dataflow/model/notebooks/Master_feature_analyzer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05e1c70e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:41.293486Z",
     "start_time": "2024-05-02T12:20:41.274314Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "126c8435",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "abf41299",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:51.399977Z",
     "start_time": "2024-05-02T12:20:41.295695Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/core/explore.py:39: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  import tqdm.autonotebook as tauton\n",
      "\u001b[33mWARNING\u001b[0m: No module named 'gluonts'\n",
      "Don't recognize host: host_os_name=Linux, am_host_os_name=Linux\n",
      "\u001b[33mWARNING\u001b[0m: No module named 'gluonts'\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import core.config as cconfig\n",
    "import core.plotting as coplotti\n",
    "import core.statistics as costatis\n",
    "import dataflow.model as dtfmod\n",
    "import helpers.hdbg as hdbg\n",
    "import helpers.henv as henv\n",
    "import helpers.hgit as hgit\n",
    "import helpers.hparquet as hparque\n",
    "import helpers.hprint as hprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eee79ad8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:51.651246Z",
     "start_time": "2024-05-02T12:20:51.402470Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[36mINFO\u001b[0m: > cmd='/venv/lib/python3.9/site-packages/ipykernel_launcher.py -f /home/.local/share/jupyter/runtime/kernel-18a0f1ac-e6af-46e9-a2c9-a3dc18dddd89.json'\n",
      "WARNING: Running in Jupyter\n",
      "INFO  # Git\n",
      "  branch_name='CmTask8000_-Add-loading-the-config-from-the-environment'\n",
      "  hash='13a32f0e4'\n",
      "  # Last commits:\n",
      "    * 13a32f0e4 tamriq   CmTask8000: add loading config from the env                       (  22 hours ago) Wed May 1 14:07:09 2024  (HEAD -> CmTask8000_-Add-loading-the-config-from-the-environment, origin/CmTask8000_-Add-loading-the-config-from-the-environment)\n",
      "    * 2e9c2e160 Shayan   Monitoring configuration modifications (#8127)                    (    2 days ago) Tue Apr 30 16:30:02 2024  (origin/CmTask8110_Rename_Tokyo_trading_DAGs)\n",
      "    * 73afee1a3 Shayan   Update infrastructure services reference (#8070)                  (    2 days ago) Tue Apr 30 16:12:17 2024           \n",
      "# Machine info\n",
      "  system=Linux\n",
      "  node name=b01b8e75f790\n",
      "  release=5.15.0-1056-aws\n",
      "  version=#61~20.04.1-Ubuntu SMP Wed Mar 13 17:40:41 UTC 2024\n",
      "  machine=x86_64\n",
      "  processor=x86_64\n",
      "  cpu count=8\n",
      "  cpu freq=scpufreq(current=2499.9959999999996, min=0.0, max=0.0)\n",
      "  memory=svmem(total=33280274432, available=21144657920, percent=36.5, used=11596828672, free=2794000384, active=10037944320, inactive=17241788416, buffers=2688651264, cached=16200794112, shared=2727936, slab=2753191936)\n",
      "  disk usage=sdiskusage(total=218506772480, used=113393041408, free=105096953856, percent=51.9)\n",
      "# Packages\n",
      "  python: 3.9.5\n",
      "  cvxopt: 1.3.2\n",
      "  cvxpy: 1.4.2\n",
      "  gluonnlp: ?\n",
      "  gluonts: ?\n",
      "  joblib: 1.3.2\n",
      "  mxnet: ?\n",
      "  numpy: 1.26.0\n",
      "  pandas: 2.1.1\n",
      "  pyarrow: 15.0.0\n",
      "  scipy: 1.11.3\n",
      "  seaborn: 0.13.0\n",
      "  sklearn: 1.3.1\n",
      "  statsmodels: 0.14.0\n"
     ]
    }
   ],
   "source": [
    "hdbg.init_logger(verbosity=logging.INFO)\n",
    "\n",
    "_LOG = logging.getLogger(__name__)\n",
    "\n",
    "_LOG.info(\"%s\", henv.get_system_signature()[0])\n",
    "\n",
    "hprint.config_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82038270",
   "metadata": {},
   "source": [
    "# Build config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db5c6f2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:51.818494Z",
     "start_time": "2024-05-02T12:20:51.655846Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO  Using hardwired config\n",
      "dir_name: /shared_data/backtest.danya/build_tile_configs.C13a.ccxt_v7_5-all.2T.2023-06-01_2024-01-31.ins.run0/tiled_results\n",
      "asset_id: 1467591036\n",
      "asset_id_col: asset_id\n",
      "resampling_frequency: 2T\n",
      "feature_column_names: ['f1']\n",
      "single_feature_column_name: f1\n"
     ]
    }
   ],
   "source": [
    "# Get config from env when running the notebook via the run_notebook.py script.\n",
    "default_config = cconfig.get_config_from_env()\n",
    "if default_config:\n",
    "    _LOG.info(\"Using config from env vars\")\n",
    "else:\n",
    "    _LOG.info(\"Using hardwired config\")\n",
    "    amp_dir = hgit.get_amp_abs_path()\n",
    "    dir_name = os.path.join(\n",
    "        amp_dir,\n",
    "        \"/shared_data/backtest.danya/build_tile_configs.C13a.ccxt_v7_5-all.2T.2023-06-01_2024-01-31.ins.run0/tiled_results\",\n",
    "    )\n",
    "    cols = [\"f1\"]\n",
    "    default_config_dict = {\n",
    "        \"dir_name\": dir_name,\n",
    "        \"asset_id\": 1467591036,\n",
    "        \"asset_id_col\": \"asset_id\",\n",
    "        \"resampling_frequency\": \"2T\",\n",
    "        \"feature_column_names\": cols,\n",
    "        \"single_feature_column_name\": \"f1\",\n",
    "    }\n",
    "    # Build config from dict.\n",
    "    default_config = cconfig.Config().from_dict(default_config_dict)\n",
    "print(default_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af139b83",
   "metadata": {},
   "source": [
    "# Report tile metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7052799",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:51.895071Z",
     "start_time": "2024-05-02T12:20:51.821338Z"
    }
   },
   "outputs": [],
   "source": [
    "parquet_tile_analyzer = dtfmod.ParquetTileAnalyzer()\n",
    "parquet_tile_metadata = parquet_tile_analyzer.collate_parquet_tile_metadata(\n",
    "    default_config[\"dir_name\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "91d1fad9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:51.962550Z",
     "start_time": "2024-05-02T12:20:51.897486Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_years</th>\n",
       "      <th>n_unique_months</th>\n",
       "      <th>n_files</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asset_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1464553467</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12.3 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467591036</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>12.2 MB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            n_years  n_unique_months  n_files     size\n",
       "asset_id                                              \n",
       "1464553467        2                8        8  12.3 MB\n",
       "1467591036        2                8        8  12.2 MB"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parquet_tile_analyzer.compute_metadata_stats_by_asset_id(parquet_tile_metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf31930c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.016377Z",
     "start_time": "2024-05-02T12:20:51.964733Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>n_asset_ids</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2023</th>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2.9 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2.9 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>3.1 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>3.2 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.3 MB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            n_asset_ids    size\n",
       "year month                     \n",
       "2023 6                2  3.0 MB\n",
       "     7                2  3.0 MB\n",
       "     8                2  2.9 MB\n",
       "     9                2  2.9 MB\n",
       "     10               2  3.1 MB\n",
       "     11               2  3.0 MB\n",
       "     12               2  3.2 MB\n",
