BENCHMARK: MARKET PERFORMANCE¶
In [10]:
bt = get_backtest('59163d3bfb7902620654d4a1')
bt.create_full_tear_sheet()
100% Time: 0:00:02|###########################################################| Entire data start date: 2003-01-02 Entire data end date: 2017-05-11 Backtest Months: 172
| Performance statistics | Backtest |
|---|---|
| annual_return | 0.09 |
| cum_returns_final | 2.54 |
| annual_volatility | 0.18 |
| sharpe_ratio | 0.57 |
| calmar_ratio | 0.17 |
| stability_of_timeseries | 0.76 |
| max_drawdown | -0.55 |
| omega_ratio | 1.12 |
| sortino_ratio | 0.81 |
| skew | -0.05 |
| kurtosis | 11.08 |
| tail_ratio | 0.98 |
| common_sense_ratio | 1.07 |
| gross_leverage | 1.00 |
| information_ratio | -0.00 |
| alpha | 0.00 |
| beta | 1.00 |
RemoteDataErrorTraceback (most recent call last) <ipython-input-10-42f1e6dc7c09> in <module>() 1 bt = get_backtest('59163d3bfb7902620654d4a1') ----> 2 bt.create_full_tear_sheet() /build/src/qexec_repo/qexec/research/results.py in create_full_tear_sheet(self, benchmark_rets, slippage, live_start_date, bayesian, round_trips, estimate_intraday, hide_positions, cone_std, bootstrap) 1147 hide_positions=hide_positions, 1148 cone_std=cone_std, -> 1149 bootstrap=bootstrap 1150 ) /usr/local/lib/python2.7/dist-packages/pyfolio/tears.pyc in create_full_tear_sheet(returns, positions, transactions, market_data, benchmark_rets, slippage, live_start_date, sector_mappings, bayesian, round_trips, estimate_intraday, hide_positions, cone_std, bootstrap, unadjusted_returns, set_context) 168 benchmark_rets=benchmark_rets, 169 bootstrap=bootstrap, --> 170 set_context=set_context) 171 172 create_interesting_times_tear_sheet(returns, /usr/local/lib/python2.7/dist-packages/pyfolio/plotting.pyc in call_w_context(*args, **kwargs) 54 if set_context: 55 with context(): ---> 56 return func(*args, **kwargs) 57 else: 58 return func(*args, **kwargs) /usr/local/lib/python2.7/dist-packages/pyfolio/tears.pyc in create_returns_tear_sheet(returns, positions, live_start_date, cone_std, benchmark_rets, bootstrap, return_fig) 358 359 plotting.plot_rolling_fama_french( --> 360 returns, ax=ax_rolling_risk) 361 362 # Drawdowns /usr/local/lib/python2.7/dist-packages/pyfolio/plotting.pyc in plot_rolling_fama_french(returns, factor_returns, rolling_window, legend_loc, ax, **kwargs) 158 returns, 159 factor_returns=factor_returns, --> 160 rolling_window=rolling_window) 161 162 rolling_beta.plot(alpha=0.7, ax=ax, **kwargs) /usr/local/lib/python2.7/dist-packages/pyfolio/timeseries.pyc in rolling_fama_french(returns, factor_returns, rolling_window) 625 if factor_returns is None: 626 factor_returns = utils.load_portfolio_risk_factors( --> 627 start=returns.index[0], end=returns.index[-1]) 628 factor_returns = factor_returns.drop(['Mkt-RF', 'RF'], 629 axis='columns') /usr/local/lib/python2.7/dist-packages/pyfolio/utils.pyc in load_portfolio_risk_factors(filepath_prefix, start, end) 352 filepath = filepath_prefix 353 --> 354 five_factors = get_returns_cached(filepath, get_fama_french, end) 355 356 return five_factors.loc[start:end] /usr/local/lib/python2.7/dist-packages/pyfolio/utils.pyc