(columns=[\"cpiaucsl\"]).dropna()\n",
|
||
"\n",
|
||
" macro_scaled = StandardScaler().fit_transform(macro_df)\n",
|
||
"\n",
|
||
" print(\"Core Indicator Statistics (raw):\")\n",
|
||
" print(macro_df.describe().round(2))\n",
|
||
"else:\n",
|
||
" macro_df = pd.DataFrame()\n",
|
||
" macro_scaled = np.array([])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d2160737",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002477,
|
||
"end_time": "2026-06-13T02:32:17.252441+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.249964+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Fit GMM with 4 Regimes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "d6669fa5",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:04.908992Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:04.908926Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.002970Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.002622Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.073699,
|
||
"end_time": "2026-06-13T02:32:17.328614+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.254915+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Macro regime silhouette score (n=4): 0.252\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(macro_df) > 0:\n",
|
||
" n_macro_regimes = 4\n",
|
||
" gmm_macro = GaussianMixture(\n",
|
||
" n_components=n_macro_regimes,\n",
|
||
" covariance_type=\"full\",\n",
|
||
" random_state=SEED,\n",
|
||
" n_init=10,\n",
|
||
" reg_covar=1e-6,\n",
|
||
" )\n",
|
||
" gmm_macro.fit(macro_scaled)\n",
|
||
" macro_labels = gmm_macro.predict(macro_scaled)\n",
|
||
" macro_probs = gmm_macro.predict_proba(macro_scaled)\n",
|
||
"\n",
|
||
" macro_silhouette = silhouette_score(macro_scaled, macro_labels)\n",
|
||
" print(f\"Macro regime silhouette score (n={n_macro_regimes}): {macro_silhouette:.3f}\")\n",
|
||
"else:\n",
|
||
" macro_labels = np.array([])\n",
|
||
" macro_probs = np.array([])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a43e34ca",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002541,
|
||
"end_time": "2026-06-13T02:32:17.333847+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.331306+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"We use 4 regimes to match the Two Sigma approach. The silhouette score measures\n",
|
||
"cluster separation — higher is better, with values above 0.25 indicating\n",
|
||
"reasonable structure. Unlike factor_regimes, we skip BIC/AIC model selection here\n",
|
||
"because the goal is interpretability (matching known economic phases), not\n",
|
||
"statistical optimality."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c1a70c9b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002444,
|
||
"end_time": "2026-06-13T02:32:17.338754+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.336310+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Regime Characteristics"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "c20207c0",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.004189Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.004057Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.008365Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.007906Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.007217,
|
||
"end_time": "2026-06-13T02:32:17.348424+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.341207+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Regime Characteristics (mean values):\n",
|
||
" unrate dff t10y2y cpi_yoy\n",
|
||
"regime \n",
|
||
"0 4.70 1.36 1.12 2.15\n",
|
||
"1 12.30 0.07 0.47 0.56\n",
|
||
"2 7.62 0.11 1.88 1.58\n",
|
||
"3 4.43 3.79 0.24 4.01\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(macro_df) > 0:\n",
|
||
" regime_chars = macro_df.copy()\n",
|
||
" regime_chars[\"regime\"] = macro_labels\n",
|
||
" regime_means = regime_chars.groupby(\"regime\").mean()\n",
|
||
"\n",
|
||
" print(\"Regime Characteristics (mean values):\")\n",
|
||
" print(regime_means.round(2))\n",
|
||
"else:\n",
|
||
" regime_means = pd.DataFrame()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c9d2ad0",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.002605,
|
||
"end_time": "2026-06-13T02:32:17.355477+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.352872+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Assign Interpretive Labels"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "b5ce4640",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.009099Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.009039Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.011593Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.011243Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.005977,
|
||
"end_time": "2026-06-13T02:32:17.363948+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.357971+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def create_regime_labels(chars: pd.DataFrame) -> dict[int, str]:\n",
|
||
" \"\"\"Create short, descriptive labels based on economic characteristics.\n",
|
||
"\n",
|
||
" Uses a priority cascade on cluster-mean values. Thresholds are approximate\n",
|
||
" and tuned for the 2002-2025 US macro environment.\n",
|
||
" \"\"\"\n",
|
||
" labels = {}\n",
|
||
" for regime in chars.index:\n",
|
||
" c = chars.loc[regime]\n",
|
||
" if c[\"unrate\"] > 10:\n",
|
||
" labels[regime] = \"Crisis\"\n",
|
||
" elif c[\"unrate\"] > 6 and c[\"dff\"] < 0.5:\n",
|
||
" labels[regime] = \"Recovery\"\n",
|
||
" elif c[\"dff\"] > 3 and c[\"t10y2y\"] < 0.5:\n",
|
||
" labels[regime] = \"Tightening\"\n",
|
||
" elif c[\"cpi_yoy\"] > 4:\n",
|
||
" labels[regime] = \"Inflation\"\n",
|
||
" elif c[\"unrate\"] < 5 and c[\"dff\"] < 2:\n",
|
||
" labels[regime] = \"Expansion\"\n",
|
||
" else:\n",
|
||
" labels[regime] = \"Transition\"\n",
|
||
"\n",
|
||
" # Ensure unique labels\n",
|
||
" seen = {}\n",
|
||
" for r, label in list(labels.items()):\n",
|
||
" if label in seen:\n",
|
||
" if chars.loc[r, \"unrate\"] > chars.loc[seen[label], \"unrate\"]:\n",
|
||
" labels[r] = f\"{label} (High Unemp.)\"\n",
|
||
" else:\n",
|
||
" labels[seen[label]] = f\"{label} (High Unemp.)\"\n",
|
||
" seen[label] = r\n",
|
||
" return labels"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "cf69a764",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.012522Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.012404Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.015082Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.014680Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.005526,
|
||
"end_time": "2026-06-13T02:32:17.372076+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.366550+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Assigned Regime Labels:\n",
|
||
" Cluster 0: Expansion\n",
|
||
" Cluster 1: Crisis\n",
|
||
" Cluster 2: Recovery\n",
|
||
" Cluster 3: Tightening\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(regime_means) > 0:\n",
|
||
" regime_labels_map = create_regime_labels(regime_means)\n",
|
||
" print(\"Assigned Regime Labels:\")\n",
|
||
" for regime, label in sorted(regime_labels_map.items()):\n",
|
||
" print(f\" Cluster {regime}: {label}\")\n",
|
||
"else:\n",
|
||
" regime_labels_map = {}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f8a9ec67",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002492,
|
||
"end_time": "2026-06-13T02:32:17.377080+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.374588+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Macro Regimes and Market Volatility\n",
|
||
"\n",
|
||
"**Key insight**: Macro regimes coincide with different VOLATILITY environments,\n",
|
||
"even when return patterns are less clear.\n",
|
||
"\n",
|
||
"**Implication**: Macro regime indicators are useful for RISK MANAGEMENT\n",
|
||
"(anticipating volatility shifts) rather than RETURN PREDICTION."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6cf7c347",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00253,
|
||
"end_time": "2026-06-13T02:32:17.382143+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.379613+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Load S&P 500 for Validation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "48163069",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.016074Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.015905Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.030700Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.030304Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.02727,
|
||
"end_time": "2026-06-13T02:32:17.412106+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.384836+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loaded S&P 500 for validation: 552 months, 1980-01-31 to 2025-12-31\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"sp500_raw = load_sp500_index().to_pandas()\n",
|
||
"sp500_raw[\"timestamp\"] = pd.to_datetime(sp500_raw[\"timestamp\"])\n",
|
||
"sp500_raw = sp500_raw.set_index(\"timestamp\")\n",
|
||
"\n",
|
||
"sp500_monthly = sp500_raw[\"close\"].resample(\"ME\").last().to_frame()\n",
|
||
"sp500_monthly[\"returns\"] = sp500_monthly[\"close\"].pct_change()\n",
|
||
"sp500_df = sp500_monthly\n",
|
||
"\n",
|
||
"print(\n",
|
||
" f\"Loaded S&P 500 for validation: {len(sp500_df)} months, \"\n",
|
||
" f\"{sp500_df.index.min().date()} to {sp500_df.index.max().date()}\"\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a6d5a650",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002636,
|
||
"end_time": "2026-06-13T02:32:17.417604+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.414968+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Compute Regime Statistics"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "01ebeb08",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.032021Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.031944Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.040502Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.040097Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.013098,
|
||
"end_time": "2026-06-13T02:32:17.433289+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.420191+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Regime Statistics (sorted by volatility):\n",
|
||
" months annual_vol max_dd_pct\n",
|
||
"regime_label \n",
|
||
"Expansion 77 12.0 14.6\n",
|
||
"Tightening 98 15.0 42.2\n",
|
||
"Recovery 98 15.2 52.6\n",
|
||
"Crisis 4 16.0 9.9\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(macro_df) > 0 and sp500_df is not None:\n",
|
||
" events = {2008: \"GFC\", 2020: \"COVID\", 2022: \"Inflation\"}\n",
|
||
"\n",
|
||
" sp500_aligned = sp500_df.reindex(macro_df.index, method=\"nearest\", tolerance=pd.Timedelta(\"5D\"))\n",
|
||
" sp500_aligned[\"regime\"] = macro_labels\n",
|
||
" sp500_aligned[\"regime_label\"] = [regime_labels_map[r] for r in macro_labels]\n",
|
||
" sp500_aligned[\"peak\"] = sp500_aligned[\"close\"].cummax()\n",
|
||
" sp500_aligned[\"drawdown\"] = (sp500_aligned[\"close\"] - sp500_aligned[\"peak\"]) / sp500_aligned[\n",
|
||
" \"peak\"\n",
|
||
" ]\n",
|
||
" sp500_aligned[\"volatility\"] = sp500_aligned[\"returns\"].rolling(12).std() * np.sqrt(12)\n",
|
||
"\n",
|
||
" regime_stats_fig = sp500_aligned.groupby(\"regime_label\").agg(\n",
|
||
" {\"returns\": [\"mean\", \"std\", \"count\"], \"drawdown\": \"min\", \"volatility\": \"mean\"}\n",
|
||
" )\n",
|
||
" regime_stats_fig.columns = [\"mean_ret\", \"std_ret\", \"months\", \"max_dd\", \"avg_vol\"]\n",
|
||
" regime_stats_fig[\"annual_vol\"] = regime_stats_fig[\"std_ret\"] * np.sqrt(12) * 100\n",
|
||
" regime_stats_fig[\"max_dd_pct\"] = -regime_stats_fig[\"max_dd\"] * 100\n",
|
||
"\n",
|
||
" regime_stats_fig = regime_stats_fig.sort_values(\"annual_vol\", ascending=True)\n",
|
||
" regime_order = regime_stats_fig.index.tolist()\n",
|
||
" n_regimes_fig = len(regime_order)\n",
|
||
"\n",
|
||
" print(\"Regime Statistics (sorted by volatility):\")\n",
|
||
" print(regime_stats_fig[[\"months\", \"annual_vol\", \"max_dd_pct\"]].round(1))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a5b071b5",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.002591,
|
||
"end_time": "2026-06-13T02:32:17.438599+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.436008+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Regime Timeline with Market Validation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "875db580",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.041590Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.041525Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.286801Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.286259Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.274799,
|
||
"end_time": "2026-06-13T02:32:17.715940+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.441141+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1400x600 with 12 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_regime_timeline_validation():\n",
|
||
" \"\"\"Plot regime swim lanes with volatility and drawdown validation bars.\"\"\"\n",
|
||
" if len(macro_df) > 0 and sp500_df is not None:\n",
|
||
" years = [pd.Timestamp(ts).year for ts in macro_df.index]\n",
|
||
" n_months = len(macro_df)\n",
|
||
"\n",
|
||
" year_ticks = []\n",
|
||
" year_labels_fig = []\n",
|
||
" for j, year in enumerate(years):\n",
|
||
" if j == 0 or years[j - 1] != year:\n",
|
||
" if year % 4 == 0:\n",
|
||
" year_ticks.append(j)\n",
|
||
" year_labels_fig.append(str(year))\n",
|
||
"\n",
|
||
" fig = plt.figure(figsize=(14, 6))\n",
|
||
" gs = GridSpec(\n",
|
||
" n_regimes_fig, 4, figure=fig, width_ratios=[3.5, 1, 1, 0.05], wspace=0.12, hspace=0.08\n",
|
||
" )\n",
|
||
"\n",
|
||
" swimlane_axes = [fig.add_subplot(gs[i, 0]) for i in range(n_regimes_fig)]\n",
|
||
" vol_axes = [fig.add_subplot(gs[i, 1]) for i in range(n_regimes_fig)]\n",
|
||
" dd_axes = [fig.add_subplot(gs[i, 2]) for i in range(n_regimes_fig)]\n",
|
||
"\n",
|
||
" # Grayscale-friendly palette (distinct in B&W print)\n",
|
||
" # Ordered from light to dark to match volatility ordering\n",
|
||
" REGIME_COLORS = [\"#e0e0e0\", \"#a0a0a0\", \"#606060\", \"#202020\"]\n",
|
||
" colors = REGIME_COLORS[:n_regimes_fig]\n",
|
||
"\n",
|
||
" for i, regime_label in enumerate(regime_order):\n",
|
||
" raw_regime = [r for r, lbl in regime_labels_map.items() if lbl == regime_label][0]\n",
|
||
" mask = macro_labels == raw_regime\n",
|
||
" stats = regime_stats_fig.loc[regime_label]\n",
|
||
" color = colors[i]\n",
|
||
"\n",
|
||
" ax_swim = swimlane_axes[i]\n",
|
||
" for j in range(n_months - 1):\n",
|
||
" if mask[j]:\n",
|
||
" ax_swim.axvspan(j, j + 1, color=color, alpha=0.85)\n",
|
||
"\n",
|
||
" ax_swim.set_xlim(0, n_months)\n",
|
||
" ax_swim.set_ylim(0, 1)\n",
|
||
" ax_swim.set_yticks([])\n",
|
||
" ax_swim.set_ylabel(regime_label, fontsize=9, rotation=0, ha=\"right\", va=\"center\")\n",
|
||
"\n",
|
||
" if i < n_regimes_fig - 1:\n",
|
||
" ax_swim.set_xticks([])\n",
|
||
" else:\n",
|
||
" ax_swim.set_xticks(year_ticks)\n",
|
||
" ax_swim.set_xticklabels(year_labels_fig, fontsize=8)\n",
|
||
" ax_swim.set_xlabel(\"Year\", fontsize=9)\n",
|
||
"\n",
|
||
" for event_year, event_label in events.items():\n",
|
||
" try:\n",
|
||
" idx = next(j for j, y in enumerate(years) if y == event_year)\n",
|
||
" ax_swim.axvline(x=idx, color=\"black\", linestyle=\"-\", alpha=0.3, linewidth=0.8)\n",
|
||
" if i == 0:\n",
|
||
" ax_swim.annotate(\n",
|
||
" event_label,\n",
|
||
" xy=(idx, 1.25),\n",
|
||
" xycoords=(\"data\", \"axes fraction\"),\n",
|
||
" ha=\"center\",\n",
|
||
" fontsize=7,\n",
|
||
" color=\"black\",\n",
|
||
" fontweight=\"bold\",\n",
|
||
" )\n",
|
||
" except StopIteration:\n",
|
||
" pass\n",
|
||
"\n",
|
||
" ax_vol = vol_axes[i]\n",
|
||
" vol_val = stats[\"annual_vol\"]\n",
|
||
" ax_vol.barh([0], [vol_val], color=color, height=0.6, edgecolor=\"black\", linewidth=0.5)\n",
|
||
" ax_vol.set_xlim(0, 22)\n",
|
||
" ax_vol.set_ylim(-0.5, 0.5)\n",
|
||
" ax_vol.set_yticks([])\n",
|
||
" ax_vol.text(\n",
|
||
" vol_val + 0.3,\n",
|
||
" 0,\n",
|
||
" f\"{vol_val:.0f}%\",\n",
|
||
" ha=\"left\",\n",
|
||
" va=\"center\",\n",
|
||
" fontsize=9,\n",
|
||
" fontweight=\"bold\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" if i == 0:\n",
|
||
" ax_vol.set_title(\"Volatility\\n(ann. %)\", fontsize=9, fontweight=\"bold\")\n",
|
||
" if i < n_regimes_fig - 1:\n",
|
||
" ax_vol.set_xticks([])\n",
|
||
" else:\n",
|
||
" ax_vol.set_xticks([0, 10, 20])\n",
|
||
" ax_vol.tick_params(labelsize=7)\n",
|
||
"\n",
|
||
" ax_dd = dd_axes[i]\n",
|
||
" dd_val = stats[\"max_dd_pct\"]\n",
|
||
" ax_dd.barh([0], [dd_val], color=color, height=0.6, edgecolor=\"black\", linewidth=0.5)\n",
|
||
" ax_dd.set_xlim(0, 60)\n",
|
||
" ax_dd.set_ylim(-0.5, 0.5)\n",
|
||
" ax_dd.set_yticks([])\n",
|
||
" ax_dd.text(\n",
|
||
" dd_val + 1,\n",
|
||
" 0,\n",
|
||
" f\"{dd_val:.0f}%\",\n",
|
||
" ha=\"left\",\n",
|
||
" va=\"center\",\n",
|
||
" fontsize=9,\n",
|
||
" fontweight=\"bold\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" if i == 0:\n",
|
||
" ax_dd.set_title(\"Max\\nDrawdown\", fontsize=9, fontweight=\"bold\")\n",
|
||
" if i < n_regimes_fig - 1:\n",
|
||
" ax_dd.set_xticks([])\n",
|
||
" else:\n",
|
||
" ax_dd.set_xticks([0, 20, 40, 60])\n",
|
||
" ax_dd.tick_params(labelsize=7)\n",
|
||
"\n",
|
||
" start_year = macro_df.index.min().year\n",
|
||
" end_year = macro_df.index.max().year\n",
|
||
" fig.suptitle(\n",
|
||
" f\"Macro Regimes and Market Volatility ({start_year}-{end_year})\",\n",
|
||
" fontsize=11,\n",
|
||
" fontweight=\"bold\",\n",
|
||
" y=0.98,\n",
|
||
" )\n",
|
||
"\n",
|
||
" fig.text(\n",
|
||
" 0.5,\n",
|
||
" 0.02,\n",
|
||
" \"Regimes from: Unemployment, Fed Funds Rate, Yield Curve (10Y-2Y), CPI | Sorted by volatility\",\n",
|
||
" ha=\"center\",\n",
|
||
" fontsize=8,\n",
|
||
" style=\"italic\",\n",
|
||
" color=\"#505050\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"plot_regime_timeline_validation()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f2102e84",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002698,
|
||
"end_time": "2026-06-13T02:32:17.721528+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.718830+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Persist Figure 1.6 inputs\n",
|
||
"\n",
|
||
"The publication-quality version of Figure 1.6 is rendered by\n",
|
||
"`book/01_process_is_edge/figures/scripts/generate_figure_1_6_macro_regimes_volatility.py`.\n",
|
||
"That script reads the arrays persisted below so the book build does not\n",
|
||
"re-fit the clustering pipeline."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "28d539a1",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.288094Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.288018Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.291893Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.291644Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.007619,
|
||
"end_time": "2026-06-13T02:32:17.731813+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.724194+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if len(macro_df) > 0 and sp500_df is not None:\n",
|
||
" ARTIFACT_DIR = OUTPUT_DIR / \"figure_1_6\"\n",
|
||
" ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)\n",
|
||
" annual_vol_by_regime = regime_stats_fig.loc[regime_order, \"annual_vol\"].to_numpy(dtype=float)\n",
|
||
" max_dd_by_regime = regime_stats_fig.loc[regime_order, \"max_dd_pct\"].to_numpy(dtype=float)\n",
|
||
" np.savez(\n",
|
||
" ARTIFACT_DIR / \"inputs.npz\",\n",
|
||
" dates=macro_df.index.astype(\"datetime64[ns]\").astype(\"int64\"),\n",
|
||
" macro_labels=np.asarray(macro_labels, dtype=np.int64),\n",
|
||
" regime_order=np.asarray(regime_order, dtype=object),\n",
|
||
" raw_regime_for_order=np.asarray(\n",
|
||
" [\n",
|
||
" next(r for r, lbl in regime_labels_map.items() if lbl == label)\n",
|
||
" for label in regime_order\n",
|
||
" ],\n",
|
||
" dtype=np.int64,\n",
|
||
" ),\n",
|
||
" annual_vol=annual_vol_by_regime,\n",
|
||
" max_dd_pct=max_dd_by_regime,\n",
|
||
" event_years=np.asarray(list(events.keys()), dtype=np.int64),\n",
|
||
" event_labels=np.asarray(list(events.values()), dtype=object),\n",
|
||
" start_year=np.int64(macro_df.index.min().year),\n",
|
||
" end_year=np.int64(macro_df.index.max().year),\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ee2a7442",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002675,
|
||
"end_time": "2026-06-13T02:32:17.737186+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.734511+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Key Insight Summary"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "603a693d",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.292966Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.292905Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.297278Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.297032Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.008799,
|
||
"end_time": "2026-06-13T02:32:17.748651+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.739852+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>Months</th>\n",
|
||
" <th>Annual vol (%)</th>\n",
|
||
" <th>Max drawdown (%)</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>regime_label</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>Expansion</th>\n",
|
||
" <td>77</td>\n",
|
||
" <td>12.0</td>\n",
|
||
" <td>14.6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Tightening</th>\n",
|
||
" <td>98</td>\n",
|
||
" <td>15.0</td>\n",
|
||
" <td>42.2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Recovery</th>\n",
|
||
" <td>98</td>\n",
|
||
" <td>15.2</td>\n",
|
||
" <td>52.6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Crisis</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>16.0</td>\n",
|
||
" <td>9.9</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Months Annual vol (%) Max drawdown (%)\n",
|
||
"regime_label \n",
|
||
"Expansion 77 12.0 14.6\n",
|
||
"Tightening 98 15.0 42.2\n",
|
||
"Recovery 98 15.2 52.6\n",
|
||
"Crisis 4 16.0 9.9"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"insight_table = (\n",
|
||
" regime_stats_fig.loc[regime_order, [\"months\", \"annual_vol\", \"max_dd_pct\"]]\n",
|
||
" .rename(\n",
|
||
" columns={\n",
|
||
" \"months\": \"Months\",\n",
|
||
" \"annual_vol\": \"Annual vol (%)\",\n",
|
||
" \"max_dd_pct\": \"Max drawdown (%)\",\n",
|
||
" }\n",
|
||
" )\n",
|
||
" .round(1)\n",
|
||
")\n",
|
||
"insight_table"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "28896296",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002706,
|
||
"end_time": "2026-06-13T02:32:17.754147+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.751441+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Correlation Heatmap"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "c941889f",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.298360Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.298299Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.392331Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.391904Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.094461,
|
||
"end_time": "2026-06-13T02:32:17.851324+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.756863+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(macro_df) > 0:\n",
|
||
" fig, ax = plt.subplots(figsize=(8, 6), layout=\"tight\")\n",
|
||
" sns.heatmap(\n",
|
||
" macro_df.corr(),\n",
|
||
" annot=True,\n",
|
||
" fmt=\".2f\",\n",
|
||
" cmap=\"RdBu_r\",\n",
|
||
" center=0,\n",
|
||
" ax=ax,\n",
|
||
" square=True,\n",
|
||
" )\n",
|
||
" ax.set_title(\"Macro Indicator Correlations\", fontsize=12)\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5a01f2d1",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002912,
|
||
"end_time": "2026-06-13T02:32:17.857305+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.854393+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Interpretation**: Unemployment moves *with* the 10y-2y spread (positive\n",
|
||
"correlation ~0.70): when the labour market deteriorates, the curve typically\n",
|
||
"steepens as the Fed cuts the front end. The Fed Funds rate moves *against*\n",
|
||
"the spread (correlation ~−0.73): hiking cycles flatten or invert the curve.\n",
|
||
"CPI YoY is largely orthogonal to the other three — inflation regimes can\n",
|
||
"coexist with both recession and expansion. These pairwise correlations show\n",
|
||
"why a single indicator is a noisy regime signal and motivate joint clustering."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "17e3b7c7",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002774,
|
||
"end_time": "2026-06-13T02:32:17.862949+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.860175+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"# Extended Analysis: All FRED Indicators\n",
|
||
"\n",
|
||
"Now we expand beyond the 4 core indicators to use all available FRED series\n",
|
||
"(~17 indicators after quality filtering). This provides a richer but noisier\n",
|
||
"view of economic conditions."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "970da604",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002765,
|
||
"end_time": "2026-06-13T02:32:17.868515+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.865750+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Prepare Full Dataset\n",
|
||
"\n",
|
||
"Using the same data loaded at the start, we now select ALL series with\n",
|
||
"sufficient data coverage (< 50% missing)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "1cd3a19a",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.393556Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.393463Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:05.407087Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:05.406665Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.016693,
|
||
"end_time": "2026-06-13T02:32:17.888004+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.871311+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Extended dataset: 277 months, 25 series\n",
|
||
"Series used: ['dff', 'dgs1', 'dgs2', 'dgs3', 'dgs5', 'dgs7', 'dgs10', 'dgs20', 'dgs30', 't10y2y', 'vixcls', 'icsa', 'walcl', 'cpiaucsl', 'cpilfesl', 'pcepi', 'unrate', 'payems', 'civpart', 'indpro', 'm2sl', 'gdp', 'gdpc1', 'YIELD_CURVE_SLOPE', 'YIELD_CURVE_5_10']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Use the already-loaded macro_raw data\n",
|
||
"value_cols_full = [c for c in macro_raw.columns if c != DATE_COL]\n",
|
||
"\n",
|
||
"macro_monthly_full = (\n",
|
||
" macro_raw.sort(DATE_COL)\n",
|
||
" .group_by_dynamic(DATE_COL, every=\"1mo\", label=\"right\")\n",
|
||
" .agg([pl.col(c).last() for c in value_cols_full])\n",
|
||
" .filter(pl.col(DATE_COL) >= pl.datetime(2002, 1, 1))\n",
|
||
")\n",
|
||
"\n",
|
||
"# Identify columns with >50% missing, exclude market price columns\n",
|
||
"total_rows = macro_monthly_full.height\n",
|
||
"null_fractions = {\n",
|
||
" col: macro_monthly_full.select(pl.col(col).is_null().sum()).item() / total_rows\n",
|
||
" for col in value_cols_full\n",
|
||
"}\n",
|
||
"# Exclude columns with too much missing data and market price columns (SP500)\n",
|
||
"exclude_cols = {\"sp500\", \"SP500\"}\n",
|
||
"good_cols = [col for col, frac in null_fractions.items() if frac < 0.5 and col not in exclude_cols]\n",
|
||
"\n",
|
||
"macro_clean_full = (\n",
|
||
" macro_monthly_full.select([DATE_COL] + good_cols).fill_null(strategy=\"forward\").drop_nulls()\n",
|
||
")\n",
|
||
"\n",
|
||
"macro_data_full = macro_clean_full.select(good_cols).to_pandas()\n",
|
||
"macro_data_full.index = macro_clean_full[DATE_COL].to_pandas()\n",
|
||
"macro_data_full = macro_data_full.apply(scale)\n",
|
||
"\n",
|
||
"print(f\"Extended dataset: {macro_data_full.shape[0]} months, {macro_data_full.shape[1]} series\")\n",
|
||
"print(f\"Series used: {list(macro_data_full.columns)}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3f33d6c8",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002879,
|
||
"end_time": "2026-06-13T02:32:17.894040+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.891161+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Visualize All Series"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "e0af1644",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:05.408139Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:05.408063Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:06.735666Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:06.735079Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 1.651853,
|
||
"end_time": "2026-06-13T02:32:19.548761+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:17.896908+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1600x800 with 20 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_cols = min(len(macro_data_full.columns), 20)\n",
|
||
"n_rows = (n_cols + 4) // 5\n",
|
||
"n_plot_cols = min(5, n_cols)\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(\n",
|
||
" nrows=n_rows, ncols=n_plot_cols, figsize=(16, 2 * n_rows), sharex=True, sharey=True\n",
|
||
")\n",
|
||
"axes = axes.flatten() if n_rows > 1 else [axes] if n_cols == 1 else axes\n",
|
||
"\n",
|
||
"for i, ax in enumerate(axes[:n_cols]):\n",
|
||
" if i < len(macro_data_full.columns):\n",
|
||
" macro_data_full.iloc[:, i].plot(ax=ax, color=\"steelblue\", linewidth=0.8)\n",
|
||
" ax.set_title(macro_data_full.columns[i], fontsize=9)\n",
|
||
" ax.tick_params(axis=\"both\", labelsize=7)\n",
|
||
" else:\n",
|
||
" ax.set_visible(False)\n",
|
||
"\n",
|
||
"for i in range(n_cols, len(axes)):\n",
|
||
" axes[i].set_visible(False)\n",
|
||
"\n",
|
||
"sns.despine()\n",
|
||
"start_yr = macro_data_full.index.min().year\n",
|
||
"end_yr = macro_data_full.index.max().year\n",
|
||
"fig.suptitle(f\"FRED Macro Series - Standardized ({start_yr}-{end_yr})\", fontsize=12, y=1.02)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "47a5a62b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003277,
|
||
"end_time": "2026-06-13T02:32:19.555558+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:19.552281+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Interpretation**: The visibly regime-bearing series are VIX (sharp spikes\n",
|
||
"in 2008 and 2020), ICSA initial claims (same crisis peaks), DFF (regime\n",
|
||
"shifts during hiking cycles), and the yield-curve spread T10Y2Y (recession\n",
|
||
"warnings ahead of 2008 and 2020). Slow-moving series (housing, monetary\n",
|
||
"aggregates) carry less regime information. Clustering will lean heavily on\n",
|
||
"the high-variance subset."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ce519f9a",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003155,
|
||
"end_time": "2026-06-13T02:32:19.561901+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:19.558746+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Hierarchical Clustering\n",
|
||
"\n",
|
||
"Cluster the correlation matrix to reveal indicator groupings."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "7884733c",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:06.736780Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:06.736705Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:06.911009Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:06.910473Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.183021,
|
||
"end_time": "2026-06-13T02:32:19.748082+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:19.565061+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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k4eGhmJgYnT9//q73z4pzID2O3q+Uv8GS9M0338jd3d1urRAXFxcNHz48zTZT+goAzsBUcwBO07BhwzsurtaoUSO98sorSkpK0s6dO/XKK6/o/PnzaS6KVLNmTdu1ePfqypUrknTPX15v/xIv/fuFLeXL6enTp3X9+nVVqlQpVb0qVao4TGpvdfDgQXXr1u2O/ViwYIGmTp2qv/76yy7pun0leUdSErcqVaqk2latWjV9++23qRZQy0i70r/XmGb0R420+uHh4aGgoCDb9hSlS5dO89y4vX8HDhyQYRgaN26cxo0b53CfuLg4lS5d2uG2I0eOaPz48Vq9enWqxOPixYtpH1QaTp8+rWvXrqX5micnJ+t///uf3eUPt59rKYlySn9atmypbt26aeLEiYqOjlarVq3UpUsXPfHEExlaCMxI55KBtD6/GUlO9u/fr+3bt6eZSMXFxdk9T+vcuptz/PaylCQso7c+S++19vX1dbiPGZ/V2936I58zWa1Wvf766xo9erRKlCihxo0bq2PHjurbt68CAgLuuH9WnQOOeHp6pmr31r/B0r9/W0qWLJlq8cWKFSum2a5hGBm6ZAkA7gWJN4BsVbRoUVsiHRISoqpVq6pjx46aMWOGRo0a5ZSYO3fulJT+F7D0uLq6OixPL4nJap988olCQ0PVpUsXPffccypevLhcXV0VFRXllIXpJPsR4PRUrVpVsbGxunHjRpavKp1eH27flnJ96LPPPpvmTIO0zoGkpCQ99NBDOnfunMaOHauqVauqQIECOnbsmEJDQ9O89jSr3elcs1gsWrZsmX755Rd98cUX+vbbbzVgwABNnTpVv/zyi212hyNFihS5p5HMjEhOTtZDDz2kMWPGONxeuXJlu+eO3te7Pcczen6mxVmf66z+rFatWlV//vmn/ve//6ls2bJ3vX9aieXtC5NJ/97fulOnTlq1apW+/fZbjRs3TlFRUfrhhx9Ut27ddONkxTmQlrTeq8w6f/78HdchAYB7ReINIEd5+OGH1bJlS7322msaOnRomresuldXrlzRypUrVbZsWVWrVi1L206Rsnry/v37U23bu3fvHfevUKGC7ceBtCxbtkxBQUFasWKF3RfpCRMm2NVL60t2+fLl0+zPX3/9paJFi97za9+pUydt3rxZy5cvV69evdKte2s/bp1+fePGDR06dChTsxtS2nN3d7/rdnbs2KF9+/ZpwYIF6tu3r63c0eUSGR0hK1asmLy9vdN8zV1cXO4pkZKkxo0bq3Hjxnr11Ve1aNEi9e7dW4sXL9agQYPS3Kdq1ao6dOjQPcW7kwoVKujKlSuZev8yeo6nJeX9v9NnKTOy8rOaUZ06ddKnn36qTz75RBEREXe9f8pI/u13Dbh9dkmKChUqaPTo0Ro9erT279+vOnXqaOrUqfrkk08kpX3+Z8U5kBnly5fX+vXrU91y8MCBA2nuc+jQIdWuXduM7gHIh7jGG0COM3bsWJ09ezbL7vWc4vr16+rTp4/OnTunF1980WlTCl1dXRUSEqJVq1bpyJEjtvI9e/bo22+/veP+3bp107Zt27Ry5cpU21JG31JGfG4djduyZYs2b95sVz/lC+ftX7JLliypOnXqaMGCBXbbdu7cqe+++04dOnS4Yz/TMmzYMJUsWVKjR4/Wvn37Um2Pi4vTK6+8Iunf6+U9PDz01ltv2R3Lhx9+qIsXL+rhhx++534UL15crVq10pw5c3TixIlU22+/LdetHL2+hmFoxowZqeqm/EBx+2vsqM22bdvq888/t92OSZJOnTqlRYsWqVmzZmlOaU7L+fPnU43IpqySnZCQkO6+TZo00c6dO+9Y71706NFDmzdvdni+X7hwQYmJiXdsI6PneFqKFSumFi1aaO7cuXafw9vbzIys/Kxm1GOPPaaaNWvq1VdfddjG5cuXHd4uLUX58uXl6uqa6hrrWbNm2T2/du2a4uPj7coqVKggHx8fu3OmQIECDs/9rDgHMiMkJEQ3b960+38kOTnZdvu/2128eFEHDx5U06ZNndovAPkXI94Acpz27dvr/vvv17Rp0zR8+HC72x399NNPqb4MSv8uCnbrok/Hjh2zjchcuXJFu3fv1meffaaTJ09q9OjRGjp0qFOPYeLEifrmm2/UvHlzPf3000pMTNTMmTNVo0YNbd++Pd19n3vuOS1btkzdu3fXgAEDFBwcrHPnzmn16tV69913Vbt2bXXs2FErVqxQ165d9fDDD+vQoUN69913Vb16dds17NK/0zerV6+uJUuWqHLlyvL399f999+v+++/X2+++abat2+vJk2aaODAgbbbifn5+aW69/TdKFy4sFauXKkOHTqoTp06evLJJxUcHCxJ+uOPP/Tpp5+qSZMmkv5NjiIiIjRx4kS1a9dOjzzyiPbu3atZs2apQYMGevLJJ++5H9K/C4Q1a9ZMNWvW1ODBgxUUFKRTp05p8+bNOnr0qLZt2+Zwv6pVq6pChQp69tlndezYMfn6+mr58uUOp2anHNuIESMUEhIiV1dX9ezZ02G7r7zyitauXatmzZrp6aeflpubm+bMmaOEhAS98cYbd318CxYs0KxZs9S1a1dVqFBBly9f1vvvvy9fX987/njSuXNnTZo0SRs2bFDbtm3vOnZ6nnvuOa1evVodO3ZUaGiogoODdfXqVe3YsUPLli3T4cOH7zilN6PneHreeustNWvWTPXq1dOQIUN033336fDhw1qzZo1iY2Oz5Diz6rOaUe7u7lqxYoXatGmjFi1aqEePHnrggQfk7u6uXbt2adGiRSpcuHCa9/L28/NT9+7dNXPmTFksFlWoUEFffvllqmuu9+3bp9atW6tHjx6qXr263NzctHLlSp06dcru/A4ODtbs2bP1yiuvqGLFiipevLj+85//ZMk5kBldunRRw4YNNXr0aB04cEBVq1bV6tWrde7cOUmpR+rXrVsnwzDUuXNnp/UJQD5n9jLqAPK+lNsR3ekWR+nd9mv+/Pl2t3y50+3Ebr0FT/ny5W3lFovF8PX1NWrUqGEMHjzY2LJlS4aPI63bid1+q7OUmLfeZsgwDGPDhg1GcHCw4eHhYQQFBRnvvvuu7dZFd9r37NmzRlhYmFG6dGnDw8PDKFOmjNGvXz/jzJkzhmH8e6ui1157zShfvrxhtVqNunXrGl9++WWqWwcZhmFs2rTJ1o/bX6t169YZDzzwgOHl5WX4+voanTp1Mnbv3m23f0qfT58+nYFX7f8cP37cCA8PNypXrmx4enoa3t7eRnBwsPHqq68aFy9etKv79ttvG1WrVjXc3d2NEiVKGE899ZRx/vx5uzqO3g/D+L9bDqV126ODBw8affv2NQICAgx3d3ejdOnSRseOHY1ly5bZ6ji6xdLu3buNNm3aGAULFjSKFi1qDB482HbruFtvRZSYmGg888wzRrFixQyLxWL3/t7+ehuGYfzxxx9GSEiIUbBgQcPb29t48MEHjU2bNtnVSeszdHs///jjD6NXr15GuXLlDKvVahQvXtzo2LGj8dtvvzl8LW5Xq1YtY+DAgRmKncLR++DoHL58+bIRERFhVKxY0fDw8DCKFi1qNG3a1JgyZYpx48YNwzDSf+8yeo7f6f3fuXOn0bVrV6NQoUKGp6enUaVKFWPcuHG27Wmd3ymvw6FDh9I9zqz8rDo6X9Jy/vx5Y/z48UbNmjUNb29vw9PT07j//vuNiIgIu9srOopz+vRpo1u3boa3t7dRuHBhY+jQocbOnTvtzu0zZ84Yw4cPN6pWrWoUKFDA8PPzMxo1amQsXbrUrq2TJ08aDz/8sOHj42NIsru1WGbPgbRuJ1agQIFUdR39bT19+rTxxBNPGD4+Poafn58RGhpqbNy40ZBkLF682K7u448/bjRr1izN1xsAMstiGCauBgQAAHKMjz/+WMOHD9eRI0dstyYD8rJVq1apa9eu+vnnn/XAAw9Ikk6ePKn77rtPixcvZsQbgNNwjTcAAPlU7969Va5cuTSvewVys+vXr9s9T0pK0syZM+Xr66t69erZyqdPn66aNWuSdANwKka8AQAAkOcMGjRI169fV5MmTZSQkKAVK1Zo06ZNeu211+5pRXgAyAwSbwAAAOQ5ixYt0tSpU3XgwAHFx8erYsWKeuqppxQWFpbdXQOQD5F4AwAAAADgRFzjDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAAABOROINAAAAAIATkXgDAAAAAOBEJN4AAAAAADgRiTcAAAAAAE5E4g0AAAAAgBOReAMAAAAA4EQk3gAAAAAAOBGJNwAAAAAATkTiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAAABOROINAAAAAIATkXjfpR9//FGdOnVSqVKlZLFYtGrVqjvuExMTo3r16slqtapixYqaP3++0/sJAAAAAMgZSLzv0tWrV1W7dm298847Gap/6NAhPfzww3rwwQcVGxurkSNHatCgQfr222+d3FMAAAAAQE5gMQzDyO5O5FYWi0UrV65Uly5d0qwzduxYrVmzRjt37rSV9ezZUxcuXNA333xjQi8BAAAAANmJEW9JCQkJunTpkt0jISEhS9revHmz2rRpY1cWEhKizZs3Z0n7AAAAAICczS0zOw+zBGZRN7JXwIRQTZw40a5swoQJioyMzHTbJ0+eVIkSJezKSpQooUuXLun69evy8vLKdAwAAAAAQM6VqcTb1ZJV3cheERERGjVqlF2Z1WrNpt4AAAAAAPKSTCbeeSPztlqtTku0AwICdOrUKbuyU6dOydfXl9FuAAAAAMgHGPF2siZNmuirr76yK1u7dq2aNGmSTT0C7EVERCg+Pj67uwEgi3h6eioqKiq7uwEAAG6RqcTbwyX/Zd5XrlzRgQMHbM8PHTqk2NhY+fv7q1y5coqIiNCxY8f00UcfSZKGDRumt99+W2PGjNGAAQP0ww8/aOnSpVqzZk12HQJgJz4+XtHR0dndDQBZJDw8PLu7AAAAbsNU87v022+/6cEHH7Q9T7k2vF+/fpo/f75OnDihI0eO2Lbfd999WrNmjcLDwzVjxgyVKVNGH3zwgUJCQkzvOwAAAADAfIx436VWrVopvVufz58/3+E+f/75pxN7BQAAAADIqTKVeHMTcAAAAAAA0seINwAAAAAATsQ13gAAAAAAOBG3EwMAAAAAwImYag4AAAAAgBMx1RwAAAAAACfK5Ih3VnUDAAAAAIC8iRFvAAAAAACciMXVAAAAAABwIka8AQAAAABwIka8AQAAAABwIm4nBgAAAACAEzHVHAAAAAAAJ8pc4u3G/cQAAAAAAEhPphJvFy7yBgAAAAAgXZlLvD1cs6ofAAAAAADkSYx4AwAAAADgRJlKvC0uXOMNANkpIiJC8fHx2d0N5CBxcXEKDw/P7m4gB/H09FRUVFR2dwMA8rXMLa7mQeINANkpPj5e0dHR2d0NADkYP8QAQPbL3Ii3K4k3AAAAAADpydyItzuJNwAAAAAA6cnk4mok3gAAAAAApCeTU81Z1RwAAAAAgPSQeAMAAAAA4ERMNQcAAAAAwIlYXA0AAAAAACfidmIAAAAAADhR5ka8PUi8AQAAAABIDyPeAAAAAAA4USYXV2NVcwAAAAAA0pPJxdVcs6ofAAAAAADkSdzHGwAAAAAAJ8rcVHP3TO0OAAAAAECel8lrvFlcDQAAAACA9GQu8fZgxBsAAAAAgPRk7hpvF0a8AQAAAABIT6YyZ4ura5543It33nlHgYGB8vT0VKNGjbR169Y0686fP18Wi8Xu4enpea8vOwAAAAAgF8nkqub5c8R7yZIlGjVqlN599101atRI06dPV0hIiPbu3avixYs73MfX11d79+61PbdYWBEeAAAAAPKDzF3jnU+nmk+bNk2DBw9W//79JUnvvvuu1qxZo7lz5+r55593uI/FYlFAQICZ3QQAAAAA5AAsriYpISFBCQkJdmVWq1VWqzVV3Rs3buj3339XRESErczFxUVt2rTR5s2b04xx5coVlS9fXsnJyapXr55ee+011ahRI+sOAgAAAACQI2XuGm8XlzzxiIqKkp+fn90jKirK4TGfOXNGSUlJKlGihF15iRIldPLkSYf7VKlSRXPnztXnn3+uTz75RMnJyWratKmOHj2amZcfAAAAAJALcI23pIjnIjRq1Ci7Mkej3feqSZMmatKkie1506ZNVa1aNc2ZM0eTJk3KsjgAAAAAgJwnU4m3ax6Zap7WtHJHihYtKldXV506dcqu/NSpUxm+htvd3V1169bVgQMH7rqvAAAAAIDchanmd7lAnIeHh4KDg/X999/bypKTk/X999/bjWqnJykpSTt27FDJkiXvKjYAAAAAIPdhcbV7MGrUKPXr10/169dXw4YNNX36dF29etW2ynnfvn1VunRp23XiL7/8sho3bqyKFSvqwoULevPNN/XPP/9o0KBB2XkYAAAAAAATZO4a73x6O7HHH39cp0+f1vjx43Xy5EnVqVNH33zzjW3BtSNHjtjdau38+fMaPHiwTp48qcKFCys4OFibNm1S9erVs+sQAAAAAAAmyeQ13u5Z1Y9cJywsTGFhYQ63xcTE2D2Pjo5WdHS0Cb0CAAAAAOQ0rGoOAAAAAIATMdUcAADkWxEREYqPj8/ubjhVXFycwsPDs7sbTuXp6WlbWwcAciIWVwMAAPlWfHw8l4PlAXn9hwUAuR8j3gAAAAAAOFHmEm83j6zqBwAAAAAAeRJD1gAAAAAAOFHmRrzd8+/txAAAAAAAyIjMrY7m4ppF3QAAAAAAIG8i8QYAAAAAwIkyubgaU80BAAAAAEgPI94AAAAAADgRI94AAAAAADgRI94AAAAAADgRtxMDAAAAAMCJGPEGAAAAAMCJMjfiTeINAAAAAEC6WFwNAAAAAAAnYqo5AAAAAABOxOJqAAAAAAA4ESPeAAAAAAA4kUt2dwAAAAAAgLyMxdUAAAAAAHAippoDAAAAAOBEmUy8M7c7AAAAAAB5XaYyZ8PCJeIAAAAAAKSHqeYAAAAAADhRJhNvRrwBAAAAAEgPU80BAAAAAHAiFlcDAAAAAMCJMpc5M+INKCIiQvHx8dndjXsWFxen8PDw7O7GPfH09FRUVFR2dwMAAABIV+ammjPiDSg+Pl7R0dHZ3Y18Kbf+YAAAAID8JZMj3pYs6gYAAAAAAHkTI94AAAAAADgRtxMDAAAAAMCJWFwNAAAAAAAnIvEGAAAAAMCJMpc5W1zyxuMevPPOOwoMDJSnp6caNWqkrVu3plv/s88+U9WqVeXp6amaNWvqq6++uqe4AAAAAIDcJVOJt+Hiliced2vJkiUaNWqUJkyYoD/++EO1a9dWSEiI4uLiHNbftGmTevXqpYEDB+rPP/9Uly5d1KVLF+3cuTMzLz8AAAAAIBfI5Ii3JW887tK0adM0ePBg9e/fX9WrV9e7774rb29vzZ0712H9GTNmqF27dnruuedUrVo1TZo0SfXq1dPbb7+dqZcfAAAAAJDzMdX8Lqea37hxQ7///rvatGnzfy+ii4vatGmjzZs3O9xn8+bNdvUlKSQkJM36AAAAAIC8g/t4S0pISFBCQoJdmdVqldVqTVX3zJkzSkpKUokSJezKS5Qoob/++sth+ydPnnRY/+TJk5nsOQAAAAAgp8vcNd4WS554REVFyc/Pz+4RFRWVVa8xAAAAACAfy9SQdVKykVX9yFYREREaNWqUXZmj0W5JKlq0qFxdXXXq1Cm78lOnTikgIMDhPgEBAXdVHwAAAACQd2RuxDuPPKxWq3x9fe0eaSXeHh4eCg4O1vfff28rS05O1vfff68mTZo43KdJkyZ29SVp7dq1adYHAAAAAOQdjHjfg1GjRqlfv36qX7++GjZsqOnTp+vq1avq37+/JKlv374qXbq0bbr6f//7X7Vs2VJTp07Vww8/rMWLF+u3337Te++9l52HAQAAAAAwQaYS73yad+vxxx/X6dOnNX78eJ08eVJ16tTRN998Y1tA7ciRI3Jx+b/JBE2bNtWiRYv00ksv6YUXXlClSpW0atUq3X///dl1CAAAIJeKiIhQfHx8dncjR4mLi1N4eHh2dyPH8PT0ZL0iIIfJ3KrmWdWLXCgsLExhYWEOt8XExKQq6969u7p37+7kXgEAgLwuPj5e0dHR2d0N5GD8CAHkPEw1BwAAAADAiTI31TyregEAAAAAQB6VuanmDHgDAAAAAJAuFlcDAAAAAMCJMneNN0PeAAAAAACki6nmAAAAAAA4USanmpN5A4AjZt1n16x713JPWAAAgHuXyanmWdUNAMhb8tp9drknLAAAwL1jqjkAAAAAAE7E4moAAAAAADgRtxMDAAAAAMCJGPEGAAAAAMCJuMYbAAAAAAAn4nZiAAAAAAA4UaYS75vcTwwAAAAAgHRxjTcAAAAAAE7EVHMAAAAAAJwocyPeyVnVDQAAAAAA8iZGvAEAAAAAcKLMLa6WTOINAAAAAEB6MjnVnMQbAAAAAID0MOINAAAAAIATcY03AAAAAABOlLkRb5Y1BwAAQD4UERGh+Pj47O6GQ3FxcQoPD8/ubqTJ09NTUVFR2d0NwFSZu8abAW8AAADkQ/Hx8YqOjs7ubuRKOflHAcBZmGoOAAAAAIATZXKqOYk3AAAAAADpYcQbAAAAAAAnYnE1AAAAAACciMXVAOQI97I67L2s2spKqgAAADBb5ka8kxnxBpA1zFodNq+tpGrW7WzMujUNP4wAAIC8KFOJN5BbODM5cWZCQhKCO8lrt7PJaz+MAAAASJmdap7MXHPkDrk1OSEJAQAAAHK/TCXeNxKZag4AAAAAQHoY8QYAAHCyrLzkKasvceKyJgBwPka8AQB3xCJuQObk5EueuKwJAJyPEW8AwB3l5KThXpBoAAAAM7lkZuekZCNPPJzl3Llz6t27t3x9fVWoUCENHDhQV65cSXefVq1ayWKx2D2GDRvmtD4CAAAAAJyLqeZO1Lt3b504cUJr167VzZs31b9/fw0ZMkSLFi1Kd7/Bgwfr5Zdftj339vZ2dlcBAAAAAE6SqcQ7kanmadqzZ4+++eYb/frrr6pfv74kaebMmerQoYOmTJmiUqVKpbmvt7e3AgICzOoqAAAAAMCJGPGWlJCQoISEBLsyq9Uqq9V6z21u3rxZhQoVsiXdktSmTRu5uLhoy5Yt6tq1a5r7Lly4UJ988okCAgLUqVMnjRs3jlFvAAAAAMilMre4mpE3RryjoqI0ceJEu7IJEyYoMjLynts8efKkihcvblfm5uYmf39/nTx5Ms39nnjiCZUvX16lSpXS9u3bNXbsWO3du1crVqy4574AAAAAALIPI9769zY5o0aNsitLa7T7+eef1+uvv55ue3v27LnnvgwZMsT275o1a6pkyZJq3bq1Dh48qAoVKtxzuwAAAACA7MHtxHR308pHjx6t0NDQdOsEBQUpICBAcXFxduWJiYk6d+7cXV2/3ahRI0nSgQMHSLwBAACQo0RERCg+Pv6u9omLi7vr2zp6enoqKirqrvYBcpJMJt55Y8T7bhQrVkzFihW7Y70mTZrowoUL+v333xUcHCxJ+uGHH5ScnGxLpjMiNjZWklSyZMl76i8AAADgLPHx8YqOjnZ6nLtN1IGchqnmTlKtWjW1a9dOgwcP1rvvvqubN28qLCxMPXv2tK1ofuzYMbVu3VofffSRGjZsqIMHD2rRokXq0KGDihQpou3btys8PFwtWrRQrVq1svmIAAAAcr57GYG9F/cyansvGOkF8oZMJd5I38KFCxUWFqbWrVvLxcVF3bp101tvvWXbfvPmTe3du1fXrl2TJHl4eGjdunWaPn26rl69qrJly6pbt2566aWXsusQAMBUeekLM1+Wgexh1gisWRjpBfKGTCXeCYx4p8vf31+LFi1Kc3tgYKCMW1aGL1u2rDZs2GBG1wAgR8pLX5j5sozswPW2AJAzsbgaAABAHsH1tgCQM3GNNwAAAGACZiQA+Rcj3gCAfMeMa8mdfR05X6yB3IcZCUD+lanE2yDxBuxk9Zf5rP7izhd14F954VpyvlgDAJB7ZG7EO4mp5sCtcvqXeb6oA+Zy5si6M0fU+ZEOAICsxYg3AABOktN/jEsLP9Ihp+CaaAB5ReZGvBNJvAEAAOAcXBMNIK/I3Ii3QeINAEBOkJXT2llfAgCArJXJEW+u8QYAICcwa2TwXhL8I0eOMPUXAJCvWYxMDFs3enltVvYl22wZ/1B2dwF3YZgl0JQ4XSv5mxJHkgoWL2BaLDPUe/ZRU+IYSUmmxGm4uaopcdw8XEyJ80qfYFPiHL5wzZQ4fx2/bEqcQt7upsQp5ms1JY4kubpYzIljMSfOkNLmnAvrrpUwJU7lIl6mxCno4WpKnCc/+dOUOAkJiabEsZh0XnuY9P4EFS9oShxJqlHa15Q4YU3uMyUOkBGZGvFOZqo5AAAAAADpylzizVRzAAAAAADSlbnEm9uJAQAAAACQLlY1BwAAAADAiTKXeDPTHAAAAACAdDHVHAAAAAAAJ2JxNQAAAAAAnIjbiQEAAAAA4ESMeAMAAABANgkMDNTIkSM1cuTI7O4KnIhrvAEAAAAgCxmGoaSkJLm5ZSrdQh7ikpmdk5OS88QDAAAAQP4VGBio6dOn25XVqVNHkZGRkiSLxaIPPvhAXbt2lbe3typVqqTVq1fb6sbExMhisejrr79WcHCwrFarfv75Zx08eFCdO3dWiRIlVLBgQTVo0EDr1q2z7deqVSv9888/Cg8Pl8VikcVisW37+eef1bx5c3l5eals2bIaMWKErl696tTXAc6TucQ72cgTDwAAAAB5S0JCgi5dumT3SEhIuOf2Jk6cqB49emj79u3q0KGDevfurXPnztnVef755zV58mTt2bNHtWrV0pUrV9ShQwd9//33+vPPP9WuXTt16tRJR44ckSStWLFCZcqU0csvv6wTJ07oxIkTkqSDBw+qXbt26tatm7Zv364lS5bo559/VlhY2L2/IMhWmbyPd1JW9QN5QEREhOLj47O7GwAAAICioqI0ceJEu7IJEybYRrHvVmhoqHr16iVJeu211/TWW29p69atateuna3Oyy+/rIceesj23N/fX7Vr17Y9nzRpklauXKnVq1crLCxM/v7+cnV1lY+PjwICAuz63rt3b9t135UqVdJbb72lli1bavbs2fL09LynY0D2yeTiajeyqh/IA+Lj4xUdHe30OMOmr3R6DAAAAORuERERGjVqlF2Z1Wq95/Zq1apl+3eBAgXk6+uruLg4uzr169e3e37lyhVFRkZqzZo1OnHihBITE3X9+nXbiHdatm3bpu3bt2vhwoW2MsMwlJycrEOHDqlatWr3fBzIHpkb8U5ixBsAAABAzmO1WjOcaLu4uMi47VbJN2/etHvu7u5u99xisSg52X69qAIFCtg9f/bZZ7V27VpNmTJFFStWlJeXlx577DHduJH+AOaVK1c0dOhQjRgxItW2cuXK3fF4kPMw4g0AAAAgXytWrJjt+mpJunTpkg4dOpTpdjdu3KjQ0FB17dpV0r8J9eHDh+3qeHh4KOm2Ac169epp9+7dqlixYqb7gJwhU4urGclJeeIBAAAAIP/6z3/+o48//lg//fSTduzYoX79+snV1TXT7VaqVEkrVqxQbGystm3bpieeeCLVKHlgYKB+/PFHHTt2TGfOnJEkjR07Vps2bVJYWJhiY2O1f/9+ff755yyulouxuBoAAACAfC0iIkKHDh1Sx44d5efnp0mTJmXJiPe0adM0YMAANW3aVEWLFtXYsWN16dIluzovv/yyhg4dqgoVKighIUGGYahWrVrasGGDXnzxRTVv3lyGYahChQp6/PHHM90nZA8SbwAAAAD5mq+vrxYvXmxX1q9fP9u/b7/+W5IuXLhg+3erVq0c1gkMDNQPP/xgVzZ8+HC7540bN9a2bdtS7dugQQN99913Geo/cr7MXeNN4g0AAAAAQLpYXA0AAAAAACfKVOJ95ovns6ofAAAAAADkSZla1RwAAAAAAKSPxBsAAAAAACci8QYAAAAAwIkydY03kB26VvI3Jc7K/edMiSNJBQ9dMC2WGRqWrmBOoHI1TQlz39mTpsQ5+s8FU+L8cdScOP+cvWZKnLhL8abEOX3Z1ZQ4cZcTTIkjSR6u5vz+7upiMSVOsmvm77mbESVKlTUnTgF3U+K4XT1jSpwzJy6bEif+6k1T4rh5mPM3wdPbnPPgeiEvU+JIkrsLY3/IfzjrAQAAAABwIhJvAAAAAACciMQbAAAAAAAnIvEGAAAAAMCJSLwBAAAAAHAiEm8AAAAAAJyIxBsAAAAAACci8QYAAAAAwIlIvAEAAADkS4ZhqE2bNgoJCUm1bdasWSpUqJA++eQTWSwWXbhwQZIUExMji8Xi8HHy5ElJUmRkpOrUqZNm3FatWtn2sVqtKl26tDp16qQVK1bc9TGsX79eHTp0UJEiReTt7a3q1atr9OjROnbsmCRp/vz5KlSokMN9LRaLVq1aJUk6fPiw3bH4+/urZcuW+umnnyRJy5cvl6urq63d21WqVEmjRo1KdXy3PoYNG5ahYwoMDEy17+TJkzO0b3x8vEJDQ1WzZk25ubmpS5cuDuvFxMSoXr16slqtqlixoubPn5+h9u+Vm1NbR4ZFREQoPj4+u7uRKXFxcdndBQAAACDDLBaL5s2bp5o1a2rOnDkaOnSoJOnQoUMaM2aMZs+erTJlyjjcd+/evfL19bUrK168eIZjDx48WC+//LISExN19OhRrVy5Uj179lRoaKjee++9DLUxZ84cPf300+rXr5+WL1+uwMBAHTlyRB999JGmTp2qadOmZbg/KdatW6caNWrozJkzevXVV9WxY0ft27dPjzzyiIoUKaIFCxbohRdesNvnxx9/1IEDBzRw4MBUx3crb2/vDPfj5Zdf1uDBg23PfXx8MrRfUlKSvLy8NGLECC1fvtxhnUOHDunhhx/WsGHDtHDhQn3//fcaNGiQSpYs6fBHmKxA4p1DxMfHKzo6Oru7kSnh4eHZ3QUAAADgrpQtW1YzZsxQWFiY2rZtq8DAQA0cOFBt27ZVnz59FBMT43C/4sWLpzmSnBHe3t4KCAiQJJUpU0aNGzdW1apVNWDAAPXo0UNt2rRJd/+jR49qxIgRGjFihF0eERgYqBYtWthG6O9WkSJFFBAQoICAAL3wwgtavHixtmzZokceeUR9+vTR/PnzUyXec+fOVaNGjVSjRg2Hx3cvfHx87mn/AgUKaPbs2ZKkjRs3Onwd3n33Xd13332aOnWqJKlatWr6+eefFR0d7bTEm6nmAAAAAPKchIQEXbp0ye6RkJDgsG6/fv3UunVrDRgwQG+//bZ27typOXPmmNzjf/tRuHDhDE05/+yzz3Tjxg2NGTPG4fbM/CggSdevX9dHH30kSfLw8JAkDRw4UPv379ePP/5oq3flyhUtW7bMbrQ7K0yePFlFihRR3bp19eabbyoxMTHL2t68eXOqHzZCQkK0efPmLItxO0a8kWOlNf2+XTb0BQAAALlLVFSUJk6caFc2YcIERUZGOqz/3nvvqUaNGvrxxx+1fPlyFStWLN32b5+CXr58ee3atStTfXZxcVHlypV1+PDhO9bdv3+/fH19VbJkyUzFvF3Tpk3l4uKia9euyTAMBQcHq3Xr1pKk6tWrq3Hjxpo7d65atGghSVq6dKkMw1DPnj3t2pk1a5Y++OADu7I5c+aod+/ed+zDiBEjVK9ePfn7+2vTpk2KiIjQiRMn7mnqvCMnT55UiRIl7MpKlCihS5cu6fr16/Ly8sqSOLci8UaOldb0+2/X1MuG3gAAACA3iYiIsC32lcJqtaZZv3jx4ho6dKhWrVqV5oJct/rpp5/srjt2d3e/577eyjAMWSyWLKt3t5YsWaKqVatq586dGjNmjObPn293bAMGDFB4eLhmzpwpHx8fzZ07V927d091DXbv3r314osv2pXdnuym5db3rVatWvLw8NDQoUMVFRWV7nuYk5F4AwAAAMhzrFbrXSdpbm5ucnPLWIp03333ZXo69+2SkpK0f/9+NWjQ4I51K1eurIsXL+rEiRPpjnr7+vrq6tWrSk5OlovL/11pnHLts5+fn139smXLqlKlSqpUqZISExPVtWtX7dy50/Za9uzZU+Hh4Vq6dKlatGihjRs3KioqKlVcPz8/VaxYMSOHfUeNGjVSYmKiDh8+rCpVqmS6vYCAAJ06dcqu7NSpU/L19XXKaLfENd4AAAAAkCMsWLBA58+fV7du3e5Y97HHHpOHh4feeOMNh9tTEusqVaooMTFRsbGxdtv/+OMPSf8m8OnFcHNz06xZs2xlPj4+6t69u+bOnat58+apcuXKat68+R37mxmxsbFycXG5q1Xj09OkSRN9//33dmVr165VkyZNsqR9RxjxBgAAAIC7FBcXl2o9oiJFitimZV+/fj1Vsuvj46MKFSpIkq5du6aTJ0/a3U4sOjpaTz31lB588ME7xi9btqyio6MVFhamS5cuqW/fvgoMDNTRo0f10UcfqWDBgpo6dapq1Kihtm3basCAAZo6daqCgoK0d+9ejRw5Uo8//rhKly6dZgyLxaIRI0YoMjJSQ4cOtd0ObODAgWrevLn27NmjsWPHOtw35fhuZbVaVbhw4XSPa/PmzdqyZYsefPBB+fj4aPPmzQoPD9eTTz55x31T7N69Wzdu3NC5c+d0+fJl2/uQcm/1YcOG6e2339aYMWM0YMAA/fDDD1q6dKnWrFmTofbvBYk3AAAAANwlR1OeN2/erMaNG0uS9u3bp7p169ptb926tdatWydJev/99/X+++/Lw8NDRYoUUXBwsJYsWaKuXbtmuA9PP/20KleurClTpqhr1666fv26AgMD1bFjR7vrpJcsWaIJEyZo6NChOn78uMqUKaOuXbtq3Lhxd4zRr18/vfjii7ZEVZKaNWumKlWq6MCBA+rbt6/D/VKO71YhISH65ptv0o1ntVq1ePFiRUZGKiEhQffdd5/Cw8NTXa+fng4dOuiff/6xPU95HwzDkPTvZQJr1qxReHi4ZsyYoTJlyuiDDz5w2q3EJMlipERHtgoPD88T9/HOymNIq71vK5uzuNrK/edMiSNJBd3y1lUfUZummxOoXE1Twjz6+ck7V8oCR/+5YEqcrq2CTInzz9lrpsSJu5T67gfO4O3hakocP28PU+JIkoerOX97XF2yfvEfR6aUOWRKnF2lWpgSp7K/pylx3K6eMSVOkxk7TYkTf/WmKXHcTPqb4OmdNQt23Um1ikVMiSNJTSqYE2too/KmxAEyIm992wcAAAAAIIch8QYAAACAHOa1115TwYIFHT7at2+f3d27Z5k9rvbt26e5/2uvvWbCEdwbrvEGAAAAgBxm2LBh6tGjh8NtzrrllRkye1wffPCBrl+/7nCbv79/pvrmTCTeAAAAAJDD+Pv75+hE8l5l9rjSW4U9J2OqOQAAAAAATkTiDQAAAACAE5F4AwAAAADgRFzjjVynYPEC5sQ5dMGUOJJ0JTHZtFhmsHiYc+9Zw8Wce6jeMOn9SbxpTpzrN5JMiXMl3px76V6+esOUONdvmHO+JZj498Dqlrd+f08ufNmUOJ4mvW6uyeZ8hixJ5sS5kWDO357Em+bEcfc052+CWfcLL+5jNSWOJBX2Mufe5EBOkrf+xwUAAAAAIIch8QYAAAAAwIlIvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJ2JVc2QZT09PhYeHZ1l7cXFxWdYWAAAAAGQXEm9kmaioqCxtLyuTeAAAAADILkw1BwAAAJCvtWrVSiNHjrQrGzFihIKDg2W1WlWnTh2H+23fvl3NmzeXp6enypYtqzfeeOOu4sbExKhz584qWbKkChQooDp16mjhwoX3eBTIyUi8AQAAAMCBAQMG6PHHH3e47dKlS2rbtq3Kly+v33//XW+++aYiIyP13nvvZbj9TZs2qVatWlq+fLm2b9+u/v37q2/fvvryyy+z6hCQQ5B4AwAAAMi3QkNDtWHDBs2YMUMWi0UWi0WHDx/WW2+9peHDhysoKMjhfgsXLtSNGzc0d+5c1ahRQz179tSIESM0bdo0SdKPP/4od3d3nTx50m6/kSNHqnnz5pKkF154QZMmTVLTpk1VoUIF/fe//1W7du20YsWKDLeB3IHEGwAAAEC+NWPGDDVp0kSDBw/WiRMndOLECZUtW/aO+23evFktWrSQh4eHrSwkJER79+7V+fPn1aJFCwUFBenjjz+2bb9586YWLlyoAQMGpNnuxYsX5e/vL0n33AZyHhJvAAAAAHlOQkKCLl26ZPdISEhIVc/Pz08eHh7y9vZWQECAAgIC5Orqesf2T548qRIlStiVpTxPGaEeOHCg5s2bZ9v+xRdfKD4+Xj169HDY5tKlS/Xrr7+qf//+trK7bQM5E4k3AAAAgDwnKipKfn5+do+svgvPnYSGhurAgQP65ZdfJEnz589Xjx49VKBAgVR1169fr/79++v9999XjRo17qkN5FzcTgwAAABAnhMREaFRo0bZlVmt1ixrPyAgQKdOnbIrS3keEBAgSSpevLg6deqkefPm6b777tPXX3+tmJiYVG1t2LBBnTp1UnR0tPr27Wu3LaNtIGcj8QYAAACQ51it1gwn2h4eHkpKSrqr9ps0aaIXX3xRN2/elLu7uyRp7dq1qlKligoXLmyrN2jQIPXq1UtlypRRhQoV9MADD9i1ExMTo44dO+r111/XkCFDHMa6UxvI+ZhqDgAAACBfCwwM1JYtW3T48GGdOXNGycnJOnDggGJjY3Xy5Eldv35dsbGxio2N1Y0bNyRJTzzxhDw8PDRw4EDt2rVLS5Ys0YwZM1KNsoeEhMjX11evvPKK3bXb0r/Tyx9++GGNGDFC3bp108mTJ3Xy5EmdO3cuw20gdyDxBgAAAJCvPfvss3J1dVX16tVVrFgxHTlyRIMGDVLdunU1Z84c7du3T3Xr1lXdunV1/PhxSf8uyvbdd9/p0KFDCg4O1ujRozV+/PhUo9YuLi4KDQ1VUlJSqmnkCxYs0LVr1xQVFaWSJUvaHo8++miG20DuwFRzAAAAAPla5cqVtXnzZruyjFxHXatWLf300093rHfs2DF16NBBJUuWtCufP3++5s+fn6E+ptUGcgcSbwAAAABwgosXL2rHjh1atGiRVq9enW1tIPuReAMAAACAE3Tu3Flbt27VsGHD9NBDD2VbG8h+JN4AAAAA4ARZcdsvbh2WN7C4GgAAAAAATkTiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYurIcfy9PRUeHh4qvLHsqEvAAAAAHCvSLyRY0VFRTks39isuck9AQAAAIB7x1RzAAAAAACciMQbAAAAAAAnIvEGAAAAAMCJsuwa74iICMXHx2dVc/lOXFxcdncBAAAAAOAEWZZ4x8fHKzo6Oquay3ccrd4NAAAAAMj9mGoOAAAAAIATkXgDAAAAyJcMw1CbNm0UEhKSatusWbNUqFAhffLJJ7JYLLpw4YIkKSYmRhaLxeHj5MmTkqTIyEjVqVMnzbitWrWy7WO1WlW6dGl16tRJK1asuOtjWL9+vTp06KAiRYrI29tb1atX1+jRo3Xs2DFJ0vz581WoUCGH+1osFq1atUqSdPjwYbtj8ff3V8uWLfXTTz9JkpYvXy5XV1dbu7erVKmSRo0aler4bn0MGzYsQ8e0YcMG/ec//5G/v7+8vb1VqVIl9evXTzdu3JD0f+9BynviyJdffqmWLVvKx8dH3t7eatCggebPn29X5/ZjLlKkiNq2bas///zTViezx5KCxBsAAABAvmSxWDRv3jxt2bJFc+bMsZUfOnRIY8aM0cyZM1WmTBmH++7du1cnTpywexQvXjzDsQcPHqwTJ07o4MGDWr58uapXr66ePXtqyJAhGW5jzpw5atOmjQICArR8+XLt3r1b7777ri5evKipU6dmuJ1brVu3TidOnNCPP/6oUqVKqWPHjjp16pQeeeQRFSlSRAsWLEi1z48//qgDBw5o4MCBqY7v1scbb7xxx/i7d+9Wu3btVL9+ff3444/asWOHZs6cKQ8PDyUlJWXoGGbOnKnOnTvrgQce0JYtW7R9+3b17NlTw4YN07PPPpvmMX/77be6cuWK2rdvb5fU3+ux3CrLrvEGAAAAgNymbNmymjFjhsLCwtS2bVsFBgZq4MCBatu2rfr06aOYmBiH+xUvXjzNkeSM8Pb2VkBAgCSpTJkyaty4sapWraoBAwaoR48eatOmTbr7Hz16VCNGjNCIESPs1toKDAxUixYt0h0NTk+RIkUUEBCggIAAvfDCC1q8eLG2bNmiRx55RH369NH8+fP1wgsv2O0zd+5cNWrUSDVq1HB4fHfju+++U0BAgF1iW6FCBbVr1y5D+//vf//T6NGjNXLkSL322mu28tGjR8vDw0MjRoxQ9+7d1ahRI4fHPGXKFFvCnjIT4l6P5VaMeAMAAADI1/r166fWrVtrwIABevvtt7Vz5067EXAz+1G4cOEMTTn/7LPPdOPGDY0ZM8bh9sz8KCBJ169f10cffSRJ8vDwkCQNHDhQ+/fv148//mird+XKFS1btsxutDszAgICbCPu92LZsmW6efOmw5HtoUOHqmDBgvr000/T3N/Ly0uSbNPaswqJNwAAAIA8JyEhQZcuXbJ7JCQkpFn/vffe086dOzVy5Ei99957KlasWLrtlylTRgULFrQ9bh3tvVcuLi6qXLmyDh8+fMe6+/fvl6+vr0qWLJnpuLdq2rSpChYsqAIFCmjKlCkKDg5W69atJUnVq1dX48aNNXfuXFv9pUuXyjAM9ezZ066dWbNm2b0+BQsW1MKFC+8Yv3v37urVq5datmypkiVLqmvXrnr77bd16dKlDPV/37598vPzc/i6eHh4KCgoSPv27XO474ULFzRp0iQVLFhQDRs2zPSx3IrEGwAAAECeExUVJT8/P7tHVFRUmvWLFy+uoUOHqlq1aurSpcsd2//pp58UGxtre3z11VdZ0m/DMGSxWLKs3t1asmSJ/vzzTy1fvlwVK1bU/Pnz5e7ubts+YMAALVu2TJcvX5b07zTz7t27y8fHx66d3r17270+sbGxeuSRR+4Y39XVVfPmzdPRo0f1xhtvqHTp0nrttddUo0YNnThxImsP9v9L+bGhcOHC2rZtm5YsWaISJUpk+lhuxTXeAAAAAPKciIgI2yrbKaxWa7r7uLm5yc0tYynSfffdl+np3LdLSkrS/v371aBBgzvWrVy5si5evKgTJ06kO+rt6+urq1evKjk5WS4u/zfumnINuJ+fn139smXLqlKlSqpUqZISExPVtWtX7dy50/ba9ezZU+Hh4Vq6dKlatGihjRs3OvxBw8/PTxUrVszIYTtUunRp9enTR3369NGkSZNUuXJlvfvuu5o4cWK6+6W8LsePH1epUqXstt24cUMHDx7Ugw8+aFe+ZMkSVa9eXUWKFHH4nmb2WCRGvAEAAADkQVarVb6+vnaPOyXe2W3BggU6f/68unXrdse6jz32mDw8PNJcXTslsa5SpYoSExMVGxtrt/2PP/6Q9G+iml4MNzc3zZo1y1bm4+Oj7t27a+7cuZo3b54qV66s5s2b37G/mVG4cGGVLFlSV69evWPdbt26yd3d3eGq7u+++66uXr2qXr162ZWXLVtWFSpUyPIfUm7FiDcAAAAA3KW4uDjFx8fblRUpUsQ2Lfv69eupkl0fHx9VqFBBknTt2jWdPHlSiYmJOnr0qFauXKno6Gg99dRTqUZkHSlbtqyio6MVFhamS5cuqW/fvgoMDNTRo0f10UcfqWDBgpo6dapq1Kihtm3basCAAZo6daqCgoK0d+9ejRw5Uo8//rhKly6dZgyLxaIRI0YoMjJSQ4cOlbe3t6R/F1lr3ry59uzZo7FjxzrcN+X4bmW1WlW4cOF0j2vOnDmKjY1V165dVaFCBcXHx+ujjz7Srl27NHPmTLu6O3bssJvibrFYVLt2bb3xxhsaPXq0PD091adPH7m7u+vzzz/XCy+8oNGjR9utaJ4R93ostyLxBgAAAIC7VKVKlVRlmzdvVuPGjSX9u8hX3bp17ba3bt1a