twStock.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# twStock notebook\n",
    "\n",
    "------\n",
    "\n",
    "\n",
    "## 目的\n",
    "1. 擷取台股即時資料分析\n",
    "2. 分析個股定期不定額\n",
    "3. **注意台股TWSE 有request limit** _(每5秒鐘3個request,過的話會被封鎖ip至少20分鐘以上)_\n",
    "4. https://www.twse.com.tw/zh/\n",
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基本功能測試"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetch from 2020/5, time:4 fetch from 2020/6, time:7 fetch from 2020/7, time:3 fetch from 2020/6, time:3 fetch from 2020/7, time:5 Time used: 22sec for request time limit\n",
      "<class 'str'>\n",
      "2000/10/27\n"
     ]
    }
   ],
   "source": [
    "import twstock\n",
    "from twstock import Stock\n",
    "from twstock import BestFourPoint\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import time\n",
    "import datetime\n",
    "\n",
    "stockNumberID = '2412' #2412 中華電    2892 第一金 \n",
    "start = time.time()\n",
    "stock = Stock(stockNumberID)          # 擷取股價\n",
    "stock.fetch_from(2020, 6)\n",
    "done = time.time()\n",
    "elapsed = int(done - start)\n",
    "print('Time used: '+ str(elapsed) + 'sec for request time limit')\n",
    "info = twstock.codes[stockNumberID] \n",
    "print(type(info.start))\n",
    "print(info.start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 近五日的收盤價"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[117.0, 116.5, 114.0, 112.0, 112.0]\n"
     ]
    }
   ],
   "source": [
    "print(stock.price[-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 近五日的最高價"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[117.0, 117.0, 114.0, 114.0, 112.5]\n"
     ]
    }
   ],
   "source": [
    "print(stock.high[-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 近五日的最低價"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[115.0, 116.0, 110.5, 112.0, 111.5]\n"
     ]
    }
   ],
   "source": [
    "print(stock.low[-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定期不定額回歸線\n",
    "* 畫出多年 \n",
    "  * 股票價\n",
    "  * 成交量\n",
    "  * 高低峰值回歸線"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fetch from 2020/5, time:7 fetch from 2020/6, time:3 fetch from 2020/7, time:8 fetch from 2000/10, time:7 fetch from 2000/11, time:7 fetch from 2000/12, time:6 fetch from 2001/1, time:6 fetch from 2001/2, time:9 fetch from 2001/3, time:9 fetch from 2001/4, time:9 fetch from 2001/5, time:7 fetch from 2001/6, time:4 fetch from 2001/7, time:3 fetch from 2001/8, time:7 fetch from 2001/9, time:9 fetch from 2001/10, time:4 fetch from 2001/11, time:6 fetch from 2001/12, time:5 fetch from 2002/1, time:3 fetch from 2002/2, time:4 fetch from 2002/3, time:4 fetch from 2002/4, time:7 fetch from 2002/5, time:4 fetch from 2002/6, time:4 fetch from 2002/7, time:7 fetch from 2002/8, time:7 fetch from 2002/9, time:9 fetch from 2002/10, time:4 fetch from 2002/11, time:9 fetch from 2002/12, time:3 fetch from 2003/1, time:7 fetch from 2003/2, time:7 fetch from 2003/3, time:5 fetch from 2003/4, time:6 fetch from 2003/5, time:3 fetch from 2003/6, time:7 fetch from 2003/7, time:7 fetch from 2003/8, time:4 fetch from 2003/9, time:3 fetch from 2003/10, time:4 fetch from 2003/11, time:4 fetch from 2003/12, time:4 fetch from 2004/1, time:8 fetch from 2004/2, time:3 fetch from 2004/3, time:5 fetch from 2004/4, time:7 fetch from 2004/5, time:7 fetch from 2004/6, time:6 fetch from 2004/7, time:4 fetch from 2004/8, time:7 fetch from 2004/9, time:4 fetch from 2004/10, time:7 fetch from 2004/11, time:5 fetch from 2004/12, time:7 fetch from 2005/1, time:3 fetch from 2005/2, time:3 fetch from 2005/3, time:4 fetch from 2005/4, time:8 fetch from 2005/5, time:9 fetch from 2005/6, time:3 