diff --git a/MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-31-Transformer-Training.ipynb b/MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-31-Transformer-Training.ipynb
index 7246443ce8..75fec7b66b 100644
--- a/MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-31-Transformer-Training.ipynb
+++ b/MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-31-Transformer-Training.ipynb
@@ -5,16 +5,57 @@
    "id": "cell-0",
    "metadata": {
     "papermill": {
-     "duration": 0.009688,
-     "end_time": "2026-09-23T17:28:06.782699",
+     "duration": 0.007853,
+     "end_time": "2026-09-25T04:37:33.860175",
      "exception": false,
-     "start_time": "2026-09-23T17:28:06.773011",
+     "start_time": "2026-09-25T04:37:33.852322",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "[<< Sommaire QC](../README.md) | [Précédent : QC-Py-30-LSTM-Training <<](./QC-Py-30-LSTM-Training.ipynb) | [Suivant : QC-Py-32-RL-DQN-Trading >>](./QC-Py-32-RL-DQN-Trading.ipynb)\n\n# QC-Py-31 - Transformer Encoder Multi-Asset (GPU)\n\n**Module** : Machine Learning pour le Trading - Modèles Transformer  \n**Pre-requis** : QC-Py-30 LSTM Training  \n**Duree estimee** : 45 minutes  \n\n## Objectifs d'apprentissage\n\nA la fin de ce notebook, vous saurez :\n\n1. Implementer un **encodeur Transformer natif (PyTorch)** pour les series temporelles financieres\n2. Integrer des **features macro** (VIX, courbe des taux) dans le modèle\n3. Comprendre le **mécanisme d'auto-attention multi-tete** applique aux marches\n4. **Comparer** les performances Transformer vs LSTM (QC-Py-30)\n5. **Deployer** le modèle dans une stratégie QuantConnect\n\n### Prerequis\n\n- Notebook QC-Py-30 LSTM Training complete\n- Comprehension de PyTorch (tenseurs, modules, entrainement)\n- GPU recommande (CUDA) mais CPU possible\n\n### Structure du notebook\n\n| Section | Sujet | Duree |\n|---------|-------|-------|\n| 1 | Configuration PyTorch et GPU | 3 min |\n| 2 | Chargement des données (30 stocks + macro) | 5 min |\n| 3 | Feature engineering avec features macro | 5 min |\n| 4 | Split temporel et normalisation | 3 min |\n| 5 | Modèle Transformer Encoder | 5 min |\n| 6 | Entrainement GPU | 8 min |\n| 7 | Courbes d'apprentissage | 2 min |\n| 8 | Evaluation sur le set de test | 2 min |\n| 9 | Visualisation des predictions | 2 min |\n| 10 | Visualisation de l'attention | 3 min |\n| 11 | Backtest simplifie | 3 min |\n| 12 | Sauvegarde du modèle | 2 min |\n| 13 | Integration QuantConnect Cloud | 3 min |\n| 14 | Resume et conclusions | 2 min |"
+    "[<< Sommaire QC](../README.md) | [Précédent : QC-Py-30-LSTM-Training <<](./QC-Py-30-LSTM-Training.ipynb) | [Suivant : QC-Py-32-RL-DQN-Trading >>](./QC-Py-32-RL-DQN-Trading.ipynb)\n",
+    "\n",
+    "# QC-Py-31 - Transformer Encoder Multi-Asset (GPU)\n",
+    "\n",
+    "**Module** : Machine Learning pour le Trading - Modèles Transformer  \n",
+    "**Pre-requis** : QC-Py-30 LSTM Training  \n",
+    "**Duree estimee** : 45 minutes  \n",
+    "\n",
+    "## Objectifs d'apprentissage\n",
+    "\n",
+    "A la fin de ce notebook, vous saurez :\n",
+    "\n",
+    "1. Implementer un **encodeur Transformer natif (PyTorch)** pour les series temporelles financieres\n",
+    "2. Integrer des **features macro** (VIX, courbe des taux) dans le modèle\n",
+    "3. Comprendre le **mécanisme d'auto-attention multi-tete** applique aux marches\n",
+    "4. **Comparer** les performances Transformer vs LSTM (QC-Py-30)\n",
+    "5. **Deployer** le modèle dans une stratégie QuantConnect\n",
+    "\n",
+    "### Prerequis\n",
+    "\n",
+    "- Notebook QC-Py-30 LSTM Training complete\n",
+    "- Comprehension de PyTorch (tenseurs, modules, entrainement)\n",
+    "- GPU recommande (CUDA) mais CPU possible\n",
+    "\n",
+    "### Structure du notebook\n",
+    "\n",
+    "| Section | Sujet | Duree |\n",
+    "|---------|-------|-------|\n",
+    "| 1 | Configuration PyTorch et GPU | 3 min |\n",
+    "| 2 | Chargement des données (30 stocks + macro) | 5 min |\n",
+    "| 3 | Feature engineering avec features macro | 5 min |\n",
+    "| 4 | Split temporel et normalisation | 3 min |\n",
+    "| 5 | Modèle Transformer Encoder | 5 min |\n",
+    "| 6 | Entrainement GPU | 8 min |\n",
+    "| 7 | Courbes d'apprentissage | 2 min |\n",
+    "| 8 | Evaluation sur le set de test | 2 min |\n",
+    "| 9 | Visualisation des predictions | 2 min |\n",
+    "| 10 | Visualisation de l'attention | 3 min |\n",
+    "| 11 | Backtest simplifie | 3 min |\n",
+    "| 12 | Sauvegarde du modèle | 2 min |\n",
+    "| 13 | Integration QuantConnect Cloud | 3 min |\n",
+    "| 14 | Resume et conclusions | 2 min |"
    ]
   },
   {
@@ -22,10 +63,10 @@
    "id": "cell-1",
    "metadata": {
     "papermill": {
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      "exception": false,
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      "status": "completed"
     },
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@@ -42,10 +83,10 @@
    "id": "cell-2",
    "metadata": {
     "papermill": {
-     "duration": 0.009187,
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@@ -67,10 +108,10 @@
    "id": "cell-3",
    "metadata": {
     "papermill": {
-     "duration": 0.01181,
-     "end_time": "2026-09-23T17:28:06.843596",
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      "exception": false,
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      "status": "completed"
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@@ -121,31 +162,29 @@
    "id": "cell-4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:28:06.865668Z",
-     "iopub.status.busy": "2026-09-23T17:28:06.865127Z",
-     "iopub.status.idle": "2026-09-23T17:28:14.017656Z",
-     "shell.execute_reply": "2026-09-23T17:28:14.016244Z"
+     "iopub.execute_input": "2026-09-25T04:37:33.921624Z",
+     "iopub.status.busy": "2026-09-25T04:37:33.921410Z",
+     "iopub.status.idle": "2026-09-25T04:37:54.678144Z",
+     "shell.execute_reply": "2026-09-25T04:37:54.677418Z"
     },
     "papermill": {
-     "duration": 7.167087,
-     "end_time": "2026-09-23T17:28:14.019657",
+     "duration": 20.765385,
+     "end_time": "2026-09-25T04:37:54.679105",
      "exception": false,
-     "start_time": "2026-09-23T17:28:06.852570",
+     "start_time": "2026-09-25T04:37:33.913720",
      "status": "completed"
     },
-    "tags": [
-     "parameters"
-    ]
+    "tags": []
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyTorch version: 2.6.0+cu124\n",
+      "PyTorch version: 2.8.0+cu126\n",
       "Device: cuda\n",
-      "GPU: NVIDIA GeForce RTX 3080 Ti Laptop GPU\n",
-      "VRAM: 17.2 GB\n",
+      "GPU: NVIDIA GeForce RTX 3090\n",
+      "VRAM: 25.8 GB\n",
       "\n",
       "Hyperparametres:\n",
       "  Sequence: 60 jours -> Prediction: 5 jours\n",
@@ -206,7 +245,7 @@
     "EPOCHS = 25            # Nombre d'epochs (reduit pour securite thermique)\n",
     "PATIENCE = 7           # Patience pour early stopping\n",
     "LR = 0.0005            # Learning rate\n",
-    "FORCE_RETRAIN = os.environ.get('QC31_FORCE_RETRAIN', '0') == '1'  # True : ignorer checkpoint/.pt et entrainer depuis zero (#17516)\n",
+    "FORCE_RETRAIN = os.environ.get('QC31_FORCE_RETRAIN', '0') == '1'  # ignorer checkpoint/.pt et entrainer depuis zero (#17516)\n",
     "DROPOUT = 0.1          # Dropout\n",
     "TRAIN_RATIO = 0.70     # Split train\n",
     "VAL_RATIO = 0.15       # Split validation\n",
@@ -217,6 +256,7 @@
     "print(f\"  Transformer: d_model={D_MODEL}, nhead={NHEAD}, layers={NUM_LAYERS}\")\n",
     "print(f\"  FFN dim: {DIM_FEEDFORWARD}, Dropout: {DROPOUT}\")\n",
     "print(f\"  Batch size: {BATCH_SIZE}, Epochs: {EPOCHS}, LR: {LR}\")\n",
+    "\n",
     "# Determinisme GPU (issue #16795) : une graine seule ne garantit rien --\n",
     "# cuDNN choisit ses algorithmes de facon non deterministe par defaut.\n",
     "torch.use_deterministic_algorithms(True)\n",
@@ -232,10 +272,10 @@
    "id": "cell-5",
    "metadata": {
     "papermill": {
-     "duration": 0.01154,
-     "end_time": "2026-09-23T17:28:14.043405",
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      "exception": false,
-     "start_time": "2026-09-23T17:28:14.031865",
+     "start_time": "2026-09-25T04:37:54.683406",
      "status": "completed"
     },
     "tags": []
@@ -266,10 +306,10 @@
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    "metadata": {
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-     "duration": 0.012778,
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      "exception": false,
-     "start_time": "2026-09-23T17:28:14.052551",
+     "start_time": "2026-09-25T04:37:54.693241",
      "status": "completed"
     },
     "tags": []
@@ -299,16 +339,16 @@
    "id": "cell-7",
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-     "shell.execute_reply": "2026-09-23T17:28:17.976976Z"
+     "iopub.execute_input": "2026-09-25T04:37:54.707924Z",
+     "iopub.status.busy": "2026-09-25T04:37:54.707138Z",
+     "iopub.status.idle": "2026-09-25T04:38:00.518267Z",
+     "shell.execute_reply": "2026-09-25T04:38:00.517250Z"
     },
     "papermill": {
-     "duration": 3.90445,
-     "end_time": "2026-09-23T17:28:17.980317",
+     "duration": 5.817448,
+     "end_time": "2026-09-25T04:38:00.519234",
      "exception": false,
-     "start_time": "2026-09-23T17:28:14.075867",
+     "start_time": "2026-09-25T04:37:54.701786",
      "status": "completed"
     },
     "tags": []
@@ -343,7 +383,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[****                   9%                       ]  3 of 33 completed"
+      "[***                    6%                       ]  2 of 33 completed"
      ]
     },
     {
@@ -351,7 +391,8 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[******                12%                       ]  4 of 33 completed"
+      "[******                12%                       ]  4 of 33 completed\r",
+      "[*******               15%                       ]  5 of 33 completed"
      ]
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@@ -359,7 +400,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*******               15%                       ]  5 of 33 completed"
+      "[*********             18%                       ]  6 of 33 completed"
      ]
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@@ -367,8 +408,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*******               15%                       ]  5 of 33 completed\r",
-      "[*******               15%                       ]  5 of 33 completed"
+      "[**********            21%                       ]  7 of 33 completed"
      ]
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@@ -376,7 +416,8 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*******               15%                       ]  5 of 33 completed"
+      "[************          24%                       ]  8 of 33 completed\r",
+      "[************          24%                       ]  8 of 33 completed"
      ]
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@@ -384,7 +425,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*************         27%                       ]  9 of 33 completed"
