结合股票数据，介绍 0 维到 4 维张量。
具体文章见我“王的机器”公众号里的 张量 101
import numpy as np
import pandas as pd
from IPython.display import Image
# 平安银行 (000001.XSHE) 在2019年1月3日的收盘价
X0 = get_price('000001.XSHE', start_date='2019-01-03', end_date='2019-01-03', frequency='daily', fields='close')
X0
| close | |
|---|---|
| 2019-01-03 | 9.28 |
# 平安银行 (000001.XSHE) 从2019年1月3日起的1年历史收盘价
X1 = get_price('000001.XSHE', start_date='2018-01-03', end_date='2019-01-03', frequency='daily', fields='close')
print( '张量形状是', X1.shape )
print( '张量压缩后形状是', np.squeeze(X1).shape )
X1.head(5).append(X1.tail(5))
张量形状是 (244, 1) 张量压缩后形状是 (244,)
| close | |
|---|---|
| 2018-01-03 | 13.13 |
| 2018-01-04 | 13.05 |
| 2018-01-05 | 13.10 |
| 2018-01-08 | 12.76 |
| 2018-01-09 | 12.88 |
| 2018-12-26 | 9.30 |
| 2018-12-27 | 9.28 |
| 2018-12-28 | 9.38 |
| 2019-01-02 | 9.19 |
| 2019-01-03 | 9.28 |
# 获取茅台 (600519.XSHG) 和平安银行 (000001.XSHE) 从2019年1月3日起的1年历史收盘价
X2 = get_price(['000001.XSHE','600519.XSHG'], start_date='2018-01-03', end_date='2019-01-03', frequency='daily', fields=['close'])
print( '张量形状是', X2.shape )
print( '张量压缩后形状是', np.squeeze(X2).shape )
X2['close'].head(5).append(X2['close'].tail(5))
张量形状是 (1, 244, 2) 张量压缩后形状是 (244, 2)
| 000001.XSHE | 600519.XSHG | |
|---|---|---|
| 2018-01-03 | 13.13 | 705.87 |
| 2018-01-04 | 13.05 | 726.79 |
| 2018-01-05 | 13.10 | 728.06 |
| 2018-01-08 | 12.76 | 741.64 |
| 2018-01-09 | 12.88 | 771.60 |
| 2018-12-26 | 9.30 | 560.08 |
| 2018-12-27 | 9.28 | 563.00 |
| 2018-12-28 | 9.38 | 590.01 |
| 2019-01-02 | 9.19 | 598.98 |
| 2019-01-03 | 9.28 | 590.00 |
Image("张量 101/QI 0-1-2D Tensor.jpg", width=800, height=800)
Image("张量 101/QI 3D Tensor.jpg", width=600, height=600)
# 获取茅台 (600519.XSHG) 和平安银行 (000001.XSHE) 从2019年1月3日起的1年历史收盘价和交易量
X3 = get_price(['000001.XSHE','600519.XSHG'], start_date='2018-01-03', end_date='2019-01-03', frequency='daily', fields=['close','volume'])
print( '张量形状是', X3.shape )
print( '张量压缩后形状是', np.squeeze(X3).shape )
X3['close'].head(5).append(X3['close'].tail(5))
张量形状是 (2, 244, 2) 张量压缩后形状是 (2, 244, 2)
| 000001.XSHE | 600519.XSHG | |
|---|---|---|
| 2018-01-03 | 13.13 | 705.87 |
| 2018-01-04 | 13.05 | 726.79 |
| 2018-01-05 | 13.10 | 728.06 |
| 2018-01-08 | 12.76 | 741.64 |
| 2018-01-09 | 12.88 | 771.60 |
| 2018-12-26 | 9.30 | 560.08 |
| 2018-12-27 | 9.28 | 563.00 |
| 2018-12-28 | 9.38 | 590.01 |
| 2019-01-02 | 9.19 | 598.98 |
| 2019-01-03 | 9.28 | 590.00 |
X3['volume'].head(5).append(X3['volume'].tail(5))
| 000001.XSHE | 600519.XSHG | |
|---|---|---|
| 2018-01-03 | 300786416.0 | 5275543.0 |
| 2018-01-04 | 188290824.0 | 7322670.0 |
| 2018-01-05 | 122884667.0 | 4055487.0 |
| 2018-01-08 | 219167653.0 | 5294319.0 |
| 2018-01-09 | 136493152.0 | 6550585.0 |
| 2018-12-26 | 42114060.0 | 1786620.0 |
| 2018-12-27 | 62459327.0 | 2949357.0 |
| 2018-12-28 | 57660400.0 | 6367837.0 |
| 2019-01-02 | 53938632.0 | 6228649.0 |
| 2019-01-03 | 41553795.0 | 3097735.0 |
Image("张量 101/QI 4D Tensor.jpg", width=700, height=700)
tick_data1 = []
tick_data2 = []
for date in pd.date_range('2018-01-04', '2019-01-04').tolist():
#['2018-01-04', '2018-01-05', '2019-01-03', '2019-01-04']:
d = get_ticks('000001.XSHE', start_dt=None, end_dt=date, count=5, fields=['time','current','volume'])
tick_data1.append(d)
d = get_ticks('600519.XSHG', start_dt=None, end_dt=date, count=5, fields=['time','current','volume'])
tick_data2.append(d)
tick_data1
[array([( 2.01801031e+13, 13.33, 2923429.),