fetch from 2005/7, time:8 fetch from 2005/8, time:5 fetch from 2005/9, time:9 fetch from 2005/10, time:8 fetch from 2005/11, time:8 fetch from 2005/12, time:8 fetch from 2006/1, time:4 fetch from 2006/2, time:9 fetch from 2006/3, time:7 fetch from 2006/4, time:3 fetch from 2006/5, time:6 fetch from 2006/6, time:7 fetch from 2006/7, time:7 fetch from 2006/8, time:9 fetch from 2006/9, time:9 fetch from 2006/10, time:8 fetch from 2006/11, time:4 fetch from 2006/12, time:5 fetch from 2007/1, time:9 fetch from 2007/2, time:7 fetch from 2007/3, time:7 fetch from 2007/4, time:6 fetch from 2007/5, time:7 fetch from 2007/6, time:8 fetch from 2007/7, time:3 fetch from 2007/8, time:3 fetch from 2007/9, time:7 fetch from 2007/10, time:6 fetch from 2007/11, time:7 fetch from 2007/12, time:8 fetch from 2008/1, time:9 fetch from 2008/2, time:3 fetch from 2008/3, time:3 fetch from 2008/4, time:3 fetch from 2008/5, time:5 fetch from 2008/6, time:5 fetch from 2008/7, time:9 fetch from 2008/8, time:4 fetch from 2008/9, time:7 fetch from 2008/10, time:3 fetch from 2008/11, time:3 fetch from 2008/12, time:9 fetch from 2009/1, time:6 fetch from 2009/2, time:7 fetch from 2009/3, time:5 fetch from 2009/4, time:3 fetch from 2009/5, time:9 fetch from 2009/6, time:4 fetch from 2009/7, time:6 fetch from 2009/8, time:4 fetch from 2009/9, time:5 fetch from 2009/10, time:3 fetch from 2009/11, time:9 fetch from 2009/12, time:5 fetch from 2010/1, time:8 fetch from 2010/2, time:8 fetch from 2010/3, time:7 fetch from 2010/4, time:6 fetch from 2010/5, time:6 fetch from 2010/6, time:7 fetch from 2010/7, time:8 fetch from 2010/8, time:7 fetch from 2010/9, time:9 fetch from 2010/10, time:4 fetch from 2010/11, time:7 fetch from 2010/12, time:8 fetch from 2011/1, time:3 fetch from 2011/2, time:8 fetch from 2011/3, time:9 fetch from 2011/4, time:5 fetch from 2011/5, time:9 fetch from 2011/6, time:8 fetch from 2011/7, time:9 fetch from 2011/8, time:8 fetch from 2011/9, time:9 fetch from 2011/10, time:7 fetch from 2011/11, time:5 fetch from 2011/12, time:9 fetch from 2012/1, time:3 fetch from 2012/2, time:7 fetch from 2012/3, time:3 fetch from 2012/4, time:7 fetch from 2012/5, time:9 fetch from 2012/6, time:3 fetch from 2012/7, time:3 fetch from 2012/8, time:8 fetch from 2012/9, time:8 fetch from 2012/10, time:5 fetch from 2012/11, time:6 fetch from 2012/12, time:9 fetch from 2013/1, time:4 fetch from 2013/2, time:7 fetch from 2013/3, time:3 fetch from 2013/4, time:3 fetch from 2013/5, time:4 fetch from 2013/6, time:4 fetch from 2013/7, time:8 fetch from 2013/8, time:8 fetch from 2013/9, time:7 fetch from 2013/10, time:9 fetch from 2013/11, time:9 fetch from 2013/12, time:8 fetch from 2014/1, time:4 fetch from 2014/2, time:9 fetch from 2014/3, time:8 fetch from 2014/4, time:8 fetch from 2014/5, time:7 fetch from 2014/6, time:5 fetch from 2014/7, time:3 fetch from 2014/8, time:5 fetch from 2014/9, time:6 fetch from 2014/10, time:6 fetch from 2014/11, time:6 fetch from 2014/12, time:9 fetch from 2015/1, time:7 fetch from 2015/2, time:5 fetch from 2015/3, time:6 fetch from 2015/4, time:3 fetch from 2015/5, time:7 fetch from 2015/6, time:8 fetch from 2015/7, time:8 fetch from 2015/8, time:7 fetch from 2015/9, time:4 fetch from 2015/10, time:8 fetch from 2015/11, time:5 fetch from 2015/12, time:3 fetch from 2016/1, time:6 fetch from 2016/2, time:8 fetch from 2016/3, time:8 fetch from 2016/4, time:6 fetch from 2016/5, time:9 fetch from 2016/6, time:9 fetch from 2016/7, time:9 fetch from 2016/8, time:3 fetch from 2016/9, time:5 fetch from 2016/10, time:5 fetch from 2016/11, time:5 fetch from 2016/12, time:8 fetch from 2017/1, time:4 fetch from 2017/2, time:5 