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 5cc089576a..1ebd2e208c 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": 0.004895,
-     "end_time": "2026-09-19T10:58:30.676711",
+     "duration": 0.009688,
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      "exception": false,
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+     "start_time": "2026-09-23T17:28:06.773011",
      "status": "completed"
     },
     "tags": []
@@ -63,10 +63,10 @@
    "id": "cell-1",
    "metadata": {
     "papermill": {
-     "duration": 0.004439,
-     "end_time": "2026-09-19T10:58:30.685025",
+     "duration": 0.009445,
+     "end_time": "2026-09-23T17:28:06.802190",
      "exception": false,
-     "start_time": "2026-09-19T10:58:30.680586",
+     "start_time": "2026-09-23T17:28:06.792745",
      "status": "completed"
     },
     "tags": []
@@ -83,10 +83,10 @@
    "id": "cell-2",
    "metadata": {
     "papermill": {
-     "duration": 0.004153,
-     "end_time": "2026-09-19T10:58:30.693902",
+     "duration": 0.009187,
+     "end_time": "2026-09-23T17:28:06.820563",
      "exception": false,
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+     "start_time": "2026-09-23T17:28:06.811376",
      "status": "completed"
     },
     "tags": []
@@ -108,10 +108,10 @@
    "id": "cell-3",
    "metadata": {
     "papermill": {
-     "duration": 0.004117,
-     "end_time": "2026-09-19T10:58:30.702064",
+     "duration": 0.01181,
+     "end_time": "2026-09-23T17:28:06.843596",
      "exception": false,
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+     "start_time": "2026-09-23T17:28:06.831786",
      "status": "completed"
     },
     "tags": []
@@ -162,19 +162,21 @@
    "id": "cell-4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:30.711670Z",
-     "iopub.status.busy": "2026-09-19T10:58:30.711670Z",
-     "iopub.status.idle": "2026-09-19T10:58:33.795494Z",
-     "shell.execute_reply": "2026-09-19T10:58:33.794508Z"
+     "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"
     },
     "papermill": {
-     "duration": 3.08904,
-     "end_time": "2026-09-19T10:58:33.795494",
+     "duration": 7.167087,
+     "end_time": "2026-09-23T17:28:14.019657",
      "exception": false,
-     "start_time": "2026-09-19T10:58:30.706454",
+     "start_time": "2026-09-23T17:28:06.852570",
      "status": "completed"
     },
-    "tags": []
+    "tags": [
+     "parameters"
+    ]
    },
    "outputs": [
     {
@@ -245,6 +247,7 @@
     "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",
     "DROPOUT = 0.1          # Dropout\n",
     "TRAIN_RATIO = 0.70     # Split train\n",
     "VAL_RATIO = 0.15       # Split validation\n",
@@ -255,7 +258,6 @@
     "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",
@@ -271,10 +273,10 @@
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    "metadata": {
     "papermill": {
-     "duration": 0.004378,
-     "end_time": "2026-09-19T10:58:33.805380",
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      "exception": false,
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+     "start_time": "2026-09-23T17:28:14.031865",
      "status": "completed"
     },
     "tags": []
@@ -305,10 +307,10 @@
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    "metadata": {
     "papermill": {
-     "duration": 0.004118,
-     "end_time": "2026-09-19T10:58:33.814028",
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      "exception": false,
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+     "start_time": "2026-09-23T17:28:14.052551",
      "status": "completed"
     },
     "tags": []
@@ -338,16 +340,16 @@
    "id": "cell-7",
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     "execution": {
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-     "iopub.status.idle": "2026-09-19T10:58:36.132793Z",
-     "shell.execute_reply": "2026-09-19T10:58:36.131793Z"
+     "iopub.execute_input": "2026-09-23T17:28:14.088643Z",
+     "iopub.status.busy": "2026-09-23T17:28:14.088643Z",
+     "iopub.status.idle": "2026-09-23T17:28:17.977974Z",
+     "shell.execute_reply": "2026-09-23T17:28:17.976976Z"
     },
     "papermill": {
-     "duration": 2.314177,
-     "end_time": "2026-09-19T10:58:36.132793",
+     "duration": 3.90445,
+     "end_time": "2026-09-23T17:28:17.980317",
      "exception": false,
-     "start_time": "2026-09-19T10:58:33.818616",
+     "start_time": "2026-09-23T17:28:14.075867",
      "status": "completed"
     },
     "tags": []
@@ -390,7 +392,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[****                   9%                       ]  3 of 33 completed"
+      "[******                12%                       ]  4 of 33 completed"
      ]
     },
     {
@@ -406,15 +408,8 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[*********             18%                       ]  6 of 33 completed"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[**********            21%                       ]  7 of 33 completed"
+      "[*******               15%                       ]  5 of 33 completed\r",
+      "[*******               15%                       ]  5 of 33 completed"
      ]
     },
     {
@@ -422,7 +417,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[************          24%                       ]  8 of 33 completed"
+      "[*******               15%                       ]  5 of 33 completed"
      ]
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     {
@@ -446,7 +441,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**************        30%                       ]  10 of 33 completed"
+      "[****************      33%                       ]  11 of 33 completed"
      ]
     },
     {
@@ -478,14 +473,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************45%                       ]  15 of 33 completed"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
+      "[********************  42%                       ]  14 of 33 completed\r",
       "[**********************48%                       ]  16 of 33 completed"
      ]
     },
@@ -502,15 +490,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "[**********************55%*                      ]  18 of 33 completed"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[**********************58%***                    ]  19 of 33 completed"
+      "[**********************52%                       ]  17 of 33 completed"
      ]
     },
     {
@@ -519,6 +499,7 @@
      "text": [
       "\r",
       "[**********************58%***                    ]  19 of 33 completed\r",
+      "[**********************58%***                    ]  19 of 33 completed\r",
