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app.py

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+from nselib import capital_market
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from keras.models import load_model
+import streamlit as st
+from sklearn.preprocessing import MinMaxScaler
+
+# Define date range for data extraction
+from_date_str = '01-01-2016'
+to_date_str = '01-08-2024'
+
+# Streamlit App Title
+st.title('Stock Trend Prediction')
+
+# Fetching Nifty 50 data
+input_field = st.text_input("Enter your input:")
+index_data = capital_market.index_data(index=input_field, from_date=from_date_str, to_date=to_date_str)
+index_data['TIMESTAMP'] = pd.to_datetime(index_data['TIMESTAMP'], format='%d-%m-%Y')
+index_data = index_data.sort_values('TIMESTAMP').reset_index(drop=True)
+
+st.subheader('Data from 2016 to 2024')
+st.write(index_data)
+
+# Visualization: Closing Price vs Time
+st.subheader('Closing Price vs Time chart')
+fig = plt.figure(figsize=(12, 6))
+plt.plot(index_data['TIMESTAMP'], index_data['CLOSE_INDEX_VAL'], label='Closing Price')
+plt.xlabel('Year')
+plt.ylabel('Closing Price')
+plt.title('Closing Price vs Time')
+plt.legend()
+st.pyplot(fig)
+
+# Add Moving Averages (100 EMA and 200 EMA)
+moving_avg_100 = index_data['CLOSE_INDEX_VAL'].ewm(span=100, adjust=False).mean()
+moving_avg_200 = index_data['CLOSE_INDEX_VAL'].ewm(span=200, adjust=False).mean()
+
+fig = plt.figure(figsize=(12, 6))
+plt.plot(index_data['TIMESTAMP'], index_data['CLOSE_INDEX_VAL'], label='Closing Price')
+plt.plot(index_data['TIMESTAMP'], moving_avg_100, label='100 EMA', color='orange')
+plt.plot(index_data['TIMESTAMP'], moving_avg_200, label='200 EMA', color='red')
+plt.xlabel('Year')
+plt.ylabel('Closing Price')
+plt.title('Closing Price vs Time with 100 EMA and 200 EMA')
+plt.legend()
+st.pyplot(fig)
+
+# Prepare data for model
+df = index_data
+
+# Splitting data into training and testing sets (70-30 split)
+data_training = pd.DataFrame(df['CLOSE_INDEX_VAL'][0:int(len(df) * 0.70)])
+data_testing = pd.DataFrame(df['CLOSE_INDEX_VAL'][int(len(df) * 0.70):])
+past_100_days = data_training.tail(100)
+
+# Scaling data
+scaler = MinMaxScaler(feature_range=(0, 1))
+data_training_array = scaler.fit_transform(data_training)
+
+# Prepare test data
+final_df = pd.concat([past_100_days, data_testing], ignore_index=True)
+input_data = scaler.transform(final_df)
+
+x_test = []
+y_test = []
+
+for i in range(100, input_data.shape[0]):
+    x_test.append(input_data[i-100:i])
+    y_test.append(input_data[i, 0])
+
+x_test, y_test = np.array(x_test), np.array(y_test)
+
+# Load pre-trained model
+model = load_model('keras_model.h5')
+
+# Predictions
+y_predicted = model.predict(x_test)
+
+# Inverse transform to original scale
+y_predicted = scaler.inverse_transform(y_predicted.reshape(-1, 1)).flatten()
+y_test = scaler.inverse_transform(y_test.reshape(-1, 1)).flatten()
+
+# Visualization: Predictions vs Original
+test_dates = df['TIMESTAMP'][len(data_training):].reset_index(drop=True)
+
+st.subheader('Prediction v/s Original')
+fig2 = plt.figure(figsize=(12, 6))
+plt.plot(test_dates, y_test, 'b', label='Original Price')
+plt.plot(test_dates, y_predicted, 'r', label='Predicted Price')
+plt.xlabel('Year')
+plt.ylabel('Closing Price')
+plt.title('Prediction vs Original Closing Price Over Time')
+plt.legend()
+plt.xticks(rotation=45)
+st.pyplot(fig2)

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed57540c248fb825e55399711ef8987135105f176fbfcc6f1882c7e5c119c888
+size 2205768

requirements.txt

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+tensorflow
+keras
+streamlit
+scikit-learn
+matplotlib
+pandas
+numpy
+nselib
+pandas_market_calendars