WorkspaceCompanyTearSheet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSEG COMPANY TEARSHEET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " IBM.N\n"
     ]
    }
   ],
   "source": [
    "RIC = input()\n",
    "# print(RIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import module and set up with App Key\n",
    "import lseg.data as ld\n",
    "APP_KEY = '<App Key>'\n",
    "ld.open_session(app_key = APP_KEY)\n",
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Instrument</th>\n",
       "      <th>Company Common Name</th>\n",
       "      <th>Country of Headquarters</th>\n",
       "      <th>TRBC Industry Group Name</th>\n",
       "      <th>GICS Sector Name</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Exchange Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>International Business Machines Corp</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Software &amp; IT Services</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>USD</td>\n",
       "      <td>NEW YORK STOCK EXCHANGE, INC.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Instrument                   Company Common Name   Country of Headquarters  \\\n",
       "0      IBM.N  International Business Machines Corp  United States of America   \n",
       "\n",
       "  TRBC Industry Group Name        GICS Sector Name Currency  \\\n",
       "0   Software & IT Services  Information Technology      USD   \n",
       "\n",
       "                   Exchange Name  \n",
       "0  NEW YORK STOCK EXCHANGE, INC.  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Request Header Information for RIC \n",
    "df = ld.get_data(RIC, \"TR.CommonName(),TR.HeadquartersCountry,TR.TRBCIndustryGroup,TR.GICSSector,TR.CompanyMarketCap.Currency\")\n",
    "#Request Exchange for Primary Quote RIC\n",
    "df_2 = ld.get_data(RIC,\"TR.PrimaryQuote\")\n",
    "df_3 = ld.get_data(df_2.at[0,'Primary Quote RIC'],\"TR.ExchangeName\")  \n",
    "df['Exchange Name'] = [df_3.at[0,'Exchange Name']]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Price Close</td>\n",
       "      <td>236.220000</td>\n",
       "      <td>Beta 5 Year</td>\n",
       "      <td>0.628409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Price 52 Week High</td>\n",
       "      <td>266.300000</td>\n",
       "      <td>Dividend yield</td>\n",
       "      <td>2.797203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52-week High Price PCT Change</td>\n",
       "      <td>-11.295531</td>\n",
       "      <td>Outstanding Shares</td>\n",
       "      <td>927.264332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Price 52 Week Low</td>\n",
       "      <td>162.650000</td>\n",
       "      <td>Free Float</td>\n",
       "      <td>926.039119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52-week Low Price PCT Change</td>\n",
       "      <td>45.232093</td>\n",
       "      <td>Free Float (Percent)</td>\n",
       "      <td>99.867900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Price Target - Median</td>\n",
       "      <td>262.495000</td>\n",
       "      <td>Company Market Cap</td>\n",
       "      <td>219038.380505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Implied Profit</td>\n",
       "      <td>11.123106</td>\n",
       "      <td>Enterprise Value (Daily Time Series)</td>\n",
       "      <td>259506.380505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               1           2  \\\n",
       "0                    Price Close  236.220000   \n",
       "1             Price 52 Week High  266.300000   \n",
       "2  52-week High Price PCT Change  -11.295531   \n",
       "3              Price 52 Week Low  162.650000   \n",
       "4   52-week Low Price PCT Change   45.232093   \n",
       "5          Price Target - Median  262.495000   \n",
       "6                 Implied Profit   11.123106   \n",
       "\n",
       "                                      3              4  \n",
       "0                           Beta 5 Year       0.628409  \n",
       "1                        Dividend yield       2.797203  \n",
       "2                    Outstanding Shares     927.264332  \n",
       "3                            Free Float     926.039119  \n",
       "4                  Free Float (Percent)      99.867900  \n",
       "5                    Company Market Cap  219038.380505  \n",
       "6  Enterprise Value (Daily Time Series)  259506.380505  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#As single Dataframe - example only\n",
    "#dfGenSingle = ld.get_data(RIC, \n",
    "#        [{'TR.PriceClose;TR.Price52WeekHigh;TR.PricePctChg52WkHigh;TR.Price52WeekLow;TR.PricePctChg52WkLow;TR.PriceTargetMedian': {'Curn':df.at[0,'Currency']}},\n",
    "#            {'TR.BetaFiveYear;TR.DividendYield;TR.SharesOutstanding;TR.FreeFloat;TR.FreeFloatPct();TR.CompanyMarketCap;TR.EV': {'Scale':6,'Curn':df.at[0,'Currency']}}])\n",
    "\n",
    "#As two Dataframes - more convenient\n",
    "dfGen = ld.get_data(RIC, 'TR.PriceClose;TR.Price52WeekHigh;TR.PricePctChg52WkHigh;TR.Price52WeekLow;TR.PricePctChg52WkLow;TR.PriceTargetMedian',{'Curn':df.at[0,'Currency']})\n",
    "dfGen2 = ld.get_data( RIC, 'TR.BetaFiveYear;TR.DividendYield;TR.SharesOutstanding;TR.FreeFloat;TR.FreeFloatPct();TR.CompanyMarketCap;TR.EV',{'Scale':6,'Curn':df.at[0,'Currency']})\n",
    "\n",
    "#calc implied profit\n",
    "dfGen['Implied Profit'] = (dfGen.at[0,'Price Target - Median']/dfGen.at[0,'Price Close'] - 1)*100\n",
    "\n",
    "# results as simple lists \n",
    "listGenNames = dfGen.columns.tolist()\n",
    "listGen = dfGen.iloc[0].tolist()\n",
    "listGen2Names = dfGen2.columns.tolist()\n",
    "listGen2 = dfGen2.iloc[0].tolist()\n",
    "\n",
    "#remove RIC\n",
    "listGenNames.pop(0)\n",
    "listGen.pop(0)\n",
    "listGen2Names.pop(0)\n",
    "listGen2.pop(0)\n",
    "\n",
    "dataDictGen= {'1': listGenNames, '2': listGen , '3': listGen2Names, '4': listGen2} \n",
    "dfGenX = pd.DataFrame(dataDictGen)  \n",
    "\n",
    "dfGenX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consensus Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Instrument</th>\n",
       "      <th>Recommendation - Mean Label</th>\n",
       "      <th>Recommendation - Number Of Total</th>\n",
       "      <th>Number of Analysts Revising Up</th>\n",
       "      <th>Number of Analysts Revising Down</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1970-01-01 00:00:00.000000023</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Instrument Recommendation - Mean Label Recommendation - Number Of Total  \\\n",
       "0      IBM.N                         BUY    1970-01-01 00:00:00.000000023   \n",
       "\n",
       "   Number of Analysts Revising Up  Number of Analysts Revising Down  \n",
       "0                               3                                 5  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = ld.get_data(RIC, 'TR.RecLabel();TR.NumOfRecommendations();TR.NumEstRevisingUp;TR.NumEstRevisingDown',{'WP':'30d','EstimateMeasure':'EPS'})\n",
    "df6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retrieve Estimates into Data Frames per Period\n",
    "\n",
    "#FY0\n",
    "dfFY0 = ld.get_data(RIC, 'TR.RevenueActValue;TR.EBITDAActValue;TR.EPSActValue',\n",
    "                       {'Period':'FY0', 'Scale':'6', 'Curn':df.at[0,'Currency']})\n",
    "#FY1\n",
    "dfFY1 = ld.get_data(RIC, \n",
    "'TR.RevenueMeanEstimate;TR.RevenueHigh;TR.RevenueLow;TR.EBITDAMean;TR.EBITDAHigh;TR.EBITDALow;TR.EPSMeanEstimate;TR.EPSHigh;TR.EPSLow',\n",
    "                       {'Period':'FY1', 'scale':'6', 'curn':df.at[0,'Currency']})\n",
    "dfFY1_2 = ld.get_data(RIC, \n",
    "                           'TR.RevenueMeanEstimate; TR.EBITDAMean; TR.EPSMeanEstimate',\n",
    "    {'Period':'FY1','RollPeriods':'false','Scale': 6,'Curn':df.at[0,'Currency'],'SDate':'-1AM'})\n",
    "#FY2\n",
    "dfFY2 = ld.get_data(RIC, \n",
    "'TR.RevenueMeanEstimate;TR.RevenueHigh;TR.RevenueLow;TR.EBITDAMean;TR.EBITDAHigh;TR.EBITDALow;TR.EPSMeanEstimate;TR.EPSHigh;TR.EPSLow',\n",
    "                       {'Period':'FY2', 'scale':'6', 'curn':df.at[0,'Currency']})\n",
    "dfFY2_2 = ld.get_data(RIC, 'TR.RevenueMeanEstimate;TR.EBITDAMean;TR.EPSMeanEstimate',\n",
    "    {'Period':'FY2','RollPeriods':'false','Scale': 6,'Curn':df.at[0,'Currency'],'SDate':'-1AM'})\n",
    "\n",
    "### DEBUG display(dfFY0,dfFY1,dfFY1_2, dfFY2, dfFY2_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Sales (MIL) Mean EST</th>\n",
       "      <th>High Est</th>\n",
       "      <th>Low Est</th>\n",
       "      <th>Est. 30 30 Days Ago</th>\n",
       "      <th>EBITDA (MIL) Mean Est.</th>\n",
       "      <th>High Est.</th>\n",
       "      <th>Low Est.</th>\n",
