Select one behavior. Every returned turn has one binary label: Present or Absent. Source: final dense boolean release.
0144cf20df9e39e337afd689Chỉ sửa lại định dạng câu hỏi và không bổ sung gì thêm
Khi thiết lập trước khi xuất bản, thông tin nào sau đây không cần thiết phải hiển thị?Khi thiết lập trước khi xuất bản, thông tin nào sau đây không cần thiết phải hiển thị?
18b47f1b2e16481063bbf80c%%writefile portfolio_optimizer.py
import streamlit as st
import pandas as pd
import plotly.graph_objects as go
import numpy as np
import yfinance as yf
import statsmodels.api as sm
import datetime
from datetime import datetime, timedelta
from pandas_datareader import data as pdr
import plotly.express as px
import warnings, json, requests, copy
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
from scipy.stats import skew, kurtosis, norm
from joblib import Parallel, delayed
from sklearn.linear_model import LinearRegression
from statsmodels.stats.stattools import durbin_watson
from functools import lru_cache, wraps
from concurrent.futures import ProcessPoolExecutor
from scipy.optimize import minimize
from requests.packages.urllib3.exceptions import InsecureRequestWarning
warnings.filterwarnings('ignore')
# ----------------------------
# Frequency Mapping
# ----------------------------
frequency_mapping = {
"Daily": "D",
"Weekly": "W",
"Monthly": "M",
"Quarterly": "Q",
"Yearly": "Y"
}
# Inverse mapping to get frequency name from code
rebalance_freq_inverse_mapping = {v: k for k, v in frequency_mapping.items()}
example_portfolios = [
{
'name': "Retirement Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*10)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': '^GSPC',
'rebalance_freq': 'M',
'selected': ['AAPL', 'MSFT', 'GOOGL', 'JPM', 'XOM'],
'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
},
{
'name': "Tech-Heavy Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*5)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': '^IXIC',
'rebalance_freq': 'M',
'selected': ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META'],
'allocations': [25.0, 25.0, 20.0, 15.0, 15.0]
},
{
'name': "Balanced Risk-Return Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*7)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': '^GSPC',
'rebalance_freq': 'M',
'selected': ['AAPL', 'JNJ', 'V', 'PG', 'XOM'],
'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
},
{
'name': "Income-Focused Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*7)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': '^DJI',
'rebalance_freq': 'Q',
'selected': ['PG', 'KO', 'JNJ', 'T', 'PFE'],
'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
},
{
'name': "Growth-Oriented Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*3)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': '^IXIC',
'rebalance_freq': 'M',
'selected': ['TSLA', 'NVDA', 'AMD', 'META', 'NFLX'],
'allocations': [30.0, 20.0, 20.0, 15.0, 15.0]
},
{
'name': "Value Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*10)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': '^DJI',
'rebalance_freq': 'Q',
'selected': ['KO', 'PFE', 'XOM', 'WMT', 'CVX'],
'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
},
{
'name': "Dividend-Focused Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*7)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': 'DVY', # Dividend Index
'rebalance_freq': 'Q',
'selected': ['T', 'VZ', 'PFE', 'KO', 'IBM'],
'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
},
{
'name': "Global Diversification Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*5)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': 'ACWI', # All Country World Index
'rebalance_freq': 'M',
'selected': ['AAPL', 'TSM', 'BABA', 'SAP', 'UL'],
'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]
},
{
'name': "High-Risk Growth Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*3)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': '^IXIC',
'rebalance_freq': 'M',
'selected': ['TSLA', 'ARKK', 'NVDA', 'SHOP', 'CRWD'],
'allocations': [30.0, 20.0, 20.0, 15.0, 15.0]
},
{
'name': "Emerging Markets Portfolio",
'start_date': pd.to_datetime(datetime.today() - timedelta(days=365*5)),
'end_date': pd.to_datetime(datetime.today()),
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': 'EEM', # Emerging Markets Index
'rebalance_freq': 'M',
'selected': ['BABA', 'TSM', 'PDD', 'INFY', 'VALE'],
'allocations': [25.0, 25.0, 20.0, 15.0, 15.0]
}
]
# ----------------------------
# Global Settings and Configurations
# ----------------------------
# Suppress only the InsecureRequestWarning, if bypassing SSL
warnings.simplefilter('ignore', InsecureRequestWarning)
requests.packages.urllib3.disable_warnings() # Use cautiously
# ----------------------------
# Global Theme Configuration
# ----------------------------
THEME = 'plotly_dark' # Options: 'plotly_dark', 'plotly_white', 'seaborn', etc.
# ----------------------------
# Global Plot Layout Configuration
# ----------------------------
FIG_HEIGHT = 600
HOVERMODE = 'x unified'
# Initialize Session State
default_session_state = {
'portfolios': [],
'backtest_results': {},
'step': "Configure Portfolio",
'edit_portfolio': None,
'add_new_portfolio': False,
'default_config': {
'rf_rate': 0.02,
'broker_fee': 0.0,
'benchmark_symbol': '^GSPC'
}
}
# Use a single loop to set defaults
for key, value in default_session_state.items():
if key not in st.session_state:
st.session_state[key] = copy.deepcopy(value)
# Initialize example portfolios if not already in session state
if 'portfolios' not in st.session_state or not st.session_state.portfolios:
st.session_state.portfolios = copy.deepcopy(example_portfolios)
# ----------------------------
# Utility Decorators
# ----------------------------
def handle_exceptions(func):
"""
Decorator to handle exceptions in Streamlit applications.
Displays errors using Streamlit's error message system.
"""
@wraps(func)
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
st.error(f"Error in {func.__name__}: {e}")
return None
return wrapper
# ----------------------------
# Helper Functions
# ----------------------------
@handle_exceptions
def get_company_name(ticker_df: pd.DataFrame, ticker: str) -> str:
"""
Retrieve the company name for a given ticker.
Parameters:
- ticker_df (pd.DataFrame): DataFrame containing 'Ticker' and 'Company Name' columns.
- ticker (str): Stock ticker symbol.
Returns:
- str: Company name or "Unknown" if not found.
"""
match = ticker_df[ticker_df['Ticker'] == ticker]
if not match.empty:
return match.iloc[0]['Company Name']
return "Unknown"
def format_asset_option(ticker: str, company_name: str) -> str:
"""
Format the asset option display string.
Parameters:
- ticker (str): Stock ticker symbol.
- company_name (str): Name of the company.
Returns:
- str: Formatted string combining ticker and company name.
"""
return f"{ticker} - {company_name}"
def handle_portfolio_data(portfolio, date_range=None):
"""
Helper function to download data and handle missing tickers.
Parameters:
- portfolio (dict): Portfolio details.
- date_range (tuple, optional): (start_date, end_date)
Returns:
- available_selected (list): Tickers with available data.
- missing_selected (set): Tickers missing data.
- price_data (pd.DataFrame): Adjusted close prices.
"""
start_date = date_range[0] if date_range else portfolio['start_date']
end_date = date_range[1] if date_range else portfolio['end_date']
price_data = download_data(portfolio['selected'], start_date, end_date)
if price_data.empty:
return [], set(portfolio['selected']), price_data
available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]
missing_selected = set(portfolio['selected']) - set(available_selected)
if missing_selected:
st.warning(f"Excluded tickers with no data: {', '.join(missing_selected)}")
return available_selected, missing_selected, price_data
def run_portfolio_comparison(comparison_type, portfolio1, portfolio2=None):
"""
Generalized function to run portfolio comparisons.
Parameters:
- comparison_type (str): "vs_benchmark" or "vs_portfolio"
- portfolio1 (dict): Primary portfolio.
- portfolio2 (dict, optional): Secondary portfolio for "vs_portfolio".
Returns:
- result (dict): Backtest results or error.
"""
try:
if comparison_type == "vs_benchmark":
available, missing, price_data = handle_portfolio_data(portfolio1)
if not available:
return {"error": "No valid tickers with available data."}
allocations = adjust_allocations([portfolio1['allocations'][portfolio1['selected'].index(t)] for t in available])
returns, cum_returns = backtest(
weights=np.array(allocations) / 100,
prices=price_data[available],
rebalance_freq=portfolio1['rebalance_freq'],
broker_fee=portfolio1['broker_fee']
)
if cum_returns.empty:
return {"error": "Cumulative returns are empty."}
# Benchmark processing...
benchmark_data = download_data(
[portfolio1['benchmark_symbol']],
portfolio1['start_date'].strftime('%Y-%m-%d'),
portfolio1['end_date'].strftime('%Y-%m-%d')
)
if benchmark_data.empty:
return {"error": "Benchmark data could not be downloaded."}
benchmark_returns, benchmark_cum_returns, benchmark_metrics = process_benchmark(benchmark_data, returns, portfolio1)
portfolio_metrics = calculate_metrics(returns, cum_returns, portfolio1['rf_rate'], benchmark_returns)
# Compile backtest results
backtest_results = {
'returns': returns,
'cum_returns': cum_returns,
'weights': allocations,
'price_data': price_data[available],
'metrics': portfolio_metrics,
'benchmark_returns': benchmark_returns,
'benchmark_cum_returns': benchmark_cum_returns,
'benchmark_metrics': benchmark_metrics
}
# **Added Return Statement**
return {"backtest_results": backtest_results}
elif comparison_type == "vs_portfolio":
overlap_start = max(portfolio1['start_date'], portfolio2['start_date'])
overlap_end = min(portfolio1['end_date'], portfolio2['end_date'])
if overlap_start >= overlap_end:
return {"error": "No overlapping date ranges."}
available_a, missing_a, price_data_a = handle_portfolio_data(portfolio1, (overlap_start, overlap_end))
available_b, missing_b, price_data_b = handle_portfolio_data(portfolio2, (overlap_start, overlap_end))
if not available_a or not available_b:
return {"error": "One of the portfolios has no valid tickers with available data."}
allocations_a = adjust_allocations([portfolio1['allocations'][portfolio1['selected'].index(t)] for t in available_a])
allocations_b = adjust_allocations([portfolio2['allocations'][portfolio2['selected'].index(t)] for t in available_b])
returns_a, cum_returns_a = backtest(
weights=np.array(allocations_a) / 100,
prices=price_data_a[available_a],
rebalance_freq=portfolio1['rebalance_freq'],
broker_fee=portfolio1['broker_fee']
)
returns_b, cum_returns_b = backtest(
weights=np.array(allocations_b) / 100,
prices=price_data_b[available_b],
rebalance_freq=portfolio2['rebalance_freq'],
broker_fee=portfolio2['broker_fee']
)
if cum_returns_a.empty or cum_returns_b.empty:
return {"error": "One of the cumulative returns is empty."}
common_index = cum_returns_a.index.intersection(cum_returns_b.index)
if common_index.empty:
return {"error": "No overlapping dates after backtesting."}
metrics_a = calculate_metrics(returns_a.loc[common_index], cum_returns_a.loc[common_index], portfolio1['rf_rate'])
metrics_b = calculate_metrics(returns_b.loc[common_index], cum_returns_b.loc[common_index], portfolio2['rf_rate'])
backtest_results = {
'returns_a': returns_a.loc[common_index],
'cum_returns_a': cum_returns_a.loc[common_index],
'weights_a': allocations_a,
'price_data_a': price_data_a[available_a],
'metrics_a': metrics_a,
'returns_b': returns_b.loc[common_index],
'cum_returns_b': cum_returns_b.loc[common_index],
'weights_b': allocations_b,
'price_data_b': price_data_b[available_b],
'metrics_b': metrics_b,
'benchmark_cum_returns': pd.Series(dtype=float)
}
return {"backtest_results": backtest_results}
except Exception as e:
return {"error": f"An unexpected error occurred: {e}"}
def adjust_allocations(allocations):
"""Adjust allocations to sum to 100% if necessary."""
total = sum(allocations)
if not np.isclose(total, 100.0, atol=1e-4):
st.warning("Allocations do not sum to 100%. Adjusting allocations proportionally.")
allocations = [a / total * 100 for a in allocations]
return allocations
def process_portfolio_allocations(portfolio, price_data):
"""Processes portfolio allocations and handles missing tickers."""
available = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]
missing = set(portfolio['selected']) - set(available)
if missing:
st.warning(f"Excluded tickers from '{portfolio['name']}': {', '.join(missing)}")
if not available:
st.error(f"No valid tickers for portfolio '{portfolio['name']}' in the overlapping period.")
st.stop()
allocations = [portfolio['allocations'][i] for i, ticker in enumerate(portfolio['selected']) if ticker in available]
allocations = adjust_allocations(allocations)
return allocations, available, missing
def initialize_benchmark_metrics():
"""Initializes benchmark metrics when benchmark data is unavailable."""
metrics = {metric: "N/A" for metric in [
'Start Balance', 'End Balance', 'Annualized Return (CAGR)', 'Best Year', 'Worst Year',
'Arithmetic Mean (Monthly)', 'Arithmetic Mean (Annualized)', 'Geometric Mean (Monthly)',
'Geometric Mean (Annualized)', 'Standard Deviation (Monthly)', 'Standard Deviation (Annualized)',
'Downside Deviation (Monthly)', 'Maximum Drawdown', 'Sharpe Ratio', 'Sortino Ratio',
'Gain/Loss Ratio', 'Skewness', 'Excess Kurtosis', 'Safe Withdrawal Rate',
'Perpetual Withdrawal Rate', 'Positive Periods', 'Benchmark Correlation', 'Beta',
'Alpha (annualized)', 'R2', 'Treynor Ratio', 'Calmar Ratio',
'Modigliani–Modigliani Measure', 'Information Ratio', 'Tracking Error',
'Active Return', 'Upside Capture Ratio', 'Downside Capture Ratio'
]}
return None, pd.Series(dtype=float), metrics
def process_benchmark(benchmark_data, returns, portfolio):
"""Processes benchmark data and calculates benchmark metrics."""
benchmark_returns = benchmark_data[portfolio['benchmark_symbol']].pct_change().dropna()
benchmark_returns = benchmark_returns.reindex(returns.index, method='ffill').dropna()
common_index = returns.index.intersection(benchmark_returns.index)
if common_index.empty:
st.error("No overlapping dates between portfolio returns and benchmark returns after alignment.")
st.stop()
returns_aligned = returns.loc[common_index]
cum_returns_aligned = (1 + benchmark_returns.loc[common_index]).cumprod()
benchmark_metrics = calculate_metrics(
benchmark_returns, cum_returns_aligned, portfolio['rf_rate'], None
)
return benchmark_returns, cum_returns_aligned, benchmark_metrics
def update_session_state(updates: dict):
"""
Helper function to update Streamlit session state.
Parameters:
- updates (dict): Dictionary of key-value pairs to update in session state.
"""
for key, value in updates.items():
st.session_state[key] = value
def display_backtest_results(results, comparison_type, portfolio1, portfolio2):
"""Displays the backtest results in the Streamlit app."""
if comparison_type == "Portfolio vs Benchmark":
display_portfolio_vs_benchmark(results)
else:
display_portfolio_vs_portfolio(results, portfolio1, portfolio2)
def display_portfolio_vs_benchmark(results):
"""Displays results for Portfolio vs Benchmark comparison."""
returns = results['returns']
cum_returns = results['cum_returns']
weights = results['weights']
price_data = results['price_data']
metrics = results['metrics']
benchmark_metrics = results['benchmark_metrics']
benchmark_cum_returns = results['benchmark_cum_returns']
# Key Metrics
st.markdown("### 🔑 Key Metrics")
key_metrics = {
'Annualized Return (CAGR)': metrics.get('Annualized Return (CAGR)', "N/A"),
'Sharpe Ratio': metrics.get('Sharpe Ratio', "N/A"),
'Maximum Drawdown': metrics.get('Maximum Drawdown', "N/A")
}
cols = st.columns(len(key_metrics))
for col, (metric, value) in zip(cols, key_metrics.items()):
col.metric(label=metric, value=value)
# Tabs for Organized Sections
tabs = st.tabs(["Overview", "Performance Statistics", "Advanced Metrics", "Drawdowns", "Visualizations"])
with tabs[0]:
st.header("📈 Portfolio Performance Overview")
st.write(f"**Portfolio Name:** {portfolio1}")
st.write(f"**Start Date:** {price_data.index.min().strftime('%Y-%m-%d')}")
st.write(f"**End Date:** {price_data.index.max().strftime('%Y-%m-%d')}")
st.write(f"**Benchmark:** {results.get('benchmark_symbol', 'N/A')}")
with tabs[1]:
st.header("📊 Performance Statistics")
st.subheader("🛠️ Basic Metrics")
performance_df_stats = create_performance_stats_df(metrics, benchmark_metrics)
st.table(performance_df_stats)
with tabs[2]:
st.header("📋 Detail Comparisons")
st.subheader("🧮 Advanced Metrics")
performance_df_advanced = create_advanced_metrics_df(metrics, benchmark_metrics)
st.table(performance_df_advanced)
# Add Explanation for "N/A" Metrics
st.write("""
**ℹ️ Note:** Metrics marked as "N/A" indicate that they are specific to the portfolio and are not applicable to the benchmark.
This is because these metrics require individual portfolio analysis and do not apply to benchmark-only data.
""")
st.subheader("🔍 Risk Factor Attribution Analysis")
perform_risk_factor_attribution(returns)
with tabs[3]:
st.header("📉 Detailed Drawdowns")
display_drawdowns(cum_returns, benchmark_cum_returns)
with tabs[4]:
st.header("📊 Visualizations")
display_visualizations(cum_returns, benchmark_cum_returns, returns, price_data, weights, metrics, benchmark_metrics)
update_session_state({
'backtest_results': results,
'show_proceed': True
})
st.success("Backtest completed!")
def display_portfolio_vs_portfolio(results, portfolio1, portfolio2):
"""Displays results for Portfolio vs Portfolio comparison."""
