{ "cells": [ { "cell_type": "markdown", "id": "40e0a986", "metadata": {}, "source": [ "# šŸ† Mitsui Commodity Prediction Challenge - Complete Template\n", "\n", "First Kaggle competition! This notebook provides a comprehensive template that combines both **TensorFlow** and **XGBoost** approaches for commodity price prediction.\n", "\n", "## šŸŽÆ Competition Overview\n", "This is a **time series prediction challenge** where I am predicting commodity price movements using:\n", "- **Commodity futures data** (Gold, Platinum, Copper, etc.)\n", "- **FX exchange rates** (USD/JPY, EUR/USD, etc.) \n", "- **US Stock prices** (Energy, Materials sectors)\n", "- **Lagged target values** (previous predictions)\n", "\n", "## šŸ§  Model Strategy\n", "1. **XGBoost**: Excellent for structured/tabular data, highly interpretable\n", "2. **TensorFlow**: Powerful for sequence patterns and complex interactions\n", "3. **Ensemble**: Combine both models for better performance\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "a5e147d5", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-08-12 12:25:35.875190: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1754994335.914167 4695 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1754994335.927499 4695 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2025-08-12 12:25:36.087399: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow version: 2.18.1\n", "[name: \"/device:CPU:0\"\n", "device_type: \"CPU\"\n", "memory_limit: 268435456\n", "locality {\n", "}\n", "incarnation: 16337200854124038957\n", "xla_global_id: -1\n", ", name: \"/device:GPU:0\"\n", "device_type: \"GPU\"\n", "memory_limit: 3758096384\n", "locality {\n", " bus_id: 1\n", " links {\n", " }\n", "}\n", "incarnation: 6641629643179090246\n", "physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\"\n", "xla_global_id: 416903419\n", "]\n", "XGBoost version: 2.1.4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "I0000 00:00:1754994341.652753 4695 gpu_device.cc:2022] Created device /device:GPU:0 with 3584 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n", "/home/asier/miniconda3/envs/tf-gpu/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [12:25:41] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost-split_1744329155408/work/src/common/error_msg.cc:45: `gpu_id` is deprecated since2.0.0, use `device` instead. E.g. device=cpu/cuda/cuda:0\n", " warnings.warn(smsg, UserWarning)\n", "/home/asier/miniconda3/envs/tf-gpu/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [12:25:41] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost-split_1744329155408/work/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n", "\n", " E.g. tree_method = \"hist\", device = \"cuda\"\n", "\n", " warnings.warn(smsg, UserWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "GPU support: True\n", "CUDA support in build: True\n", "NCCL support in build: True\n", "Available tree methods: ['exact', 'hist', 'approx', 'gpu_hist']\n", "āœ… Libraries imported successfully!\n", "TensorFlow version: 2.18.1\n", "XGBoost version: 2.1.4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/asier/miniconda3/envs/tf-gpu/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [12:25:42] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost-split_1744329155408/work/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n", "\n", " E.g. tree_method = \"hist\", device = \"cuda\"\n", "\n", " warnings.warn(smsg, UserWarning)\n" ] } ], "source": [ "import pandas as pd\n", "import tensorflow as tf\n", "import numpy as np\n", "\n", "from keras import layers, regularizers\n", "from tensorflow import keras\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import os\n", "import warnings\n", "import logging\n", "from pathlib import Path\n", "import polars as pl\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", "\n", "from sklearn.preprocessing import RobustScaler\n", "import xgboost as xgb\n", "import joblib\n", "\n", "\n", "print(\"TensorFlow version:\", tf.__version__)\n", "\n", "from tensorflow.python.client import device_lib\n", "print(device_lib.list_local_devices())\n", "\n", "\n", "print(\"XGBoost version:\", xgb.__version__)\n", "\n", "# Proper GPU support check\n", "def check_xgboost_gpu_support():\n", " \"\"\"Check if XGBoost has GPU support available\"\"\"\n", " try:\n", " # Method 1: Try to create a DMatrix and use gpu_hist\n", " dtrain = xgb.DMatrix(np.array([[1, 2], [3, 4]]), label=[1, 0])\n", " params = {'tree_method': 'gpu_hist', 'gpu_id': 0, 'objective': 'binary:logistic'}\n", " \n", " # Try to train a small model with GPU\n", " xgb.train(params, dtrain, num_boost_round=1, verbose_eval=False)\n", " return True\n", " \n", " except Exception as e:\n", " # Check specific error messages\n", " error_str = str(e).lower()\n", " if 'gpu_hist' in error_str or 'cuda' in error_str or 'gpu' in error_str:\n", " return False\n", " return False\n", "\n", "# Check GPU support\n", "gpu_available = check_xgboost_gpu_support()\n", "print(\"GPU support:\", gpu_available)\n", "\n", "# Alternative method - check build info\n", "try:\n", " build_info = xgb.build_info()\n", " if 'USE_CUDA' in build_info:\n", " print(\"CUDA support in build:\", build_info['USE_CUDA'])\n", " if 'USE_NCCL' in build_info:\n", " print(\"NCCL support in build:\", build_info['USE_NCCL'])\n", "except:\n", " print(\"Build info not available\")\n", "\n", "# Check available tree methods\n", "try:\n", " # This will show available tree methods\n", " dtrain = xgb.DMatrix(np.array([[1, 2]]), label=[1])\n", " \n", " available_methods = []\n", " for method in ['exact', 'hist', 'approx', 'gpu_hist']:\n", " try:\n", " params = {'tree_method': method, 'objective': 'reg:squarederror'}\n", " xgb.train(params, dtrain, num_boost_round=1, verbose_eval=False)\n", " available_methods.append(method)\n", " except:\n", " pass\n", " \n", " print(\"Available tree methods:\", available_methods)\n", " \n", "except Exception as e:\n", " print(\"Could not check tree methods:\", e)\n", "# Import Libraries and Setup\n", "\n", "\n", "# Set up logging and suppress warnings\n", "logging.basicConfig(level=logging.INFO)\n", "warnings.filterwarnings('ignore')\n", "\n", "# Set random seeds for reproducibility\n", "np.random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "# Set pandas display options\n", "pd.set_option('display.max_columns', None)\n", "pd.set_option('display.max_rows', 100)\n", "\n", "print(\"āœ… Libraries imported successfully!\")\n", "print(f\"TensorFlow version: {tf.__version__}\")\n", "print(f\"XGBoost version: {xgb.__version__}\")\n", "\n", "# Define paths\n", "# DATA_PATH = Path(\"/kaggle/input/mitsui-commodity-prediction-challenge\") # Kaggle path\n", "DATA_PATH = Path(\".\") # Adjust this path as needed\n", "TRAIN_CSV = DATA_PATH / \"train.csv\"\n", "TEST_CSV = DATA_PATH / \"test.csv\"\n", "TRAIN_LABELS_CSV = DATA_PATH / \"train_labels.csv\"\n", "TARGET_PAIRS_CSV = DATA_PATH / \"target_pairs.csv\"\n", "LAGGED_DIR = DATA_PATH / \"lagged_test_labels\"" ] }, { "cell_type": "markdown", "id": "33a9bbcb", "metadata": {}, "source": [ "## šŸ“Š Data Loading & Exploration\n", "\n", "The competition provides several key files:\n", "- **`train.csv`**: Historical market data (features)\n", "- **`train_labels.csv`**: Target values for training\n", "- **`test.csv`**: Test data for predictions\n", "- **`target_pairs.csv`**: Information about what each target represents\n", "- **`lagged_test_labels/`**: Previous predictions (4 lag periods)" ] }, { "cell_type": "code", "execution_count": 2, "id": "08e0f5de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "šŸ“„ Loading data files...\n", "āœ… Training data: (1917, 558)\n", "āœ… Training labels: (1917, 425)\n", "āœ… Target pairs: (424, 3)\n", "āœ… Test data: (90, 559)\n", "āœ… lag_1: (90, 108)\n", "āœ… lag_2: (90, 108)\n", "āœ… lag_3: (90, 108)\n", "āœ… lag_4: (90, 108)\n", "\n", "šŸŽÆ Data loaded successfully!\n", "Total unique dates in test: 90\n", "Total targets to predict: 424\n", "āœ… Training data: (1917, 558)\n", "āœ… Training labels: (1917, 425)\n", "āœ… Target pairs: (424, 3)\n", "āœ… Test data: (90, 559)\n", "āœ… lag_1: (90, 108)\n", "āœ… lag_2: (90, 108)\n", "āœ… lag_3: (90, 108)\n", "āœ… lag_4: (90, 108)\n", "\n", "šŸŽÆ Data loaded successfully!\n", "Total unique dates in test: 90\n", "Total targets to predict: 424\n" ] } ], "source": [ "# Load all data files\n", "print(\"šŸ“„ Loading data files...