       "2024 1                2  3.3 MB"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parquet_tile_analyzer.compute_universe_size_by_time(parquet_tile_metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "328f3f37",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.062168Z",
     "start_time": "2024-05-02T12:20:52.018608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1464553467, 1467591036]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asset_ids = parquet_tile_metadata.index.levels[0].to_list()\n",
    "display(asset_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c17e86",
   "metadata": {},
   "source": [
    "# Load features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "856544f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.107072Z",
     "start_time": "2024-05-02T12:20:52.065009Z"
    }
   },
   "outputs": [],
   "source": [
    "asset_batch_size = len(asset_ids)\n",
    "# Add the `asset_id_col` to also display the instruments. This is also required\n",
    "# to make `yield_parquet_tiles_by_assets()` work.\n",
    "requested_columns = [default_config[\"asset_id_col\"]] + default_config[\n",
    "    \"feature_column_names\"\n",
    "]\n",
    "tile_iter = hparque.yield_parquet_tiles_by_assets(\n",
    "    default_config[\"dir_name\"],\n",
    "    asset_ids,\n",
    "    default_config[\"asset_id_col\"],\n",
    "    asset_batch_size,\n",
    "    None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f96c59b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.264101Z",
     "start_time": "2024-05-02T12:20:52.112449Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c18c62844524414b36478fda181f46d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tile = next(tile_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2743b70f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.526472Z",
     "start_time": "2024-05-02T12:20:52.267195Z"
    }
   },
   "outputs": [],
   "source": [
    "feature_df = dtfmod.process_parquet_read_df(\n",
    "    tile[\n",
    "        default_config[\"feature_column_names\"] + [default_config[\"asset_id_col\"]]\n",
    "    ],\n",
    "    default_config[\"asset_id_col\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50dd995d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.573180Z",
     "start_time": "2024-05-02T12:20:52.528756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f1']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_df.columns.levels[0].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "73f06b02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.796327Z",
     "start_time": "2024-05-02T12:20:52.578808Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f6dd232481c4ef4a8859999549e5479",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing groups:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>eff_count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>autocovar</th>\n",
       "      <th>autocorr</th>\n",
       "      <th>turn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1464553467</th>\n",
       "      <th>f1</th>\n",
       "      <td>174076</td>\n",
       "      <td>174076.0</td>\n",
       "      <td>-0.003536</td>\n",
       "      <td>1.491278</td>\n",
       "      <td>0.045816</td>\n",
       "      <td>0.030723</td>\n",
       "      <td>1.392320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467591036</th>\n",
       "      <th>f1</th>\n",
       "      <td>173957</td>\n",
       "      <td>173957.0</td>\n",
       "      <td>-0.008110</td>\n",
       "      <td>1.508919</td>\n",
       "      <td>0.048123</td>\n",
       "      <td>0.031893</td>\n",
       "      <td>1.391479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count  eff_count      mean       var  autocovar  autocorr      turn\n",
       "1464553467 f1  174076   174076.0 -0.003536  1.491278   0.045816  0.030723  1.392320\n",
       "1467591036 f1  173957   173957.0 -0.008110  1.508919   0.048123  0.031893  1.391479"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_stats = costatis.compute_centered_process_stats_by_group(feature_df)\n",
