def process_data(data):
data['BB_5'] = ta.bollinger_mavg(
data['CLOSE'], 5) #bollinger_moving average 5 trading periods
data['BB_10'] = ta.bollinger_mavg(
data['CLOSE'], 10) #bollinger_moving average 10 trading periods
data['BB_20'] = ta.bollinger_mavg(
data['CLOSE'], 20) # bollinger_moving average 20 periods
data['ADX'] = ta.adx(data['HIGH'], data['LOW'], data['CLOSE'],
14) #Average Directional Index
data['ATR'] = ta.average_true_range(data['HIGH'], data['LOW'],
data['CLOSE'], 14) #Average True Range
data['CCI'] = ta.cci(data['HIGH'], data['LOW'], data['CLOSE'],
14) #Commodity Channel Index
data['DCH'] = ta.donchian_channel_hband(
data['CLOSE']) #Donchian Channel High Band
data['DCL'] = ta.donchian_channel_lband(
data['CLOSE']) #Donchian Channel Low Band
data['DPO'] = ta.dpo(data['CLOSE']) #Detrend Price Oscilator
data['EMAf'] = ta.ema_fast(
data['CLOSE']) #Expornential Moving Average fast
data['EMAs'] = ta.ema_slow(
data['CLOSE']) #Expornential Moving Average slow
data['FI'] = ta.force_index(
data['CLOSE'],
data['VOLUME']) # Force Index(reveals the value of a trend)
data['ICHa'] = ta.ichimoku_a(data['HIGH'], data['LOW']) #Ichimoku A
data['ICHb'] = ta.ichimoku_b(data['HIGH'], data['LOW']) #Ichimoku B
data['KC'] = ta.keltner_channel_central(
data['HIGH'], data['LOW'], data['CLOSE']) #Keltner channel(KC) Central
data['KST'] = ta.kst(
data['CLOSE']
) #KST Oscillator (KST) identify major stock market cycle junctures
data['MACD'] = ta.macd(
data['CLOSE']) # Moving Average convergence divergence
data['OBV'] = ta.on_balance_volume_mean(
data['CLOSE'], data['VOLUME']) # on_balance_volume_mean
data['RSI'] = ta.rsi(data['CLOSE']) # Relative Strength Index (RSI)
data['TRIX'] = ta.trix(
data['CLOSE']
) #Shows the percent rate of change of a triple exponentially smoothed moving average
data['TSI'] = ta.tsi(data['CLOSE']) #True strength index (TSI)
data['ROC1'] = (data['CLOSE'] - data['OPEN']) / data['OPEN']
data['RET'] = data['CLOSE'].pct_change()
data['y'] = np.where(data['OPEN'] <= data['CLOSE'], 1, -1)
data = data.dropna()
return data
df["Close"])
ta_df['DCH'] = ta.donchian_channel_hband(
df["Close"])
ta_df['DCL'] = ta.donchian_channel_lband(
df["Close"])
ta_df['DCHI'] = ta.donchian_channel_hband_indicator(df["Close"])
ta_df['DCLI'] = ta.donchian_channel_lband_indicator(df["Close"])
ta_df['ADI'] = ta.acc_dist_index(df["High"],
df["Low"],
df["Close"],
df["Volume BTC"])
ta_df['OBV'] = ta.on_balance_volume(df["Close"],
df["Volume BTC"])
ta_df['OBVM'] = ta.on_balance_volume_mean(
df["Close"],
df["Volume BTC"])
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"])
def OBVM(self, length):
return std_normalize(
ta.on_balance_volume_mean(self.ohclv["close"],
self.ohclv["volumeto"], length))