In [28]:

tKey = 'YOUR_TIINGO_KEY'
%matplotlib inline
import pandas as pd
import pandas_datareader.tiingo as tiingo
import datetime

# last trade day from previous year
# TODO: auto calculate
dStrLastYearClose = '12/31/2018'

# get the current date
d = datetime.datetime.today()
# convert to string format
dStrToday = d.strftime('%m/%d/%Y')
dStrYearStart = d.strftime('01/01/%Y')

# tickers of all peers
peers = ['CRZO','CDEV','DNR','LPI','OAS','PDCE','PE','RRC','SM','SWN','WLL','WPX','XEC']

# fetch the stock data
dr = tiingo.TiingoDailyReader(symbols=peers, start=dStrLastYearClose, end=dStrToday, api_key=tKey)
df = dr.read()
dr.close()

# show some stats on the data
df.describe()

Out[28]:

	adjClose	adjHigh	adjLow	adjOpen	adjVolume	close	divCash	high	low	open	splitFactor	volume
count	520.000000	520.000000	520.000000	520.000000	5.200000e+02	520.000000	520.000000	520.000000	520.000000	520.000000	520.0	5.200000e+02
mean	18.074767	18.411541	17.668075	18.006340	6.159148e+06	18.085019	0.000346	18.421913	17.678127	18.016529	1.0	6.159148e+06
std	18.095398	18.330301	17.768455	18.011581	5.056756e+06	18.125904	0.007894	18.361045	17.798370	18.041850	0.0	5.056756e+06
min	1.710000	1.780000	1.600000	1.650000	6.352690e+05	1.710000	0.000000	1.780000	1.600000	1.650000	1.0	6.352690e+05
25%	5.892500	6.032500	5.760000	5.847500	2.570019e+06	5.892500	0.000000	6.032500	5.760000	5.847500	1.0	2.570019e+06
50%	12.525000	12.782500	12.275000	12.480000	4.724221e+06	12.525000	0.000000	12.782500	12.275000	12.480000	1.0	4.724221e+06
75%	19.635000	19.902275	19.067500	19.475000	8.279120e+06	19.635000	0.000000	19.902275	19.067500	19.475000	1.0	8.279120e+06
max	76.420000	76.915083	75.189233	76.166882	3.783066e+07	76.420000	0.180000	77.100000	75.370000	76.350000	1.0	3.783066e+07

In [2]:

# show column info
df.columns

Out[2]:

Index(['adjClose', 'adjHigh', 'adjLow', 'adjOpen', 'adjVolume', 'close',
       'divCash', 'high', 'low', 'open', 'splitFactor', 'volume'],
      dtype='object')

In [3]:

# filter data down to adjusted close price
df_close = df.loc[peers,['adjClose']]

df_close.head()

Out[3]:

		adjClose
symbol	date
CRZO	2018-12-31	11.29
	2019-01-02	11.51
	2019-01-03	11.45
	2019-01-04	12.18
	2019-01-07	12.54

In [4]:

# pivot the data
df_pivot = pd.pivot_table(df_close, index='date', columns='symbol',values="adjClose")

df_pivot.head(10)

Out[4]:

symbol	CDEV	CRZO	DNR	LPI	OAS	PDCE	PE	RRC	SM	SWN	WLL	WPX	XEC
date
2018-12-31	11.02	11.29	1.71	3.62	5.53	29.76	15.98	9.57	15.48	3.41	22.69	11.35	61.502139
2019-01-02	11.30	11.51	1.96	3.74	5.54	30.42	16.22	9.96	15.34	3.72	22.69	11.63	62.559596
2019-01-03	11.28	11.45	1.97	3.66	5.64	30.96	16.15	9.95	15.32	3.64	22.80	11.54	61.691683
2019-01-04	11.81	12.18	2.19	3.99	6.04	32.71	16.88	10.69	16.63	3.90	24.77	12.13	64.774272
2019-01-07	12.68	12.54	2.20	4.12	6.18	34.25	18.12	11.13	17.48	4.07	26.65	12.87	66.200842
2019-01-08	12.89	12.49	2.19	4.17	6.42	34.02	18.74	11.00	17.96	4.11	27.18	12.93	67.268276
2019-01-09	13.22	12.93	2.23	4.19	6.58	34.04	19.19	11.60	18.78	4.37	28.63	13.06	68.545206
2019-01-10	13.21	12.64	2.29	4.18	6.59	33.99	19.01	11.44	19.44	4.30	28.67	13.18	70.291009
2019-01-11	13.03	12.24	2.17	4.01	6.33	33.57	18.72	11.50	19.31	4.35	27.47	12.85	70.470577
2019-01-14	13.30	12.34	2.15	4.03	6.41	33.57	18.46	11.87	19.34	4.38	27.87	12.72	71.857243

