Various Euler problems.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sum(a,b):\n",
    "    return a+b\n",
    "sum(2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RREF\n",
    "import numpy as np\n",
    "\n",
    "def rref(M):\n",
    "        lead = 0\n",
    "        rowCount = len(M)\n",
    "        columnCount = len(M[0])\n",
    "        for r in range(rowCount):\n",
    "                if lead >= columnCount:\n",
    "                        return\n",
    "                i = r\n",
    "                while (M[i][lead] == 0):\n",
    "                        i += 1\n",
    "                        if (i == rowCount):\n",
    "                                i = r\n",
    "                                lead += 1\n",
    "                                if (columnCount == lead):\n",
    "                                        return\n",
    "                M[[r,i]] = M[[i,r]]\n",
    "\n",
    "                if (M[r][lead] != 0):\n",
    "                        M[r] = M[r]/M[r][lead]\n",
    "                for i in range (rowCount):\n",
    "                        if (i != r):\n",
    "                                M[i] = M[i] - M[i][lead]*M[r]\n",
    "                lead += 1\n",
    "\n",
    "a = np.array([[0,-3,1,-1],[-2,1,0,3]])\n",
    "\n",
    "#prints original matrix\n",
    "print(a)\n",
    "\n",
    "#ARRAY MUST BE FLOATS FOR RREF FUNCTION\n",
    "\n",
    "a = a.astype(np.float)\n",
    "rref(a)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6857\n",
      "found in 0.1289832592010498 seconds\n"
     ]
    }
   ],
   "source": [
    "# Euler Problem 1\n",
    "\n",
    "import math \n",
    "import time\n",
    "start=time.time()\n",
    "n=600851475143\n",
    "a=[]\n",
    "while n%2==0: \n",
    "    lpf=2\n",
    "    n/=2\n",
    "\n",
    "for i in range(3, int(math.sqrt(n)) + 1, 2): \n",
    "    if n%i==0:\n",
    "        lpf=i \n",
    "        n=n/i\n",
    "if n>=i:\n",
    "    lpf=n\n",
    "print(lpf)\n",
    "end=time.time()\n",
    "print(\"found in\",end-start,\"seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104743\n",
      "found in 0.4775712490081787 seconds\n"
     ]
    }
   ],
   "source": [
    "# Euler Problem 7\n",
    "\n",
    "import math \n",
    "import time\n",
    "\n",
    "start=time.time()\n",
    "n=10001\n",
    "\n",
    "def isPrime(i):\n",
    "    if i%2==0: \n",
    "        return False\n",
    "    p=3\n",
    "    while p<int(math.sqrt(i)+1):\n",
    "        if i%p==0:\n",
    "            return False\n",
    "        p=p+2\n",
    "    return True\n",
    " \n",
    "p=2\n",
    "c=1\n",
    "i=3\n",
    "while c<n:\n",
    "    if isPrime(i):\n",
    "        p=i\n",
    "        c=c+1\n",
    "    i=i+2\n",
    "end=time.time()\n",
    "print(p)\n",
    "print(\"found in\",end-start,\"seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "142913828922\n",
      "found in 28.328092098236084 seconds\n"
     ]
    }
   ],
   "source": [
    "# Euler Problem 10\n",
    "\n",
    "import math \n",
    "import time\n",
    "\n",
    "start=time.time()\n",
    "n=2000000\n",
    "sum=2\n",
    "def isPrime(i):\n",
    "    if i%2==0: \n",
    "        return False\n",
    "    p=3\n",
    "    while p<int(math.sqrt(i)+1):\n",
    "        if i%p==0:\n",
    "            return False\n",
    "        p=p+2\n",
    "    return True\n",
    " \n",
    "p=2\n",
    "c=1\n",
    "i=3\n",
    "while i<n:\n",
    "    if isPrime(i):\n",
    "        p=i\n",
    "        c=c+1\n",
    "        sum=sum+i\n",
    "    i=i+2\n",
    "end=time.time()\n",
    "print(sum)\n",
    "print(\"found in\",end-start,\"seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4613732\n",
      "found in 0.00017905235290527344 seconds\n"
     ]
    }
   ],
   "source": [
    "# Euler Problem 2\n",
    "\n",
    "import time\n",
    "start=time.time()\n",
    "limit=4000000\n",
    "sum=0\n",
    "a=1\n",
    "b=1\n",
    "c=a+b\n",
    "while c<limit:\n",
    "    sum=sum+c\n",
    "    a=b+c\n",
    "    b=c+a\n",
    "    c=a+b\n",
    "end=time.time()\n",
    "print(sum)\n",
    "print(\"found in\",end-start,\"seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25164150\n",
      "found in 0.00020885467529296875 seconds\n"
     ]
    }
   ],
   "source": [
    "# Euler Problem 6\n",
    "\n",
    "import time\n",
