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')))