{
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{
"cell_type": "code",
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"data": {
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"data": {
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],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import xlrd\n",
"from plotly.graph_objs import Scatter, layout\n",
"import plotly\n",
"import plotly.offline as py\n",
"import plotly.graph_objs as go\n",
"import cufflinks as cf\n",
"from urllib.request import urlopen\n",
"import json\n",
"import re\n",
"cf.go_offline()###这两句是离线生成图片的设置\n",
"cf.set_config_file(offline=True, world_readable=True)\n",
"plotly.offline.init_notebook_mode(connected=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.read_csv('BCHAIN-MKPRU.csv')\n",
"df2 = pd.read_csv('LBMA-GOLD.csv')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df2 = df2.interpolate(method='linear', limit_direction='forward')"
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\n",
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" Date | \n",
" Value | \n",
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\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 9/11/16 | \n",
" 621.65 | \n",
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\n",
" \n",
" | 1 | \n",
" 9/12/16 | \n",
" 609.67 | \n",
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\n",
" \n",
" | 2 | \n",
" 9/13/16 | \n",
" 610.92 | \n",
"
\n",
" \n",
" | 3 | \n",
" 9/14/16 | \n",
" 608.82 | \n",
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\n",
" \n",
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" 9/15/16 | \n",
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"source": [
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"data": {
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"\n",
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\n",
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" Date | \n",
" USD (PM) | \n",
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\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 9/12/16 | \n",
" 1324.60 | \n",
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\n",
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" 9/13/16 | \n",
" 1323.65 | \n",
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\n",
" \n",
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" 9/14/16 | \n",
" 1321.75 | \n",
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\n",
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" 9/15/16 | \n",
" 1310.80 | \n",
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\n",
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" 9/16/16 | \n",
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"text/plain": [
" Date USD (PM)\n",
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"3 9/15/16 1310.80\n",
"4 9/16/16 1308.35"
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},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df2.head()"
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},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"data1 = df1['Value']\n",
"data2 = df2['USD (PM)']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(-0.23774252687897476, 0.9338707177253291, 24, 1801, {'1%': -3.4339861159726306, '5%': -2.863146142843122, '10%': -2.5676250581282964}, 29168.935944417746)\n",
"(-8.537735567344068, 9.973933990343592e-14, 23, 1802, {'1%': -3.4339840952648695, '5%': -2.8631452508003057, '10%': -2.567624583142913}, 29166.993491682755)\n",
"(-7.6592535830552855, 1.7059734924782906e-11, 24, 1801, {'1%': -3.4339861159726306, '5%': -2.863146142843122, '10%': -2.5676250581282964}, 29212.440500169047)\n"
]
}
],
"source": [
"from statsmodels.tsa.stattools import adfuller\n",
"print(adfuller(data1))\n",
"print(adfuller(df1['Value'].diff(1).fillna(0)))\n",
"print(adfuller(df1['Value'].diff(2).fillna(0)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(-0.5079488932343523, 0.8904559666874652, 22, 1242, {'1%': -3.4356260602190356, '5%': -2.863869899335344, '10%': -2.5680104683371323}, 10021.010556670537)\n",
"(-7.957540057520856, 3.0090552229481678e-12, 21, 1243, {'1%': -3.435621806786881, '5%': -2.8638680226791444, '10%': -2.5680094689100477}, 10019.265250961456)\n",
"(-8.180001258817041, 8.179822010278522e-13, 21, 1243, {'1%': -3.435621806786881, '5%': -2.8638680226791444, '10%': -2.5680094689100477}, 10048.910459012992)\n"
]
}
],
"source": [
"print(adfuller(data2))\n",
"print(adfuller(df2['USD (PM)'].diff(1).fillna(0)))\n",
"print(adfuller(df2['USD (PM)'].diff(2).fillna(0)))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def f(x : str)->str:\n",
" p = []\n",
" for m in re.finditer('/', x):\n",
" p.append(m.start())\n",
" return \"20\" + x[-2:] + \"-\" + x[:p[0]].zfill(2) + \"-\" + x[p[0] + 1 : p[1]].zfill(2)\n",
"df2['date'] = df2['Date'].apply(lambda x : f(x))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"df1['date'] = df1['Date'].apply(lambda x : f(x))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
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1476.7,
1479,
1482.1,
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1511.5,
1514.75,
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1527.1,
1548.75,
1573.1,
1567.85,
1571.95,
1550.75,
1553.6,
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1545.1,
1549,
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1574,
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1756.55,
1747.6,
1771.6,
1768.1,
1771.05,
1777.45,
