pandas_in_finance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讀取Google Trend 資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>djia</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-01-14</th>\n",
       "      <td>10485.18</td>\n",
       "      <td>0.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-22</th>\n",
       "      <td>10528.66</td>\n",
       "      <td>0.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-28</th>\n",
       "      <td>10702.51</td>\n",
       "      <td>0.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-04</th>\n",
       "      <td>10499.18</td>\n",
       "      <td>0.213333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-11</th>\n",
       "      <td>10579.03</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                djia      debt\n",
       "Date                          \n",
       "2004-01-14  10485.18  0.210000\n",
       "2004-01-22  10528.66  0.210000\n",
       "2004-01-28  10702.51  0.210000\n",
       "2004-02-04  10499.18  0.213333\n",
       "2004-02-11  10579.03  0.200000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('data/GoogleTrendsData.csv', index_col='Date', parse_dates=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 繪製GoogleTrend 與股價的關係"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000000003B9D518>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000D351390>], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OHj1K06ZNmTJlCt988w1t2rQB4LrrrqNFixa0aNGi7KP5MmCycxoM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\nj6UcY3f8br7e8zV3ht1pa/eu560tGGezHHKDZeR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8osGSy5FMni+PuZVz7cSu5aDFl+jz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\nFkuMYQ2/xWKxxBjW8FssFkuM8f8Kil5MevZzEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4b4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline \n",
    "data.plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 算出Search Word 三週的移動平均"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>djia</th>\n",
       "      <th>debt</th>\n",
       "      <th>debt_mavg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-01-14</th>\n",
       "      <td>10485.18</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-22</th>\n",
       "      <td>10528.66</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-28</th>\n",
       "      <td>10702.51</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-04</th>\n",
       "      <td>10499.18</td>\n",
       "      <td>0.213333</td>\n",
       "      <td>0.211111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-11</th>\n",
       "      <td>10579.03</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.207778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                djia      debt  debt_mavg\n",
       "Date                                     \n",
       "2004-01-14  10485.18  0.210000        NaN\n",
       "2004-01-22  10528.66  0.210000        NaN\n",
       "2004-01-28  10702.51  0.210000   0.210000\n",
       "2004-02-04  10499.18  0.213333   0.211111\n",
       "2004-02-11  10579.03  0.200000   0.207778"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['debt_mavg'] = pd.rolling_mean(data.debt, 3)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 將三週平均資料平移一天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>djia</th>\n",
       "      <th>debt</th>\n",
       "      <th>debt_mavg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-01-14</th>\n",
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       "    <tr>\n",
       "      <th>2004-01-22</th>\n",
       "      <td>10528.66</td>\n",
       "      <td>0.210000</td>\n",
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       "    <tr>\n",
       "      <th>2004-01-28</th>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-04</th>\n",
       "      <td>10499.18</td>\n",
       "      <td>0.213333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-11</th>\n",
       "      <td>10579.03</td>\n",
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       "      <td>0.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-19</th>\n",
       "      <td>10714.88</td>\n",
       "      <td>0.203333</td>\n",
       "      <td>0.211111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-25</th>\n",
       "      <td>10609.62</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.207778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-03</th>\n",
       "      <td>10678.14</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.205555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-10</th>\n",
       "      <td>10529.48</td>\n",
       "      <td>0.196667</td>\n",
       "      <td>0.201111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-17</th>\n",
       "      <td>10102.89</td>\n",
       "      <td>0.196667</td>\n",
       "      <td>0.201111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                djia      debt  debt_mavg\n",
       "Date                                     \n",
       "2004-01-14  10485.18  0.210000        NaN\n",
       "2004-01-22  10528.66  0.210000        NaN\n",
       "2004-01-28  10702.51  0.210000        NaN\n",
       "2004-02-04  10499.18  0.213333        NaN\n",
       "2004-02-11  10579.03  0.200000   0.210000\n",
       "2004-02-19  10714.88  0.203333   0.211111\n",
       "2004-02-25  10609.62  0.200000   0.207778\n",
       "2004-03-03  10678.14  0.200000   0.205555\n",
       "2004-03-10  10529.48  0.196667   0.201111\n",
       "2004-03-17  10102.89  0.196667   0.201111"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['debt_mavg'] = data.debt_mavg.shift(1)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 設定買賣策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
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     "data": {
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       "      <th>order</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th>2004-02-19</th>\n",
       "      <td>10714.88</td>\n",
