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 do_ta(self, data_series): open = Series(data_series['open'].astype('float64')) high = Series(data_series['high'].astype('float64')) low = Series(data_series['low'].astype('float64')) close = Series(data_series['close'].astype('float64')) # Trend # ---------------- ema30 = ta.ema(series=close, periods=30) ema50 = ta.ema(series=close, periods=50) ema100 = ta.ema(series=close, periods=100) ema200 = ta.ema(series=close, periods=200) macd_diff = ta.macd_diff(close=close, n_fast=12, n_slow=26, n_sign=9) macd_signal = ta.macd_signal(close=close, n_fast=12, n_slow=26, n_sign=9) data_series['ema30'] = ema30 data_series['ema50'] = ema50 data_series['ema100'] = ema100 data_series['ema200'] = ema200 data_series['macd_diff'] = macd_diff data_series['macd_signal'] = macd_signal # Momentum # ---------------- rsi = ta.rsi(close=close) stochastic = ta.stoch(high=high, low=low, close=close) data_series['rsi'] = rsi data_series['stochastic'] = stochastic # Volatility # ---------------- bollinger_h = ta.bollinger_hband(close=close) bollinger_l = ta.bollinger_lband(close=close) bollinger_h_indicator = ta.bollinger_hband_indicator(close=close) bollinger_l_indicator = ta.bollinger_lband_indicator(close=close) data_series['bollinger_h'] = bollinger_h data_series['bollinger_l'] = bollinger_l data_series['bollinger_h_indicator'] = bollinger_h_indicator data_series['bollinger_l_indicator'] = bollinger_l_indicator data_series['last_candle_change'] = self.lcc(close=close) return data_series
def __init__(self, prices, balance, atr_period=48, fast_period=12, slow_period=26, signal_period=9, max_entry=1, **kwargs): self.name = 'SMA strategy' self.prices = prices self.balance = balance self.signals = pd.DataFrame() self.signals['ATR'] = ta.average_true_range(self.prices['High'], self.prices['Low'], self.prices['Close'], n=atr_period) self.signals['MACD'] = ta.macd(self.prices['Close'], n_fast=fast_period, n_slow=slow_period) self.signals['MACD_SIG'] = ta.macd_signal(self.prices['Close'], n_fast=fast_period, n_slow=slow_period, n_sign=signal_period) self.signals['MACD_HIST'] = ta.macd_diff(self.prices['Close'], n_fast=fast_period, n_slow=slow_period, n_sign=signal_period) self.signals['MACD'].fillna(0) self.init_period = slow_period self.stop_period = 10 self.max_entry = max_entry self.gamma_z = 0.1 self.entrySig = [0] self.exitSig = [0] self.stopSig = [0] self.unit = np.zeros(len(self.prices)) self.entryFlag = False self.entryPrice = None self.entryCounter = 0
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
dd['s00_return_3']=dd.Close/dd.Close.shift(3)-1 dd['s03_return_1']=dd.s00_return_1.shift(3) dd['s03_return_3']=dd.s00_return_3.shift(3) dd['s05_return_1']=dd.s00_return_1.shift(5) dd['s05_return_3']=dd.s00_return_3.shift(5) dd['s00_rsi_14']=ta.rsi(dd.Close, 14) dd['s00_rsi_7']=ta.rsi(dd.Close, 7) dd['s00_willR_14']=ta.wr(dd.High,dd.Low,dd.Close,14) dd['s00_willR_7']=ta.wr(dd.High,dd.Low,dd.Close,7) dd['s00_stoch_sig_14_3']=ta.stoch_signal(dd.High,dd.Low,dd.Close,14,3) dd['s00_stoch_sig_7_3']=ta.stoch_signal(dd.High,dd.Low,dd.Close,7,3) dd['s00_cci_20_0015']=ta.cci(dd.High,dd.Low,dd.Close,20,0.015) dd['s00_cci_20_005']=ta.cci(dd.High,dd.Low,dd.Close,20,0.05) dd['s00_macd_12_26_9']=ta.macd_diff(dd.Close,12,26,9) dd['s00_macd_7_14_9']=ta.macd_diff(dd.Close,7,14, 9) dd['s00_kst_9']=ta.kst(dd.Close)-ta.kst_sig(dd.Close) dd['s01_rsi_14']=dd.s00_rsi_14.shift(+1) dd['s01_rsi_7']=dd.s00_rsi_7.shift(+1) dd['s01_willR_14']=dd.s00_willR_14.shift(+1) dd['s01_willR_7']=dd.s00_willR_7.shift(+1) dd['s01_stoch_sig_14_3']=dd.s00_stoch_sig_14_3.shift(+1) dd['s01_stoch_sig_7_3']=dd.s00_stoch_sig_7_3.shift(+1) dd['s01_cci_20_0015']=dd.s00_cci_20_0015.shift(+1) dd['s01_cci_20_005']=dd.s00_cci_20_005.shift(+1) dd['s01_macd_12_26_9']=dd.s00_macd_12_26_9.shift(+1) dd['s01_macd_7_14_9']=dd.s00_macd_7_14_9.shift(+1) dd['s01_kst_9']=dd.s00_kst_9.shift(+1)