1772.9,
1787.9,
1789.55,
1811.1,
1812.1,
1803.1,
1807.5,
1801.9,
1804.6,
1807.7,
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1815.65,
1842.55,
1852.4,
1878.3,
1902.1,
1936.65,
1940.9,
1950.9,
1957.65,
1964.9,
1958.55,
1977.9,
2048.15,
2067.15,
2031.15,
2044.5,
1939.65,
1931.9,
1944.25,
1944.75,
1972.85,
2008.75,
1981,
1927.15,
1924.35,
1943.95,
1911.15,
1932.95,
1923.85,
1957.35,
1972.35,
1947.05,
1940.45,
1926.3,
1928.45,
1910.95,
1947.2,
1966.25,
1947.4,
1958.7,
1949.35,
1961.8,
1936.25,
1950.85,
1909.35,
1906,
1873.4,
1861.75,
1859.7,
1864.3,
1883.95,
1886.9,
1902,
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},
"execution_count": 35,
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]
},
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"title": {
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"title": {
"text": "date"
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"title": {
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"source": [
"fig2 = go.Figure()\n",
"fig2.add_trace(Scatter(\n",
" x=df2['date'],\n",
" y=df2['USD (PM)'],\n",
" legendgroup='group0',\n",
" mode='lines',\n",
" name='LBMA-GOLD',\n",
" marker=dict(color='Crimson')\n",
"))\n",
"fig2.update_layout(title={'text':'Time - Value Overview of Gold','y' : 0.9,'x' : 0.5, },\n",
" xaxis_title='date',\n",
" yaxis_title='U.S. dollars($)',\n",
")\n",
"fig2.show()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"df1 = df1.drop('Date', axis=1)\n",
"df1.index = df1['date']"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"df1 = df1.drop('date', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"df2.index = df2['date']\n",
"df2 = df2.drop(['date', 'Date'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [],
"source": [
"df = df1.join([df2], how='outer')"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"# 添加表示黄金是否售卖的特征, 1就是不能买\n",
"df['stopTrade'] = np.array(df['USD (PM)'].isnull().values == True, dtype=np.int)\n",
"df = df.fillna(method='ffill')"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"df['bitcoinMargin'] = np.append( \n",
" np.array([(df['Value'][i + 1] - df['Value'][i]) / df['Value'][i] for i in range(len(df['Value']) - 1)]),\n",
" np.array([0])\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [],
"source": [
"df['bitcoinMargin'] = df['bitcoinMargin'].shift(1)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [],
"source": [
"df['goldMargin'] = np.append( \n",
" np.array([(df['USD (PM)'][i + 1] - df['USD (PM)'][i]) / df['USD (PM)'][i] for i in range(len(df['USD (PM)']) - 1)]),\n",
" np.array([0])\n",
")\n",
"df['goldMargin'] = df['goldMargin'].shift(1)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0041929000740916055"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(np.abs(df['goldMargin'].values[2:]))"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.026655062733986674"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(np.abs(df['bitcoinMargin'].values[1:]))"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1826, 5)"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [],
"source": [
"goldPrice, bitcoinPrice = df['USD (PM)'].values, df['Value'].values\n",
"df['bitcoinFive'] = np.append(\n",
" np.array([np.mean(np.array(bitcoinPrice[i : i + 5])) for i in range(len(bitcoinPrice) - 4)]),\n",
" np.array([0] * 4)\n",
")\n",
"df['bitcoinFive'] = df['bitcoinFive'].shift(4)"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [],
"source": [
"df['goldTwenty'] = np.append(\n",
" np.array([np.mean(np.array(goldPrice[i : i + 20])) for i in range(1, len(goldPrice) - 19)]),\n",
" np.array([0] * 20)\n",
")\n",
"df['goldTwenty'] = df['goldTwenty'].shift(20)"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Value | \n",
" USD (PM) | \n",
" stopTrade | \n",
" bitcoinMargin | \n",
" goldMargin | \n",
" bitcoinFive | \n",
" goldTwenty | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2016-09-11 | \n",
" 621.65 | \n",
" NaN | \n",
" 1 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | 2016-09-12 | \n",
" 609.67 | \n",
" 1324.60 | \n",
" 0 | \n",
" -0.019271 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
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\n",
" \n",
" | 2016-09-13 | \n",
" 610.92 | \n",
" 1323.65 | \n",
" 0 | \n",
" 0.002050 | \n",
" -0.000717 | \n",
" NaN | \n",
" NaN | \n",
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\n",
" \n",
" | 2016-09-14 | \n",
" 608.82 | \n",
" 1321.75 | \n",
" 0 | \n",
" -0.003437 | \n",
" -0.001435 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | 2016-09-15 | \n",
" 610.38 | \n",
" 1310.80 | \n",
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" 0.002562 | \n",
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"text/plain": [
" Value USD (PM) stopTrade bitcoinMargin goldMargin \\\n",
"2016-09-11 621.65 NaN 1 NaN NaN \n",
"2016-09-12 609.67 1324.60 0 -0.019271 NaN \n",
"2016-09-13 610.92 1323.65 0 0.002050 -0.000717 \n",
"2016-09-14 608.82 1321.75 0 -0.003437 -0.001435 \n",
"2016-09-15 610.38 1310.80 0 0.002562 -0.008284 \n",
"\n",
" bitcoinFive goldTwenty \n",
"2016-09-11 NaN NaN \n",
"2016-09-12 NaN NaN \n",
"2016-09-13 NaN NaN \n",
"2016-09-14 NaN NaN \n",
"2016-09-15 612.288 NaN "
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},
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],
"source": [
"df.head()"
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},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv('data.csv')"
]
},
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