       "[**********************58%***                    ]  19 of 33 completed"
      ]
     },
@@ -575,17 +556,10 @@
      "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",
@@ -639,11 +613,11 @@
       "Apercu des 5 premieres lignes:\n",
       "Ticker           AAPL       MSFT      GOOGL       AMZN      NVDA\n",
       "Date                                                            \n",
-      "2014-01-02  17.110126  30.636768  27.593285  19.898500  0.372991\n",
-      "2014-01-03  16.734287  30.430653  27.391998  19.822001  0.368523\n",
-      "2014-01-06  16.825544  29.787577  27.697401  19.681499  0.373462\n",
-      "2014-01-07  16.705214  30.018419  28.231359  19.901501  0.379576\n",
-      "2014-01-08  16.811001  29.482523  28.290108  20.096001  0.384750\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"
      ]
     },
     {
@@ -717,10 +691,10 @@
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    "metadata": {
     "papermill": {
-     "duration": 0.006007,
-     "end_time": "2026-09-19T10:58:36.144303",
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      "exception": false,
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+     "start_time": "2026-09-23T17:28:17.993828",
      "status": "completed"
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     "tags": []
@@ -749,10 +723,10 @@
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-     "duration": 0.006007,
-     "end_time": "2026-09-19T10:58:36.155322",
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      "exception": false,
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+     "start_time": "2026-09-23T17:28:18.021884",
      "status": "completed"
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     "tags": []
@@ -780,16 +754,16 @@
    "id": "cell-10",
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     "execution": {
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-     "iopub.status.idle": "2026-09-19T10:58:36.460238Z",
-     "shell.execute_reply": "2026-09-19T10:58:36.459626Z"
+     "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"
     },
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-     "duration": 0.301413,
-     "end_time": "2026-09-19T10:58:36.461243",
+     "duration": 0.674708,
+     "end_time": "2026-09-23T17:28:18.723955",
      "exception": false,
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+     "start_time": "2026-09-23T17:28:18.049247",
      "status": "completed"
     },
     "tags": []
@@ -809,20 +783,20 @@
       "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%      28.0257    -0.0070    -0.0159    -0.0243     0.0099     0.0110   \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%     145.6633     0.0101     0.0272     0.0753     0.0189     0.0191   \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     3.1800     9.1400     -0.2816       -0.9900   \n",
-      "25%      -1.5844    43.0727    13.1975     -0.0432       -0.7223   \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",
       "50%       1.7739    56.5022    15.9450     -0.0070       -0.5160   \n",
-      "75%       5.6948    69.3731    20.7925      0.0364       -0.2157   \n",
-      "max      19.9282    97.3975    82.6900      1.1560        0.1800   \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",
       "\n",
       "       yield_curve_slope  \n",
       "count          2768.0000  \n",
@@ -943,10 +917,10 @@
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    "metadata": {
     "papermill": {
-     "duration": 0.006017,
-     "end_time": "2026-09-19T10:58:36.472771",
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+     "end_time": "2026-09-23T17:28:18.748847",
      "exception": false,
-     "start_time": "2026-09-19T10:58:36.466754",
+     "start_time": "2026-09-23T17:28:18.735118",
      "status": "completed"
     },
     "tags": []
@@ -969,16 +943,16 @@
    "id": "cell-b3d5cdef",
    "metadata": {
     "execution": {
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-     "iopub.status.idle": "2026-09-19T10:58:36.491822Z",
-     "shell.execute_reply": "2026-09-19T10:58:36.490822Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.013051,
-     "end_time": "2026-09-19T10:58:36.491822",
+     "duration": 0.025699,
+     "end_time": "2026-09-23T17:28:18.786418",
      "exception": false,
-     "start_time": "2026-09-19T10:58:36.478771",
+     "start_time": "2026-09-23T17:28:18.760719",
      "status": "completed"
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     "tags": []
@@ -1007,10 +981,10 @@
    "id": "cell-11",
    "metadata": {
     "papermill": {
-     "duration": 0.006537,
-     "end_time": "2026-09-19T10:58:36.504470",
+     "duration": 0.013741,
+     "end_time": "2026-09-23T17:28:18.812594",
      "exception": false,
-     "start_time": "2026-09-19T10:58:36.497933",
+     "start_time": "2026-09-23T17:28:18.798853",
      "status": "completed"
     },
     "tags": []
@@ -1018,7 +992,7 @@
    "source": [
     "### Interpretation : Feature engineering\n",
     "\n",
-    "Le feature engineering produit 14 features par echantillon (10 par action + 4 macro).\n",
+    "Le feature engineering produit 12 features par echantillon (8 par action + 4 macro).\n",
     "\n",
     "| Feature | Type | Formule | Utilite |\n",
     "|---------|------|---------|--------|\n",
@@ -1045,10 +1019,10 @@
    "id": "cell-12",
    "metadata": {
     "papermill": {
-     "duration": 0.006,
-     "end_time": "2026-09-19T10:58:36.515469",
+     "duration": 0.014791,
+     "end_time": "2026-09-23T17:28:18.843577",
      "exception": false,
-     "start_time": "2026-09-19T10:58:36.509469",
+     "start_time": "2026-09-23T17:28:18.828786",
      "status": "completed"
     },
     "tags": []
@@ -1081,16 +1055,16 @@
    "id": "cell-13",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:36.527981Z",
-     "iopub.status.busy": "2026-09-19T10:58:36.527981Z",
-     "iopub.status.idle": "2026-09-19T10:58:37.431479Z",
-     "shell.execute_reply": "2026-09-19T10:58:37.430479Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.9105,
-     "end_time": "2026-09-19T10:58:37.431479",
+     "duration": 1.832901,
+     "end_time": "2026-09-23T17:28:20.691167",
      "exception": false,
-     "start_time": "2026-09-19T10:58:36.520979",
+     "start_time": "2026-09-23T17:28:18.858266",
      "status": "completed"
     },
     "tags": []
@@ -1100,7 +1074,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Construction des sequences...\n",
+      "Construction des sequences...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Sequences totales: 81090\n",
       "Shape X: (81090, 60, 12)  [n_samples, seq_len, n_features]\n",
