[Showcase2]_MSM_fitparam_kbar3_m20_RV22_mean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas import DataFrame as df\n",
    "from MSM_util import *\n",
    "from lmfit import minimize, Minimizer, Parameters, Parameter, report_fit\n",
    "import sys, scipy, numpy\n",
    "\n",
    "# print(scipy.__version__, numpy.__version__, sys.version_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OwLa9xUF1kUiGUC7zGQpGjPVvz1i1vdLHVJSahAp6JaUYE5iMvWhARzIzxD9B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbe2cb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hyper parameters\n",
    "\n",
    "# How much period taken into calculation and prediction\n",
    "m = 20\n",
    "# number of days prediction\n",
    "RV = 22\n",
    "# kbar\n",
    "kbar = 3\n",
    "vary = \"k\"\n",
    "kbar_start = 3 \n",
    "kbar_max = 8\n",
    "\n",
    "xls_data = pd.read_excel('data_GVZ_GLD.xlsx')\n",
    "date_GLD = xls_data.iloc[:,3]\n",
    "GLD = xls_data.loc[:,'GLD']\n",
    "\n",
    "GLD2 = GLD.shift(1)\n",
    "GLD2[0] = GLD[0]\n",
    "GLD_r = GLD/GLD2\n",
    "\n",
    "GLD_d = GLD_r - np.mean(GLD_r)\n",
    "plt.plot(date_GLD,GLD)\n",
    "plt.title('de-mean data')\n",
    "plt.plot(date_GLD,200*GLD_r)\n",
    "\n",
    "data = GLD_d[::-1]\n",
    "\n",
    "\n",
    "startingvals = []#[1.2, 1.2, 0.5 , 1.2 ]\n",
    "LB = [1, 1, 0.001, 0.0001]\n",
    "UB = [1.99, 50, 0.99999, 5]\n",
    "op_methods = 'slsqp'\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No starting values entered: Using grid-search\n",
      "LL = -7814.4308\n",
      "==========init params=========\n",
      "m0 =   1.4291\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1934\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4663\n",
      "b =   5.7643\n",
      "gamma_k =   0.0823\n",
      "sigma =   0.2467\n",
      "LLs = -7837.9933\n",
      "AIC =  25.9335\n",
      "BIC =  17.9335\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7815.3224\n",
      "==========init params=========\n",
      "m0 =   1.4297\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1934\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4667\n",
      "b =   5.7280\n",
      "gamma_k =   0.0815\n",
      "sigma =   0.2468\n",
      "LLs = -7838.9657\n",
      "AIC =  25.9337\n",
      "BIC =  17.9337\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7818.1649\n",
      "==========init params=========\n",
      "m0 =   1.4302\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1932\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4678\n",
      "b =   5.8804\n",
      "gamma_k =   0.0841\n",
      "sigma =   0.2472\n",
      "LLs = -7841.5591\n",
      "AIC =  25.9344\n",
      "BIC =  17.9344\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7818.9222\n",
      "==========init params=========\n",
      "m0 =   1.4310\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1932\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4679\n",
      "b =   5.8027\n",
      "gamma_k =   0.0828\n",
      "sigma =   0.2470\n",
      "LLs = -7842.3806\n",
      "AIC =  25.9346\n",
      "BIC =  17.9346\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7821.4257\n",
      "==========init params=========\n",
      "m0 =   1.4320\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1931\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4697\n",
      "b =   5.9920\n",
      "gamma_k =   0.0860\n",
      "sigma =   0.2477\n",
      "LLs = -7844.6656\n",
      "AIC =  25.9352\n",
      "BIC =  17.9352\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7820.2284\n",
      "==========init params=========\n",
      "m0 =   1.4298\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1931\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4682\n",
      "b =   5.8999\n",
      "gamma_k =   0.0843\n",
      "sigma =   0.2476\n",
      "LLs = -7843.7237\n",
      "AIC =  25.9349\n",
      "BIC =  17.9349\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7819.9275\n",
      "==========init params=========\n",
      "m0 =   1.4294\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1931\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4686\n",
      "b =   6.0697\n",
      "gamma_k =   0.0875\n",
      "sigma =   0.2480\n",
      "LLs = -7843.3132\n",
      "AIC =  25.9348\n",
      "BIC =  17.9348\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7820.5146\n",
      "==========init params=========\n",
      "m0 =   1.4294\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1931\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4678\n",
