def test_ndi_invalid_period(self): period = 128 with self.assertRaises(Exception) as cm: directional_indicators.negative_directional_index( self.close_data, self.high_data, self.low_data, period) expected = "Error: data_len < period" self.assertEqual(str(cm.exception), expected)
def test_ndi_invalid_data(self): period = 6 self.close_data.append(0) with self.assertRaises(Exception) as cm: directional_indicators.negative_directional_index(self.close_data, self.high_data, self.low_data, period) expected = ("Error: mismatched data lengths, check to ensure that all input data is the same length and valid") self.assertEqual(str(cm.exception), expected)
def double_top_double_bottom(key, price_action, time_frame): #To determine up/down trend and the strength pdi = np.array( positive_directional_index(price_action[id.close], price_action[id.high], price_action[id.low], id.window_size)) ndi = np.array( negative_directional_index(price_action[id.close], price_action[id.high], price_action[id.low], id.window_size)) adx = np.array( average_directional_index(price_action[id.close], price_action[id.high], price_action[id.low], id.window_size)) obv = np.array( on_balance_volume(price_action[id.close], price_action[id.volume])) #Calculate the local maxima and minima in the window frame local_minima, local_maxima, indices_minima, indices_maxima = get_local_min_max( np.array(price_action[id.high])) notifier = {values.double_top: False, values.double_bottom: False} if pdi[len(pdi) - 1] > ndi[len(ndi) - 1] and adx[len(adx) - 1] >= 25: notifier[values.double_top] = check_double_top(price_action, indices_maxima, obv) if pdi[len(pdi) - 1] < ndi[len(ndi) - 1] and adx[len(adx) - 1] >= 25: notifier[values.double_bottom] = check_double_bottom( price_action, indices_minima, obv) if notifier[values.double_top]: db.insert_strategy(key, time_frame, values.double_top, price_action.iloc[-1][id.time]) if notifier[values.double_bottom]: db.insert_strategy(key, time_frame, values.double_bottom, price_action.iloc[-1][id.time])
def test_ndi_period_10(self): period = 10 ndi = directional_indicators.negative_directional_index( self.close_data, self.high_data, self.low_data, period) np.testing.assert_allclose(ndi, self.ndi_period_10_expected)
def test_ndi_period_8(self): period = 8 ndi = directional_indicators.negative_directional_index(self.close_data, self.high_data, self.low_data, period) np.testing.assert_array_equal(ndi, self.ndi_period_8_expected)
def _get_ta_features(high, low, close, volume, desc): """ Returns a dict containing the technical analysis indicators calculated on the given high, low, close and volumes. """ ta = {} # Set numpy to ignore division error and invalid values (since not all features are complete) old_settings = np.seterr(divide='ignore', invalid='ignore') record_count = len(close) # Determine relative moving averages for _short, _long in desc['rsma']: if record_count < _short or record_count < _long: logging.error( "get_ta_features: not enough records for rsma (short={}, long={}, records={})" .format(_short, _long, record_count)) continue ta['rsma_{}_{}'.format(_short, _long)] = relative_sma(close, _short, _long) for _short, _long in desc['rema']: if record_count < _short or record_count < _long: logging.error( "get_ta_features: not enough records for rema (short={}, long={}, records={})" .format(_short, _long, record_count)) continue ta['rema_{}_{}'.format(_short, _long)] = relative_ema(close, _short, _long) # MACD Indicator if 'macd' in desc: for _short, _long in desc['macd']: if record_count < _short or record_count < _long: logging.error( "get_ta_features: not enough records for rema (short={}, long={}, records={})" .format(_short, _long, record_count)) continue ta['macd_{}_{}'.format( _short, _long)] = moving_average_convergence_divergence( close, _short, _long) # Aroon Indicator if 'ao' in desc: for _period in desc['ao']: if record_count < _period: logging.error( "get_ta_features: not enough records for ao (period={}, records={})" .format(_period, record_count)) continue ta['ao_{}'.format(_period)] = aroon_oscillator(close, _period) # Average