{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 买低卖空\n",
    "\n",
    "## 获取GOOG股票数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High          Low  ...     Volume    Adj Close\n",
      "Date                                  ...                        \n",
      "2013-12-31   558.406982   551.064453  ...  2725900.0   558.262512\n",
      "2014-01-02   556.788025   552.060730  ...  3656400.0   554.481689\n",
      "2014-01-03   556.379578   550.401978  ...  3345800.0   550.436829\n",
      "2014-01-06   557.340942   551.154114  ...  3551800.0   556.573853\n",
      "2014-01-07   567.717041   558.486633  ...  5124300.0   567.303589\n",
      "...                 ...          ...  ...        ...          ...\n",
      "2017-12-22  1064.199951  1059.439941  ...   755100.0  1060.119995\n",
      "2017-12-26  1060.119995  1050.199951  ...   760600.0  1056.739990\n",
      "2017-12-27  1058.369995  1048.050049  ...  1271900.0  1049.369995\n",
      "2017-12-28  1054.750000  1044.770020  ...   837100.0  1048.140015\n",
      "2017-12-29  1049.699951  1044.900024  ...   887500.0  1046.400024\n",
      "\n",
      "[1008 rows x 6 columns]\n"
     ]
    }
   ],
   "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",
    "goog_data = data.DataReader('GOOG', 'yahoo', start_date, end_date)\n",
    "print(goog_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1008 entries, 2013-12-31 to 2017-12-29\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   High       1008 non-null   float64\n",
      " 1   Low        1008 non-null   float64\n",
      " 2   Open       1008 non-null   float64\n",
      " 3   Close      1008 non-null   float64\n",
      " 4   Volume     1008 non-null   float64\n",
      " 5   Adj Close  1008 non-null   float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 55.1 KB\n"
     ]
    }
   ],
   "source": [
    "goog_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Google 数据结构如上所见，我们要用 Adj Close 来产生交易信号\n",
    "\n",
    "## 准备交易信号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1008 entries, 2013-12-31 to 2017-12-29\n",
      "Data columns (total 2 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   price             1008 non-null   float64\n",
      " 1   daily_difference  1007 non-null   float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 23.6 KB\n"
     ]
    }
   ],
   "source": [
    "# 准备新的数据集，包括时间、价格、每日价差、交易信号\n",
    "goog_data_signal = pd.DataFrame(index=goog_data.index)\n",
    "goog_data_signal['price'] = goog_data['Adj Close']\n",
    "goog_data_signal['daily_difference'] = goog_data_signal['price'].diff()\n",
    "goog_data_signal.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>daily_difference</th>\n",
       "      <th>signal</th>\n",
       "      <th>positions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>554.481689</td>\n",
       "      <td>-3.780823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>556.573853</td>\n",
       "      <td>6.137024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>567.303589</td>\n",
       "      <td>10.729736</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price  daily_difference  signal  positions\n",
       "Date                                                       \n",
       "2013-12-31  558.262512               NaN     0.0        NaN\n",
       "2014-01-02  554.481689         -3.780823     0.0        0.0\n",
       "2014-01-03  550.436829         -4.044861     0.0        0.0\n",
       "2014-01-06  556.573853          6.137024     1.0        1.0\n",
       "2014-01-07  567.303589         10.729736     1.0        0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将每日价差转换为交易信号（挂单）\n",
    "import numpy as np\n",
    "goog_data_signal['signal'] = 0.0\n",
    "goog_data_signal['signal'] = np.where(goog_data_signal['daily_difference'] > 0, 1.0, 0.0)\n",
    "goog_data_signal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 为 买入， 1 为 卖出\n",
    "\n",
    "在资金有限情况下，我们不能在市场持续上升、下降的时候不停地卖出、买入。因此要限制仓位数量\n",
    "\n",
    "为了简化，我们保证算法不会持续买卖（连续两天内不会连续购买）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>daily_difference</th>\n",
       "      <th>signal</th>\n",
       "      <th>positions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>554.481689</td>\n",
       "      <td>-3.780823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>550.436829</td>\n",
       "      <td>-4.044861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>556.573853</td>\n",
       "      <td>6.137024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>567.303589</td>\n",
       "      <td>10.729736</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price  daily_difference  signal  positions\n",
       "Date                                                       \n",
       "2013-12-31  558.262512               NaN     0.0        NaN\n",
       "2014-01-02  554.481689         -3.780823     0.0        0.0\n",
       "2014-01-03  550.436829         -4.044861     0.0        0.0\n",
       "2014-01-06  556.573853          6.137024     1.0        1.0\n",
       "2014-01-07  567.303589         10.729736     1.0        0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goog_data_signal['positions'] = goog_data_signal['signal'].diff()\n",
    "goog_data_signal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图表化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111, ylabel='Google price in $')\n",
    "\n",
    "goog_data_signal['price'].plot(ax=ax1, color='r', lw=2.)\n",
    "ax1.plot(goog_data_signal.loc[goog_data_signal.positions == 1.0].index, goog_data_signal.price[goog_data_signal.positions == 1.0], '^', markersize=5, color='m')\n",
    "ax1.plot(goog_data_signal.loc[goog_data_signal.positions == -1.0].index, goog_data_signal.price[goog_data_signal.positions == -1.0], 'v', markersize=5, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加入向上的箭头来表示买入, 加入向下箭头表示卖出加入向下箭头表示卖出\n",
    "\n",
    "## 回测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1008 entries, 2013-12-31 to 2017-12-29\n",
      "Empty DataFrame<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1008 entries, 2013-12-31 to 2017-12-29\n",
      "Empty DataFrame"
     ]
    }
   ],
   "source": [
    "initial_capital = float(1000)\n",
    "\n",
    "positions = pd.DataFrame(index=goog_data_signal.index).fillna(0.0)\n",
    "portfolio = pd.DataFrame(index=goog_data_signal.index).fillna(0.0)\n",
    "positions.info()\n",
    "portfolio.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>GOOG</th>\n",
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       "      <th>Date</th>\n",
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       "      <th>2014-01-06</th>\n",
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      "text/plain": [
       "            GOOG\n",
       "Date            \n",
       "2013-12-31   0.0\n",
       "2014-01-02   0.0\n",
       "2014-01-03   0.0\n",
       "2014-01-06   1.0\n",
       "2014-01-07   1.0"
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   ],
   "source": [
    "positions['GOOG'] = goog_data_signal['signal']\n",
    "positions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>556.573853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>567.303589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             positions\n",
       "Date                  \n",
       "2013-12-31    0.000000\n",
       "2014-01-02    0.000000\n",
       "2014-01-03    0.000000\n",
       "2014-01-06  556.573853\n",
       "2014-01-07  567.303589"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio['positions'] = positions.multiply(goog_data_signal['price'], axis=0)\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>positions</th>\n",
       "      <th>cash</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>2013-12-31</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>556.573853</td>\n",
       "      <td>443.426147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>567.303589</td>\n",
       "      <td>443.426147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             positions         cash\n",
       "Date                               \n",
       "2013-12-31    0.000000          NaN\n",
       "2014-01-02    0.000000  1000.000000\n",
       "2014-01-03    0.000000  1000.000000\n",
       "2014-01-06  556.573853   443.426147\n",
       "2014-01-07  567.303589   443.426147"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算cash\n",
    "portfolio['cash'] = initial_capital - (positions.diff().multiply(goog_data_signal['price'], axis=0)).cumsum()\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio['total'] = portfolio['positions'] + portfolio['cash']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f27efe06f10>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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