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,
)
rsv_ = kd.stoch().fillna(50)
_k = np.zeros(stock_price.shape[0])
_d = np.zeros(stock_price.shape[0])
for i, r in enumerate(rsv_):
if i == 0:
_k[i] = 50
_d[i] = 50
else:
_k[i] = _k[i - 1] * 2 / 3 + r / 3
_d[i] = _d[i - 1] * 2 / 3 + _k[i] / 3
stock_price["K"] = _k
stock_price["D"] = _d
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 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.k_days,
)
rsv_ = kd.stoch().fillna(50)
_k = np.zeros(stock_price.shape[0])
_d = np.zeros(stock_price.shape[0])
for i, r in enumerate(rsv_):
if i == 0:
_k[i] = 50
_d[i] = 50
else:
_k[i] = _k[i - 1] * 2 / 3 + r / 3
_d[i] = _d[i - 1] * 2 / 3 + _k[i] / 3
stock_price["K"] = _k
stock_price["D"] = _d
stock_price.index = range(len(stock_price))
stock_price["diff"] = stock_price["K"] - stock_price["D"]
stock_price.loc[(stock_price.index < self.k_days), "diff"] = np.nan
stock_price["diff_sign"] = stock_price["diff"].map(
lambda x: 1 if x >= 0 else (-1 if x < 0 else 0))
stock_price["diff_sign_yesterday"] = (
stock_price["diff_sign"].shift(1).fillna(0).astype(int))
stock_price["signal"] = 0
stock_price.loc[((stock_price["diff_sign"] > 0)
& (stock_price["diff_sign_yesterday"] < 0)),
"signal", ] = 1
stock_price.loc[((stock_price["diff_sign"] < 0)
& (stock_price["diff_sign_yesterday"] > 0)),
"signal", ] = -1
return stock_price
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,
)
rsv_ = kd.stoch().fillna(50)
_k = np.zeros(base_data.shape[0])
_d = np.zeros(base_data.shape[0])
for i, r in enumerate(rsv_):
if i == 0:
_k[i] = 50
_d[i] = 50
else:
_k[i] = _k[i - 1] * 2 / 3 + r / 3
_d[i] = _d[i - 1] * 2 / 3 + _k[i] / 3
base_data["K"] = _k
base_data["D"] = _d
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 calc(self, dataframe):
stoch = StochasticOscillator(close=dataframe.close,
low=dataframe.low,
high=dataframe.high,
n=self.params.get("period"),
d_n=self.params.get("d_period"))
dataframe["{}_k".format(self.col_name())] = stoch.stoch()
return dataframe
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 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,
)
rsv_ = kd.stoch().fillna(50)
_k = np.zeros(base_data.shape[0])
_d = np.zeros(base_data.shape[0])
for i, r in enumerate(rsv_):
if i == 0:
_k[i] = 50
_d[i] = 50
else:
_k[i] = _k[i - 1] * 2 / 3 + r / 3
_d[i] = _d[i - 1] * 2 / 3 + _k[i] / 3
base_data["K"] = _k
base_data["D"] = _d
base_data.index = range(len(base_data))
base_data["diff"] = base_data["K"] - base_data["D"]
base_data.loc[(base_data.index < self.kdays), "diff"] = np.nan
base_data["bool_diff"] = base_data["diff"].map(
lambda x: 1 if x >= 0 else (-1 if x < 0 else 0))
base_data["bool_diff_shift1"] = (
base_data["bool_diff"].shift(1).fillna(0).astype(int))
base_data["signal"] = 0
base_data.loc[((base_data["bool_diff"] > 0)
& (base_data["bool_diff_shift1"] < 0)), "signal", ] = 1
base_data.loc[((base_data["bool_diff"] < 0)
& (base_data["bool_diff_shift1"] > 0)), "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
iloc is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.