      "b =   5.9149\n",
      "gamma_k =   0.0846\n",
      "sigma =   0.2477\n",
      "LLs = -7844.0598\n",
      "AIC =  25.9350\n",
      "BIC =  17.9350\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7821.1650\n",
      "==========init params=========\n",
      "m0 =   1.4294\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1930\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4682\n",
      "b =   5.9978\n",
      "gamma_k =   0.0860\n",
      "sigma =   0.2480\n",
      "LLs = -7844.6450\n",
      "AIC =  25.9352\n",
      "BIC =  17.9352\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7822.0997\n",
      "==========init params=========\n",
      "m0 =   1.4295\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1930\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4680\n",
      "b =   5.9305\n",
      "gamma_k =   0.0846\n",
      "sigma =   0.2479\n",
      "LLs = -7845.6700\n",
      "AIC =  25.9354\n",
      "BIC =  17.9354\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7822.9539\n",
      "==========init params=========\n",
      "m0 =   1.4296\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1929\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4680\n",
      "b =   5.8872\n",
      "gamma_k =   0.0837\n",
      "sigma =   0.2479\n",
      "LLs = -7846.6052\n",
      "AIC =  25.9357\n",
      "BIC =  17.9357\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7824.1894\n",
      "==========init params=========\n",
      "m0 =   1.4299\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1929\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4684\n",
      "b =   5.8728\n",
      "gamma_k =   0.0832\n",
      "sigma =   0.2480\n",
      "LLs = -7847.8832\n",
      "AIC =  25.9360\n",
      "BIC =  17.9360\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7824.6492\n",
      "==========init params=========\n",
      "m0 =   1.4298\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1929\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4683\n",
      "b =   5.8293\n",
      "gamma_k =   0.0824\n",
      "sigma =   0.2480\n",
      "LLs = -7848.4391\n",
      "AIC =  25.9361\n",
      "BIC =  17.9361\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7824.8517\n",
      "==========init params=========\n",
      "m0 =   1.4296\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1929\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4680\n",
      "b =   5.7927\n",
      "gamma_k =   0.0817\n",
      "sigma =   0.2481\n",
      "LLs = -7848.7377\n",
      "AIC =  25.9362\n",
      "BIC =  17.9362\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7825.6107\n",
      "==========init params=========\n",
      "m0 =   1.4297\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1928\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.5254\n",
      "b =  49.9521\n",
      "gamma_k =   0.9529\n",
      "sigma =   0.2305\n",
      "LLs = -7863.4316\n",
      "AIC =  25.9400\n",
      "BIC =  17.9400\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7826.9820\n",
      "==========init params=========\n",
      "m0 =   1.4298\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1928\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4686\n",
      "b =   5.7676\n",
      "gamma_k =   0.0809\n",
      "sigma =   0.2483\n",
      "LLs = -7850.9974\n",
      "AIC =  25.9368\n",
      "BIC =  17.9368\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7827.6594\n",
      "==========init params=========\n",
      "m0 =   1.4296\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1927\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4686\n",
      "b =   5.7720\n",
      "gamma_k =   0.0809\n",
      "sigma =   0.2484\n",
      "LLs = -7851.7217\n",
      "AIC =  25.9370\n",
      "BIC =  17.9370\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7829.4418\n",
      "==========init params=========\n",
      "m0 =   1.4298\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1926\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4694\n",
      "b =   5.8107\n",
      "gamma_k =   0.0813\n",
      "sigma =   0.2488\n",
      "LLs = -7853.5002\n",
      "AIC =  25.9374\n",
      "BIC =  17.9374\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7830.5379\n",
      "==========init params=========\n",
      "m0 =   1.4295\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1925\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4704\n",
      "b =   6.0253\n",