       "      <th>Est. 30 30 Days Ago.</th>\n",
       "      <th>EPS Mean Est.</th>\n",
       "      <th>High Est..</th>\n",
       "      <th>Low Est..</th>\n",
       "      <th>Est. 30 30 Days Ago..</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FY0 (Actual)</th>\n",
       "      <td>62753</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>16400</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>10.33</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FY1 (Current Year)</th>\n",
       "      <td>65076.02167</td>\n",
       "      <td>66377</td>\n",
       "      <td>63800</td>\n",
       "      <td>64974.57225</td>\n",
       "      <td>17039.26313</td>\n",
       "      <td>18064</td>\n",
       "      <td>15860</td>\n",
       "      <td>17067.61713</td>\n",
       "      <td>10.76595</td>\n",
       "      <td>11.15</td>\n",
       "      <td>10.079</td>\n",
       "      <td>10.71837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% above/below mean</th>\n",
       "      <td></td>\n",
       "      <td>1.999167</td>\n",
       "      <td>-1.960817</td>\n",
       "      <td>-0.155894</td>\n",
       "      <td></td>\n",
       "      <td>6.013974</td>\n",
       "      <td>-6.920858</td>\n",
       "      <td>0.166404</td>\n",
       "      <td></td>\n",
       "      <td>3.567265</td>\n",
       "      <td>-6.380765</td>\n",
       "      <td>-0.441949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FY2 (Next Year)</th>\n",
       "      <td>68075.7141</td>\n",
       "      <td>70080</td>\n",
       "      <td>66440.044</td>\n",
       "      <td>68099.71453</td>\n",
       "      <td>17845.78863</td>\n",
       "      <td>19303.743</td>\n",
       "      <td>16800</td>\n",
       "      <td>17894.22313</td>\n",
       "      <td>11.42021</td>\n",
       "      <td>12.29</td>\n",
       "      <td>10.526</td>\n",
       "      <td>11.41222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>% above/below mean</th>\n",
       "      <td></td>\n",
       "      <td>2.944201</td>\n",
       "      <td>-2.402722</td>\n",
       "      <td>0.035255</td>\n",
       "      <td></td>\n",
       "      <td>8.169739</td>\n",
       "      <td>-5.860142</td>\n",
       "      <td>0.271406</td>\n",
       "      <td></td>\n",
       "      <td>7.616235</td>\n",
       "      <td>-7.830066</td>\n",
       "      <td>-0.069964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Sales (MIL) Mean EST  High Est    Low Est  \\\n",
       "FY0 (Actual)                      62753                        \n",
       "FY1 (Current Year)          65076.02167     66377      63800   \n",
       "% above/below mean                       1.999167  -1.960817   \n",
       "FY2 (Next Year)              68075.7141     70080  66440.044   \n",
       "% above/below mean                       2.944201  -2.402722   \n",
       "\n",
       "                   Est. 30 30 Days Ago EBITDA (MIL) Mean Est.  High Est.  \\\n",
       "FY0 (Actual)                                            16400              \n",
       "FY1 (Current Year)         64974.57225            17039.26313      18064   \n",
       "% above/below mean           -0.155894                          6.013974   \n",
       "FY2 (Next Year)            68099.71453            17845.78863  19303.743   \n",
       "% above/below mean            0.035255                          8.169739   \n",
       "\n",
       "                    Low Est. Est. 30 30 Days Ago. EPS Mean Est. High Est..  \\\n",
       "FY0 (Actual)                                              10.33              \n",
       "FY1 (Current Year)     15860          17067.61713      10.76595      11.15   \n",
       "% above/below mean -6.920858             0.166404                 3.567265   \n",
       "FY2 (Next Year)        16800          17894.22313      11.42021      12.29   \n",
       "% above/below mean -5.860142             0.271406                 7.616235   \n",
       "\n",
       "                   Low Est.. Est. 30 30 Days Ago..  \n",
       "FY0 (Actual)                                        \n",
       "FY1 (Current Year)    10.079              10.71837  \n",
       "% above/below mean -6.380765             -0.441949  \n",
       "FY2 (Next Year)       10.526              11.41222  \n",
       "% above/below mean -7.830066             -0.069964  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a dictionary out of results, and structure a single multi-row Data Frame\n",
    "\n",
    "# initialise results per column \n",
    "data = {'Sales (MIL) Mean EST':\n",
    "        [dfFY0.at[0,'Revenue - Actual'], dfFY1.at[0,'Revenue - Mean Estimate'],'',dfFY2.at[0,'Revenue - Mean Estimate'],''],\n",
    "       'High Est':\n",
    "       ['',dfFY1.at[0,'Revenue - High'],(dfFY1.at[0,'Revenue - High']/dfFY1.at[0,'Revenue - Mean Estimate']-1)*100,\n",
    "        dfFY2.at[0,'Revenue - High'],(dfFY2.at[0,'Revenue - High']/dfFY2.at[0,'Revenue - Mean Estimate']-1)*100],\n",
    "       'Low Est':\n",
    "       ['',dfFY1.at[0,'Revenue - Low'],(dfFY1.at[0,'Revenue - Low']/dfFY1.at[0,'Revenue - Mean Estimate']-1)*100,\n",
    "        dfFY2.at[0,'Revenue - Low'],(dfFY2.at[0,'Revenue - Low']/dfFY2.at[0,'Revenue - Mean Estimate']-1)*100],\n",
    "       'Est. 30 30 Days Ago':\n",
    "       ['',dfFY1_2.at[0,'Revenue - Mean Estimate'],(dfFY1_2.at[0,'Revenue - Mean Estimate']/dfFY1.at[0,'Revenue - Mean Estimate']-1)*100,\n",
    "        dfFY2_2.at[0,'Revenue - Mean Estimate'],(dfFY2_2.at[0,'Revenue - Mean Estimate']/dfFY2.at[0,'Revenue - Mean Estimate']-1)*100],\n",
    "       'EBITDA (MIL) Mean Est.':\n",
    "        [dfFY0.at[0,'EBITDA - Actual'],dfFY1.at[0,'EBITDA - Mean'],'',dfFY2.at[0,'EBITDA - Mean'],''],\n",
    "        'High Est.':\n",
    "        ['',dfFY1.at[0,'EBITDA - High'],(dfFY1.at[0,'EBITDA - High']/dfFY1.at[0,'EBITDA - Mean']-1)*100,\n",
    "         dfFY2.at[0,'EBITDA - High'],(dfFY2.at[0,'EBITDA - High']/dfFY2.at[0,'EBITDA - Mean']-1)*100],\n",
    "        'Low Est.':\n",
    "        ['',dfFY1.at[0,'EBITDA - Low'],(dfFY1.at[0,'EBITDA - Low']/dfFY1.at[0,'EBITDA - Mean']-1)*100,\n",
    "         dfFY2.at[0,'EBITDA - Low'],(dfFY2.at[0,'EBITDA - Low']/dfFY2.at[0,'EBITDA - Mean']-1)*100],\n",
    "        'Est. 30 30 Days Ago.':\n",
    "       ['',dfFY1_2.at[0,'EBITDA - Mean'],(dfFY1_2.at[0,'EBITDA - Mean']/dfFY1.at[0,'EBITDA - Mean']-1)*100,\n",
    "        dfFY2_2.at[0,'EBITDA - Mean'],(dfFY2_2.at[0,'EBITDA - Mean']/dfFY2.at[0,'EBITDA - Mean']-1)*100],\n",
    "        'EPS Mean Est.':\n",
    "        [dfFY0.at[0,'Earnings Per Share - Actual'], dfFY1.at[0,'Earnings Per Share - Mean Estimate'],'',\n",
    "         dfFY2.at[0,'Earnings Per Share - Mean Estimate'],''],\n",
    "        'High Est..':\n",
    "         ['',dfFY1.at[0,'Earnings Per Share - High'],(dfFY1.at[0,'Earnings Per Share - High']/dfFY1.at[0,'Earnings Per Share - Mean Estimate']-1)*100,\n",
    "        dfFY2.at[0,'Earnings Per Share - High'],(dfFY2.at[0,'Earnings Per Share - High']/dfFY2.at[0,'Earnings Per Share - Mean Estimate']-1)*100],\n",
    "        'Low Est..':\n",
    "         ['',dfFY1.at[0,'Earnings Per Share - Low'],(dfFY1.at[0,'Earnings Per Share - Low']/dfFY1.at[0,'Earnings Per Share - Mean Estimate']-1)*100,\n",
    "        dfFY2.at[0,'Earnings Per Share - Low'],(dfFY2.at[0,'Earnings Per Share - Low']/dfFY2.at[0,'Earnings Per Share - Mean Estimate']-1)*100],\n",
    "        'Est. 30 30 Days Ago..':\n",
    "        ['',dfFY1_2.at[0,'Earnings Per Share - Mean Estimate'],(dfFY1_2.at[0,'Earnings Per Share - Mean Estimate']/dfFY1.at[0,'Earnings Per Share - Mean Estimate']-1)*100,\n",
    "        dfFY2_2.at[0,'Earnings Per Share - Mean Estimate'],(dfFY2_2.at[0,'Earnings Per Share - Mean Estimate']/dfFY2.at[0,'Earnings Per Share - Mean Estimate']-1)*100]\n",
    "       }  \n",
    "  \n",
    "# Create Pandas DataFrame. \n",
    "dfMultiRow = pd.DataFrame(data, index =['FY0 (Actual)','FY1 (Current Year)', '% above/below mean','FY2 (Next Year)','% above/below mean' ]) \n",
    "                                             \n",
    "dfMultiRow                     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Valuation Ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python312\\Lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1056:RuntimeWarning: invalid value encountered in cast\n",
      "C:\\Python312\\Lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1080:RuntimeWarning: invalid value encountered in cast\n"
     ]
    }
   ],
   "source": [
    "# Retrieve Valuation Ratios into DataFrames per period\n",
    "\n",
    "dfLTM = ld.get_data(RIC,'TR.PE;TR.PriceToSalesPerShare;TR.EVToSales;TR.EVToEBITDA',{'Sdate':'0D'})\n",
    "dfFY1 = ld.get_data(RIC, 'TR.PToEPSMeanEst;TR.PToREVMeanEst',{'Period':'FY1'})\n",