returns_a = results['returns_a']
cum_returns_a = results['cum_returns_a']
weights_a = results['weights_a']
price_data_a = results['price_data_a']
metrics_a = results['metrics_a']
returns_b = results['returns_b']
cum_returns_b = results['cum_returns_b']
weights_b = results['weights_b']
price_data_b = results['price_data_b']
metrics_b = results['metrics_b']
# Key Metrics with Additional Metrics
st.markdown("### 🔑 Key Metrics")
key_metrics = {
'Metric': [
'Annualized Return (CAGR)',
'Sharpe Ratio',
'Maximum Drawdown',
'Benchmark Correlation',
'Beta'
],
portfolio1: [
metrics_a.get('Annualized Return (CAGR)', "N/A"),
metrics_a.get('Sharpe Ratio', "N/A"),
metrics_a.get('Maximum Drawdown', "N/A"),
metrics_a.get('Benchmark Correlation', "N/A"),
metrics_a.get('Beta', "N/A")
],
portfolio2: [
metrics_b.get('Annualized Return (CAGR)', "N/A"),
metrics_b.get('Sharpe Ratio', "N/A"),
metrics_b.get('Maximum Drawdown', "N/A"),
metrics_b.get('Benchmark Correlation', "N/A"),
metrics_b.get('Beta', "N/A")
]
}
metrics_df = pd.DataFrame(key_metrics)
metrics_df.set_index('Metric', inplace=True)
st.table(metrics_df)
# Tabs for Organized Sections with Enhanced Content
tabs = st.tabs(["Overview", "Performance Statistics", "Advanced Metrics", "Drawdowns", "Visualizations"])
with tabs[0]:
st.header("📈 Portfolio Performance Overview")
st.write(f"**Portfolio A Name:** {portfolio1}")
st.write(f"**Portfolio B Name:** {portfolio2}")
st.write(f"**Start Date:** {min(returns_a.index.min(), returns_b.index.min()).strftime('%Y-%m-%d')}")
st.write(f"**End Date:** {max(returns_a.index.max(), returns_b.index.max()).strftime('%Y-%m-%d')}")
st.write(f"**Benchmark Correlation:** {metrics_a.get('Benchmark Correlation', 'N/A')}")
st.write(f"**Beta of {portfolio1} relative to {portfolio2}:** {metrics_a.get('Beta', 'N/A')}")
with tabs[1]:
st.header("📊 Performance Statistics")
st.subheader("🛠️ Basic Metrics")
performance_df_stats = create_performance_stats_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2)
st.table(performance_df_stats)
with tabs[2]:
st.header("📉 Advanced Metrics")
performance_df_advanced = create_advanced_metrics_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2)
st.table(performance_df_advanced)
# Add Explanation for "N/A" Metrics
st.write("""
**ℹ️ Note:** Metrics marked as "N/A" indicate that certain data was unavailable or not applicable
during the comparison between the two portfolios. Ensure both portfolios have sufficient data
to calculate all metrics.
""")
with tabs[3]:
st.header("📉 Drawdowns")
st.markdown("### 📉 Detailed Drawdowns for Both Portfolios")
st.markdown(f"#### 📉 Drawdowns for {portfolio1}")
drawdowns_a = get_drawdown_details(cum_returns_a)
if drawdowns_a:
st.table(pd.DataFrame(drawdowns_a))
else:
st.write("No drawdowns detected for this portfolio.")
st.markdown(f"#### 📉 Drawdowns for {portfolio2}")
drawdowns_b = get_drawdown_details(cum_returns_b)
if drawdowns_b:
st.table(pd.DataFrame(drawdowns_b))
else:
st.write("No drawdowns detected for this portfolio.")
with tabs[4]:
st.header("📊 Visualizations")
display_visualizations_portfolio_vs_portfolio(
cum_returns_a, cum_returns_b, returns_a, returns_b,
price_data_a, price_data_b, weights_a, weights_b,
portfolio1, portfolio2, metrics_a, metrics_b
)
# Store results
update_session_state({
'backtest_results': results,
'show_proceed': True
})
st.success("Backtest completed!")
def create_performance_stats_df(metrics, benchmark_metrics):
"""Creates a DataFrame for performance statistics."""
return pd.DataFrame({
'Metric': [
'Start Balance', 'End Balance', 'Annualized Return (CAGR)',
'Standard Deviation (Annualized)', 'Best Year', 'Worst Year',
'Maximum Drawdown', 'Sharpe Ratio', 'Sortino Ratio', 'Benchmark Correlation'
],
'Portfolio': [
metrics.get('Start Balance', "N/A"),
metrics.get('End Balance', "N/A"),
metrics.get('Annualized Return (CAGR)', "N/A"),
metrics.get('Standard Deviation (Annualized)', "N/A"),
metrics.get('Best Year', "N/A"),
metrics.get('Worst Year', "N/A"),
metrics.get('Maximum Drawdown', "N/A"),
metrics.get('Sharpe Ratio', "N/A"),
metrics.get('Sortino Ratio', "N/A"),
metrics.get('Benchmark Correlation', "N/A")
],
'Benchmark': [
benchmark_metrics.get('Start Balance', "N/A"),
benchmark_metrics.get('End Balance', "N/A"),
benchmark_metrics.get('Annualized Return (CAGR)', "N/A"),
benchmark_metrics.get('Standard Deviation (Annualized)', "N/A"),
benchmark_metrics.get('Best Year', "N/A"),
benchmark_metrics.get('Worst Year', "N/A"),
benchmark_metrics.get('Maximum Drawdown', "N/A"),
benchmark_metrics.get('Sharpe Ratio', "N/A"),
benchmark_metrics.get('Sortino Ratio', "N/A"),
benchmark_metrics.get('Benchmark Correlation', "N/A")
]
}).set_index('Metric')
def create_advanced_metrics_df(metrics, benchmark_metrics):
"""Creates a DataFrame for advanced metrics."""
return pd.DataFrame({
'Metric': list(metrics.keys()),
'Portfolio': list(metrics.values()),
'Benchmark': list(benchmark_metrics.values())
}).set_index('Metric')
def create_performance_stats_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2):
"""Creates a DataFrame for Portfolio vs Portfolio performance statistics."""
return pd.DataFrame({
'Metric': [
'Start Balance', 'End Balance', 'Annualized Return (CAGR)',
'Standard Deviation (Annualized)', 'Best Year', 'Worst Year',
'Maximum Drawdown', 'Sharpe Ratio', 'Sortino Ratio', 'Benchmark Correlation'
],
portfolio1: [
metrics_a.get('Start Balance', "N/A"),
metrics_a.get('End Balance', "N/A"),
metrics_a.get('Annualized Return (CAGR)', "N/A"),
metrics_a.get('Standard Deviation (Annualized)', "N/A"),
metrics_a.get('Best Year', "N/A"),
metrics_a.get('Worst Year', "N/A"),
metrics_a.get('Maximum Drawdown', "N/A"),
metrics_a.get('Sharpe Ratio', "N/A"),
metrics_a.get('Sortino Ratio', "N/A"),
metrics_a.get('Benchmark Correlation', "N/A")
],
portfolio2: [
metrics_b.get('Start Balance', "N/A"),
metrics_b.get('End Balance', "N/A"),
metrics_b.get('Annualized Return (CAGR)', "N/A"),
metrics_b.get('Standard Deviation (Annualized)', "N/A"),
metrics_b.get('Best Year', "N/A"),
metrics_b.get('Worst Year', "N/A"),
metrics_b.get('Maximum Drawdown', "N/A"),
metrics_b.get('Sharpe Ratio', "N/A"),
metrics_b.get('Sortino Ratio', "N/A"),
metrics_b.get('Benchmark Correlation', "N/A")
]
}).set_index('Metric')
def create_advanced_metrics_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2):
"""Creates a DataFrame for Portfolio vs Portfolio advanced metrics."""
return pd.DataFrame({
'Metric': list(metrics_a.keys()),
portfolio1: list(metrics_a.values()),
portfolio2: list(metrics_b.values())
}).set_index('Metric')
def display_drawdowns(cum_returns, benchmark_cum_returns):
"""Displays drawdown details for portfolio and benchmark."""
st.markdown("### 📈 Drawdowns for Portfolio")
portfolio_drawdowns = get_drawdown_details(cum_returns)
if portfolio_drawdowns:
st.table(pd.DataFrame(portfolio_drawdowns))
else:
st.write("No drawdowns detected for the portfolio.")
if not benchmark_cum_returns.empty:
st.markdown("### 📈 Drawdowns for Benchmark")
benchmark_drawdowns = get_drawdown_details(benchmark_cum_returns)
if benchmark_drawdowns:
st.table(pd.DataFrame(benchmark_drawdowns))
else:
st.write("No drawdowns detected for the benchmark.")
else:
st.write("Benchmark data not available for drawdown analysis.")
def perform_risk_factor_attribution(returns):
"""Performs and displays risk factor attribution analysis."""
start_date = returns.index.min().strftime('%Y-%m-%d')
end_date = returns.index.max().strftime('%Y-%m-%d')
factors = fetch_fama_french_factors(start_date, end_date)
if factors.empty:
st.write("Failed to retrieve factor data.")
return
factors = factors.asfreq(returns.index.freq, method='ffill')
attribution, error = calculate_risk_factor_attribution(returns, factors)
if error:
st.error(error)
st.write("Risk factor attribution analysis is unavailable.")
elif attribution.empty:
st.write("Risk factor attribution analysis is unavailable.")
else:
st.write("Risk Factor Attribution:")
st.table(attribution)
fig_attribution = px.bar(
attribution,
x='Factor',
y='Contribution (%)',
title='Risk Factor Attribution',
labels={'Contribution (%)': 'Contribution (%)'},
template=THEME
)
st.plotly_chart(fig_attribution, use_container_width=True)
def display_visualizations(cum_returns, benchmark_cum_returns, returns, price_data, weights, metrics, benchmark_metrics):
"""Displays an enhanced set of visualizations for Portfolio vs Benchmark with improved interactivity and aesthetics."""
if not cum_returns.empty and not benchmark_cum_returns.empty:
plot_growth_comparison(cum_returns, benchmark_cum_returns, portfolio_name="Portfolio", benchmark_name="Benchmark")
else:
st.warning("Insufficient data to display Growth Comparison. Ensure both portfolio and benchmark have data.")
plot_drawdown_comparison(
(cum_returns / cum_returns.expanding().max() - 1) * 100,
(benchmark_cum_returns / benchmark_cum_returns.expanding().max() - 1) * 100,
portfolio_name="Portfolio",
benchmark_name="Benchmark"
)
st.subheader("📈 Compound Annual Growth Rate (CAGR) Over Time")
if st.session_state.selected_time_frames:
plot_cagr_over_time(cum_returns, time_frames=st.session_state.selected_time_frames)
else:
st.warning("Please select at least one time frame for CAGR.")