      "gamma_k =   0.0852\n",
      "sigma =   0.2499\n",
      "LLs = -7854.2350\n",
      "AIC =  25.9376\n",
      "BIC =  17.9376\n",
      "No starting values entered: Using grid-search\n",
      "LL = -7830.8219\n",
      "==========init params=========\n",
      "m0 =   1.4292\n",
      "b =   1.5000\n",
      "gamma_k =   0.1000\n",
      "sigma =   0.1925\n",
      "\n",
      "\n",
      " ==========fitted results==========\n",
      "optimization method = slsqp\n",
      "m0 =   1.4696\n",
      "b =   5.9005\n",
      "gamma_k =   0.0828\n",
      "sigma =   0.2495\n",
      "LLs = -7854.7534\n",
      "AIC =  25.9377\n",
      "BIC =  17.9377\n"
     ]
    }
   ],
   "source": [
    "output =msm_fitseries(data, kbar, LB, UB, op_methods, startingvals, m, RV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RV</th>\n",
       "      <th>m0</th>\n",
       "      <th>b</th>\n",
       "      <th>gamma_k</th>\n",
       "      <th>sigma</th>\n",
       "      <th>GVZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.687813</td>\n",
       "      <td>1.466268</td>\n",
       "      <td>5.764339</td>\n",
       "      <td>0.082307</td>\n",
       "      <td>0.246686</td>\n",
       "      <td>11.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.695531</td>\n",
       "      <td>1.466718</td>\n",
       "      <td>5.728050</td>\n",
       "      <td>0.081528</td>\n",
       "      <td>0.246767</td>\n",
       "      <td>12.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.738624</td>\n",
       "      <td>1.467818</td>\n",
       "      <td>5.880420</td>\n",
       "      <td>0.084104</td>\n",
       "      <td>0.247221</td>\n",
       "      <td>11.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.716048</td>\n",
       "      <td>1.467886</td>\n",
       "      <td>5.802658</td>\n",
       "      <td>0.082770</td>\n",
       "      <td>0.246984</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.784609</td>\n",
       "      <td>1.469714</td>\n",
       "      <td>5.991995</td>\n",
       "      <td>0.085954</td>\n",
       "      <td>0.247705</td>\n",
       "      <td>11.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.770267</td>\n",
       "      <td>1.468194</td>\n",
       "      <td>5.899889</td>\n",
       "      <td>0.084298</td>\n",
       "      <td>0.247554</td>\n",
       "      <td>11.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.811517</td>\n",
       "      <td>1.468556</td>\n",
       "      <td>6.069747</td>\n",
       "      <td>0.087525</td>\n",
       "      <td>0.247988</td>\n",
       "      <td>11.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.782923</td>\n",
       "      <td>1.467834</td>\n",
       "      <td>5.914861</td>\n",
       "      <td>0.084559</td>\n",
       "      <td>0.247688</td>\n",
       "      <td>11.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.809262</td>\n",
       "      <td>1.468204</td>\n",
       "      <td>5.997755</td>\n",
       "      <td>0.086020</td>\n",
       "      <td>0.247964</td>\n",
       "      <td>10.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.805972</td>\n",
       "      <td>1.468048</td>\n",
       "      <td>5.930450</td>\n",
       "      <td>0.084601</td>\n",
       "      <td>0.247930</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.806344</td>\n",
       "      <td>1.468039</td>\n",
       "      <td>5.887214</td>\n",
       "      <td>0.083659</td>\n",
       "      <td>0.247934</td>\n",
       "      <td>10.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.815212</td>\n",
       "      <td>1.468400</td>\n",
       "      <td>5.872800</td>\n",
       "      <td>0.083242</td>\n",
       "      <td>0.248027</td>\n",
       "      <td>10.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.814286</td>\n",
       "      <td>1.468256</td>\n",
       "      <td>5.829251</td>\n",
       "      <td>0.082380</td>\n",
       "      <td>0.248017</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.818054</td>\n",
       "      <td>1.468019</td>\n",
       "      <td>5.792729</td>\n",
       "      <td>0.081663</td>\n",
       "      <td>0.248056</td>\n",
       "      <td>11.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10.205748</td>\n",
       "      <td>1.525380</td>\n",
       "      <td>49.952097</td>\n",
       "      <td>0.952917</td>\n",
       "      <td>0.230515</td>\n",
       "      <td>10.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11.838105</td>\n",
       "      <td>1.468579</td>\n",
       "      <td>5.767566</td>\n",
