From f7650b3670b48a36e6c136e82ed2493047d4c6f6 Mon Sep 17 00:00:00 2001
From: jsboige <jsboige@gmail.com>
Date: Sun, 25 Oct 2026 13:08:49 +0100
Subject: [PATCH 1/2] Feat(qc,#22495): notebook research turn of the month
 (effet de calendrier, NO BEATS)

Ajoute research_turn_of_month.ipynb : effet de calendrier turn of the month (Reling & Ritter 2000) sur SPY/QQQ/EFA. Effet brut robuste au parametre de fenetre mais NO BEATS net de couts : Sharpe negatif, DM significativement negatif, effet instable out-of-sample et efface sur 2022-2026.

- 10 cellules code executees localement (yfinance), outputs reels
- walk-forward 5-fold + Diebold-Mariano + sensibilite k=2/3/5
- 3 exercices stubs (C.1)
- ligne ajoutee au tableau de research/README.md + comptes standalone 16/20

Co-Authored-By: Claude Haiku 5.5 <noreply@anthropic.com>
---
 .../QuantConnect/research/README.md           |    7 +-
 .../research/research_turn_of_month.ipynb     | 1070 +++++++++++++++++
 2 files changed, 1074 insertions(+), 3 deletions(-)
 create mode 100644 MyIA.AI.Notebooks/QuantConnect/research/research_turn_of_month.ipynb

diff --git a/MyIA.AI.Notebooks/QuantConnect/research/README.md b/MyIA.AI.Notebooks/QuantConnect/research/README.md
index a1d5071ef0e..629e28878dd 100644
--- a/MyIA.AI.Notebooks/QuantConnect/research/README.md
+++ b/MyIA.AI.Notebooks/QuantConnect/research/README.md
@@ -25,6 +25,7 @@ Recherche indépendante utilisant des données locales (yfinance, pandas, sklear
 | `research_m12_hf_dm_test.ipynb` | M12-HF : test de significativité Diebold-Mariano | (c) Standalone | BTC tick Bitstamp |
 | `research_quality_lowvol.ipynb` | Facteur Quality + Low Vol | (c) Standalone | yfinance |
 | `research_risk_parity.ipynb` | Allocation risk parity | (c) Standalone | yfinance |
+| `research_turn_of_month.ipynb` | Effet de calendrier turn of the month (brut, net de coûts, walk-forward + DM, verdict NO BEATS) | (c) Standalone | yfinance (SPY/QQQ/EFA) |
 | `research_rl_grpo.ipynb` | Agent de trading RL GRPO | (c) Standalone | yfinance |
 | `research_rl_intro.ipynb` | Introduction au RL | (c) Standalone | yfinance |
 | `research_rl_multi_asset.ipynb` | Allocation multi-actifs RL | (c) Standalone | yfinance |
@@ -33,7 +34,7 @@ Recherche indépendante utilisant des données locales (yfinance, pandas, sklear
 | `research_rl_tactical_overlay.ipynb` | Overlay tactique RL | (c) Standalone | yfinance |
 | `research_vrp_putwrite.ipynb` | Stratégie VRP put-write | (c) Standalone | yfinance |
 
-Sur l'ensemble de la série, **15 sont de type (c) standalone research** (s'exécutent localement avec `pip install yfinance pandas matplotlib scikit-learn` ; la famille M12-HF utilise des données tick BTC Bitstamp possédées agrégées en minute localement) et **4 sont de type (b) research lié au quantbook QC Cloud** (`research_btc_ml`, `research_m11ef_ensemble`, `research_m12_har_rv_j` exécutés, et la variante `_minute` qui instancie `QuantBook` sans outputs committés — à exécuter sur QC Cloud).
+Sur l'ensemble de la série, **16 sont de type (c) standalone research** (s'exécutent localement avec `pip install yfinance pandas matplotlib scikit-learn` ; la famille M12-HF utilise des données tick BTC Bitstamp possédées agrégées en minute localement) et **4 sont de type (b) research lié au quantbook QC Cloud** (`research_btc_ml`, `research_m11ef_ensemble`, `research_m12_har_rv_j` exécutés, et la variante `_minute` qui instancie `QuantBook` sans outputs committés — à exécuter sur QC Cloud).
 
 ---
 
@@ -41,12 +42,12 @@ Sur l'ensemble de la série, **15 sont de type (c) standalone research** (s'exé
 
 ### Ce que vous avez appris
 
-Ces notebooks sont le **laboratoire de recherche** de la série QuantConnect. La **majorité (15/19) sont standalone** et s'exécutent en local avec `yfinance` (données gratuites) + `pandas` / `scikit-learn` (la famille M12-HF utilise des données tick BTC possédées) ; les notebooks des familles M11/M12 (btc_ml, m11ef_ensemble, m12_har_rv_j, m12_har_rv_j_minute) chargent leurs données crypto via `QuantBook` QC Cloud (type (b)). Ils illustrent deux familles de recherche :
+Ces notebooks sont le **laboratoire de recherche** de la série QuantConnect. La **majorité (16/20) sont standalone** et s'exécutent en local avec `yfinance` (données gratuites) + `pandas` / `scikit-learn` (la famille M12-HF utilise des données tick BTC possédées) ; les notebooks des familles M11/M12 (btc_ml, m11ef_ensemble, m12_har_rv_j, m12_har_rv_j_minute) chargent leurs données crypto via `QuantBook` QC Cloud (type (b)). Ils illustrent deux familles de recherche :
 
 - **Recherche factorielle & allocation** (HAR-RV-J vol, FamaFrench + AllWeather composite, Momentum + Regime, Quality/LowVol, Risk Parity, VRP put-write) — on apprend que les modèles de volatilité et d'allocation robuste sont reproductibles en local sur données publiques, et que les composites (M12, M11ef) sont les briques des stratégies *Robuste* du catalogue. **Verdict honnête M12-HF** (`research_m12_hf_btc_local.ipynb` + `research_m12_hf_dm_test.ipynb`) : l'estimation de la realized variance en minute **bat** celle en hourly (delta médian +0.548, MSE minute ~moitié hourly), statistiquement validée par un test de Diebold-Mariano (HAC, p≈0.000) et un block-bootstrap dont l'IC95 est entièrement négatif. La cause du gain est cependant la **fréquence d'échantillonnage** (qualité de l'estimateur RV, Andersen-Bollerslev-Diebold 2003), **pas** la composante de jump — HAR-Classic sans jump montre le même gain. Leçon méthodologique : la résolution hourly (24 bars/jour) est trop bruitée pour le vol-targeting Kelly ; la minute (1440 bars/jour) l'est beaucoup moins.
 - **Recherche Reinforcement Learning** (intro, PPO, GRPO, reward shaping, multi-asset, tactical overlay) — on apprend que le RL trading se prototypé localement avant tout déploiement QC Cloud, et que le *reward shaping* est le levier le plus sensible (un reward mal spécifié fige la policy). **Leçon d'intégrité #3360** : diviser la reward portfolio-level par le nombre d'actifs (`reward / N_ASSETS`) détruit le signal du critic per-asset → la policy gèle près de l'uniforme → l'argmax collapse vers Buy (fingerprint buy-and-hold, Sharpe 0.657 identique au collapse PPO). Corrigé : la reward portfolio complète alimente chaque transition per-asset (cohérence #3359/#3360). Verdict ré-évalué honnête après fix : NO BEATS (A2C Sharpe 0.000, SAC −0.063 sur univers non-FAANG) — pour la bonne raison.
 
-Le fil rouge : **l'indépendance de la plateforme**. Ces notebooks prouvent que l'idéation et la validation ML peuvent se faire hors QC Cloud pour la majorité des sujets (15/19 standalone) ; QC Cloud devient nécessaire dès que la recherche porte sur des données crypto natives (les notebooks M11/M12 qui instancient `QuantBook`), et reste la porte d'entrée du backtest haute-fidélité sur données natives.
+Le fil rouge : **l'indépendance de la plateforme**. Ces notebooks prouvent que l'idéation et la validation ML peuvent se faire hors QC Cloud pour la majorité des sujets (16/20 standalone) ; QC Cloud devient nécessaire dès que la recherche porte sur des données crypto natives (les notebooks M11/M12 qui instancient `QuantBook`), et reste la porte d'entrée du backtest haute-fidélité sur données natives.
 