    "dfFY1_2 = ld.get_data(RIC,'TR.EV;TR.RevenueMeanEstimate,TR.EBITDAMean',{'Period':'FY1','Curn':df.at[0,'Currency']})\n",
    "dfFY2 = ld.get_data(RIC, 'TR.PToEPSMeanEst;TR.PToREVMeanEst',{'Period':'FY2'})\n",
    "dfFY2_2 = ld.get_data(RIC,'TR.EV;TR.RevenueMeanEstimate,TR.EBITDAMean',{'Period':'FY2','Curn':df.at[0,'Currency']})\n",
    "###DEBUG display(dfLTM,dfFY1,dfFY1_2,dfFY2, dfFY2_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>P/E</th>\n",
       "      <th>P/SALES</th>\n",
       "      <th>EV/REVS</th>\n",
       "      <th>EV/EBITDA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LTM (Last Twelve Months)</th>\n",
       "      <td>34.255634</td>\n",
       "      <td>3.490429</td>\n",
       "      <td>4.135296</td>\n",
       "      <td>17.837942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FY1 (Current Year)</th>\n",
       "      <td>21.941399</td>\n",
       "      <td>3.365885</td>\n",
       "      <td>3.987742</td>\n",
       "      <td>15.229906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FY2 (Next Year)</th>\n",
       "      <td>20.684383</td>\n",
       "      <td>3.217570</td>\n",
       "      <td>3.812026</td>\n",
       "      <td>14.541603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                P/E   P/SALES   EV/REVS  EV/EBITDA\n",
       "LTM (Last Twelve Months)  34.255634  3.490429  4.135296  17.837942\n",
       "FY1 (Current Year)        21.941399  3.365885  3.987742  15.229906\n",
       "FY2 (Next Year)           20.684383  3.217570  3.812026  14.541603"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a dictionary out of results, and structure a single multi-row Data Frame\n",
    "\n",
    "# Initialise results per column \n",
    "dataDict = {'P/E':\n",
    "        [dfLTM.at[0,'P/E (Daily Time Series Ratio)'], dfFY1.at[0,'Price / EPS (Mean Estimate)'], dfFY2.at[0,'Price / EPS (Mean Estimate)']],\n",
    "            'P/SALES':\n",
    "        [dfLTM.at[0,'Price To Sales Per Share (Daily Time Series Ratio)'], dfFY1.at[0,'Price / Revenue (Mean Estimate)'], dfFY2.at[0,'Price / Revenue (Mean Estimate)']],\n",
    "            'EV/REVS':\n",
    "                [dfLTM.at[0,'Enterprise Value To Sales (Daily Time Series Ratio)'], \n",
    "                 dfFY1_2.at[0,'Enterprise Value (Daily Time Series)']/dfFY1_2.at[0,'Revenue - Mean Estimate'], \n",
    "                 dfFY2_2.at[0,'Enterprise Value (Daily Time Series)']/dfFY2_2.at[0,'Revenue - Mean Estimate']],\n",
    "            \n",
    "            'EV/EBITDA':\n",
    "                [dfLTM.at[0,'Enterprise Value To EBITDA (Daily Time Series Ratio)'], \n",
    "                 dfFY1_2.at[0,'Enterprise Value (Daily Time Series)']/dfFY1_2.at[0,'EBITDA - Mean'], \n",
    "                 dfFY2_2.at[0,'Enterprise Value (Daily Time Series)']/dfFY2_2.at[0,'EBITDA - Mean']]\n",
    "}\n",
    "\n",
    "#Create Pandas DataFrame. \n",
    "dfMultiRow = pd.DataFrame(dataDict, index =['LTM (Last Twelve Months)','FY1 (Current Year)', 'FY2 (Next Year)']) \n",
    "dfMultiRow        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FY0 Per Share Metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python312\\Lib\\site-packages\\lseg\\data\\_tools\\_dataframe.py:177:FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n"
     ]
    },
    {
     "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>Instrument</th>\n",
       "      <th>Total Cash &amp; ST Invest/Share</th>\n",
       "      <th>Wk Cap/Shr</th>\n",
       "      <th>Revenue Per Share</th>\n",
       "      <th>Cash Flow/Shr (dil.)</th>\n",
       "      <th>FOCF/Shr (dil.)</th>\n",
       "      <th>DPS - Common Stock Primary Issue</th>\n",
       "      <th>BOOKVALUEPERSHARE</th>\n",
       "      <th>Total Debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>15.752085</td>\n",
       "      <td>1.446631</td>\n",
       "      <td>66.960731</td>\n",
       "      <td>11.883761</td>\n",
       "      <td>5.990431</td>\n",
       "      <td>6.67</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>59.3475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Instrument  Total Cash & ST Invest/Share  Wk Cap/Shr  Revenue Per Share  \\\n",
       "0      IBM.N                     15.752085    1.446631          66.960731   \n",
       "\n",
       "   Cash Flow/Shr (dil.)  FOCF/Shr (dil.)  DPS - Common Stock Primary Issue  \\\n",
       "0             11.883761         5.990431                              6.67   \n",
       "\n",
       "   BOOKVALUEPERSHARE  Total Debt  \n",
       "0               <NA>     59.3475  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFYOShare = ld.get_data(RIC ,['TR.TotCashAndShortTermInvestmentsperShare',\n",
    "'TR.WorkingCapitalperShare','BookValuePerShare','TR.RevenuePerShare','TR.CashFlowtoShrdil',\n",
    "'TR.FreeOperatingCashFlowperShareAvgDilutedSharesOut','TR.DPSCommonStock'], {'Period':'FY0','curn':df.at[0,'Currency']})\n",
    "dfFYOShare_2 = ld.get_data(RIC ,['TR.TotalDebt','TR.TtlCmnSharesOut'],{'Period':'FY0','curn':df.at[0,'Currency']})\n",
    "# additionally, calculate total debt\n",
    "dfFYOShare['Total Debt'] = [dfFYOShare_2.at[0,'Total Debt']/dfFYOShare_2.at[0,'Total Common Shares Outstanding']]\n",
    "dfFYOShare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FY0 Employee Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Instrument</th>\n",
       "      <th>Full-Time Employees</th>\n",
       "      <th>Revenue/Employee, FY</th>\n",
       "      <th>Empt Grth</th>\n",
       "      <th>5 yr Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>293400</td>\n",
       "      <td>209630.866878</td>\n",
       "      <td>0.038978</td>\n",
       "      <td>185091.26929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Instrument  Full-Time Employees  Revenue/Employee, FY  Empt Grth  \\\n",
       "0      IBM.N               293400         209630.866878   0.038978   \n",
       "\n",
       "       5 yr Avg  \n",
       "0  185091.26929  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfEmp = ld.get_data(RIC,'TR.Employees(Period=FY0),TR.RevenueperEmployeescalYrEnd(Period=FY0)')\n",
    "dfEmp_2= ld.get_data(RIC,'TR.Employees(Period=FY-1)')\n",
    "dfEmp_3 = ld.get_data(RIC,'TR.RevenueperEmployeescalYrEnd(Period=FY0,sdate=0D,Edate=-4FY,Frq=FY)')\n",
    "dfEmp['Empt Grth'] = (dfEmp_2.at[0,'Full-Time Employees']-dfEmp.at[0,'Full-Time Employees'])/dfEmp_2.at[0,'Full-Time Employees']\n",
    "dfEmp['5 yr Avg'] = dfEmp_3[\"Revenue/Employee, FY\"].mean()\n",
    "dfEmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abbreviated Financials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income Statement (MIL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>FY0</th>\n",
       "      <th>FY-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Instrument</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>IBM.N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Revenue</th>\n",
       "      <td>62753</td>\n",
       "      <td>61860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross Profit</th>\n",
       "      <td>35552</td>\n",
       "      <td>34300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA</th>\n",
       "      <td>14435</td>\n",
       "      <td>13818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBIT</th>\n",
       "      <td>9768</td>\n",
       "      <td>9422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Income Before Taxes</th>\n",
       "      <td>5797</td>\n",
       "      <td>8690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Income Incl Extra Before Distributions</th>\n",
       "      <td>6023</td>\n",
       "      <td>7502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Common Shares Outstanding</th>\n",
       "      <td>926.29007</td>\n",
       "      <td>915.01365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  FY0       FY-1\n",
       "Instrument                                      IBM.N      IBM.N\n",
       "Total Revenue                                   62753      61860\n",
       "Gross Profit                                    35552      34300\n",
       "EBITDA                                          14435      13818\n",
       "EBIT                                             9768       9422\n",
       "Net Income Before Taxes                          5797       8690\n",
       "Net Income Incl Extra Before Distributions       6023       7502\n",
       "Total Common Shares Outstanding             926.29007  915.01365"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfFin = ld.get_data(RIC, 'TR.TotalRevenue;TR.GrossProfit;TR.EBITDA;TR.EBIT;TR.NetIncomeBeforeTaxes;TR.NetIncome;TR.TtlCmnSharesOut',{'Period':'FY0','Scale':'6','curn':df.at[0,'Currency']})\n",