st.subheader("📦 Returns Distribution Box Plot")
plot_box(returns)
st.subheader("🔥 Correlation Heatmap of Assets")
plot_correlation_heatmap(price_data.pct_change().dropna())
st.subheader("🔍 Risk-Return Attribution Analysis")
plot_risk_return_attribution(returns, weights)
# **Corrected Rolling Beta Calculation**
st.subheader("📉 Rolling Beta Over Time")
if not benchmark_cum_returns.empty:
# Align benchmark returns with portfolio returns
benchmark_returns = benchmark_cum_returns.pct_change().dropna().reindex(returns.index, method='ffill').dropna()
# Calculate rolling covariance and variance
rolling_cov = returns.rolling(window=252).cov(benchmark_returns)
rolling_var = benchmark_returns.rolling(window=252).var()
# Compute rolling beta
rolling_beta = rolling_cov / rolling_var
# Align rolling_beta with returns
rolling_beta = rolling_beta.dropna()
fig_rolling_beta = px.line(
rolling_beta,
title='Rolling Beta (1-Year Window)',
labels={'index': 'Date', 'value': 'Beta'},
template=THEME
)
st.plotly_chart(fig_rolling_beta, use_container_width=True)
else:
st.warning("Benchmark returns not available to plot Rolling Beta.")
st.subheader("🔥 Cumulative Returns Heatmap")
plot_cumulative_returns_heatmap(cum_returns)
st.subheader("🥧 Portfolio Allocation Pie Chart")
plot_allocation_pie(weights, price_data.columns, title="🥧 Portfolio Allocation", hover_info="percent+name")
st.subheader("📉 Rolling Metrics")
if st.session_state.selected_rolling_periods:
plot_rolling_metrics(returns, windows=st.session_state.selected_rolling_periods)
else:
st.warning("Please select at least one rolling period to display metrics.")
# **Added: Rolling Sharpe Ratio Graph**
st.subheader("📈 Rolling Sharpe Ratio Over Time")
if not benchmark_cum_returns.empty:
# Calculate rolling Sharpe Ratio
rolling_sharpe = (returns.rolling(window=252).mean() - st.session_state.default_config['rf_rate']) / returns.rolling(window=252).std()
rolling_sharpe = rolling_sharpe.dropna()
fig_rolling_sharpe = px.line(
rolling_sharpe,
title='Rolling Sharpe Ratio (1-Year Window)',
labels={'index': 'Date', 'value': 'Sharpe Ratio'},
template=THEME
)
st.plotly_chart(fig_rolling_sharpe, use_container_width=True)
else:
st.warning("Benchmark returns not available to plot Rolling Sharpe Ratio.")
st.markdown("### 💡 Recommendations")
recommendations = generate_recommendations(
allocations=[alloc for alloc in weights if alloc > 0],
available_selected=price_data.columns
)
if recommendations:
for rec in recommendations:
st.markdown(f"• {rec}")
else:
st.success("🎉 Your portfolio allocations are well-balanced!")
st.markdown("### 📝 Final Score")
portfolio_score = calculate_final_score(metrics, benchmark_metrics)
st.metric("📊 Portfolio Score", f"{portfolio_score:.2f} / 100")
# Add interpretation/comment similar to Risk Factor Attribution
st.markdown("""
**📖 Interpretation:**
- The **Final Score** represents a comprehensive evaluation of your portfolio's performance based on selected metrics.
- A higher score indicates a better balance between risk and return, aligning with your investment objectives.
- Use this score to assess overall portfolio health and identify areas for improvement.
""")
def display_visualizations_portfolio_vs_portfolio(
cum_returns_a, cum_returns_b, returns_a, returns_b,
price_data_a, price_data_b, weights_a, weights_b,
portfolio1, portfolio2, metrics_a, metrics_b
):
"""Displays various visualizations for Portfolio vs Portfolio."""
# Growth Comparison
fig_growth = px.line(title='Growth Comparison')
fig_growth.add_scatter(x=cum_returns_a.index, y=cum_returns_a, mode='lines', name=portfolio1)
fig_growth.add_scatter(x=cum_returns_b.index, y=cum_returns_b, mode='lines', name=portfolio2)
st.plotly_chart(fig_growth, use_container_width=True)
# Drawdown Comparison
plot_drawdown_comparison(
(cum_returns_a / cum_returns_a.expanding().max() - 1) * 100,
(cum_returns_b / cum_returns_b.expanding().max() - 1) * 100
)
# Box Plot for Returns Distribution
st.subheader("📦 Returns Distribution Box Plot")
plot_box(returns_a, returns_b, portfolio1, portfolio2)
# Heatmap of Correlations Between Assets
st.subheader("🔥 Correlation Heatmap of Assets")
plot_correlation_heatmap(price_data_a.pct_change().dropna())
plot_correlation_heatmap(price_data_b.pct_change().dropna())
# Portfolio Allocation Pie Chart
st.subheader("🥧 Portfolio Allocation Pie Chart")
plot_allocation_pie(weights_a, price_data_a.columns, title=f"{portfolio1} Allocation", hover_info="percent+name")
plot_allocation_pie(weights_b, price_data_b.columns, title=f"{portfolio2} Allocation", hover_info="percent+name")
st.markdown("### 📝 Final Score")
score_a = calculate_final_score(metrics_a, metrics_b)
score_b = calculate_final_score(metrics_b, metrics_a)
st.write(f"**{portfolio1}:** {score_a:.2f} / 100")
st.write(f"**{portfolio2}:** {score_b:.2f} / 100")
def plot_box(
returns,
title="Returns Distribution Box Plot",
labels={"Return": "Returns (%)", "Asset": "Asset"},
color_sequence=px.colors.qualitative.Bold,
multiple=False,
portfolio_names=None
):
"""
Generic function to plot box plots for portfolio returns.
Parameters:
- returns (pd.DataFrame or pd.Series): Returns data.
- title (str): Title of the plot.
- labels (dict): Axis labels.
- color_sequence (list): Colors for the boxes.
- multiple (bool): If True, handle multiple portfolios.
- portfolio_names (list): Names of the portfolios for labeling.
"""
if isinstance(returns, pd.DataFrame):
if multiple and portfolio_names:
for i, col in enumerate(returns.columns):
fig = px.box(
returns[[col]].reset_index(drop=True),
y=col,
title=f"{title} for {portfolio_names[i]}",
labels=labels,
points='all',
template=THEME,
color_discrete_sequence=[color_sequence[i % len(color_sequence)]]
)
fig.update_traces(boxmean='sd')
fig.update_layout(xaxis_title="Assets", yaxis_title=labels.get('Return', 'Returns (%)'))
st.plotly_chart(fig, use_container_width=True)
else:
melted_returns = returns.reset_index().melt(id_vars=returns.index.name if returns.index.name else 'Date', var_name='Asset', value_name='Return')
fig = px.box(
melted_returns,
x='Asset',
y='Return',
title=title,
labels=labels,
points='all',
template=THEME,
color='Asset',
color_discrete_sequence=color_sequence
)
elif isinstance(returns, pd.Series):
fig = px.box(
returns.rename("Return"),
y='Return',
title=title,
labels=labels,
points='all',
template=THEME,
color_discrete_sequence=['cyan']
)
else:
st.warning("Returns data is neither a DataFrame nor a Series.")
return
fig.update_traces(boxmean='sd')
fig.update_layout(
xaxis_title=labels.get("Asset", "Assets"),
yaxis_title=labels.get("Return", "Returns (%)"),
legend_title="Assets"
)
st.plotly_chart(fig, use_container_width=True)
st.markdown("""
**📖 Interpretation:**
- **Boxes:** Represent the interquartile range (IQR) where the middle 50% of the data lies.
- **Median Line:** Indicates the median return.
- **Whiskers:** Extend to show the range of the data excluding outliers.
- **Outliers:** Individual points outside the whiskers represent atypical returns.
- **Box Mean (Shaded):** Shows the mean and standard deviation of returns.
""")
def plot_cumulative_returns_heatmap(cum_returns):
"""Plots the cumulative returns heatmap."""
try:
cum_returns_normalized = cum_returns / cum_returns.max()
fig_heatmap = px.imshow(
cum_returns_normalized.to_frame().T,
labels=dict(x="Date", y="Portfolio", color="Normalized Cumulative Return"),
title="Cumulative Returns Heatmap",
aspect="auto",
color_continuous_scale='Viridis',
template=THEME
)
st.plotly_chart(fig_heatmap, use_container_width=True)
st.markdown(
"**Interpretation:** The heatmap visualizes the normalized cumulative returns over time, "
"allowing for an intuitive comparison of portfolio performance across different periods."
)
except Exception as e:
st.error(f"Error plotting cumulative returns heatmap: {e}")
def plot_returns_correlation_heatmap(price_data):
"""Plots the correlation heatmap of asset returns."""
correlation_data = price_data.pct_change().dropna()
plot_correlation_heatmap(correlation_data)
def plot_correlation_heatmap(correlation_data):
"""Plots an enhanced correlation heatmap using Plotly with improved aesthetics."""
if correlation_data.empty:
st.warning("No data available to plot correlation heatmap.")
return
corr = correlation_data.corr()
fig_heatmap = px.imshow(
corr,
title="📈 Correlation Heatmap of Assets",
labels=dict(x="Asset", y="Asset", color="Correlation"),
color_continuous_scale='RdBu',
zmin=-1,
zmax=1,
text_auto=True,
aspect="auto",
template=THEME
)
fig_heatmap.update_layout(
title_x=0.5,
xaxis_title="Assets",
yaxis_title="Assets"
)
fig_heatmap.update_traces(
hovertemplate="Asset1: %{x}<br>Asset2: %{y}<br>Correlation: %{z:.2f}<extra></extra>"
)
st.plotly_chart(fig_heatmap, use_container_width=True)
def plot_growth_comparison(cum_returns, benchmark_cum_returns, portfolio_name='Portfolio', benchmark_name='Benchmark'):
"""Plots an enhanced growth comparison between portfolio and benchmark with improved interactivity."""
fig_growth = px.line(
title='📈 Growth Comparison',
labels={'value': 'Cumulative Returns', 'index': 'Date'},
template=THEME
)
fig_growth.add_scatter(
x=cum_returns.index,
y=cum_returns,
mode='lines',
name=portfolio_name,
line=dict(color='cyan', width=2)
)
fig_growth.add_scatter(
x=benchmark_cum_returns.index,
y=benchmark_cum_returns,
mode='lines',
name=benchmark_name,
line=dict(color='orange', width=2, dash='dash')
)
fig_growth.update_layout(
hovermode='x unified',
xaxis=dict(showgrid=True),
yaxis=dict(showgrid=True),
legend=dict(title="Legend", x=0.01, y=0.99),
height=600 # Added height parameter
)
st.plotly_chart(fig_growth, use_container_width=True, height=600, config={'responsive': True})
def plot_drawdown_comparison(drawdown_portfolio: pd.Series, drawdown_benchmark: pd.Series, portfolio_name='Portfolio', benchmark_name='Benchmark') -> None:
"""Plots an enhanced drawdown comparison between portfolio and benchmark with improved visuals."""
fig_drawdown = px.line(
title='📉 Drawdown Comparison',
labels={'value': 'Drawdown (%)', 'index': 'Date'},
template=THEME
)
fig_drawdown.add_scatter(
x=drawdown_portfolio.index,
y=drawdown_portfolio,
mode='lines',
name=portfolio_name,
line=dict(color='red', width=2)
)
if not drawdown_benchmark.empty:
fig_drawdown.add_scatter(
x=drawdown_benchmark.index,
y=drawdown_benchmark,
mode='lines',
name=benchmark_name,
line=dict(color='blue', width=2, dash='dash')
)
fig_drawdown.update_layout(
hovermode=HOVERMODE,
yaxis=dict(showgrid=True),
xaxis=dict(showgrid=True),
legend=dict(title="Legend", x=0.01, y=0.99),
height=FIG_HEIGHT
)
fig_drawdown.update_traces(hovertemplate="Date: %{x}<br>Drawdown: %{y:.2f}%")
st.plotly_chart(fig_drawdown, use_container_width=True, height=600, config={'responsive': True})
def plot_cagr_over_time(cum_returns, time_frames):
"""Plots CAGR over different time frames."""
for frame in time_frames:
plot_cagr(cum_returns, frame)
def plot_allocation_pie(weights, assets, title, hover_info):
"""Plots the portfolio allocation pie chart."""
fig_pie = px.pie(
names=assets,
values=weights,
title=title,
hover_data=[hover_info],
template=THEME
)
st.plotly_chart(fig_pie, use_container_width=True)
def plot_risk_return_attribution(returns, weights):
"""Plots the risk-return attribution analysis."""
fig_rra = px.scatter(
x=returns.mean() * 252,
y=returns.std() * np.sqrt(252),
size=weights*100,
color=returns.mean() / returns.std(),
hover_name=returns.index,
title='Risk-Return Attribution',
labels={'x': 'Annualized Return', 'y': 'Annualized Risk (Std Dev)'},
template='plotly_dark',
size_max=60
)
st.plotly_chart(fig_rra, use_container_width=True)
def plot_rolling_metrics(returns, windows):
"""Plots rolling metrics based on selected window periods."""
for window in windows:
rolling_mean = returns.rolling(window=window).mean()
rolling_std = returns.rolling(window=window).std()
fig = px.line(title=f'Rolling {window}-Day Mean and Std Dev')
fig.add_scatter(x=rolling_mean.index, y=rolling_mean, mode='lines', name='Rolling Mean')
fig.add_scatter(x=rolling_std.index, y=rolling_std, mode='lines', name='Rolling Std Dev')
st.plotly_chart(fig, use_container_width=True)
@handle_exceptions
def calculate_risk_factor_attribution(returns: pd.Series, factors: pd.DataFrame, annualization_factor: int = 252) -> tuple:
"""
Decompose portfolio returns by risk factors using linear regression and provide
comments on the factor contributions.
Parameters:
- returns (pd.Series): Portfolio returns.
- factors (pd.DataFrame): DataFrame where each column represents a risk factor.
- annualization_factor (int): Annualization factor (252 for daily, 12 for monthly returns).
Returns:
- pd.DataFrame: Factor names, their contribution percentages, and explanatory comments.
- None: If an exception occurs, handled by the decorator.
"""
# Ensure returns has a name, or assign one if missing
if returns.name is None:
returns = returns.rename("Portfolio_Returns")
# Convert returns to a DataFrame
returns = returns.to_frame()
# Ensure both returns and factors are in decimal format
if returns['Portfolio_Returns'].max() > 1:
returns /= 100
if factors.max().max() > 1:
factors /= 100
# Adjust returns to excess returns by subtracting RF (if it exists in factors)
if 'RF' in factors.columns:
returns['Portfolio_Returns'] -= factors['RF']
factors = factors.drop(columns=['RF'])
# Align the data using an inner join and drop any remaining NaN values
aligned_data = pd.merge(returns, factors, left_index=True, right_index=True, how='inner').dropna()
# Check that we have data after merging
if aligned_data.empty:
raise ValueError("No overlapping dates between returns and factors after dropping NaNs.")
# Prepare X and y for regression
X = aligned_data[factors.columns]
X = sm.add_constant(X) # Add intercept
y = aligned_data['Portfolio_Returns']
# Run the regression model
model = sm.OLS(y, X).fit()
coefficients = model.params.drop('const', errors='ignore')
# Calculate factor contributions (non-annualized)
factor_means = X.mean().drop('const', errors='ignore')
contributions = coefficients * factor_means
# Calculate total return
total_return = returns['Portfolio_Returns'].mean()
# Check if total_return is zero to avoid division by zero
if np.isclose(total_return, 0):
st.warning("Total portfolio return is zero. Cannot compute contribution percentages.")
attribution_df = pd.DataFrame({
'Factor': contributions.index,
'Contribution (%)': [0.0] * len(contributions)
})
else:
# Calculate percentage contributions
attribution_df = pd.DataFrame({
'Factor': contributions.index,
'Contribution (%)': (contributions / total_return * 100).round(2)
})
# Remove the constant term if it exists
if 'const' in attribution_df['Factor'].values:
attribution_df = attribution_df[attribution_df['Factor'] != 'const']
# Use full names for factors
factor_full_names = {
'Mkt-RF': 'Market Risk Premium',
'SMB': 'Small Minus Big',
'HML': 'High Minus Low'
}
attribution_df['Factor'] = attribution_df['Factor'].replace(factor_full_names)
# Generate detailed comments based on factor contributions
comments = []
for _, row in attribution_df.iterrows():
factor_name = row['Factor']
contribution = row['Contribution (%)']
# Market Risk Premium (Mkt-RF) Analysis
if factor_name == 'Market Risk Premium':
if contribution > 75:
comments.append(f"{factor_name} = {contribution}%: Extremely high sensitivity to market movements.")
elif contribution > 50:
comments.append(f"{factor_name} = {contribution}%: High sensitivity to overall market movements.")
elif contribution > 30:
comments.append(f"{factor_name} = {contribution}%: Notable sensitivity to market risk.")
elif contribution > 10:
comments.append(f"{factor_name} = {contribution}%: Moderate exposure to market risk.")
else:
comments.append(f"{factor_name} = {contribution}%: Low exposure to market risk.")
# Small Minus Big (SMB) Analysis
elif factor_name == 'Small Minus Big':
if contribution > 5:
comments.append(f"{factor_name} = {contribution}%: Strong tilt towards small-cap stocks.")
elif contribution > 0:
comments.append(f"{factor_name} = {contribution}%: Mild preference for small-cap stocks.")
elif contribution > -5:
comments.append(f"{factor_name} = {contribution}%: Slight tilt towards larger-cap stocks.")
else:
comments.append(f"{factor_name} = {contribution}%: Clear preference for large-cap stocks.")
# High Minus Low (HML) Analysis
elif factor_name == 'High Minus Low':
if contribution > 5:
comments.append(f"{factor_name} = {contribution}%: Strong tilt toward value stocks.")
elif contribution > 0:
comments.append(f"{factor_name} = {contribution}%: Slight positive tilt toward value stocks.")
elif contribution > -5:
comments.append(f"{factor_name} = {contribution}%: Minor negative tilt toward growth stocks.")
else:
comments.append(f"{factor_name} = {contribution}%: Clear preference for growth stocks.")
# Add comments to the DataFrame
attribution_df['Comment'] = comments
return attribution_df, None
@handle_exceptions
def fetch_fama_french_factors(start_date: str, end_date: str) -> pd.DataFrame:
"""
Fetch Fama-French daily factor data between specified dates.
Parameters:
- start_date (str): Start date in 'YYYY-MM-DD' format.
- end_date (str): End date in 'YYYY-MM-DD' format.
Returns:
- pd.DataFrame: DataFrame containing Fama-French factors.
"""
# [Function Implementation Remains Unchanged]
try:
# Fetch daily Fama-French factors
ff_data = pdr.get_data_famafrench('F-F_Research_Data_Factors_daily', start=start_date, end=end_date)
if not ff_data:
st.error("Fama-French data not found.")
return pd.DataFrame()
factors = ff_data[0]
factors = factors.rename(columns=lambda x: x.strip()) # Remove any leading/trailing spaces
factors /= 100 # Convert percentage returns to decimal format
# Fill missing data by forward and backward filling
factors = factors.fillna(method='ffill').fillna(method='bfill')
# Check for necessary columns
required_columns = {'Mkt-RF', 'SMB', 'HML', 'RF'}
if not required_columns.issubset(factors.columns):
missing = required_columns - set(factors.columns)
st.error(f"Missing factor columns: {', '.join(missing)}")
return pd.DataFrame()
return factors
except Exception as e:
st.error(f"Error fetching factor data: {e}")
return pd.DataFrame()
@handle_exceptions
def calculate_final_score(primary_metrics: dict, comparison_metrics: dict) -> float:
metrics_to_compare = [
'Annualized Return (CAGR)',
'Sharpe Ratio',
'Sortino Ratio',
'Treynor Ratio',
'Calmar Ratio',
'Alpha (annualized)',
'Information Ratio',
'Modigliani–Modigliani Measure',
'Upside Capture Ratio',
'Gain/Loss Ratio'
]
metric_weights = {
'Annualized Return (CAGR)': 15,
'Sharpe Ratio': 15,
'Sortino Ratio': 10,
'Treynor Ratio': 10,
'Calmar Ratio': 10,
'Alpha (annualized)': 10,
'Information Ratio': 10,
'Modigliani–Modigliani Measure': 10,
'Upside Capture Ratio': 5,
'Gain/Loss Ratio': 5
}
score = 0
total = 0
for metric in metrics_to_compare:
primary = primary_metrics.get(metric, "N/A")
comparison = comparison_metrics.get(metric, "N/A")
if primary != "N/A" and comparison != "N/A":
try:
primary_val = float(primary.strip('%')) if '%' in primary else float(primary)
comparison_val = float(comparison.strip('%')) if '%' in comparison else float(comparison)
weight = metric_weights.get(metric, 1)
if comparison_val == 0:
st.warning(f"Comparison metric '{metric}' has a value of zero. Skipping this metric to avoid division by zero.")
continue
if primary_val > comparison_val:
score += weight * (primary_val / comparison_val)
else:
score += weight * (primary_val / comparison_val) * 0.5 # Partial credit
total += weight
except ValueError:
st.warning(f"Invalid numeric conversion for metric '{metric}'. Skipping.")
continue
return (score / total) * 100 if total > 0 else 0
@handle_exceptions
def plot_cagr_over_time(cum_returns: pd.Series, time_frames: list = ['Weekly', 'Monthly', 'Quarterly', 'Annually']) -> None:
"""
Plot CAGR over multiple time frames.
Parameters:
- cum_returns (pd.Series): Cumulative returns of the portfolio.
- time_frames (list): List of time frames to calculate CAGR.
"""
frequency_map = {
'Weekly': 'W',
'Monthly': 'M',
'Quarterly': 'Q',
'Annually': 'Y'
}
fig = go.Figure()
for tf in time_frames:
freq = frequency_map.get(tf)
if not freq:
continue
rolled = cum_returns.resample(freq).last()
if rolled.empty:
st.warning(f"No data available for {tf} CAGR calculation.")
continue
years = (rolled.index[-1] - rolled.index[0]).days / 365.25
if years <= 0:
st.warning(f"Not enough data to calculate {tf} CAGR.")
continue
cagr = (rolled / rolled.iloc[0]) ** (1/years) - 1
fig.add_trace(go.Scatter(x=rolled.index, y=cagr, mode='lines', name=f'{tf} CAGR'))
fig.update_layout(
title='CAGR Over Multiple Time Frames',
xaxis_title='Date',
yaxis_title='CAGR',
hovermode=HOVERMODE
)
st.plotly_chart(fig, use_container_width=True)
@handle_exceptions
def backtest(weights: list, prices: pd.DataFrame, rebalance_freq: str = 'M', broker_fee: float = 0.0, debug: bool = False) -> tuple:
"""
Backtest the portfolio based on weights and price data.
Parameters:
- weights (list): Allocation weights for each asset.
- prices (pd.DataFrame): Adjusted closing prices of assets.
- rebalance_freq (str): Rebalancing frequency (e.g., 'D', 'W', 'M').
- broker_fee (float): Broker fee as a percentage.
- debug (bool): If True, prints debug information.
Returns:
- tuple: (portfolio_returns: pd.Series, cum_returns: pd.Series)
"""
try:
# Calculate returns and drop any NaN values
returns = prices.pct_change().dropna()
if returns.empty:
st.error("Returns data is empty after calculating percentage changes.")
return pd.Series(dtype=float), pd.Series(dtype=float)
# Calculate portfolio returns
portfolio_returns = returns.dot(weights)
if not isinstance(portfolio_returns, pd.Series):
portfolio_returns = portfolio_returns.squeeze()
# Identify rebalancing dates
if rebalance_freq.upper() == 'D':
rebalance_dates = returns.index
else:
rebalance_dates = returns.resample(rebalance_freq.upper()).last().dropna().index
# Debug: Show rebalancing dates
if debug:
st.write(f"Rebalancing Dates: {rebalance_dates.tolist()}")
# Ensure rebalance_dates are in the portfolio_returns index
valid_rebalance_dates = rebalance_dates.intersection(portfolio_returns.index)
if debug:
st.write(f"Valid Rebalancing Dates: {valid_rebalance_dates.tolist()}")
# Apply broker fees on rebalancing dates
if not valid_rebalance_dates.empty:
portfolio_returns.loc[valid_rebalance_dates] -= broker_fee / 100 # Convert to decimal
else:
st.warning("No valid rebalancing dates found within the returns data.")
# Calculate cumulative returns
cum_returns = (1 + portfolio_returns).cumprod()
if cum_returns.empty:
st.error("Cumulative returns are empty. Check the data and allocations.")
return pd.Series(dtype=float), pd.Series(dtype=float)
# Debug: Show cumulative returns
if debug:
st.write("Cumulative Returns:")
st.write(cum_returns)
return portfolio_returns, cum_returns
except Exception as e:
st.error(f"Error during backtesting: {e}")
return pd.Series(dtype=float), pd.Series(dtype=float)
def calculate_sharpe_ratio(returns: pd.Series, rf: float = 0.02) -> float:
"""
Calculate the Sharpe Ratio for a given set of returns.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- rf (float): Risk-free rate (default is 2%).
Returns:
- float: Sharpe Ratio.
"""
excess_return = returns.mean() * 252 - rf
std_dev = returns.std() * np.sqrt(252)
return excess_return / std_dev if std_dev != 0 else np.nan
def calculate_sortino_ratio(returns: pd.Series, rf: float = 0.02) -> float:
"""
Calculate the Sortino Ratio for a given set of returns.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- rf (float): Risk-free rate (default is 2%).
Returns:
- float: Sortino Ratio.
"""
excess_return = returns.mean() * 252 - rf
downside_std = returns[returns < 0].std() * np.sqrt(252)
return excess_return / downside_std if downside_std != 0 else np.nan
def calculate_treynor_ratio(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:
"""
Calculate the Treynor Ratio for a given set of returns.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
- rf (float): Risk-free rate (default is 2%).
Returns:
- float: Treynor Ratio.
"""
beta = calculate_beta(returns, benchmark_returns)
excess_return = returns.mean() * 252 - rf
return excess_return / beta if beta != 0 else np.nan
def calculate_calmar_ratio(returns: pd.Series, cum_returns: pd.Series) -> float:
"""
Calculate the Calmar Ratio for a given set of returns.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- cum_returns (pd.Series): Cumulative returns of the portfolio.
Returns:
- float: Calmar Ratio.
"""
annual_return = returns.mean() * 252
max_dd = drawdown(cum_returns)
return annual_return / abs(max_dd) if max_dd != 0 else np.nan
def calculate_beta(returns: pd.Series, benchmark_returns: pd.Series) -> float:
"""
Calculate the Beta of the portfolio relative to the benchmark.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
Returns:
- float: Beta value.
"""
if returns.empty or benchmark_returns.empty:
return np.nan
covariance_matrix = np.cov(returns, benchmark_returns)
covariance = covariance_matrix[0, 1]
benchmark_variance = covariance_matrix[1, 1]
return covariance / benchmark_variance if benchmark_variance != 0 else np.nan
def calculate_alpha(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:
"""
Calculate the Alpha of the portfolio relative to the benchmark.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
- rf (float): Risk-free rate (default is 2%).
Returns:
- float: Alpha value.
"""
beta = calculate_beta(returns, benchmark_returns)
portfolio_return = returns.mean() * 252
benchmark_return = benchmark_returns.mean() * 252
return portfolio_return - (rf + beta * (benchmark_return - rf)) if not np.isnan(beta) else np.nan
def calculate_r_squared(returns: pd.Series, benchmark_returns: pd.Series) -> float:
"""
Calculate the R-squared of the portfolio relative to the benchmark.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
Returns:
- float: R-squared value.
"""
if returns.empty or benchmark_returns.empty:
return np.nan
covariance = np.cov(returns, benchmark_returns)
var_port = covariance[0,0]
var_bench = covariance[1,1]
cov = covariance[0,1]
return (cov ** 2) / (var_port * var_bench) if var_port !=0 and var_bench !=0 else np.nan
def calculate_information_ratio(returns: pd.Series, benchmark_returns: pd.Series) -> float:
"""
Calculate the Information Ratio of the portfolio relative to the benchmark.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
Returns:
- float: Information Ratio.
"""
if returns.empty or benchmark_returns.empty:
return np.nan
active_return = (returns.mean() - benchmark_returns.mean()) * 252
tracking_error = calculate_tracking_error(returns, benchmark_returns)
return active_return / tracking_error if tracking_error != 0 else np.nan
def generate_recommendations(allocations: list, available_selected: list) -> list:
"""
Generate basic portfolio recommendations based on current allocations.
Parameters:
- allocations (list of float): Current allocation percentages.
- available_selected (list of str): List of selected asset tickers.
Returns:
- list of str: Recommendations.
"""
recommendations = []
max_alloc = max(allocations)
min_alloc = min(allocations)
total_alloc = sum(allocations)
if max_alloc > 40.0:
idx = allocations.index(max_alloc)
ticker = available_selected[idx]
recommendations.append(f"🔹 Consider reducing the allocation to **{ticker}** since it constitutes **{max_alloc:.2f}%** of your portfolio.")
if min_alloc < 5.0:
idx = allocations.index(min_alloc)
ticker = available_selected[idx]
recommendations.append(f"🔸 Consider increasing the allocation to **{ticker}** to at least **5.00%** for better diversification.")
if total_alloc < 100.0:
recommendations.append("🔹 Consider allocating the remaining funds to additional assets to reach a total of **100%**.")
return recommendations
def get_common_benchmarks(selected_tickers: list) -> dict:
"""
Define benchmark suggestions based on asset sectors or indices.
Parameters:
- selected_tickers (list of str): List of selected asset tickers.
Returns:
- dict: Dictionary of benchmark names and their corresponding tickers.
"""
# Example benchmark sets
sp500 = {"AAPL", "MSFT", "GOOGL", "AMZN", "META", "TSLA"} # Example S&P 500 tech companies
nasdaq_tech = {"AAPL", "MSFT", "GOOGL", "AMZN", "META", "TSLA", "NVDA", "AMD"}
selected_set = set(selected_tickers)
if selected_set.issubset(sp500):
return {
"S&P 500": "^GSPC",
"Dow Jones Industrial Average": "^DJI",
"Russell 2000": "^RUT",
"Custom": "CUSTOM"
}
elif selected_set.issubset(nasdaq_tech):
return {
"NASDAQ Composite": "^IXIC",
"QQQ (Invesco QQQ ETF)": "QQQ",
"Custom": "CUSTOM"
}
else:
return {
"S&P 500": "^GSPC",
"NASDAQ Composite": "^IXIC",
"Dow Jones Industrial Average": "^DJI",
"Russell 2000": "^RUT",
"Custom": "CUSTOM"
}
def calculate_tracking_error(returns: pd.Series, benchmark_returns: pd.Series) -> float:
"""
Calculate the Tracking Error of the portfolio relative to the benchmark.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
Returns:
- float: Tracking Error.
"""
if returns.empty or benchmark_returns.empty:
return np.nan
return np.std((returns - benchmark_returns)) * np.sqrt(252)
@handle_exceptions
def calculate_performance_attribution(returns: pd.Series, weights: list) -> pd.DataFrame:
"""
Calculate performance attribution based on asset contributions.
Parameters:
- returns (pd.Series or pd.DataFrame): Returns of assets.
- weights (list): Allocation weights for each asset.
Returns:
- pd.DataFrame: DataFrame containing asset names and their contribution percentages.
"""
if isinstance(returns, pd.Series):
returns = returns.to_frame('Asset') # Convert Series to DataFrame with a default column name
if len(weights) != len(returns.columns):
st.error(f"Number of weights ({len(weights)}) does not match number of assets ({len(returns.columns)}).")
return pd.DataFrame()
annual_returns = returns.mean() * 252
contributions = annual_returns * weights
attribution_df = pd.DataFrame({
'Asset': returns.columns,
'Contribution (%)': (contributions / contributions.sum() * 100).round(2)
}).sort_values(by='Contribution (%)', ascending=False)
return attribution_df
def calculate_active_return(returns: pd.Series, benchmark_returns: pd.Series) -> float:
"""
Calculate the Active Return of the portfolio relative to the benchmark.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
Returns:
- float: Active Return percentage.
"""
if returns.empty or benchmark_returns.empty:
return np.nan
return (returns.mean() - benchmark_returns.mean()) * 252 * 100
def calculate_gain_loss_ratio(returns: pd.Series) -> float:
"""
Calculate the Gain/Loss Ratio of the portfolio.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
Returns:
- float: Gain/Loss Ratio.
"""
gains = returns[returns > 0].sum()
losses = -returns[returns < 0].sum()
return gains / losses if losses != 0 else np.nan
def drawdown(cum_returns: pd.Series) -> float:
"""
Calculate the Maximum Drawdown of the portfolio.
Parameters:
- cum_returns (pd.Series): Cumulative returns of the portfolio.
Returns:
- float: Maximum Drawdown value.
"""
if cum_returns.empty:
return np.nan
peak = cum_returns.expanding(min_periods=1).max()
dd = (cum_returns / peak) - 1
return dd.min()
def calculate_capture_ratio(returns: pd.Series, benchmark_returns: pd.Series, upside: bool = True) -> float:
"""
Calculate the Upside or Downside Capture Ratio.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
- upside (bool): If True, calculates Upside Capture Ratio; otherwise, Downside.
Returns:
- float: Capture Ratio percentage.
"""
if returns.empty or benchmark_returns.empty:
return np.nan
mask = benchmark_returns > 0 if upside else benchmark_returns < 0
if mask.sum() == 0:
return np.nan
portfolio = returns[mask]
benchmark = benchmark_returns[mask]
return (portfolio.sum() / benchmark.sum()) * 100 if benchmark.sum() != 0 else np.nan
def calculate_safe_withdrawal_rate(returns: pd.Series) -> float:
"""
Calculate the Safe Withdrawal Rate based on portfolio returns.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
Returns:
- float: Safe Withdrawal Rate percentage.
"""
if returns.empty:
return np.nan
return (returns.mean() / returns.std()) * 100
def calculate_perpetual_withdrawal_rate(returns: pd.Series) -> float:
"""
Calculate the Perpetual Withdrawal Rate based on portfolio returns.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
Returns:
- float: Perpetual Withdrawal Rate percentage.
"""
if returns.empty:
return np.nan
return (returns.mean() / returns.std()) * 100
def calculate_positive_periods(returns: pd.Series) -> str:
"""
Calculate the number and percentage of positive return periods.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
Returns:
- str: String describing positive periods.
"""
if returns.empty:
return "N/A"
positive = returns > 0
return f"{positive.sum()} out of {len(returns)} ({(positive.sum()/len(returns))*100:.2f}%)"
def calculate_modigliani_miller(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:
"""
Calculate the Modigliani–Modigliani Measure (M2).
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- benchmark_returns (pd.Series): Daily returns of the benchmark.
- rf (float): Risk-free rate (default is 2%).
Returns:
- float: M2 value.
"""
sharpe = calculate_sharpe_ratio(returns, rf)
alpha = calculate_alpha(returns, benchmark_returns, rf)
return alpha / sharpe if sharpe != 0 and not np.isnan(alpha) else np.nan
def get_drawdown_details(cum_returns: pd.Series) -> list:
"""
Generate detailed drawdown information.
Parameters:
- cum_returns (pd.Series): Cumulative returns of the portfolio.
Returns:
- list: List of dictionaries containing drawdown details.
"""
drawdowns = []
if cum_returns.empty:
return drawdowns
peak = cum_returns.iloc[0]
peak_date = cum_returns.index[0]
trough = cum_returns.iloc[0]
trough_date = cum_returns.index[0]
for date, value in cum_returns.items():
if value > peak:
if trough < peak:
recovery = cum_returns[cum_returns >= peak].loc[trough_date:]
if not recovery.empty:
recovery_date = recovery.index[0]
if recovery_date > trough_date:
recovery_time = (recovery_date - trough_date).days
underwater_period = (recovery_date - peak_date).days
drawdowns.append({
'Start': peak_date.strftime('%b %Y'),
'End': trough_date.strftime('%b %Y'),
'Length': f"{(trough_date - peak_date).days // 30} months",
'Recovery By': recovery_date.strftime('%b %Y'),
'Recovery Time': f"{recovery_time // 30} months",
'Underwater Period': f"{underwater_period // 30} months",
'Drawdown': f"{((trough / peak) - 1) * 100:.2f}%"
})
else:
drawdowns.append({
'Start': peak_date.strftime('%b %Y'),
'End': trough_date.strftime('%b %Y'),
'Length': f"{(trough_date - peak_date).days // 30} months",
'Recovery By': "Not Recovered",
'Recovery Time': "N/A",
'Underwater Period': "N/A",
'Drawdown': f"{((trough / peak) - 1) * 100:.2f}%"
})
peak = value
peak_date = date
trough = value
trough_date = date
elif value < trough:
trough = value
trough_date = date
if trough < peak:
drawdowns.append({
'Start': peak_date.strftime('%b %Y'),
'End': trough_date.strftime('%b %Y'),
'Length': f"{(trough_date - peak_date).days // 30} months",
'Recovery By': "Not Recovered",
'Recovery Time': "N/A",
'Underwater Period': "N/A",
'Drawdown': f"{((trough / peak) - 1) * 100:.2f}%"
})
try:
drawdowns_sorted = sorted(drawdowns, key=lambda x: float(x['Drawdown'].strip('%')), reverse=False)
except:
drawdowns_sorted = []
drawdowns_sorted = drawdowns_sorted[:10]
for idx, dd in enumerate(drawdowns_sorted, start=1):
dd['Rank'] = idx
drawdowns_final = []
for dd in drawdowns_sorted:
drawdowns_final.append({
'Rank': dd['Rank'],
'Start': dd['Start'],
'End': dd['End'],
'Length': dd['Length'],
'Recovery By': dd['Recovery By'],
'Recovery Time': dd['Recovery Time'],
'Underwater Period': dd['Underwater Period'],
'Drawdown': dd['Drawdown']
})
return drawdowns_final
@handle_exceptions
def optimize_portfolio(
returns: pd.DataFrame,
benchmark_returns: pd.Series = None,
objectives: list = ['sharpe'],
rf: float = 0.02,
max_weight: float = 1.0,
min_weight: float = 0.0,
target_return: float = None
) -> np.ndarray:
"""
Optimize a portfolio based on specified objectives.
