From 5b8644847877dcafe0bbf2345c1a5291f55d9339 Mon Sep 17 00:00:00 2001
From: jsboige <jsboige@gmail.com>
Date: Sat, 19 Sep 2026 05:39:45 +0200
Subject: [PATCH] =?UTF-8?q?fix(qc,#16795):=20QC-Py-31=20determinisme=20?=
 =?UTF-8?q?=E2=80=94=20trio=20flags=20cuDNN=20+=20re-exec=20GPU=20locale?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Cellule 4 (site du seed 42) : CUBLAS_WORKSPACE_CONFIG avant import torch,
puis cudnn.deterministic=True / benchmark=False / use_deterministic_algorithms(True,
warn_only=True). Re-execution complete kernel coursia-ml-training (RTX 3080 Ti,
17/17 cellules, 0 erreur, yfinance installe regle F dans l'env kernel).
Ancienne execution = stack etrangere PyTorch 2.11.0+cu128 ; metriques stables
a ~6 decimales (MSE 8.636657 -> 8.636683, MAE 2.155941 -> 2.155940), donnees
Yahoo re-telechargees (ajustements retroactifs), correlation nan -> 0.0160
(valeur reelle). Prose verifiee : 0 citation stale. Sous-produits suivis
regeneres avec le notebook (training_curves.png, transformer_multiasset_model.pt).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
---
 .../QC-Py-31-Transformer-Training.ipynb       | 773 ++++++++++--------
 .../QuantConnect/Python/training_curves.png   | Bin 2486 -> 2487 bytes
 .../Python/transformer_multiasset_model.pt    |   4 +-
 3 files changed, 454 insertions(+), 323 deletions(-)

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 9706b8965c..6d075c7803 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
@@ -5,10 +5,10 @@
    "id": "cell-0",
    "metadata": {
     "papermill": {
-     "duration": 0.007475,
-     "end_time": "2026-04-29T15:28:25.747416+00:00",
+     "duration": 0.006529,
+     "end_time": "2026-09-19T03:37:36.984247",
      "exception": false,
-     "start_time": "2026-04-29T15:28:25.739941+00:00",
+     "start_time": "2026-09-19T03:37:36.977718",
      "status": "completed"
     },
     "tags": []
@@ -63,10 +63,10 @@
    "id": "cell-1",
    "metadata": {
     "papermill": {
-     "duration": 0.009052,
-     "end_time": "2026-04-29T15:28:25.762334+00:00",
+     "duration": 0.005999,
+     "end_time": "2026-09-19T03:37:36.994357",
      "exception": false,
-     "start_time": "2026-04-29T15:28:25.753282+00:00",
+     "start_time": "2026-09-19T03:37:36.988358",
      "status": "completed"
     },
     "tags": []
@@ -83,10 +83,10 @@
    "id": "cell-2",
    "metadata": {
     "papermill": {
-     "duration": 0.00771,
-     "end_time": "2026-04-29T15:28:25.779583+00:00",
+     "duration": 0.008129,
+     "end_time": "2026-09-19T03:37:37.010555",
      "exception": false,
-     "start_time": "2026-04-29T15:28:25.771873+00:00",
+     "start_time": "2026-09-19T03:37:37.002426",
      "status": "completed"
     },
     "tags": []
@@ -108,10 +108,10 @@
    "id": "cell-3",
    "metadata": {
     "papermill": {
-     "duration": 0.0098,
-     "end_time": "2026-04-29T15:28:25.798677+00:00",
+     "duration": 0.003953,
+     "end_time": "2026-09-19T03:37:37.020590",
      "exception": false,
-     "start_time": "2026-04-29T15:28:25.788877+00:00",
+     "start_time": "2026-09-19T03:37:37.016637",
      "status": "completed"
     },
     "tags": []
@@ -162,16 +162,16 @@
    "id": "cell-4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:25.814908Z",
-     "iopub.status.busy": "2026-04-29T15:28:25.814639Z",
-     "iopub.status.idle": "2026-04-29T15:28:31.577869Z",
-     "shell.execute_reply": "2026-04-29T15:28:31.576529Z"
+     "iopub.execute_input": "2026-09-19T03:37:37.031394Z",
+     "iopub.status.busy": "2026-09-19T03:37:37.031394Z",
+     "iopub.status.idle": "2026-09-19T03:37:40.315948Z",
+     "shell.execute_reply": "2026-09-19T03:37:40.315256Z"
     },
     "papermill": {
-     "duration": 5.772923,
-     "end_time": "2026-04-29T15:28:31.580324+00:00",
+     "duration": 3.291448,
+     "end_time": "2026-09-19T03:37:40.316956",
      "exception": false,
-     "start_time": "2026-04-29T15:28:25.807401+00:00",
+     "start_time": "2026-09-19T03:37:37.025508",
      "status": "completed"
     },
     "tags": []
@@ -181,7 +181,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "PyTorch version: 2.11.0+cu128\n",
+      "PyTorch version: 2.6.0+cu124\n",
       "Device: cuda\n",
       "GPU: NVIDIA GeForce RTX 3080 Ti Laptop GPU\n",
       "VRAM: 17.2 GB\n",
@@ -210,7 +210,8 @@
     "plt.style.use('seaborn-v0_8-darkgrid')\n",
     "%matplotlib inline\n",
     "\n",
-    "# PyTorch\n",
+    "# PyTorch (determinisme cuBLAS pose AVANT l'import torch)\n",
+    "os.environ.setdefault('CUBLAS_WORKSPACE_CONFIG', ':4096:8')\n",
     "import torch\n",
     "import torch.nn as nn\n",
     "import torch.optim as optim\n",
@@ -227,6 +228,11 @@
     "torch.manual_seed(42)\n",
     "np.random.seed(42)\n",
     "\n",
+    "# Determinisme : la graine seule ne fixe rien sur GPU (cuDNN non deterministe par defaut)\n",
+    "torch.backends.cudnn.deterministic = True\n",
+    "torch.backends.cudnn.benchmark = False\n",
+    "torch.use_deterministic_algorithms(True, warn_only=True)\n",
+    "\n",
     "print(f\"PyTorch version: {torch.__version__}\")\n",
     "print(f\"Device: {device}\")\n",
     "if torch.cuda.is_available():\n",
@@ -261,10 +267,10 @@
    "id": "cell-5",
    "metadata": {
     "papermill": {
-     "duration": 0.010539,
-     "end_time": "2026-04-29T15:28:31.602384+00:00",
+     "duration": 0.006001,
+     "end_time": "2026-09-19T03:37:40.331622",
      "exception": false,
-     "start_time": "2026-04-29T15:28:31.591845+00:00",
+     "start_time": "2026-09-19T03:37:40.325621",
      "status": "completed"
     },
     "tags": []
@@ -295,10 +301,10 @@
    "id": "cell-6",
    "metadata": {
     "papermill": {
-     "duration": 0.010684,
-     "end_time": "2026-04-29T15:28:31.623774+00:00",
+     "duration": 0.005002,
+     "end_time": "2026-09-19T03:37:40.340626",
      "exception": false,
-     "start_time": "2026-04-29T15:28:31.613090+00:00",
+     "start_time": "2026-09-19T03:37:40.335624",
      "status": "completed"
     },
     "tags": []
@@ -328,16 +334,16 @@
    "id": "cell-7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:31.646717Z",
-     "iopub.status.busy": "2026-04-29T15:28:31.646047Z",
-     "iopub.status.idle": "2026-04-29T15:28:34.740828Z",