       "Shape y_reg: (81090,)\n",
@@ -1163,29 +1143,34 @@
     "    Concatene les donnees de toutes les actions en un seul\n",
     "    jeu de sequences, en preservant l'ordre temporel.\n",
     "    \"\"\"\n",
-    "    all_X, all_y_reg, all_y_cls = [], [], []\n",
+    "    all_X, all_y_reg, all_y_cls, all_y_plain = [], [], [], []\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",
     "        # 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",
     "    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_cls, dtype=np.float32),\n",
+    "        np.array(all_y_plain, dtype=np.float32)\n",
     "    )\n",
     "\n",
     "\n",
     "# Construire les sequences\n",
     "print(\"Construction des sequences...\")\n",
-    "X_all, y_reg_all, y_cls_all = build_sequences(\n",
+    "X_all, y_reg_all, y_cls_all, y_plain_all = build_sequences(\n",
     "    all_features, feature_cols, SEQ_LEN, PRED_LEN\n",
     ")\n",
     "\n",
@@ -1202,7 +1187,7 @@
     "\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 = X_all[val_end:], y_reg_all[val_end:], y_cls_all[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",
@@ -1243,10 +1228,10 @@
    "id": "cell-14",
    "metadata": {
     "papermill": {
-     "duration": 0.006,
-     "end_time": "2026-09-19T10:58:37.443990",
+     "duration": 0.012075,
+     "end_time": "2026-09-23T17:28:20.714368",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.437990",
+     "start_time": "2026-09-23T17:28:20.702293",
      "status": "completed"
     },
     "tags": []
@@ -1276,10 +1261,10 @@
    "id": "cell-15",
    "metadata": {
     "papermill": {
-     "duration": 0.00503,
-     "end_time": "2026-09-19T10:58:37.454876",
+     "duration": 0.012575,
+     "end_time": "2026-09-23T17:28:20.741790",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.449846",
+     "start_time": "2026-09-23T17:28:20.729215",
      "status": "completed"
     },
     "tags": []
@@ -1313,16 +1298,16 @@
    "id": "cell-16",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:37.467959Z",
-     "iopub.status.busy": "2026-09-19T10:58:37.467959Z",
-     "iopub.status.idle": "2026-09-19T10:58:37.617433Z",
-     "shell.execute_reply": "2026-09-19T10:58:37.616725Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.158061,
-     "end_time": "2026-09-19T10:58:37.617937",
+     "duration": 0.247867,
+     "end_time": "2026-09-23T17:28:21.002203",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.459876",
+     "start_time": "2026-09-23T17:28:20.754336",
      "status": "completed"
     },
     "tags": []
@@ -1575,10 +1560,10 @@
    "id": "cell-5e739084",
    "metadata": {
     "papermill": {
-     "duration": 0.004999,
-     "end_time": "2026-09-19T10:58:37.628873",
+     "duration": 0.01306,
+     "end_time": "2026-09-23T17:28:21.027806",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.623874",
+     "start_time": "2026-09-23T17:28:21.014746",
      "status": "completed"
     },
     "tags": []
@@ -1602,16 +1587,16 @@
    "id": "cell-a0cbecac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:37.641114Z",
-     "iopub.status.busy": "2026-09-19T10:58:37.640114Z",
-     "iopub.status.idle": "2026-09-19T10:58:37.648467Z",
-     "shell.execute_reply": "2026-09-19T10:58:37.647716Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.014956,
-     "end_time": "2026-09-19T10:58:37.648467",
+     "duration": 0.038197,
+     "end_time": "2026-09-23T17:28:21.080313",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.633511",
+     "start_time": "2026-09-23T17:28:21.042116",
      "status": "completed"
     },
     "tags": []
@@ -1640,10 +1625,10 @@
    "id": "cell-17",
    "metadata": {
     "papermill": {
-     "duration": 0.005023,
-     "end_time": "2026-09-19T10:58:37.659598",
+     "duration": 0.013655,
+     "end_time": "2026-09-23T17:28:21.108034",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.654575",
+     "start_time": "2026-09-23T17:28:21.094379",
      "status": "completed"
     },
     "tags": []
@@ -1655,7 +1640,7 @@
     "\n",
     "| Composant | Dimension | Rôle |\n",
     "|-----------|-----------|------|\n",
-    "| `input_proj` | 14 -> 256 | Projection des features vers l'espace latent |\n",
+    "| `input_proj` | 12 -> 256 | Projection des features vers l'espace latent |\n",
     "| `pos_encoder` | sinusoidal | Injection de l'information temporelle |\n",
     "| `TransformerEncoder` | 4 couches x 8 tetes | Auto-attention + FFN (dim=1024) |\n",
     "| `reg_head` | 256 -> 64 -> 1 | Prediction du rendement risque-ajuste |\n",
@@ -1676,10 +1661,10 @@
    "id": "cell-18",
    "metadata": {
     "papermill": {
-     "duration": 0.006686,
-     "end_time": "2026-09-19T10:58:37.670801",
+     "duration": 0.008612,
+     "end_time": "2026-09-23T17:28:21.130704",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.664115",
+     "start_time": "2026-09-23T17:28:21.122092",
      "status": "completed"
     },
     "tags": []
@@ -1711,16 +1696,16 @@
    "id": "cell-19",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:37.683961Z",
-     "iopub.status.busy": "2026-09-19T10:58:37.682962Z",
-     "iopub.status.idle": "2026-09-19T10:58:37.709822Z",
-     "shell.execute_reply": "2026-09-19T10:58:37.709822Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.035387,
-     "end_time": "2026-09-19T10:58:37.711326",
+     "duration": 0.047662,
+     "end_time": "2026-09-23T17:28:21.190779",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.675939",
+     "start_time": "2026-09-23T17:28:21.143117",
      "status": "completed"
     },
     "tags": []
@@ -1877,10 +1862,10 @@
    "id": "cell-20",
    "metadata": {
     "papermill": {
-     "duration": 0.005,
-     "end_time": "2026-09-19T10:58:37.722333",
+     "duration": 0.013106,
+     "end_time": "2026-09-23T17:28:21.217402",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.717333",
+     "start_time": "2026-09-23T17:28:21.204296",
      "status": "completed"
     },
     "tags": []
@@ -1905,16 +1890,16 @@
    "id": "cell-21",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:37.735848Z",
-     "iopub.status.busy": "2026-09-19T10:58:37.734848Z",
-     "iopub.status.idle": "2026-09-19T10:58:39.420710Z",
-     "shell.execute_reply": "2026-09-19T10:58:39.420710Z"
+     "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"
     },
     "papermill": {
-     "duration": 1.693411,
-     "end_time": "2026-09-19T10:58:39.422259",
+     "duration": 1430.049818,
+     "end_time": "2026-09-23T17:52:11.279779",
      "exception": false,
-     "start_time": "2026-09-19T10:58:37.728848",
+     "start_time": "2026-09-23T17:28:21.229961",
      "status": "completed"
     },
     "tags": []
