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Commitb157c48

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REF: AddStraetgy.data.<array>.to_series() method
1 parent7711732 commitb157c48

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6 files changed

+16
-14
lines changed

6 files changed

+16
-14
lines changed

‎backtesting/_util.py‎

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Original file line numberDiff line numberDiff line change
@@ -2,6 +2,7 @@
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fromnumbersimportNumber
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importnumpyasnp
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importpandasaspd
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def_as_str(value):
@@ -52,6 +53,9 @@ def __float__(self):
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exceptIndexError:
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returnsuper().__float__()
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56+
defto_series(self):
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returnpd.Series(self,index=self._opts['data'].index,name=self.name)
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class_Indicator(_Array):
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pass

‎backtesting/backtesting.py‎

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -231,7 +231,9 @@ def data(self) -> _Data:
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`backtesting.backtesting.Backtest` internally),
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the last array value (e.g. `data.Close[-1]`)
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is always the _most recent_ value.
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* If you need data arrays (e.g. `data.Close`) to be indexed
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Pandas series, you can call their `.to_series()` method
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(e.g. `data.Close.to_series()`).
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"""
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returnself._data
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‎backtesting/lib.py‎

Lines changed: 5 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -22,7 +22,7 @@
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from .backtestingimportStrategy
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from ._plottingimportplot_heatmapsas_plot_heatmaps
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from ._utilimport_Indicator,_as_str
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from ._utilimport_Array,_Indicator,_as_str
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OHLCV_AGG=OrderedDict((
@@ -175,9 +175,7 @@ class System(Strategy):
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def init(self):
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# Strategy exposes `self.data` as raw NumPy arrays.
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# Let's convert closing prices back to pandas Series.
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close = pd.Series(self.data.Close,
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index=self.data.index,
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name='Close')
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close = self.data.Close.to_series()
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# Resample to daily resolution. Aggregate groups
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# using their last value (i.e. closing price at the end
@@ -200,9 +198,9 @@ def SMA(series, n):
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"""
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ifnotisinstance(series,pd.Series):
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series=pd.Series(series,
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index=series._opts['data'].index,
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name=series.name)
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assertisinstance(series,_Array), \
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'resample_apply() takes either a `pd.Series` or a `Strategy.data.*` array'
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series=series.to_series()
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resampled=series.resample(rule,label='right').agg('last').dropna()
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resampled.name=_as_str(series)+'['+rule+']'

‎backtesting/test/_test.py‎

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Original file line numberDiff line numberDiff line change
@@ -456,7 +456,7 @@ def test_plot_heatmaps(self):
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deftest_SignalStrategy(self):
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classS(SignalStrategy):
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definit(self):
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sma=pd.Series(self.data.Close).rolling(10).mean()
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sma=self.data.Close.to_series().rolling(10).mean()
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self.set_signal(self.data.Close>sma,
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self.data.Close<sma)
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@@ -469,9 +469,7 @@ def init(self):
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super().init()
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self.set_atr_periods(40)
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self.set_trailing_sl(3)
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self.sma=self.I(
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lambda:pd.Series(self.data.Close,
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index=self.data.index).rolling(10).mean())
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self.sma=self.I(lambda:self.data.Close.to_series().rolling(10).mean())
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defnext(self):
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super().next()

‎doc/examples/Quick Start User Guide.ipynb‎

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Original file line numberDiff line numberDiff line change
@@ -563,7 +563,7 @@
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"\n",
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"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",
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"\n",
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"**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",
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"**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",
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"\n",
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"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%."
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]

‎doc/examples/Quick Start User Guide.py‎

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@@ -127,7 +127,7 @@ def next(self):
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#
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# 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).
129129
#
130-
# **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)`.
130+
# **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)`).
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#
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# 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%.
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