\")\n", "\n", "# Load training data\n", "try:\n", " train_df = pd.read_csv(TRAIN_CSV)\n", " train_labels_df = pd.read_csv(TRAIN_LABELS_CSV)\n", " target_pairs_df = pd.read_csv(TARGET_PAIRS_CSV)\n", " test_df = pd.read_csv(TEST_CSV)\n", " \n", " print(f\"āœ… Training data: {train_df.shape}\")\n", " print(f\"āœ… Training labels: {train_labels_df.shape}\") \n", " print(f\"āœ… Target pairs: {target_pairs_df.shape}\")\n", " print(f\"āœ… Test data: {test_df.shape}\")\n", " \n", "except FileNotFoundError as e:\n", " print(f\"āŒ Error loading files: {e}\")\n", " print(\"Make sure you're in the correct directory with the data files\")\n", "\n", "# Load lagged test labels\n", "lagged_files = {\n", " 'lag_1': LAGGED_DIR / 'test_labels_lag_1.csv',\n", " 'lag_2': LAGGED_DIR / 'test_labels_lag_2.csv', \n", " 'lag_3': LAGGED_DIR / 'test_labels_lag_3.csv',\n", " 'lag_4': LAGGED_DIR / 'test_labels_lag_4.csv'\n", "}\n", "\n", "lagged_data = {}\n", "for lag_name, file_path in lagged_files.items():\n", " try:\n", " lagged_data[lag_name] = pd.read_csv(file_path)\n", " print(f\"āœ… {lag_name}: {lagged_data[lag_name].shape}\")\n", " except FileNotFoundError:\n", " print(f\"āš ļø Warning: {file_path} not found\")\n", "\n", "print(\"\\nšŸŽÆ Data loaded successfully!\")\n", "print(f\"Total unique dates in test: {test_df['date_id'].nunique()}\")\n", "print(f\"Total targets to predict: {len([col for col in train_labels_df.columns if col.startswith('target_')])}\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "179de3b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "šŸ” DATASET OVERVIEW\n", "==================================================\n", "\n", "šŸ“ˆ Training Features:\n", "Shape: (1917, 558)\n", "Date range: 0 to 1916\n", "Features: 557\n", "LME_metals: 4 features\n", "JPX_futures: 40 features\n", "US_stocks: 475 features\n", "FX_rates: 38 features\n", "\n", "šŸ“Š Target Information:\n", "Total targets: 424\n", "Targets by lag period:\n", " Lag 1: 106 targets\n", " Lag 2: 106 targets\n", " Lag 3: 106 targets\n", " Lag 4: 106 targets\n", "\n", "šŸ“‹ Sample Training Data:\n", " date_id LME_AH_Close LME_CA_Close LME_PB_Close LME_ZS_Close \\\n", "0 0 2264.5 7205.0 2570.0 3349.0 \n", "1 1 2228.0 7147.0 2579.0 3327.0 \n", "2 2 2250.0 7188.5 2587.0 3362.0 \n", "\n", " JPX_Gold_Mini_Futures_Open JPX_Gold_Rolling-Spot_Futures_Open \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 4684.0 4691.0 \n", "\n", " JPX_Gold_Standard_Futures_Open JPX_Platinum_Mini_Futures_Open \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 4684.0 3363.0 \n", "\n", " JPX_Platinum_Standard_Futures_Open JPX_RSS3_Rubber_Futures_Open \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 3367.0 207.0 \n", "\n", " JPX_Gold_Mini_Futures_High JPX_Gold_Rolling-Spot_Futures_High \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 4735.0 4746.0 \n", "\n", " JPX_Gold_Standard_Futures_High JPX_Platinum_Mini_Futures_High \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 4735.0 3443.0 \n", "\n", " JPX_Platinum_Standard_Futures_High JPX_RSS3_Rubber_Futures_High \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 3449.0 207.7 \n", "\n", " JPX_Gold_Mini_Futures_Low JPX_Gold_Rolling-Spot_Futures_Low \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 4679.0 4688.0 \n", "\n", " JPX_Gold_Standard_Futures_Low JPX_Platinum_Mini_Futures_Low \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 4679.0 3362.0 \n", "\n", " JPX_Platinum_Standard_Futures_Low JPX_RSS3_Rubber_Futures_Low \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 3362.0 206.4 \n", "\n", " JPX_Gold_Mini_Futures_Close JPX_Gold_Rolling-Spot_Futures_Close \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 4727.0 4739.0 \n", "\n", " JPX_Gold_Standard_Futures_Close JPX_Platinum_Mini_Futures_Close \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 4730.0 3426.0 \n", "\n", " JPX_Platinum_Standard_Futures_Close JPX_RSS3_Rubber_Futures_Close \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 3427.0 206.9 \n", "\n", " JPX_Gold_Mini_Futures_Volume JPX_Gold_Rolling-Spot_Futures_Volume \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 2681.0 37908.0 \n", "\n", " JPX_Gold_Standard_Futures_Volume JPX_Platinum_Mini_Futures_Volume \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 30656.0 624.0 \n", "\n", " JPX_Platinum_Standard_Futures_Volume JPX_RSS3_Rubber_Futures_Volume \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 13713.0 4128.0 \n", "\n", " JPX_Gold_Mini_Futures_settlement_price \\\n", "0 NaN \n", "1 NaN \n", "2 4730.0 \n", "\n", " JPX_Gold_Rolling-Spot_Futures_settlement_price \\\n", "0 NaN \n", "1 NaN \n", "2 4735.0 \n", "\n", " JPX_Platinum_Mini_Futures_settlement_price \\\n", "0 NaN \n", "1 NaN \n", "2 3423.0 \n", "\n", " JPX_RSS3_Rubber_Futures_settlement_price \\\n", "0 NaN \n", "1 NaN \n", "2 206.9 \n", "\n", " JPX_Gold_Mini_Futures_open_interest \\\n", "0 NaN \n", "1 NaN \n", "2 1768.0 \n", "\n", " JPX_Gold_Rolling-Spot_Futures_open_interest \\\n", "0 NaN \n", "1 NaN \n", "2 128380.0 \n", "\n", " JPX_Gold_Standard_Futures_open_interest \\\n", "0 NaN \n", "1 NaN \n", "2 17671.0 \n", "\n", " JPX_Platinum_Mini_Futures_open_interest \\\n", "0 NaN \n", "1 NaN \n", "2 1323.0 \n", "\n", " JPX_Platinum_Standard_Futures_open_interest \\\n", "0 NaN \n", "1 NaN \n", "2 7893.0 \n", "\n", " JPX_RSS3_Rubber_Futures_open_interest US_Stock_ACWI_adj_open \\\n", "0 NaN 63.4457 \n", "1 NaN 63.7519 \n", "2 7391.0 64.3732 \n", "\n", " US_Stock_AEM_adj_open US_Stock_AG_adj_open US_Stock_AGG_adj_open \\\n", "0 39.6837 6.7563 89.5868 \n", "1 40.0082 7.1514 89.5786 \n", "2 39.6239 6.9638 89.3901 \n", "\n", " US_Stock_ALB_adj_open US_Stock_AMP_adj_open US_Stock_BCS_adj_open \\\n", "0 117.2076 147.0081 8.1897 \n", "1 120.0349 146.4136 8.1748 \n", "2 120.2258 146.2327 8.2270 \n", "\n", " US_Stock_BKR_adj_open US_Stock_BND_adj_open US_Stock_BNDX_adj_open \\\n", "0 25.5772 66.0686 43.4882 \n", "1 26.1580 66.0524 43.4962 \n", "2 27.2469 65.9713 43.4882 \n", "\n", " US_Stock_BP_adj_open US_Stock_BSV_adj_open US_Stock_CAT_adj_open \\\n", "0 27.4272 67.0996 133.7797 \n", "1 27.6685 67.0741 132.7149 \n", "2 28.0467 67.0232 133.4332 \n", "\n", " US_Stock_CCJ_adj_open US_Stock_CLF_adj_open US_Stock_COP_adj_open \\\n", "0 9.0349 6.9508 42.4813 \n", "1 9.4531 7.4622 42.7049 \n", "2 9.4725 7.5190 43.4991 \n", "\n", " US_Stock_CVE_adj_open US_Stock_CVX_adj_open US_Stock_DE_adj_open \\\n", "0 7.9941 90.9993 140.4689 \n", "1 8.4975 92.2661 141.5628 \n", "2 9.0010 92.6208 142.7373 \n", "\n", " US_Stock_DVN_adj_open US_Stock_EEM_adj_open US_Stock_EFA_adj_open \\\n", "0 30.3211 46.6643 57.3474 \n", "1 30.7927 47.1575 57.6402 \n", "2 31.1120 47.4554 58.4289 \n", "\n", " US_Stock_EMB_adj_open US_Stock_ENB_adj_open US_Stock_EOG_adj_open \\\n", "0 81.7757 23.9998 82.8197 \n", "1 82.1634 24.5669 83.9400 \n", "2 82.1422 24.7498 84.1305 \n", "\n", " US_Stock_EWJ_adj_open US_Stock_EWT_adj_open US_Stock_EWY_adj_open \\\n", "0 53.0634 24.3186 66.7279 \n", "1 53.4962 24.5506 67.1337 \n", "2 54.5031 24.6367 66.8514 \n", "\n", " US_Stock_EWZ_adj_open US_Stock_FCX_adj_open US_Stock_FNV_adj_open \\\n", "0 27.2048 17.4460 74.4045 \n", "1 27.4936 18.1421 73.3529 \n", "2 27.8939 17.9772 71.9231 \n", "\n", " US_Stock_FXI_adj_open US_Stock_GDX_adj_open US_Stock_GDXJ_adj_open \\\n", "0 41.3834 21.6651 31.7992 \n", "1 41.6183 21.9606 32.2969 \n", "2 42.0531 21.7482 31.8269 \n", "\n", " US_Stock_GLD_adj_open US_Stock_GOLD_adj_open US_Stock_HAL_adj_open \\\n", "0 124.66 98.3121 42.6159 \n", "1 125.05 97.9900 43.3825 \n", "2 124.89 96.1060 44.3059 \n", "\n", " US_Stock_HES_adj_open US_Stock_HL_adj_open US_Stock_IAU_adj_open \\\n", "0 42.8790 3.8859 25.24 \n", "1 42.9416 4.0884 25.32 \n", "2 44.1841 3.9631 25.28 \n", "\n", " US_Stock_IEF_adj_open US_Stock_IEMG_adj_open US_Stock_IGSB_adj_open \\\n", "0 89.9489 47.5108 42.5442 \n", "1 89.8722 47.9304 42.5238 \n", "2 89.6589 48.2512 42.5076 \n", "\n", " US_Stock_JNK_adj_open US_Stock_KGC_adj_open US_Stock_KMI_adj_open \\\n", "0 69.7568 3.9328 11.9254 \n", "1 69.7947 4.0323 12.2834 \n", "2 70.1364 4.0504 12.3616 \n", "\n", " US_Stock_LQD_adj_open US_Stock_LYB_adj_open US_Stock_MBB_adj_open \\\n", "0 94.2649 61.3057 87.5953 \n", "1 93.9697 61.7308 87.6446 \n", "2 93.8453 62.2718 87.5542 \n", "\n", " US_Stock_MPC_adj_open US_Stock_MS_adj_open US_Stock_NEM_adj_open \\\n", "0 51.9964 42.0562 30.2279 \n", "1 53.0582 41.6896 30.4993 \n", "2 54.0020 42.0881 30.1321 \n", "\n", " US_Stock_NUE_adj_open US_Stock_NUGT_adj_open US_Stock_OIH_adj_open \\\n", "0 54.7107 153.7555 467.8681 \n", "1 56.9090 159.9869 478.4485 \n", "2 57.5396 155.4550 490.1049 \n", "\n", " US_Stock_OKE_adj_open US_Stock_OXY_adj_open US_Stock_PAAS_adj_open \\\n", "0 33.4078 61.6226 13.9786 \n", "1 33.9857 61.6476 14.0408 \n", "2 34.6071 62.1138 13.6676 \n", "\n", " US_Stock_RIO_adj_open US_Stock_RSP_adj_open US_Stock_RY_adj_open \\\n", "0 28.7648 89.1086 60.9880 \n", "1 29.1914 89.6449 61.3309 \n", "2 29.2553 90.1989 62.0168 \n", "\n", " US_Stock_SCCO_adj_open US_Stock_SHEL_adj_open US_Stock_SHY_adj_open \\\n", "0 34.2432 48.2084 72.4861 \n", "1 34.8575 48.7351 72.4516 \n", "2 35.1575 49.1775 72.4170 \n", "\n", " US_Stock_SLB_adj_open US_Stock_SLV_adj_open US_Stock_SPIB_adj_open \\\n", "0 55.1579 16.13 27.3825 \n", "1 56.6975 16.19 27.3505 \n", "2 58.1480 16.21 27.3505 \n", "\n", " US_Stock_SPTL_adj_open US_Stock_SPYV_adj_open US_Stock_STLD_adj_open \\\n", "0 29.9613 25.9489 37.0707 \n", "1 29.8379 26.0247 38.3800 \n", "2 29.7720 26.1595 39.1929 \n", "\n", " US_Stock_TD_adj_open US_Stock_TECK_adj_open US_Stock_TIP_adj_open \\\n", "0 42.5330 23.9531 90.8857 \n", "1 42.5763 24.5358 90.8298 \n", "2 42.9740 24.4266 90.7023 \n", "\n", " US_Stock_TRGP_adj_open US_Stock_URA_adj_open US_Stock_VALE_adj_open \\\n", "0 36.3686 12.4874 7.2118 \n", "1 36.8601 13.0318 7.3554 \n", "2 37.3888 12.8668 7.4876 \n", "\n", " US_Stock_VCIT_adj_open US_Stock_VCSH_adj_open US_Stock_VEA_adj_open \\\n", "0 67.5319 64.5564 36.2358 \n", "1 67.3851 64.5727 36.4531 \n", "2 67.3310 64.5483 36.9036 \n", "\n", " US_Stock_VGIT_adj_open US_Stock_VGK_adj_open US_Stock_VGLT_adj_open \\\n", "0 53.4186 46.7798 63.0691 \n", "1 53.3935 46.9612 62.7935 \n", "2 53.2681 47.5603 62.5989 \n", "\n", " US_Stock_VGSH_adj_open US_Stock_VT_adj_open US_Stock_VTV_adj_open \\\n", "0 50.9291 63.6198 88.4670 \n", "1 50.9038 64.0039 88.5167 \n", "2 50.8615 64.6610 88.9895 \n", "\n", " US_Stock_VWO_adj_open US_Stock_VXUS_adj_open US_Stock_VYM_adj_open \\\n", "0 37.4840 46.0581 68.3837 \n", "1 37.8304 46.3482 68.4792 \n", "2 38.1688 46.8638 68.7738 \n", "\n", " US_Stock_WMB_adj_open US_Stock_WPM_adj_open US_Stock_X_adj_open \\\n", "0 20.2599 20.0787 33.7924 \n", "1 20.5902 20.1510 35.9869 \n", "2 21.1781 20.0245 35.8634 \n", "\n", " US_Stock_XLB_adj_open US_Stock_XLE_adj_open US_Stock_XOM_adj_open \\\n", "0 52.5450 52.5465 59.1858 \n", "1 53.1495 53.2557 60.1320 \n", "2 53.6072 53.9069 61.2830 \n", "\n", " US_Stock_YINN_adj_open US_Stock_ACWI_adj_high US_Stock_AEM_adj_high \\\n", "0 669.0886 63.6732 40.2302 \n", "1 682.4271 64.0582 40.0082 \n", "2 703.6966 64.5657 40.0338 \n", "\n", " US_Stock_AG_adj_high US_Stock_AGG_adj_high US_Stock_ALB_adj_high \\\n", "0 7.1317 89.6278 119.9167 \n", "1 7.1613 89.5786 121.7621 \n", "2 6.9934 89.4967 120.8712 \n", "\n", " US_Stock_AMP_adj_high US_Stock_BCS_adj_high US_Stock_BKR_adj_high \\\n", "0 147.0770 8.2345 26.0047 \n", "1 146.6204 8.2121 27.1420 \n", "2 147.6714 8.2680 27.9647 \n", "\n", " US_Stock_BND_adj_high US_Stock_BNDX_adj_high US_Stock_BP_adj_high \\\n", "0 66.1092 43.4962 27.6685 \n", "1 66.0524 43.5042 27.9685 \n", "2 66.0038 43.5123 28.1511 \n", "\n", " US_Stock_BSV_adj_high US_Stock_CAT_adj_high US_Stock_CCJ_adj_high \\\n", "0 67.1081 134.7008 9.4433 \n", "1 67.1081 133.0952 9.5114 \n", "2 67.0656 134.8614 9.5503 \n", "\n", " US_Stock_CLF_adj_high US_Stock_COP_adj_high US_Stock_CVE_adj_high \\\n", "0 7.5380 42.8051 8.4715 \n", "1 7.5474 43.4837 8.9749 \n", "2 7.6895 44.0970 9.0617 \n", "\n", " US_Stock_CVX_adj_high US_Stock_DE_adj_high US_Stock_DVN_adj_high \\\n", "0 92.4688 141.7152 30.6549 \n", "1 93.3375 142.6477 31.3998 \n", "2 92.9104 143.5487 31.4095 \n", "\n", " US_Stock_EEM_adj_high US_Stock_EFA_adj_high US_Stock_EMB_adj_high \\\n", "0 46.9475 57.6076 82.0647 \n", "1 47.4017 57.9369 82.1634 \n", "2 47.6410 58.5915 82.2127 \n", "\n", " US_Stock_ENB_adj_high US_Stock_EOG_adj_high US_Stock_EWJ_adj_high \\\n", "0 24.5242 83.3684 53.2754 \n", "1 24.6949 85.2280 54.0350 \n", "2 24.8778 84.7555 54.7062 \n", "\n", " US_Stock_EWT_adj_high US_Stock_EWY_adj_high US_Stock_EWZ_adj_high \\\n", "0 24.4213 66.9484 27.3623 \n", "1 24.6897 67.5968 27.7233 \n", "2 24.6765 67.0631 28.0777 \n", "\n", " US_Stock_FCX_adj_high US_Stock_FNV_adj_high US_Stock_FXI_adj_high \\\n", "0 18.1237 74.5244 41.5574 \n", "1 18.1787 73.5374 41.9053 \n", "2 18.1512 72.4397 42.1835 \n", "\n", " US_Stock_GDX_adj_high US_Stock_GDXJ_adj_high US_Stock_GLD_adj_high \\\n", "0 22.0160 32.4168 125.18 \n", "1 21.9837 32.3799 125.09 \n", "2 21.9237 32.0942 125.85 \n", "\n", " US_Stock_GOLD_adj_high US_Stock_HAL_adj_high US_Stock_HES_adj_high \\\n", "0 98.7221 43.4173 42.9416 \n", "1 98.0095 44.4975 44.6042 \n", "2 96.2524 45.1030 45.2657 \n", "\n", " US_Stock_HL_adj_high US_Stock_IAU_adj_high US_Stock_IEF_adj_high \\\n", "0 4.0981 25.34 89.9745 \n", "1 4.1270 25.32 89.9233 \n", "2 3.9920 25.48 89.8210 \n", "\n", " US_Stock_IEMG_adj_high US_Stock_IGSB_adj_high US_Stock_JNK_adj_high \\\n", "0 47.7494 42.5442 69.7947 \n", "1 48.1854 42.5401 70.1364 \n", "2 48.4075 42.5320 70.2883 \n", "\n", " US_Stock_KGC_adj_high US_Stock_KMI_adj_high US_Stock_LQD_adj_high \\\n", "0 4.0504 12.2639 94.3737 \n", "1 4.0594 12.3681 94.0551 \n", "2 4.0865 12.4332 94.0241 \n", "\n", " US_Stock_LYB_adj_high US_Stock_MBB_adj_high US_Stock_MPC_adj_high \\\n", "0 61.9737 87.6035 52.8930 \n", "1 62.0620 87.6610 54.2419 \n", "2 62.6251 87.6446 54.3127 \n", "\n", " US_Stock_MS_adj_high US_Stock_NEM_adj_high US_Stock_NUE_adj_high \\\n", "0 42.0802 30.5672 56.7046 \n", "1 41.8889 30.5392 57.7100 \n", "2 42.6700 30.5392 57.6418 \n", "\n", " US_Stock_NUGT_adj_high US_Stock_OIH_adj_high US_Stock_OKE_adj_high \\\n", "0 161.2143 477.5519 34.0230 \n", "1 161.1671 490.2842 34.7004 \n", "2 159.1844 499.9679 35.0049 \n", "\n", " US_Stock_OXY_adj_high US_Stock_PAAS_adj_high US_Stock_RIO_adj_high \\\n", "0 61.8640 14.0675 29.2607 \n", "1 62.5385 14.0675 29.3247 \n", "2 62.4386 13.9342 29.3247 \n", "\n", " US_Stock_RSP_adj_high US_Stock_RY_adj_high US_Stock_SCCO_adj_high \\\n", "0 89.5131 61.3757 34.9289 \n", "1 90.0230 61.9572 35.0432 \n", "2 90.3563 62.7101 35.2497 \n", "\n", " US_Stock_SHEL_adj_high US_Stock_SHY_adj_high US_Stock_SLB_adj_high \\\n", "0 48.8158 72.4861 56.5861 \n", "1 49.3039 72.4775 58.0507 \n", "2 49.5355 72.4429 59.3067 \n", "\n", " US_Stock_SLV_adj_high US_Stock_SPIB_adj_high US_Stock_SPTL_adj_high \\\n", "0 16.23 27.3825 29.9613 \n", "1 16.26 27.3745 29.9037 \n", "2 16.30 27.3585 29.8872 \n", "\n", " US_Stock_SPYV_adj_high US_Stock_STLD_adj_high US_Stock_TD_adj_high \\\n", "0 26.0079 38.4399 42.7716 \n", "1 26.1174 39.2357 42.9198 \n", "2 26.2859 39.6379 43.3067 \n", "\n", " US_Stock_TECK_adj_high US_Stock_TIP_adj_high US_Stock_TRGP_adj_high \\\n", "0 24.5722 90.8857 36.6144 \n", "1 24.5813 90.8936 37.4112 \n", "2 24.7498 90.8777 37.6643 \n", "\n", " US_Stock_URA_adj_high US_Stock_VALE_adj_high US_Stock_VCIT_adj_high \\\n", "0 12.9823 7.3525 67.5319 \n", "1 13.1225 7.3956 67.4585 \n", "2 13.0969 7.5227 67.4083 \n", "\n", " US_Stock_VCSH_adj_high US_Stock_VEA_adj_high US_Stock_VGIT_adj_high \\\n", "0 64.5808 36.4128 53.4277 \n", "1 64.5971 36.6381 53.4269 \n", "2 64.5646 36.9800 53.3517 \n", "\n", " US_Stock_VGK_adj_high US_Stock_VGLT_adj_high US_Stock_VGSH_adj_high \\\n", "0 46.9612 63.0934 50.9291 \n", "1 47.1504 62.8818 50.9100 \n", "2 47.7337 62.8583 50.8700 \n", "\n", " US_Stock_VT_adj_high US_Stock_VTV_adj_high US_Stock_VWO_adj_high \\\n", "0 63.9271 88.5333 37.6532 \n", "1 64.3793 88.8403 38.0238 \n", "2 64.8316 89.4871 38.3218 \n", "\n", " US_Stock_VXUS_adj_high US_Stock_VYM_adj_high US_Stock_WMB_adj_high \\\n", "0 46.2273 68.5350 20.6001 \n", "1 46.5576 68.6585 21.1781 \n", "2 46.9782 69.0806 21.5381 \n", "\n", " US_Stock_WPM_adj_high US_Stock_X_adj_high US_Stock_XLB_adj_high \\\n", "0 20.3407 35.5879 53.0286 \n", "1 20.1962 36.1294 53.4431 \n", "2 20.1510 36.3669 53.9588 \n", "\n", " US_Stock_XLE_adj_high US_Stock_XOM_adj_high US_Stock_YINN_adj_high \\\n", "0 53.2050 60.1567 677.5604 \n", "1 54.0805 61.4065 695.7656 \n", "2 54.3048 61.5866 709.6449 \n", "\n", " US_Stock_ACWI_adj_low US_Stock_AEM_adj_low US_Stock_AG_adj_low \\\n", "0 63.2882 39.5898 6.6872 \n", "1 63.7432 39.2567 6.8057 \n", "2 64.3469 39.4958 6.8057 \n", "\n", " US_Stock_AGG_adj_low US_Stock_ALB_adj_low US_Stock_AMP_adj_low \\\n", "0 89.3491 116.8076 145.4660 \n", "1 89.4475 118.7440 145.3109 \n", "2 89.3529 118.2804 146.1121 \n", "\n", " US_Stock_BCS_adj_low US_Stock_BKR_adj_low US_Stock_BND_adj_low \\\n", "0 8.1599 25.4966 65.8821 \n", "1 8.1524 26.1418 65.9389 \n", "2 8.2233 26.8597 65.9064 \n", "\n", " US_Stock_BNDX_adj_low US_Stock_BP_adj_low US_Stock_BSV_adj_low \\\n", "0 43.3761 27.2642 67.0317 \n", "1 43.4642 27.6359 67.0572 \n", "2 43.4642 27.9750 67.0063 \n", "\n", " US_Stock_CAT_adj_low US_Stock_CCJ_adj_low US_Stock_CLF_adj_low \\\n", "0 131.8613 8.9765 6.9508 \n", "1 131.3289 9.1224 7.0550 \n", "2 132.6980 