Merge pull request #28 from cmorgan/autopep8

autopep8 -a -i -r .

examples/breakout/breakout.py

@@ -10,9 +10,9 @@
 import numpy as np
 from matplotlib.pyplot import show, legend, matshow
 
-bvariations=["breakout"+str(ws) for ws in [10, 20, 40, 80, 160, 320]]
-evariations=[
-            "ewmac%d_%d" % (fast, fast*4) for fast in [2,4,8,16,32, 64]]
+bvariations = ["breakout" + str(ws) for ws in [10, 20, 40, 80, 160, 320]]
+evariations = [
+    "ewmac%d_%d" % (fast, fast * 4) for fast in [2, 4, 8, 16, 32, 64]]
 
 """
 my_config = Config("examples.breakout.breakoutfuturesestimateconfig.yaml")
@@ -64,13 +64,13 @@
 ## check window size correlation and also turnover properties
 outputall=[]
 
-wslist=[4,5,6,7,8,9,10,15,20,25,30,35,40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 
+wslist=[4,5,6,7,8,9,10,15,20,25,30,35,40, 50, 60, 70, 80, 90, 100, 120, 140, 160,
            180, 200, 240, 280, 320, 360, 500]
 
 for ws in wslist:
     smoothed_output = breakout(price, ws)
-    
-    ## 
+
+    ##
     avg_forecast=float(smoothed_output.abs().mean())
     print("WS %d turnover %.2f" % (ws, turnover(smoothed_output, avg_forecast)))
     outputall.append(smoothed_output)
@@ -112,7 +112,7 @@
     all_scalar_values=[x[1] for x in all_scalars]
     print("mean %.3f std %.3f min %.3f max %.3f" % (np.nanmean(all_scalar_values), np.nanstd(all_scalar_values),
                                                     np.nanmin(all_scalar_values), np.nanmax(all_scalar_values)))
-    
+
 ## reload config so we get scalars estimated with pooling behaviour
 my_config = Config("examples.breakout.breakoutfuturesestimateconfig.yaml")
 
@@ -123,7 +123,7 @@
     scalar=float(system.forecastScaleCap.get_forecast_scalar(instrument, rule_name).tail(1).values)
     print("%s: %.3f" % (rule_name, scalar))
 
-## now turnovers    
+## now turnovers
 for rule_name in variations:
     all_turnovers=[]
     for instrument in instr_list:
@@ -133,8 +133,8 @@
     print("%s: %s %s" % (rule_name, str(all_turnovers[0]), str(all_turnovers[-1])))
     all_turnover_values=[x[1] for x in all_turnovers]
     print("mean %.3f std %.3f" % (np.nanmean(all_turnover_values), np.nanstd(all_turnover_values)))
-    
-    
+
+
 ## limit the rules to just breakout for now
 my_config = Config("examples.breakout.breakoutfuturesestimateconfig.yaml")
 my_config.trading_rules = dict([(rule_name, my_config.trading_rules[rule_name]) for rule_name in variations])
@@ -178,9 +178,9 @@
 
 allpandl=[]
 for rule_variation_name in variations:
-    
+
     allpandl.append(system.accounts.pandl_for_trading_rule(rule_variation_name).as_df())
-    
+
 allpandl=pd.concat(allpandl, axis=1)
 allpandl.columns=variations
 
@@ -190,9 +190,9 @@
 
 allpandl=[]
 for rule_variation_name in bvariations:
-    
+
     allpandl.append(system.accounts.pandl_for_trading_rule_unweighted(rule_variation_name).as_df())
-    
+
 allpandl=pd.concat(allpandl, axis=1)
 allpandl.columns=bvariations
 
@@ -201,9 +201,9 @@
 
 allpandl=[]
 for rule_variation_name in bvariations:
-    
+
     allpandl.append(system.accounts.pandl_for_trading_rule(rule_variation_name).as_df())
-    
+
 allpandl=pd.concat(allpandl, axis=1)
 
 allpandl.cumsum().sum(axis=1).plot()
@@ -217,7 +217,7 @@
 
 allrulespandl=system.accounts.pandl_for_all_trading_rules()
 
-## 
+##
 ewmac_all=allrulespandl.to_frame().loc[:,evariations].sum(axis=1)
 break_all=allrulespandl.to_frame().loc[:,bvariations].sum(axis=1)
 
@@ -227,36 +227,36 @@
 show()
 
 """
-## full backtest compare
+# full backtest compare
 
 my_config = Config("examples.breakout.breakoutfuturesestimateconfig.yaml")
-## will do all instruments we have data for
+# will do all instruments we have data for
 del(my_config.instruments)
 
-## temporarily remove breakout rules
-my_config.rule_variations=evariations
-my_config.forecast_weight_estimate["method"]="equal_weights"
+# temporarily remove breakout rules
+my_config.rule_variations = evariations
+my_config.forecast_weight_estimate["method"] = "equal_weights"
 system_old = futures_system(config=my_config, log_level="on")
 
