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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import yfinance as yf
+from datetime import date, timedelta
+import matplotlib.pyplot as plt
+from pmdarima.arima import auto_arima
+
+# Custom CSS for professional look
+st.markdown("""
+    <style>
+        .main {
+            background-color: #f5f5f5;
+            padding: 20px;
+        }
+        .sidebar .sidebar-content {
+            background-color: #fafafa;
+        }
+        .stButton>button {
+            background-color: #2e7d32;
+            color: white;
+            border-radius: 5px;
+            font-weight: bold;
+        }
+        .stButton>button:hover {
+            background-color: #388e3c;
+        }
+        h1, h2, h3 {
+            color: #333;
+        }
+        .stDataFrame {
+            background-color: #ffffff;
+        }
+        .stTable {
+            color: #333;
+        }
+        .stPlotlyChart {
+            background-color: #ffffff;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# App Title
+st.title("📈 Stock Market Forecasting")
+
+# Sidebar Inputs
+st.sidebar.header("User Input")
+
+# Inputs for stock ticker, date range, and forecast days
+ticker = st.sidebar.text_input("Enter stock ticker:", value="GOOGL")
+end_date = st.sidebar.date_input("End Date", value=date.today())
+start_date = st.sidebar.date_input("Start Date", value=date.today() - timedelta(days=365))
+forecast_days = st.sidebar.slider("Forecast days", min_value=10, max_value=30, value=15)
+
+# Ensure start_date is not later than end_date
+if start_date > end_date:
+    st.sidebar.error("❌ Start date cannot be later than end date.")
+    st.stop()
+
+# Feature selection for stock attributes (e.g., Close, High, Low, Open)
+feature_select = st.sidebar.multiselect(
+    label="Select Stock Features to Forecast",
+    options=["Close", "High", "Low", "Open"],
+    default=["Close"]
+)
+
+# Feature descriptions (explanation for each stock feature)
+feature_descriptions = {
+    "Close": "The closing price of the stock at the end of the trading day.",
+    "High": "The highest price at which the stock traded during the day.",
+    "Low": "The lowest price at which the stock traded during the day.",
+    "Open": "The price at which the stock opened at the beginning of the trading day."
+}
+
+# Fetch Stock Data based on user input
+if st.sidebar.button("Fetch Data"):
+    st.subheader(f"Stock Data for {ticker.upper()} ({start_date} to {end_date})")
+
+    try:
+        # Download stock data from Yahoo Finance
+        df = yf.download(ticker, start=start_date, end=end_date, progress=False)
+
+        # Check if data is available
+        if df.empty:
+            st.error("❌ No data found for the selected ticker or date range.")
+            st.stop()
+
+        st.success("✅ Data fetched successfully!")
+
+        # Reset index and insert 'Date' column for better readability
+        df.insert(0, "Date", df.index)
+        df.reset_index(drop=True, inplace=True)
+
+        st.dataframe(df.head())  # Display first few rows of the stock data
+
+        # Initialize an empty DataFrame to store forecasted data for all features
+        combined_forecast_df = pd.DataFrame()
+
+        # Iterate over each selected feature and generate forecasts
+        for feature in feature_select:
+            st.write(f"### 🔍 Forecasting: {feature}")
+            st.write(feature_descriptions[feature])
+
+            # Prepare the time series for forecasting (drop NaN values)
+            series = df[feature].dropna()
+
+            # Fit the ARIMA model using the selected feature's time series
+            model = auto_arima(series,
+                               start_p=1, start_q=1,
+                               max_p=2, max_q=2,
+                               m=12,  # Monthly seasonal cycle
+                               start_P=0,
+                               seasonal=True,
+                               d=1, D=1,
+                               trace=True,  # Display fitting progress
+                               error_action='ignore',
+                               suppress_warnings=True)
+
+            # Forecast future values for the specified number of days
+            forecast = model.predict(n_periods=forecast_days)
+            future_dates = pd.date_range(start=df["Date"].iloc[-1] + timedelta(days=1), periods=forecast_days)
+
+            # Create a DataFrame to hold the forecasted values
+            forecast_df = pd.DataFrame({
+                "Date": future_dates,
+                feature: forecast
+            })
+
+            # Combine the forecasted data for each feature into a single DataFrame
+            combined_forecast_df = pd.merge(combined_forecast_df, forecast_df, on="Date", how="outer") if not combined_forecast_df.empty else forecast_df
+
+            # Plot historical data and forecasted data
+            fig, ax = plt.subplots(figsize=(10, 4))
+            ax.plot(df["Date"], series, label="Historical", color='blue')  # Historical data
+            ax.plot(forecast_df["Date"], forecast_df[feature], label="Forecast", color='orange')  # Forecasted data
+            ax.set_title(f"{feature} Forecast for {ticker.upper()}")
+            ax.set_xlabel("Date")
+            ax.set_ylabel(feature)
+            ax.legend()
+
+            # Display the plot
+            st.pyplot(fig)
+
+        # Display the combined forecasted data for all selected features
+        st.subheader("Combined Forecasted Data")
+        st.dataframe(combined_forecast_df.head())
+
+    except Exception as e:
+        st.error(f"❌ An error occurred: {str(e)}")
+        st.stop()

requirements.txt

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+pandas
+numpy
+yfinance
+matplotlib
+seaborn
+plotly
+scikit-learn
+statsmodels
+streamlit
+pmdarima