{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chap 2. Deciphering the Markets with Technical Analysis\n",
    "\n",
    "### 1.1 Support and Resistance Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas_datareader import data\n",
    "\n",
    "start_date = '2014-01-01'\n",
    "end_date = '2018-01-01'\n",
    "SRC_DATA_FILENAME = 'goog_data.pkl'\n",
    "\n",
    "try:\n",
    "    goog_data2 = pd.read_pickle(SRC_DATA_FILENAME)\n",
    "except FileNotFoundError:\n",
    "    goog_data2 = data.DataReader('GOOG', 'yahoo', start_date, end_date)\n",
    "    goog_data2.to_pickle(SRC_DATA_FILENAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把GOOGLE数据下载下来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                   High          Low         Open        Close      Volume  \\\nDate                                                                         \n2015-07-17   674.468018   645.000000   649.000000   672.929993  11164900.0   \n2015-07-20   668.880005   653.010010   659.239990   663.020020   5860900.0   \n2015-07-21   673.000000   654.299988   655.210022   662.299988   3377200.0   \n2015-07-22   678.640015   659.000000   660.890015   662.099976   3929300.0   \n2015-07-23   663.630005   641.000000   661.270020   644.280029   3029100.0   \n...                 ...          ...          ...          ...         ...   \n2017-12-22  1064.199951  1059.439941  1061.109985  1060.119995    755100.0   \n2017-12-26  1060.119995  1050.199951  1058.069946  1056.739990    760600.0   \n2017-12-27  1058.369995  1048.050049  1057.390015  1049.369995   1271900.0   \n2017-12-28  1054.750000  1044.770020  1051.599976  1048.140015    837100.0   \n2017-12-29  1049.699951  1044.900024  1046.719971  1046.400024    887500.0   \n\n              Adj Close  \nDate                     \n2015-07-17   672.929993  \n2015-07-20   663.020020  \n2015-07-21   662.299988  \n2015-07-22   662.099976  \n2015-07-23   644.280029  \n...                 ...  \n2017-12-22  1060.119995  \n2017-12-26  1056.739990  \n2017-12-27  1049.369995  \n2017-12-28  1048.140015  \n2017-12-29  1046.400024  \n\n[620 rows x 6 columns]",
      "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>High</th>\n      <th>Low</th>\n      <th>Open</th>\n      <th>Close</th>\n      <th>Volume</th>\n      <th>Adj Close</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>2015-07-17</th>\n      <td>674.468018</td>\n      <td>645.000000</td>\n      <td>649.000000</td>\n      <td>672.929993</td>\n      <td>11164900.0</td>\n      <td>672.929993</td>\n    </tr>\n    <tr>\n      <th>2015-07-20</th>\n      <td>668.880005</td>\n      <td>653.010010</td>\n      <td>659.239990</td>\n      <td>663.020020</td>\n      <td>5860900.0</td>\n      <td>663.020020</td>\n    </tr>\n    <tr>\n      <th>2015-07-21</th>\n      <td>673.000000</td>\n      <td>654.299988</td>\n      <td>655.210022</td>\n      <td>662.299988</td>\n      <td>3377200.0</td>\n      <td>662.299988</td>\n    </tr>\n    <tr>\n      <th>2015-07-22</th>\n      <td>678.640015</td>\n      <td>659.000000</td>\n      <td>660.890015</td>\n      <td>662.099976</td>\n      <td>3929300.0</td>\n      <td>662.099976</td>\n    </tr>\n    <tr>\n      <th>2015-07-23</th>\n      <td>663.630005</td>\n      <td>641.000000</td>\n      <td>661.270020</td>\n      <td>644.280029</td>\n      <td>3029100.0</td>\n      <td>644.280029</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2017-12-22</th>\n      <td>1064.199951</td>\n      <td>1059.439941</td>\n      <td>1061.109985</td>\n      <td>1060.119995</td>\n      <td>755100.0</td>\n      <td>1060.119995</td>\n    </tr>\n    <tr>\n      <th>2017-12-26</th>\n      <td>1060.119995</td>\n      <td>1050.199951</td>\n      <td>1058.069946</td>\n      <td>1056.739990</td>\n      <td>760600.0</td>\n      <td>1056.739990</td>\n    </tr>\n    <tr>\n      <th>2017-12-27</th>\n      <td>1058.369995</td>\n      <td>1048.050049</td>\n      <td>1057.390015</td>\n      <td>1049.369995</td>\n      <td>1271900.0</td>\n      <td>1049.369995</td>\n    </tr>\n    <tr>\n      <th>2017-12-28</th>\n      <td>1054.750000</td>\n      <td>1044.770020</td>\n      <td>1051.599976</td>\n      <td>1048.140015</td>\n      <td>837100.0</td>\n      <td>1048.140015</td>\n    </tr>\n    <tr>\n      <th>2017-12-29</th>\n      <td>1049.699951</td>\n      <td>1044.900024</td>\n      <td>1046.719971</td>\n      <td>1046.400024</td>\n      <td>887500.0</td>\n      <td>1046.400024</td>\n    </tr>\n  </tbody>\n</table>\n<p>620 rows × 6 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "goog_data = goog_data2.tail(620)\n",
    "lows = goog_data['Low']\n",
    "highs = goog_data['High']\n",
    "goog_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111, ylabel='Google price in $')\n",
    "highs.plot(ax=ax1, color='c', lw=2.)\n",
    "lows.plot(ax=ax1, color='y', lw=2.)\n",
    "\n",
    "# Return the first 200 rows using pandas.DataFrame.head(200)\n",
    "plt.hlines(highs.head(200).max(), lows.index.values[0], lows.index.values[-1], linewidth=2, color='g') \n",
    "plt.hlines(lows.head(200).min(), lows.index.values[0], lows.index.values[-1], linewidth=2, color='r')\n",
    "plt.axvline(linewidth=2, color='b', x=lows.index.values[200], linestyle=':')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 获取2015-07-01 至2018-01-01的GOOGLE的股票价格。\n",
    "* 画出了 support line (红线), resistence line （绿线)\n",
    "* 蓝线表示数据积累点 (200天)\n",
    "\n",
    "基于这个简单的技术分析，我们可以定出策略，在200天的数据积累后（蓝色虚线）：\n",
    "\n",
    "* 股价高至 resistence line 后，挂空头仓位 (short the stock).\n",
    "* 股价低至 support line 后，挂多头仓位 (long the stock).\n",
    "\n",
    "实际效果：\n",
    "\n",
    "* 2016-08 后，GOOG 股价触发 resistance line, 算法开始持续做空 GOOGLE, 损失惨重。\n",
    "\n",
    "分析： \n",
    "\n",
    "* 即便 support & resistance indicator 有经济学逻辑，实际中需要对其进行矫正。例如：移动 support/resistance line.\n",
    "\n",
    "改进（增加特性）:\n",
    "\n",
    "* 使用 rolling window 滚动窗口\n",
    "* 数股价触及支撑线或阻力线的数量\n",
    "* 加入 tolerance margin， 从而把支撑/阻力线的空间缩小（如下图）\n",
    "\n",
    "![](../../img/2_1.jpg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "方案选择（增加两种参数）：\n",
    "\n",
    "* 价格触及 supporting/resistance line 的最少次数\n",
    "* 定一个 tolorance margine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "File data found...reading GOOG data\n"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas_datareader import data\n",
    "\n",
    "start_date = '2014-01-01'\n",
    "end_date = '2018-01-01'\n",
    "SRC_DATA_FILENAME = 'goog_data.pkl'\n",
    "\n",
    "try:\n",
    "    goog_data = pd.read_pickle(SRC_DATA_FILENAME)\n",
    "    print('File data found...reading GOOG data')\n",
    "except FileNotFoundError:\n",
    "    print('File not found...downloading the GOOG data')\n",
    "    goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)\n",
    "    goog_data.to_pickle(SRC_DATA_FILENAME)\n",