 ### Prochaines étapes
 
diff --git a/MyIA.AI.Notebooks/QuantConnect/research/research_turn_of_month.ipynb b/MyIA.AI.Notebooks/QuantConnect/research/research_turn_of_month.ipynb
new file mode 100644
index 00000000000..38f6a40e896
--- /dev/null
+++ b/MyIA.AI.Notebooks/QuantConnect/research/research_turn_of_month.ipynb
@@ -0,0 +1,1070 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7ad34bd7",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003416,
+     "end_time": "2026-10-25T11:54:51.862444+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:51.859028+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Turn of the Month : l'effet de calendrier survit-il aux coûts et out-of-sample ?\n",
+    "\n",
+    "**Navigation** : [Index QC](../README.md) | [<< research_vrp_putwrite](research_vrp_putwrite.ipynb)\n",
+    "\n",
+    "## Objectifs d'apprentissage\n",
+    "\n",
+    "A la fin de ce notebook, vous saurez :\n",
+    "1. Formuler un effet de calendrier (*turn of the month*) comme **hypothèse falsifiable**\n",
+    "2. Classer chaque jour de bourse en « TOM » / « non-TOM » **sans lookahead**\n",
+    "3. Mesurer l'edge **brut**, puis **net de coûts de transaction**\n",
+    "4. Valider par **walk-forward 5-fold** + **test de Diebold-Mariano** + sensibilité au paramètre\n",
+    "5. Porter un **verdict honnête** (BEATS / NO BEATS / INCONCLUSIVE)\n",
+    "\n",
+    "### Prerequis\n",
+    "- Python 3.10+ (pandas, numpy, matplotlib, scipy, yfinance)\n",
+    "- Bases en backtest et en test de significativité statistique\n",
+    "\n",
+    "### Durée estimée : 30 minutes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89bb9ddf",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003313,
+     "end_time": "2026-10-25T11:54:51.868860+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:51.865547+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 1. Introduction — l'effet de fin / début de mois\n",
+    "\n",
+    "Le **turn of the month (TOM)** est un effet de calendrier documenté par Reling & Ritter\n",
+    "(2000, *The Turn of the Month, Part I*). L'hypothèse : les jours **en fin de mois**\n",
+    "(début du cycle de paie) et **en début de mois** (les investisseurs institutionnels\n",
+    "déploient leurs allocations mensuelles, pensions, SIP) porteraient un rendement\n",
+    "moyen supérieur au **reste du mois**, plus « creux » en flux.\n",
+    "\n",
+    "**La question de recherche n'est PAS** « l'effet existe-t-il en brut » (c'est un\n",
+    "fact). Elle est : **l'effet survit-il (a) aux coûts de transaction d'une stratégie\n",
+    "qui l'exploite, et (b) au test out-of-sample ?** C'est ce qui sépare un artefact\n",
+    "d'échantillon d'un signal actionnable.\n",
+    "\n",
+    "| Paramètre | Valeur | Justification |\n",
+    "|-----------|--------|---------------|\n",
+    "| Univers | SPY, QQQ, EFA | 3 ETF d'indice liquides (pas de FAANG/Mag7 individuels) |\n",
+    "| Fenêtre TOM | k = 3 jours de bourse | fin + début de mois, standard de la littérature |\n",
+    "| Coût de transition | 10 bps | entrée/sortie de position (ETF liquides) |\n",
+    "| Risque-free | 3 % | pour le ratio de Sharpe |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fd014fa7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:51.874424Z",
+     "iopub.status.busy": "2026-10-25T11:54:51.874169Z",
+     "iopub.status.idle": "2026-10-25T11:54:55.729681Z",
+     "shell.execute_reply": "2026-10-25T11:54:55.729165Z"
+    },
+    "papermill": {
+     "duration": 3.859928,
+     "end_time": "2026-10-25T11:54:55.730867+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:51.870939+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Téléchargement ['SPY', 'QQQ', 'EFA'] du 2000-01-01 au 2026-10-25...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Shape: (6327, 3)\n",
+      "Période: 2001-08-27 à 2026-10-23\n",
+      "Colonnes: ['SPY', 'QQQ', 'EFA']\n",
+      "\n",
+      "Imports OK : pandas, numpy, matplotlib, scipy, yfinance\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cellule 1 — Téléchargement des données (yfinance)\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import yfinance as yf\n",
+    "from datetime import datetime\n",
+    "from scipy import stats as scipy_stats\n",
+    "\n",
+    "# Univers : 3 ETF d'indice liquides (pas de FAANG/Mag7 individuels)\n",
+    "tickers = ['SPY', 'QQQ', 'EFA']\n",
+    "start_date = '2000-01-01'\n",
+    "end_date = datetime.now().strftime('%Y-%m-%d')\n",
+    "\n",
+    "print(f\"Téléchargement {tickers} du {start_date} au {end_date}...\")\n",
+    "data = yf.download(tickers, start=start_date, end=end_date, auto_adjust=True, progress=False)\n",
+    "close = data['Close'][tickers].dropna()\n",
+    "print(f\"\\nShape: {close.shape}\")\n",
+    "print(f\"Période: {close.index[0].date()} à {close.index[-1].date()}\")\n",
+    "print(f\"Colonnes: {list(close.columns)}\")\n",
+    "print(\"\\nImports OK : pandas, numpy, matplotlib, scipy, yfinance\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98784059",
+   "metadata": {
+    "papermill": {
+     "duration": 0.001926,
+     "end_time": "2026-10-25T11:54:55.735004+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.733078+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Interprétation : données\n",
+    "\n",
+    "~26 ans de quotidiens sur 3 ETF d'indice. La période couvre plusieurs cycles de taux\n",
+    "(2000-2002 dot-com, 2008 GFC, 2020 COVID, 2022 remontée de taux) — suffisant pour\n",
+    "que l'effet de calendrier, s'il existe, ne soit pas un artefact d'une seule crise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ad959ba5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:55.740236Z",
+     "iopub.status.busy": "2026-10-25T11:54:55.739737Z",
+     "iopub.status.idle": "2026-10-25T11:54:55.753548Z",
+     "shell.execute_reply": "2026-10-25T11:54:55.752839Z"
+    },
+    "papermill": {
+     "duration": 0.017615,
+     "end_time": "2026-10-25T11:54:55.754583+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.736968+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Part de jours TOM (k=3) : 28.72%\n",
+      "Nombre de jours TOM : 1817 / 6327\n",
+      "\n",
+      "Échantillon du mois 2014-03 :\n",
+      "  2014-03-03 (Mon)   <-- TOM\n",
+      "  2014-03-04 (Tue)   <-- TOM\n",
+      "  2014-03-05 (Wed)   <-- TOM\n",
+      "  2014-03-06 (Thu)\n",
+      "  2014-03-07 (Fri)\n",
+      "  2014-03-10 (Mon)\n",
+      "  2014-03-11 (Tue)\n",
+      "  2014-03-12 (Wed)\n",
+      "  2014-03-13 (Thu)\n",
+      "  2014-03-14 (Fri)\n",
+      "  2014-03-17 (Mon)\n",
+      "  2014-03-18 (Tue)\n",
+      "  2014-03-19 (Wed)\n",
+      "  2014-03-20 (Thu)\n",
+      "  2014-03-21 (Fri)\n",
+      "  2014-03-24 (Mon)\n",
+      "  2014-03-25 (Tue)\n",
+      "  2014-03-26 (Wed)\n",
+      "  2014-03-27 (Thu)   <-- TOM\n",
+      "  2014-03-28 (Fri)   <-- TOM\n",
+      "  2014-03-31 (Mon)   <-- TOM\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cellule 2 — Classification TOM / non-TOM (SANS lookahead)\n",
+    "#\n",
+    "# Un jour de bourse est « TOM » s'il appartient aux `k` DERNIERS jours de bourse\n",
+    "# de son mois calendaire OU aux `k` PREMIERS jours de bourse de son mois.\n",
+    "# La classification ne dépend QUE de la date calendaire du jour lui-même\n",
+    "# (position dans le mois) : aucune information future n'est utilisée.\n",
+    "\n",
+    "def classify_tom(dates: pd.DatetimeIndex, k: int = 3) -> pd.Series:\n",
+    "    \"\"\"Retourne une Series boolienne (True = jour TOM) alignée sur `dates`.\"\"\"\n",
+    "    # index = dates : la masque est alignee sur l'index de dates (DatetimeIndex),\n",
+    "    # sinon un RangeIndex 0..n-1 serait inalignable avec les Series de rendements.\n",
+    "    df = pd.DataFrame({'x': 1}, index=dates)\n",
+    "    df['ym'] = dates.to_period('M')\n",
+    "    df['pos_from_start'] = df.groupby('ym')['x'].cumcount()\n",
+    "    df['pos_from_end'] = df.groupby('ym')['x'].cumcount(ascending=False)\n",
+    "    is_tom = (df['pos_from_end'] < k) | (df['pos_from_start'] < k)\n",
+    "    return is_tom\n",
+    "\n",
+    "tom_mask = classify_tom(close.index, k=3)\n",
+    "tom_share = tom_mask.mean()\n",
+    "print(f\"Part de jours TOM (k=3) : {tom_share:.2%}\")\n",
+    "print(f\"Nombre de jours TOM : {int(tom_mask.sum())} / {len(tom_mask)}\")\n",
+    "\n",
+    "# Échantillon : un mois au milieu de l'échantillon, avec le flag de chaque jour\n",
+    "sample_ym = close.index.to_period('M')[len(close) // 2]\n",
+    "sample = close.index[close.index.to_period('M') == sample_ym]\n",
+    "demo = classify_tom(sample, k=3)\n",
+    "print(f\"\\nÉchantillon du mois {sample_ym} :\")\n",
+    "for d, is_t in zip(sample, demo):\n",
+    "    flag = \"   <-- TOM\" if is_t else \"\"\n",
+    "    print(f\"  {d.strftime('%Y-%m-%d')} ({d.strftime('%a')}){flag}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b776b2b0",
+   "metadata": {
+    "papermill": {
+     "duration": 0.001984,
+     "end_time": "2026-10-25T11:54:55.759917+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.757933+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Interprétation : la classification\n",
+    "\n",
+    "Le flag ne dépend que de la **position du jour dans son mois calendaire** (les 3\n",
+    "premiers et les 3 derniers jours de bourse). C'est l'anti-lookahead structuré : on\n",
+    "ne regarde jamais les rendements futurs pour décider si un jour est TOM. Environ\n",
+    "~20 % des jours sont marqués TOM, ce qui est cohérent (6 jours sur ~21 par mois)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "414422c8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:55.765274Z",
+     "iopub.status.busy": "2026-10-25T11:54:55.764928Z",
+     "iopub.status.idle": "2026-10-25T11:54:55.776473Z",
+     "shell.execute_reply": "2026-10-25T11:54:55.775489Z"
+    },
+    "papermill": {
+     "duration": 0.015541,
+     "end_time": "2026-10-25T11:54:55.777690+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.762149+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Effet BRUT du turn of the month (k=3) :\n",
+      "     Rend. moyen TOM (bps/jour)  Rend. moyen non-TOM (bps/jour)  Écart TOM - non-TOM (bps/jour)  N jours TOM\n",
+      "SPY                       7.184                           3.236                           3.947         1817\n",
+      "QQQ                       7.903                           5.095                           2.808         1817\n",
+      "EFA                       7.042                           1.789                           5.253         1817\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cellule 3 — L'effet BRUT : rendement moyen des jours TOM vs non-TOM\n",
+    "# fillna(0.0) (et non dropna) pour GARDER l'alignement avec close.index :\n",
+    "# le premier jour n'a pas de rendement calculable -> 0.0 (1 jour sur ~6500,\n",
+    "# negligible) ; un dropna desalignerait la masque TOM d'une ligne.\n",
+    "returns = close.pct_change().fillna(0.0)\n",
+    "\n",
+    "def gross_tom_effect(ret: pd.Series, mask: pd.Series, label: str) -> dict:\n",
+    "    r_tom = ret[mask]\n",
+    "    r_other = ret[~mask]\n",
+    "    return {\n",
+    "        'Rend. moyen TOM (bps/jour)': r_tom.mean() * 1e4,\n",
+    "        'Rend. moyen non-TOM (bps/jour)': r_other.mean() * 1e4,\n",
+    "        'Écart TOM - non-TOM (bps/jour)': (r_tom.mean() - r_other.mean()) * 1e4,\n",
+    "        'N jours TOM': int(mask.sum()),\n",
+    "    }\n",
+    "\n",
+    "rows = [gross_tom_effect(returns[t], tom_mask, t) for t in tickers]\n",
+    "gross_df = pd.DataFrame(rows, index=tickers)\n",
+    "print(\"Effet BRUT du turn of the month (k=3) :\")\n",
+    "print(gross_df.to_string(float_format='{:.3f}'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20273d3b",
+   "metadata": {
+    "papermill": {
+     "duration": 0.00213,
+     "end_time": "2026-10-25T11:54:55.782097+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.779967+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Interprétation : l'effet brut\n",
+    "\n",
+    "L'écart « TOM − non-TOM » en bps par jour est le signal brut. Il est **petit**\n",
+    "(typiquement de l'ordre du bruit quotidien) : c'est normal pour un effet de\n",
+    "calendrier. La vraie question — est-il **net de coûts** et **stable out-of-sample** —\n",
+    "n'a pas encore été posée. C'est l'objet des cellules suivantes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "67558c8b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:55.788659Z",
+     "iopub.status.busy": "2026-10-25T11:54:55.788258Z",
+     "iopub.status.idle": "2026-10-25T11:54:55.802019Z",
+     "shell.execute_reply": "2026-10-25T11:54:55.801180Z"
+    },
+    "papermill": {
+     "duration": 0.018993,
+     "end_time": "2026-10-25T11:54:55.803185+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.784192+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stratégie TOM (net de 10 bps/transition) vs Buy & Hold :\n",
+      "              Sharpe   CAGR  Vol(ann)   MaxDD\n",
+      "SPY TOM(net) -0.0676 0.0235    0.0968 -0.2155\n",
+      "SPY B&H       0.3513 0.0965    0.1894 -0.5519\n",
+      "QQQ TOM(net) -0.0340 0.0258    0.1232 -0.3773\n",
+      "QQQ B&H       0.4287 0.1295    0.2320 -0.5340\n",
+      "EFA TOM(net) -0.0808 0.0214    0.1064 -0.3157\n",
+      "EFA B&H       0.1619 0.0635    0.2072 -0.6104\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cellule 4 — Stratégie réaliste NET de coûts vs Buy & Hold\n",
+    "#\n",
+    "# La stratégie : long l'actif UNIQUEMENT les jours TOM, à plat (cash) les autres.\n",
+    "# Coût de transaction : 10 bps à chaque transition (entrée/sortie de position).\n",
+    "\n",
+    "def metrics_from_returns(r: pd.Series, rf: float = 0.03) -> dict:\n",
+    "    r = r.dropna()\n",
+    "    n_years = len(r) / 252\n",
+    "    cum = (1 + r).cumprod()\n",
+    "    total = cum.iloc[-1] - 1\n",
+    "    cagr = (1 + total) ** (1 / n_years) - 1 if n_years > 0 else np.nan\n",
+    "    vol = r.std() * np.sqrt(252)\n",
+    "    sharpe = (cagr - rf) / vol if vol > 0 else 0.0\n",
+    "    dd = (cum / cum.cummax() - 1).min()\n",
+    "    return {'Sharpe': sharpe, 'CAGR': cagr, 'Vol(ann)': vol, 'MaxDD': dd}\n",
+    "\n",
+    "def tom_strategy_net(ret: pd.Series, mask: pd.Series, cost_bps: float = 10.0) -> pd.Series:\n",
+    "    \"\"\"Rendements journaliers de la stratégie TOM (long les jours TOM), net de coûts.\"\"\"\n",
+    "    cost = cost_bps / 1e4\n",
+    "    in_pos = mask.astype(float)\n",
+    "    transition = in_pos.diff().abs()\n",
+    "    transition.iloc[0] = in_pos.iloc[0]\n",
+    "    strat = in_pos * ret\n",
+    "    return strat - transition * cost\n",
+    "\n",
+    "results = {}\n",
+    "for t in tickers:\n",
+    "    strat = tom_strategy_net(returns[t], tom_mask, cost_bps=10.0)\n",
+    "    bh = returns[t]\n",
+    "    m_s = metrics_from_returns(strat)\n",
+    "    m_b = metrics_from_returns(bh)\n",
+    "    for col, m in zip([f'{t} TOM(net)', f'{t} B&H'], [m_s, m_b]):\n",
+    "        results[col] = m\n",
+    "summary = pd.DataFrame(results).T\n",
+    "print(\"Stratégie TOM (net de 10 bps/transition) vs Buy & Hold :\")\n",
+    "print(summary.to_string(float_format='{:.4f}'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2be2480",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004402,
+     "end_time": "2026-10-25T11:54:55.810110+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.805708+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Interprétation : net de coûts\n",
+    "\n",
+    "C'est le premier filtre de réalité. Une stratégie qui n'est long que ~20 % du temps\n",
+    "a un CAGR structurellement plus faible qu'un Buy & Hold permanent — l'effet de\n",
+    "calendrier doit **compenser** ce temps à plat pour offrir un **ratio de Sharpe**\n",
+    "supérieur. Le CAGR seul ne dit rien ; c'est le Sharpe (rendement ajusté au risque)\n",
+    "qui tranche. Les coûts de 10 bps par transition, multipliés par ~6 transitions/mois\n",
+    "par actif, grignotent une part non négligeable de l'edge brut."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "238814bc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:55.818855Z",
+     "iopub.status.busy": "2026-10-25T11:54:55.818512Z",
+     "iopub.status.idle": "2026-10-25T11:54:55.838745Z",
+     "shell.execute_reply": "2026-10-25T11:54:55.838062Z"
+    },
+    "papermill": {
+     "duration": 0.025454,
+     "end_time": "2026-10-25T11:54:55.839890+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.814436+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== SPY : effet TOM par fold OOS (k=3) ===\n",
+      " Fold                  Période  Écart TOM (bps/jour)  N_TOM\n",
+      "    1 2005-11-07 -> 2010-01-14                -0.187    300\n",
+      "    2 2010-01-15 -> 2014-03-25                10.828    300\n",
+      "    3 2014-03-26 -> 2018-05-31                -4.597    303\n",
+      "    4 2018-06-01 -> 2022-08-08                11.589    303\n",
+      "    5 2022-08-09 -> 2026-10-20                -1.639    300\n",
+      "\n",
+      "=== QQQ : effet TOM par fold OOS (k=3) ===\n",
+      " Fold                  Période  Écart TOM (bps/jour)  N_TOM\n",
+      "    1 2005-11-07 -> 2010-01-14                -3.334    300\n",
+      "    2 2010-01-15 -> 2014-03-25                14.138    300\n",
+      "    3 2014-03-26 -> 2018-05-31                -6.706    303\n",
+      "    4 2018-06-01 -> 2022-08-08                14.191    303\n",
+      "    5 2022-08-09 -> 2026-10-20                -0.490    300\n",
+      "\n",
+      "=== EFA : effet TOM par fold OOS (k=3) ===\n",
+      " Fold                  Période  Écart TOM (bps/jour)  N_TOM\n",
+      "    1 2005-11-07 -> 2010-01-14                 7.343    300\n",
+      "    2 2010-01-15 -> 2014-03-25                12.576    300\n",
+      "    3 2014-03-26 -> 2018-05-31                -7.991    303\n",
+      "    4 2018-06-01 -> 2022-08-08                 4.915    303\n",
+      "    5 2022-08-09 -> 2026-10-20                -0.822    300\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cellule 5 — Walk-forward 5-fold : l'effet tient-il out-of-sample ?\n",
+    "#\n",
+    "# Fenêtre expansive : on mesure l'effet BRUT (écart TOM - non-TOM) sur chaque\n",
+    "# fold OOS. Si l'écart reste du même signe sur les 5 folds, l'effet n'est pas\n",
+    "# un artefact d'une période particulière.\n",
+    "\n",
+    "def oos_tom_effect(ret: pd.Series, mask: pd.Series, n_folds: int = 5) -> pd.DataFrame:\n",
+    "    r = ret.dropna()\n",
+    "    m = mask.loc[r.index]\n",
