I have created a stock market prediction model based on super indication now in this you need give code to make predictions for upcoming prices for next 5 days using the model also in y axis insted of time make it date:

import yfinance as yf
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.callbacks import LambdaCallback

nifty = yf.download('^NSEI', start='2009-01-01', end='2021-01-01')

def supertrend(df, period=10, multiplier=3):
    hl = (df['High'] + df['Low']) / 2
    atr = df['High'].rolling(period).max() - df['Low'].rolling(period).min()
    
    up = hl - multiplier * atr
    dn = hl + multiplier * atr

    df['ST'] = 0
    df['ST'][0] = (df['High'][0] + df['Low'][0]) / 2
    
    position = 'none'
    
    for i in range(1, len(df)):
        if df['Close'][i] > up[i]:
            if position != 'buy':
                position = 'buy'
                df['ST'][i] = dn[i]
            else:
                df['ST'][i] = max(df['ST'][i - 1], dn[i])
        elif df['Close'][i] < dn[i]:
            if position != 'sell':
                position = 'sell'
                df['ST'][i] = up[i]
            else:
                df['ST'][i] = min(df['ST'][i - 1], up[i])
        else:
            df['ST'][i] = df['ST'][i - 1]

    return df

nifty = supertrend(nifty)
nifty.head()

dataset = nifty['Close'].values
dataset = dataset.astype('float32')
dataset = dataset.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back-1):
		a = dataset[i:(i+look_back), 0]
		dataX.append(a)
		dataY.append(dataset[i + look_back, 0])
	return np.array(dataX), np.array(dataY)

train_size = int(len(dataset) * 0.8)
test_size = len(dataset) - train_size
train, test = dataset[:train_size], dataset[train_size:]

look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

model = Sequential()
model.add(LSTM(100, input_shape=(1, look_back)))
model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=200, batch_size=1, verbose=0)

trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])

import matplotlib.pyplot as plt

trainPredictPlot = np.empty_like(dataset)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict

testPredictPlot = np.empty_like(dataset)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict

plt.plot(scaler.inverse_transform(dataset), label='Actual')
plt.plot(trainPredictPlot, label='Train Prediction')
plt.plot(testPredictPlot, label='Test Prediction')
plt.legend()
plt.show()

model.save('my_model.h5')