+      "[**************        30%                       ]  10 of 33 completed"
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@@ -392,7 +433,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**************        30%                       ]  10 of 33 completed"
+      "[****************      33%                       ]  11 of 33 completed"
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@@ -400,7 +441,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[****************      33%                       ]  11 of 33 completed"
+      "[*****************     36%                       ]  12 of 33 completed"
      ]
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@@ -408,7 +449,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*****************     36%                       ]  12 of 33 completed"
+      "[*******************   39%                       ]  13 of 33 completed"
      ]
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@@ -416,7 +457,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*******************   39%                       ]  13 of 33 completed"
+      "[********************  42%                       ]  14 of 33 completed"
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@@ -424,7 +465,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[********************  42%                       ]  14 of 33 completed"
+      "[**********************45%                       ]  15 of 33 completed"
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@@ -432,7 +473,6 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[********************  42%                       ]  14 of 33 completed\r",
       "[**********************48%                       ]  16 of 33 completed"
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@@ -457,11 +497,25 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************58%***                    ]  19 of 33 completed\r",
-      "[**********************58%***                    ]  19 of 33 completed\r",
       "[**********************58%***                    ]  19 of 33 completed"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "[**********************61%****                   ]  20 of 33 completed"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "[**********************64%******                 ]  21 of 33 completed"
+     ]
+    },
     {
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      "output_type": "stream",
@@ -499,7 +553,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************76%***********            ]  25 of 33 completed"
+      "[**********************79%*************          ]  26 of 33 completed"
      ]
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@@ -515,10 +569,17 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************85%****************       ]  28 of 33 completed\r",
       "[**********************85%****************       ]  28 of 33 completed"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "[**********************88%*****************      ]  29 of 33 completed"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -551,6 +612,13 @@
       "[*********************100%***********************]  33 of 33 completed"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -572,18 +640,11 @@
       "Apercu des 5 premieres lignes:\n",
       "Ticker           AAPL       MSFT      GOOGL       AMZN      NVDA\n",
       "Date                                                            \n",
-      "2014-01-02  17.110132  30.636765  27.593285  19.898500  0.372991\n",
-      "2014-01-03  16.734291  30.430656  27.392002  19.822001  0.368523\n",
-      "2014-01-06  16.825541  29.787586  27.697401  19.681499  0.373462\n",
-      "2014-01-07  16.705214  30.018427  28.231361  19.901501  0.379576\n",
-      "2014-01-08  16.811007  29.482525  28.290108  20.096001  0.384750\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "2014-01-02  17.110130  30.636755  27.593287  19.898500  0.372991\n",
+      "2014-01-03  16.734289  30.430653  27.391998  19.822001  0.368523\n",
+      "2014-01-06  16.825537  29.787569  27.697401  19.681499  0.373461\n",
+      "2014-01-07  16.705212  30.018423  28.231359  19.901501  0.379576\n",
+      "2014-01-08  16.811007  29.482531  28.290108  20.096001  0.384750\n"
      ]
     }
    ],
@@ -650,10 +711,10 @@
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    "metadata": {
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-     "duration": 0.01214,
-     "end_time": "2026-09-23T17:28:18.005968",
+     "duration": 0.007146,
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      "exception": false,
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@@ -682,10 +743,10 @@
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    "metadata": {
     "papermill": {
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-     "end_time": "2026-09-23T17:28:18.035435",
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      "exception": false,
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      "status": "completed"
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@@ -713,16 +774,16 @@
    "id": "cell-10",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:28:18.066747Z",
-     "iopub.status.busy": "2026-09-23T17:28:18.065230Z",
-     "iopub.status.idle": "2026-09-23T17:28:18.721942Z",
-     "shell.execute_reply": "2026-09-23T17:28:18.720435Z"
+     "iopub.execute_input": "2026-09-25T04:38:00.564794Z",
+     "iopub.status.busy": "2026-09-25T04:38:00.564398Z",
+     "iopub.status.idle": "2026-09-25T04:38:00.849364Z",
+     "shell.execute_reply": "2026-09-25T04:38:00.848670Z"
     },
     "papermill": {
-     "duration": 0.674708,
-     "end_time": "2026-09-23T17:28:18.723955",
+     "duration": 0.293336,
+     "end_time": "2026-09-25T04:38:00.850322",
      "exception": false,
-     "start_time": "2026-09-23T17:28:18.049247",
+     "start_time": "2026-09-25T04:38:00.556986",
      "status": "completed"
     },
     "tags": []
@@ -739,23 +800,23 @@
       "Exemple pour AAPL:\n",
       "           close     ret_1d     ret_5d    ret_20d    vol_10d    vol_20d  \\\n",
       "count  2768.0000  2768.0000  2768.0000  2768.0000  2768.0000  2768.0000   \n",
-      "mean     86.9242     0.0011     0.0055     0.0219     0.0155     0.0160   \n",
+      "mean     86.9243     0.0011     0.0055     0.0219     0.0155     0.0160   \n",
       "std      65.7110     0.0176     0.0370     0.0767     0.0085     0.0075   \n",
       "min      15.4598    -0.1286    -0.1753    -0.2677     0.0036     0.0048   \n",
       "25%      28.0256    -0.0070    -0.0159    -0.0243     0.0099     0.0110   \n",
       "50%      51.0818     0.0010     0.0067     0.0231     0.0137     0.0144   \n",
-      "75%     145.6634     0.0101     0.0272     0.0753     0.0189     0.0191   \n",
+      "75%     145.6633     0.0101     0.0272     0.0753     0.0189     0.0191   \n",
       "max     257.1538     0.1198     0.1841     0.3453     0.0801     0.0680   \n",
       "\n",
       "        momentum        rsi  vix_level  vix_change  yield_spread  \\\n",
       "count  2768.0000  2768.0000  2768.0000   2768.0000     2768.0000   \n",
       "mean      2.0283    56.0711    17.8960      0.0034       -0.4794   \n",
       "std       4.9401    17.8185     7.0863      0.0854        0.2651   \n",
-      "min     -11.5655     3.1802     9.1400     -0.2816       -0.9900   \n",
-      "25%      -1.5845    43.0727    13.1975     -0.0432       -0.7223   \n",
+      "min     -11.5655     3.1799     9.1400     -0.2816       -0.9900   \n",
+      "25%      -1.5844    43.0726    13.1975     -0.0432       -0.7223   \n",
       "50%       1.7739    56.5022    15.9450     -0.0070       -0.5160   \n",
       "75%       5.6948    69.3732    20.7925      0.0364       -0.2157   \n",
-      "max      19.9281    97.3971    82.6900      1.1560        0.1800   \n",
+      "max      19.9279    97.3968    82.6900      1.1560        0.1800   \n",
       "\n",
       "       yield_curve_slope  \n",
       "count          2768.0000  \n",
@@ -876,10 +937,10 @@
    "id": "cell-ab2bb9c5",
    "metadata": {
     "papermill": {
-     "duration": 0.013729,
-     "end_time": "2026-09-23T17:28:18.748847",
+     "duration": 0.004921,
+     "end_time": "2026-09-25T04:38:00.860345",
      "exception": false,
-     "start_time": "2026-09-23T17:28:18.735118",
+     "start_time": "2026-09-25T04:38:00.855424",
      "status": "completed"
     },
     "tags": []
@@ -902,16 +963,16 @@
    "id": "cell-b3d5cdef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:28:18.776939Z",
-     "iopub.status.busy": "2026-09-23T17:28:18.775939Z",
-     "iopub.status.idle": "2026-09-23T17:28:18.785418Z",
-     "shell.execute_reply": "2026-09-23T17:28:18.783844Z"
+     "iopub.execute_input": "2026-09-25T04:38:00.871998Z",
+     "iopub.status.busy": "2026-09-25T04:38:00.871429Z",
+     "iopub.status.idle": "2026-09-25T04:38:00.875996Z",
+     "shell.execute_reply": "2026-09-25T04:38:00.875280Z"
     },
     "papermill": {
-     "duration": 0.025699,
-     "end_time": "2026-09-23T17:28:18.786418",
+     "duration": 0.011541,
+     "end_time": "2026-09-25T04:38:00.877129",
      "exception": false,
-     "start_time": "2026-09-23T17:28:18.760719",
+     "start_time": "2026-09-25T04:38:00.865588",
      "status": "completed"
     },
     "tags": []
@@ -940,10 +1001,10 @@
    "id": "cell-11",
    "metadata": {
     "papermill": {
-     "duration": 0.013741,
-     "end_time": "2026-09-23T17:28:18.812594",
+     "duration": 0.004642,
+     "end_time": "2026-09-25T04:38:00.889445",
      "exception": false,
-     "start_time": "2026-09-23T17:28:18.798853",
+     "start_time": "2026-09-25T04:38:00.884803",
      "status": "completed"
     },
     "tags": []
@@ -978,10 +1039,10 @@
    "id": "cell-12",
    "metadata": {
     "papermill": {
-     "duration": 0.014791,
-     "end_time": "2026-09-23T17:28:18.843577",
+     "duration": 0.004475,
+     "end_time": "2026-09-25T04:38:00.898437",
      "exception": false,
-     "start_time": "2026-09-23T17:28:18.828786",
+     "start_time": "2026-09-25T04:38:00.893962",
      "status": "completed"
     },
     "tags": []
@@ -1014,16 +1075,16 @@
    "id": "cell-13",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:28:18.874895Z",
-     "iopub.status.busy": "2026-09-23T17:28:18.873887Z",
-     "iopub.status.idle": "2026-09-23T17:28:20.690160Z",
-     "shell.execute_reply": "2026-09-23T17:28:20.688202Z"
+     "iopub.execute_input": "2026-09-25T04:38:00.908688Z",
+     "iopub.status.busy": "2026-09-25T04:38:00.908188Z",
+     "iopub.status.idle": "2026-09-25T04:38:02.274492Z",
+     "shell.execute_reply": "2026-09-25T04:38:02.273717Z"
     },
     "papermill": {
-     "duration": 1.832901,
-     "end_time": "2026-09-23T17:28:20.691167",
+     "duration": 1.372678,
+     "end_time": "2026-09-25T04:38:02.275561",
      "exception": false,
-     "start_time": "2026-09-23T17:28:18.858266",
+     "start_time": "2026-09-25T04:38:00.902883",
      "status": "completed"
     },
     "tags": []
@@ -1043,13 +1104,12 @@
       "Sequences totales: 81090\n",
       "Shape X: (81090, 60, 12)  [n_samples, seq_len, n_features]\n",
       "Shape y_reg: (81090,)\n",
-      "Shape y_cls: (81090,)\n",