In [5]:

# Close price at end of previous year
df_pivot.iloc[0,:]

Out[5]:

symbol
CDEV    11.020000
CRZO    11.290000
DNR      1.710000
LPI      3.620000
OAS      5.530000
PDCE    29.760000
PE      15.980000
RRC      9.570000
SM      15.480000
SWN      3.410000
WLL     22.690000
WPX     11.350000
XEC     61.502139
Name: 2018-12-31 00:00:00, dtype: float64

In [6]:

# Latest price at close
df_pivot.iloc[-1,:]

Out[6]:

symbol
CDEV     9.66
CRZO    11.30
DNR      2.05
LPI      3.48
OAS      5.91
PDCE    37.14
PE      18.62
RRC     10.36
SM      16.65
SWN      4.29
WLL     25.01
WPX     12.59
XEC     72.64
Name: 2019-02-27 00:00:00, dtype: float64

In [21]:

# Calculate YTD performance
perf_ytd = (df_pivot / df_pivot.iloc[0,:]) - 1

perf_ytd

Out[21]:

symbol	CDEV	CRZO	DNR	LPI	OAS	PDCE	PE	RRC	SM	SWN	WLL	WPX	XEC
date
2018-12-31	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2019-01-02	0.025408	0.019486	0.146199	0.033149	0.001808	0.022177	0.015019	0.040752	-0.009044	0.090909	0.000000	0.024670	0.017194
2019-01-03	0.023593	0.014172	0.152047	0.011050	0.019892	0.040323	0.010638	0.039707	-0.010336	0.067449	0.004848	0.016740	0.003082
2019-01-04	0.071688	0.078831	0.280702	0.102210	0.092224	0.099126	0.056320	0.117032	0.074289	0.143695	0.091670	0.068722	0.053204
2019-01-07	0.150635	0.110717	0.286550	0.138122	0.117541	0.150874	0.133917	0.163009	0.129199	0.193548	0.174526	0.133921	0.076399
2019-01-08	0.169691	0.106289	0.280702	0.151934	0.160940	0.143145	0.172716	0.149425	0.160207	0.205279	0.197885	0.139207	0.093755
2019-01-09	0.199637	0.145261	0.304094	0.157459	0.189873	0.143817	0.200876	0.212121	0.213178	0.281525	0.261789	0.150661	0.114517
2019-01-10	0.198730	0.119575	0.339181	0.154696	0.191682	0.142137	0.189612	0.195402	0.255814	0.260997	0.263552	0.161233	0.142903
2019-01-11	0.182396	0.084145	0.269006	0.107735	0.144665	0.128024	0.171464	0.201672	0.247416	0.275660	0.210665	0.132159	0.145823
2019-01-14	0.206897	0.093003	0.257310	0.113260	0.159132	0.128024	0.155194	0.240334	0.249354	0.284457	0.228294	0.120705	0.168370
2019-01-15	0.225045	0.110717	0.280702	0.104972	0.175407	0.125672	0.170213	0.226750	0.282300	0.293255	0.270604	0.133040	0.189294
2019-01-16	0.248639	0.073516	0.269006	0.099448	0.182640	0.113239	0.187735	0.223615	0.293282	0.287390	0.270163	0.123348	0.199027
2019-01-17	0.262250	0.051373	0.274854	0.027624	0.198915	0.105511	0.192115	0.203762	0.313307	0.304985	0.282503	0.119824	0.188159
2019-01-18	0.288566	0.109832	0.309942	0.077348	0.226040	0.127352	0.204631	0.206897	0.365633	0.346041	0.313795	0.153304	0.212328
2019-01-22	0.196915	0.067316	0.192982	-0.011050	0.110307	0.126008	0.168335	0.136886	0.293928	0.252199	0.204936	0.081057	0.191727
2019-01-23	0.174229	0.005314	0.169591	-0.024862	0.095841	0.135753	0.136421	0.106583	0.239664	0.234604	0.163508	0.066960	0.174047
2019-01-24	0.160617	0.014172	0.181287	0.000000	0.124774	0.127016	0.135169	0.129572	0.235788	0.275660	0.205818	0.077533	0.188483
