def create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame:
stock_price = stock_price.sort_values("date")
kd = StochasticOscillator(
high=stock_price["max"],
low=stock_price["min"],
close=stock_price["close"],
n=self.kdays,
d_n=self.ddays,
)
stock_price["K"] = kd.stoch()
stock_price["D"] = kd.stoch_signal()
stock_price.index = range(len(stock_price))
stock_price["signal"] = 0
stock_price.loc[stock_price["K"] <= self.kd_lower, "signal"] = 1
stock_price.loc[stock_price["K"] >= self.kd_upper, "signal"] = -1
return stock_price
def analyze(self):
stochastic_df = StochasticOscillator(self.df.high,
self.df.low,
self.df.close,
n=5)
self.df["stoch_k"] = stochastic_df.stoch()
self.df["stoch_d"] = stochastic_df.stoch_signal()
self.df["high_close"] = ((self.df.high - self.df.close) /
(self.df.high + self.df.close / 2) * 100)
self.df["buy_signal"] = (
((self.df['stoch_k'] > self.df['stoch_d']) &
(self.df['stoch_k'].shift(1) < self.df['stoch_d'].shift(1))) &
(self.df['stoch_d'] < 26))
self.df["stoch_buy_signal_early"] = (
(self.df['stoch_k'] < self.df['stoch_d']) &
((self.df["stoch_d"]) - self.df["stoch_k"] < 3) &
(self.df['stoch_d'] < 26))
def init(self, base_data):
base_data = base_data.sort_values("date")
kd = StochasticOscillator(
high=base_data["max"],
low=base_data["min"],
close=base_data["close"],
n=self.kdays,
d_n=self.ddays,
)
base_data["K"] = kd.stoch()
base_data["D"] = kd.stoch_signal()
base_data.index = range(len(base_data))
base_data["signal"] = 0
base_data.loc[base_data["K"] <= self.kd_lower, "signal"] = 1
base_data.loc[base_data["K"] >= self.kd_upper, "signal"] = -1
return base_data
def add_momentum_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""Adds the momentum indicators.
Parameters
----------
data : pd.DataFrame
A dataframe with daily stock values. Must include: open, high,
low, close and volume. It should also be sorted in a descending
manner.
Returns
-------
pd.DataFrame
The input dataframe with the indicators added.
"""
rsi = RSIIndicator(data['close'])
stoch_osc = StochasticOscillator(data['high'], data['low'], data['close'])
data.loc[:, 'rsi'] = rsi.rsi()
data.loc[:, 'stoch_osc'] = stoch_osc.stoch()
data.loc[:, 'stoch_osc_signal'] = stoch_osc.stoch_signal()
return data
class TestStochasticOscillator(unittest.TestCase):
"""
https://school.stockcharts.com/doku.php?id=technical_indicators:stochastic_oscillator_fast_slow_and_full
"""
_filename = 'ta/tests/data/cs-soo.csv'
def setUp(self):
self._df = pd.read_csv(self._filename, sep=',')
self._indicator = StochasticOscillator(
high=self._df['High'], low=self._df['Low'], close=self._df['Close'], n=14, d_n=3, fillna=False)
def tearDown(self):
del(self._df)
def test_so(self):
target = 'SO'
result = self._indicator.stoch()
pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
def test_so_signal(self):
target = 'SO_SIG'
result = self._indicator.stoch_signal()
pd.testing.assert_series_equal(self._df[target].tail(), result.tail(), check_names=False)
def STOCHASTIC(self, df):
df_STOCH = df.copy()
stoch = StochasticOscillator(df['High'], df['Low'], df['Close'])
df['%K'] = stoch.stoch()
df['%D'] = stoch.stoch_signal()
return df
def add_momentum_ta(
df: pd.DataFrame,
high: str,
low: str,
close: str,
volume: 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.
volume (str): Name of 'volume' 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.
