In [3]:
# Get backtest object
bt = get_backtest('57bbdfdc1b2ff1100ff72694')
# Create all tear sheets
bt.create_full_tear_sheet()
100% Time: 0:05:13|###########################################################|
Entire data start date: 2007-01-05
Entire data end date: 2016-08-18
Backtest Months: 115
Backtest
annual_return 0.07
annual_volatility 0.03
sharpe_ratio 2.10
calmar_ratio 1.53
stability 0.94
max_drawdown -0.05
omega_ratio 1.45
sortino_ratio 3.33
skewness 0.32
kurtosis 5.31
information_ratio 0.00
alpha 0.07
beta 0.06
Worst Drawdown Periods
net drawdown in % peak date valley date recovery date duration
0 4.77 2015-05-21 2016-02-02 NaT NaN
1 3.52 2011-07-07 2011-08-08 2011-09-15 51
2 2.84 2010-04-23 2010-08-31 2011-01-10 187
4 1.85 2011-11-08 2011-12-19 2012-01-12 48
3 1.82 2012-04-02 2012-06-11 2012-08-21 102
2-sigma returns daily -0.004
2-sigma returns weekly -0.008
dtype: float64
Stress Events
mean min max
Lehmann 0.000 -0.008 0.012
US downgrade/European Debt Crisis 0.001 -0.012 0.016
Fukushima 0.001 -0.004 0.003
EZB IR Event 0.000 -0.002 0.005
Aug07 0.001 -0.002 0.003
Mar08 0.000 -0.005 0.004
Sept08 -0.000 -0.008 0.012
2009Q1 0.001 -0.007 0.006
2009Q2 0.001 -0.006 0.008
Flash Crash 0.001 -0.003 0.005
Apr14 0.000 -0.003 0.004
Oct14 0.000 -0.004 0.003
Fall2015 -0.000 -0.003 0.003
Low Volatility Bull Market 0.000 -0.003 0.004
GFC Crash 0.001 -0.010 0.012
Recovery 0.000 -0.012 0.016
New Normal 0.000 -0.006 0.006
Top 10 long positions of all time (and max%) [u'KO-4283' u'DUK-2351' u'D-2071' u'BBT-16850' u'NEE-2968' u'KR-4297' u'SO-7011' u'JNJ-4151' u'CL-1582' u'UPS-20940'] [ 0.059 0.057 0.055 0.055 0.055 0.051 0.049 0.048 0.047 0.047] Top 10 short positions of all time (and max%) [u'D-2071' u'BBT-16850' u'NEE-2968' u'SO-7011' u'KMB-4263' u'BRK_B-11100' u'HON-25090' u'JNJ-4151' u'XOM-8347' u'CMCS_A-1637'] [-0.062 -0.062 -0.057 -0.051 -0.051 -0.049 -0.048 -0.048 -0.046 -0.046] Top 10 positions of all time (and max%) [u'D-2071' u'BBT-16850' u'KO-4283' u'NEE-2968' u'DUK-2351' u'SO-7011' u'KR-4297' u'KMB-4263' u'BRK_B-11100' u'JNJ-4151'] [ 0.062 0.062 0.059 0.057 0.057 0.051 0.051 0.051 0.049 0.048] All positions ever held [u'D-2071' u'BBT-16850' u'KO-4283' u'NEE-2968' u'DUK-2351' u'SO-7011' u'KR-4297' u'KMB-4263' u'BRK_B-11100' u'JNJ-4151' u'HON-25090' u'CL-1582' u'UPS-20940' u'T-6653' u'PG-5938' u'XOM-8347' u'CMCS_A-1637' u'ADP-630' u'ITW-4080' u'UTX-7883' u'USB-25010' u'VZ-21839' u'MET-21418' u'MO-4954' u'CVX-23112' u'BAC-700' u'TRV-7041' u'PNC-6068' u'C-1335' u'AEP-161' u'WFC-8151' u'TWC-33133' u'STI-7152' u'ABT-62' u'PEP-5885' u'WMT-8229' u'BK-903' u'PM-35902' u'EXC-22114' u'BEN-812