def DMI(df, n1): #trend
df_DMI = df.copy()
adxi = ADXIndicator(df['High'], df['Low'], df['Close'], n1, False)
df['plusDI'] = adxi.adx_pos()
df['minusDI'] = adxi.adx_neg()
df['adx'] = adxi.adx()
return df
def get_current_info(self, history: pd.DataFrame, newData: pd.DataFrame, isTD: bool = False) -> Tuple[float, float, float, float, float, datetime, pd.DataFrame]:
"""
get the latest ADX, pos/neg indicator and date
"""
history = history.dropna()
newData = self.data_preparation(newData)
history = history.append(newData)
smoothed = 14
adxI = ADXIndicator(history['premium_high'], history['premium_low'], history['premium_close'], smoothed, False)
history['pos_directional_indicator'] = adxI.adx_pos()
history['neg_directional_indicator'] = adxI.adx_neg()
history['adx'] = adxI.adx()
adx = history.iloc[-1]['adx']
posDI = history.iloc[-1]['pos_directional_indicator']
negDI = history.iloc[-1]['neg_directional_indicator']
marketPrice = history.iloc[-1]['gbtc_open_price']
curPremium = history.iloc[-1]['premium_close']
date = history.index[-1]
return (adx, posDI, negDI, marketPrice, curPremium,
datetime.strptime(date, '%Y-%m-%dT%H:%M:%SZ') if not isTD else datetime.strptime(date, '%Y-%m-%d %H:%M:%S'), history)
def getADXList(data):
adxI = ADXIndicator(data['High'], data['Low'], data['Close'], 14, False)
data['pos_directional_indicator'] = adxI.adx_pos()
data['neg_directional_indicator'] = adxI.adx_neg()
data['adx'] = adxI.adx()
return data[[
'pos_directional_indicator', 'neg_directional_indicator', 'adx'
]]
def DMI(self, df, n): #trend
df_DMI = df.copy()
adxi = ADXIndicator(df['High'], df['Low'], df['Close'], n, False)
df['plusDI'] = adxi.adx_pos()
df['minusDI'] = adxi.adx_neg()
df['adx'] = adxi.adx()
# df['DMI'] = np.where((df_DMI['+DI']>df_DMI['-DI'])&(df_DMI['+DI'].shift()<df_DMI['-DI'].shift()),'buy',
# (np.where((df_DMI['+DI'] < df_DMI['-DI']) & (df_DMI['+DI'].shift()>df_DMI['-DI'].shift()),'sell','wait or hold')))
return df
def adx_indicator(data):
data['Adj Open'] = data.Open * data['Adj Close'] / data['Close']
data['Adj High'] = data.High * data['Adj Close'] / data['Close']
data['Adj Low'] = data.Low * data['Adj Close'] / data['Close']
data.dropna(inplace=True)
adxI = ADXIndicator(data['Adj High'], data['Adj Low'], data['Adj Close'],
14, False)
data['pos_directional_indicator'] = adxI.adx_pos()
data['neg_directional_indicator'] = adxI.adx_neg()
data['adx'] = adxI.adx()
data['di_pos'] = adxI.adx_pos()
data['di_neg'] = adxI.adx_neg()
#print(data['di_neg'])
data['trend_signal'] = np.where(data['adx'] > 25, 1, 0)
data['adx_indicator'] = np.where(
(data['adx'] >= 25) & (data['di_pos'] >= data['di_neg']), 1, 0)
return data
def preprocess_data(self, data: pd.DataFrame) -> pd.DataFrame:
btc_per_share = 0.00094498
data["nav_open_price"] = data["open_price_x"] * \
btc_per_share
data["nav_close_price"] = data["close_price_x"] * btc_per_share
data["nav_high_price"] = data["high_price_x"] * \
btc_per_share
data["nav_low_price"] = data["low_price_x"] * \
btc_per_share
# Calculate premium high
data["nav_high_price"] = data["high_price_x"] * btc_per_share
data["premium_high"] = (data["high_price_y"] - data["nav_high_price"]
) / data["nav_high_price"]
data["premium_high"].dropna()
# Calculate premium low
data["nav_low_price"] = data["low_price_x"] * btc_per_share
data["premium_low"] = (data["low_price_y"] -
data["nav_low_price"]) / data["nav_low_price"]
data["premium_low"].dropna()
# Calculate premium close
data["nav_close_price"] = data["close_price_x"] * btc_per_share
data["premium_close"] = (
data["close_price_y"] -
data["nav_close_price"]) / data["nav_close_price"]
data["premium_close"].dropna()
print(data)
# Calculate ADX, pos/neg direction indicators
smoothed = 70
adxI = ADXIndicator(data['premium_high'], data['premium_low'],
data['premium_close'], smoothed, False)
data['pos_directional_indicator'] = adxI.adx_pos()
data['neg_directional_indicator'] = adxI.adx_neg()
data['adx'] = adxI.adx()
data.dropna()
return data
def add_trend_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""Adds the trend 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.
