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Finished Chap2 Deciphering the Market with Technical Analysis
2020-07-17 12:42:54 +10:00
{
"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": [
{
"output_type": "display_data",
"data": {
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},
"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
}
],
"source": [
"goog_data_signal"
]
},
{
"cell_type": "code",
"execution_count": 8,
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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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},
"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"
]
}
],
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"name": "python",
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