fetch from 2017/3, time:9 fetch from 2017/4, time:3 fetch from 2017/5, time:9 fetch from 2017/6, time:5 fetch from 2017/7, time:5 fetch from 2017/8, time:4 fetch from 2017/9, time:4 fetch from 2017/10, time:9 fetch from 2017/11, time:7 fetch from 2017/12, time:3 fetch from 2018/1, time:9 fetch from 2018/2, time:6 fetch from 2018/3, time:3 fetch from 2018/4, time:3 fetch from 2018/5, time:4 fetch from 2018/6, time:4 fetch from 2018/7, time:3 fetch from 2018/8, time:5 fetch from 2018/9, time:7 fetch from 2018/10, time:5 fetch from 2018/11, time:4 fetch from 2018/12, time:4 fetch from 2019/1, time:3 fetch from 2019/2, time:6 fetch from 2019/3, time:4 fetch from 2019/4, time:9 fetch from 2019/5, time:8 fetch from 2019/6, time:8 fetch from 2019/7, time:9 fetch from 2019/8, time:8 fetch from 2019/9, time:9 fetch from 2019/10, time:3 fetch from 2019/11, time:7 fetch from 2019/12, time:7 fetch from 2020/1, time:4 fetch from 2020/2, time:8 fetch from 2020/3, time:9 fetch from 2020/4, time:4 fetch from 2020/5, time:5 fetch from 2020/6, time:4 fetch from 2020/7, time:9 "
     ]
    }
   ],
   "source": [
    "import twstock\n",
    "import pandas as pd\n",
    "\n",
    "# 這是抓取歷史資料\n",
    "stock = twstock.Stock(stockNumberID)\n",
    "info = twstock.codes[stockNumberID] \n",
    "stock_from = stock.fetch_from(2000,10)     # 獲取 2010 年 01 月至今日之股票資料\n",
    "stock_from_pd = pd.DataFrame(stock_from)\n",
    "stock_from_pd = stock_from_pd.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "124.0\n",
      "58.1\n",
      "2560\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>capacity</th>\n",
       "      <th>turnover</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>change</th>\n",
       "      <th>transaction</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>14947670</td>\n",
       "      <td>885880317</td>\n",
       "      <td>59.5</td>\n",
       "      <td>59.6</td>\n",
       "      <td>59.1</td>\n",
       "      <td>59.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>5186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>16020545</td>\n",
       "      <td>950201764</td>\n",
       "      <td>59.3</td>\n",
       "      <td>59.5</td>\n",
       "      <td>59.2</td>\n",
       "      <td>59.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>21835678</td>\n",
       "      <td>1297482332</td>\n",
       "      <td>59.3</td>\n",
       "      <td>59.7</td>\n",
       "      <td>59.2</td>\n",
       "      <td>59.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>6301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>58817538</td>\n",
       "      <td>3449001637</td>\n",
       "      <td>59.2</td>\n",
       "      <td>59.3</td>\n",
       "      <td>58.2</td>\n",
       "      <td>58.4</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>19530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>44405291</td>\n",
       "      <td>2586840886</td>\n",
       "      <td>58.2</td>\n",
       "      <td>58.8</td>\n",
       "      <td>57.9</td>\n",
       "      <td>58.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>10103</td>\n",
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      "text/plain": [
       "            capacity    turnover  open  high   low  close  change  transaction\n",
       "date                                                                          \n",
       "2010-01-04  14947670   885880317  59.5  59.6  59.1   59.3    -0.2         5186\n",
       "2010-01-05  16020545   950201764  59.3  59.5  59.2   59.4     0.1         4787\n",
       "2010-01-06  21835678  1297482332  59.3  59.7  59.2   59.6     0.2         6301\n",
       "2010-01-07  58817538  3449001637  59.2  59.3  58.2   58.4    -1.2        19530\n",
       "2010-01-08  44405291  2586840886  58.2  58.8  57.9   58.1    -0.3        10103"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(stock_from_pd))\n",
    "print(stock_from_pd.loc[stock_from_pd['close'].idxmax()].close)\n",
    "print(stock_from_pd.loc[stock_from_pd['close'].idxmin()].close)\n",
    "print(len(stock_from_pd))\n",
    "stock_from_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joyts\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site-packages\\matplotlib\\figure.py:445: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  % get_backend())\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.signal import argrelextrema\n",
    "from sklearn import linear_model\n",
    "import datetime as dt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#設定中文字型\n",
    "font = {'family' : 'DFKai-SB',\n",
    "'weight' : 'bold',\n",
    "'size'  : '14'}\n",
    "plt.rc('font', **font) # pass in the font dict as kwargs\n",
    "plt.rc('axes',unicode_minus=False)\n",
    "\n",
    "#畫圖\n",
    "fig, (ax1, ax2) = plt.subplots(2, gridspec_kw={'height_ratios': [3, 1]},figsize=(10, 10))\n",
    "stockTitle = info.name + '股份 開盤/收盤 價曲線'\n",
    "fig.suptitle(stockTitle)\n",
    "\n",
    "#setting index as date\n",
    "# print(stock_from_pd.info(verbose=True))\n",
    "# print(stock_from_pd.head())\n",
    "# df = stock_from_pd.reset_index()\n",
    "# print(df.head())\n",
    "\n",
    "# Find local peaks\n",
    "n=5 # number of points to be checked before and after \n",
    "stock_from_pd['min'] = stock_from_pd.iloc[argrelextrema(stock_from_pd.close.values, np.less_equal, order=n)[0]]['close']\n",
    "stock_from_pd['max'] = stock_from_pd.iloc[argrelextrema(stock_from_pd.close.values, np.greater_equal, order=n)[0]]['close']\n",
    "\n",
    "# Plot results\n",
    "ax1.scatter(stock_from_pd.index, stock_from_pd['max'], label=\"高峰值\", c='tab:red')\n",
    "ax1.scatter(stock_from_pd.index, stock_from_pd['min'], label=\"低峰值\", c='tab:green')\n",
    "\n",
    "#Linear Regression\n",
    "regr=linear_model.LinearRegression()\n",
    "highData = stock_from_pd['max'].dropna()\n",
    "X = highData.index.factorize()[0].reshape(-1,1)\n",
    "y = highData.values\n",
    "regr.fit(X,y)\n",
    "arrPredict = regr.predict(X)\n",
    "dataframePredict = pd.DataFrame({'Predict':arrPredict})\n",
    "dataframehighData = pd.DataFrame(highData)\n",
    "dataframehighData['Predict'] = dataframePredict['Predict'].values\n",
    "ax1.plot(dataframehighData.Predict, '-' , label=\"高峰回歸線\", c='tab:red')\n",
    "\n",
    "lowData = stock_from_pd['min'].dropna()\n",
    "_X = lowData.index.factorize()[0].reshape(-1,1)\n",
    "_y = lowData.values\n",
    "regr.fit(_X,_y)\n",
    "_arrPredict = regr.predict(_X)\n",
    "_dataframePredict = pd.DataFrame({'Predict':_arrPredict})\n",
    "dataframelowData = pd.DataFrame(lowData)\n",
    "dataframelowData['Predict'] = _dataframePredict['Predict'].values\n",
    "ax1.plot(dataframelowData.Predict, '-' , label=\"低峰回歸線\", c='tab:green')\n",
    "\n",
    "\n",
    "ax1.plot(stock_from_pd.open, '-' , label=\"開盤價\", c='tab:olive')\n",
    "ax1.plot(stock_from_pd.close, '-' , label=\"收盤價\", c='tab:cyan')\n",
    "ax1.set_ylabel('價位')\n",
    "ax1.grid(True, axis='y')\n",
    "ax1.legend()\n",
    "ax2.set_xlabel('日期')\n",
    "ax2.set_ylabel('張數')\n",
    "ax2.grid(True, axis='y')\n",
    "ax2.plot(stock_from_pd.capacity, '-' , label=\"交易量\", c='m')\n",
    "ax2.legend()\n",
    "\n",
    "fig.savefig('day200706.png')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  瑞耘\n",
