version 0.11.0

Author: Robert Carver

DONE_TO_DO.md

@@ -1,5 +1,13 @@
 # Release notes
 
+## Version 0.11.0
+
+* Included option to show account curves as cumulative (compounding): somecurve.cumulative()
+* removed percentage options, now a method for account curves: somecurve.percent()
+* Incorporated capital into account curves: anycurve.capital
+* General clean up of the way capital dealt with in accounting
+
+
 ## Version 0.10.3
 
 * More speed up, couple of tweaks...
@@ -187,7 +195,6 @@
 
 * Simulation:
 
-  * vol targeting with capital adjustment
   * add other trading rules (some in private...?) - cross sectional carry
   * quandl data
   * stitch futures contracts 

README.md

@@ -7,10 +7,10 @@ Rob Carver
 [http://qoppac.blogspot.co.uk/p/pysystemtrade.html](http://qoppac.blogspot.co.uk/p/pysystemtrade.html)
 
 
-Version 0.10.2
+Version 0.11.0
 
 
-20160526
+20160527
 
 
 ## Release notes

docs/introduction.md

@@ -133,43 +133,43 @@ Did we make any money?
 ```python
 from syscore.accounting import accountCurve
 account = accountCurve(price, forecast=ewmac, percentage=True)
-account.stats()
+account.percent().stats()
 ```
 
-```
-[[('min', '-0.07911'),
-  ('max', '0.0522'),
+
+[[('min', '-7.911'),
+  ('max', '5.22'),
   ('median', '0'),
-  ('mean', '0.0001644'),
-  ('std', '0.005173'),
+  ('mean', '0.01644'),
+  ('std', '0.5173'),
   ('skew', '-0.5757'),
-  ('ann_mean', '0.04208'),
-  ('ann_std', '0.08276'),
+  ('ann_mean', '4.208'),
+  ('ann_std', '8.276'),
   ('sharpe', '0.5084'),
   ('sortino', '0.569'),
-  ('avg_drawdown', '-0.1193'),
+  ('avg_drawdown', '-11.93'),
   ('time_in_drawdown', '0.9731'),
   ('calmar', '0.1269'),
   ('avg_return_to_drawdown', '0.3526'),
-  ('avg_loss', '-0.003307'),
-  ('avg_gain', '0.003418'),
+  ('avg_loss', '-0.3307'),
+  ('avg_gain', '0.3418'),
   ('gaintolossratio', '1.034'),
   ('profitfactor', '1.12'),
   ('hitrate', '0.5201'),
   ('t_stat', '2.929'),
   ('p_value', '0.003405')],
- ('You can also plot:', ['rolling_ann_std', 'drawdown', 'curve'])]
-```
-
+ ('You can also plot / print:',
+  ['rolling_ann_std', 'drawdown', 'curve', 'percent', 'cumulative'])]
 
 Looks like we did make a few bucks. `account`, by the way inherits from a pandas data frame. Here are some other things we can do with it:
 
 ```python
 account.sharpe() ## get the Sharpe Ratio (annualised), and any other statistic which is in the stats list
 account.curve().plot() ## plot the cumulative account curve (equivalent to account.cumsum().plot() inicidentally)
-account.drawdown().plot() ## see the drawdowns
+account.curve().percent() ## gives a % curve
+account.percent().drawdown().plot() ## see the drawdowns as a percentage
 account.weekly ## weekly returns (also daily [default], monthly, annual)
-account.costs.ann_mean() ## annual mean for costs (there are none in this simple example)
+account.gross.ann_mean() ## annual mean for gross returns, also costs (there are none in this simple example)
 ```
 
 
@@ -582,7 +582,7 @@ from systems.account import Account
 account=Account()
 my_system=System([ fcs, empty_rules, combiner, possizer, portfolio, account], data, my_config)
 profits=my_system.account.portfolio()
-profits.stats()
+profits.percent().stats()
 ```
 
 ```
@@ -595,8 +595,8 @@ Once again we have the now familiar accounting object. Some results have been re
 These are profits net of tax. You can see the gross profits and costs:
 
 ```python
-profits.gross.stats() ## all other things work eg profits.gross.sharpe()
-profits.costs.stats()
+profits.gross.percent().stats() ## all other things work eg profits.gross.sharpe()
+profits.costs.percent().stats()
 ```
 
