def enrich_sampleset(df):
df['OBV'] = ta.on_balance_volume(df['close'], df['volume'])
df['ADX'] = ta.adx(df['high'], df['low'], df['close'], n=14)
df['VPT'] = ta.volume_price_trend(df['close'], df['volume'])
df['ATR'] = ta.average_true_range(df['high'], df['low'], df['close'], n=14)
df['MFI'] = ta.money_flow_index(df['high'], df['low'], df['close'],
df['volume'])
df['KST'] = ta.kst(df['close'])
return ta.utils.dropna(df)
def adx():
adx = ta.adx(high, low, close, n=14, fillna=False)
adxn = ta.adx_neg(high, low, close, n=14, fillna=False)
adxp = ta.adx_pos(high, low, close, n=14, fillna=False)
if adxp[-1] > adxn[-1]:
trn_adx_status = " Buy"
elif adxp[-1] < adxn[-1]:
trn_adx_status = " Sell"
else:
trn_adx_status = " Hold"
return trn_adx_status
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
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
"""
self.synthetic_return = np.exp(
self.drift + self.stdev * norm.ppf(np.random.rand((t_intervals * self.frequency) + self.frequency, 1)))
self.initial_close = self.close.iloc[-1]
self.synthetic_close = np.zeros_like(self.synthetic_return)
self.synthetic_close[0] = self.initial_close
for t in range(1, self.synthetic_return.shape[0]):
self.synthetic_close[t] = self.synthetic_close[t - 1] * self.synthetic_return[t]
OHLC = []
for t in range(self.synthetic_return.shape[0]):
if t % self.frequency == 0 and t > 0:
open = self.synthetic_close[t - self.frequency]
high = np.max(self.synthetic_close[t - self.frequency: t])
low = np.min(self.synthetic_close[t - self.frequency: t])
close = self.synthetic_close[t]
OHLC.append([open, high, close, low])
data = pd.DataFrame(data=OHLC, columns=["open", "high", "low", "close"])
close_prices = data.close
if self.context.include_ha:
ha = heikenashi(data)
data = pd.concat((data, ha), axis=1)
if self.context.include_wadl:
wad = wadl(data, 15)
wad = wad.fillna(method="bfill")
data = pd.concat((data, wad), 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["adx" + str(period)] = ta.adx(high=data.high,
low=data.low,
close=data.close,
n=period,
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 periiod 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["cci"] = ta.cci(high=data.high,
low=data.low,
close=data.close,
n=15,
c=0.015,
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["ichimoku_b"] = ta.ichimoku_b(high=data.high,
low=data.low,
n1=9,
n2=26,
fillna=True)
# Normalizar los valores
for feature in self.context.features:
norm_feature = preprocessing.normalize(data[feature].values.reshape(-1, 1), axis=0)
data[feature] = pd.DataFrame(data=norm_feature, index=data.index, columns=[feature])
return data.values, close_prices