    "dfFin2 = ld.get_data(RIC, 'TR.TotalRevenue;TR.GrossProfit;TR.EBITDA;TR.EBIT;TR.NetIncomeBeforeTaxes;TR.NetIncome;TR.TtlCmnSharesOut',{'Period':'FY-1','Scale':'6','curn':df.at[0,'Currency']})\n",
    "\n",
    "# results as simple lists \n",
    "listFin = dfFin.iloc[0].tolist() #select row 0, all columns\n",
    "listFin2 = dfFin2.iloc[0].tolist()\n",
    "\n",
    "# results as single dictionary\n",
    "dataDict2 = {'FY0': listFin, 'FY-1': listFin2 }\n",
    "\n",
    "# Pandas DataFrame from dictionary \n",
    "dfIncome = pd.DataFrame(dataDict2, index = dfFin.columns) \n",
    "dfIncome        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balance Sheet (MIL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>FY0</th>\n",
       "      <th>FY-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Instrument</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>IBM.N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cash and Short Term Investments</th>\n",
       "      <td>14591</td>\n",
       "      <td>13441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property/Plant/Equipment, Total - Net</th>\n",
       "      <td>8928</td>\n",
       "      <td>8721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Assets, Reported</th>\n",
       "      <td>137175</td>\n",
       "      <td>135241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current Liabilities</th>\n",
       "      <td>33142</td>\n",
       "      <td>34122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Long Term Debt</th>\n",
       "      <td>49884</td>\n",
       "      <td>50121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Liabilities</th>\n",
       "      <td>109868</td>\n",
       "      <td>112708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Equity</th>\n",
       "      <td>27307</td>\n",
       "      <td>22533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          FY0    FY-1\n",
       "Instrument                              IBM.N   IBM.N\n",
       "Cash and Short Term Investments         14591   13441\n",
       "Property/Plant/Equipment, Total - Net    8928    8721\n",
       "Total Assets, Reported                 137175  135241\n",
       "Current Liabilities                     33142   34122\n",
       "Total Long Term Debt                    49884   50121\n",
       "Total Liabilities                      109868  112708\n",
       "Total Equity                            27307   22533"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfBal = ld.get_data(RIC, 'TR.CashAndSTInvestments;TR.PropertyPlantEquipmentTotalNet;TR.TotalAssetsReported;TR.CurrentLiabilities;TR.TotalLongTermDebt;TR.TotalLiabilities;TR.TotalEquity',{'Period':'FY0','Scale':6,'Curn':df.at[0,'Currency']})\n",
    "dfBal2 = ld.get_data(RIC, 'TR.CashAndSTInvestments;TR.PropertyPlantEquipmentTotalNet;TR.TotalAssetsReported;TR.CurrentLiabilities;TR.TotalLongTermDebt;TR.TotalLiabilities;TR.TotalEquity',{'Period':'FY-1','Scale':6,'Curn':df.at[0,'Currency']})\n",
    "\n",
    "# results as single dictionary\n",
    "dataDict3 = {'FY0': list(dfBal.iloc[0, :]), 'FY-1': list(dfBal2.iloc[0, :]) }\n",
    "\n",
    "# Pandas DataFrame from dictionary \n",
    "dfBalance= pd.DataFrame(dataDict3, index = dfBal.columns) \n",
    "dfBalance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cash Flow Statement (MIL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "        vertical-align: middle;\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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FY0</th>\n",
       "      <th>FY-1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Instrument</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>IBM.N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cash from Operating Activities, Cumulative</th>\n",
       "      <td>13446</td>\n",
       "      <td>13931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capital Expenditures, Cumulative</th>\n",
       "      <td>-1685</td>\n",
       "      <td>-1810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cash from Investing Activities, Cumulative</th>\n",
       "      <td>-4937</td>\n",
       "      <td>-7070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR.CASHDIVIDENDSPAID*-1</th>\n",
       "      <td>-6147</td>\n",
       "      <td>-6040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cash from Financing Activities, Cumulative</th>\n",
       "      <td>-7079</td>\n",
       "      <td>-1769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Income</th>\n",
       "      <td>6023</td>\n",
       "      <td>7502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cash From Operations</th>\n",
       "      <td>12132</td>\n",
       "      <td>11747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                              FY0   FY-1\n",
       "Instrument                                  IBM.N  IBM.N\n",
       "Cash from Operating Activities, Cumulative  13446  13931\n",
       "Capital Expenditures, Cumulative            -1685  -1810\n",
       "Cash from Investing Activities, Cumulative  -4937  -7070\n",
       "TR.CASHDIVIDENDSPAID*-1                     -6147  -6040\n",
       "Cash from Financing Activities, Cumulative  -7079  -1769\n",
       "Net Income                                   6023   7502\n",
       "Cash From Operations                        12132  11747"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCash = ld.get_data(RIC, 'TR.NetIncomeStartingLine',{'Period':'FY0','Scale':'6','curn':df.at[0,'Currency']})\n",
    "dfCash_2 = ld.get_data(RIC, 'TR.NetIncomeStartingLine,TR.DepreciationDepletion,TR.Amortization,TR.NonCashItems,TR.DeferredTaxes',{'Period':'FY0','Scale':'6','Curn':df.at[0,'Currency']})\n",
    "dfCash_3 = ld.get_data(RIC, 'TR.CashFromOperatingAct;TR.CapitalExpenditures;TR.CashFromInvestingAct;TR.CashDividendsPaid*-1;TR.CashFromFinancingAct',{'Period':'FY0','Scale':6,'Curn':df.at[0,'Currency']})\n",
    "#insert as columns, entincome and  sum of cash from operations\n",
    "dfCash_3['Net Income'] = [dfCash.at[0,'Net Income/Starting Line, Cumulative']]\n",
    "dfCash_3['Cash From Operations'] = [sum(list(dfCash_2.iloc[0, 1:]))]\n",
    "\n",
    "dfCash2 = ld.get_data(RIC, 'TR.NetIncomeStartingLine',{'Period':'FY-1','Scale':'6','curn':df.at[0,'Currency']})\n",
    "dfCash2_2 = ld.get_data(RIC, 'TR.NetIncomeStartingLine,TR.DepreciationDepletion,TR.Amortization,TR.NonCashItems,TR.DeferredTaxes',{'Period':'FY-1','Scale':'6','Curn':df.at[0,'Currency']})\n",
    "dfCash2_3 = ld.get_data(RIC, 'TR.CashFromOperatingAct;TR.CapitalExpenditures;TR.CashFromInvestingAct;TR.CashDividendsPaid*-1;TR.CashFromFinancingAct',{'Period':'FY-1','Scale':6,'Curn':df.at[0,'Currency']})\n",
    "#insert as columns, entincome and  sum of cash from operations\n",
    "dfCash2_3['Net Income'] = [dfCash2.at[0,'Net Income/Starting Line, Cumulative']]\n",
    "dfCash2_3['Cash From Operations'] = [sum(list(dfCash2_2.iloc[0, 1:]))]\n",
    "\n",
    "#DEBUG display(dfCash_3,dfCash2_3)\n",
    "\n",
    "# results as simple lists\n",
    "listCash = dfCash_3.iloc[0, :].tolist()\n",
    "listCash2 = dfCash2_3.iloc[0, :].tolist()\n",
    "\n",
    "\n",
    "# results as single dictionary\n",
    "dataDict3 = {'FY0': listCash, 'FY-1': listCash2 }\n",
    "#dataDict3\n",
    "\n",
    "# Pandas DataFrame from dictionary \n",
    "dfCashFlow = pd.DataFrame(dataDict3, index = dfCash_3.columns) \n",
    "dfCashFlow  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review of Stock Performance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price Actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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   "source": [
    "# retrieve percentage changes\n",
    "dfPriceActions = ld.get_data(RIC, 'TR.PricePctChg1M/100;TR.PricePctChg3M/100;TR.PricePctChg6M/100;TR.PricePctChg1Y/100')\n",
    "dfPriceActions\n",
    "\n",
    "#create lists out of results \n",
    "listNames = dfPriceActions.columns.tolist()\n",
    "listPA = dfPriceActions.iloc[0, :].tolist()\n",
    "\n",
    "# remove the first element, it's header\n",
    "listNames.pop(0)\n",
    "listPA.pop(0)\n",
    "#display(listNames, listPA)\n",
    "\n",
    "# Pandas DataFrame from dictionary\n",
    "dataDictPA = {'Names': listNames, 'Periods': listPA } \n",
    "dfPA = pd.DataFrame(dataDictPA, index = listNames) \n",
    "\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import plotly.offline as pyof\n",
    "pyof.init_notebook_mode(connected=True)\n",
    "\n",
    "fig = px.bar(dfPA, x='Names', y='Periods')\n",
    "fig\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Profitability "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>FY0</th>\n",
