REF: Add Straetgy.data.<array>.to_series() method

backtesting/_util.py

@@ -2,6 +2,7 @@
 from numbers import Number
 
 import numpy as np
+import pandas as pd
 
 
 def _as_str(value):
@@ -52,6 +53,9 @@ class _Array(np.ndarray):
         except IndexError:
             return super().__float__()
 
+    def to_series(self):
+        return pd.Series(self, index=self._opts['data'].index, name=self.name)
+
 
 class _Indicator(_Array):
     pass

backtesting/backtesting.py

@@ -231,7 +231,9 @@ class Strategy(metaclass=ABCMeta):
           `backtesting.backtesting.Backtest` internally),
           the last array value (e.g. `data.Close[-1]`)
           is always the _most recent_ value.
-
+        * If you need data arrays (e.g. `data.Close`) to be indexed
+          Pandas series, you can call their `.to_series()` method
+          (e.g. `data.Close.to_series()`).
         """
         return self._data
 

backtesting/lib.py

@@ -22,7 +22,7 @@ import pandas as pd
 
 from .backtesting import Strategy
 from ._plotting import plot_heatmaps as _plot_heatmaps
-from ._util import _Indicator, _as_str
+from ._util import _Array, _Indicator, _as_str
 
 
 OHLCV_AGG = OrderedDict((
@@ -175,9 +175,7 @@ def resample_apply(rule: str,
             def init(self):
                 # Strategy exposes `self.data` as raw NumPy arrays.
                 # Let's convert closing prices back to pandas Series.
-                close = pd.Series(self.data.Close,
-                                  index=self.data.index,
-                                  name='Close')
+                close = self.data.Close.to_series()
 
                 # Resample to daily resolution. Aggregate groups
                 # using their last value (i.e. closing price at the end
@@ -200,9 +198,9 @@ def resample_apply(rule: str,
 
     """
     if not isinstance(series, pd.Series):
-        series = pd.Series(series,
-                           index=series._opts['data'].index,
-                           name=series.name)
+        assert isinstance(series, _Array), \
+            'resample_apply() takes either a `pd.Series` or a `Strategy.data.*` array'
+        series = series.to_series()
 
     resampled = series.resample(rule, label='right').agg('last').dropna()
     resampled.name = _as_str(series) + '[' + rule + ']'

backtesting/test/_test.py

@@ -456,7 +456,7 @@ class TestLib(TestCase):
     def test_SignalStrategy(self):
         class S(SignalStrategy):
             def init(self):
-                sma = pd.Series(self.data.Close).rolling(10).mean()
+                sma = self.data.Close.to_series().rolling(10).mean()
                 self.set_signal(self.data.Close > sma,
                                 self.data.Close < sma)
 
@@ -469,9 +469,7 @@ class TestLib(TestCase):
                 super().init()
                 self.set_atr_periods(40)
                 self.set_trailing_sl(3)
-                self.sma = self.I(
-                    lambda: pd.Series(self.data.Close,
-                                      index=self.data.index).rolling(10).mean())
+                self.sma = self.I(lambda: self.data.Close.to_series().rolling(10).mean())
 
             def next(self):
                 super().next()

doc/examples/Quick Start User Guide.ipynb

@@ -563,7 +563,7 @@
     "\n",
     "In `init()`, the whole series of points was available, whereas **in `next()`, the length of `self.data` and any declared indicator arrays is adjusted** on each `next()` call so that `array[-1]` (e.g. `self.data.Close[-1]` or `self.sma1[-1]`) always contains the most recent value, `array[-2]` the previous value, etc. (ordinary Python indexing of ascending-sorted 1D arrays).\n",
     "\n",
-    "**Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are **NumPy arrays** for performance reasons. If you need `pandas.Series`, use, e.g., `pd.Series(self.data.Close, index=self.data.index)`.\n",
+    "**Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are **NumPy arrays for performance reasons**. If you need `pandas.Series`, use `.to_series()` method (e.g. `self.data.Close.to_series()`) or construct the series manually (e.g. `pd.Series(self.data.Close, index=self.data.index)`).\n",
     "\n",
     "Let's see how our strategy performs on historical Google data. We begin with 10,000 units of cash and set broker's commission to realistic 0.2%."
    ]

doc/examples/Quick Start User Guide.py

@@ -127,7 +127,7 @@ class SmaCross(Strategy):
 #
 # In `init()`, the whole series of points was available, whereas **in `next()`, the length of `self.data` and any declared indicator arrays is adjusted** on each `next()` call so that `array[-1]` (e.g. `self.data.Close[-1]` or `self.sma1[-1]`) always contains the most recent value, `array[-2]` the previous value, etc. (ordinary Python indexing of ascending-sorted 1D arrays).
 #
-# **Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are **NumPy arrays** for performance reasons. If you need `pandas.Series`, use, e.g., `pd.Series(self.data.Close, index=self.data.index)`.
+# **Note**: `self.data` and any indicators wrapped with `self.I` (e.g. `self.sma1`) are **NumPy arrays for performance reasons**. If you need `pandas.Series`, use `.to_series()` method (e.g. `self.data.Close.to_series()`) or construct the series manually (e.g. `pd.Series(self.data.Close, index=self.data.index)`).
 #
 # Let's see how our strategy performs on historical Google data. We begin with 10,000 units of cash and set broker's commission to realistic 0.2%.