def Update():
global pricedata_stadistics
global pricedata_stadistics_sup
print(str(dt.datetime.now()) + " " + timeframe + " Bar Closed - Running Update Function...")
pricedata_stadistics['index'] = pricedata['bidclose'].index
pricedata_stadistics['bidclose'] = pricedata['bidclose'].values
pricedata_stadistics_sup['index'] = pricedata_sup['bidclose'].index
pricedata_stadistics_sup['bidclose'] = pricedata_sup['bidclose'].values
# Calculate Indicators
iFastSMA = sma(pricedata['bidopen'], fast_sma_periods)
iSlowSMA = sma(pricedata['bidclose'], slow_sma_periods)
iSlowSMA2 = sma(pricedata_sup['bidopen'], slow_sma_periods2)
pricedata_stadistics['emaFast'] = iFastSMA
pricedata_stadistics['emaSlow'] = iSlowSMA
pricedata_stadistics_sup['emaSlow2'] = iSlowSMA2
# Print Price/Indicators
print("Close Price: " + str(pricedata_stadistics['bidclose'][len(pricedata) - 1]))
#print("Fast SMA: " + str(iFastSMA[len(iFastSMA) - 1]))
#print("Slow SMA Open SUP: " + str(iSlowSMA[len(iSlowSMA) - 1]))
#print("Slow SMA Open SUP2: " + str(iSlowSMA2[len(iSlowSMA2) - 1]))
# ***********************************************************
# * Regresion al precio de cierre las velas ================
# ***********************************************************
pricedata_stadistics['x'] = np.arange(len(pricedata_stadistics))
# ************* Calcular la poscion Relativa Y
for index, row in pricedata_stadistics.iterrows():
pricedata_stadistics.loc[index, 'y'] = int(
'{:.5f}'.format((pricedata_stadistics.loc[index, 'bidclose'])).replace('.', ''))
max_value = max(np.array(pricedata_stadistics['y'].values))
min_value = min(np.array(pricedata_stadistics['y'].values))
for index, row in pricedata_stadistics.iterrows():
value = pricedata_stadistics.loc[index, 'y'] - min_value
NewPricePosition = ((value * 100) / max_value) * 100
pricedata_stadistics.loc[index, 'y'] = NewPricePosition
# *********** Calcular la poscion Relativa X
max_value = max(np.array(pricedata_stadistics['x'].values))
min_value = min(np.array(pricedata_stadistics['x'].values))
for index, row in pricedata_stadistics.iterrows():
value = pricedata_stadistics.loc[index, 'x'] - min_value
NewPricePosition = ((value * 100) / max_value)
pricedata_stadistics.loc[index, 'x'] = NewPricePosition
regresionLineal_xx = np.array(pricedata_stadistics['x'].values)
regresionLineal_yy = np.array(pricedata_stadistics['y'].values)
regresionLineal_bb = regresionlineal2.estimate_b0_b1(regresionLineal_xx, regresionLineal_yy)
y_pred_sup = regresionLineal_bb[0] + regresionLineal_bb[1] * regresionLineal_xx
pricedata_stadistics['y_pred'] = y_pred_sup
# Recreacion del Eje X para Presentacion de la Regresion.
# for index, row in pricedata_stadistics.iterrows():
# pricedata_stadistics.loc[index, 'x_pred'] = pricedata_stadistics.loc[0, 'y_pred']
# Calculo de Angulo
vx = np.array(pricedata_stadistics['x'])
vy = np.array(pricedata_stadistics['y_pred'])
x1 = vx[0]
y1 = vy[0]
x2 = vx[-1]
y2 = vy[-1]
x = x2 - x1
y = y2 - y1
angle = math.atan2(y, x) * (180.0 / math.pi)
angle = round(angle, 2)
# angle2 = np.rad2deg(np.arctan2(vy[-1] - vy[0], vx[-1] - vx[0]))
print("\nAngulo: " + str(angle))
lv_Tendency = "Lateral"
if pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] < \
pricedata_stadistics.iloc[1]['y_pred'] and \
pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] < \
pricedata_stadistics.iloc[1]['y_pred']:
lv_Tendency = "Bajista"
elif pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] > \
pricedata_stadistics.iloc[1]['y_pred'] and \
pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] > \
pricedata_stadistics.iloc[1]['y_pred']:
lv_Tendency = "Alcista"
print("\nTendencia Regresion Lineal: " + lv_Tendency)
# # TRADING LOGIC
#
# print("Slow SMA Open SUP: " + str(iSlowSMA[len(iSlowSMA) - 1]))
# print("Slow SMA Open SUP2: " + str(iSlowSMA2[len(iSlowSMA2) - 1]))
message_text = ""
#if crossesOver(iFastSMA, iSlowSMA) and angle >= 1:
if crossesOver(iFastSMA, iSlowSMA):# and lv_Tendency == "Alcista":
message_text = message_text + "\n BUY SIGNAL!"
if countOpenTrades("S") > 0:
message_text = message_text + "\n Closing Sell Trade(s)..."
exit("S")
if countOpenTrades("B") == 0:
message_text = message_text + "\n Opening Buy Trade..."
enter("B")
#if crossesUnder(iFastSMA, iSlowSMA) and angle <= -1:
if crossesUnder(iFastSMA, iSlowSMA):# and lv_Tendency == "Bajista":
message_text = message_text + "\n SELL SIGNAL!"
if countOpenTrades("B") > 0:
message_text = message_text + "\n Closing Buy Trade(s)..."
exit("B")
if countOpenTrades("S") == 0:
message_text = message_text + "\n Opening Sell Trade..."
enter("S")
print(message_text)
print(str(dt.datetime.now()) + " " + timeframe + " Update Function Completed.\n")
print("\n")
def Update(self):
self.logMessages = self.logMessages + "\n" + (
str(dt.datetime.now()) + " " + self.timeframe +
" Vela Formada - Analizando - Running Update Function...")
