def makeOutY(FinalM, xVals, startYear, yVals,
currentData): # Make a Y array with the fucntion x
Current_Data = float(currentData[0]['LastTradePrice']) # Gets current data
y = len(yVals) # length of yVals array
x10 = float(FinalM.item(10, 0)) #Coeffcient 10
x9 = float(FinalM.item(9, 0)) #Coeffcient 9
x8 = float(FinalM.item(8, 0)) #Coeffcient 8
x7 = float(FinalM.item(7, 0)) #Coeffcient 7
x6 = float(FinalM.item(6, 0)) #Coeffcient 6
x5 = float(FinalM.item(5, 0)) #Coeffcient 5
x4 = float(FinalM.item(4, 0)) #Coeffcient 4
x3 = float(FinalM.item(3, 0)) #Coeffcient 3
x2 = float(FinalM.item(2, 0)) #Coeffcient 2
x1 = float(FinalM.item(1, 0)) #Coeffcient 1
xo = float(FinalM.item(0, 0)) #Coeffcient 0
Generated_Data = np.array(yVals[y - xVals])
for x in range(0, 30):
Generated_Data = np.append(
Generated_Data,
(float(x10 * pow(x, 10) + x9 * pow(x, 9) + x8 * pow(x, 8) +
x7 * pow(x, 7) + x6 * pow(x, 6) + x5 * pow(x, 5) +
x4 * pow(x, 4) + x3 * pow(x, 3) + x2 * pow(x, 2) +
x1 * pow(x, 1) + xo * pow(x, 0))))
pass
return ArrayNCalc.Normalize(
ArrayNCalc.differenceBetweenDataPoints(Generated_Data), yVals)
def totalTogether(stockSymbol, Webster, currentInfo, Predict, Pointy,
Rsi): #plots all the graphs together
old_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
# Does not work in linux!!!
temp = Webster.index
temp = temp.tz_localize("UTC")
figure = obj.Scatter(y=Webster.High,
x=temp,
line=dict(color=('rgb(0,50,100)')),
name="Past Data for " + stockSymbol) #Past Data Line
currentFigure = obj.Trace(y=currentInfo[0]['LastTradePrice'],
x=currentInfo[0]['LastTradeDateTime'],
line=dict(color=('rgb(0,0,0)')),
name='Current Data for ' +
stockSymbol) #Current Point
Predicts = obj.Scatter(y=Predict,
x=ArrayNCalc.getWorkDates(len(Predict)),
line=dict(color=('rgb(255,165,0)')),
name="Prediction " + stockSymbol) #Prediction Line
point = obj.Trace(y=Pointy,
x=datetime.now(pytz.utc) + timedelta(days=1),
line=dict(color=pointColorMaker(Pointy, currentInfo)),
name="Prediction " + stockSymbol) #point of prediciton
RSI = obj.Scatter(y=Rsi,
x=ArrayNCalc.getWorkDates(len(Rsi)),
line=dict(color=('rgb(0,0,0)')),
name="RSI for " + stockSymbol) #Prediction Line
fig = tools.make_subplots(rows=2, cols=1)
#data=Data([figure,currentFigure,Predicts, point]) #Data Array of Figures
data = Data([Predicts, point, currentFigure, figure])
fig.append_trace(data[0], 1, 1)
fig.append_trace(data[1], 1, 1)
fig.append_trace(data[2], 1, 1)
fig.append_trace(data[3], 1, 1)
fig.append_trace(RSI, 2, 1)
fig['layout']['yaxis1'].update(title='Dollars ($)', domain=[0, 0.7])
fig['layout']['yaxis2'].update(title='RSI percentage (%)',
range=[0, 100],
domain=[0.8, 1])
sys.stdout.close()
sys.stdout = old_stdout
return py.plot(fig, filename=stockSymbol + '_Line',
auto_open=False) #Plot The Function
def getPointY(FinalM, date, prev, yahoorecent):
i = ArrayNCalc.getWorkDates(45)
i = len(i)
prev = float(prev)
Max = .25 * prev - prev # Maximum
Min = .25 * prev + prev # Minimum
x10 = float(FinalM.item(10, 0)) #Coeffcient 10
x9 = float(FinalM.item(9, 0)) #Coeffcient 9
x8 = float(FinalM.item(8, 0)) #Coeffcient 8
x7 = float(FinalM.item(7, 0)) #Coeffcient 7
x6 = float(FinalM.item(6, 0)) #Coeffcient 6
x5 = float(FinalM.item(5, 0)) #Coeffcient 5
x4 = float(FinalM.item(4, 0)) #Coeffcient 4
x3 = float(FinalM.item(3, 0)) #Coeffcient 3
x2 = float(FinalM.item(2, 0)) #Coeffcient 2
x1 = float(FinalM.item(1, 0)) #Coeffcient 1
xo = float(FinalM.item(0, 0)) #Coeffcient 0
change = float(
(x10 * pow(i, 10) + x9 * pow(i, 9) + x8 * pow(i, 8) + x7 * pow(i, 7) +
x6 * pow(i, 6) + x5 * pow(i, 5) + x4 * pow(i, 4) + x3 * pow(i, 3) +
x2 * pow(i, 2) + x1 * pow(i, 1) + xo * pow(i, 0)))
if (yahoorecent < prev):
change += yahoorecent + (Max - Min) # Predicited Increase