-     "shell.execute_reply": "2026-04-29T15:28:34.739612Z"
+     "iopub.execute_input": "2026-09-19T03:37:40.349626Z",
+     "iopub.status.busy": "2026-09-19T03:37:40.349626Z",
+     "iopub.status.idle": "2026-09-19T03:37:42.216807Z",
+     "shell.execute_reply": "2026-09-19T03:37:42.215572Z"
     },
     "papermill": {
-     "duration": 3.108098,
-     "end_time": "2026-04-29T15:28:34.742273+00:00",
+     "duration": 1.872711,
+     "end_time": "2026-09-19T03:37:42.217337",
      "exception": false,
-     "start_time": "2026-04-29T15:28:31.634175+00:00",
+     "start_time": "2026-09-19T03:37:40.344626",
      "status": "completed"
     },
     "tags": []
@@ -388,17 +394,10 @@
      "output_type": "stream",
      "text": [
       "\r",
+      "[*******               15%                       ]  5 of 33 completed\r",
       "[*******               15%                       ]  5 of 33 completed"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[*********             18%                       ]  6 of 33 completed"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -420,7 +419,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[************          24%                       ]  8 of 33 completed"
+      "[*************         27%                       ]  9 of 33 completed"
      ]
     },
     {
@@ -428,7 +427,6 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**************        30%                       ]  10 of 33 completed\r",
       "[**************        30%                       ]  10 of 33 completed"
      ]
     },
@@ -437,7 +435,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*****************     36%                       ]  12 of 33 completed"
+      "[****************      33%                       ]  11 of 33 completed"
      ]
     },
     {
@@ -445,6 +443,8 @@
      "output_type": "stream",
      "text": [
       "\r",
+      "[****************      33%                       ]  11 of 33 completed\r",
+      "[*******************   39%                       ]  13 of 33 completed\r",
       "[*******************   39%                       ]  13 of 33 completed"
      ]
     },
@@ -453,8 +453,6 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*******************   39%                       ]  13 of 33 completed\r",
-      "[**********************45%                       ]  15 of 33 completed\r",
       "[**********************45%                       ]  15 of 33 completed"
      ]
     },
@@ -463,15 +461,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************52%                       ]  17 of 33 completed"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[**********************52%                       ]  17 of 33 completed"
+      "[**********************48%                       ]  16 of 33 completed"
      ]
     },
     {
@@ -479,7 +469,9 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************58%***                    ]  19 of 33 completed"
+      "[**********************48%                       ]  16 of 33 completed\r",
+      "[**********************48%                       ]  16 of 33 completed\r",
+      "[**********************48%                       ]  16 of 33 completed"
      ]
     },
     {
@@ -503,7 +495,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************67%*******                ]  22 of 33 completed"
+      "[**********************64%******                 ]  21 of 33 completed"
      ]
     },
     {
@@ -559,7 +551,14 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************88%*****************      ]  29 of 33 completed\r",
+      "[**********************88%*****************      ]  29 of 33 completed"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
       "[**********************88%*****************      ]  29 of 33 completed"
      ]
     },
@@ -576,7 +575,14 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************97%********************** ]  32 of 33 completed\r",
+      "[**********************97%********************** ]  32 of 33 completed"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
       "[*********************100%***********************]  33 of 33 completed"
      ]
     },
@@ -608,11 +614,11 @@
       "Apercu des 5 premieres lignes:\n",
       "Ticker           AAPL       MSFT      GOOGL       AMZN      NVDA\n",
       "Date                                                            \n",
-      "2014-01-02  17.140663  30.760904  27.627512  19.898500  0.373844\n",
-      "2014-01-03  16.764154  30.553986  27.425978  19.822001  0.369365\n",
-      "2014-01-06  16.855570  29.908302  27.731756  19.681499  0.374315\n",
-      "2014-01-07  16.735023  30.140079  28.266376  19.901501  0.380444\n",
-      "2014-01-08  16.841005  29.602013  28.325201  20.096001  0.385630\n"
+      "2014-01-02  17.110134  30.636765  27.593287  19.898500  0.372991\n",
+      "2014-01-03  16.734299  30.430653  27.392002  19.822001  0.368523\n",
+      "2014-01-06  16.825544  29.787580  27.697399  19.681499  0.373461\n",
+      "2014-01-07  16.705221  30.018433  28.231359  19.901501  0.379576\n",
+      "2014-01-08  16.811007  29.482525  28.290110  20.096001  0.384750\n"
      ]
     }
    ],
@@ -679,10 +685,10 @@
    "id": "cell-8",
    "metadata": {
     "papermill": {
-     "duration": 0.006105,
-     "end_time": "2026-04-29T15:28:34.754968+00:00",
+     "duration": 0.006587,
+     "end_time": "2026-09-19T03:37:42.230905",
      "exception": false,
-     "start_time": "2026-04-29T15:28:34.748863+00:00",
+     "start_time": "2026-09-19T03:37:42.224318",
      "status": "completed"
     },
     "tags": []
@@ -711,10 +717,10 @@
    "id": "cell-9",
    "metadata": {
     "papermill": {
-     "duration": 0.006194,
-     "end_time": "2026-04-29T15:28:34.770610+00:00",
+     "duration": 0.009512,
+     "end_time": "2026-09-19T03:37:42.249421",
      "exception": false,
-     "start_time": "2026-04-29T15:28:34.764416+00:00",
+     "start_time": "2026-09-19T03:37:42.239909",
      "status": "completed"
     },
     "tags": []
@@ -742,16 +748,16 @@
    "id": "cell-10",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:34.792192Z",
-     "iopub.status.busy": "2026-04-29T15:28:34.791246Z",
-     "iopub.status.idle": "2026-04-29T15:28:35.358023Z",