ta_df = pd.DataFrame() ta_df['RSI'] = ta.rsi(df["Close"]) ta_df['MFI'] = ta.money_flow_index( df["High"], df["Low"], df["Close"], df["Volume BTC"]) ta_df['TSI'] = ta.tsi(df["Close"]) ta_df['UO'] = ta.uo(df["High"], df["Low"], df["Close"]) ta_df['Stoch'] = ta.stoch(df["High"], df["Low"], df["Close"]) ta_df['Stoch_Signal'] = ta.stoch_signal(df["High"], df["Low"], df["Close"]) ta_df['WR'] = ta.wr(df["High"], df["Low"], df["Close"]) ta_df['AO'] = ta.ao(df["High"], df["Low"]) ta_df['MACD'] = ta.macd(df["Close"]) ta_df['MACD_signal'] = ta.macd_signal(df["Close"]) ta_df['MACD_diff'] = ta.macd_diff(df["Close"]) ta_df['EMA_fast'] = ta.ema_indicator(df["Close"]) ta_df['EMA_slow'] = ta.ema_indicator(df["Close"]) ta_df['Vortex_pos'] = ta.vortex_indicator_pos( df["High"], df["Low"], df["Close"]) ta_df['Vortex_neg'] = ta.vortex_indicator_neg( df["High"], df["Low"], df["Close"]) ta_df['Vortex_diff'] = abs( ta_df['Vortex_pos'] - ta_df['Vortex_neg']) ta_df['Trix'] = ta.trix(df["Close"]) ta_df['Mass_index'] = ta.mass_index(df["High"], df["Low"]) ta_df['CCI'] = ta.cci(df["High"], df["Low"], df["Close"]) ta_df['DPO'] = ta.dpo(df["Close"]) ta_df['KST'] = ta.kst(df["Close"]) ta_df['KST_sig'] = ta.kst_sig(df["Close"])
def get_data(context, data_, window): # Crear ventana de datos. h1 = data_.history( context.symbols, context.row_features, bar_count=window, frequency=str(context.bar_period) + "T", ) h1 = h1.swapaxes(2, 0) norm_data = [] close_prices = [] for i, asset in enumerate(context.assets): data = h1.iloc[i] close = h1.iloc[i].close if context.include_ha: ha = heikenashi(data) data = pd.concat((data, ha), axis=1) for period in [3, 6, 8, 10, 15, 20]: data["rsi" + str(period)] = ta.rsi(data.close, n=period, fillna=True) data["stoch" + str(period)] = ta.stoch(data.high, data.low, data.close, n=period, fillna=True) data["stoch_signal" + str(period)] = ta.stoch_signal( high=data.high, low=data.low, close=data.close, n=period, d_n=3, fillna=True) data["dpo" + str(period)] = ta.dpo(close=data.close, n=period, fillna=True) data["atr" + str(period)] = ta.average_true_range(high=data.high, low=data.low, close=data.close, n=period, fillna=True) for period in [6, 7, 8, 9, 10]: data["williams" + str(period)] = ta.wr(high=data.high, low=data.low, close=data.close, lbp=period, fillna=True) for period in [12, 13, 14, 15]: data["proc" + str(period)] = ta.trix(close=data.close, n=period, fillna=True) data["macd_diff"] = ta.macd_diff(close=data.close, n_fast=15, n_slow=30, n_sign=9, fillna=True) data["macd_signal"] = ta.macd_signal(close=data.close, n_fast=15, n_slow=30, n_sign=9, fillna=True) data["bb_high_indicator"] = ta.bollinger_hband_indicator( close=data.close, n=15, ndev=2, fillna=True) data["bb_low_indicator"] = ta.bollinger_lband_indicator( close=data.close, n=15, ndev=2, fillna=True) data["dc_high_indicator"] = ta.donchian_channel_hband_indicator( close=data.close, n=20, fillna=True) data["dc_low_indicator"] = ta.donchian_channel_lband_indicator( close=data.close, n=20, fillna=True) data["ichimoku_a"] = ta.ichimoku_a(high=data.high, low=data.low, n1=9, n2=26, fillna=True) data.fillna(method="bfill") # Normalizar los valores for feature in data.columns: norm_feature = preprocessing.normalize( data[feature].values.reshape(-1, 1), axis=0) data[feature] = pd.DataFrame(data=norm_feature, index=data.index, columns=[feature]) norm_data.append(data.values) close_prices.append(close) context.features = data.columns return np.array(norm_data), np.array(close_prices)
