def techIndicator(df1):
#OFtrader
# Initialize Bollinger Bands Indicator
from ta.volatility import BollingerBands
indicator_bb = BollingerBands(close=df1["Close"], window=10, window_dev=2)
# Add Bollinger Bands features
df1['bb_bbh'] = indicator_bb.bollinger_hband()
df1['bb_bbl'] = indicator_bb.bollinger_lband()
# Initialize Bollinger Bands Indicator
from ta.trend import PSARIndicator
indicator_SAR = PSARIndicator(high=df1["high"],
low=df1["low"],
close=df1["Close"])
# Add Bollinger Bands features
df1['sar_high'] = indicator_SAR.psar_up()
df1['sar_low'] = indicator_SAR.psar_down()
from ta.trend import EMAIndicator
indicator_EMA = EMAIndicator(close=df1["Close"], window=7)
df1['Media7'] = indicator_EMA.ema_indicator()
df1['sar_low'] = df1['sar_low'].fillna(0)
df1['sar_high'] = df1['sar_high'].fillna(0)
df1['Distancia_M7'] = df1['Close'] / df1['Media7']
df1['Distancia_BBH'] = df1['Close'] / df1['bb_bbh']
df1['Distancia_BBL'] = df1['Close'] / df1['bb_bbl']
df1['Distancia_SAR'] = np.where(df1['sar_high'] > 0,
df1['Close'] / df1['sar_high'],
df1['sar_low'] / df1['Close'])
df1['posicao_sar'] = np.where(df1['sar_high'] > 0, '1', '0')
corte = 3
df1["Distancia_M7"] = pd.qcut(df1["Distancia_M7"], corte, labels=False)
df1["Distancia_BBH"] = pd.qcut(df1["Distancia_BBH"], corte, labels=False)
df1["Distancia_BBL"] = pd.qcut(df1["Distancia_BBL"], 15, labels=False)
df1["Distancia_SAR"] = pd.qcut(df1["Distancia_SAR"], 15, labels=False)
#
# Padrão Bom com M7: 3 - BBH: 3 - BBL: 15 - DSAR: 15
df1 = df1.drop(["sar_high", "sar_low", "bb_bbh", "bb_bbl", "Media7"],
axis=1)
#df1.tail(50)
return df1
class TestPSARIndicator(unittest.TestCase):
"""
https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
"""
_filename = 'ta/tests/data/cs-psar.csv'
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = PSARIndicator(high=self._df['High'],
low=self._df['Low'],
close=self._df['Close'],
fillna=False)
def tearDown(self):
del (self._df)
def test_psar_up(self):
target = 'psar_up'
result = self._indicator.psar_up()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def test_psar_down(self):
target = 'psar_down'
result = self._indicator.psar_down()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def test_psar_up_indicator(self):
target = 'psar_up_ind'
result = self._indicator.psar_up_indicator()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def test_psar_down_indicator(self):
target = 'psar_down_ind'
result = self._indicator.psar_down_indicator()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def add_trend_ta(
df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = "",
vectorized: bool = False,
) -> pd.DataFrame:
"""Add trend technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
vectorized(bool): if True, use only vectorized functions indicators
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# MACD
indicator_macd = MACD(close=df[close],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}trend_macd"] = indicator_macd.macd()
df[f"{colprefix}trend_macd_signal"] = indicator_macd.macd_signal()
df[f"{colprefix}trend_macd_diff"] = indicator_macd.macd_diff()
# SMAs
df[f"{colprefix}trend_sma_fast"] = SMAIndicator(
close=df[close], window=12, fillna=fillna).sma_indicator()
df[f"{colprefix}trend_sma_slow"] = SMAIndicator(
close=df[close], window=26, fillna=fillna).sma_indicator()
# EMAs
df[f"{colprefix}trend_ema_fast"] = EMAIndicator(
close=df[close], window=12, fillna=fillna).ema_indicator()
