Week 1 Coursera Stock.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the package \"Pandas\" into Jupyter Notebook\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We import the stock data of Facebook into Jupyter Notebook. The CSV file is located in the folder called \"Data\" in your Workspace\n",
    "#We then name the DataFrame name as 'fb'\n",
    "fb = pd.read_csv('facebook.csv',index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms = pd.read_csv('microsoft.csv',parse_dates=False,index_col = 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.73\n"
     ]
    }
   ],
   "source": [
    "# run this cell to ensure Microsoft's stock data is imported\n",
    "print(ms.iloc[0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>46.730000</td>\n",
       "      <td>47.439999</td>\n",
       "      <td>46.450001</td>\n",
       "      <td>46.450001</td>\n",
       "      <td>42.848763</td>\n",
       "      <td>21552500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>46.660000</td>\n",
       "      <td>47.419998</td>\n",
       "      <td>46.540001</td>\n",
       "      <td>46.759998</td>\n",
       "      <td>43.134731</td>\n",
       "      <td>27913900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>46.369999</td>\n",
       "      <td>46.730000</td>\n",
       "      <td>46.250000</td>\n",
       "      <td>46.330002</td>\n",
       "      <td>42.738068</td>\n",
       "      <td>39673900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>46.380001</td>\n",
       "      <td>46.750000</td>\n",
       "      <td>45.540001</td>\n",
       "      <td>45.650002</td>\n",
       "      <td>42.110783</td>\n",
       "      <td>36447900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>45.980000</td>\n",
       "      <td>46.459999</td>\n",
       "      <td>45.490002</td>\n",
       "      <td>46.230000</td>\n",
       "      <td>42.645817</td>\n",
       "      <td>29114100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2014-12-31  46.730000  47.439999  46.450001  46.450001  42.848763  21552500\n",
       "2015-01-02  46.660000  47.419998  46.540001  46.759998  43.134731  27913900\n",
       "2015-01-05  46.369999  46.730000  46.250000  46.330002  42.738068  39673900\n",
       "2015-01-06  46.380001  46.750000  45.540001  45.650002  42.110783  36447900\n",
       "2015-01-07  45.980000  46.459999  45.490002  46.230000  42.645817  29114100"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ms.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>20.400000</td>\n",
       "      <td>20.510000</td>\n",
       "      <td>19.990000</td>\n",
       "      <td>20.049999</td>\n",
       "      <td>19.459270</td>\n",
       "      <td>4157500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>20.129999</td>\n",
       "      <td>20.280001</td>\n",
       "      <td>19.809999</td>\n",
       "      <td>20.129999</td>\n",
       "      <td>19.536913</td>\n",
       "      <td>2842000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>20.129999</td>\n",
       "      <td>20.190001</td>\n",
       "      <td>19.700001</td>\n",
       "      <td>19.790001</td>\n",
       "      <td>19.206934</td>\n",
       "      <td>4948800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>19.820000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>19.170000</td>\n",
       "      <td>19.190001</td>\n",
       "      <td>18.624611</td>\n",
       "      <td>4944100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>19.330000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>19.080000</td>\n",
       "      <td>19.139999</td>\n",
       "      <td>18.576082</td>\n",
       "      <td>8045200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close   Volume\n",
       "Date                                                                      \n",
       "2014-12-31  20.400000  20.510000  19.990000  20.049999  19.459270  4157500\n",
       "2015-01-02  20.129999  20.280001  19.809999  20.129999  19.536913  2842000\n",
       "2015-01-05  20.129999  20.190001  19.700001  19.790001  19.206934  4948800\n",
       "2015-01-06  19.820000  19.840000  19.170000  19.190001  18.624611  4944100\n",
       "2015-01-07  19.330000  19.500000  19.080000  19.139999  18.576082  8045200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(780, 6)\n"
     ]
    }
   ],
   "source": [
    "print(fb.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(780, 6)\n"
     ]
    }
   ],
   "source": [
    "print(ms.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Open        High         Low       Close   Adj Close  \\\n",
      "count  780.000000  780.000000  780.000000  780.000000  780.000000   \n",
      "mean    80.212705   81.285654   79.022397   80.264897   79.914215   \n",
      "std     64.226121   65.048907   63.190963   64.198375   64.327846   \n",
      "min     19.250000   19.500000   18.940001   19.139999   18.576082   \n",
      "25%     25.525000   26.085000   24.845000   25.475000   25.134513   \n",
      "50%     53.379999   54.034999   52.930000   53.420000   53.035403   \n",
      "75%    113.322502  115.779999  110.297499  113.702501  113.261238   \n",
      "max    245.770004  249.270004  244.449997  246.850006  246.850006   \n",