9.3364 7.3013 \n", "\n", " US_Stock_COP_adj_low US_Stock_CVE_adj_low US_Stock_CVX_adj_low \\\n", "0 42.2731 7.9854 90.8763 \n", "1 42.6663 8.4975 91.8608 \n", "2 43.2524 8.8881 92.0924 \n", "\n", " US_Stock_DE_adj_low US_Stock_DVN_adj_low US_Stock_EEM_adj_low \\\n", "0 140.3613 30.2087 46.6155 \n", "1 141.3386 30.7202 47.1478 \n", "2 141.0786 30.6846 47.4359 \n", "\n", " US_Stock_EFA_adj_low US_Stock_EMB_adj_low US_Stock_ENB_adj_low \\\n", "0 57.2580 81.7687 23.8900 \n", "1 57.6076 81.9073 24.3412 \n", "2 58.3882 81.9872 24.6888 \n", "\n", " US_Stock_EOG_adj_low US_Stock_EWJ_adj_low US_Stock_EWT_adj_low \\\n", "0 82.4767 52.9928 24.3020 \n", "1 83.6961 53.4609 24.5506 \n", "2 83.6435 54.4456 24.5903 \n", "\n", " US_Stock_EWY_adj_low US_Stock_EWZ_adj_low US_Stock_FCX_adj_low \\\n", "0 66.6220 27.1326 17.3819 \n", "1 67.1248 27.3721 17.5468 \n", "2 66.7720 27.7430 17.6887 \n", "\n", " US_Stock_FNV_adj_low US_Stock_FXI_adj_low US_Stock_GDX_adj_low \\\n", "0 72.7533 41.2530 21.6281 \n", "1 71.2866 41.5835 21.4619 \n", "2 71.8401 41.9618 21.6004 \n", "\n", " US_Stock_GDXJ_adj_low US_Stock_GLD_adj_low US_Stock_GOLD_adj_low \\\n", "0 31.7808 124.390 97.7948 \n", "1 31.3522 124.101 95.0126 \n", "2 31.5227 124.740 95.1395 \n", "\n", " US_Stock_HAL_adj_low US_Stock_HES_adj_low US_Stock_HL_adj_low \\\n", "0 42.4417 42.0334 3.8667 \n", "1 43.1386 42.7628 3.8859 \n", "2 43.7571 43.8623 3.8956 \n", "\n", " US_Stock_IAU_adj_low US_Stock_IEF_adj_low US_Stock_IEMG_adj_low \\\n", "0 25.1800 89.6504 47.4285 \n", "1 25.1202 89.7570 47.9304 \n", "2 25.2400 89.5906 48.2101 \n", "\n", " US_Stock_IGSB_adj_low US_Stock_JNK_adj_low US_Stock_KGC_adj_low \\\n", "0 42.5035 69.6998 3.9328 \n", "1 42.5116 69.7758 3.9464 \n", "2 42.4954 70.1174 4.0097 \n", "\n", " US_Stock_KMI_adj_low US_Stock_LQD_adj_low US_Stock_LYB_adj_low \\\n", "0 11.8408 93.6278 61.1070 \n", "1 12.1728 93.7832 61.1125 \n", "2 12.2769 93.8065 61.7253 \n", "\n", " US_Stock_MBB_adj_low US_Stock_MPC_adj_low US_Stock_MS_adj_low \\\n", "0 87.5048 51.8941 41.5859 \n", "1 87.5542 53.0188 41.3867 \n", "2 87.5287 53.7975 41.8570 \n", "\n", " US_Stock_NEM_adj_low US_Stock_NUE_adj_low US_Stock_NUGT_adj_low \\\n", "0 30.1880 54.6170 153.6611 \n", "1 29.9246 56.6108 149.1254 \n", "2 29.8288 56.7472 152.4809 \n", "\n", " US_Stock_OIH_adj_low US_Stock_OKE_adj_low US_Stock_OXY_adj_low \\\n", "0 463.8332 33.2835 61.1855 \n", "1 474.1446 33.9111 61.6143 \n", "2 485.2630 34.4549 61.7017 \n", "\n", " US_Stock_PAAS_adj_low US_Stock_RIO_adj_low US_Stock_RSP_adj_low \\\n", "0 13.8364 28.6955 89.0118 \n", "1 13.5254 29.0207 89.4251 \n", "2 13.4899 29.1327 90.1011 \n", "\n", " US_Stock_RY_adj_low US_Stock_SCCO_adj_low US_Stock_SHEL_adj_low \\\n", "0 60.8166 34.2432 48.0960 \n", "1 61.3309 34.5289 48.7351 \n", "2 61.8304 34.8075 49.1575 \n", "\n", " US_Stock_SHY_adj_low US_Stock_SLB_adj_low US_Stock_SLV_adj_low \\\n", "0 72.4343 54.7771 16.1200 \n", "1 72.4300 56.0134 16.0939 \n", "2 72.3997 57.7428 16.1900 \n", "\n", " US_Stock_SPIB_adj_low US_Stock_SPTL_adj_low US_Stock_SPYV_adj_low \\\n", "0 27.2945 29.6321 25.8815 \n", "1 27.3265 29.7473 25.9910 \n", "2 27.2945 29.7144 26.1593 \n", "\n", " US_Stock_STLD_adj_low US_Stock_TD_adj_low US_Stock_TECK_adj_low \\\n", "0 37.0707 42.3160 23.8985 \n", "1 38.0548 42.5763 24.1079 \n", "2 38.9362 42.8583 24.3628 \n", "\n", " US_Stock_TIP_adj_low US_Stock_TRGP_adj_low US_Stock_URA_adj_low \\\n", "0 90.5866 35.9889 12.4709 \n", "1 90.7342 36.6739 12.7761 \n", "2 90.6385 37.1878 12.7183 \n", "\n", " US_Stock_VALE_adj_low US_Stock_VCIT_adj_low US_Stock_VCSH_adj_low \\\n", "0 7.1888 67.2228 64.4994 \n", "1 7.2865 67.2847 64.5564 \n", "2 7.3669 67.2538 64.5076 \n", "\n", " US_Stock_VEA_adj_low US_Stock_VGIT_adj_low US_Stock_VGK_adj_low \\\n", "0 36.1956 53.2856 46.6892 \n", "1 36.4370 53.3433 46.9265 \n", "2 36.8392 53.2676 47.5524 \n", "\n", " US_Stock_VGLT_adj_low US_Stock_VGSH_adj_low US_Stock_VT_adj_low \\\n", "0 62.3743 50.8869 63.5857 \n", "1 62.5502 50.8700 64.0039 \n", "2 62.4855 50.8531 64.6439 \n", "\n", " US_Stock_VTV_adj_low US_Stock_VWO_adj_low US_Stock_VXUS_adj_low \\\n", "0 88.1850 37.4196 45.9934 \n", "1 88.4006 37.8224 46.3321 \n", "2 88.9563 38.1607 46.8316 \n", "\n", " US_Stock_VYM_adj_low US_Stock_WMB_adj_low US_Stock_WPM_adj_low \\\n", "0 68.2164 20.1740 19.9884 \n", "1 68.3200 20.5703 19.7265 \n", "2 68.7101 21.0195 19.8574 \n", "\n", " US_Stock_X_adj_low US_Stock_XLB_adj_low US_Stock_XLE_adj_low \\\n", "0 33.6213 52.4069 52.3801 \n", "1 34.5807 53.0286 53.1978 \n", "2 35.5594 53.5813 53.7115 \n", "\n", " US_Stock_XOM_adj_low US_Stock_YINN_adj_low US_Stock_ACWI_adj_close \\\n", "0 59.0729 664.5823 63.6732 \n", "1 59.8919 682.0378 63.9969 \n", "2 61.0288 698.6496 64.4432 \n", "\n", " US_Stock_AEM_adj_close US_Stock_AG_adj_close US_Stock_AGG_adj_close \\\n", "0 39.9057 7.1317 89.5131 \n", "1 39.6752 6.9539 89.5213 \n", "2 40.0167 6.9638 89.4639 \n", "\n", " US_Stock_ALB_adj_close US_Stock_AMP_adj_close US_Stock_BCS_adj_close \\\n", "0 119.5712 146.3792 8.2270 \n", "1 119.8076 145.6555 8.1972 \n", "2 118.3804 146.1896 8.2419 \n", "\n", " US_Stock_BKR_adj_close US_Stock_BND_adj_close US_Stock_BNDX_adj_close \\\n", "0 25.9966 65.9794 43.4562 \n", "1 27.0452 66.0362 43.4882 \n", "2 27.9325 66.0038 43.4962 \n", "\n", " US_Stock_BP_adj_close US_Stock_BSV_adj_close US_Stock_CAT_adj_close \\\n", "0 27.6359 67.0656 132.7149 \n", "1 27.9554 67.0741 132.9177 \n", "2 28.0663 67.0232 134.7431 \n", "\n", " US_Stock_CCJ_adj_close US_Stock_CLF_adj_close US_Stock_COP_adj_close \\\n", "0 9.4239 7.4811 42.6586 \n", "1 9.4142 7.4338 43.4452 \n", "2 9.5114 7.6137 43.9464 \n", "\n", " US_Stock_CVE_adj_close US_Stock_CVX_adj_close US_Stock_DE_adj_close \\\n", "0 8.4541 92.3530 141.6704 \n", "1 8.9402 93.0262 142.3159 \n", "2 8.9923 92.7367 143.1318 \n", "\n", " US_Stock_DVN_adj_close US_Stock_EEM_adj_close US_Stock_EFA_adj_close \\\n", "0 30.6404 46.8889 57.5914 \n", "1 30.9959 47.3382 57.8678 \n", "2 31.3659 47.5726 58.5020 \n", "\n", " US_Stock_EMB_adj_close US_Stock_ENB_adj_close US_Stock_EOG_adj_close \\\n", "0 82.0365 24.4754 83.2998 \n", "1 82.0013 24.6400 84.5421 \n", "2 82.2127 24.8595 84.5573 \n", "\n", " US_Stock_EWJ_adj_close US_Stock_EWT_adj_close US_Stock_EWY_adj_close \\\n", "0 53.2489 24.3915 66.9308 \n", "1 53.9820 24.6367 67.5218 \n", "2 54.6444 24.6301 67.0366 \n", "\n", " US_Stock_EWZ_adj_close US_Stock_FCX_adj_close US_Stock_FNV_adj_close \\\n", "0 27.3557 18.1054 73.3437 \n", "1 27.6905 17.8307 71.8124 \n", "2 27.8742 18.0780 72.4397 \n", "\n", " US_Stock_FXI_adj_close US_Stock_GDX_adj_close US_Stock_GDXJ_adj_close \\\n", "0 41.5487 21.9975 32.3891 \n", "1 41.9053 21.7667 31.8361 \n", "2 42.0444 21.9052 32.0573 \n", "\n", " US_Stock_GLD_adj_close US_Stock_GOLD_adj_close US_Stock_HAL_adj_close \\\n", "0 125.15 98.3219 43.2170 \n", "1 124.82 96.4281 43.9923 \n", "2 125.46 95.7448 44.9767 \n", "\n", " US_Stock_HES_adj_close US_Stock_HL_adj_close US_Stock_IAU_adj_close \\\n", "0 42.7718 4.0884 25.34 \n", "1 44.1573 3.9438 25.28 \n", "2 45.2210 3.9920 25.42 \n", "\n", " US_Stock_IEF_adj_close US_Stock_IEMG_adj_close US_Stock_IGSB_adj_close \\\n", "0 89.7613 47.7165 42.5116 \n", "1 89.8551 48.1360 42.5198 \n", "2 89.8124 48.3335 42.5238 \n", "\n", " US_Stock_JNK_adj_close US_Stock_KGC_adj_close US_Stock_KMI_adj_close \\\n", "0 69.7758 4.0504 12.2379 \n", "1 70.1174 4.0323 12.3420 \n", "2 70.2123 4.0775 12.3746 \n", "\n", " US_Stock_LQD_adj_close US_Stock_LYB_adj_close US_Stock_MBB_adj_close \\\n", "0 93.8220 61.9572 87.5870 \n", "1 93.9930 61.9737 87.6199 \n", "2 93.9619 62.2718 87.5788 \n", "\n", " US_Stock_MPC_adj_close US_Stock_MS_adj_close US_Stock_NEM_adj_close \\\n", "0 52.8379 41.6019 30.4594 \n", "1 53.9627 41.7135 30.2040 \n", "2 54.0177 42.3512 30.5392 \n", "\n", " US_Stock_NUE_adj_close US_Stock_NUGT_adj_close US_Stock_OIH_adj_close \\\n", "0 56.6790 161.0727 476.2966 \n", "1 57.4203 156.4463 488.1322 \n", "2 57.2754 159.1844 499.0713 \n", "\n", " US_Stock_OKE_adj_close US_Stock_OXY_adj_close US_Stock_PAAS_adj_close \\\n", "0 33.9609 61.5976 14.0408 \n", "1 34.5139 62.4469 13.7120 \n", "2 34.9241 62.4136 13.9342 \n", "\n", " US_Stock_RIO_adj_close US_Stock_RSP_adj_close US_Stock_RY_adj_close \\\n", "0 29.1914 89.4955 61.3757 \n", "1 29.2980 89.9527 61.7559 \n", "2 29.1647 90.2605 62.5610 \n", "\n", " US_Stock_SCCO_adj_close US_Stock_SHEL_adj_close US_Stock_SHY_adj_close \\\n", "0 34.9289 48.7632 72.4602 \n", "1 34.9932 49.2547 72.4602 \n", "2 34.8147 49.4162 72.4256 \n", "\n", " US_Stock_SLB_adj_close US_Stock_SLV_adj_close US_Stock_SPIB_adj_close \\\n", "0 56.3329 16.21 27.3505 \n", "1 57.7995 