tidy

Author: Chris Morgan

examples/introduction/asimpletradingrule.py

@@ -11,9 +11,8 @@
 """"
 Let's get some data
 
-We can get data from various places; however for now we're going to use prepackaged 'legacy' data stored
-   in csv files
-
+We can get data from various places; however for now we're going to use
+prepackaged 'legacy' data stored in csv files
 """
 
 data = csvFuturesData()
@@ -22,7 +21,6 @@
 
 """
 We get stuff out of data with methods
-
 """
 print(data.get_instrument_list())
 print(data.get_raw_price("EDOLLAR").tail(5))
@@ -46,7 +44,8 @@
 print(data.get_instrument_raw_carry_data("EDOLLAR").tail(6))
 
 """
-Technical note: csvFuturesData inherits from FuturesData which itself inherits from Data
+Technical note: csvFuturesData inherits from FuturesData which itself inherits
+from Data
 The chain is 'data specific' <- 'asset class specific' <- 'generic'
 
 So there are also
@@ -59,7 +58,6 @@
 Let's create a simple trading rule
 
 No capping or scaling
-
 """
 
 import pandas as pd
@@ -68,11 +66,13 @@
 
 def calc_ewmac_forecast(price, Lfast, Lslow=None):
     """
-    Calculate the ewmac trading fule forecast, given a price and EWMA speeds Lfast, Lslow and vol_lookback
+    Calculate the ewmac trading fule forecast, given a price and EWMA speeds
+    Lfast, Lslow and vol_lookback
 
     """
     # price: This is the stitched price series
-    # We can't use the price of the contract we're trading, or the volatility will be jumpy
+    # We can't use the price of the contract we're trading, or the volatility
+    # will be jumpy
     # And we'll miss out on the rolldown. See
     # http://qoppac.blogspot.co.uk/2015/05/systems-building-futures-rolling.html
 
@@ -83,13 +83,11 @@ def calc_ewmac_forecast(price, Lfast, Lslow=None):
 
     # We don't need to calculate the decay parameter, just use the span
     # directly
-
     fast_ewma = pd.ewma(price, span=Lfast)
     slow_ewma = pd.ewma(price, span=Lslow)
     raw_ewmac = fast_ewma - slow_ewma
 
     vol = robust_vol_calc(price.diff())
-
     return raw_ewmac / vol
 
 """

examples/introduction/simplesystem.py

@@ -14,10 +14,11 @@
 
 For this we need to build a system
 
-A system is made up of SystemStages - essentially stages in the process, and it needs data, and perhaps a configuration
+A system is made up of SystemStages - essentially stages in the process, and it
+needs data, and perhaps a configuration
 
-
-The minimum stage you would have would be Rules - which is where you put trading rules
+The minimum stage you would have would be Rules - which is where you put
+trading rules
 """
 
 

examples/smallaccountsize/smallaccount.yaml

@@ -12,6 +12,7 @@ trading_rules:
          Lfast: 2
          Lslow: 8
      forecast_scalar: 10.6
+
   ewmac4_16:
      function: systems.provided.futures_chapter15.rules.ewmac
      data:
@@ -21,6 +22,7 @@ trading_rules:
          Lfast: 4
          Lslow: 16
      forecast_scalar: 7.5
+
   ewmac8_32:
      function: systems.provided.futures_chapter15.rules.ewmac
      data:
@@ -30,6 +32,7 @@ trading_rules:
          Lfast: 8
          Lslow: 32
      forecast_scalar: 5.3
+
   ewmac16_64:
      function: systems.provided.futures_chapter15.rules.ewmac
      data:
@@ -39,6 +42,7 @@ trading_rules:
          Lfast: 16
          Lslow: 64
      forecast_scalar: 3.75
+
   ewmac32_128:
      function: systems.provided.futures_chapter15.rules.ewmac
      data:
@@ -48,6 +52,7 @@ trading_rules:
          Lfast: 32
          Lslow: 128
      forecast_scalar: 2.65
+
   ewmac64_256:
      function: systems.provided.futures_chapter15.rules.ewmac
      data:

syscore/algos.py

@@ -1,7 +1,8 @@
 """
 Algos.py
 
-Basic building blocks of trading rules, like volatility measurement and crossovers
+Basic building blocks of trading rules, like volatility measurement and
+crossovers
 
 """
 import pandas as pd
@@ -38,18 +39,19 @@ def apply_with_min_periods(xcol, my_func=np.nanmean, min_periods=0):
 
 def vol_estimator(x, using_exponent=True, min_periods=20, ew_lookback=250):
     """
-    Generic vol estimator used for optimisation, works on data frames, produces a single answer
+    Generic vol estimator used for optimisation, works on data frames, produces
+    a single answer
 
     :param x: data
     :type x: Tx1 pd.DataFrame
 
-    :param using_exponent: Use exponential or normal vol (latter recommended for bootstrapping)
+    :param using_exponent: Use exponential or normal vol (latter recommended
+    for bootstrapping)
     :type using_exponent: bool
 
     :param min_periods: The minimum number of observations (*default* 10)
     :type min_periods: int
 
-
     :returns: pd.DataFrame -- volatility measure
 
     """
@@ -75,7 +77,8 @@ def mean_estimator(x, using_exponent=True, min_periods=20, ew_lookback=500):
     """
     Generic mean estimator used for optimisation, works on data frames
 
-    :param using_exponent: Use exponential or normal vol (latter recommended for bootstrapping)
+    :param using_exponent: Use exponential or normal vol (latter recommended
+    for bootstrapping)
     :type using_exponent: bool
 
