def add_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""
This method creates technical indicators, based on the OHLC and volume bars
:param data: pandas DataFrame, containing open, high, low and close and
optional volume columns
:return: DataFrame with added technical indicators
"""
assert 'open' in data.columns, "open column not present or with different name"
assert 'high' in data.columns, "high column not present or with different name"
assert 'low' in data.columns, "low column not present or with different name"
assert 'close' in data.columns, "close column not present or with different name"
try:
data['RSI'] = ta.rsi(data["close"])
data['TSI'] = ta.tsi(data["close"])
data['UO'] = ta.uo(data["high"], data["low"], data["close"])
data['AO'] = ta.ao(data["high"], data["low"])
data['MACD_diff'] = ta.macd_diff(data["close"])
data['Vortex_pos'] = ta.vortex_indicator_pos(data["high"], data["low"], data["close"])
data['Vortex_neg'] = ta.vortex_indicator_neg(data["high"], data["low"], data["close"])
data['Vortex_diff'] = abs(data['Vortex_pos'] - data['Vortex_neg'])
data['Trix'] = ta.trix(data["close"])
data['Mass_index'] = ta.mass_index(data["high"], data["low"])
data['CCI'] = ta.cci(data["high"], data["low"], data["close"])
data['DPO'] = ta.dpo(data["close"])
data['KST'] = ta.kst(data["close"])
data['KST_sig'] = ta.kst_sig(data["close"])
data['KST_diff'] = (data['KST'] - data['KST_sig'])
data['Aroon_up'] = ta.aroon_up(data["close"])
data['Aroon_down'] = ta.aroon_down(data["close"])
data['Aroon_ind'] = (data['Aroon_up'] - data['Aroon_down'])
data['BBH'] = ta.bollinger_hband(data["close"])
data['BBL'] = ta.bollinger_lband(data["close"])
data['BBM'] = ta.bollinger_mavg(data["close"])
data['BBHI'] = ta.bollinger_hband_indicator(data["close"])
data['BBLI'] = ta.bollinger_lband_indicator(data["close"])
data['KCHI'] = ta.keltner_channel_hband_indicator(data["high"], data["low"], data["close"])
data['KCLI'] = ta.keltner_channel_lband_indicator(data["high"], data["low"], data["close"])
data['DCHI'] = ta.donchian_channel_hband_indicator(data["close"])
data['DCLI'] = ta.donchian_channel_lband_indicator(data["close"])
data['DR'] = ta.daily_return(data["close"])
data['DLR'] = ta.daily_log_return(data["close"])
if 'volume' in data.columns:
data['MFI'] = ta.money_flow_index(data["high"], data["low"], data["close"], data["volume"])
data['ADI'] = ta.acc_dist_index(data["high"], data["low"], data["close"], data["volume"])
data['OBV'] = ta.on_balance_volume(data["close"], data["volume"])
data['CMF'] = ta.chaikin_money_flow(data["high"], data["low"], data["close"], data["volume"])
data['FI'] = ta.force_index(data["close"], data["volume"])
data['EM'] = ta.ease_of_movement(data["high"], data["low"], data["close"], data["volume"])
data['VPT'] = ta.volume_price_trend(data["close"], data["volume"])
data['NVI'] = ta.negative_volume_index(data["close"], data["volume"])
data.fillna(method='bfill', inplace=True)
return data