2019-01-25	0.186025	0.004429	0.210526	0.019337	0.132007	0.123320	0.155194	0.177638	0.258398	0.328446	0.258704	0.099559	0.227899
2019-01-28	0.172414	-0.011515	0.152047	-0.005525	0.106691	0.075269	0.126408	0.177638	0.247416	0.287390	0.241075	0.069604	0.211841
2019-01-29	0.179673	0.010629	0.181287	0.008287	0.097649	0.065860	0.132666	0.183908	0.271318	0.310850	0.236227	0.070485	0.217032
2019-01-30	0.221416	0.101860	0.233918	0.066298	0.139241	0.146169	0.177096	0.228840	0.319121	0.348974	0.297929	0.088106	0.238767
2019-01-31	0.195100	0.087688	0.187135	0.049724	0.088608	0.094422	0.162703	0.152560	0.267442	0.281525	0.261789	0.080176	0.222060
2019-02-01	0.208711	0.137290	0.210526	0.077348	0.108499	0.113575	0.164581	0.144201	0.275194	0.284457	0.293521	0.102203	0.235523
2019-02-04	0.209619	0.154119	0.216374	0.091160	0.110307	0.129032	0.180851	0.137931	0.257752	0.275660	0.325253	0.144493	0.237307
2019-02-05	0.170599	0.124004	0.181287	0.058011	0.050633	0.115591	0.150188	0.120167	0.217700	0.243402	0.267078	0.138326	0.226926
2019-02-06	0.166969	0.108946	0.152047	0.041436	0.041591	0.108535	0.149562	0.059561	0.209302	0.167155	0.269282	0.135683	0.212814
2019-02-07	0.130672	0.031887	0.081871	-0.044199	0.010850	0.072245	0.118273	0.009404	0.125969	0.090909	0.187748	0.078414	0.160746
2019-02-08	0.112523	-0.023915	0.000000	-0.049724	-0.001808	0.035618	0.103880	-0.013584	0.082687	0.114370	0.162627	0.049339	0.145499
2019-02-11	0.118875	-0.001771	0.046784	0.022099	0.021700	0.044691	0.128911	0.081505	0.127907	0.155425	0.175408	0.072247	0.172263
2019-02-12	0.118875	0.029229	0.081871	0.002762	0.043400	0.130376	0.119524	0.087774	0.179587	0.175953	0.200970	0.086344	0.182482
2019-02-13	0.116152	0.055802	0.122807	0.046961	0.061483	0.188172	0.140175	0.085684	0.228682	0.187683	0.221684	0.122467	0.205353
2019-02-14	0.138838	0.078831	0.216374	0.063536	0.070524	0.227487	0.154568	0.084639	0.217700	0.175953	0.262230	0.147137	0.217356
2019-02-15	0.177858	0.123118	0.257310	0.066298	0.124774	0.284946	0.172090	0.135841	0.279716	0.228739	0.293962	0.181498	0.235567
2019-02-19	0.170599	0.113375	0.269006	0.038674	0.108499	0.279906	0.177722	0.137931	0.232558	0.222874	0.290877	0.166520	0.220608
2019-02-20	0.188748	0.116032	0.309942	0.055249	0.148282	0.319220	0.160200	0.157785	0.271964	0.269795	0.316439	0.180617	0.242558
2019-02-21	0.120690	0.052259	0.251462	-0.008287	0.097649	0.302083	0.122028	0.131661	0.099483	0.255132	0.281181	0.140969	0.192154
2019-02-22	0.138838	0.043401	0.251462	0.000000	0.112116	0.313172	0.209637	0.108673	0.091085	0.243402	0.281181	0.131278	0.206137
2019-02-25	0.153358	0.029229	0.222222	-0.008287	0.101266	0.262433	0.172716	0.150470	0.091085	0.293255	0.249008	0.098678	0.198658
2019-02-26	-0.107078	0.058459	0.181287	-0.033149	0.066908	0.244624	0.143304	0.107628	0.060724	0.272727	0.239753	0.083700	0.180447
2019-02-27	-0.123412	0.000886	0.198830	-0.038674	0.068716	0.247984	0.165207	0.082550	0.075581	0.258065	0.102248	0.109251	0.181097