       "      <th>5 Year Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gross Margin</th>\n",
       "      <td>56.653870</td>\n",
       "      <td>55.393184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R/D % Sales</th>\n",
       "      <td>0.119182</td>\n",
       "      <td>0.112762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGA % Sales</th>\n",
       "      <td>0.303507</td>\n",
       "      <td>0.314336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA Margin</th>\n",
       "      <td>23.002884</td>\n",
       "      <td>22.602059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Operating Margin</th>\n",
       "      <td>9.237805</td>\n",
       "      <td>7.744094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pretax Margin</th>\n",
       "      <td>9.237810</td>\n",
       "      <td>7.744094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Margin</th>\n",
       "      <td>10.310260</td>\n",
       "      <td>8.199293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CF/Sales</th>\n",
       "      <td>0.177474</td>\n",
       "      <td>0.172620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tax Rate</th>\n",
       "      <td>-11.609453</td>\n",
       "      <td>-0.058780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROA</th>\n",
       "      <td>4.750088</td>\n",
       "      <td>3.511109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROE</th>\n",
       "      <td>25.963082</td>\n",
       "      <td>22.262804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        FY0  5 Year Avg\n",
       "Gross Margin      56.653870   55.393184\n",
       "R/D % Sales        0.119182    0.112762\n",
       "SGA % Sales        0.303507    0.314336\n",
       "EBITDA Margin     23.002884   22.602059\n",
       "Operating Margin   9.237805    7.744094\n",
       "Pretax Margin      9.237810    7.744094\n",
       "Net Margin        10.310260    8.199293\n",
       "CF/Sales           0.177474    0.172620\n",
       "Tax Rate         -11.609453   -0.058780\n",
       "ROA                4.750088    3.511109\n",
       "ROE               25.963082   22.262804"
      ]
     },
     "execution_count": 20,
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     "output_type": "execute_result"
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   "source": [
    "dfProfit = ld.get_data(RIC, 'TR.GrossMargin;TR.ResearchAndDevelopment;TR.TotalRevenue;TR.SGAExpenseTotal;TR.EBITDAMarginPercent;TR.OperProfitMarginPct;TR.PretaxMarginPercent;TR.NetProfitMargin;TR.CashFlowMarginPercent/100;TR.IncomeTaxRatePct;TR.ROATotalAssetsPercent;TR.ReturnonAvgTotEqtyPctNetIncomeBeforeExtraItems',{'Period':'FY0'})\n",
    "#dfProfit   \n",
    "dfProfit2 = ld.get_data(RIC, \n",
    "    'TR.GrossProfitMarginPct5YrAvg();TR.EBITDAMarginPct5YrAvg;TR.OperatingProfitMarginPct5YrAvg;TR.IncomeBefTaxMarginpct5YrAvg();TR.IncomeAftTaxMarginpct5YrAvg();TR.CashFlowMarginPct5YrAvg()/100;TR.IncomeTaxRatePct5YrAvg()/100;TR.ReturnonAvgTotAssetsPctIncomeAfterTax5YrAvg')\n",
    "dfProfit3 = ld.get_data(RIC, 'TR.ResearchAndDevelopment(),TR.TotalRevenue(),TR.SGAExpenseTotal,TR.ReturnonAvgTotEqtyPctNetIncomeBeforeExtraItems',{'Period':'FY0','Sdate':0,'Edate':-4,'Frq':'FY'})\n",
    "dfProfit3['RAD over TR'] = dfProfit3['Research And Development']/dfProfit3['Total Revenue']\n",
    "dfProfit3['SGA over TR'] = dfProfit3['Selling/General/Administrative Expense, Total']/dfProfit3['Total Revenue']\n",
    "#dfProfit2\n",
    "\n",
    "#Create a dictionary out of results, and structure a single multi-row Data Frame\n",
    "# initialise results per column \n",
    "dataDictProfit = {'FY0':\n",
    "        [dfProfit.at[0,'Gross Margin, Percent'],  dfProfit.at[0,'Research And Development']/dfProfit.at[0,'Total Revenue'],\n",
    "        dfProfit.at[0,'Selling/General/Administrative Expense, Total']/dfProfit.at[0,'Total Revenue'],\n",
    "        dfProfit.at[0,'EBITDA Margin, Percent'],dfProfit.at[0,'Operating Profit Margin, Percent'],\n",
    "        dfProfit.at[0,'Pretax Margin, Percent'],dfProfit.at[0,'Net Profit Margin, (%)'],\n",
    "        dfProfit.at[0,'TR.CASHFLOWMARGINPERCENT/100'],dfProfit.at[0,'Income Tax Rate, %'],\n",
    "        dfProfit.at[0,'ROA Total Assets, Percent'],dfProfit.at[0,'ROE Total Equity %']\n",
    "    ]\n",
    "        ,'5 Year Avg':[dfProfit2.at[0,'Gross Profit Margin, %, 5 Yr Avg'],dfProfit3['RAD over TR'].mean(),\n",
    "                       dfProfit3['SGA over TR'].mean(), dfProfit2.at[0,'EBITDA Margin, %, 5 Yr Avg'],\n",
    "                       dfProfit2.at[0,'Oper Profit Margin, %, 5 Yr Avg'],dfProfit2.at[0,'Income Bef Tax Margin, %, 5 Yr Avg'],\n",
    "                       dfProfit2.at[0,'Income Aft Tax Margin, %, 5 Yr Avg'],dfProfit2.at[0,'TR.CASHFLOWMARGINPCT5YRAVG()/100'],\n",
    "                       dfProfit2.at[0,'TR.INCOMETAXRATEPCT5YRAVG()/100'],dfProfit2.at[0,'ROA Tot Assets, %, 5 Yr Avg'],\n",
    "                       dfProfit3['ROE Total Equity %'].mean()\n",
    "]\n",
    "                       \n",
    "}\n",
    "#dataDictProfit\n",
    "\n",
    "# structure as required multi-row\n",
    "dfMultiProfit = pd.DataFrame(dataDictProfit, index =[\n",
    "    'Gross Margin','R/D % Sales','SGA % Sales','EBITDA Margin','Operating Margin','Pretax Margin','Net Margin',\n",
    "    'CF/Sales','Tax Rate','ROA','ROE'\n",
    "    ]) \n",
    "dfMultiProfit   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FY0 Sales By Region "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Instrument</th>\n",
       "      <th>Segment Name</th>\n",
       "      <th>Geographic Total Revenues (Calculated)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>Americas</td>\n",
       "      <td>31266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>Europe/Middle East/Africa (EMEA)</td>\n",
       "      <td>19429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>Asia/Pacific</td>\n",
       "      <td>12058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>Segment Total</td>\n",
       "      <td>62753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>Consolidated Total</td>\n",
       "      <td>62753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Instrument                      Segment Name  \\\n",
       "0      IBM.N                          Americas   \n",
       "1      IBM.N  Europe/Middle East/Africa (EMEA)   \n",
       "2      IBM.N                      Asia/Pacific   \n",
       "3      IBM.N                     Segment Total   \n",
       "4      IBM.N                Consolidated Total   \n",
       "\n",
       "   Geographic Total Revenues (Calculated)  \n",
       "0                                   31266  \n",
       "1                                   19429  \n",
       "2                                   12058  \n",
       "3                                   62753  \n",
       "4                                   62753  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfSalesByRegion = ld.get_data(RIC, 'TR.BGS.GeoTotalRevenue.segmentName;TR.BGS.GeoTotalRevenue()',{'Scale':'6','curn':df.at[0,'Currency']})\n",
    "dfSalesByRegion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Price Chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pricing Data  - 1 -  Closing Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Instrument</th>\n",
       "      <th>Calc Date</th>\n",
       "      <th>Price Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>113.960617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>115.909151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>119.128054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>120.274251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2020-04-28</td>\n",
       "      <td>120.608558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-14</td>\n",
       "      <td>239.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-15</td>\n",
       "      <td>240.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-16</td>\n",
       "      <td>238.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-17</td>\n",
       "      <td>238.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-21</td>\n",
       "      <td>236.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1256 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Instrument  Calc Date  Price Close\n",
       "0         IBM.N 2020-04-22   113.960617\n",
       "1         IBM.N 2020-04-23   115.909151\n",
       "2         IBM.N 2020-04-24   119.128054\n",
       "3         IBM.N 2020-04-27   120.274251\n",
       "4         IBM.N 2020-04-28   120.608558\n",
       "...         ...        ...          ...\n",
       "1251      IBM.N 2025-04-14       239.06\n",
       "1252      IBM.N 2025-04-15        240.7\n",
       "1253      IBM.N 2025-04-16       238.57\n",