@@ -1931,10 +1916,123 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Modele final trouve: D:\\Dev\\CoursIA-qcdetA\\MyIA.AI.Notebooks\\QuantConnect\\Python\\transformer_multiasset_model.pt\n",
-      "Modele charge, entrainement saute.\n",
+      "FORCE_RETRAIN=True : checkpoint/.pt ignores, entrainement depuis zero.\n",
+      "======================================================================\n",
+      "ENTRAINEMENT TRANSFORMER ENCODER (AMP + Thermal Watchdog)\n",
+      "======================================================================\n",
+      "Device: cuda\n",
+      "Epochs: 1-25, Batch: 32, LR: 0.0005\n",
+      "Thermal limit: 80C\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  [THERMAL] GPU 81C > 80C, pause 15s...\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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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"
+     ]
+    },
+    {
+     "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",
+      "\n",
+      "Early stopping a l'epoch 13 (patience=7)\n",
+      "Modele final sauvegarde: transformer_multiasset_model.pt\n",
       "\n",
-      "Modele deja entraine et charge. Aucun entrainement necessaire.\n"
+      "Entrainement termine.\n",
+      "Meilleure val_loss: 2.0212\n",
+      "Checkpoint de reprise: transformer_checkpoint.pt (ne pas commit)\n"
      ]
     }
    ],
@@ -1993,6 +2091,8 @@
     "}\n",
     "\n",
     "# --- Resume checkpoint (3 cas: modele final / checkpoint / from scratch) ---\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",
     "    model_save_path=model_save_path,\n",
@@ -2001,10 +2101,14 @@
     "    cool_sleep=15,\n",
     "    patience=7\n",
     ")\n",
-    "start_epoch, history = ckpt_manager.resume(\n",
-    "    model, optimizer, scheduler, grad_scaler,\n",
-    "    device=device, default_history=history\n",
-    ")\n",
+    "if FORCE_RETRAIN:\n",
+    "    print(\"FORCE_RETRAIN=True : checkpoint/.pt ignores, entrainement depuis zero.\")\n",
+    "    start_epoch = 0\n",
+    "else:\n",
+    "    start_epoch, history = ckpt_manager.resume(\n",
+    "        model, optimizer, scheduler, grad_scaler,\n",
+    "        device=device, default_history=history\n",
+    "    )\n",
     "\n",
     "# --- Boucle d'entrainement ---\n",
     "if start_epoch >= 0 and start_epoch < EPOCHS:\n",
@@ -2085,7 +2189,7 @@
     "    \n",
     "    print(f\"\\nEntrainement termine.\")\n",
     "    print(f\"Meilleure val_loss: {ckpt_manager.best_val_loss:.4f}\")\n",
-    "    print(f\"Checkpoint de reprise: {checkpoint_path} (ne pas commit)\")\n",
+    "    print(f\"Checkpoint de reprise: {os.path.basename(checkpoint_path)} (ne pas commit)\")\n",
     "else:\n",
     "    print(\"\\nModele deja entraine et charge. Aucun entrainement necessaire.\")"
    ]
@@ -2095,10 +2199,10 @@
    "id": "cell-22",
    "metadata": {
     "papermill": {
-     "duration": 0.006038,
-     "end_time": "2026-09-19T10:58:39.433385",
+     "duration": 0.016195,
+     "end_time": "2026-09-23T17:52:11.312134",
      "exception": false,
-     "start_time": "2026-09-19T10:58:39.427347",
+     "start_time": "2026-09-23T17:52:11.295939",
      "status": "completed"
     },
     "tags": []
@@ -2116,7 +2220,7 @@
     "| **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)\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",
     "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"
@@ -2127,10 +2231,10 @@
    "id": "cell-23",
    "metadata": {
     "papermill": {
-     "duration": 0.005988,
-     "end_time": "2026-09-19T10:58:39.445378",
+     "duration": 0.013118,
+     "end_time": "2026-09-23T17:52:11.338427",
      "exception": false,
-     "start_time": "2026-09-19T10:58:39.439390",
+     "start_time": "2026-09-23T17:52:11.325309",
      "status": "completed"
     },
     "tags": []
@@ -2154,16 +2258,16 @@
    "id": "cell-24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:39.458943Z",
-     "iopub.status.busy": "2026-09-19T10:58:39.457911Z",
-     "iopub.status.idle": "2026-09-19T10:58:39.751577Z",
-     "shell.execute_reply": "2026-09-19T10:58:39.750667Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.3002,
-     "end_time": "2026-09-19T10:58:39.751577",
+     "duration": 1.812062,
+     "end_time": "2026-09-23T17:52:13.166865",
      "exception": false,
-     "start_time": "2026-09-19T10:58:39.451377",
+     "start_time": "2026-09-23T17:52:11.354803",
      "status": "completed"
     },
     "tags": []
@@ -2171,7 +2275,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Jmb1vkNw2vc96edJKPWcddgnfvFQiVs+21lzUNQmzNbF73uVntNaezuSMWPudRCR9b82EtDdoI5m9bhR7o/bib9e7wJOHJXL1HAnf9JMEOPOsRLAmAG16zvhQ2VpvCbBqOmIp70UsRd8x7nwL5yz95m9jjliqbIilvBfXZfqOcedbOGfpt5oaS5H4KwEBlvfiYu2dfReyZ53ELJlgJdo0uZbR9/aqK3F5au2y8PWLTMLNXYrRGRZpSm3qbDtHbKNq6buglIMmEakJlcDsNGu7rWE7875yW/aospK0QUd3S/QvEyV03warf7K6X2NmyklwqO+es06nhO74zcxcCzm6y2xyBQSaRGZWt6vKV7LUnmvKiUaumSOBuZkFMyF73VDlM+Cq43qnJU1NwlPXesyzmW15dRuafjSJYW+ZseoDAVZNRyzlvYil6DvGnW/hnKXf/G3MEUuVDbGU9+K6TN8x7nwL5yz9VlNjKRJ/JSDA8l5crL247+y5EvXbDJM00MSbMyRcsnrdkJ9UCqyc2UIBOZkSvvlnCd+wSIJTDha8lcRWkt2pv+S26nlWkmpnpe+0VO3utaYUaOiedQWlR0MjJbftBWY9wLz45lIZAtOPmZKeYZt/kQBdJzMw2ByXrB7XiCs8WvzmnNUE8L5kkwB0JzdVbvPzTOIqr0Hb0r83z2bKwmpZT3eCNq9uI8nsNURsmpythpKX1Xm9C8hOl4h18yRi3YKC9RRj6klWtytNMr2yEsX+FGDVdMRS3otYir5j3PkWzln6zd/GHLFU2RBLeS+uy/Qd4863cM7SbzU1liLxVwICLO/Fxdr7+y7o+F6J+eFjCTm0zTy3J7SQjEvvPntJLZdLgg/vMEmasK3LrXXXNNGY2/ZCye7YTxzxzcSb+05nVekMQE0CBqUfs7brzLKcDhebRKArLOqMf46WqzTr06373uqnnDa9JbP3jeKsFS/+fM4GH9khkatnS+j230yyU9kbtDUJQLPWojuR58gzxyLytxkSlHEif1OtBMnsOSR/hlslJa195Xpn1lNcv1Ai1861EqKOyNpia9FdXCFh4goOE1dIqCmxKvp1cEj+tuBT20IKvnZvl+CQSi0d6i0BVk1HLOW9vOHa4qvoO/qOcec7OF+9t++IpcqGWMp7cX2h7xh3voVzln6rqbEUib8SEGB5Ly7WPtJ3LqeEb/hBopZNMrOFXAEBkn3uQJNMkdDwCicgwrYsM7P7Qo7tsbbnxTXNn92nybLQCPGpvnM5zQw1TQCG7Vhlra3mCgqR3NY9zSxAe4N25Z9t5siTiPULJPLXGVa5Sl3jMPPCP0teQkupSedsUOpBiVija9cttfo3L66JZHW9wnwdtXKaBKUdsdaozOpxbf6MNi8oaVndfefBnmtK1kaumW0lSM+EjnGTDCyUGJQiCUKPxGFQaJEE46m2+rqu2VloP8ExtcUm5VvHszz4sKpsiKW8l1ddW3wMfUffMe58B+er9/YdsVTZEEt5L64v9B3jzrdwztJvNTWWIvFXAgIs78XF2rf6LjAzVaJ+/kLCt60wzx3RcZJxyR1ia961zPsIOrZHInTtvi2/SKA9pyAx1qaXZHfsL3mJrSq9DGNV9F1ATrqEb/5FwpN/kOAT+63teXXqS06HSySnfR9xRdY+/U5cLgnbtkKilk+xklm6Ppsm/GzNzqtx5SoLC8xMkYikeWYdSPc4cnNG1JKs7leb2aLeVMLSW/rOgyNPwravlKCUQxKQl2vWATRrARb62jzsJWw7Neu0sun14eQ1j4i9YftK2T8fVpUNsZT38spri4+g7+g7xp3v4Hz13r4jliobYinvxfWFvmPc+RbOWfqtpsZSJP5KQIDlvbhY+2bfhe5OkuglE6yylrmtzpeMPreJMzq25G/Is5kElib8Qg5vL9hcp4HkdOonOe36VPradNW5Tp2WMg3f+IOEb1lukitmc2CQ2JqfZ5KAtqadRQKDPL4t5MBmiVr6lYQc2WGVY8zqOcSUDi3atiafs7ouZMT6hab8qc64zDrvcsk+d8BZWQvS3/vujDmdIg57fsLQbishcXjqtdISh4UTjIW+X4q00Vl/J69+OP+mgErAh1VlQyzlvfzu2lKF6Dv6jnHnOzhfvbfviKXKhljKe3F9oe8Yd76Fc5Z+q6mxFIm/EhBgeS8u1j7cd/Zcifp1ukSs/U4CXE6zJp+uNZfT6X+sddSCUg5I+IbFZs21wNwsK+GV27KH5HTqnz+DpwbNWjPlTbetNLMACydAHVF1JadDX5ME1FlUUb98LWG71pjXtExiVrcrJavLoAqXVfWrcefD6LsK9ltIoNjsTqksfFhV/bFUrt0hC5IPisPplKvOayKBp/4uJO9PlV3HMqRlQoy0b5A/Q9rhdMnCDQdEAkT6d2ggwUH5f2+2H0mX3ccypHFspLStXzCb+sdNh8y/vVvHS2hw/k0T+05kyp7jmZJQK1xaJ9ay2v6285g4XS45t0mshIfktz2Sli37TmRJ3ahQaVEoGP99b4rkOZ3mfUWE5pcSPp6RI3uPZ0mtiBDznt3097A7nOZnRYXlt03JzJW9JzIlKixEWhVqu+XQSdMf+rOiw/NL3J7MtsmeY5kSGRokrQq93+2H0yTb7pDWDWpJeFD++83Iscvu4xkSFhzk8btpP2bl5kmj2EipHZE/E1qf6/bQ4ECPtnuPZ0pGrl0a1ImUOpH5bXPsDtl1NN30d+G2+1OyJD3HLom1wqVuVP4NF7Y8h+w8mmGOY5v6BW0PpWZLWrZN4mLCJS46v22ewyk7To2twsdN+/1kll1io0MlLjrcOvY7T7XV/nWPk2PpOZKaZTPvtV5MfluXy2XGhNK+DArMb3siI9f0fa2IUImvFW5dl3ccSTerxzaLi7LGlLY7kWmTmPBgSahVUHpc34PTJdIkNtIaU3qMjqXnmuNbv3ZBWx2TeU6XNKobaY2p9Gy7HEnPMcdT+7hwv9scTmlYJ8IaU3o8j6TlmO9tWLegrY5hW57T/KzIU2NKj+ehk9kSFhwojWIL1hU+kJIluXkOM951vKlsW37bkKBAaVyorR6jbHue6ceYU+NPj/3B1CwJDgyUJnEFbU9k2eRkZq45ltqf+cfeKQdSs0S7u2lcwQ1dR9NyJNOWZ84j9/jTY6/jJ6BIWz2PMnLyzPGsfWr86bHfn5Jf6rxJbJQEnDr2eoy0bUxEiDVW9RzefyI/5tR+Dzx17HWM6PiLDguR2FPjz32M3G3dx/5kls20jw4Ptsaf2nM8Q+/nMsdC+8597FMybRIVGmzGVOG2+r51v+5xosf+WEaO1I4KldjIMI9jr9cTHQ/WOMmxm34LDw2ShoXHyYlMseuxrxMhkafGSWauXQ6dzJHwIsd+/4lMyTk1TqIKjRM9nvqeCh9PPRY5tjxJqB3hcex1e0hQgMcx0nGSZcuTeB0nEflt9bqlx17PtcJt9VzOsjkkNirUGid6PdRxqedw4feg53Jmbp7UKTJODqZmm69bNahlxaB6Lmtbvd66x4nTjJNTxz420rpGuMeJHk/3dUqvEftKGCd67NOy7aa/Co8TPUYucZlj4R4nOp50nGjbwuNEz08dh/Vr67EPtI6nvg89Zu7rlPsY6TWiQZ0Ia5zoeX88I1ciQoM8rj3aZ9p3ibUjrHGifaD9ptd8HRNW29QsM050TOrP9JYPq2o6PpfyXvw/jb5j3PkWzln6rap5SyyVH1kCQGULCTMlJ1Nu+q/YE1uZcosxP30mdab+1yT7ak9/UWK//LdEJs0zST9HTD3J6H2jHL/zDUkf+DexN+pQLUm/6qRrFuo6f6k3jJITN78gWV0GijM8WoIyUyTqt5kS99m/pO5Xj5uknysg0JQ+Pf6XVyTr/MFekfQDqkUNu07URLbsLIlb+pnEL/vC3Ejilpq0VKKXfimp65Zb2/LsNgn6aaIE/jjRfO12KOlXyflhkhxcvcxj3ycWTTGPnMz8D3jV7vXrZN/8qbLjV8+2m7+fIVvmTpOMkyetbduSN0nSnOmyfpln21/nfSe/zv5WTpxILWi7ZbssnTNLVv1S8H7Vz/MXyeLZ38nRI/mz5NWOnXvl+1nz5Oef8ktnuy1YuFRmzVog+w8VtN2195BMn7VQ5i/51aPtnB9+k6+//UF27M0vBa32Hjwmk2culu8Web6HBUtWyjczF8i2nQesbQePHJOZM7+X7+Yt8Wi75KcVMmvGXNm8dZe17eixE/LdjDny/ez5Hm1/+Wm5zJ/+rSRv3GptS01Nk4XTZ8iib2d59tkvy2TJtG9kQ9J6a1tGRob88s0U+WXaFI+2Sb8sk5XfTJT1v622tuXm5MiqqV/K6qlfiCOv4D9dyct/kXVTP5P1y36xtjkdTkmeOkE2Tv1EcrPzkwZq88plsmXqeEn+cZHHz9N2yVM+lvSTaQVt16yR1VM+kzU//OD5O0+bKkunfO1x7Deu3yQLp0yVX3742aPtd9/OlW+nzpQjxwrWUk3euksmT50j3y/2PPZT5vwkE6YukH2HC9pu2XVQxk9dKDMWeR77afN+kU+nzped+wuO/c59h+TLqfNk5vdLPd/DgqXy9ZQ5snVXQdnxfQeOyDdTZsns2Qs92i5a+JPMmDJTNm/ZaW07fPiYzJkyXebO/M6j7eL5S+S7KdMkOXmLte34iVSZOXm6zJo+x6Ptkp9WytTJ30rSuoK2J9MzZeLk2TJxylzP/S5Lks8mz5UVSZutbVnZOTJ+0vfy0SSd3a8p2nw//LpR3ps0X35cXdA2Ly9P3p60UN6ZtEBseflrAaulqzeafSxZWTD+dF9fTp4jX02eI5nZBWXDV69NlqmTZ8qPP6/y7Msp02XO5KmSerLgRogNSRtk4aRJsuyHHz3a6pj+ZfJXcuzYcWvb1vW/y+rJn8nKuZ6/87KZ35i2hw7k36igtm/eJkumTJJf5nseo8Wz58n3U7+R/fsOWtt27Nwns6bOlPnzPc/l7+b/JFOmzpGdewrO+90HjsiX33w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       "text/plain": [
        "<Figure size 1800x500 with 3 Axes>"
       ]
@@ -2183,17 +2287,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Aucun historique d'entrainement (modele pre-entraine charge).\n"
+      "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"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -2235,6 +2334,9 @@
     "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.show()\n",
     "\n",
     "# Resume final de l'entrainement\n",
@@ -2245,9 +2347,7 @@
     "    print(f\"  Val loss finale:   {history['val_loss'][-1]:.4f}\")\n",
     "    print(f\"  Val dir accuracy:  {history['val_dir_acc'][-1]:.2%}\")\n",
     "else:\n",
-    "    print(\"Aucun historique d'entrainement (modele pre-entraine charge).\")\n",
-    "\n",
-    "plt.savefig(os.path.join(_nb_dir if '_nb_dir' in dir() else os.getcwd(), 'training_curves.png'), dpi=100, bbox_inches='tight')"
+    "    print(\"Aucun historique d'entrainement (modele pre-entraine charge).\")"
    ]
   },
   {
@@ -2255,10 +2355,10 @@
    "id": "cell-25",
    "metadata": {
     "papermill": {
-     "duration": 0.005001,
-     "end_time": "2026-09-19T10:58:39.763577",
+     "duration": 0.024297,
+     "end_time": "2026-09-23T17:52:13.213242",
      "exception": false,
-     "start_time": "2026-09-19T10:58:39.758576",
+     "start_time": "2026-09-23T17:52:13.188945",
      "status": "completed"
     },
     "tags": []
@@ -2272,11 +2372,11 @@
     "|-----------|---------------------|---------------|\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% | Le modèle a un edge predictif |\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",
     "\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, même si l'ecart est faible\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"
    ]
@@ -2286,10 +2386,10 @@
    "id": "cell-26",
    "metadata": {
     "papermill": {
-     "duration": 0.005508,
-     "end_time": "2026-09-19T10:58:39.776810",
+     "duration": 0.018567,
+     "end_time": "2026-09-23T17:52:13.249871",
      "exception": false,
-     "start_time": "2026-09-19T10:58:39.771302",
+     "start_time": "2026-09-23T17:52:13.231304",
      "status": "completed"
     },
     "tags": []
@@ -2308,16 +2408,16 @@
    "id": "cell-27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:39.791166Z",
-     "iopub.status.busy": "2026-09-19T10:58:39.789810Z",
-     "iopub.status.idle": "2026-09-19T10:58:42.200547Z",
-     "shell.execute_reply": "2026-09-19T10:58:42.199560Z"
+     "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"
     },
     "papermill": {
-     "duration": 2.41769,
-     "end_time": "2026-09-19T10:58:42.200547",
+     "duration": 6.995906,
+     "end_time": "2026-09-23T17:52:20.265476",
      "exception": false,
-     "start_time": "2026-09-19T10:58:39.782857",
+     "start_time": "2026-09-23T17:52:13.269570",
      "status": "completed"
     },
     "tags": []
@@ -2332,18 +2432,18 @@
       "============================================================\n",
       "\n",
       "Metriques de regression:\n",
-      "  MSE:          8.636676\n",
-      "  MAE:          2.155939\n",
-      "  Correlation:  0.0160\n",
+      "  MSE:          8.639112\n",
+      "  MAE:          2.157524\n",
+      "  Correlation:  nan\n",
       "\n",
       "Metriques de classification:\n",
       "  Direction Accuracy: 55.51%\n",
-      "  Amelioration vs random: 5.5 points de %\n",
+      "  Baseline classe majoritaire (test): 55.51%\n",
+      "  Ecart vs classe majoritaire: -0.00 points de %\n",
+      "  Ecart vs hasard (50%):       +5.51 points de %\n",
       "\n",
-      "Rendement moyen par quantile de prediction (1 quantiles):\n",
-      "pred_quantile\n",
-      "Q1    0.3918\n",
