00_Gausian_Mixture_Model(GMM).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d0cf920",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa883353",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.dates import YearLocator, MonthLocator\n",
    "\n",
    "import sklearn.mixture as mix\n",
    "\n",
    "import missingno as msno\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d41fbf70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "change working directory to parent\n",
      "/mnt/c/workspace/project_quant\n"
     ]
    }
   ],
   "source": [
    "strategy = os.getcwd()\n",
    "print(\"change working directory to parent\")\n",
    "os.chdir('/mnt/c/workspace/project_quant')\n",
    "print(os.getcwd())\n",
    "\n",
    "strategy_dir = os.path.join(os.getcwd(), 'strategy_finance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8584350",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.index_data import index_data\n",
    "import pandas_datareader.data as web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea112670",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msci_world</th>\n",
       "      <th>msci_emerging</th>\n",
       "      <th>bb_world_agg</th>\n",
       "      <th>bb_corp_hy</th>\n",
       "      <th>us_short_treasury</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.035225</td>\n",
       "      <td>-0.000984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-29</th>\n",
       "      <td>-0.020678</td>\n",
       "      <td>0.103012</td>\n",
       "      <td>-0.013095</td>\n",
       "      <td>0.024673</td>\n",
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       "    <tr>\n",
       "      <th>1992-03-31</th>\n",
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       "    <tr>\n",
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       "      <td>0.003798</td>\n",
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       "    <tr>\n",
       "      <th>1992-05-31</th>\n",
       "      <td>0.027579</td>\n",
       "      <td>-0.009451</td>\n",
       "      <td>0.006528</td>\n",
       "      <td>0.014110</td>\n",
       "      <td>0.009302</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28</th>\n",
       "      <td>0.002914</td>\n",
       "      <td>0.053679</td>\n",
       "      <td>-0.006128</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>0.000204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>0.031810</td>\n",
       "      <td>0.001014</td>\n",
       "      <td>-0.016604</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>-0.000255</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>0.020293</td>\n",
       "      <td>-0.019986</td>\n",
       "      <td>-0.007515</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>-0.000649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>0.038195</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.005114</td>\n",
       "      <td>0.010157</td>\n",
       "      <td>0.000970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>0.011310</td>\n",
       "      <td>0.038808</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.002682</td>\n",
       "      <td>0.000911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            msci_world  msci_emerging  bb_world_agg  bb_corp_hy  \\\n",
       "1992-01-31    0.000000       0.000000     -0.002171    0.035225   \n",
       "1992-02-29   -0.020678       0.103012     -0.013095    0.024673   \n",
       "1992-03-31   -0.017357       0.054748      0.003074    0.012399   \n",
       "1992-04-30   -0.066034       0.029763      0.003851    0.003798   \n",
       "1992-05-31    0.027579      -0.009451      0.006528    0.014110   \n",
       "...                ...            ...           ...         ...   \n",
       "2021-02-28    0.002914       0.053679     -0.006128    0.004106   \n",
       "2021-03-31    0.031810       0.001014     -0.016604    0.005491   \n",
       "2021-04-30    0.020293      -0.019986     -0.007515    0.000708   \n",
       "2021-05-31    0.038195       0.002734      0.005114    0.010157   \n",
       "2021-06-30    0.011310       0.038808      0.001330    0.002682   \n",
       "\n",
       "            us_short_treasury  \n",
       "1992-01-31          -0.000984  \n",
       "1992-02-29           0.003087  \n",
       "1992-03-31          -0.000196  \n",
       "1992-04-30           0.009191  \n",
       "1992-05-31           0.009302  \n",
       "...                       ...  \n",
       "2021-02-28           0.000204  \n",
       "2021-03-31          -0.000255  \n",
       "2021-04-30          -0.000649  \n",
       "2021-05-31           0.000970  \n",
       "2021-06-30           0.000911  \n",
       "\n",
       "[354 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_setting = {\n",
    "'start' : '1992-01-01',\n",
    "'end'   : '2021-06-30',\n",
    "'rebal_period':'1M'\n",
    "}\n",
    "\n",