-## new system has all trading rules
+# new system has all trading rules
 new_config = Config("examples.breakout.breakoutfuturesestimateconfig.yaml")
-new_config.rule_variations=bvariations
-new_config.forecast_weight_estimate["method"]="equal_weights"
+new_config.rule_variations = bvariations
+new_config.forecast_weight_estimate["method"] = "equal_weights"
 del(new_config.instruments)
 
 system_new = futures_system(config=new_config, log_level="on")
 
-curve1=system_old.accounts.portfolio()
-curve2=system_new.accounts.portfolio()
+curve1 = system_old.accounts.portfolio()
+curve2 = system_new.accounts.portfolio()
 
 print(curve1.stats())
 print(curve2.stats())
 
 print(account_test(curve2, curve1))
 
 
-curves_to_plot=pd.concat([curve1.as_df(), curve2.as_df()], axis=1)
-curves_to_plot.columns=["ewmac", "breakout"]
+curves_to_plot = pd.concat([curve1.as_df(), curve2.as_df()], axis=1)
+curves_to_plot.columns = ["ewmac", "breakout"]
 
 print(curves_to_plot.corr())
 curves_to_plot.cumsum().plot()

examples/forecastscaling/forecastscaling.py

@@ -6,56 +6,64 @@
   cross sectional
 """
 
-system=futures_system()
+system = futures_system()
 
-## don't pool
-system.config.forecast_scalar_estimate['pool_instruments']=False
+# don't pool
+system.config.forecast_scalar_estimate['pool_instruments'] = False
 
-instrument_list=system.get_instrument_list()
+instrument_list = system.get_instrument_list()
 print(instrument_list)
 
-results=[]
+results = []
 for instrument_code in instrument_list:
-    results.append(round(float(system.forecastScaleCap.get_forecast_scalar(instrument_code, "ewmac2_8").tail(1).values),2))
+    results.append(
+        round(
+            float(
+                system.forecastScaleCap.get_forecast_scalar(
+                    instrument_code,
+                    "ewmac2_8").tail(1).values),
+            2))
 print(results)
 
-results=[]
+results = []
 for instrument_code in instrument_list:
-    results.append(round(float(system.forecastScaleCap.get_forecast_scalar(instrument_code, "carry").tail(1).values),2))
+    results.append(
+        round(
+            float(
+                system.forecastScaleCap.get_forecast_scalar(
+                    instrument_code,
+                    "carry").tail(1).values),
+            2))
 print(results)
 
 
 """
  Use an expanding window
 """
 
-system=futures_system()
+system = futures_system()
 
-## Let's use a one year rolling window instead
-system.config.forecast_scalar_estimate['window']=250
-system.config.forecast_scalar_estimate['min_periods']=250
+# Let's use a one year rolling window instead
+system.config.forecast_scalar_estimate['window'] = 250
+system.config.forecast_scalar_estimate['min_periods'] = 250
 
 
 system.forecastScaleCap.get_forecast_scalar("EDOLLAR", "ewmac64_256").plot()
 show()
 
 
-
-
 """
  Goldilocks amount of minimum data - not too much, not too little
 """
 
 
+system = futures_system()
 
+# stupidly small number of min periods
+system.config.forecast_scalar_estimate['min_periods'] = 50
 
-system=futures_system()
-
-## stupidly small number of min periods
-system.config.forecast_scalar_estimate['min_periods']=50
-
-## don't pool
-system.config.forecast_scalar_estimate['pool_instruments']=False
+# don't pool
+system.config.forecast_scalar_estimate['pool_instruments'] = False
 
 
 system.forecastScaleCap.get_forecast_scalar("EDOLLAR", "ewmac64_256").plot()
@@ -67,4 +75,4 @@
 
 """
 
-"""
+"""

examples/introduction/asimpletradingrule.py

@@ -76,7 +76,7 @@ def calc_ewmac_forecast(price, Lfast, Lslow=None):
     # And we'll miss out on the rolldown. See
     # http://qoppac.blogspot.co.uk/2015/05/systems-building-futures-rolling.html
 
-    price=price.resample("1B", how="last")
+    price = price.resample("1B", how="last")
 
     if Lslow is None:
         Lslow = 4 * Lfast
@@ -90,7 +90,7 @@ def calc_ewmac_forecast(price, Lfast, Lslow=None):
 
     vol = robust_vol_calc(price.diff())
 
-    return raw_ewmac /  vol
+    return raw_ewmac / vol
 
 """
 Try it out
@@ -100,7 +100,7 @@ def calc_ewmac_forecast(price, Lfast, Lslow=None):
 instrument_code = 'EDOLLAR'
 price = data.daily_prices(instrument_code)
 ewmac = calc_ewmac_forecast(price, 32, 128)
-ewmac.columns=['forecast']
+ewmac.columns = ['forecast']
 print(ewmac.tail(5))
 
 from matplotlib.pyplot import show
@@ -118,4 +118,4 @@ def calc_ewmac_forecast(price, Lfast, Lslow=None):
 account.curve().plot()
 show()
 
