def test_candlestick_chart(data):
df = data[100:300]
ind = indicator.Indicate(data)
sma = ind.smooth_moving_average(14)['close_sma_14'][100:300]
rsi = ind.relative_strength_index()
rsi_overlay = observe.trend_relative_strength_index(rsi)[100:300]
candlestick_chart(df, sma, rsi['rsi'][100:300], rsi_overlay, 'AUD_JPY')
def test_indicate_init_np_columns(data):
ts = data.to_numpy()
columns = np.split(ts, ts.shape[1], axis=1)
del ts
ts = np.asarray([np.concatenate(col) for col in columns])
indicator.Indicate(data=ts,
labels=['open', 'high', 'low'],
orient='columns')
def test_sma(data):
arr = data["close"].to_numpy(copy=True).astype(float)
ti_sma = np.round(np.append([np.nan for i in range(9)],
ti.sma(arr, period=10)),
decimals=3)
sma = indicator.Indicate(data["close"])\
.smooth_moving_average(10)['close_sma_10']\
.astype(float)
assert np.allclose(ti_sma, sma, atol=1e-03, equal_nan=True)
def test_indicate_init_np_rows_integrity(data):
df_og = data.astype(float).copy()
ts = data.to_numpy()
d = indicator.Indicate(data=ts,
labels=['open', 'high', 'low', 'close'],
orient='rows')
df_proc = pd.DataFrame(d.data)
df_og.reset_index(drop=True, inplace=True)
pd.testing.assert_frame_equal(df_og, df_proc)
def test_adx(data):
"""Spot check against ti seems accurate, slight differences (1e-02) with
Oanda display"""
arr_high = data["high"].to_numpy().astype(float)
arr_low = data["low"].to_numpy().astype(float)
arr_close = data["close"].to_numpy().astype(float)
ti_adx = np.append([np.nan for i in range(26)],
ti.adx(arr_high, arr_low, arr_close, period=14))
adx = indicator.Indicate(data, exp=6)\
.average_directional_movement()['adx']\
.astype(float)
assert np.allclose(ti_adx[-10:], adx[-10:], atol=1e-03, equal_nan=True)
def test_atr(data):
arr_high = data["high"].to_numpy().astype(float)
arr_low = data["low"].to_numpy().astype(float)
arr_close = data["close"].to_numpy().astype(float)
ti_atr = np.round(np.append([np.nan for i in range(13)],
ti.atr(arr_high, arr_low, arr_close,
period=14)),
decimals=3)
atr = indicator.Indicate(data)\
.average_true_range()['atr']\
.astype(float)
assert np.allclose(ti_atr, atr, atol=1e-03, equal_nan=True)
def test_indicate_init_np_columns_integrity(data):
df_og = data.astype(float).copy()
ts = data.to_numpy()
columns = np.split(ts, ts.shape[1], axis=1)
del ts
ts = np.asarray([np.concatenate(col) for col in columns])
d = indicator.Indicate(data=ts,
labels=['open', 'high', 'low', 'close'],
orient='columns')
df_proc = pd.DataFrame(d.data)
df_og.reset_index(drop=True, inplace=True)
pd.testing.assert_frame_equal(df_og, df_proc)
def test_stoch(data):
arr_high = data["high"].to_numpy().astype(float)
arr_low = data["low"].to_numpy().astype(float)
arr_close = data["close"].to_numpy().astype(float)
ti_percK, ti_percD = ti.stoch(arr_high, arr_low, arr_close, 14, 1, 3)
stoch = indicator.Indicate(data).stochastic()
percK = stoch['percK'].astype(float)
percD = stoch['percD'].astype(float)
assert np.allclose(ti_percK[-250:],
percK[-250:],
atol=1e-03,
equal_nan=True)
assert np.allclose(ti_percD[-250:],
percD[-250:],
atol=1e-03,
equal_nan=True)
def test_macd(data):
"""Values match approx Oanda via spot check but not tulipy, hence marked to
fail"""
arr = data["close"].to_numpy(copy=True).astype(float)
ti_macd, ti_signal, ti_histogram = ti.macd(arr, 12, 26, 9)
s = indicator.Indicate(data["close"], exp=6)\
.moving_average_convergence_divergence()
assert np.allclose(ti_macd[-250:],
s['macd'][-250:].astype(float),
atol=1e-05,
equal_nan=True)
assert np.allclose(ti_signal[-250:],
s['signal'][-250:].astype(float),
atol=1e-05,
equal_nan=True)
assert np.allclose(ti_histogram[-250:],
s['histogram'][-250:].astype(float),
atol=1e-05,
equal_nan=True)
def test_candles_indicators_relationship(save_to_table, dbsession):
"""Test indicators table functionality, relationship with candles table
and data integrity."""
data = save_to_table[1].drop('batch_id', axis=1)
ikh = indicator.Indicate(data, exp=6).ichimoku_kinko_hyo()
ikh['senkou_A'] = ikh['senkou_A'][:len(data)]
ikh['senkou_B'] = ikh['senkou_B'][:len(data)]
df = pd.DataFrame(ikh, index=data.timestamp)
df.reset_index(inplace=True)
df.rename(columns={'index': 'timestamp'}, inplace=True)
df['batch_id'] = save_to_table[0]
rows = df.to_dict('records')
for entry in rows:
entry['timestamp'] = entry['timestamp'].to_pydatetime()
dbsession.bulk_insert_mappings(Indicators, rows)
assert len(
dbsession.query(Indicators).filter_by(
batch_id=save_to_table[0]).all()) == len(df)
tenkan = []
kijun = []
chikou = []
senkou_A = []
senkou_B = []
for row in dbsession.query(Candles).filter_by(batch_id=save_to_table[0])\
.all():
tenkan.append(row.indicators.tenkan)
kijun.append(row.indicators.kijun)
chikou.append(row.indicators.chikou)
senkou_A.append(row.indicators.senkou_A)
senkou_B.append(row.indicators.senkou_B)
np_ikh = np.stack((tenkan, kijun, chikou, senkou_A, senkou_B), axis=1)
df_ikh = pd.DataFrame(
np_ikh, columns=['tenkan', 'kijun', 'chikou', 'senkou_A', 'senkou_B'])
# print(df_ikh.tail())
df.drop(['timestamp', 'batch_id'], inplace=True, axis=1)
pd.testing.assert_frame_equal(df, df_ikh)
def indicate(mid):
return indicator.Indicate(mid)
def test_rsi(data):
arr = data["close"].to_numpy(copy=True).astype(float)
ti_rsi = np.round(ti.rsi(arr, 14), decimals=3)
rsi = indicator.Indicate(data["close"]).relative_strength_index()['rsi']\
.astype(float)
assert np.allclose(ti_rsi[-250:], rsi[-250:], atol=1e-03, equal_nan=True)
def test_indicate_init_df_integrity(data):
df_og = data.astype(float).copy()
d = indicator.Indicate(data=data)
df_proc = pd.DataFrame(d.data)
df_og.reset_index(drop=True, inplace=True)
pd.testing.assert_frame_equal(df_og, df_proc)
def test_indicate_init_np_rows(data):
ts = data.to_numpy()
indicator.Indicate(data=ts, labels=['open', 'high', 'low'], orient='rows')
def test_indicate_init(ts, labels, orient):
d = indicator.Indicate(data=ts, labels=labels, orient=orient)
assert isinstance(d.data, dict)
def test_indicate_init_none():
indicator.Indicate()