self.pricedata_stadistics['index'] = self.pricedata['bidclose'].index
self.pricedata_stadistics['bidclose'] = self.pricedata[
'bidclose'].values
self.pricedata_stadistics['bidhigh'] = self.pricedata['bidhigh'].values
self.pricedata_stadistics['askclose'] = self.pricedata[
'askclose'].values
self.pricedata_stadistics['askhigh'] = self.pricedata['askhigh'].values
self.pricedata_stadistics['asklow'] = self.pricedata['asklow'].values
self.pricedata_stadistics['askclose'] = self.pricedata[
'askclose'].values
self.pricedata_stadistics['bidlow'] = self.pricedata['bidlow'].values
self.pricedata_stadistics['tickqty'] = self.pricedata['tickqty'].values
# Calculate Indicators
iFastSMA = sma(self.pricedata['bidclose'], self.fast_sma_periods)
iSlowSMA = sma(self.pricedata['bidclose'], self.slow_sma_periods)
self.pricedata_stadistics['emaFast'] = iFastSMA
self.pricedata_stadistics['emaSlow'] = iSlowSMA
# Adds a "n_high" column with max value of previous 14 periods
self.pricedata_stadistics['n_high'] = self.pricedata_stadistics[
'bidhigh'].rolling(self.stoK).max()
# Adds an "n_low" column with min value of previous 14 periods
self.pricedata_stadistics['n_low'] = self.pricedata_stadistics[
'bidlow'].rolling(self.stoK).min()
# Uses the min/max values to calculate the %k (as a percentage)
self.pricedata_stadistics['per_k'] = \
(
(self.pricedata_stadistics['bidclose'] - self.pricedata_stadistics['n_low']) / \
(self.pricedata_stadistics['n_high'] - self.pricedata_stadistics['n_low'])
) * 100
# Uses the %k to calculates a SMA over the past 3 values of %k
self.pricedata_stadistics['per_d'] = self.pricedata_stadistics[
'per_k'].rolling(self.stoD).mean()
# data_per_k = per_k(pricedata['bidclose'], stoK)
# data_per_d = per_d(pricedata['bidclose'], stoD)
data_per_k = self.pricedata_stadistics['per_k']
data_per_d = self.pricedata_stadistics['per_d']
# pricedata_stadistics['per_k'] = data_per_k
# pricedata_stadistics['per_d'] = data_per_d
# self.pricedata_stadistics.loc[index, 'lower_sto'] = 20
# self.pricedata_stadistics.loc[index, 'upper_sto'] = 80
self.logMessages = self.logMessages + "\n" + (
"STO K " + str(data_per_k[len(data_per_k) - 1]))
self.logMessages = self.logMessages + "\n" + (
"STO D " + str(data_per_d[len(data_per_d) - 1]))
# Calcular Indicador
iRSI = rsi(self.pricedata_stadistics['bidclose'], 15)
self.logMessages = self.logMessages + "\n" + ("RSI: " +
str(iRSI[len(iRSI) - 1]))
for index, row in self.pricedata_stadistics.iterrows():
self.pricedata_stadistics.loc[index, 'lower_sto'] = 20
self.pricedata_stadistics.loc[index, 'upper_sto'] = 80
self.pricedata_stadistics.loc[index, 'macdline0'] = 0.00
self.pricedata_stadistics.loc[index, 'macdsub'] = self.macdsub
self.pricedata_stadistics.loc[index, 'macdupper'] = self.macduper
# ***********************************************************
# * Regresion al precio de cierre las velas ================
# ***********************************************************
self.pricedata_stadistics['x'] = np.arange(
len(self.pricedata_stadistics))
# ************* Calcular la poscion Relativa Y
for index, row in self.pricedata_stadistics.iterrows():
self.pricedata_stadistics.loc[index, 'y'] = int('{:.5f}'.format(
(self.pricedata_stadistics.loc[index,
'bidclose'])).replace('.', ''))
max_value = max(np.array(self.pricedata_stadistics['y'].values))
min_value = min(np.array(self.pricedata_stadistics['y'].values))
for index, row in self.pricedata_stadistics.iterrows():
value = self.pricedata_stadistics.loc[index, 'y'] - min_value
NewPricePosition = ((value * 100) / max_value) * 100
self.pricedata_stadistics.loc[index, 'y'] = NewPricePosition
# *********** Calcular la poscion Relativa X
max_value = max(np.array(self.pricedata_stadistics['x'].values))
min_value = min(np.array(self.pricedata_stadistics['x'].values))
for index, row in self.pricedata_stadistics.iterrows():
value = self.pricedata_stadistics.loc[index, 'x'] - min_value
NewPricePosition = ((value * 100) / max_value)
self.pricedata_stadistics.loc[index, 'x'] = NewPricePosition
regresionLineal_xx = np.array(self.pricedata_stadistics['x'].values)
regresionLineal_yy = np.array(self.pricedata_stadistics['y'].values)
regresionLineal_bb = regresionlineal2.estimate_b0_b1(
regresionLineal_xx, regresionLineal_yy)
y_pred_sup = regresionLineal_bb[
0] + regresionLineal_bb[1] * regresionLineal_xx
self.pricedata_stadistics['y_pred'] = y_pred_sup
if self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['y_pred'] < \
self.pricedata_stadistics.iloc[1]['y_pred'] and \
self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['y_pred'] < \
self.pricedata_stadistics.iloc[1]['y_pred']:
lv_Tendency = "Bajista"
elif self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['y_pred'] > \
self.pricedata_stadistics.iloc[1]['y_pred'] and \
self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['y_pred'] > \
self.pricedata_stadistics.iloc[1]['y_pred']:
lv_Tendency = "Alcista"
# MACD ########################################################################
exp1 = self.pricedata_stadistics['bidclose'].ewm(span=self.macdFast,
adjust=False).mean()
exp2 = self.pricedata_stadistics['bidclose'].ewm(span=self.macdSlow,
adjust=False).mean()
macd = exp1 - exp2
self.pricedata_stadistics[
'macd'] = self.pricedata_stadistics.index.map(macd)
self.pricedata_stadistics['signal'] = pd.DataFrame(
self.pricedata_stadistics['macd'].ewm(span=self.macdSmooth,
adjust=False).mean())
self.pricedata_stadistics['hist'] = pd.DataFrame(
self.pricedata_stadistics['macd'] -
self.pricedata_stadistics['signal'])
# Imprimir Precio/Indicador
# self.logMessages = self.logMessages + "\n" + ("Precio Cierre: " + str(pricedata['bidclose'][len(pricedata) - 1]))
# self.logMessages = self.logMessages + "\n" + ("Tendencia Regresion Lineal: " + lv_Tendency)
lv_signal = self.pricedata_stadistics.iloc[
len(self.pricedata_stadistics) - 1]['signal']
self.logMessages = self.logMessages + "\n" + (
"MACD Signal: " + str(lv_signal) + " SubValuacion: " +
str(self.macdsub) + " SobreValuacion: " + str(self.macduper))
self.logMessages = self.logMessages + "\n" + ("RSI " +
str(iRSI[len(iRSI) - 1]))
# data_per_d['lower_sto']
# if self.crossesOver(data_per_k, data_per_d) and lv_signal <= self.macdsub and \
# self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['signal'] > \
# self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['macd']:
# if self.crossesOver(data_per_k, data_per_d) and lv_signal <= self.macdsub and \
# self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['signal'] > \
# self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['macd']:
# if self.crossesOver(data_per_k, data_per_d) and data_per_d[len(data_per_d) - 1] <= 20 and \
# self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['bidclose'] > iFastSMA[
# len(iFastSMA) - 1]:
if self.crossesOver(self.pricedata_stadistics['y'],
self.pricedata_stadistics['y_pred']
) and lv_Tendency == "Alcista":
self.logMessages = self.logMessages + "\n" + " SEÑAL DE COMPRA ! \n"
self.logMessages = self.logMessages + "\n" + ('''
__,_,
[_|_/
//
_// __
(_|) |@@|
\ \__ \--/ __
\o__|----| | __
\ }{ /\ )_ / _\_
/\__/\ \__O (__
(--/\--) \__/
_)( )(_
`---''---`
''')
self.logMessages = self.logMessages + "\n" + " SEÑAL DE COMPRA !"
