def inciso3v(self):
codigo = int(
input("Ingrese codigo del producto que desea cambiar stock: "))
cantid = int(input("Ingrese la cantidad: "))
stock1 = Stock(codigo, cantid)
for i in range(len(self.__listaStock)):
if self.__listaStock[i].getCodigoProducto() == codigo:
stock2 = Stock(codigo, self.__listaStock[i].getCantidad())
restar = stock1 - stock2
self.inciso3a(codigo, restar)
def main():
chef = LineChef('BTC-USD', 20, 8, [1, 2, 3, 4])
startDate = datetime.datetime(2018, 2, 4, 8)
endDate = startDate + datetime.timedelta(days=60)
chef.train(startDate, endDate)
strongCount = 0
for pat in chef.patterns:
if (pat.isStrong()):
strongCount += 1
print("# Matches: ", pat.numMatches)
print(pat.changes)
print(pat)
print("STRONG COUNT: ", strongCount)
testStartDate = endDate
testEndDate = testStartDate + datetime.timedelta(days=14)
predictionDelta = 4
simBoi = RepeatPredictor(5, Stock('BTC-USD'), 1000, 20, testStartDate,
datetime.timedelta(hours=predictionDelta))
chef.test(testStartDate, testEndDate, predictionDelta, simBoi)
def manejadorArchiStock(self):
archi = open("Stock.csv")
reader = csv.reader(archi, delimiter=';')
next(reader)
for i in reader:
codP = int(i[0])
cant = int(i[1])
objeto = Stock(codP, cant)
self.agregadorStock(objeto)
archi.close()
def main(transaction_history):
# this method assumes the transaction history is sent in the form of a dictionary.
# It processes the transaction history of each instrument and creates a portfolio based on it.
for key, value in transaction_history.iteritems():
for all_bonds, tran_history in value.iteritems():
transaction = get_current_quantity(tran_history)
if key == 'Stock':
instrument = Stock(all_bonds, transaction)
elif key == 'Bonds':
instrument = Bonds(all_bonds, transaction)
elif key == 'Cash':
instrument = Cash(all_bonds, transaction)
else:
raise ValueError('Instrument not defined')
all_instrument[all_bonds] = instrument
return create_portfolio()
def loadValuesIntoOneStock(self):
testStock = Stock('TEST', 'testValues.txt')
testStock.record = {
"2019-03-18": {
"06:00:00": (0.5, 45.10),
"06:15:00": (73, 12)
},
"2019-03-19": {
"06:15:00": (83, 12)
},
"2019-03-20": {
"06:30:00": (14, -6.0),
"06:31:00": (18, 9)
}
}
return testStock
def main():
# ts = TimeSeries(key=API_KEY)
# for key, stock in ALL_STOCKS.items():
# data, metaData = ts.get_intraday(symbol=stock.symbol, interval="5min", outputsize="full")
# for dataKey, item in data.items():
# stock.addValue(dataKey.split(" ")[1], item["4. close"], item["5. volume"], dataKey.split(" ")[0])
# stock.saveValues()
# startDate = datetime.datetime(2018, 2, 4, 8) # bull?
# startDate = datetime.datetime(2018, 2, 17, 8) # ?
startDate = datetime.datetime(2018, 3, 6, 8) # bear
endDate = startDate + datetime.timedelta(hours=100)
testStartDate = datetime.datetime(2018, 5, 12, 8)
testEndDate = testStartDate + datetime.timedelta(hours=168)
ratBoi = RatBoi(Stock("BTC-USD"))
ratBoi.train(startDate, endDate, iterations=3, learningRate=0.9)
# ratBoi.test(testStartDate, testEndDate)
ratBoi.simulate(testStartDate, testEndDate, 0)
return
def loadValsForSkewKurtosis(self):
testStock = Stock('TEST', 'testValues.txt')
#3 days, 32 mins span
testStock.record = {
"2019-03-18": {
"06:00:00": (61, 0),
"06:01:00": (61, 0),
"06:02:00": (61, 0),
"06:03:00": (61, 0),
"06:04:00": (61, 0),
"06:05:00": (64, 0),
"06:06:00": (64, 0),
"06:07:00": (64, 0),
"06:08:00": (64, 0),
"06:09:00": (64, 0),
"06:10:00": (64, 0),
"06:11:00": (64, 0),
"06:12:00": (64, 0),
"06:13:00": (64, 0),
"06:14:00": (64, 0),
"06:15:00": (64, 0),
"06:16:00": (64, 0),
"06:17:00": (64, 0),
"06:18:00": (64, 0),
"06:19:00": (64, 0),
"06:20:00": (64, 0),
"06:21:00": (64, 0),
"06:22:00": (64, 0),
"06:23:00": (67, 0),
"06:24:00": (67, 0),
"06:25:00": (67, 0),
"06:26:00": (67, 0),
"06:27:00": (67, 0),
"06:28:00": (67, 0),
"06:29:00": (67, 0),
"06:30:00": (67, 0),
"06:31:00": (67, 0),
"06:32:00": (67, 0),
"06:33:00": (67, 0),
"06:34:00": (67, 0),
"06:35:00": (67, 0),
"06:36:00": (67, 0),
"06:37:00": (67, 0),
"06:38:00": (67, 0),
"06:39:00": (67, 0),
"06:40:00": (67, 0),
"06:41:00": (67, 0),
"06:42:00": (67, 0),
"06:43:00": (67, 0),
"06:44:00": (67, 0),
"06:45:00": (67, 0),
"06:46:00": (67, 0),
"06:47:00": (67, 0),
"06:48:00": (67, 0),
"06:49:00": (67, 0),
"06:50:00": (67, 0),
"06:51:00": (67, 0),
"06:52:00": (67, 0),
"06:53:00": (67, 0),
"06:54:00": (67, 0),
"06:55:00": (67, 0),
"06:56:00": (67, 0),
"06:57:00": (67, 0),
"06:58:00": (67, 0),
"06:59:00": (67, 0),
},
"2019-03-19": {
"06:15:00": (73, 0),
"06:16:00": (73, 0),
"06:17:00": (73, 0),
"06:18:00": (73, 0),
"06:19:00": (73, 0),
"06:20:00": (73, 0),
"06:21:00": (73, 0),
"06:22:00": (73, 0)
},
"2019-03-21": {
"06:01:00": (67, 0),
"06:02:00": (67, 0),
"06:03:00": (67, 0),
"06:04:00": (67, 0),
"06:05:00": (67, 0),
"06:06:00": (70, 0),
"06:07:00": (70, 0),
"06:08:00": (70, 0),
"06:09:00": (70, 0),
"06:10:00": (70, 0),
"06:11:00": (70, 0),
"06:12:00": (70, 0),
"06:13:00": (70, 0),
"06:14:00": (70, 0),
"06:15:00": (70, 0),
"06:16:00": (70, 0),
"06:17:00": (70, 0),
"06:18:00": (70, 0),
"06:19:00": (70, 0),
"06:20:00": (70, 0),
"06:21:00": (70, 0),
"06:22:00": (70, 0),
"06:23:00": (70, 0),
"06:24:00": (70, 0),
"06:25:00": (70, 0),
"06:26:00": (70, 0),
"06:27:00": (70, 0),
"06:28:00": (70, 0),
"06:29:00": (70, 0),
"06:30:00": (70, 0),
"06:31:00": (70, 0),
"06:32:00": (70, 0)
}
}
return testStock
d_bar = 5
interest_rate = 0.02
init_holding = 1
initialcash = 20000
theta = 75
gene_length = 64
risk_coef = 0.5
num_strategies = 80
max_stockprice = 200
min_stockprice = 0.01
min_excess = 0.005
eta = 0.005
specialist_iterations = 10
the_market = Stock(num_shares, init_price, dividend_startvalue, rho, noise_sd,
d_bar, interest_rate)
agents = [
Agent(init_holding, initialcash, num_strategies, theta, gene_length,
risk_coef, num_shares, init_price, dividend_startvalue, rho,
noise_sd, d_bar, interest_rate) for _ in range(100)
]
the_specialist = Specialist(max_stockprice, min_stockprice, min_excess, eta,
specialist_iterations, num_shares, init_price,
dividend_startvalue, rho, noise_sd, d_bar,
interest_rate)
for t in range(1000):
the_market.advance_arprocess()
the_specialist.clear_market(agents)
ma = the_market.calculate_ma(query='dividend', period=50)
from Stocks import Stock
ALL_STOCKS = {
'amazon': Stock('AMZN', 'amazonValues.txt'),
'walmart': Stock('WMT', 'walmartValues.txt'),
'generalElectric': Stock('GE', 'generalElectricValues.txt'),
'cocaCola': Stock('KO', 'cocaColaValues.txt'),
'google': Stock('GOOGL', 'googleValues.txt'),
'microsoft': Stock('MSFT', 'microsoftValues.txt'),
'tesla': Stock('TSLA', 'teslaValues.txt'),
'apple': Stock('AAPL', 'appleValues.txt'),
'facebook': Stock('FB', 'facebookValues.txt'),
}