    "display(feature_stats.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c28e31d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.844612Z",
     "start_time": "2024-05-02T12:20:52.798535Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>eff_count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>autocovar</th>\n",
       "      <th>autocorr</th>\n",
       "      <th>turn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>174016.5</td>\n",
       "      <td>174016.5</td>\n",
       "      <td>-0.005823</td>\n",
       "      <td>1.500098</td>\n",
       "      <td>0.04697</td>\n",
       "      <td>0.031308</td>\n",
       "      <td>1.3919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count  eff_count      mean       var  autocovar  autocorr    turn\n",
       "f1  174016.5   174016.5 -0.005823  1.500098    0.04697  0.031308  1.3919"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_stats.groupby(level=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7269da01",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:52.912803Z",
     "start_time": "2024-05-02T12:20:52.847336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>eff_count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>autocovar</th>\n",
       "      <th>autocorr</th>\n",
       "      <th>turn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1464553467</th>\n",
       "      <td>174076.0</td>\n",
       "      <td>174076.0</td>\n",
       "      <td>-0.003536</td>\n",
       "      <td>1.491278</td>\n",
       "      <td>0.045816</td>\n",
       "      <td>0.030723</td>\n",
       "      <td>1.392320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467591036</th>\n",
       "      <td>173957.0</td>\n",
       "      <td>173957.0</td>\n",
       "      <td>-0.008110</td>\n",
       "      <td>1.508919</td>\n",
       "      <td>0.048123</td>\n",
       "      <td>0.031893</td>\n",
       "      <td>1.391479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count  eff_count      mean       var  autocovar  autocorr      turn\n",
       "1464553467  174076.0   174076.0 -0.003536  1.491278   0.045816  0.030723  1.392320\n",
       "1467591036  173957.0   173957.0 -0.008110  1.508919   0.048123  0.031893  1.391479"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_stats.groupby(level=0).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "74e38e16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:53.077018Z",
     "start_time": "2024-05-02T12:20:52.915121Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1cd7a68ce3e48c5bc84233435f6d22b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing groups:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>eff_count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>autocovar</th>\n",
       "      <th>autocorr</th>\n",
       "      <th>turn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1464553467</th>\n",
       "      <th>f1</th>\n",
       "      <td>174076</td>\n",
       "      <td>174076.0</td>\n",
       "      <td>-0.003536</td>\n",
       "      <td>1.491278</td>\n",
       "      <td>0.045816</td>\n",
       "      <td>0.030723</td>\n",
       "      <td>1.392320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467591036</th>\n",
       "      <th>f1</th>\n",
       "      <td>173957</td>\n",
       "      <td>173957.0</td>\n",
       "      <td>-0.008110</td>\n",
       "      <td>1.508919</td>\n",
       "      <td>0.048123</td>\n",
       "      <td>0.031893</td>\n",
       "      <td>1.391479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count  eff_count      mean       var  autocovar  autocorr      turn\n",
       "1464553467 f1  174076   174076.0 -0.003536  1.491278   0.045816  0.030723  1.392320\n",
       "1467591036 f1  173957   173957.0 -0.008110  1.508919   0.048123  0.031893  1.391479"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_stats = costatis.compute_centered_process_stats_by_group(feature_df)\n",
    "display(feature_stats.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ef0bf620",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:53.127806Z",
