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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)
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