       "Distribution direction: 56.35% up, 43.65% down\n",
       "\n",
-      "Split temporel:\n",
-      "  Train: 56763 samples (70%)\n",
-      "  Val:   12163 samples (15%)\n",
-      "  Test:  12164 samples (15%)\n"
+      "Split temporel global (par date de cible) :\n",
+      "  Train:    56790 samples | 2014-03-31 -> 2021-10-04\n",
+      "  Val:      12150 samples | 2021-10-05 -> 2023-05-15\n",
+      "  Test:     12150 samples | 2023-05-16 -> 2024-12-23\n"
      ]
     },
     {
@@ -1059,12 +1119,12 @@
       "\n",
       "Normalisation appliquee (StandardScaler fitte sur train)\n",
       "  Features: 12\n",
-      "  Train mean: -0.0000, std: 1.0000\n",
+      "  Train mean: 0.0000, std: 1.0000\n",
       "\n",
       "DataLoaders crees:\n",
-      "  Train batches: 1774\n",
-      "  Val batches:   381\n",
-      "  Test batches:  381\n"
+      "  Train batches: 1775\n",
+      "  Val batches:   380\n",
+      "  Test batches:  380\n"
      ]
     }
    ],
@@ -1074,19 +1134,19 @@
     "class TimeSeriesDataset(Dataset):\n",
     "    \"\"\"\n",
     "    Dataset PyTorch pour sequences temporelles multi-actifs.\n",
-    "    \n",
+    "\n",
     "    Chaque echantillon contient SEQ_LEN jours de features\n",
     "    pour une action donnee, avec les targets associes.\n",
     "    \"\"\"\n",
-    "    \n",
+    "\n",
     "    def __init__(self, sequences, targets_reg, targets_cls):\n",
     "        self.sequences = torch.FloatTensor(sequences)\n",
     "        self.targets_reg = torch.FloatTensor(targets_reg)\n",
     "        self.targets_cls = torch.FloatTensor(targets_cls)\n",
-    "    \n",
+    "\n",
     "    def __len__(self):\n",
     "        return len(self.sequences)\n",
-    "    \n",
+    "\n",
     "    def __getitem__(self, idx):\n",
     "        return (\n",
     "            self.sequences[idx],\n",
@@ -1097,61 +1157,85 @@
     "\n",
     "def build_sequences(all_features, feature_cols, seq_len=60, pred_len=5):\n",
     "    \"\"\"\n",
-    "    Construit les sequences d'entraiement a partir des features.\n",
-    "    \n",
-    "    Concatene les donnees de toutes les actions en un seul\n",
-    "    jeu de sequences, en preservant l'ordre temporel.\n",
+    "    Construit les sequences d'entrainement a partir des features.\n",
+    "\n",
+    "    Concatene les sequences de toutes les actions ET fait remonter, pour\n",
+    "    chaque sequence, son actif et la DATE de sa cible : c'est cette date\n",
+    "    (globale a l'univers) qui servira au decoupage temporel -- jamais\n",
+    "    l'ordre d'insertion des actifs dans le dictionnaire.\n",
     "    \"\"\"\n",
-    "    all_X, all_y_reg, all_y_cls, all_y_plain = [], [], [], []\n",
-    "    \n",
+    "    all_X, all_y_reg, all_y_cls = [], [], []\n",
+    "    all_y_ret, all_tickers, all_dates = [], [], []\n",
+    "\n",
     "    for ticker, df in all_features.items():\n",
     "        features = df[feature_cols].values\n",
     "        target_ret = df['target_risk_adj'].values\n",
-    "        target_plain = df['target_return'].values  # rendement simple (fraction)\n",
     "        target_dir = df['target_direction'].values\n",
-    "        \n",
+    "        target_plain = df['target_return'].values\n",
+    "\n",
     "        # Creer les sequences avec sliding window\n",
     "        for i in range(seq_len, len(features) - pred_len):\n",
     "            all_X.append(features[i - seq_len:i])\n",
     "            all_y_reg.append(target_ret[i])\n",
-    "            all_y_plain.append(target_plain[i])\n",
     "            all_y_cls.append(target_dir[i])\n",
-    "    \n",
-    "    # y_reg : rendements risque-ajustes ( apprentissage / tri )\n",
-    "    # y_plain : rendements simples ( composition du backtest, #17516 )\n",
+    "            all_y_ret.append(target_plain[i])\n",
+    "            all_tickers.append(ticker)\n",
+    "            all_dates.append(df.index[i])\n",
+    "\n",
     "    return (\n",
     "        np.array(all_X, dtype=np.float32),\n",
     "        np.array(all_y_reg, dtype=np.float32),\n",
     "        np.array(all_y_cls, dtype=np.float32),\n",
-    "        np.array(all_y_plain, dtype=np.float32)\n",
+    "        np.array(all_y_ret, dtype=np.float32),\n",
+    "        np.array(all_tickers),\n",
+    "        pd.DatetimeIndex(all_dates),\n",
     "    )\n",
     "\n",
     "\n",
     "# Construire les sequences\n",
     "print(\"Construction des sequences...\")\n",
-    "X_all, y_reg_all, y_cls_all, y_plain_all = build_sequences(\n",
+    "X_all, y_reg_all, y_cls_all, y_ret_all, tickers_all, dates_all = build_sequences(\n",
     "    all_features, feature_cols, SEQ_LEN, PRED_LEN\n",
     ")\n",
     "\n",
     "print(f\"Sequences totales: {len(X_all)}\")\n",
     "print(f\"Shape X: {X_all.shape}  [n_samples, seq_len, n_features]\")\n",
     "print(f\"Shape y_reg: {y_reg_all.shape}\")\n",
-    "print(f\"Shape y_cls: {y_cls_all.shape}\")\n",
     "print(f\"Distribution direction: {y_cls_all.mean():.2%} up, {1-y_cls_all.mean():.2%} down\")\n",
     "\n",
-    "# Split temporel strict (les sequences sont deja ordonnees par date)\n",
-    "n_total = len(X_all)\n",
-    "train_end = int(n_total * TRAIN_RATIO)\n",
-    "val_end = int(n_total * (TRAIN_RATIO + VAL_RATIO))\n",
-    "\n",
-    "X_train, y_reg_train, y_cls_train = X_all[:train_end], y_reg_all[:train_end], y_cls_all[:train_end]\n",
-    "X_val, y_reg_val, y_cls_val = X_all[train_end:val_end], y_reg_all[train_end:val_end], y_cls_all[train_end:val_end]\n",
-    "X_test, y_reg_test, y_cls_test, y_plain_test = X_all[val_end:], y_reg_all[val_end:], y_cls_all[val_end:], y_plain_all[val_end:]\n",
-    "\n",
-    "print(f\"\\nSplit temporel:\")\n",
-    "print(f\"  Train: {len(X_train)} samples ({TRAIN_RATIO:.0%})\")\n",
-    "print(f\"  Val:   {len(X_val)} samples ({VAL_RATIO:.0%})\")\n",
-    "print(f\"  Test:  {len(X_test)} samples ({1-TRAIN_RATIO-VAL_RATIO:.0%})\")\n",
+    "# Split temporel GLOBAL : on decoupe l'axe calendaire de l'univers (tous\n",
+    "# actifs confondus). Chaque sequence est affectee selon la date de sa\n",
+    "# cible : le test est une fenetre temporelle sur l'univers entier, pas\n",
+    "# la queue d'une concatenation par actif.\n",
+    "dates_sorted = np.sort(dates_all.values)\n",
+    "i_train = int(len(dates_sorted) * TRAIN_RATIO)\n",
+    "i_val = int(len(dates_sorted) * (TRAIN_RATIO + VAL_RATIO))\n",
+    "date_train_end = dates_sorted[max(i_train - 1, 0)]\n",
+    "date_val_end = dates_sorted[max(i_val - 1, 0)]\n",
+    "\n",
+    "mask_train = dates_all <= date_train_end\n",
+    "mask_val = (dates_all > date_train_end) & (dates_all <= date_val_end)\n",
+    "mask_test = dates_all > date_val_end\n",
+    "\n",
+    "assert dates_all[mask_train].max() <= dates_all[mask_val].min() <= dates_all[mask_test].min(), \\\n",
+    "    \"Fuite temporelle : les bornes du split ne se suivent pas\"\n",
+    "\n",
+    "print(f\"\\nSplit temporel global (par date de cible) :\")\n",
+    "print(f\"  Train:   {mask_train.sum():6d} samples | {dates_all[mask_train].min().date()} -> {dates_all[mask_train].max().date()}\")\n",
+    "print(f\"  Val:     {mask_val.sum():6d} samples | {dates_all[mask_val].min().date()} -> {dates_all[mask_val].max().date()}\")\n",
+    "print(f\"  Test:    {mask_test.sum():6d} samples | {dates_all[mask_test].min().date()} -> {dates_all[mask_test].max().date()}\")\n",
+    "\n",
+    "X_train = X_all[mask_train]\n",
+    "y_reg_train, y_cls_train = y_reg_all[mask_train], y_cls_all[mask_train]\n",
+    "X_val = X_all[mask_val]\n",
+    "y_reg_val, y_cls_val = y_reg_all[mask_val], y_cls_all[mask_val]\n",
+    "X_test = X_all[mask_test]\n",
+    "y_reg_test, y_cls_test = y_reg_all[mask_test], y_cls_all[mask_test]\n",
+    "\n",
+    "# Identites conservees pour le backtest cross-sectionnel (section 11)\n",
+    "tickers_test = tickers_all[mask_test]\n",
+    "dates_test = dates_all[mask_test]\n",
+    "y_ret_test = y_ret_all[mask_test]\n",
     "\n",
     "# Normalisation : fit sur train uniquement\n",
     "n_features = X_train.shape[2]\n",
@@ -1187,10 +1271,10 @@
    "id": "cell-14",
    "metadata": {
     "papermill": {
-     "duration": 0.012075,
-     "end_time": "2026-09-23T17:28:20.714368",
+     "duration": 0.005668,
+     "end_time": "2026-09-25T04:38:02.286738",
      "exception": false,
-     "start_time": "2026-09-23T17:28:20.702293",
+     "start_time": "2026-09-25T04:38:02.281070",
      "status": "completed"
     },
     "tags": []
@@ -1202,17 +1286,17 @@
     "\n",
     "| Étape | Detail |\n",
     "|-------|--------|\n",
-    "| **Construction** | Sliding window de 60 jours sur chaque action, concatenees |\n",
-    "| **Split** | Temporel strict (70/15/15), jamais de shuffle entre splits |\n",
+    "| **Construction** | Sliding window de 60 jours par action, avec (actif, date de cible) conserve par sequence |\n",
+    "| **Split** | Temporel global 70/15/15 sur l'axe calendaire de l'univers : chaque sequence est affectee selon la date de sa cible |\n",
     "| **Normalisation** | StandardScaler fitte uniquement sur le train set |\n",
     "\n",
     "**Points cles** :\n",
-    "1. Le split temporel est essentiel en finance : on ne peut pas utiliser des données futures pour entrainer\n",
+    "1. Le decoupage porte sur la DATE, pas sur l'ordre d'insertion des actifs : la fenetre de test est une periode calendaire recente commune a tout l'univers -- sinon on s'entrainerait sur 2014-2022 de 27 actions et on testerait sur 2014-2025 de 3 autres\n",
     "2. Le scaler est fitte sur le train uniquement et applique (transform) sur val et test\n",
-    "3. La distribution des targets (up/down) est proche de 50/50, ce qui est attendu sur un marche efficient\n",
+    "3. La distribution des targets (up/down) est a 56.35% up / 43.65% down (mesure ci-dessus) : un leger biais haussier, coherent avec la decennie etudiee, mais assez equilibre pour l'apprentissage\n",
     "4. Le DataLoader shuffle les batchs d'entrainement mais preserve l'ordre en validation/test\n",
     "\n",
-    "> *Ancre savante -- Fama, E.F. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance 25(2):383-417. DOI 10.1111/j.1540-6261.1970.tb00518.x. (hypothese de marche efficient.)*\n"
+    "> *Ancre savante -- Fama, E.F. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance 25(2):383-417. (marche efficient : la direction future est difficile a predire, un modele honnete reste proche de 50 %.)*"
    ]
   },
   {
@@ -1220,10 +1304,10 @@
    "id": "cell-15",
    "metadata": {
     "papermill": {
-     "duration": 0.012575,
-     "end_time": "2026-09-23T17:28:20.741790",
+     "duration": 0.006756,
+     "end_time": "2026-09-25T04:38:02.301942",
      "exception": false,
-     "start_time": "2026-09-23T17:28:20.729215",
+     "start_time": "2026-09-25T04:38:02.295186",
      "status": "completed"
     },
     "tags": []
@@ -1257,16 +1341,16 @@
    "id": "cell-16",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:28:20.769152Z",
-     "iopub.status.busy": "2026-09-23T17:28:20.768156Z",
-     "iopub.status.idle": "2026-09-23T17:28:21.000696Z",
-     "shell.execute_reply": "2026-09-23T17:28:20.999693Z"
+     "iopub.execute_input": "2026-09-25T04:38:02.316642Z",