' u'STT-7139' u'JPM-25006' u'GOOG_L-26578' u'DHR-2170' u'PX-6272' u'PSA-24962' u'LMT-12691' u'HD-3496' u'MMM-4922' u'COST-1787' u'BLK-20689' u'K-4192' u'APC-455' u'PFE-5923' u'GE-3149' u'COP-23998' u'BAX-734' u'COV-34010' u'MRK-5029' u'LLY-4487' u'KMP-2546' u'EMR-2530' u'AIG-239' u'CB-8580' u'RAI-20277' u'MCD-4707' u'FDX-2765' u'SPG-10528' u'CAT-1267' u'WMB-8214' u'GIS-3214' u'UNP-7800' u'PEG-5862' u'BDX-794' u'WBA-8089' u'FE-17850' u'CVS-4799' u'APA-448' u'OXY-5729' u'MDT-4758' u'MA-32146' u'MMC-4914' u'DIS-2190' u'GS-20088' u'RTN-6583' u'LOW-4521' u'HAL-3443' u'DOW-2263' u'AXP-679' u'ETR-2637' u'AMGN-368' u'TWX-357' u'MS-17080' u'CB-1274' u'ALL-24838' u'MSFT-5061' u'INTC-3951' u'SYK-7178' u'PRU-23328' u'GD-3136' u'CSCO-1900' u'L-4569' u'IBM-3766' u'ACN-25555' u'HNZ-3617' u'DVN-2368' u'TGT-21090' u'HES-216' u'APD-460' u'DD-2119' u'BNI-995' u'NOC-5387' u'HPQ-3735' u'SYY-7285' u'SLB-6928' u'ANTM-23103' u'VIA-27873' u'NSC-5442' u'EPD-19079' u'F-2673' u'TMO-7493' u'MCK-12350' u'NKE-5328' u'JCI-4117' u'HIG-14064' u'FITB-2855' u'AFL-185' u'FRE-2988' u'WYE-23555' u'BIIB-3806' u'BA-698' u'MHS-25445' u'UNH-7792' u'GILD-3212' u'COF-12160' u'YUM-17787' u'BHI-858' u'KRFT-43405' u'SBUX-6683' u'BMY-980' u'TXN-7671' u'DTV-26111' u'DE-2127' u'XTO-9074' u'RF-34913' u'AAPL-24' u'MDLZ-22802' u'EMC-2518' u'BBY-754' u'MSI-4974' u'QCOM-6295' u'AET-168' u'KSS-4313' u'CTL-1960' u'MER-4796' u'EOG-2564' u'PCG-5792' u'ORCL-5692' u'SPGI-4849' u'ADM-128' u'KMI-40852' u'PPL-6119' u'WFT-19336' u'SCHW-6704' u'LYB-39546' u'TJX-7457' u'PCP-5822' u'GENZ-3166' u'CSX-1937' u'SPLS-7061' u'FOXA-12213' u'MON-22140' u'STJ-7156' u'CBS_A-27970' u'GLW-3241' u'ESRX-2618' u'ECL-2427' u'VLO-7990' u'V-35920' u'PCAR-5787' u'AMT-24760' u'ADBE-114' u'CTSH-18870' u'NOV-24809' u'CCL-24692' u'SGP-6836' u'ETN-2633' u'AGN-205' u'MRO-5035' u'EBAY-24819' u'STZ-24873' u'CI-1539' u'FB-42950' u'SCCO-14284' u'AA-2' u'PCLN-19917' u'FCX-13197' u'DELL-25317' u'WM-19181' u'BX-34067' u'CELG-1406' u'CHK-8461' u'GM-40430' u'PSX-42788' u'AMAT-337' u'HCA-41047' u'FNM-2925' u'MOS-41462' u'YHOO-14848' u'SE-33030' u'ZBH-23047' u'CME-24475' u'CMX-1680' u'ABBV-43694' u'WWY-8323' u'VMW-34545' u'SHLD-26169' u'EL-13841' u'AMZN-16841' u'BSX-1131' u'BRCM-18529' u'COH-22099' u'NTRS-5479' u'TXU-7677' u'M-2754' u'NEM-5261' u'DNA-24847' u'LBTY_A-27357' u'NUE-5488' u'LVS-26882' u'DAL-33729' u'REGN-6413' u'CFC-1359' u'AGN-8572' u'JNPR-20239' u'ETE-28056' u'ALXN-14328' u'ISRG-25339' u'WU-32603' u'JAVA-7216' u'MOS-26721' u'CRM-26401' u'CMI-1985' u'AVGO-38650' u'TMUS-33698' u'DISH-13017' u'ATVI-9883' u'EP-2568' u'MCO-22139' u'GRMN-22316' u'MPC-41636' u'MNST-3450' u'MGM-4831' u'SYMC-7272' u'AAL-45971' u'NTAP-13905' u'APOL-24829' u'MU-5121' u'MYL-5166' u'NYX-28145' u'FSLR-32902' u'PYPL-49242' u'NFLX-23709' u'PD-5829' u'FDC-2755' u'GOOG-46631' u'HPE-49547' u'S-2938'] [ 