def test_money_flow_invalid_data(self):
self.close_data.append(1)
with self.assertRaises(Exception) as cm:
money_flow.money_flow(self.close_data, self.high_data,
self.low_data, self.volume)
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 money_flow_index(close_data, high_data, low_data, volume, period):
"""
Money Flow Index.
Formula:
MFI = 100 - (100 / (1 + PMF / NMF))
"""
catch_errors.check_for_input_len_diff(
close_data, high_data, low_data, volume
)
catch_errors.check_for_period_error(close_data, period)
mf = money_flow(close_data, high_data, low_data, volume)
tp = typical_price(close_data, high_data, low_data)
flow = [tp[idx] > tp[idx-1] for idx in range(1, len(tp))]
pf = [mf[idx] if flow[idx] else 0 for idx in range(0, len(flow))]
nf = [mf[idx] if not flow[idx] else 0 for idx in range(0, len(flow))]
pmf = [sum(pf[idx+1-period:idx+1]) for idx in range(period-1, len(pf))]
nmf = [sum(nf[idx+1-period:idx+1]) for idx in range(period-1, len(nf))]
# Dividing by 0 is not an issue, it turns the value into NaN which we would
# want in that case
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
money_ratio = np.array(pmf) / np.array(nmf)
mfi = 100 - (100 / (1 + money_ratio))
mfi = fill_for_noncomputable_vals(close_data, mfi)
return mfi
def test_money_flow(self):
mf = money_flow.money_flow(self.close_data, self.high_data,
self.low_data, self.volume)
np.testing.assert_array_equal(mf, self.mf_expected)