      "\n",
      "             Volume  \n",
      "count  7.800000e+02  \n",
      "mean   1.204453e+07  \n",
      "std    8.221848e+06  \n",
      "min    1.311200e+06  \n",
      "25%    7.215200e+06  \n",
      "50%    9.728700e+06  \n",
      "75%    1.408885e+07  \n",
      "max    9.232320e+07  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "# print summary statistics of Facebook\n",
    "print(fb.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Open        High         Low       Close   Adj Close  \\\n",
      "count  780.000000  780.000000  780.000000  780.000000  780.000000   \n",
      "mean    58.912308   59.372308   58.465000   58.945397   56.931553   \n",
      "std     12.852040   12.868060   12.802831   12.830350   13.670080   \n",
      "min     40.340000   40.740002   39.720001   40.290001   37.267815   \n",
      "25%     47.894998   48.295000   47.535001   47.974998   44.792073   \n",
      "50%     56.150002   56.640001   55.700001   56.209999   53.775458   \n",
      "75%     67.892500   68.174997   67.447500   67.740000   66.565508   \n",
      "max     95.139999   96.070000   93.720001   95.010002   94.565781   \n",
      "\n",
      "             Volume  \n",
      "count  7.800000e+02  \n",
      "mean   2.978900e+07  \n",
      "std    1.522319e+07  \n",
      "min    7.425600e+06  \n",
      "25%    2.096685e+07  \n",
      "50%    2.623125e+07  \n",
      "75%    3.408020e+07  \n",
      "max    1.691640e+08  \n"
     ]
    }
   ],
   "source": [
    "# print summary statistics of Microsoft\n",
    "print(ms.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select all the price information of Facebook in 2016.\n",
    "fb_2015=fb.loc['2015-01-01':'2015-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Open         2.288000e+01\n",
      "High         2.311000e+01\n",
      "Low          2.273000e+01\n",
      "Close        2.297000e+01\n",
      "Adj Close    2.237908e+01\n",
      "Volume       5.923900e+06\n",
      "Name: 2015-03-16, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# print the price of Facebook on '2015-03-16'\n",
    "print(fb_2015.loc['2015-03-16'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select all the price information of Microsoft in 2016.\n",
    "ms_2016 = ms.loc['2016-01-01':'2016-12-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Open         5.345000e+01\n",
      "High         5.460000e+01\n",
      "Low          5.340000e+01\n",
      "Close        5.435000e+01\n",
      "Adj Close    5.187095e+01\n",
      "Volume       3.169170e+07\n",
      "Name: 2016-03-16, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# print the price of Microsoft on '2016-03-16'\n",
    "print(ms_2016.loc['2016-03-16'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.4\n"
     ]
    }
   ],
   "source": [
    "# print the opening price of the first row\n",
    "print(fb.iloc[0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90.559998\n"
     ]
    }
   ],
   "source": [
    "# print the opening price of the last row\n",
    "print(ms.iloc[-1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-3e486ae091d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Plot the stock data using plot() method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Open'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Low'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# Plot the stock data using plot() method\n",
    "data[['Open', 'Low']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa711c5cf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(10, 8))\n",
    "# plot only the Close price of 2016 of Microsoft, 1 line \n",
    "ms.loc['2016-01-01':'2016-12-31','Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column in the DataFrame (1) - Price difference\n",
    "#Create a new column PriceDiff in the DataFrame fb\n",
    "fb['PriceDiff'] = fb['Close'].shift(-1) - fb['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Your turn to create PriceDiff in the DataFrame ms\n",
    "ms['PriceDiff'] = ms['Close'].shift(-1) - ms['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.6799999999999997\n"
     ]
    }
   ],
   "source": [
    "#Run this code to display the price difference of Microsoft on 2015-01-05\n",
    "print(ms['PriceDiff'].loc['2015-01-05'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column in the DataFrame (2) - Daily return\n",
    "\n",
    "# Daily Return is calcuated as PriceDiff/Close\n",
    "#Create a new column Return in the DataFrame fb\n",
    "fb['Return'] = fb['PriceDiff'] /fb['Close']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Your turn to create a new column Return in the DataFrame MS\n",
    "ms['Return'] = ms['PriceDiff'] /ms['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.014677314281143345\n"
     ]
    }
   ],
   "source": [
    "#Run this code to print the return on 2015-01-05\n",
    "print(ms['Return'].loc['2015-01-05'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new column in the DataFrame using List Comprehension - Direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a new column Direction. \n",