       "      <td>0.080934</td>\n",
       "      <td>0.248267</td>\n",
       "      <td>11.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.853212</td>\n",
       "      <td>1.468616</td>\n",
       "      <td>5.772030</td>\n",
       "      <td>0.080907</td>\n",
       "      <td>0.248425</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.893520</td>\n",
       "      <td>1.469401</td>\n",
       "      <td>5.810697</td>\n",
       "      <td>0.081332</td>\n",
       "      <td>0.248847</td>\n",
       "      <td>11.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.992756</td>\n",
       "      <td>1.470378</td>\n",
       "      <td>6.025277</td>\n",
       "      <td>0.085231</td>\n",
       "      <td>0.249883</td>\n",
       "      <td>11.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.957893</td>\n",
       "      <td>1.469625</td>\n",
       "      <td>5.900540</td>\n",
       "      <td>0.082791</td>\n",
       "      <td>0.249520</td>\n",
       "      <td>11.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RV        m0          b   gamma_k     sigma    GVZ\n",
       "0   11.687813  1.466268   5.764339  0.082307  0.246686  11.70\n",
       "1   11.695531  1.466718   5.728050  0.081528  0.246767  12.99\n",
       "2   11.738624  1.467818   5.880420  0.084104  0.247221  11.67\n",
       "3   11.716048  1.467886   5.802658  0.082770  0.246984  12.28\n",
       "4   11.784609  1.469714   5.991995  0.085954  0.247705  11.38\n",
       "5   11.770267  1.468194   5.899889  0.084298  0.247554  11.39\n",
       "6   11.811517  1.468556   6.069747  0.087525  0.247988  11.79\n",
       "7   11.782923  1.467834   5.914861  0.084559  0.247688  11.02\n",
       "8   11.809262  1.468204   5.997755  0.086020  0.247964  10.64\n",
       "9   11.805972  1.468048   5.930450  0.084601  0.247930  10.72\n",
       "10  11.806344  1.468039   5.887214  0.083659  0.247934  10.58\n",
       "11  11.815212  1.468400   5.872800  0.083242  0.248027  10.75\n",
       "12  11.814286  1.468256   5.829251  0.082380  0.248017  10.84\n",
       "13  11.818054  1.468019   5.792729  0.081663  0.248056  11.18\n",
       "14  10.205748  1.525380  49.952097  0.952917  0.230515  10.62\n",
       "15  11.838105  1.468579   5.767566  0.080934  0.248267  11.02\n",
       "16  11.853212  1.468616   5.772030  0.080907  0.248425  11.75\n",
       "17  11.893520  1.469401   5.810697  0.081332  0.248847  11.61\n",
       "18  11.992756  1.470378   6.025277  0.085231  0.249883  11.53\n",
       "19  11.957893  1.469625   5.900540  0.082791  0.249520  11.68"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = output\n",
    "a[\"GVZ\"] = xls_data[\"GVZ\"]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oYeGrLXt4dsm3vJO/A2MMZ+b15/KThjA2u1fAj5UQG82IfqkB329rRF5zcyAI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbe2d73c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbe293278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbd72b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vtu23B1u3g3fdpYePuQcB3whDJBiBhiPO9ttt3xtNglHswEJgD5fyFFM4NE26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\nEdmCFO4iIluQwl1EZAtSuIuIbEH/H/4VtkbWjg7WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbddd7ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vtu23B1u3g3fdpYePuQcB3whDJBiBhiPO9ttt3xtNglHswEJgD5fyFFM4NE26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\nEdmCFO4iIluQwl1EZAtSuIuIbEH/H/4VtkbWjg7WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbde058d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bY3FqImVUlpdm5Xzu+HjPbOdcVuWiL4VPgVpYHiicc1nVHyiy2E7RFotT79VO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\nzjnnhvT/A/p/BE89Bw/cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbd809ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datainreg = xls_data[\"GVZ\"][::-1]\n",
    "data_lastm = datainreg\n",
    "data_lastm = data_lastm.iloc[-m:]\n",
    " \n",
    "msm_plot(data_lastm, output, m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope :  0.119141530313\n",
      "Intercept :  9.95948356095\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.11914153031294192, 9.9594835609536787)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datainreg = xls_data[\"GVZ\"][::-1]\n",
    "data_lastm = datainreg\n",
    "data_lastm = data_lastm.iloc[-m:]\n",
    "\n",
    "linreg(df(output['RV']), df(data_lastm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XLSX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180210MSM_fitparam_kbar3_RV22_m20.xlsx\n"
     ]