df[f"{colprefix}trend_ema_slow"] = EMAIndicator(
close=df[close], window=26, fillna=fillna).ema_indicator()
# Vortex Indicator
indicator_vortex = VortexIndicator(high=df[high],
low=df[low],
close=df[close],
window=14,
fillna=fillna)
df[f"{colprefix}trend_vortex_ind_pos"] = indicator_vortex.vortex_indicator_pos(
)
df[f"{colprefix}trend_vortex_ind_neg"] = indicator_vortex.vortex_indicator_neg(
)
df[f"{colprefix}trend_vortex_ind_diff"] = indicator_vortex.vortex_indicator_diff(
)
# TRIX Indicator
df[f"{colprefix}trend_trix"] = TRIXIndicator(close=df[close],
window=15,
fillna=fillna).trix()
# Mass Index
df[f"{colprefix}trend_mass_index"] = MassIndex(high=df[high],
low=df[low],
window_fast=9,
window_slow=25,
fillna=fillna).mass_index()
# DPO Indicator
df[f"{colprefix}trend_dpo"] = DPOIndicator(close=df[close],
window=20,
fillna=fillna).dpo()
# KST Indicator
indicator_kst = KSTIndicator(
close=df[close],
roc1=10,
roc2=15,
roc3=20,
roc4=30,
window1=10,
window2=10,
window3=10,
window4=15,
nsig=9,
fillna=fillna,
)
df[f"{colprefix}trend_kst"] = indicator_kst.kst()
df[f"{colprefix}trend_kst_sig"] = indicator_kst.kst_sig()
df[f"{colprefix}trend_kst_diff"] = indicator_kst.kst_diff()
# Ichimoku Indicator
indicator_ichi = IchimokuIndicator(
high=df[high],
low=df[low],
window1=9,
window2=26,
window3=52,
visual=False,
fillna=fillna,
)
df[f"{colprefix}trend_ichimoku_conv"] = indicator_ichi.ichimoku_conversion_line(
)
df[f"{colprefix}trend_ichimoku_base"] = indicator_ichi.ichimoku_base_line()
df[f"{colprefix}trend_ichimoku_a"] = indicator_ichi.ichimoku_a()
df[f"{colprefix}trend_ichimoku_b"] = indicator_ichi.ichimoku_b()
# Schaff Trend Cycle (STC)
df[f"{colprefix}trend_stc"] = STCIndicator(
close=df[close],
window_slow=50,
window_fast=23,
cycle=10,
smooth1=3,
smooth2=3,
fillna=fillna,
).stc()
if not vectorized:
# Average Directional Movement Index (ADX)
indicator_adx = ADXIndicator(high=df[high],
low=df[low],
close=df[close],
window=14,
fillna=fillna)
df[f"{colprefix}trend_adx"] = indicator_adx.adx()
df[f"{colprefix}trend_adx_pos"] = indicator_adx.adx_pos()
df[f"{colprefix}trend_adx_neg"] = indicator_adx.adx_neg()
# CCI Indicator
df[f"{colprefix}trend_cci"] = CCIIndicator(
high=df[high],
low=df[low],
close=df[close],
window=20,
constant=0.015,
fillna=fillna,
).cci()
# Ichimoku Visual Indicator
indicator_ichi_visual = IchimokuIndicator(
high=df[high],
low=df[low],
window1=9,
window2=26,
window3=52,
visual=True,
fillna=fillna,
)
df[f"{colprefix}trend_visual_ichimoku_a"] = indicator_ichi_visual.ichimoku_a(
)
df[f"{colprefix}trend_visual_ichimoku_b"] = indicator_ichi_visual.ichimoku_b(
)
# Aroon Indicator
indicator_aroon = AroonIndicator(close=df[close],
window=25,
fillna=fillna)
df[f"{colprefix}trend_aroon_up"] = indicator_aroon.aroon_up()
df[f"{colprefix}trend_aroon_down"] = indicator_aroon.aroon_down()
df[f"{colprefix}trend_aroon_ind"] = indicator_aroon.aroon_indicator()
# PSAR Indicator
indicator_psar = PSARIndicator(
high=df[high],
low=df[low],
close=df[close],
step=0.02,
max_step=0.20,
fillna=fillna,
)
# df[f'{colprefix}trend_psar'] = indicator.psar()
df[f"{colprefix}trend_psar_up"] = indicator_psar.psar_up()
df[f"{colprefix}trend_psar_down"] = indicator_psar.psar_down()
df[f"{colprefix}trend_psar_up_indicator"] = indicator_psar.psar_up_indicator(
)
df[f"{colprefix}trend_psar_down_indicator"] = indicator_psar.psar_down_indicator(
)
return df