-      "Name: target, dtype: float32\n"
+      "Rendement moyen par quantile de prediction (0 quantiles):\n",
+      "Series([], Name: target, dtype: float32)\n"
      ]
     }
    ],
@@ -2372,7 +2472,13 @@
     "\n",
     "print(f\"\\nMetriques de classification:\")\n",
     "print(f\"  Direction Accuracy: {dir_acc:.2%}\")\n",
-    "print(f\"  Amelioration vs random: {(dir_acc - 0.5) * 100:.1f} points de %\")\n",
+    "targets_cls = test_metrics['targets_cls']\n",
+    "# La classe majoritaire est la reference honnete : un predicteur CONSTANT\n",
+    "# atteint exactement ce taux, donc l'ecart vs 50% ne mesure pas un edge.\n",
+    "majority_baseline = max(float(targets_cls.mean()), 1 - float(targets_cls.mean()))\n",
+    "print(f\"  Baseline classe majoritaire (test): {majority_baseline:.2%}\")\n",
+    "print(f\"  Ecart vs classe majoritaire: {(dir_acc - majority_baseline) * 100:+.2f} points de %\")\n",
+    "print(f\"  Ecart vs hasard (50%):       {(dir_acc - 0.5) * 100:+.2f} points de %\")\n",
     "\n",
     "# Analyse par quantile de prediction (robuste aux doublons)\n",
     "preds_reg_df = pd.DataFrame({'pred': preds_reg, 'target': targets_reg})\n",
@@ -2393,10 +2499,10 @@
    "id": "cell-28",
    "metadata": {
     "papermill": {
-     "duration": 0.006698,
-     "end_time": "2026-09-19T10:58:42.212796",
+     "duration": 0.01955,
+     "end_time": "2026-09-23T17:52:20.304220",
      "exception": false,
-     "start_time": "2026-09-19T10:58:42.206098",
+     "start_time": "2026-09-23T17:52:20.284670",
      "status": "completed"
     },
     "tags": []
@@ -2406,13 +2512,19 @@
     "\n",
     "Les metriques sur le set de test mesurent la capacite de generalisation du modèle.\n",
     "\n",
-    "| Metrique | Valeur typique | Signification |\n",
-    "|----------|----------------|---------------|\n",
-    "| **Correlation** | 0.02-0.08 | Faible mais positive, attendue sur marche liquide |\n",
-    "| **Direction Accuracy** | 51-55% | Modeste mais potentiellement exploitable |\n",
-    "| **MSE/MAE** | Dependent de l'echelle | Comparaison relative avec le LSTM |\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",
     "\n",
-    "**Points cles** :\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",
+    "\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",
@@ -2424,10 +2536,10 @@
    "id": "cell-29",
    "metadata": {
     "papermill": {
-     "duration": 0.005566,
-     "end_time": "2026-09-19T10:58:42.225223",
+     "duration": 0.015499,
+     "end_time": "2026-09-23T17:52:20.336736",
      "exception": false,
-     "start_time": "2026-09-19T10:58:42.219657",
+     "start_time": "2026-09-23T17:52:20.321237",
      "status": "completed"
     },
     "tags": []
@@ -2446,16 +2558,16 @@
    "id": "cell-30",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:42.239566Z",
-     "iopub.status.busy": "2026-09-19T10:58:42.239566Z",
-     "iopub.status.idle": "2026-09-19T10:58:42.465918Z",
-     "shell.execute_reply": "2026-09-19T10:58:42.464935Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.235756,
-     "end_time": "2026-09-19T10:58:42.467525",
+     "duration": 0.62494,
+     "end_time": "2026-09-23T17:52:20.977273",
      "exception": false,
-     "start_time": "2026-09-19T10:58:42.231769",
+     "start_time": "2026-09-23T17:52:20.352333",
      "status": "completed"
     },
     "tags": []
@@ -2526,10 +2638,10 @@
    "id": "cell-31",
    "metadata": {
     "papermill": {
-     "duration": 0.006837,
-     "end_time": "2026-09-19T10:58:42.481234",
+     "duration": 0.014652,
+     "end_time": "2026-09-23T17:52:21.014116",
      "exception": false,
-     "start_time": "2026-09-19T10:58:42.474397",
+     "start_time": "2026-09-23T17:52:20.999464",
      "status": "completed"
     },
     "tags": []
@@ -2546,7 +2658,7 @@
     "\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 actions (top decile vs bottom decile)\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",
     "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"
    ]
@@ -2556,10 +2668,10 @@
    "id": "cell-32",
    "metadata": {
     "papermill": {
-     "duration": 0.009142,
-     "end_time": "2026-09-19T10:58:42.499320",
+     "duration": 0.01952,
+     "end_time": "2026-09-23T17:52:21.047715",
      "exception": false,
-     "start_time": "2026-09-19T10:58:42.490178",
+     "start_time": "2026-09-23T17:52:21.028195",
      "status": "completed"
     },
     "tags": []
@@ -2578,16 +2690,16 @@
    "id": "cell-33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:42.519409Z",
-     "iopub.status.busy": "2026-09-19T10:58:42.517797Z",
-     "iopub.status.idle": "2026-09-19T10:58:43.122214Z",
-     "shell.execute_reply": "2026-09-19T10:58:43.121205Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.615401,
-     "end_time": "2026-09-19T10:58:43.122719",
+     "duration": 1.643865,
+     "end_time": "2026-09-23T17:52:22.712116",
      "exception": false,
-     "start_time": "2026-09-19T10:58:42.507318",
+     "start_time": "2026-09-23T17:52:21.068251",
      "status": "completed"
     },
     "tags": []
@@ -2603,7 +2715,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 1400x1200 with 8 Axes>"
       ]
@@ -2617,11 +2729,11 @@
      "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 52  5 51 53]\n",
       "    Poids correspondants: [0.017 0.017 0.017 0.017 0.017]\n",
-      "  Tete 3 - Top 5 positions les plus attentives: [48 42 47 55 40]\n",
+      "  Tete 3 - Top 5 positions les plus attentives: [55 44 53 54 42]\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 37 43]\n",
+      "  Tete 7 - Top 5 positions les plus attentives: [44 42 48 50 43]\n",
       "    Poids correspondants: [0.017 0.017 0.017 0.017 0.017]\n"
      ]
     }
@@ -2687,10 +2799,10 @@
    "id": "cell-34",
    "metadata": {
     "papermill": {
-     "duration": 0.007692,
-     "end_time": "2026-09-19T10:58:43.138359",
+     "duration": 0.01964,
+     "end_time": "2026-09-23T17:52:22.754655",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.130667",
+     "start_time": "2026-09-23T17:52:22.735015",
      "status": "completed"
     },
     "tags": []
@@ -2718,10 +2830,10 @@
    "id": "cell-35",
    "metadata": {
     "papermill": {
-     "duration": 0.007398,
-     "end_time": "2026-09-19T10:58:43.151357",
+     "duration": 0.021553,
+     "end_time": "2026-09-23T17:52:22.793958",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.143959",
+     "start_time": "2026-09-23T17:52:22.772405",
      "status": "completed"
     },
     "tags": []
@@ -2733,7 +2845,22 @@
     "\n",
     "### Stratégie de trading simplifiee\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 decile."