( 2.01801031e+13, 13.32, 2924188.),
( 2.01801031e+13, 13.33, 2924578.),
( 2.01801031e+13, 13.33, 2925430.),
( 2.01801032e+13, 13.33, 2962498.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801041e+13, 13.24, 1843835.),
( 2.01801041e+13, 13.24, 1844204.),
( 2.01801041e+13, 13.25, 1844353.),
( 2.01801041e+13, 13.25, 1844477.),
( 2.01801042e+13, 13.25, 1854509.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801051e+13, 13.29, 1198209.),
( 2.01801051e+13, 13.29, 1198615.),
( 2.01801051e+13, 13.3 , 1199005.),
( 2.01801051e+13, 13.3 , 1199827.),
( 2.01801052e+13, 13.3 , 1210313.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801051e+13, 13.29, 1198209.),
( 2.01801051e+13, 13.29, 1198615.),
( 2.01801051e+13, 13.3 , 1199005.),
( 2.01801051e+13, 13.3 , 1199827.),
( 2.01801052e+13, 13.3 , 1210313.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801051e+13, 13.29, 1198209.),
( 2.01801051e+13, 13.29, 1198615.),
( 2.01801051e+13, 13.3 , 1199005.),
( 2.01801051e+13, 13.3 , 1199827.),
( 2.01801052e+13, 13.3 , 1210313.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801081e+13, 12.95, 2138312.),
( 2.01801081e+13, 12.96, 2138421.),
( 2.01801081e+13, 12.96, 2138432.),
( 2.01801081e+13, 12.96, 2138835.),
( 2.01801082e+13, 12.96, 2158621.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801091e+13, 13.08, 1323836.),
( 2.01801091e+13, 13.08, 1324113.),
( 2.01801091e+13, 13.08, 1324292.),
( 2.01801091e+13, 13.08, 1324562.),
( 2.01801092e+13, 13.08, 1344345.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801101e+13, 13.46, 2341550.),
( 2.01801101e+13, 13.46, 2341788.),
( 2.01801101e+13, 13.46, 2352157.),
( 2.01801101e+13, 13.47, 2354443.),
( 2.01801102e+13, 13.47, 2403277.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801111e+13, 13.37, 1432720.),
( 2.01801111e+13, 13.38, 1433239.),
( 2.01801111e+13, 13.38, 1433292.),
( 2.01801111e+13, 13.38, 1433345.),
( 2.01801112e+13, 13.4 , 1443878.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801121e+13, 13.55, 1338290.),
( 2.01801121e+13, 13.54, 1338660.),
( 2.01801121e+13, 13.54, 1340557.),
( 2.01801121e+13, 13.54, 1340702.),
( 2.01801122e+13, 13.55, 1353991.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801121e+13, 13.55, 1338290.),
( 2.01801121e+13, 13.54, 1338660.),
( 2.01801121e+13, 13.54, 1340557.),
( 2.01801121e+13, 13.54, 1340702.),
( 2.01801122e+13, 13.55, 1353991.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801121e+13, 13.55, 1338290.),
( 2.01801121e+13, 13.54, 1338660.),
( 2.01801121e+13, 13.54, 1340557.),
( 2.01801121e+13, 13.54, 1340702.),
( 2.01801122e+13, 13.55, 1353991.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801151e+13, 14.21, 3079918.),
( 2.01801151e+13, 14.21, 3081286.),
( 2.01801151e+13, 14.21, 3081560.),
( 2.01801151e+13, 14.2 , 3082521.),
( 2.01801152e+13, 14.2 , 3122394.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801161e+13, 14.33, 2299300.),
( 2.01801161e+13, 14.33, 2299720.),
( 2.01801161e+13, 14.33, 2299932.),
( 2.01801161e+13, 14.33, 2304576.),
( 2.01801162e+13, 14.2 , 2444549.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801171e+13, 14.21, 2637907.),
( 2.01801171e+13, 14.22, 2638338.),
( 2.01801171e+13, 14.22, 2638502.),
( 2.01801171e+13, 14.23, 2638888.),
( 2.01801172e+13, 14.23, 2656294.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801181e+13, 14.69, 2083673.),
( 2.01801181e+13, 14.68, 2084720.),
( 2.01801181e+13, 14.69, 2088249.),