     """
@@ -98,9 +101,9 @@ def mean_estimator(x, using_exponent=True, min_periods=20, ew_lookback=500):
     return mean_list
 
 
-def robust_vol_calc(x, days=35, min_periods=10, vol_abs_min=0.0000000001, vol_floor=True,
-                    floor_min_quant=0.05, floor_min_periods=100,
-                    floor_days=500):
+def robust_vol_calc(x, days=35, min_periods=10, vol_abs_min=0.0000000001,
+                    vol_floor=True, floor_min_quant=0.05,
+                    floor_min_periods=100, floor_days=500):
     """
     Robust exponential volatility calculation, assuming daily series of prices
     We apply an absolute minimum level of vol (absmin);
@@ -115,21 +118,27 @@ def robust_vol_calc(x, days=35, min_periods=10, vol_abs_min=0.0000000001, vol_fl
     :param min_periods: The minimum number of observations (*default* 10)
     :type min_periods: int
 
-    :param vol_abs_min: The size of absolute minimum (*default* =0.0000000001) 0.0= not used
+    :param vol_abs_min: The size of absolute minimum (*default* =0.0000000001)
+      0.0= not used
     :type absmin: float or None
 
     :param vol_floor Apply a floor to volatility (*default* True)
     :type vol_floor: bool
-    :param floor_min_quant: The quantile to use for volatility floor (eg 0.05 means we use 5% vol) (*default 0.05)
+
+    :param floor_min_quant: The quantile to use for volatility floor (eg 0.05
+      means we use 5% vol) (*default 0.05)
     :type floor_min_quant: float
-    :param floor_days: The lookback for calculating volatility floor, in days (*default* 500)
+
+    :param floor_days: The lookback for calculating volatility floor, in days
+      (*default* 500)
     :type floor_days: int
-    :param floor_min_periods: Minimum observations for floor - until reached floor is zero (*default* 100)
+
+    :param floor_min_periods: Minimum observations for floor - until reached
+      floor is zero (*default* 100)
     :type floor_min_periods: int
 
     :returns: pd.DataFrame -- volatility measure
 
-
     """
 
     # Standard deviation will be nan for first 10 non nan values
@@ -197,7 +206,8 @@ def apply_buffer_single_period(
     """
     Apply a buffer to a position, single period
 
-    If position is outside the buffer, we eithier trade to the edge of the buffer, or to the optimal
+    If position is outside the buffer, we eithier trade to the edge of the
+    buffer, or to the optimal
 
     :param last_position: last position we had
     :type last_position: float
@@ -239,7 +249,8 @@ def apply_buffer(optimal_position, pos_buffers,
     """
     Apply a buffer to a position
 
-    If position is outside the buffer, we eithier trade to the edge of the buffer, or to the optimal
+    If position is outside the buffer, we eithier trade to the edge of the
+    buffer, or to the optimal
 
     If we're rounding positions, then we floor and ceiling the buffers.
 
@@ -255,7 +266,6 @@ def apply_buffer(optimal_position, pos_buffers,
     :param round_positions: Produce rounded positions
     :type round_positions: bool
 
-
     :returns: pd.Series
     """
 

syscore/pdutils.py

@@ -92,7 +92,8 @@ def pd_readcsv_frompackage(filename):
     """
     Run pd_readcsv on a file in python
 
-    :param args: List showing location in project directory of file eg systems, provided, tests.csv
+    :param args: List showing location in project directory of file eg systems,
+      provided, tests.csv
     :type args: str
 
     :returns: pd.DataFrame

systems/portfolio.py

@@ -14,7 +14,8 @@ class Portfolios(SystemStage):
     """
     Stage for portfolios
 
-    This is a 'switching' class which selects eithier the fixed or the estimated flavours
+    This is a 'switching' class which selects eithier the fixed or the
+    estimated flavours
 
     """
 
@@ -48,7 +49,8 @@ class PortfoliosFixed(SystemStage):
     """
     Stage for portfolios
 
-    Gets the position, accounts for instrument weights and diversification multiplier
+    Gets the position, accounts for instrument weights and diversification
+    multiplier
 
     This version involves fixed weights and multipliers.
 
@@ -70,11 +72,10 @@ class PortfoliosFixed(SystemStage):
     def __init__(self):
         """
         Create a SystemStage for creating portfolios
-
-
         """
         protected = ["get_instrument_weights",
-                     "get_instrument_diversification_multiplier", "get_raw_instrument_weights"]
+                     "get_instrument_diversification_multiplier",
+                     "get_raw_instrument_weights"]
 
         setattr(self, "_protected", protected)
 
@@ -83,7 +84,8 @@ def __init__(self):
 
     def get_subsystem_position(self, instrument_code):
         """
-        Get the position assuming all capital in one position, from a previous module
+        Get the position assuming all capital in one position, from a previous
+        module
 
         :param instrument_code: instrument to get values for
         :type instrument_code: str

systems/provided/futures_chapter15/estimatedsystem.py

@@ -1,5 +1,6 @@
 '''
-This is a variation of the chapter 15 system which estimates rather than uses fixed parameters
+This is a variation of the chapter 15 system which estimates rather than uses
+fixed parameters
 
 A system consists of a system, plus a config
 
@@ -44,8 +45,9 @@ def futures_system(data=None, config=None,
 
     rules = Rules(trading_rules)
 
-    system = System([Account(), Portfolios(), PositionSizing(), FuturesRawData(), ForecastCombine(),
-                     ForecastScaleCap(), rules], data, config)
+    system = System([Account(), Portfolios(), PositionSizing(),
+                     FuturesRawData(), ForecastCombine(), ForecastScaleCap(),
+                     rules], data, config)
 
     system.set_logging_level(log_level)