69atkyS9//77ev/99+Xh4aEiRYooODhYS5YsUdeuXTPch6efflqVK1fWlClT1LVrV12/fl2BgYHq2LGj3TT7JUuWaMKECRo6dKiOHz+uMmXKqGvXrho3btwdY/Tr108vvvii3n77bdsK6s2aNVOVKlV04MAB9e3b1+F+Kcd3q5CQEH3zzTfpxmvYsKF+/vlnDRs2TMePH7ctXLdq1Sq1bNnSrm6LFi3snru6uioxMVEjR45UUFCQpkyZohkzZigpKUk1atTQ7Nmz1b9//zsec1Ydy60shmEYdx3ZgfDwcLv7x+Hu8Ppl3MZmzp3KkmLllmOmxJGkK4nJpsUyw9ux75kSxyhV1ZQ4HT/7nylxjh++YEqcji3vMyXO36evmBLn1PnrpsRxc3c1JU5BT/N+E7e65a0rzj4K3G9KnEPVOpkS576CWb9okiMuV86YEqfe1N2mxLlx/aYpcbx8PEyJU8DX05Q4D9QocedKWaR++YyPEmZGj1ql7lwJMEne+h8XAAAAAIAchsQbAAAAAHKY1157LdW9o1Me7du3z+7u3bO8elx3wjXeAAAAAJDDDBs2TD169HC4zcvLy+TeZJ28elx3QuINAAAAADmMv7+//P39s7sbWS6vHtedMNUcAAAAAAAnIvEGAAAAAMCJSLwBAAAAAHAirvFGrlPv2UdNidOwdAVT4kiSxcOce3SaJazOEFPiTJ7zhClxHqzW35Q4XbrVNCWOWfcGTnZ1NyXOhfgkU+K4uZjzurnzk/g9s9wsZ0qcm/GGKXFOJ5hzMsS7FDUlzhsD6psSx8Vizmc1r/F0M++Pj9XEWEBOwVkPAAAAAIATkXgDAAAAAOBEJN4AAAAAADgRiTcAAAAAAE5E4g0AAAAAgBOReAMAAAAA4EQk3gAAAACQhsjISNWpUyfL2rNYLFq1alWWtYfcgcQbAAAAANLw7LPP6vvvv8/ubiCXc8vuDgAAAABATlWwYEEVLFgwu7uBXI4RbwAAAAD51nvvvadSpUopOTnZrrxz584aMGCA3VTz+Ph41ahRQ0OGDLHVO3jwoHx8fDR37lxb2dy5c1WjRg1ZrVaVLFlSYWFhDmPfuHFDYWFhKlmypDw9PVW+fHlFRUVl/UEi25F4AwAAAMi3unfvrrNnz2r9+vW2snPnzumbb75R79697ep6enpq4cKFWrBggT7//HMlJSXpySef1EMPPaQBAwZIkmbPnq3hw4dryJAh2rFjh1avXq2KFSs6jP3WW29p9erVWrp0qfbu3auFCxcqMDDQaceK7MNUcwAAAAB5TkJCghISEuzKrFarrFarXVnhwoXVvn17LVq0SK1bt5YkLVu2TEWLFtWDDz6on376ya5+nTp19Morr2jQoEHq2bOn/vnnH3355Ze27a+88opGjx6t//73v7ayBg0aOOzjkSNHVKlSJTVr1kwWi0Xly5fP1DEj5yLxRqZFREQoPj7etHivtSxnWiwAAADkTlFRUZo4caJd2YQJExQZGZmqbu/evTV48GDNmjVLVqtVCxcuVM+ePeXi4niC8OjRo7Vq1Sq9/fbb+vrrr1WkSBFJUlxcnI4fP25L4O8kNDRUDz30kKpUqaJ27dqpY8eOatu27d0dKHIFEm9kWnx8vKKjo02Ld32VebEAAACQO0VERGjUqFF2ZbePdqfo1KmTDMPQmjVr1KBBA/3000/pfr+Ni4vTvn375Orqqv3796tdu3aSJC8vr7vqY7169XTo0CF9/fXXWrdunXr06KE2bdpo2bJld9UOcj6u8QYAAACQ51itVvn6+to90kq8PT099eijj2rhwoX69NNPVaVKFdWrVy/NtgcMGKCaNWtqwYIFGjt2rPbs2SNJ8vHxUWBg4F3dfszX11ePP/643n//fS1ZskTLly/XuXPn7u5gkeMx4g0AAAAg3+vdu7c6duyoXbt26cknn0yz3jvvvKPNmzdr+/btKlu2rNasWaPevXvrl19+kYeHhyIjIzVs2DAVL15c7du31+XLl7Vx40Y988wzqdqaNm2aSpYsqbp168rFxUWfffaZAgICVKhQISceKbIDI94AAAAA8r3//Oc/8vf31969e/XEE084rPPXX3/pueee06xZs1S2bFlJ0qxZs3TmzBmNGzdOktSvXz9Nnz5ds2bNUo0aNdSxY0ft37/fYXs+Pj564403VL9+fTVo0ECHDx/WV199lea15ci9GPEGAAAAkO+5uLjo+PHjqcojIyNtC7JVrVpV165ds9teqFAhHTlyxK5s6NChGjp0qMM4hmHY/j148GANHjw4kz1HbsBPKQAAAAAAOBGJNwAAAAAATkTiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAAABOROINAAAAAIATcR/vHMLT01Ph4eHZ3Y17EhcXZ2o8IynJnEDlapoTR5Lh4mpaLDNMnvOEKXGeH7rIlDitYoebEifZuHOdrGC4mPOnP8msAzJJomnHYzEpjpRk5K336IaLjylxTl25dudKWeByQt4aHynq7WFKnJvJyabESTInjJJN+pzeNPFvdnx8ommxgJyCxDuHiIqKyu4u3LPc+oMBAAAAAJghb/2UCgAAAABADkPiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAAABOROINAAAAIF9r1aqVRo4cmd3dQB7G7cQAAAAA5GsrVqyQu7t7dncDeRiJNwAAAIB8zd/fP7u7gDyOqeYAAAAA8rVbp5onJCRo7NixKlu2rKxWqypWrKgPP/xQknT+/Hn17t1bxYoVk5eXlypVqqR58+bZ2hk7dqwqV64sb29vBQUFady4cbp582Z2HBJyGEa8AQAAAOD/69u3rzZv3qy33npLtWvX1qFDh3TmzBlJ0rhx47R79259/fXXKlq0qA4cOKDr16/b9vXx8dH8+fNVqlQp7dixQ4MHD5aPj4/GjBmTXYeDHILEGwAAAECek5CQoISEBLsyq9Uqq9Wa5j779u3T0qVLtXbtWrVp00aSFBQUZNt+5MgR1a1bV/Xr15ckBQYG2u3/0ksv2f4dGBioZ599VosXLybxBok3Ms/T01Ph4eGmxXu1WWnTYgEAACB3ioqK0sSJE+3KJkyYoMjIyDT3iY2Nlaurq1q2bOlw+1NPPaVu3brpjz/+UNu2bdWlSxc1bdrUtn3JkiV66623dPDgQV25ckWJiYny9fXNkuNB7kbijUyLiooyNd615VNMjQcAAIDcJyIiQqNGjbIrS2+0W5K8vLzS3d6+fXv9888/+uqrr7R27Vq1bt1aw4cP15QpU7R582b17t1bEydOVEhIiPz8/LR48WJNnTo108eC3I/F1QAAAADkOVarVb6+vnaPOyXeNWvWVHJysjZs2JBmnWLFiqlfv3765JNPNH36dL333nuSpE2bNql8+fJ68cUXVb9+fVWqVEn//PNPlh4Tci9GvAEAAABA/16X3a9fPw0YMMC2uNo///yjuLg49ejRQ+PHj1dwcLBq1KihhIQEffnll6pWrZokqVKlSjpy5IgWL16sBg0aaM2aNVq5cmU2HxFyCka8AQAAAOD/mz17th577DE9/fTTqlq1qgYPHqyrV69Kkjw8PBQREaFatWqpRYsWcnV11eLFiyVJjzzyiMLDwxUWFqY6depo06ZNGjduXHYeCnIQi2EYRlY0FB4erujo6KxoCkiXWdd4uzfrZkocSZKLq3mxTHB15bumxHl+6CJT4rSK3WhKnFolfEyJE+TnbkqcxCz53+XOLt9INieQSdxcLKbFSsqarwA5hlmjCdvjrpkSx9s9b42PuLuYczw3k835m5Bk0p+eZJM+pzeTzft7kGRSrJAqxU2JA2RE3vqLDgAAAABADkPiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAAABOROINAAAAAIATkXgDAAAAAOBEbtndAeBuNdxc1ZQ49509aUocSbqRmLfuQ/xgtf6mxGkVO9yUODF1HjAlTvkHy5sSJ3Fga1PixJ+9aEqcq/v+Z0ocayFz7rPu4uttShxJSr6RaFosM8xu8F9T4gxvXNaUOP9cvGFKnHiT/g+qGzvflDhy8zAljEvBQqbEcfUxJ44CgsyJIynJr5RpsYCcghFvAAAAAACciMQbAAAAAAAnIvEGAAAAAMCJSLwBAAAAAHAiEm8AAAAAAJyIxBsAAAAAACci8QYAAACQr7Vq1UojR4685/0PHz4si8Wi2NjYLOsT8hbu4w0AAAAgX1uxYoXc3d2zuxvIw0i8AQAAAORr/v7+2d0FJSUlyWKxyMWFScl5Ee8qAAAAgHzt1qnmgYGBeu211zRgwAD5+PioXLlyeu+99+zqb926VXXr1pWnp6fq16+vP//80257TEyMLBaL1qxZo1q1asnT01ONGzfWzp07bXXmz5+vQoUKafXq1apevbqsVquOHDmi8+fPq2/fvipcuLC8vb3Vvn177d+/3+mvAZyLxBsAAAAAbjF16lRbQv3000/rqaee0t69eyVJV65cUceOHVW9enX9/vvvioyM1LPPPuuwneeee05Tp07Vr7/+qmLFiqlTp066efOmbfu1a9f0+uuv64MPPtCuXbtUvHhxhYaG6rffftPq1au1efNmGYahDh062O2H3IfEGwAAAECek5CQoEuXLtk9EhISMrRvhw4d9PTTT6tixYoaO3asihYtqvXr10uSFi1apOTkZH344YeqUaOGOnbsqOeee85hOxMmTNBDDz2kmjVrasGCBTp16pRWrlxp237z5k3NmjVLTZs2VZUqVXTs2DGtXr1aH3zwgZo3b67atWtr4cKFOnbsmFatWpXp1wTZh2u884mIiAjFx8dndzeyhmvr7O4BAAAAcrioqChNnDjRrmzChAmKjIy84761atWy/dtisSggIEBxcXGSpD179timj6do0qSJw3ZuLff391eVKlW0Z88eW5mHh4ddrD179sjNzU2NGjWylRUpUiTVfsh9SLzzifj4eEVHR2d3N7LE/c9+md1dAAAAQA4XERGhUaNG2ZVZrdYM7Xv7CucWi0XJyclZ1rcUXl5eslgsWd4uch6mmgMAAADIc6xWq3x9fe0eGU2801OtWjVt377dbjbpL7/84rDureXnz5/Xvn37VK1atXTbTkxM1JYtW2xlZ8+e1d69e1W9evVM9x3Zh8QbAAAAADLoiSeekMVi0eDBg7V792599dVXmjJlisO6L7/8sr7//nvt3LlToaGhKlq0qLp06ZJm25UqVVLnzp01ePBg/fzzz9q2bZuefPJJlS5dWp07d3bSEcEMJN4AAAAAkEEFCxbUF198oR07dqhu3bp68cUX9frrrzusO3nyZP33v/9VcHCwTp48qS+++EIeHh7ptj9v3jwFBwerY8eOatKkiQzD0FdffZVq+jtyF67xBgAAAJCvxcTE2P59+PDhVNtjY2Ptnjdu3DhVmWEYqfZr1qyZ3b27bxUaGqrQ0NBU5YULF9ZHH310py4jl2HEGwAAAAAAJyLxBgAAAADAiZhqDgAAAABZqFWrVg6nniP/YsQbAAAAAAAnIvEGAAAAAMCJSLwBAAAAAHAiEm8AAAAAAJyIxdWQ67h5mPN70dF/LpgSR5ISbyabFssMXbrVNCVOsklrlpR/sLwpcRas/8eUOB3/+syUOAev3DAlzvH4m6bE8XCxmBLH3WJOHMm8YzLLV2O6mhJnVDNz/iYU8HA1Jc7NZHP+D7p65KgpcZJvJJoSx6t4YVPiuBXyNyWORwFfU+JIkou7lzmBCvqZEwfIAEa8AQAAAABwIhJvAAAAAACciMQbAAAAAAAnIvEGAAAAAMCJSLwBAAAAAHAiEm8AAAAAAJyIxBsAAAAAACci8QYAAAAAwIlIvAEAAAAAcCISbwAAAAAAnIjEGwAAAEC+1qpVK4WFhSksLEx+fn4qWrSoxo0bJ8MwJEkff/yx6tevLx8fHwUEBOiJJ55QXFycJMkwDFWsWFFTpkyxazM2NlYWi0UHDhyQJF24cEGDBg1SsWLF5Ovrq//85z/atm2brX5kZKTq1KmjuXPnqly5cipYsKCefvppJSUl6Y033lBAQICKFy+uV1991baPYRiKjIxUuXLlZLVaVapUKY0YMcLZLxfuAYk3AAAAgDwnISFBly5dsnskJCSkWX/BggVyc3PT1q1bNWPGDE2bNk0ffPCBJOnmzZuaNGmStm3bplWrVunw4cMKDQ2VJFksFg0YMEDz5s2za2/evHlq0aKFKlasKEnq3r274uLi9PXXX+v3339XvXr11Lp1a507d862z8GDB/X111/rm2++0aeffqoPP/xQDz/8sI4ePaoNGzbo9ddf10svvaQtW7ZIkpYvX67o6GjNmTNH+/fv16pVq1SzZs2sfBmRRdyyuwPpiYiIUHx8fHZ3I09I+UXOWUx9r7weMicOAAAAcq2oqChNnDjRrmzChAmKjIx0WL9s2bKKjo6WxWJRlSpVtGPHDkVHR2vw4MEaMGCArV5QUJDeeustNWjQQFeuXFHBggUVGhqq8ePHa+vWrWrYsKFu3rypRYsW2UbBf/75Z23dulVxcXGyWq2SpClTpmjVqlVatmyZhgwZIklKTk7W3Llz5ePjo+rVq+vBBx/U3r179dVXX8nFxUVVqlTR66+/rvXr16tRo0Y6cuSIAgIC1KZNG7m7u6tcuXJq2LChE15NZFaOTrzj4+MVHR2d3d3IE8LDw53avpnvVZ0XvjIlDgAAAHKviIgIjRo1yq4sJel1pHHjxrJYLLbnTZo00dSpU5WUlKTY2FhFRkZq27ZtOn/+vJKTkyVJR44cUfXq1VWqVCk9/PDDmjt3rho2bKgvvvhCCQkJ6t69uyRp27ZtunLliooUKWIX8/r16zp48KDteWBgoHx8fGzPS5QoIVdXV7m4uNiVpQyqde/eXdOnT1dQUJDatWunDh06qFOnTnJzy9FpXr7EOwIAAAAgz7Farekm2hkVHx+vkJAQhYSEaOHChSpWrJiOHDmikJAQ3bhxw1Zv0KBB6tOnj6KjozVv3jw9/vjj8vb2liRduXJFJUuWVExMTKr2CxUqZPu3u7u73TaLxeKwLCXxL1u2rPbu3at169Zp7dq1evrpp/Xmm29qw4YNqfZD9iLxBgAAAJDvpVw3neKXX35RpUqV9Ndff+ns2bOaPHmyypYtK0n67bffUu3foUMHFShQQLNnz9Y333yjH3/80batXr16OnnypNzc3BQYGJil/fby8lKnTp3UqVMnDR8+XFWrVtWOHTtUr169LI2DzGFxNQAAAAD53pEjRzRq1Cjt3btXn376qWbOnKn//ve/KleunDw8PDRz5kz9/fffWr16tSZNmpRqf1dXV4WGhioiIkKVKlVSkyZNbNvatGmjJk2aqEuXLvruu+90+PBhbdq0SS+++KLDJD6j5s+frw8//FA7d+7U33//rU8++UReXl4qX778PbcJ5yDxBgAAAJDv9e3bV9evX1fDhg01fPhw/fe//9WQIUNUrFgxzZ8/X5999pmqV6+uyZMnp7p1WIqBAwfqxo0b6t+/v125xWLRV199pRYtWqh///6qXLmyevbsqX/++UclSpS45z4XKlRI77//vh544AHVqlVL69at0xdffJHqWnJkP6aaAwAAAMj33N3dNX36dM2ePTvVtl69eqlXr152ZSn3+L7VsWPH5O7urr59+6ba5uPjo7feektvvfWWw/iRkZGpVlyfP39+qnq3XifepUsXdenSxWF7yFlIvAEAAAAgExISEnT69GlFRkaqe/fumRrFRt7EVHMAAAAAyIRPP/1U5cuX14ULF/TGG29kd3eQAzHiDQAAACBfc3Sbr7sRGhqq0NDQLOkL8iZGvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJ+Iab+Q6r/QJNiXOH0cvmBJHkq7fSDItlhnuK2gxJY7hYs6fsMSBrU2J0/Gvz0yJ8+WJy6bE8XI15zwo6mHOeWDS4Sgp9d1p8kQsMxz67Q9T4ny2q4opcY5fjDclzgmT4szoF2FKHCXdMCWMYfUxJc51w9WUOP9cTTQljiSdumzOe9SyuClhgAxhxBsAAAAAACci8QYAAAAAwIlIvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJyLxBgAAAADAiUi8AQAAAABwIhJvAAAAAMgirVq10siRIzNUNyYmRhaLRRcuXHBqn5D9SLwBAAAAAHAiEm8AAAAAAJyIxBsAAABAvvXll1+qUKFCSkpKkiTFxsbKYrHo+eeft9UZNGiQnnzySZ09e1a9evVS6dKl5e3trZo1a+rTTz9Nt/2EhASNHTtWZcuWldVqVcWKFfXhhx869ZiQ87hldwcAAAAAIKslJCQoISHBrsxqtcpqtdqVNW/eXJcvX9aff/6p+vXra8OGDSpatKhiYmJsdTZs2KCxY8cqPj5ewcHBGjt2rHx9fbVmzRr16dNHFSpUUMOGDR32o2/fvtq8ebPeeust1a5dW4cOHdKZM2ey/HiRs5F45xOenp4KDw93WvtxcXGpyiIiIhQfH5/lsVoPef7OlQAAAJCvRUVFaeLEiXZlEyZMUGRkpF2Zn5+f6tSpo5iYGNWvX18xMTEKDw/XxIkTdeXKFV28eFEHDhxQy5YtVbp0aT377LO2fZ955hl9++23Wrp0qcPEe9++fVq6dKnWrl2rNm3aSJKCgoKy/mCR45F45xNRUVFObd9RUh8fH6/o6Ogsj/XlnlNZ3iYAAADyloiICI0aNcqu7PbR7hQtW7ZUTEyMRo8erZ9++klRUVFaunSpfv75Z507d06lSpVSpUqVlJSUpNdee01Lly7VsWPHdOPGDSUkJMjb29thu7GxsXJ1dVXLli2z/PiQu5B4AwAAAMhzHE0rT0urVq00d+5cbdu2Te7u7qpatapatWqlmJgYnT9/3pY4v/nmm5oxY4amT5+umjVrqkCBAho5cqRu3LjhsF0vL68sOx7kbiyuBgAAACBfS7nOOzo62pZkpyTeMTExatWqlSRp48aN6ty5s5588knVrl1bQUFB2rdvX5rt1qxZU8nJydqwYYMZh4EcjMQbAAAAQL5WuHBh1apVSwsXLrQl2S1atNAff/yhffv22ZLxSpUqae3atdq0aZP27NmjoUOH6tSptC+DDAwMVL9+/TRgwACtWrVKhw4dUkxMjJYuXWrGYSEHIfEGAAAAkO+1bNlSSUlJtsTb399f1atXV0BAgKpUqSJJeumll1SvXj2FhISoVatWCggIUJcuXdJtd/bs2Xrsscf09NNPq2rVqho8eLCuXr3q5KNBTsM13gAAAADyvenTp2v69Ol2ZbGxsXbP/f39tWrVqnTbufU2ZNK/dxeaNm2apk2blqpuq1atZBjGPfQWuQ0j3gAAAAAAOBGJNwAAAAAATkTiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAAABOxO3EkOscvnDNlDj/nDUnjiRdib9pWiwzJLu6mxInKdmc22/En71oSpyDV26YEsfL1WJKnOtJ5rw/pxISTYmTF5l0KpjGw9vPlDhB/t6mxLG6uZoSx8vDnDhupw+YEsdINOn/1AKFTAnj7VXYlDjFvf1NiSNJbi557I8PkAGMeAMAAAAA4EQk3gAAAAAAOBGJNwAAAAAATkTiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAAABOROINAAAAACaaP3++ChUqlN3dgIlIvAEAAADgLmzbtk29evVS2bJl5eXlpWrVqmnGjBnZ3S3kYG7Z3QEAAAAAyE1+//13FS9eXJ988onKli2rTZs2aciQIXJ1dVVYWFh2dw85ECPeAAAAAPK1Vq1a6ZlnntHIkSNVuHBhlShRQu+//76uXr2q/v37y8fHRxUrVtTXX38tSRowYIBmzJihli1bKigoSE8++aT69++vFStW2Nrctm2bHnzwQfn4+MjX11fBwcH67bffsusQkc1IvAEAAADkOQkJCbp06ZLdIyEhIc36CxYsUNGiRbV161Y988wzeuqpp9S9e3c1bdpUf/zxh9q2bas+ffro2rVrDve/ePGi/P39bc979+6tMmXK6Ndff9Xvv/+u559/Xu7u7ll+nMgdmGqOLOHp6anw8HC7sri4uAzvHxERofj4+AzVrdBjxF31DQAAAPlPVFSUJk6caFc2YcIERUZGOqxfu3ZtvfTSS5L+/W46efJkFS1aVIMHD5YkjR8/XrNnz9b27dvVuHFju303bdqkJUuWaM2aNbayI0eO6LnnnlPVqlUlSZUqVcqqQ0MuROKNLBEVFZWq7PZEPD3x8fGKjo7OUN23Nx/KcLsAAADInyIiIjRq1Ci7MqvVmmb9WrVq2f7t6uqqIkWKqGbNmrayEiVKSEo9uLRz50517txZEyZMUNu2bW3lo0aN0qBBg/Txxx+rTZs26t69uypUqJCpY0LuxVRzAAAAAHmO1WqVr6+v3SO9xPv2aeAWi8WuzGKxSJKSk5NtZbt371br1q01ZMgQ22h5isjISO3atUsPP/ywfvjhB1WvXl0rV67MikNDLkTiDQAAAAB3adeuXXrwwQfVr18/vfrqqw7rVK5cWeHh4fruu+/06KOPat68eSb3EjkFiTcAAAAA3IWdO3fqwQcfVNu2bTVq1CidPHlSJ0+e1OnTpyVJ169fV1hYmGJiYvTPP/9o48aN+vXXX1WtWrVs7jmyC9d4AwAAAMBdWLZsmU6fPq1PPvlEn3zyia28fPnyOnz4sFxdXXX27Fn17dtXp06dUtGiRfXoo4+mWuwN+QeJNwAAAIB8LSYmJlXZ4cOHU5UZhiFJ6tKlS5qro0uSh4eHPv300zS3h4aGKjQ09C57idyMqeYAAAAAADgRiTcAAAAAAE5E4g0AAAAAgBOReAMAAAAA4EQk3gAAAAAAOBGJNwAAAAAATsTtxJDr/HX8silx4i7FmxJHki5fvWFaLDNciE/K7i5kqav7/mdKnOPxN02JU9TDnD/9pxISTYlzI9kwJU5e5GrJ7h5kLTevgqbEKVbAw5Q4V26Y87c0IdHdlDiJJw6bEsdIMud1cytqzv/dronm/N9QwNPHlDiSdMOk/4eAnIQRbwAAAAAAnIjEGwAAAAAAJyLxBgAAAADAiUi8AQAAAABwIhJvAAAAAACciMQbAAAAAAAnIvEGAAAAAMCJSLwBAAAA4C4FBgZq+vTp2d0N5BIk3gAAAACQzU6cOKEnnnhClStXlouLi0aOHJndXUIWIvEGAAAAgGyWkJCgYsWK6aWXXlLt2rWzuzvIYiTeAAAAAPK1y5cvq3fv3ipQoIBKliyp6OhotWrVyjbqHBcXp06dOsnLy0v33XefFi5cmKoNi8Wi2bNnq3379vLy8lJQUJCWLVtmV+fo0aPq1auX/P39VaBAAdWvX19btmyR9O/U9RkzZqhv377y8/Nz+jHDXCTeAAAAAPKchIQEXbp0ye6RkJDgsO6oUaO0ceNGrV69WmvXrtVPP/2kP/74w7Y9NDRU//vf/7R+/XotW7ZMs2bNUlxcXKp2xo0bp27dumnbtm3q3bu3evbsqT179kiSrly5opYtW+rYsWNavXq1tm3bpjFjxig5Odk5LwByFLfs7gDyLk9PT4WHh2eo7u1/uCIiIhQfH++4crP+me0aAAAA8rioqChNnDjRrmzChAmKjIy0K7t8+bIWLFigRYsWqXXr1pKkefPmqVSpUpKkffv26euvv9bWrVvVoEEDSdKHH36oatWqpYrZvXt3DRo0SJI0adIkrV27VjNnztSsWbO0aNEinT59Wr/++qv8/f0lSRUrVszSY0bOReINp4mKispw3dsT9Pj4eEVHRzusG7Z8e6b6BQAAgLwvIiJCo0aNsiuzWq2p6v3999+6efOmGjZsaCvz8/NTlSpVJEl79uyRm5ubgoODbdurVq2qQoUKpWqrSZMmqZ7HxsZKkmJjY1W3bl1b0o38hcQbAAAAQJ5jtVodJtrZxcvLK7u7gGzENd4AAAAA8q2goCC5u7vr119/tZVdvHhR+/btk/Tv6HZiYqJ+//132/a9e/fqwoULqdr65ZdfUj1PmZJeq1YtxcbG6ty5c044CuR0JN4AAAAA8i0fHx/169dPzz33nNavX69du3Zp4MCBcnFxkcViUZUqVdSuXTsNHTpUW7Zs0e+//65BgwY5HMH+7LPPNHfuXO3bt08TJkzQ1q1bFRYWJknq1auXAgIC1KVLF23cuFF///23li9frs2bN9v2j42NVWxsrK5cuaLTp08rNjZWu3fvNu21gPOQeAMAAADI16ZNm6YmTZqoY8eOatOmjR544AFVq1ZNnp6ekv5vsbWWLVvq0Ucf1ZAhQ1S8ePFU7UycOFGLFy9WrVq19NFHH+nTTz9V9erVJUkeHh767rvvVLx4cXXo0EE1a9bU5MmT5erqatu/bt26qlu3rn7//XctWrRIdevWVYcOHcx5EeBUXOMNAAAAIF/z8fGxuzf31atXNXHiRA0ZMkSSFBAQoC+//NJunz59+qRqp1SpUvruu+/SjFO+fPlU9/a+lWEYd9t15BIk3gAAAADytT///FN//fWXGjZsqIsXL+rll1+WJHXu3Dmbe4a8gsQbAAAAQL43ZcoU7d27Vx4eHgoODtZPP/2kokWLZne3kEeQeAMAAADI11Kuq84MpokjPSyuBgAAAACAE5F4AwAAAADgRCTeAAAAAAA4Edd4I9cp5O1uSpzTl13vXCmLXL9hXiwzuLlYTImTmGzOtVTWQj6mxPEw6XVzNScMcoGkPHY5ood3AVPi+FnN+Ztd2sfTlDhWV3PGYZIPXTIljpKTTAmTdNmc98fVxZzzzRJ/2ZQ4kuRbgAXLkP8w4g0AAAAAgBOReAMAAAAA4EQk3gAAAAAAOBGJNwAAAAAATkTiDQAAAACAE5F4AwAAAADgRCTeAAAAAAA4EYk3AAAAANylwMBATZ8+Pbu7gVyCxBsAAAAAACci8QYAAAAAwIlIvAEAAADka5cvX1bv3r1VoEABlSxZUtHR0WrVqpVGjhwpSYqLi1OnTp3k5eWl++67TwsXLkzVhsVi0ezZs9W+fXt5eXkpKChIy5YtM/lIkFO5ZXcHAEny9PRUeHi47XlcXFw29gYAAAC5XUJCghISEuzKrFarrFZrqrqjRo3Sxo0btXr1apUoUULjx4/XH3/8oTp16kiSQkNDdfz4ca1fv17u7u4aMWKEw++r48aN0+TJkzVjxgx9/PHH6tmzp3bs2KFq1ao55RiRe5B4I0eIioqye56ShEdERCg+Pt5uW4G2Q0zrFwAAAHKnqKgoTZw40a5swoQJioyMtCu7fPmyFixYoEWLFql169aSpHnz5qlUqVKSpH379unrr7/W1q1b1aBBA0nShx9+6DCZ7t69uwYNGiRJmjRpktauXauZM2dq1qxZWX14yGVIvJGjxcfHKzo62q7spa/3ZFNvAAAAkFtERERo1KhRdmWORrv//vtv3bx5Uw0bNrSV+fn5qUqVKpKkPXv2yM3NTcHBwbbtVatWVaFChVK11aRJk1TPY2NjM3EUyCtIvAEAAADkOWlNKweyA4urAQAAAMi3goKC5O7url9//dVWdvHiRe3bt0/Sv6PbiYmJ+v33323b9+7dqwsXLqRq65dffkn1nOu7ITHiDQAAACAf8/HxUb9+/fTcc8/J399fxYsX14QJE+Ti4iKLxaIqVaqoXbt2Gjp0qGbPni03NzeNHDlSXl5eqdr67LPPVL9+fTVr1kwLFy7U1q1b9eGHH2bDUSGnYcQbAAAAQL42bdo0NWnSRB07dlSbNm30wAMPqFq1avL09JT0f4uttWzZUo8++qiGDBmi4sWLp2pn4sSJWrx4sWrVqqWPPvpIn376qapXr2724SAHYsQbAAAAQL7m4+Njd2/uq1evauLEiRoy5N+76QQEBOjLL7+026dPnz6p2ilVqpS+++4753YWuRKJNwAAAIB87c8//9Rff/2lhg0b6uLFi3r55ZclSZ07d87mniGvIPEGAAAAkO9NmTJFe/fulYeHh4KDg/XTTz+paNGi2d0t5BEk3gAAAADytbp169qtWn4vDMPIot4gL2JxNQAAAAAAnIjEGwAAAAAAJyLxBgAAAADAibjGG7lOMV+rKXHiLieYEkeSEhKTTYtlBnfTftKzmBLFxdfblDjuFnOOJ4lL0JBHJd64aUqchERzPkQJSUmmxLl205w4LgV8TYljFlefQuYE8jYnjuFhzv91khRv0vce844IuDNGvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJyLxBgAAAADAiUi8AQAAAABwIhJvAAAAAACciMQbAAAAAAAnIvEGAAAAgHsUExMji8WiCxcuSJLmz5+vQoUK2dV57733VLZsWbm4uGj69OmZjhkYGJgl7cA8btndAQAAAADIrZo2baoTJ07Iz8/P4fZLly4pLCxM06ZNU7du3dKsh7yNxBs5kqenp8LDwxUXF5fdXQEAAADS5OHhoYCAgDS3HzlyRDdv3tTDDz+skiVLmtgz5CRMNUeOFBUVpejoaBUvXjy7uwIAAIA8Ljk5WW+88YYqVqwoq9WqcuXK6dVXX9Xhw4dlsVi0ePFiNW3aVJ6enrr//vu1YcMG2763TzW/1fz581WzZk1JUlBQkCwWiw4fPixJ+vzzz1WvXj15enoqKChIEydOVGJioiTJMAxFRkaqXLlyslqtKlWqlEaMGOH01wHOw4g3AAAAgHwtIiJC77//vqKjo9WsWTOdOHFCf/31l237c889p+nTp6t69eqaNm2aOnXqpEOHDqlIkSLptvv444+rbNmyatOmjbZu3aqyZcuqWLFi+umnn9S3b1+99dZbat68uQ4ePKghQ4ZIkiZMmKDly5crOjpaixcvVo0aNXTy5Elt27bNqa8BnIvEGwAAAECek5CQoISEBLsyq9Uqq9VqV3b58mXNmDFDb7/9tvr16ydJqlChgpo1a2YbnQ4LC1O3bt0kSbNnz9Y333yjDz/8UGPGjEm3D15eXrbkvFixYrYp6RMnTtTzzz9vixcUFKRJkyZpzJgxmjBhgo4cOaKAgAC1adNG7u7uKleunBo2bJi5FwTZiqnmyDUiIiIUHh6e3d0AAABALhAVFSU/Pz+7R1RUVKp6e/bsUUJCglq3bp1mW02aNLH9283NTfXr19eePXvuuW/btm3Tyy+/rIIFC9oegwcP1okTJ3Tt2jV1795d169fV1BQkAYPHqyVK1fapqEjdyLxRq4RHx+v6Ojo7O4GAAAAcoGIiAhdvHjR7hEREZGqnpeXl+l9u3LliiZOnKjY2FjbY8eOHdq/f788PT1VtmxZ7d27V7NmzZKXl5eefvpptWjRQjdv3jS9r8gaWTbVPGUV6qzEitYAAAAA7oWjaeWOVKpUSV5eXvr+++81aNAgh3V++eUXtWjRQpKUmJio33//XWFhYffct3r16mnv3r2qWLFimnW8vLzUqVMnderUScOHD1fVqlW1Y8cO1atX757jIvtkWeLtaNpGZjGtGAAAAIAzeXp6auzYsRozZow8PDz0wAMP6PTp09q1a5dt+vk777yjSpUqqVq1aoqOjtb58+c1YMCAe445fvx4dezYUeXKldNjjz0mFxcXbdu2TTt37tQrr7yi+fPnKykpSY0aNZK3t7c++eQTeXl5qXz58ll12DAZi6sBAAAAyNfGjRsnNzc3jR8/XsePH1fJkiU1bNgw2/bJkydr8uTJio2NVcWKFbV69WoVLVr0nuOFhIToyy+/1Msvv6zXX39d7u7uqlq1qm3EvVChQpo8ebJGjRqlpKQk1axZU1988cUdV1FHzkXiDQAAACBfc3Fx0YsvvqgXX3zRrjxlVfNq1appy5YtDvdt1aqVDMOwPQ8NDVVoaKjteZ06dey2pwgJCVFISIjDNrt06aIuXbqk2d+UfiH3YHE1AAAAAACciMQbAAAAAAAnYqo5AAAAADgQGBjocJo4cLcY8QYAAAAAwIlIvAEAAAAAcCISbwAAAAAAnIjEGwAAAAAAJ2JxNeQ6ri4WU+J4uJr3u5TVjd/A7kWSSYudJN9INCWOh0nndpJJa8S4mnM4psUx63UDzGbW/6tyMen/uuRkc+K4uJoTxywWvosAzsQnDAAAAAAAJyLxBgAAAADAiUi8AQAAAABwIhJvAAAAAACciMQbAAAAAAAnYlVz5Gienp4KDw+XJMXFxWVzbwAAAADg7jHijRwtKipK0dHRio6OVvHixbO7OwAAAICdmJgYWSwWXbhwIbu7Iinn9Qf/IvEGAAAAgHvUtGlTnThxQn5+ftndFeRgTDUHAAAAgHvk4eGhgICA7O4GcjhGvAEAAADka8nJyXrjjTdUsWJFWa1WlStXTq+++qoOHz4si8WixYsXq2nTpvL09NT999+vDRs22Pa9fWr32bNn1atXL5UuXVre3t6qWbOmPv30U7t4gYGBmj59ul1ZnTp1FBkZaXt+4cIFDR06VCVKlLDF/fLLLyVJ//zzjzp16qTChQurQIECqlGjhr766iunvDbIGox4AwAAAMjXIiIi9P777ys6OlrNmjXTiRMn9Ndff9m2P/fcc5o+fbqqV6+uadOmqVOnTjp06JCKFCmSqq34+HgFBwdr7Nix8vX11Zo1a9SnTx9VqFBBDRs2zFB/kpOT1b59e12+fFmffPKJKlSooN27d8vV1VWSNHz4cN24cUM//vijChQooN27d6tgwYJZ82LAKUi8AQAAAOQ5CQkJSkhIsCuzWq2yWq12ZZcvX9aMGTP09ttvq1+/fpKkChUqqFmzZjp8+LAkKSwsTN26dZMkzZ49W998840+/PBDjRkzJlXc0qVL69lnn7U9f+aZZ/Ttt99q6dKlGU68161bp61bt2rPnj2qXLmyJCkoKMi2/ciRI+rWrZtq1qyZahtyJqaaI1eJiIjI7i4AAAAgF4iKipKfn5/dIyoqKlW9PXv2KCEhQa1bt06zrSZNmtj+7ebmpvr162vPnj0O6yYlJWnSpEmqWbOm/P39VbBgQX377bc6cuRIhvseGxurMmXK2JLu240YMUKvvPKKHnjgAU2YMEHbt2/PcNvIHiTeyFXi4+OzuwsAAADIBSIiInTx4kW7h6NBHC8vryyN++abb2rGjBkaO3as1q9fr9jYWIWEhOjGjRu2Oi4uLjIMw26/mzdvZrhPgwYN0t9//