except (AssertionError, Exception) as error:
raise IndicatorsError(error)
LOGGER.error(error)
def add_indicators(df):
df['RSI'] = ta.rsi(df["Close"])
df['MFI'] = ta.money_flow_index(df["High"], df["Low"], df["Close"],
df["Volume"])
df['TSI'] = ta.tsi(df["Close"])
df['UO'] = ta.uo(df["High"], df["Low"], df["Close"])
df['AO'] = ta.ao(df["High"], df["Low"])
df['MACD_diff'] = ta.macd_diff(df["Close"])
df['Vortex_pos'] = ta.vortex_indicator_pos(df["High"], df["Low"],
df["Close"])
df['Vortex_neg'] = ta.vortex_indicator_neg(df["High"], df["Low"],
df["Close"])
df['Vortex_diff'] = abs(df['Vortex_pos'] - df['Vortex_neg'])
df['Trix'] = ta.trix(df["Close"])
df['Mass_index'] = ta.mass_index(df["High"], df["Low"])
df['CCI'] = ta.cci(df["High"], df["Low"], df["Close"])
df['DPO'] = ta.dpo(df["Close"])
df['KST'] = ta.kst(df["Close"])
df['KST_sig'] = ta.kst_sig(df["Close"])
df['KST_diff'] = (df['KST'] - df['KST_sig'])
df['Aroon_up'] = ta.aroon_up(df["Close"])
df['Aroon_down'] = ta.aroon_down(df["Close"])
df['Aroon_ind'] = (df['Aroon_up'] - df['Aroon_down'])
df['BBH'] = ta.bollinger_hband(df["Close"])
df['BBL'] = ta.bollinger_lband(df["Close"])
df['BBM'] = ta.bollinger_mavg(df["Close"])
df['BBHI'] = ta.bollinger_hband_indicator(df["Close"])
df['BBLI'] = ta.bollinger_lband_indicator(df["Close"])
df['KCHI'] = ta.keltner_channel_hband_indicator(df["High"], df["Low"],
df["Close"])
df['KCLI'] = ta.keltner_channel_lband_indicator(df["High"], df["Low"],
df["Close"])
df['DCHI'] = ta.donchian_channel_hband_indicator(df["Close"])
df['DCLI'] = ta.donchian_channel_lband_indicator(df["Close"])
df['ADI'] = ta.acc_dist_index(df["High"], df["Low"], df["Close"],
df["Volume"])
df['OBV'] = ta.on_balance_volume(df["Close"], df["Volume"])
df['CMF'] = ta.chaikin_money_flow(df["High"], df["Low"], df["Close"],
df["Volume"])
df['FI'] = ta.force_index(df["Close"], df["Volume"])
df['EM'] = ta.ease_of_movement(df["High"], df["Low"], df["Close"],
df["Volume"])
df['VPT'] = ta.volume_price_trend(df["Close"], df["Volume"])
df['NVI'] = ta.negative_volume_index(df["Close"], df["Volume"])
df['DR'] = ta.daily_return(df["Close"])
df['DLR'] = ta.daily_log_return(df["Close"])
df.fillna(method='bfill', inplace=True)
return df
def add_candle_indicators(df, l, ck, hk, lk, vk):
df[l + 'rsi'] = ta.rsi(df[ck])
df[l + 'mfi'] = ta.money_flow_index(df[hk], df[lk], df[ck], df[vk])
df[l + 'tsi'] = ta.tsi(df[ck])
df[l + 'uo'] = ta.uo(df[hk], df[lk], df[ck])
df[l + 'ao'] = ta.ao(df[hk], df[lk])
df[l + 'macd_diff'] = ta.macd_diff(df[ck])
df[l + 'vortex_pos'] = ta.vortex_indicator_pos(df[hk], df[lk], df[ck])
df[l + 'vortex_neg'] = ta.vortex_indicator_neg(df[hk], df[lk], df[ck])
df[l + 'vortex_diff'] = abs(df[l + 'vortex_pos'] - df[l + 'vortex_neg'])
df[l + 'trix'] = ta.trix(df[ck])
df[l + 'mass_index'] = ta.mass_index(df[hk], df[lk])
df[l + 'cci'] = ta.cci(df[hk], df[lk], df[ck])
df[l + 'dpo'] = ta.dpo(df[ck])
df[l + 'kst'] = ta.kst(df[ck])
df[l + 'kst_sig'] = ta.kst_sig(df[ck])