In [34]:

perf_ytd.plot(title='Peer YTD Performance', figsize=(20,10))

Out[34]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f00114f6630>

In [23]:

# Daily performance
perf_daily = df_pivot.pct_change(1)

perf_daily

Out[23]:

symbol	CDEV	CRZO	DNR	LPI	OAS	PDCE	PE	RRC	SM	SWN	WLL	WPX	XEC
date
2018-12-31	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2019-01-02	0.025408	0.019486	0.146199	0.033149	0.001808	0.022177	0.015019	0.040752	-0.009044	0.090909	0.000000	0.024670	0.017194
2019-01-03	-0.001770	-0.005213	0.005102	-0.021390	0.018051	0.017751	-0.004316	-0.001004	-0.001304	-0.021505	0.004848	-0.007739	-0.013873
2019-01-04	0.046986	0.063755	0.111675	0.090164	0.070922	0.056525	0.045201	0.074372	0.085509	0.071429	0.086404	0.051127	0.049968
2019-01-07	0.073666	0.029557	0.004566	0.032581	0.023179	0.047080	0.073460	0.041160	0.051112	0.043590	0.075898	0.061006	0.022024
2019-01-08	0.016562	-0.003987	-0.004545	0.012136	0.038835	-0.006715	0.034216	-0.011680	0.027460	0.009828	0.019887	0.004662	0.016124
2019-01-09	0.025601	0.035228	0.018265	0.004796	0.024922	0.000588	0.024013	0.054545	0.045657	0.063260	0.053348	0.010054	0.018983
2019-01-10	-0.000756	-0.022428	0.026906	-0.002387	0.001520	-0.001469	-0.009380	-0.013793	0.035144	-0.016018	0.001397	0.009188	0.025469
2019-01-11	-0.013626	-0.031646	-0.052402	-0.040670	-0.039454	-0.012357	-0.015255	0.005245	-0.006687	0.011628	-0.041856	-0.025038	0.002555
2019-01-14	0.020721	0.008170	-0.009217	0.004988	0.012638	0.000000	-0.013889	0.032174	0.001554	0.006897	0.014561	-0.010117	0.019677
2019-01-15	0.015038	0.016207	0.018605	-0.007444	0.014041	-0.002085	0.013001	-0.010952	0.026370	0.006849	0.034446	0.011006	0.017909
2019-01-16	0.019259	-0.033493	-0.009132	-0.005000	0.006154	-0.011045	0.014973	-0.002555	0.008564	-0.004535	-0.000347	-0.008554	0.008183
2019-01-17	0.010901	-0.020627	0.004608	-0.065327	0.013761	-0.006942	0.003688	-0.016225	0.015485	0.013667	0.009715	-0.003137	-0.009064
2019-01-18	0.020848	0.055602	0.027523	0.048387	0.022624	0.019757	0.010499	0.002604	0.039843	0.031461	0.024399	0.029898	0.020341
2019-01-22	-0.071127	-0.038308	-0.089286	-0.082051	-0.094395	-0.001192	-0.030130	-0.058009	-0.052507	-0.069717	-0.082858	-0.062643	-0.016992
2019-01-23	-0.018954	-0.058091	-0.019608	-0.013966	-0.013029	0.008654	-0.027317	-0.026654	-0.041937	-0.014052	-0.034382	-0.013040	-0.014836
2019-01-24	-0.011592	0.008811	0.010000	0.025496	0.026403	-0.007692	-0.001101	0.020774	-0.003127	0.033254	0.036364	0.009909	0.012296
2019-01-25	0.021892	-0.009607	0.024752	0.019337	0.006431	-0.003280	0.017641	0.042553	0.018296	0.041379	0.043860	0.020442	0.033165
2019-01-28	-0.011477	-0.015873	-0.048309	-0.024390	-0.022364	-0.042776	-0.024919	0.000000	-0.008727	-0.030905	-0.014006	-0.027244	-0.013078
