[python] 2021 NASDAQ CORRELATION HISTOGRAM

2021_01_correlation_histogram.ipynb

0.08MB

Start Date : 2016/11/30

End Date : 2021/01/07

6,212 NASDAQ, NYSE Stocks Close Price 

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

In [2]:

data=pd.read_csv('allstock_t.csv')

In [3]:

data.head()

Out[3]:

	A	AA	AACG	AACQ	AACQU	AAIC	AAL	AAMC	AAME	AAN	...	ZTO	ZTR	ZTS	ZUMZ	ZUO	ZVO	ZYME	ZYNE	ZYXI	TSLA
0	126.16	25.23	1.27	10.54	11.15	3.93	15.38	22.39	2.36	21.37	...	28.20	8.76	167.00	39.39	13.73	4.83	53.54	3.49	15.18	816.04
1	122.89	25.21	1.22	10.62	11.25	3.88	15.52	22.85	2.30	20.85	...	28.48	8.87	167.15	39.21	13.42	4.84	51.67	3.41	14.42	755.98
2	119.61	23.67	1.23	10.56	11.29	3.77	15.43	23.84	2.60	19.57	...	28.82	8.92	164.73	36.08	13.40	5.13	50.88	3.39	13.51	735.11
3	118.64	23.25	1.18	10.66	11.30	3.73	15.13	23.90	2.14	18.81	...	28.05	8.79	163.59	35.70	13.55	4.61	48.97	3.33	13.88	729.77
4	118.49	23.05	1.19	10.63	11.24	3.78	15.77	23.58	2.06	18.96	...	29.16	8.78	165.50	36.78	13.93	4.74	47.26	3.30	13.46	705.67

5 rows × 6212 columns

In [4]:

data.describe()

Out[4]:

	A	AA	AACG	AACQ	AACQU	AAIC	AAL	AAMC	AAME	AAN	...	ZTO	ZTR	ZTS	ZUMZ	ZUO	ZVO	ZYME	ZYNE	ZYXI	TSLA
count	1033.000000	1033.000000	1033.000000	86.000000	124.000000	1033.000000	1033.000000	1033.000000	1033.000000	29.000000	...	1033.000000	1033.000000	1033.000000	1033.000000	691.000000	438.000000	931.000000	1033.000000	1033.000000	1033.000000
mean	73.185063	29.839206	2.754318	9.993721	10.388468	8.635886	34.291549	44.361007	2.772043	18.648621	...	20.198645	10.833621	99.770716	23.707028	16.884819	3.150502	22.721740	9.291249	6.798984	118.857686
std	15.442046	13.207211	1.698425	0.281709	0.354555	4.086326	13.508238	27.777008	0.676280	2.025071	...	6.484080	1.684192	33.651160	5.874606	5.603138	1.309655	13.272675	5.287056	6.098869	135.816838
min	43.210000	5.480000	0.670000	9.620000	10.000000	1.700000	9.040000	7.990000	1.500000	16.370000	...	11.230000	5.050000	49.100000	11.450000	7.190000	1.110000	6.480000	2.890000	0.290000	35.790000
25%	64.730000	20.070000	1.130000	9.782500	10.127500	5.600000	27.220000	16.760000	2.230000	17.620000	...	15.360000	9.870000	71.770000	19.780000	12.515000	2.012500	12.430000	5.030000	2.780000	55.840000
50%	69.700000	29.290000	2.020000	9.940000	10.315000	8.380000	34.810000	38.800000	2.640000	18.120000	...	18.470000	11.130000	91.470000	23.600000	15.360000	3.000000	16.290000	7.860000	4.000000	65.320000
75%	79.700000	41.640000	4.600000	10.067500	10.452500	11.780000	46.280000	68.700000	3.390000	18.960000	...	22.680000	11.830000	126.200000	27.130000	20.570000	4.117500	34.680000	11.900000	10.260000	85.050000
max	126.160000	60.230000	6.870000	10.660000	11.330000	17.080000	58.470000	109.350000	4.100000	26.500000	...	38.640000	14.110000	174.350000	39.390000	37.090000	6.790000	54.990000	25.190000	28.580000	816.040000

