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Finished chap1
2020-07-12 11:03:39 +10:00
{
"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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" <th></th>\n",
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" <th>2014-01-02</th>\n",
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" <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",
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" <th>2014-01-06</th>\n",
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"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": [
{
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"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": [
{
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"text/plain": [
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"metadata": {
"needs_background": "light"
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"output_type": "display_data"
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],
"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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"text/plain": [
" GOOG\n",
"Date \n",
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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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"text/plain": [
" positions\n",
"Date \n",
"2013-12-31 0.000000\n",
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"2014-01-06 556.573853\n",
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"source": [
"portfolio['positions'] = positions.multiply(goog_data_signal['price'], axis=0)\n",
"portfolio.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
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"text/plain": [
" positions cash\n",
"Date \n",
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"2014-01-03 0.000000 1000.000000\n",
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},
"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,
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