       "1254      IBM.N 2025-04-17       238.81\n",
       "1255      IBM.N 2025-04-21       236.22\n",
       "\n",
       "[1256 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dfPricingIntermediate1 = ld.get_data(RIC, 'TR.PriceClose.calcdate;TR.PriceClose',{'sdate':'-5AY','edate':'0d','frq':'D'})\n",
    "dfPricingIntermediate1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pricing Data - 2 - Dates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2025-04-21 00:00:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2023-04-21 00:00:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2020-04-22 00:00:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2022-03-22 00:00:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2023-04-21 00:00:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'2022-03-22 00:00:00'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Intermediate dates to use in the next step\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "\n",
    "def workdays(d, end, excluded=(6, 7)):\n",
    "    days = []\n",
    "    while d.date() <= end.date():\n",
    "        if d.isoweekday() not in excluded:\n",
    "            days.append(d)\n",
    "        d += timedelta(days=1)\n",
    "    return days\n",
    "\n",
    "RIC2 = 'MSFT.O'\n",
    "\n",
    "dfDatesMSFTIntermediate1 = ld.get_data(RIC2, 'TR.PriceClose.date',{'sdate':'0d'})\n",
    "dfDatesMSFTIntermediate2 = ld.get_data(RIC2, 'TR.PriceClose.date',{'sdate':'-24AM'})\n",
    "\n",
    "\n",
    "# number of work days between the two dates + 250\n",
    "daysBetween = len(workdays(dfDatesMSFTIntermediate2.at[0,'Date'],\n",
    "               dfDatesMSFTIntermediate1.at[0,'Date'])) + 250\n",
    "daysBetween\n",
    "dfDatesMSFTIntermediate4 = ld.get_data(RIC2, 'TR.PriceClose.date',{'sdate':'-'+str(daysBetween)+'D'})\n",
    "\n",
    "display(str(dfDatesMSFTIntermediate1.at[0,'Date']),\n",
    "str(dfDatesMSFTIntermediate2.at[0,'Date']),\n",
    "str(dfPricingIntermediate1.at[0,'Calc Date']),\n",
    "str(dfDatesMSFTIntermediate4.at[0,'Date']))\n",
    "\n",
    "if dfPricingIntermediate1.at[0,'Calc Date'] > dfDatesMSFTIntermediate2.at[0,'Date']:\n",
    "    laterDate1 = str(dfPricingIntermediate1.at[0,'Calc Date'])\n",
    "else:\n",
    "    laterDate1 = str(dfDatesMSFTIntermediate2.at[0,'Date'])\n",
    "    \n",
    "\n",
    "if dfPricingIntermediate1.at[0,'Calc Date'] > dfDatesMSFTIntermediate4.at[0,'Date']:\n",
    "    laterDate2 = str(dfPricingIntermediate1.at[0,'Calc Date'])\n",
    "else:\n",
    "    laterDate2 = str(dfDatesMSFTIntermediate4.at[0,'Date'])\n",
    "    \n",
    "display(laterDate1,laterDate2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pricing Data - 3 - Volumes, Prices, 50 days, 200 days "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Instrument</th>\n",
       "      <th>Calc Date</th>\n",
       "      <th>Price Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-21</td>\n",
       "      <td>236.22</td>\n",
       "      <td>1270.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-17</td>\n",
       "      <td>238.81</td>\n",
       "      <td>1275.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-16</td>\n",
       "      <td>238.57</td>\n",
       "      <td>1281.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-15</td>\n",
       "      <td>240.7</td>\n",
       "      <td>820.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2025-04-14</td>\n",
       "      <td>239.06</td>\n",
       "      <td>949.827</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>768</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2022-03-28</td>\n",
       "      <td>131.47</td>\n",
       "      <td>767.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2022-03-25</td>\n",
       "      <td>131.35</td>\n",
       "      <td>1000.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2022-03-24</td>\n",
       "      <td>129.25</td>\n",
       "      <td>902.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2022-03-23</td>\n",
       "      <td>128.3</td>\n",
       "      <td>1085.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>IBM.N</td>\n",
       "      <td>2022-03-22</td>\n",
       "      <td>129.06</td>\n",
       "      <td>869.989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>773 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Instrument  Calc Date  Price Close    Volume\n",
       "0        IBM.N 2025-04-21       236.22  1270.741\n",
       "1        IBM.N 2025-04-17       238.81  1275.239\n",
       "2        IBM.N 2025-04-16       238.57  1281.134\n",
       "3        IBM.N 2025-04-15        240.7   820.351\n",
       "4        IBM.N 2025-04-14       239.06   949.827\n",
       "..         ...        ...          ...       ...\n",
       "768      IBM.N 2022-03-28       131.47   767.957\n",
       "769      IBM.N 2022-03-25       131.35  1000.289\n",
       "770      IBM.N 2022-03-24       129.25   902.799\n",
       "771      IBM.N 2022-03-23        128.3  1085.209\n",
       "772      IBM.N 2022-03-22       129.06   869.989\n",
       "\n",
       "[773 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([248.414 , 248.7584, 249.2482, 249.766 , 250.1666, 250.4994,\n",
       "       250.9552, 250.9368, 250.7438, 250.8058, 250.7862, 250.7574,\n",
       "       250.3528, 249.8384, 249.3274, 248.8074, 248.328 , 247.7588,\n",
       "       247.1   , 246.497 , 245.9916, 245.5934, 245.1804, 244.5876,\n",
       "       244.0474, 243.3846, 242.8226, 242.3622, 241.8674, 241.3766,\n",
       "       240.6772, 239.9136, 239.4182, 238.7946, 238.3098, 237.8926,\n",
       "       237.4602, 237.0408, 236.5264, 236.0058, 235.3684, 234.8996,\n",
       "       234.2998, 233.6832, 233.0018, 232.324 , 231.6884, 231.1106,\n",
       "       230.5932, 230.1304, 229.543 , 228.9222, 227.9482, 226.864 ,\n",
       "       225.8112, 224.797 , 223.8114, 223.4572, 223.1612, 222.95  ,\n",
       "       222.7284, 222.4814, 222.2882, 221.9544, 221.585 , 221.2968,\n",
       "       221.0306, 220.7736, 220.6342, 220.4974, 220.3272, 220.2158,\n",
       "       220.4174, 220.6094, 220.8456, 221.093 , 221.3456, 221.5634,\n",
       "       221.7248, 221.9418, 222.1684, 222.3616, 222.5692, 222.7382,\n",
       "       222.7012, 222.6346, 222.4726, 222.222 , 222.0066, 221.7938,\n",
       "       221.6106, 221.3184, 221.048 , 220.7976, 220.6276, 220.4338,\n",
       "       220.1634, 219.9238, 219.6298, 219.4504, 219.2868, 219.071 ,\n",
       "       218.9768, 218.8782, 218.787 , 218.702 , 218.574 , 218.4378,\n",
       "       218.2462, 218.0174, 217.721 , 217.4164, 217.119 , 216.9272,\n",
       "       216.7228, 216.477 , 216.2868, 216.1094, 215.7954, 215.4128,\n",
       "       214.9984, 214.477 , 213.6418, 212.7864, 211.9804, 211.1886,\n",
       "       210.267 , 209.3296, 208.3366, 207.4138, 206.5418, 205.7242,\n",
       "       204.859 , 204.1166, 203.4092, 202.7288, 201.9548, 201.2422,\n",
       "       200.5382, 199.7816, 199.0692, 198.3496, 197.6412, 196.8794,\n",
       "       196.126 , 195.3382, 194.6172, 193.848 , 193.1182, 192.2954,\n",
       "       191.5142, 190.828 , 190.1322, 189.4848, 188.8312, 188.2538,\n",
       "       187.654 , 187.072 , 186.4956, 185.9314, 185.3644, 184.7852,\n",
       "       184.1948, 183.6176, 183.0756, 182.5428, 182.0062, 181.4858,\n",
       "       180.9552, 180.4272, 179.8644, 179.3236, 178.8408, 178.3638,\n",
       "       177.8758, 177.4168, 177.0986, 176.776 , 176.5836, 176.2706,\n",
       "       175.8758, 175.4136, 174.9722, 174.5074, 174.0196, 173.5312,\n",
       "       173.1938, 172.8372, 172.5522, 172.2548, 171.9226, 171.4878,\n",
       "       171.0654, 170.6964, 170.3638, 170.1462, 169.932 , 169.7806,\n",
       "       169.9098, 170.0332, 170.1566, 170.2422, 170.3696, 170.5726,\n",
       "       170.8306, 171.0182, 171.2116, 171.4294, 171.701 , 172.0088,\n",
       "       172.3942, 172.787 , 173.1616, 173.5972, 173.9948, 174.405 ,\n",
       "       174.8166, 175.2324, 175.6384, 176.0666, 176.5672, 177.0996,\n",
       "       177.6418, 178.196 , 178.6888, 179.117 , 179.5678, 180.0884,\n",
       "       180.5702, 180.9354, 181.456 , 182.0062, 182.55  , 183.0238,\n",
       "       183.5378, 183.9506, 184.3082, 184.6888, 184.9882, 185.3032,\n",
       "       185.6454, 186.0154, 186.3156, 186.6958, 187.1246, 187.5134,\n",
       "       187.8422, 188.138 , 188.1792, 188.2622, 188.3114, 188.3546,\n",
       "       188.3934, 188.3998, 188.4406, 188.5536, 188.5814, 188.6208,\n",
       "       188.6428, 188.605 , 188.6172, 188.313 , 188.033 , 187.6716,\n",
       "       187.3236, 186.8638, 186.3662, 185.8894, 185.4354, 184.9028,\n",
       "       184.3106, 183.6742, 183.0178, 182.3342, 181.7176, 181.0982,\n",