8 rows × 6212 columns

In [5]:

data.corr()

Out[5]:

	A	AA	AACG	AACQ	AACQU	AAIC	AAL	AAMC	AAME	AAN	...	ZTO	ZTR	ZTS	ZUMZ	ZUO	ZVO	ZYME	ZYNE	ZYXI	TSLA
A	1.000000	-0.546866	-0.575601	0.658820	0.648158	-0.820908	-0.749643	-0.610791	-0.740684	0.022504	...	0.812895	-0.622723	0.905127	0.683145	-0.577249	0.409858	0.814755	-0.695498	0.783201	0.839405
AA	-0.546866	1.000000	0.815143	0.768057	0.726503	0.782478	0.873741	0.830392	0.682925	-0.118194	...	-0.718033	0.717891	-0.738771	-0.368700	0.842965	0.356939	-0.774016	0.409851	-0.767670	-0.442666
AACG	-0.575601	0.815143	1.000000	0.447957	0.180494	0.768677	0.812953	0.799256	0.693058	0.422086	...	-0.599905	0.727005	-0.696504	-0.468258	0.607671	0.259030	-0.656486	0.566730	-0.627059	-0.397638
AACQ	0.658820	0.768057	0.447957	1.000000	0.985981	0.879144	0.821862	0.238698	0.298408	-0.333966	...	-0.550040	0.836415	-0.082277	0.621965	0.848117	0.421777	0.583873	-0.087699	-0.191017	0.872929
AACQU	0.648158	0.726503	0.180494	0.985981	1.000000	0.878924	0.756595	0.260480	0.305446	-0.431639	...	-0.484003	0.775781	0.050401	0.647299	0.514447	-0.046324	0.606103	-0.140728	-0.196754	0.773121
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
ZVO	0.409858	0.356939	0.259030	0.421777	-0.046324	0.014492	-0.049150	0.573502	0.053076	-0.138094	...	0.176974	-0.169982	0.223559	0.018655	0.259352	1.000000	-0.230148	0.150186	0.081227	0.431805
ZYME	0.814755	-0.774016	-0.656486	0.583873	0.606103	-0.845485	-0.829789	-0.773132	-0.804468	-0.160786	...	0.788578	-0.648268	0.917772	0.732296	-0.723101	-0.230148	1.000000	-0.556448	0.802557	0.673927
ZYNE	-0.695498	0.409851	0.566730	-0.087699	-0.140728	0.754595	0.656618	0.556456	0.758193	0.295205	...	-0.647344	0.610665	-0.696765	-0.491461	0.423136	0.150186	-0.556448	1.000000	-0.604850	-0.456071
ZYXI	0.783201	-0.767670	-0.627059	-0.191017	-0.196754	-0.892040	-0.877533	-0.753387	-0.808730	0.226944	...	0.946738	-0.733390	0.884515	0.532797	-0.750800	0.081227	0.802557	-0.604850	1.000000	0.629662
TSLA	0.839405	-0.442666	-0.397638	0.872929	0.773121	-0.571510	-0.634259	-0.351846	-0.456120	0.000435	...	0.696491	-0.617307	0.712225	0.495106	-0.497623	0.431805	0.673927	-0.456071	0.629662	1.000000

6212 rows × 6212 columns

In [6]:

cortable=data.corr()

In [7]:

plt.figure(figsize=(20,10))
sns.histplot(cortable['FB'])
plt.show()

 

In [8]:

plt.figure(figsize=(20,10))
sns.histplot(cortable['AAPL'])
plt.show()

 

In [9]:

plt.figure(figsize=(20,10))
sns.histplot(cortable['TSLA'])
plt.show()

 

In [11]:

plt.figure(figsize=(20,10))
sns.histplot(cortable['ZVO'])
plt.show()

 

 

 

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