       "      <td>0.203333</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2004-02-25</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2004-03-03</th>\n",
       "      <td>10678.14</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.205555</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-10</th>\n",
       "      <td>10529.48</td>\n",
       "      <td>0.196667</td>\n",
       "      <td>0.201111</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2004-03-17</th>\n",
       "      <td>10102.89</td>\n",
       "      <td>0.196667</td>\n",
       "      <td>0.201111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                djia      debt  debt_mavg  order\n",
       "Date                                            \n",
       "2004-01-14  10485.18  0.210000        NaN      0\n",
       "2004-01-22  10528.66  0.210000        NaN      0\n",
       "2004-01-28  10702.51  0.210000        NaN      0\n",
       "2004-02-04  10499.18  0.213333        NaN      0\n",
       "2004-02-11  10579.03  0.200000   0.210000      1\n",
       "2004-02-19  10714.88  0.203333   0.211111      1\n",
       "2004-02-25  10609.62  0.200000   0.207778      1\n",
       "2004-03-03  10678.14  0.200000   0.205555      1\n",
       "2004-03-10  10529.48  0.196667   0.201111      1\n",
       "2004-03-17  10102.89  0.196667   0.201111      1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['order'] = 0\n",
    "# 黃金交叉做多\n",
    "data['order'][data.debt > data.debt_mavg] = -1 \n",
    "# 死亡交叉做空\n",
    "data['order'][data.debt < data.debt_mavg] = 1\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 計算道瓊報酬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>ret_djia</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <th>2004-01-28</th>\n",
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       "      <td>10499.18</td>\n",
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       "      <td>0</td>\n",
       "      <td>-0.018998</td>\n",
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       "    <tr>\n",
       "      <th>2004-02-11</th>\n",
       "      <td>10579.03</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                djia      debt  debt_mavg  order  ret_djia\n",
       "Date                                                      \n",
       "2004-01-14  10485.18  0.210000        NaN      0       NaN\n",
       "2004-01-22  10528.66  0.210000        NaN      0  0.004147\n",
       "2004-01-28  10702.51  0.210000        NaN      0  0.016512\n",
       "2004-02-04  10499.18  0.213333        NaN      0 -0.018998\n",
       "2004-02-11  10579.03  0.200000       0.21      1  0.007605"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ret_djia'] = data.djia.pct_change()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 根據買賣日調整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['ret_djia'] = data['ret_djia'].shift(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 計算根據Search Term 買賣的報酬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.012841</td>\n",
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       "      <td>1</td>\n",
       "      <td>-0.009824</td>\n",
       "      <td>-0.009824</td>\n",
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       "    <tr>\n",
       "      <th>2004-02-25</th>\n",
       "      <td>10609.62</td>\n",
       "      <td>0.200000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.006458</td>\n",
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       "      <th>2004-03-03</th>\n",
       "      <td>10678.14</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.205555</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.013922</td>\n",
       "      <td>-0.013922</td>\n",
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       "    <tr>\n",
       "      <th>2004-03-10</th>\n",
       "      <td>10529.48</td>\n",
       "      <td>0.196667</td>\n",
       "      <td>0.201111</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.040514</td>\n",
       "      <td>-0.040514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-17</th>\n",
       "      <td>10102.89</td>\n",
       "      <td>0.196667</td>\n",
       "      <td>0.201111</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.003775</td>\n",
       "      <td>-0.003775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                djia      debt  debt_mavg  order  ret_djia  ret_google\n",
       "Date                                                                  \n",
       "2004-01-14  10485.18  0.210000        NaN      0  0.004147    0.000000\n",
       "2004-01-22  10528.66  0.210000        NaN      0  0.016512    0.000000\n",
       "2004-01-28  10702.51  0.210000        NaN      0 -0.018998   -0.000000\n",
       "2004-02-04  10499.18  0.213333        NaN      0  0.007605    0.000000\n",
       "2004-02-11  10579.03  0.200000   0.210000      1  0.012841    0.012841\n",
       "2004-02-19  10714.88  0.203333   0.211111      1 -0.009824   -0.009824\n",
       "2004-02-25  10609.62  0.200000   0.207778      1  0.006458    0.006458\n",
       "2004-03-03  10678.14  0.200000   0.205555      1 -0.013922   -0.013922\n",
       "2004-03-10  10529.48  0.196667   0.201111      1 -0.040514   -0.040514\n",
       "2004-03-17  10102.89  0.196667   0.201111      1 -0.003775   -0.003775"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ret_google'] = data.order * data.ret_djia\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 觀察資金變化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mYNDGQawzuw5J3bujQgXdE6ftog4s90NDVq0ew86dyRkzyLZtyfBwcvZs8uzZ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\n0ndWWPffOtb+uTZLjitJl4k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x4WEAHUkOJ3kRQDsANt16NX7vCd9pIQBnSN4CEAbgULLt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\nSfIWQgg7JMlbCCHskCRvIYSwQ/8Pwe1nvVTtKvUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc50a208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(1 + data.ret_google).cumprod().plot();\n",
    "(1 + data.ret_djia).cumprod().plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}