    "start=time.time()\n",
    "def sosq(n):\n",
    "    tot=0\n",
    "    i=1\n",
    "    for i in range(1,n+1):\n",
    "        sq=i**2\n",
    "        #print(\"*\",sq)\n",
    "        tot=tot+sq\n",
    "    return tot\n",
    "\n",
    "def sqos(n):\n",
    "    sum=0\n",
    "    i=1\n",
    "    for i in range(1,n+1):\n",
    "        sum=sum+i\n",
    "    sq=sum*sum\n",
    "    return sq\n",
    "\n",
    "def diff(n):\n",
    "    res=sqos(n)-sosq(n)\n",
    "    return res\n",
    "\n",
    "c=diff(100)\n",
    "end=time.time()\n",
    "print(c)\n",
    "print(\"found in\",end-start,\"seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum path sum is  425185\n",
      "reached in  13 iterations\n"
     ]
    }
   ],
   "source": [
    "# Euler Problem 83\n",
    "\n",
    "f = open('p081_matrix.txt','r')\n",
    "M = [[ int(num) for num in line.split(',')] for line in f]\n",
    "N = [[10000000 for x in range(len(M))]for y in range(len(M))]\n",
    "\n",
    "N[0][0] = M[0][0]\n",
    "flag = True\n",
    "count = 0\n",
    "while flag:\n",
    "    flag = False\n",
    "    count += 1\n",
    "    for i in range(len(M)):\n",
    "        for j in range(len(M)):\n",
    "            paths = []\n",
    "            if (i-1)>=0: \n",
    "                paths.append(N[i-1][j])\n",
    "            if (i+1)<len(M):\n",
    "                paths.append(N[i+1][j])\n",
    "            if (j-1)>=0:\n",
    "                paths.append(N[i][j-1])\n",
    "            if (j+1)<len(M):\n",
    "                paths.append(N[i][j+1])\n",
    "            mp = min(paths)\n",
    "            if mp+M[i][j] < N[i][j]:\n",
    "                N[i][j] = mp + M[i][j]\n",
    "                flag = True\n",
    "\n",
    "a = len(N)-1\n",
    "print ('minimum path sum is ',N[a][a])\n",
    "print('reached in ',count,'iterations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "427337\n"
     ]
    }
   ],
   "source": [
    "# Euler Problem 81\n",
    "\n",
    "def prettyprint(B):\n",
    "    for i in range(len(B)):\n",
    "        for j in range(len(B[i])):\n",
    "            print(B[i][j],end=\"\\t\")\n",
    "        print(\"\")\n",
    "    print(\"---\")\n",
    "    return None\n",
    "\n",
    "f = open('p081_matrix.txt','r')\n",
    "A = [[ int(num) for num in line.split(',')] for line in f]\n",
    "B = [[None for x in range(len(A))]for y in range(len(A))]\n",
    "\n",
    "\n",
    "\n",
    "B[79][79]  = A[79][79]\n",
    "N=80\n",
    "def update(B):\n",
    "    for i in range(N):\n",
    "        for j in range(N):\n",
    "            if ( B[i][j] == None):\n",
    "                if ( i == N-1 and j == N-1 ):\n",
    "                    pass\n",
    "                elif ( i == N-1 and j < N-1 ):\n",
    "                    if (  B[i][j+1] != None):\n",
    "                        B[i][j] = A[i][j]+B[i][j+1]\n",
    "                elif ( i < N-1 and j == N-1 ):\n",
    "                    if(  B[i+1][j] != None):\n",
    "                        B[i][j] = A[i][j]+B[i+1][j]\n",
    "                else:\n",
    "                    if ( B[i][j+1] != None and B[i+1][j] != None ):\n",
    "                        B[i][j] = min(A[i][j]+B[i][j+1], A[i][j]+B[i+1][j] )\n",
    "for i in range(1000):\n",
    "    update(B)\n",
    "\n",
    "print(B[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'recDC' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-79a51f862179>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mminCoins\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmakechange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m487\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m488\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-10-79a51f862179>\u001b[0m in \u001b[0;36mmakechange\u001b[1;34m(coinValueList, change, knownResults)\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mc\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcoinValueList\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mchange\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m             numCoins = 1 + recDC(coinValueList, change-i,\n\u001b[0m\u001b[0;32m     13\u001b[0m                               knownResults)\n\u001b[0;32m     14\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mnumCoins\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mminCoins\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'recDC' is not defined"