    }
   ],
   "source": [
    "from datetime import date\n",
    "text = 'MSM_fitparam'\n",
    "namew=text + \"_kbar\" + str(kbar)+ \"_RV\" + str(22) +'_m' +str(m)+\".xlsx\"\n",
    "writer = pd.ExcelWriter(\"\".join((date.today().strftime('%y%m%d'),namew)))\n",
    "print(writer.path)\n",
    "output.to_excel(writer, 'Sheet2')\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output2 = msm_averageparams(output, kbar, m, data, RV, LB , UB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RV</th>\n",
       "      <th>m0</th>\n",
       "      <th>b</th>\n",
       "      <th>gamma_k</th>\n",
       "      <th>sigma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.726989</td>\n",
       "      <td>1.471197</td>\n",
       "      <td>8.079518</td>\n",
       "      <td>0.126936</td>\n",
       "      <td>0.247099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RV        m0         b   gamma_k     sigma\n",
       "0   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "1   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "2   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "3   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "4   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "5   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "6   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "7   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "8   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "9   11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "10  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "11  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "12  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "13  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "14  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "15  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "16  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "17  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "18  11.726989  1.471197  8.079518  0.126936  0.247099\n",
       "19  11.726989  1.471197  8.079518  0.126936  0.247099"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l8OGH5vKJx3ff2bpsCH/nztCihQu/4zhOQiIDt+rTEXriifY+dmz89YsWWQ7/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\n0pjsUF4OnTrBvvuGbYmTw7j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47UAS90JGUNXlqvqdqm4F/p7guGFfv8ZAGfBkojphXb/6\nkI/C/x6wr4j0CFqEp2FpoqN5AfhpEJkyEPgy6i95xgl8go8An6jqHxPU2SOoh4gMwL6L1VmyL5mU\n2aFew4CELa0wr18UtaYnJ7n7NSOIyFDg18Dxqro+QZ3Q0qfH9BmdmOC4oV2/gFJglqoujrcyzOtX\nL8LuXU7lhUWcVGG9/VcHZecD5wefBXggWP8RUJJl+w7H/vZ/CEwPXsNibLwI+BiLUqgADsuifXsF\nx/0gsCEXr2FLTMh3jSoL7fphD6BlwGbMz/xzoC3wBjAHeB1oE9TtBIyr6X7Nkn1zMf945B78a6x9\nie6FLNn3r+De+hAT8465dP2C8lGRey6qbtavX7pfnrLBcRynwMhHV4/jOI5TD1z4HcdxCgwXfsdx\nnALDhd9xHKfAcOF3HMcpMFz4HcdxCgwXfsdxnALj/wNHNbrBpKTCkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbd88e860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbde595f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbda50518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbde0f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbde6a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23fbde8e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datainreg = xls_data[\"GVZ\"][::-1]\n",
    "data_lastm = datainreg\n",
    "data_lastm = data_lastm.iloc[-m:]\n",
    "\n",
    "msm_plot(xls_data[\"GVZ\"], output2, m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope :  -1.42070790877e+13\n",
      "Intercept :  1.6660625706e+14\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-14207079087658.77, 166606257060242.06)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datainreg = xls_data[\"GVZ\"][::-1]\n",
    "data_lastm = datainreg\n",
    "data_lastm = data_lastm.iloc[-m:]\n",
    "\n",
    "linreg(df(output2['RV']), df(data_lastm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19    11.68\n",
       "18    11.53\n",
       "17    11.61\n",
       "16    11.75\n",
       "15    11.02\n",
       "14    10.62\n",
       "13    11.18\n",
       "12    10.84\n",
       "11    10.75\n",
       "10    10.58\n",
       "9     10.72\n",
       "8     10.64\n",
       "7     11.02\n",
       "6     11.79\n",
       "5     11.39\n",
       "4     11.38\n",
       "3     12.28\n",
       "2     11.67\n",
       "1     12.99\n",
       "0     11.70\n",
       "Name: GVZ, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_lastm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
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