16.17 27.3665 \n", "2 59.2338 16.23 27.3425 \n", "\n", " US_Stock_SPTL_adj_close US_Stock_SPYV_adj_close US_Stock_STLD_adj_close \\\n", "0 29.7473 26.0079 38.3885 \n", "1 29.8708 26.1005 39.0303 \n", "2 29.8708 26.2438 39.3213 \n", "\n", " US_Stock_TD_adj_close US_Stock_TECK_adj_close US_Stock_TIP_adj_close \\\n", "0 42.5980 24.5176 90.7421 \n", "1 42.7860 24.2081 90.8298 \n", "2 43.2633 24.6815 90.8538 \n", "\n", " US_Stock_TRGP_adj_close US_Stock_URA_adj_close US_Stock_VALE_adj_close \\\n", "0 36.5548 12.9328 7.3382 \n", "1 37.1803 12.9575 7.3841 \n", "2 37.5080 13.0565 7.3727 \n", "\n", " US_Stock_VCIT_adj_close US_Stock_VCSH_adj_close US_Stock_VEA_adj_close \\\n", "0 67.3465 64.5564 36.4048 \n", "1 67.4005 64.5646 36.6301 \n", "2 67.3851 64.5320 36.9599 \n", "\n", " US_Stock_VGIT_adj_close US_Stock_VGK_adj_close US_Stock_VGLT_adj_close \\\n", "0 53.3600 46.9375 62.5584 \n", "1 53.3768 47.1031 62.8097 \n", "2 53.3350 47.6549 62.8421 \n", "\n", " US_Stock_VGSH_adj_close US_Stock_VT_adj_close US_Stock_VTV_adj_close \\\n", "0 50.9038 63.9271 88.4504 \n", "1 50.8742 64.3623 88.7904 \n", "2 50.8615 64.7463 89.3046 \n", "\n", " US_Stock_VWO_adj_close US_Stock_VXUS_adj_close US_Stock_VYM_adj_close \\\n", "0 37.6210 46.2273 68.4155 \n", "1 37.9835 46.5496 68.6385 \n", "2 38.2171 46.9363 68.9411 \n", "\n", " US_Stock_WMB_adj_close US_Stock_WPM_adj_close US_Stock_X_adj_close \\\n", "0 20.5703 20.1420 35.5499 \n", "1 21.1384 20.0065 35.6164 \n", "2 21.5017 20.1058 35.9774 \n", "\n", " US_Stock_XLB_adj_close US_Stock_XLE_adj_close US_Stock_XOM_adj_close \\\n", "0 53.0113 53.1471 60.0402 \n", "1 53.3826 53.9431 61.2194 \n", "2 53.8489 54.2687 61.3042 \n", "\n", " US_Stock_YINN_adj_close US_Stock_ACWI_adj_volume US_Stock_AEM_adj_volume \\\n", "0 676.2986 2086655.0 958931.0 \n", "1 694.5039 3217386.0 1095086.0 \n", "2 702.6151 2044202.0 891723.0 \n", "\n", " US_Stock_AG_adj_volume US_Stock_AGG_adj_volume US_Stock_ALB_adj_volume \\\n", "0 4063026.0 8851239.0 1118561.0 \n", "1 3287805.0 2441732.0 1042125.0 \n", "2 2555393.0 2945384.0 820939.0 \n", "\n", " US_Stock_AMP_adj_volume US_Stock_BCS_adj_volume US_Stock_BKR_adj_volume \\\n", "0 812370.0 2479483.0 3653271.0 \n", "1 798721.0 2093172.0 4954241.0 \n", "2 870171.0 2435374.0 6213650.0 \n", "\n", " US_Stock_BND_adj_volume US_Stock_BNDX_adj_volume US_Stock_BP_adj_volume \\\n", "0 3104529.0 3335706.0 4204919.0 \n", "1 2216727.0 879852.0 4753619.0 \n", "2 2152595.0 1469575.0 6113799.0 \n", "\n", " US_Stock_BSV_adj_volume US_Stock_CAT_adj_volume US_Stock_CCJ_adj_volume \\\n", "0 1303818.0 5108362.0 2599140.0 \n", "1 1561255.0 4146054.0 2688710.0 \n", "2 1738090.0 4865769.0 1419745.0 \n", "\n", " US_Stock_CLF_adj_volume US_Stock_COP_adj_volume US_Stock_CVE_adj_volume \\\n", "0 17206204.0 3985183.0 3291948.0 \n", "1 15880018.0 4105840.0 5904620.0 \n", "2 18622576.0 4897240.0 3587570.0 \n", "\n", " US_Stock_CVX_adj_volume US_Stock_DE_adj_volume US_Stock_DVN_adj_volume \\\n", "0 5626785.0 1897594.0 4782523.0 \n", "1 5805458.0 1392277.0 3908743.0 \n", "2 4598264.0 1747032.0 4243507.0 \n", "\n", " US_Stock_EEM_adj_volume US_Stock_EFA_adj_volume US_Stock_EMB_adj_volume \\\n", "0 68969445.0 26529992.0 5965341.0 \n", "1 56228656.0 33384936.0 3111122.0 \n", "2 52154899.0 23974373.0 2974468.0 \n", "\n", " US_Stock_ENB_adj_volume US_Stock_EOG_adj_volume US_Stock_EWJ_adj_volume \\\n", "0 2464147.0 1899830.0 8155709.0 \n", "1 1839100.0 3068424.0 11572894.0 \n", "2 2025684.0 2124966.0 10920627.0 \n", "\n", " US_Stock_EWT_adj_volume US_Stock_EWY_adj_volume US_Stock_EWZ_adj_volume \\\n", "0 2288820.0 2409894.0 19866772.0 \n", "1 3400464.0 2176740.0 19379453.0 \n", "2 3300094.0 2258013.0 19276273.0 \n", "\n", " US_Stock_FCX_adj_volume US_Stock_FNV_adj_volume US_Stock_FXI_adj_volume \\\n", "0 21899857.0 529286.0 14290869.0 \n", "1 20673587.0 1000554.0 13556338.0 \n", "2 19540525.0 650930.0 11832265.0 \n", "\n", " US_Stock_GDX_adj_volume US_Stock_GDXJ_adj_volume US_Stock_GLD_adj_volume \\\n", "0 43172042.0 14051187.0 11762515.0 \n", "1 45780941.0 13739513.0 7904326.0 \n", "2 31300269.0 8099208.0 7329718.0 \n", "\n", " US_Stock_GOLD_adj_volume US_Stock_HAL_adj_volume US_Stock_HES_adj_volume \\\n", "0 517148.0 7269233.0 2874217.0 \n", "1 541989.0 11181926.0 3694948.0 \n", "2 396352.0 10142711.0 3839434.0 \n", "\n", " US_Stock_HL_adj_volume US_Stock_IAU_adj_volume US_Stock_IEF_adj_volume \\\n", "0 5134266.0 5181470.5 3902185.0 \n", "1 5287284.0 7576264.5 2135976.0 \n", "2 3053921.0 6067366.5 2430030.0 \n", "\n", " US_Stock_IEMG_adj_volume US_Stock_IGSB_adj_volume \\\n", "0 10928291.0 1390474.0 \n", "1 10018637.0 1691482.0 \n", "2 18278286.0 1253936.0 \n", "\n", " US_Stock_JNK_adj_volume US_Stock_KGC_adj_volume US_Stock_KMI_adj_volume \\\n", "0 3.825092e+06 8927913.0 16189076.0 \n", "1 8.505088e+06 12718030.0 13893583.0 \n", "2 2.887354e+06 8780206.0 12718909.0 \n", "\n", " US_Stock_LQD_adj_volume US_Stock_LYB_adj_volume US_Stock_MBB_adj_volume \\\n", "0 8480804.0 1127775.0 497374.0 \n", "1 5087608.0 1437330.0 815582.0 \n", "2 7710116.0 1630994.0 904625.0 \n", "\n", " US_Stock_MPC_adj_volume US_Stock_MS_adj_volume US_Stock_NEM_adj_volume \\\n", "0 3055886.0 6644643.0 5325831.0 \n", "1 4844035.0 6955511.0 5143550.0 \n", "2 3156419.0 8678825.0 3876001.0 \n", "\n", " US_Stock_NUE_adj_volume US_Stock_NUGT_adj_volume US_Stock_OIH_adj_volume \\\n", "0 3756643.0 1209120.4 346158.7 \n", "1 3772513.0 1262889.6 666077.9 \n", "2 2529263.0 1108731.2 607154.2 \n", "\n", " US_Stock_OKE_adj_volume US_Stock_OXY_adj_volume US_Stock_PAAS_adj_volume \\\n", "0 2548565.0 2829956.0 1549909.0 \n", "1 2539138.0 3463407.0 1479705.0 \n", "2 2790821.0 2706051.0 1657160.0 \n", "\n", " US_Stock_RIO_adj_volume US_Stock_RSP_adj_volume US_Stock_RY_adj_volume \\\n", "0 3908206.0 1132667.0 555237.0 \n", "1 2849232.0 553456.0 713464.0 \n", "2 2377675.0 577397.0 1016622.0 \n", "\n", " US_Stock_SCCO_adj_volume US_Stock_SHEL_adj_volume \\\n", "0 932009.0 818213.0 \n", "1 829856.0 1306782.0 \n", "2 632187.0 1386012.0 \n", "\n", " US_Stock_SHY_adj_volume US_Stock_SLB_adj_volume US_Stock_SLV_adj_volume \\\n", "0 1786046.0 8678208.0 8943159.0 \n", "1 1040937.0 9529205.0 9149509.0 \n", "2 955446.0 12252324.0 7466259.0 \n", "\n", " US_Stock_SPIB_adj_volume US_Stock_SPTL_adj_volume \\\n", "0 911414.0 2450873.0 \n", "1 594183.0 295799.0 \n", "2 2792148.0 1701918.0 \n", "\n", " US_Stock_SPYV_adj_volume US_Stock_STLD_adj_volume US_Stock_TD_adj_volume \\\n", "0 874915.0 2868565.0 1424536.0 \n", "1 444574.0 4305494.0 853992.0 \n", "2 904759.0 1959521.0 1233808.0 \n", "\n", " US_Stock_TECK_adj_volume US_Stock_TIP_adj_volume \\\n", "0 4510381.0 1525065.0 \n", "1 4350134.0 3322391.0 \n", "2 3990943.0 1946494.0 \n", "\n", " US_Stock_TRGP_adj_volume US_Stock_URA_adj_volume \\\n", "0 1844452.0 608348.0 \n", "1 2900595.0 655209.0 \n", "2 2291888.0 276964.0 \n", "\n", " US_Stock_VALE_adj_volume US_Stock_VCIT_adj_volume \\\n", "0 19189400.0 967681.0 \n", "1 20449625.0 1101686.0 \n", "2 22033143.0 1354024.0 \n", "\n", " US_Stock_VCSH_adj_volume US_Stock_VEA_adj_volume \\\n", "0 1772292.0 9308030.0 \n", "1 1533054.0 10178879.0 \n", "2 1439293.0 9883496.0 \n", "\n", " US_Stock_VGIT_adj_volume US_Stock_VGK_adj_volume \\\n", "0 239750.0 3203054.0 \n", "1 372129.0 3227641.0 \n", "2 122940.0 4297473.0 \n", "\n", " US_Stock_VGLT_adj_volume US_Stock_VGSH_adj_volume US_Stock_VT_adj_volume \\\n", "0 122068.0 201319.0 1267705.0 \n", "1 34298.0 414231.0 768549.0 \n", "2 591445.0 161725.0 819369.0 \n", "\n", " US_Stock_VTV_adj_volume US_Stock_VWO_adj_volume US_Stock_VXUS_adj_volume \\\n", "0 1925989.0 17804562.0 1937809.0 \n", "1 1236706.0 17896178.0 1391297.0 \n", "2 1501979.0 13003543.0 1298665.0 \n", "\n", " US_Stock_VYM_adj_volume US_Stock_WMB_adj_volume US_Stock_WPM_adj_volume \\\n", "0 1534451.0 4480305.0 1918509.0 \n", "1 1170263.0 8969474.0 2899390.0 \n", "2 964661.0 7298642.0 2029410.0 \n", "\n", " US_Stock_X_adj_volume US_Stock_XLB_adj_volume US_Stock_XLE_adj_volume \\\n", "0 11994695.0 8979239.0 14891354.0 \n", "1 12109805.0 4952274.0 19299630.0 \n", "2 9990157.0 4120092.0 14306843.0 \n", "\n", " US_Stock_XOM_adj_volume US_Stock_YINN_adj_volume FX_AUDJPY FX_AUDUSD \\\n", "0 11469254.0 51983.65 87.933498 0.783393 \n", "1 13957739.0 39273.00 88.130730 0.782779 \n", "2 10863030.0 43778.95 88.709278 0.786472 \n", "\n", " FX_CADJPY FX_CHFJPY FX_EURAUD FX_EURGBP FX_EURJPY FX_EURUSD \\\n", "0 89.764583 115.545884 1.540368 0.887622 135.449910 1.206713 \n", "1 89.731507 115.235160 1.534906 0.889248 135.272384 1.201492 \n", "2 90.306741 115.750255 1.535084 0.890830 136.176212 1.207300 \n", "\n", " FX_GBPAUD FX_GBPJPY FX_GBPUSD