else:
change = yahoorecent - change # Predicted decrease
return prev + (change / (Max - Min))
def getPointY(FinalM, date, prev, yahoorecent):
i = ArrayNCalc.getWorkDates(45)
i = len(i)
prev = float(prev)
Max = .25 * prev - prev
Min = .25 * prev + prev
x10 = float(FinalM.item(10, 0))
x9 = float(FinalM.item(9, 0))
x8 = float(FinalM.item(8, 0))
x7 = float(FinalM.item(7, 0))
x6 = float(FinalM.item(6, 0))
x5 = float(FinalM.item(5, 0))
x4 = float(FinalM.item(4, 0))
x3 = float(FinalM.item(3, 0))
x2 = float(FinalM.item(2, 0))
x1 = float(FinalM.item(1, 0))
xo = float(FinalM.item(0, 0))
change = float(
(x10 * pow(i, 10) + x9 * pow(i, 9) + x8 * pow(i, 8) + x7 * pow(i, 7) +
x6 * pow(i, 6) + x5 * pow(i, 5) + x4 * pow(i, 4) + x3 * pow(i, 3) +
x2 * pow(i, 2) + x1 * pow(i, 1) + xo * pow(i, 0)))
if (yahoorecent < prev):
change += yahoorecent + (Max - Min)
else:
change = yahoorecent - change
return prev + (change / (Max - Min))
def makeXVals_Matrix(
percision, startYear, DataSet
): #Number of elements,percison what power of X do we want to go to. Returns One's Matrix
i = len(ArrayNCalc.getWorkDates(45))
matrixx = np.array(i - DataSet)
matrixx = np.append(matrixx, 0)
for q in range(2, percision + 1):
matrixx = np.append(matrixx, pow(0, q))
pass
for x in range(int(i - DataSet + 1), int(i)):
matrixx = np.append(matrixx, [1])
for y in range(1, percision + 1):
matrixx = np.append(matrixx, pow(x, y))
pass
pass
return (np.reshape(matrixx, (DataSet, percision + 1)))
def rsiPlot(stockSymbol, Webster, Predict,
startYear): #plots all the graphs together
old_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
figure = obj.Scatter(y=Webster.High,
x=Webster.index,
line=dict(color=('rgb(0,50,100)')),
name="Past Data for " + stockSymbol) #Past Data Line
Predicts = obj.Scatter(y=Predict,
x=ArrayNCalc.getWorkDates(len(Predict)),
line=dict(color=('rgb(255,165,0)')),
name="RSI for " + stockSymbol) #Prediction Line
fig = tools.make_subplots(rows=2, cols=1)
fig.append_trace(figure, 1, 1) # subplot positions
fig.append_trace(Predicts, 2, 1)
fig['layout']['xaxis2'].update(
range=[datetime(int(startYear), 1, 1),
datetime.now()])
fig['layout']['yaxis1'].update(title='Dollars ($)') # format the axes
fig['layout']['yaxis2'].update(title='RSI percentage (%)', range=[0, 100])
sys.stdout.close()
sys.stdout = old_stdout
return py.plot(fig, filename=stockSymbol + '_Line')
def getRelativeAcc():
stockName = request.args.get('s')
var=stockName
old_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
if var != 'null': # ONLY PRECED IF WE HAVE A COMPANY
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.High) # Lenght of the predictin Data to save the recalc of it
Coefficients=LinearAlgebra.coeffcients_Generator(LinearAlgebra.makeXVals_Matrix(10,timeBegin,Prediction_Data_Length),LinearAlgebra.makeY_Matrix(Prediction_Data.Low)) #coeffcients for prediction fucntion a0-a10
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
ret = str(ArrayNCalc.CalculateRelativeACC(Prediction_Model,Prediction_Data.High))
sys.stdout.close()
sys.stdout = old_stdout
return "The relative accuracy is: " + ret + "%"
else:
sys.stdout.close()
sys.stdout = old_stdout
return ""
def getRelativeAcc():
stockName = request.args.get('s')
var=stockName
old_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
if var != 'null': # ONLY PRECED IF WE HAVE A COMPANY
timeBegin=2010
Coefficients = numpy.zeros(shape =(11,1))
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
#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
ret = str(ArrayNCalc.CalculateRelativeACC(Prediction_Model,Prediction_Data.High))
sys.stdout.close()
sys.stdout = old_stdout
return "The relative error is: " + ret + "%"
else:
sys.stdout.close()
sys.stdout = old_stdout
return ""
