bondtrading.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2aa4901b358>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7gDuAa2ld2VsJzG4WfiHJubRW/XyG1i2mi5v4q4HLgZ2Bm6vq7Ca+E3AVcDjw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\nDeT/KEqSel0Nsf2L/fnAZUn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aa48bd0630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7gDuAa2ld2VsJzG4WfiHJubRW/XyG1i2mi5v4q4HLgZ2Bm6vq7Ca+E3AVcDjw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\nDeT/KEqSel0Nsf2L/fnAZUn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aa48bd07f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7gDuAa2ld2VsJzG4WfiHJubRW/XyG1i2mi5v4q4HLgZ2Bm6vq7Ca+E3AVcDjw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aa48bd0240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import string\n",
    "import random\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (15,3)\n",
    "\n",
    "def isin(size=6, chars=string.ascii_uppercase + string.digits):\n",
    "    return ''.join(random.choice(chars) for _ in range(size))\n",
    "def vol():\n",
    "    return random.randint(200000,10000000)\n",
    "def month():\n",
    "    return random.randrange(1,12)\n",
    "isin()\n",
    "vol()\n",
    "month()\n",
    "data = []\n",
    "for i in range(1,100):\n",
    "    data.append({\"isin\":isin(size=3,chars='ABC'),\"vol\":vol(),\"month\":month()})\n",
    "bdf = pd.DataFrame(data)\n",
    "bgp1 = bdf[(bdf.month == 1)].groupby(['month','isin']).agg(['sum'])\n",
    "bgp2 = bdf[(bdf.month == 1)].groupby(['month','isin']).agg(['sum'])\n",
    "bgp3 = bdf[(bdf.month == 1)].groupby(['month','isin']).agg(['sum'])\n",
    "\n",
    "bgp1.plot(kind='bar')\n",
    "bgp2.plot(kind='bar')\n",
    "bgp3.plot(kind='bar')\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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