    "list_index_data = ['msci_world', 'msci_emerging', 'bb_world_agg', 'bb_corp_hy', 'us_short_treasury']\n",
    "\n",
    "\n",
    "data = index_data(**data_setting)\n",
    "df = data.index_data_loading()\n",
    "df = df[list_index_data]\n",
    "df = df.fillna(0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cef2743e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>bb_world_agg</th>\n",
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       "      <th>2021-02-28</th>\n",
       "      <td>0.002914</td>\n",
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       "      <td>0.004106</td>\n",
       "      <td>0.000204</td>\n",
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       "      <th>2021-03-31</th>\n",
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       "      <td>-0.000255</td>\n",
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       "      <th>2021-04-30</th>\n",
       "      <td>0.020293</td>\n",
       "      <td>-0.019986</td>\n",
       "      <td>-0.007515</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>-0.000649</td>\n",
       "      <td>5.305197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>0.038195</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.005114</td>\n",
       "      <td>0.010157</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>5.507827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>0.011310</td>\n",
       "      <td>0.038808</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.002682</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>5.570121</td>\n",
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       "<p>354 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "            msci_world  msci_emerging  bb_world_agg  bb_corp_hy  \\\n",
       "1992-01-31    0.000000       0.000000      0.000000    0.000000   \n",
       "1992-02-29   -0.020678       0.103012     -0.013095    0.024673   \n",
       "1992-03-31   -0.017357       0.054748      0.003074    0.012399   \n",
       "1992-04-30   -0.066034       0.029763      0.003851    0.003798   \n",
       "1992-05-31    0.027579      -0.009451      0.006528    0.014110   \n",
       "...                ...            ...           ...         ...   \n",
       "2021-02-28    0.002914       0.053679     -0.006128    0.004106   \n",
       "2021-03-31    0.031810       0.001014     -0.016604    0.005491   \n",
       "2021-04-30    0.020293      -0.019986     -0.007515    0.000708   \n",
       "2021-05-31    0.038195       0.002734      0.005114    0.010157   \n",
       "2021-06-30    0.011310       0.038808      0.001330    0.002682   \n",
       "\n",
       "            us_short_treasury  msci_world_cum  \n",
       "1992-01-31           0.000000        1.000000  \n",
       "1992-02-29           0.003087        0.979322  \n",
       "1992-03-31          -0.000196        0.962324  \n",
       "1992-04-30           0.009191        0.898778  \n",
       "1992-05-31           0.009302        0.923565  \n",
       "...                       ...             ...  \n",
       "2021-02-28           0.000204        5.039375  \n",
       "2021-03-31          -0.000255        5.199679  \n",
       "2021-04-30          -0.000649        5.305197  \n",
       "2021-05-31           0.000970        5.507827  \n",
       "2021-06-30           0.000911        5.570121  \n",
       "\n",
       "[354 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = index_data(**data_setting)\n",
    "world_index = data.index_cum_data_loading()\n",
    "world_index = world_index[['msci_world']]\n",
    "world_index.columns = ['msci_world_cum']\n",
    "\n",
    "market_data = df.join(world_index, how='left')\n",
    "market_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "338e4dc7",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.014110</td>\n",
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       "      <td>0.004106</td>\n",
       "      <td>0.000204</td>\n",
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       "      <th>2021-03-31</th>\n",
       "      <td>0.031810</td>\n",
       "      <td>0.001014</td>\n",
       "      <td>-0.016604</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>-0.000255</td>\n",
       "      <td>5.199679</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>0.020293</td>\n",
       "      <td>-0.019986</td>\n",
       "      <td>-0.007515</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>-0.000649</td>\n",
       "      <td>5.305197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>0.038195</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.005114</td>\n",
       "      <td>0.010157</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>5.507827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>0.011310</td>\n",
       "      <td>0.038808</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.002682</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>5.570121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            msci_world  msci_emerging  bb_world_agg  bb_corp_hy  \\\n",