     "start_time": "2024-05-02T12:20:53.081227Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>eff_count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>autocovar</th>\n",
       "      <th>autocorr</th>\n",
       "      <th>turn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f1</th>\n",
       "      <td>174016.5</td>\n",
       "      <td>174016.5</td>\n",
       "      <td>-0.005823</td>\n",
       "      <td>1.500098</td>\n",
       "      <td>0.04697</td>\n",
       "      <td>0.031308</td>\n",
       "      <td>1.3919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count  eff_count      mean       var  autocovar  autocorr    turn\n",
       "f1  174016.5   174016.5 -0.005823  1.500098    0.04697  0.031308  1.3919"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_asset_stats_per_feature = feature_stats.groupby(level=1).mean()\n",
    "display(mean_asset_stats_per_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09b48ec2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:53.600036Z",
     "start_time": "2024-05-02T12:20:53.131044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='mean asset per feature'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rr77a1THaePHFF7Ny5cqujtHj9OvXL0OHDu3qGO9o8ODB2Wmnnbo6BrAWNTV9ujoCQEmZa0C5MdeAcmOu0Z10q/KlpqYmDQ0NbbavWLEiAwYMeNfzVq9enTfffLPV1S/19fWpqKh413PfTXNzIfX1rxV17gfVokULM3PmzIwaNaqro7Sx2WabdXWEHmv16tVdHeEdzZw5M71798+mm/pvC91JVVVlamr6pL7+9TQ1NXd1HIB1Zq4B5cZcA8qNuUZnqanp0+4rrLpV+TJs2LA2z3ZpaGjIkiVL2jzP5Z/PS966suFjH/tYy/b58+dn0003LfqWY0nS2Og3a0cMHrxJxo3bP3V1S7s6ShsvvbQgDQ3d78qXioqK9OpVldWrm1IoFLo6Thv9+/fLFlt8uKtjvKNx4/bP4MGb+H0K3VRTU7Pfn0BZMdeAcmOuAeXGXKM76Vbly9ixY3PllVe2evbLrFmzUllZmTFjxqz1vFGjRqVfv3659957W8qXNWvW5P7778/YsWM7JTv/a7vtRnZ1hB6luroygwb1zbJlq/zhAAAAAABQBrpV+XLUUUflpptuyuTJkzNp0qQsXrw406dPz1FHHZUhQ4a0HDdx4sQsWrQos2fPTpKsv/76mTRpUi6//PLU1tbmox/9aG655ZYsX748X/rSl7rq7QAAAAAAAB9A3ap8GTBgQG644YZMmzYtkydPTt++fXPYYYdlypQprY5rbm5OU1NTq21f+cpXUigUct1116Wuri7bbLNNrr322myxxRad+RYAAAAAAIAPuIpCd3zIRDfR1NScurpVXR2DMue2Y0C5MdeAcmOuAeXGXAPKjblGZ6mt7Zuqqsp2Hdu+owAAAAAAAGgX5QsAAAAAAEAJKV8AAAAAAABKSPkCAAAAAABQQsoXAAAAAACAElK+AAAAAAAAlJDyBQAAAAAAoISULwAAAAAAACWkfAEAAAAAACgh5QsAAAAAAEAJKV8AAAAAAABKqKJQKBS6OkR3VSgU0tzsp4f3X1VVZZqamrs6BkDJmGtAuTHXgHJjrgHlxlyjM1RWVqSioqJdxypfAAAAAAAASshtxwAAAAAAAEpI+QIAAAAAAFBCyhcAAAAAAIASUr4AAAAAAACUkPIFAAAAAACghJQvAAAAAAAAJaR8AQAAAAAAKCHlCwAAAAAAQAkpXwAAAAAAAEpI+QIAAAAAAFBCyhcAAAAAAIASUr4AAAAAAACUkPIFAAAoSw888EB+/OMfd3UMgPedeQeUEzONcqF8AQAAytIDDzyQW265patjALzvzDugnJhplAvlCwDQpd54442ujgDQI5mfQHe3tjllfgGdxbyhKylfoAhnnnlmDjnkkDzyyCP59Kc/ne233z7HHntsXn755Sxfvjxf//rXM2rUqOy333655557Wp370EMP5fDDD8/222+f3XffPd/61rfy2muvtex/7bXX8u1vfzsHHnhgRo4cmX333TdTp05NQ0NDq3X23XfffPvb386Pf/zj7LPPPtlpp51y8sknp66urlN+DoAPlp/+9Kf5+Mc/nr///e+tti9fvjzbbbddbr311jz11FM56aSTsueee2aHHXbIhAkTcuedd7Y6/rHHHsuIESPy0EMP5dRTT82oUaPy9a9/vRPfCdATvNc8+elPf5oRI0a0+dwzYcKEnHnmmUne+rz2s5/9LC+88EJGjBiRESNGtOxLkvvvvz8TJkzIJz7xiey555658MIL8+abb7Zar76+PtOmTcvYsWOz3XbbZd99980ll1zS6phbb701Bx54YMv+//zP/0xzc3OS5OWXX86IESMya9asNu/xc5/7XE4//fQkyauvvpqzzjorn/zkJ7P99tvngAMOyPe+972sXr261TkjRozI1VdfnYsvvjhjxozJ6NGjO/gzC3Q35TLv/jHrU089lS9+8YvZYYcdMn36dJ//gA5Z20w77rjjMmnSpFbHPv/88xkxYkQee+yxlm1r+7zk79HoCtVdHQB6qiVLluSiiy7KV7/61VRXV+e8887LN77xjfTp0yc777xzjjjiiNx+++3513/914wcOTKbbbZZZs2alSlTpuRzn/tcvva1r2XJkiW55JJLUl9fn0svvTTJW418U1NTpkyZktra2vztb3/LlVdemZNPPjk33XRTqwwPPvhgFixYkKlTp2bZsmW58MILM23atJa1AEpl//33z7e+9a3MmjUrxx57bMv2+++/P0nyqU99KnPnzs2oUaNy9NFHp1evXnnyySfzzW9+M4VCIYceemir9f7jP/4jn/nMZ3LFFVekstL3ggCtLVq0qN3zZG3e/p/p+fPn57vf/W6SpLa2Nknyi1/8IqeeemoOPvjgnHHGGZk/f34uvfTS/O1vf8tll12WJFm9enUmTpyYhQsXZvLkyfnoRz+aV155Jb/5zW9avsZNN92U8847L8cdd1zGjRuXp556KjNmzEhDQ0P+7d/+LZtvvnl22GGH3HPPPfnUpz7Vct5f/vKXPPfccznllFOSJMuWLcvAgQNz1llnpaamJn/5y19y+eWXZ8mSJbnwwgtbva8bb7wxI0eOzPnnn5/Gxsbif5KBbqFc5t0/OuOMM3LkkUdm0qRJ6dOnT8t3nfv8B7TH2mbav//7v7d7jbV9XvL3aHQ25QsUacWKFfnRj36Uj3zkI0ne+o7FadOm5Stf+UomT56cJPnEJz6R2bNn54EHHsgXvvCFTJ8+PQcddFDOP//8lnU22mijnHjiiTn55JPzkY98JLW1tTn33HNb9jc2NmbzzTfPMccckxdffDFDhw5t2VcoFPKDH/wgvXr1SpIsXLgwV111VZqbm32YBUqqf//+2XvvvXPXXXe1Kl/uuuuujBkzJgMHDszBBx/csr1QKGSXXXbJ4sWLc9ttt7X5y4N99903//qv/9pp+YGepSPzZG223HLL1NbWZtGiRdlhhx1a7ZsxY0Z22GGHlu/qHjt2bPr06ZOpU6fmj3/8Y0aMGJE777wzv//973Prrbdmxx13bDn37a/f1NSUK664IgcffHC++c1vJkn23HPPrFmzJtddd11OPPHEDBo0KAcffHC++93vZuXKlenXr1+St2bngAEDsueeeyZ56zs0//EvL0eNGpU+ffrkzDPPzNSpU9OnT5+WfQMGDMiMGTNSUVHR3p9OoBsrp3n3tqOOOionnnhiy+u3vyPd5z+gPd5tprXX2j4v+Xs0OptfVVCkwYMHtxQvSbLVVlslSfbYY4+WbTU1Namtrc0rr7ySF198MQsXLsz48ePT2NjY8s+uu+6aysrK/O53v2s5784778xnP/vZ7Ljjjtl2221zzDHHJHnruyT/0S677NLyB0aSDB8+PGvWrMnSpUvfh3cMfNAdfPDBefrpp7No0aIkb5XOjz/+eMtfGqxYsSLnnXde9tlnn2y77bbZdtttc9ttt+XFF19ss9a4ceM6MzrQw3RknnTUqlWr8vzzz+fAAw9stf2ggw5Kkpbv9H700UczfPjwVn8R+Y/mz5+fZcuWtbqi5e111qxZk9/+9rdJkvHjx2fNmjV54IEHWo655557csABB7R8jisUCrn++utz0EEHZfvtt8+2226bb3zjG2lsbMxLL73Uav2xY8cqXqCMlNO8e9vaPuf5/Ad0lrV9XvL3aHQ2V75AkWpqalq9Xm+99ZK89d3h/6hXr1558803s2zZsiRpuSrmn/3tb39LksyePTv/9m//liOPPDJTpkzJwIEDs2TJkkyePLnNfXn/OcPbf4D883EApbDPPvukT58+ufvuu/OVr3wl9957b9Zff/3st99+Sd66N+9TTz2VyZMnZ+utt06/fv1yyy235N57722z1oYbbtjZ8YEepCPzpKMaGhpSKBTazKH+/funV69eWbFiRZK3nmk1ePDgta7z9nH/vM7br9/ev9FGG2W33XbL3Xffnc9+9rP5wx/+kHnz5mXq1Kkt59xwww35zne+ky9/+cvZbbfdUlNTk2effTbf/va323yuMz+hvJTTvHvbhz70oXdcx/wCOsva5o2/R6OzKV+gkwwcODBJMnXq1Gy//fZt9r/9YXfWrFnZZptt8u1vf7tl3//8z/90SkaAd9O7d+/st99+ueeee/KVr3wl99xzT/bZZ59ssMEGefPNN/PQQw+1PAjxbTfffPM7ruW7toG1ac88WX/99ZMka9asaXVufX39e67fv3//VFRUtHm4akNDQ1avXp0BAwYkeeuz2x//+Me1rvP2Z7t/Xuft75x8e53krSsHzz333Cxbtix33313Ntpoo+y6664t+2fNmpV99903Z5xxRsu2efPmvePXNT+hfJTjvHs35hewLnr16tVmFv5z+fs284buwm3HoJMMGzYsG2+8cV566aV84hOfaPPPkCFDkiRvvPFGy1U0b/u///f/dkVkgDYOOeSQ/P73v88vf/nLPP300y23HFu9enWam5tbza+VK1fmwQcf7KqoQA/Vnnny9uem+fPnt2ybN29ey5XEb1tvvfXafCdj3759s80222TWrFmttr/9XeY77bRTkrduJTtv3rw888wz75hz6NChqa2tfcd11ltvvVbfbHPAAQckSe67777cfffdOeigg1rdV9znP/hgKsd5B1AK7zTTNt5447z44ospFAot2+bOndvZ0aBDXPkCnaSioiJnnnlmvvGNb+S1117LuHHj0qdPnyxatChz5szJlClTMnTo0Oyxxx759re/nSuuuCI77rhj5syZk0cffbSr4wMkeet/zgcOHJizzz47NTU1GTt2bJK3vrPyE5/4RK655prU1tamuro6V199dfr169fmuyQB3k175snIkSOzySab5IILLsgZZ5yRlStX5uqrr2757uy3DR8+PHfccUfuuuuufPjDH86gQYOy+eab55RTTsnkyZPzjW98I5/5zGfy4osv5tJLL82BBx6YESNGJEkmTJiQm2++OSeeeGJOOeWUfOQjH8nixYvzxBNPZNq0aamqqsrJJ5+c8847L7W1tdl7773z9NNP55prrsnEiRNbPXx6wIAB2WuvvXLFFVfk1VdfzSGHHNIq5x577JEbb7wxP/rRj7LVVltl5syZWbBgwfv7Ew10uXKcdwCl8E4z7cADD8x///d/Z9q0adlvv/3y5JNP5r777uvqqPCulC/QicaPH5+amppceeWVLd/NuNlmm2WvvfZquS/uUUcdlZdffjk/+tGPcu2112bPPffMJZdckiOOOKIrowMkees7kA488MDcdtttOeyww1o9rPCSSy7J1KlTc+aZZ2bgwIE57rjj8tprr+W6667rwsRAT/Re82S99dbLjBkzcs455+TrX/96ttxyy5x99tm56KKLWq1z2GGH5be//W2mTZuW5cuX59BDD81FF12UT37yk/n+97+fK664IieffHIGDhyYI444otVtv3r16pXrr78+l156aa666qosX748G2+8ccsVf0ly3HHHpbq6Otdff31uueWWbLTRRjnllFNy0kkntXlPhxxySB588MFsueWWbb5LfPLkyVm2bFkuu+yyJMmBBx6Yb37zm++4DlBeynHeAayrtc20f/3Xf82PfvSj/OxnP8vYsWNz7rnn5vjjj+/quLBWFYV/vFYLAAAAAACAdeKZLwAAAAAAACWkfAEAAAAAACgh5QsAAAAAAEAJKV8AAAAAAABKSPkCAAAAAABQQsoXAAAAAACAElK+AAAAAAAAlJDyBQAAAAAAoISULwAAAAAAACWkfAEAAAAAACgh5QsAAAAAAEAJKV8AAAAAAABK6P8DDis9yLt54VsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots()\n",
    "mean_asset_stats_per_feature[\n",
    "    [\"mean\", \"var\", \"autocovar\", \"autocorr\", \"turn\"]\n",
    "].boxplot(ylabel=\"mean asset per feature\", ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6c8a2fdb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:53.654989Z",
     "start_time": "2024-05-02T12:20:53.603901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>eff_count</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>autocovar</th>\n",
       "      <th>autocorr</th>\n",
       "      <th>turn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1464553467</th>\n",
       "      <td>174076.0</td>\n",
       "      <td>174076.0</td>\n",
       "      <td>-0.003536</td>\n",
       "      <td>1.491278</td>\n",
       "      <td>0.045816</td>\n",
       "      <td>0.030723</td>\n",
       "      <td>1.392320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467591036</th>\n",
       "      <td>173957.0</td>\n",
       "      <td>173957.0</td>\n",
       "      <td>-0.008110</td>\n",
       "      <td>1.508919</td>\n",
       "      <td>0.048123</td>\n",
       "      <td>0.031893</td>\n",