if self.countOpenTrades("S") > 0:
self.logMessages = self.logMessages + "\n" + " Cerrando Ventas Abiertas..."
self.exit("S")
self.logMessages = self.logMessages + "\n" + " Abrir Operacion de Compra..."
if self.countOpenTrades("B") == 0:
self.enter("B")
# playsound("file_example_MP3_700KB.mp3")
self.operacioncompra = True
# Verifica el Cruce del SMA para Abajo.
if self.crossesUnder(self.pricedata_stadistics['y'],
self.pricedata_stadistics['y_pred']
) and lv_Tendency == "Bajista":
# if crossesUnder(pricedata_stadistics['signal'], 0.0004):
# if self.crossesUnder(data_per_k, data_per_d) and lv_signal >= self.macduper and \
# self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['signal'] < \
# self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['macd']:
# if self.crossesUnder(data_per_k, data_per_d) and data_per_d[len(data_per_d) - 1] >= 80 and \
# self.pricedata_stadistics.iloc[len(self.pricedata_stadistics) - 1]['bidclose'] < iFastSMA[
# len(iFastSMA) - 1]:
self.logMessages = self.logMessages + "\n" + " SEÑAL DE VENTA ! \n"
self.logMessages = self.logMessages + "\n" + ('''
__
_ |@@|
/ \ \--/ __
) O|----| | __
/ / \ }{ /\ )_ / _\_
)/ /\__/\ \__O (__
|/ (--/\--) \__/
/ _)( )(_
`---''---`
''')
self.logMessages = self.logMessages + "\n" + " SEÑAL DE VENTA ! "
if self.countOpenTrades("B") > 0:
self.logMessages = self.logMessages + "\n" + " Cerrando Operacion de Compras..."
self.exit("B")
self.logMessages = self.logMessages + "\n" + " Abrir Operacion de Venta..."
if self.countOpenTrades("S") == 0:
self.enter("S")
# playsound("file_example_MP3_700KB.mp3")
self.operacionventa = True
self.logMessages = self.logMessages + "\n" + (
str(dt.datetime.now()) + " " + self.timeframe + "Verificacion "
"Realizada.\n")
def Update():
global message_text
message_text = message_text + (
'\n' + str(dt.datetime.now()) + " " + timeframe + " Bar Closed - Running Update Function..." + symbol)
# *********************************************************************
# ** Estadistica General - Regresion Lineal Simple 1
# *********************************************************************
pricedata_stadistics.iloc[0:0]
pricedata_stadistics['bidclose'] = pricedata['bidclose'].values
pricedata_stadistics['bidopen'] = pricedata['bidopen'].values
pricedata_stadistics['x'] = np.arange(len(pricedata_stadistics))
# ************* Calcular la poscion Relativa Y
for index, row in pricedata_stadistics.iterrows():
pricedata_stadistics.loc[index, 'y'] = int(
'{:.5f}'.format((pricedata_stadistics.loc[index, 'bidclose'])).replace('.', ''))
max_value = max(np.array(pricedata_stadistics['y'].values))
min_value = min(np.array(pricedata_stadistics['y'].values))
for index, row in pricedata_stadistics.iterrows():
value = pricedata_stadistics.loc[index, 'y'] - min_value
NewPricePosition = ((value * 100) / max_value) * 100
pricedata_stadistics.loc[index, 'y'] = NewPricePosition
# *********** Calcular la poscion Relativa X
max_value = max(np.array(pricedata_stadistics['x'].values))
min_value = min(np.array(pricedata_stadistics['x'].values))
for index, row in pricedata_stadistics.iterrows():
value = pricedata_stadistics.loc[index, 'x'] - min_value
NewPricePosition = ((value * 100) / max_value)
pricedata_stadistics.loc[index, 'x'] = NewPricePosition
# ***********************************************************
# * EMA'S================
# ***********************************************************
iFastSMA = sma(pricedata_stadistics['y'], fast_sma_periods)
iSlowSMA = sma(pricedata_stadistics['y'], slow_sma_periods)
iTooSlowSMA = sma(pricedata_stadistics['y'], too_slow_sma_periods)
pricedata_stadistics['emaFast'] = iFastSMA
pricedata_stadistics['emaSlow'] = iSlowSMA
pricedata_stadistics['emaTooSlow'] = iTooSlowSMA
# ***********************************************************
# * Regresion al precio de cierre las velas ================
# ***********************************************************
regresionLineal_xx = np.array(pricedata_stadistics['x'].values)
regresionLineal_yy = np.array(pricedata_stadistics['y'].values)
regresionLineal_bb = regresionlineal2.estimate_b0_b1(regresionLineal_xx, regresionLineal_yy)