-     "shell.execute_reply": "2026-04-29T15:28:35.356775Z"
+     "iopub.execute_input": "2026-09-19T03:37:42.263073Z",
+     "iopub.status.busy": "2026-09-19T03:37:42.263073Z",
+     "iopub.status.idle": "2026-09-19T03:37:42.561090Z",
+     "shell.execute_reply": "2026-09-19T03:37:42.560059Z"
     },
     "papermill": {
-     "duration": 0.578469,
-     "end_time": "2026-04-29T15:28:35.359636+00:00",
+     "duration": 0.307149,
+     "end_time": "2026-09-19T03:37:42.562074",
      "exception": false,
-     "start_time": "2026-04-29T15:28:34.781167+00:00",
+     "start_time": "2026-09-19T03:37:42.254925",
      "status": "completed"
     },
     "tags": []
@@ -768,23 +774,23 @@
       "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     87.0794     0.0011     0.0055     0.0219     0.0155     0.0160   \n",
-      "std      65.8283     0.0176     0.0370     0.0767     0.0085     0.0075   \n",
-      "min      15.4874    -0.1286    -0.1753    -0.2677     0.0036     0.0048   \n",
-      "25%      28.0757    -0.0070    -0.0159    -0.0243     0.0099     0.0110   \n",
-      "50%      51.1730     0.0010     0.0067     0.0231     0.0137     0.0144   \n",
-      "75%     145.9233     0.0101     0.0272     0.0753     0.0189     0.0191   \n",
-      "max     257.6127     0.1198     0.1841     0.3453     0.0801     0.0680   \n",
+      "mean     86.9242     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",
+      "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     3.1801     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    97.3974    82.6900      1.1560        0.1800   \n",
+      "min     -11.5655     3.1800     9.1400     -0.2816       -0.9900   \n",
+      "25%      -1.5844    43.0727    13.1975     -0.0432       -0.7223   \n",
+      "50%       1.7740    56.5023    15.9450     -0.0070       -0.5160   \n",
+      "75%       5.6948    69.3731    20.7925      0.0364       -0.2157   \n",
+      "max      19.9280    97.3973    82.6900      1.1560        0.1800   \n",
       "\n",
       "       yield_curve_slope  \n",
       "count          2768.0000  \n",
@@ -902,39 +908,77 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "cell-ab2bb9c5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.009788,
+     "end_time": "2026-09-19T03:37:42.579874",
+     "exception": false,
+     "start_time": "2026-09-19T03:37:42.570086",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
-    "### Exercice 1 : Ajouter des features de correlation inter-actifs\n\nLe Transformer peut exploiter les relations entre actifs. Ajoutez une feature de **correlation rolling 20j** entre chaque action et SPY.\n\n**Indices** :\n- `# Indice` : Utilisez `close.pct_change().rolling(20).corr(spy_returns)`\n- `# Étape 1` : Calculer les returns de SPY comme reference\n- `# Étape 2` : Pour chaque ticker, calculer la correlation rolling 20j avec SPY\n- `# Étape 3` : Ajouter la colonne `corr_spy_20d` au DataFrame"
-   ],
-   "id": "cell-ab2bb9c5"
+    "### Exercice 1 : Ajouter des features de correlation inter-actifs\n",
+    "\n",
+    "Le Transformer peut exploiter les relations entre actifs. Ajoutez une feature de **correlation rolling 20j** entre chaque action et SPY.\n",
+    "\n",
+    "**Indices** :\n",
+    "- `# Indice` : Utilisez `close.pct_change().rolling(20).corr(spy_returns)`\n",
+    "- `# Étape 1` : Calculer les returns de SPY comme reference\n",
+    "- `# Étape 2` : Pour chaque ticker, calculer la correlation rolling 20j avec SPY\n",
+    "- `# Étape 3` : Ajouter la colonne `corr_spy_20d` au DataFrame"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 4,
+   "id": "cell-b3d5cdef",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-19T03:37:42.599724Z",
+     "iopub.status.busy": "2026-09-19T03:37:42.599724Z",
+     "iopub.status.idle": "2026-09-19T03:37:42.609089Z",
+     "shell.execute_reply": "2026-09-19T03:37:42.608403Z"
+    },
+    "papermill": {
+     "duration": 0.022715,
+     "end_time": "2026-09-19T03:37:42.611137",
+     "exception": false,
+     "start_time": "2026-09-19T03:37:42.588422",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exercice a completer\n"
+      "Exercice a completer : Features de correlation inter-actifs\n"
      ]
     }
    ],
    "source": [
-    "# Exercice 1 : Features de correlation inter-actifs\n# TODO etudiant : Ajouter la correlation rolling 20j avec SPY\n# Etape 1 : Calculer les returns SPY comme reference\n# Etape 2 : Calculer corr(returns_ticker, returns_spy).rolling(20)\n# Etape 3 : Ajouter au DataFrame\ncorr_features = None  # TODO etudiant : remplacer par le calcul\nprint(\"Exercice a completer : Features de correlation inter-actifs\")"
-   ],
-   "id": "cell-b3d5cdef"
+    "# Exercice 1 : Features de correlation inter-actifs\n",
+    "# TODO etudiant : Ajouter la correlation rolling 20j avec SPY\n",
+    "# Etape 1 : Calculer les returns SPY comme reference\n",
+    "# Etape 2 : Calculer corr(returns_ticker, returns_spy).rolling(20)\n",
+    "# Etape 3 : Ajouter au DataFrame\n",
+    "corr_features = None  # TODO etudiant : remplacer par le calcul\n",
+    "print(\"Exercice a completer : Features de correlation inter-actifs\")"
+   ]
   },
   {
    "cell_type": "markdown",
    "id": "cell-11",
    "metadata": {
     "papermill": {
-     "duration": 0.009636,
-     "end_time": "2026-04-29T15:28:35.376305+00:00",
+     "duration": 0.005622,
+     "end_time": "2026-09-19T03:37:42.624282",
      "exception": false,
-     "start_time": "2026-04-29T15:28:35.366669+00:00",
+     "start_time": "2026-09-19T03:37:42.618660",
      "status": "completed"
     },
     "tags": []
@@ -969,10 +1013,10 @@
    "id": "cell-12",
    "metadata": {
     "papermill": {
-     "duration": 0.011782,
-     "end_time": "2026-04-29T15:28:35.399558+00:00",
+     "duration": 0.00557,
+     "end_time": "2026-09-19T03:37:42.638058",
      "exception": false,
-     "start_time": "2026-04-29T15:28:35.387776+00:00",
+     "start_time": "2026-09-19T03:37:42.632488",
      "status": "completed"
     },
     "tags": []
@@ -1001,20 +1045,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "cell-13",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:35.422268Z",
-     "iopub.status.busy": "2026-04-29T15:28:35.421860Z",
