def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(close=cls._df["Close"],
window_slow=25,
window_fast=13,
fillna=False)
cls._indicator = TSIIndicator(**cls._params)
class TestTSIIndicator(unittest.TestCase):
"""
https://school.stockcharts.com/doku.php?id=technical_indicators:true_strength_index
"""
_filename = 'ta/tests/data/cs-tsi.csv'
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = TSIIndicator(close=self._df['Close'], r=25, s=13, fillna=False)
def tearDown(self):
del(self._df)
def test_kama(self):
target = 'TSI'
result = self._indicator.tsi()
pd.testing.assert_series_equal(
self._df[target].tail(), result.tail(), check_names=False, check_less_precise=True)
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(close=cls._df['Close'], r=25, s=13, fillna=False)
cls._indicator = TSIIndicator(**cls._params)
def calculate_Momentum_Indicators():
JSON_sent = request.get_json()
df = pd.DataFrame(JSON_sent[0])
_, RSI, TSI, UO, STOCH, STOCH_SIGNAL, WR, AO, KAMA, ROC = JSON_sent
indicator_RSI = RSIIndicator(close=df["close"], n=RSI['N'])
df['rsi'] = indicator_RSI.rsi()
if TSI['displayTSI']:
indicator_TSI = TSIIndicator(close=df["close"],
r=TSI['rTSI'],
s=TSI['sTSI'])
df['tsi'] = indicator_TSI.tsi()
if UO['displayUO']:
indicator_UO = uo(high=df['high'],
low=df['low'],
close=df['close'],
s=UO['sForUO'],
m=UO['mForUO'],
len=UO['lenForUO'],
ws=UO['wsForUO'],
wm=UO['wmForUO'],
wl=UO['wlForUO'])
df['uo'] = indicator_UO
if STOCH['displaySTOCH']:
indicator_Stoch = stoch(high=df['high'],
low=df['low'],
close=df['close'],
n=STOCH['nForSTOCH'],
d_n=STOCH['dnForSTOCH'])
df['stoch'] = indicator_Stoch
if STOCH_SIGNAL['displayStochSignal']:
indicator_StochSignal = stoch_signal(
high=df['high'],
low=df['low'],
close=df['close'],
n=STOCH_SIGNAL['nForStochSignal'],
d_n=STOCH_SIGNAL['dnForStochSignal'])
df['stoch_signal'] = indicator_StochSignal
if WR['displayWR']:
indicator_wr = wr(high=df['high'],
low=df['low'],
close=df['close'],
lbp=WR['lbpForWR'])
df['wr'] = indicator_wr
if AO['displayAO']:
indicator_ao = ao(high=df['high'],
low=df['low'],
s=AO['sForAO'],
len=AO['lenForAO'])
df['ao'] = indicator_ao
if KAMA['displayKama']:
indicator_kama = kama(close=df['close'],
n=KAMA['nForKama'],
pow1=KAMA['pow1ForKama'],
pow2=KAMA['pow2ForKama'])
df['kama'] = indicator_kama
if ROC['displayROC']:
indicator_roc = roc(close=df['close'], n=ROC['nForROC'])
df['roc'] = indicator_roc
df.fillna(0, inplace=True)
export_df = df.drop(columns=['open', 'high', 'low', 'close', 'volume'])
return (json.dumps(export_df.to_dict('records')))
def add_momentum_ta(
df: pd.DataFrame,
high: str,
low: str,
close: str,
volume: str,
fillna: bool = False,
colprefix: str = "",
vectorized: bool = False,
) -> pd.DataFrame:
"""Add trend technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
volume (str): Name of 'volume' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
vectorized(bool): if True, use only vectorized functions indicators
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Relative Strength Index (RSI)
df[f"{colprefix}momentum_rsi"] = RSIIndicator(close=df[close],
window=14,
fillna=fillna).rsi()
# Stoch RSI (StochRSI)
indicator_srsi = StochRSIIndicator(close=df[close],
window=14,
smooth1=3,
smooth2=3,
fillna=fillna)
df[f"{colprefix}momentum_stoch_rsi"] = indicator_srsi.stochrsi()
df[f"{colprefix}momentum_stoch_rsi_k"] = indicator_srsi.stochrsi_k()
df[f"{colprefix}momentum_stoch_rsi_d"] = indicator_srsi.stochrsi_d()
# TSI Indicator
df[f"{colprefix}momentum_tsi"] = TSIIndicator(close=df[close],
window_slow=25,
window_fast=13,
fillna=fillna).tsi()
# Ultimate Oscillator
df[f"{colprefix}momentum_uo"] = UltimateOscillator(
high=df[high],
low=df[low],
close=df[close],
window1=7,
window2=14,
window3=28,
weight1=4.0,
weight2=2.0,
weight3=1.0,
fillna=fillna,
).ultimate_oscillator()
# Stoch Indicator
indicator_so = StochasticOscillator(
high=df[high],
low=df[low],
close=df[close],
window=14,
smooth_window=3,
fillna=fillna,
)
df[f"{colprefix}momentum_stoch"] = indicator_so.stoch()
df[f"{colprefix}momentum_stoch_signal"] = indicator_so.stoch_signal()
# Williams R Indicator
df[f"{colprefix}momentum_wr"] = WilliamsRIndicator(
high=df[high], low=df[low], close=df[close], lbp=14,
fillna=fillna).williams_r()
# Awesome Oscillator
df[f"{colprefix}momentum_ao"] = AwesomeOscillatorIndicator(
high=df[high], low=df[low], window1=5, window2=34,
fillna=fillna).awesome_oscillator()
# Rate Of Change
df[f"{colprefix}momentum_roc"] = ROCIndicator(close=df[close],
window=12,
fillna=fillna).roc()
# Percentage Price Oscillator
indicator_ppo = PercentagePriceOscillator(close=df[close],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}momentum_ppo"] = indicator_ppo.ppo()
df[f"{colprefix}momentum_ppo_signal"] = indicator_ppo.ppo_signal()
df[f"{colprefix}momentum_ppo_hist"] = indicator_ppo.ppo_hist()
# Percentage Volume Oscillator
indicator_pvo = PercentageVolumeOscillator(volume=df[volume],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}momentum_pvo"] = indicator_pvo.pvo()
df[f"{colprefix}momentum_pvo_signal"] = indicator_pvo.pvo_signal()
df[f"{colprefix}momentum_pvo_hist"] = indicator_pvo.pvo_hist()
if not vectorized:
# KAMA
df[f"{colprefix}momentum_kama"] = KAMAIndicator(close=df[close],
window=10,
pow1=2,
pow2=30,
fillna=fillna).kama()
return df
def add_momentum_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
volume: str,
fillna: bool = False,
colprefix: str = ""):
"""Add trend technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Relative Strength Index (RSI)
df[f'{colprefix}momentum_rsi'] = RSIIndicator(close=df[close],
n=14,
fillna=fillna).rsi()
# Money Flow Indicator
df[f'{colprefix}momentum_mfi'] = MFIIndicator(
high=df[high],
low=df[low],
close=df[close],
volume=df[volume],
n=14,
fillna=fillna).money_flow_index()
# TSI Indicator
df[f'{colprefix}momentum_tsi'] = TSIIndicator(close=df[close],
r=25,
s=13,
fillna=fillna).tsi()
# Ultimate Oscillator
df[f'{colprefix}momentum_uo'] = UltimateOscillatorIndicator(
high=df[high],
low=df[low],
close=df[close],
s=7,
m=14,
len=28,
ws=4.0,
wm=2.0,
wl=1.0,
fillna=fillna).uo()
# Stoch Indicator
indicator = StochIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
d_n=3,
fillna=fillna)
df[f'{colprefix}momentum_stoch'] = indicator.stoch()
df[f'{colprefix}momentum_stoch_signal'] = indicator.stoch_signal()
# Williams R Indicator
df[f'{colprefix}momentum_wr'] = WilliamsRIndicator(high=df[high],
low=df[low],
close=df[close],
lbp=14,
fillna=fillna).wr()
# Awesome Oscillator
df[f'{colprefix}momentum_ao'] = AwesomeOscillatorIndicator(
high=df[high], low=df[low], s=5, len=34, fillna=fillna).ao()
# KAMA
df[f'{colprefix}momentum_kama'] = KAMAIndicator(close=df[close],
n=10,
pow1=2,
pow2=30,
fillna=fillna).kama()
# Rate Of Change
df[f'{colprefix}momentum_roc'] = ROCIndicator(close=df[close],
n=12,
fillna=fillna).roc()
return df
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = TSIIndicator(close=self._df['Close'], r=25, s=13, fillna=False)
