Spaces:
Sleeping
Sleeping
import streamlit as st | |
import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import yfinance as yf | |
yf.pdr_override() | |
from pandas_datareader import data as pdr | |
import tensorflow as tf | |
from keras.models import load_model | |
from sklearn.preprocessing import MinMaxScaler | |
# Start and the End dates and the stock ticker | |
start = '2000-01-01' | |
end = '2023-12-31' | |
stock_ticker = '005930.KS' | |
st.title("Stock Market Trend Predictor") | |
use_input = st.text_input('Enter Stock Ticker', stock_ticker) | |
if st.button('Analyze'): | |
df = pdr.get_data_yahoo(use_input, start) | |
#View Data | |
st.subheader("Data from year 2000 to till date:") | |
st.dataframe(df.sort_index(ascending=False),use_container_width=True) | |
#Plot Graph for Closing Price Vs the Time | |
st.subheader("Closing Price VS Time Chart:") | |
fig = plt.figure(figsize=(12,6)) | |
plt.plot(df.Close,label="Closing Price") | |
plt.legend() | |
st.pyplot(fig) | |
#Plot Graph for Closing Price Vs the Time with 100 Moving Average | |
moving_avg_100 = df.Close.rolling(100).mean() | |
st.subheader("Closing Price VS Time Chart With 100Moving Average:") | |
fig = plt.figure(figsize=(12,6)) | |
plt.plot(df.Close, label="Closing Price") | |
plt.plot(moving_avg_100,'red', label="100 Moving Average") | |
plt.legend() | |
st.pyplot(fig) | |
#Plot Graph for Closing Price Vs the Time with 100 moving Average and 200 Moving Average | |
moving_avg_200 = df.Close.rolling(200).mean() | |
st.subheader("Closing Price VS Time Chart With 100Moving Average and 200Moving Average:") | |
fig = plt.figure(figsize=(10,5)) | |
plt.plot(df.Close, label="Closing Price") | |
plt.plot(moving_avg_100,'red', label="100 Moving Average") | |
plt.plot(moving_avg_200,'green', label="200 Moving Average") | |
plt.legend() | |
st.pyplot(fig) | |
#Spliting Data in Training and Testing Data | |
data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)]) | |
data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))]) | |
#Scale the training data between 0 and 1 | |
scaler = MinMaxScaler(feature_range = (0,1)) | |
data_training_array = scaler.fit_transform(data_training) | |
#Load the pre-trained model | |
model = load_model('model.h5') | |
#Testing Past | |
past_100_days = data_training.tail(100) | |
final_df = pd.concat([past_100_days, data_testing], ignore_index=True) | |
input_test_data = scaler.fit_transform(final_df) | |
x_test = [] | |
y_test = [] | |
for i in range(100 , input_test_data.shape[0]): | |
x_test.append(input_test_data[i-100:i]) | |
y_test.append(input_test_data[i,0]) | |
x_test, y_test = np.array(x_test), np.array(y_test) | |
#Make Predictions | |
y_predicted = model.predict(x_test) | |
#Get the scale factor from the scaler and get the original value from the scaled values | |
scaler = scaler.scale_ | |
scale_factor = 1/scaler[0] | |
y_predicted = y_predicted*scale_factor | |
y_test = y_test*scale_factor | |
#Plot Final Graph | |
def plot_final_graph(): | |
st.subheader("Original Stock Price Vs Predicted Stock Price:") | |
fig2 = plt.figure(figsize= (12,6)) | |
plt.plot(y_test, 'blue', label="Original Stock Price") | |
plt.plot(y_predicted, 'red', label="Predicted Stock Price") | |
plt.xlabel('Time') | |
plt.ylabel('Price') | |
plt.legend() | |
st.pyplot(fig2) | |
def main(): | |
st.title('Stock Price Predictive Analysis') | |
#Call the function to plot the final graph | |
plot_final_graph() | |
df_test = pd.DataFrame(y_test, columns=['Original_Price']) | |
df_predicted = pd.DataFrame(y_predicted, columns=['Predicted_Price']) | |
df_predictions = pd.concat([df_test, df_predicted], axis=1) | |
st.subheader("Original and Predicted Stock Price:") | |
st.dataframe(df_predictions.sort_index(ascending=False),use_container_width=True, hide_index=True) | |
if __name__ == "__main__": | |
main() | |