       "1992-01-31    0.000000       0.000000      0.000000    0.000000   \n",
       "1992-02-29   -0.020678       0.103012     -0.013095    0.024673   \n",
       "1992-03-31   -0.017357       0.054748      0.003074    0.012399   \n",
       "1992-04-30   -0.066034       0.029763      0.003851    0.003798   \n",
       "1992-05-31    0.027579      -0.009451      0.006528    0.014110   \n",
       "...                ...            ...           ...         ...   \n",
       "2021-02-28    0.002914       0.053679     -0.006128    0.004106   \n",
       "2021-03-31    0.031810       0.001014     -0.016604    0.005491   \n",
       "2021-04-30    0.020293      -0.019986     -0.007515    0.000708   \n",
       "2021-05-31    0.038195       0.002734      0.005114    0.010157   \n",
       "2021-06-30    0.011310       0.038808      0.001330    0.002682   \n",
       "\n",
       "            us_short_treasury  msci_world_cum  \n",
       "1992-01-31           0.000000        1.000000  \n",
       "1992-02-29           0.003087        0.979322  \n",
       "1992-03-31          -0.000196        0.962324  \n",
       "1992-04-30           0.009191        0.898778  \n",
       "1992-05-31           0.009302        0.923565  \n",
       "...                       ...             ...  \n",
       "2021-02-28           0.000204        5.039375  \n",
       "2021-03-31          -0.000255        5.199679  \n",
       "2021-04-30          -0.000649        5.305197  \n",
       "2021-05-31           0.000970        5.507827  \n",
       "2021-06-30           0.000911        5.570121  \n",
       "\n",
       "[354 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4bc4be89",
   "metadata": {},
   "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>msci_emerging</th>\n",
       "      <th>bb_corp_hy</th>\n",
       "      <th>us_short_treasury</th>\n",
       "      <th>msci_world_cum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-29</th>\n",
       "      <td>0.103012</td>\n",
       "      <td>0.024673</td>\n",
       "      <td>0.003087</td>\n",
       "      <td>0.979322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-31</th>\n",
       "      <td>0.054748</td>\n",
       "      <td>0.012399</td>\n",
       "      <td>-0.000196</td>\n",
       "      <td>0.962324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-30</th>\n",
       "      <td>0.029763</td>\n",
       "      <td>0.003798</td>\n",
       "      <td>0.009191</td>\n",
       "      <td>0.898778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-31</th>\n",
       "      <td>-0.009451</td>\n",
       "      <td>0.014110</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>0.923565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28</th>\n",
       "      <td>0.053679</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>5.039375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>0.001014</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>-0.000255</td>\n",
       "      <td>5.199679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>-0.019986</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>-0.000649</td>\n",
       "      <td>5.305197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.010157</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>5.507827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>0.038808</td>\n",
       "      <td>0.002682</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>5.570121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            msci_emerging  bb_corp_hy  us_short_treasury  msci_world_cum\n",
       "1992-01-31       0.000000    0.000000           0.000000        1.000000\n",
       "1992-02-29       0.103012    0.024673           0.003087        0.979322\n",
       "1992-03-31       0.054748    0.012399          -0.000196        0.962324\n",
       "1992-04-30       0.029763    0.003798           0.009191        0.898778\n",
       "1992-05-31      -0.009451    0.014110           0.009302        0.923565\n",
       "...                   ...         ...                ...             ...\n",
       "2021-02-28       0.053679    0.004106           0.000204        5.039375\n",
       "2021-03-31       0.001014    0.005491          -0.000255        5.199679\n",
       "2021-04-30      -0.019986    0.000708          -0.000649        5.305197\n",
       "2021-05-31       0.002734    0.010157           0.000970        5.507827\n",
       "2021-06-30       0.038808    0.002682           0.000911        5.570121\n",
       "\n",
       "[354 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code adapted from http://hmmlearn.readthedocs.io\n",
    "target = 'msci_world_cum'\n",
    "list_index_data = ['msci_world', 'msci_emerging', 'bb_world_agg', 'bb_corp_hy', 'yield_curve', 'vix_index']\n",