+     "iopub.status.busy": "2026-09-25T04:38:02.316241Z",
+     "iopub.status.idle": "2026-09-25T04:38:02.507067Z",
+     "shell.execute_reply": "2026-09-25T04:38:02.506073Z"
     },
     "papermill": {
-     "duration": 0.247867,
-     "end_time": "2026-09-23T17:28:21.002203",
+     "duration": 0.199577,
+     "end_time": "2026-09-25T04:38:02.508093",
      "exception": false,
-     "start_time": "2026-09-23T17:28:20.754336",
+     "start_time": "2026-09-25T04:38:02.308516",
      "status": "completed"
     },
     "tags": []
@@ -1519,10 +1603,10 @@
    "id": "cell-5e739084",
    "metadata": {
     "papermill": {
-     "duration": 0.01306,
-     "end_time": "2026-09-23T17:28:21.027806",
+     "duration": 0.006008,
+     "end_time": "2026-09-25T04:38:02.518927",
      "exception": false,
-     "start_time": "2026-09-23T17:28:21.014746",
+     "start_time": "2026-09-25T04:38:02.512919",
      "status": "completed"
     },
     "tags": []
@@ -1546,16 +1630,16 @@
    "id": "cell-a0cbecac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:28:21.058926Z",
-     "iopub.status.busy": "2026-09-23T17:28:21.058926Z",
-     "iopub.status.idle": "2026-09-23T17:28:21.079311Z",
-     "shell.execute_reply": "2026-09-23T17:28:21.077089Z"
+     "iopub.execute_input": "2026-09-25T04:38:02.532721Z",
+     "iopub.status.busy": "2026-09-25T04:38:02.532136Z",
+     "iopub.status.idle": "2026-09-25T04:38:02.536945Z",
+     "shell.execute_reply": "2026-09-25T04:38:02.536267Z"
     },
     "papermill": {
-     "duration": 0.038197,
-     "end_time": "2026-09-23T17:28:21.080313",
+     "duration": 0.013294,
+     "end_time": "2026-09-25T04:38:02.538260",
      "exception": false,
-     "start_time": "2026-09-23T17:28:21.042116",
+     "start_time": "2026-09-25T04:38:02.524966",
      "status": "completed"
     },
     "tags": []
@@ -1584,10 +1668,10 @@
    "id": "cell-17",
    "metadata": {
     "papermill": {
-     "duration": 0.013655,
-     "end_time": "2026-09-23T17:28:21.108034",
+     "duration": 0.004749,
+     "end_time": "2026-09-25T04:38:02.548873",
      "exception": false,
-     "start_time": "2026-09-23T17:28:21.094379",
+     "start_time": "2026-09-25T04:38:02.544124",
      "status": "completed"
     },
     "tags": []
@@ -1620,10 +1704,10 @@
    "id": "cell-18",
    "metadata": {
     "papermill": {
-     "duration": 0.008612,
-     "end_time": "2026-09-23T17:28:21.130704",
+     "duration": 0.007641,
+     "end_time": "2026-09-25T04:38:02.564704",
      "exception": false,
-     "start_time": "2026-09-23T17:28:21.122092",
+     "start_time": "2026-09-25T04:38:02.557063",
      "status": "completed"
     },
     "tags": []
@@ -1655,16 +1739,16 @@
    "id": "cell-19",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:28:21.159678Z",
-     "iopub.status.busy": "2026-09-23T17:28:21.159678Z",
-     "iopub.status.idle": "2026-09-23T17:28:21.189780Z",
-     "shell.execute_reply": "2026-09-23T17:28:21.187236Z"
+     "iopub.execute_input": "2026-09-25T04:38:02.577681Z",
+     "iopub.status.busy": "2026-09-25T04:38:02.577370Z",
+     "iopub.status.idle": "2026-09-25T04:38:02.587344Z",
+     "shell.execute_reply": "2026-09-25T04:38:02.586368Z"
     },
     "papermill": {
-     "duration": 0.047662,
-     "end_time": "2026-09-23T17:28:21.190779",
+     "duration": 0.018046,
+     "end_time": "2026-09-25T04:38:02.588451",
      "exception": false,
-     "start_time": "2026-09-23T17:28:21.143117",
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      "status": "completed"
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     "tags": []
@@ -1821,10 +1905,10 @@
    "id": "cell-20",
    "metadata": {
     "papermill": {
-     "duration": 0.013106,
-     "end_time": "2026-09-23T17:28:21.217402",
+     "duration": 0.006421,
+     "end_time": "2026-09-25T04:38:02.601214",
      "exception": false,
-     "start_time": "2026-09-23T17:28:21.204296",
+     "start_time": "2026-09-25T04:38:02.594793",
      "status": "completed"
     },
     "tags": []
@@ -1849,16 +1933,16 @@
    "id": "cell-21",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:28:21.243707Z",
-     "iopub.status.busy": "2026-09-23T17:28:21.242706Z",
-     "iopub.status.idle": "2026-09-23T17:52:11.270330Z",
-     "shell.execute_reply": "2026-09-23T17:52:11.268634Z"
+     "iopub.execute_input": "2026-09-25T04:38:02.616325Z",
+     "iopub.status.busy": "2026-09-25T04:38:02.615886Z",
+     "iopub.status.idle": "2026-09-25T06:01:35.486239Z",
+     "shell.execute_reply": "2026-09-25T06:01:35.485371Z"
     },
     "papermill": {
-     "duration": 1430.049818,
-     "end_time": "2026-09-23T17:52:11.279779",
+     "duration": 5012.883725,
+     "end_time": "2026-09-25T06:01:35.490507",
      "exception": false,
-     "start_time": "2026-09-23T17:28:21.229961",
+     "start_time": "2026-09-25T04:38:02.606782",
      "status": "completed"
     },
     "tags": []
@@ -1889,108 +1973,134 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  [THERMAL] GPU 81C > 80C, pause 15s...\n"
+      "Epoch  1/25 | Train Loss: 2.0117 (reg=1.6696, cls=0.6843) | Val Loss: 2.1193 | Dir Acc: 53.00% | Time: 205.7s | GPU: 46C\n",
+      "  -> Nouveau meilleur modele (val_loss=2.1193)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch  2/25 | Train Loss: 2.0100 (reg=1.6682, cls=0.6836) | Val Loss: 2.1196 | Dir Acc: 53.00% | Time: 256.3s | GPU: 48C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  1/25 | Train Loss: 2.0621 (reg=1.7190, cls=0.6860) | Val Loss: 2.0234 | Dir Acc: 57.34% | Time: 145.0s | GPU: 62C\n",
-      "  -> Nouveau meilleur modele (val_loss=2.0234)\n"
+      "Epoch  3/25 | Train Loss: 2.0088 (reg=1.6672, cls=0.6833) | Val Loss: 2.1205 | Dir Acc: 53.00% | Time: 270.6s | GPU: 48C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  2/25 | Train Loss: 2.0605 (reg=1.7177, cls=0.6856) | Val Loss: 2.0232 | Dir Acc: 57.35% | Time: 116.1s | GPU: 62C\n",
-      "  -> Nouveau meilleur modele (val_loss=2.0232)\n"
+      "Epoch  4/25 | Train Loss: 2.0088 (reg=1.6672, cls=0.6832) | Val Loss: 2.1189 | Dir Acc: 53.00% | Time: 298.9s | GPU: 48C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  3/25 | Train Loss: 2.0603 (reg=1.7176, cls=0.6855) | Val Loss: 2.0225 | Dir Acc: 57.34% | Time: 109.2s | GPU: 64C\n",
-      "  -> Nouveau meilleur modele (val_loss=2.0225)\n"
+      "  -> Nouveau meilleur modele (val_loss=2.1189)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  4/25 | Train Loss: 2.0600 (reg=1.7173, cls=0.6854) | Val Loss: 2.0228 | Dir Acc: 57.34% | Time: 109.0s | GPU: 64C\n"
+      "Epoch  5/25 | Train Loss: 2.0090 (reg=1.6673, cls=0.6833) | Val Loss: 2.1230 | Dir Acc: 53.00% | Time: 362.4s | GPU: 48C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  5/25 | Train Loss: 2.0599 (reg=1.7173, cls=0.6853) | Val Loss: 2.0239 | Dir Acc: 57.34% | Time: 106.1s | GPU: 64C\n"
+      "Epoch  6/25 | Train Loss: 2.0138 (reg=1.6711, cls=0.6854) | Val Loss: 2.1240 | Dir Acc: 53.00% | Time: 385.9s | GPU: 48C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  6/25 | Train Loss: 2.0597 (reg=1.7170, cls=0.6853) | Val Loss: 2.0212 | Dir Acc: 57.34% | Time: 110.0s | GPU: 64C\n",
-      "  -> Nouveau meilleur modele (val_loss=2.0212)\n"
+      "Epoch  7/25 | Train Loss: 2.0088 (reg=1.6672, cls=0.6832) | Val Loss: 2.1272 | Dir Acc: 53.00% | Time: 637.9s | GPU: 48C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  7/25 | Train Loss: 2.0598 (reg=1.7171, cls=0.6853) | Val Loss: 2.0229 | Dir Acc: 57.34% | Time: 112.5s | GPU: 64C\n"
+      "Epoch  8/25 | Train Loss: 2.0087 (reg=1.6671, cls=0.6832) | Val Loss: 2.1200 | Dir Acc: 53.00% | Time: 847.2s | GPU: 48C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  8/25 | Train Loss: 2.0597 (reg=1.7171, cls=0.6852) | Val Loss: 2.0212 | Dir Acc: 57.34% | Time: 130.1s | GPU: 62C\n"
+      "Epoch  9/25 | Train Loss: 2.0087 (reg=1.6671, cls=0.6832) | Val Loss: 2.1226 | Dir Acc: 53.00% | Time: 505.0s | GPU: 48C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch  9/25 | Train Loss: 2.0597 (reg=1.7171, cls=0.6853) | Val Loss: 2.0236 | Dir Acc: 57.34% | Time: 108.9s | GPU: 63C\n"
+      "Epoch 10/25 | Train Loss: 2.0085 (reg=1.6669, cls=0.6832) | Val Loss: 2.1174 | Dir Acc: 53.00% | Time: 262.0s | GPU: 48C\n",
+      "  -> Nouveau meilleur modele (val_loss=2.1174)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 10/25 | Train Loss: 2.0597 (reg=1.7170, cls=0.6853) | Val Loss: 2.0216 | Dir Acc: 57.34% | Time: 112.1s | GPU: 64C\n"
+      "Epoch 11/25 | Train Loss: 2.0088 (reg=1.6672, cls=0.6832) | Val Loss: 2.1201 | Dir Acc: 53.00% | Time: 178.2s | GPU: 46C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 11/25 | Train Loss: 2.0596 (reg=1.7169, cls=0.6852) | Val Loss: 2.0238 | Dir Acc: 57.34% | Time: 103.7s | GPU: 63C\n"
+      "Epoch 12/25 | Train Loss: 2.0088 (reg=1.6672, cls=0.6832) | Val Loss: 2.1231 | Dir Acc: 53.00% | Time: 143.1s | GPU: 45C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 12/25 | Train Loss: 2.0598 (reg=1.7172, cls=0.6852) | Val Loss: 2.0218 | Dir Acc: 57.34% | Time: 89.0s | GPU: 63C\n"
+      "Epoch 13/25 | Train Loss: 2.0088 (reg=1.6672, cls=0.6832) | Val Loss: 2.1201 | Dir Acc: 53.00% | Time: 118.4s | GPU: 44C\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 13/25 | Train Loss: 2.0596 (reg=1.7170, cls=0.6852) | Val Loss: 2.0216 | Dir Acc: 57.34% | Time: 73.5s | GPU: 63C\n",
+      "Epoch 14/25 | Train Loss: 2.0085 (reg=1.6670, cls=0.6832) | Val Loss: 2.1211 | Dir Acc: 53.00% | Time: 130.1s | GPU: 44C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15/25 | Train Loss: 2.0086 (reg=1.6670, cls=0.6831) | Val Loss: 2.1216 | Dir Acc: 53.00% | Time: 125.1s | GPU: 44C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16/25 | Train Loss: 2.0086 (reg=1.6670, cls=0.6831) | Val Loss: 2.1217 | Dir Acc: 53.00% | Time: 117.9s | GPU: 43C\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17/25 | Train Loss: 2.0083 (reg=1.6668, cls=0.6830) | Val Loss: 2.1218 | Dir Acc: 53.00% | Time: 140.6s | GPU: 43C\n",
       "\n",
-      "Early stopping a l'epoch 13 (patience=7)\n",
+      "Early stopping a l'epoch 17 (patience=7)\n",
       "Modele final sauvegarde: transformer_multiasset_model.pt\n",
       "\n",
       "Entrainement termine.\n",
-      "Meilleure val_loss: 2.0212\n",
+      "Meilleure val_loss: 2.1174\n",
       "Checkpoint de reprise: transformer_checkpoint.pt (ne pas commit)\n"
      ]
     }
@@ -2050,7 +2160,7 @@
     "}\n",
     "\n",
     "# --- Resume checkpoint (3 cas: modele final / checkpoint / from scratch) ---\n",
-    "# FORCE_RETRAIN : execution de reference depuis zero (#17516) — un .pt residuel\n",
+    "# FORCE_RETRAIN : execution de reference depuis zero (#17516) -- un .pt residuel\n",
     "# sur la machine ne doit jamais produire une courbe d'apprentissage vide.\n",
     "ckpt_manager = TrainingCheckpoint(\n",
     "    checkpoint_path=checkpoint_path,\n",
@@ -2158,10 +2268,10 @@
    "id": "cell-22",
    "metadata": {
     "papermill": {
-     "duration": 0.016195,
-     "end_time": "2026-09-23T17:52:11.312134",
+     "duration": 0.005738,
+     "end_time": "2026-09-25T06:01:35.501403",
      "exception": false,
-     "start_time": "2026-09-23T17:52:11.295939",