 For more see the costs and accountCurve section of the userguide.

docs/userguide.md

@@ -56,7 +56,8 @@ from systems.provided.futures_chapter15.basesystem import futures_system
 system=futures_system()
 system.accounts.portfolio().stats() ## see some statistics
 system.accounts.portfolio().curve().plot() ## plot an account curve
-system.accounts.portfolio().pandl_for_instrument("US10").stats() ## produce statistics for a 10 year bond
+system.accounts.portfolio().percent().curve().plot() ## plot an account curve in percentage terms
+system.accounts.portfolio().pandl_for_instrument("US10").percent().stats() ## produce % statistics for a 10 year bond
 system.accounts.pandl_for_instrument_forecast("EDOLLAR", "carry").sharpe() ## Sharpe for a specific trading rule variation
 ```
 
@@ -2846,7 +2847,6 @@ These classes share some useful arguments (all boolean):
 
 - `delayfill`: Assume we trade at the next days closing price. Always defaults to True (more conservative)
 - `roundpositions`: Round positions to nearest instrument block. Defaults to True for portfolios and instruments, defaults to False for subsystems. Not used in `pandl_for_instrument_forecast` or `pandl_for_trading_rule` (always False)
-- `percentage`: Return the p&l as a percentage of notional capital, rather than in cash amounts. Defaults to True. Not used in in `pandl_for_instrument_forecast` or `pandl_for_trading_rule`(always False)
 
 All p&l methods return an object of type `accountCurve` (for instruments, subsystems and instrument forecasts) or `accountCurveGroup` (for portfolio and trading rule), or even nested `accountCurveGroup` (`pandl_for_all_trading_rules`, `pandl_for_all_trading_rules_unweighted`). This inherits from a pandas data frame, so it can be plotted, averaged and so on. It also has some special methods. To see what they are use the `stats` method:
 
@@ -2857,27 +2857,29 @@ system.accounts.portfolio().stats()
 ```
 
 ```
-[[('min', '-0.764'),
-  ('max', '0.1589'),
-  ('median', '0'),
-  ('mean', '0.0006711'),
-  ('std', '0.02224'),
-  ('skew', '-5.726'),
-  ('ann_daily_mean', '0.1718'),
-  ('ann_daily_std', '0.3559'),
-  ('sharpe', '0.4827'),
-  ('sortino', '0.4677'),
-  ('avg_drawdown', '-0.339'),
-  ('time_in_drawdown', '0.9864'),
-  ('calmar', '0.1109'),
-  ('avg_return_to_drawdown', '0.5068'),
-  ('avg_loss', '-0.01581'),
-  ('avg_gain', '0.01534'),
-  ('gaintolossratio', '0.9702'),
-  ('profitfactor', '1.113'),
-  ('hitrate', '0.5343')],
- ('You can also plot:', ['rolling_ann_std', 'drawdown', 'curve']),
- ('You can also print:', ['weekly', 'monthly', 'annual'])]
+[[('min', '-1.997e+05'),
+  ('max', '4.083e+04'),
+  ('median', '-1.631'),
+  ('mean', '156.9'),
+  ('std', '5226'),
+  ('skew', '-7.054'),
+  ('ann_mean', '4.016e+04'),
+  ('ann_std', '8.361e+04'),
+  ('sharpe', '0.4803'),
+  ('sortino', '0.5193'),
+  ('avg_drawdown', '-1.017e+05'),
+  ('time_in_drawdown', '0.9621'),
+  ('calmar', '0.1199'),
+  ('avg_return_to_drawdown', '0.395'),
+  ('avg_loss', '-3016'),
+  ('avg_gain', '3371'),
+  ('gaintolossratio', '1.118'),
+  ('profitfactor', '1.103'),
+  ('hitrate', '0.4968'),
+  ('t_stat', '2.852'),
+  ('p_value', '0.004349')],
+ ('You can also plot / print:',
+  ['rolling_ann_std', 'drawdown', 'curve', 'as_percent', 'as_cumulative'])]
 ```
 