+    "    n = len(r)\n",
+    "    fold_size = n // (n_folds + 1)\n",
+    "    rows = []\n",
+    "    for f in range(1, n_folds + 1):\n",
+    "        s, e = fold_size * f, min(fold_size * (f + 1), n)\n",
+    "        rs, ms = r.iloc[s:e], m.iloc[s:e]\n",
+    "        if ms.sum() < 5:\n",
+    "            continue\n",
+    "        d_bps = (rs[ms].mean() - rs[~ms].mean()) * 1e4\n",
+    "        rows.append({'Fold': f,\n",
+    "                     'Période': f\"{rs.index[0].date()} -> {rs.index[-1].date()}\",\n",
+    "                     'Écart TOM (bps/jour)': d_bps, 'N_TOM': int(ms.sum())})\n",
+    "    return pd.DataFrame(rows)\n",
+    "\n",
+    "for t in tickers:\n",
+    "    print(f\"\\n=== {t} : effet TOM par fold OOS (k=3) ===\")\n",
+    "    print(oos_tom_effect(returns[t], tom_mask).to_string(index=False, float_format='{:.3f}'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ccd6a5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002106,
+     "end_time": "2026-10-25T11:54:55.844340+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.842234+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Interprétation : stabilité out-of-sample\n",
+    "\n",
+    "Un effet de calendrier **robuste** garde le même signe (ici : positif) sur la\n",
+    "plupart des 5 folds OOS. S'il est positif sur 2 folds et négatif sur 3, c'est du\n",
+    "bruit, pas un signal. Cette stabilité temporelle est l'argument le plus fort en\n",
+    "faveur (ou contre) l'effet — plus que n'importe quelle moyenne globale."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ed94ac59",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:55.852136Z",
+     "iopub.status.busy": "2026-10-25T11:54:55.851868Z",
+     "iopub.status.idle": "2026-10-25T11:54:55.881275Z",
+     "shell.execute_reply": "2026-10-25T11:54:55.880153Z"
+    },
+    "papermill": {
+     "duration": 0.035811,
+     "end_time": "2026-10-25T11:54:55.882661+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.846850+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== (a) Test de Diebold-Mariano : la stratégie TOM bat-elle significativement B&H ? ===\n",
+      "  SPY: stat=-2.528, p=0.0115  ->  SIGNIFICATIF (p<0.05)\n",
+      "  QQQ: stat=-2.944, p=0.0032  ->  SIGNIFICATIF (p<0.05)\n",
+      "  EFA: stat=-1.584, p=0.1131  ->  non significatif\n",
+      "\n",
+      "=== (b) Sensibilité à la fenêtre k (effet BRUT, bps/jour) ===\n",
+      "Actif       EFA   QQQ   SPY\n",
+      "k (jours)                  \n",
+      "2         7.747 5.095 5.042\n",
+      "3         5.253 2.808 3.947\n",
+      "5         3.908 2.166 4.220\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cellule 6 — Test de Diebold-Mariano + sensibilité au paramètre k\n",
+    "#\n",
+    "# (a) DM : teste si la stratégie TOM bat significativement le Buy & Hold\n",
+    "#     (différence de rendement moyen, avec autocovariances HAC jusqu'à h-1).\n",
+    "# (b) Sensibilité : on re-mesure l'effet BRUT pour k = 2, 3, 5 jours.\n",
+    "\n",
+    "def dm_test(d: pd.Series, h: int = 1) -> tuple:\n",
+    "    \"\"\"DM test (HAC, h-1 lag) sur la série d'écart d. Retourne (stat, p-value).\"\"\"\n",
+    "    d = d.dropna()\n",
+    "    n = len(d)\n",
+    "    if n < 10:\n",
+    "        return np.nan, np.nan\n",
+    "    # autocovariance manuelle : pandas Series n'a pas de methode .autocov()\n",
+    "    def _autocov(lag):\n",
+    "        dev = d - d.mean()\n",
+    "        return float((dev.iloc[lag:] * dev.iloc[:n - lag]).sum()) / n\n",
+    "    gamma0 = _autocov(0)\n",
+    "    gammas = sum(_autocov(lag) for lag in range(1, h))\n",
+    "    var = (gamma0 + 2 * gammas) / n\n",
+    "    if var <= 0:\n",
+    "        return np.nan, np.nan\n",
+    "    stat = d.mean() / np.sqrt(var)\n",
+    "    p = 2 * (1 - scipy_stats.norm.cdf(abs(stat)))\n",
+    "    return stat, p\n",
+    "\n",
+    "print(\"=== (a) Test de Diebold-Mariano : la stratégie TOM bat-elle significativement B&H ? ===\")\n",
+    "for t in tickers:\n",
+    "    strat = tom_strategy_net(returns[t], tom_mask, cost_bps=10.0)\n",
+    "    bh = returns[t]\n",
+    "    common = strat.index.intersection(bh.index)\n",
+    "    d = (strat.loc[common] - bh.loc[common]).dropna()\n",
+    "    stat, p = dm_test(d)\n",
+    "    if p is not None and not np.isnan(p) and p < 0.05:\n",
+    "        sig = \"SIGNIFICATIF (p<0.05)\"\n",
+    "    else:\n",
+    "        sig = \"non significatif\"\n",
+    "    print(f\"  {t}: stat={stat:.3f}, p={p:.4f}  ->  {sig}\")\n",
+    "\n",
+    "print(\"\\n=== (b) Sensibilité à la fenêtre k (effet BRUT, bps/jour) ===\")\n",
+    "sens_rows = []\n",
+    "for k in [2, 3, 5]:\n",
+    "    m = classify_tom(close.index, k=k)\n",
+    "    for t in tickers:\n",
+    "        d_bps = (returns[t][m].mean() - returns[t][~m].mean()) * 1e4\n",
+    "        sens_rows.append({'k (jours)': k, 'Actif': t, 'Écart TOM (bps/jour)': d_bps})\n",
+    "sens_df = pd.DataFrame(sens_rows)\n",
+    "print(sens_df.pivot(index='k (jours)', columns='Actif',\n",
+    "                    values='Écart TOM (bps/jour)').to_string(float_format='{:.3f}'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e62d8cf",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002202,
+     "end_time": "2026-10-25T11:54:55.887456+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.885254+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Interprétation : significativité et robustesse au paramètre\n",
+    "\n",
+    "Le test de Diebold-Mariano répond à la question décisive : la différence de\n",
+    "rendement journalier (stratégie TOM − B&H) est-elle **statistiquement** différente\n",
+    "de zéro, en tenant compte de l'autocorrélation des rendements ? **Le signe du\n",
+    "statistic est porteur** : un stat **négatif** avec p < 0.05 signifie que la\n",
+    "stratégie **perd** significativement face au B&H (ici SPY p=0.012, QQQ p=0.003) —\n",
+    "la significativité confirme le déficit, elle ne l'infirme pas. La sensibilité à k,\n",
+    "elle, montre que l'effet **brut** garde un signe stable (positif) quand k varie de\n",
+    "2 à 5 : le phénomène est réel, même s'il n'est pas exploitable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "72be4c6c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:55.894364Z",
+     "iopub.status.busy": "2026-10-25T11:54:55.894058Z",
+     "iopub.status.idle": "2026-10-25T11:54:56.535455Z",
+     "shell.execute_reply": "2026-10-25T11:54:56.534618Z"
+    },
+    "papermill": {
+     "duration": 0.646779,
+     "end_time": "2026-10-25T11:54:56.536478+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:55.889699+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eZEzDSa9I//xPuerLi6SDW1Je36p/1o9h7/OGFAKjQBFEJiEAAAAAAFk172vv+HZJA4QmUMXhnNa6v/TYDqljkm65fd/2Tm+ZJx3c5p3fOCP5fjb+l3zZjI+k0Qlj+qXmwNaUxxzs+UjK5yc+zn22SsRhpbzLz3tTemyn1LRP2scGkCEECQEAAAAAyCwLsA2JlP58KPm6tld6p0NCleusO26xcKlYmP/ymp2kNle40zbWn28xkBNHku/HM4Ziy37S6U9LV/4k/X6fNPNj6UQqRT+2L5ZeayKNvDb5us2zpX+fc6eLl5UGfOcGKCe/4T9u4tW/ewOGxsZZTPp+AGQLuhsDAAAAAJAZNlbfx70Dr7voI3e8v8rNpbK1la+El5LaXiHN+0o6tl86ssu77oTPeIVm5wopNtqbxWfBu4Pbvet9A3hJ7VrhPi8eJfUb5r9u7lfe6QfWuFWWLRg5/hmpzeVSi4ul4wfctt48WVr8o9T8QqlC/Sy8cQCpIUgIAAAAAEBmLPhO2r/RO9/vc2nEVW6BEsu6MyffonzJqhWbVWOlvT6VhWOP+2+3Y7H7fNpj3uw+z/atB/pXSvZ1/KA04mp3uufD/uv2rpfmJnTPNlvnSR+d5p1/tZFUtY104z/uvAUGu9+XiTcJICPobgwAAAAAQGasm+w+W9bb7XPcwJip3CLrlXdzWomy7rNvgNDEJGQNeni6E4cmBAMXjpQ+PdOdDi+d8v6tWrLHkd3e/Vr25Yc9vMHI84dKR/f5v9ayE7fM8XZzBpArCBICAAAAAJBRVozDU6zk1EfdbLd/X3Dn10xQvmfjAAYS49Pd+Pgh6aeETMjQEgnrfTINV4+XogOMYbh5jjThRe/8jA+lZ6Pcba1r89G97vIqrdxuzw16BW5LGZ+KzAByXJENEg4dOlTNmjVTx44d87opAAAAAICC5sAW73T5eu7zBe9KkbWkXk8o3wst7j8fESWd+Zw77l/STEln+5LusxVC8di90s0STGrOF4GPuWGad3xD0+Ea7/QF7yffvmT5NN8GgOxTZIOEgwcP1pIlSzRz5sy8bgoAAAAAoKCx7rDmlLu8XYvr9ZDuXihVaqICoVhCdqApW0vqPNh9D4nrfaoIh5UMnN0XqEtw3InAx7Ouy5E1pIs/ced9uxm3GSBd+KF3/pQ7veMmAsgVRTZICAAAAABApu3f5D5XbKgCq+N13unNs5Ovjz7snfZ0N651sv82R/d4p2NjpLFDpO1L3Plbp0uP7fCuP5HQNTk4oYbqnjX++/IUezHtfbIMAeQKgoQAAAAAAKTX3nXSz4Ol1v2lB9b6d88taKxisYdl9w2/Uvr3xcBBwvL13WfLmvStVlzMp9vyjiXS5Ne9WZYhYW735HNe8QYJ3ztFGvuk1PspqccD/u0JDpYu+ljqeo9Uvm52vlMA6ZAQvgcAAAAAAAFZoQ0bk88CXhNfllb+Lc39SrrmD6l2FxVYlh3Y+Fxp+Wi3u/HSX9yHBULrdpOmv+du1+d1/6Bdz4ekUpWk3+6Wdi7zLvedNiGh7nNYQmXkX+/0rut6V+A2tfLJJgSQq8gkBAAAAAAgJXvWSi/Wkaa+LY26SVo2Wjq03V33/VUq8Pq+Ld08Ware3tIE3WXzv3GrGm9b4M6XC5DV1/ZKqXRVt3CJVXo2o27w3yYk3L+wC4B8jSAhAAAAAAAp+f1+93n8M9KC79ysQo/4WBV4ERWkKi2l4BCpeGTgbeqfmnyZZQlG1pQW/+gWJAkkvLT77AQgAeR3BAkBAAAAAAgkLk5aPS7l9d3uVaGS0WrCxcu4z8cPBn69pyKyBRQfTij0Ym6ckKVmAsgZjEkIAAAAAEBSVoDDKvWaej2l0tXcbrim+UXS6U9LkTVUqOxdm7HtPZmCFiQsXVmq2Ula8WfK217yqRQUIlVrk/W2Ash2BAkBAAAAAEjKEyA0Nvaeb5bc0T1S2Zoq1Kp3kE5/SipRPu0g4TsJ3YmH7JcmvSqNe9qdt7EKrRqyR4uLc7LFALKI7sYAAAAAACTtZuzLMgajD3nnm56nQqm3T2B00E9Sna5S5WYpbx+e0N3YY0ikVLurNHiGdPXv/gFCAPkeQUIAAAAAADxmfy497ZM1GBImNekjdbrJu6z9NSqUQiO8FY89WYKpCbTN94OkqMZSnVOyv30AchRBQgAAAAAAPPas8Z+PjXbH0KvcXApOGLEr5rgKJSswYmJPpG/7jjckX3ZoW/a2CUCuIUgIAAAAAIBnDL0tc93pUlWSr793uXTrf96qvYXNvvXu89wv07d9RIXky6zIC4ACiSAhAAAAAKBos+DgqBulnwdLaye4y24Y710fE+0+R1SUKjVRodXiEik8Uup2b+b3cfkP2dkiALmI6sYAAAAAgKJrxRhp4ffSwhH+y0tXkcrXc7sfFwtTkVClhfTwhoy9xsZnnD3MOx9CmAEoqPj0AgAAAACKrvFPS9uXeOfL1ZFOukkKDpFunS7FxeZl6/K/896QejwovVaIMyyBIoIgIQAAAACg6Nq5XIqP9Y5DOHiGVCzcnfc8I3VlqkpnvyyFFs/rlgDIAoKEAAAAAICiZ+0k6fM+/svOfpHAYGZ1ujGvWwAgiwgSAgAAAACKHt8AYbm60qCfpMhaedkiAMhTBAkBAAAAAEVL7AnvdOlq0jW/S2Wq5WWLACDPBed1AwAAAAAAyFYzP5F+GizFxwdev/of73TV1gQIAYAgIQAAAACg0Bl9jzTvK+nY/sDr/3rYfxxCAABBQgAAAABAEehW/H5X6etL3enjB93nQT9L5WrnTdsAIJ9hTEIAAAAAQOESVkqKPiTFHPUu27bQfcQclw5tl4qVkOr2yMtWAkC+QiYhAAAAAKDwOLpXqtnJnT5xzLu84Rnu88YZ7rMFEIOC8qCBAJA/ESQEAAAAABQeU9+WVo9zC5KUKOsuswImNm/WJBQtaXZ+3rURAPIhgoQAAAAAgMJj30b3+fx3pVKV3OmZH0sTX3anJ73qZhV6goYAAAdjEgIAAAAACo9dK9xn34IkR/f5b9P7Kalys9xtFwDkc2QSAgAAAAAKh+gj0vZF7vRv90jvdpE2zpSOH0jYIGEMwgXD86yJAJBfESQEAAAAABQOE16Q4mLc6YXfSzsWS5/0lqa+5S47+yX3ecob0tYFeddOAMiHCBICAAAAAHLfga1uQZHssn+TNG2oO129Q+BtioV7p1eOyb5jA0AhQJAQAAAAAJC7VoyRXmsiTX83+/b5enM3i7DVZVKf1wNvU6erdzq0RPYdGwAKAYKEAAAAAIDcExsjHdntrTScHU4c805H1pTK1ky+Tb/PpLBS3nmChADghyAhAAAAACD3vFhb+ukWd7pW5+zZ5+6V3unOg6US5aSrf5dqdXGX1ewkNb9QCgn1blc8MnuODQCFRLG8bgAAAAAAoIiwjL/oQ9754wezvs/Du6SFI9zp05+RSpZ3p+ucIpWp5k7Hx7nPvpmElVtm/dgAUIgU2SDh0KFDnUdsbGxeNwUAAAAACrXgQ9ulIyulXcv8Vxzbl/Wdv1zfO13/NP91Tc+TFo30di0uFiadfKu0cYZUoUHWjw0AhUiRDRIOHjzYeRw4cECRkaSZAwAAAECOiItVpa+6Jw/erf5XOpoNQcKw0lL0QenmyVLl5v7rThxxn9dO9C476/msHxMACiHGJAQAAAAA5JwF3yVfFh8vnfGMdNrj6d9PXKw0+l5p2e/eZcv/cAOE5v2uUlCQ/2sanSVVaiZd+kVmWw8ARUaRzSQEAAAAAOSwaUMV/NcjzmR8UIiC4hOGeypfV+pwTcb2tX6qNPNj9zFkv7ts9uepv8bGJ7x1WqaaDgBFDUFCAAAAAED22/CflBAgNPFXj1bQvK+kBqdLDc/I+P72rk0yv05a8Yc7fcqd0il3ZbXFAFCkESQEAAAAAGQ/TwZhh2u1p+bZKlezk1S7s3f9hJekjf9JF33krUicmoPb/OffbO2d7v1U8q7GAIAMIUgIAAAAAMheR/ZIm2dJxSMVf/YrOrFzZ/Jtts6XVo2Vjh9MX5CwenupQkOpzQApLs5/HQFCAMgyCpcAAAAAALKHFSQ5tl+a9ak737RvygG8kDD3OTY6ffuu0026/m8pNkZ6upy3svFVv2ZHywGgyCOTEAAAAACQPZb+In0/yDvfeXDK2xYLd58nvixd+EHa2YC/3iHN/9Z/2Uk3SHW7Z6XFAIAEZBICAAAAALLHhJe90x2vl8rXS3lbTybhguHShulp73vXiuTLylTLTCsBAAEQJAQAAAAAZJ2NE3hwq3f+rBe82YKB+K47uift/dvYhUmVrJDRVgIAUkCQEAAAAACQNbM/kz45Xbp5knfZ7tWpv6ZYcZ/pVIKJHod3JV9Wr2dGWgkASAVjEgIAAAAAMm/fBunXO93pxT9KV4ySNs2Sohqn/roznpGmvuVOFyuR+rZzvwqcbZieqsgAgHQhSAgAAAAAyLxJr3qnO90sBYdIDXplbB9hEamvH/+/5MsqphGEBABkCN2NAQAAAACZExfrdjU2N09xA4TpdWiHdzqqSerbnvW8FFnLf9netRlpKQAgDWQSAgAAAAC8YmOk1eOl2l2k8FKpb7vHJ1BXoUHGjjP5dZ9jRkuhPmMUJtWkj9T8Amn5H9KWuVKHa6Vgfs4CQHbiWxUAAAAA4PV5H2nDNKlEOenBdf7rRt8r7VgqXfWrW5jkuwHSXYvcwiOpBfkCscCgb1Zh8TKBt1sxRvqmn9TvczdQ2PjsTLwpAEBaCBICAAAAQFEXHy+Ne1qq3NwNEJq63f3XH9ntZg6unyI97VMwZNQN0rV/Zu6YHusnSxUbJM9oDApyA4RmxFVS8/0ZPw4AIGeDhBs2bND69et15MgRRUVFqXnz5goPT0fZegAAAABA/rJnjTT5Nf9lF37ond4wXZr7pXT8QPLXtrg4c8fsdo806xN3OsYnq9DjzVbSgc3e+QoNM3ccAED2Fy5Zt26dHnzwQdWuXVt169ZVjx49dPbZZ6tDhw6KjIzU6aefrhEjRiguLi4juwUAAAAA5JXjB6U1//ovO+Uu/+7Du1dKayZIzS9M/vr212TuuJE1pAs/cKdjjydf7xsgNN3vy9xxAADZGyS844471Lp1a61du1bPPvuslixZov379ys6Olrbtm3T77//rq5du+qJJ55Qq1atNHPmzPTuGgAAAACQ0w5sdQt/JGXdjEff4073fFh6cp/1BZaGRErvdZUO7ZRWjZUObJJKV5Ee2uh9ba3OUkgWRrEKCXOfYwIECRMFSQNHSC0vzfxxAABpSve3eUREhNasWaMKFSokW1epUiWddtppzuPJJ5/Un3/+qY0bN6pjx47p3T0AAAAAIKcsGiWNTMj46/GQdOrD3nWb5/h3HbZxAOMTeodtXyi94jNWYEQld71H1dZZa5cVPDFrJ/pnCvr1Tot3g5SNzsjasQAA2RMkfP7559O7qc4666x0bwsAAAAAyGGeAKE5vMN/3aHt7vM5r0jl67vTJZMnhzjKVHMDdh5nv5i1doV4goQTvMv2b5L+eNB/uxkfSOe8lLVjAQCyb0xCAAAAAEA22rVK+vNhacZH0tYF2b//Qzukpb9Kg2d4l3W71zs98RVp/0apXF3ppBuk4ISfiCV8qheb4GLSmc9JFepLFRu7y6p3yHr7AmUi/vOctOy3rO8bAJAhWRg8IrmlS5fq3HPPdbolAwAAAABSsHCkVKOD9NfD0sox7rI2V0gXDM3e47ySUBG4WjtpyH5p1jDpiwuka/9yuw2Pf8ZdX6qS/+uSZhLGxUidbnanKzeTbp4ila+b9faVikq+bN7XWd8vACBvg4RWxGT9+vXZuUsAAAAAKFysSvAP1yVf3iJA5eCsOLrXO126qvv8213u88v13KrEVVpK2xZK1yQpaBKou3GQT0e0Ki2UK8583g2ktuqfO8cDgCIsQ0HCe+5JqHiVgp07d2a1PQAAAABQuK2fknzZ1b9LdU7JvmP8do806xN3uvYpUv+E7LyGZ0or/3KnZw9zswsDKZmku/EZz/oXLMkue9dJwaFSg17u/Imj/uvrdpfuXuwNcgIA8keQ8M0331SbNm1UpkyZgOsPHTqUXe0CAAAAgMLl+CEpNlqakFDsw4JfB7e7y6y777cDpHo9pU43Ze048fHeAKFp2c8b4Kt/qjdImJqoxtKA4W5l4WptpdJVlCPiYqW4E94qx3O+9F9fsZFULCxnjg0AyHyQsEGDBrr77rt1xRVXBFw/b948tW/fPiO7BAAAAICi4fsrpdXjvfORNdyH2TpfWv67VKJc1o9j4wf6qtHRO33sgP+6/z6QmvSRIqsn30/js5TjPMHLJT+7zyGh/usJEAJA/qxu3KFDB82ePTvF9UFBQYq3v1oBAAAAAFw7lkp/P+EfIOz7tv82IQmZdDHH3edDO93XZURcnPTlhdLYIVKjs91lN/zjP36gVTCu1UXq/43U/mrpjwekfXk4rnxQSEorpJsm5XJjAKBoy1Am4auvvqrjxxP+0QqgdevWirN/mAAAAACgoDm4TfrlDqnFRVLrbCqUERsjvXeKFB/rXRZZU2o3KHDG3KKRUpNzpOnvSZtmSncucLMLd6+UqqfSayv6iHTiiBuItEef193XbFsgVW/nP9bgtX9Iu1dL3w10l4WVUp4pnmQoK0+Q9Px3pKqt8qRJAFBUZShIWKVKDo1DAQAAAAB5be6X7nh99rDAWdM+Wd/nN5f6BwhbXCJ1uT35dp5MQjPxVWnHYnd6z2rpr0elHUukm6ekXFV4zGNuULF+L2n1OGnsU9JDqWQI+lYqDs/DIGHS7tU2/mHXe6TKuVQ9GQCQue7GAAAAAFBojX/WO20BN4+MDKm0dpI0JFLaudw71qApUd4N8l3yiVStTfLXeQp3mFPu8E5XaeUGCM3hnSkf1wqVWNZg7S7u/LF9qbezVGXvdPGyylOlq7nP1iutViep95OBzxEAIEcRJAQAAACAjTNSLv4x7Gzpw1OlE0dT38fhXdLnCdmHQ0+Sjh+Ujuxy5x9cm3IWoAnxKdARe0Kq0EAqVtw/087mU6oQ7NHwDPfZqiSnJqykdP146fx33S7IeengFvd5aULxEgBA/u9uDAAAAACFknXX9WUBwSN73ODhhmnusg3Tpfqnpj/Q+M1lUpka0gVD0z5+eGmf/fwn7V7lTj9dXmrVX1rwnRQbnXJw0tixbBy/W6ZJZWulfcwa7d1HfrFrpTTvWzeLs/NgqUL9vG4RABQpZBICAAAAwAXvSTU7ST0fdudjjrlBq28v824ze1jg11rGoI0BuHiUO9/9fumaP93qxAc2uV2G0xIUJD2xR2rQ2x0bse0V3nUHt0o1TpLCIpK/zgqQfHG+O121tftcuVnejjOYWSUrSEt+crtOx1MQEwAKVJAwOjpay5cvV0yMTyo+AAAAABQkNuagZa1dN0Zq2c9dZtmD//zPfzvLLExq5Vjp1SbS5NekhSOk9tdIXe6Qhp0lHd0TuDhHSoJDpGoJlYjnf+ddvnaC1KyvVKND8tfM+lTaudSdPvURFUjd7vVWfbYKzZ6AIQAg/wcJjxw5ouuuu04lS5ZU8+bNtWHDBmf57bffrhdeeCG72wgAAAAAOee3u6TXmkk7lrlFPGp0lPascYNzngy9x3dJV/+W/LVWLCT6kHd+5RipeBnvvI0taFmC6VUszDsmYnmf7rYnjiUPbP7znDTtHXf+xn9TH/MwPwsOdZ/jTkgxx1MffxEAkL+ChA8//LDmz5+vf//9V8WLe7+8e/fureHDh2dn+wAAAAAgZ1lA8MBmN3stooJ0xSj/Mf1iY6SQhECWiYmWlvwsLf9DOrjNXRbV1H0+kFCEo8eDbnDxlqkZa8t8n99Tg36SzkpIwlg0Ulo40rvO2jvhRe98uE9gsqDZtUIKCpa+G+iOx2gIEgJAwQgS/vTTT3rnnXfUtWtXBfn8VcyyClevXp2d7QMAAACAnBEXJ837Rlo7UQoJlyIqusu/6Cvtc3tLOXYslmZ8JL3W3A0MKl76fpD0bX9p43R3m75vSdU7SJd96e36e9NEqVh4xtp0aId3OqyU9OdD7vTOZdIP10kr/nLnj+71f51lQBZUNpZj0jEIgxk+HwByW6a+eXfu3KlKlSolW3748GG/oCEAAAAA5FtT35J+usWdjj3u7Ra8Za773P9b77ZWyMSKkIy8VnrW57fQ1vluMM+Kk9wwTmp6XtbadP3f3ulAxTtWjUseJGx4RvrHPQQAIDuDhB06dNDo0aMT5z2BwY8//lidO3fOzC4BAAAAIPesGiuNfdI7f8P45NvU6yHdPke6Y563+6unsIav1v2l0GzqHhvV2O3ufPEnbmZjhYZut+WgEHd9maru85Hd7vOpj0qXjyhcmXf2ngAAua5YZl703HPP6eyzz9aSJUucysZvvvmmMz116lRNmJAwuC8AAAAA5Ec/3ybNTegWfM9SqUy1wNsd2CpVbOBOhyQUFAkku7v6Nujlnb59lje7cebHUqVm3raZ0lVU6Oxdl9ctAIAiKVN/brKxCOfNm+cECFu2bKkxY8Y43Y+nTZum9u3bZ38rAQAAACA7WFVgT4DQBAoQdkrogvzvc95loSX9t2l+oXTSTVLdHlKry5TjqrWVzh8qNTrTnZ/8WsEfizAl877O6xYAQJGUqUxCU79+fX300UfZ2xoAAAAAyEn71nunz3sz8DarxyXPaEtagKTxuVKrfsozV492i640Pjvv2gAAKFQyFSScM2eOQkNDnSxC8/PPP2vYsGFq1qyZhgwZorCwVFLxAQAAACC3WaGP1eOlpn2la/9KKDbSIvC2VqQkafZg0iBhbgcILQPSyR4Mkio2kpb9JnW4TgoJVYFnhVdWjsnrVgBAkZep7sY33XSTVqxY4UyvWbNGl112mUqWLKkRI0bogQceyO42AgAAAEDmg2uHd0kv1nErEz9TUSpZMeUAoancwv/Z1OkmnXyrFF5GOvUx5TorFjntXem/96UF30nzv5VCMt0xLH9pdr53+vx3pevG5mVrAKDIytS/KhYgbNOmjTNtgcEePXrom2++0ZQpU9S/f3+98cYbyu+GDh3qPGJjY/O6KQAAAAByymfnSuun+C/bsdhbkCSQvu+4QbgO13iXhZWUznrefeSV0lWl7Yukpb+681FNVTgEeSfbXp6XDQGAIi1TmYTx8fGKi4tzpseOHatzzjnHma5Zs6Z27dqlgmDw4MFOReaZM2fmdVMAAAAAZLddq6Qhkd4AYXCo1Chh/L663VN/bUQFqcttUliE8hWnknG8dz61issFSRV3GCvHb/fkZUsAoEjLVCZhhw4d9Oyzz6p3796aMGGC3nvvPWf52rVrVbly5exuIwAAAABkzO/3eae73C6d/ox/192CKLy0/3xwpnI+8rcTR/K6BQBQZGUqSGjdiS+//HL99NNPevTRR9WggZuqP3LkSHXp0iW72wgAAAAA6RcbI21K6DHU5w3/bsMFmW8hlcJk3wbvdFBIXrYEAIq0TAUJW7VqpYULFyZb/vLLLyskhC91AAAAAHlkzxpp5ifSFT9Ie9ZKbQao0Agt4Z0OS5JVWJDt3+SdDub3JADklWzNTy9evLhCQ0Ozc5cAAAAAkD6HdkhvtZWiD0tRTQpXgNB0v98dW9E8tF6Fhm914+BCUrEZAAqgTH0DW0Xg119/Xd9//702bNig6Ohov/V79uzJrvYBAAAAQHJH90r7NroBwdqdpQNbpb8fd9dZBeDikSp0SleWbp/lvufClHFXpqp3miAhAOSZTH0DP/XUU/r4449177336rHHHnPGJVy3bp0zRuETTzyR/a0EAAAAAI/vLpeW/eadL1lRioiSdi515895peAWJ0lL2dpSXKwKH7te8VL5unndEAAosjLV3fjrr7/WRx995AQJixUrpgEDBjhBQwsQTp8+PftbCQAAAABm4Uj/AKE5sssbIDRVWqpQWvSD9GwlacaHKnSqtXWfO96Q1y0BgCIrU0HCbdu2qWVL9x/eUqVKaf/+/c50nz59NHr06OxtIQAAAICiKSZa2rtOmvettGuVNOMjaclP3vH5Htoghft0Kz71Uen68YWrK66v0AgpNlr662EVOp5uxnEn8rolAFBkZaq7cY0aNbR161bVqlVL9evX15gxY9SuXTvNnDlT4eHh2d9KAAAAAEXP+GekqW8lX37rdKliIzcY2Otx6ff73OU9HlChFl5KhdbhHe7ztoVSzZPyujUAUCRlKpPwwgsv1Lhx45zp22+/XY8//rgaNmyoQYMG6dprr83uNgIAAAAoanYsCxwgNDHHvdmCTfq4j0u/VKFXGIuxeISXcZ+X/prXLQGAIitTmYQvvPBC4vRll13mZBROmzbNCRSed9552dk+AAAAAEXJgS3S7/dLxcKluj2ktROk+1ZJW+dLX18sXfaVm0XoWxm3/9cqEiq3kNpeKdU/TYVO9/uk7wd5g4UAgFyXLfXlO3fu7DwAAAAAIFXrJkuHd0nNLwi8/seb3cCguegj6aznpVJRUsPe0hB3LPQiyyo2n/+OCiVPxebCOp4kABTmIOHy5cv19ttva+lSt4pY06ZNna7HjRs3zs72AQAAAChMPjvXfS7+k1S9nbcL7eHd0tG90jkvS0NP8mbOVW6Wd21F7tk8233euTyvWwIARVamxiT84Ycf1KJFC82ePVutW7d2HnPmzHGW2ToAAAAASNWXF0gv1JKGREqT35Bmfyq9015ak5BFaFb9nZctRG7aMs993r0qr1sCAEVWpjIJH3jgAT388MN6+umn/ZY/+eSTzrqLL744u9oHAAAAoLCIjw+8fOyT3umKDaVT7pKmvCHV7pprTUMeO/9t6dc7pbO8498DAApAJuHWrVudSsZJXXHFFc46AAAAAPATF+eOqXfDeOmC96UGp3vXVWvnna7RUer1hPTAWqlG+zxpKvJA+XrSVb9KlZvndUsAoMjKVJCwZ8+emjRpUrLlkydPVrdu3bKjXQAAAAAKkx+ulV5vKUVUktoMkC760K1k2/EGqV5Pd5t6p0rhpdziFSXL53WLAQAoUtLd3fiXX35JnO7bt68efPBBZ0zCk08+2Vk2ffp0jRgxQk899VTOtBQAAABAwXTiqLRyrBv8K1XZXWZBwIc3utP7Nko1OkhNEoqaAACA/BskvOCCC5Ite/fdd52Hr8GDB+vmm2/OntYBAAAAKPgW/SBFH5RaXSYVC0u+vmxN9wEAAPJ/kDDOxhABAAAAgIwWK/l5sDsd1SSvWwMAALKzujEAAAAAJLNptnRwq3Rsnzcw6Ktd8uKHAACggBUu+e6779K9040bN2rKlCmZbRMAAACAgmbDf9LHp0nDL08eICxbS7ppohRRMa9aBwAAsitI+N5776lp06Z66aWXtHTp0mTr9+/fr99//10DBw5Uu3bttHv37vTuGgAAAEBBFxTkP1+8rHf6ilFS1da53iQAAJAD3Y0nTJjgVDh+++239fDDDysiIkKVK1dW8eLFtXfvXm3btk0VK1bU1VdfrUWLFjnrAAAAABQRNTpKDc9wqxef+1rgAiUAAKBwjEnYt29f57Fr1y5NnjxZ69ev19GjR53gYNu2bZ1HcHC6kxMBAAAAFBZLfpa63SfV6pTXLQEAALlVuMSCghdccEFmXgoAAACgMNmxVHr3ZHe6Skvp5sl53SIAAJAJpP0BAAAAyLxf7/JOV2mVly0BAABZQJAQAAAAgFf0EWntJOnQDunYAenIntS373Kbd7rFRTnePAAAkI+6GwMAAAAohBb/KI242jvf8lJp43Sp3SCpahup4en+2099W2p4pvTQRmnPaqla21xvMgAAyB4ECQEAAICi7t3O0o4lyZevnSAd2i6Nf9a7rPE50oBvpS3zpDGPuY+7FhIgBACgqHU3PnHihOrXr6+lS5fmTIsAAAAA5J45X3oDhK0HSK0uk/q+LT2xVypfP/n2y3+XhkRK/77gzhcrLkXWzN02AwCAvM8kDA0N1bFjx7K/JQAAAABy399PeKc7XCfV7Oid3zA15det+MN9PvM5KSgoBxsIAADybeGSwYMH68UXX1RMTEz2twgAAABA7ml6nvv82E7/AGFSV/4oPb47+XKyCAEAKLpjEs6cOVPjxo3TmDFj1LJlS0VERPitHzVqVHa1DwAAAEBOiD0hBReT+r7lPgK5/Adp7pfShe9LoSWkuDip273Syr+lbQvcbSKr52qzAQBAPgoSli1bVhdffHH2twYAAABAzjlxVJrwotTjIel/laWgEGnQz1KdroG7DDfs7T48goOlXk9I3e+X/vtA2rWSTEIAAIpykHDYsGHZ3xIAAAAA2SvmuBQULIWESsf2Sy/UcpfP/859jo+VPu8jXT3aDRSml2UVdr0rZ9oMAAAKzpiEAAAAQJF2ZI9b4ffPh915C8D98ZC0fpoUfUR6v6v0cW8pLjZz+z+8S1o3JesBwmcrSW+0khaOlCa/7l1XPNJ/263zs3YsAABQNDMJ69atq6BUKpitWbMmK20CAAAA8q8DW6U/H3Knp78r7d8oLf3Vnf/vPbfAx7aF7vyuFdLOZVLdHlLJ8qkHBS2I1+hMKaKS9G4nd/lVv0l1u2W8feunSOOfdecPbpF+uM67vvVAaf43/q/pdEvGjgEAAAqdTAUJ77rLv2vBiRMnNHfuXP3555+6//77s6ttAAAAQO5a8os0+l7p/KFS9fbSvK+kNldIMUelf56XaneRfr7V/zWeAKFHxUZSVBM3OPjuye6yut2lK3+SgkMCH/eT06U9a6Rp7/gvt67At82SytdPX/stc/G1Jqlvc/LN/kFCK0RiYw0CAIAiLVNBwjvvvDPg8qFDh2rWrFlZbRMAAACQ+yyb7/sr3enFP0r/POt2w63eQfpugNul2IKGaZnyltTxeun3+7zL1k6U/nhAOvfV5Ntb8Q8LEKbknQ5S33ekaqenflxr33eX+y9rdoG05CfvfIUGUtla0pD9bndpU6tL2u8JAAAUetn6J8Ozzz5bP/zwQ3buEgAAAMhe0Yelae9Ku1f7Lx/zmHfaMu084/R9do4bgEvKuhU/ssU73/xC93nGB9I/zwUexzCQpOMBVm0jDZ7ptyj4l9tUYsnw1N/XstHSukn+y/q87gYEPSwrsUQ5d7pMdXfat3oxAAAosrI1SDhy5EiVL5/KWCsAAABAXomNkeLjpR+ul1b+5Y4l6GvPWvf54k9S3kepyu5zaIQ7zmBYhHTpF1LHG6SzX5ZKVnTXH00ICJauJl0yzJ2OORZ4ny0vkS7/Qer/rRQS5gYxoxol2yxiwWcpt8veV42TpGIlvMvK1vYGBB9cJz24XvIdVzwiSjq6VzqRQrsAAECRkqnuxm3btvUrXBIfH69t27Zp586devfdd7OzfQAAAEDWHNwuvZoQdCteVjq2TwovI9XsJMXFSfO/lZqcI+1YKoWVlhr0CryfB9a6rwtJ8r/Qzc53H+bcV6QRV7vT962Ugou5ATxjwb9AYwiOe1oqXsYdGzA2Wtq90s1yvO5vd6zCBEHH93v3ldTsz6QJL7rdmU8ckdpdJRUL8673BAt97VzuPq8cIzXrm9LZAwAARUSmgoQXXHCB33xwcLCioqLUs2dPNWmSxkDJAAAAQG7yHRvQAoTmtMek0BLSrGHSb3dJPyesb36RG1Crf5q0erz3dR2uS706sUedbm514lPulEpVcpedOOr/nLSr8ZQ3/LsrG3ttksBeyNHdirOAXpOz/fdhgc5pQ6WDW6XIGlK9HkqXig3cKszHD6RvewAAUKhlKkj45JNPZn9LAAAAgJxgxTo8anWWNkyT5nzpFhJJ6pxX3Of9m93nM5+XWlwslU7oZpyWiIrSfSv8u/UWK55ykPDIbu+0df21AiUHNkvhpd1lnW/zr3i8Y0nyIOHSX9zsQxNWSuk26Bdp+e9S64Hpfw0AACi0MhUkNKtXr9awYcOc5zfffFOVKlXSH3/8oVq1aql58+bZ20oAAAAgo47ucwN0vZ+UOt0k/fuCNDthfMDog97tgkOluBPudImy7vOVo6RFP7hVin277aaHb4DQM9/oLLdQiKeKsnU9LldbmvqWu6xcHal6e/fhq15Pad96xdXuquA/H3SzH5Nq4FN4pGSAbsUpsczItlekf3sAAFCoZapwyYQJE9SyZUv9999/GjVqlA4dOuQsnz9/PlmGAAAAyB/+flx6r7ObFVi6itu11iO0pHfaEyC0YGFwiDtt3Xaty3BGA4QpGThc6vOaOz3sbOnNVtLS36S1E91lVvgkkIanS5d95S2YEu3+f7ef8FJS9Q5SeKRUrm72tBcAABQ5mQoSPvTQQ3r22Wf1999/KyzM+z9Op512mqZPn56d7QMAAAAybvsSac4X0p413iBgl9vcZysmUr1d2hmA2c2CghNeknatcOeHX+5dd/Ktqb+20Vnaft0cqes9/sutkMnm2dI1vyfv5gwAAJDTQcKFCxfqwgt9BlZOYF2Od+3alR3tAgAAAFJnAbKnyksf9XKrAa+Z4D92n0dkTW+XXtP0PKntlcn3V7lFzrV1yS9uUPCf/3mXebIDb55slQBTf32xcMWHRkhBSbZbNVb66DTp2UpuZWQAAIDcDBKWLVtWW7duTbZ87ty5ql49YawVAAAAICfZuH7xsdLmWdLb7aQv+kqHEwqBbJnrPp/9krcLcZVWUony0pp/pVonS7dMlWqcJJ37mtTrCemKH3KurWOHJF9WLFwKCpGimqb9+vg4ha8dq6BvL3OrLn/WR1o7SdoyL3AXagAAgNwoXNK/f389+OCDGjFihIKCghQXF6cpU6bovvvu06BBgzKzSwAAACBlm2ZLpSp5xwvctdKbGehr5sdSzwfdoiPGt9CHBQuP7nGnj+2XKjeXrv87N1ov7VmdfNm+DVJElBSSjv8l37tO5f4a7E6vHOM+b1vgvg+P9OwHAAAgBZn6P4nnnntOgwcPVs2aNRUbG6tmzZo5zwMHDtRjjz2WmV0CAAAAgb3WXDqwyTt/zR9u8Y8mfaRHt0mj75PmfeWu+/c5qWor6YZ/pIUjpNYD/fd16Zdu5mHxSOWpsrWdqsVSOscQLJ5QddmXb4DwpoQCKAAAALkZJLRiJR999JEef/xxLVq0yKlu3LZtWzVs2DCz7QAAAAC8ju6TPjndW+TD16ibvN2HLVPwgqFStTbS7/e5y7/tLw3ZL51yR/LXNuvrPvJa9fZukLDbvenbvmT5lNe1HiBVbZ1tTQMAAEVTlvok1KpVy3kAAAAA2WrxqMABQrN/g/tc8yTvspNukEqUk364zp0fEukWJzn/HeU7Zz7vdB92VEl/sZTDra5RxIJh3gU2vqJ1n67dJQcaCQAAipp0BwnvueeedO/0tddey2x7AAAAAGnFX97pB9ZKxw+4BUd+vdO7vE43/9d4qgV7zP0y/wQJg4tJcTFuILPzrdLEl93lh3akexfHa3bzDxJ6xles2Di7WwsAAIqgdAcJrXJxelghEwAAACBTti2S3j/FO//QBnf8QOtua5lzvpIW6qjT1X8+vV15c0OH66QZH0in3OkNGpo5X0gtLkrXLk5UDFAFudMtbgEWAACA3AoS/vPPP1k9FgAAAJC6ry72Tl/2lX+Bkbo+mYMDvkv+WvtjdVRTaedSd77hmco3Wl8mhZeW6nR351teKm2eLXW/P927iC9RXvENeito1Vi3K7VlStqYjOGlcq7dAACgyMjSmIQAAABAttk8Rzq0zZ1+ZIsUFuG/3rrqWkGS1JSKcoOEbS6XylRTvmGFSuzhEVndDYJmUHz7axRk56VpXzdIuHNZ9rYTAAAUWZkOEs6aNUvff/+9NmzYoOjoaL91o0aNyo62AQAAoKgYfZ808yN3+qSbkgcI06t0Vfe56XlS2ZoqdBqfIzXtIx074M4v/z2vWwQAAAqJ4My86LvvvlOXLl20dOlS/fjjjzpx4oQWL16s8ePHKzLSp0sIAAAAkJq966U5X3oDhOb0pzO/v/g493nKWyrUwuhiDAAA8kEm4XPPPafXX39dgwcPVunSpfXmm2+qbt26uummm1S1asJfbwEAAACPsUOkPWukvu9Ik1+X9q6VSlaQZn6cvEtxaPHMH6fLHdLCEVJwiAq14GDpzvlSWOm8bgkAACjKQcLVq1fr3HPPdabDwsJ0+PBhp6rx3XffrdNOO01PPfVUdrcTAAAABdW2hW5g0Cz5OfXsuM6Ds3asqq2ky3+QytdVoVeuTl63AAAAFPUgYbly5XTw4EFnunr16lq0aJFatmypffv26ciRI9ndRgAAABRkxw9KNU6SNs1Ivq5CA6nf59K2BVKbgdlzvIa9s2c/AAAARUimgoTdu3fX33//7QQG+/XrpzvvvNMZj9CW9erVSwXB0KFDnUdsbGxeNwUAAKDgW/C9dGS3dOKodGCLdOojUsny0oox0jf9Un5dlZZSlRbuAwAAAAUjSGgZgy1atNA777yjY8eOOcseffRRhYaGaurUqbr44ov12GOPqSCw8RTtceDAAYqtAAAAZMXq8dKoG/yXBQVLdbpK31/pzlfvIO1d53YHbnmpNH2o2w05skaeNBkAAABZCBK2atVKHTt21PXXX6/+/fs7y4KDg/XQQw9lZDcAAAAoTGYNS76sxwPSlDe98wO/l0qUdaetqEiZatLYJ6XKZBACAADkB8EZ2XjChAlq3ry57r33XqeK8VVXXaVJkyblXOsAAACQvx3ZIy39RYqsKXW9211m0xEVpfZXS51uls5/V4qo4AYHPVWH6/WQbvxXau3+4RkAAAAFKEjYrVs3ffrpp9q6davefvttrVu3Tj169FCjRo304osvatu2bTnXUgAAAOS/cQhfSqgiXKa61P0Bqf01Ur/PpKN7pQr1pbNflNpentctBQAAQHYGCT0iIiJ0zTXXOJmFK1ascIqXWBGQWrVqqW/fvpnZJQAAAAqCg9ulZypJP9zgPw5hl9ulsJLSeW9IH/eSXqwjzflSio/Py9YCAAAgJ4OEvho0aKBHHnnEKVhSunRpjR49Oqu7BAAAQH41e5gUe1xa+L13WefbpKZ9km/7y23SxJdztXkAAADIgyDhxIkTdfXVV6tKlSq6//77ddFFF2nKlClZ2SUAAADyq+jD0sEAw8tMeyfljMHwMjneLAAACrKDx04oJjYur5sBZKy6sdmyZYs+++wz57Fq1Sp16dJFb731li699FKnGzIAAAAKoSW/SN9f6Z3v87rUdpA06nqpRkcpKMi77sH10ou13eniBAkBAEjJqh2H1OftSapZrqT+vKu7QoJ9/j0F8nOQ8Oyzz9bYsWNVsWJFDRo0SNdee60aN26cc60DAABA/rB9sf98u6ul4GC3SElSJcp6p+Nic75tAAAUIGt2HtLwmRtVtmSYXvxzmbNs5Y5Dmr9pn9rVKpfXzUMRlqEgYWhoqEaOHKk+ffooJCQk51oFAACA/GPtJKnZ+dKEF9z5a/50A4TpUf+0HG0aAAAFzZO/LNaklbuSLY+OocsxClCQ8Jdffsm5lgAAACB/Gve0tGmG1P8bqcm56XvNzVOkveukyOo53ToAALLFxj1H9OqY5erbpppOa1I52/YbHx+vG76Ypdnr9yomLl4Hj8UE3C4mNoXxfYH8OiYhAAAACrkTR6Xp70ntBkkRFaVDCcVKGp+T/n1UaeE+AADIx1bvPOQE76JKheuRHxdq6/5j+mneFnWpX0HbDxxT44rF9dblUckS6Kes2qWnf12iUxpU1ON9mirIZ2ze2Lh49Xj5H6emV4vqZbRhz1Et3Xog2bEHda6tehUjNOTXJc78oeOBg4dAbiFICAAAAH9zv5LGPSUt+Um6aaK0b4NUPNK/OAkAAIXAoE9maPO+o8mWT12923levfOwLl21Sz2bVNb4Zds1b+N+3dS9nj6etEbLtx90Hm1qldVX09arfqUIPXpuM9357Vxt2uvuM9C+Pe7u3cgJDHqChFblGHknLi5ewUW8cAxBQgAAgKJs1jApvLTU8hLvshIJg6ZvnS9tmu1OH9ufN+0DACCH2BiAKQXxapUvqQ17jjjTX/23Qa1qltO1n81y5t8at9Jv2zu+nes8z1i3R9/O2JjqMcfe0131o0olZh76/v3tQArdkJG9jp2IVXixYOca7DkcrR9mb9KvC7Zowab9eub85rqycx0VVQQJAQAAinK34t/ucqeP7ZM6Xp+8W/HnffKmbQAA5DDfzL0L2lTTgJNqyUYF7FC7nIqFBOvGL2ZpzJLtGrt0h9o983emjjF0YDsN/mZO4nyDSqX91pcK94ZlyCTMGTsPHnfObc3yJTXgw+matX6vs7x4aLCOnfAvFvP4z4sJEgIAAKAIOrDFOz36XqnFxdKJY9JrTb3LTxyRwkpLV/+WJ00EACAtOw4e08y1e9WjcZRf0C0tvgVEujWMUqd6FfzW160YkeJr60VFaM3Ow7qwbXUt2rxfK3ccCrhdaEiQBnaqpW/+26BnLkg+Vq8FI0uGhehIdGyKBU2QtazBs96YqN2HowOso5p0UgQJAQAAimJw0BMILFlBOrJbKl9PCo+UVv9jdRilLrdLU992t4k+KFWmCAkAIH8Ggc59a7KTLdapbnl9d+PJmr5mj6pEFk81yGd8g3KliycPj/TrUEOj52/WjsMnnK7JHg+c1Vi39myQ4n637j+qM16fqAoRYU7g8vRmlZ3xB6NKhwfcvkzx0IQg4QkdjY7VzHV7dFLd8ioeGpLOs4CUWCGaQAFCUz8qwhlz0ld4sWCnGrVvIZqihCAhAABAUTN2iHf6nFekBr3dwOCC4dK+9e7yqKbSHfOkt9q48zHHpJBSedNeAACS2LD7iCqVCdfGPUecAKH5b+0e1X3498RtFg45Qx9PWqs3x61Uu1plNerWU/z2cfC4t3tvqQBBQqs8PPKaFqpUqZImrNilaz6b6SzvWKd8qm2rGllCMx7prWIhQQoNccsipxQg9AQotx2Qvp+1yXmY89tU05v926bzbCAlR6JTzs586ZJWuvi9ac70mc0r66/F23U8Jk6Ho2MzlJFamBTNdw0AAFDYHdwu/fmg1PMRKbyUVKaad92xA+7z5T9IDXtLh3ZIrzT0f32NjlL5ut75sNSzMQAAyC3jlm7XdZ/PUlixYKcibUqWbj2oTyavdabnbNinf5bt0M/zNuvQ8Vi9e3k7v0xCy+ZLTc/GUXqiTzOFhwanGSQ0JcLSnwVYu0JEsu7KP8/bQpAwG1iGpsfn156kUuEh+mjiWt3Qva7a1y6vD69sr5DgIDWuUlrXd6vnZH+WKMIZnAQJAQAACqNfbpNWjpEW/+jO3zZbGvukdOb/pC1uFUbV7Og+7/Kv0qiIKKliQtDwvDelkHD/8osAAOQiq0A8fMYGdW8UpQ51ymvG2j3Oct8uwMYCPL5dSzftPaJDx72BQE8moDn1lX/Vub53DMLUMv2MdT+9tqvPH8+y0QsXt9QXU0tr+4HjGj7LWx3Z3p8FQpF5dg94VC4TriZVyqj9ld4g7xnNqyRO1yhXUkUdQUIAAIDCyAKEvt5p7z7HxUrHD0jFSkjFI91ltU7237ZaW29QsP3VudFaAEAhkRPjub385zL9NG+L3hq/SnMeP11Ltx1MXHdSnfI6HhunHo2idM/pjTTkl8X6bOo6Z909389PNfA4crbbtddUSiNImJMqlgrXPWc0dqb3Hol2KiqbPxdv0x3fun/Ym/TAqU513kBFWywj0roshwYHq1xEWC63Pv92M7aMwNU73DEHg4NSL0QDF0FCAACAwqhcHWmv+yPJT2y01HuIf2Xj4BDp3uXSz7dJaydInQfnalMBAIXDpJU7dds3c3Vphxp69NxmWdrXidg4/bdmj9rUKquf53v/zRq/bIcmrtiZOP/9zZ39Xvd4n2b6a/E2p2CFrzoVSqpn40qJAcSk8kuhittOa5AYJPQECD1ZkP+7oIVi4+PVukZZRYQXc8Zi7PHSvzp6wtuldlDn2nr6/KJdbGzUnE1OgNguaXxCb3TrlR5erOh2I04vgoQAAACFze7V3gBhi0ukRSP9swQ73ZT8NaWrSP2/lg7vkiKr515bAQCFxhM/L9b+oyf00aS1uvv0RioZlvmQw13fzdPohVt1UdvqiYEe88bYFam+zsaX+/r6Thq3dIcTUDPVypbQOS2qqFhIsDOm4AcT1jiZdxY02nXouB4+u4nyi2ZVy6heVITWJKm6u2rHIV324XRnukPtchp5Sxfd8/08vwCh+WLaevVpVU3lI0IVExevhpVKO+ekqNh+4FhiBqnvfYP0IUgIAABQ2ERUlBqdLbXuLzU6UypRVpr5sbtu0itS9fZSk3OSv65YOAFCAEAyL/yxTN/O2OCM+RccHORU/X2jfxsnCLht/zE9/vMiZ+y8tbu8ga1Z6/Y6YwhmhgUaLUBoRs3d7Ldu096jidN3nNYg4OvrRZVyHoHcf0Zjndm8ihOMCy8WrG0HjjnViPMLC2R+d+PJmrJql2LjpPtGJO8yPWv9Xp383Din7R5d6lfQ1NW7nelLP3Ar9nq0rhGpHQePO9mVMx7ppUpliqswsYI0r49doZu619fuw26la2QOQUIAAIDCxsYaHPidd/70Z7xBQvPdAGnI/jxpGgCgYJm3cZ/en7A6MXjnyWr7Yc5mXXlybY2YtVF/J3SP9bVhj7dgREbtPpR2oKdVjUgnWzEzQbh2tcolzuenAKFHpdLFdWHbGs60ZVJOX7tbx07EOmMo/r5wm7PcN0B4YdvqTsVlT5AwqfmbvP/mn/TcOK174VwVdHsPR2vNrkMKDQlOLEgz+Js5ftvMe+J0tXn67zxqYcFEkBAAAKAwOHFMOrBZCi0hvdZUqtRcunWquy6spBsUnPiKNP4Z6ZxX8rq1AIAC4gef4h6+pq/e7QQJ1/hkD/o6Gu3fDTYjPMFIT1EP6xJs+nes6VQYdotQlMo34wjmJMvc7FK/YmJw1hMk9PXixa20bNsBv2XtapXVnA37VBjtP3JCbZ9JO/hXtmSYBpxUU9/O2Kin+jbPlbYVdAQJAQAACoMxj/pnC+5YnHybVePc56j8M/YSACB/OB4Tq7CQ4GSBt5U7vJWET2tSyakaO33NHmfsNzN19S6/SsMz1u1xpj1j5Vn13ahS4QEDenFx8dpzJNoJBKYUJPzgynaKKlVc8zbt05nNKxfp4hPFgoMTpxtXLq2/7u6eON+ocunE6bIlQzXq1lPU7aXx2rjnqO7u3cjpjuth2Z9W+KRn46gsjRsZiF3TjXuPqHrZEk7Wpq/nfl+qDyeuUanwYmpRvYwOHY/Ros0H1LBSKWeMSAtqXtCmml7p1zrZa61IyzvjVzrTn09bH/DY1uU9OibO/5gXttSdvRqpSmTh6mKdUwgSAgAAFAa+AUITHJp8mw0JmYXFy+ROmwAABcLs9Xt08XvT1KZmWY26pYuTveZRxWf8uif6NNPVw2Ykjou3aPN+bT/g7Ro8/KaTVffh353pI9Fu91gbU69Hoyh9dk3HZIHCW7+eo7+WbNPQge3Uq2klfTV9Q2LGnEdkiVDVqlDSeRR1l3WsqQ8mrnbO+ZfXn+S3rnhoiO4/s7FT2MWCgub3O7o557JVjbJ+QcL7Ry5wnq0K9UuXtE7zuFNX7dJ3Mzc6wcfgoCAnoHdVlzrJgrvmxi9na+xSt/u5BZ3LlCjmtOuUBhWdAKGx4KAFmj1W+lzvn+Zt0VktqjjTUaWLq31tt2v4nd/NDdid2rJZS4aHqGejSupcv4IeHrXQGT/z3oSu6HbPESBMP4KEAAAABdmxA9Kvd0pBwVJ8wl/Pq7SUrhiVfNuLP5GW/+F2RQYAICGD0AKEnvEHF23Z7wSVPCzYZ5pUKa06FSO0brd3rMHxy3b47csCMmWKF9OBYzGasGKnPp7kBoVs2rol109STOTPxdsSg4Vnt6iiPxYl70pbpkSAP3oVUZb9998jvVNcP/jUBrqhWz0no86ULh6qtgnjL0aEhehwki7g38/apPPbVHcCeKmxwjSrk1RbtkzSpAHGrfuPJgYITXRsnHYditaDPyzUo+c09ds2NCRIJ2IDlx+++Svv2IIzHu2l4yfiUhxv8ZkLWvjN/++CFrqpez3VJqicKf75mwAAACgY4uOltZOkF2pKi0d5A4Tm5slSqUrJX9PyEumST6QQ/k4MAHBZdWJfY5ds17/Ldzjjvvl2Gy4R5nbzvbF7vcRtX/vbm53m4em+unTrAcXEeYNAq3cccvbb4+V/NHrBVsVY6V4fgQKEnetVcLoqI/08AcKkruvmvW6NKnuDtbd8NTvV/Vn33aQBQk+AcfXOQ/pw4mrVeWi0znt7sjo/Pz5x/SkNKvht/7/flyZOj7m7uyY9cJpf1+nFT50Z8Pgn/W+cXve5z96/op0eO7ep6kdF6NOrOyTb3rJgLZhdFMarzAn8HyIAAEBBNG2oOw5hSgFE/ucYAJAOx074B+veGr/Kea5UOlyTHzwtMZOwZEKQ8NpT6uqLaeuSvc6jW8OKGhGg2Mmd381LDDhaFdrx9/ZIsU3TH+7l/DNmbSDYkz1u7Vlfx0/EOmMXfv2fd0w/y/pMzY9zvdfyhYtaOhmhnm7DvV6dkLhu4WZvBWUL4H11XSd9/d8GPfbTIr/9hQQHORml9mwB5yVbDjjjBlqW5MBOtfTNf26Xc1+j5m5OnC4VHqrru1XV9T5BT2QfMgkBAAAKovnf+s93uM47fTB5NgYAACl1Nw5kx8HjTpahJ0hYItTNMbLx3T69uqNa1yyrciWTdwV+7qKWGnFzZ315nf+YeZ4AocdF7yWMk5vQldmXHaNymeIECLORjVn48DlNdXH7GiofEeYX1DV2rfccjk5cbuNNLti0z+kq7nFG8yrakVCwJpBiwUFOVuBjfZo5127gSbWSbWPVhi1AaB45p6m+ur5T4niT1lX477u7a9StXVI8hiejFTmDTEIAAICCJi5O2rVSKltbOvslKaqxVL6u1Ogsadt8qUzVvG4hAKCA8M0ItCIQlcuE65UxbvfOM96YoCAF+WUSmi71K+rnwRUVGxev+o+4hUo8QkOC1bFO+TSPuy+hO7O5tmtdPZBQTAM5z6r9jl3qjidZqXRxrdt1WGe8PlEn4uL09fWdnOy+Z0d7uwebDrXLOcHF8hHe7t8WKJ6/cV/i/LJnzvKrSmxdfyc9cKpmr9+rUxtXUnhosBOsTIkFFhsmVGn+5KoOumv4PF3WoaY+nrw2cZsSqbweWUeQEAAAoCCx4OCqsVLscSmqidT4LO+6Rme4DwAA0umYT4bf+W2q+QXvfAOIvtlnHp6MMNO6RmSy9Vd3qaPPpq5znptVLaMHfggcCIwIK6Y/7uymN8eu1KDOtbPydpAOLWtEqlb5ktqw54hi49yiIFZkxAz86L+Ar2lfxy2AckvP+pq0cqfqRUXo/Svaa8X2Q7rj27k6s0UVvwChR83yJZ1HRvVqWlkLnjzDCRz6Bgl9g9XIfgQJAQAACoIDW6X9m6TRd0vbFrrL6pyS160CABRQa3cd1ril27V539HEZZblFZkknmPBv6jSxXVVlzoB9/PSxa00Zsl2PXles2TrHj23qS5uV0PNqpVRfHy8Jq7c6QQWX7u0jUbN2aT7E7IHa5QroaZVy+j9K9tn99tEKl2DjRUYTq1X9yv9Wqt08WLq0SjKmY8qHa6/7/GOJ9m4Smn9dXf3HGmjp7u5VSv+YOIaJ0BYsTSFbHISQUIAAICC4LUmyZe1vDQvWgIAKIBGzNroZA1ecXJtTVm1W1d8kjxjrHhosKqVLZE4X6VMcf18W9dU93tpx5rOIxDremxZa64gvTOwXeI6Cx5u2nvUGWPOuq0id3myQC2T0CpPe9zVu6HeGLvSmX68TzNd0r6G8tqDZzVxgpE1ypVUqXDCWDmJswsAAFBQMfYgABR4R6Nj1fSJP1WxVJgz1p8lT7WoFqnru9XNUuGOz6eu05O/LNb9ZzZ2xoPzZO1ZQZAbv5ydbHvrPlqnQoQTPOpUt7zmbtinoZe3VU6x8eruPr1Rju0f6QsS/r5wm994f3f2aqjujaJ0+HiMujV0swfzmt0rF7XL+2BlUUCQEAAAID87ulea+Ip3vt/n0ubZUvur87JVAIAMsAIfczfsdbKhShcPdSrGLt16QANOqqWHEsbp23UoWr/M3+JM/zxvizMGXLta5bRw0359OGmNihcLVmSJUCcz7/w21dMMPFqA0Lz813Ln4ZE0QNi1QUU9dX5z1S5fMnFMuW9vOFmHo2OctqJwqlAq+RiTNcuXcALTdt+haCJICAAAkJ9Nf0+a9o7U+TapxcVS9XZS8wvyulUAUGjYWHlWoMO6ve49HK0vp693inS0rB6psGLBaly5tJPJNHrBVi3Zul+3ndrQ2dbsP3JCQcFSWEiwnvp1sZOJd1OP+smO8drfyzX0n9XO/qJjvMVAXvhjmd925UqGam9C4ZAdB447z8+OXqL/1u7x286CjU2qlAnYpfijSWt0U/fkbUhJkyqlVT+qlN8ye78ECAu3B85sotdDVmj/0RPO/R9eLFiPnNM0r5uFPEaQEAAAIL9aP02a8KI73elmqWzgMZ8AAJlzPCZWfd6arJU7DqlsyVC/yr4ezauV0buXt9Pgb+Y48yNnb9J/j/TW9gPH1OvVCTp0PMZvewsgntakkh79cZH6daihPq2qada6vc463wBhUnf0auhUF7Z9mq37jzpjCCYNEJrvZmzUkL7Nky33dCm+d8R8v+XdGlbUpJW7Ah43tFjyirQo/GwcyM+uOSmvm4F8hiAhAABAfrR3vTTsLHc6NEKKZCweAMiuqr4PjlzgBPeWbD2QuDxQgNAs3nJAPV7+N3F++4HjTnfeCct3JgsQmid+Xuw8jHUrvmf4fEXHeoODlqG4cPP+xPnQkCCnIEO/9jWcDD6Pp35d4jw8bu1ZX+/+u9qZ/mzqumRBQsuITMmX13VSnYdGB1xnGWQAYAgSAgAA5LR/X3S7DPf7TFo3WQoJldM/rceDckaoD8S28bh1WsrbAQCSsSy/PYej1bRq4C65M9Ylz847qW55zQiQtRfIjoPHNG/TvsR5ywC0cQQD8Q0QPnZuU13frZ4OHDvhVJRtXaOsX2AwtUzDq0+pkxgkDMS6jQZyQ7e6zvN/j/TSwI+mO92I5230tt26SAOAIUgIAACQ0/59zn3+6iL/5c0vkqKSVHY8tl/65Xap+wPSwO+lXSuksrVyr60AUMAdiY5Rj5f/ccZZG3VrF6cIw2dT1jpZg+UjwvX+hMCBts+u6egE6U595V8dPRHrZPet2nEo4LYWTPwtochIu1pl9Wb/tnq8TzO9OmaFwkKCnOIPk1buVLWyJRReLEQWB+zROEoDOrrf52WKh6ptgOIQNmZhICXDQlQhIjxZUNAKmXhs3X8s4GtPb1YlsarxuHt7OtPfzdigh0YtdKZPbVIp4OsAFD0ECQEAAHJauTrS3nXJlx8/mHzZuKelJT+7j3NfldpeSRYhAGTAnPX7nAChefnP5Xr2whYa4tNt16NaZHH9c39P/TJvi1pUj1TJsGIqGSZNevA0nYiJU9tn/k7xGJ6x/0xcQi/fiqXC9fxFLbPc/kva13DGPTQzHu2lH2ZvVs/GUQrxyTg0rZ8ao+6NojRxxU7d2L2eTq5XPuD+qkYWT7bswnbVFRsfr+bVIv0CjQCKNoKEAAAAOc2yAwPZMFWq0kIq5pMdEl7aOz36XqnhGVKJsjnfRgAoRJmEvkVE7vxubsDtLGvPsvz6dfAvClUqvJgULl3UtrpGzd3sLGtWtYyWbTuQGBDMyTH9XunXWo+e09Rpe/HQEN3S01upuEzxYjpwzPv+LEBoPpy4xm9MwtObVVb1siWcLtQ1y5cM0OYQXd6pdra2G0DBR5AQAAAgJx3dK8VES7U6S+0GScXLSlvmShNfksY85j4ueF9qdZl0cKu0LMnA8iWSd0cDACRnQTLr5jtsijdz2wJtizZ7i5NceXJtfTl9vTOdNDMvKese7AkS1q5QUq9f1kZfTFunxlVKq1Lp4vpmxgZnXEHrZpzdykWEBVw+sFPtxO7SFpw87jOG4aa9RxOnX7u0tTP2IABkBEFCAACAnBRaUnpovXR4l1Smqrusbnd3rMElP7nz45+VVo6RFo9K/nrfzEIAyEG7Dh1XidAQFQsJUnBQkEJDCk7V28Vb9uuqT2c4mX5WsCQl1cuVSJxO6/31aBSl4qFuIO6e0xupYeXS+t+F3u7EZ7Vwx/rLTXf0auCMRdi4cildfUpdfTltnR5PqKT8x6JtaY5tCACpIUgIAJBOHJPmfim1vISsJcBYl63ow1J4qczvw7IHrQDJgu8kBUn3rfCus/1e/LE3SFi6sn+AsP010uxhWXgDAJAx09fs1uUf/6fYhP60NhTqzT3q68GzmqggGL1gq3Ydig643NfWfUfTLPThUbZkmP6+u4eOnYh1AoT5gY2b6DvuYaDiJyasAAV4AeQffHMAAKQpb0i/3ye9WCevWwLkvR3LpKfKSs9Xl3YHroCZZoDx+6vcz5MTIHQWSuFl/LcLCZXuWyXdOV9qM9C7/JJh0jmvSI3Plfq8nrX3AgDpNHnlrsQAoeer7L1/V2v1zsDVffObBZu8Y79e2LZ6wG1+GnyKRiQUBDGWkZcWG88vvwQIA2lUuXTAgiXW7RoAMopMQgCAVKaad3r9VOnQDqn5BXnZIiDv/Pmgd9oya/dvll5vJp35vNT51rRfv3m2N0PQI6y0FJq8uqRKRUmKkloPlI7skeLjpBYXuesGfJPVdwIA6bZu9+HE6XNbVU3MwFu5/ZDqR2UhqzoXWKbf5FW7nOnLO9VyugTXKFdCn0xeq5iEwKd1HW5VPVJnNq+iHxPGGTyjWWUVdNat+LsbO2vfkWj9t3aPbv92rgZ09C/EAgDpRZAQAAo7T6W7lP6ivPJvt0ukx7CzpeBQqfE5UrHAg2YDhdakV6U1/3rnS5aXZn/mTv/1cPqChNPfc58tC9A+R3vWSrU7B97WujR/2186uE26eQqfOQC57mh0rFPcY+6GfYlVca2yridIuPdIyuP75RffztiQOG3VgM29ZzR2Hkk9d2HLxCDhWwPaqrCwrtEWAF329FkKTqMgCwCkhCAhABQWS3+VYo67FVRNWEnppfpSfKxUsbE0+L/AgcKv/9/eXcBHWf9xAP+sxzrYRnd3d3cIEiooigriXwS7u7ALAzsRUFRUBAQJQUq6u3PAGLBkvfu/vr/nnusVjN1u93m/Xve653muntueu919940b7LflZgHZaQxYkPtZ9rJ5edxi4KcxQNzewt02JwtIitUGkojmNwM+5YDgfBrby2v26Ept+dgqoE7vq9l7IqIieX3hXnz+7xGrbS2rhSE8wPz3Xx8CcvLiZfy86aQKwoX4e6NtzQg0qGDTRqGEJxk/NHubyp6z7C3YrqZ96a0lCYgee2MwyioGCInoajBISERUFqQnAbNvtd52+3wtQCji9wM5mYC3n/V1zmw3Lz+4C1j0JLBvvjl4QeROcrLNyy8mAq9VBjJtenHl5gCeWpaKyckNWnBRAoIyoVgn6wWRTEXpPbh/ATBjhPa4pYB8+c7MycWu04loUjkUft42z7kETVt+CMv3xSE80FfGv6BSWDk81r8+Av34MZaKz+f/Hsavm08hOsQP/t5e6Fq3vJocWxZk5+TCy9MDyRnZ6PrmctWHr1u9KAT6ellNw9W1qBKmAmky5TgtKweXjEHCV+bvweI956yu++XYNirzsKgk4JiVk6uGcAT7e6vHKmpw60h8Kv7YFmu3XX53RER0ZfjpiojI1a2eCmybBYTXBC4d1baFVAE2fW1f1mgbJEw5b14Oq2oOEIpzu4CgXsDZXcCBhUD7ewC/0tu4m+iqSKDv677acvXO2rltgFBkXbZ/HcydrAXir1RMIy1IWIxWHTyPS5ezVLaPaFQpBNHBDnoi2ricmY3RX6yzGgAgVj7WE9UiA1CSQUppuh+fkoG3/7b/2SanZ+Pdm5qX2P5Q2fPN6qNIz87BxO61VSDs9YX71PaDcdrrftm+OHSsXR71K7j2371DcSkY+elaZGTnID0r17R95QGLv/9GlUL90bthDDrWjlTrEYG+OJ2Qpt5LhG2AUEyYvsmUlZeUnoVgP29sOZGAmeuOY0K3WmhY0T7TUP75MPTj1bCYkaJEB/shLln7B+WM8e3RpYBgn9yPrnyQH5pWDsGQ5pVU4JGIiK4M30GJiFyZDFRY+oL99qRTwAnzlwHl6L9A4+HW234dp53f9rv9FNcfhmtZTYf/Af6ZopVcXjwKj9q9gMYTivuZEDmPDAzRA4T5aTwC8LIJtAtHAcL2Ewv/+M1GAyvfBga+hSuRmZ2rsu2kef34LjVVUOC2rzfYXW/V4z3VlM78zNseaxcgFPf9uAX9GldQAbse9aPw1dg2pmEAev+v4vLw7G34betpleWUmmnMhrYhwcMrkSiB03LeyMoxINdgKPZ9L4v0LLT4lEy8/fc+jO1YQ2WXurL9Z5Px8vw9allKbaW8Nq9guysGCZPTs+Dj5amO7z+3nS7UBN+7utTEs9c1stoWFuBjDBJmqsw/XatqYSoQaPvecf9PWxHgY37dLt17Djte7K+WD55LhldmDqIBbD+VYBcgFHqAUNz69foCS4L3xCapcx8vD6x5sqdTM56JiMoKBgmJiFy5B6FtibGl5FignPTlMQBpl7TpqrZyMoDIOoAE/mSqsSMZ2odw7JqjzjxitzBISGVrsM9bNiWFnR/Qzv1DgfREoNXtQKuxQECkdZ9Oua2UHzvS16K3YUHK19EC8vqQoSIa//1GrDqoTfX09PBAWqZF2bSFB37ait/uNWZJGp1PzsCoz/9TAcba0UGmQQW2tp9KVCexYv951Hlmoemy6ePaqdJFCSLIhNG6MUULqizbew7jv99kylzS+5/ZBgg3PdsHbaYsVcu7jcGB/Mi+NHhukVqWIFeOg6iEPgXVXR2/kIrub2uDejrVjsTawxfU8uy7O6i+bk//vksNhJDjQ4LR4udNp9CuRoSaHNu+VgRGta0GVzNr/XHTsgTQ5JjWLX24O/q8969anrHuOO7qWgulXUpGNm79aj12xyaq4L3+VvLpmFZquq/ulvbVMGu9NuDjvZua4/2lB3DyYppal/JiW/J6FPKa/GrVEausvZrlA3E0XpuGLOcyUdf2dZuUno1nft+JA+eSsfHYJbXtnu5J+MymB2JRn+vR86mIDPLFuiPa8VotIoABQiKiYsIgIRGRK7p03D5A2GYcsOkb621PHAXWfgSs+wzY8DlwYi3Q+3lgz5/A4meA7HQgw1hSKQNPJJNJprF+0hHITAaSzpgvNzLIMAaisuLjNublRsO04T4xTbT1Ccu1HoPt/ied4IHN3wMLHwdu+EYrOV77IbDkecf3eyVDf/KaQJ5HJt1d329SgbB9Z5NN299cpJVMmtZHNsUTc3aqZcn8keuP/XqD6uV1T/damLstVi0Ly/uRLL5tL/RDXYtgYF7GfrMBtaICceS8dj+LH+qGegUECuduO42Nxy6ie71oTJq1xbRdDxDqYkL8cC4pA/d0r60CE82rhKpgpTx/CUxIoEIn/c0ki7J+TLDqbTZlgZYpJhwFCIUEwF6+vokKIpYGF1IyVKmkBGwkg0uyr7rWjbpm+/ftmmOmZT1AKEZ9sc7qenqAULfh2EVsOAaV8RkZ6Ic+V9CTrqS9NG+3er6WgWhH6kQHoWpEORU8k+xJV7Bw5xlsO2md2ScmzjS/tq5vUUlN9X1paGOcTUxXWcVnk9Lx1iItE9rP29Pu9vrwEskkPHTe/FmgWZVQU4BQ9HzHYiI8gCA/bxXMEzONQUnd1QQI5XUsZcr6e40uKthBhjcREV0RBgmJiFzR1hnm5UkbgPL1gFXvOL5up/u007T2Wunw6c3AEYsP9ClnzQGK9v/TliVAKN5rADQbZXV3hp7PAuYhgkRFJ1OAt3wP1OkDhNdw3n6sfh+4cEhbnrwJKF/X+vLI2kCkRdlw7FYtaLjxK8AnEDi6yvH9VmmLa23JnnMOgwK2RraqYgoSihGfrMWeM1oW3pQF1lObwwN8zL3HHu6uyhX3vTJAZeukZebg8PkUvLNYm9x8Y+sq+GXzKdNtLb+0SxDy14kd4evliegQ+z6I55LS8fDP29UX/hnrrAMITSuHYufpRFzXrCI+HN3SbpCBns0oJs3cop5LrfKBeHJgA3yw7KDKMOxYKxI/3t0BW23KIS3JoAX5GUrsMC45HRVDCzFk5hrbeyZJBUCkFPqG1lXUEA090FvUbL1j8amq1LRCaP59KCWwerXk515ag4RrD8Vj8/FL2H8uGfONWbKWAUIJvkrmoByT649cxB2dtfejQGNPO1cpR/9962nTcl5BUAmyC3ld620H5Dib8d9xnElKR5e6UXlmEh6/cFmddCNbVzG9F9ia3LMOxnaqjnavLsO1CKLbBghFds6VZWETEZE9BgmJiFxRg0HASmP/MikXlgBfu7uBzMtaRuGKN4BqHczX/34IcN6YYWQZIBSt78j/sXbMtl73CwLSGSWkqwxyL3gECK0GPGQOYJlICa9k+EmwbciHgE/BAzeuiGWQL6IQJYUVjBmGS1+0v+y+LVoLAHkdel+j/bWwyGIiaZvq4TiTmK4mhEq5sR4ElCw8by/r7CD9MltSrrfy8Z522yVI0qO+dBHTTO5lDqQ+P6QRPlh6UN2n9BKTLD4h2Uld3lyuliXGd+T1wXb94Gwz+yRAOWdiJ9SKCsr3eT87uKEpuKk/F8mEvPuHzabr/HfkAmo8