def insert():
share = StringVar
quantity = IntVar
share = variable1.get()
quantity = E1.get()
amount = round(get_data_of_stocks(share)[2][0], 2)
invest = round(float(quantity) * float(amount), 2)
stock = db.Stocks(share, quantity, amount, invest)
db.insert_stocks(stock)
message = "Company: {}\nQuantity: {}\nPrice: {}\nInvestment: {}".format(share, quantity, amount, invest)
tab1_display.insert(INSERT, message)
def generate_data(company):
close, opens, low, high, dates, volume = get_data_of_stocks(company)
random_close = np.random.randint(low=min(close), high=max(close), size=30)
random_opens = np.random.randint(low=min(opens), high=max(opens), size=30)
random_high = np.random.randint(low=min(high), high=max(high), size=30)
random_low = np.random.randint(low=min(low), high=max(low), size=30)
random_volume = np.random.randint(low=min(volume),
high=max(volume),
size=30)
return random_close, random_opens, random_high, random_low, random_volume
def buttonClick1():
close , opens , low , high , dates ,volume = get_data_of_stocks(variable1.get())
new_dates = []
new_close = []
for i in range(0, len(close), 15):
new_dates.append(dates[i])
new_close.append(close[i])
plt.plot(new_dates,new_close)
plt.xlabel("Dates($)")
plt.ylabel("Closing Price($)")
plt.title(variable1.get())
plt.show()
def predict_data(company):
close, opens, low, high, dates, volume = get_data_of_stocks(company)
X, Y = convert_to_dataFrame(opens, high, low, close, volume)
reg = LinearRegression()
reg = reg.fit(X[['opens', 'high', 'low', 'volume']], Y['close'])
random_close, random_opens, random_high, random_low, random_volume = generate_data(
company)
test_x, test_y = convert_to_dataFrame(random_opens, random_high,
random_low, random_close,
random_volume)
predict = reg.predict(test_x[['opens', 'high', 'low', 'volume']])
return predict