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va2577/price-channel.ipynb

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price-channel.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Price Channel, Donchian Channel, High Low(HL) Bands"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from highcharts import Highstock"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## データ\n",
"\n",
"[ヒストリカルデータ取得 :: デューカスコピー・ジャパン | 自動売買も裁量もJForex!](https://www.dukascopy.jp/marketwatch/historical/)\n",
"\n",
"```\n",
"$ head USDJPY_1\\ Min_Bid_2000.01.01_2018.01.01.csv\n",
"Time (UTC),Open,High,Low,Close,Volume\n",
"2003.05.04 21:00:00,118.94,118.952,118.94,118.952,253\n",
"2003.05.04 21:01:00,118.961,118.967,118.958,118.967,154.6\n",
"2003.05.04 21:02:00,118.972,118.972,118.955,118.955,219.7\n",
"2003.05.04 21:03:00,118.953,118.961,118.949,118.949,309.9\n",
"2003.05.04 21:04:00,118.953,118.953,118.946,118.946,229.4\n",
"2003.05.04 21:05:00,118.952,118.954,118.944,118.944,112.2\n",
"2003.05.04 21:06:00,118.95,118.952,118.945,118.945,170.2\n",
"2003.05.04 21:07:00,118.947,118.956,118.947,118.947,124.5\n",
"2003.05.04 21:08:00,118.946,118.954,118.934,118.934,355\n",
"$ wc -l USDJPY_1\\ Min_Bid_2000.01.01_2018.01.01.csv\n",
"5509561 USDJPY_1 Min_Bid_2000.01.01_2018.01.01.csv\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## データを読み込む"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 15.4 s, sys: 3.3 s, total: 18.7 s\n",
"Wall time: 18.9 s\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
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" 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>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</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>2003-05-04 21:00:00</th>\n",
" <td>118.940</td>\n",
" <td>118.952</td>\n",
" <td>118.940</td>\n",
" <td>118.952</td>\n",
" <td>253.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-04 21:01:00</th>\n",
" <td>118.961</td>\n",
" <td>118.967</td>\n",
" <td>118.958</td>\n",
" <td>118.967</td>\n",
" <td>154.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-04 21:02:00</th>\n",
" <td>118.972</td>\n",
" <td>118.972</td>\n",
" <td>118.955</td>\n",
" <td>118.955</td>\n",
" <td>219.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-04 21:03:00</th>\n",
" <td>118.953</td>\n",
" <td>118.961</td>\n",
" <td>118.949</td>\n",
" <td>118.949</td>\n",
" <td>309.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-04 21:04:00</th>\n",
" <td>118.953</td>\n",
" <td>118.953</td>\n",
" <td>118.946</td>\n",
" <td>118.946</td>\n",
" <td>229.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close volume\n",
"time \n",
"2003-05-04 21:00:00 118.940 118.952 118.940 118.952 253.0\n",
"2003-05-04 21:01:00 118.961 118.967 118.958 118.967 154.6\n",
"2003-05-04 21:02:00 118.972 118.972 118.955 118.955 219.7\n",
"2003-05-04 21:03:00 118.953 118.961 118.949 118.949 309.9\n",
"2003-05-04 21:04:00 118.953 118.953 118.946 118.946 229.4"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filepath1 = 'USDJPY_1 Min_Bid_2000.01.01_2018.01.01.csv'\n",
"dtype1 = { 'time': str, 'open': float, 'high': float, 'low': float, 'close': float, 'volume': float }\n",
"names1 = ['time', 'open', 'high', 'low', 'close', 'volume']\n",
"%time df = pd.read_csv(filepath_or_buffer=filepath1, dtype=dtype1, header=0, index_col='time', names=names1, parse_dates=['time'])\n",
"# CPU times: user 15.5 s, sys: 3.77 s, total: 19.2 s\n",
"# Wall time: 20.5 s\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 日足のローソク足を 5 本にするため時間の計算\n",
"\n",
"UTC → UTC+3"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 78.1 ms, sys: 578 ms, total: 656 ms\n",
"Wall time: 662 ms\n"
]
},
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</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>2003-05-05 00:00:00</th>\n",
" <td>118.940</td>\n",
" <td>118.952</td>\n",
" <td>118.940</td>\n",
" <td>118.952</td>\n",
" <td>253.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-05 00:01:00</th>\n",
" <td>118.961</td>\n",
" <td>118.967</td>\n",
" <td>118.958</td>\n",
" <td>118.967</td>\n",
" <td>154.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-05 00:02:00</th>\n",