( 2.01801181e+13, 14.69, 2089455.),
( 2.01801182e+13, 14.72, 2148027.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801191e+13, 14.73, 2553405.),
( 2.01801191e+13, 14.73, 2554164.),
( 2.01801191e+13, 14.74, 2554398.),
( 2.01801191e+13, 14.73, 2554467.),
( 2.01801192e+13, 14.8 , 2571147.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801191e+13, 14.73, 2553405.),
( 2.01801191e+13, 14.73, 2554164.),
( 2.01801191e+13, 14.74, 2554398.),
( 2.01801191e+13, 14.73, 2554467.),
( 2.01801192e+13, 14.8 , 2571147.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801191e+13, 14.73, 2553405.),
( 2.01801191e+13, 14.73, 2554164.),
( 2.01801191e+13, 14.74, 2554398.),
( 2.01801191e+13, 14.73, 2554467.),
( 2.01801192e+13, 14.8 , 2571147.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801221e+13, 14.45, 2022733.),
( 2.01801221e+13, 14.43, 2023162.),
( 2.01801221e+13, 14.44, 2024568.),
( 2.01801221e+13, 14.44, 2025370.),
( 2.01801222e+13, 14.44, 2073867.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801231e+13, 14.64, 2331994.),
( 2.01801231e+13, 14.64, 2333733.),
( 2.01801231e+13, 14.65, 2336905.),
( 2.01801231e+13, 14.65, 2337526.),
( 2.01801232e+13, 14.65, 2388792.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801241e+13, 14.65, 2558653.),
( 2.01801241e+13, 14.64, 2558732.),
( 2.01801241e+13, 14.64, 2558861.),
( 2.01801241e+13, 14.64, 2559187.),
( 2.01801242e+13, 14.64, 2591292.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801251e+13, 14.17, 2354605.),
( 2.01801251e+13, 14.17, 2354816.),
( 2.01801251e+13, 14.16, 2355117.),
( 2.01801251e+13, 14.16, 2355299.),
( 2.01801252e+13, 14.2 , 2369985.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801261e+13, 14.04, 1981053.),
( 2.01801261e+13, 14.04, 1981632.),
( 2.01801261e+13, 14.05, 1982837.),
( 2.01801261e+13, 14.05, 1984083.),
( 2.01801262e+13, 14.05, 2032989.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801261e+13, 14.04, 1981053.),
( 2.01801261e+13, 14.04, 1981632.),
( 2.01801261e+13, 14.05, 1982837.),
( 2.01801261e+13, 14.05, 1984083.),
( 2.01801262e+13, 14.05, 2032989.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801261e+13, 14.04, 1981053.),
( 2.01801261e+13, 14.04, 1981632.),
( 2.01801261e+13, 14.05, 1982837.),
( 2.01801261e+13, 14.05, 1984083.),
( 2.01801262e+13, 14.05, 2032989.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801291e+13, 13.73, 2076393.),
( 2.01801291e+13, 13.73, 2076528.),
( 2.01801291e+13, 13.74, 2077488.),
( 2.01801291e+13, 13.73, 2077590.),
( 2.01801292e+13, 13.74, 2090547.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801301e+13, 13.61, 1078904.),
( 2.01801301e+13, 13.61, 1079131.),
( 2.01801301e+13, 13.61, 1079566.),
( 2.01801301e+13, 13.61, 1080161.),
( 2.01801302e+13, 13.65, 1094740.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01801311e+13, 14.04, 1713083.),
( 2.01801311e+13, 14.04, 1714212.),
( 2.01801311e+13, 14.04, 1714551.),
( 2.01801311e+13, 14.04, 1715289.),
( 2.01801312e+13, 14.05, 1747729.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802011e+13, 14.03, 1992267.),
( 2.01802011e+13, 14.03, 1992536.),
( 2.01802011e+13, 14.04, 1992743.),
( 2.01802011e+13, 14.04, 1992934.),
( 2.01802012e+13, 14.03, 2005615.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802021e+13, 14.03, 1159938.),
( 2.01802021e+13, 14.04, 1160384.),
( 2.01802021e+13, 14.01, 1161564.),
( 2.01802021e+13, 14.02, 1161704.),
