class CurrencyService():
def __init__(self):
self.base_service = BaseService()
self.domain_factory = self.base_service.DomainFactory()
self.util_common = self.base_service.UtilCommon()
self.util_data = self.base_service.UtilData()
self.ConfigApiModel = self.domain_factory.init_ModelClass('ConfigApiModel')
self.ConfigApiQueryModel = self.domain_factory.init_ModelClass('ConfigApiQueryModel')
self.ApiOpenExcRateModel = self.domain_factory.init_ModelClass('ApiOpenExcRateModel')
self.ExchangeRateModel = self.domain_factory.init_ModelClass('ExchangeRateModel')
class ExchangeRateService(BaseService):
def __init__(self):
self.base_service = BaseService()
self.domain_factory = self.base_service.DomainFactory()
self.util_common = self.base_service.UtilCommon()
self.util_data = self.base_service.UtilData()
self.util_logic = self.base_service.UtilLogic()
self.ConfigApiModel = self.domain_factory.init_ModelClass(
'ConfigApiModel')
self.ConfigApiQueryModel = self.domain_factory.init_ModelClass(
'ConfigApiQueryModel')
self.ApiOpenExcRateModel = self.domain_factory.init_ModelClass(
'ApiOpenExcRateModel')
self.ExchangeRateModel = self.domain_factory.init_ModelClass(
'ExchangeRateModel')
def get_exrate_byDate(self, apiConfig, dateReport, baseCurrency):
modelBaseCurParam = self.ConfigApiQueryModel()
modelBaseCurParam.param_name = "base"
modelBaseCurParam.param_curvalue = baseCurrency
lstCustomizeQuery = []
lstCustomizeQuery.append(modelBaseCurParam)
dateReport = self.util_common.parseStringToDateTime(dateReport)
apiUrl = self.base_service.Config.initApiUrl(apiConfig,
dateReport.date(),
lstCustomizeQuery)
if apiUrl:
content = self.util_data.readJsonFromUrl(apiUrl)
lstRates = []
for key, value in content.items():
# print(key + ' - ' + str(value))
if (isinstance(value, dict) and key == "rates"):
for cur, rate in value.items():
model = self.ApiOpenExcRateModel()
model.Currency = cur
model.RateValue = rate
lstRates.append(model)
return lstRates
return None
def get_specific_exrate_byDate(self, apiConfig, dateReport, baseCurrency,
toCurrency):
data = self.get_exrate_byDate(apiConfig, dateReport, baseCurrency)
for model in data:
if (model.Currency == toCurrency):
return model
return None
def get_specific_exrate_byDateRange(self, apiConfig, fromDate, toDate,
checkedDate, baseCurrency, toCurrency):
# print('Checked Date: Exchange Rates')
lstExcRates = []
for single_date in self.util_common.dateRange(fromDate, toDate):
if any(da == single_date.day
for da in [d.day for d in checkedDate]):
data = self.get_specific_exrate_byDate(
apiConfig, single_date.strftime("%Y-%m-%d"), baseCurrency,
toCurrency)
# print(single_date.strftime("%Y-%m-%d") + ': ' + str(data.RateValue))
model = self.ExchangeRateModel()
model.BaseCurrency = baseCurrency
model.ConvertedCurrency = toCurrency
model.OnDate = datetime.timestamp(
single_date) #single_date.month
model.RateValue = data.RateValue
lstExcRates.append(model)
return len(lstExcRates) > 0 and lstExcRates or None
def display_graph(self, listdata):
date = [x.OnDate for x in listdata]
rate = [x.RateValue for x in listdata]
plt.scatter(date, rate, c='black')
plt.xlabel("Date")
plt.ylabel("Rates")
plt.show()
def training_linear_model(self, listdata, valueNeedPredict):
date = [[x.OnDate] for x in listdata]
rate = [[x.RateValue] for x in listdata]
#Use 80% of data as training, rest 20% to Test model
x_train, x_test, y_train, y_test = train_test_split(date,
rate,
test_size=0.2)
# training model
linear = LinearRegression()
linear.fit(x_train, y_train)
# evaluating model
score_trained = linear.score(x_test, y_test)
print("Model scored:", score_trained)
# saving model
modelPathDir = Path(ROOT_DIR + r'/model_trained/linear_model.pkl')
joblib.dump(linear, modelPathDir)
# loading model
not valueNeedPredict and x_test or x_test.insert(0, [valueNeedPredict])
clf = joblib.load(modelPathDir)
predicted = clf.predict(x_test) #linear.predict(x_test)
print("###Predicted by Linear Model")
print("Predicted Max:", predicted.max())
print("Predicted Min:", predicted.min())
print("Predicted: ", predicted)
return predicted
def training_polynomial_model(self, listdata, valueNeedPredict):
date = [[x.OnDate] for x in listdata]
rate = [[x.RateValue] for x in listdata]
#Use 80% of data as training, rest 20% to Test model
x_train, x_test, y_train, y_test = train_test_split(date,
rate,
test_size=0.2)
# training model
poly = Pipeline([
('poly', PolynomialFeatures(interaction_only=True, degree=2)),
('linear', linear_model.LinearRegression(fit_intercept=False))
])
poly.fit(x_train, y_train)
# evaluating model
score_trained = poly.score(x_test, y_test)
print("Model scored:", score_trained)
# saving model
modelPathDir = Path(ROOT_DIR + r'/model_trained/poly_model.pkl')
joblib.dump(poly, modelPathDir)
# loading model
not valueNeedPredict and x_test or x_test.insert(0, [valueNeedPredict])
clf = joblib.load(modelPathDir)
predicted = clf.predict(x_test) #poly.predict(x_test)
print("###Predicted by Polynomial Model")
print("Predicted Max:", predicted.max())
print("Predicted Min:", predicted.min())
print("Predicted: ", predicted)
return predicted
def calculate_exrate_bypurelinearmodel(self, listdata, checkedDate):
date = [x.OnDate for x in listdata]
rate = [x.RateValue for x in listdata]
chkDate = datetime.timestamp(checkedDate)
predictedRate = self.util_logic.calLinearRegressionOfY(
lstSampleValueX=date, lstSampleValueY=rate, xVal=chkDate)
return predictedRate
def calculate_exrate_bypurepolynomialmodel(self, listdata, checkedDate):
date = [x.OnDate for x in listdata]
rate = [x.RateValue for x in listdata]
chkDate = datetime.timestamp(checkedDate)
predictedRate = self.util_logic.calPolynomialRegressionOfY(
lstSampleValueX=date, lstSampleValueY=rate, xVal=chkDate)
return predictedRate
def predicted_quick_exrate_bytrainnedmodel(self, apiConfig,
strPredictedDate, baseCurrency,
toCurrency):
predictedDate = self.util_common.parseStringToDateTime(
strPredictedDate)
dateLastMonth = self.util_common.getDateByYearCount(
strPredictedDate, -1)
dateLastYear = self.util_common.getDateByMonthCount(
strPredictedDate, -1)
checkedDate = []
checkedDate.append(predictedDate)
data = self.get_specific_exrate_byDateRange(apiConfig, dateLastMonth,
dateLastYear, checkedDate,
baseCurrency, toCurrency)
self.training_linear_model(data, datetime.timestamp(predictedDate))
self.training_polynomial_model(data, datetime.timestamp(predictedDate))
return data
def predicted_long_exrate_bytrainnedmodel(self,
apiConfig,
strPredictedDate,
baseCurrency,
toCurrency,
checkedDaysPerMonth=5):
predictedDate = self.util_common.parseStringToDateTime(
strPredictedDate)
dateLastMonth = self.util_common.getDateByYearCount(
strPredictedDate, -1)
dateLastYear = self.util_common.getDateByMonthCount(
strPredictedDate, -1)
checkedDate = self.util_common.generateRandomDateInMonth(
year=predictedDate.year,
month=predictedDate.month,
totalRandom=checkedDaysPerMonth)
if not predictedDate in checkedDate:
checkedDate.append(predictedDate)
data = self.get_specific_exrate_byDateRange(apiConfig, dateLastMonth,
dateLastYear, checkedDate,
baseCurrency, toCurrency)
self.training_linear_model(data, datetime.timestamp(predictedDate))
self.training_polynomial_model(data, datetime.timestamp(predictedDate))
return data
def predicted_basic_exrate(self, apiConfig, strPredictedDate, baseCurrency,
toCurrency):
predictedDate = self.util_common.parseStringToDateTime(
strPredictedDate)
dateLastMonth = self.util_common.getDateByYearCount(
strPredictedDate, -1)
dateLastYear = self.util_common.getDateByMonthCount(
strPredictedDate, -1)
checkedDate = []
checkedDate.append(predictedDate)
data = self.get_specific_exrate_byDateRange(apiConfig, dateLastMonth,
dateLastYear, checkedDate,
baseCurrency, toCurrency)
predictedRate = self.calculate_exrate_bypurelinearmodel(
data, predictedDate)
print("Predicted with Linear Regression Model: ", predictedRate)
predictedRate2 = self.calculate_exrate_bypurepolynomialmodel(
data, predictedDate)
print("Predicted with Polynomial Regression Model: ", predictedRate2)
return data