+     "start_time": "2026-09-25T06:01:35.495665",
      "status": "completed"
     },
     "tags": []
@@ -2179,7 +2289,7 @@
     "| **torch.cuda.empty_cache()** | Limite l'accumulation memoire entre epochs |\n",
     "\n",
     "**Points cles** :\n",
-    "1. La direction accuracy en validation devrait se situer entre 50% et 56% (les marches sont difficiles a predire). Dans le run committe elle vaut **57.34% des l'epoch 1 et ne bouge plus** : au-dessus de la fourchette, mais parce que le modele predit la classe majoritaire — c'est la limite mesuree suivie par **#17584**, pas une performance\n",
+    "1. La direction accuracy en validation devrait se situer entre 50% et 56% (les marches sont difficiles a predire)\n",
     "2. La loss devrait decroitre de maniere monotone sur le train set, la val loss peut stagner ou remonter\n",
     "3. Le temps par epoch depend du GPU ; sur CPU, attendez 2-5x plus lent\n",
     "4. Le early stopping est essentiel : les Transformers sur-apprennent vite sur les series financieres bruitees"
@@ -2190,10 +2300,10 @@
    "id": "cell-23",
    "metadata": {
     "papermill": {
-     "duration": 0.013118,
-     "end_time": "2026-09-23T17:52:11.338427",
+     "duration": 0.004685,
+     "end_time": "2026-09-25T06:01:35.510763",
      "exception": false,
-     "start_time": "2026-09-23T17:52:11.325309",
+     "start_time": "2026-09-25T06:01:35.506078",
      "status": "completed"
     },
     "tags": []
@@ -2217,16 +2327,16 @@
    "id": "cell-24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:52:11.373999Z",
-     "iopub.status.busy": "2026-09-23T17:52:11.373999Z",
-     "iopub.status.idle": "2026-09-23T17:52:13.165344Z",
-     "shell.execute_reply": "2026-09-23T17:52:13.163339Z"
+     "iopub.execute_input": "2026-09-25T06:01:35.522457Z",
+     "iopub.status.busy": "2026-09-25T06:01:35.522134Z",
+     "iopub.status.idle": "2026-09-25T06:01:36.107228Z",
+     "shell.execute_reply": "2026-09-25T06:01:36.106694Z"
     },
     "papermill": {
-     "duration": 1.812062,
-     "end_time": "2026-09-23T17:52:13.166865",
+     "duration": 0.591245,
+     "end_time": "2026-09-25T06:01:36.107977",
      "exception": false,
-     "start_time": "2026-09-23T17:52:11.354803",
+     "start_time": "2026-09-25T06:01:35.516732",
      "status": "completed"
     },
     "tags": []
@@ -2234,7 +2344,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1800x500 with 3 Axes>"
       ]
@@ -2247,10 +2357,10 @@
      "output_type": "stream",
      "text": [
       "Resume de l'entrainement:\n",
-      "  Epochs effectues: 13\n",
-      "  Train loss finale: 2.0596\n",
-      "  Val loss finale:   2.0216\n",
-      "  Val dir accuracy:  57.34%\n"
+      "  Epochs effectues: 17\n",
+      "  Train loss finale: 2.0083\n",
+      "  Val loss finale:   2.1218\n",
+      "  Val dir accuracy:  53.00%\n"
      ]
     }
    ],
@@ -2292,10 +2402,10 @@
     "ax3.grid(True, alpha=0.3)\n",
     "ax3.set_ylim(0.45, 0.65)\n",
     "\n",
-    "plt.tight_layout()\n",
     "# Sauvegarder AVANT plt.show() : le backend inline ferme la figure au show,\n",
     "# un savefig apres produit un png vide (2.4 Ko sur main, #17516)\n",
     "plt.savefig(os.path.join(_nb_dir if '_nb_dir' in dir() else os.getcwd(), 'training_curves.png'), dpi=100, bbox_inches='tight')\n",
+    "plt.tight_layout()\n",
     "plt.show()\n",
     "\n",
     "# Resume final de l'entrainement\n",
@@ -2314,10 +2424,10 @@
    "id": "cell-25",
    "metadata": {
     "papermill": {
-     "duration": 0.024297,
-     "end_time": "2026-09-23T17:52:13.213242",
+     "duration": 0.00642,
+     "end_time": "2026-09-25T06:01:36.121000",
      "exception": false,
-     "start_time": "2026-09-23T17:52:13.188945",
+     "start_time": "2026-09-25T06:01:36.114580",
      "status": "completed"
     },
     "tags": []
@@ -2325,19 +2435,20 @@
    "source": [
     "### Interpretation : Courbes d'apprentissage\n",
     "\n",
-    "Les trois graphiques permettent de diagnostiquer la qualite de l'entrainement :\n",
+    "Lecture des courbes de CE run (entrainement from scratch, early stopping a 17 epochs sur 25 prevues, patience=7) :\n",
     "\n",
-    "| Graphique | Observation attendue | Signification |\n",
-    "|-----------|---------------------|---------------|\n",
-    "| **Loss totale** | Decroissance puis plateau | Le modèle converge |\n",
-    "| **Reg/Cls decomposition** | Reg domine au debut, Cls stabilise | La regression est plus difficile |\n",
-    "| **Direction accuracy** | Au-dessus de 50% **et croissante** | Le modèle a un edge predictif. Dans le run committe elle vaut 57.34% de bout en bout : c'est le taux de la classe majoritaire, pas un edge (limite mesuree, #17584) |\n",
+    "| Graphique | Ce que le run mesure | Lecture |\n",
+    "|-----------|---------------------|---------|\n",
+    "| **Loss totale** | Train 2.0117 -> 2.0083, val plate ~2.12 | Decroissance quasi nulle : rien ne s'apprete a emerger |\n",
+    "| **Reg/Cls decomposition** | reg ~1.667 domine, cls ~0.683 immobile | La tete de regression n'extrait aucun signal |\n",
+    "| **Direction accuracy** | **53.00% constant a CHAQUE epoch** | Le modele predit la classe majoritaire — pas un edge (limite mesuree, suivie par #17584) |\n",
+    "\n",
+    "**Diagnostic** : une direction accuracy rigoureusement constante sur 17 epochs est la signature d'un **predicteur constant**. La loss bouge a la 4e decimale parce que le modele ajuste son biais, pas parce qu'il apprend une structure. C'est le resultat honnete d'un entrainement reel sur ce split : mieux qu'un artefact de convergence affiche, il nomme la limite de la capacite du modele sur ces features.\n",
     "\n",
     "**Points cles** :\n",
-    "1. Un ecart croissant entre train et val loss indique de l'overfitting\n",
-    "2. La direction accuracy superieure a 50% est un signal positif **seulement si elle evolue entre epochs** : dans le run committe elle reste a 57.34% de l'epoch 1 a l'epoch 13, ce qui est le taux de la classe majoritaire et non un apprentissage (limite mesuree, #17584)\n",
-    "3. La loss de classification (BCE) converge généralement plus vite que la loss de regression\n",
-    "4. En finance, une direction accuracy de 53-55% est déjà exploitable avec un bon money management"
+    "1. Un ecart croissant train/val signalerait de l'overfitting — ici les deux courbes sont plates et collees : ni overfitting ni apprentissage\n",
+    "2. « Au-dessus de 50% » ne vaut edge que si la valeur BOUGE avec l'entrainement ; figee, elle mesure l'equilibre des classes\n",
+    "3. Le early stopping (patience=7) a arrete l'entrainement des que la val loss a cesse de s'ameliorer — comportement nominal du watchdog"
    ]
   },
   {
@@ -2345,10 +2456,10 @@
    "id": "cell-26",
    "metadata": {
     "papermill": {
-     "duration": 0.018567,
-     "end_time": "2026-09-23T17:52:13.249871",
+     "duration": 0.005197,
+     "end_time": "2026-09-25T06:01:36.131280",
      "exception": false,
-     "start_time": "2026-09-23T17:52:13.231304",
+     "start_time": "2026-09-25T06:01:36.126083",
      "status": "completed"
     },
     "tags": []
@@ -2367,16 +2478,16 @@
    "id": "cell-27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:52:13.293391Z",
-     "iopub.status.busy": "2026-09-23T17:52:13.292881Z",
-     "iopub.status.idle": "2026-09-23T17:52:20.263399Z",
-     "shell.execute_reply": "2026-09-23T17:52:20.261152Z"
+     "iopub.execute_input": "2026-09-25T06:01:36.143290Z",
+     "iopub.status.busy": "2026-09-25T06:01:36.143090Z",
+     "iopub.status.idle": "2026-09-25T06:01:38.488008Z",
+     "shell.execute_reply": "2026-09-25T06:01:38.487621Z"
     },
     "papermill": {
-     "duration": 6.995906,
-     "end_time": "2026-09-23T17:52:20.265476",
+     "duration": 2.351662,
+     "end_time": "2026-09-25T06:01:38.488740",
      "exception": false,
-     "start_time": "2026-09-23T17:52:13.269570",
+     "start_time": "2026-09-25T06:01:36.137078",
      "status": "completed"
     },
     "tags": []
@@ -2391,18 +2502,20 @@
       "============================================================\n",
       "\n",
       "Metriques de regression:\n",
-      "  MSE:          8.639112\n",
-      "  MAE:          2.157524\n",
-      "  Correlation:  nan\n",
+      "  MSE:          9.937829\n",
+      "  MAE:          2.300780\n",
+      "  Correlation:  0.0098\n",
       "\n",
       "Metriques de classification:\n",
-      "  Direction Accuracy: 55.51%\n",
-      "  Baseline classe majoritaire (test): 55.51%\n",
+      "  Direction Accuracy: 56.23%\n",
+      "  Baseline classe majoritaire (test): 56.23%\n",
       "  Ecart vs classe majoritaire: -0.00 points de %\n",
-      "  Ecart vs hasard (50%):       +5.51 points de %\n",
+      "  Ecart vs hasard (50%):       +6.23 points de %\n",
       "\n",
-      "Rendement moyen par quantile de prediction (0 quantiles):\n",
-      "Series([], Name: target, dtype: float32)\n"
+      "Rendement moyen par quantile de prediction (1 quantiles):\n",
+      "pred_quantile\n",
+      "Q1    0.5491\n",
+      "Name: target, dtype: float32\n"
      ]
     }
    ],
@@ -2458,10 +2571,10 @@
    "id": "cell-28",
    "metadata": {
     "papermill": {
-     "duration": 0.01955,
-     "end_time": "2026-09-23T17:52:20.304220",
+     "duration": 0.005099,
+     "end_time": "2026-09-25T06:01:38.499334",
      "exception": false,
-     "start_time": "2026-09-23T17:52:20.284670",
+     "start_time": "2026-09-25T06:01:38.494235",
      "status": "completed"
     },
     "tags": []
@@ -2469,25 +2582,20 @@
    "source": [
     "### Interpretation : Evaluation test\n",
     "\n",
-    "Les metriques sur le set de test mesurent la capacite de generalisation du modèle.\n",
+    "Metriques mesurees sur CE run (test) : MSE 9.94, MAE 2.30, **correlation 0.0098**, direction accuracy 56.23%, **quantiles effondres en un seul bac**.\n",
     "\n",
-    "| Metrique | Valeur de CE run | Valeur typique annoncee | Signification |\n",
-    "|----------|------------------|--------------------------|---------------|\n",
-    "| **Correlation** | `nan` | 0.02-0.08 | **Non definie ici** : la suite de predictions est constante (cf. limite ci-dessous) |\n",
-    "| **Direction Accuracy** | 55.51% | 51-55% | **Taux de la classe majoritaire**, pas un edge (cf. limite ci-dessous) |\n",
-    "| **MSE/MAE** | 8.639112 / 2.157524 | Dependent de l'echelle | Comparaison relative avec le LSTM |\n",
+    "| Metrique | Valeur mesuree | Lecture |\n",
+    "|----------|----------------|---------|\n",
+    "| **Correlation** | **0.0098** | Quasi nulle (~0.01 % de variance expliquee) : le modele ne classe pas, il repete. Elle etait indefinie (NaN) sur le run precedent : la variance des predictions suffit tout juste a faire calculer Pearson, pas a ordonner |\n",
+    "| **Direction Accuracy** | 56.23% | **Egale a la baseline classe majoritaire du test (ecart -0.00 point, imprime ci-dessus)** : c'est l'equilibre des classes, pas un pouvoir discriminant (limite mesuree, suivie par #17584) |\n",
+    "| **Rendement par quantile** | **1 seul quantile (Q1 : 0.5491)** | `qcut` ne forme qu'un bac : toutes les predictions tombent dans le meme intervalle — le classement top/bottom n'existe pas |\n",
     "\n",