       "      <td>1.391479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count  eff_count      mean       var  autocovar  autocorr      turn\n",
       "1464553467  174076.0   174076.0 -0.003536  1.491278   0.045816  0.030723  1.392320\n",
       "1467591036  173957.0   173957.0 -0.008110  1.508919   0.048123  0.031893  1.391479"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_feature_stats_per_asset = feature_stats.groupby(level=0).mean()\n",
    "display(mean_feature_stats_per_asset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d235d8d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:54.126460Z",
     "start_time": "2024-05-02T12:20:53.657349Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='mean feature per asset'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots()\n",
    "mean_feature_stats_per_asset[\n",
    "    [\"mean\", \"var\", \"autocovar\", \"autocorr\", \"turn\"]\n",
    "].boxplot(ylabel=\"mean feature per asset\", ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc07f7b7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:54.244384Z",
     "start_time": "2024-05-02T12:20:54.131091Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "mean_feature_corr = costatis.compute_mean_pearson_correlation_by_group(\n",
    "    feature_df, 1\n",
    ")\n",
    "# Plot heatmap if correlation matrix contains >1 feature.\n",
    "if mean_feature_corr.shape[1] > 1:\n",
    "    coplotti.plot_heatmap(mean_feature_corr, mode=\"clustermap\", figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "865cb28c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:55.134103Z",
     "start_time": "2024-05-02T12:20:54.247846Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/venv/lib/python3.9/site-packages/seaborn/matrix.py:1124: UserWarning: ``square=True`` ignored in clustermap\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_asset_corr = costatis.compute_mean_pearson_correlation_by_group(\n",
    "    feature_df, 0\n",
    ")\n",
    "coplotti.plot_heatmap(mean_asset_corr, mode=\"clustermap\", figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fe46b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-12T14:51:45.786990Z",
     "start_time": "2021-10-12T14:51:45.712122Z"
    }
   },
   "source": [
    "# Per-asset cross-sectional feature analysis (all features for a single asset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6dc2e3e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:55.212369Z",
     "start_time": "2024-05-02T12:20:55.136550Z"
    }
   },
   "outputs": [],
   "source": [
    "single_asset_feature_df = feature_df.T.xs(default_config[\"asset_id\"], level=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "32f09c9a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:55.268866Z",
     "start_time": "2024-05-02T12:20:55.214721Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "single_asset_feature_df_corr = single_asset_feature_df.corr()\n",
    "# Plot heatmap if correlation matrix contains >1 feature.\n",
    "if single_asset_feature_df_corr.shape[1] > 1:\n",
    "    coplotti.plot_heatmap(\n",
    "        single_asset_feature_df.corr(), mode=\"clustermap\", figsize=(10, 10)\n",
    "    )\n",
    "# Alternative method (prints correlation matrix and does not reorder columns):\n",
    "# coplotti.plot_correlation_matrix(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ef8c6c29",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:55.756652Z",
     "start_time": "2024-05-02T12:20:55.273175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.0, 1.0]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coplotti.plot_effective_correlation_rank(single_asset_feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "29362b8b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:58.357726Z",
     "start_time": "2024-05-02T12:20:55.759893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coplotti.plot_projection(\n",
    "    single_asset_feature_df.resample(default_config[\"resampling_frequency\"]).sum(\n",
