Update app.py

app.py

@@ -1,135 +1,136 @@
-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)
+import streamlit as st
+import pandas as pd
+from textblob import TextBlob
+import joblib
+import matplotlib.pyplot as plt
+import datetime
+
+# 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 = 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
+
+    # 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)