    "\n",
    "goog_data_signal = pd.DataFrame(index=goog_data.index)\n",
    "goog_data_signal['price'] = goog_data['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def trading_support_resistance(data, bin_width=20):\n",
    "\n",
    "    data['sup_tolerance'] = pd.Series(np.zeros(len(data))) # tolerance of support line\n",
    "    data['res_tolerance'] = pd.Series(np.zeros(len(data))) # tolerance of resistance line\n",
    "\n",
    "    data['sup_count'] = pd.Series(np.zeros(len(data))) # number of hitting support line\n",
    "    data['res_count'] = pd.Series(np.zeros(len(data)))\n",
    "\n",
    "    data['sup'] = pd.Series(np.zeros(len(data))) # support line value (constant)\n",
    "    data['res'] = pd.Series(np.zeros(len(data)))\n",
    "\n",
    "    data['position'] = pd.Series(np.zeros(len(data)))\n",
    "    data['signal'] = pd.Series(np.zeros(len(data)))\n",
    "\n",
    "    in_support = 0\n",
    "    in_resistance = 0\n",
    "\n",
    "    for x in range((bin_width-1) + bin_width, len(data)):\n",
    "        data_section = data[x-bin_width : x+1] # get data within rolling window\n",
    "        \n",
    "        support_level = min(data_section['price'])\n",
    "        resistance_level = max(data_section['price'])\n",
    "        range_level = resistance_level - support_level\n",
    "\n",
    "        data['res'][x] = resistance_level\n",
    "        data['sup'][x] = support_level\n",
    "\n",
    "        data['sup_tolerance'][x] = support_level + 0.2*range_level # 20% tolorance\n",
    "        data['res_tolerance'][x] = resistance_level - 0.2*range_level\n",
    "\n",
    "        if (data['price'][x] >= data['res_tolerance'][x]) and (data['price'][x] <= data['res'][x]):\n",
    "            # if price is within resistance tolerance region\n",
    "            in_resistance += 1\n",
    "            data['res_count'][x] = in_resistance\n",
    "        elif (data['price'][x] <= data['sup_tolerance'][x]) and (data['price'][x] >= data['sup'][x]):\n",
    "            # price is within support tolerance region\n",
    "            in_support += 1\n",
    "            data['sup_count'][x] = in_support\n",
    "        else:\n",
    "            # if not within any region, clear count\n",
    "            in_support = 0\n",
    "            in_resistance = 0\n",
    "\n",
    "        if in_resistance > 2:\n",
    "            # If enter resistance region twice\n",
    "            data['signal'][x] = 1\n",
    "        elif in_support > 2:\n",
    "            data['signal'][x] = 0\n",
    "        else:\n",
    "            data['signal'][x] = data['signal'][x-1]\n",
    "    \n",
    "    data['position'] = data['signal'].diff()\n",
    "\n",
    "trading_support_resistance(goog_data_signal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述代码完成了\n",
    "\n",
    "* 在规定的滚动时间窗口内（默认20天）基于过去的数据计算支撑线、阻力线。\n",
    "* 基于支撑线、阻力线，计算了20%的区域。\n",
    "* 用diff()计算了下单的时间\n",