 The `stats` method lists three kinds of output:
@@ -2941,6 +2943,50 @@ You probably won't need it but acc_curve.calc_data() returns a dict of all the i
 acc_curve.calc_data()['trades'] ## simulated trades
 ```
 
+Personally I prefer looking at statistics in percentage terms. This is easy. Just use the .percent() method before you use any statistical method:
+
+```python
+acc_curve.capital ## tells me the capital I will use to calculate %
+acc_curve.percent()
+acc_curve.gross.daily.percent()
+acc_curve.net.daily.percent() 
+acc_curve.costs.monthly.percent()
+acc_curve.gross.daily.percent().stats() 
+acc_curve.monthly.percent().sharpe() 
+acc_curve.gross.weekly.percent().std() 
+acc_curve.daily.percent().ann_std() 
+acc_curve.costs.annual.percent().median() 
+acc_curve.percent().rolling_ann_std() 
+acc_curve.gross.percent().curve() 
+acc_curve.net.monthly.percent().drawdown() 
+acc_curve.costs.weekly.percent().curve() 
+```
+
+
+You may also want to use *cumulated* returns, which use compound interest rather than the simple addition I normally use. See this blog for more information FIX ME
+
+```python
+acc_curve.cumulative()
+acc_curve.gross.daily.cumulative() 
+acc_curve.net.daily.cumulative() 
+acc_curve.costs.monthly.cumulative()
+acc_curve.gross.daily.cumulative().stats() 
+acc_curve.monthly.cumulative().sharpe() 
+acc_curve.gross.weekly.cumulative().std() 
+acc_curve.daily.cumulative().ann_std() 
+acc_curve.costs.annual.cumulative().median() 
+acc_curve.cumulative().rolling_ann_std() 
+acc_curve.gross.cumulative().curve() 
+acc_curve.net.monthly.cumulative().drawdown() 
+acc_curve.costs.weekly.cumulative().curve() 
+```
+
+Or both
+```python
+acc_curve.cumulative().percent().stats()
+acc_curve.percent().cumulative().stats() ## these are equivalent
+```
+
 #### `accountCurveGroup` in more detail
 
 `accountCurveGroup`, is the output you get from `systems.account.portfolio`, `systems.account.pandl_across_subsystems`, pandl_for_instrument_rules_unweighted`, `pandl_for_trading_rule` and `pandl_for_trading_rule_unweighted`. For example:
@@ -3001,6 +3047,8 @@ acc_curve_group.get_stats("sharpe", "net", "daily") ## get all annualised sharpe
 acc_curve_group.get_stats("sharpe", freq="daily") ## equivalent
 acc_curve_group.get_stats("sharpe", curve_type="net") ## equivalent
 acc_curve_group.net.get_stats("sharpe", freq="daily") ## equivalent
+acc_curve_group.net.get_stats("sharpe", percent=False) ## defaults to giving stats in % terms, this turns it off
+
 ```
 
 *Warning see [weighted and unweighted account curve groups](#weighted_acg)

examples/breakout/temp.py

@@ -0,0 +1,16 @@
+from systems.provided.futures_chapter15.basesystem import futures_system
+from matplotlib.pyplot import show
+
+system = futures_system(log_level="on")
+print(system.accounts.portfolio().sharpe())
+system.accounts.portfolio().curve().plot()
+show()
+
+system.accounts.portfolio().as_cumulative().plot()
+show()
+
+system.accounts.portfolio().as_cum_percent().plot()
+show()
+
+system.accounts.portfolio().as_percent().cumsum().plot()
+show()

examples/introduction/asimpletradingrule.py

@@ -112,10 +112,10 @@ def calc_ewmac_forecast(price, Lfast, Lslow=None):
 """
 
 from syscore.accounting import accountCurve
-account = accountCurve(price, forecast=ewmac, percentage=True)
+account = accountCurve(price, forecast=ewmac)
 account.curve()
 
 account.curve().plot()
 show()
 
-print(account.stats())
+print(account.percent().stats())

examples/introduction/simplesystem.py

@@ -186,11 +186,11 @@
 my_system = System([fcs, my_rules, combiner, possizer,
                     portfolio, my_account], data, my_config)
 profits = my_system.accounts.portfolio()
-print(profits.stats())
+print(profits.percent().stats())
 
 ## have costs data now
-print(profits.gross.stats())
-print(profits.net.stats())
+print(profits.gross.percent().stats())
+print(profits.net.percent().stats())
 
 """
 Another approach is to create a config object