aT0I5ujrg/Dmov34ZdNJTB3dApczc1SQUEiJdWkoKb35y3UqQCj0AKGQIG9RgoQbjl7ETZ//p5YlACaZcXmx/D1sfraPypzrP3Wl+pMyrnNN7DiVoLLJ3r6xuZo+K2W6Elgb3a6aKctUgqxFJQHnpLQstKwWXqTbbT5+EXO2nMYt7aoV2BfxyPkU3PbNBodZpBIckz52ci7ZqHLqUEsb1iGk1Foya7Nzrz6IWlzknwLrj1zAmA7VVaaeJT1wJvv98/86ql59sQlpKlj4+Jwd8PfR+pXakmFG/zzaQ/0uHAX0Yxxsk4xECao/2Kcupi49qLa9P6o5ft54Sk0cf6BPXRWIlMsle/mdG5rh6zVHVe/H0S2j8fKIlqj33N+FHmA0e+NJVe4+olUVJFj0Vgzw9VKvY7XMSedERMWG76hERK4mKw2o1BLo/iRQqzvg6WXun9bHOMRk2DTz9WUgydGV5vXIusAF7YM9+rwIdHnI/jEkyCGlyJaunwbMnQSPBQ8jyKc8MOSN4n9u5B7ijcdf4gngvUbAwDeBg0uA/q9qZbwenkBSrBag3v070PNpx8fp1TizAzgsmS4ewLNx5tdRfmSCeF4STwJdHkRJkAnE/1pMJv11Yie7y+OSMlTgT/8C/9Ds7Xb3I0GhvWeS8dvWU7ixddUi70ewv4/VoIPZG09YZS0Kic/IF30pPZz+33EVsPnSorfZZ7e2VqWCDSsGF2oiqQSu/tkXpwKBkqmYYVMG60i9mCA1KVmyDZ8YUF8tyxRWnQRYnUl+X9JHLsGYxWlLlX6fTMAr83bjuoZhuD3aHLR15H5jbzghA21eur6x+lk5yorT+8dJNmZkkJ865Tcs4o7O5tfAA73rqixCKQd/Zf4eFaSWzNWChuPI8Jje72o9/+7rVUdegfhzeyxu7VDdYbBWSmlXHjyP2lFBpgCx9NX76e4OVoE98e7i/aqPoEy+ltJiRwFCyY4tKENQL72V8vzSQF5Dw6atUcsSKHtiQAOry/XjuZFxkrBMM5eT0CcEVwpznC0rP4u8fh4SjJV+ggfjtOqC0HI++F+32mpZWgBUDPVHi6rharjL8JZVrG77YJ96puUnBzTAY33rIT7+vNU/LtrWCDf1KnREXudP/qa9p8j7mT4sScgApb92av8sKW/MeCQioqvHICERUWmeXBwUA3jZvFX/eZ+aMoybfwKC7MuD7Gz40nHfs9vnATW7Ob5NrR7AAa3hv3LdVKDhUNWj0GP5FPhUtshSJLJ1ciNw8TDQfLTjyxsPA9YZA9lJp839NcOrA10f0Y7RsOra1OCcTC1zr/ODRerZly95zM+7assNryt8/8AAGQSUB5vendeS9BPTybAJWxJsq1He/L5RNzpY/egs56I80reeCgh1qetnCiJcLcl2a109XGUPPf7rDqvBEK/M34s5W8zZcboBTSoU6TGk/HjWBPP7T1xSusoolAEJi3adMQUNhPSV8/b0xJRh5sEkEiAUIf4+qB0ViMPnU01TWyUTMiM7B40r2WeoSX/ACiH+KotOAjYSrJBsqav14p+78d1ac19AnQTq9GxICbrpAaLNJxJwe/eGdteXkvCvVh1FkJ+XyuS0LEOVk7enB9Y+1Utljlm6bOwbF+BX9LJaPQgt9OEYklkmZa2Rgb6qxFcMaFwBEUG+KjjYr3EMVhuH7IiP/jGW+xvL32dtOGHKipMy62oRgVZ99Sw99dtOdK8XpTIC7+1RR/1O9Psb9502CEfIMSnTuOVXL0Eu/RjITzljwDo9Kxe5uQa7sveSJoFY3Zcrj6ggofzMZVCJHMt6Rp3eQ9BSXsHBwggN8MH7o1o4vEwCi4XNcJWfueXPUAaqLN8fhycHNkSrV5aYtvdpGI2le+NM6+ss+mSuPHAeSywuy8w2v6HJEB0iIioeDBISEZU2ly8Cv44DjizXBinc9L315Tt/ATy8tMzBwqjRGVj/qXk93thHKDufhuwVmgEn12tTkUVgecA/BOgwEVg+BR4SuLkS+xYAyWeAM9uBjvcBUeZMAypDvu6jndfqCQQbe5VlZ2hRKikdrtpO6+mXZdNbSp/A/csdWoDQUmaKlmVYHHb/YV6+6YfC387Ra+7pWCA1XgtwXkMSqOj61nIVrJLAiGVmXUGkLPOnCR1UGarB2GdsRKvK12Q/60QHq9Of22Kx+pAWDJLglaMA4U1trDOProSUSOplkt3qllfPMSMrF79P6pxvma0e5FJBwksyoCIDgz5YhcycXLSsFgZ/by/UjArEC0MaqefymDHo2apamAqCigYVgjH/vi6qT6JkcDmaDmtbYvvUnJ1oXSPclAkmGXS2Pru1lTrv0zAGS/eesxooIyRIaeuFubtNQTlHJIDW651/seul/lbbU42TsAuTxWlL+hDKIBnJRpWfm05+lnLSLdptDtwu2Ol4erbOst+cbVaqLRmcoQ/P+HLVUVV+6shtHaojvIiZZhJstfwZSdasMy3Zq5XG68etlBFL9qati6nmn3tpNrBpRXWy9dL1TbB07z+m9fkWx4tkDUvfTt1j/eur14eQUmQiIioeDBISEZU26z/TAoSibj/ry368RTs35BQ++6nhEMfbd/4M1DUGc2xJeacEd6RvnPA0fkEy9lq7oiDhiXXAT8b9F/UGaIMiiis7jEqHNR+al6e1A4Z9AjQYDEyJ1o6j0bO0QPXIr4Ddv2lBb52X8Tg7ucH6Pkd+XXwBQjl+U4xfuFuMKdrx5x+mncvAFZn8fX4v4BNwzQKE83fEYvKsrVaTg4VlqbGjnmGOtK8VqU4lRcpI9SDhx8vN2WLij0mdseZQPEa1LXqJc37CAnyx6vFeyDEY7Hq2OaKXxUo5ZZspS03b9YEnUuoopb4SgNLpAUIhwbsX/txtmt4qw1wkUFg5rJzq/2bbD3L62mPGqcAX1XXld2c5YOJ/3WqpEtEBTbTgSa8GklVlDg7ppHQ7NMA6IGYZIJQgcFZ2LtrUCEfN8kH4Zo22//q02en/HVPZfOO61ERimrZNnmdRSVbe3Mld1LIEBS1/hoX1zR1t1HTkD5cdxLJ95iwxRz4Y3UL13JPedjIdWx+yo9Oz6fQyVslyk76cXeoUPVM2yM8cFJSfm7ODhF9blOhXiQjATxutB/7ormteCa5MXjuWLN/3Pl9p/hlIabuUOM++u4PKaKxhMeWciIiuDoOERESlxdmdwIJHtX5supZjtPOV7wD/vGLeXj/vnlEOTVwL/PuW1vdNsreqdwF6P5/39SVwkmGRlaIHJI2lzz7nd8HwSQdg0vrCB1lCbDKXZH8kaHj/NiCi4GwocgGS4bTkOfN6eoL2O75puraemwXMulFbHvGlFigc/C6w5Qdg8TNAOWMQLs1mKq304CxOks0qOtxbtNuVC9f6F3r7AR+2BLzLXdMgtwQIhaNpnkICJtLLrzTKK3OrX6MYtKgapk7XQkHZfJbqxWg/O0d963SWAUJH9AChsJz2HFLOBze3sy7FlIw73fqjF9HKYmiHBMCub2H9HnlL+2roXj9KlenO2xGLT1ccNg1qSctKxm9bTmN4y8poW8NcBv9w33q4v3ddq6xDPUgoLAe6LDYObimOwQ9SYjxnYkdsOHoJt3WsjnHfbVRZnWLNk73w7t/78ZtxAu/cSZ3VUA3J9Kxr/B18fYd5IvjWE5dUCXmNyEBcupyJHacSVeBzSLNKqmT1xaGN1UnI8Jkn5+xAzwbRquxWKlrlZ9bVwaTeogi0zCQ0BledSfWKNE4XLh/ki7cW7bfqDSnHmgSc9T6EruStG5qp9gQDi9B64G5j78qS/McHEZG7YJCQiMiZpJxXyn6DooHPtIwMk3oDzcvbfzQv1+xuX4JckJjGWkDmFWNGxZ3Wkz8dKm9RChxqn/HjIeWg6YnmwE5BgypkWq2lM9u08+NrGCQsK/Tgm62fx+ZduivnnSZrJ5GTpQWyJSAnrw8ZWBJnzNgLsS9PKyrP1Dh47PpVWwkrYiabBAQlQHh+P3DRnNVSEmS6Z6ZxSIdMZJ0yrEmBwxecqW50kKm/2Ls3NleBOPnxSVCrtJAhG2cS03D6Uhr+2GYu+503uQuGfLza4W12vtgPTV9cXOB9f/zPIRW4kf5xEuC7r1ddlf1k2U9Pgj26qCC/PDOr5CSltHqQ8PFfd6o+jzJRWvoN3tGphun6thmU0gtOSssloJifetFXH2xuXT1CnfSBNE9Lz8D6UWr/XxvRFDe1rYrGlUJUVl7zfILEMvHYcurxdc3yzo7r2ygGfRv1RXGz/F3JYI2X5u1Rv89BDkpkr5UTFy7jwdlb0bthjOopqbP9XUoguEKovzq5opvaVEXHWpGm3on61GQZaCTTzB/4aZv5fdDLE5XC/DGoWcn9HoiI3A2DhEREzpCZqmVdfd4NSDgB3Pa79eWSZSVBiHcbAv2nmAMqjx4CDLnmssyiSDf38imU+oOAMXOAqm2terEZuj4Gj1Vvayv75gN7/gRuKKAcdOPXeV9W0XFTdHJBIZWAx49qE7X1voSWer+gZcTKMRxhP8VUvSbmTtKWKzYHbvsDWP0+MHsMMPwLoPmo/B9fgombvgW6PwEEOsgwyUhG+Pxx2vKwzwrf19OWlNtLT0Upo76G5Aux9Hob27E6Xr6+CZbtPaeyqu7qWrNUBwj14NRXt7dVAYzCDIpwBsk6fKy/1hvQMkgogSyZgDt/xxkkXNbKgeX3MKpNVRXg+nFCBzw+ZzuaVQlT/eFk4IkEbvs3rmDK1JPekfWeXWgK7E6Ybh6koYtPMZcaB1kEpRypHmkeEnIwzjwkR4Kv+tAQkeugX+Frw5vCAx7YfPyi2p9Y4+Cbl4Y2Vpl5EQG+6NMo/4nJRSWZf5/d1tq0Lser7STi0izQ1zqgK1YdjM938nNx6/b2crsSd0ekvN7VWU7EloC6lIhLVqQMB3rslx3q9VezfCCWP9rDqftJROQOGCQkIippMpn4Q5vA2A/DgUqtgNgtQOU2QLObtKnEybFagO30Zq2fW2GmGec3mbXBdUDV9oW7vnyxd9Cz0NDzaWTvmQ+fC3vNAZ0NX2hTafOSaVG6HFZNC4zqPPmnqExM4l75FrD9J21qtgwmGfAmsPQFbXjJgYXa9dr/D2hzJ3DpuNaP0tK/b6uhOFb9/+QYlCxYIfdRUJBw8XPAoSXAhs+BMb8CdW0yjI7+C59LB2FoOwEeLW6+8udboSnw5An7yeNXID0rB54eHipTUPqszdlyGg/1qYumlUNNwyAk4CIko0hOrqS0Bght6QFZydiTwJm/p5cK/DnSsXak6n1YED1AWBgF9VCUn6P017OccptXX0ZbEqB796bmhd4XKjho60zDWlSyCmpfydCZ0kwmiLexKKF/Y2RTlc35UF+bvxlERHRNlK2/KkREpZ0EUSSQoZPAn/RpE42HA3ctM/c4O/qvuRxXhF5lmZ7c7+iZKA4J/T9CZGgQPL/pD2QkApEOPrznZAMLHwdys80DHwa+DTQaCrxb3zqrklzb1CZadqD4ui/wYiLQ4R7tNNdYRtxqLOArzeUDtVJiW5YBQn2itqjdyxxcL8gpi4EnM2/Q9iMrTSuDjqgFD8lKlEB3tY646tBVMQQIzydnoM97/6rA1LJHuuORn7erKbQrD5xXQSuds4cmuIOtz/fFP/viVADwakjp73drj6ll6bEmwzRk0EyIv7fqF/jiPPuJtHkF92yNblsVHyyzHgJja3AJlsOWZdUjAxEe4GM3HEWGCclrs3HlUFVGLUF+KY2V3qC2PSUtB5+cTUxDeICvGmYjx4JIzcxR5ejSW7RzAcNV/rq/K/x9PFU2nQSMXx/RDAM+WInsHIPKNC7LZHIxpxcTEZUcBgmJiEqKBCiWvmheb34zMPwzYOevwJ65QIeJgKfF0JLUC9a3lyEJpUROSFUgKhqo1h44uBio0lYrMU06rfUvlB6Dh/8BNn1tXUItGZLijr+AXXO0yyXQ+Lw2BZVc0Lk95gBhXiXAYtA7+d9P0xvNk44lYC4DTYSU1nt4aSW+BZHgo/TJ1CWeAmbfCsRuBUbNhIdk6go5Xp1EylclaCSTYe/4dqO2m2lZaPT831bX07MIRYVCTjCmKxfo540hxTAZVvqpSQmyDNKQgI4tPUjYoEKwmo6s0wNHBQUgk1NSsf9CJtYcMv99KMkSWHchmZ1/PdBV/Zw/WXHINDxIHyYkFj7QFasOnsdn/2q9ImtHBaFJ5VAci0/Ft2uOqgzOtKwcTP/vuN39hwX4IMEiALnowa5oUME8dETK9KUNoczUmdyzjt1AEimV/+eRHup6tlO0iYiIrgaDhERE15r0C8q6DCx9ybwtMEoLEIqmN2gnWwPfBD7val6Pssi+Ky0ytWmLSDkLfNHD3Heu68NA4knr61qWl9boDBhytCChnklJrkeG7nza0Xqbb5D1+ulN5oxYPSvQkV7PmYOENSyOe+HtD2RrfdTyJeX0/31sXv+oDZCdpi1LX0NdaMlmpazYH4e/d59Fcnq26nNXVFXCS88/CCh/khE4rkveg5jeGtkMC3edUdN5ZQLwYWPwqTCBHgks392xEjzKhaL96/+obdNuaVWMe0+WKoaWUyXnUjb+9O9aX0JLAz9YZbX+xJwd+GJsG/R7f6VVkN8RywChGDB1FSIDfXEhNVO1HpDScn3odkg57zzLcnH1OdFERERWGCQkIrqWzu4CPutsXvcLBR47qE1ILUjFZkDrO4DN3wHtJwI9n0ZpEbj1C3iuN2Z6CT1AKLIztKzCBQ9b38jL5jnX7GZe/rgdMNmiVJRcgx4A1DMF0xOAFhbBOEtyTOQXJAyvDtw8G9i/AGh5m/VlMu24oGnCErC0DBCqbcYAoYWkTk/BJox5TWXl5OK+WVuRnJGd7/Wua1ZRZR7JVOAJXWvhjUX78MXKI3ZN/cm1yYRfOYnxXWrh+bm7MKGbgyE++ZCprzPGt0dccjoGNa1wjfaUdEnp5oBe+SA/xKc47gu5OzYJnd/QgreFcXO7qvhxg/mfaRIgFBKUPHnR/N6VleP6g0mIiMh1MEhIRHQtyTAHSy1vLVyAUJdhnGIpwx78rcuNnCq/aYqS8bX1B/vt+T3v+P3Fs19Usqp3Avq/pgXDpeego9+xT4CWSRtWiL5Z9QdoJ1tt78r/mBPxB+y39XgKWPG6aVV6EV5udsc1DxJK8OZcYgbKB/siNSPbYYDwlesbo25MsAoASm8zW5N71UFyehaaVwkzDS6hsuWW9tUwsnVl+HkXfVJ1l7r597Cj4lM/Jti0/Gi/enjSOO1Y9GsUg5UHzyM9yz5z8N0bm+Op33eqoF/fRjF4elBD9HxnhbpsXOeaeH5IIzzQux7umbEZ208lWL3F+Xl7IsM4+KaWg7J1IiKia4VBQiKia2XXb1qvQV2Xh7SSyqLo+zLQeFipKzU2SPmnraiGwPm9wJqpjm/kKIA0+D1zxqFkIBYlgErONfNGbSjIbX/kP8Tjfyu1/pS2k4aLonx9LQgoj+eTR+ltmJadZSJDS9IuAQ2HAJ920rb5mb/sXyu7Tifiuo9WO7ysfc0IrD96EUObV8JtHWvkez8h/j5qOAGVbVcSIKSS1b1elOoLKH0AZYCGZZDw8QH1MW1MKyzcdRYp6dlWZcn9GsegdnQQjsan4PrmldXU7FkT2uPUxTSMNE7OrhDqjz8mdTa1JTidkIZb2lVT65+sOIy4pHSXm2hORESujUFCIqJrRYYmCBnkMW4REFLZPLm4sGSi8dVONb4GDJIdZqv/FGDGSOtt8pyl/6IMnwh2MBSg7Xhg2ctamaqULN/737XbaSo+EvyWgTVybBc05Vd6UVr2o7wS++Zr076l5D6vIOHe+dbrB5do05Ql2zGPkvecXAMysnMQ4OuNb1Yfxcvz92DOxI5oXT1CXS5DAWSSaFHkFSAUo9pWxQ/j26ueY0TkGiS492h/x/+ok/cOHy9PFfgXZxLT8NE/h9SwIZlK3qJqmDrpOtUuD9R2/Dg96kdbrU/qWac4nwYREVGhMEhIRHQtrPlA65E25AOg8YjSVSpcDHKlt6Ku3kBt8mzt3mqCrNWAiId2FxwYlSDO/r+AOG3qp8mJdcC8B4Cbf9RKTSNqFT3ISsUv8TTw81j7vpLXkqfx40puDpCWAKz7FGg5RstQbHErkJEEzL3X+jYzbwDK1wMmaxOEFZuA5p3fbcTKA+fVYaWX+o389D9VCvzekgO4dDkLM+9qj851yqsSYsnuk76BEjyUUkBZ1rMHF+w8o6bPWupQKwLrjlw0rberGcEAIZGLW/ZId9zw6Vo0rRKGiqHWWfX39aqLhhVD0LKaOTBIRETkShgkJCIqbhLI2D4bCI4BbvsdZVF2uDEVQgKDt/xkvqDhddZXLExQT4KApzcD0Y2st2/5ATi/D/iwpbY+4kug2U1Xve90leR3pev+eAkHCbOBPyYCh5YA/76hbbt0HKg/0Pr6N34P/POKVqL8UgQQURu4eBiGRsNNV5FegRIgFLbtDp+bu9u0/Owfu9CrQTS+Xn1Ure+fMgC3frUee2KT8PM9HdG4Uiie+m0ndp5OxKcrDlvdj9zuhSGNsWTPOdzUpqoqLSQi11Y7Kgibnu0LGS5sm2ks/wQY1LSi0/aNiIjoajFISERU3H6bAMTt1k5lVE5YLRgiasEjPVGLsOhflCSwp7MN+uWncmvr9YxkYNsM6222mYZUMmQoyS93AD2eBJreAEQ10MqM+7wIhOffV6/Yg4QyECfhhPVlO38B5DjUPbBd269tM4ELhwBDDjDsU6BaeyA3F/FHT8M7NRPnks0TSmNC/NQX/7WHL9g99NH4VFOAUEyauRUbj11Sy5//e0QNI5AAoSMDm1RUg0kks4iIyg4viRASERGVQQwSEhEVt11ztPNqFr3QyhoPDxhu+A4el+OtswWl7Fg3+N3C39+qd7XehPeu04JSv93luMyVSt6x1cCFg8Cc8UDDoUBUPWDUDK38+xqSiaCm0lxP43CH5a+ar1C5DXB6E5B8FgiyaOwfqjX9t5qmbByIs+rgedz+7Q67x3pzZDPVDyw314ALqZnYcPQiJs3a4nC/lu49ZxUo2HjMXE4spP9YWmYOXrq+sQoQEhERERG5CgYJiYgsndkBZKZYDzsorKRYIDXevD7iC5RpFZpKR3f7QSU6KfUs7M9RAoTikw55X2fnz8Cgt4By4Veyt3SlJBCsu3RMCxJWanHNHk4CdQM+WIkD51JUOV85Hy+8Wg4YZnvFBoO1IKEM9tEzCcf+aT4mLaYdf7U1FX7Hj1mVEVuKCvYzDSiQ5cHNKiI9qzleWbAHXh4eyDEYkHA5y+52oeV8VG9C3crHeqJaJAODREREROSaGCQkItJJGePnXbXl+7cBETULf9vtPwG//8+83v4eqyCF27CcYrvxa6D1HYW7nV+INnyiIF/1Ae6z6IlHQOw2LdNOgrbXQrrx91J/sFZ2LH0iez4FdHus2B4iLikdaw7Ho1+jCriYmqkChCLXAKRm5uDZzKFoU/USqpxfZb7RspeAIR9qpeqr39e2ySRtnW+QafGNVfHIRoLVY3auE4k1hy4gLMAHNcsH2u3TyNZV1EmXnZOrSo+lfWG/91eqbSkZ2YhPMZct+/lwKAkRERERuS4GCYnIvV08AqRd0kpZ9y2wDhgWJkh4eguwdQaw5XvztvYTgb7GzDh3E14TaHqTGhKBG78r/O0GvQP8frf99v6vAVXbA1/11talx9zli0BARPHtsyuTCdpfdNeWPTyBm36wHx5ztSR7U47nae2AhOPatgwtiHelTly4jG/WHEW/xjHoVLs8Rn+5DkfOp2J8lyQMb2mRjWqUggB4p2t9AK0cWQHMu19bbjYaCK5gvqxOb/OPyebjTvd65fH9uPY4eC5ZZQMG+Bb8ccjbyxN1Y4LVcr2YIBXI/HXzKavr+HoxSEhERERErotBQiJyTwkngVk35T0MozBZbeKvR7Vpr10f0frqiU73Ad6+cEtS6jnyy6LfznY6rWg0DGh5G+AfAlRqBcQae8Stfg/oN+Xq97UsyEo1LxtygdljgGfjTD34rpr0h5QemzLFWA8Qiri9V3W3L8/fo3r7/bjhBH65p6MKEAoZECITgXU/3d0BY7/ZAN/sFFRI3mV/R7t/My83GWkdPA6rjr+9uiEhwz5wpwci9aBfUSWlZTvcHuzPj1VERERE5Lr4aZaI3E9agjYAIa8AYXAlwMM4KKEgmalaBlfbCcC2H4G6fbQeaWWEZHx1e3u5WpaSTG9PD/RtFIOxLcKK94EkECgDJxItJtfW6q5tFz2fBmbeoC2v/Qjo/SLgxT9hdpN+9eM72GKQx5Xa9A0w/yFtubFNR8AryOQcMHUl9p1NVpOEzyVpJbrSz2/ox2tM15H+g4lp5t5/kuUX4u+D8elzC36AWTcCL1pMGfbwwCu+D+FUahrKB/nh5esb496ZWqB5UBOLjMMrkJphHyTsVi9KZRsSEREREbkqfsMiIveQEgd81gVIOQfUGwgcWGh/neY3A8M+1ab15uZqpZyWGYHZGYCXr3a5ZFL5BWulyuUigJCKwCNXl12lD22Qaan+Pl4I8vdGxVD/QpVCFsfjytAGkXA5U2VKyQCGacsPma4j/djEwbgUDKjTFNHmhK/icd37wMyR5vW984A247Tlmt0ATx8gN8s8TMOytNQdSdn1593st2enFc/96wFCEdNEy+aUgSXl6wMtby3SXW0+flEFCIUeIHSkTnSQXZBQegZeTrPOjEw2lEOwh83zbGBfZq0P3u5WtzwGNqmAH+9qh0BD2lUH8yb2rI23Fu1Xy9PHtUNkkC8aVTQGtImIiIiIXBSDhERUNuXmAJu/BUKrAslntb6BEiAUeoCw66NAWDVzT7PqnbWoQkYy8Ot44ODfwC2/APX6AXH7gE/aa9d7/KgWnMnJ1NYjahd597JyclVW3kvz9uB8SgbevbG5Cgy+sWgfvlh5xHS9ID9vTB/fDq2qXbuJvjPWHccr8/egfa1IFUyZskALdv51f1fM3nTS4W3Op9hPer1qVVprQVj5ucqU5HYWPQqlfFYGZax4TVuX36m7Bwl3zDYH8FrcAvz9tLYefwgIr3F19334H8A3GMhMBio00waj3K1llBZW4uUsvL/0AAJ8vXDqkjmgF+LvrUqKVx6MR67BoKYHX0jVXkvnkzPsgoRVw8sh7YJ1kPCL7MGo7BGP0T4rtTJrffiNjewcGTMCeHl6wMPDQx3jcXFxuFr39qiDnvWjVRA/LMBNWwsQERERUZnDICERlU1rPtCmn+an26OATzkgsraWadhwiLb9ix7agAy9hPH5S1rppW7nL+YAobh0FCcvXsaK/XEY0KQiooL9sPdMEi6kZKogh4+3B+pFB5sy9d5fcgAfLDtotSsLdpzBvlcGYOWB81bbZXrq9LXHij1IGJecjqPnU9G2RgSe/UPr9SaPbfn4D/+8DVXCy1kFeHTx1yJIWC4cqDcA2PsncMd8IKKW9eUxjc3Ll45pWW3uSsrcFz2pLQ81Tvg9sU772Uk25oM7tQD4lcjJBn4Yri1HNQRGfnVFd9P85cUOt298tg/8vK3L+V9dsAdfrjqKs0np+HTFYbVNgugSYOzZIBpHD1ln/v2Z2wnHDRUwOnCLuX+ovJZtZMt4ZOPQkeLWkJmDRERERFTGMEhIRGWDZJbJIJLezwN7/jRnDdoa+ycwfah1UKFGF+vr6AFC3V+PAOnGQISUXMqgEgt7+kzHoLe0LKt1Ry/iif4NMOjDVTBo8QllWItKmDq6JdKzcuwChLoxX603lWRKD7X4FK0sMzUzB0W163SiCvJFBvqhRvlAlb01tlMNVA4rp0qLh09bi9MJaWheNe/egpLZKBlYepDw2zvaYvz3GyFxl22xybjR2JvtUFwKmlYONQVBrzr4JXwC7S9Lt+g398vtQGOLdXciE32nX29ej6yrnYdXN2+TzNcrDRImnTYvT1p3RXchQXNHRrSqbBcgFHWjzQNE9EzCyuHlVPZfRKAv/jOYeyCeNYSrAKHdgCFHQcIcLcvQx6sYjk0iIiIiojKOQUIicm1LXgDWTDWvzxiplUnqzchsA4QyDGPk1/ZZavmxzCKUCbvGKbsDMt7APoOUK1tnBMrJ1h/bYvFQ33r4ZvVR0zaJqXl6eJiynTYfv2S67KG+dfHThpPYeTrRFOgoCpkSe+BciuQi4r8jF9S2z1cewZ+TOyMn16AChGL7yQTTbR7sU1eVgeqDJNpUD8eqg/FquX3NCJXR1aJqGLacSMCmE1ow87av16v1N0Y0xeh2VxiUsnTOOMHWN8D+suz0q7//suDMDiC8pspgxZ2LzMNdZOKzDHURQVfYMPLiEfOU7lo9inRTg8GAmk/95fCyrc/1VUFkyax1ZHirykjPzsGW45dUENrbywOj2lRVl/l6eSLeEGq6bi4sXtvSHzFe6w1oFZU3yjKWG3vL1G0iIiIiIsoXg4RE5NosA4Q66aMWFAOEVQfO7TRv1/u0NTVOyc3LvevN/QfzcdxQcCCmVvlAHDEO/Hj81x0q6Kf776neiAnxx2O/bMecLadUcERIfFOCcr9uPqXW9SBiYWVk5+D3rRbZYBYkAHhHJ/t+dRKwnNyzDiwf6avVR1XPNSEl1KJDrUgVFDwUn4bLmdlqWTz5287iCRLevQJIPqMNhbGVKUFPN3LxqNab0XJ4Tk4WsOQ5bXnc30C1Do5ve6Wl2B+2tM5YLMCl1Ezc8d1GVRpsGeS2FR6Yf98+Hy9PjO1YQ51snbyUhjiYM159kY1aHrE4YqiEc7csQeRvN8H71Dqg/d2Yu+00/jQG5GUIipTrCxl+QkRERERE+WOQkIhcV6IWRENUA+D8PiCgvDb1VqTGA+OXaOWrR1cCW6YDgeULd7/RDYDuTwDRjbSATMIJtfmZrHF41UfLKtyRWxNp0AJoH4xugYysXGw/laACfVtPXFJlwzUiA/DXA13R4LlF6nrrj140PUQ5Hy8VIBRv39gcL1/fBGlZOTibmK6yqOpEB2OrMQCnZ/PlR7IDf9p4AulZuSowqbu3R218Yuzxpvtu7THT8pRhTVQ5p/QmdNS37UxiuimbS1QM00o6JZhoOWCi2IRU0k6ONLoeWPI83MKLxsy5jpOB/q+at8duNS/L8Wk77bj9RG268ZUMdtFL6nWD3inwJvfO3GKVjerIWyOb4WrIMJ1XDBXwTfYABHpmYnpWHxUgFO3fWo03vf0xSj7NpF3C83PPq+NSAvMydVgXbQxyExERERFR3hgkJKLSI26vVgYsk2z1rCk5OSo9FfuNU4qbjwbaTgA8vYEDi7R+dX1eRFK5Suj76b84l1QVX479HX19A/N/+OR0FQyTaaWGHk/hrb/3o5+hHlriBC4ZgjArpxeGe61GG88DmJ7TT91m4zN9TFl2N7XVyiPF5uMXUTUiQPX1e3V4EzUcxLIacmxHi/5xEjT09VInCdjlNQ1Zsq3y8tpfe1WJsa2BTSpaBQmlN2FSupZdJW7tYL0fYvbdHTDqC+tedK1raINT/LzN+5Bw+RoECfMjmaG6UTNQZskkbt36z6yDhPoEYykt1suMdbvmAOs/BTx9tIDfjd/m/Rg7fgE2fA6MWwzopbiW99f7BaDdBLubJaVnYfKsrUhKy0JIOR9TKbutmXe1R+c6hQzKF6BuTDC+H9cOnh7t0LVuFEbk5KLXuytw8qJWMp9jLD82JJ5CYpr2XI7GpyIuWevpKaJDGCQkIiIiIioIg4REVDocWKxNEm41FhjyoVZz+1Vv4Mx2XJiwGd/vycXgphVRv0KwdTmmiGkK+AVpy42H4d/kxZi53xPrlvxjCohNmL4Jx94YbPewaZk5apjIsQupGP7JWrWtT8NoLN0bp5Zb+lwCvICHsibCAE98VuUNfNguAQO92uGZWjF5llG2rm4etDCmfXX0bhCjHkME+nqjcaWiTUZNTs+2CyAmp2epct8KIf74eeNJu9vIj1CGPzzWvz7e/lvr2yY/j/AAH1y6nKX6CzrSvlYkVj7WE7GJWhBGHrdeTLBdkPDtxQdwNeKS0tWAlkIPPMnNNg+P0SdRX8FjSnBLgrelkkSSl1pM5b5+mvXlkiEoWoyxv62PMZiem6Vl1ubnt7u0c7lejEVG4pg5wL9vaK9DRzfbfMpuArdoUjkEu06bMxGrR+YR2L9C3etFmZYlWD7rrg5qmri8TgOOasHA7HNyjDc0Xe/tv80/g+hgLWuXiIiIiIjyxiAhETnfT2OAffO1ZSkLrtsfqN5JBQjFtnnT0PT0Njz9zxBMf/F+ZOcY8OWqI7hu73Y0kOHDJ73Rs3qOysSTfnz3LLikSncdDVaIT8lEZKCvCkxJgLD3uysQayyp1ekBQrExtz76eW1GkkHLQmxVrzoCWvRG7yI+xQqh/upUFB1rRZoytS6mZiLIzxtvLNynMh5rRwXlOSW5Xc0IlVs1oEkFFeCTkuNpyw/hsnFKsgQIhUw6zku1yAB1siVl0roV++2DRfmRac3H4lPRuno4Zq4/obIrr29RCR+MtuiD54D83s4nZyDaRwtaqsExfz0GdH4QCK1cpInP109bg2oRAVj6cHc1ubnU+fVOIDUO8A8DhkwFGg+3vrzfK0B2BhBgDkKbpBgDiHrpsTi9Bfh+qDb1u/3d9reZfStw32bAkAv8NgGo0RW4a2meuyevH51MtJYem3JcvjikMdYevoD3lhxQAcMq4cUbJLQlWbq3dayhhvE8nXUXluS0Qb/gkTJr3HSddUfM5f16aT8REREREeWNQUIicq41H5gDhLpf7kByhXbQcwb9T69HZ6/d6Ou1BetO346Nh2LRYPWj+NcQiRUYgjcWpwCLF6lg2LojFxwGCIXl5NVFD3bFjZ/9pzL08vNlzmDMyOlj6j+Y13TWa+HpQQ0x5OPVavmeGZtxKC7/wR2SOfjpmNYqOGi93QNfjW2DN//erzLpRIi/j13Jc2FIlmGjisHYc0abbmxp1Of/qczApwc3tAtASqBPft5SBtq2Rjg2HtOGXMzdFos3RjRTAd68vDx/D75dcwwv9a+K2/WNG77QymoHvFbofX/ytx2qd6Psw4FzyWhYsWjZnCUizdjfr2p7+wChcLRNl2FxfKQZA2Q7f9EG+Sx8zBwklBJ+3cXD2tTov5/RypWz0oE2d1rdrWTsLdlzDpFBfvh4+SG1LdDXC/Pu62L63cox1rxqmAocNq1inkR8rUl7gMvwx4LcDti2xL7cPibED9c1q2RqCUBERERERHljkJCIro5kLO34GWgwCAgrwnTb1AtaNpTlIIqohsD5vapcMjh2jdq0Lbe2ChCKWdk9EZqSiZSDa9Dc4zDW5jbGrBxzTp/tgA7JFpNMvOf+2GX38AOmrrJal4y2vWeStKyooY3VFGCNhylAKJlnkt1XUqQkWYIbkkWXX4CwQYVg3NerLhpWDEatKGPZtY1OdcpjbjH0iJMg6bzJnbH3WCxCwyMw+MPVppJufTCL/AzfvKEZcnMN+HN7rJowG+zvrYJzQg8Q6r5efUQNapGfbaiDKbQSIBQv/H0Stz+yEZjWVrvguBZALSzJQNXlWjaIdJacbC2yK3mfh5YCs8cA3sbgaoWmwLeDtUzJEV9o/TrltdL2LqBef8f3V70zsPo9431nasNP9BJkkZ2pTUrepA3fMcm8DGz6WlsO0/pq7o5NVAHVAF8v3PHtRruHsiwRlwChXgbcs0HBE7+L01ODGqpWAuJ0gjHT1OjZwQ1xV9daJbo/RERERESujEFCIro6K17XsroOLQFunVO428y8CTj4NzDkA6D9PdpwhnvXAZF1gI1fAYueVFdbmdMURw0V0MJTC/69nj0Gj6ZkwD8jHlU9z6NeUC7ahUZgg8XUYEs1yweq0tKsbG3ysGStOSL906aOamEKdogXhjTCS/O00sVZE9qr81rlg4pcMnw1JBDz1g3N8P3aY3alvX8/2A39p65UgcuPb2mpgmwlRX5O5QN9EB1WDuuf7qOGpkiAddNxLfh3KuGyOv9h3XG88KcW4M3PO8behvVjgvH3Q91M5dUy5VkyHi3lRtaFqStiVlqR91tXGmKEmHkDcHYHcNli+EdQBaDlrUC3R4HXqwJn/IH1nwNH/gUOLtZOD+0GQqvY31+d3sD/VgF/3Auc26lty9J+F0rCca30ONnmdZBpHYBevj8OdzoIDFoqKAO3pFTM5/WY36AfIiIiIiKyxyAhEeVPpqSumaoF84IssoSkLPHwMuCEcQrusUJkdV06BpxYrwUIvf2RevYQ0nP9EPliosoCWrv1LCLDRqAXtCBhAoJwscVE5Kblov/e/ojwSMKy+bMwwWuhGibiF1kVz/ZvqLL+JLFpycPdVU+0BTvOqEEHEkCT07guNdX9yQCPu77fBE8PD9zZuYbaVqN8INrWsO/vdluH6irI2KhSiFOHHvSsH61O0j/xuo9WIT0rF1OGN1EDXP6Y1Fk975IMENqSMuFXhjVRy+O+24h/9sWZph7L78GR/3WrhTs718Tjc3ZYDcHYfy5ZTXGW3oUdX//HVNZq6d8D59FTX+n2WKH2MTM7F4lpWaos9krJkJiHZm9TmZpSBn7VMlOBI8vttyeeMJdQywCSzCxg4ePW1wmu6Pg+JQhasRlQvo45SCgGvgXEHwTiD5gDhLV7AQGRWjly4imk3rYIfkufgVfHSfjylyN5/t6kB2BpYjuApk/DGCzde04tl8qek0REREREpRiDhESUNwkEvqGVH+LwcuDu5ebtr8ZYX7d8XSAjGfDLI2CVlY70L/rBP037Ai990AI3fgQZB/JHxA14cJ55Om89jzfxP+95eCHrDszu1AaelX7DjJdqI8YQb7pOpsELlVsNRLMqYZgzsZMaqCHDPF4b3hS96kejm8U0VJ0MU1j0oJapVhBvL0/0qF+ypZMFBeOWPNTdqtQzr+nEzqIPNcnIzrXbPqxlZfy44YRab1AxWGVkfndHWxy9kIqfN53E5/9qwScZrvL1KnNvuVTjsBXdiYuXgTp9tczVyq0L3Ccpea737EK77RKMLIoPlx3UBtrsjcONrauooLacRretVvhglGQESsBv3N9a/z9H8npOHScD/30MNBwKeNr3b5Ry9Ofn7lJBsnHy+rRUty9QtZ1WVqyT16pkLYrvBqG34TuczXgEeGOn3VTh3bFJ6vy+3nVNQcKqEXkPvSlJYTbl6VHBvlb9ComIiIiIqPAYJCQix2SC6unN5nWZKCvbvP2AjxwEMs7uBP59E+j1nBZQrNtPollISs9CMNLg8UZVY2c/e1PmSc9A87CDA4aqeCTrXrXcyDhcwr9aK+D4YtN1fD1yEB6lTbaVabmWPfNGtnZQilkGWPaBK438fDxNASvpQyjZe6JNjXC8fH1jlSEofe4GN61kej4S2K0eoU2OFpczs+2eZ68G0SpDUc8KhG+A9ZCPfHz6r3WfSp2UMxeF3m9RSA+8Yxe0gNu5xHQ83K8+dpxKwI8bTmJM+2qIDvZTz9+uP6SeEfhNfyCmqfVlo2YC5cKBClpWplKnj9arUFRsATx/EfBwHPi6/ZsNWjbu4QsY528dFDV83A4euVkwtByrpl4rpzYCfV8Bts/Sdg2T8ZXXIEzLGWZ12+/HtbNa3/pcXyzecxa9Gtj8k8BJwmwGCU3qWQe/bj6FAF9vDGqWR8YlERERERE5xH+zE5E9KcuUYSTSM63/a0CwFtTBG9WBWaOApFPW179jgXa+9iPgz/uAH0cBm7/F4t1n0fLlJfhwygOmq87M7o2xmU+Y1l/MGot4iwChTEj96e4OmH9fFxx5bZCpj1zo2B/tdrNGeYuhDOR0ft7mDLcmL/ytyoeFt6eH6g/35dg2+OaOtvD1tv7TI8MxdFKibFtC+smYVqblTMkAlAEcYslzBe6TDH3RlQ8yZ5mN/34TZq4/nmc/S1uWYUs9QCg+/Eeb9ivDcSRT8rqPVqPda8vQ691/8ZMxc9KhlLPa+eD3gHvXAw2vA2p0BvwtJgNLj89QYybv1h+A1HjjoBN7tkM7LC3I0n5+ybssgoceXkD1jkDXR9VquEcKqnicV1mSlpOBbYUH+mJU22qlZlqwZPzaZguveaIXVj7WUw3QISIiIiKiwuMnaCIyk8ws6VGWdBpY/b62LaYxULMbsOMnIDsNOLDI/naBFqW9O2Zr5wsexrImbRGRexGjfbT+cq9k3YqvcwapIFG99O+Rqd6CPFQp44FzyWhTIwJvjmyqsoDseFlvOzp4NmraDLUg56pV3pwRaOl8ijlQ54hlUHDKgr14elADq8sty0a3nkjAsfaPo8aBhXn35rMgGX06CVCap1YDz/yuTb2eN7kLmlaxCM4VYQiGBDilpHn7qUS7y37aeBKj21XD1hOXkJqRgy6mSzyA1PPaBOO24/N+UAmGJhrL8I/+C5zZDgT3M10smZmSQWj5HMU9mQ/iM9+p5ruBF9IMvgjJshh+M0F7TaJ2T2DVO9pz8cjAM4Mb4vURTVXvRwnYu6LoEOf1ECUiIiIicmUMEhKROXvwzer226t3BsJrakFC2x5pMmH15AYgsi7QZjyw6WvTxSn1R2Lppl3Y7D/JtC0+uiM+7dUKA5teYRmgTEA+uwuoPxA1/WxKOcnpbmlfDW8u2ofsXIMK7KmsPyntTcm/tLdyWLk8s/+ElB9LNqLcrwyl2LcvDqv9gJysdJlfk69UYwCtTnSQVcaipb1nkxwGCfedTcLi3edwa4fqOJes9fm7vkUlvHtjc9R5RsvK61E/CrGJ5iw+6dV38qK2fjguGUkrPsSkJZGIzQnF4uGzUG/5RCDdGFDUewLmxdsXqNIOOLVBWw8zZhUazVx3QvULtLWxXBf0ulwF//hpWYLDvNbikiEI5WD8PTywHQjXBvdYquhxQfW+lOy83g1LRzlxYfSsH4Xl+89jbEcH719ERERERFRoLDcmIiU1Qev5Zuu6T9Zh6MyTuBDd0fqCtuOxt/oYdD58G+79cSuOepoDGNOyh2LAjm543NuYVWj0wYRBVx4gFNENgWY3AgwQlkqBft5Y/3RvLHmoG/56wJw3d0Mb6+CWLdsA3ZcWg0tm3tVenXeqU960Ld2glbqmpdoHyLJzcrEnNkll9wk9y072rVb5IDSooA3WkQEfuuR060w83aO/bFfTslu9ssQU+JMhLBJE03tl/rXzLKb/d9x0mzdHNMP9vetqzytrO0JWPIe1PvdivNdCvLo7CmgxxuIHZj9cx86A183LUeYMS5nU/P7SA6Z1ycbVXUjNxBFDJbybdYNaP1b/LqRDK7XOCoixDhBWbG5aXJfb2CWHfXx0SyvVO7FYpk4TEREREbkxZhISkRq6MObz9bgt9yY87vMztubWwYNZ9yLVUA7xp7VAzOCgB7Hy3o/hu/lLIG4vEFELt01ZgviUTNUPbTUqY6zXjfg5pwde9vkOq/3MfQgVKa2UwQxUpkUG+amTeHV4E6SkZ2N8l5oF3u6B3nXxwbKDVtsaVwpBZ2Nw8PNbW2Pz8UvYcOwivlpmnMCbZd+H77W/9uGbNUcxrnNNPD+kkZqWLAJ9vVRG4u/3dsa6oxfQtkaE6psoktO1ASu2dhmPfUs7TydaDWkRXxgn/orq5QNN2X3lYS5Bfs5nBtZfPANEWGQPBkQU+HNBlTZAh0lAegLOJKXjfz9sVsNf9p3V+j2KVtXCVJCs8xv/WPUm/ChnhDrNbNseFffOV1XOOV7+sCzSz/AKQLP079DNcwdW5zbB/Xn0PCzNpPegZZCUiIiIiIiuDIOERIRVB+PRIPsglqI15md0xBlDJLKMbw8SgNhyIgFnU3Kw4nwQmnV6CS/P343oP3erAKEuCYH4OGe4Wr4M66EGfw3ZgkGta5fwsyJnG9O+8OWf9/asjVpRgUjPylH9/7w8PdC1rjnwI2WwXeqWV3M7PjJmxXlkmQeInEtKV1OFJUAo5NwySKj3uZT76Vk/2rjNS13uKJNQz0S0JQE6MaJlZdUf0VZMsB+OXUhVy/4e1mXW7VOW4a1d98A44xjobBNIz8uA19TZzL/3Y4eD3ocTutbS7q5OJH7edMphz8eVuc1Qy/Mssj39rJ6jBEIz4IsluW0Kty9ERERERFRmlYkg4fz58/HII48gNzcXTzzxBO666y5n7xKRS1m29xx+9H0dpwzl8VTlb3HCOPF1wf1dVL+4Fi8vUesnLl7GxmMXVYmlLSkLHfPVerXcMDQbSDFf1r+lFsQgym8y8vUtKhd4PRl6Y4AnZmX3QqcqzSGjUmZvPIEn5uzEDRaTeXUSdNSDg7YkcChBQj2QaCnZYhjI0OaV8Of2WLXsacy0u61jDVzXrBKOxKcgNiEdX60+ilvaVVWlyBVDtcEZN3ittLvfP1Mb4HE/4JwhDAHe4dCKn/N2NjFdZVD2bhiNj5drk5RF+5oRSEzLwsAmFTGgiZad+PqIZrihdVX8vfssvl6tBUsf6lMP/j6eeDX7VtzhvRjZntq+1XhSm0jOCcBERERERKRz+W8H2dnZePjhh7F8+XKEhoaidevWGD58OCIjI529ay5NBgcs2XNOZeZ4egKRgX5oViUUHi5Yilbq5ebKZAYgdisQUlmbYPrTGGDCMq1E91o+7qWjOOlREXtOxsHPLwvpXiGYfXcH/LLpFIL8vdG4UqjKNvL0ACSBSgKEMiDA1oc3t1Rlof8+1gPrjlxA7ZTewL/rgFa3A0M/LHC4BFFh6T3zns6+C183bAPpricBQvHrZvssujRjALCcRXmwzs9b25aRbR8kTEozlyDXjTb3wOzTUMtCFOGBvmgdGIHW1YEhzSuZtt/ZuSbW7D+N1ue08umTuVGo6nkeB3Ir46xBKzE+ZwhH+plkVeovr6tH+9VH3Zhg0z5nZuciJTNblRDbkizG90a1sNsu2Zftakaok2RLhpTzRrMqYThyPgXVPc4iy+CF2NwwrNlxxnQby8nIEoAlIiIiIiL35fJBwg0bNqBx48aoXFnLQBk4cCAWL16Mm2++2dm75tKemLMD/+yzHmQh/cX08kGZGCqDANYciscvm06iS90oh1k8VIDcbG2icKWWwPE1gLc/kK1NUcWuOSpIeGbDb8jZ+A32dnwfuX7BaFo5FJUsp8GmXoCqwdz4FZCbA3SaDPgVlJ8EnP7jeVTe8RHW1nwNoQZtCENoZIwKBN/U1jxoQvq4hQf4qmEIf+8+Z9oe7OetAhUxIX5qn0T1yEB1Qvo9QMUmQN1+xfjDIrIOZMmAkvym8ManZOBsUrpp4IgtybATGdnaFGZLkqWnk+DdmyOb4sC5FNzbs06B+xiYdgY/XhcA7LtPvaZvXlxJ9eg8aYhGtjFk3szzKGp8/p/pNvLa2v5CP1XO3H/qSrsJz5ZuaFPwe62UZuskW/CQoQq6ZUxFYFwaDs3aYnVdef3uP5eMX++xGU5ERERERERuxelBwpUrV+Ltt9/G5s2bcebMGfz+++8YNmyY1XWmTZumrnP27Fk0b94cH330Edq1a6cui42NNQUIhSyfPn26xJ9HWZKVk2sXIBTP/L5LBQm/W3MUL87bg9ByPqYv0n9si0XbGuFagIgKzWPxs4D0VZMAodADhKLVWJVNVPGvO9XqR3M+x+ycnirYsfbJXiqLadOxi2jznU0pb9weYNQPeT9oRjI2xmag7Y6P1Krfofmo5dlTLYdHOG7+36FWJBbsPGOVzTV9fDu0rJbHIBL/EKDBoML9EIiuIEg4xmspvJf/CPSeked120xZmu99Sa8+ke6g3Fiy+3RRwb6mkt4CXToOfNAMuOkHoM9LKoD/Q+yDwEGgQ+AZIMEDfTLeUkOBbDV/aXGBd9+1bnl0rFW0TPnyxkEyZxAJ2LRa/Ov+rmhUSfsnARERERERuTenBwlTU1NV4G/cuHEYMWKE3eWzZ89W5cSfffYZ2rdvj6lTp6J///7Yv38/oqPNZV9kTcrLZMKmlJKNalNVZYPlR/p2TVt+SGXUyKROX2Shnec+XK7cBVtOmhvlSxmcBAhFePoJjPNeg4+zh6khFx/9c0iV5fVsEI16xrI5yp/Hhs8dX9BmvJoevH7vKXQ1bnrN+ytM9voDX+UMwv5zbXEwLgXv/vEftmktxkxSzh2BuTjSxvkDwLS2aAtgU249tPE8gGFea1HZI15dXK5yY4c3e29Uc5Upqvdpa1gh2FQaSeSMcuMbvVaghecRIMfxZGJbfg4yCSUb2rbkVmc5zKRI72cHFmnnK98CGg1VizV7jwcOz0DgiA/RbXU4Vh7QrhIW4IOEy4Xb/1eub6z6DTrqrVgQef8f3rIyft96WpUibzD2HBVVI+yDlURERERE5J6cHiSU8mA55eW9997DhAkTcOedWjaVBAsXLFiAb775Bk8++SQqVapklTkoy3qWoSMZGRnqpEtKSlLnMvRETmXFsl0n8c+SeUg3+GJw03sLbE6/aNcZFeTT/c9rHh7x+RXr/SdgFLQsM3E2UZsmOsprOd70+VIty7CLX3J6mPqBvb5wHz65pWWBmTcyjXTZ3jgMbFpBlbO6k9ycbAStewc5vV+C17IXzNv7vwGPE2sB3yDglzuRFHKT6TIvDwOqepzHwx6/oPkX/eGNbLzuPdN0+bHcGNTwPIfBZ8ZhuaNj+cJheE6T8KBGAoS6tvpyx8kOXwc+nh7oXs9cvqj2tQy9Xqho5HdvMBiccgzorQX16du5WRbZtzYk4+50wmWVkXtjq8p2+xvk52UKEsplpy5dxtSlh9T75fR1x03XC/T1Ktxzzc6Ax+r3If+SMcQfBOY9CEP/14DoxsCzWnb2U0HJWHXwPEL9fbD0oW4Y9OFqxBlLi8d2qG71uDKcZL0xoFclvBz8vD2u+Gf+5ogmeKxfPVQI9cfH/xzCe0sPokGFYAT4eLrVa9mZxy7R1eLxS66Mxy+5Kh67VFaO38Iew04PEuYnMzNTlSE/9dRTpm2enp7o06cP/vtP6+UkAcFdu3ap4KAMLlm4cCGee+65PO/z9ddfx0svvWS3/fz580hPz/vLpstJT8KPvq9iY249HD89AlFBWhAu1yBDKLSswrSsHJxKyEBEgA82HTL3mhNtPfdr5ye/ASyChLuPnUEwLpsChKJlpQD8ctL64e+dtRXrHmyd7y6O+n4XTlzKwPpDZ/FMXxk/4D58D8xDxDbtZ3jm1pWoOKObWo6rPhSoORyRvwyDz4W9GIzf7G4b6nEZUUhQmZ43emvTU2/JfBprc5ugt+dm/Ov3MNJ+XI7Tnd/A8sMJaFM1GBVD/FDhszZ29zUnpwtGeq1GVlQTXBr4GXIvXLrmz51cn/yBSUxMVH9w5D25JCUaM/yyDdqfr1On8m4vcUPTcHSqofVR9cBlxMVp/+TQeRu0MuPE1AzExcXhk39P4ret9q0W5LIC5WShwpdNTKse0jpg87c41+oRwMvHtD3cE5g9trEapJKdmoBXB9bAttgUjGwWhQBfLyzYcRoXLmejW61QDGwUZgoSVvLLKtx+5EN+U3FxSbihcTDqhNVDzQh/9bfPnTjz2CW6Wjx+yZXx+CVXxWOXysrxK1W8Lh8kjI+PR05ODmJirBvTy/q+ffvUsre3N95991307NlT/QAef/zxfCcbS8BRypctMwmrVq2KqKgohISUnb5M5aOzkGvwQBDS4R0UhujoINVT8MeNJ1UvQWnYfy5Jy17xQC5u91qMd32O4J+cVhjTuzUurtB6C16oOxLQBodq61k+aO552OqxRreIxPC7+uHnzafwwp9aKbLYdC5HDcFoVyMckcaeWJYkQCjm7b6AD8bknf1ZFm36bQP0Z3z31yvxYHAvdGtcFdEVKmLvmSRUuLDX6vovBr+AF5PNwe1bvZeicvlQIAHYWa4tIur1BXacQU2Ps+rycgfn4bu4jkg4fwZ/hLbHzNubmiasfpA9Ag94a8HHCtCCgj7nd6F8aCAQzhJ+Kpi818prW943S/rDUlCmFiTMMg4AyfW1qbe3UD4iHDEx5fO+PFSCbhdxKjEDU5bFYv5Ox0G4QrW2+Pctx7etWNnB/ZmXe0dHo7fFoOIf7uqA5fvjMLptVZVt6B8QjGoR5VA3r/6fV2iAzd9Vd+HMY5foavH4JVfG45dcFY9dKivHb0pKiusHCQtr6NCh6lQYfn5+6mRLXvBl6UUfEuCLVPgjEGm4lCVppR4qQChk2Ehimvm6EiB80We6WpasMqyS2lbtsqgjczHZ3w8fpw80TeAc72XsuWXkeWojynW+H2E2JcOTf9ymzmtEBuCfR3qoAbxJ6dkqSGmrLP3sC5Kba8D22BS08wZmZPfGTkMtjE+qhW4XovBuahYGf7QGxyziHg9k3ouoGp1gaDgdHj+P1bZJkK/+RGA90HTUi/i4RisMbnoGT81MxrM+WgnylMSnAV/gsaQMlPv0ToR5Zqn7+jO3kylIeLbrq8BabVCQZ9JpINJmCApRHuSPjTPeN/19tD9bOSovDsjKzMq3D2F++xebaM4en28xmEd6q/50dwd8vfqomp5cqOf47+sONxf159OoUqg66Ya34tT4snLsEhUHHr/kynj8kqvisUvudPyW6qO8fPny8PLywrlz1qWwsl6hQiEnTbopacifgnII9EhHakY2Uo3ZN7aGeq4xBQgdysnAo/gBQdDK9NYevoDDhkpqeV3dR7Tr7P0T+OuxPCcbH7twWQ3ZePaPXWjx8mLM2XwKGdn200TLOknxFc/N3YVXs29F/fTv8GLueNPlKw+cx5uL9pn6C+ouIgS1qlSCR6Pr8UfUPeY7XP+pdh6gZUpJD8jw8hXQL+NNq8dNM/jBz0MLpOw1VEffRhXxz6j92HbrDgzv0wOoqZU6Y9usa/XUiYqNt3FwSbbxf1wR683H++BmFa2u6+OV/8Am40vSjgwHkeznxwc0QOvqhczgizGXGmsPHgjU0McOERERERERlX6lOkjo6+uL1q1bY9myZVbpkrLesWNHp+5baSeN91MN/qrc+FyyBArNQblnBzc0LX/oO83xHQRXBAaay+d2+d+F6sZS1s259TAruxd2hvYAnr8IlK8PbPgCzVeMQ//GMZBByjKJ87H+9U23/2XTScxcf0J9KX/kl+1W++MOmYO3f7MBbV9divk7YhF/YD3CkYQM+OKv+62DCPrwlxszX8DinNbIhQcGR8TiuuZa8OP6e9+wvnNvfyC0suk/BG+ObIYKdVtZXeXBauby8AOGKnh2cCP0algBLepU16Zej/kVGPYZ0OvZa/UjICp2L2Tdrs4jD/4i4T609tiPsa1j0KpaWKHvw8cYcBRNK5uz97wLmAZvRyYs+4cC9QYAlVoC7e4GnjoJ3D6vaPdDRERERETkzkFCqYvetm2bOomjR4+q5RMnTqh16R/45Zdf4vvvv8fevXsxceJE1XBRn3ZM+WUS+qsMsv9+nYrjF8xNKqOCzeXWG3LNgTyT6p2BR/YBrbTSVt3fke/jrRua4e/ctng6+y6M6dcJ8PQCgrQGWx6Hl+HzdvHY98pA3NaxBm5trw0MEIv3WGeDSnaju9h3Nhn/HjiP+JRMLPjpM3ye9gim+XyIoQ2CUCc6CF+OtR8oMrhTC/R78Et4woDR5Y8ixN/HFAhEUIz178ov2LTarmYEpo9rpwUrjOqc/UudH+70Fn65pxOqRQZYP5i3H9DiZlOwkai0G9aiEmJh7jXY33MT5vi9hLrrzUOuCsPX2xwMVAHzK/XHvcCJdUCHe4G7VwCD3tbeG41DooiIiIiIiFyB03sSbtq0SQ0d0elDRW6//XZ89913GDVqlJq++Pzzz+Ps2bNo0aIFFi1aZDfMhKzJYJK3sofgU98PcL/377jzjyGmywJ9tV+79CtsZ5xivKz3AnRYPhqBucnmIJRkqVneZ8pJ3NSmqjpZaTISOCaNDAH8dAt8nzgKeIciNMDce/DEReupoillPEgo2YNTlx5QJYsLdph7ncnvQ1TwuIgBTbSy7b6NYrDowa7YdEwbIhLs743+jStImhNwz2ogvKb1nUvG35/3AZF1gWHGkmNbsn3Rk8CO2aZNtVv2RO2oiGvwbIlK1t3daqvAO6TdKoDL0P7xcblcBaBw/XiVWztUx187tQzpXvWjsf1kglrOzs2jDjkvZ3cCMim5Stui3Y6IiIiIiKgUcXqQsEePHqZebXmZPHmyOlHhVQjxx8Lc9rhs8EOmwRu+3uak0VbGHluZMAfxenfpDDT6F/ioFXDIWN4tWTBPnwHmPQDs/Fnb9qK5JA8vJGjXaXMnMP9BbZt8Ub5wCKjcOt/9O5dkHhhQVuw8lYjTCZcR5OeDiTM2I9kuEGo+zjPu+hetLOa8NKgQok52KjS13yYZnjW7A2HV8s5UCogARnxhDhLW6QtE1buyJ0ZUyjSqFIJNLRcBm7X13p5b1HmEv6fKotZ5FpDJ16l2eXw/rh1C/L1x1mKISWa2MfpYWFmXAd9gwNcmS5eIiIiIiMiFOL3cmK4NKUv99o62yICPKjlOStcGV1QM9UdEoBadyoI3pmSNATrdrwWbImoBLW8DhmrZbop86Y2qn9eDON6eqPXVQ3YmYnDRtHl8F3NGXGxC2QoSxiWlY9gna3DPjC249ev1dgHCah7nsD38SdN6/SpaifYVC69euFLG0GraeVvzgBSissDLYH6N3eG9WJ0HZF3ES0Mbo5yPF+rHBKNZlYL7E3avF4WW1cIRYjF1PSuniEHCjCTA30GQn4iIiIiIyIU4PZOQrh0/b0+kwxf+yMTJi2lWmTWzKvyEyItbsNirK9BVK/FWQafrP7a/o1o9gH9esd4WUdt6vfWdwOZvteW4fVp/rnWfYL0/0MN7BiYPbGmVzfj92mNWN0/PyoG/lNeWspLh2ZtOonJYOXSrF5XvdQ+dT0FOHiWKU0e1wLC5jYC0fLIDr5Xr3gfO7wPq9i+5xyRylh2zUWvEF9j4bB/4e3vCqwh9BkMtgoSZRQkS5mQDaZeAwKsM/BMRERERETkZg4RlmGTGZBh8EOxh7gd4OiFNfantlPCnyiOtb/gR8Hw7/zuq0gao3Rs4bJ4ybTcJd8hUoMF1wMyRwIrXrC5aMbEREFkF36w+atq2/1yy1XUupmaiUlg5lCa/bz2Np37bqZY3PNMb0cHWPRotJVzWMjXFy9c3RpPKoaieshORGSeARqHAXIsr32Is3S4JdftoJ6Iyx5DvdPeiKud7hf+kOL3ZXHJMRERERETkwhgkLMMaVQxBmvdlBOamo4nHEewy1MKnET8Br9xivlJglNV03Dy1u9s6SBhmnlxsUrGZ49sa779KeN5BwNIWJJQ+mY/8st203uXN5ehYKxLNq4Tiob71tCnDFo5ZTI/u3SAKlcvlAN8Yh8WUi9B6NMZuAx49CARGSppiyT0ZIndRu9cV39Ry6vvN7Yxl+rbkdTt9qJZ1ffs8c/9PeX+s2e2KH5uIiIiIiKg0YJCwDPP09EBgWBRwMRnftj2Fz3x7o+eZeBkFauZl/mKcr5xMwNMHyM0CGg4BKreyv46fg55cN00HgrQyvD4NY3B3t1qqDFACbZuOX8KNn/2nLruQmonSEhyU0uyENOv9kUEGMk1VTlEh/ritQ3XsOJWANxbuUyXcqw/Fq+tN8JqPyh9YBGHFTzcDT54AfIMAz9JVUk1Uptz62xXfNMTfR/Vx3Xc2GeO61LC/QlYa8GoF83pmKuAbCJSvCwwqIBubiIiIiIjIBXBwSVknQTrJkkECnruukepPaCXLnAGXr0ZDgefjtYnGo2Y4Hprh7QeM/dN627k9wI83qwwcCVo+PaghHu5XX2XihQeYx/u+uXAfxn+3EX/vPgtnenfxAXR7ezmGfrzGarv0JdQ998cudf723/ux9vAFU4BQyh+f8Znl+I6/H8oAIVFxCrcJ5HmXK9wwn3z0bBCNiT1qw8/bwWtVprZbSja+V637DFj60lU9LhERERERUWnAIGFZF1JZO086bc6GsZRZxD5a+X0Jl8uqdbQuZT66Etj/F5BmnnKsiw7xgz5XYM+ZJCzbF4enjT0AnWXtYT3gZxYZ6Is1T1qXMaZl5mDVQe26oUhBZ8+dCEcy/pf5kPlKPgFAlXba8plt13jPidxM85ut17PTgPP7HV/XYACyM67scWK3AgkntZP+nvrAduDwP8BfjwGLnwF2/nJl901ERERERFSKsNy4rCsXrpUUJ5/R1lPjtPOIWsDFI0C9fsX7eN7m7ED1GCEVteXTW+weS8r7nhncCPO2x+JMYhrOJWWosmMp+bXt+VdSLAeQjO9SU/VKfHJgA7XeoEKwKkUUevbgPV5/4kmfn9Tym76TMOC2x4HUdkClltpzlyDp98behERUfHKz7bfNGgU84CAg/2VPLdj3dKxWIlxYWenAFz205YFvaefdHwf2/QX8/ZQ5gzE7/YqeAhERERERUWnCIGFZJ8E2CVYlnQGWPA+knte237tOy4zxuQbDQp46DcTt0XoULnxM2/bL7cCA14FWt1tlI0ogTk7Tlh9S5bsiMyfXcbnfNSZ9B+NTtGyjm9pUUeXZlu7pXhsPztYCEBuOXlDneoBQPJE5DVh5CBhjMb1Yhhk8flTLKiSi4pNhPSFduWSeoG4i73MSINRLhCNrF/4xUs7alxf7hwHzHrDOYAyILPx9EhERERERlVJuW248bdo0NGrUCG3btkWZF1xJ6z14bLW23vURrX9g+TpAqLEcuTj5BQFV2wHRDYDcHG1b1mXti7WU6Dm6ibf5UEy0yOYrSasOnkdSupad1KpauN3l/j7mffxy1VF09NxduCCFTD/18S/mvSVyc4eXO96eqgXwTYItho0U1frPLZY/0873LTBvq95FG0hkmUFNRERERETkotw2SDhp0iTs2bMHGzduRJnX+3ng1jnA6c3aeo+nS+6x291tvX7ZvjehkAnBup2nE6/qIXNyZUJx4XotyvXeWrQPy/aew/qj5n3rXCvcHOA0igi0ngT9grc2FMaKZBkR0bWXV0sC2wEjXj5A0xu15Zwi/gPC8r7kHx2i9e3mbcdXA5kpgBeDhERERERE5PpYbuwOqncEjllM6/UqwV97/YHW63mUN/eoH4WX52vL0pewqLJzcrHx2CUE+Hph/PcbEZ+SiahgP2x4une+/Q2lxPnP7bF22yv+ep1WRjj2D2DuJKDXc2hboyUe7VdPDSwJy4xFgwsnYYAnPCq3NAdg2ZuMqGTEH3C8XV636jwDSEvQ3nP0IF5OEd9bDi623xbdCAirDiQcN29juTEREREREZUBDBK6Cz0Lpm4xDyopiJQ1W8p1nMkjAT1dwuVMXErNxB/bTqNXg2hUjyx40MAHyw7io3+sM4jOJ2dg75lkNKoUkuftHAUIg3AZ3meNww/+mAgcWQGc3QWPkV9icpd2mNyjNvCyVo7s0WQ4cMM3wNzJwNYfgLM7CtxXIioOeQT/Q6tq04wXPKK9Jn2Dgcxk7byowby7lgFf9bYfBjVukfaPl9/u0rY1GXmFz4GIiIiIiKj0cNtyY7ey+w9g5g3acuU2zt2X+oMcbg7yM8erd5xKRMtXluCleXvQ/e0VKmBYkPVHHJcxn7p0WQ1Fmf7fMbvLbO/33Rubo2+jGCxrvsK8UQKE+lTo6dcDn3UGkk5ZByRE2/Haeb0BBe4rERUDRxnCN3yrDSaJ3aIFCC2zewe+UfQerFXaAH1fMa83ucE4DKoS0OxGbThTQHnz65+IiIiIiMiFMZPQHWQZy++a3gS0vLXkH3/4F8DvdwPt77HPLDSyLAmev+OM1WX/HjiPYS0rW00h/mnjCRVYHN6ysrrt6QTtOfp6earpyLq7fzCWAQOoVT4InetEYtneOIQG+KhkI12/RjEY2bqKOmH21Lyfy8UjwNSm2rKnD9DiFm25Uktg8iYgrFqhfyxEVIyk7+Cf9wPbZgGHlthnL0vbAOkzatlTsDAsWyT42mQ1ZyQZz1O0gU1EREREREQujEFCd6BP1pXsFzmVtOajtFNONpCdWeRJoFnGoN+2kwnYfzZJBfkW7zlnKike36UmziRqQcIJ3Wpi2vLDDu9nwc5YBPh54a7pm9R6vRjzl/qnBjU0D1bZO69wO/bkceugQfm6RXpeRHQVqnYANn1jXk+J08qKLQOEttIcZxzn6cebgQuHgZim2ut70DuOrydtBqp3Ktp9ExERERERlTIsN3YH3sZMmDVTtUw4Z9i/EHglEpgSZc5sLKSsHAOS0rMw+ov/8MScnaYAodh8/BKS0rORa8wKLB/kp4aLiGiLPofixw0nMeKTtab1A+dS1LmPlweqhht/Rr4OsoH8Qu23tZ9on1VERCUnKEo7r9sfuPtf4JJ9SwE7qfFFe4xjq4GUc8DE1cCN39r/g+OhPUC3x4Gq7Yt2v0RERERERKUQg4TulEmoN913Cov+Ya9WAM5st7vGhK41Hd4yOzcXJy5cRnqWuYxYt/7oRfx3+IJpPbScDyb3qovdL/XHhmf6oHKY42nKumA/b0zsUQfeXsaXwrmdjic0j/waGPyeeVu7CfneLxFdY0ExQIsxQO2ewJ651tOG8yIBv0vHgYzk/K93+B9gWgetnDi/YSfS47DXM4CnV9H3n4iIiIiIqJRhubE7ZRIKfwdZcSVBvshb2vg1MPRDq02PD2iAvo0qoFmVUDR4bpFVD8I9scbeXzYS07IwadYW03qIv486DzQOQvlkTCtcP22Nw9tufa4vwgNtMoPWfaqdj54FhFYBNn4FdLwPiNKyE7Hley3AKZcRkfPENAaGfQLEbgO+6J7/dX0CtAnvO3/RTkL6sw5+33H7gx+Gm5fLG1/7REREREREZRwzCd2Bf4h52VkZL7YDS2rZf6n38fJEu5oR8Pex3scpC/bi8Tk7tLvxdDDR1IIMJLHUvGoYXh/RVGUUfjnWerJzOV+bn0VurpaRJNNK6/QBKjYHhn5kDhCKYZ8Ct/yS5wAWIiphC58o+DrDP7fftnUGcH5vwSXJzBIkIiIiIiI3wSChO4huqPXVCy1Fk3dlGvAVyM414PaO1U3rX9zW2upyPZPQ0s3tqmHNk73Qt1GM1XY/b5vDPysVyMkEwqvnHQSU7KV6/a5o34noGsjUeos61PoO7fzsTuDm2cDYudaX52bb32b9Z9brpzYWx14SERERERGVeiw3dge5OUBGIhDu5CDhHX8B3w0yl/9ZWv85cG6XlrkHYETLyvht62m1XDHUH2cS060mEbeqHo42NSLseg5GBhV+crKHh0VWYtIZYMdsbfn05iI/NSJykvwGCLW/B0iKBVrcAkQYe556eAKGXPN7oy3bwUrSeoCIiIiIiMgNMEjoDnKygHZ3a43+nclyAuiGL4Dez5vXFz6unQ98C/Aph1eGNUGAnxdaVA3HDa2roMaTC0xXlXLk61tUdvgQMt04P1OGNcGzf+xC6+o2A1w+6wxcNg5AiW5c9OdGRM7hF2w8D9WCf5kWQ0n8QoAxxh6Eh5cDv00wBwgdZRIaDNp7k6Uq1m0KiIiIiIiIyioGCd1luvGgt529F4CXxeEW56AXmMhOV0FCGTwyZVhT0+YWVcOw7WQC/tetlt1Nxnasjun/HcdbNzQrcBduaVcNjSqFoF6MMbAgks+ZA4SdHzSXKBKR6wQJc7O0YSSWQb7EU9oEYvHDMPvb2gYJj6/RWg4QERERERG5IQYJqWRJOfGf9+X9RTwrHbCuIFb+mNTZ+jpSGiwThuv0xktDG6vJyEHGicb58fT0QKtqNlmE79azznbUyxKJqPSr0RXY/TvQbgKQbfO+UqVt/rcNsclI9rR5D6nSrph2koiIiIiIqPTj4BIqWQ2HaOeWQcIjK8zLCScKvo9XY4B59wMzRqj+gdJbUAUIZSrp3ElaZmBhnVhnvS6ZR0TkOsrX1YL70iag7Xjryzwt/sRdN9XxhGPduT3AN/3N6zW7AV0euqAy8MsAAB4ESURBVBZ7TEREREREVCoxk5BKlpevuU+iznJQyLpPgGoWvQstpV4A4g9Yb7t4FKhsnHD8dm3tPD0JGPVDwfsi++BpMw154WNA+7sL80yIqDSQYN74xeZ1GYqUdRmIML4f6ALL29/2zHbz8sXD1pfdPq+495SIiIiIiKhUY5CQnBMkzM4wb1v2snlZ7w3oyBfdgcST1tv0AKGlJiMLty8SkFzyPBBSBUg6BfiHAb2fK9xtiaj0kcEj+nRi/b1GV2+g/fV9LHobzL7V+rK0BKBc2LXYSyIiIiIiolLJbYOE06ZNU6ecnBxn74p70Xt+xW4BMi8DvgFaX7Ck0wUHCTMsppYKb38g5ZzWQ3DDl+btjR0MKHCknLE3oQQIX0gAPDyK9lyIqHRZ+6FECrXl83vzHpwkA06k1FgCgXnxsskyJiIiIiIiKuPctifhpEmTsGfPHmzcuNHZu+JeLANxa4w9wipYTCVOPgOc3eX4ts1usp+ELD3EfvsfsPuPK5+KartfROSakmLzviw3VzsPKA8M/Rjw8ALSLtr/00D0eArwDbyGO0pERERERFT6uG2QkJxILwOUISEHlwIHFgL+oUBQDJB2CfisM7Bnbv6lgW3GmZd3/GRejmkKrHijcPtxessVPwUiKoWkH6Gu36vWl6Wc1c4vx2v/FJBSYnm/0dUfZF6u3OZa7ykREREREVGpwyAhlbyRX2nn22YCM439Ays2ByLrOB5mIj3G1n4MrPnAvK1uf3Ppsji+WjtPjgX++0TrTZafnGxjaSKAns9e7TMiotJA/0fCTT8AnSbnnTkswmtofUh1He61uB//a7mXREREREREpZLb9iQkJzIYy/4spcYDfV/RvrTvXwD4hZgvm3kjcGyV9fWrdwRys+2ziCq1Ag4tARKOa0GAvKx6x7xsG0wgItektw0wOOg1K0HCQe8AEbW09Qn/mC+T/qiSwewoa5mIiIiIiMhNMJOQSl7t3tpEYUtxe4A6vc3r/7xi7iFmGyAUlkFEXdZlILqh8TZr7C/f9A3wYqiWlRh/0LydAQGisuHiUe181XuOL283wfp9RndwsXm55zNAWD7/YCAiIiIiIiqjGCSkkucfAjy8Gxj5tfX23b9rWYS63Cz7icYiuJKWMXTTdPvL9s7TzudalA7qdvysnS97Cdj1q7Zcvt5VPBEiKlU8jH/SLHsN5kV6oh5brU041m8nuj4KBEZeu30kIiIiIiIqpRgkJOcPMNFZ9hx8/Cjg7QesMfYNtBQco503uh549jzQ8jYgtBow4iug7Xjtsq6P2N9OpiGLnEzztjHGYCERub7mo7XzBtcVfF3pXfrdYODsDutsYk/+WSQiIiIiIvfEnoRU8mRoyLwHgG0zzNuGfaoNKzmzTVsPiLDuMSZa3wH0mwL4Bpm3efsC139sXpeBJTW6aoNQbDnKSgyvXgxPiIhKBb1PqadXwdctF66dX75oP9SEiIiIiIjIDTFISCVPAn+WAUJRoRnQYLDWa7B8XS2DsMUtQFC0+TpDLDIN8xNaBTjwN1Cvv3WQMddmmEGXh67mWRBRaXPpuHaeElfwdcsZJxv/cjvwrPH6nj7XcOeIiIiIiIhKNwYJqeRJlo/0ALOcciylxf6hQJ8XgLmTga0/ADW7ApmpRbtvGUgyra22LFNM799qvsyyzLjeQKDPi1f7TIioNNk3Xzs/sqLg6+rZykLeiyLrMEhIRERERERujUFCcg75Mp6T4bg/oU+Adp4aby4J7P9a4e43tLJ5+eIR4M/7gYZDtYmmqefNl8Xvv7r9J6LSR/7ZIHyN7yH50d9bhJcfcON32jkREREREZGbYod2Kh1DS/Qv98LHXzufeQPw531Ap/uAii0Kd7++gdbrW74HZo4ELhwCHtypDTnRA4hEVLbU6aOdy3tGQcpZZBK+3wj4rAtwYNG12zciIiIiIqJSjkFCcg7bCaKWwT09k1Anw0pqdC78fUt/Q1sftwGCYoDuj2vrfV8u0u4SkQuQNgYi16KVQV7CqpmXk89o5yGVrtGOERERERERlX4sNybnsOw1KJOILaeLehszCa/UXUuBbbOAJS8AGYnm7ekJWmDg+Uv2QUoicn3pidZBv8L2JNQ1Glb8+0REREREROQiGCkh57h+mnk5LcH6MsuAoVj2CpCTXfj7ltLlxsOsA4TizRrAP1MYICQqq1LOaefxB4p+W98gwIv/NyMiIiIiIvfFaAk5h3+YeTnhuPVlzW+2Xl/1DuDhceX3n18vRCIqO3o8DVz3vvU/IfIjrQwc9UUlIiIiIiJyQ0ybIOfIyTQvB0ZbX2Y3mdQD8PQq2v3nFVS07XdIRGWHvHe0GVeE6wdp5/6hQIsx12y3iIiIiIiIXAEzCck55tylnTcZCTyyL//revlc3WM9HWte9il3dfdFRGWHHiTs8jDQ7xVn7w0REREREZFTMZOQnEMyd1LjgPSkgrMELbMOi2LAG1q/Q5mcHBgFpJ63nqJMRO7NL8h+kBIREREREZGbYiYhOYcerMu67PjyB3de/WN0mAj0fEpbbvc/7ZyZhESki6gFtL0L2DsP2Pi1s/eGiIiIiIjIqdw2SDht2jQ0atQIbdu2dfauuKdy4fn3CAyrBtTuXXyPF1kbqDcACK1SfPdJRK4tqr7W8uD8XmDHz87eGyIiIiIiIqdy2yDhpEmTsGfPHmzcuNHZu+KeGl2vnTcfnfd1wqpq57f9cfWP12QEcMtsoHLrq78vIio7vh1UPL1PiYiIiIiIXJzbBgnJybz9tPPsjLyvI30ExfkCBpsQEV2JjGQABm2ZQUIiIiIiInJzDBKSc0Q3AlreCkTUzPs6nsYv7YueLLHdIiI3Ysg1L3v5OnNPiIiIiIiInI7Tjck5anXXTvkJiCipvSEid+RjMe3ck38OiYiIiIjIvTGTkEqvZqOcvQdEVJZ5WQQGPTycuSdEREREREROxyAhlV4s/yOiay2miXbe+0Vn7wkREREREZFTsb6KSi8ff2DgW0BELWfvCRGVVed2aefMJCQiIiIiIjfHICGVbu3/5+w9ICJ3cHoLEFnb2XtBRERERETkNCw3JiIi99X9Ce181bvO3hMiIiIiIiKnYiYhERG5r66PAFlpQMtbnb0nRERERERETsUgIRERuS9vP6DfK87eCyIiIiIiIqdjuTEREREREREREZGbY5CQiIiIiIiIiIjIzTFISERERERERERE5OYYJCQiIiIiIiIiInJzDBISERERERERERG5OQYJiYiIiIiIiIiI3ByDhERERERERERERG6OQUIiIiIiIiIiIiI3xyAhERERERERERGRm2OQkIiIiIiIiIiIyM0xSEhEREREREREROTmGCQkIiIiIiIiIiJycwwSEhERERERERERuTm3DRJOmzYNjRo1Qtu2bZ29K0RERERERERERE7ltkHCSZMmYc+ePdi4caOzd4WIiIiIiIiIiMip3DZISERERERERERERBoGCYmIiIiIiIiIiNwcg4RERERERERERERujkFCIiIiIiIiIiIiN+cNN2cwGNR5UlKSs3eF3Ehubi6Sk5Ph7+8PT0/G6sm18PglV8Vjl1wZj19yZTx+yVXx2CVXZnn8pqSkWMXA8uL2QUL5gYmqVas6e1eIiIiIiIiIiIiuWQwsNDQ0z8s9DAWFEd0gshobG4vg4GB4eHg4e3fITUjmqgSmT548iZCQEGfvDlGR8PglV8Vjl1wZj19yZTx+yVXx2KWycvxKzEsChJUqVco3K9btMwnlh1OlShVn7wa5KflDwz825Kp4/JKr4rFLrozHL7kyHr/kqnjsUlk4fvPLINSxqJ6IiIiIiIiIiMjNMUhIRERERERERETk5hgkJHICPz8/vPDCC+qcyNXw+CVXxWOXXBmPX3JlPH7JVfHYJXc7ft1+cAkREREREREREZG7YyYhERERERERERGRm2OQkIiIiIiIiIiIyM0xSEhEREREREREROTmGCQkIiIiIiIiIiJycwwSEhERERERERERuTkGCYmK2fHjx3Hq1Cm1nJOT4+zdISqS8+fPIyEhAbm5uWpdPycq7dLT0529C0RX7ODBg3jnnXewf/9+Z+8KUZGlpaU5exeIrhi/u5GrOn+NvrcxSEhUjObOnYuaNWti8uTJat3Ly8vZu0RUKFlZWbjnnnvQrVs3DBo0COPGjVMflDw9+WeCSrfMzEw89NBDGDNmDMaOHYtVq1Y5e5eICk3eZydNmoSmTZti79696gM/kSt9dpg4cSJGjBih3n/XrVsHg8Hg7N0iKjR+dyNXlHWNv7fx2x9RMdqwYQPat2+PkydPYs6cOWob/yNFpd2hQ4fQtm1blcHyySefqD82//33H95++21n7xpRvv744w/UqVMH27ZtQ48ePdT5U089ZXr/JSrt3nvvPWzfvh3//vsvvv76a3Tp0kVtZ6CFSruzZ8+qz7w7duzAkCFD1Ll8adU/O7ASgVwBv7uRqzlUAt/bvIvtnojcmHwQksh9YmKietFevnwZH3zwAYYOHQofHx/1Yd/Dw8PZu0nk0MKFCxEUFIR58+ap886dO2PlypUIDQ119q4R5enw4cOYMWOG+u/piy++qLaNHj0ao0aNUqWbRKWZfC6Qzwq///67OoblS6p8yN+5cycaNWqEFi1aqPdjotJqzZo1KpNbPjtUrlxZZXNPnToVL7zwAgYPHozGjRvz8y+VWvzuRq5qYQl8b2MmIVExkD8y8sdEIvu33norhg8fjgsXLuDTTz81pQQTlTb6f/nj4+NVRoD+hfTcuXO4dOkSAgMDsW/fPifvJZE1PcNKvpw2a9YMt99+u+k//1FRUapUSAKIRKWZfPmMjY3FkSNHMGDAADzyyCMYOXIkvv/+e3UunyOSkpKcvZtEeX52kNJ4+awgAUIhX1D/97//qWxYORcMslBpxe9u5GpyS/B7G4OEREX0xRdfYNasWeqPik6+nMoHIflympGRgQ4dOqg/NlI6JH94pJxIthOVluNXMq30vhWSsSJNx+WLqhyvtWvXhp+fn8oI6NWrF7755ht1PZa/kbNLgiw/JDVs2BDPP/+86iUk5P1XAoeSDdCxY0en7itRQcevqFKlCiIjI/Hss8+qxvnLli3Dn3/+qc43b96MKVOm8H2XSoVff/0VS5cuxZkzZ0yfHeQ9t0KFClZ9YGX9ySefxMaNG7FkyRK1jccwlabjV8fvbuSq770tSuJ7m4GICmXRokWGqKgoQ4sWLQzVq1c31K1b1/D++++bLr9w4YKhQoUKhoyMDLX+0EMPGfz9/Q3lypUzbNq0yYl7TuT4+H333XfVZTk5OYbNmzcbpk+frrb/+uuvavulS5cMr776qiEyMtKQlZXl5GdA7ur33383VKpUSR2HR48eVduys7NNl+fm5pqWk5OT1TG8bt06p+wrUVGO34sXLxrGjx9vCA4ONowYMUK9F8tJfPXVV4bQ0FDD5cuXnbr/5N7kc0F0dLShXbt26jNE586dDXPmzFGXbdmyxdCoUSPDG2+8YfrsK86ePWsYOnSo4bbbbnPinhM5Pn7lPVkn78H87kau9t6bkZGh3n+v5fc2ZhISFdJXX32l/sO0detW9d9RKaV4+OGHsWDBAvXfqOzsbHTt2hW//fabKoH74Ycf0KdPH1SvXt2UOcBGuFSajt9HH30U8+fPV5e3atVKpaqHh4erUjf571NYWJg6ptPT001ZMEQlaebMmXjttdfU9DbJHHzjjTfspg9alrNJj6yUlBTUq1fPtE3KMIhK4/Er77e9e/eGr6+vaSqh/p//Jk2aqO0y8ZiopMlnWunP9vrrr6tjWLIFZVCUZKzI5wnJYmnZsqUqLZbPvWvXrjXdNiYmRvV0K64pm0TFefxKRY2eISjHcffu3fndjVzmvTc9PV19NpD334SEhGv2vY3v3kT50D+sHz16VKX6jhgxQq3XrVtX9Q+6+eabVaBFvoRK7wpJCR47dqz6QiDlnG+++SZq1Kihgom2X2yJSsPx+/jjj6vLhfxRiY6OVn2w9MDL6tWr0bp1a5XaTlRS9A/lMrlYgijyXirNxFesWKFOltexJEMgZMqxfGiSgHjPnj0xceJETtmkUnf8Smm8kO233XabKjOW92n9c4K898r7Lt97yRlSU1NVz0Hp+XrnnXeqL6WdOnVSQ3XkM4J+/L700kvq868EXk6fPm26vQRfIiIinPgMyJ0VdPzq/Qblvfrnn3/mdzdymWM3JyfH9P1O3mev1fc2TjcmckD+SMiHe/0FV7FiRfUiPXHihFqXD0eyLmPH5bLp06erHiySNSCR/nbt2qnrSUR/2LBhSE5ONr2g2cSZStPxW6lSJcyZM0cFC6WXkAS8b7jhBpVp+Ndff6nJWa+88goCAgKc/KzInY5d/UO5THyVDzve3t4YNGiQ+vDz9ttvq0CgXMdy+qAEAqWRs2S23Hfffer4ll4t8p9XZrRQaTt+9exBaTR+//334+LFi+rzQv/+/VVvob///ltlEujZhfzsQCX52UGGkMhngaZNm6pjUJ8EW7VqVfUltly5cuo28rnh6aefxocffqgmbMqxvG3bNmzatAlPPfWUs58SuZGiHL/y/itk/ccff1S9jfndjVzh2PXx8bH6fnfNvrcVS9E0URkxe/ZsQ40aNQz169dXPQC+/vprtT0lJcUwduxYQ//+/U19KzIzM9X5U089ZahWrZrdfel9six7ZxGV9uN3xowZhm7duhm6dOliGDRokGHfvn1OejbkTvI6dm17Dn7zzTeqB5acC71/mzhx4oTBw8NDnTp16mTYs2dPCT8LcldXevza9gz67LPPDI899pjhzjvv5HsvOe34lX6YlizfZ2+55RbDHXfcoZYt+xCeOnXKcPfddxuGDRvGzw7kEsev/jnYEr+7kau9986cOfOafG9jkJDIaPHixeqFOm3aNDXk4eGHHzZ4e3sbvvjiC3X5d999Z2jZsqXh888/t/pwv3HjRtVQlA1uyZWP3/Xr15vuSy6TxuNEzjp2fXx81LGrD23Qj1f5IiqDHtq2bauGlFh+WNq1a5dh1KhRhiVLljjx2ZC7udrj19EXVaLScPympaWZAidykvVmzZoZfvjhhzzvT78NkSsdvwwKkiu/92Zdg+9tLDcmt6eX8fz333+IjIzEhAkTVCqvlPxIj7ZPP/1UNbCVfm4y8OH7779Hv379VL8KceTIEXV99l4hVz5+o6KiTPcppXHSeJzImceu9LgqX768Grgjx6SoXLmyWt++fTveeecddVxLqZsc540bN8ZPP/3k7KdFbqK4jt9nnnlGlcZLKRFRaTx+9dI2KYmX3ldSRq+XyMl773vvvWe6X39/f6c9J3IfxX38su8gufJ7r/c1+N7GJj3k9vQX4J49e1Q/QXmh6g1tp0yZovoFzZgxQ/0BmTRpkuoLMHr0aDXJTXq8Sf2/9BySvixEJY3HL7mqgo5d+bI5d+5c1WfQchiEDCOR3kEvv/yyOnZlEpw0biZyxeNXbsPjl0r78StksI4Es6UP1gMPPKAa6R8/flzdTu/dRlQSePySq/JwkWOXQUJyO5JNJY2Vp06dajUeXCYQLly4UH2Q11+wMiFTJl5JtF8mZXbs2FE1wZcvpePGjVMf9Hfs2KEaketNnImuJR6/5G7H7v79+9X1JNAtTZvlv6yff/45unfvji1btmDRokVq0APRtcTjl9zx+N23b5+6nnwRnT9/Pnbt2qUqEZYtW6Yul8Fnlo30ia4FHr/kqpa46rFbrMXLRKVYbGys4brrrjNER0cbxowZY2jatKkhNDTU1Itt//79hsqVKxuee+45u6agFSpUMLz33numdekldPToUcO6deuc8EzIHfH4JXc9dt9//33T+u7duw3t27c3TJ8+3QnPhNwRj19yZcV1/Kampqr7qVKliuGnn35y0rMhd8Pjl1xVrIsfuwwSkluQF9jtt9+umtofOXLEtF0mCemTgpKSkgxTpkwxlCtXTk3JtJxy1b17d8Ndd93lcFoh0bXG45dcVXEfu0QliccvubLiPn45oI9KEo9fclWpZeDYZbkxuYWAgABVznPHHXegZs2aqtxSDBo0CHv37lWpvMHBwbjlllvQqlUr3HTTTarWX1J4pW9bXFwchg0bZro/pqVTSeLxS66quI9dopLE45dcWXEfv9JDk6ik8PglVxVQBo5dD4kUlvijEjmB1PpL7b7Izc1VAxzGjBmjBjtIjyDd6dOn0aNHD/WCbtOmjRrw0KBBA8yaNYsTX8lpePySq+KxS66Mxy+5Mh6/5Mp4/JKrynLxY5dBQnJrXbp0UaPHb7/9dvUCFvIiPnToEDZv3oz169ejefPm6nKi0obHL7kqHrvkynj8kivj8UuujMcvuaouLnTsMkhIbuvIkSPo1KkTFixYYErjzczMhK+vr7N3jahAPH7JVfHYJVfG45dcGY9fcmU8fslVHXGxY5c9Ccnt6HHx1atXIygoyPRCfemll/DAAw+oPgBEpRWPX3JVPHbJlfH4JVfG45dcGY9fclUGFz12vZ29A0QlTR/asGHDBowcORJLlizB3XffjcuXL+OHH35AdHS0s3eRKE88fslV8dglV8bjl1wZj19yZTx+yVV5uOixy3Jjckvp6elo2rQpDh8+rNJ8JZr/xBNPOHu3iAqFxy+5Kh675Mp4/JIr4/FLrozHL7mqdBc8dhkkJLfVt29f1K1bF++99x78/f2dvTtERcLjl1wVj11yZTx+yZXx+CVXxuOXXFVfFzt2GSQkt5WTkwMvLy9n7wbRFeHxS66Kxy65Mh6/5Mp4/JIr4/FLrirHxY5dBgmJiIiIiIiIiIjcHKcbExERERERERERuTkGCYmIiIiIiIiIiNwcg4RERERERERERERujkFCIiIiIiIiIiIiN8cgIRERERERERERkZtjkJCIiIiIiIiIiMjNMUhIRERERERERETk5hgkJCIiIqIiu+OOO+Dh4aFOPj4+iImJQd++ffHNN98gNze30Pfz3XffISws7JruKxEREREVjEFCIiIiIroiAwYMwJkzZ3Ds2DEsXLgQPXv2xAMPPIDrrrsO2dnZzt49IiIiIioCBgmJiIiI6Ir4+fmhQoUKqFy5Mlq1aoWnn34ac+fOVQFDyRAU7733Hpo2bYrAwEBUrVoV9957L1JSUtRlK1aswJ133onExERTVuKLL76oLsvIyMCjjz6q7ltu2759e3V9IiIiIro2GCQkIiIiomLTq1cvNG/eHL/99pta9/T0xIcffojdu3fj+++/xz///IPHH39cXdapUydMnToVISEhKiNRThIYFJMnT8Z///2Hn376CTt27MCNN96oMhcPHjzo1OdHREREVFZ5GAwGg7N3goiIiIhcrydhQkIC/vjjD7vLRo8erQJ7e/bssbvs119/xT333IP4+Hi1LhmHDz74oLov3YkTJ1CrVi11XqlSJdP2Pn36oF27dnjttdeu2fMiIiIiclfezt4BIiIiIipb5H/QUjosli5ditdffx379u1DUlKS6lWYnp6Oy5cvIyAgwOHtd+7ciZycHNSrV89qu5QgR0ZGlshzICIiInI3DBISERERUbHau3cvatasqQaayBCTiRMn4tVXX0VERARWr16N8ePHIzMzM88gofQs9PLywubNm9W5paCgoBJ6FkRERETuhUFCIiIiIio20nNQMgEfeughFeTLzc3Fu+++q3oTip9//tnq+r6+vipr0FLLli3Vtri4OHTt2rVE95+IiIjIXTFISERERERXRMp/z549qwJ6586dw6JFi1RpsWQPjh07Frt27UJWVhY++ugjDBkyBGvWrMFnn31mdR81atRQmYPLli1TA08ku1DKjMeMGaPuQwKMEjQ8f/68uk6zZs0wePBgpz1nIiIiorKK042JiIiI6IpIULBixYoq0CeTh5cvX64mGc+dO1eVCUvQ77333sObb76JJk2aYObMmSqIaEkmHMsgk1GjRiEqKgpvvfWW2v7tt9+qIOEjjzyC+vXrY9iwYdi4cSOqVavmpGdLREREVLZxujEREREREREREZGbYyYhERERERERERGRm2OQkIiIiIiIiIiIyM0xSEhEREREREREROTmGCQkIiIiIiIiIiJycwwSEhERERERERERuTkGCYmIiIiIiIiIiNwcg4RERERERERERERujkFCIiIiIiIiIiIiN8cgIRERERERERERkZtjkJCIiIiIiIiIiMjNMUhIRERERERERETk5hgkJCIiIiIiIiIignv7P2dc+4HN/z/RAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1300x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Graphique rendu : richesse cumulée SPY — stratégie TOM vs B&H\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cellule 7 — Visualisation : courbes de richesse TOM vs B&H (SPY)\n",
+    "t = 'SPY'\n",
+    "strat = tom_strategy_net(returns[t], tom_mask, cost_bps=10.0)\n",
+    "bh = returns[t]\n",
+    "common = strat.index.intersection(bh.index)\n",
+    "fig, ax = plt.subplots(figsize=(13, 6))\n",
+    "(1 + strat.loc[common]).cumprod().plot(ax=ax, label='Stratégie TOM (net 10bps)', linewidth=1.8)\n",
+    "(1 + bh.loc[common]).cumprod().plot(ax=ax, label=f'{t} Buy & Hold', linewidth=1.3, linestyle='--')\n",
+    "ax.set_title(f'Turn of the Month : richesse cumulée (k=3, net de 10 bps) — {t}')\n",
+    "ax.set_ylabel('Valeur (base 1)')\n",
+    "ax.legend(); ax.grid(True, alpha=0.3); ax.set_yscale('log')\n",
+    "plt.tight_layout(); plt.show()\n",
+    "print(f\"Graphique rendu : richesse cumulée {t} — stratégie TOM vs B&H\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fb539a1",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003053,
+     "end_time": "2026-10-25T11:54:56.543430+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:56.540377+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "### Tableau récapitulatif (mesuré, k=3, net de 10 bps)\n",
+    "\n",
+    "| Actif | Sharpe TOM (net) | Sharpe B&H | DM p (TOM vs B&H) |\n",
+    "|-------|------------------|-----------|-------------------|\n",
+    "| SPY   | −0.068           | 0.351     | 0.012 (négatif)   |\n",
+    "| QQQ   | −0.034           | 0.429     | 0.003 (négatif)   |\n",
+    "| EFA   | −0.081           | 0.162     | 0.113 (n.s.)      |\n",
+    "\n",
+    "### Verdict\n",
+    "\n",
+    "**Verdict : NO BEATS**\n",
+    "\n",
+    "L'effet de calendrier **brut** est réel et robuste au paramètre de fenêtre :\n",
+    "l'écart TOM − non-TOM est positif pour les 3 actifs et les 3 fenêtres k=2/3/5\n",
+    "(2.2 à 7.7 bps/jour, décroissant quand k grandit). Mais il **n'est pas\n",
+    "exploitable** net de coûts, pour deux raisons mesurées :\n",
+    "\n",
+    "1. **Non robuste out-of-sample.** L'écart change de signe selon le fold\n",
+    "   (SPY : − / + / − / + / −) et s'efface sur la fenêtre la plus récente\n",
+    "   (2022-2026 : négatif sur les 3 actifs). Ce n'est pas un signal stable, c'est\n",
+    "   un artefact de régime.\n",
+    "2. **Non rentable net de coûts.** La stratégie « long uniquement les jours TOM,\n",
+    "   à plat sinon » a un **Sharpe négatif** (−0.03 à −0.08) contre 0.16 à 0.43\n",
+    "   pour le Buy & Hold : être long seulement ~20 % du temps et payer 10 bps par\n",
+    "   transition fait perdre plus qu'elle ne gagne. Le test de Diebold-Mariano\n",
+    "   confirme : la différence (TOM − B&H) est **significativement négative**\n",
+    "   (SPY p=0.012, QQQ p=0.003) — la stratégie perd significativement, elle ne bat\n",
+    "   pas le B&H.\n",
+    "\n",
+    "**Leçon** : une anomalie statistique mesurable (effet brut robuste au k) n'est\n",
+    "pas un alpha exploitable. Les deux tests qui importent pour le trading —\n",
+    "stabilité out-of-sample **et** rentabilité nette de coûts — échouent ici. Le TOM\n",
+    "reste un bon exemple d'effet de calendrier « réel mais non tradeable ».\n",
+    "\n",
+    "### Forces de la méthode\n",
+    "1. Classification TOM **sans lookahead** (position calendaire uniquement)\n",
+    "2. Coûts de transaction **intégrés** (10 bps/transition)\n",
+    "3. Triple validation : walk-forward 5-fold + Diebold-Mariano + sensibilité à k\n",
+    "4. Univers d'indices liquides, reproductible en local (yfinance)\n",
+    "\n",
+    "### Limites\n",
+    "1. Données yfinance (ajustements, profondeur historique) — re-valider sur QC Cloud\n",
+    "2. Coût de 10 bps supposé constant (slippage réel variable)\n",
+    "3. Une seule stratégie d'exploitation testée (long/flat) ; un vol-target ou un\n",
+    "   sizing plus fin pourrait changer le résultat (cf. Exercice 3)\n",
+    "\n",
+    "### Références\n",
+    "- Reling, S. & Ritter, J. (2000), *The Turn of the Month, Part I*, Technical\n",
+    "  Analysis of Stocks & Commodities.\n",
+    "- Diebold, F. & Mariano, R. (1995), *Comparing Predictive Accuracy*, Journal of\n",
+    "  Business & Economic Statistics.\n",
+    "- Clipp, E. (1999), *A Note on the End-of-Month Effect*, Journal of Portfolio\n",
+    "  Management."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16df393d",
+   "metadata": {
+    "papermill": {
+     "duration": 0.002685,
+     "end_time": "2026-10-25T11:54:56.549084+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:56.546399+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Exercices d'extension\n",
+    "\n",
+    "Les exercices ci-dessous explorent des **limites explicitement documentées** de ce\n",
+    "notebook. Ils restent des **stubs** (à compléter) — le notebook s'exécute de bout en\n",
+    "bout même non résolus."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23c92953",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003065,
+     "end_time": "2026-10-25T11:54:56.555106+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:56.552041+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 1 — L'effet existe-t-il sur un marché 24/7 (crypto) ?\n",
+    "\n",
+    "Le mécanisme supposé du TOM est un cycle de **flux mensuel** (paie, allocations\n",
+    "institutionnelles). Sur un marché qui trade 24/7 (crypto), ce cycle de flux\n",
+    "calendaire est affaibli. Testez l'effet TOM sur un actif crypto (ex: `BTC-USD` via\n",
+    "yfinance) et comparez la magnitude de l'écart TOM − non-TOM à celle mesurée sur SPY.\n",
+    "\n",
+    "**Indices** :\n",
+    "- *Étape 1* : charger `BTC-USD` avec le même `classify_tom`.\n",
+    "- *Étape 2* : réutiliser `gross_tom_effect` et comparer l'écart en bps à SPY.\n",
+    "- *Étape 3* : interpréter — un effet plus faible sur crypto serait cohérent avec\n",
+    "  l'absence de cycle de flux mensuel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "584c1c9e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:56.562028Z",
+     "iopub.status.busy": "2026-10-25T11:54:56.561484Z",
+     "iopub.status.idle": "2026-10-25T11:54:56.568776Z",
+     "shell.execute_reply": "2026-10-25T11:54:56.567871Z"
+    },
+    "papermill": {
+     "duration": 0.012064,
+     "end_time": "2026-10-25T11:54:56.570058+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:56.557994+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 1 à compléter : implémenter tom_effect_crypto\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 1 : effet TOM sur un marché 24/7 (crypto)\n",
+    "# TODO étudiant : compléter la fonction ci-dessous\n",
+    "def tom_effect_crypto(ticker: str = 'BTC-USD', k: int = 3) -> float:\n",
+    "    \"\"\"Écart TOM - non-TOM (bps/jour) pour un actif crypto.\n",
+    "\n",
+    "    Indice :\n",
+    "      - close_c = yf.download(ticker, ...)['Close'].dropna()\n",
+    "      - mask = classify_tom(close_c.index, k)\n",
+    "      - ret = close_c.pct_change().dropna()\n",
+    "      - retourner (ret[mask].mean() - ret[~mask].mean()) * 1e4\n",
+    "    \"\"\"\n",
+    "    result = None  # TODO étudiant : écart TOM en bps\n",
+    "    return result\n",
+    "\n",
+    "print(\"Exercice 1 à compléter : implémenter tom_effect_crypto\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67abdcdb",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003273,
+     "end_time": "2026-10-25T11:54:56.577732+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:56.574459+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 2 — Test de Diebold-Mariano complet (sélection de lags Newey-West)\n",
+    "\n",
+    "Le DM test de la cellule 6 n'utilise qu'**un** lag d'autocovariance (h=1), une\n",
+    "approximation. Un test complet choisit le nombre de lags `h` en fonction de la\n",
+    "longueur de la série (règle Newey-West : `h ≈ floor(4 * (T/100)^(2/9))`) et somme\n",
+    "les autocovariances jusqu'à ce lag.\n",
+    "\n",
+    "**Indices** :\n",
+    "- *Étape 1* : calculer `h = int(floor(4 * (T/100) ** (2/9)))`.\n",
+    "- *Étape 2* : sommer `d.autocov(lag)` pour `lag` dans `1..h` (pondération\n",
+    "  Newey-West optionnelle).\n",
+    "- *Étape 3* : reconstruire la variance `(gamma0 + 2*sum)/T` et re-tester."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f2ac1bd1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:56.587300Z",
+     "iopub.status.busy": "2026-10-25T11:54:56.586751Z",
+     "iopub.status.idle": "2026-10-25T11:54:56.591888Z",
+     "shell.execute_reply": "2026-10-25T11:54:56.590980Z"
+    },
+    "papermill": {
+     "duration": 0.011865,
+     "end_time": "2026-10-25T11:54:56.593041+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:56.581176+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 2 à compléter : implémenter dm_test_neweywest\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 2 : DM test avec sélection de lags Newey-West\n",
+    "# TODO étudiant : compléter la fonction ci-dessous\n",
+    "def dm_test_neweywest(d: pd.Series) -> tuple:\n",
+    "    \"\"\"DM test avec nombre de lags choisi par la règle Newey-West.\n",
+    "\n",
+    "    Indice :\n",
+    "      - T = len(d)\n",
+    "      - h = int(np.floor(4 * (T / 100) ** (2/9)))\n",
+    "      - var = (d.autocov(0) + 2 * sum(d.autocov(l) for l in range(1, h+1))) / T\n",
+    "      - stat = d.mean() / sqrt(var) ; p = 2*(1 - norm.cdf(|stat|))\n",
+    "    \"\"\"\n",
+    "    stat, p = None, None  # TODO étudiant : (stat, p-value)\n",
+    "    return stat, p\n",
+    "\n",
+    "print(\"Exercice 2 à compléter : implémenter dm_test_neweywest\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0097abeb",
+   "metadata": {
+    "papermill": {
+     "duration": 0.003681,
+     "end_time": "2026-10-25T11:54:56.601634+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:56.597953+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 3 — Combiner le signal TOM avec un vol-target\n",
+    "\n",
+    "La stratégie TOM brute est à plat ~80 % du temps, ce qui bride son CAGR. Un\n",
+    "**vol-target** (re-échelle dynamique vers une volatilité annualisée cible, ex: 15 %)\n",
+    "re-engage le risque. Vérifiez si superposer un vol-target à la stratégie TOM\n",
+    "**améliore le Sharpe net** — attention au **lookahead** : la volatilité utilisée\n",
+    "pour fixer le levier du jour t ne doit inclure que des données strictement\n",
+    "antérieures (`shift(1)`).\n",
+    "\n",
+    "**Indices** :\n",
+    "- *Étape 1* : `vol_realisee = strat.rolling(60).std() * sqrt(252)`.\n",
+    "- *Étape 2* : `levier = clip(target_vol / vol_realisee.shift(1), 0, 3)`.\n",
+    "- *Étape 3* : `strat_leve = levier * strat`, re-calculer le Sharpe net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b7320fc1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-25T11:54:56.612537Z",
+     "iopub.status.busy": "2026-10-25T11:54:56.612079Z",
+     "iopub.status.idle": "2026-10-25T11:54:56.618292Z",
+     "shell.execute_reply": "2026-10-25T11:54:56.617409Z"
+    },
+    "papermill": {
+     "duration": 0.013704,
+     "end_time": "2026-10-25T11:54:56.620068+00:00",
+     "exception": false,
+     "start_time": "2026-10-25T11:54:56.606364+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 3 à compléter : implémenter tom_with_voltarget\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 3 : TOM + vol-target\n",
+    "# TODO étudiant : compléter la fonction ci-dessous\n",
+    "def tom_with_voltarget(strat: pd.Series, target_vol: float = 0.15,\n",
+    "                       vol_window: int = 60, max_leverage: float = 3.0) -> pd.Series:\n",
+    "    \"\"\"Re-échelle la stratégie TOM vers une volatilité annualisée cible.\n",
+    "\n",
+    "    Indice :\n",
+    "      - vol_realisee = strat.rolling(vol_window).std() * np.sqrt(252)\n",
+    "      - levier = (target_vol / vol_realisee.shift(1)).clip(0, max_leverage)\n",
+    "        (le shift(1) est OBLIGATOIRE : anti-lookahead)\n",
+    "      - retourner levier * strat\n",
+    "    \"\"\"\n",
+    "    levered = None  # TODO étudiant : série de rendements re-échalée\n",
+    "    return levered\n",
+    "\n",
+    "print(\"Exercice 3 à compléter : implémenter tom_with_voltarget\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 7.257041,
+   "end_time": "2026-10-25T11:54:57.077355+00:00",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "research_turn_of_month.ipynb",
+   "output_path": "research_turn_of_month.ipynb",
+   "parameters": {},
+   "start_time": "2026-10-25T11:54:49.820314+00:00",
+   "version": "2.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 2af0a91d4f008d91caf50bd34a996fd205a1f3a8 Mon Sep 17 00:00:00 2001
From: jsboige <jsboige@gmail.com>
Date: Wed, 28 Oct 2026 06:39:37 +0100
Subject: [PATCH 2/2] Fix(qc,#22495): research TOM -- reference fantome
 retiree, prose alignee sur la sortie
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- La reference « Reling & Ritter (2000), The Turn of the Month, Part I » n'existe
  pas : remplacee par Ariel (1987, JFE 18(1):161-174) et Lakonishok & Smidt
  (1988, RFS 1(4):403-425), verifies sur Crossref (DOI dans la bibliographie).
  La reference « Clipp (1999) » du meme bloc, introuvable elle aussi, est retiree.
  Diebold & Mariano (1995) completee : JBES 13(3):253-263.
- Ajout de Han, Han & Tian (2025), The disappearing turn-of-month effect, FRL
  71:106461 : la litterature recente documente l'effacement que le verdict mesure.
- Prose alignee sur la sortie : la part de jours TOM est 28,7 % (1817 / 6329), et
  non « ~20 % » (cellules 5, 9, conclusion) ; critere de stabilite OOS reformule
  sur « les 5 folds » ; reserve ajoutee sur k=5 (~48 % des jours).
- Cellule 5 renommee : decoupage OOS par blocs disjoints, premier bloc exclu.
- Re-execution papermill complete (23/23 cellules, 0 erreur) : deux jours de
  bourse de plus que la tete precedente, chiffres re-alignes dans la conclusion.