df[l + 'kst_diff'] = (df[l + 'kst'] - df[l + 'kst_sig'])
df[l + 'aroon_up'] = ta.aroon_up(df[ck])
df[l + 'aroon_down'] = ta.aroon_down(df[ck])
df[l + 'aroon_ind'] = (df[l + 'aroon_up'] - df[l + 'aroon_down'])
df[l + 'bbh'] = ta.bollinger_hband(df[ck])
df[l + 'bbl'] = ta.bollinger_lband(df[ck])
df[l + 'bbm'] = ta.bollinger_mavg(df[ck])
df[l + 'bbhi'] = ta.bollinger_hband_indicator(df[ck])
df[l + 'bbli'] = ta.bollinger_lband_indicator(df[ck])
df[l + 'kchi'] = ta.keltner_channel_hband_indicator(df[hk], df[lk], df[ck])
df[l + 'kcli'] = ta.keltner_channel_lband_indicator(df[hk], df[lk], df[ck])
df[l + 'dchi'] = ta.donchian_channel_hband_indicator(df[ck])
df[l + 'dcli'] = ta.donchian_channel_lband_indicator(df[ck])
df[l + 'adi'] = ta.acc_dist_index(df[hk], df[lk], df[ck], df[vk])
df[l + 'obv'] = ta.on_balance_volume(df[ck], df[vk])
df[l + 'cmf'] = ta.chaikin_money_flow(df[hk], df[lk], df[ck], df[vk])
df[l + 'fi'] = ta.force_index(df[ck], df[vk])
df[l + 'em'] = ta.ease_of_movement(df[hk], df[lk], df[ck], df[vk])
df[l + 'vpt'] = ta.volume_price_trend(df[ck], df[vk])
df[l + 'nvi'] = ta.negative_volume_index(df[ck], df[vk])
df[l + 'dr'] = ta.daily_return(df[ck])
df[l + 'dlr'] = ta.daily_log_return(df[ck])
df[l + 'ma50'] = df[ck].rolling(window=50).mean()
df[l + 'ma100'] = df[ck].rolling(window=100).mean()
df[l + '26ema'] = df[[ck]].ewm(span=26).mean()
df[l + '12ema'] = df[[ck]].ewm(span=12).mean()
df[l + 'macd'] = (df[l + '12ema'] - df[l + '26ema'])
df[l + '100sd'] = df[[ck]].rolling(100).std()
df[l + 'upper_band'] = df[l + 'ma100'] + (df[l + '100sd'] * 2)
df[l + 'lower_band'] = df[l + 'ma100'] - (df[l + '100sd'] * 2)
df[l + 'ema'] = df[ck].ewm(com=0.5).mean()
df[l + 'momentum'] = df[ck] - 1
return df
def preproc(self):
self.dat = df = pd.read_csv(self.path)
s = np.asanyarray(ta.stoch(df["High"],df["Low"],df["Close"],14)).reshape((-1, 1)) - np.asanyarray(ta.stoch_signal(df["High"],df["Low"],df["Close"],14)).reshape((-1, 1))
x = np.asanyarray(ta.daily_return(df["Close"])).reshape((-1,1))
m = np.asanyarray(ta.macd_diff(df["Close"])).reshape((-1,1))
cross1 = np.asanyarray(ta.ema(self.dat["Close"],20)).reshape((-1, 1)) - np.asanyarray(ta.ema(self.dat["Close"],5)).reshape((-1, 1))
x = np.concatenate([x], 1)
y = np.asanyarray(self.dat[["Open"]])
gen = tf.keras.preprocessing.sequence.TimeseriesGenerator(x, y, self.window_size)
self.x = []
self.y = []
for i in gen:
self.x.extend(i[0].tolist())
self.y.extend(i[1].tolist())
self.x = np.asanyarray(self.x)#.reshape((-1, self.window_size, x.shape[-1]))
self.y = np.asanyarray(self.y)
self.df = self.x
self.trend = self.y
def add_indicators(df):
df['RSI'] = ta.rsi(df["Close"])
df['TSI'] = ta.tsi(df["Close"])
df['UO'] = ta.uo(df["High"], df["Low"], df["Close"])
df['AO'] = ta.ao(df["High"], df["Low"])
df['MACD_diff'] = ta.macd_diff(df["Close"])
df['Vortex_pos'] = ta.vortex_indicator_pos(df["High"], df["Low"],
df["Close"])
df['Vortex_neg'] = ta.vortex_indicator_neg(df["High"], df["Low"],