    "* 当价格低于支撑线，到达支撑区间后（或者高于阻力线，到达阻力区间），我们会挂多头仓位（空头仓位）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                  price  sup_tolerance  res_tolerance  sup_count  res_count  \\\nDate                                                                          \n2013-12-31   558.262512            NaN            NaN        NaN        NaN   \n2014-01-02   554.481689            NaN            NaN        NaN        NaN   \n2014-01-03   550.436829            NaN            NaN        NaN        NaN   \n2014-01-06   556.573853            NaN            NaN        NaN        NaN   \n2014-01-07   567.303589            NaN            NaN        NaN        NaN   \n...                 ...            ...            ...        ...        ...   \n2017-12-22  1060.119995    1014.371997     1061.44801        NaN        NaN   \n2017-12-26  1056.739990    1014.371997     1061.44801        NaN        NaN   \n2017-12-27  1049.369995    1014.371997     1061.44801        NaN        NaN   \n2017-12-28  1048.140015    1014.371997     1061.44801        NaN        NaN   \n2017-12-29  1046.400024    1014.371997     1061.44801        NaN        NaN   \n\n                   sup          res  position  signal  \nDate                                                   \n2013-12-31         NaN          NaN       NaN     NaN  \n2014-01-02         NaN          NaN       NaN     NaN  \n2014-01-03         NaN          NaN       NaN     NaN  \n2014-01-06         NaN          NaN       NaN     NaN  \n2014-01-07         NaN          NaN       NaN     NaN  \n...                ...          ...       ...     ...  \n2017-12-22  998.679993  1077.140015       0.0     1.0  \n2017-12-26  998.679993  1077.140015       0.0     1.0  \n2017-12-27  998.679993  1077.140015       0.0     1.0  \n2017-12-28  998.679993  1077.140015       0.0     1.0  \n2017-12-29  998.679993  1077.140015       0.0     1.0  \n\n[1008 rows x 9 columns]",
      "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>price</th>\n      <th>sup_tolerance</th>\n      <th>res_tolerance</th>\n      <th>sup_count</th>\n      <th>res_count</th>\n      <th>sup</th>\n      <th>res</th>\n      <th>position</th>\n      <th>signal</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2013-12-31</th>\n      <td>558.262512</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-02</th>\n      <td>554.481689</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>550.436829</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>556.573853</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>567.303589</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2017-12-22</th>\n      <td>1060.119995</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-26</th>\n      <td>1056.739990</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-27</th>\n      <td>1049.369995</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-28</th>\n      <td>1048.140015</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2017-12-29</th>\n      <td>1046.400024</td>\n      <td>1014.371997</td>\n      <td>1061.44801</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>998.679993</td>\n      <td>1077.140015</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1008 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "goog_data_signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                  price  sup_tolerance  res_tolerance  sup_count  res_count  \\\nDate                                                                          \n2013-12-31   558.262512            NaN            NaN        NaN        NaN   \n2014-01-02   554.481689            NaN            NaN        NaN        NaN   \n2014-01-03   550.436829            NaN            NaN        NaN        NaN   \n2014-01-06   556.573853            NaN            NaN        NaN        NaN   \n2014-01-07   567.303589            NaN            NaN        NaN        NaN   \n...                 ...            ...            ...        ...        ...   \n2017-12-22  1060.119995    1014.371997     1061.44801        NaN        NaN   \n2017-12-26  1056.739990    1014.371997     1061.44801        NaN        NaN   \n2017-12-27  1049.369995    1014.371997     1061.44801        NaN        NaN   \n2017-12-28  1048.140015    1014.371997     1061.44801        NaN        NaN   \n2017-12-29  1046.400024    1014.371997     1061.44801        NaN        NaN   \n\n                   sup          res  position  signal  \nDate                                                   \n2013-12-31         NaN          NaN       NaN     NaN  \n2014-01-02         NaN          NaN       NaN     NaN  \n2014-01-03         NaN          NaN       NaN     NaN  \n2014-01-06         NaN          NaN       NaN     NaN  \n2014-01-07         NaN          NaN       NaN     NaN  \n...                ...          ...       ...     ...  \n2017-12-22  998.679993  1077.140015       0.0     1.0  \n2017-12-26  998.679993  1077.140015       0.0     1.0  \n2017-12-27  998.679993  1077.140015       0.0     1.0  \n2017-12-28  998.679993  1077.140015       0.0     1.0  \n2017-12-29  998.679993  1077.140015       0.0     1.0  \n\n[1008 rows x 9 columns]",
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     },
     "metadata": {},
     "execution_count": 7
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   "source": [
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109.434088 168.03436 \nL 109.642699 168.676754 \nL 109.85131 171.382698 \nL 110.059921 164.913295 \nL 110.685755 166.613224 \nL 110.894366 165.016315 \nL 111.102977 164.640585 \nL 111.311588 165.437519 \nL 111.520199 166.74957 \nL 112.146033 166.270801 \nL 112.354644 167.782865 \nL 112.563255 167.234403 \nL 112.771866 172.028116 \nL 112.980477 173.703806 \nL 113.606311 171.558434 \nL 113.814922 174.006819 \nL 114.023533 173.611672 \nL 114.232144 174.524978 \nL 114.440755 172.885656 \nL 115.066588 173.155343 \nL 115.2752 174.715866 \nL 115.483811 171.064533 \nL 115.692422 171.103923 \nL 115.901033 171.458438 \nL 116.526866 168.82827 \nL 116.735477 167.404093 \nL 117.1527 168.461609 \nL 117.987144 169.422169 \nL 118.195755 170.131235 \nL 118.404367 172.209935 \nL 118.612978 172.755356 \nL 118.821589 172.028116 \nL 119.864644 170.106996 \nL 120.281867 167.640436 \nL 120.9077 166.537466 \nL 121.116311 169.176739 \nL 121.324922 168.543431 \nL 121.533533 169.073701 \nL 121.742145 170.809997 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182.360975 \nL 136.970757 181.824622 \nL 137.179368 181.276179 \nL 137.387979 181.491306 \nL 137.805201 181.048905 \nL 138.431035 183.482138 \nL 138.639646 183.497289 \nL 138.848257 184.233617 \nL 139.056868 182.41854 \nL 139.265479 186.069892 \nL 139.891313 185.54871 \nL 140.099924 183.612419 \nL 140.308535 185.827484 \nL 140.517146 185.136593 \nL 140.725757 188.069793 \nL 141.351591 189.542447 \nL 141.560202 195.120984 \nL 141.977424 190.360597 \nL 142.186035 188.769771 \nL 142.811869 186.188063 \nL 143.02048 184.454808 \nL 143.229091 185.006293 \nL 143.646313 183.412444 \nL 144.272146 184.533589 \nL 144.480758 184.506327 \nL 144.689369 185.724446 \nL 145.106591 186.206238 \nL 145.732424 189.52125 \nL 145.941035 193.13017 \nL 146.149647 193.390761 \nL 146.358258 192.911992 \nL 146.566869 194.884632 \nL 147.192702 195.981547 \nL 