     ]
    }
   ],
   "source": [
    "# Dynamic Programming for make change\n",
    "\n",
    "def makechange(coinValueList,change,knownResults):\n",
    "    minCoins = change\n",
    "    if change in coinValueList:\n",
    "        knownResults[change] = 1\n",
    "        return 1\n",
    "    elif knownResults[change] > 0:\n",
    "        return knownResults[change]\n",
    "    else:\n",
    "        for i in [c for c in coinValueList if c <= change]:\n",
    "            numCoins = 1 + recDC(coinValueList, change-i,\n",
    "                              knownResults)\n",
    "            if numCoins < minCoins:\n",
    "                minCoins = numCoins\n",
    "                knownResults[change] = minCoins\n",
    "    return minCoins\n",
    "\n",
    "print(makechange([1,5,10,25],487,[0]*488))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260324"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Euler Problem 82\n",
    "\n",
    "f = open('matrix82.txt','r')\n",
    "matrix = [[ int(num) for num in line.split(',')] for line in f]\n",
    "def least_sum(matrix):    \n",
    "\n",
    "    size = len(matrix)\n",
    "    A = [matrix[row][0] for row in range(size)]\n",
    "    for col in range(1, size):\n",
    "        B = [matrix[row][col] for row in range(size)]\n",
    "        t = B[:]\n",
    "        for i in range(size):\n",
    "            B[i] += A[i]\n",
    "            for j in range(0, i): \n",
    "                if sum([A[j]]+t[j:i+1]) < B[i]:\n",
    "                    B[i] = sum([A[j]]+t[j:i+1])\n",
    "            for k in range(i, size):\n",
    "                if sum([A[k]] +t[i:k+1]) < B[i]:\n",
    "                    B[i] = sum([A[k]] +t[i:k+1])\n",
    "        A = B\n",
    "    return min(A)\n",
    "least_sum(matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trading Problem\n",
    "# BSE URL and download using wget\n",
    "# works for all days from 01/01/2007\n",
    "\n",
    "\n",
    "import datetime, time, os, wget, zipfile\n",
    "os.chdir(\"/Users/rohith/Documents/Term2/introductiontoprogramming/\")\n",
    "\n",
    "def getBSEURL(d):\n",
    "    try:\n",
    "        today = d\n",
    "        urlto = 'https://www.bseindia.com/download/BhavCopy/Equity/EQ'+today+'_CSV.ZIP'\n",
    "        os.system(\"wget \"+ urlto + \" -U\\\"X11/Mozilla\\\" --mirror -P BSEdata/ -nd\")\n",
    "    except:\n",
    "        print(\"Does not exist on \" + d)\n",
    "\n",
    "\n",
    "start_date = datetime.date(2020, 1, 1)\n",
    "end_date = datetime.date(2020, 1, 15)\n",
    "delta = datetime.timedelta(days=1)\n",
    "while start_date <= end_date:\n",
    "    d = start_date.strftime(\"%d%m%y\")\n",
    "    getBSEURL(d)\n",
    "    start_date += delta\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "Done!\n",
      "Done!\n",
      "File not found on: 040120\n",
      "File not found on: 050120\n",
      "Done!\n",
      "Done!\n",
      "Done!\n",
      "Done!\n",
      "Done!\n",
      "File not found on: 110120\n",
      "File not found on: 120120\n",
      "Done!\n",
      "Done!\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# unzipping all BSE files\n",
    "\n",
    "def BSEunzip(d):\n",
    "    filename = \"EQ\"+d+\"_CSV.ZIP\"\n",
    "    try:\n",
    "        with zipfile.ZipFile(filename, 'r') as zip: \n",
    "            zip.extractall(\"/Users/rohith/Documents/Term2/introductiontoprogramming/BSEcsvdata/\")\n",
    "            print('Done!')\n",
    "    except:\n",
    "        print(\"File not found on: \" + d)\n",
    "        \n",
    "os.chdir(\"/Users/rohith/Documents/Term2/introductiontoprogramming/BSEdata/\")       \n",
    "start_date = datetime.date(2020, 1, 1)\n",
    "end_date = datetime.date(2020, 1, 15)\n",
    "delta = datetime.timedelta(days=1)\n",
    "while start_date <= end_date:\n",
    "    d = start_date.strftime(\"%d%m%y\")\n",
    "    BSEunzip(d)\n",
    "    start_date += delta\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EQ130120.CSV',\n",
       " 'EQ030120.CSV',\n",
       " 'EQ020120.CSV',\n",
       " 'combined_csv.csv',\n",
       " 'EQ100120.CSV',\n",
       " 'EQ090120.CSV',\n",
       " 'EQ080120.CSV',\n",
       " 'EQ010120.CSV',\n",
       " 'EQ140120.CSV',\n",
       " 'EQ150120.CSV',\n",