-     "iopub.status.idle": "2026-04-29T15:28:36.899978Z",
-     "shell.execute_reply": "2026-04-29T15:28:36.898860Z"
+     "iopub.execute_input": "2026-09-19T03:37:42.649865Z",
+     "iopub.status.busy": "2026-09-19T03:37:42.649865Z",
+     "iopub.status.idle": "2026-09-19T03:37:43.890312Z",
+     "shell.execute_reply": "2026-09-19T03:37:43.888450Z"
     },
     "papermill": {
-     "duration": 1.48864,
-     "end_time": "2026-04-29T15:28:36.901881+00:00",
+     "duration": 1.248261,
+     "end_time": "2026-09-19T03:37:43.891318",
      "exception": false,
-     "start_time": "2026-04-29T15:28:35.413241+00:00",
+     "start_time": "2026-09-19T03:37:42.643057",
      "status": "completed"
     },
     "tags": []
@@ -1173,10 +1217,10 @@
    "id": "cell-14",
    "metadata": {
     "papermill": {
-     "duration": 0.008779,
-     "end_time": "2026-04-29T15:28:36.925355+00:00",
+     "duration": 0.007145,
+     "end_time": "2026-09-19T03:37:43.909314",
      "exception": false,
-     "start_time": "2026-04-29T15:28:36.916576+00:00",
+     "start_time": "2026-09-19T03:37:43.902169",
      "status": "completed"
     },
     "tags": []
@@ -1206,10 +1250,10 @@
    "id": "cell-15",
    "metadata": {
     "papermill": {
-     "duration": 0.006732,
-     "end_time": "2026-04-29T15:28:36.938480+00:00",
+     "duration": 0.006366,
+     "end_time": "2026-09-19T03:37:43.920684",
      "exception": false,
-     "start_time": "2026-04-29T15:28:36.931748+00:00",
+     "start_time": "2026-09-19T03:37:43.914318",
      "status": "completed"
     },
     "tags": []
@@ -1239,20 +1283,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "cell-16",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:36.953156Z",
-     "iopub.status.busy": "2026-04-29T15:28:36.952851Z",
-     "iopub.status.idle": "2026-04-29T15:28:37.204955Z",
-     "shell.execute_reply": "2026-04-29T15:28:37.203104Z"
+     "iopub.execute_input": "2026-09-19T03:37:43.932444Z",
+     "iopub.status.busy": "2026-09-19T03:37:43.932444Z",
+     "iopub.status.idle": "2026-09-19T03:37:44.106881Z",
+     "shell.execute_reply": "2026-09-19T03:37:44.105869Z"
     },
     "papermill": {
-     "duration": 0.26228,
-     "end_time": "2026-04-29T15:28:37.207439+00:00",
+     "duration": 0.1821,
+     "end_time": "2026-09-19T03:37:44.107899",
      "exception": false,
-     "start_time": "2026-04-29T15:28:36.945159+00:00",
+     "start_time": "2026-09-19T03:37:43.925799",
      "status": "completed"
     },
     "tags": []
@@ -1502,39 +1546,78 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "cell-5e739084",
+   "metadata": {
+    "papermill": {
+     "duration": 0.005569,
+     "end_time": "2026-09-19T03:37:44.123935",
+     "exception": false,
+     "start_time": "2026-09-19T03:37:44.118366",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
-    "### Exercice 2 : Modifier le nombre de têtes d'attention\n\nChangez le **nombre de têtes d'attention** de 4 a 8 et observez l'impact sur les performances. Plus de tetes permet de capturer des patterns différents mais augmente le cout de calcul.\n\n**Indices** :\n- `# Indice` : Le paramètre `nhead` est défini dans la configuration du modèle\n- `# Indice` : `d_model` doit etre divisible par `nhead`\n- `# Étape 1` : Verifier que d_model est divisible par 8\n- `# Étape 2` : Modifier nhead=8 dans la configuration\n- `# Étape 3` : Relancer l'entrainement et comparer"
-   ],
-   "id": "cell-5e739084"
+    "### Exercice 2 : Modifier le nombre de têtes d'attention\n",
+    "\n",
+    "Changez le **nombre de têtes d'attention** de 4 a 8 et observez l'impact sur les performances. Plus de tetes permet de capturer des patterns différents mais augmente le cout de calcul.\n",
+    "\n",
+    "**Indices** :\n",
+    "- `# Indice` : Le paramètre `nhead` est défini dans la configuration du modèle\n",
+    "- `# Indice` : `d_model` doit etre divisible par `nhead`\n",
+    "- `# Étape 1` : Verifier que d_model est divisible par 8\n",
+    "- `# Étape 2` : Modifier nhead=8 dans la configuration\n",
+    "- `# Étape 3` : Relancer l'entrainement et comparer"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 7,
+   "id": "cell-a0cbecac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-19T03:37:44.137222Z",
+     "iopub.status.busy": "2026-09-19T03:37:44.137222Z",
+     "iopub.status.idle": "2026-09-19T03:37:44.154554Z",
+     "shell.execute_reply": "2026-09-19T03:37:44.153666Z"
+    },
+    "papermill": {
+     "duration": 0.025142,
+     "end_time": "2026-09-19T03:37:44.155599",
+     "exception": false,
+     "start_time": "2026-09-19T03:37:44.130457",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exercice a completer\n"
+      "Exercice a completer : Impact du nombre de tetes d'attention\n"
      ]
     }
    ],
    "source": [
-    "# Exercice 2 : Impact du nombre de tetes d'attention\n# TODO etudiant : Tester nhead=8 et comparer avec nhead=4\n# Etape 1 : Verifier d_model % 8 == 0\n# Etape 2 : Modifier nhead dans la config\n# Etape 3 : Comparer les performances\nnhead_comparison = None  # TODO etudiant : remplacer par les resultats\nprint(\"Exercice a completer : Impact du nombre de tetes d'attention\")"
-   ],
-   "id": "cell-a0cbecac"
+    "# Exercice 2 : Impact du nombre de tetes d'attention\n",
+    "# TODO etudiant : Tester nhead=8 et comparer avec nhead=4\n",
+    "# Etape 1 : Verifier d_model % 8 == 0\n",
+    "# Etape 2 : Modifier nhead dans la config\n",
+    "# Etape 3 : Comparer les performances\n",
+    "nhead_comparison = None  # TODO etudiant : remplacer par les resultats\n",
+    "print(\"Exercice a completer : Impact du nombre de tetes d'attention\")"
+   ]
   },
   {
    "cell_type": "markdown",
    "id": "cell-17",
    "metadata": {
     "papermill": {
-     "duration": 0.012569,
-     "end_time": "2026-04-29T15:28:37.235425+00:00",
+     "duration": 0.005391,
+     "end_time": "2026-09-19T03:37:44.166058",
      "exception": false,
-     "start_time": "2026-04-29T15:28:37.222856+00:00",
+     "start_time": "2026-09-19T03:37:44.160667",
      "status": "completed"
     },
     "tags": []
@@ -1567,10 +1650,10 @@
    "id": "cell-18",
    "metadata": {
     "papermill": {
-     "duration": 0.01175,
-     "end_time": "2026-04-29T15:28:37.257760+00:00",