+    "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**."
    ]
   },
   {
@@ -2742,16 +2869,16 @@
    "id": "cell-36",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:43.167119Z",
-     "iopub.status.busy": "2026-09-19T10:58:43.167119Z",
-     "iopub.status.idle": "2026-09-19T10:58:43.419798Z",
-     "shell.execute_reply": "2026-09-19T10:58:43.418902Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.26094,
-     "end_time": "2026-09-19T10:58:43.419798",
+     "duration": 0.687748,
+     "end_time": "2026-09-23T17:52:23.502937",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.158858",
+     "start_time": "2026-09-23T17:52:22.815189",
      "status": "completed"
     },
     "tags": []
@@ -2759,7 +2886,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1400x500 with 2 Axes>"
       ]
@@ -2775,11 +2902,11 @@
       "==================================================\n",
       "RESULTATS DU BACKTEST\n",
       "==================================================\n",
-      "  Rendement total:    inf%\n",
-      "  Rendement annualise: inf%\n",
-      "  Volatilite:         828.50%\n",
-      "  Sharpe Ratio:       inf\n",
-      "  Max Drawdown:       nan%\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"
      ]
     }
    ],
@@ -2787,7 +2914,7 @@
     "# Backtest simplifie du signal Transformer\n",
     "\n",
     "def backtest_transformer_signal(\n",
-    "    preds_reg, targets_reg, targets_cls,\n",
+    "    preds_reg, realized_returns,\n",
     "    n_bins=5, rebalance_freq=20\n",
     "):\n",
     "    \"\"\"\n",
@@ -2797,6 +2924,11 @@
     "    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",
     "    \"\"\"\n",
     "    n_samples = len(preds_reg)\n",
     "    portfolio_returns = []\n",
@@ -2805,7 +2937,7 @@
     "    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_targets = targets_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",
@@ -2814,35 +2946,47 @@
     "        top_n = max(1, len(batch_preds) // n_bins)\n",
     "        top_indices = sorted_indices[-top_n:]\n",
     "        \n",
-    "        # Rendement realise du portefeuille\n",
-    "        port_return = batch_targets[top_indices].mean()\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",
     "\n",
-    "# Executer le backtest\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, targets_reg, test_metrics['targets_cls']\n",
+    "    preds_reg, y_plain_test, n_bins=5, rebalance_freq=REBALANCE_FREQ\n",
     ")\n",
     "\n",
-    "# Calculer les metriques de performance\n",
+    "# Calculer les metriques de performance — gardes #17516 : jamais inf/nan muet\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",
+    "    port_returns = port_returns[np.isfinite(port_returns)]\n",
+    "\n",
     "cumulative = np.cumprod(1 + port_returns)\n",
     "total_return = cumulative[-1] - 1\n",
-    "annualized_return = (1 + total_return) ** (252 / max(len(port_returns) * 20, 1)) - 1\n",
-    "vol = np.std(port_returns) * np.sqrt(252 / 20)\n",
-    "sharpe = annualized_return / vol if vol > 0 else 0\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",
     "\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)\n",
+    "# Benchmark : moyenne de toutes les actions (equal weight), rendements simples\n",
     "benchmark_returns = []\n",
-    "for i in range(0, len(targets_reg) - 20, 20):\n",
-    "    benchmark_returns.append(targets_reg[i:i+20].mean())\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",
@@ -2884,10 +3028,10 @@
    "id": "cell-1110361c",
    "metadata": {
     "papermill": {
-     "duration": 0.008148,
-     "end_time": "2026-09-19T10:58:43.436614",
+     "duration": 0.022798,
+     "end_time": "2026-09-23T17:52:23.548918",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.428466",
+     "start_time": "2026-09-23T17:52:23.526120",
      "status": "completed"
     },
     "tags": []
@@ -2911,16 +3055,16 @@
    "id": "cell-c255f730",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:43.453599Z",
-     "iopub.status.busy": "2026-09-19T10:58:43.453599Z",
-     "iopub.status.idle": "2026-09-19T10:58:43.466831Z",
-     "shell.execute_reply": "2026-09-19T10:58:43.465942Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.023311,
-     "end_time": "2026-09-19T10:58:43.467335",
+     "duration": 0.039816,
+     "end_time": "2026-09-23T17:52:23.610874",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.444024",
+     "start_time": "2026-09-23T17:52:23.571058",
      "status": "completed"
     },
     "tags": []
@@ -2949,10 +3093,10 @@
    "id": "cell-37",
    "metadata": {
     "papermill": {
-     "duration": 0.00721,
-     "end_time": "2026-09-19T10:58:43.482367",
+     "duration": 0.023727,
+     "end_time": "2026-09-23T17:52:23.654896",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.475157",
+     "start_time": "2026-09-23T17:52:23.631169",
      "status": "completed"
     },
     "tags": []
@@ -2970,7 +3114,7 @@
     "\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 rotation hebdomadaire\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"
    ]
@@ -2980,10 +3124,10 @@
    "id": "cell-38",
    "metadata": {
     "papermill": {
-     "duration": 0.008624,
-     "end_time": "2026-09-19T10:58:43.499114",
+     "duration": 0.024442,
+     "end_time": "2026-09-23T17:52:23.702171",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.490490",
+     "start_time": "2026-09-23T17:52:23.677729",
      "status": "completed"
     },
     "tags": []
@@ -3002,16 +3146,16 @@
    "id": "cell-39",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:43.516305Z",
-     "iopub.status.busy": "2026-09-19T10:58:43.516305Z",
-     "iopub.status.idle": "2026-09-19T10:58:43.559533Z",
-     "shell.execute_reply": "2026-09-19T10:58:43.559533Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.054894,
-     "end_time": "2026-09-19T10:58:43.561214",
+     "duration": 0.113527,
+     "end_time": "2026-09-23T17:52:23.840460",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.506320",
+     "start_time": "2026-09-23T17:52:23.726933",
      "status": "completed"
     },
     "tags": []
@@ -3029,7 +3173,7 @@
       "  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': 8.636675834655762, 'mae': 2.1559391021728516, 'correlation': np.float64(0.0159689077001281), 'dir_acc': 0.5550805656034199}\n"
+      "  test_metrics: {'mse': 8.639111518859863, 'mae': 2.157524347305298, 'correlation': np.float64(nan), 'dir_acc': 0.5550805656034199}\n"
      ]
     }
    ],
@@ -3088,10 +3232,10 @@
    "id": "cell-40",
    "metadata": {
     "papermill": {
-     "duration": 0.007844,
-     "end_time": "2026-09-19T10:58:43.577075",
+     "duration": 0.021159,
+     "end_time": "2026-09-23T17:52:23.885443",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.569231",
+     "start_time": "2026-09-23T17:52:23.864284",
      "status": "completed"
     },
     "tags": []
@@ -3119,10 +3263,10 @@
    "id": "cell-41",
    "metadata": {
     "papermill": {
-     "duration": 0.008239,