"""
# Relative Strength Index (RSI)
df[f"{colprefix}momentum_rsi"] = RSIIndicator(close=df[close],
window=14,
fillna=fillna).rsi()
# Stoch RSI (StochRSI)
indicator_srsi = StochRSIIndicator(close=df[close],
window=14,
smooth1=3,
smooth2=3,
fillna=fillna)
df[f"{colprefix}momentum_stoch_rsi"] = indicator_srsi.stochrsi()
df[f"{colprefix}momentum_stoch_rsi_k"] = indicator_srsi.stochrsi_k()
df[f"{colprefix}momentum_stoch_rsi_d"] = indicator_srsi.stochrsi_d()
# TSI Indicator
df[f"{colprefix}momentum_tsi"] = TSIIndicator(close=df[close],
window_slow=25,
window_fast=13,
fillna=fillna).tsi()
# Ultimate Oscillator
df[f"{colprefix}momentum_uo"] = UltimateOscillator(
high=df[high],
low=df[low],
close=df[close],
window1=7,
window2=14,
window3=28,
weight1=4.0,
weight2=2.0,
weight3=1.0,
fillna=fillna,
).ultimate_oscillator()
# Stoch Indicator
indicator_so = StochasticOscillator(
high=df[high],
low=df[low],
close=df[close],
window=14,
smooth_window=3,
fillna=fillna,
)
df[f"{colprefix}momentum_stoch"] = indicator_so.stoch()
df[f"{colprefix}momentum_stoch_signal"] = indicator_so.stoch_signal()
# Williams R Indicator
df[f"{colprefix}momentum_wr"] = WilliamsRIndicator(
high=df[high], low=df[low], close=df[close], lbp=14,
fillna=fillna).williams_r()
# Awesome Oscillator
df[f"{colprefix}momentum_ao"] = AwesomeOscillatorIndicator(
high=df[high], low=df[low], window1=5, window2=34,
fillna=fillna).awesome_oscillator()
# Rate Of Change
df[f"{colprefix}momentum_roc"] = ROCIndicator(close=df[close],
window=12,
fillna=fillna).roc()
# Percentage Price Oscillator
indicator_ppo = PercentagePriceOscillator(close=df[close],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}momentum_ppo"] = indicator_ppo.ppo()
df[f"{colprefix}momentum_ppo_signal"] = indicator_ppo.ppo_signal()
df[f"{colprefix}momentum_ppo_hist"] = indicator_ppo.ppo_hist()
# Percentage Volume Oscillator
indicator_pvo = PercentageVolumeOscillator(volume=df[volume],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}momentum_pvo"] = indicator_pvo.pvo()
df[f"{colprefix}momentum_pvo_signal"] = indicator_pvo.pvo_signal()
df[f"{colprefix}momentum_pvo_hist"] = indicator_pvo.pvo_hist()
if not vectorized:
# KAMA
df[f"{colprefix}momentum_kama"] = KAMAIndicator(close=df[close],
window=10,
pow1=2,
pow2=30,
fillna=fillna).kama()
return df
def add_momentum_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
volume: str,
fillna: bool = False,
colprefix: str = "") -> 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
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Relative Strength Index (RSI)
df[f'{colprefix}momentum_rsi'] = RSIIndicator(close=df[close],
n=14,
fillna=fillna).rsi()
# TSI Indicator
df[f'{colprefix}momentum_tsi'] = TSIIndicator(close=df[close],
r=25,
s=13,
fillna=fillna).tsi()
# Ultimate Oscillator
df[f'{colprefix}momentum_uo'] = UltimateOscillator(high=df[high],
low=df[low],
close=df[close],
s=7,
m=14,
len=28,
ws=4.0,
wm=2.0,
wl=1.0,
fillna=fillna).uo()
# Stoch Indicator
indicator = StochasticOscillator(high=df[high],
low=df[low],
close=df[close],
n=14,
d_n=3,
fillna=fillna)
df[f'{colprefix}momentum_stoch'] = indicator.stoch()
df[f'{colprefix}momentum_stoch_signal'] = indicator.stoch_signal()
# Williams R Indicator
df[f'{colprefix}momentum_wr'] = WilliamsRIndicator(high=df[high],
low=df[low],
close=df[close],
lbp=14,
fillna=fillna).wr()
# Awesome Oscillator
df[f'{colprefix}momentum_ao'] = AwesomeOscillatorIndicator(
high=df[high], low=df[low], s=5, len=34, fillna=fillna).ao()
# KAMA
df[f'{colprefix}momentum_kama'] = KAMAIndicator(close=df[close],
n=10,
pow1=2,
pow2=30,
fillna=fillna).kama()
# Rate Of Change
df[f'{colprefix}momentum_roc'] = ROCIndicator(close=df[close],
n=12,
fillna=fillna).roc()
return df