148.027147 191.275711 \nL 148.861591 191.633267 \nL 149.487425 181.618583 \nL 150.113258 183.05487 \nL 150.53048 190.693914 \nL 150.739091 190.493929 \nL 150.947703 183.263951 \nL 151.573536 185.09418 \nL 151.782147 184.863883 \nL 151.990758 186.827435 \nL 152.40798 184.330572 \nL 153.033814 185.291132 \nL 153.242425 182.530665 \nL 153.451036 182.824591 \nL 153.868258 178.873249 \nL 154.702703 180.742868 \nL 154.911314 181.694341 \nL 155.119925 180.733781 \nL 155.328536 181.921596 \nL 155.95437 184.054839 \nL 156.162981 182.788224 \nL 156.788814 176.027936 \nL 157.623259 171.40996 \nL 157.83187 171.491782 \nL 158.040481 170.897865 \nL 158.249092 173.214429 \nL 158.874925 172.861417 \nL 159.083537 177.055148 \nL 159.292148 178.215702 \nL 159.500759 176.903651 \nL 159.70937 179.385343 \nL 160.335203 177.206665 \nL 160.543814 178.318741 \nL 160.752426 175.694619 \nL 160.961037 176.152172 \nL 161.169648 175.434019 \nL 161.795481 175.9037 \nL 162.004092 172.455365 \nL 162.212703 175.911267 \nL 162.421315 177.006671 \nL 162.629926 179.076265 \nL 163.255759 177.958144 \nL 163.672981 180.827713 \nL 163.881593 182.957915 \nL 164.924648 182.506426 \nL 165.133259 181.115575 \nL 165.550482 181.600408 \nL 166.176315 181.854926 \nL 166.384926 184.515414 \nL 166.802148 183.482138 \nL 167.010759 186.436535 \nL 167.636593 183.003369 \nL 167.845204 183.430619 \nL 168.262426 179.482317 \nL 168.471037 174.009842 \nL 169.514093 178.395241 \nL 169.722704 181.962415 \nL 169.931315 181.792261 \nL 170.557149 180.917177 \nL 170.974371 185.94892 \nL 171.391593 181.695046 \nL 172.017427 182.46073 \nL 172.226038 184.484369 \nL 172.434649 184.308132 \nL 172.64326 181.640337 \nL 172.851871 183.022861 \nL 173.477704 183.493822 \nL 173.686316 181.956351 \nL 173.894927 181.37599 \nL 174.103538 180.391525 \nL 174.312149 181.120768 \nL 175.146593 183.487739 \nL 175.355205 181.218002 \nL 175.563816 181.221025 \nL 175.772427 183.551553 \nL 176.39826 182.980317 \nL 176.606871 181.403345 \nL 176.815482 181.059995 \nL 177.232705 183.180849 \nL 177.858538 185.155862 \nL 178.067149 185.198405 \nL 178.27576 182.159924 \nL 179.318816 185.04344 \nL 179.736038 184.417513 \nL 179.944649 182.147776 \nL 180.153261 182.159924 \nL 180.779094 181.704151 \nL 180.987705 181.008346 \nL 181.613538 183.679164 \nL 182.239372 186.769295 \nL 182.447983 187.076185 \nL 182.865205 186.198059 \nL 183.69965 186.362149 \nL 183.908261 185.705826 \nL 184.116872 188.194344 \nL 184.325483 187.024536 \nL 184.534094 184.153167 \nL 185.159928 179.163985 \nL 185.368539 174.742998 \nL 185.57715 175.010386 \nL 185.785761 169.045845 \nL 185.994372 140.763654 \nL 186.620206 143.774781 \nL 187.037428 144.054334 \nL 187.45465 155.764636 \nL 188.289095 154.415549 \nL 188.497706 153.221428 \nL 188.706317 153.020878 \nL 188.914928 155.141751 \nL 189.540761 153.44019 \nL 189.749372 154.035739 \nL 189.957984 149.620816 \nL 190.166595 149.95506 \nL 190.375206 152.197461 \nL 191.001039 152.674505 \nL 191.20965 144.455398 \nL 191.418261 144.826102 \nL 191.626873 145.771065 \nL 191.835484 145.567492 \nL 192.461317 144.428062 \nL 192.669928 145.868299 \nL 192.878539 144.418937 \nL 193.08715 148.694083 \nL 193.295762 159.131278 \nL 194.130206 168.374334 \nL 194.338817 154.227165 \nL 194.547428 151.495573 \nL 194.75604 153.692389 \nL 195.381873 157.378069 \nL 195.590484 163.594809 \nL 195.799095 158.566107 \nL 196.216317 162.7106 \nL 197.050762 