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.streamlit/config.toml

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+[theme]
+primaryColor="#254336"
+backgroundColor="#Ffffff"
+secondaryBackgroundColor="#B7B597"
+textColor="#000000"
+font="sans serif"

SARIMAX_model_NIO.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:049bdbb96f201b05d6ae0952f40a6cbec5a3454337325852faca28e0d97ce8c1
+size 15751906

SARIMAX_model_NVDA.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:076546363600ff1215746c24dc8c956b0c085f2f5679214e2c75b7523bc469b3
+size 15751906

SARIMAX_model_TSLA.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:08932ee8f840d9c4fc209bbe11101dc5da900b103ca5b422d40148e409022084
+size 15751906

app.py

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+import streamlit as st
+import pandas as pd
+import yfinance as yf
+import joblib
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from PIL import Image
+
+# Load the banner image
+banner = Image.open("StocKnock.png")
+banner1 = Image.open("StocKnock2.png")
+Tesla = Image.open('Tesla.png')
+NVDA = Image.open('Nvidia.png')
+Nio = Image.open('Nio.png')
+
+# Load the model pipeline
+model_pipeline = joblib.load('model_LinReg.pkl')
+
+# Load SARIMA models for each company
+sarima_models = {
+    'TSLA': joblib.load('SARIMAX_model_TSLA.pkl'),
+    'NVDA': joblib.load('SARIMAX_model_NVDA.pkl'),
+    'NIO': joblib.load('SARIMAX_model_NIO.pkl')
+}
+
+# Initialize VADER sentiment analyzer
+sia = SentimentIntensityAnalyzer()
+
+def analyze_sentiment(text):
+    return sia.polarity_scores(text)
+
+def categorize_sentiment(compound_score):
+    if compound_score >= 0.05:
+        return 'Positive'
+    elif compound_score <= -0.05:
+        return 'Negative'
+    else:
+        return 'Neutral'
+
+def get_stock_data(ticker):
+    stock_data = yf.download(ticker, period='1y', interval='1d')  # Get 1 year of data for better SARIMA forecasting
+    if stock_data.empty:
+        return None
+    return stock_data
+
+def create_input_df(company, headlines):
+    company_ticker = {'Tesla': 'TSLA', 'Nvidia': 'NVDA', 'NIO': 'NIO'}
+    ticker = company_ticker.get(company)
+    if not ticker:
+        return None
+
+    stock_data = get_stock_data(ticker)
+    if stock_data is None:
+        return None
+
+    # Filter stock data to include only entries from 2024
+    stock_data_2024 = stock_data[stock_data.index.year == 2024]
+    if stock_data_2024.empty:
+        return None
+
+    latest_stock = stock_data_2024.iloc[-1]
+
+    data = {
+        'Company_ID': [ticker],
+        'Open': [latest_stock['Open']],
+        'High': [latest_stock['High']],
+        'Low': [latest_stock['Low']],
+        'Close': [latest_stock['Close']],
+        'Volume': [latest_stock['Volume']],
+        'news_count': [len(headlines)]
+    }
+
+    # Initialize sentiment scores
+    pos_score = neg_score = neu_score = compound_score = 0
+
+    # Calculate sentiment scores for each headline
+    for headline in headlines:
+        sentiment = analyze_sentiment(headline)
+        pos_score += sentiment['pos']
+        neg_score += sentiment['neg']
+        neu_score += sentiment['neu']
+        compound_score += sentiment['compound']
+
+    # Calculate average sentiment scores
+    num_headlines = len(headlines)
+    avg_pos_score = pos_score / num_headlines
+    avg_neg_score = neg_score / num_headlines
+    avg_neu_score = neu_score / num_headlines
+    avg_compound_score = compound_score / num_headlines
+
+    # Categorize sentiment based on the average compound score
+    sentiment_category = categorize_sentiment(avg_compound_score)
+
+    # Add sentiment scores and category to the data dictionary
+    data.update({
+        'positive': [avg_pos_score],
+        'negative': [avg_neg_score],
+        'neutral': [avg_neu_score],
+        'compound': [avg_compound_score],
+        'sentiment_category': [sentiment_category]
+    })
+
+    return pd.DataFrame(data), stock_data_2024
+
+def predict_stock_price(company, headlines):
+    if len(headlines) > 10:
+        return "Please provide up to 10 headlines."
+
+    input_df, stock_data_2024 = create_input_df(company, headlines)
+    if input_df is None:
+        return "Invalid company selected or no data available for 2024."
+
+    st.write("Input DataFrame:")
+    st.write(input_df)  # Display the input DataFrame for debugging
+
+    # Predict the next closing price
+    predicted_next_close = model_pipeline.predict(input_df)[0]
+
+    # Perform SARIMA forecast
+    ticker = input_df['Company_ID'][0]
+    sarima_model = sarima_models.get(ticker)
+    if sarima_model is None:
+        return "SARIMA model not available for the selected company."
+
+    # Prepare data for SARIMA forecast with predicted value
+    history_with_predicted = stock_data_2024['Adj Close']
+    future_with_predicted = np.append(history_with_predicted, predicted_next_close)
+
+    # Prepare data for SARIMA forecast without predicted value
+    history_without_predicted = stock_data_2024['Adj Close']
+
+    # Forecast future prices with predicted value
+    forecast_steps = 30
+    forecast_with_predicted = sarima_model.forecast(steps=forecast_steps, exog=[predicted_next_close])
+
+    # Plot the results
+    fig = make_subplots(rows=1, cols=1)
+
+    # Historical data
+    fig.add_trace(go.Scatter(x=history_without_predicted.index, y=history_without_predicted, mode='lines', name='Historical Data'))
+
+    # Predicted next close price
+    predicted_date = history_with_predicted.index[-1] + pd.Timedelta(days=1)
+    fig.add_trace(go.Scatter(x=[predicted_date], y=[predicted_next_close], mode='markers', name='Predicted Next Close'))
+
+    # Forecast data with predicted value
+    forecast_index_with_predicted = [predicted_date + pd.Timedelta(days=i) for i in range(1, forecast_steps + 1)]
+    forecast_with_predicted_line = go.Scatter(x=forecast_index_with_predicted, y=forecast_with_predicted, mode='lines', name='Forecast')
+    fig.add_trace(forecast_with_predicted_line)
+
+    fig.update_layout(title=f"SARIMA Forecast for {company}", xaxis_title="Date", yaxis_title="Price")
+
+    st.plotly_chart(fig)
+
+    return f"Predicted Next Close Price: {predicted_next_close}"
+
+def main():
+    st.sidebar.image(banner1, use_column_width=True)
+    st.sidebar.title("**StocKnock**")
+    st.sidebar.write("Welcome to **StocKnock**, where we use sentiment analysis on social media to predict stock prices. Join us for smarter investing!")
+    st.sidebar.title("What model do we use?")
+    st.sidebar.write("We utilize **Linear Regression** to predict the stock for the next day and **Sarimax** to forecast future stock prices, including the predicted results.")
+    st.sidebar.title("Stocks you can predict")
+    st.sidebar.write("For the time being, these are the stock that you can predict!")
+    st.sidebar.image(Tesla, use_column_width=True)
+    st.sidebar.image(NVDA, use_column_width=True)
+    st.sidebar.image(Nio, use_column_width=True)
+    st.image(banner, use_column_width=True)
+    st.title("Stock Price Prediction App")
+    st.write("Select a company and provide up to 10 headlines to predict the next stock price based on tweets.")
+
+    company_options = ['Tesla', 'Nvidia', 'NIO']
+    company = st.selectbox("Select Company", company_options, key="company_select")
+
+    headlines = st.text_area("Enter Headlines (up to 10 headlines)", key="headlines_input")
+
+    if st.button("Predict", key="predict_button"):
+        if headlines:
+            headlines = headlines.split("\n")
+        else:
+            st.error("Please enter headlines.")
+
+        prediction = predict_stock_price(company, headlines)
+        st.success(prediction)
+
+if __name__ == "__main__":
+    main()

model_LinReg.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:16a59828a4054330b584f8462c952c79b70d517ffd261bd84bbfa4b7b814fc22
+size 4174

requirements.txt

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+streamlit
+pandas
+yfinance
+joblib
+vaderSentiment
+numpy
+plotly
+Pillow
+scikit-learn
+statsmodels
+transformers