       "       180.4596, 179.7966, 179.116 , 178.5506, 177.8958, 177.2078,\n",
       "       176.5002, 175.8622, 175.2322, 174.723 , 174.267 , 173.8192,\n",
       "       173.3942, 173.0058, 172.5616, 172.1166, 171.727 , 171.2638,\n",
       "       170.7388, 170.2234, 169.763 , 169.2602, 168.6652, 168.0514,\n",
       "       167.4756, 166.9044, 166.3388, 165.7486, 165.1198, 164.4396,\n",
       "       163.8276, 163.1218, 162.3872, 161.6008, 160.7726, 160.2264,\n",
       "       159.7082, 159.2282, 158.778 , 158.3992, 158.0178, 157.5866,\n",
       "       157.1634, 156.7728, 156.3986, 156.0722, 155.591 , 155.1636,\n",
       "       154.674 , 154.2152, 153.7454, 153.2738, 152.8052, 152.3202,\n",
       "       151.8252, 151.4072, 151.0562, 150.6974, 150.3102, 149.896 ,\n",
       "       149.4818, 149.045 , 148.5804, 148.1022, 147.638 , 147.2304,\n",
       "       146.8894, 146.5486, 146.2504, 145.9666, 145.7032, 145.5286,\n",
       "       145.3308, 145.1196, 144.928 , 144.7714, 144.5998, 144.4476,\n",
       "       144.3282, 144.224 , 144.1132, 144.0228, 143.9772, 143.974 ,\n",
       "       143.9302, 143.935 , 143.9034, 143.8472, 143.7748, 143.6878,\n",
       "       143.6158, 143.5376, 143.4904, 143.466 , 143.4288, 143.3664,\n",
       "       143.4622, 143.5446, 143.6794, 143.8012, 143.8908, 144.0096,\n",
       "       144.1268, 144.2274, 144.3472, 144.4058, 144.4078, 144.4492,\n",
       "       144.4742, 144.493 , 144.484 , 144.4692, 144.4522, 144.415 ,\n",
       "       144.3766, 144.2546, 144.0984, 143.9184, 143.6568, 143.397 ,\n",
       "       143.1062, 142.7984, 142.526 , 142.2658, 141.9892, 141.727 ,\n",
       "       141.4694, 141.2196, 140.9332, 140.6148, 140.3112, 139.9768,\n",
       "       139.6028, 139.2674, 139.0046, 138.7866, 138.6072, 138.4548,\n",
       "       138.2918, 138.1728, 138.033 , 137.9092, 137.7518, 137.6112,\n",
       "       137.4518, 137.2918, 137.1028, 136.861 , 136.5704, 136.295 ,\n",
       "       136.023 , 135.64  , 135.23  , 134.9088, 134.5698, 134.2316,\n",
       "       133.888 , 133.5186, 133.1188, 132.7266, 132.362 , 131.9734,\n",
       "       131.623 , 131.2676, 130.968 , 130.7314, 130.4756, 130.2598,\n",
       "       130.095 , 129.9384, 129.8098, 129.6604, 129.5194, 129.3956,\n",
       "       129.2604, 129.0902, 128.944 , 128.7942, 128.6686, 128.5898,\n",
       "       128.5058, 128.437 , 128.4192, 128.4042, 128.351 , 128.2418,\n",
       "       128.135 , 127.999 , 127.8962, 127.766 , 127.622 , 127.5102,\n",
       "       127.4088, 127.3074, 127.1594, 126.974 , 126.8066, 126.7416,\n",
       "       126.6886, 126.5728, 126.489 , 126.4194, 126.3988, 126.3468,\n",
       "       126.3058, 126.2838, 126.3218, 126.3726, 126.5072, 126.6328,\n",
       "       126.7546, 126.9004, 127.046 , 127.2324, 127.3758, 127.5186,\n",
       "       127.6866, 127.8518, 128.049 , 128.2272, 128.427 , 128.6078,\n",
       "       128.8378, 129.032 , 129.199 , 129.404 , 129.5936, 129.7908,\n",
       "       129.974 , 130.1454, 130.2844, 130.421 , 130.5562, 130.6356,\n",
       "       130.704 , 130.9092, 131.0962, 131.3014, 131.4842, 131.6748,\n",
       "       131.8986, 132.2082, 132.5392, 132.864 , 133.2634, 133.692 ,\n",
       "       134.082 , 134.4246, 134.728 , 135.1062, 135.4432, 135.7954,\n",
       "       136.1236, 136.4124, 136.7518, 137.0616, 137.3182, 137.596 ,\n",
       "       137.8178, 138.0024, 138.227 , 138.5104, 138.9216, 139.3232,\n",
       "       139.696 , 140.0212, 140.3574, 140.6686, 140.9182, 141.1664,\n",
       "       141.4118, 141.6748, 141.9058, 142.1236, 142.3722, 142.62  ,\n",
       "       142.8782, 143.1366, 143.3314, 143.5564, 143.7764, 143.9722,\n",
       "       144.153 , 144.3492, 144.5236, 144.533 , 144.451 , 144.4146,\n",
       "       144.3574, 144.2842, 144.1654, 143.9982, 143.8444, 143.6992,\n",
       "       143.5642, 143.3636, 143.1928, 142.9774, 142.809 , 142.555 ,\n",
       "       142.29  , 141.9224, 141.56  , 141.19  , 140.7424, 140.3452,\n",
       "       139.879 , 139.3922, 138.9216, 138.5206, 138.162 , 137.8296,\n",
       "       137.3424, 136.7612, 136.1532, 135.6448, 135.1444, 134.6338,\n",
       "       134.124 , 133.63  , 133.1628, 132.6782, 132.2262, 131.851 ,\n",
       "       131.4728, 131.0152, 130.594 , 130.157 , 129.8366, 129.4676,\n",
       "       129.1152, 128.779 , 128.4266, 128.0984, 127.8282, 127.5726,\n",
       "       127.4164, 127.2218, 127.0626, 127.003 , 126.9782, 126.9364,\n",
       "       126.8834, 126.885 , 126.8962, 126.9566, 126.9876, 127.0276,\n",
       "       127.054 , 127.1068, 127.1708, 127.31  , 127.5034, 127.7226,\n",
       "       127.9546, 128.1656, 128.4504, 128.7352, 128.996 , 129.204 ,\n",
       "       129.3418, 129.3886, 129.4494, 129.5842, 129.751 , 129.902 ,\n",
       "       130.0644, 130.3922, 130.7504, 131.0774, 131.3148, 131.5998,\n",
       "       131.8938, 132.1486, 132.4198, 132.6716, 132.8702, 133.1476,\n",
       "       133.3582, 133.5886, 133.8564, 134.1582, 134.465 , 134.678 ,\n",
       "       134.8264, 135.0144, 135.1232, 135.2304, 135.364 , 135.3988,\n",
       "       135.4364, 135.4654, 135.5136, 135.5628, 135.637 , 135.7388,\n",
       "       135.8312, 135.9356, 136.044 , 136.17  , 136.3254, 136.4738,\n",
       "       136.5094, 136.5358, 136.5264, 136.4492, 136.4062, 136.4242,\n",
       "       136.5758, 136.692 , 136.7816, 136.878 , 136.9222, 136.9398,\n",
       "       137.0856, 137.2554, 137.3562, 137.3416, 137.1936, 137.0732,\n",
       "       136.9738, 136.905 , 136.7862, 136.7006, 136.666 , 136.6694,\n",
       "       136.714 , 136.658 , 136.4172, 136.1264, 135.8204, 135.4872,\n",
       "       135.1656, 134.9242, 134.7372, 134.5512, 134.4386, 134.303 ,\n",
       "       134.1672, 134.0558, 133.954 , 133.8728, 133.7524, 133.5652,\n",
       "       133.3366, 133.064 , 132.8064, 132.5846])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "array([222.1895 , 221.8949 , 221.57635, 221.24825, 220.899  , 220.56305,\n",
       "       220.24865, 219.97595, 219.6617 , 219.42615, 219.15   , 218.8601 ,\n",
       "       218.4887 , 218.0844 , 217.6777 , 217.281  , 216.9129 , 216.5319 ,\n",
       "       216.1212 , 215.7086 , 215.2954 , 214.90245, 214.5201 , 214.0868 ,\n",
       "       213.6873 , 213.27075, 212.88345, 212.5078 , 212.1281 , 211.7507 ,\n",
       "       211.3158 , 210.85325, 210.45465, 210.0392 , 209.60995, 209.1968 ,\n",
       "       208.77035, 208.33555, 207.90585, 207.459  , 206.9927 , 206.51385,\n",
       "       206.0136 , 205.51415, 205.0298 , 204.56055, 204.10025, 203.66575,\n",
       "       203.31275, 202.97735, 202.62515, 202.26585, 201.8567 , 201.4499 ,\n",
       "       201.065  , 200.69275, 200.31275, 200.0991 , 199.901  , 199.7269 ,\n",
       "       199.552  , 199.3675 , 199.1909 , 199.0241 , 198.84455, 198.6804 ,\n",
       "       198.53505, 198.4003 , 198.2558 , 198.101  , 197.9393 , 197.779  ,\n",
       "       197.63545, 197.4889 , 197.34765, 197.20385, 197.06975, 196.93935,\n",
       "       196.8038 , 196.6404 , 196.5105 , 196.3764 , 196.2376 , 196.0965 ,\n",
       "       195.91695, 195.7113 , 195.48235, 195.24755, 195.0213 , 194.78335,\n",
       "       194.56195, 194.2928 , 194.01755, 193.7673 , 193.5605 , 193.3579 ,\n",
       "       193.1387 , 192.9226 , 192.70925, 192.5103 , 192.31725, 192.12395,\n",
       "       191.968  , 191.83385, 191.72235, 191.6319 , 191.50525, 191.39   ,\n",
       "       191.2714 , 191.14065, 191.0242 , 190.8254 , 190.6271 , 190.4534 ,\n",
       "       190.2792 , 190.07215, 189.86895, 189.67925, 189.4561 , 189.20235,\n",
       "       188.93515, 188.6436 , 188.28555, 187.9201 , 187.56565, 187.20515,\n",
       "       186.84825, 186.49765, 186.1516 , 185.7926 , 185.44235, 185.08795,\n",
       "       184.72035, 184.3775 , 184.0497 , 183.7334 , 183.43095, 183.14685,\n",
       "       182.8682 , 182.58635, 182.2997 , 181.99235, 181.6873 , 181.38385,\n",
       "       181.0883 , 180.8053 , 180.5386 , 180.2567 , 179.9681 , 179.66055,\n",
       "       179.36445, 179.0823 , 178.8085 , 178.55145, 178.30555, 178.0663 ,\n",
       "       177.81865, 177.561  , 177.30665, 177.0365 , 176.7871 , 176.5279 ,\n",
       "       176.2744 , 176.02865, 175.793  , 175.5527 , 175.3017 , 175.04855,\n",
       "       174.7981 , 174.54235, 174.2852 , 174.0424 , 173.77285, 173.5144 ,\n",
       "       173.23905, 172.9618 , 172.71785, 172.4837 , 172.26875, 172.0192 ,\n",
       "       171.7632 , 171.5087 , 171.26965, 171.0227 , 170.77495, 170.5252 ,\n",
       "       170.3127 , 170.09755, 169.87875, 169.6665 , 169.4419 , 169.21255,\n",
       "       168.99935, 168.80115, 168.6194 , 168.4624 , 168.3101 , 168.17685,\n",
       "       168.02125, 167.8666 , 167.7179 , 167.56815, 167.4254 , 167.29215,\n",
