def data():
global symbol
global start
global end
global data
global comp_name
if request.method=='POST':
print(request)
# if symbol != request.form['search']:
symbol = request.form['search']
source = request.form['sourcery']
start = request.form['trip-start']
end = request.form['trip-end']
data = gatherer.data(symbol, source, start, end)
comp_name = company.get_symbol(symbol)
return render_template("home1.html", key=stripe_keys['publishable_key'])
def data():
global symbol
global start
global end
global data
global comp_name
if request.method == 'POST':
print(request)
# if symbol != request.form['search']:
symbol = request.form['search']
source = request.form['sourcery']
start = request.form['trip-start']
end = request.form['trip-end']
data = gatherer.data(symbol, source, start, end)
comp_name = company.get_symbol(symbol)
return chart1()
def chart3():
global start
global end
global data
global comp_name
global symbol
global pred_Price
if request.method == 'POST':
print(request)
# if symbol != request.form['search']:
symbol = request.form['search']
source = request.form['sourcery']
start = request.form['trip-start']
end = request.form['trip-end']
comp_name = company.get_symbol(symbol)
dt, dd, reg, knn = logica.task2(data)
#inital setup for model
df = web.DataReader(name=symbol, data_source='yahoo', start=start, end=end)
#creating a new dataframe with the close column only
data = df.filter(['Close'])
#Converting the dataframe to numpy array
dataset = data.values
#Geting the number of 80 percent of data as traning len
training_data_len = math.ceil(len(dataset) * .8)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
training_data = scaled_data[0:training_data_len]
x_train = []
y_train = []
for i in range(60, len(training_data)):
x_train.append(training_data[i - 60:i, 0])
y_train.append(training_data[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
#Building the LSTM model
model = Sequential()
model.add(
LSTM(100, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(100, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, batch_size=1, epochs=1)
#predicting the closing price value for APPL comapny for 30 March 2021
apple_qoute = web.DataReader(name=symbol,
data_source='yahoo',
start=start,
end=end)
#creating new dataframe
new_df = apple_qoute.filter(['Close'])
#get the last 60 days value and then convert into numpy array
last_60_days = new_df[-60:].values
#scale the data between 0 and 1
last_60_days_scaled = scaler.transform(last_60_days)
#create and empty list
X_test = []
#APPEND THE past 60 days
X_test.append(last_60_days_scaled)
#Convert the X_test data set to numpy array
X_test = np.array(X_test)
#Reshape the data
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
#Geting the predicted price
pred_price = model.predict(X_test)
pred_price = scaler.inverse_transform(pred_price)
print("The predicted price for 30th March 2021 :: " + str(pred_price))
return render_template('chart3.html',
stock_date=dt,
stock_data=dd,
reg=reg,
knn=knn,
company=comp_name,
start=start,
end=end,
pred_Price=pred_price)