y_pred_sup = regresionLineal_bb[0] + regresionLineal_bb[1] * regresionLineal_xx
pricedata_stadistics['y_pred'] = y_pred_sup
# Recreacion del Eje X para Presentacion de la Regresion.
for index, row in pricedata_stadistics.iterrows():
pricedata_stadistics.loc[index, 'x_pred'] = pricedata_stadistics.loc[0, 'y_pred']
# Calculo de Angulo
vx = np.array(pricedata_stadistics['x'])
vy = np.array(pricedata_stadistics['y_pred'])
x1 = vx[0]
y1 = vy[0]
x2 = vx[-1]
y2 = vy[-1]
x = x2 - x1
y = y2 - y1
angle = math.atan2(y, x) * (180.0 / math.pi)
angle = round(angle, 2)
# angle2 = np.rad2deg(np.arctan2(vy[-1] - vy[0], vx[-1] - vx[0]))
message_text = message_text + "\nAngulo: " + str(angle)
#
# pricedata_stadistics['y_bidhigh'] = pricedata['bidhigh'].values
# pricedata_stadistics['y_bidlow'] = pricedata['bidlow'].values
# # ************* Calcular la poscion Relativa Y
# for index, row in pricedata_stadistics.iterrows():
# pricedata_stadistics.loc[index, 'y_bidhigh'] = int(
# '{:.5f}'.format((pricedata_stadistics.loc[index, 'y_bidhigh'])).replace('.', ''))
# pricedata_stadistics.loc[index, 'y_bidlow'] = int(
# '{:.5f}'.format((pricedata_stadistics.loc[index, 'y_bidlow'])).replace('.', ''))
#
# max_value = max(np.array(pricedata_stadistics['y_bidhigh'].values))
# min_value = min(np.array(pricedata_stadistics['y_bidhigh'].values))
# for index, row in pricedata_stadistics.iterrows():
# value = pricedata_stadistics.loc[index, 'y_bidhigh'] - min_value
# NewPricePosition = ((value * 100) / max_value) * 100
# pricedata_stadistics.loc[index, 'y_bidhigh'] = NewPricePosition
#
# max_value = max(np.array(pricedata_stadistics['y_bidlow'].values))
# min_value = min(np.array(pricedata_stadistics['y_bidlow'].values))
# for index, row in pricedata_stadistics.iterrows():
# value = pricedata_stadistics.loc[index, 'y_bidlow'] - min_value
# NewPricePosition = ((value * 100) / max_value) * 100
# pricedata_stadistics.loc[index, 'y_bidlow'] = NewPricePosition
#
# # Regresion al precio mas alto velas ======================
# regresionLineal_xx = np.array(pricedata_stadistics['x'].values)
# regresionLineal_yy = np.array(pricedata_stadistics['y_bidhigh'].values)
# regresionLineal_bb = regresionlineal2.estimate_b0_b1(regresionLineal_xx, regresionLineal_yy)
#
# y_pred_sup = regresionLineal_bb[0] + regresionLineal_bb[1] * regresionLineal_xx
# pricedata_stadistics['y_pred_bidhigh'] = y_pred_sup
#
# # Regresion al precio de cierre las velas ======================
# regresionLineal_xx = np.array(pricedata_stadistics['x'].values)
# regresionLineal_yy = np.array(pricedata_stadistics['y_bidlow'].values)
# regresionLineal_bb = regresionlineal2.estimate_b0_b1(regresionLineal_xx, regresionLineal_yy)
# y_pred_sup = regresionLineal_bb[0] + regresionLineal_bb[1] * regresionLineal_xx
# pricedata_stadistics['y_pred_bidlow'] = y_pred_sup
# # *********************************************************************
# # ** Estadistica General - Regresion Lineal
# # *********************************************************************
# pricedata_stadistics_sup.iloc[0:0]
# pricedata_stadistics_sup['bidclose'] = pricedata_sup['bidclose'].values
# pricedata_stadistics_sup['bidhigh'] = pricedata_sup['bidhigh'].values
# pricedata_stadistics_sup['bidlow'] = pricedata_sup['bidlow'].values
# pricedata_stadistics_sup['rowid'] = np.arange(len(pricedata_stadistics_sup))
#
# # *************BIDHIGH
# # ************* Calcular la poscion Relativa Y
# for index, row in pricedata_stadistics_sup.iterrows():
# pricedata_stadistics_sup.loc[index, 'Y_bidhigh'] = int(
# '{:.5f}'.format((pricedata_stadistics_sup.loc[index, 'bidhigh'])).replace('.', ''))
#
# max_value = max(np.array(pricedata_stadistics_sup['Y_bidhigh'].values))
# min_value = min(np.array(pricedata_stadistics_sup['Y_bidhigh'].values))
# for index, row in pricedata_stadistics_sup.iterrows():
# value = pricedata_stadistics_sup.loc[index, 'Y_bidhigh'] - min_value
# NewPricePosition = (value * 100) / max_value
# pricedata_stadistics_sup.loc[index, 'Y_bidhigh'] = NewPricePosition
#
# # *********** Calcular la poscion Relativa X
# max_value = max(np.array(pricedata_stadistics_sup['rowid'].values))
# for index, row in pricedata_stadistics_sup.iterrows():
# value = pricedata_stadistics_sup.loc[index, 'rowid']
# NewPricePosition = (value * 100) / max_value
# pricedata_stadistics_sup.loc[index, 'X_bidhigh'] = NewPricePosition
#
# # Regresion al precio mas Alto de las velas ======================
# regresionLineal_xx_sup = np.array(pricedata_stadistics_sup['X_bidhigh'].values)
# regresionLineal_yy_sup = np.array(pricedata_stadistics_sup['Y_bidhigh'].values)
# regresionLineal_bb_sup = regresionlineal2.estimate_b0_b1(regresionLineal_xx_sup, regresionLineal_yy_sup)
# y_pred_sup = regresionLineal_bb_sup[0] + regresionLineal_bb_sup[1] * regresionLineal_xx_sup
#
# pricedata_stadistics_sup['y_pred_bidhigh'] = y_pred_sup
# pricedata_stadistics_sup['x_pred_bidhigh'] = regresionLineal_xx_sup
#
#
#
#
# # *************BIDLOW
# # ************* Calcular la poscion Relativa Y
# for index, row in pricedata_stadistics_sup.iterrows():
# pricedata_stadistics_sup.loc[index, 'Y_bidlow'] = int(
# '{:.5f}'.format((pricedata_stadistics_sup.loc[index, 'bidlow'])).replace('.', ''))
#
# max_value = max(np.array(pricedata_stadistics_sup['Y_bidlow'].values))
# min_value = min(np.array(pricedata_stadistics_sup['Y_bidlow'].values))
# for index, row in pricedata_stadistics_sup.iterrows():
# value = pricedata_stadistics_sup.loc[index, 'Y_bidlow'] - min_value
# NewPricePosition = (value * 100) / max_value