in get_returns_cached(filepath, update_func, latest_dt, **kwargs) 191 192 if update_cache: --> 193 returns = update_func(**kwargs) 194 try: 195 ensure_directory(cache_dir()) /usr/local/lib/python2.7/dist-packages/pyfolio/utils.pyc in get_fama_french() 316 start = '1/1/1970' 317 research_factors = web.DataReader('F-F_Research_Data_Factors_daily', --> 318 'famafrench', start=start)[0] 319 momentum_factor = web.DataReader('F-F_Momentum_Factor_daily', 320 'famafrench', start=start)[0] /usr/local/lib/python2.7/dist-packages/pandas_datareader/data.pyc in DataReader(name, data_source, start, end, retry_count, pause, session) 113 return FamaFrenchReader(symbols=name, start=start, end=end, 114 retry_count=retry_count, pause=pause, --> 115 session=session).read() 116 117 elif data_source == "oecd": /usr/local/lib/python2.7/dist-packages/pandas_datareader/base.pyc in read(self) 73 def read(self): 74 """ read data """ ---> 75 return self._read_one_data(self.url, self.params) 76 77 def _read_one_data(self, url, params): /usr/local/lib/python2.7/dist-packages/pandas_datareader/famafrench.pyc in _read_one_data(self, url, params) 95 96 doc_chunks, tables = [], [] ---> 97 data = self._read_zipfile(url) 98 99 for chunk in data.split(2 * '\r\n'): /usr/local/lib/python2.7/dist-packages/pandas_datareader/famafrench.pyc in _read_zipfile(self, url) 63 64 def _read_zipfile(self, url): ---> 65 raw = self._get_response(url).content 66 67 with tempfile.TemporaryFile() as tmpf: /usr/local/lib/python2.7/dist-packages/pandas_datareader/base.pyc in _get_response(self, url, params) 115 time.sleep(self.pause) 116 --> 117 raise RemoteDataError('Unable to read URL: {0}'.format(url)) 118 119 def _read_lines(self, out): RemoteDataError: Unable to read URL: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_daily_CSV.zip
In [9]:
bt = get_backtest('590fa9d2fd011b61d31281a7')
bt.create_full_tear_sheet()
100% Time: 0:00:07|###########################################################| Entire data start date: 2003-01-02 Entire data end date: 2017-05-05 Backtest Months: 171
| Performance statistics | Backtest |
|---|---|
| annual_return | 0.49 |
| cum_returns_final | 315.17 |
| annual_volatility | 0.29 |
| sharpe_ratio | 1.55 |
| calmar_ratio | 1.71 |
| stability_of_timeseries | 0.97 |
| max_drawdown | -0.29 |
| omega_ratio | 1.43 |
| sortino_ratio | 2.55 |
| skew | 1.21 |
| kurtosis | 25.73 |
| tail_ratio | 1.21 |
| common_sense_ratio | 1.80 |
| gross_leverage | 1.09 |
| information_ratio | 0.07 |
| alpha | 0.41 |
| beta | 0.25 |
RemoteDataErrorTraceback (most recent call last) <ipython-input-9-98d9df1c12c5> in <module>() 1 bt = get_backtest('590fa9d2fd011b61d31281a7') ----> 2 bt.create_full_tear_sheet() /build/src/qexec_repo/qexec/research/results.py in create_full_tear_sheet(self, benchmark_rets, slippage, live_start_date, bayesian, round_trips, estimate_intraday, hide_positions, cone_std, bootstrap) 1147 hide_positions=hide_positions, 1148 cone_std=cone_std, -> 1149 bootstrap=bootstrap 1150 ) /usr/local/lib/python2.7/dist-packages/pyfolio/tears.pyc in