2019-01-29	0.006192	0.022401	0.025381	0.013889	-0.008170	-0.008750	0.005556	0.005324	0.019161	0.018223	-0.003906	0.000824	0.004283
2019-01-30	0.035385	0.090272	0.044554	0.057534	0.037891	0.075347	0.039227	0.037952	0.037602	0.029083	0.049911	0.016461	0.017860
2019-01-31	-0.021545	-0.012862	-0.037915	-0.015544	-0.044444	-0.045148	-0.012228	-0.062075	-0.039177	-0.050000	-0.027844	-0.007287	-0.013487
2019-02-01	0.011390	0.045603	0.019704	0.026316	0.018272	0.017501	0.001615	-0.007253	0.006116	0.002288	0.025148	0.020392	0.011017
2019-02-04	0.000751	0.014798	0.004831	0.012821	0.001631	0.013881	0.013971	-0.005479	-0.013678	-0.006849	0.024532	0.038369	0.001444
2019-02-05	-0.032258	-0.026094	-0.028846	-0.030380	-0.053746	-0.011905	-0.025967	-0.015611	-0.031844	-0.025287	-0.043898	-0.005389	-0.008390
2019-02-06	-0.003101	-0.013396	-0.024752	-0.015666	-0.008606	-0.006325	-0.000544	-0.054104	-0.006897	-0.061321	0.001739	-0.002322	-0.011502
2019-02-07	-0.031104	-0.069489	-0.060914	-0.082228	-0.029514	-0.032737	-0.027218	-0.047337	-0.068910	-0.065327	-0.064236	-0.050427	-0.042932
2019-02-08	-0.016051	-0.054077	-0.075676	-0.005780	-0.012522	-0.034159	-0.012871	-0.022774	-0.038439	0.021505	-0.021150	-0.026961	-0.013136
2019-02-11	0.005710	0.022686	0.046784	0.075581	0.023551	0.008761	0.022676	0.096398	0.041766	0.036842	0.010993	0.021830	0.023364
2019-02-12	0.000000	0.031056	0.033520	-0.018919	0.021239	0.082020	-0.008315	0.005797	0.045819	0.017766	0.021747	0.013147	0.008717
2019-02-13	-0.002433	0.025818	0.037838	0.044077	0.017331	0.051130	0.018446	-0.001921	0.041621	0.009975	0.017248	0.033252	0.019342
2019-02-14	0.020325	0.021812	0.083333	0.015831	0.008518	0.033088	0.012623	-0.000962	-0.008938	-0.009877	0.033189	0.021978	0.009958
2019-02-15	0.034263	0.041051	0.033654	0.002597	0.050676	0.046811	0.015176	0.047206	0.050928	0.044888	0.025140	0.029954	0.014959
2019-02-19	-0.006163	-0.008675	0.009302	-0.025907	-0.014469	-0.003923	0.004805	0.001840	-0.036850	-0.004773	-0.002384	-0.012677	-0.012107
2019-02-20	0.015504	0.002387	0.032258	0.015957	0.035889	0.030717	-0.014878	0.017447	0.031971	0.038369	0.019802	0.012085	0.017983
2019-02-21	-0.057252	-0.057143	-0.044643	-0.060209	-0.044094	-0.012990	-0.032902	-0.022563	-0.135602	-0.011547	-0.026783	-0.033582	-0.040565
2019-02-22	0.016194	-0.008418	0.000000	0.008357	0.013180	0.008516	0.078081	-0.020314	-0.007638	-0.009346	0.000000	-0.008494	0.011729
2019-02-25	0.012749	-0.013582	-0.023364	-0.008287	-0.009756	-0.038639	-0.030523	0.037700	0.000000	0.040094	-0.025112	-0.028816	-0.006201
2019-02-26	-0.225806	0.028399	-0.033493	-0.025070	-0.031199	-0.014107	-0.025080	-0.037239	-0.027827	-0.015873	-0.007410	-0.013633	-0.015193
2019-02-27	-0.018293	-0.054393	0.014851	-0.005714	0.001695	0.002700	0.019157	-0.022642	0.014007	-0.011521	-0.110914	0.023577	0.000551

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