-    "> **Limite mesuree de ce run — a lire avant les chiffres.** L'entrainement committe n'apprend rien : la `Direction Accuracy` de validation vaut **57.34% aux treize epochs**, identique des l'epoch 1, et la perte de regression ne bouge que de **0.1%** (`reg` 1.7190 -> 1.7170). Les deux tetes sortent donc une **constante** : la tete de classification predit la classe majoritaire (d'ou les 57.34%, qui ne sont pas un edge predictif), et la tete de regression a une **variance nulle** — ce qui rend `np.corrcoef` indefini (`nan`) et laisse la serie de quantiles **vide** (`0 quantiles`).\n",
-    ">\n",
-    "> Ce n'est **pas** un artefact de checkpoint repris : la cellule d'entrainement porte `Epochs: 1-25` et la banniere `FORCE_RETRAIN=True`, donc le modele a bien ete entraine depuis zero. Le meme notebook sur `main` donnait `Correlation 0.0160` avec `1 quantiles` et un `MSE` a **0.03%** pres : la degenerescence est **preexistante**, la re-execution honnete l'a rendue visible au lieu de la masquer derriere une correlation presque nulle.\n",
-    ">\n",
-    "> Cause suivie par **#17584**. Les quatre points ci-dessous decrivent ce qu'une **execution qui apprend** montrerait : ils ne sont pas observes ici.\n",
+    "**Diagnostic** : la correlation quasi nulle et le quantile unique sont **coherents entre eux** — des predictions a variance quasi nulle ne peuvent etre ni utilement correlees a la cible ni ordonnees en section transversale. Le print d'evaluation affiche « Ecart vs hasard (50%) : +6.23 points », mais aussi « Ecart vs classe majoritaire : -0.00 points » : pour un predicteur quasi constant, ces 6.23 points mesurent seulement la part de la classe majoritaire dans le test.\n",
     "\n",
-    "**Points cles** (a titre de repere, non observes dans ce run) :\n",
-    "1. L'analyse par quantile est cruciale : si le rendement moyen croit monotone de Q1 a Q5, le modèle classe correctement les actions\n",
-    "2. Une correlation positive même faible (0.03-0.05) peut generer des profits si combinee avec une bonne gestion du risque\n",
-    "3. En pratique, la direction accuracy et la correlation sont plus importantes que le MSE pour le trading\n",
-    "4. Les résultats sur données de marche reelles sont toujours plus modestes que sur des données synthetiques"
+    "**Points cles** :\n",
+    "1. En finance, une correlation positive faible mais REELLE (0.02-0.08) peut etre exploitable — 0.0098 n'y entre pas\n",
+    "2. L'analyse par quantile est le test decisif : monotonie Q1->Q5 = classement informe ; un bac unique = aucun classement\n",
+    "3. Ces metriques restent la base de comparaison avec le LSTM (meme split, meme oracle) : c'est l'ecart entre modeles, pas la valeur absolue, qui instruit\n"
    ]
   },
   {
@@ -2495,10 +2603,10 @@
    "id": "cell-29",
    "metadata": {
     "papermill": {
-     "duration": 0.015499,
-     "end_time": "2026-09-23T17:52:20.336736",
+     "duration": 0.005016,
+     "end_time": "2026-09-25T06:01:38.509440",
      "exception": false,
-     "start_time": "2026-09-23T17:52:20.321237",
+     "start_time": "2026-09-25T06:01:38.504424",
      "status": "completed"
     },
     "tags": []
@@ -2517,16 +2625,16 @@
    "id": "cell-30",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:52:20.374791Z",
-     "iopub.status.busy": "2026-09-23T17:52:20.374791Z",
-     "iopub.status.idle": "2026-09-23T17:52:20.974752Z",
-     "shell.execute_reply": "2026-09-23T17:52:20.972597Z"
+     "iopub.execute_input": "2026-09-25T06:01:38.520586Z",
+     "iopub.status.busy": "2026-09-25T06:01:38.520391Z",
+     "iopub.status.idle": "2026-09-25T06:01:38.666755Z",
+     "shell.execute_reply": "2026-09-25T06:01:38.666379Z"
     },
     "papermill": {
-     "duration": 0.62494,
-     "end_time": "2026-09-23T17:52:20.977273",
+     "duration": 0.152841,
+     "end_time": "2026-09-25T06:01:38.667441",
      "exception": false,
-     "start_time": "2026-09-23T17:52:20.352333",
+     "start_time": "2026-09-25T06:01:38.514600",
      "status": "completed"
     },
     "tags": []
@@ -2534,7 +2642,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -2597,10 +2705,10 @@
    "id": "cell-31",
    "metadata": {
     "papermill": {
-     "duration": 0.014652,
-     "end_time": "2026-09-23T17:52:21.014116",
+     "duration": 0.005412,
+     "end_time": "2026-09-25T06:01:38.678421",
      "exception": false,
-     "start_time": "2026-09-23T17:52:20.999464",
+     "start_time": "2026-09-25T06:01:38.673009",
      "status": "completed"
     },
     "tags": []
@@ -2617,7 +2725,7 @@
     "\n",
     "**Points cles** :\n",
     "1. Le scatter plot ne montre PAS une correlation lineaire forte -- c'est normal en finance\n",
-    "2. Ce qui compte est la capacite du modèle a **classer** les fenêtres par rendement attendu (top quintile, `n_bins=5`). Dans ce run l'ordre porte sur des fenêtres d'une **même action**, pas sur des actions entre elles — limite mesurée, cf. section 11 et #17571\n",
+    "2. Ce qui compte est la capacite du modèle a **classer** les actions (top decile vs bottom decile)\n",
     "3. La direction accuracy est la metrique la plus actionnable pour le trading\n",
     "4. L'asymetrie des erreurs (erreur sur les baisses vs erreur sur les hausse) peut indiquer un biais du modèle"
    ]
@@ -2627,10 +2735,10 @@
    "id": "cell-32",
    "metadata": {
     "papermill": {
-     "duration": 0.01952,
-     "end_time": "2026-09-23T17:52:21.047715",
+     "duration": 0.005437,
+     "end_time": "2026-09-25T06:01:38.689276",
      "exception": false,
-     "start_time": "2026-09-23T17:52:21.028195",
+     "start_time": "2026-09-25T06:01:38.683839",
      "status": "completed"
     },
     "tags": []
@@ -2649,16 +2757,16 @@
    "id": "cell-33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:52:21.088877Z",
-     "iopub.status.busy": "2026-09-23T17:52:21.088877Z",
-     "iopub.status.idle": "2026-09-23T17:52:22.709705Z",
-     "shell.execute_reply": "2026-09-23T17:52:22.707608Z"
+     "iopub.execute_input": "2026-09-25T06:01:38.700976Z",
+     "iopub.status.busy": "2026-09-25T06:01:38.700762Z",
+     "iopub.status.idle": "2026-09-25T06:01:39.254244Z",
+     "shell.execute_reply": "2026-09-25T06:01:39.253866Z"
     },
     "papermill": {
-     "duration": 1.643865,
-     "end_time": "2026-09-23T17:52:22.712116",
+     "duration": 0.560424,
+     "end_time": "2026-09-25T06:01:39.254986",
      "exception": false,
-     "start_time": "2026-09-23T17:52:21.068251",
+     "start_time": "2026-09-25T06:01:38.694562",
      "status": "completed"
     },
     "tags": []
@@ -2674,7 +2782,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 1400x1200 with 8 Axes>"
       ]
@@ -2688,11 +2796,11 @@
      "text": [
       "\n",
       "Analyse des poids d'attention:\n",
-      "  Tete 0 - Top 5 positions les plus attentives: [55 52  5 51 53]\n",
+      "  Tete 0 - Top 5 positions les plus attentives: [37 40 47  0  9]\n",
       "    Poids correspondants: [0.017 0.017 0.017 0.017 0.017]\n",
-      "  Tete 3 - Top 5 positions les plus attentives: [55 44 53 54 42]\n",
+      "  Tete 3 - Top 5 positions les plus attentives: [14 17 15 19 13]\n",
       "    Poids correspondants: [0.017 0.017 0.017 0.017 0.017]\n",
-      "  Tete 7 - Top 5 positions les plus attentives: [44 42 48 50 43]\n",
+      "  Tete 7 - Top 5 positions les plus attentives: [37 40  9 19 59]\n",
       "    Poids correspondants: [0.017 0.017 0.017 0.017 0.017]\n"
      ]
     }
@@ -2758,10 +2866,10 @@
    "id": "cell-34",
    "metadata": {
     "papermill": {
-     "duration": 0.01964,
-     "end_time": "2026-09-23T17:52:22.754655",
+     "duration": 0.006157,
+     "end_time": "2026-09-25T06:01:39.267915",
      "exception": false,
-     "start_time": "2026-09-23T17:52:22.735015",
+     "start_time": "2026-09-25T06:01:39.261758",
      "status": "completed"
     },
     "tags": []
@@ -2789,10 +2897,10 @@
    "id": "cell-35",
    "metadata": {
     "papermill": {
-     "duration": 0.021553,
-     "end_time": "2026-09-23T17:52:22.793958",
+     "duration": 0.006197,
+     "end_time": "2026-09-25T06:01:39.280095",
      "exception": false,
-     "start_time": "2026-09-23T17:52:22.772405",
+     "start_time": "2026-09-25T06:01:39.273898",
      "status": "completed"
     },
     "tags": []
@@ -2800,26 +2908,11 @@
    "source": [
     "***\n",
     "\n",
-    "## Section 11 : Backtest simplifie du signal\n",
+    "## Section 11 : Backtest cross-sectionnel du signal\n",
     "\n",
-    "### Stratégie de trading simplifiee\n",
+    "### Stratégie de trading simplifiée\n",
     "\n",
-    "Nous appliquons un backtest simplifie : a chaque date de rebalancement, le modèle predit le rendement futur de chaque action, et on investit dans le top quintile (n_bins=5).\n",
-    "\n",
-    "### Limite declaree : ce que ce backtest fait, et ce qu'il ne fait pas\n",
-    "\n",
-    "Les chiffres qui suivent sont lus comme des mesures : ils viennent donc avec leur limite, **mesuree dans le code** (suivi #17571), et non avec une reserve de principe.\n",
-    "\n",
-    "| Ce que la section annonce | Ce que le code fait |\n",
-    "|---|---|\n",
-    "| « a chaque date de rebalancement, ... le rendement futur de **chaque action** » | La cellule *Construction des sequences multi-actifs* concatene les actions les unes apres les autres : le tableau est ordonne **(action, date)**, jamais (date, action). Les 20 echantillons consecutifs d'un rebalancement sont donc 20 fenetres de la **meme action** : le tri porte sur des **jours d'une action**, pas sur des actions entre elles. C'est un *timing intra-actif*, sans selection transversale. |\n",
-    "| Un axe de temps calendaire | `n_days = len(port_returns) * REBALANCE_FREQ` totalise 608 x 20 = 12 160 « jours », obtenus en mettant les 30 historiques **bout a bout** : environ 48 ans affiches, alors que chaque action ne couvre que 2014-2025. |\n",
-    "\n",
-    "**En pratique** : `Rendement total` et `Rendement annualise` ne sont **pas comparables a une performance calendaire** — l'annualise suppose une duree qui n'a pas eu lieu. Lisez-les comme un **controle de bon sens** (valeurs finies, signe et ordre de grandeur coherents), pas comme le resultat d'une strategie. `Volatilite`, `Sharpe` et `Max Drawdown`, calcules sur le meme axe, heritent de la meme reserve.\n",
-    "\n",
-    "Ce que la section etablit **reste valide** : les rendements sont finis (plus de `inf%` ni de `nan%`) et c'est bien le rendement simple qui est compose. C'est un correctif d'unites et de gardes, pas de perimetre.\n",
-    "\n",
-    "Le refactor attendu — propager `(ticker, date)` dans la construction du jeu de sequences, decouper par date, trier les actifs entre eux a chaque date de rebalancement, metriques sur un axe calendaire — est suivi par **#17571**."
+    "A chaque date de rebalancement (tous les 20 jours de bourse), le modèle predit le rendement futur de chaque action PRESENTE A CETTE DATE ; on trie les actifs **entre eux** (cross-section) et on investit en equal-weight dans le top quintile jusqu'au prochain rebalancement. Les métriques sont calculées sur l'axe calendaire de la fenêtre de test -- la somme des rendements forward 5 jours non chevauchants des actifs détenus."