FX_NZDJPY FX_NZDUSD FX_USDCHF \\\n", "0 1.735387 152.598635 1.359490 79.721293 0.710231 0.971450 \n", "1 1.726072 152.119947 1.351132 79.815128 0.708920 0.977019 \n", "2 1.723206 152.864365 1.355253 80.734616 0.715770 0.974460 \n", "\n", " FX_USDJPY FX_ZARJPY FX_ZARUSD FX_NOKUSD FX_NOKEUR FX_CADUSD \\\n", "0 112.247002 9.028141 0.080431 0.123146 0.102051 0.799706 \n", "1 112.586998 9.111194 0.080926 0.123355 0.102668 0.796997 \n", "2 112.793999 9.177484 0.081365 0.123975 0.102688 0.800634 \n", "\n", " FX_AUDNZD FX_EURCHF FX_EURCAD FX_AUDCAD FX_GBPCHF FX_EURNZD \\\n", "0 1.103011 1.172261 1.508946 0.979601 1.320676 1.699043 \n", "1 1.104186 1.173881 1.507524 0.982160 1.320083 1.694821 \n", "2 1.098776 1.176466 1.507930 0.982311 1.320640 1.686714 \n", "\n", " FX_AUDCHF FX_GBPNZD FX_GBPCAD FX_CADCHF FX_NZDCAD FX_NZDCHF \\\n", "0 0.761027 1.914152 1.699987 0.776874 0.888115 0.689954 \n", "1 0.764790 1.905904 1.695279 0.778682 0.889488 0.692628 \n", "2 0.766385 1.893418 1.692724 0.780186 0.894004 0.697490 \n", "\n", " FX_ZAREUR FX_NOKGBP FX_NOKCHF FX_ZARCHF FX_NOKJPY FX_ZARGBP \n", "0 0.066653 0.090582 0.119630 0.078135 13.822740 0.059163 \n", "1 0.067354 0.091297 0.120520 0.079066 13.888146 0.059895 \n", "2 0.067394 0.091478 0.120809 0.079287 13.983675 0.060037 \n", "\n", "šŸŽÆ Sample Targets:\n", " date_id target_0 target_1 target_2 target_3 target_4 target_5 \\\n", "0 0 0.005948 -0.002851 -0.004675 -0.000639 NaN NaN \n", "1 1 0.005783 -0.024118 -0.007052 -0.018955 -0.031852 -0.019452 \n", "2 2 0.001048 0.023836 -0.008934 -0.022060 NaN NaN \n", "\n", " target_6 target_7 target_8 target_9 target_10 target_11 target_12 \\\n", "0 -0.006729 0.006066 NaN 0.003446 NaN -0.005674 NaN \n", "1 0.003002 -0.006876 -0.002042 0.021284 0.017698 0.004791 0.010499 \n", "2 0.037449 0.007658 NaN -0.026844 NaN -0.002100 NaN \n", "\n", " target_13 target_14 target_15 target_16 target_17 target_18 \\\n", "0 0.000306 NaN -0.005366 -0.004691 NaN 0.004416 \n", "1 -0.018310 0.013664 0.023292 0.007293 -0.00872 -0.021583 \n", "2 0.029436 NaN 0.010725 -0.008480 NaN -0.004744 \n", "\n", " target_19 target_20 target_21 target_22 target_23 target_24 \\\n", "0 NaN 0.008382 0.006122 NaN -0.000908 NaN \n", "1 0.008823 -0.003377 -0.020721 -0.022573 0.012265 -0.01707 \n", "2 NaN 0.009616 -0.008239 NaN 0.001071 NaN \n", "\n", " target_25 target_26 target_27 target_28 target_29 target_30 \\\n", "0 0.000863 0.001459 0.001350 -0.013582 -0.005273 0.000193 \n", "1 -0.026115 0.011370 -0.000112 0.000741 0.028171 -0.003402 \n", "2 -0.012131 -0.006961 0.000797 -0.004581 0.011770 0.033271 \n", "\n", " target_31 target_32 target_33 target_34 target_35 target_36 \\\n", "0 0.005351 -0.003309 NaN NaN NaN -0.001916 \n", "1 -0.001346 -0.010453 -0.002559 -0.022646 -0.0017 -0.020349 \n", "2 0.009655 0.014027 NaN NaN NaN 0.027851 \n", "\n", " target_37 target_38 target_39 target_40 target_41 target_42 \\\n", "0 -0.005857 NaN -0.009557 -0.006358 -0.008902 -0.021810 \n", "1 -0.016194 -0.019999 0.011118 0.012549 -0.010465 0.032790 \n", "2 0.003358 NaN 0.008446 0.020502 -0.035575 0.055146 \n", "\n", " target_43 target_44 target_45 target_46 target_47 target_48 \\\n", "0 -0.007458 NaN 0.005595 0.001842 -0.000073 -0.000822 \n", "1 0.020437 0.009641 -0.014834 0.026920 0.013685 -0.018111 \n", "2 0.004530 NaN -0.001383 -0.024885 0.004354 0.004539 \n", "\n", " target_49 target_50 target_51 target_52 target_53 target_54 \\\n", "0 -0.004331 NaN NaN NaN -0.009646 NaN \n", "1 -0.010689 -0.0037 0.007103 0.005167 0.032273 -0.017246 \n", "2 0.016804 NaN NaN NaN 0.017368 NaN \n", "\n", " target_55 target_56 target_57 target_58 target_59 target_60 \\\n", "0 -0.004707 -0.000849 NaN NaN NaN 0.007431 \n", "1 -0.002005 0.004097 0.015988 -0.011252 0.003843 -0.023090 \n", "2 0.013645 -0.008489 NaN NaN NaN -0.012845 \n", "\n", " target_61 target_62 target_63 target_64 target_65 target_66 \\\n", "0 0.009642 NaN NaN -0.003436 -0.010998 NaN \n", "1 -0.021337 -0.020045 -0.012447 0.029862 -0.003103 -0.012308 \n", "2 -0.012748 NaN NaN 0.013185 -0.000947 NaN \n", "\n", " target_67 target_68 target_69 target_70 target_71 target_72 \\\n", "0 NaN NaN -0.001205 NaN NaN -0.001268 \n", "1 0.006842 -0.013901 -0.010884 -0.010778 -0.015615 -0.029991 \n", "2 NaN NaN -0.003700 NaN NaN -0.010075 \n", "\n", " target_73 target_74 target_75 target_76 target_77 target_78 \\\n", "0 -0.007163 -0.005196 0.001768 0.008688 0.004642 NaN \n", "1 -0.015137 0.013063 -0.010369 0.002142 -0.038396 -0.005941 \n", "2 0.004955 -0.003464 0.008065 0.014626 0.007475 NaN \n", "\n", " target_79 target_80 target_81 target_82 target_83 target_84 \\\n", "0 -0.001401 NaN NaN NaN -0.002078 -0.003610 \n", "1 0.000395 0.004019 -0.009377 -0.005723 0.027899 0.006087 \n", "2 0.006256 NaN NaN NaN -0.024164 -0.011051 \n", "\n", " target_85 target_86 target_87 target_88 target_89 target_90 \\\n", "0 NaN 0.007626 -0.003806 -0.001211 0.016399 -0.005027 \n", "1 0.033952 -0.017688 0.000688 -0.024991 -0.020762 0.035161 \n", "2 NaN -0.010906 -0.012993 -0.011520 -0.011776 0.010287 \n", "\n", " target_91 target_92 target_93 target_94 target_95 target_96 \\\n", "0 -0.012353 NaN 0.003808 0.002370 0.001051 -0.003365 \n", "1 0.030814 0.011525 -0.020937 0.024080 0.017528 0.026374 \n", "2 0.018147 NaN -0.018549 0.018351 -0.015451 -0.027554 \n", "\n", " target_97 target_98 target_99 target_100 target_101 target_102 \\\n", "0 -0.013944 0.004406 NaN -0.014940 NaN 0.010940 \n", "1 -0.015568 -0.008627 0.01368 0.029102 0.015813 -0.001147 \n", "2 0.021193 -0.003924 NaN 0.017236 NaN 0.009971 \n", "\n", " target_103 target_104 target_105 target_106 target_107 target_108 \\\n", "0 -0.000315 NaN -0.000267 0.014945 -0.006862 0.023320 \n", "1 0.012957 -0.02454 0.007860 0.006160 0.000954 0.000563 \n", "2 0.000717 NaN -0.000562 0.000341 -0.006022 -0.007648 \n", "\n", " target_109 target_110 target_111 target_112 target_113 target_114 \\\n", "0 NaN NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN NaN \n", "2 -0.0194 0.022963 0.002118 -0.000891 -0.001069 -0.006342 \n", "\n", " target_115 target_116 target_117 target_118 target_119 target_120 \\\n", "0 NaN 0.004554 -0.007867 -0.018033 -0.012145 -0.014590 \n", "1 NaN 0.004006 -0.009600 -0.021452 -0.017904 -0.034777 \n", "2 0.001811 -0.003643 -0.006552 0.002907 -0.006873 -0.024671 \n", "\n", " target_121 target_122 target_123 target_124 target_125 target_126 \\\n", "0 NaN NaN NaN 0.001010 -0.003504 -0.025786 \n", "1 NaN NaN NaN -0.006408 0.007509 -0.034827 \n", "2 -0.000547 0.00999 -0.00023 -0.027811 0.003392 -0.031249 \n", "\n", " target_127 target_128 target_129 target_130 target_131 target_132 \\\n", "0 0.009532 NaN 0.024422 NaN 0.009494 0.007206 \n", "1 0.012420 NaN -0.011014 NaN 0.008368 -0.002414 \n", "2 -0.007107 0.002992 -0.008398 -0.016986 -0.002695 -0.006060 \n", "\n", " target_133 target_134 target_135 target_136 target_137 target_138 \\\n", "0 -0.019386 -0.005648 0.030032 0.007995 -0.027956 NaN \n", "1 0.004516 -0.007113 -0.011077 0.008343 -0.039170 NaN \n", "2 -0.000023 0.002164 0.001606 -0.001371 -0.023792 -0.004714 \n", "\n", " target_139 target_140 target_141 target_142 target_143 target_144 \\\n", "0 0.001006 NaN -0.018092 0.014101 0.008300 -0.069601 \n", "1 -0.006651 NaN 0.008870 0.004050 0.006678 -0.030318 \n", "2 -0.007630 0.044917 0.007047 -0.010231 0.005122 -0.003769 \n", "\n", " target_145 target_146 target_147 target_148 target_149 target_150 \\\n", "0 NaN 0.020856 NaN NaN NaN -0.002438 \n", "1 NaN -0.000966 NaN NaN NaN -0.005193 \n", "2 0.008884 0.001495 -0.02785 -0.001519 0.000527 0.018903 \n", "\n", " target_151 target_152 target_153 target_154 target_155 target_156 \\\n", "0 -0.022161 0.023370 NaN NaN 0.022898 -0.022300 \n", "1 -0.046343 -0.007532 NaN NaN 0.039941 -0.010183 \n", "2 -0.014066 -0.014312 -0.006752 0.011204 0.017460 0.000651 \n", "\n", " target_157 target_158 target_159 target_160 target_161 target_162 \\\n", "0 0.031094 -0.046566 NaN -0.025036 -0.021201 0.020832 \n", "1 0.062026 -0.016439 NaN -0.042487 0.008668 0.000795 \n", "2 0.044942 -0.015542 0.002878 -0.021334 0.009963 -0.001405 \n", "\n", " target_163 target_164 target_165 target_166 target_167 target_168 \\\n", "0 -0.022849 -0.026458 0.026446 -0.004135 -0.002602 0.013527 \n", "1 0.018624 -0.042925 0.027106 -0.008688 0.001187 -0.008260 \n", "2 0.050504 -0.022963 -0.004118 0.000942 -0.009002 -0.007211 \n", "\n", " target_169 target_170 target_171 target_172 target_173 target_174 \\\n", "0 -0.014975 -0.025797 NaN -0.011665 0.041561 0.024776 \n", "1 -0.011593 0.004401 NaN 0.000949 0.033563 0.001663 \n", "2 0.000691 0.011718 0.005635 0.002998 -0.004047 0.016325 \n", "\n", " target_175 