+     "duration": 0.006001,
+     "end_time": "2026-09-19T03:37:44.178067",
      "exception": false,
-     "start_time": "2026-04-29T15:28:37.246010+00:00",
+     "start_time": "2026-09-19T03:37:44.172066",
      "status": "completed"
     },
     "tags": []
@@ -1598,20 +1681,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "cell-19",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:37.280617Z",
-     "iopub.status.busy": "2026-04-29T15:28:37.280153Z",
-     "iopub.status.idle": "2026-04-29T15:28:37.296183Z",
-     "shell.execute_reply": "2026-04-29T15:28:37.295146Z"
+     "iopub.execute_input": "2026-09-19T03:37:44.191147Z",
+     "iopub.status.busy": "2026-09-19T03:37:44.191147Z",
+     "iopub.status.idle": "2026-09-19T03:37:44.216946Z",
+     "shell.execute_reply": "2026-09-19T03:37:44.215289Z"
     },
     "papermill": {
-     "duration": 0.029238,
-     "end_time": "2026-04-29T15:28:37.297892+00:00",
+     "duration": 0.033399,
+     "end_time": "2026-09-19T03:37:44.217466",
      "exception": false,
-     "start_time": "2026-04-29T15:28:37.268654+00:00",
+     "start_time": "2026-09-19T03:37:44.184067",
      "status": "completed"
     },
     "tags": []
@@ -1768,10 +1851,10 @@
    "id": "cell-20",
    "metadata": {
     "papermill": {
-     "duration": 0.009724,
-     "end_time": "2026-04-29T15:28:37.318109+00:00",
+     "duration": 0.006186,
+     "end_time": "2026-09-19T03:37:44.230270",
      "exception": false,
-     "start_time": "2026-04-29T15:28:37.308385+00:00",
+     "start_time": "2026-09-19T03:37:44.224084",
      "status": "completed"
     },
     "tags": []
@@ -1792,20 +1875,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "cell-21",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:37.337012Z",
-     "iopub.status.busy": "2026-04-29T15:28:37.336601Z",
-     "iopub.status.idle": "2026-04-29T15:28:40.048971Z",
-     "shell.execute_reply": "2026-04-29T15:28:40.047551Z"
+     "iopub.execute_input": "2026-09-19T03:37:44.243944Z",
+     "iopub.status.busy": "2026-09-19T03:37:44.243944Z",
+     "iopub.status.idle": "2026-09-19T03:37:46.088036Z",
+     "shell.execute_reply": "2026-09-19T03:37:46.086711Z"
     },
     "papermill": {
-     "duration": 2.725582,
-     "end_time": "2026-04-29T15:28:40.050591+00:00",
+     "duration": 1.852057,
+     "end_time": "2026-09-19T03:37:46.088036",
      "exception": false,
-     "start_time": "2026-04-29T15:28:37.325009+00:00",
+     "start_time": "2026-09-19T03:37:44.235979",
      "status": "completed"
     },
     "tags": []
@@ -1822,7 +1905,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Modele final trouve: d:\\dev\\CoursIA\\MyIA.AI.Notebooks\\QuantConnect\\Python\\transformer_multiasset_model.pt\n",
+      "Modele final trouve: D:\\Dev\\CoursIA-qcdet31\\MyIA.AI.Notebooks\\QuantConnect\\Python\\transformer_multiasset_model.pt\n",
       "Modele charge, entrainement saute.\n",
       "\n",
       "Modele deja entraine et charge. Aucun entrainement necessaire.\n"
@@ -1986,10 +2069,10 @@
    "id": "cell-22",
    "metadata": {
     "papermill": {
-     "duration": 0.013664,
-     "end_time": "2026-04-29T15:28:40.074316+00:00",
+     "duration": 0.006,
+     "end_time": "2026-09-19T03:37:46.100643",
      "exception": false,
-     "start_time": "2026-04-29T15:28:40.060652+00:00",
+     "start_time": "2026-09-19T03:37:46.094643",
      "status": "completed"
     },
     "tags": []
@@ -2018,10 +2101,10 @@
    "id": "cell-23",
    "metadata": {
     "papermill": {
-     "duration": 0.006841,
-     "end_time": "2026-04-29T15:28:40.094690+00:00",
+     "duration": 0.005015,
+     "end_time": "2026-09-19T03:37:46.110658",
      "exception": false,
-     "start_time": "2026-04-29T15:28:40.087849+00:00",
+     "start_time": "2026-09-19T03:37:46.105643",
      "status": "completed"
     },
     "tags": []
@@ -2041,20 +2124,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "cell-24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:40.120651Z",
-     "iopub.status.busy": "2026-04-29T15:28:40.119987Z",
-     "iopub.status.idle": "2026-04-29T15:28:40.619400Z",
-     "shell.execute_reply": "2026-04-29T15:28:40.618173Z"
+     "iopub.execute_input": "2026-09-19T03:37:46.128716Z",
+     "iopub.status.busy": "2026-09-19T03:37:46.128174Z",
+     "iopub.status.idle": "2026-09-19T03:37:46.614250Z",
+     "shell.execute_reply": "2026-09-19T03:37:46.613507Z"
     },
     "papermill": {
-     "duration": 0.514825,
-     "end_time": "2026-04-29T15:28:40.620966+00:00",
+     "duration": 0.496664,
+     "end_time": "2026-09-19T03:37:46.614250",
      "exception": false,
-     "start_time": "2026-04-29T15:28:40.106141+00:00",
+     "start_time": "2026-09-19T03:37:46.117586",
      "status": "completed"
     },
     "tags": []
@@ -2062,7 +2145,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1800x500 with 3 Axes>"
       ]
@@ -2146,10 +2229,10 @@
    "id": "cell-25",
    "metadata": {
     "papermill": {
-     "duration": 0.007252,
-     "end_time": "2026-04-29T15:28:40.640527+00:00",
+     "duration": 0.005218,
+     "end_time": "2026-09-19T03:37:46.626410",
      "exception": false,
-     "start_time": "2026-04-29T15:28:40.633275+00:00",
+     "start_time": "2026-09-19T03:37:46.621192",
      "status": "completed"
     },
     "tags": []
@@ -2177,10 +2260,10 @@
    "id": "cell-26",
    "metadata": {
     "papermill": {
-     "duration": 0.014198,
-     "end_time": "2026-04-29T15:28:40.670059+00:00",
+     "duration": 0.00668,
+     "end_time": "2026-09-19T03:37:46.639334",
      "exception": false,
-     "start_time": "2026-04-29T15:28:40.655861+00:00",
+     "start_time": "2026-09-19T03:37:46.632654",
      "status": "completed"
     },
     "tags": []
@@ -2195,20 +2278,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "cell-27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:40.692568Z",
-     "iopub.status.busy": "2026-04-29T15:28:40.692020Z",
-     "iopub.status.idle": "2026-04-29T15:28:45.710054Z",
-     "shell.execute_reply": "2026-04-29T15:28:45.708691Z"
+     "iopub.execute_input": "2026-09-19T03:37:46.651909Z",
+     "iopub.status.busy": "2026-09-19T03:37:46.651909Z",
+     "iopub.status.idle": "2026-09-19T03:37:49.326344Z",
+     "shell.execute_reply": "2026-09-19T03:37:49.325333Z"