# pricedata_stadistics_sup.loc[index, 'Y_bidlow'] = NewPricePosition
#
# # *********** Calcular la poscion Relativa X
# max_value = max(np.array(pricedata_stadistics_sup['rowid'].values))
# for index, row in pricedata_stadistics_sup.iterrows():
# value = pricedata_stadistics_sup.loc[index, 'rowid']
# NewPricePosition = (value * 100) / max_value
# pricedata_stadistics_sup.loc[index, 'X_bidlow'] = NewPricePosition
#
#
#
#
# # Regresion al precio mas Alto de las velas ======================
# regresionLineal_xx_sup = np.array(pricedata_stadistics_sup['X_bidlow'].values)
# regresionLineal_yy_sup = np.array(pricedata_stadistics_sup['Y_bidhigh'].values)
# regresionLineal_bb_sup = regresionlineal2.estimate_b0_b1(regresionLineal_xx_sup, regresionLineal_yy_sup)
# y_pred_sup = regresionLineal_bb_sup[0] + regresionLineal_bb_sup[1] * regresionLineal_xx_sup
#
# pricedata_stadistics_sup['y_pred_bidlow'] = y_pred_sup
# pricedata_stadistics_sup['x_pred_bidlow'] = regresionLineal_xx_sup
#
#
#
#
#
# # create circle
# c = plt.Circle((x1, y1), radius=10, color='red', alpha=.3)
# plt.gca().add_artist(c)
#
# #plt.text(x1, y1, str(round(angle, 2)) + ' °')
# # Regresion al mas bajo de las velas ======================
# regresionLineal_xx_sup = np.array(pricedata_stadistics_sup['rowid'].tail(numberofregresion_sup).values)
# regresionLineal_yy_sup = np.array(pricedata_stadistics_sup['bidlow'].tail(numberofregresion_sup).values)
# regresionLineal_bb_sup = regresionlineal2.estimate_b0_b1(regresionLineal_xx_sup, regresionLineal_yy_sup)
# y_pred_sup = regresionLineal_bb_sup[0] + regresionLineal_bb_sup[1] * regresionLineal_xx_sup
#
# numberRegx = len(pricedata_stadistics_sup) - numberofregresion_sup
# posreg = 0
# for index, row in pricedata_stadistics_sup.iterrows():
# if numberRegx <= pricedata_stadistics_sup.loc[index, 'rowid']:
# pricedata_stadistics_sup.loc[index, 'y_pred_bidlow'] = y_pred_sup[posreg]
# posreg = posreg + 1
#
# # *********************************************************************
# # *** Proyecion de Precios * Se puede Mejorar con Ciclo
# # *********************************************************************
# lv_index_1 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 1]['date']
# lv_rowid_1 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 1]['rowid']
# lv_y_pred_askhigh_1 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 1]['y_pred_bidhigh']
# lv_y_pred_asklow_1 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 1]['y_pred_bidlow']
#
# lv_index_2 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 2]['date']
# lv_rowid_2 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 2]['rowid']
# lv_y_pred_askhigh_2 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 2]['y_pred_bidhigh']
# lv_y_pred_asklow_2 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 2]['y_pred_bidlow']
#
# lv_index_base = lv_index_1 - lv_index_2
# lv_rowid_base = lv_rowid_1 - lv_rowid_2
# lv_y_pred_askhigh_base = lv_y_pred_askhigh_1 - lv_y_pred_askhigh_2
# lv_y_pred_asklow_base = lv_y_pred_asklow_1 - lv_y_pred_asklow_2
#
# pricedata_stadistics_proyeccion.iloc[0:0]
# for proyect_times in range(2):
# pricedata_stadistics_proyeccion.loc[lv_index_1] = pd.Series(
# {'rowid': lv_rowid_1,
# 'y_pred_bidhigh': lv_y_pred_askhigh_1,
# 'y_pred_bidlow': lv_y_pred_asklow_1
# })
# lv_index_1 = lv_index_1 + lv_index_base
# lv_rowid_1 = lv_rowid_1 + lv_rowid_base
# lv_y_pred_askhigh_1 = lv_y_pred_askhigh_1 + lv_y_pred_askhigh_base
# lv_y_pred_asklow_1 = lv_y_pred_asklow_1 + lv_y_pred_asklow_base
#
# pricedata_stadistics_proyeccion_tenden.iloc[0:0]
# for proyect_times in range(3):
# pricedata_stadistics_proyeccion_tenden.loc[lv_index_1] = pd.Series(
# {'rowid': lv_rowid_1,
# 'y_pred_bidhigh': lv_y_pred_askhigh_1,
# 'y_pred_bidlow': lv_y_pred_asklow_1
# })
# lv_index_1 = lv_index_1 + lv_index_base
# lv_rowid_1 = lv_rowid_1 + lv_rowid_base
# lv_y_pred_askhigh_1 = lv_y_pred_askhigh_1 + lv_y_pred_askhigh_base
# lv_y_pred_asklow_1 = lv_y_pred_asklow_1 + lv_y_pred_asklow_base
#
# Calculamos La tendencia con los valores de de la proyection las velas mas altas y mas bajas.
lv_Tendency = "Lateral"
if pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] < \
pricedata_stadistics.iloc[1]['y_pred'] and \
pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] < \
pricedata_stadistics.iloc[1]['y_pred']:
lv_Tendency = "Bajista"
elif pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] > \
pricedata_stadistics.iloc[1]['y_pred'] and \
pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] > \
pricedata_stadistics.iloc[1]['y_pred']:
lv_Tendency = "Alcista"
message_text = message_text + "\nTendencia Regresion Lineal: " + lv_Tendency
lv_posicion_venta = False
lv_posicion_compra = False
if lv_Tendency == "Bajista" and (pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['emaTooSlow'] >
pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y']):
lv_posicion_venta = True
lv_posicion_compra = False
elif lv_Tendency == "Alcista" and (pricedata_stadistics.iloc[len(pricedata_stadistics) - 1][
'emaTooSlow'] < pricedata_stadistics.iloc[len(pricedata_stadistics) - 1][
'y']):
lv_posicion_venta = False
lv_posicion_compra = True
message_text = message_text + "\nPosicion de Venta: " + str(lv_posicion_venta) + " Posicion de Compra: " + str(
lv_posicion_compra)
# # Print Price/Indicators
# print("Close Price: " + str(pricedata['bidclose'][len(pricedata) - 1]))
# # print("Fast SMA: " + str(iFastSMA[len(iFastSMA) - 1]))
# # print("Slow SMA: " + str(iSlowSMA[len(iSlowSMA) - 1]))
#
# # TRADING LOGIC
#
if crossesOver(iFastSMA, iSlowSMA) and lv_posicion_compra and angle >= angulo_plus:
message_text = message_text + "\n BUY SIGNAL!"
if countOpenTrades("S") > 0:
message_text = message_text + "\n Closing Sell Trade(s)..."
exit("S")
if countOpenTrades("B") == 0:
message_text = message_text + "\n Opening Buy Trade..."
enter("B")
if crossesUnder(iFastSMA, iSlowSMA) and lv_posicion_venta and angle <= angulo_minus:
message_text = message_text + "\n SELL SIGNAL!"
if countOpenTrades("B") > 0:
message_text = message_text + "\n Closing Buy Trade(s)..."
exit("B")
if countOpenTrades("S") == 0:
message_text = message_text + "\n Opening Sell Trade..."
enter("S")
message_text = message_text + "\n" + str(
dt.datetime.now()) + " " + timeframe + " Update Function Completed.========= \n"
print(message_text)
message_text = ''
def Update():
print(
str(dt.datetime.now()) + " " + timeframe +
" Bar Closed - Running Update Function...")
# *********************************************************************
# ** Estadistica General - Regresion Lineal Simple 1
# *********************************************************************
pricedata_stadistics['bidclose'] = pricedata['bidclose'].values
pricedata_stadistics['bidopen'] = pricedata['bidopen'].values
pricedata_stadistics['date'] = pricedata['bidclose'].index
# pricedata_stadistics['emaFast'] = pricedata_stadistics['bidclose'].rolling(window=fast_sma_periods).mean()
# pricedata_stadistics['emaSlow'] = pricedata_stadistics['bidclose'].rolling(window=slow_sma_periods).mean()
# Calculate Indicators
iFastSMA = sma(pricedata['bidclose'], fast_sma_periods)
iSlowSMA = sma(pricedata['bidclose'], slow_sma_periods)
pricedata_stadistics['emaFast'] = iFastSMA
pricedata_stadistics['emaSlow'] = iSlowSMA
pricedata_stadistics.index = pricedata['bidclose'].index
pricedata_stadistics['rowid'] = np.arange(len(pricedata_stadistics))
# *********************************************************************
# ** Estadistica General - Regresion Lineal
# *********************************************************************
pricedata_stadistics_sup['bidclose'] = pricedata_sup['bidclose'].values
pricedata_stadistics_sup['date'] = pricedata_sup['bidclose'].index
pricedata_stadistics_sup['bidhigh'] = pricedata_sup['bidhigh'].values
pricedata_stadistics_sup['bidlow'] = pricedata_sup['bidlow'].values
pricedata_stadistics_sup.index = pricedata_sup['bidclose'].index
pricedata_stadistics_sup['rowid'] = np.arange(
len(pricedata_stadistics_sup))
# Regresion al mas Alto de las velas ======================
regresionLineal_xx_sup = np.array(
pricedata_stadistics_sup['rowid'].tail(numberofregresion_sup).values)
regresionLineal_yy_sup = np.array(
pricedata_stadistics_sup['bidhigh'].tail(numberofregresion_sup).values)
regresionLineal_bb_sup = regresionlineal2.estimate_b0_b1(
regresionLineal_xx_sup, regresionLineal_yy_sup)
y_pred_sup = regresionLineal_bb_sup[
0] + regresionLineal_bb_sup[1] * regresionLineal_xx_sup
numberRegx = len(pricedata_stadistics_sup) - numberofregresion_sup
posreg = 0
for index, row in pricedata_stadistics_sup.iterrows():
if numberRegx <= pricedata_stadistics_sup.loc[index, 'rowid']:
pricedata_stadistics_sup.loc[index,
'y_pred_bidhigh'] = y_pred_sup[posreg]
posreg = posreg + 1
# Regresion al mas bajo de las velas ======================
regresionLineal_xx_sup = np.array(
pricedata_stadistics_sup['rowid'].tail(numberofregresion_sup).values)
regresionLineal_yy_sup = np.array(
pricedata_stadistics_sup['bidlow'].tail(numberofregresion_sup).values)
regresionLineal_bb_sup = regresionlineal2.estimate_b0_b1(
regresionLineal_xx_sup, regresionLineal_yy_sup)
y_pred_sup = regresionLineal_bb_sup[
0] + regresionLineal_bb_sup[1] * regresionLineal_xx_sup
numberRegx = len(pricedata_stadistics_sup) - numberofregresion_sup
posreg = 0
for index, row in pricedata_stadistics_sup.iterrows():
if numberRegx <= pricedata_stadistics_sup.loc[index, 'rowid']:
pricedata_stadistics_sup.loc[index,
'y_pred_bidlow'] = y_pred_sup[posreg]
posreg = posreg + 1
# *********************************************************************
# *** Proyecion de Precios * Se puede Mejorar con Ciclo
# *********************************************************************
lv_index_1 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) -
1]['date']
lv_rowid_1 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) -
1]['rowid']
lv_y_pred_askhigh_1 = pricedata_stadistics_sup.iloc[
len(pricedata_stadistics_sup) - 1]['y_pred_bidhigh']
lv_y_pred_asklow_1 = pricedata_stadistics_sup.iloc[
len(pricedata_stadistics_sup) - 1]['y_pred_bidlow']
lv_index_2 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) -
2]['date']
lv_rowid_2 = pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) -
2]['rowid']
lv_y_pred_askhigh_2 = pricedata_stadistics_sup.iloc[
len(pricedata_stadistics_sup) - 2]['y_pred_bidhigh']
lv_y_pred_asklow_2 = pricedata_stadistics_sup.iloc[
len(pricedata_stadistics_sup) - 2]['y_pred_bidlow']
lv_index_base = lv_index_1 - lv_index_2
lv_rowid_base = lv_rowid_1 - lv_rowid_2
lv_y_pred_askhigh_base = lv_y_pred_askhigh_1 - lv_y_pred_askhigh_2
lv_y_pred_asklow_base = lv_y_pred_asklow_1 - lv_y_pred_asklow_2
pricedata_stadistics_proyeccion.loc[lv_index_1] = pd.Series({
'rowid':
lv_rowid_1,
'y_pred_bidhigh':
lv_y_pred_askhigh_1,
'y_pred_bidlow':
lv_y_pred_asklow_1
})
pricedata_stadistics_proyeccion.loc[lv_index_1 +
lv_index_base] = pd.Series({
'rowid':
lv_rowid_1 + lv_rowid_base,
'y_pred_bidhigh':
lv_y_pred_askhigh_1 +
lv_y_pred_askhigh_base,
'y_pred_bidlow':
lv_y_pred_asklow_1 +
lv_y_pred_asklow_base
})
# Calculamos La tendencia con los valores de de la proyection las velas mas altas y mas bajas.
lv_Tendency = "Lateral"
if pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 1]['y_pred_bidhigh'] < \
pricedata_stadistics_sup.iloc[1]['y_pred_bidhigh'] and \
pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 1]['y_pred_bidlow'] < \
pricedata_stadistics_sup.iloc[1]['y_pred_bidlow']:
lv_Tendency = "Bajista"
elif pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 1]['y_pred_bidhigh'] > \
pricedata_stadistics_sup.iloc[1]['y_pred_bidhigh'] and \
pricedata_stadistics_sup.iloc[len(pricedata_stadistics_sup) - 1]['y_pred_bidlow'] > \
pricedata_stadistics_sup.iloc[1]['y_pred_bidlow']:
lv_Tendency = "Alcista"
print("Tendencia Regresion Lineal: " + lv_Tendency)
lv_posicion_venta = False
lv_posicion_compra = False
if lv_Tendency == "Bajista" and pricedata_stadistics_proyeccion.iloc[
len(pricedata_stadistics_proyeccion) -
1]['y_pred_bidhigh'] < pricedata_stadistics.iloc[
len(pricedata_stadistics) - 1]['bidclose']:
lv_posicion_venta = True
lv_posicion_compra = False
elif lv_Tendency == "Alcista" and pricedata_stadistics_proyeccion.iloc[
len(pricedata_stadistics_proyeccion) -
1]['y_pred_bidlow'] > pricedata_stadistics.iloc[
len(pricedata_stadistics) - 1]['bidclose']:
lv_posicion_venta = False
lv_posicion_compra = True
print("Posicion de Venta: " + str(lv_posicion_venta) +
" Posicion de Compra: " + str(lv_posicion_compra))
# Print Price/Indicators
print("Close Price: " + str(pricedata['bidclose'][len(pricedata) - 1]))
#print("Fast SMA: " + str(iFastSMA[len(iFastSMA) - 1]))
#print("Slow SMA: " + str(iSlowSMA[len(iSlowSMA) - 1]))
# TRADING LOGIC
if crossesOver(iFastSMA, iSlowSMA) and lv_posicion_compra:
print(" BUY SIGNAL!")
if countOpenTrades("S") > 0:
print(" Closing Sell Trade(s)...")
exit("S")
if countOpenTrades("B") == 0:
print(" Opening Buy Trade...")
enter("B")
if crossesUnder(iFastSMA, iSlowSMA) and lv_posicion_venta:
print(" SELL SIGNAL!")
if countOpenTrades("B") > 0:
print(" Closing Buy Trade(s)...")
exit("B")
if countOpenTrades("S") == 0:
print(" Opening Sell Trade...")
enter("S")
print(
str(dt.datetime.now()) + " " + timeframe +
" Update Function Completed.\n")
print("\n")
def Update():
global pricedata_stadistics, operacionventa, operacioncompra
print(str(dt.datetime.now()) + " " + timeframe + " Vela Formada - Analizando - Running Update Function...")
pricedata_stadistics['index'] = pricedata['bidclose'].index
pricedata_stadistics['bidclose'] = pricedata['bidclose'].values
pricedata_stadistics['bidhigh'] = pricedata['bidhigh'].values
pricedata_stadistics['askclose'] = pricedata['askclose'].values
pricedata_stadistics['askhigh'] = pricedata['askhigh'].values
pricedata_stadistics['asklow'] = pricedata['asklow'].values
pricedata_stadistics['askclose'] = pricedata['askclose'].values
pricedata_stadistics['bidlow'] = pricedata['bidlow'].values
pricedata_stadistics['tickqty'] = pricedata['tickqty'].values
# Calculate Indicators
iFastSMA = sma(pricedata['bidclose'], fast_sma_periods)
iSlowSMA = sma(pricedata['bidclose'], slow_sma_periods)
pricedata_stadistics['emaFast'] = iFastSMA
pricedata_stadistics['emaSlow'] = iSlowSMA
# Adds a "n_high" column with max value of previous 14 periods
pricedata_stadistics['n_high'] = pricedata_stadistics['bidhigh'].rolling(stoK).max()
# Adds an "n_low" column with min value of previous 14 periods
pricedata_stadistics['n_low'] = pricedata_stadistics['bidlow'].rolling(stoK).min()
# Uses the min/max values to calculate the %k (as a percentage)
pricedata_stadistics['per_k'] = (pricedata_stadistics['bidclose'] - pricedata_stadistics['n_low']) * 100 / (
pricedata_stadistics['n_high'] - pricedata_stadistics['n_low'])
# Uses the %k to calculates a SMA over the past 3 values of %k
pricedata_stadistics['per_d'] = pricedata_stadistics['per_k'].rolling(stoD).mean()
# data_per_k = per_k(pricedata['bidclose'], stoK)
# data_per_d = per_d(pricedata['bidclose'], stoD)
data_per_k = pricedata_stadistics['per_k']
data_per_d = pricedata_stadistics['per_d']
# pricedata_stadistics['per_k'] = data_per_k
# pricedata_stadistics['per_d'] = data_per_d
# Calcular Indicador
iRSI = rsi(pricedata_stadistics['bidclose'], 15)
print("RSI: " + str(iRSI[len(iRSI) - 1]))
for index, row in pricedata_stadistics.iterrows():
pricedata_stadistics.loc[index, 'lower_sto'] = 20
pricedata_stadistics.loc[index, 'upper_sto'] = 80
pricedata_stadistics.loc[index, 'macdline0'] = 0.00
# ***********************************************************
# * Regresion al precio de cierre las velas ================
# ***********************************************************
pricedata_stadistics['x'] = np.arange(len(pricedata_stadistics))
# ************* Calcular la poscion Relativa Y
for index, row in pricedata_stadistics.iterrows():
pricedata_stadistics.loc[index, 'y'] = int(
'{:.5f}'.format((pricedata_stadistics.loc[index, 'bidclose'])).replace('.', ''))
max_value = max(np.array(pricedata_stadistics['y'].values))
min_value = min(np.array(pricedata_stadistics['y'].values))
for index, row in pricedata_stadistics.iterrows():
value = pricedata_stadistics.loc[index, 'y'] - min_value
NewPricePosition = ((value * 100) / max_value) * 100
pricedata_stadistics.loc[index, 'y'] = NewPricePosition
# *********** Calcular la poscion Relativa X
max_value = max(np.array(pricedata_stadistics['x'].values))
min_value = min(np.array(pricedata_stadistics['x'].values))
for index, row in pricedata_stadistics.iterrows():
value = pricedata_stadistics.loc[index, 'x'] - min_value
NewPricePosition = ((value * 100) / max_value)
pricedata_stadistics.loc[index, 'x'] = NewPricePosition
regresionLineal_xx = np.array(pricedata_stadistics['x'].values)
regresionLineal_yy = np.array(pricedata_stadistics['y'].values)
regresionLineal_bb = regresionlineal2.estimate_b0_b1(regresionLineal_xx, regresionLineal_yy)
y_pred_sup = regresionLineal_bb[0] + regresionLineal_bb[1] * regresionLineal_xx
pricedata_stadistics['y_pred'] = y_pred_sup
if pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] < \
pricedata_stadistics.iloc[1]['y_pred'] and \
pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] < \
pricedata_stadistics.iloc[1]['y_pred']:
lv_Tendency = "Bajista"
elif pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] > \
pricedata_stadistics.iloc[1]['y_pred'] and \
pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['y_pred'] > \
pricedata_stadistics.iloc[1]['y_pred']:
lv_Tendency = "Alcista"
# MACD ########################################################################
exp1 = pricedata_stadistics['bidclose'].ewm(span=macdFast, adjust=False).mean()
exp2 = pricedata_stadistics['bidclose'].ewm(span=macdSlow, adjust=False).mean()
macd = exp1 - exp2
pricedata_stadistics['macd'] = pricedata_stadistics.index.map(macd)
pricedata_stadistics['signal'] = pd.DataFrame(
pricedata_stadistics['macd'].ewm(span=macdSmooth, adjust=False).mean())
pricedata_stadistics['hist'] = pd.DataFrame(pricedata_stadistics['macd'] - pricedata_stadistics['signal'])
# Imprimir Precio/Indicador
# print("Precio Cierre: " + str(pricedata['bidclose'][len(pricedata) - 1]))
# print("Tendencia Regresion Lineal: " + lv_Tendency)
lv_signal = pricedata_stadistics.iloc[len(pricedata_stadistics) - 1]['signal']
print("MACD Signal: " + str(lv_signal) + " SubValuacion: " + str(macdsub) + " SobreValuacion: " + str(macduper))
print("STO D" + str(data_per_d[len(data_per_d) - 1]))
if iRSI[len(iRSI) - 1] <= 40 and crossesOver(data_per_d,
pricedata_stadistics['lower_sto']) and lv_signal <= macdsub:
print(" SEÑAL DE COMPRA ! \n")
print('''
__,_,
[_|_/
//
_// __
(_|) |@@|
\ \__ \--/ __
\o__|----| | __
\ }{ /\ )_ / _\_
/\__/\ \__O (__
(--/\--) \__/
_)( )(_
`---''---`
''')
print(" SEÑAL DE COMPRA ! \n")
if countOpenTrades("S") > 0:
print(" Cerrando Ventas Abiertas...\n")
exit("S")
print(" Abrir Operacion de Compra...\n")
if countOpenTrades("B") == 0:
enter("B")
operacioncompra = True
# Verifica el Cruce del SMA para Abajo.
# if crossesUnder(data_per_d, 0.80):
# if crossesUnder(pricedata_stadistics['signal'], 0.0004):
if iRSI[len(iRSI) - 1] >= 60 and crossesUnder(data_per_d, pricedata_stadistics['upper_sto']) and lv_signal >= macduper:
print(" SEÑAL DE VENTA ! \n")
print('''
__
_ |@@|
/ \ \--/ __
) O|----| | __
/ / \ }{ /\ )_ / _\_
)/ /\__/\ \__O (__
|/ (--/\--) \__/
/ _)( )(_
`---''---`
''')
print(" SEÑAL DE VENTA ! \n")
if countOpenTrades("B") > 0:
print(" Cerrando Operacion de Compras...\n")
exit("B")
print(" Abrir Operacion de Venta...\n")
if countOpenTrades("S") == 0:
enter("S")
operacionventa = True
# Cerrar Ventas #########################################
if operacionventa and crossesOver(data_per_d, pricedata_stadistics['lower_sto']):
if countOpenTrades("S") > 0:
print(" Cerrando Ventas Abiertas...\n")
operacionventa = False
exit("S")
# Cerrar Compras #########################################
if operacioncompra and crossesUnder(data_per_d, pricedata_stadistics['upper_sto']):
if countOpenTrades("B") > 0:
print(" Cerrando Compras Abiertas...\n")
operacioncompra = False
exit("B")
print(str(dt.datetime.now()) + " " + timeframe + " Verificacion Realizada.\n")