Co-Authored-By: Claude Haiku 5.5 <noreply@anthropic.com>
---
 .../research/research_turn_of_month.ipynb     | 352 +++++++++---------
 1 file changed, 185 insertions(+), 167 deletions(-)

diff --git a/MyIA.AI.Notebooks/QuantConnect/research/research_turn_of_month.ipynb b/MyIA.AI.Notebooks/QuantConnect/research/research_turn_of_month.ipynb
index 38f6a40e896..e1027376d1a 100644
--- a/MyIA.AI.Notebooks/QuantConnect/research/research_turn_of_month.ipynb
+++ b/MyIA.AI.Notebooks/QuantConnect/research/research_turn_of_month.ipynb
@@ -5,10 +5,10 @@
    "id": "7ad34bd7",
    "metadata": {
     "papermill": {
-     "duration": 0.003416,
-     "end_time": "2026-10-25T11:54:51.862444+00:00",
+     "duration": 0.005444,
+     "end_time": "2026-10-28T05:38:22.746956+00:00",
      "exception": false,
-     "start_time": "2026-10-25T11:54:51.859028+00:00",
+     "start_time": "2026-10-28T05:38:22.741512+00:00",
      "status": "completed"
     },
     "tags": []
@@ -39,10 +39,10 @@
    "id": "89bb9ddf",
    "metadata": {
     "papermill": {
-     "duration": 0.003313,
-     "end_time": "2026-10-25T11:54:51.868860+00:00",
+     "duration": 0.00432,
+     "end_time": "2026-10-28T05:38:22.756840+00:00",
      "exception": false,
-     "start_time": "2026-10-25T11:54:51.865547+00:00",
+     "start_time": "2026-10-28T05:38:22.752520+00:00",
      "status": "completed"
     },
     "tags": []
@@ -50,8 +50,8 @@
    "source": [
     "## 1. Introduction — l'effet de fin / début de mois\n",
     "\n",
-    "Le **turn of the month (TOM)** est un effet de calendrier documenté par Reling & Ritter\n",
-    "(2000, *The Turn of the Month, Part I*). L'hypothèse : les jours **en fin de mois**\n",
+    "Le **turn of the month (TOM)** est un effet de calendrier documenté par Ariel (1987), nommé et mesuré sur\n",
+    "quatre-vingt-dix ans par Lakonishok & Smidt (1988). L'hypothèse : les jours **en fin de mois**\n",
     "(début du cycle de paie) et **en début de mois** (les investisseurs institutionnels\n",
     "déploient leurs allocations mensuelles, pensions, SIP) porteraient un rendement\n",
     "moyen supérieur au **reste du mois**, plus « creux » en flux.\n",
@@ -75,16 +75,16 @@
    "id": "fd014fa7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-25T11:54:51.874424Z",
-     "iopub.status.busy": "2026-10-25T11:54:51.874169Z",
-     "iopub.status.idle": "2026-10-25T11:54:55.729681Z",
-     "shell.execute_reply": "2026-10-25T11:54:55.729165Z"
+     "iopub.execute_input": "2026-10-28T05:38:22.769180Z",
+     "iopub.status.busy": "2026-10-28T05:38:22.768621Z",
+     "iopub.status.idle": "2026-10-28T05:38:38.472399Z",
+     "shell.execute_reply": "2026-10-28T05:38:38.471262Z"
     },
     "papermill": {
-     "duration": 3.859928,
-     "end_time": "2026-10-25T11:54:55.730867+00:00",
+     "duration": 15.711429,
+     "end_time": "2026-10-28T05:38:38.473635+00:00",
      "exception": false,
-     "start_time": "2026-10-25T11:54:51.870939+00:00",
+     "start_time": "2026-10-28T05:38:22.762206+00:00",
      "status": "completed"
     },
     "tags": []
@@ -94,7 +94,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Téléchargement ['SPY', 'QQQ', 'EFA'] du 2000-01-01 au 2026-10-25...\n"
+      "Téléchargement ['SPY', 'QQQ', 'EFA'] du 2000-01-01 au 2026-10-28...\n"
      ]
     },
     {
@@ -102,8 +102,8 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Shape: (6327, 3)\n",
-      "Période: 2001-08-27 à 2026-10-23\n",
+      "Shape: (6329, 3)\n",
+      "Période: 2001-08-27 à 2026-10-27\n",
       "Colonnes: ['SPY', 'QQQ', 'EFA']\n",
       "\n",
       "Imports OK : pandas, numpy, matplotlib, scipy, yfinance\n"
@@ -138,10 +138,10 @@
    "id": "98784059",
    "metadata": {
     "papermill": {
-     "duration": 0.001926,
-     "end_time": "2026-10-25T11:54:55.735004+00:00",
+     "duration": 0.004979,
+     "end_time": "2026-10-28T05:38:38.481344+00:00",
      "exception": false,
-     "start_time": "2026-10-25T11:54:55.733078+00:00",
+     "start_time": "2026-10-28T05:38:38.476365+00:00",
      "status": "completed"
     },
     "tags": []
@@ -160,16 +160,16 @@
    "id": "ad959ba5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-25T11:54:55.740236Z",
-     "iopub.status.busy": "2026-10-25T11:54:55.739737Z",
-     "iopub.status.idle": "2026-10-25T11:54:55.753548Z",
-     "shell.execute_reply": "2026-10-25T11:54:55.752839Z"
+     "iopub.execute_input": "2026-10-28T05:38:38.492859Z",
+     "iopub.status.busy": "2026-10-28T05:38:38.492076Z",
+     "iopub.status.idle": "2026-10-28T05:38:38.518921Z",
+     "shell.execute_reply": "2026-10-28T05:38:38.517729Z"
     },
     "papermill": {
-     "duration": 0.017615,
-     "end_time": "2026-10-25T11:54:55.754583+00:00",
+     "duration": 0.034763,
+     "end_time": "2026-10-28T05:38:38.520525+00:00",
      "exception": false,
-     "start_time": "2026-10-25T11:54:55.736968+00:00",
+     "start_time": "2026-10-28T05:38:38.485762+00:00",
      "status": "completed"
     },
     "tags": []
@@ -179,8 +179,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Part de jours TOM (k=3) : 28.72%\n",
-      "Nombre de jours TOM : 1817 / 6327\n",
+      "Part de jours TOM (k=3) : 28.71%\n",
+      "Nombre de jours TOM : 1817 / 6329\n",
       "\n",
       "Échantillon du mois 2014-03 :\n",
       "  2014-03-03 (Mon)   <-- TOM\n",
@@ -246,10 +246,10 @@
    "id": "b776b2b0",
    "metadata": {
     "papermill": {
-     "duration": 0.001984,
-     "end_time": "2026-10-25T11:54:55.759917+00:00",
+     "duration": 0.004034,
+     "end_time": "2026-10-28T05:38:38.528721+00:00",
      "exception": false,
-     "start_time": "2026-10-25T11:54:55.757933+00:00",
+     "start_time": "2026-10-28T05:38:38.524687+00:00",
      "status": "completed"
     },
     "tags": []
@@ -259,8 +259,9 @@
     "\n",
     "Le flag ne dépend que de la **position du jour dans son mois calendaire** (les 3\n",
     "premiers et les 3 derniers jours de bourse). C'est l'anti-lookahead structuré : on\n",
-    "ne regarde jamais les rendements futurs pour décider si un jour est TOM. Environ\n",
-    "~20 % des jours sont marqués TOM, ce qui est cohérent (6 jours sur ~21 par mois)."
+    "ne regarde jamais les rendements futurs pour décider si un jour est TOM. La sortie\n",
+    "ci-dessus mesure **28,7 %** de jours TOM (1817 sur 6329), ce qui est cohérent :\n",
+    "6 jours sur ~21 jours de bourse par mois font 28,6 %."
    ]
   },
   {
@@ -269,16 +270,16 @@
    "id": "414422c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-25T11:54:55.765274Z",
-     "iopub.status.busy": "2026-10-25T11:54:55.764928Z",
-     "iopub.status.idle": "2026-10-25T11:54:55.776473Z",
-     "shell.execute_reply": "2026-10-25T11:54:55.775489Z"
+     "iopub.execute_input": "2026-10-28T05:38:38.540923Z",
+     "iopub.status.busy": "2026-10-28T05:38:38.540337Z",
+     "iopub.status.idle": "2026-10-28T05:38:38.558332Z",
+     "shell.execute_reply": "2026-10-28T05:38:38.557032Z"
     },
     "papermill": {
-     "duration": 0.015541,
-     "end_time": "2026-10-25T11:54:55.777690+00:00",
+     "duration": 0.026346,
+     "end_time": "2026-10-28T05:38:38.560405+00:00",
      "exception": false,
-     "start_time": "2026-10-25T11:54:55.762149+00:00",
+     "start_time": "2026-10-28T05:38:38.534059+00:00",
      "status": "completed"
     },
     "tags": []
@@ -290,9 +291,9 @@
      "text": [
       "Effet BRUT du turn of the month (k=3) :\n",
       "     Rend. moyen TOM (bps/jour)  Rend. moyen non-TOM (bps/jour)  Écart TOM - non-TOM (bps/jour)  N jours TOM\n",
-      "SPY                       7.184                           3.236                           3.947         1817\n",
-      "QQQ                       7.903                           5.095                           2.808         1817\n",
-      "EFA                       7.042                           1.789                           5.253         1817\n"
+      "SPY                       7.290                           3.220                           4.069         1817\n",
+      "QQQ                       8.133                           5.053                           3.080         1817\n",
+      "EFA                       7.122                           1.776                           5.345         1817\n"
      ]
     }
    ],
@@ -324,10 +325,10 @@
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@@ -347,16 +348,16 @@
    "id": "67558c8b",
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-     "end_time": "2026-10-25T11:54:55.803185+00:00",
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      "exception": false,
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      "status": "completed"
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     "tags": []
@@ -368,12 +369,12 @@
      "text": [
       "Stratégie TOM (net de 10 bps/transition) vs Buy & Hold :\n",
       "              Sharpe   CAGR  Vol(ann)   MaxDD\n",
-      "SPY TOM(net) -0.0676 0.0235    0.0968 -0.2155\n",
-      "SPY B&H       0.3513 0.0965    0.1894 -0.5519\n",
-      "QQQ TOM(net) -0.0340 0.0258    0.1232 -0.3773\n",
-      "QQQ B&H       0.4287 0.1295    0.2320 -0.5340\n",
-      "EFA TOM(net) -0.0808 0.0214    0.1064 -0.3157\n",
-      "EFA B&H       0.1619 0.0635    0.2072 -0.6104\n"
+      "SPY TOM(net) -0.0596 0.0242    0.0968 -0.2155\n",
+      "SPY B&H       0.3541 0.0971    0.1894 -0.5519\n",
+      "QQQ TOM(net) -0.0201 0.0275    0.1231 -0.3773\n",
+      "QQQ B&H       0.4332 0.1305    0.2320 -0.5340\n",
+      "EFA TOM(net) -0.0753 0.0220    0.1064 -0.3157\n",
+      "EFA B&H       0.1637 0.0639    0.2071 -0.6104\n"
      ]
     }
    ],
@@ -421,10 +422,10 @@
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      "status": "completed"
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@@ -432,7 +433,7 @@
    "source": [
     "### Interprétation : net de coûts\n",
     "\n",
-    "C'est le premier filtre de réalité. Une stratégie qui n'est long que ~20 % du temps\n",
+    "C'est le premier filtre de réalité. Une stratégie qui n'est long que ~28,7 % du temps\n",
     "a un CAGR structurellement plus faible qu'un Buy & Hold permanent — l'effet de\n",
     "calendrier doit **compenser** ce temps à plat pour offrir un **ratio de Sharpe**\n",
     "supérieur. Le CAGR seul ne dit rien ; c'est le Sharpe (rendement ajusté au risque)\n",
@@ -446,16 +447,16 @@
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      "exception": false,
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      "status": "completed"
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@@ -470,7 +471,7 @@
       " Fold                  Période  Écart TOM (bps/jour)  N_TOM\n",
       "    1 2005-11-07 -> 2010-01-14                -0.187    300\n",
       "    2 2010-01-15 -> 2014-03-25                10.828    300\n",
-      "    3 2014-03-26 -> 2018-05-31                -4.597    303\n",
+      "    3 2014-03-26 -> 2018-05-31                -4.598    303\n",
       "    4 2018-06-01 -> 2022-08-08                11.589    303\n",
       "    5 2022-08-09 -> 2026-10-20                -1.639    300\n",
       "\n",
@@ -495,9 +496,12 @@
    "source": [
     "# Cellule 5 — Walk-forward 5-fold : l'effet tient-il out-of-sample ?\n",
     "#\n",
-    "# Fenêtre expansive : on mesure l'effet BRUT (écart TOM - non-TOM) sur chaque\n",
-    "# fold OOS. Si l'écart reste du même signe sur les 5 folds, l'effet n'est pas\n",
-    "# un artefact d'une période particulière.\n",
+    "# Découpage OOS par blocs DISJOINTS (et non une fenêtre expansive : il n'y a\n",
+    "# rien à ré-entraîner pour une règle calendaire). Le premier bloc est exclu — il\n",
+    "# sert de marge, ce qui explique que le fold 1 ne commence pas au début de la\n",
+    "# série. On mesure l'effet BRUT (écart TOM - non-TOM) sur chaque bloc : si\n",
+    "# l'écart reste du même signe sur les 5 blocs, ce n'est pas un artefact d'une\n",
+    "# seule période.\n",
     "\n",
     "def oos_tom_effect(ret: pd.Series, mask: pd.Series, n_folds: int = 5) -> pd.DataFrame:\n",
     "    r = ret.dropna()\n",
@@ -526,10 +530,10 @@
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     "papermill": {
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-     "end_time": "2026-10-25T11:54:55.844340+00:00",
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      "exception": false,
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      "status": "completed"
     },
     "tags": []
@@ -537,9 +541,13 @@
    "source": [
     "### Interprétation : stabilité out-of-sample\n",
     "\n",
-    "Un effet de calendrier **robuste** garde le même signe (ici : positif) sur la\n",
-    "plupart des 5 folds OOS. S'il est positif sur 2 folds et négatif sur 3, c'est du\n",
-    "bruit, pas un signal. Cette stabilité temporelle est l'argument le plus fort en\n",
+    "Un effet de calendrier **robuste** garderait le même signe (ici : positif) sur **les\n",
+    "5 folds** OOS. Le critère appliqué ici est donc le plus exigeant : trois folds sur\n",
+    "cinq du même signe ne suffisent pas — sur quatre-vingt-dix ans de littérature, un\n",
+    "effet qui s'inverse deux fois sur cinq périodes consécutives n'est pas un signal\n",
+    "stable, c'est un épisode. C'est cette stabilité temporelle qui est l'argument le\n",
+    "plus fort en faveur (ou contre) l'effet — plus que n'importe quelle moyenne\n",
+    "globale. Cette stabilité temporelle est l'argument le plus fort en\n",
     "faveur (ou contre) l'effet — plus que n'importe quelle moyenne globale."
    ]
   },
@@ -549,16 +557,16 @@
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-     "shell.execute_reply": "2026-10-25T11:54:55.880153Z"
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      "exception": false,
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      "status": "completed"
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     "tags": []
@@ -569,16 +577,22 @@
      "output_type": "stream",
      "text": [
       "=== (a) Test de Diebold-Mariano : la stratégie TOM bat-elle significativement B&H ? ===\n",
-      "  SPY: stat=-2.528, p=0.0115  ->  SIGNIFICATIF (p<0.05)\n",
-      "  QQQ: stat=-2.944, p=0.0032  ->  SIGNIFICATIF (p<0.05)\n",
-      "  EFA: stat=-1.584, p=0.1131  ->  non significatif\n",
+      "  SPY: stat=-2.520, p=0.0117  ->  SIGNIFICATIF (p<0.05)\n",
+      "  QQQ: stat=-2.924, p=0.0035  ->  SIGNIFICATIF (p<0.05)\n",
+      "  EFA: stat=-1.578, p=0.1145  ->  non significatif\n",
       "\n",
-      "=== (b) Sensibilité à la fenêtre k (effet BRUT, bps/jour) ===\n",
+      "=== (b) Sensibilité à la fenêtre k (effet BRUT, bps/jour) ===\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Actif       EFA   QQQ   SPY\n",
       "k (jours)                  \n",
-      "2         7.747 5.095 5.042\n",
-      "3         5.253 2.808 3.947\n",
-      "5         3.908 2.166 4.220\n"
+      "2         7.966 5.586 5.322\n",
+      "3         5.345 3.080 4.069\n",
+      "5         3.931 2.177 4.215\n"
      ]
     }
    ],
@@ -638,25 +652,25 @@
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    "metadata": {
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      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "### Interprétation : significativité et robustesse au paramètre\n",
-    "\n",
-    "Le test de Diebold-Mariano répond à la question décisive : la différence de\n",
-    "rendement journalier (stratégie TOM − B&H) est-elle **statistiquement** différente\n",
-    "de zéro, en tenant compte de l'autocorrélation des rendements ? **Le signe du\n",
-    "statistic est porteur** : un stat **négatif** avec p < 0.05 signifie que la\n",
-    "stratégie **perd** significativement face au B&H (ici SPY p=0.012, QQQ p=0.003) —\n",
-    "la significativité confirme le déficit, elle ne l'infirme pas. La sensibilité à k,\n",
     "elle, montre que l'effet **brut** garde un signe stable (positif) quand k varie de\n",
-    "2 à 5 : le phénomène est réel, même s'il n'est pas exploitable."
+    "2 à 5 : le phénomène est réel, même s'il n'est pas exploitable.\n",
+    "\n",
+    "**Une réserve sur k=5, à ne pas lire comme une robustesse.** À k=5 la fenêtre TOM\n",
+    "couvre 10 jours de bourse par mois, soit ~48 % des jours : on ne mesure plus une\n",
+    "anomalie de calendrier sur une frange du mois, mais une moyenne sur près de la\n",
+    "moitié de l'échantillon, qui se rapproche mécaniquement du marché entier. La\n",
+    "décroissance de l'écart quand k grandit (7,97 → 5,35 → 3,93 bps sur EFA) dit donc\n",
+    "aussi que le signal se **dilue** avec la taille de la fenêtre : c'est la valeur\n",
+    "portée par k=2 et k=3 qui compte, pas la stabilité apparente de k=5."
    ]
   },
   {
@@ -665,16 +679,16 @@
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@@ -682,7 +696,7 @@
    "outputs": [
     {
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",
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       "text/plain": [
        "<Figure size 1300x600 with 1 Axes>"
       ]
@@ -719,10 +733,10 @@
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@@ -734,9 +748,9 @@
     "\n",
     "| Actif | Sharpe TOM (net) | Sharpe B&H | DM p (TOM vs B&H) |\n",
     "|-------|------------------|-----------|-------------------|\n",
-    "| SPY   | −0.068           | 0.351     | 0.012 (négatif)   |\n",
-    "| QQQ   | −0.034           | 0.429     | 0.003 (négatif)   |\n",
-    "| EFA   | −0.081           | 0.162     | 0.113 (n.s.)      |\n",
+    "| SPY   | −0.060           | 0.354     | 0.012 (négatif)   |\n",
+    "| QQQ   | −0.020           | 0.433     | 0.004 (négatif)   |\n",
+    "| EFA   | −0.075           | 0.164     | 0.115 (n.s.)      |\n",
     "\n",
     "### Verdict\n",
     "\n",
@@ -744,7 +758,7 @@
     "\n",
     "L'effet de calendrier **brut** est réel et robuste au paramètre de fenêtre :\n",
     "l'écart TOM − non-TOM est positif pour les 3 actifs et les 3 fenêtres k=2/3/5\n",
-    "(2.2 à 7.7 bps/jour, décroissant quand k grandit). Mais il **n'est pas\n",
+    "(2,2 à 8,0 bps/jour, décroissant quand k grandit). Mais il **n'est pas\n",
     "exploitable** net de coûts, pour deux raisons mesurées :\n",
     "\n",
     "1. **Non robuste out-of-sample.** L'écart change de signe selon le fold\n",
@@ -752,11 +766,11 @@
     "   (2022-2026 : négatif sur les 3 actifs). Ce n'est pas un signal stable, c'est\n",
     "   un artefact de régime.\n",
     "2. **Non rentable net de coûts.** La stratégie « long uniquement les jours TOM,\n",
-    "   à plat sinon » a un **Sharpe négatif** (−0.03 à −0.08) contre 0.16 à 0.43\n",
-    "   pour le Buy & Hold : être long seulement ~20 % du temps et payer 10 bps par\n",
+    "   à plat sinon » a un **Sharpe négatif** (−0.02 à −0.08) contre 0.16 à 0.43\n",
+    "   pour le Buy & Hold : être long seulement ~28,7 % du temps et payer 10 bps par\n",
     "   transition fait perdre plus qu'elle ne gagne. Le test de Diebold-Mariano\n",
     "   confirme : la différence (TOM − B&H) est **significativement négative**\n",
-    "   (SPY p=0.012, QQQ p=0.003) — la stratégie perd significativement, elle ne bat\n",
+    "   (SPY p=0.012, QQQ p=0.004) — la stratégie perd significativement, elle ne bat\n",
     "   pas le B&H.\n",
     "\n",
     "**Leçon** : une anomalie statistique mesurable (effet brut robuste au k) n'est\n",
@@ -777,12 +791,16 @@
     "   sizing plus fin pourrait changer le résultat (cf. Exercice 3)\n",
     "\n",
     "### Références\n",
-    "- Reling, S. & Ritter, J. (2000), *The Turn of the Month, Part I*, Technical\n",
-    "  Analysis of Stocks & Commodities.\n",
+    "- Ariel, R. A. (1987), *A monthly effect in stock returns*, Journal of Financial\n",
+    "  Economics 18(1):161-174, doi:10.1016/0304-405X(87)90066-3.\n",
+    "- Lakonishok, J. & Smidt, S. (1988), *Are Seasonal Anomalies Real? A Ninety-Year\n",
+    "  Perspective*, Review of Financial Studies 1(4):403-425, doi:10.1093/rfs/1.4.403.\n",
+    "- Han, L., Han, Y. & Tian, S. (2025), *The disappearing turn-of-month effect*,\n",
+    "  Finance Research Letters 71:106461, doi:10.1016/j.frl.2024.106461 — la\n",
+    "  littérature récente documente l'effacement de l'effet, ce qui va dans le sens\n",
+    "  du verdict mesuré ici.\n",
     "- Diebold, F. & Mariano, R. (1995), *Comparing Predictive Accuracy*, Journal of\n",
-    "  Business & Economic Statistics.\n",
-    "- Clipp, E. (1999), *A Note on the End-of-Month Effect*, Journal of Portfolio\n",
-    "  Management."
+    "  Business & Economic Statistics 13(3):253-263, doi:10.1080/07350015.1995.10524599."
    ]
   },
   {
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@@ -840,16 +858,16 @@
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@@ -886,10 +904,10 @@
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@@ -915,16 +933,16 @@
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@@ -961,10 +979,10 @@
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@@ -991,16 +1009,16 @@
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@@ -1054,14 +1072,14 @@
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-   "start_time": "2026-10-25T11:54:49.820314+00:00",
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