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))
m = np.asanyarray(ta.macd(df["Close"])).reshape(
(-1, 1)) - np.asanyarray(ta.macd_signal(df["Close"])).reshape(
(-1, 1))
trend3 = np.asanyarray(self.dat[["Close"]]) - np.asanyarray(
ta.ema(self.dat["Close"], 20)).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))
y = np.asanyarray(self.dat[["Open"]])
x = np.concatenate([s, m, cross1], 1)
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 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 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)[1000::]
self.y = np.asanyarray(self.y)[1000::]
self.df = self.x[-self.STEP_SIZE::]
self.trend = self.y[-self.STEP_SIZE::]
lista.append({
'TimeStamp': A1_Hist['candles'][i]['time'],
'Open': A1_Hist['candles'][i]['mid']['o'],
'High': A1_Hist['candles'][i]['mid']['h'],
'Low': A1_Hist['candles'][i]['mid']['l'],
'Close': A1_Hist['candles'][i]['mid']['c']
})
pd_hist = pd.DataFrame(lista)
pd_hist = pd_hist[['TimeStamp', 'Open', 'High', 'Low', 'Close']]
pd_hist['TimeStamp'] = pd.to_datetime(pd_hist['TimeStamp'])
# -- ---------------------------------------------------------------- Agregar analisis tecnico -- #
# -- ---------------------------------------------------------------- ------------------------ -- #
pd_hist['ema_Close'] = ta.ema(series=pd_hist['Close'], periods=20)
# -- ------------------------------------------------------------------- --------------------- -- #
# -- ------------------------------------------------------------------- --------------------- -- #
trace0 = go.Scatter(x=pd_hist['TimeStamp'],
y=pd_hist['ema_Close'],
mode='lines',
name='lines0')
trace1 = go.Scatter(x=pd_hist['TimeStamp'],
y=pd_hist['Close'],
mode='lines',
name='lines1')
data = [trace0, trace1]