df["Close"])
df['Vortex_diff'] = abs(df['Vortex_pos'] - df['Vortex_neg'])
df['Trix'] = ta.trix(df["Close"])
df['Mass_index'] = ta.mass_index(df["High"], df["Low"])
df['CCI'] = ta.cci(df["High"], df["Low"], df["Close"])
df['DPO'] = ta.dpo(df["Close"])
df['KST'] = ta.kst(df["Close"])
df['KST_sig'] = ta.kst_sig(df["Close"])
df['KST_diff'] = (df['KST'] - df['KST_sig'])
df['Aroon_up'] = ta.aroon_up(df["Close"])
df['Aroon_down'] = ta.aroon_down(df["Close"])
df['Aroon_ind'] = (df['Aroon_up'] - df['Aroon_down'])
df['BBH'] = ta.bollinger_hband(df["Close"])
df['BBL'] = ta.bollinger_lband(df["Close"])
df['BBM'] = ta.bollinger_mavg(df["Close"])
df['BBHI'] = ta.bollinger_hband_indicator(df["Close"])
df['BBLI'] = ta.bollinger_lband_indicator(df["Close"])
df['KCHI'] = ta.keltner_channel_hband_indicator(df["High"], df["Low"],
df["Close"])
df['KCLI'] = ta.keltner_channel_lband_indicator(df["High"], df["Low"],
df["Close"])
df['DCHI'] = ta.donchian_channel_hband_indicator(df["Close"])
df['DCLI'] = ta.donchian_channel_lband_indicator(df["Close"])
df['DR'] = ta.daily_return(df["Close"])
df['DLR'] = ta.daily_log_return(df["Close"])
df.fillna(method='bfill', inplace=True)
return df
ta_df['CMF'] = ta.chaikin_money_flow(df["High"],
df["Low"],
df["Close"],
df["Volume BTC"])
ta_df['FI'] = ta.force_index(df["Close"],
df["Volume BTC"])
ta_df['EM'] = ta.ease_of_movement(df["High"],
df["Low"],
df["Close"],
df["Volume BTC"])
ta_df['VPT'] = ta.volume_price_trend(df["Close"],
df["Volume BTC"])
ta_df['NVI'] = ta.negative_volume_index(df["Close"],
df["Volume BTC"])
ta_df['DR'] = ta.daily_return(df["Close"])
ta_df['DLR'] = ta.daily_log_return(df["Close"])
ta_df['CR'] = ta.cumulative_return(df["Close"])
corr = ta_df.corr()
corr.describe().to_csv('All_indicators.csv')
sns.heatmap(corr,
cmap="viridis",
xticklabels=corr.columns.values,
yticklabels=corr.columns.values)
plt.title("All Indicators")
plt.show()
wm=2,
wl=1,
fillna=True)
X['stoch_signal'] = ta.stoch_signal(price['High'],
price['Low'],
price['Adj. Close'],
n=14,
d_n=3,
fillna=True)
X['wr'] = ta.wr(price['High'],
price['Low'],
price['Adj. Close'],
lbp=14,
fillna=True)
X['ao'] = ta.ao(price['Low'], price['Adj. Close'], s=5, l=34, fillna=True)
X['daily_return'] = ta.daily_return(price['Adj. Close'], fillna=True)
# </editor-fold>
print('creating TA test set')
# <editor-fold desc="Create the test set">
X_test = pd.DataFrame(index=price_test.index,
data={
'bollinger_hband_indicator':
np.array([np.nan] * price_test.index.shape[0])
})
X_test['bollinger_hband_indicator'] = ta.bollinger_hband_indicator(
price_test['Adj. Close'], n=20, ndev=2, fillna=True)
X_test['bollinger_lband_indicator'] = ta.bollinger_lband_indicator(
price_test['Adj. Close'], n=20, ndev=2, fillna=True)
X_test['ema_indicator_21'] = ta.ema_indicator(price_test['Adj. Close'],
n=21,
def add_technical_indicators(df):
"""
Args:
df (pd.DataFrame): The processed dataframe returned by `process_data`.