     },
     "papermill": {
-     "duration": 5.031274,
-     "end_time": "2026-04-29T15:28:45.711364+00:00",
+     "duration": 2.68342,
+     "end_time": "2026-09-19T03:37:49.327854",
      "exception": false,
-     "start_time": "2026-04-29T15:28:40.680090+00:00",
+     "start_time": "2026-09-19T03:37:46.644434",
      "status": "completed"
     },
     "tags": []
@@ -2223,16 +2306,18 @@
       "============================================================\n",
       "\n",
       "Metriques de regression:\n",
-      "  MSE:          8.636657\n",
-      "  MAE:          2.155941\n",
-      "  Correlation:  nan\n",
+      "  MSE:          8.636683\n",
+      "  MAE:          2.155940\n",
+      "  Correlation:  0.0160\n",
       "\n",
       "Metriques de classification:\n",
       "  Direction Accuracy: 55.51%\n",
       "  Amelioration vs random: 5.5 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    0.3918\n",
+      "Name: target, dtype: float32\n"
      ]
     }
    ],
@@ -2282,10 +2367,10 @@
    "id": "cell-28",
    "metadata": {
     "papermill": {
-     "duration": 0.014637,
-     "end_time": "2026-04-29T15:28:45.741615+00:00",
+     "duration": 0.007613,
+     "end_time": "2026-09-19T03:37:49.346698",
      "exception": false,
-     "start_time": "2026-04-29T15:28:45.726978+00:00",
+     "start_time": "2026-09-19T03:37:49.339085",
      "status": "completed"
     },
     "tags": []
@@ -2313,10 +2398,10 @@
    "id": "cell-29",
    "metadata": {
     "papermill": {
-     "duration": 0.011409,
-     "end_time": "2026-04-29T15:28:45.766292+00:00",
+     "duration": 0.008055,
+     "end_time": "2026-09-19T03:37:49.363878",
      "exception": false,
-     "start_time": "2026-04-29T15:28:45.754883+00:00",
+     "start_time": "2026-09-19T03:37:49.355823",
      "status": "completed"
     },
     "tags": []
@@ -2331,20 +2416,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "cell-30",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:45.790349Z",
-     "iopub.status.busy": "2026-04-29T15:28:45.789618Z",
-     "iopub.status.idle": "2026-04-29T15:28:46.239646Z",
-     "shell.execute_reply": "2026-04-29T15:28:46.238449Z"
+     "iopub.execute_input": "2026-09-19T03:37:49.390284Z",
+     "iopub.status.busy": "2026-09-19T03:37:49.388761Z",
+     "iopub.status.idle": "2026-09-19T03:37:49.762407Z",
+     "shell.execute_reply": "2026-09-19T03:37:49.760892Z"
     },
     "papermill": {
-     "duration": 0.467093,
-     "end_time": "2026-04-29T15:28:46.241010+00:00",
+     "duration": 0.388414,
+     "end_time": "2026-09-19T03:37:49.763414",
      "exception": false,
-     "start_time": "2026-04-29T15:28:45.773917+00:00",
+     "start_time": "2026-09-19T03:37:49.375000",
      "status": "completed"
     },
     "tags": []
@@ -2352,7 +2437,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -2415,10 +2500,10 @@
    "id": "cell-31",
    "metadata": {
     "papermill": {
-     "duration": 0.010183,
-     "end_time": "2026-04-29T15:28:46.262460+00:00",
+     "duration": 0.010425,
+     "end_time": "2026-09-19T03:37:49.781671",
      "exception": false,
-     "start_time": "2026-04-29T15:28:46.252277+00:00",
+     "start_time": "2026-09-19T03:37:49.771246",
      "status": "completed"
     },
     "tags": []
@@ -2445,10 +2530,10 @@
    "id": "cell-32",
    "metadata": {
     "papermill": {
-     "duration": 0.008224,
-     "end_time": "2026-04-29T15:28:46.285173+00:00",
+     "duration": 0.010685,
+     "end_time": "2026-09-19T03:37:49.799452",
      "exception": false,
-     "start_time": "2026-04-29T15:28:46.276949+00:00",
+     "start_time": "2026-09-19T03:37:49.788767",
      "status": "completed"
     },
     "tags": []
@@ -2463,20 +2548,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "cell-33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:46.309614Z",
-     "iopub.status.busy": "2026-04-29T15:28:46.308941Z",
-     "iopub.status.idle": "2026-04-29T15:28:47.482858Z",
-     "shell.execute_reply": "2026-04-29T15:28:47.481607Z"
+     "iopub.execute_input": "2026-09-19T03:37:49.821353Z",
+     "iopub.status.busy": "2026-09-19T03:37:49.820340Z",
+     "iopub.status.idle": "2026-09-19T03:37:50.553022Z",
+     "shell.execute_reply": "2026-09-19T03:37:50.551914Z"
     },
     "papermill": {
-     "duration": 1.190443,
-     "end_time": "2026-04-29T15:28:47.484102+00:00",
+     "duration": 0.743856,
+     "end_time": "2026-09-19T03:37:50.553591",
      "exception": false,
-     "start_time": "2026-04-29T15:28:46.293659+00:00",
+     "start_time": "2026-09-19T03:37:49.809735",
      "status": "completed"
     },
     "tags": []
@@ -2492,7 +2577,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 1400x1200 with 8 Axes>"
       ]
@@ -2506,11 +2591,11 @@
      "text": [
       "\n",
       "Analyse des poids d'attention:\n",
-      "  Tete 0 - Top 5 positions les plus attentives: [55  7 42 52 10]\n",
+      "  Tete 0 - Top 5 positions les plus attentives: [55  7 42 52 44]\n",
       "    Poids correspondants: [0.017 0.017 0.017 0.017 0.017]\n",
-      "  Tete 3 - Top 5 positions les plus attentives: [48 42 55 47 40]\n",
+      "  Tete 3 - Top 5 positions les plus attentives: [48 55 42 47 40]\n",
       "    Poids correspondants: [0.017 0.017 0.017 0.017 0.017]\n",
-      "  Tete 7 - Top 5 positions les plus attentives: [49 47 40 41 37]\n",
+      "  Tete 7 - Top 5 positions les plus attentives: [49 47 40 37 43]\n",
       "    Poids correspondants: [0.017 0.017 0.017 0.017 0.017]\n"
      ]
     }
@@ -2576,10 +2661,10 @@
    "id": "cell-34",
    "metadata": {
     "papermill": {
-     "duration": 0.11842,
-     "end_time": "2026-04-29T15:28:47.614087+00:00",
+     "duration": 0.00709,
+     "end_time": "2026-09-19T03:37:50.568609",
      "exception": false,
-     "start_time": "2026-04-29T15:28:47.495667+00:00",
+     "start_time": "2026-09-19T03:37:50.561519",
      "status": "completed"
     },
     "tags": []
@@ -2607,10 +2692,10 @@
    "id": "cell-35",
    "metadata": {
     "papermill": {
-     "duration": 0.025146,
-     "end_time": "2026-04-29T15:28:47.660136+00:00",
+     "duration": 0.007118,
+     "end_time": "2026-09-19T03:37:50.585302",
      "exception": false,
-     "start_time": "2026-04-29T15:28:47.634990+00:00",
