def _plot_bollinger_bands(data, axes, period = 50, bandwidth = 1):
_check_int(period, raise_err = True)
_check_int(bandwidth, raise_err = True)
_check_pandas_series(data, raise_err = True)
lowr, mean, uppr = _get_bollinger_bands(data, period = period, bandwidth = bandwidth)
axes.plot(lowr, color = 'r', linestyle = '--')
axes.plot(mean, color = 'g', linestyle = '--')
axes.plot(uppr, color = 'r', linestyle = '--')
def _plot_bollinger_bands(data, axes, period = 50, bandwidth = 1):
_check_int(period, raise_err = True)
_check_int(bandwidth, raise_err = True)
_check_pandas_series(data, raise_err = True)
lowr, mean, uppr = _get_bollinger_bands(data, period = period, bandwidth = bandwidth)
axes.plot(lowr, color = 'r', linestyle = '--')
axes.plot(mean, color = 'g', linestyle = '--')
axes.plot(uppr, color = 'r', linestyle = '--')
def _get_bollinger_bands(data, period=50, bandwidth=1):
_check_int(period, raise_err=True)
_check_int(bandwidth, raise_err=True)
_check_pandas_series(data, raise_err=True)
roll = data.rolling(window=period)
std, mean = roll.std(), roll.mean()
upper = mean + bandwidth * std
lower = mean - bandwidth * std
return (lower, mean, upper)
def _get_bollinger_bands(data, period = 50, bandwidth = 1):
_check_int(period, raise_err = True)
_check_int(bandwidth, raise_err = True)
_check_pandas_series(data, raise_err = True)
roll = data.rolling(window = period)
std, mean = roll.std(), roll.mean()
upper = mean + bandwidth * std
lower = mean - bandwidth * std
return (lower, mean, upper)
def split(share,
attrs = 'Close',
window = 0.01,
train = 0.60,
shift = 1,
normalize = False,
provider = 'quandl'):
'''
:param attrs: `str` or `list` of attribute names of a share, defaults to *Close* attribute
:type attrs: :obj: `str`, :obj:`list`
'''
_check_type(share, type_ = bb.Share, raise_err = True, expected_type_name = 'bulbea.Share')
_check_iterable(attrs, raise_err = True)
_check_int(shift, raise_err = True)
_check_real(window, raise_err = True)
_check_real(train, raise_err = True)
_validate_in_range(window, 0, 1, raise_err = True)
_validate_in_range(train, 0, 1, raise_err = True)
if(provider == 'alphavantage'):
df = pd.DataFrame(share.data)
data = df.ix[3]
data = pd.to_numeric(data)
else:
data = share.data[attrs]
length = len(share)
window = int(np.rint(length * window))
offset = shift - 1
splits = np.array([data[i if i is 0 else i + offset: i + window] for i in range(length - window)])
if normalize:
splits = np.array([_get_cummulative_return(split) for split in splits])
size = len(splits)
split = int(np.rint(train * size))
train = splits[:split,:]
test = splits[split:,:]
Xtrain, Xtest = train[:,:-1], test[:,:-1]
ytrain, ytest = train[:, -1], test[:, -1]
return (Xtrain, Xtest, ytrain, ytest)
def split(sharedata,
attrs = 'Close',
window = 0.01,
train = 0.60,
shift = 1,
normalize = False):
'''
:type attrs: :obj: `str`, :obj:`list`
'''
_check_iterable(attrs, raise_err = True)
_check_int(shift, raise_err = True)
_check_real(window, raise_err = True)
_check_real(train, raise_err = True)
_validate_in_range(train, 0, 1, raise_err = True)
data = sharedata[attrs]
length = len(sharedata)
if window >=0 and window <= 1:
window = int(np.rint(length * window))
print('====Actual window is ' + str(window))
offset = shift - 1
splits = np.array([data[i if i is 0 else i + offset: i + window] for i in range(length - window)])
normsplit = np.array([ [split[0],split[len(split)-1]] for split in splits])
if normalize:
splits = np.array([_get_cummulative_return(split) for split in splits])
size = len(splits)
split = int(np.rint(train * size))
train = splits[:split,:]
test = splits[split:,:]
Xtrain, Xtest = train[:,:-1], test[:,:-1]
XtrainNorm, XtestNorm = normsplit[:split,:],normsplit[split:,:]
ytrain, ytest = train[:, -1], test[:, -1]
return (Xtrain, Xtest, ytrain, ytest, XtrainNorm, XtestNorm)
def split(share,
attrs = 'Close',
window = 0.01,
train = 0.60,
shift = 1,
normalize = False):
'''
:param attrs: `str` or `list` of attribute names of a share, defaults to *Close* attribute
:type attrs: :obj: `str`, :obj:`list`
'''
_check_type(share, type_ = bb.Share, raise_err = True, expected_type_name = 'bulbea.Share')
_check_iterable(attrs, raise_err = True)
_check_int(shift, raise_err = True)
_check_real(window, raise_err = True)
_check_real(train, raise_err = True)
_validate_in_range(window, 0, 1, raise_err = True)
_validate_in_range(train, 0, 1, raise_err = True)
data = share.data[attrs]
length = len(share)
window = int(np.rint(length * window))
if window <= 1: window = 2
offset = shift - 1
splits = np.array([data[i if i is 0 else i + offset: i + window] for i in range(length - window)])
print("{} {}".format(window,len(splits)))
if normalize:
splits = np.array([_get_cummulative_return(split) for split in splits])
size = len(splits)
split = int(np.rint(train * size))
train = splits[:split,:]
test = splits[split:,:]
Xtrain, Xtest = train[:,:-1], test[:,:-1]
ytrain, ytest = train[:, -1], test[:, -1]
return (Xtrain, Xtest, ytrain, ytest)
def split(share,
attrs = 'Close',
window = 0.01,
train = 0.60,
shift = 1,
normalize = False):
'''
:param attrs: `str` or `list` of attribute names of a share, defaults to *Close* attribute
:type attrs: :obj: `str`, :obj:`list`
'''
_check_type(share, type_ = bb.Share, raise_err = True, expected_type_name = 'bulbea.Share')
_check_iterable(attrs, raise_err = True)
_check_int(shift, raise_err = True)
_check_real(window, raise_err = True)
_check_real(train, raise_err = True)
_validate_in_range(window, 0, 1, raise_err = True)
_validate_in_range(train, 0, 1, raise_err = True)
data = share.data[attrs]
length = len(share)
window = int(np.rint(length * window))
offset = shift - 1
splits = np.array([data[i if i is 0 else i + offset: i + window] for i in range(length - window)])
if normalize:
splits = np.array([_get_cummulative_return(split) for split in splits])
size = len(splits)
split = int(np.rint(train * size))
train = splits[:split,:]
test = splits[split:,:]
Xtrain, Xtest = train[:,:-1], test[:,:-1]
ytrain, ytest = train[:, -1], test[:, -1]
return (Xtrain, Xtest, ytrain, ytest)