    "#The List Comprehension means : if the price difference is larger than 0, denote as 1, otherwise, denote as 0,\n",
    "#for every record in the DataFrame - fb\n",
    "\n",
    "fb['Direction'] = [1 if fb['PriceDiff'].loc[ei] > 0 else 0 for ei in fb.index ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your turn to create a new column Direction for MS\n",
    "\n",
    "ms['Direction'] = [1 if ms['PriceDiff'].loc[ei] > 0 else 0 for ei in ms.index ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price difference on 2015-01-05 is -0.6799999999999997. direction is 0\n"
     ]
    }
   ],
   "source": [
    "# Run the following code to show the price difference on 2015-01-05\n",
    "print('Price difference on {} is {}. direction is {}'.format('2015-01-05', ms['PriceDiff'].loc['2015-01-05'], ms['Direction'].loc['2015-01-05']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create a new column in the DataFrame using Rolling Window calculation (.rolling()) - Moving average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fb['ma50'] = fb['Close'].rolling(50).mean()\n",
    "\n",
    "#plot the moving average\n",
    "plt.figure(figsize=(10, 8))\n",
    "fb['ma50'].loc['2015-01-01':'2015-12-31'].plot(label='MA50')\n",
    "fb['Close'].loc['2015-01-01':'2015-12-31'].plot(label='Close')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can use .rolling() to calculate any numbers of days' Moving Average. This is your turn to calculate \"60 days\"\n",
    "# moving average of Microsoft, rename it as \"ma60\". And follow the codes above in plotting a graph\n",
    "\n",
    "ms['ma60'] = ms['Close'].rolling(60).mean()\n",
    "\n",
    "#plot the moving average\n",
    "plt.figure(figsize=(10, 8))\n",
    "ms['ma60'].loc['2015-01-01':'2015-12-31'].plot(label='MA60')\n",
    "ms['Close'].loc['2015-01-01':'2015-12-31'].plot(label='Close')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 1. Munging the stock data and add two columns - MA10 and MA50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MA10</th>\n",
       "      <th>MA50</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-13</th>\n",
       "      <td>22.559999</td>\n",
       "      <td>22.760000</td>\n",
       "      <td>22.250000</td>\n",
       "      <td>22.700001</td>\n",
       "      <td>22.116024</td>\n",
       "      <td>8982200</td>\n",
       "      <td>22.648</td>\n",
       "      <td>21.0174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-16</th>\n",
       "      <td>22.879999</td>\n",
       "      <td>23.110001</td>\n",
       "      <td>22.730000</td>\n",
       "      <td>22.969999</td>\n",
       "      <td>22.379078</td>\n",
       "      <td>5923900</td>\n",
       "      <td>22.685</td>\n",
       "      <td>21.0758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-17</th>\n",
       "      <td>22.920000</td>\n",
       "      <td>23.260000</td>\n",
       "      <td>22.760000</td>\n",
       "      <td>23.250000</td>\n",
       "      <td>22.651876</td>\n",
       "      <td>7497500</td>\n",
       "      <td>22.792</td>\n",
       "      <td>21.1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-18</th>\n",
       "      <td>23.250000</td>\n",
       "      <td>23.370001</td>\n",
       "      <td>22.660000</td>\n",
       "      <td>22.870001</td>\n",
       "      <td>22.281652</td>\n",
       "      <td>10337600</td>\n",
       "      <td>22.836</td>\n",
       "      <td>21.1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-19</th>\n",
       "      <td>22.950001</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>22.780001</td>\n",
       "      <td>23.219999</td>\n",
       "      <td>22.622650</td>\n",
       "      <td>7768900</td>\n",
       "      <td>22.872</td>\n",
       "      <td>21.2804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume  \\\n",
       "Date                                                                          \n",
       "2015-03-13  22.559999  22.760000  22.250000  22.700001  22.116024   8982200   \n",
       "2015-03-16  22.879999  23.110001  22.730000  22.969999  22.379078   5923900   \n",
       "2015-03-17  22.920000  23.260000  22.760000  23.250000  22.651876   7497500   \n",
       "2015-03-18  23.250000  23.370001  22.660000  22.870001  22.281652  10337600   \n",
       "2015-03-19  22.950001  23.299999  22.780001  23.219999  22.622650   7768900   \n",
       "\n",
       "              MA10     MA50  \n",
       "Date                         \n",
       "2015-03-13  22.648  21.0174  \n",
       "2015-03-16  22.685  21.0758  \n",
       "2015-03-17  22.792  21.1382  \n",
       "2015-03-18  22.836  21.1998  \n",
       "2015-03-19  22.872  21.2804  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fb = pd.read_csv('facebook.csv',index_col = 0)\n",
    "fb['MA10'] = fb['Close'].rolling(10).mean()\n",
    "fb['MA50'] = fb['Close'].rolling(50).mean()\n",
    "fb = fb.dropna()\n",
    "fb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 2. Add \"Shares\" column to make decisions base on the strategy¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add a new column \"Shares\", if MA10>MA50, denote as 1 (long one share of stock), otherwise, denote as 0 (do nothing)\n",
    "\n",