( 2.01802022e+13, 14.05, 1176513.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802021e+13, 14.03, 1159938.),
( 2.01802021e+13, 14.04, 1160384.),
( 2.01802021e+13, 14.01, 1161564.),
( 2.01802021e+13, 14.02, 1161704.),
( 2.01802022e+13, 14.05, 1176513.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802021e+13, 14.03, 1159938.),
( 2.01802021e+13, 14.04, 1160384.),
( 2.01802021e+13, 14.01, 1161564.),
( 2.01802021e+13, 14.02, 1161704.),
( 2.01802022e+13, 14.05, 1176513.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802051e+13, 14.55, 2299330.),
( 2.01802051e+13, 14.54, 2301106.),
( 2.01802051e+13, 14.55, 2301596.),
( 2.01802051e+13, 14.56, 2302765.),
( 2.01802052e+13, 14.55, 2331998.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802061e+13, 14.03, 2556569.),
( 2.01802061e+13, 14.02, 2557004.),
( 2.01802061e+13, 14.02, 2557721.),
( 2.01802061e+13, 14.01, 2557898.),
( 2.01802062e+13, 14. , 2582872.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802071e+13, 12.9 , 3321709.),
( 2.01802071e+13, 12.92, 3322153.),
( 2.01802071e+13, 12.9 , 3322745.),
( 2.01802071e+13, 12.92, 3323140.),
( 2.01802072e+13, 12.92, 3345717.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802081e+13, 12.56, 2102504.),
( 2.01802081e+13, 12.55, 2104468.),
( 2.01802081e+13, 12.56, 2106259.),
( 2.01802081e+13, 12.56, 2106620.),
( 2.01802082e+13, 12.54, 2137815.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802091e+13, 11.69, 2787271.),
( 2.01802091e+13, 11.69, 2787663.),
( 2.01802091e+13, 11.68, 2788257.),
( 2.01802091e+13, 11.68, 2788398.),
( 2.01802092e+13, 11.69, 2824950.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802091e+13, 11.69, 2787271.),
( 2.01802091e+13, 11.69, 2787663.),
( 2.01802091e+13, 11.68, 2788257.),
( 2.01802091e+13, 11.68, 2788398.),
( 2.01802092e+13, 11.69, 2824950.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802091e+13, 11.69, 2787271.),
( 2.01802091e+13, 11.69, 2787663.),
( 2.01802091e+13, 11.68, 2788257.),
( 2.01802091e+13, 11.68, 2788398.),
( 2.01802092e+13, 11.69, 2824950.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802121e+13, 11.72, 1219966.),
( 2.01802121e+13, 11.72, 1219999.),
( 2.01802121e+13, 11.71, 1220135.),
( 2.01802121e+13, 11.71, 1220252.),
( 2.01802122e+13, 11.72, 1228782.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802131e+13, 11.93, 1286225.),
( 2.01802131e+13, 11.94, 1286234.),
( 2.01802131e+13, 11.94, 1286280.),
( 2.01802131e+13, 11.94, 1286302.),
( 2.01802132e+13, 11.94, 1298179.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802141e+13, 12., 849668.),
( 2.01802141e+13, 12., 850226.),
( 2.01802141e+13, 12., 850933.),
( 2.01802141e+13, 12., 851014.),
( 2.01802142e+13, 12., 864190.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802141e+13, 12., 849668.),
( 2.01802141e+13, 12., 850226.),
( 2.01802141e+13, 12., 850933.),
( 2.01802141e+13, 12., 851014.),
( 2.01802142e+13, 12., 864190.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802141e+13, 12., 849668.),
( 2.01802141e+13, 12., 850226.),
( 2.01802141e+13, 12., 850933.),
( 2.01802141e+13, 12., 851014.),
( 2.01802142e+13, 12., 864190.)],
dtype=(numpy.record, [('time', '<f8'), ('current', '<f8'), ('volume', '<f8')])),
array([( 2.01802141e+13, 12., 849668.),
( 2.01802141e+13, 12., 850226.),
( 2.01802141e+13, 12., 850933.),
( 2.01802141e+13, 12., 851014.),
( 2.01802142e+13, 12., 864190.)],
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