def get_trayectory(self, t_intervals): """ :param t_intervals: número de intervalos en cada trayectoria :return: Datos con características de la trayectoria sintética y precios de cierre en bruto de al misma """ trayectories = [] closes = [] p = True for i, asset in enumerate(self.context.assets): synthetic_return = np.exp( self.drift[i] + self.stdev[i] * norm.ppf(np.random.rand((t_intervals * self.frequency) + self.frequency, 1))) initial_close = self.close[i, -1] synthetic_close = np.zeros_like(synthetic_return) synthetic_close[0] = initial_close for t in range(1, synthetic_return.shape[0]): synthetic_close[t] = synthetic_close[t - 1] * synthetic_return[t] OHLC = [] for t in range(synthetic_return.shape[0]): if t % self.frequency == 0 and t > 0: open = synthetic_close[t - self.frequency] high = np.max(synthetic_close[t - self.frequency: t]) low = np.min(synthetic_close[t - self.frequency: t]) close = synthetic_close[t] OHLC.append([open, high, close, low]) data = pd.DataFrame(data=OHLC, columns=["open", "high", "low", "close"]) close = data.close if self.context.include_ha: ha = heikenashi(data) data = pd.concat((data, ha), axis=1) for period in [3, 6, 8, 10, 15, 20]: data["rsi" + str(period)] = ta.rsi(data.close, n=period, fillna=True) data["stoch" + str(period)] = ta.stoch(data.high, data.low, data.close, n=period, fillna=True) data["stoch_signal" + str(period)] = ta.stoch_signal(high=data.high, low=data.low, close=data.close, n=period, d_n=3, fillna=True) data["dpo" + str(period)] = ta.dpo(close=data.close, n=period, fillna=True) data["atr" + str(period)] = ta.average_true_range(high=data.high, low=data.low, close=data.close, n=period, fillna=True) for period in [6, 7, 8, 9, 10]: data["williams" + str(period)] = ta.wr(high=data.high, low=data.low, close=data.close, lbp=period, fillna=True) for period in [12, 13, 14, 15]: data["proc" + str(period)] = ta.trix(close=data.close, n=period, fillna=True) data["macd_diff"] = ta.macd_diff(close=data.close, n_fast=15, n_slow=30, n_sign=9, fillna=True) data["macd_signal"] = ta.macd_signal(close=data.close, n_fast=15, n_slow=30, n_sign=9, fillna=True) data["bb_high_indicator"] = ta.bollinger_hband_indicator(close=data.close, n=15, ndev=2, fillna=True) data["bb_low_indicator"] = ta.bollinger_lband_indicator(close=data.close, n=15, ndev=2, fillna=True) data["dc_high_indicator"] = ta.donchian_channel_hband_indicator(close=data.close, n=20, fillna=True) data["dc_low_indicator"] = ta.donchian_channel_lband_indicator(close=data.close, n=20, fillna=True) data["ichimoku_a"] = ta.ichimoku_a(high=data.high, low=data.low, n1=9, n2=26, fillna=True) data.fillna(method="bfill") # Normalizar los valores for feature in data.columns: norm_feature = preprocessing.normalize(data[feature].values.reshape(-1, 1), axis=0) data[feature] = pd.DataFrame(data=norm_feature, index=data.index, columns=[feature]) self.assets = data.columns trayectories.append(data.values) closes.append(close) return np.array(trayectories), np.array(closes)
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