def observadorRelog(self):
while (True):
time.sleep(1)
now = datetime.now()
hora_actual = now.strftime('%H:%M:%S')
print(hora_actual)
if hora_actual == '16:42:00':
i = Indicadores()
print(i.trm())
if hora_actual == '16:42:30':
l = LeerExcel()
l.leerExcelFuncion()
exit()
from Indicadores import *
valor = float(input('ingrese el valor que dolares que quiere convertir: '))
i = Indicadores()
resultado = float(i.indicadoresEconomicos()) * valor
print(resultado)
def createTrainsDataCedears(self, dateToStart):
DAYS = 30
date_time = datetime.fromisoformat(dateToStart)
xTrains = []
yTrains = []
yTest = []
xTest = []
trainOrTest = True
for NroCedear in tqdm(range(30)):
if NroCedear == 27:
continue
cedearTicket = self.cedears[NroCedear]
prom = tools.getInfoFewDaysAgo(1, date_time + timedelta(days=1), cedearTicket, 10)
if type(prom) != type("") :
prom = [prom["open"][0], prom["close"][0], prom["high"][0], prom["low"][0]]
prom = np.mean(prom)
data = tools.getInfoFewDaysAgo(DAYS, date_time, cedearTicket, 10)
Bands = indicator.getBollingerBands(data)
xQuantity = Bands.size
heatMap = np.zeros((100, xQuantity))
for xPos in range(5, xQuantity):
yPos = tools.acotation(np.max(data['high']), np.min(data['low']), Bands['BBM_5_2.0'][xPos])
yPosClose = tools.acotation(np.max(data['high']), np.min(data['low']), data['close'][xPos])
yPosOpen = tools.acotation(np.max(data['high']), np.min(data['low']), data['open'][xPos])
if yPos >= 100:
yPos = 99
if yPosClose >= 100:
yPosClose = 99
if yPosOpen >= 100:
yPosOpen = 99
if yPosOpen < yPosClose:
heat = 100
plus = 80 / (yPosClose - yPosOpen)
for y in range(yPosOpen, yPosClose):
heatMap[y][xPos] = int(heat)
heat += plus
elif yPosClose < yPosOpen:
heat = 180
substrac = 80 / (yPosOpen - yPosClose)
for y in range(yPosClose, yPosOpen):
heatMap[y][xPos] = int(heat)
heat -= substrac
heatMap[yPos][xPos] = 255
acotationVar = tools.acotation(np.max(data["high"]), np.min(data["low"]), prom)
if acotationVar >= 100:
acotationVar = 99
elif acotationVar < 0:
acotationVar = 0
if trainOrTest:
yTrains.append(acotationVar)
xTrains.append(heatMap.copy())
else:
yTest.append(acotationVar)
xTest.append(heatMap.copy())
trainOrTest = not trainOrTest
plt.imshow(heatMap, cmap='hot', interpolation='nearest')
plt.show()
xTrains = np.array(xTrains)
yTrains = np.array(yTrains)
xTest = np.array(xTest)
yTest = np.array(yTest)
return xTrains, yTrains, xTest, yTest
{'User-agent' : 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de; rv:1.9.1.5) Gecko/20091102 Firefox/3.5.5'}
jonathan campos lozano16:32
from Indicadores import *
i = Indicadores()
i.indicadoresEconomicos()
class Form:
inid = Indicadores()
def __init__(self):
self.root = Tk()
self.calculadora = Calculadora()
self.dorlares = StringVar()
self.n1 = StringVar()
self.n2 = StringVar()
self.resultado = StringVar()
self.formulario()
def sumar(self):
self.resultado.set(
self.calculadora.suma(float(self.n1.get()), float(self.n2.get())))
self.borrar()
def restar(self):
self.resultado.set(
self.calculadora.resta(float(self.n1.get()), float(self.n2.get())))
self.borrar()
def multiplicar(self):
self.resultado.set(
self.calculadora.multiplicacion(float(self.n1.get()),
float(self.n2.get())))
self.borrar()
def dividir(self):
self.resultado.set(
self.calculadora.divicion(float(self.n1.get()),
float(self.n2.get())))
self.borrar()
def borrar(self):
self.n1.set("")
self.n2.set("")
def divisas(self):
trm = self.inid.trm()
self.resultado.set(round(float(trm) * float(self.dorlares.get()), 3))
def formulario(self):
self.root.config(bd=20)
Label(self.root, text="VALOR EN DOLARES").pack()
Entry(self.root, textvariable=self.dorlares).pack()
Label(self.root, text="NUMERO 1").pack()
Entry(self.root, textvariable=self.n1).pack()
Label(self.root, text="NUMERO 2").pack()
Entry(self.root, textvariable=self.n2).pack()
Label(self.root, text="RESULTADO").pack()
Entry(self.root, textvariable=self.resultado, state="disabled").pack()
Button(self.root, text="+", command=self.sumar).pack(side="left")
Button(self.root, text="-", command=self.restar).pack(side="left")
Button(self.root, text="*", command=self.multiplicar).pack(side="left")
Button(self.root, text="/", command=self.dividir).pack(side="left")
Button(self.root, text="Valor en Pesos",
command=self.divisas).pack(side="left")
self.root.mainloop()
from BaseDeDatos import BaseDeDatos
import Indicadores as Indicator
import Herramientas as tool
MyBase = BaseDeDatos("mongodb://localhost:27017/", "Market", "ticketsCedears",
"cedearPureData")
MyBase.updateTicketsCedears()
MyBase.subirIndicesYDatosDelDia()
TICKET = "BPAT.BA"
DAYS = 30
data = tool.getInfoFewDaysAgo(DAYS, datetime.now(), TICKET, 10)
stochData = Indicator.getStochasticIndicator(data)
ADXData = Indicator.getADXIndicator(data)
bBand = Indicator.getBollingerBands(data)
print(stochData)
print("-----------------------------")
print(ADXData)
print("-----------------------------")
print(bBand)
print("-----------------------------")
"""
#save in database
for mapAndResult in range(len(xTrains)):
mydict = {
"heatmap": binary.Binary(pickle.dumps(xTrains[mapAndResult], protocol=2)),
def subirIndicesYDatosDelDia(self):
ticketNro = 0
total = self.MTickets.find().count()
for ticket in self.MTickets.find():
ticketNro += 1
if ticketNro == 21:
a = 1
print("ticket nro: " + str(ticketNro) + " de: " + str(total))
data = tool.getInfoFewDaysAgo(20, datetime.now(), ticket["TicketName"], 10)
if type(data) != type(""):
bbans = indicator.getBollingerBandsToday(data)
stoch = indicator.getStochasticToday(data)
adx = indicator.getADXToday(data)
dataToday = data.iloc[-1]
cantidad = self.MData.find({"ticket": ticket["TicketName"]}).count()
actionCursor = self.MData.find({"ticket": ticket["TicketName"]})
for action in actionCursor:
if cantidad > 0:
subirFecha = action["fechas"]
if subirFecha[-1] != datetime.now().strftime("%m/%d/%Y"):
subirFecha.append(datetime.now().strftime("%m/%d/%Y"))
#Indicadores
indicadoresGuardados = action["indicadores"]
indicadoresGuardados[0]["curvas"]["slowk"].append(stoch["STOCHk_5"])
indicadoresGuardados[0]["curvas"]["slowd"].append(stoch["STOCHd_3"])
indicadoresGuardados[1]["curvas"]["adx"].append(adx["ADX_14"])
indicadoresGuardados[1]["curvas"]["di_plus"].append(stoch["DMP_14"])
indicadoresGuardados[1]["curvas"]["di_minus"].append(stoch["DMN_14"])
indicadoresGuardados[2]["curvas"]["BBUp"].append(bbans["BBU_5_2.0"])
indicadoresGuardados[2]["curvas"]["BBMed"].append(stoch["BBM_5_2.0"])
indicadoresGuardados[2]["curvas"]["BBLow"].append(stoch["BBL_5_2.0"])
#Datos
datosGuardados = action["datos"]
datosGuardados["open"].append(dataToday["open"])
datosGuardados["close"].append(dataToday["close"])
datosGuardados["high"].append(dataToday["high"])
datosGuardados["low"].append(dataToday["low"])
else:
subirFecha[-1] = datetime.now().strftime("%m/%d/%Y")
#Indicadores
indicadoresGuardados = action["indicadores"]
indicadoresGuardados[0]["curvas"]["slowk"][-1] = stoch["STOCHk_5"]
indicadoresGuardados[0]["curvas"]["slowk"][-1] = stoch["STOCHd_3"]
indicadoresGuardados[1]["curvas"]["adx"][-1] = adx["ADX_14"]
indicadoresGuardados[1]["curvas"]["di_plus"][-1] = adx["DMP_14"]
indicadoresGuardados[1]["curvas"]["di_minus"][-1] = adx["DMN_14"]
indicadoresGuardados[2]["curvas"]["BBUp"][-1] = bbans["BBU_5_2.0"]
indicadoresGuardados[2]["curvas"]["BBMed"][-1] = bbans["BBM_5_2.0"]
indicadoresGuardados[2]["curvas"]["BBLow"][-1] = bbans["BBL_5_2.0"]
#Datos
datosGuardados = action["datos"]
datosGuardados["open"][-1] = dataToday["open"]
datosGuardados["close"][-1] = dataToday["close"]
datosGuardados["high"][-1] = dataToday["high"]
datosGuardados["low"][-1] = dataToday["low"]
self.MData.update_one({"ticket": ticket["TicketName"]},
{"$set": {"indicadores": indicadoresGuardados, "datos": datosGuardados}})
else:
jsonIndicadoresASubir = {
"tipo_activo": "cedear",
"activo": "",
"ticket": ticket["TicketName"],
"fechas": [datetime.now().strftime("%m/%d/%Y")],
"indicadores": [
{
"indicador": "STOCH",
"curvas": {
"slowk": [stoch["STOCHk_5"]],
"slowd": [stoch["STOCHd_3"]],
},
"lineas_punteadas": [
{
"nombre": "80",
"posicion_y": 80
},
{
"nombre": "20",
"posicion_y": 20
}
],
"etiquetas": [
{
"nombre": "",
"posiciones": []
}
]
},
{
"indicador": "DMI",
"curvas": {
"adx": [adx["ADX_14"]],
"di_plus": [adx["DMP_14"]],
"di_minus": [adx["DMN_14"]]
}
},
{
"indicador": "BollingerBands",
"curvas": {
"BBUp": [bbans["BBU_5_2.0"]],
"BBMed": [bbans["BBM_5_2.0"]],
"BBLow": [bbans["BBL_5_2.0"]]
}
}
],
"datos": {
"open":[dataToday["open"]],
"close":[dataToday["close"]],
"high":[dataToday["high"]],
"low":[dataToday["low"]]
}
}
self.MData.insert_one(jsonIndicadoresASubir)