    "        min_count=1\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c90a3a85",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:58.424335Z",
     "start_time": "2024-05-02T12:20:58.361101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.008110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.228357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>0.016697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>0.820156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                f1\n",
       "mean     -0.008110\n",
       "std       1.228357\n",
       "skew      0.016697\n",
       "kurtosis  0.820156"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sc = dtfmod.StatsComputer()\n",
    "# single_asset_feature_df.apply(sc.compute_summary_stats).round(3)\n",
    "# Alternative method:\n",
    "single_asset_feature_df.apply(costatis.compute_moments)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935c6604",
   "metadata": {},
   "source": [
    "# Single feature analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fc70ed1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:58.473386Z",
     "start_time": "2024-05-02T12:20:58.427305Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO  feature=f1\n"
     ]
    }
   ],
   "source": [
    "feature = default_config[\"single_feature_column_name\"]\n",
    "_LOG.info(\"feature=%s\", feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e89d1de7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:20:59.658368Z",
     "start_time": "2024-05-02T12:20:58.475781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coplotti.plot_qq(single_asset_feature_df[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e51b114",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:21:04.121043Z",
     "start_time": "2024-05-02T12:20:59.661211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coplotti.plot_histograms_and_lagged_scatterplot(\n",
    "    single_asset_feature_df[feature], lag=2, figsize=(20, 10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8ec7fca2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:21:06.830910Z",
     "start_time": "2024-05-02T12:21:04.124715Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coplotti.plot_time_series_by_period(\n",
    "    single_asset_feature_df[feature],\n",
    "    \"hour\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f7500e",
   "metadata": {},
   "source": [
    "# Feature cross-section (single feature across all assets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9b7b2f69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:21:06.876074Z",
     "start_time": "2024-05-02T12:21:06.835126Z"
    }
   },
   "outputs": [],
   "source": [
    "xs_feature_df = feature_df[feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "74775935",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:21:07.244854Z",
     "start_time": "2024-05-02T12:21:06.878480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>asset_id</th>\n",
       "      <th>1464553467</th>\n",
       "      <th>1467591036</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asset_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1464553467</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.360535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467591036</th>\n",
       "      <td>0.360535</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "asset_id    1464553467  1467591036\n",
       "asset_id                          \n",
       "1464553467    1.000000    0.360535\n",
       "1467591036    0.360535    1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coplotti.plot_correlation_matrix(xs_feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "71de7c6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-02T12:21:07.760526Z",
     "start_time": "2024-05-02T12:21:07.264964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.8713643315804314, 1.7699345210469046, 1.470010230525826]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coplotti.plot_effective_correlation_rank(xs_feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "420bd5c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}