def get_analysis(self, data, bollinger_function):
if bollinger_function == 'upper':
return bollinger_bands.upper_bollinger_band(
data, self.params['period'])
if bollinger_function == 'middle':
return bollinger_bands.middle_bollinger_band(
data, self.params['period'])
if bollinger_function == 'lower':
return bollinger_bands.lower_bollinger_band(
data, self.params['period'])
if bollinger_function == 'bandwidth':
return bollinger_bands.bandwidth(data, self.params['period'])
if bollinger_function == 'range':
return bollinger_bands.bb_range(data, self.params['period'])
if bollinger_function == 'percent_bandwidth':
return bollinger_bands.percent_bandwidth(data,
self.params['period'])
if bollinger_function == 'percent_b':
return bollinger_bands.percent_b(data, self.params['period'])
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
count = 10 # count in days in the past
gran = '1h'
symbol = 'BTC/USD'
price_prom = 0.15
params = {"count": count, "granularity": gran}
if __name__ == "__main__":
df = hlp.get_bitmex(symbol, gran, count, m15=False)
# dfdt = pd.Series(df['close'].values, index=pd.DatetimeIndex(
# start='2017-01-1', periods=len(df['close']), freq='1d'))
# dfseas = seasonal_decompose(dfdt)
# finding BB%
dfflist = df['close'].tolist()
dfreturns = hlp.returns(df['close'])
hlp.plot_histogram(dfreturns, 20)
print(df.head())
bbl = bollinger_bands.percent_b(dfflist, 70, 2.0)
bbdivs_bear, bbdivs_bull, bpf, pbf = hlp.divergence2(bbl,
df,
prominence=5,
hei=20)
# SRSI and its peaks/bottoms
K, D = hlp.StochRSI(df, price='close')
kdivs_bear, kdivs_bull, kbf, kpf = hlp.divergence2(K,
df,
prominence=5,
hei=20)
# peaks of close : resistance and support levels
dfflist2 = df['close'].values
prom = price_prom * (np.max(dfflist2) - np.min(dfflist2))
print(prom)
def test_percent_b_invalid_period(self):
period = 128
with self.assertRaises(Exception) as cm:
bollinger_bands.percent_b(self.data, period)
expected = "Error: data_len < period"
self.assertEqual(str(cm.exception), expected)
def test_percent_b_period_6(self):
period = 6
percent_b = bollinger_bands.percent_b(self.data, period)
np.testing.assert_array_equal(percent_b,
self.percent_b_period_6_expected)
# In[5]: data = socket.get_candles(instrument = 'GBP/USD', period = 'D1', start = dt.datetime(2016,1,1), end = dt.datetime(2018, 6, 10)) # In[6]: #Define useful variables data['upper_band'] = ubb(data['askclose'], period = 20) data['mid_band'] = mbb(data['askclose'], period = 20 ) data['lower_band'] = lbb(data['askclose'], period = 20 ) data['percent_b'] = percent_b(data['askclose'], period =20) data # In[7]: fig = plt.figure(figsize=(12,8)) ax1 = fig.add_subplot(111, xlabel = 'Date',ylabel='Close') data['askclose'].plot(ax=ax1, color='r', lw=1) data['upper_band'].plot(ax=ax1, color = 'b', lw= 1) data['mid_band'].plot(ax=ax1, color = 'g', lw= 1) data['lower_band'].plot(ax=ax1, color = 'y', lw= 1)