    "* 四大買賣點分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import twstock\n",
    "from twstock import Stock\n",
    "from twstock import BestFourPoint\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "print(twstock.codes['6532'].name)\n",
    "print(twstock.codes['6532'].start)\n",
    "print(twstock.codes['6532'])   \n",
    "\n",
    "stock = Stock('6532')               # 擷取股價\n",
    "bfp = BestFourPoint(stock)\n",
    "print('\\n判斷是否為四大 買點')\n",
    "print(bfp.best_four_point_to_buy())    # 判斷是否為四大買點\n",
    "print('\\n判斷是否為四大 賣點')\n",
    "print(bfp.best_four_point_to_sell())   # 判斷是否為四大賣點\n",
    "print('\\n綜合判斷')\n",
    "print(bfp.best_four_point())           # 綜合判斷\n",
    "\n",
    "ma_p = stock.moving_average(stock.price, 5)       # 計算五日均價\n",
    "ma_c = stock.moving_average(stock.capacity, 5)    # 計算五日均量\n",
    "ma_p_cont = stock.continuous(ma_p)                # 計算五日均價持續天數\n",
    "ma_br = stock.ma_bias_ratio(5, 10)                # 計算五日、十日乖離值\n",
    "d = {'ma_p': ma_p, 'ma_c': ma_c}\n",
    "df = pd.DataFrame(data=d)\n",
    "print('\\n計算五日均價持續天數:')\n",
    "print(ma_p_cont)\n",
    "print('\\n計算 ma_p(五日均價) ma_c(五日均量):')\n",
    "print(df)\n",
    "\n",
    "twstock.realtime.get('6532')    # 擷取當前股票資訊\n",
    "twstock.realtime.get(['6532'])  # 擷取當前三檔資訊"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本操作\n",
    "* 匯入twstock library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from twstock import Stock\n",
    "\n",
    "stock = Stock('2892')                             # 擷取第一金股價\n",
    "ma_p = stock.moving_average(stock.price, 5)       # 計算五日均價\n",
    "ma_c = stock.moving_average(stock.capacity, 5)    # 計算五日均量\n",
    "ma_p_cont = stock.continuous(ma_p)                # 計算五日均價持續天數\n",
    "ma_br = stock.ma_bias_ratio(5, 10)                # 計算五日、十日乖離值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_p_cont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "d = {'ma_p': ma_p, 'ma_c': ma_c}\n",
    "df = pd.DataFrame(data=d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "d = {'ma_br': ma_br}\n",
    "df = pd.DataFrame(data=d)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 擷取自 2015 年 1 月至今之資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock.fetch_from(2015, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 基本資料之使用:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock.capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock.data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "\n",
    "## 附件：Juypter Notebook 基本操作 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jupyter Notebook 基本操作介紹影片:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%HTML\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/HW29067qVWk\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "### Juypter % 及 %% 外掛程式運用:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 圖表基本操作 (matplot library):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = np.pi * (15 * np.random.rand(N))**2\n",
    "\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 計算時間:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit\n",
    "square_evens = [n*n for n in range(1000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 資料呈現(panda):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 解決ImportError: cannot import name 'nosetester'問題:\n",
    "  * numpy 1.11.1 version\n",
    "    * pip3 uninstall numpy\n",
    "    * pip3 install numpy==1.11.1\n",
    "  * pandas 0.19.2 version \n",
    "    * pip3 uninstall pandas\n",
    "    * pip3 install pandas==0.19.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(10,5))\n",
    "df.head()\n"
   ]
  }
 ],
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