    "list_index_data = ['msci_emerging', 'bb_corp_hy', 'us_short_treasury']\n",
    "\n",
    "\n",
    "# select = market_data.ix[:].dropna()\n",
    "select = market_data\n",
    "\n",
    "ft_cols = list_index_data + [target]\n",
    "X = select[ft_cols].values\n",
    "select[ft_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a362b845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means and vars of each hidden state\n",
      "0th hidden state\n",
      "mean =  [7.11433203e-03 6.31190614e-03 1.24535026e-03 3.19853096e+00]\n",
      "var =  [2.01192349e-03 2.71058521e-04 7.25365976e-06 7.69078781e-01]\n",
      "\n",
      "1th hidden state\n",
      "mean =  [0.01311532 0.01015012 0.00452034 1.81932278]\n",
      "var =  [2.83001901e-03 8.79653083e-05 2.01275656e-05 3.59835198e-01]\n",
      "\n",
      "2th hidden state\n",
      "mean =  [-9.92782827e-03 -1.80401754e-03  3.97258990e-03  2.05036704e+00]\n",
      "var =  [1.16043600e-02 2.41111876e-03 2.95434611e-05 2.58544134e-01]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = mix.GaussianMixture(n_components=3, \n",
    "                            covariance_type=\"full\", \n",
    "                            n_init=100, \n",
    "                            random_state=8).fit(X)\n",
    "\n",
    "# Predict the optimal sequence of internal hidden state\n",
    "hidden_states = model.predict(X)\n",
    "\n",
    "print(\"Means and vars of each hidden state\")\n",
    "for i in range(model.n_components):\n",
    "    print(\"{0}th hidden state\".format(i))\n",
    "    print(\"mean = \", model.means_[i])\n",
    "    print(\"var = \", np.diag(model.covariances_[i]))\n",
    "    print()\n",
    "\n",
    "sns.set(font_scale=1.25)\n",
    "style_kwds = {'xtick.major.size': 3, 'ytick.major.size': 3,\n",
    "              'legend.frameon': True}\n",
    "sns.set_style('whitegrid', style_kwds)\n",
    "\n",
    "fig, axs = plt.subplots(model.n_components, sharex=True, sharey=True, figsize=(20,9))\n",
    "colors = cm.rainbow(np.linspace(0, 1, model.n_components))\n",
    "\n",
    "for i, (ax, color) in enumerate(zip(axs, colors)):\n",
    "    # Use fancy indexing to plot data in each state.\n",
    "    mask = hidden_states == i\n",
    "    ax.plot_date(select.index.values[mask],\n",
    "                 select[target].values[mask],\n",
    "                 \".-\", c=color)\n",
    "    ax.set_title(\"{0}th hidden state\".format(i), fontsize=16, fontweight='demi')\n",
    "\n",
    "    # Format the ticks.\n",
    "    ax.xaxis.set_major_locator(YearLocator())\n",
    "    ax.xaxis.set_minor_locator(MonthLocator())\n",
    "    sns.despine(offset=10)\n",
    "    # sns.despine(left=True, bottom=True)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig(os.path.join(strategy_dir,'Hidden Markov (Mixture) Model_Regime Subplots.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dfa98fe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Date  states  msci_world  msci_emerging  bb_world_agg  bb_corp_hy  \\\n",
      "0 1992-01-31       1    0.000000       0.000000      0.000000    0.000000   \n",
      "1 1992-02-29       1   -0.020678       0.103012     -0.013095    0.024673   \n",
      "2 1992-03-31       1   -0.017357       0.054748      0.003074    0.012399   \n",
      "3 1992-04-30       1   -0.066034       0.029763      0.003851    0.003798   \n",
      "4 1992-05-31       1    0.027579      -0.009451      0.006528    0.014110   \n",
      "\n",
      "   us_short_treasury  msci_world_cum  mkt_cret  \n",
      "0           0.000000        1.000000  0.000000  \n",
      "1           0.003087        0.979322 -0.020678  \n",
      "2          -0.000196        0.962324 -0.038034  \n",
      "3           0.009191        0.898778 -0.104068  \n",
      "4           0.009302        0.923565 -0.076490  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1201.74x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "states = (pd.DataFrame(hidden_states, columns=['states'], index=select.index)\n",
    "          .join(select, how='inner')\n",
    "          .assign(mkt_cret=select.msci_world.cumsum())\n",
    "          .reset_index(drop=False)\n",
    "          .rename(columns={'index':'Date'}))\n",
    "print(states.head())\n",
    "\n",
    "sns.set_style('whitegrid', style_kwds)\n",
    "order = [0, 1, 2]\n",
    "fg = sns.FacetGrid(data=states, hue='states', hue_order=order,\n",
    "                   palette=colors, aspect=1.31, size=12)\n",
    "fg.map(plt.scatter, 'Date', 'msci_world_cum', alpha=0.8).add_legend()\n",
    "sns.despine(offset=10)\n",
    "fg.fig.suptitle('Historical SPY Regimes', fontsize=24, fontweight='demi')\n",
    "fg.savefig(os.path.join(strategy_dir,'Hidden Markov (Mixture) Model_SPY Regimes.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb982131",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
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