"""
adx = ADXIndicator(data['high'], data['low'], data['close'])
ema = EMAIndicator(data['close'])
ema_200 = EMAIndicator(data['close'], n=200)
ichimoku = IchimokuIndicator(data['high'], data['low'])
macd = MACD(data['close'])
sma = SMAIndicator(data['close'], n=14)
sma_200 = SMAIndicator(data['close'], n=200)
data.loc[:, 'adx'] = adx.adx()
data.loc[:, 'adx_pos'] = adx.adx_pos()
data.loc[:, 'adx_neg'] = adx.adx_neg()
data.loc[:, 'ema'] = ema.ema_indicator()
data.loc[:, 'ema_200'] = ema_200.ema_indicator()
data.loc[:, 'ichimoku_a'] = ichimoku.ichimoku_a()
data.loc[:, 'ichimoku_b'] = ichimoku.ichimoku_b()
data.loc[:, 'ichimoku_base_line'] = ichimoku.ichimoku_base_line()
data.loc[:, 'ichimoku_conversion_line'] = (
ichimoku.ichimoku_conversion_line())
data.loc[:, 'macd'] = macd.macd()
data.loc[:, 'macd_diff'] = macd.macd_diff()
data.loc[:, 'macd_signal'] = macd.macd_signal()
data.loc[:, 'sma'] = sma.sma_indicator()
data.loc[:, 'sma_200'] = sma_200.sma_indicator()
return data
#df = ta.add_all_ta_features(df, open = "open_price" , high = "high_price" , low = "low_price" , close = "close_price" , # volume= "volume", fillna= True) low = df["low_price"].to_list() print(low) high = df["high_price"] print (high) close = df["close_price"] print(close) #print(df["low_price"]) print(df.columns) indicator = ADXIndicator( high= df["high_price"], low = df["low_price"] , close = df["close_price"], n=14, fillna= True) print(indicator) df['ADX_NEG'] = indicator.adx_neg() df['ADX_POSVT'] = indicator.adx_pos() #df['RSI']=indicator.rsi() df['ADX'] = indicator.adx() print(df) ohlc = df.loc[:, ['date_recorded', 'open_price', 'high_price', 'low_price', 'close_price']] #print(ohlc) #Now got the price of the tatamotors #aapl['Date'] = aapl.index.map(mdates.date2num) ohlc['date_recorded'] = pd.to_datetime(ohlc['date_recorded']) ohlc['date_recorded'] = ohlc['date_recorded'].apply(mpl_dates.date2num) #ohlc = ohlc.astype(float)
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
def add_trend_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = ""):
"""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.
"""
# MACD
indicator_macd = MACD(close=df[close],
n_fast=12,
n_slow=26,
n_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()
# EMAs
df[f'{colprefix}trend_ema_fast'] = EMAIndicator(
close=df[close], n=12, fillna=fillna).ema_indicator()
df[f'{colprefix}trend_ema_slow'] = EMAIndicator(
close=df[close], n=26, fillna=fillna).ema_indicator()
# Average Directional Movement Index (ADX)
indicator = ADXIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
fillna=fillna)
df[f'{colprefix}trend_adx'] = indicator.adx()
df[f'{colprefix}trend_adx_pos'] = indicator.adx_pos()
df[f'{colprefix}trend_adx_neg'] = indicator.adx_neg()
# Vortex Indicator
indicator = VortexIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
fillna=fillna)
df[f'{colprefix}trend_vortex_ind_pos'] = indicator.vortex_indicator_pos()
df[f'{colprefix}trend_vortex_ind_neg'] = indicator.vortex_indicator_neg()
df[f'{colprefix}trend_vortex_ind_diff'] = indicator.vortex_indicator_diff()
# TRIX Indicator
indicator = TRIXIndicator(close=df[close], n=15, fillna=fillna)
df[f'{colprefix}trend_trix'] = indicator.trix()
# Mass Index
indicator = MassIndex(high=df[high],
low=df[low],
n=9,
n2=25,
fillna=fillna)
df[f'{colprefix}trend_mass_index'] = indicator.mass_index()
# CCI Indicator
indicator = CCIIndicator(high=df[high],
low=df[low],
close=df[close],
n=20,
c=0.015,
fillna=fillna)
df[f'{colprefix}trend_cci'] = indicator.cci()
# DPO Indicator
indicator = DPOIndicator(close=df[close], n=20, fillna=fillna)
df[f'{colprefix}trend_dpo'] = indicator.dpo()
# KST Indicator
indicator = KSTIndicator(close=df[close],
r1=10,
r2=15,
r3=20,
r4=30,
n1=10,
n2=10,
n3=10,
n4=15,
nsig=9,
fillna=fillna)
df[f'{colprefix}trend_kst'] = indicator.kst()
df[f'{colprefix}trend_kst_sig'] = indicator.kst_sig()
df[f'{colprefix}trend_kst_diff'] = indicator.kst_diff()
# Ichimoku Indicator
indicator = IchimokuIndicator(high=df[high],
low=df[low],
n1=9,
n2=26,
n3=52,
visual=False,
fillna=fillna)
df[f'{colprefix}trend_ichimoku_a'] = indicator.ichimoku_a()
df[f'{colprefix}trend_ichimoku_b'] = indicator.ichimoku_b()
indicator = IchimokuIndicator(high=df[high],
low=df[low],
n1=9,
n2=26,
n3=52,
visual=True,
fillna=fillna)
df[f'{colprefix}trend_visual_ichimoku_a'] = indicator.ichimoku_a()
df[f'{colprefix}trend_visual_ichimoku_b'] = indicator.ichimoku_b()
# Aroon Indicator
indicator = AroonIndicator(close=df[close], n=25, fillna=fillna)
df[f'{colprefix}trend_aroon_up'] = indicator.aroon_up()
df[f'{colprefix}trend_aroon_down'] = indicator.aroon_down()
df[f'{colprefix}trend_aroon_ind'] = indicator.aroon_indicator()
# PSAR Indicator
indicator = 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_up()
df[f'{colprefix}trend_psar_down'] = indicator.psar_down()
df[f'{colprefix}trend_psar_up_indicator'] = indicator.psar_up_indicator()
df[f'{colprefix}trend_psar_down_indicator'] = indicator.psar_down_indicator(
)
return df