'''
period = 30
df['SMA_Close'] = talib.SMA(df["Close"], timeperiod=period)
print('Time #1 batch TI: ' + str(time.time() - start_time) + ' seconds')
start_time = time.time()
# Stochastic Oscillator (SO)
period = 14
sma_period = 3
StochasticOscillator = ta.momentum.StochasticOscillator(high=df["High"],
low=df["Low"],
close=df["Close"],
n=period,
d_n=sma_period,
fillna=False)
df['SO'] = StochasticOscillator.stoch()
# Momentum (M)
period = 3
df['Momentum'] = talib.MOM(df["Close"], timeperiod=period)
# Price Rate Of Change (ROC)
'''
Description:
is a pure momentum oscillator that measures the percent change in price from one period to the next
The ROC calculation compares the current price with the price “n” periods ago
'''
period = 12
RateOfChange = ta.momentum.ROCIndicator(close=df["Close"],
n=period,
fillna=False)
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 applyIndicator(self, full_company_price):
self.data = full_company_price
high = self.data['high']
low = self.data['low']
close = self.data['close']
volume = self.data['volume']
EMA12 = EMAIndicator(close, 12, fillna=False)
EMA30 = EMAIndicator(close, 20, fillna=False)
EMA60 = EMAIndicator(close, 60, fillna=False)
MACD1226 = MACD(close, 26, 12, 9, fillna=False)
MACD2452 = MACD(close, 52, 24, 18, fillna=False)
ROC12 = ROCIndicator(close, 12, fillna=False)
ROC30 = ROCIndicator(close, 30, fillna=False)
ROC60 = ROCIndicator(close, 60, fillna=False)
RSI14 = RSIIndicator(close, 14, fillna=False)
RSI28 = RSIIndicator(close, 28, fillna=False)
RSI60 = RSIIndicator(close, 60, fillna=False)
AROON25 = AroonIndicator(close, 25, fillna=False)
AROON50 = AroonIndicator(close, 50, fillna=False)
AROON80 = AroonIndicator(close, 80, fillna=False)
MFI14 = MFIIndicator(high, low, close, volume, 14, fillna=False)
MFI28 = MFIIndicator(high, low, close, volume, 28, fillna=False)
MFI80 = MFIIndicator(high, low, close, volume, 80, fillna=False)
CCI20 = CCIIndicator(high, low, close, 20, 0.015, fillna=False)
CCI40 = CCIIndicator(high, low, close, 40, 0.015, fillna=False)
CCI100 = CCIIndicator(high, low, close, 100, 0.015, fillna=False)
WILLR14 = WilliamsRIndicator(high, low, close, 14, fillna=False)
WILLR28 = WilliamsRIndicator(high, low, close, 28, fillna=False)
WILLR60 = WilliamsRIndicator(high, low, close, 60, fillna=False)
BBANDS20 = BollingerBands(close, 20, 2, fillna=False)
KC20 = KeltnerChannel(high, low, close, 20, 10, fillna=False)
STOCH14 = StochasticOscillator(high, low, close, 14, 3, fillna=False)
STOCH28 = StochasticOscillator(high, low, close, 28, 6, fillna=False)
STOCH60 = StochasticOscillator(high, low, close, 60, 12, fillna=False)
CMI20 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
20,
fillna=False)
CMI40 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
40,
fillna=False)
CMI100 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
100,
fillna=False)
self.data['ema12'] = (close - EMA12.ema_indicator()) / close
self.data['ema30'] = (close - EMA30.ema_indicator()) / close
self.data['ema60'] = (close - EMA60.ema_indicator()) / close
self.data['macd1226'] = MACD1226.macd() - MACD1226.macd_signal()
self.data['macd2452'] = MACD2452.macd() - MACD2452.macd_signal()
self.data['roc12'] = ROC12.roc()
self.data['roc30'] = ROC30.roc()
self.data['roc60'] = ROC60.roc()
self.data['rsi14'] = RSI14.rsi()
self.data['rsi28'] = RSI28.rsi()
self.data['rsi60'] = RSI60.rsi()
self.data['aroon25'] = AROON25.aroon_indicator()
self.data['aroon50'] = AROON50.aroon_indicator()
self.data['aroon80'] = AROON80.aroon_indicator()
self.data['mfi14'] = MFI14.money_flow_index()
self.data['mfi28'] = MFI28.money_flow_index()
self.data['mfi80'] = MFI80.money_flow_index()
self.data['cci20'] = CCI20.cci()
self.data['cci40'] = CCI40.cci()
self.data['cci100'] = CCI100.cci()
self.data['willr14'] = WILLR14.wr()
self.data['willr28'] = WILLR28.wr()
self.data['willr60'] = WILLR60.wr()
self.data['bband20up'] = (BBANDS20.bollinger_hband() - close) / close
self.data['bband20down'] = (close - BBANDS20.bollinger_lband()) / close
self.data['stoch14'] = STOCH14.stoch()
self.data['stoch28'] = STOCH28.stoch()
self.data['stoch60'] = STOCH60.stoch()
self.data['cmi20'] = CMI20.chaikin_money_flow()
self.data['cmi40'] = CMI40.chaikin_money_flow()
self.data['cmi100'] = CMI100.chaikin_money_flow()
self.data['kc20up'] = (KC20.keltner_channel_hband() - close) / close
self.data['kc20down'] = (close - KC20.keltner_channel_lband()) / close
return self.data
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