-print(account.percent().stats())
+print(account.percent().stats())

examples/introduction/prebakedsystems.py

@@ -42,7 +42,8 @@
 system.pickle_cache("private.this_system_name.pck")
 show()
 
-del(system) ## just to make sure
+del(system)  # just to make sure
 system = futures_system(log_level="on")
 system.unpickle_cache("private.this_system_name.pck")
-system.accounts.portfolio().sharpe() ## this will run much faster and reuse previous calculations
+# this will run much faster and reuse previous calculations
+system.accounts.portfolio().sharpe()

examples/introduction/simplesystem.py

@@ -78,17 +78,20 @@
 from systems.forecast_scale_cap import ForecastScaleCap
 
 
-## we can estimate these ourselves
-my_config.instruments=[ "US10", "EDOLLAR", "CORN", "SP500"]
-my_config.use_forecast_scale_estimates=True
-fcs=ForecastScaleCap()
+# we can estimate these ourselves
+my_config.instruments = ["US10", "EDOLLAR", "CORN", "SP500"]
+my_config.use_forecast_scale_estimates = True
+fcs = ForecastScaleCap()
 my_system = System([fcs, my_rules], data, my_config)
-print(my_system.forecastScaleCap.get_forecast_scalar("EDOLLAR", "ewmac32").tail(5))
+print(
+    my_system.forecastScaleCap.get_forecast_scalar(
+        "EDOLLAR",
+        "ewmac32").tail(5))
 
-## or we can use the values from the book
+# or we can use the values from the book
 my_config.forecast_scalars = dict(ewmac8=5.3, ewmac32=2.65)
-my_config.use_forecast_scale_estimates=False
-fcs=ForecastScaleCap()
+my_config.use_forecast_scale_estimates = False
+fcs = ForecastScaleCap()
 my_system = System([fcs, my_rules], data, my_config)
 print(my_system.forecastScaleCap.get_capped_forecast(
     "EDOLLAR", "ewmac32").tail(5))
@@ -99,36 +102,34 @@
 
 from systems.forecast_combine import ForecastCombine
 
-## defaults
+# defaults
 combiner = ForecastCombine()
 my_system = System([fcs, my_rules, combiner], data, my_config)
 print(my_system.combForecast.get_forecast_weights("EDOLLAR").tail(5))
 print(my_system.combForecast.get_forecast_diversification_multiplier("EDOLLAR").tail(5))
 
 
-## estimates:
+# estimates:
 from systems.account import Account
 my_account = Account()
 combiner = ForecastCombine()
 
-my_config.forecast_weight_estimate=dict(method="one_period")
-my_config.use_forecast_weight_estimates=True
+my_config.forecast_weight_estimate = dict(method="one_period")
+my_config.use_forecast_weight_estimates = True
 
 my_system = System([my_account, fcs, my_rules, combiner], data, my_config)
 
-## this is a bit slow, better to know what's going on
+# this is a bit slow, better to know what's going on
 my_system.set_logging_level("on")
 
 print(my_system.combForecast.get_forecast_weights("EDOLLAR").tail(5))
 print(my_system.combForecast.get_forecast_diversification_multiplier("EDOLLAR").tail(5))
 
 
-
-
-## fixed:
+# fixed:
 my_config.forecast_weights = dict(ewmac8=0.5, ewmac32=0.5)
 my_config.forecast_div_multiplier = 1.1
-my_config.use_forecast_weight_estimates=False
+my_config.use_forecast_weight_estimates = False
 
 combiner = ForecastCombine()
 my_system = System([fcs, empty_rules, combiner], data, my_config)
@@ -155,10 +156,11 @@
 
 # portfolio - estimated
 from systems.portfolio import Portfolios
-portfolio= Portfolios()
+portfolio = Portfolios()
 
-my_config.use_instrument_weight_estimates=True
-my_config.instrument_weight_estimate=dict(method="shrinkage", date_method="in_sample")
+my_config.use_instrument_weight_estimates = True
+my_config.instrument_weight_estimate = dict(
+    method="shrinkage", date_method="in_sample")
 
 my_system = System([my_account, fcs, my_rules, combiner, possizer,
                     portfolio], data, my_config)
@@ -168,9 +170,9 @@
 print(my_system.portfolio.get_instrument_weights().tail(5))
 print(my_system.portfolio.get_instrument_diversification_multiplier().tail(5))
 
-## or fixed
+# or fixed
 portfolio = Portfolios()
-my_config.use_instrument_weight_estimates=False
+my_config.use_instrument_weight_estimates = False
 my_config.instrument_weights = dict(US10=.1, EDOLLAR=.4, CORN=.3, SP500=.2)
 my_config.instrument_div_multiplier = 1.5
 
@@ -188,7 +190,7 @@
 profits = my_system.accounts.portfolio()
 print(profits.percent().stats())
 
-## have costs data now
+# have costs data now
 print(profits.gross.percent().stats())
 print(profits.net.percent().stats())