60+ffpox44dql+/vmbOnJmlx4GsReINAAAAIM+xWq3y9fW1e9w+zVySKlWqJC8vL33//fdptvXLL7/Y/p2YmKjff/9d1apVc1h348aN6ty5s5588knVrl1bQUFB2rdvn12dYsWK6cSJE7bnly5d0qFDh2zPa9WqpaNHj6ba71Zly5bVsGHDtGLFCo0ePVrvv/9+mnWR/bjGGwAAAEC+5enpqbFjx2rMmDHy8PDQAw88oNOnT2vXrl226efvvPOOKlWqpGrVqik6Olrnz5/XgAEDHLZXqVIlLVu2TJs2bVLhwoU1bdo0nTp1StWrV7fV+c9//qP58+erU6dOKlSokMaPH29bOE2SWrZsqRYtWqhbt26aNm2aKlasqL/++ksWi0Xt2rXTyJEj1b59e1WuXFnnz5/X+vXr0/whADkDiTcAAACAfG3cuHFyc3PT+PHjdfz4cZUsWVLDhg2zbZ88ebImT56s2NhYVaxYUatXr1bRokUdtvXSSy/p77//VkhIiLy9vTVkyBB16dJFFy9etNWJiIjQoUOH1LFjR/n5+WnSpEl2I96StHz5cj377LPq1auXrl69qooVK2ry5MmS/r2OfPjw4Tp69Kh8fX3Vrl07RUdHO+GVQVYh8QYAAACQr7m4uOjFF1/Uiy++aFeesqp5tWrVtGXLFof7tmrVyu56bX9/f61atSrdeL6+vlq8eLFdWcqK6re2M3fuXIf7p3c99+39Qc7ANd4AAAAAADgRiTcAAAAAAE7EVHMAAAAAcCAwMJBp28gSjHgDAAAAAOBEJN4AAAAAADgRiTcAAAAAAE5E4g0AAAAAgBOxuBpyDU9PTx05ckSVLRZT4rm6mBMHAJA1jOQkU+Iky5yFlpKSTQmjpGSTFo5ycTUnTl5j0vce0+LIvHMbyEkY8UauERUVpeLFi2d3NwAAAADgrpB4AwAAAADgRCTeAAAAAAA4EYk3AAAAAAD/j727j6v5/v8H/jjV6XTqdE2qKUnJVS5icrU02lxmLpaPiw9G+GyuRYghu2pj1MxcjJBcjWnDrhmFLc3kelMKiym5SFZ0/fr94ed8HcWo93kf5XG/3c7t5rze57wfr/dxOvU8r/f79dIjFt5EREREREREesTCm4iIiIiIiEiPWHgTEREREREZmJubG6KiogzdDdITruNNRERERERkYEeOHIGFhYWhu0F6wsKbiIiIiIjIwGrXrm3oLpAe8VRzIiIiIiJ6rvn7+2PChAmYMGECrK2tUatWLcydOxdCCABAYWEhZs6cCRcXF6hUKnh4eCA6Olr7/NOnT6NHjx7QaDSoU6cOhg0bhuvXrz/x/gGeal7TsfAmIiIiIqLnXkxMDExMTPDbb7/hk08+wZIlS7BmzRoAwPDhw7FlyxYsXboUf/75J1atWgWNRgMAuHXrFrp06YJWrVrh999/xw8//ICrV69i4MCBT7x/qvl4qjkREREREdU4hYWFKCws1GlTqVRQqVQVPt7FxQWRkZFQKBTw8vLCqVOnEBkZic6dO2Pbtm3Ys2cPAgICAADu7u7a5y1btgytWrXCBx98oG1bu3YtXFxckJqaioYNGz52/2PGjJH60OkZxBFvqlbMzMwM3QUiIiIiqgYiIiJgbW2tc4uIiHjk49u1aweFQqG93759e5w7dw7Hjh2DsbExOnfuXOHzTpw4gf3790Oj0WhvjRo1AgCkp6f/6/5LS0ureqhUDXDEm6qViIgIrDh80dDdICIiIqJnXFhYGEJCQnTaHjXa/Tj/NvCTl5eHwMBAfPTRR+W2OTk5PXUe1UwsvImIiIiIqMZ53GnlFUlKStK5f/jwYXh6eqJFixYoKytDQkKC9lTzB/n4+GDHjh1wc3ODicmjy6tH7d/Y2PiJ+0jVF081JyIiIiKi515GRgZCQkKQkpKCLVu24NNPP8XkyZPh5uaGESNGYNSoUfj6669x4cIFxMfHY9u2bQCA8ePH4+bNmxg8eDCOHDmC9PR0/Pjjjxg5cqTOaeSP2j89HzjiTUREREREz73hw4fj7t27aNu2LYyNjTF58mSMHTsWALBixQrMnj0b48aNw40bN+Dq6orZs2cDAJydnfHLL79g5syZePXVV1FYWIh69eqhe/fuMDIyeqL9U83HwpuIiIiIiJ57SqUSUVFRWLFiRbltZmZmWLJkCZYsWVLhcz09PREXF1fp/QPAxYsXn7rPVH3wVHMiIiIiIiIiPWLhTURERERERKRHPNWciIiIiIiea/Hx8dV6//Ts44g3ERERERERkR6x8CYiIiIiIiLSIxbeRERERERERHrEwpuIiIiIiIhIjzi5GlU7Y1/4R5acMuMLsuQAQJmtPMckF0Wxqyw5RUaWsuSseHGyLDnfzegnS86F35NlyTE1t5Ylx0StkSXH1NxClpySomJZcuQkykplyemxbrYsOUV3N8qSY3wlT5acWkXy/P+YxCyUJQfGSlliym5dkyWn5HK6LDn5qfK8rwEg/1K2LDm1P4qRJYfoSXDEm4iIiIiIiEiPWHgTERERERER6RELbyIiIiIiIiI9YuFNREREREREpEcsvImIiIiIiIj0iIU3ERERERERkR6x8CYiIiIiIqrAxYsXoVAocPz4cUN3hao5Ft5EREREREQVcHFxQWZmJpo1a2borgAA/P39MWXKFEN3gyqBhTcREREREVEFjI2N4ejoCBMTE4P2o6ioyKD5VHUsvImIiIiI6LlWVlaGhQsXwsPDAyqVCq6urnj//fd1TjUvKytD3bp1sWLFCp3nHjt2DEZGRvjrr78AAAqFAitWrECPHj2gVqvh7u6OL7/8Uuc5M2fORMOGDWFubg53d3fMnTsXxcXF2u3h4eFo2bIl1qxZg/r168PMzAxvvPEGEhIS8Mknn0ChUEChUODixYt6f21IGob96oaIiIiIiMjAwsLCsHr1akRGRqJTp07IzMzE2bNndR5jZGSEwYMHY/PmzXjrrbe07Zs2bULHjh1Rr149bdvcuXPx4Ycf4pNPPkFsbCwGDRqEU6dOoXHjxgAAS0tLrF+/Hs7Ozjh16hTGjBkDS0tLzJgxQ7uPtLQ07NixA3FxcTA2Nka9evWQmpqKZs2a4Z133gEA1K5dW58vC0mIhTcREREREdU4hYWFKCws1GlTqVRQqVQ6bf/88w8++eQTLFu2DCNGjAAANGjQAJ06dSo3ojx06FAsXrwYGRkZcHV1RVlZGbZu3Yq3335b53FBQUEYPXo0AODdd9/Fnj178Omnn2L58uUAoPN4Nzc3TJ8+HVu3btUpvIuKirBhwwad4trU1BTm5uZwdHSs5KtChsLC+xHCwsJQUFBg6G5QBT4OGWXoLhARERHRMy4iIgILFizQaZs/fz7Cw8N12v78808UFhaia9eu/7rPli1bonHjxti8eTNmzZqFhIQEZGdnIygoSOdx7du3L3f/wZnRv/jiCyxduhTp6enIy8tDSUkJrKysdJ5Tr149jmjXICy8H6GgoACRkZGG7gZVoPTSKUN3gYiIiIiecWFhYQgJCdFpe3i0GwDUavVT7Xfo0KHawnvz5s3o3r077O3tn/j5iYmJGDp0KBYsWIBu3brB2toaW7duxeLFi3UeZ2Fh8VT9omcbJ1cjIiIiIqIaR6VSwcrKSudWUeHt6ekJtVqNn3/++Yn2O2TIEJw+fRpHjx7Fl19+iaFDh5Z7zOHDh8vdv39996+//op69ephzpw5aNOmDTw9PbUTs/0bU1NTlJaWPtFj6dnCEW8iIiIiInpumZmZYebMmZgxYwZMTU3RsWNHXLt2DWfOnKnw9HM3Nzd06NABwcHBKC0tRZ8+fco9Zvv27WjTpg06deqETZs24bfffkN0dDSAe4V+RkYGtm7dihdffBHffvstvvrqqyfqq5ubG5KSknDx4kVoNBrY2dnByIhjqdUB/5eIiIiIiOi5NnfuXEybNg3z5s1D48aN8Z///AfZ2dmPfPzQoUNx4sQJ9OvXr8JT1RcsWICtW7eiefPm2LBhA7Zs2YImTZoAAPr06YOpU6diwoQJaNmyJX799VfMnTv3ifo5ffp0GBsbo0mTJqhduzYyMjIqd8AkO454ExERERHRc83IyAhz5szBnDlzym0TQpRre+utt3SWFHuYs7Mzfvrpp0duX7hwIRYuXKjTNmXKFO2/w8PDy00CBwANGzZEYmLiI/dLzy6OeBMRERERERHpEQtvIiIiIiIiIj3iqeZEREREREQSqejUdCKOeBMRERERERHpEQtvIiIiIiIiIj1i4U1ERERERESkRyy8iYiIiIiIiPSIk6tRtbP3Th1Zcuo4u8iSAwBmJjXrO7DiAnkmFbmad0eWnPHt5HkvhHSqJ0vO9jNesuS425nLklPbwlSWHGuVsSw5hSU1b1KeMshzTEV3N8qS89nWP2TJMTVSyJKjNpYnp+Q/IfLkFJTIkpOXUyBLzs2iUlly0vOLZckBgNxieY7ps49kiSF6IjXrr30iIiIiIiKiZwwLbyIiIiIiIiI9YuFNREREREREpEcsvImIiIiIiIj0iIU3ERERERERkR6x8CYiIiIiIiLSIxbeRERERERED/H398eUKVMM3Q2qIVh4ExERERER6VFcXBxeffVV2NvbQ6FQ4Pjx4+UeU1BQgPHjx8Pe3h4ajQYDBgzA1atX5e8s6QULbyIiIiIiIj3Kz89Hp06d8NFHHz3yMVOnTsXu3buxfft2JCQk4MqVK+jfv7+MvSR9YuFNRERERETPtfz8fAwfPhwajQZOTk5YvHixzvbMzEz06tULarUa9evXx+bNm+Hm5oaoqCgAgBAC4eHhcHV1hUqlgrOzMyZNmqR9/rBhwzBv3jwEBARUmJ+bm4vo6GgsWbIEXbp0QevWrbFu3Tr8+uuvOHz4sN6Om+RjYugOEBERERERGVJoaCgSEhKwc+dOODg4YPbs2UhOTkbLli0BAMOHD8f169cRHx8PpVKJkJAQZGdna5+/Y8cOREZGYuvWrWjatCmysrJw4sSJJ84/evQoiouLdQrzRo0awdXVFYmJiWjXrp1kx0qGwcKbiIiIiIhqnMLCQhQWFuq0qVQqqFQqnba8vDxER0dj48aN6Nq1KwAgJiYGdevWBQCcPXsWe/fuxZEjR9CmTRsAwJo1a+Dp6andR0ZGBhwdHREQEAClUglXV1e0bdv2ifualZUFU1NT2NjY6LTXqVMHWVlZT7wfenax8Ca9CAsLQ0FBgV723f3NML3sl4iIiIhqjoiICCxYsECnbf78+QgPD9dpS09PR1FREXx9fbVtdnZ28PLyAgCkpKTAxMQEPj4+2u0eHh6wtbXV3g8KCkJUVBTc3d3RvXt39OzZE4GBgTAxYblF9/CdQHpRUFCAyMhIvez7x5Tsf38QERERET3XwsLCEBISotP28Gi3VFxcXJCSkoK9e/diz549GDduHBYtWoSEhAQolcp/fb6joyOKiopw69YtnVHvq1evwtHRUS99JnlxcjUiIiIiIqpxVCoVrKysdG4VFd4NGjSAUqlEUlKSti0nJwepqakAAC8vL5SUlODYsWPa7WlpacjJydHZj1qtRmBgIJYuXYr4+HgkJibi1KlTT9TX1q1bQ6lU4ueff9a2paSkICMjA+3bt3+q46ZnE0e8iYiIiIjouaXRaBAcHIzQ0FDY29vDwcEBc+bMgZHRvTHKRo0aISAgAGPHjsWKFSugVCoxbdo0qNVqKBQKAMD69etRWloKX19fmJubY+PGjVCr1ahXrx4A4ObNm8jIyMCVK1cA3CuqgXsj3Y6OjrC2tkZwcDBCQkJgZ2cHKysrTJw4Ee3bt+fEajUER7yJiIiIiOi5tmjRIrz00ksIDAxEQEAAOnXqhNatW2u3b9iwAXXq1IGfnx/69euHMWPGwNLSEmZmZgAAGxsbrF69Gh07dkTz5s2xd+9e7N69G/b29gCAXbt2oVWrVujVqxcAYNCgQWjVqhVWrlypzYiMjETv3r0xYMAA+Pn5wdHREXFxcTK+CqRPHPEmIiIiIqLnmkajQWxsLGJjY7VtoaGh2n87OTnhu+++096/fPkysrOz4eHhAQDo27cv+vbt+8j9v/HGG3jjjTce2wczMzN89tln+Oyzzyp3EPRMY+FNRERERET0GPv27UNeXh68vb2RmZmJGTNmwM3NDX5+fobuGlUTLLyJiIiIiIgeo7i4GLNnz8b58+dhaWmJDh06YNOmTU80YzkRwMKbiIiIiIjosbp164Zu3boZuhtUjXFyNSIiIiIiIiI9YuFNREREREREpEcsvImIiIiIiIj0iIU3ERERERERkR5xcjWqdhraq2XJqWMh3yyVxmXFsmXJ4VqhPN/p/SNTzl+5RbLkWJgay5JzJbdAlhyViTzHk1dUKkvOC5ZmsuQUlspzPHIqLZMnx/hKniw5pkYKWXKKykSNyqndpJYsOaXF8rzhNNfvyJJjff2uLDm4fFueHAB/35XnZ4joWcIRbyIiIiIiIiI9YuFNREREREREpEcsvImIiIiIiIj0iIU3ERERERERkR6x8CYiIiIiIiLSI85qXkOEhYWhoECemYqfRHZ2tqG7QERERERE9Exg4V1DFBQUIDIy0tDd0Jo6daqhu0BEREREVGn+/v5o2bIloqKiDN0VqgF4qjkREREREZGeFBcXY+bMmfD29oaFhQWcnZ0xfPhwXLlyRedxN2/exNChQ2FlZQUbGxsEBwcjLy/PQL0mqbHwJiIiIiIi0pM7d+4gOTkZc+fORXJyMuLi4pCSkoI+ffroPG7o0KE4c+YM9uzZg2+++QYHDhzA2LFjDdRrkhoLbyIiIiIieq7l5+dj+PDh0Gg0cHJywuLFi3W2Z2ZmolevXlCr1ahfvz42b94MNzc37WnoQgiEh4fD1dUVKpUKzs7OmDRpEgDA2toae/bswcCBA+Hl5YV27dph2bJlOHr0KDIyMgAAf/75J3744QesWbMGvr6+6NSpEz799FNs3bq13Mg4VU8svImIiIiI6LkWGhqKhIQE7Ny5Ez/99BPi4+ORnJys3X7/1PD4+Hjs2LEDn3/+uc5kwjt27EBkZCRWrVqFc+fO4euvv4a3t/cj83Jzc6FQKGBjYwMASExMhI2NDdq0aaN9TEBAAIyMjJCUlCT9AZPsOLkaERERERHVOIWFhSgsLNRpU6lUUKlUOm15eXmIjo7Gxo0b0bVrVwBATEwM6tatCwA4e/Ys9u7diyNHjmgL4zVr1sDT01O7j4yMDDg6OiIgIABKpRKurq5o27Zthf0qKCjAzJkzMXjwYFhZWQEAsrKy4ODgoPM4ExMT2NnZISsrqwqvAj0rWHjTM+NJl0SbNOcdGXpDRERERNVZREQEFixYoNM2f/58hIeH67Slp6ejqKgIvr6+2jY7Ozt4eXkBAFJSUmBiYgIfHx/tdg8PD9ja2mrvBwUFISoqCu7u7ujevTt69uyJwMBAmJjollvFxcUYOHAghBBYsWKFVIdK1QALb3pmPOmSaBeu/yNDb4iIiIioOgsLC0NISIhO28Oj3VJxcXFBSkoK9u7diz179mDcuHFYtGgREhISoFQqAfxf0f3XX39h37592tFuAHB0dNQ5dR0ASkpKcPPmTTg6OuqlzyQvXuNNREREREQ1jkqlgpWVlc6tosK7QYMGUCqVOtdS5+TkIDU1FQDg5eWFkpISHDt2TLs9LS0NOTk5OvtRq9UIDAzE0qVLER8fj8TERJw6dQrA/xXd586dw969e2Fvb6/z3Pbt2+PWrVs4evSotm3fvn0oKyvTGYmn6osj3kRERERE9NzSaDQIDg5GaGgo7O3t4eDggDlz5sDI6N4YZaNGjRAQEICxY8dixYoVUCqVmDZtGtRqNRQKBQBg/fr1KC0tha+vL8zNzbFx40ao1WrUq1cPxcXFeP3115GcnIxvvvkGpaWl2uu27ezsYGpqisaNG6N79+4YM2YMVq5cieLiYkyYMAGDBg2Cs7OzwV4bkg4LbyIiIiIieq4tWrQIeXl5CAwMhKWlJaZNm4bc3Fzt9g0bNiA4OBh+fn5wdHREREQEzpw5AzMzMwCAjY0NPvzwQ4SEhKC0tBTe3t7YvXs37O3tcfHiRezatQsA0LJlS53c/fv3w9/fHwCwadMmTJgwAV27doWRkREGDBiApUuXynL8pH/PdOFtZmaGqVOnGiT74WssiIiIiIioZtJoNIiNjUVsbKy2LTQ0VPtvJycnfPfdd9r7ly9fRnZ2Njw8PAAAffv2Rd++fSvct5ubG4QQ/9oHOzs7bN68uZJHQM+6Z7rwjoiIMFi2oQp+IiIiIiJ6tuzbtw95eXnw9vZGZmYmZsyYATc3N/j5+Rm6a1RNPNOFNxERERERkaEVFxdj9uzZOH/+PCwtLdGhQwds2rRJO2M50b9h4U1ERERERPQY3bp1Q7du3QzdDarGuJwYERERERERkR6x8CYiIiIiIiLSIxbeRERERERERHrEwpuIiIiIiIhIjzi5GulFZdZgf9K10zWmxpXp0lMzyb8uSw4AKEqLZcuSQ4FRLUN3QVIFJWWy5BSXyZOTmVsgS45app/VwhJ5ZpRVGcvzXfWd4lJZcgDA2EghS05p2b+vXyuFWkXyvHZqY3letyKZXje5WDjZGboLklKa3ZIlx0imz57a1+7IkgMAeSU1671N9CRYeJNeVGYNdq6dTkRERERENRFPNSciIiIiIiLSIxbeRERERERERHrEwpuIiIiIiIhIj1h4ExEREREREekRC28iIiIiIiIiPWLhTURERERERKRHLLyJiIiIiIge4u/vjylTphi6G1RDsPAmIiIiIiLSo/DwcDRq1AgWFhawtbVFQEAAkpKSdB5z8+ZNDB06FFZWVrCxsUFwcDDy8vIM1GOSGgtvIiIiIiIiPWrYsCGWLVuGU6dO4dChQ3Bzc8Orr76Ka9euaR8zdOhQnDlzBnv27ME333yDAwcOYOzYsQbsNUmJhTcRERERET3X8vPzMXz4cGg0Gjg5OWHx4sU62zMzM9GrVy+o1WrUr18fmzdvhpubG6KiogAAQgiEh4fD1dUVKpUKzs7OmDRpkvb5Q4YMQUBAANzd3dG0aVMsWbIEt2/fxsmTJwEAf/75J3744QesWbMGvr6+6NSpEz799FNs3boVV65cke11IP0xMXQHiIiIiIiIpFZYWIjCwkKdNpVKBZVKVe6xoaGhSEhIwM6dO+Hg4IDZs2cjOTkZLVu2BAAMHz4c169fR3x8PJRKJUJCQpCdna19/o4dOxAZGYmtW7eiadOmyMrKwokTJyrsV1FRET7//HNYW1ujRYsWAIDExETY2NigTZs22scFBATAyMgISUlJ6NevX1VfDjIwFt70zDAzM8PUqVP/9XGzF7wvQ2+IiIiIqDqLiIjAggULdNrmz5+P8PBwnba8vDxER0dj48aN6Nq1KwAgJiYGdevWBQCcPXsWe/fuxZEjR7SF8Zo1a+Dp6andR0ZGBhwdHREQEAClUglXV1e0bdtWJ+ebb77BoEGDcOfOHTg5OWHPnj2oVasWACArKwsODg46jzcxMYGdnR2ysrKq/mKQwbHwpmdGRETEEz3u2u07eu4JEREREVV3YWFhCAkJ0WmraLQ7PT0dRUVF8PX11bbZ2dnBy8sLAJCSkgITExP4+Phot3t4eMDW1lZ7PygoCFFRUXB3d0f37t3Rs2dPBAYGwsTk/8qtl19+GcePH8f169exevVqDBw4EElJSeUKbqqZeI03ERERERHVOCqVClZWVjq3igpvKbi4uCAlJQXLly+HWq3GuHHj4Ofnh+LiYu1jLCws4OHhgXbt2iE6OhomJiaIjo4GADg6Ouqcug4AJSUluHnzJhwdHfXSZ5IXC28iIiIiInpuNWjQAEqlUmd5r5ycHKSmpgIAvLy8UFJSgmPHjmm3p6WlIScnR2c/arUagYGBWLp0KeLj45GYmIhTp049MresrEx7DXr79u1x69YtHD16VLt93759KCsr0xmJp+qLp5oTEREREdFzS6PRIDg4GKGhobC3t4eDgwPmzJkDI6N7Y5SNGjVCQEAAxo4dixUrVkCpVGLatGlQq9VQKBQAgPXr16O0tBS+vr4wNzfHxo0boVarUa9ePeTn5+P9999Hnz594OTkhOvXr+Ozzz7D33//jaCgIABA48aN0b17d4wZMwYrV65EcXExJkyYgEGDBsHZ2dlgrw1Jh4U3ERERERE91xYtWoS8vDwEBgbC0tIS06ZNQ25urnb7hg0bEBwcDD8/Pzg6OiIiIgJnzpyBmZkZAMDGxgYffvghQkJCUFpaCm9vb+zevRv29vYoKCjA2bNnERMTg+vXr8Pe3h4vvvgiDh48iKZNm2ozNm3ahAkTJqBr164wMjLCgAEDsHTpUtlfC9IPFt5ERERERPRc02g0iI2NRWxsrLYtNDRU+28nJyd899132vuXL19GdnY2PDw8AAB9+/ZF3759K9y3mZkZ4uLi/rUPdnZ22Lx5cyWPgJ51LLyJiIiIiIgeY9++fcjLy4O3tzcyMzMxY8YMuLm5wc/Pz9Bdo2qChTcREREREdFjFBcXY/bs2Th//jwsLS3RoUMHbNq0CUql0tBdo2qChTcREREREdFjdOvWDd26dTN0N6ga43JiRERERERERHrEwpuIiIiIiIhIj1h4ExEREREREekRr/Gmaue/G4/JknM98x9ZcgCgqLBUtiw5LBzVRpacWuamsuS0Or5elpz8jMuy5HwyIkyWHJNrabLklGRelCWn7MJtWXKMLKxkybkXJtP370bGssSYxCyUJafkPyGy5NRuUkuWHAsnO1lyPlp8UJYcY4UsMVAby/Pzo5bpgOqo5CsLXlCzBKHnD0e8iYiIiIiIiPSIhTcRERERERGRHrHwJiIiIiIiItIjFt5EREREREREesTCm4iIiIiIiEiPWHgTERERERER6RELbyIiIiIiIiI9YuFNRERERET0EH9/f0yZMgUAcOfOHQwYMABWVlZQKBS4detWhW1Ej8LV64mIiIiIiB4jJiYGBw8exK+//opatWrB2toaK1euLNdG9CgsvImIiIiIiB4jPT0djRs3RrNmzR7bRvQoLLyJiIiIiOi5lp+fj7feegtxcXGwtLTE9OnTtdv8/f2RkJAAAFAoFOjcuTMAlGuLj4+Xvd9UfbDwJiIiIiKiGqewsBCFhYU6bSqVCiqVqtxjQ0NDkZCQgJ07d8LBwQGzZ89GcnIyWrZsibi4OMyaNQunT59GXFwcTE1NAaDCNqJHYeFNz7ywsDAUFBT8X4Pn64brDBERERFVCxEREViwYIFO2/z58xEeHq7TlpeXh+joaGzcuBFdu3YFcO+a7rp16wIA7OzsYG5uDlNTUzg6OmqfV1Eb0aOw8KZnXkFBASIjI7X3uy3/xYC9ISIiIqLqICwsDCEhITptFY12p6eno6ioCL6+vto2Ozs7eHl56b2P9Pxg4U1ERERERDXOo04rJzIEruNNRERERETPrQYNGkCpVCIpKUnblpOTg9TUVAP2imoajngTEREREdFzS6PRIDg4GKGhobC3t4eDgwPmzJkDIyOOUZJ0WHgTEREREdFzbdGiRcjLy0NgYCAsLS0xbdo05ObmGrpbVIOw8CYiIiIioueaRqNBbGwsYmNjtW2hoaHaf0dFRZV7TkVtRI/C8yeIiIiIiIiI9IiFNxEREREREZEesfAmIiIiIiIi0iNe4/0IZmZmmDp1qqG78cSys7MN3QUiIiIiIiKqAAvvR4iIiDB0F55KdfqSgIiIiIiI6HnCU82JiIiIiIiI9Igj3mRwYWFhKCgoeOT2h0+jLyws0XeXAAAF+cWy5ABASXGpbFlyMFIoZMkpLiuTJQcmprLElBXJ895GaZEsMaJEnp8hUSrTz09Zzfo5BQDI9TMkF2OlLDElBfL8rJYW16z/H2N5fjWgVMiTk1ciz/9PUZk8L5yZkXzvN41JzXpvEz0JFt5kcAUFBYiMjHzkdp5GT0RERERE1RlPNSciIiIiIiLSIxbeRERERERERHrEwpuIiIiIiIhIj1h4ExEREREREekRC28iIiIiIiIiPWLhTURERERERKRHLLyJiIiIiIge4u/vjylTphi6G1RDsPAmIiIiIiLSo7i4OLz66quwt7eHQqHA8ePHDd0lkhkLbyIiIiIiIj3Kz89Hp06d8NFHHxm6K2QgLLyJiIiIiOi5lp+fj+HDh0Oj0cDJyQmLFy/W2Z6ZmYlevXpBrVajfv362Lx5M9zc3BAVFQUAEEIgPDwcrq6uUKlUcHZ2xqRJk7TPHzZsGObNm4eAgAA5D4ueISaG7gAREREREZHUCgsLUVhYqNOmUqmgUqnKPTY0NBQJCQnYuXMnHBwcMHv2bCQnJ6Nly5YAgOHDh+P69euIj4+HUqlESEgIsrOztc/fsWMHIiMjsXXrVjRt2hRZWVk4ceKEXo+PqhcW3vTMMzMzw9SpU/+vwbWvwfpCRERERNVDREQEFixYoNM2f/58hIeH67Tl5eUhOjoaGzduRNeuXQEAMTExqFu3LgDg7Nmz2Lt3L44cOYI2bdoAANasWQNPT0/tPjIyMuDo6IiAgAAolUq4urqibdu2ejw6qm5YeNMzLyIiQue+f2SCgXpCRERERNVFWFgYQkJCdNoqGu1OT09HUVERfH19tW12dnbw8vICAKSkpMDExAQ+Pj7a7R4eHrC1tdXeDwoKQlRUFNzd3dG9e3f07NkTgYGBMDFhuUX38BpvIiIiIiKqcVQqFaysrHRuFRXeUnBxcUFKSgqWL18OtVqNcePGwc/PD8XFxXrJo+qHhTcRERERET23GjRoAKVSiaSkJG1bTk4OUlNTAQBeXl4oKSnBsWPHtNvT0tKQk5Ojsx+1Wo3AwEAsXboU8fHxSExMxKlTp+Q5CHrm8dwHIiIiIiJ6bmk0GgQHByM0NBT29vZwcHDAnDlzYGR0b4yyUaNGCAgIwNixY7FixQoolUpMmzYNarUaCoUCALB+/XqUlpbC19cX5ubm2LhxI9RqNerVqwcAuHnzJjIyMnDlyhUA905fBwBHR0c4Ojoa4KhJbhzxJiIiIiKi59qiRYvw0ksvITAwEAEBAejUqRNat26t3b5hwwbUqVMHfn5+6NevH8aMGQNLS0uYmZkBAGxsbLB69Wp07NgRzZs3x969e7F7927Y29sDAHbt2oVWrVqhV69eAIBBgwahVatWWLlypfwHSwbBEW8iIiIiInquaTQaxMbGIjY2VtsWGhqq/beTkxO+++477f3Lly8jOzsbHh4eAIC+ffuib9++j9z/G2+8gTfeeEPyflP1wcKbiIiIiIjoMfbt24e8vDx4e3sjMzMTM2bMgJubG/z8/AzdNaomWHgTERERERE9RnFxMWbPno3z58/D0tISHTp0wKZNm6BUKg3dNaomWHgTERERERE9Rrdu3dCtWzdDd4OqMU6uRkRERERERKRHLLyJiIiIiIiI9IiFNxEREREREZEe8RpvemJhYWEoKCiQfL/Z2dlP9XiFQiF5HypiYmosSw4AKM3ky6pJSsvkyTHS2MiSo3awlSVHqCxlyYGFjSwxJrWKZMkp/cdMlhxjSxtZcgAARjXrs6fs1jVZcvJypP9dWBHN9Tuy5CjNbsmSozaWZ7wnr0SmXw4yKSoTsuTcLimVJQcA1EXy/C1H9Cxh4U1PrKCgAJGRkZLvd+rUqZLvk4iIiIiI6FnBU82JiIiIiIiI9IiFNxEREREREZEesfAmIiIiIiIi0iMW3kRERERERER6xMKbiIiIiIiISI9YeBMRERERERHpEQtvIiIiIiKih/j7+2PKlCmG7gbVECy8iYiIiIiI9KS4uBgzZ86Et7c3LCws4OzsjOHDh+PKlSuG7hrJiIU3ERERERGRnty5cwfJycmYO3cukpOTERcXh5SUFPTp08fQXSMZsfAmIiIiIqLnWn5+PoYPHw6NRgMnJycsXrxYZ3tmZiZ69eoFtVqN+vXrY/PmzXBzc0NUVBQAQAiB8PBwuLq6QqVSwdnZGZMmTQIAWFtbY8+ePRg4cCC8vLzQrl07LFu2DEePHkVGRobch0oGYmLoDhAREREREUmtsLAQhYWFOm0qlQoqlarcY0NDQ5GQkICdO3fCwcEBs2fPRnJyMlq2bAkAGD58OK5fv474+HgolUqEhIQgOztb+/wdO3YgMjISW7duRdOmTZGVlYUTJ048sm+5ublQKBSwsbGR5Fjp2cfCm545YWFhKCgoePQD6vWTrzNEREREVC1FRERgwYIFOm3z589HeHi4TlteXh6io6OxceNGdO3aFQAQExODunXrAgDOnj2LvXv34siRI2jTpg0AYM2aNfD09NTuIyMjA46OjggICIBSqYSrqyvatm1bYb8KCgowc+ZMDB48GFZWVlIdLj3jWHjTM6egoACRkZGP3P5y1AEZe0NERERE1VFYWBhCQkJ02ioa7U5PT0dRURF8fX21bXZ2dvDy8gIApKSkwMTEBD4+PtrtHh4esLW11d4PCgpCVFQU3N3d0b17d/Ts2ROBgYEwMdEtt4qLizFw4EAIIbBixQpJjpOqB17jTURERERENY5KpYKVlZXOraLCWwouLi5ISUnB8uXLoVarMW7cOPj5+aG4uFj7mPtF919//YU9e/ZwtPs5w8KbiIiIiIieWw0aNIBSqURSUpK2LScnB6mpqQAALy8vlJSU4NixY9rtaWlpyMnJ0dmPWq1GYGAgli5divj4eCQmJuLUqVMA/q/oPnfuHPbu3Qt7e3sZjoyeJTzVnIiIiIiInlsajQbBwcEIDQ2Fvb09HBwcMGfOHBgZ3RujbNSoEQICAjB27FisWLECSqUS06ZNg1qthkKhAACsX78epaWl8PX1hbm5OTZu3Ai1Wo169eqhuLgYr7/+OpKTk/HNN9+gtLQUWVlZAO6d0m5qamqwYyf5sPAmIiIiIqLn2qJFi5CXl4fAwEBYWlpi2rRpyM3N1W7fsGEDgoOD4efnB0dHR0RERODMmTMwMzMDANjY2ODDDz9ESEgISktL4e3tjd27d8Pe3h4XL17Erl27AEA7S/p9+/fvh7+/v1yHSQbEwpuIiIiIiJ5rGo0GsbGxiI2N1baFhoZq/+3k5ITvvvtOe//y5cvIzs6Gh4cHAKBv377o27dvhft2c3ODEEI/Hadqg4U3ERERERHRY+zbtw95eXnw9vZGZmYmZsyYATc3N/j5+Rm6a1RNsPAmIiIiIiJ6jOLiYsyePRvnz5+HpaUlOnTogE2bNkGpVBq6a1RNsPAmIiIiIiJ6jG7duqFbt26G7gZVY1xOjIiIiIiIiEiPWHgTERERERER6RELbyIiIiIiIiI94jXeZHBmZmaYOnWq9n52dvZjH29qaqzvLgEAzMzlmyzDRKZjqmnKZFqaw9jSRpYcExs7WXLuCnneb+ZqW1lyjEuK5ckxkunn1NxGnhw5KRSyxJRcTpcl52ZRqSw51tfvypJjZCzPOIzaWJ73QVGZXDk1a3mou6XyHc/tkjLZsoieFSy8yeAiIiJ07j9YhBMREREREVV3PNWciIiIiIiISI9YeBMRERERERHpEQtvIiIiIiIiIj1i4U1ERERERESkRyy8iYiIiIiIiPSIhTcRERERERGRHrHwJiIiIiIieoi/vz+mTJli6G5QDcHCm4iIiIiISI/Cw8PRqFEjWFhYwNbWFgEBAUhKSjJ0t0hGLLyJiIiIiIj0qGHDhli2bBlOnTqFQ4cOwc3NDa+++iquXbtm6K6RTFh4ExERERHRcy0/Px/Dhw+HRqOBk5MTFi9erLM9MzMTvXr1glqtRv369bF582a4ubkhKioKACCEQHh4OFxdXaFSqeDs7IxJkyZpnz9kyBAEBATA3d0dTZs2xZIlS3D79m2cPHlSzsMkAzIxdAeIiIiIiIikVlhYiMLCQp02lUoFlUpV7rGhoaFISEjAzp074eDggNmzZyM5ORktW7YEAAwfPhzXr19HfHw8lEolQkJCkJ2drX3+jh07EBkZia1bt6Jp06bIysrCiRMnKuxXUVERPv/8c1hbW6NFixbSHTA901h41xBmZmaYOnWqXjMe/HDRp389Fs/XZekHEREREVVfERERWLBggU7b/PnzER4ertOWl5eH6OhobNy4EV27dgUAxMTEoG7dugCAs2fPYu/evThy5AjatGkDAFizZg08PT21+8jIyICjoyMCAgKgVCrh6uqKtm3b6uR88803GDRoEO7cuQMnJyfs2bMHtWrVkvqw6RnFwruGiIiI0HuGvgv7+/7tWLot/0WWfhARERFR9RUWFoaQkBCdtopGu9PT01FUVARfX19tm52dHby8vAAAKSkpMDExgY+Pj3a7h4cHbG1ttfeDgoIQFRUFd3d3dO/eHT179kRgYCBMTP6v3Hr55Zdx/PhxXL9+HatXr8bAgQORlJQEBwcHyY6Znl28xpuIiIiIiGoclUoFKysrnVtFhbcUXFxckJKSguXLl0OtVmPcuHHw8/NDcXGx9jEWFhbw8PBAu3btEB0dDRMTE0RHR+ulP/TsYeFNRERERETPrQYNGkCpVOos75WTk4PU1FQAgJeXF0pKSnDs2DHt9rS0NOTk5OjsR61WIzAwEEuXLkV8fDwSExNx6tSpR+aWlZWVuwadai6eak5ERERERM8tjUaD4OBghIaGwt7eHg4ODpgzZw6MjO6NUTZq1AgBAQEYO3YsVqxYAaVSiWnTpkGtVkOhUAAA1q9fj9LSUvj6+sLc3BwbN26EWq1GvXr1kJ+fj/fffx99+vSBk5MTrl+/js8++wx///03goKCDHnoJCMW3kRERERE9FxbtGgR8vLyEBgYCEtLS0ybNg25ubna7Rs2bEBwcDD8/Pzg6OiIiIgInDlzBmZmZgAAGxsbfPjhhwgJCUFpaSm8vb2xe/du2Nvbo6CgAGfPnkVMTAyuX78Oe3t7vPjiizh48CCaNm1qqEMmmbHwJiIiIiKi55pGo0FsbCxiY2O1baGhodp/Ozk54bvvvtPev3z5MrKzs+Hh4QEA6Nu3L/r27Vvhvs3MzBAXF6efjlO1wcKbiIiIiIjoMfbt24e8vDx4e3sjMzMTM2bMgJubG/z8/AzdNaomWHgTERERERE9RnFxMWbPno3z58/D0tISHTp0wKZNm6BUKg3dNaomWHgTERERERE9Rrdu3dCtWzdDd4OqMS4nRkRERERERKRHLLyJiIiIiIiI9IiFNxEREREREZEe8RpvqnbcHTSy5Ny1UcuSAwAOlirZsuRgZiLPd3rFZUKWHDi6yxJjamElS85f+SWy5DiY28mSY2FmKUuOouAfWXKEqbksOQAAhUzfvysUssTkp26UJSc9v1iWHFy+LUtM7Wt3ZMmpo5Lnz04zozJZcm6XlMqSc7dUnt91RXL9TgWQVSDP7yGiZwlHvImIiIiIiIj0iIU3ERERERERkR6x8CYiIiIiIiLSIxbeRERERERERHrEwpuIiIiIiIhIj1h4ExEREREREekRC28iIiIiIiIiPWLhTURERERE9BB/f39MmTLF0N2gGoKFNxERERERkR698cYbUCgUOrfu3bsbulskIxNDd4CIiIiIiKim6969O9atW6e9r1KpDNgbkhtHvImIiIiI6LmWn5+P4cOHQ6PRwMnJCYsXL9bZnpmZiV69ekGtVqN+/frYvHkz3NzcEBUVBQAQQiA8PByurq5QqVRwdnbGpEmTdPahUqng6Oiovdna2sp1ePQM4Ig3ERERERE910JDQ5GQkICdO3fCwcEBs2fPRnJyMlq2bAkAGD58OK5fv474+HgolUqEhIQgOztb+/wdO3YgMjISW7duRdOmTZGVlYUTJ07oZMTHx8PBwQG2trbo0qUL3nvvPdjb28t5mGRALLyJiIiIiKjGKSwsRGFhoU6bSqUqd4p3Xl4eoqOjsXHjRnTt2hUAEBMTg7p16wIAzp49i7179+LIkSNo06YNAGDNmjXw9PTU7iMjIwOOjo4ICAiAUqmEq6sr2rZtq93evXt39O/fH/Xr10d6ejpmz56NHj16IDExEcbGxno5fnq2sPAmgwkLC0NBQcHTP7HjG5L3hYiIiIhqloiICCxYsECnbf78+QgPD9dpS09PR1FREXx9fbVtdnZ28PLyAgCkpKTAxMQEPj4+2u0eHh46p4oHBQUhKioK7u7u6N69O3r27InAwECYmNwrtwYNGqR9rLe3N5o3b44GDRogPj5eW+xTzcbCmwymoKAAkZGRT/28t7488e8PIiIiIqLnWlhYGEJCQnTa9DWhmYuLC1JSUrB3717s2bMH48aNw6JFi5CQkAClUlnu8e7u7qhVqxbS0tJYeD8nOLkaERERERHVOCqVClZWVjq3igrvBg0aQKlUIikpSduWk5OD1NRUAICXlxdKSkpw7Ngx7fa0tDTk5OTo7EetViMwMBBLly5FfHw8EhMTcerUqQr7dvnyZdy4cQNOTk5SHCpVAxzxJiIiIiKi55ZGo0FwcDBCQ0Nhb28PBwcHzJkzB0ZG98YoGzVqhICAAIwdOxYrVqyAUqnEtGnToFaroVAoAADr169HaWkpfH19YW5ujo0bN0KtVqNevXrIy8vDggULMGDAADg6OiI9PR0zZsyAh4cHunXrZshDJxlxxJuIiIiIiJ5rixYtwksvvYTAwEAEBASgU6dOaN26tXb7hg0bUKdOHfj5+aFfv34YM2YMLC0tYWZmBgCwsbHB6tWr0bFjRzRv3hx79+7F7t27YW9vD2NjY5w8eRJ9+vRBw4YNERwcjNatW+PgwYNcy/s5whFvIiIiIiJ6rmk0GsTGxiI2NlbbFhoaqv23k5MTvvvuO+39y5cvIzs7Gx4eHgCAvn37om/fvhXuW61W48cff9RPx6naYOFNRERERET0GPv27UNeXh68vb2RmZmJGTNmwM3NDX5+fobuGlUTLLyJiIiIiIgeo7i4GLNnz8b58+dhaWmJDh06YNOmTRXOWE5UERbeREREREREj9GtWzdOhEZVwsnViIiIiIiIiPSIhTcRERERERGRHrHwJiIiIiIiItIjFt5EREREREREesTJ1eiJmZmZYerUqZLtLzs7u1LPa/qClWR9eBylkXzfS9mqa9aMmCoTeV67goISWXJKrZ1lyTFSqmXJufpPkSw5JkYKWXKKTOX5VWZlUUuWnIKSMlly5FQq0yHlX6rc75WnlVtcKkvO33fl+RnKKxGy5LygludnVWMizxtOXSTP/89tmT4TsmT6nUr0vGLhTU8sIiJC0v1JWcQTERERERE9q3iqOREREREREZEesfAmIiIiIiIi0iMW3kRERERERER6xMKbiIiIiIiISI9YeBMRERERERHpEQtvIiIiIiKih/j7+2PKlCmG7gbVECy8iYiIiIiI9EihUFR4W7RokaG7RjLhOt5ERERERER6lJmZqXP/+++/R3BwMAYMGGCgHpHcOOJNRERERETPtfz8fAwfPhwajQZOTk5YvHixzvbMzEz06tULarUa9evXx+bNm+Hm5oaoqCgAgBAC4eHhcHV1hUqlgrOzMyZNmqR9vqOjo85t586dePnll+Hu7i7nYZIBccSbiIiIiIiea6GhoUhISMDOnTvh4OCA2bNnIzk5GS1btgQADB8+HNevX0d8fDyUSiVCQkKQnZ2tff6OHTsQGRmJrVu3omnTpsjKysKJEycqzLp69Sq+/fZbxMTEyHFo9Ixg4U1ERERERDVOYWEhCgsLddpUKhVUKpVOW15eHqKjo7Fx40Z07doVABATE4O6desCAM6ePYu9e/fiyJEjaNOmDQBgzZo18PT01O4jIyMDjo6OCAgIgFKphKurK9q2bVthv2JiYmBpaYn+/ftLdqz07GPhTQZjZmaGqVOnPvXzGgyc9O8PIiIiIqLnWkREBBYsWKDTNn/+fISHh+u0paeno6ioCL6+vto2Ozs7eHl5AQBSUlJgYmICHx8f7XYPDw/Y2tpq7wcFBSEqKgru7u7o3r07evbsicDAQJiYlC+31q5di6FDh8LMzEyKw6RqgoU3GUxERESlnrcs8YLEPSEiIiKimiYsLAwhISE6bQ+PdkvFxcUFKSkp2Lt3L/bs2YNx48Zh0aJFSEhIgFKp1D7u4MGDSElJwRdffKGXftCzi5OrERERERFRjaNSqWBlZaVzq6jwbtCgAZRKJZKSkrRtOTk5SE1NBQB4eXmhpKQEx44d025PS0tDTk6Ozn7UajUCAwOxdOlSxMfHIzExEadOndJ5THR0NFq3bo0WLVpIeahUDXDEm4iIiIiInlsajQbBwcEIDQ2Fvb09HBwcMGfOHBgZ3RujbNSoEQICAjB27FisWLECSqUS06ZNg1qthkKhAACsX78epaWl8PX1hbm5OTZu3Ai1Wo169eppc27fvo3t27eXmzGdng8svImIiIiI6Lm2aNEi5OXlITAwEJaWlpg2bRpyc3O12zds2IDg4GD4+fnB0dEREREROHPmjPY6bRsbG3z44YcICQlBaWkpvL29sXv3btjb22v3sXXrVgghMHjwYNmPjwxPIYQQhu4E0dOQ6xpvpZF8V2LYqpX//qBqxNVanslCcgtKZMnxf0E/14M9zOhOzr8/SAK//qORJecFK3leN42pPD+rVqbGsuQUlJTJkiOnUpn+0sh//y1ZchYtSpAlp45KnvERa6U87+0X1PIcT55MP0M3i0plybkt0/FkyfQ7VU4rxUW97fvy5ctwcXHB3r17tTOhEz0OR7yJiIiIiIgeY9++fcjLy4O3tzcyMzMxY8YMuLm5wc/Pz9Bdo2qChTcREREREdFjFBcXY/bs2Th//jwsLS3RoUMHbNq0SWfGcqLHYeFNRERERET0GN26dUO3bt0M3Q2qxricGBEREREREZEesfAmIiIiIiIi0iMW3kRERERERER6xMKbiIiIiIiISJ8EUQ1XUFAg5s+fLwoKCmpEjpxZzGEOc579HDmzmMMc5jDHEFlyHhORviiEEMLQxT+RPt2+fRvW1tbIzc2FlZVVtc+RM4s5zGHOs58jZxZzmMMc5hgiS85jItIXnmpOREREREREpEcsvImIiIiIiIj0iIU3ERERERERkR6x8KYaT6VSYf78+VCpVDUiR84s5jCHOc9+jpxZzGEOc5hjiCw5j4lIXzi5GhEREREREZEeccSbiIiIiIiISI9YeBMRERERERHpEQtvIiIiIiIiIj1i4U1URYWFhSgsLDR0N4iIiKql27dvG7oLRER6x8KbqBL27NmDnj17wtbWFubm5jA3N4etrS169uyJvXv3Grp7T23cuHHIy8vT3t+yZQvy8/O192/duoWePXvqtQ+3bt3S6/6JiAAgIyMDFc0rK4RARkaGAXpEtra2yM7OBgB06dKFvw+IqEbirOZU4xUVFeHChQto0KABTExMqry/mJgYjB49Gq+//jq6deuGOnXqAACuXr2Kn376CV9++SWio6MxbNiwKmfd17lzZwQHByMoKAhqtVqy/d5nbGyMzMxMODg4AACsrKxw/PhxuLu7A7h3bM7OzigtLZUk76OPPoKbmxv+85//AAAGDhyIHTt2wNHREd999x1atGghSc7zoLS0FKdOnUK9evVga2tbpX09zaiTlZVVlbJqspSUFHz66af4888/AQCNGzfGxIkT4eXlVaX9Ll269IkfO2nSpErn1PT3wcOfd/fduHEDDg4Okn3O/fbbb0hMTERWVhYAwNHREe3bt0fbtm0l2f99WVlZSEpK0snx9fWFo6NjtcmxtrbG4cOH0bhxYxgZGeHq1auoXbt2lfdbGVevXsWqVaswb948SfZ3+fJl2NjYQKPR6LQXFxcjMTERfn5+Vc64ceMGTp48iRYtWsDOzg7Xr19HdHQ0CgsLERQUhMaNG1c542FCCMTHxyMtLQ1OTk7o1q0blEpllfe7ePFivP7666hXr54EvSR6trDwphrrzp07mDhxImJiYgAAqampcHd3x8SJE/HCCy9g1qxZldpvw4YNMXnyZIwfP77C7cuXL0dkZCTOnTtX6b4/bMqUKdi8eTMKCwsxcOBABAcHo127dpLt38jICFlZWdo/RC0tLXHixAm9Fd7169fHpk2b0KFDB+zZswcDBw7EF198gW3btiEjIwM//fSTJDlyKy0txddff60tuJo2bYo+ffrA2NhYsowpU6bA29sbwcHBKC0tRefOnfHrr7/C3Nwc33zzDfz9/Su9byMjIygUisc+RggBhUIh2XvB0P7880/06tUL58+fl2R/O3bswKBBg9CmTRu0b98eAHD48GEcOXIEW7duxYABAyq97/r16z/R4xQKRZWOR873wdKlSzF27FiYmZn96xcLVfky4UGPKuz++usvNGnSROdsn8rIzs7GgAED8Msvv8DV1VXny9mMjAx07NgRO3bsKFf4P638/Hz873//w9atW6FQKGBnZwcAuHnzJoQQGDx4MFatWgVzc/NnPuf+69W4cWMkJCSgQ4cOMDU1rfCx+/btq3TOkzhx4gR8fHyq/N7OzMzEa6+9hqNHj0KhUGDIkCFYvny5tgCX6vfqb7/9hldffRW3b9+GjY0N9uzZg6CgIJiYmKCsrAxXrlzBoUOH4OPjU6Wcnj17YsuWLbC2tsbNmzfRs2dP/Pbbb6hVqxZu3LiBhg0b4sCBA1X+wsTIyAhGRkZ4+eWXMXr0aPTr1++R7wWi6oaFN9VYkydPxi+//IKoqCh0794dJ0+ehLu7O3bu3Inw8HAcO3asUvs1MzPDiRMnHjl6lZKSgpYtW+Lu3btV6X45JSUl2LVrF2JiYvD999/Dw8MDo0aNwrBhw7R/2FWW3IW3Wq1GamoqXFxcMHnyZBQUFGDVqlVITU2Fr68vcnJyJMkB7hXDkZGR2qK+qKhIZ/vNmzclyUlLS0OvXr1w+fJl7XsjJSUFLi4u+Pbbb9GgQQNJcurWrYuvv/4abdq0wddff43x48dj//79iI2Nxb59+/DLL79Uet8JCQlP/NjOnTtXOsfW1vZfC7v7pPr/eRSp/si+r0GDBhg6dCjeeecdnfb58+dj48aNSE9PlyRHn+R6HwD3vkz4/fffYW9v/9gvFqr6ZQIAhISEAAA++eQTjBkzRqdQLC0tRVJSEoyNjav0MwQAr7/+Oq5cuYJ169aV+z2RkpKCUaNGwdnZGdu3b69SzujRo3HgwAF8+umnCAgI0H7BV1paip9//hkTJ06En58fVq9e/czn3L17FzExMUhPT8fixYvL/f88KDIystI5AHDy5MnHbj979iwGDx5c5c+EESNGICUlBcuWLcOtW7cwa9YsKBQK/PTTT7C1tcXVq1fh5OSEsrKyKuW88sorcHNzw5IlS7Bq1Sp88skn6N69u/b/Y9SoUcjJycFXX31VpZwH/04YN24cEhIS8M0336B+/fq4fPky+vbtixdffBErVqyocs7atWvx9ddf47vvvoOVlRX++9//YvTo0WjWrFmV9k1kaCy8qcaqV68evvjiC7Rr106nkExLS4OPj0+lJ3Np3bo1unbtioULF1a4febMmdi7dy+OHj1ale4/VnZ2Nj7//HO8//77KC0tRc+ePTFp0iR06dKlUvuTu/B2dnbGl19+iQ4dOsDLywvvvfcegoKCkJKSghdffFHSiXbmzZuHNWvWYNq0aXj77bcxZ84cXLx4EV9//TXmzZsn2Shaz549IYTApk2btCNCN27cwH//+18YGRnh22+/lSTHzMwMaWlpqFu3LsaOHQtzc3NERUXhwoULaNGiRbWYpOj+WShPYsSIEVXKul9sPcq1a9ewefNmyd7b5ubmOHnyJDw8PHTaz507hxYtWuDOnTuS5DxMyksOaqqXX34ZwL0vFtq3b68zimZqago3NzdMnz4dnp6eVcqxtLTEgQMH0KpVqwq3Hz16FP7+/vjnn3+qlGNra4tvv/0WHTp0qHD7L7/8gt69e1f5i0w5cm7fvq29bOHll1/GV199BRsbm0rv73Hun9FR0Z+/99ulOJvjhRdewFdffaW9tOD+ad+XLl3Czz//jOLiYkl+r9rZ2WnPFiguLoaZmRkSExO1ucnJyejTpw8uX75cpZwH/05o1KgRFi5ciD59+mi3//zzzxgzZkyVvyB7MCc7Oxvr16/HunXrkJqaitatW2PMmDEYNGgQLC0tq5RDZAhVv+CV6Bl17dq1Ck/ly8/Pf+LRtoosXrwYvXv3xg8//ICAgACd0wh//vlnnD9/XrIiqyK//fYb1q1bh61bt8LBwQFvvPEG/v77b/Tu3Rvjxo3Dxx9/XKn9zps3TzvCUFRUhPfffx/W1tYAIHmx0L9/fwwZMgSenp64ceMGevToAQA4duxYuYKlqjZt2oTVq1ejV69eCA8Px+DBg9GgQQM0b94chw8flqzwTkhIwOHDh7VFNwDY29vjww8/RMeOHSXJAIA6dergjz/+gJOTE3744Qft6MKdO3ckPaV93bp10Gg0CAoK0mnfvn077ty5U6WCuKrF9NP45JNP0LJly0dei/zgpIJS8Pf3x8GDB8u9jw8dOoSXXnpJspyHLznw8/NDYmKiJJccPOiHH36ARqNBp06dAACfffYZVq9ejSZNmuCzzz7TW5F/vyiqymf1w/bv3w8AGDlyJJYuXaq3P9xVKtVjvwD7559/oFKpqpxTVlb22FNwTU1NqzyaKleOra2t9rp7Kf/PK2JnZ4eFCxeia9euFW4/c+YMAgMDq5yTm5ur8/OhUqkQFxeHoKAgvPzyy9i4cWOVM4B7v6/vz/2iVCphbm6OWrVqabffPxVcCvf/b3JycsqdxeXh4YErV65IknOfg4MDZsyYgRkzZuDgwYOIjo7G1KlTMXXqVMk/u4lkIYhqqJdeekksXbpUCCGERqMR58+fF0IIMWHCBNGtW7cq7fvChQtixowZws/PTzRs2FA0bNhQ+Pn5iZkzZ4oLFy5UtevlXL16VXz88ceiadOmwtTUVAwYMEB8//33oqysTPuYgwcPCgsLi0rtv3PnzsLf3/9fb1IpKioSixYtEpMmTRLJycna9iVLlojVq1dLliOEEObm5uKvv/4SQgjh6Ogojh49KoQQIj09XVhZWUmWY2trK3755Zdy7YcOHRK2traS5cyfP19YW1uLRo0aCVdXV1FQUCCEECI6Olq0a9dOshxPT0+xb9++cu3x8fGiYcOGkuUcPXpUnDx5Unv/66+/Fq+99poICwsThYWFVd5/w4YNRWxs7CO3Hzt2TBgZGVU5574VK1aI2rVri/Hjx4vY2FgRGxsrxo8fLxwcHMSKFSvEzp07tbeqeOGFF8SRI0eEEEJ89dVXwtnZWaSkpIi3335bdOjQQYpDEUII0axZM/Htt98KIYQ4efKkMDU1FWFhYaJdu3bijTfekCznvjVr1mg/50xNTUXTpk0l/UwoKioSxsbG4tSpU5Lt82Hjxo0T9erVE3FxcSI3N1fbnpubK+Li4oSbm5uYMGFClXOGDBkiWrVqpfMZel9ycrJo3bq1GDp0aLXIsbKyEn/88YcQQggjIyORnZ1dpf09zquvvirefffdR24/fvy4UCgUVc7x9vYWX375Zbn24uJi0bdvX+Hq6irJZ0+jRo3Ezz//rL3/zTffiDt37mjvHz58WNStW7fKOQqFQvTs2VP069dP2Nrait27d+tsP3z4sKhTp06Vc4yMjMTVq1cfuT03N1d8/vnnVc4hMgQW3lRjHTx4UGg0GvHmm28KMzMzMXnyZPHKK68ICwsL8fvvvxu6e09FqVSKRo0aiYULFz7yD5Lc3FxJi+OaomHDhuLw4cNCCCE6duwoIiIihBBCbN26VdSuXVuynGHDhommTZuKw4cPi7KyMlFWViYSExNFs2bNxIgRIyTLEUKI7du3iyVLlohLly5p29avXy++/vpryTJUKlWFXyJduHBBmJmZSZbTpk0b7R+n6enpQqVSicGDBwsPDw8xefLkKu9/yJAhYsqUKY/cLtUf2fcpFIonulX1D26VSqX9/x8zZoz2tTp//rywtLSs6mFoWVhYaN8H8+fPFwMGDBBC3PvCRIo/sh80d+5cYWFhIWbNmqX9cmLWrFlCo9GIuXPnSpZTv359cfz4ccn297CCggLx5ptvClNTU2FkZCTMzMyEmZmZMDIyEqampuKtt97SfmFWFTdv3hTdu3cXCoVC2NnZiUaNGolGjRoJOzs7YWRkJHr06CFycnKqRU7//v1FnTp1hL+/v1AoFKJjx47i5ZdfrvBWVXFxcY/9Mu7mzZti/fr1Vc6ZMWOGePXVVyvcVlxcLPr06SNJ4R0eHi62bNnyyO2zZ88W/fv3r3LOG2+8oXP74osvdLaHhoZWeVBDiHufoY8rvImqM17jTTVaeno6PvzwQ5w4cQJ5eXnw8fHBzJkz4e3tbeiuPZWDBw9KeppqRW7fvo2kpCQUFRWhbdu2ki/lsmvXrid+7IPXjVXVrFmzYGVlhdmzZ+OLL77Af//7X7i5uSEjIwNTp07Fhx9+KEnOrVu3MGLECOzevVu7pEpJSQn69OmD9evXa0/bry5cXV2xbNmycv8XO3fuxPjx46t8veB91tbWSE5ORoMGDfDRRx9h3759+PHHH/HLL79g0KBBuHTpUpX2n5WVhcLCwhq3NE29evWwevVqdO3aFfXr18eKFSvQq1cvnDlzBp06dZJsgkI7OzscOnQITZo0QadOnTB8+HCMHTsWFy9eRJMmTSS9DKV27dpYunQpBg8erNO+ZcsWTJw4EdevX5ckJzo6GnFxcYiNjdW5NERqt2/fxtGjR3WW32rdurXkS7D9+eefOHz4cLllyxo1alRtcu5PrpaWloYlS5bodXI1uZSUlODOnTuP/P8uKSnB33//rffPpvuXIUlxecPj5Ofnw9jYGGZmZnrNIarOWHgTSUzqWZLlcPz4cfTs2VP7B5WlpSW2bduGbt26SZZhZGT0RI/T91JViYmJSExMhKenpyTX8QH3rke9dOkSateujb///ltn/Wapr1kHgCNHjmD//v3Izs4ud23lkiVLJMmYOXMmvvjiC6xbt067zmxCQgJGjRqF119/vdJzCTzMysoKR48ehaenJ1555RX07t0bkydPRkZGBry8vCRfHaCmCA8PR1RUFJycnHDnzh2kpqZCpVJh7dq1WL16NRITEyXJ6dOnD4qKitCxY0e8++67uHDhAl544QX89NNPmDBhAlJTUyXJAQAbGxscOXKk3ORmqampaNu2LW7duiVJTqtWrZCWlobi4mLUq1cPFhYWOtuTk5MlyaHK0ffkao8i1wSFzHm2c4j0iYU31VjGxsbayVoedOPGDTg4OOituDtx4gRatWolyaQ2APDdd98hLi4OdnZ2GDlyJBo3bqzdlpOTgwEDBlR5XdNu3bohLy8PH3/8MczMzPDuu+/i1KlTkq5FXpOVlZXBzMwMZ86cqfKMyP/mgw8+wNtvvw0vLy/UqVNHZyIihUIh2Rq3RUVFGDZsGLZv3w4Tk3vzcJaVlWH48OFYuXKlZOuqdunSBS4uLggICEBwcDD++OMPeHh4ICEhASNGjMDFixclyTly5AjKysrg6+ur035/Cak2bdpUet//tv70g6SazA8AvvzyS1y6dAlBQUGoW7cugHszxtvY2OC1116TJCMjIwPjxo3DpUuXMGnSJAQHBwMApk6ditLS0qc69n8zceJEKJXKcl8eTZ8+HXfv3sVnn30mSc6CBQseu33+/PmS5DzK1atXsWrVKsybN0+S/V2+fBk2Njba9aHvKy4uRmJiovaLs6q4ceMGTp48iRYtWsDOzg7Xr19HdHS0dqbuB38vVca/rT7wIKm+XHx4gsLOnTvj119/lXyCQuY82zlEsjLgae5EevWo64T+/vvvKl2j2q9fv8feunTpItlkTZs2bRLGxsaiV69eolOnTsLMzExs3LhRuz0rK0uSLHt7e+2kY0IIkZOTIxQKhc7EQNXV+vXrxTfffKO9HxoaKqytrUX79u3FxYsXJctp0qSJSExMlGx/j+Lg4CDWrVun95z7UlJSxLZt28Tu3bslfb3uO3HihGjWrJmwsrIS4eHh2vYJEyaIwYMHS5bz4osviu3bt5dr37Fjh2jbtm2V9u3m5qZzs7CwEAqFQtja2gpbW1uhUCiEhYWFqF+/fpVyHuXu3bt62a/cJkyYIKysrETTpk1FcHCwCA4O1r43JkyYIKZOnaq9VWfHjx+X5HP7ypUr4sUXXxRGRkbC2NhYDBs2TPzzzz/a7VL9fkhKShLW1tba9/Tvv/8u6tevLzw9PUWDBg2EWq3W+f1RGQ9P5GllZSXMzc1Fq1atRKtWrYSFhYWwsrKS5Brv++SaoJA5z3YOkZw44k01zv0RmKlTp+Ldd9/VGQUoLS3FgQMHcPHiRRw7dqxS+1cqlXjllVe0y4g97ObNm/jmm28kGVFv1aoVRo4cqR0l27ZtG0aNGoVPPvkEwcHBkq2v/fA63sC9081PnjyJ+vXrV2nfFZk0aRI8PDzKjf4tW7YMaWlpiIqKkizLy8sLK1asQJcuXZCYmIiuXbsiKioK33zzDUxMTBAXFydJzu7du7Fw4UKsWLECzZo1k2SfFXFycsKBAwf0PrJuaAUFBTA2NtZeL19VGo0GJ0+e1K5Nf9+FCxfQvHnzKq+pfN/mzZuxfPlyREdHw8vLCwCQkpKCMWPG4H//+x+GDh0qSU5paSk++OADrFy5ElevXkVqairc3d0xd+5cuLm5aUemq+q7776DsbFxuctOfvrpJ5SWlmqXApTC/XW2/42UZ3bow8mTJx+7/ezZsxg8eHCVP7dHjBiBlJQULFu2DLdu3cKsWbOgUCjw008/wdbWFlevXoWTk1OVz7565ZVX4ObmhiVLlmDVqlX45JNP0L17d6xevRoAMGrUKOTk5OCrr76qUs59S5YsQXx8PGJiYrSnFOfk5GDkyJF46aWXMG3aNElyzMzMkJaWhrp162Ls2LEwNzdHVFQULly4gBYtWjx2STjm1JwcIlkZuvInktr9USeFQiFcXFx0RqIaNmwoXn31Ve0s15Xh7e0t1qxZ88jtUi5PZGFhoV0G7b59+/YJjUYjVqxYIdmIhkKhEPv37xcnTpzQ3iwsLMS3336r0yYVZ2fnCmeWP3r0qHjhhRckyxFCCLVarV1ObMaMGWLYsGFCCCFOnz4tatWqJVmOjY2NzizG90c779+k8tFHH0ky23dFHhxR/LdbdWNnZyd+/fXXcu2//PKLsLGxkSzH3d29wmWXfv/9d+Hm5iZZzoIFC4S7u7vYuHGjUKvVIj09XQhxb7Z+KZeV8/b21i4n9qDvv/9eNG/eXLIcOZWUlIhFixaJF198UdSpU0fyn9X7s9Y/ajZ7KWa1F+Le52hSUpL2fkFBgQgMDBQtW7YUN27ckOz3g62trXapr6KiImFkZKSTK/XntrOzszh9+nS59lOnTgknJyfJclxdXcWPP/4oSkpKhIuLi/bMqNOnT0v6mcCcZzuHSE4mhi78iaR24cIFAPdGT+Li4iSfhKN169ZITk5+5IiSSqWCq6urJFlWVla4evWqzqjzyy+/jG+++Qa9e/eWbGZpAOjatSvEQyfA9O7dGwqFAkIISSc9u3HjRoWzfFtZWUk2c/F9Go0GN27cgKurK3766SfttYRmZmaSTtwl5Sj940yfPh29evVCgwYN0KRJk3IjwlUZwX/Ss0AevK68KpYvX66dv+B///sfunbtqt12/fp1tG3bFufPn5ck69VXX0VYWBh27typfe/dunULs2fPxiuvvCJJBgBkZmaipKSkXHtpaSmuXr0qWc6GDRvw+eefo2vXrnjzzTe17S1atMDZs2clyzl37hyaNGlSrr1Ro0ZIS0uTLOdBaWlpSE9Ph5+fH9RqtfbzRyoLFizAmjVrMG3aNLz99tuYM2cOLl68iK+//lqS667t7OywcOFCnffzg86cOSPJxI65ubk6v99UKhXi4uIQFBSEl19+GRs3bqxyBnBvvge1Wg3g3hlf5ubmqFWrlnZ7rVq1cOPGDUmygHuzwV+7dq1c+7Vr1yQ7MwUARo4ciYEDB8LJyQkKhQIBAQEA7s37IOWM8Mx5tnOI5MTCm2qs/fv362W/K1eufGwB2rhxY23xX1Vt27bF999/j3bt2um0d+7cGbt370bv3r0lyZGqv0/Kw8MDP/zwAyZMmKDT/v3335c7FbiqXnnlFYwePRqtWrVCamoqevbsCeDeH79ubm6S5YwYMUKyfT3OpEmTsH//frz88suwt7eXtCDR189MRZYuXYqwsDCMHDkSubm56NmzJ8LDwxEWFgbgXqH6119/SZb38ccfw8/PD/Xq1UOrVq0A3JvNv06dOoiNjZUsp2vXrvjf//6HNWvWwMfHBwBw9OhRvPXWW9o/HKXw999/VzhjfllZGYqLiyXLsba2xvnz58v9rKSlpZWbDbyqbty4gYEDB2L//v1QKBQ4d+4c3N3dERwcDFtbWyxevFiSnE2bNmH16tXo1asXwsPDMXjwYDRo0ADNmzfH4cOHqzwBXuvWrXHlypVHLhN169atcl9yVoa7uztOnjypc9mJiYkJtm/fjqCgIMl+P7i4uOi8B7Zu3QonJyft9szMTJ1CvKr69euHkSNHYvHixWjbti2Ae8VWaGgo+vfvL1lOeHg4mjVrpp2g8P5yW8bGxpg1axZznpMcIlkZdsCdSL8uXbokPvvsMzFz5kyDnSobEREhcnJyKvXc+Ph48cEHHzxy+759+8Qbb7xRyZ4ZTnR0tFCr1WLevHkiPj5exMfHi7lz5wpzc3Px+eefS5qVk5Mjxo8fL/r06SO+//57bfu8efPEe++9J2lWSUmJ2L59u3jnnXfEO++8I7788ktRXFwsaYZGo9GZLK66atKkidi0aZP2/i+//CJq164t5s6dK4SQbmKoB+Xl5YlVq1aJcePGiWnTpomYmBhRVFQkaUZ2drbo0aOHUCgUwtTUVJiamgqFQiF69OghsrKyJMvx8fERsbGxQoh774n7p5ovWLBAdOrUSbKcsWPHCm9vb5GWlqZtO3funGjevLkIDg6WLEcIIYYNGya6desmLl26pHNMP/zwg2jSpIlkOebm5trLTxwdHbUTg6WnpwsrK6sq7z8uLk77f1ORmzdvivXr11c5Z8aMGeLVV1+tcFtxcbHo06ePJD9D4eHhYsuWLY/cPnv2bNG/f/8q59yXn58v3nrrLaFSqYSRkZEwMjISpqam4q233hJ5eXmS5RARyY2Tq1GN9fPPP6NPnz5wd3fH2bNn0axZM1y8eBFCCPj4+Mg2OY+VlRWOHz8u+UiuPhw5cgRbtmzRrs3bsGFDDBkypEpLLT3KihUr8P777+PKlSsAADc3N4SHh2P48OGSZ8nhzJkz6NOnD7KysrSTaqWmpqJ27drYvXu3ZBOu1atXDz/++KMsp9r9/vvv2LZtGzIyMlBUVKSzraqT0pmbm+OPP/7QGUk9ffo0AgICMHLkSEyZMkWSiQMN5dy5c9r13Bs1aoSGDRtKuv+dO3dixIgRCAsLwzvvvIMFCxYgJSUFGzZswDfffCPZ6fO5ubno3r07fv/9d+2SZZcvX8ZLL72EuLg4SddadnR0xI8//ogWLVrA0tISJ06cgLu7O86fP4/mzZsjLy9PkhwvLy9s2LABvr6+6NSpE3r37o1Zs2bhiy++wMSJE5GdnS1Jjr6VlJTgzp07sLKyeuT2v//++5Ej71K5c+cOjI2NtSOSUsnPz0d6ejoAoEGDBpKfYfGopfAUCgXMzMzg4eEBPz8/GBsbM6cG5xDJysCFP5HevPjii2LevHlCiP8bEfrnn39Enz59xPLly2Xrx4MjN5Xl5+cnYmJixJ07dyTqVXmhoaFCoVAIS0tL0aJFC9GiRQuh0WiEkZGRmDFjht5ys7OzdZbAkdratWvFtm3byrVv27ZNklGn+9q1aycCAwPFzZs3tW03b94Uffr0Ee3bt5csZ+3atWLgwIEiPz9fsn1WZMuWLUKpVIrevXsLU1NT0bt3b9GwYUNhbW0tyVkWLi4u4sCBA+Xaz5w5I+rUqSOGDx8u+Yj32bNnxfjx40WXLl1Ely5dxPjx48Wff/4pacajJqMLCQkRs2fPFmvXrhU3btyQJOvAgQMiICBA1K5dW6jVatGxY0fx448/SrLvB5WVlYkff/xRLFy4UHz66aciISFB8gwh7n1Wpqamav99/3PzyJEjws7OTrKcmTNnivfff18IcW8yOhMTE+Hh4SFMTU3FzJkzJct5WElJiTh27JjOZwRzDOfBpf/s7OyEnZ2ddum/OnXqCIVCIRo0aCAyMjKYU4NziOTEwptqLI1Goz090sbGRjtL6vHjx0W9evVk7UdVC+/JkyeL2rVrCysrKzF69GjJ14tev369MDMzE59++qnOqbdFRUXik08+EWZmZiImJkayvEetQ11cXCxmzZolWY4QQnh6eop9+/aVa4+PjxcNGzaULMfMzOyRM/FWZd34h7Vs2VJYWloKjUYjmjVrpl3n9v5NKt7e3mLZsmVCiP97D5eVlYkxY8Zov9CqisGDB4spU6ZUuO306dOidu3akhbeX375pTAxMRHt2rXTFsPt27cXJiYm4ssvv5Qs5/4axBYWFsLHx0f4+PgIjUYjrK2tha+vr7CxsRG2trbizJkzT73vTz75RLtm919//SXKysok6/ezoEePHuLtt98WQtx7z50/f16UlpaKoKAgMWDAAL3l/vrrr2Lx4sVi165dku538uTJ2hUwSkpKRMeOHbWFw/79+5ljYJs3bxb+/v7lLqPo0qWL2Lp1q7h06ZLo2LFjld97zHm2c4jkxMKbaqw6depol0Bp3Lix2LlzpxDiXuFtYWEhWz+kKLyFuFeU7tixQ/Tp00colUrRuHFjsWjRIkmuG33xxRfFkiVLHrl98eLF4sUXX6xyzn2Wlpbi9ddf1xkpOXv2rPDx8ZH8SxGVSiUuXLhQrv3ChQuSFsTNmzcXP//8c7n2n3/+WTRr1kyynPDw8MfepGJubq593ezs7MTJkyeFEEL88ccfwtHRscr7P3HihFi7du0jt58+fVrS43F3d9deP/6gefPmCXd3d8lyIiMjRf/+/UVubq627datW+L1118XUVFRIj8/X7z22muPvDb3cYyNjcXVq1eFEEIYGRlp/61PCxYseOxNSqdOnRIODg6ie/fuwtTUVLz++uuicePGok6dOjp/fFcXL7zwgjhy5IgQQoivvvpKODs7i5SUFPH222+LDh06MMfA3N3dxbFjx8q1Jycni/r16wsh7s09UdXPO+Y82zlEcmLhTTXWa6+9pp2oa9q0acLDw0O89957wsfHR3Tt2lW2fkhVeD/o6tWr4t133xVmZmZCqVSK1157rcKi70mZm5s/to/p6enC3Ny80vt/WFpammjXrp144YUXxE8//SSWLVsmzM3NxZAhQ8StW7ckyxHi3inN9790edDXX38t6dqz3377rWjatKnYvn27uHTpkrh06ZLYvn27dh3k3Nxc7a06eOGFF7TFtre3t9i8ebMQ4t7ooBQTUD3o7t27IikpSezevVvs3LlT5yYVtVotzp07V649NTVVqNVqyXKcnZ0rHM0+ffq0cHZ2FkLcW/fY3t7+qfft4uIili9fLi5evCgUCoU4evSo+Ouvvyq8SaVly5Y6t6ZNmwpzc3NhZWUl6RkW9926dUu89957IigoSPTo0UPMmTNHXLlyRfKcii47OHv2rKQZKpVKXLp0SQghxJgxY8TkyZOFEEKcP39eWFpaMsfA1Gq19ouEB/3222/az4QLFy5U+Yt65jzbOURyYuFNNVZ6ero4ceKEEOLebMb/+9//hLe3t+jfv7+4ePGibP2QuvBOSkoSb775prCxsRGurq5i3rx5Ijg4WKjVajFt2rRK7dPS0vKx17qePXtW8j+sSktLxcSJE4WRkZFQKpXawk5qM2bMEPXq1RP79u0TJSUloqSkRPz888+iXr16lX69KqJQKLS3+zPxVnRfitOnc3JyxOrVq8WsWbO01wsfPXpUXL58ucr7vm/w4MFi8eLFQggh3nnnHVG7dm0xevRoUa9ePdGvXz/Jcr7//ntRu3ZtndfvwddNKj169KhwhH3t2rWVGn1+lEeddrt//36h0WiEEPc+myrz87Rq1SphamqqfT9VdJP6datIbm6u6Nevn9iwYYNec/RFrssOXF1dxY8//ihKSkqEi4uLdjWC06dPCxsbG+YYWM+ePYWPj49ITk7WtiUnJ4vWrVuLXr16CSGE2LVrV5XPWGLOs51DJCcW3lQjlZSUiISEhEov4yWlHj16VHnE5urVq+Ljjz8WTZs2FaampmLAgAHi+++/17nG8+DBg5X+5rdz587aaysrMmfOHNG5c+dK7ftRdu3aJWrXri06duwoateuLbp27Sr+/vtvSTOEEKKwsFAMHDhQKBQKoVQqhVKpFEZGRmLkyJGioKBAspz7y6I9ya0qTpw4IWrXri08PDyEiYmJ9kudOXPmiGHDhklxKEIIIW7cuKH9/ygtLRUREREiMDBQhISESDqZkoeHhxg3bpykS21VZMWKFaJ27dpi/PjxIjY2VsTGxorx48cLBwcHsWLFCslG2YcMGSLq168v4uLitGc+xMXFCXd3d/Hf//5XCHFv4rrWrVtXav+3b98Wp06dEgqFQvz888/i+PHjFd707eTJk5JfFiLXRIhyXXYwf/58YW1tLRo1aiRcXV21nzfR0dGiXbt2zDGwzMxMERAQUG7pv1deeUVkZmYKIe4t2VnVCQuZ82znEMmJhTfVWCqVSpw/f16WrKKiIpGamir5adL3KZVK0ahRI7Fw4UKRnZ1d4WNyc3OFv79/pfa/e/duYWxsLEJDQ3UKoMzMTDF9+nRhYmIidu/eXal9V2Ts2LFCpVKJjz/+WJSVlYnMzEzRo0cPYWdnJ7744gvJch6Umpoqtm3bJnbv3i3rGQ9S69q1qwgNDRVC6J5N8csvv0haCA0bNkysXbtW79fWWlpaynL9bkUj6voYZf/nn3/E6NGjdUamTU1NxZgxY7RrEB87dqzCaxefxvr16yX94uhpHTx4UPJRTrkmQpTrsgMhhNi+fbtYsmSJ9hRtIe7933399dfMeUacPXtW+6Wb1JcbMKf65BDJget4U43Vpk0bfPTRR+jatauk+124cCEmTpwItVqN0tJSzJw5E59++ilKSkpgZGSEYcOGYdWqVVAqlZJlHjx4EC+99JJk+6vIp59+iunTp6OkpATW1tYA7q3ha2JigoULF2Ly5MmSZTVr1gybNm1CixYtdNo/++wzzJw5s8rr9YaEhODdd9+FhYUFQkJCHvvYJUuWVDrn5MmTT/zY5s2bVzrnQdbW1khOTkaDBg101jr+66+/4OXlhYKCAklyRo8ejQMHDiAtLQ0vvPACOnfuDH9/f3Tu3Bmenp6SZADAqFGj0LFjRwQHB0u2z2dBXl4ezp8/DwBwd3eHRqMxcI8q5+G1dIUQyMzMRGxsLDp37ozNmzdLlmVmZoazZ8/qrO0OABcvXkTjxo1x9+5dSXJ69uyJoKAgjBw5Uqd93bp12Lp1K3788UdJcujZ82+/Dx5Uld8NzHm2c4gMxcTQHSDSl/feew/Tp0/Hu+++i9atW8PCwkJnu5WVVaX2GxYWhjfeeANqtRqRkZFYu3YtVq5cCV9fXxw7dgwhISGIjIzEjBkzpDgMAMBLL72E3NxcZGVlAQAcHR21xbFUJk6ciH79+mH79u04d+4cAKBhw4YYMGAAXFxcJM06evQoVCpVufbx48cjICCgyvs/duwYiouLtf9+FIVCUaWcli1bQqFQQAjxr/sqLS2tUtZ9KpUKt2/fLteempqK2rVrS5IBAGvWrAEA/P333zhw4AASEhKwePFi/O9//4OTkxMuX74sSc6yZcsQFBSEgwcPwtvbu9wXVpMmTZIkR24ajUayL1vus7W1feL37M2bNyXJjIyM1LlvZGSE2rVrY8SIEQgLC5Mk4z4HBwecPHmyXOF94sQJ2NvbS5bTp08fzJw5E0ePHkW7du0AAIcPH8b27duxYMEC7Nq1S+exlfXwlxb3KRQKmJmZwcPDA35+fjA2Nq50Rk3M0aeHfx8kJyejpKQEXl5eAO59jhobG6N169bMqcE5RIbCEW+qsYyMjLT/fvCP1ftFUmULISMjI2RlZcHBwQE+Pj548803MXbsWO32TZs2ISIiAqdPn6585x+wZs0aLFmyBCkpKTrtXl5emDZtmiwjhSdPnkSbNm1QVFSk96zq5K+//tL++9ixY5g+fTpCQ0PRvn17AEBiYiIWL16MhQsXom/fvpJkjh49Gjdu3MC2bdtgZ2eHkydPwtjYGH379oWfnx+ioqIkybnvzp07OHToEPbv34/4+HgkJyejSZMmj/1C42lER0fjzTffhJmZGezt7XV+VhUKhXbUuDIeVShUpDoU+DExMU/82BEjRuixJ/oxc+ZMfPHFF1i3bh38/PwAAAkJCRg1ahRef/11fPzxx5LkPPi74XGq8nsCAOrXr49r167hzp07sLW1BQDk5OTA3NwcGo0G2dnZcHd3x/79+6v05WZNy5HLkiVLEB8fj5iYGJ3jGTlyJF566SVMmzaNOc9BDpGsDHiaO5Fe6WuCK4VCob3O2t7eXpw6dUpn+/nz5yVbemvhwoXC3NxczJo1S+zfv1/88ccf4o8//hD79+8XYWFhwsLCQixatEiSrMc5fvx4la99bdWqlXZSrpYtW4pWrVo98lYdvfjii+Lbb78t1/7tt98KHx8fyXJu3bolAgIChI2NjTA2NhYuLi5CqVQKPz8/7TXEUggLCxPt27cXZmZmolWrVmLKlCni66+/lnRiNSGEqFOnjnj//fdFaWmppPsVQgg3Nzedm4WFhVAoFMLW1lbY2toKhUIhLCwstGvCkmE9biLEwsJCQ3fvqW3evFn4+/vrzGFw7tw50aVLF7F161Zx6dIl0bFjRzFgwADmGICzs7M4ffp0ufZTp04JJycn5jwnOURy4qnmVGN17txZb/tevXo1NBoNTE1Ny53S+c8//1R4GnVlLFu2DOvWrcPAgQN12hs3bgx/f3+0aNECoaGhmD59uiR5+vTaa69pXxepRn+fJadOnUL9+vXLtdevXx9//PGHZDnW1tbYs2cPDh06hJMnTyIvLw8+Pj6SnKL/oA8//BC1a9fG/Pnz0b9/fzRs2FDS/d9XVFSE//znP088Cvk0Lly4oP335s2bsXz5ckRHR2tPW0xJScGYMWPwv//9T/Jsfbh9+7b2EpmKLjd4UGUvpanI77//jm3btiEjI6PcWS9xcXGS5ZiamuKLL77Ae++9h2PHjkGtVqN58+aoV6+eZBkAcOnSJVlGZN9++23s2LEDDRo00LZ5eHjg448/xoABA3D+/HksXLgQAwYMYI4B3L59G9euXSvXfu3aNfzzzz/MeU5yiGRl6MqfSJ9ycnLEjz/+KGJjY0VMTIzOrbLq1aunM4oWGRmpsz0qKkqypVXMzMzEH3/88cjtZ86ckXwW3opIMeL9oODg4ApnL67OWrVqJYYNG6YzMldYWCiGDRsm6Si+lGvCP87x48fFJ598Ivr16ydq1aolnJ2dxeDBg8WqVatESkqKZDlTpkwR77//vmT7exR3d3ed9WDv+/3334Wbm5ve86VgZGQkrl69KoQQOuvD63Md7y1btgilUil69+4tTE1NRe/evUXDhg2FtbW1eOONNyTLuW/NmjXaZRNNTU1F06ZNxerVqyXNMDIyEn5+fuLzzz+X/AyOB6nVanHkyJFy7b/99pv2c/vChQuVXgaypubIZdiwYcLNzU3s2LFDu/Tfl19+KerXry+GDx/OnOckh0hOLLypxtq1a5ewtLQUCoVCWFtbCxsbG+3N1tZWb7mJiYkV/oFfGS+99JIYPny4KC4uLretpKREDB8+XPj5+VU5Jzc397G3gwcPSvrHfJ8+fYRKpRJ169YVoaGhsqw7rG9JSUnCwcFBuyZ5165dRe3atYWDg4NISkqSLEehUAh/f38RGxsr7t69K9l+/83x48fFiBEjhImJiaTvhYkTJwpra2vh5+cnJkyYIKZOnapzk4parRa//fZbufakpCRZvrySQnx8vPazQJ9rxT/I29tbLFu2TAjxf8vXlZWViTFjxoh58+ZJliOEEHPnzhUWFhZi1qxZ2uWDZs2aJTQaTYXrbldWcnKymD59uqhbt65QqVTitddeE9u3b5d8ebaePXsKHx8fnd8HycnJonXr1qJXr15CiHu/p5o1a8YcA8jPzxdvvfWWUKlUOkv/vfXWW5JetsOcZzuHSE6cXI1qrIYNG6Jnz5744IMPYG5ubrB+9OrVC2vWrIGTk9NTP/fkyZPo1q0biouL4efnhzp16gAArl69igMHDsDU1BQ//fQTmjVrVqU+GhkZPXa2ZFHFCekqkpOTg+3bt2Pz5s04ePAgGjVqhKFDh2LIkCHlZjWuLvLz87Fp0yacPXsWwL1LAoYMGVJuRv2qOH78ONatW4ctW7ZoT9MeNWoUfH19JcsA7v2fHzt2DPHx8YiPj8ehQ4dw+/ZtNG/eHJ07dy4323Vlvfzyy4/cplAosG/fPklyAgMD8ffff2PNmjXw8fEBcG92/bFjx+KFF17Qmcma/o+FhQXOnDkDNzc32NvbIz4+Ht7e3vjzzz/RpUsXZGZmSpZVu3ZtLF26FIMHD9Zp37JlCyZOnIjr169LlgXce4/Hx8dj8+bN2LFjB8rKytC/f3+sXbtWkv1nZWVh2LBh+Pnnn7Wz9RcXFyMgIAAbNmyAo6Mj9u/fj+LiYrz66qvMMZD8/Hykp6cDABo0aCDp5zVzqk8OkRxYeFONZWFhgVOnTsHd3d2g/XhwreXK+Oeff7Bx40YcPnxYZzmx9u3bY8iQIZJcy5mQkPBEj9PXdfOXL1/Gli1bsHbtWpw7dw4lJSV6yalJSkpKsGvXLqxfvx4//PADGjZsiFGjRmHYsGGSLCtma2uLvLw8tGjRQruG90svvQQbG5uqd94Arl27hhEjRuCHH37QKRq6d++OdevWab/Uqk5ycnIQHR2NP//8EwDQpEkTjBw5EnZ2dpJl1K1bF99//z28vb3RvHlzhIWFYfDgwUhMTET37t2Rm5srWZaNjQ2OHDlSbp341NRUtG3bFrdu3ZIs62HJyckIDg7GyZMnJf2CEbg3l8D9VSm8vLy0cwxIrablEBHVNCy8qcbq378/Bg0aVG5iMrlVtfCu6YqLi/Htt99i48aN+Pbbb2FnZ4e///7b0N2qlHPnzmH//v3Izs5GWVmZzrZ58+bpJbOwsBDLly9HWFgYioqKYGpqioEDB+Kjjz6q1FkW93377bd46aWXJJ2k61lw7tw5baHaqFEjvU0ap28HDhxAYGAgrK2t0aZNGwD3RvBv3bqF3bt3a5fjqqohQ4agTZs2CAkJwbvvvotPP/0Ur732Gvbs2QMfHx9JJ1ebOHEilEollixZotM+ffp03L17F5999plkWcC9L/w2b96MzZs34/Tp02jfvj2GDh2KN998s9L7DAkJeeLHPnycz3MOEdHzgIU31VjR0dF45513MHLkSHh7e2tHue7r06ePLP3QZ+FdXFyMzMxMuLq6Vmk/JSUlKC0t1ZmN/erVq1i5ciXy8/PRp08fdOrUqard1bF///5yp3gOHToUXbp0eexp78+q1atX46233kKtWrXg6OhYbj3q5ORkSfN+//13rF27Flu3boWFhQVGjBiB4OBgXL58GQsWLMDt27fx22+/SZpZnT2qgFAoFDAzM4OHhwdee+01SUeL9cnb2xvt27fHihUrYGxsDAAoLS3FuHHj8Ouvv+LUqVOS5Ny8eRMFBQVwdnZGWVkZFi5ciF9//RWenp54++23tevrSmHixInYsGEDXFxc0K5dOwBAUlISMjIyMHz4cJ3P8KoUeatWrcLmzZvxyy+/6FziIsXs6Q9fOpGcnIySkhLtqHBqaiqMjY3RunXrKl1GUdNyiIieByy8qcZ63PJEUl+v/Dj6LLxPnDgBHx+fKh/LyJEjYWpqilWrVgG4d3p706ZNUVBQACcnJ/zxxx/YuXMnevbsKUW38cILL+DmzZvo3r07hg4disDAQMmWYDOUevXqYdy4cZg5c6Zec5YsWYJ169YhJSUFPXv2xOjRo9GzZ0+d9/vly5fh5ubGU/Yf8PLLLyM5ORmlpaXlioZGjRohJSUFCoUChw4dQpMmTQzc23+nVqtx/Pjxcqf5pqSkoGXLlrh7966BelZ5j7ve/0FVvfbfxcUFgwcPxtChQ9GiRYtK7+ffLFmyBPHx8YiJidF+QZGTk4ORI0fipZdewrRp05hDRPQ8MciUbkTPkfszAeuDVMt8eXp6ih9//FF7f9myZcLZ2VncunVLCCHEjBkzhL+/f5Vz7vv8889FTk6OZPt7FlhaWsqy1JeHh4f44IMPxJUrVx75mMLCQrF+/Xq996U6iYyMFP379xe5ubnatlu3bonXX39dREVFifz8fPHaa6+JV1991YC9fHIdOnQQX331Vbn2r776Svj6+lZp3w++Rv+24kF1VFZWJkuOs7OzOH36dLn2U6dOCScnJ+YQET1nTAxd+BPpyzvvvPPIbQqFAnPnzpWxN5Vzf/blR5FqVOvvv//WmdDo559/xoABA2BtbQ0AGDFiBNatWydJFgCMGTNGsn09K4KCgvDTTz9V6frQJ3Hu3Ll/fYypqSlGjBih135UN4sWLcKePXt0rlm3trZGeHg4Xn31VUyePBnz5s2rNrMxT5o0CZMnT0ZaWpr2tOzDhw/js88+w4cffoiTJ09qH9u8efOn2retrS0yMzPh4OAAGxubCi/9EHpY6UCfTp48iWbNmsHIyOhfT8N/2tfrUW7fvo1r166Va7927Rr++ecfSTJqYg4RUU3FwptqrK+++krnfnFxMS5cuAATExM0aNBAtsJ79uzZlb5u9I8//sCgQYNQv379CrdnZmYiNTW1Kt0DAJiZmekU8YcPH8aiRYt0tufl5VU5pybz8PDA3Llzcfjw4QrnFJg0aZKkeXfu3EFGRgaKiop02qUqGmqa3NxcZGdnlzuN/Nq1a7h9+zaAe7NqP/x6PqvuL7k1Y8aMCrcpFIpKF8f79u3Tfmbt37+/6p19BrRs2RJZWVlwcHBAy5Ytta/PfVV5vR6lX79+GDlyJBYvXoy2bdsCuHfNemhoKPr37y9JRk3MISKqqXiNNz1Xbt++jTfeeAP9+vXDsGHDKrWPcePGYeHChdBoNADurTHbp08f7dqSt27dwpAhQ/Ddd99Vub9t2rRBcHAw3nrrrQq3Hz9+HK1bt67yH4pdu3ZF27ZtERERgYMHD8Lf3x+XL1/Wzoq9Z88evPXWW0hLS6tSTk32qC9HgHt/1J8/f16SnGvXruGNN97ADz/8UOH26jICKbehQ4ciMTERixcvxosvvggAOHLkCKZPn44OHTogNjYWW7duxccff4zff//dwL39d3/99dcTP1aKScOqu7/++guurq5QKBT/+tpJ9XrduXMH06dPx9q1a1FcXAwAMDExQXBwMBYtWiTZesQ1LYeIqKZi4U3PnVOnTiEwMBAXL16s1PONjY21p2ECgJWVFY4fP66dPO3q1atwdnaWpACaPHkyFAoFoqKiKtyenp6O0aNHV3lUKiEhAT169ICTkxMyMzMxePBgREdHa7ePGzcO+fn5iImJqVIOVd3QoUPx119/ISoqCv7+/vjqq69w9epVvPfee1i8eDF69epl6C4+k/Ly8jB16lRs2LBBO+mciYkJRowYgcjISFhYWOD48eMA7o2OPusiIiJQp04djBo1Sqd97dq1uHbtmqST/MmxXric5HztACA/Px/p6ekAgAYNGuitQK1pOURENQ0Lb3ruHDp0CIGBgcjJyanU842MjLSnLALlZy2XsvC+LyMjAy4uLuWutRRC4NKlS1VeTgwA/vzzT/z0009wdHREUFCQzizZn3/+Odq2bVstChI53V/b2MLC4rHr3SoUCixevFiSTCcnJ+zcuRNt27aFlZUVfv/9dzRs2BC7du3CwoULcejQIUlyaqq8vDzt2Qfu7u7aM1eqGzc3N2zevBkdOnTQaU9KSsKgQYNw4cIFSXLkWi9cTnK9dkRERA/iNd5UYy1dulTnvhACmZmZiI2NRY8ePQzUq8qpX7++zij7fTdv3kT9+vUlKfIbN26Mxo0bV7ht7NixOvd79eqFNWvWaE9Ff14dO3ZMe8rlsWPHHvk4Kdclz8/P174PbG1tce3aNTRs2BDe3t6SrxVeE2k0mhpxHXxWVlaFP3+1a9dGZmamZDnjx4/Hf/7znwrXCx8/frxk64XLSa7XjoiI6EEsvKnGioyM1LlvZGSE2rVrY8SIEQgLCzNQryrn/qQ/D8vLy4OZmZns/Tlw4EC1XCdYag+e4i/XJFReXl5ISUmBm5sbWrRogVWrVsHNzQ0rV6587r8IeZ64uLjgl19+KTe3wC+//AJnZ2fJctLS0vDll19qi27g3uU2ISEh2LBhg2Q5cpLrtSMiInoQC2+qsfR5uuC8efNgbm4OACgqKsL777+vXXrrzp07kuXcP335/vJn9zOBe6NOSUlJPP37OTN58mTtqNz8+fPRvXt3bNy4EaamprwG/zkyZswYTJkyBcXFxejSpQuAe8sAzpgxA9OmTZMsx8fHB3/++Se8vLx02v/880+0aNFCshw5yfXaERERPYjXeBM9JX9//yc6dViKEdCXX34ZwL3Jz9q3bw9TU1PtNlNTU7i5uWH69Ok6a3DL4eHr2skwhBC4e/cuzp49C1dXV9SqVcvQXSKZCCEwa9YsLF26VLsEmpmZGWbOnIl58+ZJlvPFF19gxowZmDhxYoXrhT94eUp1OYVfrteOiIjoQSy8iaqBkSNH4pNPPoGVlZWhuwKAhbehRUdHIzIyEufOnQMAeHp6YsqUKRg9erSBe0Zyy8vLw59//gm1Wg1PT0+oVCpJ9//gJIsV0cf613LR92tHRET0IBbeRE9p+vTpGD16NBo1amTorhgMC2/DmTdvHpYsWYKJEyeiffv2AIDExEQsW7YMU6dOxTvvvGPgHlJNwvXCiYiIpMHCm+gpeXp64vz58/D19cXo0aPxn//857lbx5SFt+HUrl0bS5cuxeDBg3Xat2zZgokTJ+L69esG6hnVRHKveU1ERFRTPf4cMiIq59y5c9i/fz8aNmyIyZMnw9HREaNGjcKvv/5q6K7JZvbs2bCzszN0N55LxcXF2vWUH9S6dWuUlJQYoEdUk61atarCs3uaNm2KlStXGqBHRERE1RMLb6JK8PPzw/r165GVlYVPPvkE586dQ6dOndC4cWN8/PHHuHr1qqG7+FTGjRuHvLw87f0tW7YgPz9fe//WrVvo2bOn9n5YWBhsbGzk7CL9f8OGDcOKFSvKtX/++ecYOnSoAXpENRnXvCYiIpIGTzUnkkhaWhrWrVuHlStXIi8vD4WFhYbu0hMzNjZGZmYmHBwcAABWVlY4fvy49lTyq1evwtnZudpNnlQTTZw4ERs2bICLi4t2lumkpCRkZGRg+PDhUCqV2scuWbLEUN2kGsLT0xPz58/Hf//7X5322NhYzJ8/H+fPnzdQz4iIiKoXruNNJIH8/HwcPHgQCQkJyMnJKbfm7bPu4e/f+H3cs+v06dPw8fEBAKSnpwMAatWqhVq1auH06dPaxz3JkndE/4ZrXhMREUmDhTdRFRw6dAhr167Fl19+CSEEgoKC8NFHH6Fjx46G7hrVUFKsD0/0pEJDQ3Hjxg2MGzeu3JrXYWFhBu4dERFR9cFTzYmeUmZmJmJiYrB+/XqkpqaiXbt2GDVqFAYNGgSNRmPo7lWKkZERsrKytKeaPzxrOU81J3q+cc1rIiKiquGIN9FTcnFxgb29PYYNG4bg4GA0btzY0F2SxLx582Bubg4AKCoqwvvvvw9ra2sAwJ07dwzZNSIyMI1GgxdffNHQ3SAiIqq2OOJN9JTi4uLQp08fmJhU/L1VQUEBli1bhunTp8vcs8rz9/d/omuCeZozEREREdHTY+FNVAnXrl1DUlISTE1N0bVrVxgbG6O4uBjLly9HREQESkpKcP36dUN3k4iIiIiIngFcx5voKR06dAienp7o06cPevTogQ4dOuCPP/5A06ZNsWrVKoSHh+PSpUuG7uZTmT59Os6ePWvobhARERER1Ugc8SZ6Sv7+/nB2dsbs2bMRExODxYsXw9PTE++//z5ef/11Q3evUjw9PXH+/Hn4+vpi9OjR+M9//gMLCwtDd4uIiIiIqEZg4U30lOzt7XHw4EE0adIEd+/ehUajQVxcHF577TVDd61KDhw4gLVr12LHjh0AgKCgIIwePRodOnQwcM+IiIiIiKo3Ft5ET6mipbeOHz+OBg0aGLhn0sjPz8cXX3yBdevW4ZdffoGXlxeCg4MxbNgw1KlTx9DdIyIiIiKqdlh4Ez0lIyMj7Nu3D3Z2dgCADh06YNu2bahbt67O45o3b26I7kkqLS0N69atw8qVK5GXl4fCwkJDd4mIiIiIqNrhOt5EldC1a1c8+J1V7969AQAKhQJCCCgUCpSWlhqqe5LIz8/HwYMHkZCQgJycHHh5eRm6S0RERERE1RILb6KndOHCBUN3Qa8OHTqEtWvX4ssvv4QQAkFBQfjoo4/QsWNHQ3eNiIiIiKha4qnmRITMzEzExMRg/fr1SE1NRbt27TBq1CgMGjQIGo3G0N0jIiIiIqrWWHgTPaVdu3ZV2G5tbY2GDRvCyclJ5h5VnYmJCezt7TFs2DAEBwejcePGhu4SEREREVGNwcKb6CkZGRk9cptCocCgQYOwevVqmJuby9irqomLi0OfPn1gYlLx1ScFBQVYtmwZpk+fLnPPiIiIiIiqPxbeRBLJzc3F0aNHMX78ePTr1w8ffPCBobv0VK5du4akpCSYmpqia9euMDY2RnFxMZYvX46IiAiUlJTg+vXrhu4mEREREVG1w8KbSGI//PADpkyZgrNnzxq6K0/s0KFD6N27N27fvg2FQoE2bdpg3bp16Nu3L0xMTDBp0iSMGDECarXa0F0lIiIiIqp2WHgTSezixYto1qwZ8vLyDN2VJ+bv7w9nZ2fMnj0bMTExWLx4MTw9PfH+++/j9ddfN3T3iIiIiIiqNRbeRBLbt28f3nzzTaSmphq6K0/M3t4eBw8eRJMmTXD37l1oNBrExcXhtddeM3TXiIiIiIiqvUfPEkVET+348eOYPn06evXqZeiuPJWcnBzUqlULAKBWq2Fubo5mzZoZuFdERERERDVDxVMYE9Ej2draQqFQlGvPz89HSUkJXnnlFSxYsMAAPauaP/74A1lZWQAAIQRSUlKQn5+v85jmzZsbomtERERERNUaTzUnekoxMTEVtltZWcHLyx362AUAAAoxSURBVAtNmjSRuUdVZ2RkBIVCgYo+Du63KxQKlJaWGqB3RERERETVGwtvIsJff/31RI+rV6+enntCRERERFTzsPAmekpXrlzBkiVLMG/ePFhZWelsy83NxXvvvYfp06ejTp06BuohERERERE9S3iNN9FTWrJkCW7fvl2u6AYAa2tr/PPPP1iyZAk++ugjA/Sucnbt2lVhu7W1NRo2bAgnJyeZe0REREREVHNwxJvoKTVr1gwrV65Ep06dKtz+66+/YsyYMThz5ozMPas8I6NHL3CgUCgwaNAgrF69Gubm5jL2ioiIiIioZuByYkRP6cKFC3B1dX3k9rp16+LixYvydUgCZWVlFd5ycnKwZ88eJCcn47333jN0N4mIiIiIqiUW3kRPSa1WP7awvnjxItRqtXwd0iNra2t06dIFkZGRiIuLM3R3iIiIiIiqJRbeRE/J19cXsbGxj9y+YcMGtG3bVsYe6V+jRo1w+fJlQ3eDiIiIiKha4uRqRE9p+vTpeOWVV2BtbY3Q0FDt7OVXr17FwoULsX79evz0008G7qW0zp8/D2dnZ0N3g4iIiIioWuLkakSVsGrVKkyePBnFxcWwsrKCQqFAbm4ulEolIiMj8dZbbxm6i5I5fvw4Ro0ahc6dOyMyMtLQ3SEiIiIiqnZYeBNV0t9//41t27YhLS0NQgg0bNgQr7/+OurWrWvorj01W1tbKBSKcu35+fkoKSnBK6+8gm3btlW4hBoRERERET0eC28iPevVqxfWrFnzTK+FHRMTU2G7lZUVvLy80KRJE5l7RERERERUc7DwJtIzS0tL/L/27ie06/qPA/jz6x9QD5MJM5IZrMNAxcM6rJsVLBGCaWtCeFhHdRARKpLkUYQOdmge5qKkEUHBxAyJoCldCqEvOAgMw+ahZLpJbmLgXPudEvwzf37Xts++Xx6Py/jy+h6el8Gee/+7ePFinn/++aKjAAAABXCrOZA///wz+/fvz/j4+COzW7du5cCBAxkZGSkgGQAAVD/FG8ixY8cyPj7+2DPcq1evzsTERI4dO1ZAMgAAqH6KN5Bvv/02XV1dM867urryzTffLGAiAACoHYo3kN9//z3PPffcjPPGxsYMDw8vXCAAAKghijeQlStXPrFYDw8PZ+XKlQsXCAAAaojiDfPs0KFDWbNmTdExnujFF19Mf3//jPPPPvssra2tC5gIAABqx7KiA0C1GhwczMDAQIaHh1MqldLU1JTOzs5s2bLlge+99957BSV8evv378+rr76a1atX58CBA3nmmWeSJCMjI/nggw9y8uTJfPfddwWnBACA6uQdb5iFPXv25MSJE6mvr09zc3Omp6dz+fLl/PXXX+nu7s5HH31UdMSK9fb25p133snk5GTq6upSKpVy69atLF++PB9++GH27t1bdEQAAKhKijdU6NSpU3nzzTfT29ubt956K6VSKUnyzz//5OTJk9m7d2+++uqrtLe3F5y0cn/88Ue+/PLL/Pbbb5menk5zc3M6OzvT2NhYdDQAAKhaijdUqL29PZs2bcrRo0cfOz948GAuXbqU06dPL3CyhfPaa6/l448/zrPPPlt0FAAAWPRcrgYVKpfLef3112ecd3R05Oeff17ARAvvhx9+yN9//110DAAAqAqKN1RodHT0iVuvGxsbMzY2toCJAACAxUzxhgrdvXs3y5cvn3G+bNmy3L17dwETAQAAi5nnxGAWDh8+nFWrVj12dufOnQVOAwAALGaKN1Roy5Yt+fXXX//vdwAAABLFGyp2/vz5oiMAAABVxBlvmGNXrlzJ1q1bi44xrw4dOpQ1a9YUHQMAAKqCd7xhjl28eDEvvPBCpqamio5SscHBwQwMDGR4eDilUilNTU3p7Oy0dR4AAP4DK95AkmTPnj1pa2vLF198kbGxsdy4cSOff/55Xnnllbz99ttFxwMAgKqleAM5depUPv3003zyyScZHR3Njz/+mJ9++ik3btxIX19fTpw4ka+//rromAAAUJVsNYc5Vo1bzdvb27Np06YcPXr0sfODBw/m0qVLOX369AInAwCA6udWc6hQS0tLSqXSjPNqfMe7XC7n/fffn3He0dGRN954YwETAQBA7VC8oUI7duwoOsKcGx0dTWNj44zzxsbGjI2NLWAiAACoHbaaA1myZElGRkbS0NDw2PnIyEjWrVtXVdvnAQBgsbDiDRW6fv161q5dO+P83r17KZfLaW1tXcBU/93hw4ezatWqx86qcfs8AAAsFla8oUJLly7NtWvX7pfvzZs35+zZs1m/fn2S6lwdfvnll594bv1f586dW4A0AABQW6x4Q4Ue/l/V8PBwJicnn/idxe78+fNFRwAAgJrlHW+YB0+zelxNrly5kq1btxYdAwAAqpLiDfxfExMT+f7774uOAQAAVclWc6hQqVTKxMREVqxYkenp6ZRKpdy+fTvj4+NJcv8nAABAonhDxaanp9Pc3PzA55aWlgc+19pWcwAAYPYUb6iQm70BAIBKeE4MSEtLyxNX6e/cuZPLly9X1RNpAACwWFjxhgo97Rnuurq6eU4yd3bs2FF0BAAAqFlWvKFCS5YseeLq8L9nvK0OAwAAiRVvqFh/f38aGxuLjjGnrl+/nrVr1844v3fvXsrlclpbWxcwFQAA1AYr3lCh+vr6HD9+PLt27So6ypxZunRprl27dr98b968OWfPns369euTJCMjI1m3bp1VfAAAmIUlRQeAanPkyJHs3r07O3fuzM2bN4uOMyce/v/b8PBwJicnn/gdAADg6SjeUKHu7u4MDQ1lbGwsGzduzJkzZ4qOtCC8TQ4AALPjjDfMQlNTUwYHB9PT05OOjo5s2LAhy5Y9+OtULpcLSgcAACwmijfM0tWrVzMwMJD6+vps3779keJdTUqlUiYmJrJixYr7t7Lfvn37/tNpT/uEGgAA8CiXq8Es9PX1Zd++fWlra0tvb28aGhqKjvSfPPxE2r/l++HPLlcDAIDKVe8SHRRk27ZtuXDhQnp6etLV1VV0nDlx7ty5oiMAAEDNUryhQlNTUxkaGqqpt7xfeumloiMAAEDNstUceOoz3HV1dfOcBAAAao/iDTxyxvthzngDAMDs2WoOpL+/v6a2zgMAwGJixRtIfX19jh8/nl27dhUdBQAAas6SogMAxTty5Eh2796dnTt35ubNm0XHAQCAmqJ4A+nu7s7Q0FDGxsaycePGnDlzpuhIAABQM2w1Bx7Q09OTd999Nxs2bMiyZQ9eA1EulwtKBQAA1cvlasB9V69ezcDAQOrr67N9+/ZHijcAAFA5f1UDSZK+vr7s27cvbW1t+eWXX9LQ0FB0JAAAqAmKN5Bt27blwoUL6enpSVdXV9FxAACgpijeQKampjI0NOQtbwAAmAcuVwMAAIB55DkxAAAAmEeKNwAAAMwjxRsAAADmkeINAAAA80jxBgAAgHmkeAMAAMA8UrwBAABgHineAAAAMI8UbwAAAJhH/wMKl5vSB04GcAAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 1000x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# clustermap manages its own gridspec; disable constrained_layout to avoid\n",
|
||
"# matplotlib colorbar/engine-switch errors under non-interactive backends.\n",
|
||
"with plt.rc_context({\"figure.constrained_layout.use\": False}):\n",
|
||
" fig = sns.clustermap(macro_data_full.corr(), cmap=\"RdBu_r\", center=0, figsize=(10, 10))\n",
|
||
" fig.fig.suptitle(\"FRED Indicator Correlations (Hierarchical Clustering)\", y=1.02)\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4b9b8592",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003525,
|
||
"end_time": "2026-06-13T02:32:19.755466+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:19.751941+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Interpretation**: Four blocks emerge: a labour/yield-curve block\n",
|
||
"(CIVPART, UNRATE, T10Y2Y), a stress block (VIX, ICSA, high-yield spread), a\n",
|
||
"growth/price-level block (INDPRO, CPI, M2), and a short-rate block\n",
|
||
"(DFF, T10YIE). Most off-diagonal correlations are modest in magnitude, and\n",
|
||
"the dendrogram's high cophenetic correlation confirms that this block\n",
|
||
"structure is genuinely hierarchical."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "14f8d11e",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003541,
|
||
"end_time": "2026-06-13T02:32:19.762522+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:19.758981+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Comparing GMM and K-Means\n",
|
||
"\n",
|
||
"Both methods identify 4 regimes. The key difference:\n",
|
||
"- **GMM**: Soft assignments with probabilities (uncertainty quantified)\n",
|
||
"- **K-Means**: Hard assignments (each month belongs to exactly one regime)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3c771109",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003354,
|
||
"end_time": "2026-06-13T02:32:19.769394+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:19.766040+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Fit Both Models"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "593abc34",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:06.912215Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:06.912151Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:07.577325Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:07.576816Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.419609,
|
||
"end_time": "2026-06-13T02:32:20.192358+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:19.772749+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"GMM silhouette: 0.417\n",
|
||
"K-Means silhouette: 0.448\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"n_regimes_full = 4\n",
|
||
"\n",
|
||
"# GMM\n",
|
||
"gmm_full = GaussianMixture(\n",
|
||
" n_components=n_regimes_full,\n",
|
||
" covariance_type=\"full\",\n",
|
||
" random_state=SEED,\n",
|
||
" n_init=10,\n",
|
||
" reg_covar=1e-6,\n",
|
||
")\n",
|
||
"gmm_full.fit(macro_data_full)\n",
|
||
"gmm_probs_full = gmm_full.predict_proba(macro_data_full)\n",
|
||
"gmm_labels_full = gmm_full.predict(macro_data_full)\n",
|
||
"\n",
|
||
"# K-Means\n",
|
||
"kmeans_full = KMeans(n_clusters=n_regimes_full, random_state=SEED, n_init=10)\n",
|
||
"kmeans_labels_full = kmeans_full.fit_predict(macro_data_full)\n",
|
||
"\n",
|
||
"print(f\"GMM silhouette: {silhouette_score(macro_data_full, gmm_labels_full):.3f}\")\n",
|
||
"print(f\"K-Means silhouette: {silhouette_score(macro_data_full, kmeans_labels_full):.3f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5de572c4",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.008513,
|
||
"end_time": "2026-06-13T02:32:20.209462+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.200949+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### GMM Regime Probabilities"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "af64bbc8",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:07.579098Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:07.578988Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:07.709278Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:07.708682Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.189912,
|
||
"end_time": "2026-06-13T02:32:20.409706+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.219794+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_regime_heatmap(\n",
|
||
" gmm_probs_full,\n",
|
||
" macro_data_full.index,\n",
|
||
" f\"GMM Regime Probabilities ({start_yr}-{end_yr})\",\n",
|
||
" n_regimes=n_regimes_full,\n",
|
||
" is_probability=True,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "189f92eb",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003607,
|
||
"end_time": "2026-06-13T02:32:20.417241+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.413634+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### K-Means Regime Assignments"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "11470f83",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:07.710453Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:07.710384Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:07.796855Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:07.796426Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.091791,
|
||
"end_time": "2026-06-13T02:32:20.512572+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.420781+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_regime_heatmap(\n",
|
||
" kmeans_labels_full,\n",
|
||
" macro_data_full.index,\n",
|
||
" f\"K-Means Regime Assignments ({start_yr}-{end_yr})\",\n",
|
||
" n_regimes=n_regimes_full,\n",
|
||
" is_probability=False,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4c9dad70",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003704,
|
||
"end_time": "2026-06-13T02:32:20.521244+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.517540+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Agglomerative Clustering\n",
|
||
"\n",
|
||
"Hierarchical clustering on observations (not features) shows how months group together."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b6df6347",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003617,
|
||
"end_time": "2026-06-13T02:32:20.528543+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.524926+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Linkage Matrix"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "d7fae9a2",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:07.798350Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:07.798217Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:07.801978Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:07.801618Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.009264,
|
||
"end_time": "2026-06-13T02:32:20.541386+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.532122+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Cophenetic correlation: 0.710\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"Z_full = linkage(macro_data_full, \"ward\")\n",
|
||
"pairwise_dist = pdist(macro_data_full)\n",
|
||
"c, _ = cophenet(Z_full, pairwise_dist)\n",
|
||
"\n",
|
||
"print(f\"Cophenetic correlation: {c:.3f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7f7dcc47",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003816,
|
||
"end_time": "2026-06-13T02:32:20.549209+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.545393+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Note**: The cophenetic correlation measures how faithfully the dendrogram\n",
|
||
"preserves the original pairwise distances; good dendrograms score above 0.7.\n",
|
||
"Here it reaches 0.71, so the Ward-linkage tree is a reliable summary of the\n",
|
||
"indicator distance structure — the block grouping above reflects real\n",
|
||
"hierarchy rather than an artefact of the linkage choice."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6da694bc",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003921,
|
||
"end_time": "2026-06-13T02:32:20.556920+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.552999+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Dendrogram"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "a44dfc4b",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:07.803029Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:07.802968Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:07.845666Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:07.845149Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.062474,
|
||
"end_time": "2026-06-13T02:32:20.623229+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.560755+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1600x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, ax = plt.subplots(figsize=(16, 5))\n",
|
||
"# no_labels=True since the point of this figure is the cophenetic-correlation\n",
|
||
"# summary in the title, not per-observation identity.\n",
|
||
"dendrogram(Z_full, orientation=\"top\", no_labels=True, ax=ax)\n",
|
||
"ax.set_title(f\"Ward Linkage Dendrogram | Cophenetic Correlation: {c:.2f}\", fontsize=12)\n",
|
||
"ax.set_ylabel(\"Distance\")\n",
|
||
"sns.despine()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "dea0a5b2",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003696,
|
||
"end_time": "2026-06-13T02:32:20.631397+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.627701+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## PCA Preprocessing\n",
|
||
"\n",
|
||
"Reduce dimensionality before clustering to filter noise."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ec343e11",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003755,
|
||
"end_time": "2026-06-13T02:32:20.638788+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.635033+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Fit PCA"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "905b25cf",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:07.846945Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:07.846879Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:07.851770Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:07.851465Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.023437,
|
||
"end_time": "2026-06-13T02:32:20.665995+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.642558+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Cumulative Explained Variance:\n",
|
||
" PC1: 45.6%\n",
|
||
" PC2: 80.4%\n",
|
||
" PC3: 87.9%\n",
|
||
" PC4: 93.6%\n",
|
||
" PC5: 96.0%\n",
|
||
" PC6: 97.8%\n",
|
||
" PC7: 98.8%\n",
|
||
" PC8: 99.4%\n",
|
||
" PC9: 99.7%\n",
|
||
" PC10: 99.8%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"n_pca = min(10, macro_data_full.shape[1])\n",
|
||
"pca = PCA(n_components=n_pca)\n",
|
||
"reduced = pca.fit_transform(macro_data_full)\n",
|
||
"\n",
|
||
"print(\"Cumulative Explained Variance:\")\n",
|
||
"cumvar = pd.Series(pca.explained_variance_ratio_).cumsum()\n",
|
||
"for i, v in enumerate(cumvar):\n",
|
||
" print(f\" PC{i + 1}: {v:.1%}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "27c2b540",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.0037,
|
||
"end_time": "2026-06-13T02:32:20.673468+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.669768+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### GMM on PCA-Reduced Data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "b9594074",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:07.852871Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:07.852809Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:07.959093Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:07.958845Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.119629,
|
||
"end_time": "2026-06-13T02:32:20.796725+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.677096+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"GMM on PCA silhouette: 0.396\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"gmm_pca = GaussianMixture(\n",
|
||
" n_components=n_regimes_full,\n",
|
||
" covariance_type=\"full\",\n",
|
||
" random_state=SEED,\n",
|
||
" n_init=10,\n",
|
||
" reg_covar=1e-6,\n",
|
||
")\n",
|
||
"gmm_pca.fit(reduced)\n",
|
||
"pca_probs = gmm_pca.predict_proba(reduced)\n",
|
||
"pca_labels = gmm_pca.predict(reduced)\n",
|
||
"\n",
|
||
"print(f\"GMM on PCA silhouette: {silhouette_score(reduced, pca_labels):.3f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "5b9b93ef",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:07.965916Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:07.965684Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:08.093463Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:08.092791Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.125209,
|
||
"end_time": "2026-06-13T02:32:20.933938+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.808729+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_regime_heatmap(\n",
|
||
" pca_probs,\n",
|
||
" macro_data_full.index,\n",
|
||
" f\"GMM Regime Probabilities (PCA-reduced, {start_yr}-{end_yr})\",\n",
|
||
" n_regimes=n_regimes_full,\n",
|
||
" is_probability=True,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3f9a6e03",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003967,
|
||
"end_time": "2026-06-13T02:32:20.942141+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.938174+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"# Comparison: 4 Indicators vs Extended Dataset\n",
|
||
"\n",
|
||
"How do results differ between the simple (4-indicator) and extended approaches?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4302bf8f",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003731,
|
||
"end_time": "2026-06-13T02:32:20.949768+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.946037+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Visual Comparison\n",
|
||
"\n",
|
||
"Side-by-side heatmaps showing regime probabilities from both approaches."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "102a5703",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:08.094599Z",
|
||
"iopub.status.busy": "2026-06-16T02:17:08.094538Z",
|
||
"iopub.status.idle": "2026-06-16T02:17:08.205813Z",
|
||
"shell.execute_reply": "2026-06-16T02:17:08.205208Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.138239,
|
||
"end_time": "2026-06-13T02:32:21.092006+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:20.953767+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1400x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create side-by-side comparison (if both datasets available)\n",
|
||
"if len(macro_probs) > 0 and len(gmm_probs_full) > 0:\n",
|
||
" fig, axes = plt.subplots(2, 1, figsize=(14, 6))\n",
|
||
"\n",
|
||
" # Panel 1: Core 4 indicators\n",
|
||
" ax1 = axes[0]\n",
|
||
" im1 = ax1.imshow(macro_probs.T, aspect=\"auto\", cmap=\"Blues\", vmin=0, vmax=1)\n",
|
||
" years_core = [pd.Timestamp(ts).year for ts in macro_df.index]\n",
|
||
" year_ticks_core = [j for j, y in enumerate(years_core) if j == 0 or years_core[j - 1] != y]\n",
|
||
" year_labels_core = [str(years_core[j]) for j in year_ticks_core]\n",
|
||
" ax1.set_xticks(year_ticks_core[::2])\n",
|
||
" ax1.set_xticklabels(year_labels_core[::2], fontsize=8)\n",
|
||
" ax1.set_yticks(range(n_macro_regimes))\n",
|
||
" ax1.set_yticklabels([f\"Regime {i + 1}\" for i in range(n_macro_regimes)])\n",
|
||
" ax1.set_title(f\"Core 4 Indicators (Silhouette: {macro_silhouette:.3f})\", fontsize=11)\n",
|
||
"\n",
|
||
" # Panel 2: Extended dataset\n",
|
||
" ax2 = axes[1]\n",
|
||
" im2 = ax2.imshow(gmm_probs_full.T, aspect=\"auto\", cmap=\"Blues\", vmin=0, vmax=1)\n",
|
||
" years_ext = macro_data_full.index.year.tolist()\n",
|
||
" year_ticks_ext = [j for j, y in enumerate(years_ext) if j == 0 or years_ext[j - 1] != y]\n",
|
||
" year_labels_ext = [str(years_ext[j]) for j in year_ticks_ext]\n",
|
||
" ax2.set_xticks(year_ticks_ext[::2])\n",
|
||
" ax2.set_xticklabels(year_labels_ext[::2], fontsize=8)\n",
|
||
" ax2.set_yticks(range(n_regimes_full))\n",
|
||
" ax2.set_yticklabels([f\"Regime {i + 1}\" for i in range(n_regimes_full)])\n",
|
||
" ax2.set_xlabel(\"Year\")\n",
|
||
" ext_sil = silhouette_score(macro_data_full, gmm_labels_full)\n",
|
||
" ax2.set_title(\n",
|
||
" f\"Extended {macro_data_full.shape[1]} Indicators (Silhouette: {ext_sil:.3f})\", fontsize=11\n",
|
||
" )\n",
|
||
"\n",
|
||
" fig.suptitle(\n",
|
||
" \"GMM Regime Comparison: Core vs Extended Indicators\", fontsize=12, fontweight=\"bold\"\n",
|
||
" )\n",
|
||
" plt.show()\n",
|
||
"else:\n",
|
||
" print(\"Comparison visualization skipped - insufficient data\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e811511b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.004014,
|
||
"end_time": "2026-06-13T02:32:21.104114+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:32:21.100100+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Quantitative Comparison"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "934b6869",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-16T02:17:08.207111Z",
|
||
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"text": [
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"Extended indicators provide better separation; the broader panel captures meaningful economic variation despite added noise.\n"
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]
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" <th id=\"T_dd124_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n",
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"core_sil = macro_silhouette if len(macro_df) > 0 else float(\"nan\")\n",
|
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"extended_sil = silhouette_score(macro_data_full, gmm_labels_full)\n",
|
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"pca_sil = silhouette_score(reduced, pca_labels)\n",
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"\n",
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" \"Extended indicators provide better separation; the broader panel captures \"\n",
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" \"meaningful economic variation despite added noise.\"\n",
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" )\n",
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"print(interpretation)\n",
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"source": [
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"## Key Takeaways\n",
|
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"\n",
|
||
"- **Macro indicators line up with realised volatility**: The four regimes' mean\n",
|
||
" annualised volatility runs from 12.0% (Expansion, 77 months) to 16.0% (Crisis,\n",
|
||
" 4 months), with Tightening at 15.0% and Recovery at 15.2%. The Crisis cell\n",
|
||
" covers only 4 months on this 2002-2024 panel, so the 16.0% figure is an\n",
|
||
" anecdotal upper bound rather than a regime-level statistic.\n",
|
||
"- **Extended indicators improve cluster quality**: The 25-indicator model achieves\n",
|
||
" higher silhouette (0.42) than the 4-indicator model (0.25), but the core model\n",
|
||
" is more interpretable — the trade-off depends on the use case.\n",
|
||
"- **Hierarchical structure is genuine**: A cophenetic correlation of 0.71, above the\n",
|
||
" 0.7 quality bar, means the dendrogram faithfully represents the indicator distance\n",
|
||
" structure rather than imposing an arbitrary tree.\n",
|
||
"- **Use rates, not levels**: We use CPI year-over-year change rather than the CPI level,\n",
|
||
" because clustering on a trending level would capture \"early vs late\" rather than\n",
|
||
" \"inflationary vs non-inflationary.\"\n",
|
||
"- **GMM vs K-Means**: Similar performance (silhouette 0.42 vs 0.45), but GMM provides\n",
|
||
" probability assignments that quantify regime uncertainty.\n",
|
||
"\n",
|
||
"**Book reference**: §1.4 of Chapter 1 frames these regimes as inputs to risk\n",
|
||
"management — exposure caps, hedging triggers, and de-risking rules — rather than\n",
|
||
"as return forecasts. Figure 1.6 in the chapter is the macro-regime panel rendered\n",
|
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"above."
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]
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