create_full_tear_sheet(returns, positions, transactions, market_data, benchmark_rets, slippage, live_start_date, sector_mappings, bayesian, round_trips, estimate_intraday, hide_positions, cone_std, bootstrap, unadjusted_returns, set_context) 168 benchmark_rets=benchmark_rets, 169 bootstrap=bootstrap, --> 170 set_context=set_context) 171 172 create_interesting_times_tear_sheet(returns, /usr/local/lib/python2.7/dist-packages/pyfolio/plotting.pyc in call_w_context(*args, **kwargs) 54 if set_context: 55 with context(): ---> 56 return func(*args, **kwargs) 57 else: 58 return func(*args, **kwargs) /usr/local/lib/python2.7/dist-packages/pyfolio/tears.pyc in create_returns_tear_sheet(returns, positions, live_start_date, cone_std, benchmark_rets, bootstrap, return_fig) 358 359 plotting.plot_rolling_fama_french( --> 360 returns, ax=ax_rolling_risk) 361 362 # Drawdowns /usr/local/lib/python2.7/dist-packages/pyfolio/plotting.pyc in plot_rolling_fama_french(returns, factor_returns, rolling_window, legend_loc, ax, **kwargs) 158 returns, 159 factor_returns=factor_returns, --> 160 rolling_window=rolling_window) 161 162 rolling_beta.plot(alpha=0.7, ax=ax, **kwargs) /usr/local/lib/python2.7/dist-packages/pyfolio/timeseries.pyc in rolling_fama_french(returns, factor_returns, rolling_window) 625 if factor_returns is None: 626 factor_returns = utils.load_portfolio_risk_factors( --> 627 start=returns.index[0], end=returns.index[-1]) 628 factor_returns = factor_returns.drop(['Mkt-RF', 'RF'], 629 axis='columns') /usr/local/lib/python2.7/dist-packages/pyfolio/utils.pyc in load_portfolio_risk_factors(filepath_prefix, start, end) 352 filepath = filepath_prefix 353 --> 354 five_factors = get_returns_cached(filepath, get_fama_french, end) 355 356 return five_factors.loc[start:end] /usr/local/lib/python2.7/dist-packages/pyfolio/utils.pyc in get_returns_cached(filepath, update_func, latest_dt, **kwargs) 191 192 if update_cache: --> 193 returns = update_func(**kwargs) 194 try: 195 ensure_directory(cache_dir()) /usr/local/lib/python2.7/dist-packages/pyfolio/utils.pyc in get_fama_french() 316 start = '1/1/1970' 317 research_factors = web.DataReader('F-F_Research_Data_Factors_daily', --> 318 'famafrench', start=start)[0] 319 momentum_factor = web.DataReader('F-F_Momentum_Factor_daily', 320 'famafrench', start=start)[0] /usr/local/lib/python2.7/dist-packages/pandas_datareader/data.pyc in DataReader(name, data_source, start, end, retry_count, pause, session) 113 return FamaFrenchReader(symbols=name, start=start, end=end, 114 retry_count=retry_count, pause=pause, --> 115 session=session).read() 116 117 elif data_source == "oecd": /usr/local/lib/python2.7/dist-packages/pandas_datareader/base.pyc in read(self) 73 def read(self): 74 """ read data """ ---> 75 return self._read_one_data(self.url, self.params) 76 77 def _read_one_data(self, url, params): /usr/local/lib/python2.7/dist-packages/pandas_datareader/famafrench.pyc in _read_one_data(self, url, params) 95 96 doc_chunks, tables = [], [] ---> 97 data = self._read_zipfile(url) 98 99 for chunk in data.split(2 * '\r\n'): /usr/local/lib/python2.7/dist-packages/pandas_datareader/famafrench.pyc in _read_zipfile(self, url) 63 64 def _read_zipfile(self, url): ---> 65 raw = self._get_response(url).content 66 67 with tempfile.TemporaryFile() as tmpf: /usr/local/lib/python2.7/dist-packages/pandas_datareader/base.pyc in _get_response(self, url, params) 115 time.sleep(self.pause) 116 --> 117 raise RemoteDataError('Unable to read URL: {0}'.format(url)) 118 119 def _read_lines(self, out): RemoteDataError: Unable to read URL: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_daily_CSV.zip
In [21]:
bt = get_backtest('590fad1c04563461cc15c71b')
bt.create_returns_tear_sheet()
100% Time: 0:00:05|###########################################################| Entire data start date: 2003-01-02 Entire data end date: 2006-12-29 Backtest Months: 47
| Performance statistics | Backtest |
|---|---|
| annual_return | 0.39 |
| cum_returns_final | 2.68 |
| annual_volatility | 0.19 |
| sharpe_ratio | 1.83 |
| calmar_ratio | 2.31 |
| stability_of_timeseries | 0.82 |
| max_drawdown | -0.17 |
| omega_ratio | 1.42 |
| sortino_ratio | 3.03 |
| skew | 0.59 |
| kurtosis | 3.66 |
| tail_ratio | 1.25 |
| common_sense_ratio | 1.73 |
| gross_leverage | 1.10 |
| information_ratio | 0.09 |
| alpha | 0.21 |
| beta | 0.97 |
| Worst drawdown periods | net drawdown in % | peak date | valley date | recovery date | duration |
|---|---|---|---|---|---|
| 0 | 16.72 | 2006-05-09 | 2006-08-11 | 2006-10-09 | 110 |
| 1 | 15.61 | 2004-03-05 | 2004-07-08 | 2004-12-02 | 195 |
| 2 | 13.95 | 2005-08-02 | 2005-11-01 | 2005-12-14 | 97 |
| 3 | 7.89 | 2004-12-31 | 2005-02-01 | 2005-03-07 | 47 |
| 4 | 7.70 | 2005-03-07 | 2005-05-12 | 2005-07-08 | 90 |
[-0.022 -0.045]
In [22]:
bt = get_backtest('590faa55fcfa7f61ce648968')
bt.create_returns_tear_sheet()
100% Time: 0:00:12|###########################################################| Entire data start date: 2007-01-03 Entire data end date: 2017-05-05 Backtest Months: 124
| Performance statistics | Backtest |
|---|---|
| annual_return | 0.54 |
| cum_returns_final | 83.95 |
| annual_volatility | 0.31 |
| sharpe_ratio | 1.53 |
| calmar_ratio | 1.86 |
| stability_of_timeseries | 0.95 |
| max_drawdown | -0.29 |
| omega_ratio | 1.44 |
| sortino_ratio | 2.51 |
| skew | 1.20 |
| kurtosis | 23.77 |
| tail_ratio | 1.16 |
| common_sense_ratio | 1.78 |
| gross_leverage | 1.07 |
| information_ratio | 0.07 |
| alpha | 0.47 |
| beta | 0.14 |
| Worst drawdown periods | net drawdown in % | peak date | valley date | recovery date | duration |
|---|---|---|---|---|---|
| 0 | 28.85 | 2011-08-08 | 2011-10-07 | 2012-10-17 | 313 |
| 1 | 27.23 | 2008-05-16 | 2008-11-11 | 2008-11-19 | 134 |
| 2 | 22.59 | 2008-12-01 | 2009-01-14 | 2009-01-20 | 37 |
| 3 | 19.09 | 2015-09-01 | 2015-09-25 | 2015-12-18 | 79 |
| 4 | 18.08 | 2014-12-03 | 2015-05-06 | 2015-08-21 | 188 |
[-0.038 -0.072]
In [24]:
bt = get_backtest('590facd3b9b30961eefcca98')
bt.create_returns_tear_sheet()
100% Time: 0:00:02|###########################################################| Entire data start date: 2008-01-02 Entire data end date: 2009-12-31 Backtest Months: 24
| Performance statistics | Backtest |
|---|---|
| annual_return | 1.25 |
