Create long_term_analysis.py

pages/long_term_analysis.py

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+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from datetime import datetime, timedelta
+
+# Page Configuration
+st.set_page_config(page_title="10-Year Market Analysis", page_icon="📊", layout="wide")
+
+# Sidebar Controls
+st.sidebar.title("Long-Term Analysis")
+symbol = st.sidebar.text_input("Enter Stock Symbol", value="AAPL", help="Example: AAPL, MSFT, TSLA, NIFTY50")
+
+# Get Date Range (Last 10 Years)
+end_date = datetime.today()
+start_date = end_date - timedelta(days=365*10)
+
+# Fetch Data Function
+@st.cache_data
+def fetch_long_term_data(symbol):
+    try:
+        data = yf.download(symbol, start=start_date.strftime('%Y-%m-%d'), end=end_date.strftime('%Y-%m-%d'))
+        return data
+    except Exception as e:
+        st.error(f"Error fetching data: {e}")
+        return None
+
+# Fetch Data
+df = fetch_long_term_data(symbol)
+
+# Check if Data Exists
+if df is not None and not df.empty:
+    st.write(f"### {symbol} - Historical Data (Last 10 Years)")
+    
+    # Convert index to date column
+    df.reset_index(inplace=True)
+
+    # Exploratory Data Analysis (EDA)
+    st.subheader("1️⃣ Exploratory Data Analysis (EDA)")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.write("**Basic Statistics**")
+        st.write(df.describe())
+
+    with col2:
+        st.write("**Missing Values**")
+        st.write(df.isnull().sum())
+
+    # Data Visualization
+    st.subheader("2️⃣ Data Visualization")
+
+    # Plot Closing Price
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(df["Date"], df["Close"], label="Close Price", color="blue")
+    ax.set_title(f"{symbol} - Closing Price Trend (10 Years)")
+    ax.set_xlabel("Year")
+    ax.set_ylabel("Stock Price (USD)")
+    ax.legend()
+    st.pyplot(fig)
+
+    # Moving Averages
+    df["SMA_50"] = df["Close"].rolling(window=50).mean()
+    df["SMA_200"] = df["Close"].rolling(window=200).mean()
+
+    # Plot Moving Averages
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(df["Date"], df["Close"], label="Close Price", color="gray", alpha=0.5)
+    ax.plot(df["Date"], df["SMA_50"], label="50-Day SMA", color="blue")
+    ax.plot(df["Date"], df["SMA_200"], label="200-Day SMA", color="red")
+    ax.set_title(f"{symbol} - Moving Averages (50 & 200 Days)")
+    ax.set_xlabel("Year")
+    ax.set_ylabel("Stock Price (USD)")
+    ax.legend()
+    st.pyplot(fig)
+
+    # Volatility Analysis
+    df["Volatility"] = df["Close"].pct_change().rolling(window=30).std()
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(df["Date"], df["Volatility"], label="30-Day Volatility", color="purple")
+    ax.set_title(f"{symbol} - Volatility Over Time (30-Day Rolling)")
+    ax.set_xlabel("Year")
+    ax.set_ylabel("Volatility")
+    ax.legend()
+    st.pyplot(fig)
+
+    # Volume Analysis
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.bar(df["Date"], df["Volume"], color="green", alpha=0.5)
+    ax.set_title(f"{symbol} - Trading Volume Over 10 Years")
+    ax.set_xlabel("Year")
+    ax.set_ylabel("Volume")
+    st.pyplot(fig)
+
+    # Correlation Heatmap
+    st.subheader("3️⃣ Correlation Analysis")
+    correlation_matrix = df[["Open", "High", "Low", "Close", "Volume"]].corr()
+    fig, ax = plt.subplots(figsize=(8, 6))
+    sns.heatmap(correlation_matrix, annot=True, cmap="coolwarm", fmt=".2f", linewidths=0.5, ax=ax)
+    st.pyplot(fig)
+
+    # Show Data Table
+    st.write("### Raw Data (Last 10 Rows)")
+    st.dataframe(df.tail(10))
+
+else:
+    st.warning("No data found. Please check the stock symbol.")