In [1]:
# Get backtest object
bt = get_backtest('59859055e3e8b44fbeddad93')
100% Time: 0:00:31|###########################################################|
In [5]:
bt.create_full_tear_sheet(estimate_intraday=True, slippage=1)
Entire data start date: 2007-08-01 Entire data end date: 2015-07-31 Backtest Months: 95
| Performance statistics | Backtest |
|---|---|
| annual_return | 0.08 |
| cum_returns_final | 0.81 |
| annual_volatility | 0.03 |
| sharpe_ratio | 2.37 |
| calmar_ratio | 2.15 |
| stability_of_timeseries | 0.92 |
| max_drawdown | -0.04 |
| omega_ratio | 1.55 |
| sortino_ratio | 3.99 |
| skew | 0.88 |
| kurtosis | 11.10 |
| tail_ratio | 1.23 |
| common_sense_ratio | 1.33 |
| gross_leverage | 1.00 |
| information_ratio | -0.00 |
| alpha | 0.07 |
| beta | 0.01 |
| Worst drawdown periods | net drawdown in % | peak date | valley date | recovery date | duration |
|---|---|---|---|---|---|
| 0 | 3.60 | 2008-10-09 | 2008-11-21 | 2008-12-30 | 59 |
| 1 | 3.60 | 2014-11-24 | 2014-12-23 | NaT | NaN |
| 2 | 2.81 | 2011-10-11 | 2012-07-20 | 2012-12-03 | 300 |
| 3 | 1.48 | 2011-07-01 | 2011-07-28 | 2011-08-26 | 41 |
| 4 | 1.40 | 2008-06-20 | 2008-07-02 | 2008-07-15 | 18 |
[-0.004 -0.008]
| Stress Events | mean | min | max |
|---|---|---|---|
| Lehmann | 0.08% | -0.72% | 1.11% |
| US downgrade/European Debt Crisis | 0.07% | -0.28% | 0.34% |
| Fukushima | 0.05% | -0.14% | 0.34% |
| EZB IR Event | 0.03% | -0.19% | 0.36% |
| Aug07 | 0.18% | -0.37% | 2.05% |
| Mar08 | 0.03% | -0.32% | 0.35% |
| Sept08 | 0.12% | -0.72% | 1.11% |
| 2009Q1 | 0.09% | -0.39% | 1.38% |
| 2009Q2 | 0.09% | -0.68% | 0.69% |
| Flash Crash | 0.16% | -0.07% | 0.31% |
| Apr14 | 0.04% | -0.31% | 0.28% |
| Oct14 | 0.07% | -0.26% | 0.54% |
| Low Volatility Bull Market | 0.04% | 0.04% | 0.04% |
| GFC Crash | 0.07% | -1.42% | 2.05% |
| Recovery | 0.02% | -0.50% | 0.76% |
| New Normal | 0.02% | -0.82% | 0.56% |
| Top 10 long positions of all time | max |
|---|---|
| WSM-8284 | 1.32% |
| VSEA-20037 | 1.31% |
| MMC-4914 | 1.31% |
| LNCR-4501 | 1.31% |
| PPDI-14288 | 1.31% |
| BMY-980 | 1.31% |
| VIVO-4244 | 1.31% |
| MRVL-21666 | 1.31% |
| PSS-15005 | 1.31% |
| ELS-8516 | 1.31% |
| Top 10 short positions of all time | max |
|---|---|
| THOR-15228 | -1.31% |
| SOHU-21813 | -1.31% |
| GDP-13363 | -1.31% |
| DEI-32770 | -1.31% |
| IVZ-16589 | -1.31% |
| ESE-2608 | -1.31% |
| CHS-8612 | -1.31% |
| ATHR-25967 | -1.31% |
| OXPS-26978 | -1.31% |
| IN-17948 | -1.31% |
| Top 10 positions of all time | max |
|---|---|
| WSM-8284 | 1.32% |
| VSEA-20037 | 1.31% |
| THOR-15228 | 1.31% |
| SOHU-21813 | 1.31% |
| MMC-4914 | 1.31% |
| GDP-13363 | 1.31% |
| LNCR-4501 | 1.31% |
| DEI-32770 | 1.31% |
| IVZ-16589 | 1.31% |
| PPDI-14288 | 1.31% |
| All positions ever held | max |
|---|---|
| WSM-8284 | 1.32% |
| VSEA-20037 | 1.31% |
| THOR-15228 | 1.31% |
| SOHU-21813 | 1.31% |
| MMC-4914 | 1.31% |
| GDP-13363 | 1.31% |
| LNCR-4501 | 1.31% |
| DEI-32770 | 1.31% |
| IVZ-16589 | 1.31% |
| PPDI-14288 | 1.31% |
| BMY-980 | 1.31% |
| ESE-2608 | 1.31% |
| VIVO-4244 | 1.31% |
| CHS-8612 | 1.31% |
| ATHR-25967 | 1.31% |
| MRVL-21666 | 1.31% |
| OXPS-26978 | 1.31% |
| IN-17948 | 1.31% |
| ANF-15622 | 1.31% |
| PSS-15005 | 1.31% |
| ELS-8516 | 1.31% |
| UNM-7797 | 1.31% |
| CYBS-20197 | 1.31% |
| ENS-26530 | 1.31% |
| HCBK-20374 | 1.31% |
| FEIC-13030 | 1.31% |
| SXT-22272 | 1.31% |
| MTD-17895 | 1.31% |
| RGS-6453 | 1.31% |
| YUM-17787 | 1.31% |
| ... | ... |
| OUT-46644 | 0.83% |
| CLNS-47230 | 0.83% |
| HYH-47929 | 0.83% |
| SSCC-39821 | 0.82% |
| CDK-47752 | 0.82% |
| BRX-45755 | 0.82% |
| NAVI-46782 | 0.82% |
| LPT-11514 | 0.82% |
| OA-600 | 0.82% |
| AEC-10073 | 0.82% |
| INFO-47163 | 0.82% |
| KLXI-48169 | 0.82% |
| SIR-42606 | 0.82% |
| REX-9642 | 0.82% |
| STAG-41271 | 0.82% |
| ART-38765 | 0.82% |
| AMTG-41738 | 0.81% |
| ARNA-21724 | 0.81% |
| ARR-35162 | 0.81% |
| KEM-4265 | 0.81% |
| RSO-28076 | 0.81% |
| CIM-35081 | 0.80% |
| SAIA-24115 | 0.80% |
| ARI-38759 | 0.80% |
| OGS-46180 | 0.80% |
| SYA-39160 | 0.80% |
| CPE-12011 | 0.80% |
| WAL-27421 | 0.79% |
| GPT-26520 | 0.79% |
| BTH-11258 | 0.78% |
2230 rows × 1 columns
In [6]:
import pyfolio as pf
import numpy as np
import scipy.stats as st
returns = bt.daily_performance.returns
import pandas as pd
print 'median', np.median(returns)
print 'skew', st.skew(returns)
print pd.Series(returns).describe()
median 0.00046013658893 skew 0.878222156722 count 2015.000000 mean 0.000498 std 0.001992 min -0.013991 25% -0.000505 50% 0.000460 75% 0.001435 max 0.020658 Name: returns, dtype: float64
In [ ]: