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 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')))
