app.py

import streamlit as st
import pandas as pd
import yfinance as yf
from textblob import TextBlob
import joblib
import matplotlib.pyplot as plt
import datetime

# Function to load stock data using yfinance
@st.cache_data(ttl=86400)
def load_yfinance_data():
    # List of stock tickers
    tickers = ['TSLA', 'MSFT', 'PG', 'META', 'AMZN', 'GOOG', 'AMD', 'AAPL', 'NFLX', 'TSM',
               'KO', 'F', 'COST', 'DIS', 'VZ', 'CRM', 'INTC', 'BA', 'BX', 'NOC', 'PYPL', 'ENPH', 'NIO', 'ZS', 'XPEV']
    
    # Set the start and end dates for the past 1 year
    start_date = (datetime.datetime.now() - datetime.timedelta(days=365)).strftime('%Y-%m-%d')
    end_date = datetime.datetime.today().strftime('%Y-%m-%d')

    # Download the stock data using yfinance
    data = yf.download(tickers, start=start_date, end=end_date, group_by='ticker')

    # Process and format the data for each ticker
    all_data = []
    for ticker in tickers:
        df = data[ticker].copy()
        df.reset_index(inplace=True)
        df['Stock Name'] = ticker
        all_data.append(df)
    
    # Concatenate all the data into a single DataFrame
    all_stock_data = pd.concat(all_data, ignore_index=True)

    return all_stock_data

# Load the data
data = load_yfinance_data()

# Perform sentiment analysis on tweets (assuming you still have your tweets data)
tweets_data = pd.read_csv('data/stock_tweets.csv')

# Convert the Date columns to datetime
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(data, daily_sentiment, how='left', on=['Date', 'Stock Name'])

# Fill missing sentiment values with 0 (neutral sentiment)
merged_data['Sentiment'] = merged_data['Sentiment'].fillna(0)

# Sort the data by date
merged_data = merged_data.sort_values(by='Date')

# 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)

# Load the best model
model_filename = 'model/best_model.pkl'
model = joblib.load(model_filename)

# Streamlit application layout
st.title("Stock Price Prediction Using Sentiment Analysis")

# User input for stock data
st.header("Input Stock Data")
stock_names = merged_data['Stock Name'].unique()
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 = merged_data[merged_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 = datetime.datetime.now()

    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

    # 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)
    st.dataframe(prediction_df)
    # Plot predictions using Plotly
    import plotly.express as px
    fig = px.line(prediction_df, x='Date', y='Predicted Close Price',
                  markers=True, title=f"{selected_stock} Predicted Close Prices")
    st.plotly_chart(fig, use_container_width=True)

    # ----------------------------------------
    # Enhanced Visualizations
    st.header(" Enhanced Stock Analysis")
    stock_history = data[data['Stock Name'] == selected_stock]

    # Date filter slider
    min_date = stock_history['Date'].min()
    max_date = stock_history['Date'].max()
    date_range = st.slider("Select Date Range for Visualizations",
                           min_value=min_date, max_value=max_date,
                           value=(min_date, max_date))
    filtered_data = stock_history[(stock_history['Date'] >= date_range[0]) & (stock_history['Date'] <= date_range[1])]

    with st.expander(" Price vs Sentiment Trend"):
        fig1 = px.line(filtered_data, x='Date', y=['Close', 'Sentiment'],
                       labels={'value': 'Price / Sentiment', 'variable': 'Metric'},
                       title=f"{selected_stock} - Close Price & Sentiment")
        st.plotly_chart(fig1, use_container_width=True)

    with st.expander(" Volatility Over Time"):
        fig2 = px.line(filtered_data, x='Date', y='Volatility',
                       title=f"{selected_stock} - 7-Day Rolling Volatility")
        st.plotly_chart(fig2, use_container_width=True)

    with st.expander(" Moving Averages (MA7 vs MA14)"):
        fig3 = px.line(filtered_data, x='Date',
                       y=['MA7', 'MA14'],
                       labels={'value': 'Price', 'variable': 'Moving Average'},
                       title=f"{selected_stock} - Moving Averages")
        st.plotly_chart(fig3, use_container_width=True)

    with st.expander(" Daily Price Change"):
        fig4 = px.line(filtered_data, x='Date', y='Daily_Change',
                       title=f"{selected_stock} - Daily Price Change")
        st.plotly_chart(fig4, use_container_width=True)

    with st.expander(" Sentiment Distribution"):
        fig5 = px.histogram(filtered_data, x='Sentiment', nbins=30,
                            title=f"{selected_stock} - Sentiment Score Distribution")
        st.plotly_chart(fig5, use_container_width=True)