       "       167.1798 , 167.05885, 166.93345, 166.7987 , 166.67705, 166.54715,\n",
       "       166.42855, 166.31535, 166.20155, 166.08605, 165.9678 , 165.83895,\n",
       "       165.7041 , 165.5615 , 165.4319 , 165.30205, 165.1763 , 165.0531 ,\n",
       "       164.9282 , 164.8096 , 164.68995, 164.5579 , 164.4159 , 164.2921 ,\n",
       "       164.15455, 164.0084 , 163.88105, 163.75675, 163.62855, 163.50815,\n",
       "       163.3886 , 163.26565, 163.13525, 163.00555, 162.85375, 162.70655,\n",
       "       162.5554 , 162.40425, 162.2576 , 162.10665, 161.94265, 161.7751 ,\n",
       "       161.60365, 161.4313 , 161.1753 , 160.92475, 160.67605, 160.43935,\n",
       "       160.20035, 159.9539 , 159.70545, 159.458  , 159.20835, 158.93555,\n",
       "       158.6525 , 158.3618 , 158.08115, 157.81525, 157.56295, 157.30045,\n",
       "       157.04205, 156.7809 , 156.5082 , 156.2307 , 155.96025, 155.6882 ,\n",
       "       155.39745, 155.10115, 154.79345, 154.47585, 154.16035, 153.8524 ,\n",
       "       153.5297 , 153.18   , 152.8195 , 152.50175, 152.1595 , 151.8131 ,\n",
       "       151.46305, 151.13185, 150.78385, 150.45965, 150.1487 , 149.8267 ,\n",
       "       149.51245, 149.19765, 148.88605, 148.58325, 148.2976 , 147.99765,\n",
       "       147.68525, 147.38135, 147.09555, 146.8119 , 146.51035, 146.2081 ,\n",
       "       145.9133 , 145.62325, 145.338  , 145.0525 , 144.76245, 144.46705,\n",
       "       144.18945, 143.8896 , 143.5966 , 143.3116 , 143.0146 , 142.79745,\n",
       "       142.58845, 142.3823 , 142.1852 , 142.00645, 141.82215, 141.6359 ,\n",
       "       141.4536 , 141.28935, 141.10965, 140.9261 , 140.74065, 140.5777 ,\n",
       "       140.4031 , 140.22105, 140.03705, 139.8357 , 139.6402 , 139.4508 ,\n",
       "       139.262  , 139.0821 , 138.91845, 138.75945, 138.6026 , 138.4371 ,\n",
       "       138.2706 , 138.097  , 137.9254 , 137.7543 , 137.5896 , 137.43375,\n",
       "       137.2875 , 137.14465, 137.0128 , 136.8823 , 136.76155, 136.6488 ,\n",
       "       136.5535 , 136.45325, 136.34415, 136.24815, 136.1517 , 136.06305,\n",
       "       135.976  , 135.8935 , 135.80365, 135.7144 , 135.63885, 135.5703 ,\n",
       "       135.49745, 135.46815, 135.43545, 135.4006 , 135.36175, 135.32535,\n",
       "       135.29235, 135.2913 , 135.29755, 135.31215, 135.32585, 135.33105,\n",
       "       135.3634 , 135.39295, 135.4166 , 135.4438 , 135.4615 , 135.4661 ,\n",
       "       135.4698 , 135.47385, 135.49365, 135.4957 , 135.48395, 135.4841 ,\n",
       "       135.4795 , 135.4637 , 135.4569 , 135.46335, 135.5107 , 135.55955,\n",
       "       135.6041 , 135.63145, 135.6545 , 135.67465, 135.67975, 135.68225,\n",
       "       135.6887 , 135.68535, 135.69725, 135.70425, 135.7052 , 135.7103 ,\n",
       "       135.7213 , 135.7353 , 135.7268 , 135.7266 , 135.71945, 135.70175,\n",
       "       135.6828 , 135.6641 , 135.6458 , 135.61765, 135.57235, 135.54245,\n",
       "       135.5074 , 135.47445, 135.42975, 135.40645, 135.38605, 135.37045,\n",
       "       135.3597 , 135.33035, 135.29605, 135.25115, 135.199  , 135.13225,\n",
       "       135.0613 , 134.9443 , 134.8281 , 134.7145 , 134.59245, 134.48055,\n",
       "       134.35175, 134.21985, 134.09135, 133.9706 , 133.8764 , 133.80345,\n",
       "       133.73325, 133.6461 , 133.54825, 133.479  , 133.416  , 133.3535 ,\n",
       "       133.29655, 133.2405 , 133.20285, 133.1553 , 133.1223 , 133.10055,\n",
       "       133.0761 , 133.03235, 133.00245, 132.96965, 132.95265, 132.9398 ,\n",
       "       132.92045, 132.9023 , 132.88875, 132.87185, 132.8517 , 132.81415,\n",
       "       132.77465, 132.7342 , 132.7001 , 132.682  , 132.66605, 132.66325,\n",
       "       132.66895, 132.6889 , 132.7208 , 132.74655, 132.76725, 132.7928 ,\n",
       "       132.8199 , 132.8352 , 132.84825, 132.8618 , 132.89645, 132.91795,\n",
       "       132.93865, 132.96405, 132.99235, 133.024  , 133.06065, 133.08995,\n",
       "       133.12135, 133.15725, 133.18985, 133.22525, 133.244  , 133.27165,\n",
       "       133.3132 , 133.3866 , 133.4604 , 133.52525, 133.5791 , 133.64015,\n",
       "       133.7159 , 133.7888 , 133.86595, 133.9277 , 133.984  , 134.058  ,\n",
       "       134.12505, 134.1895 , 134.2581 , 134.3326 , 134.4002 , 134.4403 ,\n",
       "       134.47055, 134.5073 , 134.5217 , 134.54205, 134.56705, 134.5902 ,\n",
       "       134.61965, 134.65205, 134.69515, 134.75275, 134.8402 , 134.93775,\n",
       "       135.0234 , 135.0913 , 135.15875, 135.2345 , 135.30735, 135.3754 ,\n",
       "       135.4241 , 135.4652 , 135.4938 , 135.5054 , 135.51345, 135.5369 ,\n",
       "       135.5778 , 135.60475, 135.6281 , 135.65165, 135.6589 , 135.6521 ,\n",
       "       135.67145, 135.70585, 135.7306 , 135.75905, 135.74655, 135.73675,\n",
       "       135.7158 , 135.71445, 135.703  , 135.70595, 135.7327 , 135.74405,\n",
       "       135.7641 , 135.7748 , 135.73585, 135.68475, 135.6421 , 135.59915,\n",
       "       135.55255, 135.51245, 135.47885, 135.4178 , 135.3573 , 135.29245,\n",
       "       135.2378 , 135.18545, 135.1335 , 135.0682 , 134.99845, 134.92805,\n",
       "       134.8585 , 134.78075, 134.7045 , 134.6313 ])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfDtVolPrice = ld.get_data(df_2.at[0,'Primary Quote RIC'], 'TR.PriceClose.CalcDate,TR.PriceClose',{'Sdate':'0D','Edate':laterDate2[:10],'Frq':'D'})\n",
    "\n",
    "dfDtVolPrice2 = ld.get_data(df_2.at[0,'Primary Quote RIC'], 'TR.Volume',{'Sdate':'0D','Edate':laterDate2[:10],'Frq':'D','Scale':3})\n",
    "dfDtVolPrice['Volume'] = dfDtVolPrice2['Volume']\n",
    "\n",
    "#calculate moving averages\n",
    "convolve50 = np.convolve(dfDtVolPrice['Price Close'], np.ones((50,))/50, mode='valid')\n",
    "convolve200 = np.convolve(dfDtVolPrice['Price Close'], np.ones((200,))/200, mode='valid')\n",
    "display(dfDtVolPrice,convolve50,convolve200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Price Chart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Price Close",
         "type": "scatter",
         "x": [
          "2025-04-21T00:00:00.000000000",
          "2025-04-17T00:00:00.000000000",
          "2025-04-16T00:00:00.000000000",
          "2025-04-15T00:00:00.000000000",
          "2025-04-14T00:00:00.000000000",
          "2025-04-11T00:00:00.000000000",
          "2025-04-10T00:00:00.000000000",
          "2025-04-09T00:00:00.000000000",
          "2025-04-08T00:00:00.000000000",
          "2025-04-07T00:00:00.000000000",
          "2025-04-04T00:00:00.000000000",
          "2025-04-03T00:00:00.000000000",
          "2025-04-02T00:00:00.000000000",
          "2025-04-01T00:00:00.000000000",
          "2025-03-31T00:00:00.000000000",
          "2025-03-28T00:00:00.000000000",
          "2025-03-27T00:00:00.000000000",
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          "2025-03-05T00:00:00.000000000",
          "2025-03-04T00:00:00.000000000",
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          "2025-02-25T00:00:00.000000000",
          "2025-02-24T00:00:00.000000000",
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{\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermap\":[{\"type\":\"scattermap\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('601e0ca5-05f2-4476-b703-3800c4d4f014');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "\n",
    "#fig = go.Figure()\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "#       x=dfDtVolPrice['Calc Date'],  #we can optionally chart the complete set\n",
    "        x = dfDtVolPrice.loc[:525,['Calc Date']]['Calc Date'],    # we are cutting off\n",
    "        y=dfDtVolPrice['Price Close'],\n",
    "        name = 'Price Close'), \n",
    "    secondary_y=False,\n",
    ")\n",
    "fig.add_trace(go.Scatter(\n",
    "#    x=dfDtVolPrice['Calc Date'],\n",
    "    x = dfDtVolPrice.loc[:525,['Calc Date']]['Calc Date'],\n",
    "    y=dfDtVolPrice['Volume'],\n",
    "    name = 'Volume'), \n",
    "    secondary_y=True,\n",
    ")\n",
    "fig.add_trace(go.Scatter(\n",
    "#    x=dfDtVolPrice['Calc Date'],\n",
    "    x = dfDtVolPrice.loc[:525,['Calc Date']]['Calc Date'],\n",
    "    y=convolve50,\n",
    "    name='50 Day MA',),\n",
    "    secondary_y=False,\n",
    ")\n",
    "fig.add_trace(go.Scatter(\n",
    "    x = dfDtVolPrice.loc[:525,['Calc Date']]['Calc Date'],\n",
    "#    x=dfDtVolPrice['Calc Date'],\n",
    "    y=convolve200,\n",
    "    name='200 Day MA',),\n",
    "    secondary_y=False,\n",
    ")\n",
    "\n",
    "fig.update_yaxes(title_text=\"Price\", secondary_y=False)\n",
    "fig.update_yaxes(title_text=\"Volume\", secondary_y=True)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}