" <td>118.972</td>\n",
" <td>118.972</td>\n",
" <td>118.955</td>\n",
" <td>118.955</td>\n",
" <td>219.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-05 00:03:00</th>\n",
" <td>118.953</td>\n",
" <td>118.961</td>\n",
" <td>118.949</td>\n",
" <td>118.949</td>\n",
" <td>309.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-05 00:04:00</th>\n",
" <td>118.953</td>\n",
" <td>118.953</td>\n",
" <td>118.946</td>\n",
" <td>118.946</td>\n",
" <td>229.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close volume\n",
"time \n",
"2003-05-05 00:00:00 118.940 118.952 118.940 118.952 253.0\n",
"2003-05-05 00:01:00 118.961 118.967 118.958 118.967 154.6\n",
"2003-05-05 00:02:00 118.972 118.972 118.955 118.955 219.7\n",
"2003-05-05 00:03:00 118.953 118.961 118.949 118.949 309.9\n",
"2003-05-05 00:04:00 118.953 118.953 118.946 118.946 229.4"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#%time pd.DataFrame({ 'open': df['open'], 'high': df['high'], 'low': df['low'], 'close': df['close'], 'volume': df['volume'] }, index=df.index + pd.DateOffset(hours=3))\n",
"#%time pd.DataFrame({ 'open': df['open'].values, 'high': df['high'].values, 'low': df['low'].values, 'close': df['close'].values, 'volume': df['volume'].values }, index=df.index + pd.DateOffset(hours=3))\n",
"# index を変える場合、 Series だと DataFrame を生成するのに 6 秒程度、 ndarray だと DataFrame を生成するのに 1 秒程度\n",
"\n",
"data2 = { 'open': df['open'].values, 'high': df['high'].values, 'low': df['low'].values, 'close': df['close'].values, 'volume': df['volume'].values }\n",
"columns2 = ['open', 'high', 'low', 'close', 'volume']\n",
"index2 = df.index + pd.DateOffset(hours=3)\n",
"%time df2 = pd.DataFrame(data=data2, columns=columns2, index=index2)\n",
"# CPU times: user 62.5 ms, sys: 453 ms, total: 516 ms\n",
"# Wall time: 524 ms\n",
"\n",
"df2.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## リサンプリング"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 250 ms, sys: 125 ms, total: 375 ms\n",
"Wall time: 410 ms\n",
"CPU times: user 203 ms, sys: 78.1 ms, total: 281 ms\n",
"Wall time: 309 ms\n",
"CPU times: user 15.6 ms, sys: 0 ns, total: 15.6 ms\n",
"Wall time: 3.83 ms\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</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>2003-05-05</th>\n",
" <td>118.940</td>\n",
" <td>119.046</td>\n",
" <td>118.461</td>\n",
" <td>118.603</td>\n",
" <td>592866.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-06</th>\n",
" <td>118.591</td>\n",
" <td>118.751</td>\n",
" <td>117.290</td>\n",
" <td>117.500</td>\n",
" <td>581707.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-07</th>\n",
" <td>117.456</td>\n",
" <td>117.830</td>\n",
" <td>116.052</td>\n",
" <td>116.303</td>\n",
" <td>584496.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-08</th>\n",
" <td>116.311</td>\n",
" <td>116.969</td>\n",
" <td>115.940</td>\n",
" <td>116.823</td>\n",
" <td>588236.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-05-09</th>\n",
" <td>116.835</td>\n",
" <td>117.612</td>\n",
" <td>116.794</td>\n",
" <td>117.151</td>\n",
" <td>583132.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close volume\n",
"time \n",
"2003-05-05 118.940 119.046 118.461 118.603 592866.9\n",
"2003-05-06 118.591 118.751 117.290 117.500 581707.0\n",
"2003-05-07 117.456 117.830 116.052 116.303 584496.2\n",
"2003-05-08 116.311 116.969 115.940 116.823 588236.7\n",
"2003-05-09 116.835 117.612 116.794 117.151 583132.9"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 日足の始値、高値、安値、終値を計算する\n",
"# Series ごとに始値、高値、安値、終値の計算結果が得られる\n",
"# 始値を参照する場合は df['open']['open'] のように記述する\n",
"%time df3 = df2.resample(rule='D').ohlc()\n",
"# CPU times: user 250 ms, sys: 78.1 ms, total: 328 ms\n",
"# Wall time: 348 ms\n",
"\n",
"# 日足の出来高を計算する\n",
"%time df4 = df2.resample(rule='D').sum()\n",
"# CPU times: user 156 ms, sys: 93.8 ms, total: 250 ms\n",
"# Wall time: 241 ms\n",
"\n",
"# 日足の DataFrame\n",
"# 土日も計算結果に含まれるため dropna で取り除く\n",
"data5 = { 'open': df3['open']['open'].values, 'high': df3['high']['high'].values, 'low': df3['low']['low'].values, 'close': df3['close']['close'].values, 'volume': df4['volume'].values }\n",
