decile-market-cap-distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Market-cap Deciles and Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "library(DBI)\n",
    "library(plutoDbR)\n",
    "library(plutoR)\n",
    "library(tidyverse)\n",
    "options(\"scipen\"=999)\n",
    "options(stringsAsFactors = FALSE)\n",
    "source(\"/usr/share/pluto/config.R\")\n",
    "source(\"/usr/share/pluto/goofy/plot.common.R\")\n",
    "source(\"/usr/share/pluto/goofy/misc.common.R\")\n",
    "\n",
    "library(pracma)\n",
    "library(ggthemes)\n",
    "library(reshape2)\n",
    "library(quantmod)\n",
    "library(lubridate)\n",
    "library(ggrepel)\n",
    "library(PerformanceAnalytics)\n",
    "library(ggpubr)\n",
    "\n",
    "options(repr.plot.width=16, repr.plot.height=8)\n",
    "options(tibble.width = Inf)\n",
    "\n",
    "equities <- EquitiesIndiaNse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "startEndMisc <- equities$MarketCapDecile() %>%\n",
    "    summarize(S = min(TIME_STAMP), E = max(TIME_STAMP)) %>%\n",
    "    collect()\n",
    "\n",
    "startDate <- as.Date(startEndMisc$S[1])\n",
    "endDate <- as.Date(startEndMisc$E[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "startAggMktCap <- equities$MarketCapDecile() %>%\n",
    "    inner_join(equities$MiscInfo(), by=c('TIME_STAMP', 'SYMBOL')) %>%\n",
    "    filter(TIME_STAMP == startDate & DECILE < 10) %>%\n",
    "    group_by(DECILE) %>%\n",
    "    summarize(FF = sum(FF_MKT_CAP_CR), N = n()) %>%\n",
    "    select(DECILE, FF, N) %>%\n",
    "    collect()\n",
    "\n",
    "endAggMktCap <- equities$MarketCapDecile() %>%\n",
    "    inner_join(equities$MiscInfo(), by=c('TIME_STAMP', 'SYMBOL')) %>%\n",
    "    filter(TIME_STAMP == endDate & DECILE < 10) %>%\n",
    "    group_by(DECILE) %>%\n",
    "    summarize(FF = sum(FF_MKT_CAP_CR), N = n()) %>%\n",
    "    select(DECILE, FF, N) %>%\n",
    "    collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 10 x 6\u001b[39m\n",
      "   DECILE     FF.x   N.x     FF.y   N.y CHANGE\n",
      "    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m  \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m\n",
      "\u001b[90m 1\u001b[39m      0 3\u001b[4m4\u001b[24m\u001b[4m3\u001b[24m\u001b[4m1\u001b[24m516.   150 6\u001b[4m2\u001b[24m\u001b[4m1\u001b[24m\u001b[4m8\u001b[24m028.   159 81.2  \n",
      "\u001b[90m 2\u001b[39m      1  \u001b[4m3\u001b[24m\u001b[4m0\u001b[24m\u001b[4m8\u001b[24m404.   147  \u001b[4m5\u001b[24m\u001b[4m4\u001b[24m\u001b[4m1\u001b[24m794.   157 75.7  \n",
      "\u001b[90m 3\u001b[39m      2  \u001b[4m1\u001b[24m\u001b[4m2\u001b[24m\u001b[4m4\u001b[24m792.   148  \u001b[4m1\u001b[24m\u001b[4m9\u001b[24m\u001b[4m4\u001b[24m948.   157 56.2  \n",
      "\u001b[90m 4\u001b[39m      3   \u001b[4m6\u001b[24m\u001b[4m1\u001b[24m726.   148   \u001b[4m9\u001b[24m\u001b[4m2\u001b[24m970.   157 50.6  \n",
      "\u001b[90m 5\u001b[39m      4   \u001b[4m3\u001b[24m\u001b[4m2\u001b[24m304.   148   \u001b[4m4\u001b[24m\u001b[4m7\u001b[24m355.   157 46.6  \n",
      "\u001b[90m 6\u001b[39m      5   \u001b[4m1\u001b[24m\u001b[4m7\u001b[24m661.   148   \u001b[4m2\u001b[24m\u001b[4m4\u001b[24m100.   157 36.5  \n",
      "\u001b[90m 7\u001b[39m      6    \u001b[4m9\u001b[24m198.   148   \u001b[4m1\u001b[24m\u001b[4m1\u001b[24m686.   157 27.0  \n",
      "\u001b[90m 8\u001b[39m      7    \u001b[4m4\u001b[24m985.   148    \u001b[4m6\u001b[24m175.   157 23.9  \n",
      "\u001b[90m 9\u001b[39m      8    \u001b[4m2\u001b[24m390.   148    \u001b[4m2\u001b[24m644.   157 10.6  \n",
      "\u001b[90m10\u001b[39m      9     791.   146     786.   152 -\u001b[31m0\u001b[39m\u001b[31m.\u001b[39m\u001b[31m555\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "aggMktCap <- startAggMktCap %>% \n",
    "    inner_join(endAggMktCap, by=c('DECILE'))\n",
    "    \n",
    "aggMktCap %>% \n",
    "    mutate(CHANGE = 100*(`FF.y`/`FF.x`-1)) %>%\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggMktCapDf <- data.frame(aggMktCap[, c('DECILE', 'FF.x', 'FF.y')])\n",
    "names(aggMktCapDf) <- c('DECILE', 'BeginCap', 'EndCap')\n",
    "aggMktCapMelt <- melt(aggMktCapDf, id='DECILE')\n",
    "aggMktCapMelt$value <- aggMktCapMelt$value/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
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uCQAAAACAvcHuHMHR6v8d/fDggc/8dXhEdO/cvhSzyKoAACAASURBVERy8o5zJkz7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\nevsfkJyc/J933t66d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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(aggMktCapMelt, aes(x=DECILE, y=value, fill=variable)) +\n",
    "    theme_economist() +\n",
    "    geom_bar(stat=\"identity\", position=position_dodge()) +\n",
    "    scale_x_continuous(labels=aggMktCapDf$DECILE, breaks=aggMktCapDf$DECILE) +\n",
    "    scale_y_log10() +\n",
    "    geom_text_repel(aes(label= round(value, 2)), position = position_dodge(0.9)) +\n",
    "    labs(x='', y=\"log(free-float '000 crore)\", fill='', title=\"Market-cap Deciles\", subtitle=sprintf(\"Free-float %s:%s\", startDate, endDate)) +\n",
    "    annotate(\"text\", x=max(aggMktCapDf$DECILE), y=0, \n",
    "             label = \"@StockViz\", hjust=1.1, vjust=-1.1, \n",
    "             col=\"white\", cex=6, fontface = \"bold\", alpha = 0.8)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was created using [pluto](https://pluto.studio). Thank you for playing fair!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}