mirror of
https://github.com/kernc/backtesting.py.git
synced 2024-01-28 15:29:30 +03:00
136 lines
3.4 KiB
Python
136 lines
3.4 KiB
Python
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from numbers import Number
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import numpy as np
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def _as_str(value):
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if isinstance(value, (Number, str)):
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return str(value)
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name = str(getattr(value, 'name', '') or '')
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if callable(value):
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name = value.__name__.replace('<lambda>', '')
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if name in ('Open', 'High', 'Low', 'Close'):
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name = name[:1]
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if len(name) > 10:
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name = name[:9] + '…'
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return name
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def _data_period(df):
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"""Return data index period as pd.Timedelta"""
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return df.index[:100].to_series().diff().median()
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class _Array(np.ndarray):
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"""
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ndarray extended to supply .name and other arbitrary properties
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in ._opts dict.
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"""
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def __new__(cls, array, name=None, write=False, **kwargs):
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obj = np.asarray(array).view(cls)
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obj.name = name or array.name
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obj._opts = kwargs
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if not write:
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obj.setflags(write=False)
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return obj
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def __array_finalize__(self, obj):
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if obj is not None:
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self.name = getattr(obj, 'name', '')
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self._opts = getattr(obj, '_opts', {})
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def __bool__(self):
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try:
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return bool(self[-1])
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except IndexError:
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return super().__bool__()
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def __float__(self):
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try:
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return float(self[-1])
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except IndexError:
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return super().__float__()
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class _Indicator(_Array):
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pass
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class _Data:
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"""
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A data array accessor. Provides access to OHLCV "columns"
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as a standard `pd.DataFrame` would, except it's not a DataFrame
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and the returned "series" are _not_ `pd.Series` but `np.ndarray`
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for performance reasons.
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"""
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def __init__(self, df):
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self.__i = len(df)
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self.__pip = None
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self.__cache = {}
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self.__arrays = {col: _Array(arr, data=self)
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for col, arr in df.items()}
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# Leave index as Series because pd.Timestamp nicer API to work with
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self.__arrays['__index'] = df.index.copy()
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def __getitem__(self, item):
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return getattr(self, item)
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def __getattr__(self, item):
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try:
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return self.__get_array(item)
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except KeyError:
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raise KeyError("Column '{}' not in data".format(item)) from None
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def _set_length(self, i):
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self.__i = i
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self.__cache.clear()
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def __len__(self):
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return self.__i
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@property
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def pip(self):
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if self.__pip is None:
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self.__pip = 10**-np.median([len(s.partition('.')[-1])
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for s in self.__arrays['Close'].astype(str)])
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return self.__pip
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def __get_array(self, key):
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arr = self.__cache.get(key)
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if arr is None:
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arr = self.__cache[key] = self.__arrays[key][:self.__i]
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return arr
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@property
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def Open(self):
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return self.__get_array('Open')
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@property
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def High(self):
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return self.__get_array('High')
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@property
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def Low(self):
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return self.__get_array('Low')
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@property
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def Close(self):
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return self.__get_array('Close')
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@property
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def Volume(self):
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return self.__get_array('Volume')
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@property
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def index(self):
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return self.__get_array('__index')
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# Make pickling in Backtest.optimize() work with our catch-all __getattr__
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def __getstate__(self):
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return self.__dict__
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def __setstate__(self, state):
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self.__dict__ = state
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