"columns5 = ['open', 'high', 'low', 'close', 'volume']\n",
"%time df5 = pd.DataFrame(data=data5, columns=columns5, index=df3.index).dropna()\n",
"# CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
"# Wall time: 2.91 ms\n",
"\n",
"df5.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 高値と安値を計算する\n",
"\n",
"* [pandas.DataFrame.rolling — pandas 0.23.0 documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
"Wall time: 4.02 ms\n",
"CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
"Wall time: 1.34 ms\n",
"CPU times: user 15.6 ms, sys: 15.6 ms, total: 31.2 ms\n",
"Wall time: 2.93 ms\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
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"<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",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-12-28</th>\n",
" <td>113.75</td>\n",
" <td>111.993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-29</th>\n",
" <td>113.75</td>\n",
" <td>112.030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-30</th>\n",
" <td>113.75</td>\n",
" <td>112.030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>113.75</td>\n",
" <td>112.030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-02</th>\n",
" <td>113.75</td>\n",
" <td>112.030</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" high low\n",
"time \n",
"2017-12-28 113.75 111.993\n",
"2017-12-29 113.75 112.030\n",
"2017-12-30 113.75 112.030\n",
"2018-01-01 113.75 112.030\n",
"2018-01-02 113.75 112.030"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# high = pd.rolling_max(df2, 20)\n",
"# /path/to/dir/lib/python3.5/site-packages/ipykernel_launcher.py:1: FutureWarning: pd.rolling_max is deprecated for DataFrame and will be removed in a future version, replace with \n",
"# \tDataFrame.rolling(window=20,center=False).max()\n",
"# \"\"\"Entry point for launching an IPython kernel.\n",
"# pd.rolling_max と pd.rolling_min は deprecated になったみたい\n",
"\n",
"period = 20\n",
"# %time df5.rolling(window=period).max()\n",
"# CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
"# Wall time: 48.3 ms\n",
"%time high = df5['high'].rolling(window=period).max()\n",
"%time low = df5['low'].rolling(window=period).min()\n",
"%time pd.DataFrame(data={ 'high': high, 'low': low }).tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Highstock 向けの DataFrame を作る"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>hlhigh</th>\n",
" <th>hllow</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4098</th>\n",
" <td>1514419200000</td>\n",
" <td>113.279</td>\n",
" <td>113.351</td>\n",
" <td>112.664</td>\n",
" <td>112.895</td>\n",
" <td>113.75</td>\n",
" <td>111.993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4099</th>\n",
" <td>1514505600000</td>\n",
" <td>112.894</td>\n",
" <td>112.969</td>\n",
" <td>112.472</td>\n",
" <td>112.678</td>\n",
" <td>113.75</td>\n",
" <td>112.030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4100</th>\n",
" <td>1514592000000</td>\n",
" <td>112.680</td>\n",
" <td>112.714</td>\n",
" <td>112.647</td>\n",
" <td>112.658</td>\n",
" <td>113.75</td>\n",
" <td>112.030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4101</th>\n",
" <td>1514764800000</td>\n",
" <td>112.658</td>\n",
" <td>112.658</td>\n",
" <td>112.658</td>\n",
" <td>112.658</td>\n",
" <td>113.75</td>\n",
" <td>112.030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4102</th>\n",
" <td>1514851200000</td>\n",
" <td>112.658</td>\n",
" <td>112.789</td>\n",
" <td>112.570</td>\n",
" <td>112.773</td>\n",
" <td>113.75</td>\n",
" <td>112.030</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time open high low close hlhigh hllow\n",
"4098 1514419200000 113.279 113.351 112.664 112.895 113.75 111.993\n",
"4099 1514505600000 112.894 112.969 112.472 112.678 113.75 112.030\n",
"4100 1514592000000 112.680 112.714 112.647 112.658 113.75 112.030\n",
"4101 1514764800000 112.658 112.658 112.658 112.658 113.75 112.030\n",
"4102 1514851200000 112.658 112.789 112.570 112.773 113.75 112.030"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"time6 = (df5.index.values // pd.Timedelta('1ms')).astype(np.int64)\n",
"data6 = { 'time': time6, 'open': df5['open'].values, 'high': df5['high'].values, 'low': df5['low'].values, 'close': df5['close'].values }\n",
