def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(close=cls._df['Close'],
n_slow=26,
n_fast=12,
n_sign=9,
fillna=True)
cls._indicator = PercentagePriceOscillator(**cls._params)
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(
close=cls._df["Close"],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=True,
)
cls._indicator = PercentagePriceOscillator(**cls._params)
if len(sys.argv) > 1:
batch_size = 31
symbol = sys.argv[1]
end = datetime.today()
start = datetime(2000, 9, 1)
ETH = pdr.DataReader(symbol,'yahoo',start,end)
df = pd.DataFrame(data=ETH)
kama_indicator = KAMAIndicator(close = df["Close"], window = 10, pow1 = 2, pow2 = 30, fillna = False)
df['kama'] = kama_indicator.kama()
ppo_indicator = PercentagePriceOscillator(close = df["Close"], window_slow = 20, window_fast = 10, window_sign = 9, fillna = False)
df['ppo'] = ppo_indicator.ppo()
roc_indicator = ROCIndicator(close = df["Close"], window = 12, fillna = False)
df['roc'] = roc_indicator.roc()
macd_indicator = MACD(close = df["Close"], window_slow = 20, window_fast = 12, window_sign = 9, fillna = False)
df['macd'] = macd_indicator.macd()
rsi_indicator = RSIIndicator(close = df["Close"], window = 14, fillna = False)
df['rsi'] = rsi_indicator.rsi()
aroon_indicator = AroonIndicator(close = df["Close"], window = 20, fillna = False)
df['aroon'] = aroon_indicator.aroon_indicator()
boll_indicator = BollingerBands(close = df["Close"], window = 20, window_dev = 2, 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 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()
# Stoch RSI (StochRSI)
indicator = StochRSIIndicator(close=df[close], n=14, d1=3, d2=3, fillna=fillna)
df[f'{colprefix}momentum_stoch_rsi'] = indicator.stochrsi()
df[f'{colprefix}momentum_stoch_rsi_k'] = indicator.stochrsi_k()
df[f'{colprefix}momentum_stoch_rsi_d'] = indicator.stochrsi_d()
# 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()
# Percentage Price Oscillator
indicator = PercentagePriceOscillator(close=df[close], n_slow=26, n_fast=12, n_sign=9, fillna=fillna)
df[f'{colprefix}momentum_ppo'] = indicator.ppo()
df[f'{colprefix}momentum_ppo_signal'] = indicator.ppo_signal()
df[f'{colprefix}momentum_ppo_hist'] = indicator.ppo_hist()
# Percentage Volume Oscillator
indicator = PercentageVolumeOscillator(volume=df[volume], n_slow=26, n_fast=12, n_sign=9, fillna=fillna)
df[f'{colprefix}momentum_ppo'] = indicator.pvo()
df[f'{colprefix}momentum_ppo_signal'] = indicator.pvo_signal()
df[f'{colprefix}momentum_ppo_hist'] = indicator.pvo_hist()
return df
def features_df(stocks,
rsi_vals=False,
macd_vals=False,
ppo=False,
awesome_oscillator_val=False,
daily_log_return=False,
_return=False,
change=False,
volatility=False,
min5=False,
min10=False,
min30=False,
hband_indicator=False,
lband_indicator=False,
corr=False):
"""
Calculates the momentum, RSI, moving average convergence/divergence (MACD), Percentage Price Oscillator,
awesome oscillator indicator, daily log return column, return, change, volatility, 5-10 and 30 mins moving average,
high band indicator, lower band indicator, correlation of the given stock dataframe.
:param stocks: dataframe of stocks
:param rsi_vals: Default value False. If you want the RSI column, set True
:param macd_vals: Default value False. If you want the MACD column, set True
:param ppo: Default value False. If you want the Percentage Price Oscillator column, set True
:param awesome_oscillator_val: Default value False. If you want the awesome oscillator indicator column, set True
:param daily_log_return: Default value False. If you want the daily log return column, set True
:param _return: Default value False. If you want the return column, set True
:param change: Default value False. If you want the change column, set True
:param volatility: Default value False. If you want the volatility column, set True
:param min5: Default value False. If you want the min5 column, set True
:param min10: Default value False. If you want the min10 column, set True
:param min30: Default value False. If you want the min30 column, set True
:param hband_indicator: Default value False. If you want the high band indicator column, set True
:param lband_indicator: Default value False. If you want the lower band indicator column, set True
:param corr: Default value False. If you want the correlation column, set True
:return: a dataframe with the different features of said stock Default value False.
If you want the RSI column, set True
"""
if rsi_vals is True:
stocks['rsi_vals'] = RSIIndicator(close=stocks.Close, window=10).rsi()
if macd_vals is True:
stocks['macd_vals'] = macd(stocks.Close,
window_slow=26,
window_fast=12)
if ppo is True:
stocks['Ppo'] = np.array(
PercentagePriceOscillator(stocks.Close,
window_slow=26,
window_fast=12).ppo())
if awesome_oscillator_val is True:
stocks['awesome_oscillator'] = awesome_oscillator(stocks.High,
stocks.Low,
window1=5,
window2=29)
if daily_log_return is True:
stocks['daily_log_return'] = daily_return(close=stocks.Close)
if _return is True:
stocks['Return'] = round(stocks['Close'] / stocks['Open'] - 1, 3)
if change is True:
stocks['Change'] = (stocks.Close - stocks.Close.shift(1)).fillna(0)
if volatility is True:
stocks['Volatility'] = stocks.Close.ewm(21).std()
if min5 is True:
stocks['min5'] = stocks.Close.rolling(5).mean()
if min10 is True:
stocks['min10'] = stocks.Close.rolling(10).mean()
if min30 is True:
stocks['min30'] = stocks.Close.rolling(30).mean()
if hband_indicator is True:
stocks['hband_indicator'] = BollingerBands(
close=stocks.Close).bollinger_hband_indicator()
if lband_indicator is True:
stocks['lband_indicator'] = BollingerBands(
close=stocks.Close).bollinger_lband_indicator()
if corr is True:
stocks['Corr'] = stocks.Close.rolling(window=10).corr(stocks['min10'])
return stocks.iloc[-1, :]
for k in range(500):
if k == 344: # This index gives an error when fitting the model
pass
else:
print(k, Symbols[k])
data = yf.Ticker(Symbols[k])
reader = data.history(day, interval='1m')
y = np.where(reader['Close'].shift(-1) > reader['Close'], 1, -1)[30:]
reader['rsi_vals'] = RSIIndicator(close=reader.Close, window=10).rsi()
reader['macd_vals'] = macd(reader.Close,
window_slow=26,
window_fast=12)
reader['Ppo'] = PercentagePriceOscillator(reader.Close,
window_slow=26,
window_fast=12).ppo()
reader['awesome_oscillator'] = awesome_oscillator(reader.High,
reader.Low,
window1=5,
window2=29)
reader['daily_log_return'] = daily_return(close=reader.Close)
reader['Return'] = round(reader['Close'] / reader['Open'] - 1, 3)
reader['Change'] = (reader.Close - reader.Close.shift(1)).fillna(0)
reader['Volatility'] = reader.Close.ewm(21).std()
reader['min5'] = reader.Close.rolling(5).mean()
reader['min10'] = reader.Close.rolling(10).mean()
reader['min30'] = reader.Close.rolling(30).mean()
reader['hband_indicator'] = BollingerBands(
close=reader.Close).bollinger_hband_indicator()
reader['lband_indicator'] = BollingerBands(