    "fb['Shares'] = [1 if fb.loc[ei, 'MA10']>fb.loc[ei, 'MA50'] else 0 for ei in fb.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7ffa710b6fd0>"
      ]
     },
     "execution_count": 41,
     "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": [
    "#Add a new column \"Profit\" using List Comprehension, for any rows in fb, if Shares=1, the profit is calculated as the close price of \n",
    "#tomorrow - the close price of today. Otherwise the profit is 0.\n",
    "\n",
    "#Plot a graph to show the Profit/Loss\n",
    "\n",
    "fb['Close1'] = fb['Close'].shift(-1)\n",
    "fb['Profit'] = [fb.loc[ei, 'Close1'] - fb.loc[ei, 'Close'] if fb.loc[ei, 'Shares']==1 else 0 for ei in fb.index]\n",
    "fb['Profit'].plot()\n",
    "plt.axhline(y=0, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 3. Use .cumsum() to display our model's performance if we follow the strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MA10</th>\n",
       "      <th>MA50</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Close1</th>\n",
       "      <th>Profit</th>\n",
       "      <th>wealth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>241.110001</td>\n",
       "      <td>246.419998</td>\n",
       "      <td>238.410004</td>\n",
       "      <td>242.720001</td>\n",
       "      <td>242.720001</td>\n",
       "      <td>14270800</td>\n",
       "      <td>235.692003</td>\n",
       "      <td>210.030001</td>\n",
       "      <td>1</td>\n",
       "      <td>245.800003</td>\n",
       "      <td>3.080002</td>\n",
       "      <td>177.820011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>245.770004</td>\n",
       "      <td>249.270004</td>\n",
       "      <td>244.449997</td>\n",
       "      <td>245.800003</td>\n",
       "      <td>245.800003</td>\n",
       "      <td>11964400</td>\n",
       "      <td>237.800003</td>\n",
       "      <td>210.713801</td>\n",
       "      <td>1</td>\n",
       "      <td>240.500000</td>\n",
       "      <td>-5.300003</td>\n",
       "      <td>172.520008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>238.520004</td>\n",
       "      <td>246.899994</td>\n",
       "      <td>238.059998</td>\n",
       "      <td>240.500000</td>\n",
       "      <td>240.500000</td>\n",
       "      <td>12980600</td>\n",
       "      <td>239.406003</td>\n",
       "      <td>211.296601</td>\n",
       "      <td>1</td>\n",
       "      <td>233.520004</td>\n",
       "      <td>-6.979996</td>\n",
       "      <td>165.540012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <td>237.000000</td>\n",
       "      <td>237.970001</td>\n",
       "      <td>231.169998</td>\n",
       "      <td>233.520004</td>\n",
       "      <td>233.520004</td>\n",
       "      <td>17961600</td>\n",
       "      <td>239.747003</td>\n",
       "      <td>211.685401</td>\n",
       "      <td>1</td>\n",
       "      <td>213.699997</td>\n",
       "      <td>-19.820007</td>\n",
       "      <td>145.720005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>227.000000</td>\n",
       "      <td>233.229996</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>213.699997</td>\n",
       "      <td>213.699997</td>\n",
       "      <td>28869000</td>\n",
       "      <td>237.748003</td>\n",
       "      <td>211.638401</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2018-01-30  241.110001  246.419998  238.410004  242.720001  242.720001   \n",
       "2018-01-31  245.770004  249.270004  244.449997  245.800003  245.800003   \n",
       "2018-02-01  238.520004  246.899994  238.059998  240.500000  240.500000   \n",
       "2018-02-02  237.000000  237.970001  231.169998  233.520004  233.520004   \n",
       "2018-02-05  227.000000  233.229996  205.000000  213.699997  213.699997   \n",
       "\n",
       "              Volume        MA10        MA50  Shares      Close1     Profit  \\\n",
       "Date                                                                          \n",
       "2018-01-30  14270800  235.692003  210.030001       1  245.800003   3.080002   \n",
       "2018-01-31  11964400  237.800003  210.713801       1  240.500000  -5.300003   \n",
       "2018-02-01  12980600  239.406003  211.296601       1  233.520004  -6.979996   \n",
       "2018-02-02  17961600  239.747003  211.685401       1  213.699997 -19.820007   \n",
       "2018-02-05  28869000  237.748003  211.638401       1         NaN        NaN   \n",
       "\n",
       "                wealth  \n",
       "Date                    \n",
       "2018-01-30  177.820011  \n",
       "2018-01-31  172.520008  \n",
       "2018-02-01  165.540012  \n",
       "2018-02-02  145.720005  \n",
       "2018-02-05         NaN  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use .cumsum() to calculate the accumulated wealth over the period\n",
    "\n",
    "fb['wealth'] = fb['Profit'].cumsum()\n",
    "fb.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the wealth to show the growth of profit over the period\n",
    "\n",
    "fb['wealth'].plot()\n",
    "plt.title('Total money you win is {}'.format(fb.loc[fb.index[-2], 'wealth']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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