"columns6 = ['time', 'open', 'high', 'low', 'close']\n",
"df6 = pd.DataFrame(data=data6, columns=columns6)\n",
"data7 = { 'time': time6, 'hlhigh': high.values }\n",
"columns7 = ['time', 'hlhigh']\n",
"df7 = pd.DataFrame(data=data7, columns=columns7)\n",
"data8 = { 'time': time6, 'hllow': low.values }\n",
"columns8 = ['time', 'hllow']\n",
"df8 = pd.DataFrame(data=data8, columns=columns8)\n",
"df6.merge(df7).merge(df8).tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Highstock のローソク足を表示する"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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111.069], [1511568000000.0, 111.069], [1511740800000.0, 110.84100000000001], [1511827200000.0, 110.84100000000001], [1511913600000.0, 110.84100000000001], [1512000000000.0, 110.84100000000001], [1512086400000.0, 110.84100000000001], [1512172800000.0, 110.84100000000001], [1512345600000.0, 110.84100000000001], [1512432000000.0, 110.84100000000001], [1512518400000.0, 110.84100000000001], [1512604800000.0, 110.84100000000001], [1512691200000.0, 110.84100000000001], [1512777600000.0, 110.84100000000001], [1512950400000.0, 110.84100000000001], [1513036800000.0, 110.84100000000001], [1513123200000.0, 110.84100000000001], [1513209600000.0, 110.84100000000001], [1513296000000.0, 110.84100000000001], [1513382400000.0, 110.84100000000001], [1513555200000.0, 110.84100000000001], [1513641600000.0, 110.84100000000001], [1513728000000.0, 110.93], [1513814400000.0, 111.346], [1513900800000.0, 111.40799999999999], [1513987200000.0, 111.40799999999999], [1514160000000.0, 111.993], [1514246400000.0, 111.993], [1514332800000.0, 111.993], [1514419200000.0, 111.993], [1514505600000.0, 112.03], [1514592000000.0, 112.03], [1514764800000.0, 112.03], [1514851200000.0, 112.03]], &quot;type&quot;: &quot;line&quot;}]; var dataLen = data.length; for (var ix = 0; ix &lt; dataLen; ix++) { chart.addSeries(data[ix]); } }); &lt;/script&gt; &lt;/body&gt; &lt;/html&gt;\" height=520 width=820></iframe>"
],
"text/plain": [
"<highcharts.highstock.highstock.Highstock at 0x7fab0d78e048>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"H = Highstock()\n",
"\n",
"H.add_data_set(df6.values.tolist(), 'candlestick', '米ドル/円')\n",
"H.add_data_set(df7.values.tolist(), 'line', 'High')\n",
"H.add_data_set(df8.values.tolist(), 'line', 'Low')\n",
"\n",
"options = {\n",
" 'plotOptions': {\n",
" 'line': {\n",
" 'lineWidth': 1\n",
" }\n",
" },\n",
" 'rangeSelector': {\n",
" 'selected': 1\n",
" },\n",
" 'title': {\n",
" 'text': '米ドル/円 daily'\n",
" }\n",
"}\n",
"H.set_dict_options(options)\n",
"\n",
"H"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Highstock の SMA を表示する\n",
"\n",
"chart types の sma に対応していない。\n",
"[highstock_types.py](https://github.com/kyper-data/python-highcharts/blob/developer/highcharts/highstock/highstock_types.py) の `PLOT_OPTION_ALLOWED_ARGS` を見ると sma がない。"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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112.789, 112.57, 112.773]], &quot;type&quot;: &quot;candlestick&quot;}]; var dataLen = data.length; for (var ix = 0; ix &lt; dataLen; ix++) { chart.addSeries(data[ix]); } }); &lt;/script&gt; &lt;/body&gt; &lt;/html&gt;\" height=520 width=820></iframe>"
],
"text/plain": [
"<highcharts.highstock.highstock.Highstock at 0x7fab0d766748>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"H2 = Highstock()\n",
"\n",
"H2.add_data_set(df6.values.tolist(), 'candlestick', '米ドル/円', id='USDJPY')\n",
"#H2.add_data_set(linkedTo='USDJPY', params={ 'index': 3, 'period': 20 }, type='sma')\n",
"#KeyError: 'sma'\n",
"#TypeError: add_data_set() missing 1 required positional argument: 'data'\n",
"\n",
"options2 = {\n",
"# 'plotOptions': {\n",
"# 'sma': {\n",
"# 'linkedTo': 'USDJPY',\n",
"# 'lineWidth': 1\n",
"# }\n",
"# },\n",
" 'rangeSelector': {\n",
" 'selected': 1\n",
" },\n",
" 'title': {\n",
" 'text': '米ドル/円 daily'\n",
" }\n",
"}\n",
"#OptionTypeError: Not An Accepted Option Type: sma\n",
"H2.set_dict_options(options2)\n",
"\n",
"H2"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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