Python talib.STOCHF Examples
The following are 9
code examples of talib.STOCHF().
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Example #1
| Source File: talib_indicators.py From QUANTAXIS with MIT License | 5 votes |
|
def STOCHF(DataFrame, fastk_period=5, fastd_period=3, fastd_matype=0):
fastk, fastd = talib.STOCHF(DataFrame.high.values, DataFrame.low.values, DataFrame.close.values,
fastk_period, fastd_period, fastd_matype)
return pd.DataFrame({'STOCHF_FASTK': fastk, 'STOCHF_FASTD': fastd}, index=DataFrame.index)
Example #2
| Source File: technical_indicator.py From NowTrade with MIT License | 5 votes |
|
def __str__(self):
return 'STOCHF(symbol=%s, fast_k_period=%s, fast_d_period=%s, \
fast_d_ma_type=%s)' %(self.symbol, self.fast_k_period, \
self.fast_d_period, self.fast_d_ma_type)
Example #3
| Source File: technical_indicator.py From NowTrade with MIT License | 5 votes |
|
def results(self, data_frame):
try:
fastk, fastd = talib.STOCHF(data_frame['%s_High' %self.symbol].values,
data_frame['%s_Low' %self.symbol].values,
data_frame['%s_Close' %self.symbol].values,
self.fast_k_period, self.fast_d_period,
self.fast_d_ma_type)
data_frame[self.fastk] = fastk
data_frame[self.fastd] = fastd
except KeyError:
data_frame[self.fastk] = np.nan
data_frame[self.fastd] = np.nan
Example #4
| Source File: ta.py From dash-technical-charting with MIT License | 5 votes |
|
def add_STOCHF(self, fastk_period=5, fastd_period=3, fastd_matype=0,
types=['line', 'line'],
colors=['primary', 'tertiary'],
**kwargs):
"""Fast Stochastic Oscillator.
Note that the first argument of types and colors refers to Fast Stoch %K,
while second argument refers to Fast Stoch %D
(signal line of %K obtained by MA).
"""
if not (self.has_high and self.has_low and self.has_close):
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if 'kind' in kwargs:
kwargs['type'] = kwargs['kind']
if 'kinds' in kwargs:
types = kwargs['type']
if 'type' in kwargs:
types = [kwargs['type']] * 2
if 'color' in kwargs:
colors = [kwargs['color']] * 2
name = 'STOCHF({},{})'.format(str(fastk_period),
str(fastd_period))
fastk = name + r'[%k]'
fastd = name + r'[%d]'
self.sec[fastk] = dict(type=types[0], color=colors[0])
self.sec[fastd] = dict(type=types[1], color=colors[1], on=fastk)
self.ind[fastk], self.ind[fastd] = talib.STOCHF(self.df[self.hi].values,
self.df[self.lo].values,
self.df[self.cl].values,
fastk_period, fastd_period,
fastd_matype)
Example #5
| Source File: stochf.py From jesse with MIT License | 5 votes |
|
def stochf(candles: np.ndarray, fastk_period=5, fastd_period=3, fastd_matype=0, sequential=False) -> StochasticFast:
"""
Stochastic Fast
:param candles: np.ndarray
:param fastk_period: int - default=5
:param fastd_period: int - default=3
:param fastd_matype: int - default=0
:param sequential: bool - default=False
:return: StochasticFast(k, d)
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
k, d = talib.STOCHF(
candles[:, 3],
candles[:, 4],
candles[:, 2],
fastk_period=fastk_period,
fastd_period=fastd_period,
fastd_matype=fastd_matype
)
if sequential:
return StochasticFast(k, d)
else:
return StochasticFast(k[-1], d[-1])
Example #6
| Source File: talib_wrapper.py From tia with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def STOCHF(frame, fastk=5, fastd=3, fastd_matype=0, high_col='high', low_col='low', close_col='close'):
return _frame_to_frame(frame, [high_col, low_col, close_col], ['FAST_K', 'FAST_D'], talib.STOCHF, fastk, fastd,
fastd_matype)
Example #7
| Source File: test_indicator_momentum.py From pandas-ta with MIT License | 5 votes |
|
def test_stoch(self):
result = pandas_ta.stoch(self.high, self.low, self.close, fast_k=14, slow_k=14, slow_d=14)
self.assertIsInstance(result, DataFrame)
self.assertEqual(result.name, 'STOCH_14_14_14')
self.assertEqual(len(result.columns), 4)
result = pandas_ta.stoch(self.high, self.low, self.close)
self.assertIsInstance(result, DataFrame)
self.assertEqual(result.name, 'STOCH_14_5_3')
try:
tal_stochf = tal.STOCHF(self.high, self.low, self.close)
tal_stoch = tal.STOCH(self.high, self.low, self.close)
tal_stochdf = DataFrame({'STOCHF_14': tal_stochf[0], 'STOCHF_3': tal_stochf[1], 'STOCH_5': tal_stoch[0], 'STOCH_3': tal_stoch[1]})
pdt.assert_frame_equal(result, tal_stochdf)
except AssertionError as ae:
try:
stochfk_corr = pandas_ta.utils.df_error_analysis(result.iloc[:,0], tal_stochdf.iloc[:,0], col=CORRELATION)
self.assertGreater(stochfk_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:,0], CORRELATION, ex)
try:
stochfd_corr = pandas_ta.utils.df_error_analysis(result.iloc[:,1], tal_stochdf.iloc[:,1], col=CORRELATION)
self.assertGreater(stochfd_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:,1], CORRELATION, ex, newline=False)
try:
stochsk_corr = pandas_ta.utils.df_error_analysis(result.iloc[:,2], tal_stochdf.iloc[:,2], col=CORRELATION)
self.assertGreater(stochsk_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:,2], CORRELATION, ex, newline=False)
try:
stochsd_corr = pandas_ta.utils.df_error_analysis(result.iloc[:,3], tal_stochdf.iloc[:,3], col=CORRELATION)
self.assertGreater(stochsd_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:,3], CORRELATION, ex, newline=False)
Example #8
| Source File: talib_indicators.py From qtpylib with Apache License 2.0 | 5 votes |
|
def STOCHF(data, **kwargs):
_check_talib_presence()
_, phigh, plow, pclose, _ = _extract_ohlc(data)
return talib.STOCHF(phigh, plow, pclose, **kwargs)
Example #9
| Source File: test_technical.py From catalyst with Apache License 2.0 | 4 votes |
|
def test_fso_expected_with_talib(self, seed):
"""
Test the output that is returned from the fast stochastic oscillator
is the same as that from the ta-lib STOCHF function.
"""
window_length = 14
nassets = 6
rng = np.random.RandomState(seed=seed)
input_size = (window_length, nassets)
# values from 9 to 12
closes = 9.0 + (rng.random_sample(input_size) * 3.0)
# Values from 13 to 15
highs = 13.0 + (rng.random_sample(input_size) * 2.0)
# Values from 6 to 8.
lows = 6.0 + (rng.random_sample(input_size) * 2.0)
expected_out_k = []
for i in range(nassets):
fastk, fastd = talib.STOCHF(
high=highs[:, i],
low=lows[:, i],
close=closes[:, i],
fastk_period=window_length,
fastd_period=1,
)
expected_out_k.append(fastk[-1])
expected_out_k = np.array(expected_out_k)
today = pd.Timestamp('2015')
out = np.empty(shape=(nassets,), dtype=np.float)
assets = np.arange(nassets, dtype=np.float)
fso = FastStochasticOscillator()
fso.compute(
today, assets, out, closes, lows, highs
)
assert_equal(out, expected_out_k, array_decimal=6)