0.062 0.062 0.059 0.057 0.057 0.051 0.051 0.051 0.049 0.048 0.048 0.047 0.047 0.047 0.047 0.046 0.046 0.046 0.046 0.045 0.045 0.045 0.045 0.045 0.045 0.045 0.044 0.044 0.043 0.043 0.042 0.042 0.042 0.042 0.041 0.041 0.041 0.041 0.041 0.041 0.041 0.041 0.04 0.04 0.04 0.04 0.04 0.039 0.039 0.038 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.036 0.036 0.036 0.035 0.035 0.035 0.035 0.035 0.035 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.034 0.033 0.033 0.033 0.033 0.033 0.033 0.032 0.032 0.032 0.032 0.032 0.032 0.031 0.031 0.031 0.031 0.031 0.031 0.031 0.031 0.03 0.03 0.03 0.029 0.029 0.029 0.029 0.029 0.029 0.029 0.029 0.029 0.029 0.028 0.028 0.028 0.028 0.028 0.028 0.028 0.028 0.028 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.024 0.024 0.024 0.024 0.024 0.024 0.024 0.024 0.024 0.024 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.021 0.021 0.021 0.021 0.021 0.021 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.019 0.019 0.019 0.018 0.018 0.018 0.018 0.018 0.018 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.015 0.015 0.015 0.015 0.014 0.013 0.013 0.013 0.013 0.013 0.012 0.012 0.012 0.011 0.011 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.009 0.009 0.009 0.008 0.008 0.008 0.008 0.007 0.006 0.006 0.006 0.004 0.004 0.003 0.001 0.001]
In [4]:
bt.create_returns_tear_sheet(live_start_date='2014-1-1')
Entire data start date: 2007-01-03
Entire data end date: 2016-08-18
Out-of-Sample Months: 31
Backtest Months: 83
Backtest Out_of_Sample All_History
annual_return 0.09 0.04 0.07
annual_volatility 0.04 0.02 0.03
sharpe_ratio 2.29 1.49 2.10
calmar_ratio 2.48 0.74 1.53
stability 0.94 0.51 0.94
max_drawdown -0.04 -0.05 -0.05
omega_ratio 1.50 1.27 1.45
sortino_ratio 3.69 2.20 3.34
skewness 0.32 -0.24 0.32
kurtosis 4.77 1.14 5.32
information_ratio 0.01 -0.02 0.00
alpha NaN 0.03 NaN
beta NaN 0.09 NaN
Worst Drawdown Periods
net drawdown in % peak date valley date recovery date duration
0 4.77 2015-05-21 2016-02-02 NaT NaN
1 3.52 2011-07-07 2011-08-08 2011-09-15 51
2 2.84 2010-04-23 2010-08-31 2011-01-10 187
4 1.85 2011-11-08 2011-12-19 2012-01-12 48
3 1.82 2012-04-02 2012-06-11 2012-08-21 102
2-sigma returns daily -0.004
2-sigma returns weekly -0.008
dtype: float64
In [ ]:
bt.create_bayesian_tear_sheet(live_start_date='2014-1-1')
In [ ]:
import pyfolio as pf
In [ ]:
returns = bt.daily_performance.returns
pf.timeseries.cum_returns(returns).plot();