158.468892 \nL 197.259373 159.058358 \nL 197.676595 155.093125 \nL 198.302429 155.861869 \nL 198.51104 152.246069 \nL 198.719651 151.990847 \nL 198.928262 149.888204 \nL 199.136873 154.035739 \nL 199.762707 152.154918 \nL 199.971318 156.028982 \nL 200.179929 156.129257 \nL 200.38854 155.084019 \nL 200.597151 159.286244 \nL 201.222984 164.475957 \nL 201.431596 164.451663 \nL 201.640207 160.364901 \nL 201.848818 159.492859 \nL 202.057429 154.746752 \nL 202.891874 149.116437 \nL 203.309096 151.024612 \nL 203.517707 149.672484 \nL 204.14354 148.742709 \nL 204.352151 147.032043 \nL 204.560763 147.378434 \nL 204.769374 144.163715 \nL 204.977985 144.023938 \nL 205.603818 142.838942 \nL 206.02104 149.976332 \nL 206.229652 147.187008 \nL 206.438263 131.930785 \nL 207.064096 128.655293 \nL 207.272707 129.958814 \nL 207.689929 127.397376 \nL 207.898541 129.253884 \nL 208.524374 126.124252 \nL 208.732985 125.805215 \nL 209.150207 123.043227 \nL 209.358819 122.280565 \nL 209.984652 124.975697 \nL 210.193263 123.9335 \nL 210.401874 121.78225 \nL 210.610485 123.04931 \nL 210.819096 127.373063 \nL 211.44493 123.739032 \nL 211.653541 124.851127 \nL 211.862152 120.384555 \nL 212.070763 120.867682 \nL 212.279374 115.340683 \nL 212.905208 115.529068 \nL 213.113819 117.868684 \nL 213.32243 117.908186 \nL 213.739652 117.26707 \nL 214.365486 119.594558 \nL 214.574097 112.168508 \nL 214.782708 113.584432 \nL 214.991319 116.574306 \nL 215.19993 112.238387 \nL 216.034375 113.587474 \nL 216.242986 116.856883 \nL 216.451597 117.510145 \nL 216.660208 120.727905 \nL 217.286041 118.023649 \nL 217.494653 119.351464 \nL 217.703264 114.887934 \nL 218.120486 120.594211 \nL 218.95493 117.346074 \nL 219.163542 117.251882 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130.997969 \nL 233.349098 133.216057 \nL 233.557709 126.823099 \nL 233.766321 126.810951 \nL 233.974932 128.764693 \nL 234.183543 129.229571 \nL 234.809376 134.009115 \nL 235.017987 134.370695 \nL 235.643821 124.389272 \nL 236.269654 123.274154 \nL 236.478265 123.930458 \nL 236.686876 121.572594 \nL 236.895487 121.059089 \nL 237.104099 121.113798 \nL 237.729932 119.749505 \nL 237.938543 120.156669 \nL 238.355765 121.812646 \nL 239.19021 122.350444 \nL 239.607432 117.185025 \nL 239.816043 118.880503 \nL 240.024654 117.373428 \nL 240.650488 118.777205 \nL 240.859099 121.053025 \nL 241.06771 118.655659 \nL 241.276321 120.299469 \nL 242.110766 121.569571 \nL 242.527988 116.823464 \nL 242.736599 116.373756 \nL 242.94521 114.61144 \nL 243.571044 112.29916 \nL 243.779655 116.151953 \nL 243.988266 116.534805 \nL 244.196877 114.568897 \nL 244.405488 126.835246 \nL 245.031321 125.50439 \nL 245.239933 130.065153 \nL 245.448544 130.764 \nL 245.657155 135.267032 \nL 246.491599 133.082363 \nL 246.70021 134.859886 \nL 246.908822 133.845025 \nL 247.117433 132.103981 \nL 247.326044 129.159692 \nL 247.951877 128.618833 \nL 248.160488 125.495284 \nL 248.3691 127.89265 \nL 248.786322 129.247801 \nL 249.412155 127.528029 \nL 249.620766 130.645514 \nL 249.829377 130.523967 \nL 250.037989 132.441248 \nL 250.2466 129.579004 \nL 250.872433 131.250169 \nL 251.081044 126.434164 \nL 251.289655 124.860233 \nL 251.498266 125.209666 \nL 251.706878 122.614809 \nL 252.541322 121.685034 \nL 252.749933 122.16206 \nL 252.958544 123.30149 \nL 253.167155 125.750506 \nL 253.792989 127.509798 \nL 254.0016 127.479403 \nL 254.210211 123.945647 \nL 254.418822 123.854496 \nL 254.627433 126.640797 \nL 255.461878 126.98717 \nL 255.670489 126.78968 \nL 255.8791 129.390619 \nL 256.087711 135.054353 \nL 256.713545 134.44968 \nL 257.130767 133.310249 \nL 257.339378 131.970287 \nL 257.547989 140.067848 \nL 258.173823 142.182619 \nL 258.382434 138.603298 \nL 258.591045 137.366633 \nL 259.008267 132.778515 \nL 259.842712 134.072911 \nL 260.051323 133.216057 \nL 260.259934 133.948342 \nL 260.468545 130.827815 \nL 261.094378 127.953405 \nL 261.302989 126.267051 \nL 261.511601 127.379146 \nL 261.720212 126.172859 \nL 261.928823 126.507103 \nL 262.971879 120.022975 \nL 263.18049 120.800826 \nL 263.389101 119.552014 \nL 264.223545 120.864641 \nL 264.640768 118.588821 \nL 264.849379 111.636774 \nL 265.475212 110.394027 \nL 265.683823 110.943991 \nL 265.892434 110.302876 \nL 266.101045 110.77992 \nL 266.309657 107.556095 \nL 266.93549 107.695853 \nL 267.144101 106.936233 \nL 267.561323 106.756973 \nL 267.769934 107.252247 \nL 268.395768 107.489239 \nL 268.604379 109.09963 \nL 268.81299 108.257984 \nL 269.230212 109.622259 \nL 269.856046 110.61583 \nL 270.064657 110.637101 \nL 270.273268 111.378492 \nL 270.69049 111.408888 \nL 271.316324 110.61583 \nL 271.733546 112.165467 \nL 272.150768 110.825486 \nL 272.985213 108.206316 \nL 273.193824 108.12429 \nL 273.402435 109.652636 \nL 273.611046 114.410909 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111, ylabel='Google price in $')\n",
    "goog_data_signal['sup'].plot(ax=ax1, color='g', lw=2.)\n",
    "goog_data_signal['res'].plot(ax=ax1, color='b', lw=2.)\n",
    "goog_data_signal['price'].plot(ax=ax1, color='r', lw=2.)\n",
    "\n",
    "ax1.plot(goog_data_signal.loc[goog_data_signal.position == 1.0].index,\n",
    "        goog_data_signal.price[goog_data_signal.position == 1.0], '^', markersize=7, color='k', label='buy')\n",
    "ax1.plot(goog_data_signal.loc[goog_data_signal.position  == -1.0].index,\n",
    "        goog_data_signal.price[goog_data_signal.position == -1.0], 'v', markersize=7, color='k', label='sell')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "交易策略：\n",
    "\n",
    "* 当价格进入支撑线区域后连续两天，就可以买多仓\n",
    "* 价格进入阻力线区域后连续两天，买空仓"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Creating trading signals based on fundamental technical analysis\n",
    "\n",
    "## SMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import statistics as stats\n",
    "\n",
    "time_period = 20 # number of days over which to average\n",
    "history = [] # to track a history of prices\n",
    "sma_values = [] \n",
    "\n",
    "for close_price in goog_data['Adj Close']:\n",
    "    history.append(close_price)\n",
    "    if len(history) > time_period:\n",
    "        del(history[0])\n",
    "    sma_values.append(stats.mean(history))\n",
    "\n",
    "goog_data = goog_data.assign(ClosePrice=pd.Series(goog_data['Adj Close'], index=goog_data.index))\n",
    "goog_data = goog_data.assign(Simple20DayMovingAverage=pd.Series(sma_values, index=goog_data.index))\n",
    "close_price = goog_data['ClosePrice']\n",
    "sma = goog_data['Simple20DayMovingAverage']\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111, ylabel='Google price in $')\n",
    "close_price.plot(ax=ax1, color='g', lw=2., legend=True)\n",
    "sma.plot(ax=ax1, color='r', lw=2., legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "20日SMA线消除了部分噪音"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}