    ]
   },
   {
@@ -2828,16 +2921,16 @@
    "id": "cell-36",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:52:22.838122Z",
-     "iopub.status.busy": "2026-09-23T17:52:22.836452Z",
-     "iopub.status.idle": "2026-09-23T17:52:23.501414Z",
-     "shell.execute_reply": "2026-09-23T17:52:23.499144Z"
+     "iopub.execute_input": "2026-09-25T06:01:39.294290Z",
+     "iopub.status.busy": "2026-09-25T06:01:39.293973Z",
+     "iopub.status.idle": "2026-09-25T06:01:39.627315Z",
+     "shell.execute_reply": "2026-09-25T06:01:39.626889Z"
     },
     "papermill": {
-     "duration": 0.687748,
-     "end_time": "2026-09-23T17:52:23.502937",
+     "duration": 0.341617,
+     "end_time": "2026-09-25T06:01:39.628024",
      "exception": false,
-     "start_time": "2026-09-23T17:52:22.815189",
+     "start_time": "2026-09-25T06:01:39.286407",
      "status": "completed"
     },
     "tags": []
@@ -2845,7 +2938,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -2859,112 +2952,132 @@
      "text": [
       "\n",
       "==================================================\n",
-      "RESULTATS DU BACKTEST\n",
+      "RESULTATS DU BACKTEST CROSS-SECTIONNEL\n",
       "==================================================\n",
-      "  Rendement total:    3852.04%\n",
-      "  Rendement annualise: 7.92%\n",
-      "  Volatilite:         16.20%\n",
-      "  Sharpe Ratio:       0.489\n",
-      "  Max Drawdown:       -37.59%\n"
+      "  Fenetre de test : 2023-05-16 -> 2024-11-18\n",
+      "  20 fenetres de rebalancement (400 jours de bourse)\n",
+      "  Univers de test : 30 actifs\n",
+      "  Strategie  | total  114.81% | annualise   61.88% | vol 18.83% | Sharpe  3.286 | MaxDD  -5.95%\n",
+      "  Benchmark  | total   61.82% | annualise   35.42% | vol 10.14% | Sharpe  3.495\n",
+      "  Exces vs benchmark (fenetre, non annualise) : +52.99%\n"
      ]
     }
    ],
    "source": [
-    "# Backtest simplifie du signal Transformer\n",
+    "# Backtest cross-sectionnel du signal Transformer (axe calendaire)\n",
     "\n",
-    "def backtest_transformer_signal(\n",
-    "    preds_reg, realized_returns,\n",
-    "    n_bins=5, rebalance_freq=20\n",
-    "):\n",
+    "def backtest_transformer_signal(preds_reg, tickers, dates, realized_ret,\n",
+    "                                 n_bins=5, rebalance_freq=20, pred_len=5):\n",
     "    \"\"\"\n",
-    "    Backtest simplifie : long le top quintile des predictions.\n",
-    "    \n",
-    "    A chaque pas de rebalancement :\n",
-    "    1. Trier les actions par prediction de rendement\n",
-    "    2. Acheter le top quintile (short le bottom si souhaite)\n",
-    "    3. Calculer le rendement du portefeuille\n",
-    "    \n",
-    "    Diagnostic #17516 : on trie sur les predictions (risque-ajustees)\n",
-    "    mais on COMPOSE les rendements simples realises. Composer une serie\n",
-    "    risque-ajustee (type Sharpe, |x| > 1 courant) via cumprod(1 + x)\n",
-    "    explose vers inf/nan — d'ou les inf% sur main.\n",
+    "    Backtest cross-sectionnel sur axe calendaire.\n",
+    "\n",
+    "    A chaque date de rebalancement d :\n",
+    "    1. CROSS-SECTION : les actifs presents a la date d sont tries ENTRE\n",
+    "       EUX par prediction (rendement risque-ajuste attendu) ;\n",
+    "    2. portefeuille equal-weight du top quintile, detenu jusqu'au\n",
+    "       prochain rebalancement ;\n",
+    "    3. rendement realise de la fenetre = somme, par actif selectionne,\n",
+    "       de ses rendements forward pred_len jours NON CHEVAUCHANTS dans la\n",
+    "       fenetre (rebalance_freq = 20 jours, pred_len = 5 => 4 pas).\n",
+    "\n",
+    "    Benchmark : equal-weight de l'univers entier, meme protocole.\n",
+    "    Les rendements composes sont les rendements forward simples\n",
+    "    (target_return), pas les cibles risque-ajustees.\n",
     "    \"\"\"\n",
-    "    n_samples = len(preds_reg)\n",
-    "    portfolio_returns = []\n",
-    "    dates_indices = []\n",
-    "    \n",
-    "    for i in range(0, n_samples - rebalance_freq, rebalance_freq):\n",
-    "        # Prendre les predictions de cette periode\n",
-    "        batch_preds = preds_reg[i:i+rebalance_freq]\n",
-    "        batch_realized = realized_returns[i:i+rebalance_freq]\n",
-    "        \n",
-    "        # Trier par prediction (meilleur rendement attendu)\n",
-    "        sorted_indices = np.argsort(batch_preds)\n",
-    "        \n",
-    "        # Top quintile\n",
-    "        top_n = max(1, len(batch_preds) // n_bins)\n",
-    "        top_indices = sorted_indices[-top_n:]\n",
-    "        \n",
-    "        # Rendement realise du portefeuille (rendements simples)\n",
-    "        port_return = batch_realized[top_indices].mean()\n",
-    "        portfolio_returns.append(port_return)\n",
-    "        dates_indices.append(i)\n",
-    "    \n",
-    "    return np.array(portfolio_returns), dates_indices\n",
-    "\n",
+    "    bt = pd.DataFrame({\n",
+    "        'pred': preds_reg,\n",
+    "        'ticker': tickers,\n",
+    "        'date': dates,\n",
+    "        'ret': realized_ret,\n",
+    "    })\n",
+    "    unique_dates = np.sort(bt['date'].unique())\n",
+    "    n_steps = max(1, rebalance_freq // pred_len)\n",
     "\n",
-    "# Executer le backtest : tri sur preds_reg, PnL compose sur y_plain_test\n",
-    "# (rendements simples — cf diagnostic #17516 ci-dessus)\n",
-    "REBALANCE_FREQ = 20\n",
-    "port_returns, port_dates = backtest_transformer_signal(\n",
-    "    preds_reg, y_plain_test, n_bins=5, rebalance_freq=REBALANCE_FREQ\n",
+    "    portfolio_returns = []\n",
+    "    benchmark_returns = []\n",
+    "    rebalance_dates = []\n",
+    "\n",
+    "    k = 0\n",
+    "    while k + rebalance_freq < len(unique_dates):\n",
+    "        d0 = unique_dates[k]\n",
+    "        xs = bt[bt['date'] == d0]\n",
+    "        if len(xs) < n_bins:\n",
+    "            k += rebalance_freq\n",
+    "            continue\n",
+    "\n",
+    "        # Cross-section a date fixee : tri des actifs entre eux\n",
+    "        top_n = max(1, len(xs) // n_bins)\n",
+    "        selected = set(xs.nlargest(top_n, 'pred')['ticker'])\n",
+    "\n",
+    "        # Pas forward non chevauchants de la fenetre de detention\n",
+    "        step_dates = {unique_dates[k + j * pred_len] for j in range(n_steps)}\n",
+    "        win = bt[bt['date'].isin(step_dates)]\n",
+    "\n",
+    "        per_asset = win.groupby('ticker')['ret'].sum()\n",
+    "        port_ret = per_asset[per_asset.index.isin(selected)].mean()\n",
+    "        bench_ret = per_asset.mean()\n",
+    "\n",
+    "        portfolio_returns.append(port_ret)\n",
+    "        benchmark_returns.append(bench_ret)\n",
+    "        rebalance_dates.append(d0)\n",
+    "        k += rebalance_freq\n",
+    "\n",
+    "    return (np.array(portfolio_returns), np.array(benchmark_returns),\n",
+    "            pd.DatetimeIndex(rebalance_dates))\n",
+    "\n",
+    "\n",
+    "# Executer le backtest cross-sectionnel sur la fenetre de test\n",
+    "port_returns, bench_returns, rebalance_dates = backtest_transformer_signal(\n",
+    "    preds_reg, tickers_test, dates_test, y_ret_test,\n",
+    "    n_bins=5, rebalance_freq=20, pred_len=PRED_LEN\n",
     ")\n",
     "\n",
-    "# Calculer les metriques de performance — gardes #17516 : jamais inf/nan muet\n",
+    "# Gardes #17516 (jamais inf/nan muet) : un rendement non fini fausserait\n",
+    "# cumprod en silence. Filtre AVANT le comptage des fenetres.\n",
     "if not np.isfinite(port_returns).all():\n",
     "    n_bad = int((~np.isfinite(port_returns)).sum())\n",
-    "    print(f\"ATTENTION : {n_bad} rendements non finis exclues du backtest\")\n",
+    "    print(f\"ATTENTION : {n_bad} rendements non finis exclus du backtest\")\n",
     "    port_returns = port_returns[np.isfinite(port_returns)]\n",
     "\n",
+    "# Metriques sur axe CALENDAIRE : chaque element de port_returns couvre\n",
+    "# rebalance_freq jours de bourse de la fenetre de test -- plus\n",
+    "# d'empilement des 30 historiques d'actifs.\n",
+    "n_windows = len(port_returns)\n",
+    "n_trading_days = n_windows * 20\n",
+    "\n",
     "cumulative = np.cumprod(1 + port_returns)\n",
+    "bench_cumulative = np.cumprod(1 + bench_returns)\n",
     "total_return = cumulative[-1] - 1\n",
-    "n_days = max(len(port_returns) * REBALANCE_FREQ, 1)\n",
     "if total_return <= -1:\n",
     "    annualized_return = float('nan')  # ruine totale : annualisation indefinie\n",
     "    print(\"ATTENTION : rendement total <= -100%, annualisation indefinie.\")\n",
     "else:\n",
-    "    annualized_return = (1 + total_return) ** (252 / n_days) - 1\n",
-    "vol = np.std(port_returns) * np.sqrt(252 / REBALANCE_FREQ)\n",
-    "sharpe = annualized_return / vol if vol > 0 else 0.0\n",
+    "    annualized_return = (1 + total_return) ** (252 / max(n_trading_days, 1)) - 1\n",
+    "vol = np.std(port_returns) * np.sqrt(252 / 20)\n",
+    "sharpe = annualized_return / vol if vol > 0 else 0\n",
     "\n",
-    "# Max drawdown\n",
+    "# Max Drawdown\n",
     "running_max = np.maximum.accumulate(cumulative)\n",
     "drawdown = (cumulative - running_max) / running_max\n",
     "max_dd = drawdown.min()\n",
     "\n",
-    "# Benchmark : moyenne de toutes les actions (equal weight), rendements simples\n",
-    "benchmark_returns = []\n",
-    "for i in range(0, len(y_plain_test) - REBALANCE_FREQ, REBALANCE_FREQ):\n",
-    "    benchmark_returns.append(y_plain_test[i:i+REBALANCE_FREQ].mean())\n",
-    "benchmark_cumulative = np.cumprod(1 + np.array(benchmark_returns))\n",
-    "\n",
     "# Visualisation\n",
     "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
     "\n",
     "# 1. Rendement cumule\n",
     "ax1 = axes[0]\n",
-    "ax1.plot(cumulative, label='Transformer (Top Quintile)', color='steelblue', linewidth=1.5)\n",
-    "ax1.plot(benchmark_cumulative, label='Benchmark (Equal Weight)', color='gray', linewidth=1, alpha=0.7)\n",
-    "ax1.set_xlabel('Periodes de rebalancement')\n",
+    "ax1.plot(cumulative, label='Transformer (Top Quintile, cross-section)', color='steelblue', linewidth=1.5)\n",
+    "ax1.plot(bench_cumulative, label='Benchmark (Equal Weight, univers)', color='gray', linewidth=1, alpha=0.7)\n",
+    "ax1.set_xlabel('Fenetres de rebalancement (20 jours de bourse)')\n",
     "ax1.set_ylabel('Rendement cumule')\n",
-    "ax1.set_title('Backtest Simplifie', fontweight='bold')\n",
+    "ax1.set_title('Backtest Cross-Sectionnel (fenetre de test)', fontweight='bold')\n",
     "ax1.legend()\n",
     "ax1.grid(True, alpha=0.3)\n",
     "\n",
     "# 2. Drawdown\n",
     "ax2 = axes[1]\n",
     "ax2.fill_between(range(len(drawdown)), drawdown * 100, alpha=0.5, color='coral')\n",
-    "ax2.set_xlabel('Periodes de rebalancement')\n",
+    "ax2.set_xlabel('Fenetres de rebalancement (20 jours de bourse)')\n",
     "ax2.set_ylabel('Drawdown (%)')\n",
     "ax2.set_title('Drawdown', fontweight='bold')\n",
     "ax2.grid(True, alpha=0.3)\n",
@@ -2973,13 +3086,21 @@
     "plt.show()\n",
     "\n",
     "print(\"\\n\" + \"=\" * 50)\n",
-    "print(\"RESULTATS DU BACKTEST\")\n",
+    "print(\"RESULTATS DU BACKTEST CROSS-SECTIONNEL\")\n",
     "print(\"=\" * 50)\n",
-    "print(f\"  Rendement total:    {total_return:.2%}\")\n",
-    "print(f\"  Rendement annualise: {annualized_return:.2%}\")\n",
-    "print(f\"  Volatilite:         {vol:.2%}\")\n",
-    "print(f\"  Sharpe Ratio:       {sharpe:.3f}\")\n",
-    "print(f\"  Max Drawdown:       {max_dd:.2%}\")"
+    "print(f\"  Fenetre de test : {rebalance_dates.min().date()} -> {rebalance_dates.max().date()}\")\n",
+    "print(f\"  {n_windows} fenetres de rebalancement ({n_trading_days} jours de bourse)\")\n",
+    "print(f\"  Univers de test : {len(np.unique(tickers_test))} actifs\")\n",
+    "bench_total = bench_cumulative[-1] - 1\n",
+    "bench_annualized = (1 + bench_total) ** (252 / max(n_trading_days, 1)) - 1\n",
+    "bench_vol = np.std(bench_returns) * np.sqrt(252 / 20)\n",
+    "bench_sharpe = bench_annualized / bench_vol if bench_vol > 0 else 0\n",
+    "\n",
+    "print(f\"  Strategie  | total {total_return:8.2%} | annualise {annualized_return:8.2%} \"\n",
+    "      f\"| vol {vol:.2%} | Sharpe {sharpe:6.3f} | MaxDD {max_dd:7.2%}\")\n",
+    "print(f\"  Benchmark  | total {bench_total:8.2%} | annualise {bench_annualized:8.2%} \"\n",
+    "      f\"| vol {bench_vol:.2%} | Sharpe {bench_sharpe:6.3f}\")\n",
+    "print(f\"  Exces vs benchmark (fenetre, non annualise) : {total_return - bench_total:+.2%}\")"
    ]
   },
   {
@@ -2987,10 +3108,10 @@
    "id": "cell-1110361c",
    "metadata": {
     "papermill": {
-     "duration": 0.022798,
-     "end_time": "2026-09-23T17:52:23.548918",
+     "duration": 0.006921,
+     "end_time": "2026-09-25T06:01:39.642270",
      "exception": false,
-     "start_time": "2026-09-23T17:52:23.526120",
+     "start_time": "2026-09-25T06:01:39.635349",
      "status": "completed"
     },
     "tags": []
@@ -3014,16 +3135,16 @@
    "id": "cell-c255f730",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:52:23.596209Z",
-     "iopub.status.busy": "2026-09-23T17:52:23.595663Z",
-     "iopub.status.idle": "2026-09-23T17:52:23.608773Z",
-     "shell.execute_reply": "2026-09-23T17:52:23.606636Z"
+     "iopub.execute_input": "2026-09-25T06:01:39.656837Z",
+     "iopub.status.busy": "2026-09-25T06:01:39.656643Z",
+     "iopub.status.idle": "2026-09-25T06:01:39.659790Z",
+     "shell.execute_reply": "2026-09-25T06:01:39.659183Z"
     },
     "papermill": {
-     "duration": 0.039816,
-     "end_time": "2026-09-23T17:52:23.610874",
+     "duration": 0.011482,
+     "end_time": "2026-09-25T06:01:39.660478",
      "exception": false,
-     "start_time": "2026-09-23T17:52:23.571058",
+     "start_time": "2026-09-25T06:01:39.648996",
      "status": "completed"
     },
     "tags": []
@@ -3052,30 +3173,33 @@
    "id": "cell-37",
    "metadata": {
     "papermill": {
-     "duration": 0.023727,
-     "end_time": "2026-09-23T17:52:23.654896",
+     "duration": 0.00693,
+     "end_time": "2026-09-25T06:01:39.674360",
      "exception": false,
-     "start_time": "2026-09-23T17:52:23.631169",
+     "start_time": "2026-09-25T06:01:39.667430",
      "status": "completed"
     },
     "tags": []
    },
    "source": [
-    "### Interpretation : Backtest simplifie\n",
+    "### Interpretation : Backtest cross-sectionnel\n",
     "\n",
-    "Le backtest simplifie compare la stratégie Transformer (top quintile) a un benchmark equal-weight.\n",
+    "Resultats mesures sur CE run (fenetre de test, top quintile equal-weight vs benchmark equal-weight de l'univers) :\n",
     "\n",
-    "| Metrique | Signification |\n",
-    "|----------|---------------|\n",
-    "| **Rendement total** | Performance brute sur la periode de test |\n",
-    "| **Sharpe Ratio** | Rendement ajuste par le risque (>0.5 est bon) |\n",
-    "| **Max Drawdown** | Plus grosse perte depuis un pic |\n",
+    "| Ligne | Total | Annualise | Vol | Sharpe | MaxDD |\n",
+    "|-------|-------|-----------|-----|--------|-------|\n",
+    "| **Strategie (top quintile)** | 114.81% | 61.88% | 18.83% | **3.286** | -5.95% |\n",
+    "| **Benchmark (univers)** | 61.82% | 35.42% | 10.14% | **3.495** | — |\n",
+    "\n",
+    "**Verdict : NO BEATS risque-ajuste.** La strategie rend presque deux fois le benchmark en brut (+52.99 % d'exces sur la fenetre), mais avec une volatilite quasi double (18.83% vs 10.14%) : rapportee au risque, elle rend MOINS (Sharpe 3.286 < 3.495).\n",
+    "\n",
+    "**Lecture coherente avec l'evaluation** : la correlation quasi nulle et le quantile unique de la section precedente disent que le tri du top quintile n'est pas une selection informee — le portefeuille est un tirage de l'univers largement corrige par le beta du marche en bull run. Le rendement brut eleve mesure l'exposition, pas une capacite predictive.\n",
     "\n",
     "**Points cles** :\n",
-    "1. Ce backtest est simplifie et ne tient pas compte des couts de transaction, slippage, ou contraintes de liquidite\n",
-    "2. Le top quintile represente une stratégie long-only avec rebalancement toutes les 20 seances\n",
-    "3. Le Sharpe ratio est la metrique la plus importante : un Sharpe > 0.5 est exploitable en pratique\n",
-    "4. Pour un backtest realiste, utiliser QuantConnect avec les couts de transaction reels"
+    "1. Un Sharpe strategy < Sharpe benchmark est le refus attendu ici : le trio (corr quasi nulle, quantile unique, Sharpe sous benchmark) raconte la meme histoire trois fois\n",
+    "2. Ce backtest est simplifie : ni couts de transaction, ni slippage, ni contraintes de liquidite — ces frictions degraderaient encore la ligne strategie\n",
+    "3. La selection est transversale (comparaison des actifs entre eux a chaque date), le tri porte sur la prediction risque-ajustee\n",
+    "4. Pour un backtest realiste avec couts reels : QuantConnect (voir la serie QC)\n"
    ]
   },
   {
@@ -3083,10 +3207,10 @@
    "id": "cell-38",
    "metadata": {
     "papermill": {
-     "duration": 0.024442,
-     "end_time": "2026-09-23T17:52:23.702171",
+     "duration": 0.006983,
+     "end_time": "2026-09-25T06:01:39.688178",
      "exception": false,
-     "start_time": "2026-09-23T17:52:23.677729",
+     "start_time": "2026-09-25T06:01:39.681195",
      "status": "completed"
     },
     "tags": []
@@ -3105,16 +3229,16 @@
    "id": "cell-39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:52:23.754550Z",
-     "iopub.status.busy": "2026-09-23T17:52:23.754018Z",
-     "iopub.status.idle": "2026-09-23T17:52:23.838310Z",
-     "shell.execute_reply": "2026-09-23T17:52:23.836112Z"
+     "iopub.execute_input": "2026-09-25T06:01:39.703341Z",
+     "iopub.status.busy": "2026-09-25T06:01:39.703139Z",
+     "iopub.status.idle": "2026-09-25T06:01:39.731637Z",
+     "shell.execute_reply": "2026-09-25T06:01:39.731080Z"
     },
     "papermill": {
-     "duration": 0.113527,
-     "end_time": "2026-09-23T17:52:23.840460",
+     "duration": 0.037287,
+     "end_time": "2026-09-25T06:01:39.732395",
      "exception": false,
-     "start_time": "2026-09-23T17:52:23.726933",
+     "start_time": "2026-09-25T06:01:39.695108",
      "status": "completed"
     },
     "tags": []
@@ -3132,7 +3256,7 @@
       "  model_state_dict: 59 couches\n",
       "  scaler: StandardScaler (12 features)\n",
       "  config: {'input_dim': 12, 'd_model': 256, 'nhead': 8, 'num_layers': 4, 'dim_feedforward': 1024, 'dropout': 0.1, 'seq_len': 60, 'pred_len': 5, 'feature_cols': ['close', 'ret_1d', 'ret_5d', 'ret_20d', 'vol_10d', 'vol_20d', 'momentum', 'rsi', 'vix_level', 'vix_change', 'yield_spread', 'yield_curve_slope'], 'tickers': ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'NVDA', 'META', 'TSLA', 'BRK-B', 'JPM', 'V', 'UNH', 'JNJ', 'WMT', 'XOM', 'MA', 'PG', 'HD', 'CVX', 'MRK', 'ABBV', 'AVGO', 'KO', 'PEP', 'COST', 'ADBE', 'CRM', 'AMD', 'NFLX', 'TMO', 'CSCO']}\n",
-      "  test_metrics: {'mse': 8.639111518859863, 'mae': 2.157524347305298, 'correlation': np.float64(nan), 'dir_acc': 0.5550805656034199}\n"
+      "  test_metrics: {'mse': 9.93782901763916, 'mae': 2.3007802963256836, 'correlation': np.float64(0.009831443711406356), 'dir_acc': 0.5623045267489712}\n"
      ]
     }
    ],
@@ -3191,10 +3315,10 @@
    "id": "cell-40",
    "metadata": {
     "papermill": {
-     "duration": 0.021159,
-     "end_time": "2026-09-23T17:52:23.885443",
+     "duration": 0.00744,
+     "end_time": "2026-09-25T06:01:39.747833",
      "exception": false,
-     "start_time": "2026-09-23T17:52:23.864284",
+     "start_time": "2026-09-25T06:01:39.740393",
      "status": "completed"
     },
     "tags": []
@@ -3222,10 +3346,10 @@
    "id": "cell-41",
    "metadata": {
     "papermill": {
-     "duration": 0.023752,
-     "end_time": "2026-09-23T17:52:23.931863",
+     "duration": 0.007829,
+     "end_time": "2026-09-25T06:01:39.763903",
      "exception": false,
-     "start_time": "2026-09-23T17:52:23.908111",
+     "start_time": "2026-09-25T06:01:39.756074",
      "status": "completed"
     },
     "tags": []
@@ -3258,10 +3382,10 @@
    "id": "cell-43",
    "metadata": {
     "papermill": {
-     "duration": 0.023803,
-     "end_time": "2026-09-23T17:52:23.976099",
+     "duration": 0.012932,
+     "end_time": "2026-09-25T06:01:39.785052",
      "exception": false,
-     "start_time": "2026-09-23T17:52:23.952296",
+     "start_time": "2026-09-25T06:01:39.772120",
      "status": "completed"
     },
     "tags": []
@@ -3290,10 +3414,10 @@
    "id": "cell-44",
    "metadata": {
     "papermill": {
-     "duration": 0.027648,
-     "end_time": "2026-09-23T17:52:24.027811",
+     "duration": 0.007641,
+     "end_time": "2026-09-25T06:01:39.800587",
      "exception": false,
-     "start_time": "2026-09-23T17:52:24.000163",
+     "start_time": "2026-09-25T06:01:39.792946",
      "status": "completed"
     },
     "tags": []
@@ -3310,16 +3434,16 @@
    "id": "cell-45",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-23T17:52:24.077045Z",
-     "iopub.status.busy": "2026-09-23T17:52:24.076528Z",
-     "iopub.status.idle": "2026-09-23T17:52:24.099159Z",
-     "shell.execute_reply": "2026-09-23T17:52:24.097533Z"
+     "iopub.execute_input": "2026-09-25T06:01:39.816197Z",
+     "iopub.status.busy": "2026-09-25T06:01:39.815902Z",
+     "iopub.status.idle": "2026-09-25T06:01:39.822567Z",
+     "shell.execute_reply": "2026-09-25T06:01:39.821804Z"
     },
     "papermill": {
-     "duration": 0.050527,
-     "end_time": "2026-09-23T17:52:24.100769",
+     "duration": 0.015428,
+     "end_time": "2026-09-25T06:01:39.823375",
      "exception": false,
-     "start_time": "2026-09-23T17:52:24.050242",
+     "start_time": "2026-09-25T06:01:39.807947",
      "status": "completed"
     },
     "tags": []
@@ -3349,13 +3473,13 @@
       "  Periode:      2014-01-01 a 2025-01-01\n",
       "\n",
       "RESULTATS TEST\n",
-      "  MSE:                8.639112\n",
-      "  MAE:                2.157524\n",
-      "  Correlation:        nan\n",
-      "  Direction Accuracy: 55.51%\n",
+      "  MSE:                9.937829\n",
+      "  MAE:                2.300780\n",
+      "  Correlation:        0.0098\n",
+      "  Direction Accuracy: 56.23%\n",
       "\n",
       "ENTRAINEMENT\n",
-      "  Epochs:       13 (early stopping: patience=7)\n",
+      "  Epochs:       17 (early stopping: patience=7)\n",
       "  Optimiseur:   AdamW (lr=0.0005, weight_decay=0.01)\n",
       "  Scheduler:    CosineAnnealingLR\n",
       "  Loss:         HuberLoss + 0.5*BCELoss\n",
@@ -3455,10 +3579,10 @@
    "id": "cell-46",
    "metadata": {
     "papermill": {
-     "duration": 0.031746,
-     "end_time": "2026-09-23T17:52:24.155878",
+     "duration": 0.008075,
+     "end_time": "2026-09-25T06:01:39.841761",
      "exception": false,
-     "start_time": "2026-09-23T17:52:24.124132",
+     "start_time": "2026-09-25T06:01:39.833686",
      "status": "completed"
     },
     "tags": []
@@ -3566,23 +3690,21 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.19"
+   "version": "3.13.3"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 1461.900736,
-   "end_time": "2026-09-23T17:52:26.680572",
+   "duration": 5050.938168,
+   "end_time": "2026-09-25T06:01:42.711822",
    "environment_variables": {},
    "exception": null,
    "input_path": "QC-Py-31-Transformer-Training.ipynb",
-   "output_path": "qcpy31_out.ipynb",
-   "parameters": {
-    "FORCE_RETRAIN": true
-   },
-   "start_time": "2026-09-23T17:28:04.779836",
+   "output_path": "QC-Py-31-Transformer-Training.ipynb",
+   "parameters": {},
+   "start_time": "2026-09-25T04:37:31.773654",
    "version": "2.7.0"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/MyIA.AI.Notebooks/QuantConnect/Python/training_curves.png b/MyIA.AI.Notebooks/QuantConnect/Python/training_curves.png
index c52599786e..6648e02f3c 100644
Binary files a/MyIA.AI.Notebooks/QuantConnect/Python/training_curves.png and b/MyIA.AI.Notebooks/QuantConnect/Python/training_curves.png differ
diff --git a/MyIA.AI.Notebooks/QuantConnect/Python/transformer_multiasset_model.pt b/MyIA.AI.Notebooks/QuantConnect/Python/transformer_multiasset_model.pt
index 5ce47896e6..1beec2e3d9 100644
--- a/MyIA.AI.Notebooks/QuantConnect/Python/transformer_multiasset_model.pt
+++ b/MyIA.AI.Notebooks/QuantConnect/Python/transformer_multiasset_model.pt
@@ -1,3 +1,3 @@
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