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app (2).py

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+pip install --upgrade pip
+pip install -r requirements.txt
+
+import streamlit as st 
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import pandas_datareader as data 
+import yfinance as yf
+from keras.models import load_model
+
+
+
+st.title('Stock Trend predictor')
+
+user_input = st.text_input('Enter Stock Ticker','AAPL' )
+script = yf.download(tickers=user_input,period='15y',interval='1d') 
+
+#describing data
+st.subheader('Data of last 15 years')
+st.write(script.describe())
+
+#visualization
+st.subheader('closing price vs time chart')
+fig = plt.figure(figsize=(17,7))
+plt.plot(script.Close, 'b')
+st.pyplot(fig)
+
+st.subheader('Closing price vs time chart with 100MA')#100 ma
+ma100 = script.Close.rolling(100).mean()
+fig = plt.figure(figsize=(17,7))
+plt.plot(ma100, 'g')
+plt.plot(script.Close, 'b')
+st.pyplot(fig) 
+
+st.subheader('Closing price vs time chart with 100MA and 200MA')#200&100 ma
+ma200 = script.Close.rolling(200).mean()
+#ma100 = script.Close.rolling(100).mean()
+fig = plt.figure(figsize=(17,7))
+plt.plot(ma200, 'r')
+plt.plot(ma100, 'g')
+plt.plot(script.Close, 'b')
+st.pyplot(fig) 
+
+# splitting data into training and testing 
+
+data_training = pd.DataFrame(script['Close'][0:int(len(script)*0.70)])
+data_testing = pd.DataFrame(script['Close'][int(len(script)*0.70): int(len(script))])
+
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler(feature_range=(0,1))  
+
+data_training_array = scaler.fit_transform(data_training)
+
+#load my model 
+model = load_model('stock_model.h5')
+
+
+
+#testing part
+past_100_days = data_training.tail(100)
+final_script = pd.concat([past_100_days, data_testing], ignore_index=True)
+input_data = scaler.fit_transform(final_script)
+
+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)
+
+# making prediction
+
+y_predicted = model.predict(x_test) 
+scaler.scale_
+
+scale_factor = 1/scaler.scale_
+y_predicted = y_predicted * scale_factor
+y_test = y_test * scale_factor
+#final graph 
+
+st.subheader('Prediction vs original')
+fig2 = plt.figure(figsize=(17,7))
+plt.plot(y_test, 'b', label = 'Original Price')
+plt.plot(y_predicted, 'r', label = 'Predicted Price')
+plt.xlabel('Time')
+plt.ylabel('Price')
+plt.legend()
+st.pyplot(fig2)

requirements.txt

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+streamlit
+numpy
+pandas
+matplotlib
+pandas_datareader
+yfinance
+keras.models
+tensorflow
+sklearn
+
+

stock_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a544bf19f84d80f99017ddde84028ebb0508375f9baa55c222a5f6a7a3ddf76
+size 2214648