app.py

import streamlit as st 
import pandas as pd 
from textblob import TextBlob 
import joblib 
import matplotlib.pyplot as plt 
 
# Load the data 
@st.cache_data 
def load_data(): 
 stock_data = pd.read_csv('data/stock_yfinance_data.csv') 
 tweets_data = pd.read_csv('data/stock_tweets.csv') 
 
 # Convert the Date columns to datetime 
 stock_data['Date'] = pd.to_datetime(stock_data['Date']) 
 tweets_data['Date'] = pd.to_datetime(tweets_data['Date']).dt.date 
 
 # Perform sentiment analysis on tweets 
 def get_sentiment(tweet): 
 analysis = TextBlob(tweet) 
 return analysis.sentiment.polarity 
 
 tweets_data['Sentiment'] = tweets_data['Tweet'].apply(get_sentiment) 
 
 # Aggregate sentiment by date and stock 
 daily_sentiment = tweets_data.groupby(['Date', 'Stock Name']).mean(numeric_only=True).reset_index() 
 
 # Convert the Date column in daily_sentiment to datetime64[ns] 
 daily_sentiment['Date'] = pd.to_datetime(daily_sentiment['Date']) 
 
 # Merge stock data with sentiment data 
 merged_data = pd.merge(stock_data, daily_sentiment, how='left', left_on=['Date', 'Stock Name'], right_on=['Date', 'Stock Name']) 
 
 # Fill missing sentiment values with 0 (neutral sentiment) 
 merged_data['Sentiment'].fillna(0, inplace=True) 
 
 # Sort the data by date 
 merged_data.sort_values(by='Date', inplace=True) 
 
 # Create lagged features 
 merged_data['Prev_Close'] = merged_data.groupby('Stock Name')['Close'].shift(1) 
 merged_data['Prev_Sentiment'] = merged_data.groupby('Stock Name')['Sentiment'].shift(1) 
 
 # Create moving averages 
 merged_data['MA7'] = merged_data.groupby('Stock Name')['Close'].transform(lambda x: x.rolling(window=7).mean()) 
 merged_data['MA14'] = merged_data.groupby('Stock Name')['Close'].transform(lambda x: x.rolling(window=14).mean()) 
 
 # Create daily price changes 
 merged_data['Daily_Change'] = merged_data['Close'] - merged_data['Prev_Close'] 
 
 # Create volatility 
 merged_data['Volatility'] = merged_data.groupby('Stock Name')['Close'].transform(lambda x: x.rolling(window=7).std()) 
 
 # Drop rows with missing values 
 merged_data.dropna(inplace=True) 
 
 return merged_data 
 
data = load_data() 
stock_names = data['Stock Name'].unique() 
 
# Load the best model 
model_filename = 'model/best_model.pkl' 
model = joblib.load(model_filename) 
 
st.title("Stock Price Prediction Using Sentiment Analysis") 
 
# User input for stock data 
st.header("Input Stock Data") 
selected_stock = st.selectbox("Select Stock Name", stock_names) 
days_to_predict = st.number_input("Number of Days to Predict", 
min_value=1, max_value=30, value=10) 
 
# Get the latest data for the selected stock 
latest_data = data[data['Stock Name'] == selected_stock].iloc[-1] 
prev_close = latest_data['Close'] 
prev_sentiment = latest_data['Sentiment'] 
ma7 = latest_data['MA7'] 
ma14 = latest_data['MA14'] 
daily_change = latest_data['Daily_Change'] 
volatility = latest_data['Volatility'] 
 
# Display the latest stock data in a table 
latest_data_df = pd.DataFrame({ 
 'Metric': ['Previous Close Price', 'Previous Sentiment', '7-day Moving Average', '14-day Moving Average', 'Daily Change', 'Volatility'], 
 'Value': [prev_close, prev_sentiment, ma7, ma14, daily_change, volatility] 
}) 
 
st.write("Latest Stock Data:") 
st.write(latest_data_df) 
 
st.write("Use the inputs above to predict the next days close prices of the stock.") 
if st.button("Predict"): 
 predictions = [] 
 latest_date = latest_data['Date'] 
 
 for i in range(days_to_predict): 
 X_future = pd.DataFrame({ 
 'Prev_Close': [prev_close], 
 'Prev_Sentiment': [prev_sentiment], 
 'MA7': [ma7], 
 'MA14': [ma14], 
 'Daily_Change': [daily_change], 
 'Volatility': [volatility] 
 }) 
 
 next_day_prediction = model.predict(X_future)[0] 
 predictions.append(next_day_prediction) 
 
 # Update features for next prediction 
 prev_close = next_day_prediction 
 ma7 = (ma7 * 6 + next_day_prediction) / 7 # Simplified rolling calculation 
 ma14 = (ma14 * 13 + next_day_prediction) / 14 # Simplified rolling calculation 
 daily_change = next_day_prediction - prev_close 
 
 # st.write(f"Predicted next {days_to_predict} days close prices: {predictions}") 
 # Prepare prediction data for display 
 # Prepare prediction data for display 
 prediction_dates = pd.date_range(start=latest_date + pd.Timedelta(days=1), periods=days_to_predict) 
 prediction_df = pd.DataFrame({ 
 'Date': prediction_dates, 
 'Predicted Close Price': predictions 
 }) 
 
 st.subheader("Predicted Prices") 
 st.write(prediction_df) 
 
 # Plotting the results 
 st.subheader("Prediction Chart") 
 plt.figure(figsize=(10, 6)) 
 plt.plot(prediction_df['Date'], prediction_df['Predicted Close Price'], marker='o', linestyle='--', label="Predicted Close Price") 
 plt.xlabel("Date") 
 plt.ylabel("Close Price") 
 plt.title(f"{selected_stock} Predicted Close Prices") 
 plt.legend() 
 st.pyplot(plt)