       " '.ipynb_checkpoints',\n",
       " 'EQ070120.CSV',\n",
       " 'EQ060120.CSV']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# putting the csv file into a pandas dataframe\n",
    "\n",
    "import pandas as pd\n",
    "os.chdir(\"/Users/rohith/Documents/Term2/introductiontoprogramming/BSEcsvdata/\")   \n",
    "data = pd.read_csv(\"EQ010120.csv\")\n",
    "\n",
    "csvFiles = list(os.listdir())\n",
    "csvFiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combining multiple csv files into a single file\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "os.chdir('/Users/rohith/Documents/Term2/introductiontoprogramming/BSEcsvdata/')\n",
    "extension = 'CSV'\n",
    "all_filenames = [i for i in glob.glob('*.{}'.format(extension))]\n",
    "combined_csv = pd.concat([pd.read_csv(f) for f in all_filenames ])\n",
    "combined_csv.to_csv( \"combined_csv.csv\", index=False, encoding='utf-8-sig')"
   ]
  },
  {
   "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>Date</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>WAP</th>\n",
       "      <th>No.of Shares</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Total Turnover (Rs.)</th>\n",
       "      <th>Deliverable Quantity</th>\n",
       "      <th>% Deli. Qty to Traded Qty</th>\n",
       "      <th>Spread High-Low</th>\n",
       "      <th>Spread Close-Open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1280.4</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>1279.05</td>\n",
       "      <td>1277.217288</td>\n",
       "      <td>44876</td>\n",
       "      <td>2304</td>\n",
       "      <td>57316403.0</td>\n",
       "      <td>21684</td>\n",
       "      <td>48.32</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>1286.8</td>\n",
       "      <td>1286.8</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>1271.80</td>\n",
       "      <td>1273.143424</td>\n",
       "      <td>45369</td>\n",
       "      <td>3413</td>\n",
       "      <td>57761244.0</td>\n",
       "      <td>23784</td>\n",
       "      <td>52.42</td>\n",
       "      <td>17.8</td>\n",
       "      <td>-15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>1288.5</td>\n",
       "      <td>1274.8</td>\n",
       "      <td>1280.85</td>\n",
       "      <td>1279.356194</td>\n",
       "      <td>713086</td>\n",
       "      <td>6298</td>\n",
       "      <td>912290991.0</td>\n",
       "      <td>326730</td>\n",
       "      <td>45.82</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>1274.90</td>\n",
       "      <td>1275.153841</td>\n",
       "      <td>83333</td>\n",
       "      <td>3238</td>\n",
       "      <td>106262395.0</td>\n",
       "      <td>42217</td>\n",
       "      <td>50.66</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>1297.4</td>\n",
       "      <td>1297.4</td>\n",
       "      <td>1265.1</td>\n",
       "      <td>1271.55</td>\n",
       "      <td>1279.164735</td>\n",
       "      <td>106498</td>\n",
       "      <td>4892</td>\n",
       "      <td>136228486.0</td>\n",
       "      <td>45371</td>\n",
       "      <td>42.60</td>\n",
       "      <td>32.3</td>\n",
       "      <td>-25.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Open Price  High Price  Low Price  Close Price          WAP  \\\n",
       "0 2020-01-01      1275.0      1280.4     1271.0      1279.05  1277.217288   \n",
       "1 2019-12-31      1286.8      1286.8     1269.0      1271.80  1273.143424   \n",
       "2 2019-12-30      1280.0      1288.5     1274.8      1280.85  1279.356194   \n",
       "3 2019-12-27      1278.0      1279.0     1270.0      1274.90  1275.153841   \n",
       "4 2019-12-26      1297.4      1297.4     1265.1      1271.55  1279.164735   \n",
       "\n",
       "   No.of Shares  No. of Trades  Total Turnover (Rs.)  Deliverable Quantity  \\\n",
       "0         44876           2304            57316403.0                 21684   \n",
       "1         45369           3413            57761244.0                 23784   \n",
       "2        713086           6298           912290991.0                326730   \n",
       "3         83333           3238           106262395.0                 42217   \n",
       "4        106498           4892           136228486.0                 45371   \n",
       "\n",
       "   % Deli. Qty to Traded Qty  Spread High-Low  Spread Close-Open  \n",