| cum_returns_final | 4.09 |
| annual_volatility | 0.44 |
| sharpe_ratio | 2.05 |
| calmar_ratio | 4.64 |
| stability_of_timeseries | 0.86 |
| max_drawdown | -0.27 |
| omega_ratio | 1.69 |
| sortino_ratio | 3.79 |
| skew | 1.74 |
| kurtosis | 14.76 |
| tail_ratio | 1.52 |
| common_sense_ratio | 3.41 |
| gross_leverage | 0.85 |
| information_ratio | 0.11 |
| alpha | 0.91 |
| beta | 0.03 |
| Worst drawdown periods | net drawdown in % | peak date | valley date | recovery date | duration |
|---|---|---|---|---|---|
| 0 | 26.96 | 2008-05-16 | 2008-11-11 | 2008-11-19 | 134 |
| 1 | 22.60 | 2008-12-01 | 2009-01-14 | 2009-01-20 | 37 |
| 2 | 13.63 | 2009-06-12 | 2009-06-24 | 2009-07-16 | 25 |
| 3 | 12.75 | 2009-01-20 | 2009-01-22 | 2009-03-02 | 30 |
| 4 | 9.10 | 2009-10-22 | 2009-10-28 | 2009-11-11 | 15 |
[-0.052 -0.094]
In [23]:
bt = get_backtest('590fab4f1fdca661ab9b84f2')
bt.create_returns_tear_sheet()
100% Time: 0:00:06|###########################################################| Entire data start date: 2012-01-03 Entire data end date: 2017-05-05 Backtest Months: 64
| Performance statistics | Backtest |
|---|---|
| annual_return | 0.43 |
| cum_returns_final | 5.79 |
| annual_volatility | 0.24 |
| sharpe_ratio | 1.62 |
| calmar_ratio | 2.26 |
| stability_of_timeseries | 0.97 |
| max_drawdown | -0.19 |
| omega_ratio | 1.39 |
| sortino_ratio | 2.56 |
| skew | 0.56 |
| kurtosis | 10.41 |
| tail_ratio | 1.21 |
| common_sense_ratio | 1.74 |
| gross_leverage | 1.17 |
| information_ratio | 0.06 |
| alpha | 0.33 |
| beta | 0.42 |
| Worst drawdown periods | net drawdown in % | peak date | valley date | recovery date | duration |
|---|---|---|---|---|---|
| 0 | 19.09 | 2015-09-01 | 2015-09-25 | 2015-12-18 | 79 |
| 1 | 18.07 | 2016-01-14 | 2016-01-21 | 2016-03-30 | 55 |
| 2 | 17.98 | 2014-12-03 | 2015-05-06 | 2015-08-21 | 188 |
| 3 | 14.28 | 2012-04-02 | 2012-04-23 | 2012-08-16 | 99 |
| 4 | 14.27 | 2016-05-02 | 2016-06-22 | 2016-06-30 | 44 |
[-0.029 -0.057]
In [25]:
bt = get_backtest('590fac5599f11f61ab83608a')
bt.create_returns_tear_sheet()
100% Time: 0:00:02|###########################################################| Entire data start date: 2016-05-05 Entire data end date: 2017-05-05 Backtest Months: 12
| Performance statistics | Backtest |
|---|---|
| annual_return | 0.52 |
| cum_returns_final | 0.52 |
| annual_volatility | 0.20 |
| sharpe_ratio | 2.16 |
| calmar_ratio | 4.64 |
| stability_of_timeseries | 0.88 |
| max_drawdown | -0.11 |
| omega_ratio | 1.55 |
| sortino_ratio | 4.02 |
| skew | 2.01 |
| kurtosis | 14.86 |
| tail_ratio | 1.27 |
| common_sense_ratio | 1.93 |
| gross_leverage | 1.14 |
| information_ratio | 0.08 |
| alpha | 0.38 |
| beta | 0.31 |
| Worst drawdown periods | net drawdown in % | peak date | valley date | recovery date | duration |
|---|---|---|---|---|---|
| 0 | 11.23 | 2016-05-10 | 2016-06-22 | 2016-06-27 | 35 |
| 1 | 8.38 | 2016-09-09 | 2016-11-14 | 2016-12-05 | 62 |
| 2 | 6.12 | 2017-03-15 | 2017-05-04 | NaT | NaN |
| 3 | 4.21 | 2016-08-15 | 2016-08-31 | 2016-09-09 | 20 |
| 4 | 3.97 | 2017-03-01 | 2017-03-09 | 2017-03-15 | 11 |
[-0.024 -0.053]
In [ ]: