def main():
var = '' #just want update to repeat not entire thing
while var != 'null':
company_name = raw_input(
"Enter the name of the company you're searching for "
) #THis asks for a company name
var = get_companysymbol(
company_name
) #this searchs the company list we have and returns null if not found
var = company_name
if var != 'null': # ONLY PRECED IF WE HAVE A COMPANY
timeBegin = int(
input('Enter Start year ')
) - 2 #Gets the start date and pushes it back 2 years to show enough data to fit the screen nicely
totalDataCurrent = Fetching.fetchDataToday(
var, timeBegin
) # This gets all the data from the start year to 3 days ago (give or take a work day)
googData = Fetching.fetchGoogData(
var) # Fetch Todays data from google finance
Prediction_Data = Fetching.fetchDataSpec(
var, (datetime.now() + timedelta(days=-45))
) # Get the data from just the past month for the prediciton part
Prediction_Data_Length = len(
Prediction_Data.High
) # Lenght of the predictin Data to save the recalc of it
Coeffcients = LinearAlgebra.coeffcients_Generator(
LinearAlgebra.makeXVals_Matrix(10, timeBegin,
Prediction_Data_Length),
LinearAlgebra.makeY_Matrix(Prediction_Data.High)
) #coeffcients for prediction fucntion a0-a10
Prediction_Model = LinearAlgebra.makeOutY(
Coeffcients, Prediction_Data_Length, timeBegin,
totalDataCurrent.High, googData
) # Gets Prediciton Model or scatter of predicted points these points are also normalized
pointY = LinearAlgebra.getPointY(
Coeffcients, timeBegin, googData[0]['LastTradePrice'],
totalDataCurrent.High[len(totalDataCurrent) - 1]
) #gets Predictiion Point for the next day independently so I can calculate individual days
R = RSI.PredictRSI(totalDataCurrent.Close)
Graphing.totalTogether(
var, totalDataCurrent, googData, Prediction_Model, pointY,
R) #Print Final Graph with everything together
pass
def getGraph():
stockName = request.args.get('s')
var=stockName
old_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
if var != 'null': # ONLY PRECEDE IF WE HAVE A COMPANY
Coefficients = numpy.zeros(shape =(11,1))
timeBegin=2010
totalDataCurrent=Fetching.fetchDataToday(var,timeBegin) # This gets all the data from the start year to 3 days ago (give or take a work day)
googData=Fetching.fetchGoogData(var) # Fetch Todays data from google finance
Prediction_Data=Fetching.fetchDataSpec(var,(datetime.now()+timedelta(days=-45))) # Get the data from just the past month for the prediciton part
Prediction_Data_Length=len(Prediction_Data.Close) # Lenght of the predictin Data to save the recalc of it
#if Cache2.Search(var) ==0:
Coefficients=LinearAlgebra.coefficients_Generator(LinearAlgebra.makeXVals_Matrix(10,timeBegin,Prediction_Data_Length),LinearAlgebra.makeY_Matrix(Prediction_Data.Low)) #coeffcients for prediction fucntion a0-a10
#Cache2.Cache_Predictions(var,Coefficients) # after calculating store the data in cache
#else:
#Coefficients = Cache2.Fetch_Cache(var) # fetch from cache if the company data is stored and it's recent ( less than 3 days from prediction)
Prediction_Model=LinearAlgebra.makeOutY(Coefficients,Prediction_Data_Length,timeBegin,totalDataCurrent.High,googData) # Gets Prediciton Model or scatter of predicted points these points are also normalized
pointY=LinearAlgebra.getPointY(Coefficients,timeBegin,googData[0]['LastTradePrice'],totalDataCurrent.High[len(totalDataCurrent)-1]) #gets Predictiion Point for the next day independently so I can calculate individual days
R=RSI.PredictRSI(totalDataCurrent.Close)
pointY=LinearAlgebra.getPointY(Coefficients,timeBegin,googData[0]['LastTradePrice'],totalDataCurrent.Low[len(totalDataCurrent)-1]) #gets Predictiion Point for the next day independently so I can calculate individual days
url=Graphing.totalTogether(var,totalDataCurrent,googData,Prediction_Model,pointY,R) #Print Final Graph with everything together
url = url + ".embed?width=640&height=480"
ret = "<iframe width='640' height='480' frameborder='0' scrolling='no' src='" + url + "'> </iframe>"
sys.stdout.close()
sys.stdout = old_stdout
return ret
else:
sys.stdout.close()
sys.stdout = old_stdout
return "false"