       "0                      48.32              9.4               4.05  \n",
       "1                      52.42             17.8             -15.00  \n",
       "2                      45.82             13.7               0.85  \n",
       "3                      50.66              9.0              -3.10  \n",
       "4                      42.60             32.3             -25.85  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading HDFC stock data from a csv file into a pandas dataframe\n",
    "import os\n",
    "import pandas as pd\n",
    "os.chdir(\"/Users/rohith/Documents/Term2/introductiontoprogramming/\")   \n",
    "data = pd.read_csv(\"hdfcdata.csv\")\n",
    "data['Date'] = pd.to_datetime(data.Date)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'WAP')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting WAP vs date\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(data.Date,data.WAP)\n",
    "plt.xlabel('date')\n",
    "plt.ylabel('WAP')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E[X] =  0.1 E[Y] =  0.07999999999999999\n",
      "Var[X] =  0.0076 Var[Y] =  0.007080000000000001\n",
      "Cov[X,Y] =  -0.0024\n",
      "Corr[X,Y] =  -0.3271808050833462\n",
      "Minimum Var Portfolio: a =  0.486652977412731\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'stdev')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Portfolio return and variance\n",
    "# Finding the minimum variance portfolio\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "prob = [0.2]*5\n",
    "\n",
    "retX = [0.11, 0.09, 0.25, 0.07, -0.02]\n",
    "retY = [-0.03, 0.15, 0.02, 0.20, 0.06]\n",
    "\n",
    "asset, assetX, assetY = [], [], []\n",
    "def expRet(asset):\n",
    "    return sum(x for x in asset)/len(asset)\n",
    "\n",
    "def var(asset,prob):\n",
    "    q = [ (x-expRet(asset))**2 for x in asset]\n",
    "    for i in range(len(asset)):\n",
    "        q[i] = q[i]*prob[i]\n",
    "    return sum(q)\n",
    "\n",
    "\n",
    "def covar(assetX,assetY,prob):\n",
    "    covariance = 0\n",
    "    for i in range(len(assetX)):\n",
    "        prod = prob[i]*(assetX[i]-expRet(assetX))*(assetY[i]-expRet(assetY))\n",
    "        covariance+=prod\n",
    "    return covariance\n",
    "\n",
    "def corr(cov,stdevX,stdevY):\n",
    "    r = cov/(stdevX*stdevY)\n",
    "    return r\n",
    "    \n",
    "\n",
    "\n",
    "def minVarPort(retX,retY,prob):\n",
    "    varX = var(retX,prob)\n",
    "    varY = var(retY,prob)\n",
    "    covXY = covar(retX,retY,prob)\n",
    "    a = (varY - covXY)/(varX + varY - 2*covXY)\n",
    "    return a\n",
    "\n",
    "\n",
    "\n",
    "#Rp = aX + bY\n",
    "\n",
    "portRet = []\n",
    "portVar = []\n",
    "portStdev = []\n",
    "weights = []\n",
    "a = -0.5 # make zero for no short selling condition\n",
    "while a <= 1.5:\n",
    "    b = 1 - a\n",
    "    retRp = (a*expRet(retX)) + (b*expRet(retY))\n",
    "    varRp = (((a**2)*var(retX,prob)) + ((b**2)*var(retY,prob))\\\n",
    "             + (2*a*b*covar(retX,retY,prob)))\n",
    "    stdevRp = math.sqrt(varRp)\n",
    "    portRet.append(retRp)\n",
    "    portVar.append(varRp)\n",
    "    portStdev.append(stdevRp)\n",
    "    weights.append(a)\n",
    "    a = a + 0.05\n",
    "    \n",
    "\n",
    "\n",
    "EX = expRet(retX)\n",
    "EY = expRet(retY)\n",
    "varX = var(retX,prob)\n",
    "varY = var(retY,prob)\n",
    "covXY = covar(retX,retY,prob)\n",
    "corrXY = corr(covar(retX,retY,prob),math.sqrt(var(retX,prob)),math.sqrt(var(retY,prob)))\n",
    "minvarXY = minVarPort(retX,retY,prob)\n",
    "\n",
    "\n",
    "print('E[X] = ',EX,'E[Y] = ',EY)\n",
    "print('Var[X] = ',varX,'Var[Y] = ',varY)\n",
    "print('Cov[X,Y] = ',covXY)\n",
    "print('Corr[X,Y] = ',corrXY)\n",
    "print('Minimum Var Portfolio: a = ',minvarXY)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(weights,portRet)\n",
    "plt.ylabel('return')\n",
    "plt.xlabel('weights')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(weights,portStdev)\n",
    "plt.ylabel('stdev')\n",
    "plt.xlabel('weights')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(portStdev,portRet)\n",
    "plt.ylabel('return')\n",
    "plt.xlabel('stdev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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