+     "start_time": "2026-09-19T03:37:50.578184",
      "status": "completed"
     },
     "tags": []
@@ -2627,20 +2712,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "id": "cell-36",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:47.705026Z",
-     "iopub.status.busy": "2026-04-29T15:28:47.704324Z",
-     "iopub.status.idle": "2026-04-29T15:28:48.083213Z",
-     "shell.execute_reply": "2026-04-29T15:28:48.082079Z"
+     "iopub.execute_input": "2026-09-19T03:37:50.612079Z",
+     "iopub.status.busy": "2026-09-19T03:37:50.612079Z",
+     "iopub.status.idle": "2026-09-19T03:37:50.912113Z",
+     "shell.execute_reply": "2026-09-19T03:37:50.910561Z"
     },
     "papermill": {
-     "duration": 0.405909,
-     "end_time": "2026-04-29T15:28:48.085418+00:00",
+     "duration": 0.31658,
+     "end_time": "2026-09-19T03:37:50.913122",
      "exception": false,
-     "start_time": "2026-04-29T15:28:47.679509+00:00",
+     "start_time": "2026-09-19T03:37:50.596542",
      "status": "completed"
     },
     "tags": []
@@ -2648,7 +2733,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -2770,39 +2855,78 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "cell-1110361c",
+   "metadata": {
+    "papermill": {
+     "duration": 0.014638,
+     "end_time": "2026-09-19T03:37:50.942286",
+     "exception": false,
+     "start_time": "2026-09-19T03:37:50.927648",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
-    "### Exercice 3 : Ajouter une metrique de Calmar Ratio au backtest\n\nLe **Calmar Ratio** = CAGR / |Max Drawdown|. Il mesure le rendement ajuste au risque de drawdown. Calculez-le pour les predictions du Transformer.\n\n**Indices** :\n- `# Indice` : CAGR = (valeur_finale / valeur_initiale)^(252/nb_jours) - 1\n- `# Indice` : Max Drawdown = min(cumulative / cummax - 1)\n- `# Étape 1` : Calculer le CAGR a partir de la courbe de portefeuille\n- `# Étape 2` : Calculer le Max Drawdown\n- `# Étape 3` : Calculer le Calmar Ratio = CAGR / |MaxDD|"
-   ],
-   "id": "cell-1110361c"
+    "### Exercice 3 : Ajouter une metrique de Calmar Ratio au backtest\n",
+    "\n",
+    "Le **Calmar Ratio** = CAGR / |Max Drawdown|. Il mesure le rendement ajuste au risque de drawdown. Calculez-le pour les predictions du Transformer.\n",
+    "\n",
+    "**Indices** :\n",
+    "- `# Indice` : CAGR = (valeur_finale / valeur_initiale)^(252/nb_jours) - 1\n",
+    "- `# Indice` : Max Drawdown = min(cumulative / cummax - 1)\n",
+    "- `# Étape 1` : Calculer le CAGR a partir de la courbe de portefeuille\n",
+    "- `# Étape 2` : Calculer le Max Drawdown\n",
+    "- `# Étape 3` : Calculer le Calmar Ratio = CAGR / |MaxDD|"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 15,
+   "id": "cell-c255f730",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-19T03:37:50.972995Z",
+     "iopub.status.busy": "2026-09-19T03:37:50.972487Z",
+     "iopub.status.idle": "2026-09-19T03:37:50.989748Z",
+     "shell.execute_reply": "2026-09-19T03:37:50.988765Z"
+    },
+    "papermill": {
+     "duration": 0.033147,
+     "end_time": "2026-09-19T03:37:50.991765",
+     "exception": false,
+     "start_time": "2026-09-19T03:37:50.958618",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exercice a completer\n"
+      "Exercice a completer : Calmar Ratio du Transformer\n"
      ]
     }
    ],
    "source": [
-    "# Exercice 3 : Calmar Ratio du Transformer\n# TODO etudiant : Calculer le Calmar Ratio (CAGR / |MaxDD|) des predictions Transformer\n# Etape 1 : Calculer le CAGR\n# Etape 2 : Calculer le Max Drawdown\n# Etape 3 : Calmar = CAGR / |MaxDD|\ncalmar_ratio = None  # TODO etudiant : remplacer par le calcul\nprint(\"Exercice a completer : Calmar Ratio du Transformer\")"
-   ],
-   "id": "cell-c255f730"
+    "# Exercice 3 : Calmar Ratio du Transformer\n",
+    "# TODO etudiant : Calculer le Calmar Ratio (CAGR / |MaxDD|) des predictions Transformer\n",
+    "# Etape 1 : Calculer le CAGR\n",
+    "# Etape 2 : Calculer le Max Drawdown\n",
+    "# Etape 3 : Calmar = CAGR / |MaxDD|\n",
+    "calmar_ratio = None  # TODO etudiant : remplacer par le calcul\n",
+    "print(\"Exercice a completer : Calmar Ratio du Transformer\")"
+   ]
   },
   {
    "cell_type": "markdown",
    "id": "cell-37",
    "metadata": {
     "papermill": {
-     "duration": 0.012511,
-     "end_time": "2026-04-29T15:28:48.118946+00:00",
+     "duration": 0.00751,
+     "end_time": "2026-09-19T03:37:51.013836",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.106435+00:00",
+     "start_time": "2026-09-19T03:37:51.006326",
      "status": "completed"
     },
     "tags": []
@@ -2830,10 +2954,10 @@
    "id": "cell-38",
    "metadata": {
     "papermill": {
-     "duration": 0.022983,
-     "end_time": "2026-04-29T15:28:48.157565+00:00",
+     "duration": 0.015162,
+     "end_time": "2026-09-19T03:37:51.040225",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.134582+00:00",
+     "start_time": "2026-09-19T03:37:51.025063",
      "status": "completed"
     },
     "tags": []
@@ -2848,20 +2972,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "id": "cell-39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:48.201024Z",
-     "iopub.status.busy": "2026-04-29T15:28:48.200464Z",
-     "iopub.status.idle": "2026-04-29T15:28:48.251971Z",
-     "shell.execute_reply": "2026-04-29T15:28:48.250645Z"
+     "iopub.execute_input": "2026-09-19T03:37:51.072790Z",
+     "iopub.status.busy": "2026-09-19T03:37:51.071791Z",
+     "iopub.status.idle": "2026-09-19T03:37:51.114672Z",
+     "shell.execute_reply": "2026-09-19T03:37:51.113089Z"
     },
     "papermill": {
-     "duration": 0.073644,
-     "end_time": "2026-04-29T15:28:48.254193+00:00",
+     "duration": 0.059534,
+     "end_time": "2026-09-19T03:37:51.115178",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.180549+00:00",
+     "start_time": "2026-09-19T03:37:51.055644",
      "status": "completed"
     },
     "tags": []
@@ -2871,7 +2995,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Modele sauvegarde: transformer_multiasset_model.pt\n",
+      "Modele sauvegarde: transformer_multiasset_model.pt"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
       "Taille: 17.10 MB\n",
       "Compatible ObjectStore QC (<9 MB): Non\n",
