Correlation¶

In [1]:

import pandas as pd
import numpy as np
import seaborn as sns

import pandas as pd import numpy as np import seaborn as sns

In [2]:

sp500_px = pd.read_csv("../data/sp500_data.csv", index_col=0, parse_dates=True)
sp500_px

sp500_px = pd.read_csv("../data/sp500_data.csv", index_col=0, parse_dates=True) sp500_px

Out[2]:

	ADS	CA	MSFT	RHT	CTSH	CSC	EMC	IBM	XRX	ALTR	...	WAT	ALXN	AMGN	BXLT	BIIB	CELG	GILD	REGN	VRTX	HSIC
1993-01-29	0.000000	0.060124	-0.022100	0.000000	0.000000	0.018897	0.007368	0.092165	0.259140	-0.007105	...	0.000000	0.000000	0.347160	0.000000	0.041670	0.000000	0.015564	1.750000	0.125000	0.000000
1993-02-01	0.000000	-0.180389	0.027621	0.000000	0.000000	0.018889	0.018425	0.115207	-0.100775	0.063893	...	0.000000	0.000000	-0.231440	0.000000	0.000000	-0.010410	0.007782	1.250000	0.125000	0.000000
1993-02-02	0.000000	-0.120257	0.035900	0.000000	0.000000	-0.075573	0.029482	-0.023041	0.028796	-0.014192	...	0.000000	0.000000	-0.115720	0.000000	0.000000	0.000000	-0.007792	-0.250000	0.000000	0.000000
1993-02-03	0.000000	0.060124	-0.024857	0.000000	0.000000	-0.151128	0.003689	-0.253454	-0.043190	-0.007105	...	0.000000	0.000000	-0.086790	0.000000	0.041670	-0.041670	-0.038919	-0.500000	0.062500	0.000000
1993-02-04	0.000000	-0.360770	-0.060757	0.000000	0.000000	0.113350	-0.022114	0.069862	0.000000	-0.007096	...	0.000000	0.000000	0.144650	0.000000	-0.041660	-0.031260	-0.046711	0.000000	0.062500	0.000000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2015-06-25	0.019989	-0.529999	-0.379997	0.080002	0.350003	-0.580002	-0.457999	-1.720001	-0.009936	0.039997	...	-2.500000	1.889999	-2.060012	0.150002	-1.630004	-0.990005	-2.250000	1.270019	-1.919998	-0.080002
2015-06-26	-1.299988	-0.330000	-0.390004	-1.820000	0.190003	0.330002	-0.328565	-0.769989	0.020000	0.000000	...	-0.070007	-2.380005	-1.390000	0.550004	-3.509979	-1.080002	-0.800003	-1.900024	-3.629997	0.440002
2015-06-29	-10.299988	-0.410000	-0.670002	-1.250000	-1.919998	-0.510002	-0.139999	-0.949997	-0.100000	-0.040001	...	-2.250000	-5.490005	-5.029999	0.090000	-8.290009	-1.209999	-2.419998	-9.620026	-3.770004	-2.479996
2015-06-30	-2.109986	-0.049999	-0.559997	-0.099999	-0.689999	-0.610001	0.239999	-1.330001	-0.200000	0.020001	...	-0.299988	0.270004	0.279999	-1.710001	0.390014	0.439995	-0.209999	-2.070007	-0.849999	-1.360001
2015-07-01	-1.210022	0.020000	-0.009998	0.869995	0.329998	-0.190003	-0.039999	0.520004	-0.030000	-0.079998	...	-0.250000	1.270005	0.050003	-1.380001	-2.910003	1.180000	-2.099999	-8.719970	2.080001	0.940002

5647 rows × 517 columns

In [3]:

sp500_sym = pd.read_csv("../data/sp500_sectors.csv")
sp500_sym

sp500_sym = pd.read_csv("../data/sp500_sectors.csv") sp500_sym

Out[3]:

	sector	sector_label	sub_sector	symbol
0	information_technology	Technology	data_processing_&_outsourced_services	ADS
1	information_technology	Technology	systems_software	CA
2	information_technology	Technology	systems_software	MSFT
3	information_technology	Technology	systems_software	RHT
4	information_technology	Technology	it_consulting_&_services	CTSH
...	...	...	...	...
512	health_care	Health Care	biotechnology	CELG
513	health_care	Health Care	biotechnology	GILD
514	health_care	Health Care	biotechnology	REGN
515	health_care	Health Care	biotechnology	VRTX
516	health_care	Health Care	health_care_distributors	HSIC

517 rows × 4 columns

In [4]:

etfs = sp500_px.loc[sp500_px.index > '2012-07-01', sp500_sym[sp500_sym['sector'] == "etf"]["symbol"]]
etfs

etfs = sp500_px.loc[sp500_px.index > '2012-07-01', sp500_sym[sp500_sym['sector'] == "etf"]["symbol"]] etfs

Out[4]:

	XLI	QQQ	SPY	DIA	GLD	VXX	USO	IWM	XLE	XLY	XLU	XLB	XTL	XLV	XLP	XLF	XLK
2012-07-02	-0.376098	0.096313	0.028223	-0.242796	0.419998	-10.400000	0.000000	0.534641	0.028186	0.095759	0.098311	-0.093713	0.019076	-0.009529	0.313499	0.018999	0.075668
2012-07-03	0.376099	0.481576	0.874936	0.728405	0.490006	-3.520000	0.250000	0.926067	0.995942	0.000000	-0.044686	0.337373	0.000000	0.000000	0.129087	0.104492	0.236462
2012-07-05	0.150440	0.096313	-0.103487	0.149420	0.239991	6.560000	-0.070000	-0.171848	-0.460387	0.306431	-0.151938	0.103086	0.019072	-0.142955	-0.073766	-0.142490	0.066211
2012-07-06	-0.141040	-0.491201	0.018819	-0.205449	-0.519989	-8.800000	-0.180000	-0.229128	0.206706	0.153214	0.080437	0.018744	-0.429213	-0.095304	0.119865	0.066495	-0.227003
2012-07-09	0.244465	-0.048160	-0.056445	-0.168094	0.429992	-0.480000	0.459999	-0.190939	-0.234892	-0.201098	-0.035751	-0.168687	0.000000	0.352630	-0.064548	0.018999	0.009457
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2015-06-25	-0.379997	-0.669998	-1.270004	-1.398679	0.010002	0.279999	-0.069999	-0.497896	-0.750000	-0.200005	-0.309997	-0.059997	-0.229999	0.189994	-0.279999	-0.220002	-0.330002
2015-06-26	-0.040001	-0.700004	-0.389999	-0.079926	0.360001	-0.020001	0.189999	-0.587512	0.259995	-0.010002	0.340000	-0.290001	-0.280002	-0.220001	0.029998	-0.030001	-0.330002
2015-06-29	-0.590000	-1.320000	-2.580002	-1.918189	0.260002	1.829999	-0.080000	-2.280327	-0.500000	-1.089996	-0.160000	-0.769996	-0.700001	-1.180000	-0.500000	-0.330000	-0.450000
2015-06-30	-0.480000	-0.419998	-1.369996	-1.168887	0.130005	0.889999	0.139999	-0.338568	-0.169998	-0.430001	-0.310001	-0.270001	0.259999	-0.510002	-0.580002	-0.150002	-0.269996
2015-07-01	-0.269996	-0.279999	-0.159989	-0.169852	-0.140000	-0.309999	-0.440001	-0.537713	-1.110001	0.220001	0.200001	-0.219997	-1.029998	0.029998	0.290001	-0.010000	-0.090000

754 rows × 17 columns

In [5]:

sns.heatmap(etfs.corr(), vmin=-1, vmax=1)

sns.heatmap(etfs.corr(), vmin=-1, vmax=1)

Out[5]:

<Axes: >

Scatterplots¶

In [6]:

telecom = sp500_px.loc[sp500_px.index > "2012-07-01", sp500_sym[sp500_sym["sector"] == "telecommunications_services"]["symbol"]]
telecom

telecom = sp500_px.loc[sp500_px.index > "2012-07-01", sp500_sym[sp500_sym["sector"] == "telecommunications_services"]["symbol"]] telecom

Out[6]:

	T	CTL	FTR	VZ	LVLT
2012-07-02	0.422496	0.140847	0.070879	0.554180	-0.519998
2012-07-03	-0.177448	0.066280	0.070879	-0.025976	-0.049999
2012-07-05	-0.160548	-0.132563	0.055128	-0.051956	-0.180000
2012-07-06	0.342205	0.132563	0.007875	0.140106	-0.359999
2012-07-09	0.136883	0.124279	-0.023626	0.253943	0.180000
...	...	...	...	...	...
2015-06-25	0.049342	-1.600000	-0.040000	-0.187790	-0.330002
2015-06-26	-0.256586	0.039999	-0.070000	0.029650	-0.739998
2015-06-29	-0.098685	-0.559999	-0.060000	-0.504063	-1.360000
2015-06-30	-0.503298	-0.420000	-0.070000	-0.523829	0.199997
2015-07-01	-0.019737	0.080000	-0.050000	0.355811	0.139999

754 rows × 5 columns

In [7]:

ax = telecom.plot.scatter(x='T', y='VZ', figsize=(4,4), marker="2")
ax.set_xlabel("ATT (T)")
ax.set_ylabel("Verizon (VZ)")
ax.axhline(0, color="grey", lw=1)
ax.axvline(0, color="grey", lw=1)

ax = telecom.plot.scatter(x='T', y='VZ', figsize=(4,4), marker="2") ax.set_xlabel("ATT (T)") ax.set_ylabel("Verizon (VZ)") ax.axhline(0, color="grey", lw=1) ax.axvline(0, color="grey", lw=1)

Out[7]:

<matplotlib.lines.Line2D at 0x7f3168526cf0>