Returns:
pd.DataFrame: The updated dataframe with the technical indicators inside.
Acknowledgements:
- Thanks for Adam King for this compilation of technical indicators!
The original file and code can be found here:
https://github.com/notadamking/RLTrader/blob/e5b83b1571f9fcfa6a67a2a810222f1f1751996c/util/indicators.py
"""
# Add momentum indicators
df["AO"] = ta.ao(df["High"], df["Low"])
df["MFI"] = ta.money_flow_index(df["High"], df["Low"], df["Close"],
df["Volume"])
df["RSI"] = ta.rsi(df["Close"])
df["TSI"] = ta.tsi(df["Close"])
df["UO"] = ta.uo(df["High"], df["Low"], df["Close"])
# Add trend indicators
df["Aroon_up"] = ta.aroon_up(df["Close"])
df["Aroon_down"] = ta.aroon_down(df["Close"])
df["Aroon_ind"] = (df["Aroon_up"] - df["Aroon_down"])
df["CCI"] = ta.cci(df["High"], df["Low"], df["Close"])
df["DPO"] = ta.dpo(df["Close"])
df["KST"] = ta.kst(df["Close"])
df["KST_sig"] = ta.kst_sig(df["Close"])
df["KST_diff"] = (df["KST"] - df["KST_sig"])
df["MACD_diff"] = ta.macd_diff(df["Close"])
df["Mass_index"] = ta.mass_index(df["High"], df["Low"])
df["Trix"] = ta.trix(df["Close"])
df["Vortex_pos"] = ta.vortex_indicator_pos(df["High"], df["Low"],
df["Close"])
df["Vortex_neg"] = ta.vortex_indicator_neg(df["High"], df["Low"],
df["Close"])
df["Vortex_diff"] = abs(df["Vortex_pos"] - df["Vortex_neg"])
# Add volatility indicators
df["BBH"] = ta.bollinger_hband(df["Close"])
df["BBL"] = ta.bollinger_lband(df["Close"])
df["BBM"] = ta.bollinger_mavg(df["Close"])
df["BBHI"] = ta.bollinger_hband_indicator(df["Close"])
df["BBLI"] = ta.bollinger_lband_indicator(df["Close"])
df["KCHI"] = ta.keltner_channel_hband_indicator(df["High"], df["Low"],
df["Close"])
df["KCLI"] = ta.keltner_channel_lband_indicator(df["High"], df["Low"],
df["Close"])
df["DCHI"] = ta.donchian_channel_hband_indicator(df["Close"])
df["DCLI"] = ta.donchian_channel_lband_indicator(df["Close"])
# Volume indicators
df["ADI"] = ta.acc_dist_index(df["High"], df["Low"], df["Close"],
df["Volume"])
df["CMF"] = ta.chaikin_money_flow(df["High"], df["Low"], df["Close"],
df["Volume"])
df["EM"] = ta.ease_of_movement(df["High"], df["Low"], df["Close"],
df["Volume"])
df["FI"] = ta.force_index(df["Close"], df["Volume"])
df["NVI"] = ta.negative_volume_index(df["Close"], df["Volume"])
df["OBV"] = ta.on_balance_volume(df["Close"], df["Volume"])
df["VPT"] = ta.volume_price_trend(df["Close"], df["Volume"])
# Add miscellaneous indicators
df["DR"] = ta.daily_return(df["Close"])
df["DLR"] = ta.daily_log_return(df["Close"])
# Fill in NaN values
df.fillna(method="bfill", inplace=True) # First try `bfill`
df.fillna(value=0,
inplace=True) # Then replace the rest of the NANs with 0s
return df