Directional Movement Index (ADX) if 'adx' in desc: for _period in desc['adx']: if record_count < _period: logging.error( "get_ta_features: not enough records for adx (period={}, records={})" .format(_period, record_count)) continue ta['adx_{}'.format(_period)] = average_directional_index( close, high, low, _period) # Difference between Positive Directional Index(DI+) and Negative Directional Index(DI-) if 'wd' in desc: for _period in desc['wd']: if record_count < _period: logging.error( "get_ta_features: not enough records for wd (period={}, records={})" .format(_period, record_count)) continue ta['wd_{}'.format(_period)] = \ positive_directional_index(close, high, low, _period) \ - negative_directional_index(close, high, low, _period) # Percentage Price Oscillator if 'ppo' in desc: for _short, _long in desc['ppo']: if record_count < _short or record_count < _long: logging.error( "get_ta_features: not enough records for ppo (short={}, long={}, records={})" .format(_short, _long, record_count)) continue ta['ppo_{}_{}'.format(_short, _long)] = price_oscillator( close, _short, _long) # Relative Strength Index if 'rsi' in desc: for _period in desc['rsi']: if record_count < _period: logging.error( "get_ta_features: not enough records for rsi (period={}, records={})" .format(_period, record_count)) continue ta['rsi_{}'.format(_period)] = relative_strength_index( close, _period) # Money Flow Index if 'mfi' in desc: for _period in desc['mfi']: if record_count < _period: logging.error( "get_ta_features: not enough records for mfi (period={}, records={})" .format(_period, record_count)) continue ta['mfi_{}'.format(_period)] = money_flow_index( close, high, low, volume, _period) # True Strength Index if 'tsi' in desc and len(close) >= 40: if record_count < 40: logging.error( "get_ta_features: not enough records for tsi (period={}, records={})" .format(40, record_count)) else: ta['tsi'] = true_strength_index(close) if 'boll' in desc: for _period in desc['stoch']: if record_count < _period: logging.error( "get_ta_features: not enough records for boll (period={}, records={})" .format(_period, record_count)) continue ta['boll_{}'.format(_period)] = percent_b(close, _period) # Stochastic Oscillator if 'stoch' in desc: for _period in desc['stoch']: if record_count < _period: logging.error( "get_ta_features: not enough records for stoch (period={}, records={})" .format(_period, record_count)) continue ta['stoch_{}'.format(_period)] = percent_k(close, _period) # ta.py['stoch'] = percent_k(high, low, close, 14) # Chande Momentum Oscillator ## Not available in ta.py if 'cmo' in desc: for _period in desc['cmo']: if record_count < _period: logging.error( "get_ta_features: not enough records for cmo (period={}, records={})" .format(_period, record_count)) continue ta['cmo_{}'.format(_period)] = chande_momentum_oscillator( close, _period) # Average True Range Percentage if 'atrp' in desc: for _period in desc['atrp']: if record_count < _period: logging.error( "get_ta_features: not enough records for atrp (period={}, records={})" .format(_period, record_count)) continue ta['atrp_{}'.format(_period)] = average_true_range_percent( close, _period) # Percentage Volume Oscillator if 'pvo' in desc: for _short, _long in desc['pvo']: if record_count < _short or record_count < _long: logging.error( "get_ta_features: not enough records for pvo (short={}, long={}, records={})" .format(_short, _long, record_count)) continue ta['pvo_{}_{}'.format(_short, _long)] = volume_oscillator( volume, _short, _long) # Force Index if 'fi' in desc: fi = force_index(close, volume) for _period in desc['fi']: if record_count < _period: logging.error( "get_ta_features: not enough records for atrp (period={}, records={})" .format(_period, record_count)) continue ta['fi_{}'.format(_period)] = exponential_moving_average( fi, _period) # Accumulation Distribution Line if 'adi' in desc: ta['adi'] = accumulation_distribution(close, high, low, volume) # On Balance Volume if 'obv' in desc: ta['obv'] = on_balance_volume(close, volume) # Restore numpy error settings np.seterr(**old_settings) return ta