       "\n",
@@ -2879,7 +3010,7 @@
       "  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': 8.636656761169434, 'mae': 2.1559407711029053, 'correlation': np.float64(nan), 'dir_acc': 0.5550805656034199}\n"
+      "  test_metrics: {'mse': 8.636682510375977, 'mae': 2.1559395790100098, 'correlation': np.float64(0.01596901937187109), 'dir_acc': 0.5550805656034199}\n"
      ]
     }
    ],
@@ -2938,10 +3069,10 @@
    "id": "cell-40",
    "metadata": {
     "papermill": {
-     "duration": 0.011228,
-     "end_time": "2026-04-29T15:28:48.286793+00:00",
+     "duration": 0.01616,
+     "end_time": "2026-09-19T03:37:51.142912",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.275565+00:00",
+     "start_time": "2026-09-19T03:37:51.126752",
      "status": "completed"
     },
     "tags": []
@@ -2969,10 +3100,10 @@
    "id": "cell-41",
    "metadata": {
     "papermill": {
-     "duration": 0.016968,
-     "end_time": "2026-04-29T15:28:48.322691+00:00",
+     "duration": 0.008287,
+     "end_time": "2026-09-19T03:37:51.160240",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.305723+00:00",
+     "start_time": "2026-09-19T03:37:51.151953",
      "status": "completed"
     },
     "tags": []
@@ -3005,10 +3136,10 @@
    "id": "cell-43",
    "metadata": {
     "papermill": {
-     "duration": 0.01183,
-     "end_time": "2026-04-29T15:28:48.396976+00:00",
+     "duration": 0.008126,
+     "end_time": "2026-09-19T03:37:51.175876",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.385146+00:00",
+     "start_time": "2026-09-19T03:37:51.167750",
      "status": "completed"
     },
     "tags": []
@@ -3037,10 +3168,10 @@
    "id": "cell-44",
    "metadata": {
     "papermill": {
-     "duration": 0.013302,
-     "end_time": "2026-04-29T15:28:48.422829+00:00",
+     "duration": 0.009274,
+     "end_time": "2026-09-19T03:37:51.193122",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.409527+00:00",
+     "start_time": "2026-09-19T03:37:51.183848",
      "status": "completed"
     },
     "tags": []
@@ -3053,20 +3184,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "id": "cell-45",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-04-29T15:28:48.453185Z",
-     "iopub.status.busy": "2026-04-29T15:28:48.452521Z",
-     "iopub.status.idle": "2026-04-29T15:28:48.463822Z",
-     "shell.execute_reply": "2026-04-29T15:28:48.462810Z"
+     "iopub.execute_input": "2026-09-19T03:37:51.213484Z",
+     "iopub.status.busy": "2026-09-19T03:37:51.212484Z",
+     "iopub.status.idle": "2026-09-19T03:37:51.223125Z",
+     "shell.execute_reply": "2026-09-19T03:37:51.221697Z"
     },
     "papermill": {
-     "duration": 0.029019,
-     "end_time": "2026-04-29T15:28:48.465106+00:00",
+     "duration": 0.019505,
+     "end_time": "2026-09-19T03:37:51.223125",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.436087+00:00",
+     "start_time": "2026-09-19T03:37:51.203620",
      "status": "completed"
     },
     "tags": []
@@ -3096,9 +3227,9 @@
       "  Periode:      2014-01-01 a 2025-01-01\n",
       "\n",
       "RESULTATS TEST\n",
-      "  MSE:                8.636657\n",
-      "  MAE:                2.155941\n",
-      "  Correlation:        nan\n",
+      "  MSE:                8.636683\n",
+      "  MAE:                2.155940\n",
+      "  Correlation:        0.0160\n",
       "  Direction Accuracy: 55.51%\n",
       "\n",
       "ENTRAINEMENT\n",
@@ -3202,10 +3333,10 @@
    "id": "cell-46",
    "metadata": {
     "papermill": {
-     "duration": 0.024807,
-     "end_time": "2026-04-29T15:28:48.505758+00:00",
+     "duration": 0.009284,
+     "end_time": "2026-09-19T03:37:51.239967",
      "exception": false,
-     "start_time": "2026-04-29T15:28:48.480951+00:00",
+     "start_time": "2026-09-19T03:37:51.230683",
      "status": "completed"
     },
     "tags": []
@@ -3281,6 +3412,23 @@
   }
  ],
  "metadata": {
+  "cost": {
+   "api_provider": "none",
+   "api_usd_est": 0.0,
+   "cpu_min": 4,
+   "external_account": "none",
+   "free_alternative": "self",
+   "gpu_min": 5,
+   "gpu_required": true,
+   "metadata_written": "2026-08-03",
+   "network": false,
+   "notes": "Transformer encoder training (SEQ_LEN=60, BATCH=32, EPOCHS=25). torch.cuda thermal-safe laptop GPU. ~15 min GPU.",
+   "reduced_pedagogical": null,
+   "reproducibility": "HIGH",
+   "validator": "manual",
+   "vram_gb": 4,
+   "vram_tier": "LOW"
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -3296,38 +3444,21 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.12"
+   "version": "3.10.19"
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 26.60323,
-   "end_time": "2026-04-29T15:28:50.063095+00:00",
+   "duration": 16.736885,
+   "end_time": "2026-09-19T03:37:52.365033",
    "environment_variables": {},
    "exception": null,
    "input_path": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-31-Transformer-Training.ipynb",
-   "output_path": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-31-Transformer-Training-executed.ipynb",
+   "output_path": "QC-Py-31-Transformer-Training.ipynb",
    "parameters": {},
-   "start_time": "2026-04-29T15:28:23.459865+00:00",
+   "start_time": "2026-09-19T03:37:35.628148",
    "version": "2.7.0"
-  },
-  "cost": {
-   "api_usd_est": 0.0,
-   "api_provider": "none",
-   "cpu_min": 4,
-   "gpu_min": 5,
-   "gpu_required": true,
-   "vram_gb": 4,
-   "vram_tier": "LOW",
-   "network": false,
-   "external_account": "none",
-   "free_alternative": "self",
-   "reduced_pedagogical": null,
-   "reproducibility": "HIGH",
-   "metadata_written": "2026-08-03",
-   "validator": "manual",
-   "notes": "Transformer encoder training (SEQ_LEN=60, BATCH=32, EPOCHS=25). torch.cuda thermal-safe laptop GPU. ~15 min GPU."
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
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diff --git a/MyIA.AI.Notebooks/QuantConnect/Python/transformer_multiasset_model.pt b/MyIA.AI.Notebooks/QuantConnect/Python/transformer_multiasset_model.pt
index 5ce47896e6..ce451dcb69 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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-size 17928263
+oid sha256:eb1b9e859b5eaa597925f18fcd2f9d1ccc19d29a489c471422fd7cae1d03e720
+size 17927888