def init(self):
self.sma = self.I(SMA, self.data.Close, 10)
self.remains_indicator = np.r_[2] * np.cumsum(self.sma * 5 + 1) * np.r_[2]
self.transpose_invalid = self.I(lambda: np.column_stack((self.data.Open,
self.data.Close)))
resampled = resample_apply('W', SMA, self.data.Close, 3)
resampled_ind = resample_apply('W', SMA, self.sma, 3)
assert np.unique(resampled[-5:]).size == 1
assert np.unique(resampled[-6:]).size == 2
assert resampled in self._indicators, "Strategy.I not called"
assert resampled_ind in self._indicators, "Strategy.I not called"
assert 1 == try_(lambda: self.data.X, 1, AttributeError)
assert 1 == try_(lambda: self.data['X'], 1, KeyError)
assert self.data.pip == .01
assert float(self.data.Close) == self.data.Close[-1]
def loop_through_data(broker, data, indicator_attrs, start, strategy,
self_data):
with np.errstate(invalid='ignore'):
for i in range(start, len(self_data)):
data._set_length(i + 1)
for attr, indicator in indicator_attrs:
setattr(strategy, attr, indicator[..., :i + 1])
try:
broker.next()
except _OutOfMoneyError:
break
strategy.next()
else:
for trade in broker.trades:
trade.close()
if start < len(self_data):
try_(broker.next, exception=_OutOfMoneyError)
return get_perf_metrics(broker, data, strategy, self_data)
def next(self):
try_(self.step.__next__, None, StopIteration)
def I(
self,
func: Callable,
*args,
name=None,
plot=True,
overlay=None,
color=None,
scatter=False,
**kwargs,
) -> np.ndarray:
if name is None:
params = ','.join(filter(None, map(_as_str, chain(args, kwargs.values()))))
func_name = _as_str(func)
name = f'{func_name}({params})' if params else f'{func_name}'
else:
name = name.format(
*map(_as_str, args),
**dict(zip(kwargs.keys(), map(_as_str, kwargs.values()))),
)
try:
value = func(*args, **kwargs)
except Exception as e:
raise RuntimeError(f'I "{name}" errored with exception: {e}')
if isinstance(value, pd.DataFrame):
value = value.values.T
if value is not None:
value = try_(lambda: np.asarray(value, order='C'), None)
is_arraylike = value is not None
# Optionally flip the array if the user returned e.g. `df.values`
if is_arraylike and np.argmax(value.shape) == 0:
value = value.T
if not is_arraylike or not 1 <= value.ndim <= 2 or value.shape[-1] != len(self._data.Close):
raise ValueError(
'Indicators must return (optionally a tuple of) numpy.arrays of same '
f'length as `data` (data shape: {self._data.Close.shape}; indicator "{name}"'
f'shape: {getattr(value, "shape" , "")}, returned value: {value})'
)
if plot and overlay is None and np.issubdtype(value.dtype, np.number):
x = value / self._data.Close
# By default, overlay if strong majority of indicator values
# is within 30% of Close
with np.errstate(invalid='ignore'):
overlay = ((x < 1.4) & (x > 0.6)).mean() > 0.6
value = _Indicator(
value,
name=name,
plot=plot,
overlay=overlay,
color=color,
scatter=scatter,
index=self.data.index,
)
self._indicators.append(value)
return value