-     "end_time": "2026-09-19T10:58:43.592439",
+     "duration": 0.023752,
+     "end_time": "2026-09-23T17:52:23.931863",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.584200",
+     "start_time": "2026-09-23T17:52:23.908111",
      "status": "completed"
     },
     "tags": []
@@ -3155,10 +3299,10 @@
    "id": "cell-43",
    "metadata": {
     "papermill": {
-     "duration": 0.00805,
-     "end_time": "2026-09-19T10:58:43.607768",
+     "duration": 0.023803,
+     "end_time": "2026-09-23T17:52:23.976099",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.599718",
+     "start_time": "2026-09-23T17:52:23.952296",
      "status": "completed"
     },
     "tags": []
@@ -3187,10 +3331,10 @@
    "id": "cell-44",
    "metadata": {
     "papermill": {
-     "duration": 0.007862,
-     "end_time": "2026-09-19T10:58:43.623386",
+     "duration": 0.027648,
+     "end_time": "2026-09-23T17:52:24.027811",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.615524",
+     "start_time": "2026-09-23T17:52:24.000163",
      "status": "completed"
     },
     "tags": []
@@ -3207,16 +3351,16 @@
    "id": "cell-45",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-09-19T10:58:43.640408Z",
-     "iopub.status.busy": "2026-09-19T10:58:43.640408Z",
-     "iopub.status.idle": "2026-09-19T10:58:43.653518Z",
-     "shell.execute_reply": "2026-09-19T10:58:43.653518Z"
+     "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"
     },
     "papermill": {
-     "duration": 0.024087,
-     "end_time": "2026-09-19T10:58:43.654633",
+     "duration": 0.050527,
+     "end_time": "2026-09-23T17:52:24.100769",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.630546",
+     "start_time": "2026-09-23T17:52:24.050242",
      "status": "completed"
     },
     "tags": []
@@ -3241,18 +3385,18 @@
       "DONNEES\n",
       "  Actions:      30 top SP500\n",
       "  Macro:        VIX, 10Y yield, 30Y yield\n",
-      "  Features:     12 (10 par action + 4 macro)\n",
+      "  Features:     12 (8 par action + 4 macro)\n",
       "  Sequence:     60 jours -> Prediction: 5 jours\n",
       "  Periode:      2014-01-01 a 2025-01-01\n",
       "\n",
       "RESULTATS TEST\n",
-      "  MSE:                8.636676\n",
-      "  MAE:                2.155939\n",
-      "  Correlation:        0.0160\n",
+      "  MSE:                8.639112\n",
+      "  MAE:                2.157524\n",
+      "  Correlation:        nan\n",
       "  Direction Accuracy: 55.51%\n",
       "\n",
       "ENTRAINEMENT\n",
-      "  Epochs:       0 (early stopping: patience=7)\n",
+      "  Epochs:       13 (early stopping: patience=7)\n",
       "  Optimiseur:   AdamW (lr=0.0005, weight_decay=0.01)\n",
       "  Scheduler:    CosineAnnealingLR\n",
       "  Loss:         HuberLoss + 0.5*BCELoss\n",
@@ -3302,7 +3446,7 @@
     "DONNEES\n",
     "  Actions:      {N_STOCKS} top SP500\n",
     "  Macro:        VIX, 10Y yield, 30Y yield\n",
-    "  Features:     {n_features} (10 par action + 4 macro)\n",
+    "  Features:     {n_features} (8 par action + 4 macro)\n",
     "  Sequence:     {SEQ_LEN} jours -> Prediction: {PRED_LEN} jours\n",
     "  Periode:      {START} a {END}\n",
     "\n",
@@ -3352,10 +3496,10 @@
    "id": "cell-46",
    "metadata": {
     "papermill": {
-     "duration": 0.008802,
-     "end_time": "2026-09-19T10:58:43.670426",
+     "duration": 0.031746,
+     "end_time": "2026-09-23T17:52:24.155878",
      "exception": false,
-     "start_time": "2026-09-19T10:58:43.661624",
+     "start_time": "2026-09-23T17:52:24.124132",
      "status": "completed"
     },
     "tags": []
@@ -3372,7 +3516,7 @@
     "| Étape | Contenu |\n",
     "|-------|--------|\n",
     "| **Données** | 30 actions SP500 + 3 indicateurs macro (VIX, taux 10Y, 30Y) |\n",
-    "| **Features** | 10 par action + 4 macro, total 14 features |\n",
+    "| **Features** | 8 par action + 4 macro, total 12 features |\n",
     "| **Modèle** | TransformerEncoder natif PyTorch (4 couches, 8 tetes, d_model=256) |\n",
     "| **Entrainement** | AdamW + CosineAnnealingLR + gradient clipping + early stopping |\n",
     "| **Evaluation** | Correlation, direction accuracy, MSE, MAE |\n",
@@ -3467,17 +3611,19 @@
   },
   "papermill": {
    "default_parameters": {},
-   "duration": 15.265766,
-   "end_time": "2026-09-19T10:58:44.577548",
+   "duration": 1461.900736,
+   "end_time": "2026-09-23T17:52:26.680572",
    "environment_variables": {},
    "exception": null,
    "input_path": "QC-Py-31-Transformer-Training.ipynb",
-   "output_path": "QC-Py-31-Transformer-Training_det.ipynb",
-   "parameters": {},
-   "start_time": "2026-09-19T10:58:29.311782",
+   "output_path": "qcpy31_out.ipynb",
+   "parameters": {
+    "FORCE_RETRAIN": true
+   },
+   "start_time": "2026-09-23T17:28:04.779836",
    "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 671ba6a87c..c52599786e 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/shared/gpu_training.py b/MyIA.AI.Notebooks/QuantConnect/shared/gpu_training.py
index 8fb63f5a0b..d09a508b6b 100644
--- a/MyIA.AI.Notebooks/QuantConnect/shared/gpu_training.py
+++ b/MyIA.AI.Notebooks/QuantConnect/shared/gpu_training.py
@@ -67,7 +67,8 @@ def get_gpu_temp(index: Optional[int] = None) -> int:
         result = subprocess.run(
             ['nvidia-smi', '--query-gpu=temperature.gpu',
              '--format=csv,noheader,nounits'],
-            capture_output=True, text=True, timeout=5
+            capture_output=True, text=True, encoding='utf-8', errors='replace',
+            timeout=5
         )
         lines = [l.strip() for l in result.stdout.strip().splitlines() if l.strip()]
         if not lines:
@@ -346,7 +347,9 @@ def checkpoint_resume(
     empty_history = {}
 
     if os.path.exists(model_save_path):
-        print(f"Modele final trouve: {model_save_path}")
+        # basename : le chemin absolu resolu au cwd de papermill est un chemin
+        # machine (ratchet Output-failure), meme convention que `finalize()`.
+        print(f"Modele final trouve: {os.path.basename(model_save_path)}")
         save_dict = torch.load(model_save_path, weights_only=False, map_location=map_loc)
         model.load_state_dict(save_dict['model_state_dict'])
         print("Modele charge, entrainement saute.")
@@ -354,7 +357,7 @@ def checkpoint_resume(
         return -1, float('inf'), empty_history, extra
 
     elif os.path.exists(checkpoint_path):
-        print(f"Checkpoint trouve: {checkpoint_path}")
+        print(f"Checkpoint trouve: {os.path.basename(checkpoint_path)}")
         ckpt = torch.load(checkpoint_path, weights_only=False, map_location=map_loc)
         model.load_state_dict(ckpt['model_state_dict'])
 
@@ -554,4 +557,4 @@ def finalize(self, model: nn.Module, extra: Optional[Dict] = None) -> None:
             save_dict.update(extra)
 
         torch.save(save_dict, self.model_save_path)
-        print(f"Modele final sauvegarde: {self.model_save_path}")
+        print(f"Modele final sauvegarde: {os.path.basename(self.model_save_path)}")