Made improvements

app.py

@@ -4,11 +4,6 @@ import yfinance as yf
 import plotly.graph_objects as go
 from datetime import datetime, timedelta
 import numpy as np
-import base64
-from io import BytesIO
-import matplotlib.pyplot as plt
-import matplotlib
-matplotlib.use('Agg')
 
 # Import utility functions
 from utils.yfinance_utils import fetch_yfinance_daily
@@ -29,7 +24,7 @@ st.set_page_config(page_title="Asset Class Comparison", layout="wide")
 # Title and description
 st.title("Asset Class Performance Comparison")
 st.write("Compare the performance of different asset classes over time")
-st.write("Note: Cryptocurrencies (BTC, ETH, SOL, DOGE) are highly volatile and should be considered high-risk investments")
+# st.write("Note: Cryptocurrencies (BTC, ETH, SOL, DOGE) are highly volatile and should be considered high-risk investments")
 
 # Sidebar for user inputs
 st.sidebar.header("Investment Parameters")
@@ -37,9 +32,15 @@ currency = st.sidebar.selectbox("Display Currency", ["USD", "SGD"], index=0)
 initial_investment = st.sidebar.number_input(f"Initial Investment Amount ({currency})", min_value=1000, value=10000, step=1000)
 start_date = st.sidebar.date_input("Start Date", value=datetime.now() - timedelta(days=365*25))
 user_end_date = st.sidebar.date_input("End Date", value=datetime.now())
+
 fd_rate = st.sidebar.number_input("Fixed Deposit Rate (%)", min_value=0.0, value=2.9, step=0.1) / 100
 use_log_scale = st.sidebar.checkbox("Use Log Scale", value=True)
 
+# Calculate and display investment period
+investment_days = (user_end_date - start_date).days
+investment_years = investment_days / 365
+st.write(f"Investment Period: {investment_days} days ({investment_years:.1f} years)")
+
 # Asset selection
 selected_assets = st.sidebar.multiselect(
     "Select Assets to Compare",
@@ -207,33 +208,18 @@ for asset in selected_assets:
         annualized_return = ((final_value / initial_investment) ** (1/years) - 1) * 100
         
         # Calculate yearly return statistics
-        yearly_data = valid_series.resample('Y').first()
+        yearly_data = valid_series.resample('YE').first()
         yearly_returns = yearly_data.pct_change().dropna()
         positive_years = (yearly_returns > 0).sum()
         total_years = len(yearly_returns)
         positive_percentage = (positive_years / total_years) * 100
         
-        # Create sparkline using matplotlib
-        plt.figure(figsize=(2, 0.5))
-        plt.plot(valid_series.index, valid_series, linewidth=1)
-        plt.axis('off')
-        plt.margins(0)
-        plt.tight_layout(pad=0)
-        
-        # Convert plot to base64 string
-        buf = BytesIO()
-        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0, transparent=True)
-        buf.seek(0)
-        sparkline = base64.b64encode(buf.read()).decode('utf-8')
-        plt.close()
-        
         summary_data.append({
             "Asset": asset,
             f"Final Value ({currency_symbol})": final_value,
             "Annualized Return (%)": annualized_return,
             "Positive Years": f"{positive_years}/{total_years}",
             "Positive Years %": positive_percentage,
-            "Performance": f'<img src="data:image/png;base64,{sparkline}" width="100" height="30">'
         })
     else:
         summary_data.append({
@@ -242,7 +228,6 @@ for asset in selected_assets:
             "Annualized Return (%)": None,
             "Positive Years": "N/A",
             "Positive Years %": None,
-            "Performance": "N/A"
         })
 
 # Convert to DataFrame
@@ -306,7 +291,7 @@ for asset in selected_assets:
     valid_series = returns_data[asset].dropna()
     if len(valid_series) > 1:
         # Resample to yearly data
-        yearly_data = valid_series.resample('Y').first()
+        yearly_data = valid_series.resample('YE').first()
         
         # Calculate yearly returns
         yearly_returns = yearly_data.pct_change().dropna()