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)