target_176 target_177 target_178 target_179 target_180 \\\n", "0 NaN -0.035469 0.018539 -0.001668 NaN NaN \n", "1 NaN -0.053472 0.032949 -0.006998 NaN NaN \n", "2 -0.002156 -0.033666 0.015254 -0.006061 0.011783 -0.000469 \n", "\n", " target_181 target_182 target_183 target_184 target_185 target_186 \\\n", "0 -0.006115 0.001410 NaN NaN NaN 0.008601 \n", "1 0.005512 -0.006123 NaN NaN NaN 0.002304 \n", "2 0.047367 -0.004353 -0.002383 -0.006311 -0.004019 0.005523 \n", "\n", " target_187 target_188 target_189 target_190 target_191 target_192 \\\n", "0 NaN 0.012352 0.011356 -0.029917 0.019282 0.014227 \n", "1 NaN -0.003558 0.013336 -0.037864 0.039271 0.019157 \n", "2 -0.007541 0.007738 -0.002008 -0.021812 0.018952 -0.002243 \n", "\n", " target_193 target_194 target_195 target_196 target_197 target_198 \\\n", "0 -0.050278 NaN 0.001676 0.006983 NaN -0.015695 \n", "1 -0.002120 NaN -0.008022 0.027719 NaN -0.035258 \n", "2 0.028128 0.000368 0.020502 0.022683 0.004396 -0.020321 \n", "\n", " target_199 target_200 target_201 target_202 target_203 target_204 \\\n", "0 -0.022267 0.018504 0.014163 -0.000810 -0.021239 NaN \n", "1 -0.020419 0.014699 0.046339 0.000782 -0.033387 NaN \n", "2 -0.009468 -0.002729 0.021740 -0.009238 -0.032906 -0.032403 \n", "\n", " target_205 target_206 target_207 target_208 target_209 target_210 \\\n", "0 0.019315 0.007748 NaN 0.029447 -0.037083 NaN \n", "1 -0.007746 0.010668 NaN 0.021574 0.003010 NaN \n", "2 -0.006765 0.019797 -0.007337 0.008252 0.052467 0.000433 \n", "\n", " target_211 target_212 target_213 target_214 target_215 target_216 \\\n", "0 0.005343 -0.002165 0.011812 NaN NaN NaN \n", "1 -0.017568 -0.004150 -0.012218 -0.019059 -0.023321 -0.06137 \n", "2 -0.006881 -0.011642 0.015571 0.018387 0.017833 0.00103 \n", "\n", " target_217 target_218 target_219 target_220 target_221 target_222 \\\n", "0 NaN 0.007931 0.013517 -0.024444 NaN NaN \n", "1 0.051253 0.008305 -0.002369 -0.043932 -0.011774 0.009604 \n", "2 -0.004553 -0.009310 -0.003205 -0.007879 0.002301 -0.006357 \n", "\n", " target_223 target_224 target_225 target_226 target_227 target_228 \\\n", "0 -0.020364 0.026606 NaN -0.035387 -0.015713 0.013177 \n", "1 -0.010111 0.043366 -0.00753 -0.041235 0.011444 -0.006559 \n", "2 0.012910 0.005319 0.00615 0.003099 -0.006663 -0.001293 \n", "\n", " target_229 target_230 target_231 target_232 target_233 target_234 \\\n", "0 -0.012428 -0.066126 0.004703 NaN 0.003148 0.017918 \n", "1 -0.010256 -0.083603 -0.016022 0.000552 0.017233 -0.003857 \n", "2 0.022251 -0.018721 0.017608 -0.016460 0.018194 -0.007291 \n", "\n", " target_235 target_236 target_237 target_238 target_239 target_240 \\\n", "0 NaN 0.005072 0.041336 NaN NaN -0.000643 \n", "1 0.054489 -0.006581 0.047827 -0.020035 -0.023810 0.010321 \n", "2 -0.032226 0.021170 0.036959 -0.025651 0.007002 -0.019782 \n", "\n", " target_241 target_242 target_243 target_244 target_245 target_246 \\\n", "0 -0.053281 0.033392 -0.002054 0.002682 NaN 0.031399 \n", "1 -0.026264 0.001951 -0.024599 0.000280 -0.016924 0.050842 \n", "2 -0.013794 0.005983 0.001161 -0.025739 0.001040 0.016247 \n", "\n", " target_247 target_248 target_249 target_250 target_251 target_252 \\\n", "0 NaN -0.037722 -0.018347 0.017515 0.000394 NaN \n", "1 0.016103 -0.040373 -0.039519 0.019119 -0.000443 -0.009408 \n", "2 -0.043424 -0.007787 -0.009944 -0.007557 0.010595 -0.027349 \n", "\n", " target_253 target_254 target_255 target_256 target_257 target_258 \\\n", "0 -0.042190 NaN 0.015616 NaN 0.009401 0.003878 \n", "1 -0.052129 -0.006298 0.020403 -0.008417 0.031792 0.023325 \n", "2 -0.003127 0.003768 0.011306 0.010245 0.031394 -0.011899 \n", "\n", " target_259 target_260 target_261 target_262 target_263 target_264 \\\n", "0 -0.008184 NaN NaN NaN -0.022696 0.014552 \n", "1 -0.016134 0.009278 0.059499 -0.019505 -0.017253 0.020641 \n", "2 0.018190 -0.019195 0.009414 0.004964 -0.007571 -0.003708 \n", "\n", " target_265 target_266 target_267 target_268 target_269 target_270 \\\n", "0 0.013675 0.062873 -0.056658 0.002107 NaN NaN \n", "1 0.013437 0.066721 -0.029267 -0.022330 0.010243 0.011439 \n", "2 -0.014658 0.058822 0.000530 -0.000005 -0.008954 -0.009241 \n", "\n", " target_271 target_272 target_273 target_274 target_275 target_276 \\\n", "0 0.017345 0.001784 0.013630 -0.018237 NaN NaN \n", "1 0.014487 0.002081 -0.001749 -0.020922 0.016086 0.031397 \n", "2 -0.012811 -0.005891 -0.003014 0.008882 0.013473 -0.064458 \n", "\n", " target_277 target_278 target_279 target_280 target_281 target_282 \\\n", "0 -0.002114 -0.006275 -0.003660 NaN NaN 0.038958 \n", "1 0.009283 0.012712 -0.021579 0.019946 0.003210 0.080954 \n", "2 -0.020986 0.007830 0.006097 -0.017966 -0.015291 0.037761 \n", "\n", " target_283 target_284 target_285 target_286 target_287 target_288 \\\n", "0 -0.016258 NaN NaN NaN -0.037854 NaN \n", "1 0.015573 -0.011448 -0.010115 -0.003891 -0.040693 -0.004531 \n", "2 -0.000149 -0.011418 0.002418 0.016169 -0.022883 -0.043373 \n", "\n", " target_289 target_290 target_291 target_292 target_293 target_294 \\\n", "0 0.013660 NaN -0.040243 -0.013814 0.022867 0.018692 \n", "1 -0.002228 0.003551 -0.050675 -0.020157 -0.004372 0.017714 \n", "2 -0.002985 0.016134 -0.008244 0.002646 -0.001686 -0.009425 \n", "\n", " target_295 target_296 target_297 target_298 target_299 target_300 \\\n", "0 -0.006041 -0.057223 -0.000397 0.019764 -0.025543 -0.037914 \n", "1 -0.014809 -0.055013 0.033606 -0.001583 -0.032291 -0.021359 \n", "2 0.004300 -0.019371 0.037513 0.000149 0.010439 -0.007981 \n", "\n", " target_301 target_302 target_303 target_304 target_305 target_306 \\\n", "0 NaN NaN NaN -0.010677 0.003239 -0.002690 \n", "1 0.009175 0.065608 -0.001578 0.018146 0.009996 -0.014720 \n", "2 -0.011354 0.029575 -0.013380 -0.002442 0.001441 -0.001323 \n", "\n", " target_307 target_308 target_309 target_310 target_311 target_312 \\\n", "0 0.051024 NaN 0.011686 NaN -0.000320 NaN \n", "1 0.052664 -0.027229 -0.012754 -0.025139 0.008038 -0.002489 \n", "2 0.012048 0.008357 0.006601 0.017176 -0.009551 -0.017465 \n", "\n", " target_313 target_314 target_315 target_316 target_317 target_318 \\\n", "0 0.034720 0.018816 -0.065809 0.003011 NaN 0.006156 \n", "1 0.047495 0.004336 -0.056798 0.035202 0.011551 0.009334 \n", "2 0.024674 0.007026 -0.023597 0.023916 -0.019277 0.004108 \n", "\n", " target_319 target_320 target_321 target_322 target_323 target_324 \\\n", "0 -0.040631 NaN -0.036876 -0.021203 -0.006955 NaN \n", "1 -0.040023 0.004375 -0.022925 -0.016283 -0.009455 -0.037377 \n", "2 -0.016443 -0.021069 -0.021830 -0.015872 0.000802 -0.010037 \n", "\n", " target_325 target_326 target_327 target_328 target_329 target_330 \\\n", "0 -0.040590 NaN -0.001146 NaN NaN -0.009070 \n", "1 -0.035228 0.018944 -0.003990 0.001439 0.005442 0.018436 \n", "2 -0.018760 0.031047 0.003446 -0.003856 -0.082631 0.024440 \n", "\n", " target_331 target_332 target_333 target_334 target_335 target_336 \\\n", "0 NaN 0.012078 -0.035902 -0.031057 -0.022328 0.029666 \n", "1 -0.017276 0.013023 -0.045350 -0.003960 -0.012070 0.023311 \n", "2 -0.060391 -0.002704 -0.032089 -0.010500 0.045023 0.011767 \n", "\n", " target_337 target_338 target_339 target_340 target_341 target_342 \\\n", "0 0.012200 NaN 0.025222 NaN NaN -0.038690 \n", "1 0.001610 -0.015401 0.019943 0.010631 0.003091 -0.033982 \n", "2 -0.012111 -0.024611 0.006784 -0.002298 -0.005530 -0.025162 \n", 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-0.040243 -0.013814 0.022867 0.018692 \n", "1 -0.002228 0.003551 -0.050675 -0.020157 -0.004372 0.017714 \n", "2 -0.002985 0.016134 -0.008244 0.002646 -0.001686 -0.009425 \n", "\n", " target_295 target_296 target_297 target_298 target_299 target_300 \\\n", "0 -0.006041 -0.057223 -0.000397 0.019764 -0.025543 -0.037914 \n", "1 -0.014809 -0.055013 0.033606 -0.001583 -0.032291 -0.021359 \n", "2 0.004300 -0.019371 0.037513 0.000149 0.010439 -0.007981 \n", "\n", " target_301 target_302 target_303 target_304 target_305 target_306 \\\n", "0 NaN NaN NaN -0.010677 0.003239 -0.002690 \n", "1 0.009175 0.065608 -0.001578 0.018146 0.009996 -0.014720 \n", "2 -0.011354 0.029575 -0.013380 -0.002442 0.001441 -0.001323 \n", "\n", " target_307 target_308 target_309 target_310 target_311 target_312 \\\n", "0 0.051024 NaN 0.011686 NaN -0.000320 NaN \n", "1 0.052664 -0.027229 -0.012754 -0.025139 0.008038 -0.002489 \n", "2 0.012048 0.008357 0.006601 0.017176 -0.009551 -0.017465 \n", "\n", " target_313 target_314 target_315 target_316 target_317 target_318 \\\n", "0 0.034720 0.018816 -0.065809 0.003011 NaN 0.006156 \n", "1 0.047495 0.004336 -0.056798 0.035202 0.011551 0.009334 \n", "2 0.024674 0.007026 -0.023597 0.023916 -0.019277 0.004108 \n", "\n", " target_319 target_320 target_321 target_322 target_323 target_324 \\\n", "0 -0.040631 NaN -0.036876 -0.021203 -0.006955 NaN \n", "1 -0.040023 0.004375 -0.022925 -0.016283 -0.009455 -0.037377 \n", "2 -0.016443 -0.021069 -0.021830 -0.015872 0.000802 -0.010037 \n", "\n", " target_325 target_326 target_327 target_328 target_329 target_330 \\\n", "0 -0.040590 NaN -0.001146 NaN NaN -0.009070 \n", "1 -0.035228 0.018944 -0.003990 0.001439 0.005442 0.018436 \n", "2 -0.018760 0.031047 0.003446 -0.003856 -0.082631 0.024440 \n", "\n", " target_331 target_332 target_333 target_334 target_335 target_336 \\\n", "0 NaN 0.012078 -0.035902 -0.031057 -0.022328 0.029666 \n", "1 -0.017276 0.013023 -0.045350 -0.003960 -0.012070 0.023311 \n", "2 -0.060391 -0.002704 -0.032089 -0.010500 0.045023 0.011767 \n", "\n", " target_337 target_338 target_339 target_340 target_341 target_342 \\\n", "0 0.012200 NaN 0.025222 NaN NaN -0.038690 \n", "1 0.001610 -0.015401 0.019943 0.010631 0.003091 -0.033982 \n", "2 -0.012111 -0.024611 0.006784 -0.002298 -0.005530 -0.025162 \n", "\n", " target_343 target_344 target_345 target_346 target_347 target_348 \\\n", "0 -0.006624 -0.007629 0.020645 NaN -0.006132 NaN \n", "1 -0.016824 0.012047 -0.000923 0.009749 -0.003478 0.001487 \n", "2 -0.010426 0.026367 -0.028056 0.018295 0.004207 0.003930 \n", "\n", " target_349 target_350 target_351 target_352 target_353 target_354 \\\n", "0 -0.049421 -0.008647 NaN 0.004848 0.048748 -0.043554 \n", "1 -0.035106 -0.008274 -0.003159 -0.004567 0.055604 -0.032553 \n", "2 -0.014420 0.010339 0.014611 0.014157 0.060654 -0.034887 \n", "\n", " target_355 target_356 target_357 target_358 target_359 target_360 \\\n", "0 -0.026801 NaN -0.028981 -0.001919 0.003485 -0.048480 \n", "1 -0.043308 0.005173 -0.023901 -0.001651 -0.003429 -0.038367 \n", "2 -0.040393 -0.013646 -0.019227 0.005090 0.012471 -0.021991 \n", "\n", " target_361 target_362 target_363 target_364 target_365 target_366 \\\n", "0 -0.082990 NaN NaN NaN -0.039969 0.041481 \n", "1 -0.085240 0.028655 -0.008811 -0.046645 -0.040424 0.040197 \n", "2 -0.097312 0.045188 0.003952 -0.036997 -0.034077 0.038468 \n", "\n", " target_367 target_368 target_369 target_370 target_371 target_372 \\\n", "0 0.014812 NaN 0.044623 NaN NaN NaN \n", "1 0.011455 -0.021626 0.051413 0.012968 -1.171004e-02 -0.005510 \n", "2 -0.005488 -0.014796 0.040806 0.015353 -2.795074e-07 0.005538 \n", "\n", " target_373 target_374 target_375 target_376 target_377 target_378 \\\n", "0 0.055153 -0.019665 NaN 0.009049 0.022842 NaN \n", "1 0.038579 -0.014224 0.021975 0.009566 0.006713 -0.005479 \n", "2 0.043075 -0.000577 0.014431 -0.009259 0.004082 -0.022966 \n", "\n", " target_379 target_380 target_381 target_382 target_383 target_384 \\\n", "0 0.046370 0.046350 0.028058 NaN 0.019017 0.004705 \n", "1 0.031304 0.036766 0.024670 -0.004608 0.004908 -0.007125 \n", "2 0.010501 0.023470 0.010894 -0.022604 -0.010525 -0.030055 \n", "\n", " target_385 target_386 target_387 target_388 target_389 target_390 \\\n", "0 NaN -0.012729 NaN -0.011773 0.023571 NaN \n", "1 0.046980 -0.000298 0.004824 -0.010374 0.006768 -0.009518 \n", "2 0.028862 0.017025 0.041938 0.005172 -0.000601 -0.031099 \n", "\n", " target_391 target_392 target_393 target_394 target_395 target_396 \\\n", "0 -0.091022 0.006293 0.022177 0.040977 0.002379 -0.051436 \n", "1 -0.070225 -0.001248 0.059259 0.020991 0.008073 -0.040249 \n", "2 -0.056066 -0.000063 0.115478 0.036307 0.015766 -0.052541 \n", "\n", " target_397 target_398 target_399 target_400 target_401 target_402 \\\n", "0 0.005262 NaN -0.066416 0.007124 -0.014872 NaN \n", "1 -0.001909 0.009720 -0.051488 -0.007198 -0.012201 -0.028174 \n", "2 0.010377 0.020045 -0.045181 -0.012679 0.003707 -0.011758 \n", "\n", " target_403 target_404 target_405 target_406 target_407 target_408 \\\n", "0 NaN NaN 0.035734 0.020090 NaN -0.042561 \n", "1 0.028013 -0.037905 0.019292 0.017704 -0.018676 -0.022584 \n", "2 0.038694 -0.037981 0.005764 -0.001951 -0.012845 -0.007365 \n", "\n", " target_409 target_410 target_411 target_412 target_413 target_414 \\\n", "0 -0.012987 0.027634 -0.041252 0.031637 NaN NaN \n", "1 -0.005958 0.021191 -0.040295 0.029351 -0.006528 0.003377 \n", "2 0.008149 0.013421 -0.090242 0.016779 -0.003223 -0.006712 \n", "\n", " target_415 target_416 target_417 target_418 target_419 target_420 \\\n", "0 0.021239 -0.005595 NaN -0.004628 0.033793 NaN \n", "1 0.021372 -0.001517 0.012846 0.010547 0.030527 -0.000764 \n", "2 0.009308 0.001857 -0.012761 -0.002345 0.017529 -0.005394 \n", "\n", " target_421 target_422 target_423 \n", "0 0.038234 NaN 0.027310 \n", "1 0.025021 0.003548 0.020940 \n", "2 0.004835 -0.009075 0.001706 \n" ] } ], "source": [ "# Data Exploration\n", "print(\"šŸ” DATASET OVERVIEW\")\n", "print(\"=\" * 50)\n", "\n", "# Display basic info about training data\n", "print(\"\\nšŸ“ˆ Training Features:\")\n", "print(f\"Shape: {train_df.shape}\")\n", "print(f\"Date range: {train_df['date_id'].min()} to {train_df['date_id'].max()}\")\n", "print(f\"Features: {train_df.shape[1] - 1}\") # -1 for date_id\n", "\n", "# Group features by type\n", "feature_groups = {\n", " 'LME_metals': [col for col in train_df.columns if 'LME_' in col],\n", " 'JPX_futures': [col for col in train_df.columns if 'JPX_' in col],\n", " 'US_stocks': [col for col in train_df.columns if 'US_Stock_' in col],\n", " 'FX_rates': [col for col in train_df.columns if 'FX_' in col]\n", "}\n", "\n", "for group, features in feature_groups.items():\n", " print(f\"{group}: {len(features)} features\")\n", " \n", "print(f\"\\nšŸ“Š Target Information:\")\n", "target_cols = [col for col in train_labels_df.columns if col.startswith('target_')]\n", "print(f\"Total targets: {len(target_cols)}\")\n", "\n", "# Show target distribution by lag\n", "target_pairs_df['lag'] = target_pairs_df['lag'].astype(int)\n", "lag_counts = target_pairs_df['lag'].value_counts().sort_index()\n", "print(\"Targets by lag period:\")\n", "for lag, count in lag_counts.items():\n", " print(f\" Lag {lag}: {count} targets\")\n", "\n", "# Display first few rows\n", "print(f\"\\nšŸ“‹ Sample Training Data:\")\n", "print(train_df.head(3))\n", "\n", "print(f\"\\nšŸŽÆ Sample Targets:\")\n", "print(train_labels_df.head(3))" ] }, { "cell_type": "markdown", "id": "30791dc1", "metadata": {}, "source": [ "## šŸ”§ Feature Engineering\n", "\n", "For commodity prediction, we'll create powerful features that capture:\n", "- **Price relationships** between different assets\n", "- **Technical indicators** (moving averages, volatility)\n", "- **Market regime indicators** (trend, momentum)\n", "- **Cross-asset correlations**" ] }, { "cell_type": "code", "execution_count": 4, "id": "5b9efa46", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "šŸ”§ Lightweight feature engineering for EDA (subset of columns)\n", "Train engineered shape: (1917, 633)\n" ] } ], "source": [ "# Feature Engineering (EDA-friendly minimal version)\n", "# For production training use src.features.create_technical_features_fast or full pipeline in other notebooks\n", "from src.features import create_technical_features_fast\n", "\n", "print(\"šŸ”§ Lightweight feature engineering for EDA (subset of columns)\")\n", "train_engineered = create_technical_features_fast(train_df, limit_cols=25)\n", "# Only keep engineered test if needed for quick comparisons\n", "test_engineered = create_technical_features_fast(test_df, limit_cols=25)\n", "print(f\"Train engineered shape: {train_engineered.shape}\")" ] }, { "cell_type": "markdown", "id": "75a7e20e", "metadata": {}, "source": [ "## šŸ”„ Data Preprocessing\n", "\n", "Prepare the data for both XGBoost and TensorFlow models:\n", "- Handle missing values appropriately\n", "- Scale features for neural networks\n", "- Create proper train/validation splits" ] }, { "cell_type": "markdown", "id": "83a131ca", "metadata": {}, "source": [ "## šŸŽ‰ Conclusion (EDA Only)\n", "This notebook is now limited to exploratory data analysis and lightweight feature inspection.\n", "\n", "Model training, hyperparameter tuning, full preprocessing, ensembling and submission logic have been moved to:\n", "- 01_hyperparameter_optimization.ipynb (tuning)\n", "- 02_training_submission.ipynb (final training + predict())\n", "\n", "Use this notebook to:\n", "- Inspect raw & lightly engineered features\n", "- Understand distributions, correlations, temporal coverage\n", "- Prototype additional candidate feature ideas\n", "\n", "No model artifacts are created here.\n" ] } ], "metadata": { "kernelspec": { "display_name": "tf-gpu", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.18" } }, "nbformat": 4, "nbformat_minor": 5 }