Parameters:
- returns (pd.DataFrame): Historical returns of assets.
- benchmark_returns (pd.Series, optional): Returns of the benchmark index.
- objectives (list): Objectives to optimize.
Options include 'sharpe', 'min_variance', 'max_return', 'min_drawdown', 'maximize_alpha', 'minimize_beta'.
- rf (float): Risk-free rate for alpha calculation.
- max_weight (float): Maximum weight per asset.
- min_weight (float): Minimum weight per asset.
- target_return (float, optional): Target return for the portfolio.
Returns:
- np.ndarray: Optimized asset weights or None if optimization fails.
"""
from scipy.optimize import minimize
def calculate_beta(portfolio_returns: pd.Series, benchmark_returns: pd.Series) -> float:
covariance = np.cov(portfolio_returns, benchmark_returns)
variance = covariance[1, 1]
return covariance[0, 1] / variance if variance != 0 else 0
def calculate_var(portfolio_returns: np.ndarray, confidence_level: float = 0.95) -> float:
return np.percentile(portfolio_returns, (1 - confidence_level) * 100)
def calculate_cvar(portfolio_returns: np.ndarray, confidence_level: float = 0.95) -> float:
var = calculate_var(portfolio_returns, confidence_level)
return portfolio_returns[portfolio_returns <= var].mean()
# Define individual objective functions
def sharpe_ratio(weights: np.ndarray) -> float:
portfolio_return = np.dot(returns.mean(), weights) * 252
portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
return -portfolio_return / portfolio_volatility if portfolio_volatility > 1e-6 else 0 # Negative for maximization
def min_variance(weights: np.ndarray) -> float:
return np.dot(weights.T, np.dot(returns.cov() * 252, weights))
def max_return(weights: np.ndarray) -> float:
return -np.dot(returns.mean(), weights) * 252 # Negative for maximization
def min_drawdown(weights: np.ndarray) -> float:
portfolio_returns = returns.dot(weights)
cvar = calculate_cvar(portfolio_returns, 0.95)
return cvar # Minimizing CVaR as a proxy for drawdown
# Initialize list of objective functions
objective_functions = []
# Map objectives to functions
for obj in objectives:
if obj == 'sharpe':
objective_functions.append(sharpe_ratio)
elif obj == 'min_variance':
objective_functions.append(min_variance)
elif obj == 'max_return':
objective_functions.append(max_return)
elif obj == 'min_drawdown':
objective_functions.append(min_drawdown)
elif obj == 'maximize_alpha':
if benchmark_returns is None:
st.error("benchmark_returns must be provided for 'maximize_alpha' objective.")
return None
benchmark_return = benchmark_returns.mean() * 252 # Assuming daily returns annualized
def maximize_alpha(weights: np.ndarray) -> float:
portfolio_return = np.dot(returns.mean(), weights) * 252
portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
portfolio_returns = returns.dot(weights)
beta = calculate_beta(portfolio_returns, benchmark_returns)
alpha = portfolio_return - (rf + beta * (benchmark_return - rf))
return -alpha # Negative for maximization
objective_functions.append(maximize_alpha)
elif obj == 'minimize_beta':
if benchmark_returns is None:
st.error("benchmark_returns must be provided for 'minimize_beta' objective.")
return None
def minimize_beta(weights: np.ndarray) -> float:
portfolio_returns = returns.dot(weights)
beta = calculate_beta(portfolio_returns, benchmark_returns)
return beta
objective_functions.append(minimize_beta)
else:
st.error(f"Invalid optimization objective: {obj}")
return None
# Composite objective function: weighted sum of individual objectives
def composite_objective(weights: np.ndarray) -> float:
return sum(fn(weights) for fn in objective_functions)
# Define constraints
constraints = [
{'type': 'eq', 'fun': lambda x: np.sum(x) - 1},
]
if target_return is not None:
def target_return_constraint(x: np.ndarray) -> float:
return np.dot(x, returns.mean()) * 252 - target_return
constraints.append({
'type': 'ineq',
'fun': target_return_constraint
})
# Define bounds for weights
bounds = tuple((min_weight, max_weight) for _ in range(returns.shape[1]))
# Initial guess (equally distributed weights)
initial_guess = np.array([1.0 / returns.shape[1]] * returns.shape[1])
# Perform optimization
result = minimize(
composite_objective,
initial_guess,
method='SLSQP',
bounds=bounds,
constraints=constraints
)
if result.success:
optimized_weights = result.x
portfolio_volatility = np.sqrt(np.dot(optimized_weights.T, np.dot(returns.cov() * 252, optimized_weights)))
portfolio_return = np.dot(returns.mean(), optimized_weights) * 252
sharpe_ratio_val = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else np.nan
if portfolio_volatility <= 1e-6:
st.error("Optimized portfolio has near-zero volatility. Optimization constraints may be too restrictive.")
return None
# Store optimized results in session state
st.session_state.backtest_results['optimized_weights'] = optimized_weights
st.session_state.backtest_results['optimized_return'] = portfolio_return
st.session_state.backtest_results['optimized_volatility'] = portfolio_volatility
st.session_state.backtest_results['optimized_sharpe'] = sharpe_ratio_val
return optimized_weights
else:
st.error("Optimization failed. Try adjusting your constraints or target return.")
return None
def monte_carlo_simulation(
returns: pd.DataFrame,
num_simulations: int = 1000,
periods: int = 252,
mean_returns: pd.Series = None,
cov_matrix: pd.DataFrame = None,
mean_reversion: bool = False,
mean_reversion_speed: float = 0.1,
long_term_mean: pd.Series = None,
time_varying_vol: bool = False,
vol_change_rate: float = 0.0,
stress_shocks: dict = None,
stress_period: int = None,
return_distribution: str = 'normal'
) -> np.ndarray:
"""
Perform Monte Carlo simulations for portfolio returns.
Parameters:
- returns (pd.DataFrame): Historical returns of assets.
- num_simulations (int): Number of simulation paths.
- periods (int): Number of periods to simulate.
- mean_returns (pd.Series, optional): Mean returns of assets.
- cov_matrix (pd.DataFrame, optional): Covariance matrix of asset returns.
- mean_reversion (bool): If True, applies mean reversion.
- mean_reversion_speed (float): Speed of mean reversion.
- long_term_mean (pd.Series, optional): Long-term mean for mean reversion.
- time_varying_vol (bool): If True, allows volatility to change over time.
- vol_change_rate (float): Rate at which volatility changes.
- stress_shocks (dict, optional): Dict of shocks to apply at specified period.
- stress_period (int, optional): Period at which to apply stress shocks.
- return_distribution (str): Distribution type ('normal' or 'log-normal').
Returns:
- np.ndarray: Array of simulated cumulative returns.
"""
if mean_returns is None:
mean_returns = returns.mean()
if cov_matrix is None:
cov_matrix = returns.cov()
num_assets = returns.shape[1]
available_selected = returns.columns.tolist()
def simulate():
weights = np.random.random(num_assets)
weights /= np.sum(weights)
simulated_prices = [1] # Initial price
current_mean = mean_returns.copy()
current_vol = np.sqrt(np.diag(cov_matrix))
for t in range(periods):
if mean_reversion and long_term_mean is not None:
current_mean += mean_reversion_speed * (long_term_mean - current_mean)
if time_varying_vol:
current_vol += vol_change_rate
current_vol = np.clip(current_vol, 0, None) # Ensure volatility doesn't go negative
adjusted_cov_matrix = np.outer(current_vol, current_vol) * np.corrcoef(returns.T)
if return_distribution == 'log-normal':
simulated_returns = np.random.lognormal(mean=np.log(1 + current_mean), sigma=current_vol) - 1
else:
simulated_returns = np.random.multivariate_normal(current_mean, adjusted_cov_matrix)
# Apply stress shocks if applicable
if stress_shocks and t == stress_period:
for idx, ticker in enumerate(available_selected):
simulated_returns[idx] += stress_shocks.get(ticker, 0)
portfolio_return = np.dot(simulated_returns, weights)
simulated_prices.append(simulated_prices[-1] * (1 + portfolio_return))
cumulative_return = simulated_prices[-1] - 1
return cumulative_return
portfolio_returns = Parallel(n_jobs=-1)(
delayed(simulate)() for _ in range(num_simulations)
)
return np.array(portfolio_returns)
@handle_exceptions
def plot_efficient_frontier(returns: pd.DataFrame, num_portfolios: int = 1000, rf: float = 0.0, portfolio_name='Portfolio') -> None:
"""
Plot an enhanced Efficient Frontier with optimized portfolio marked.
Parameters:
- returns (pd.DataFrame): Historical returns of assets.
- num_portfolios (int): Number of portfolios to simulate.
- rf (float): Risk-free rate for Sharpe Ratio calculation.
- portfolio_name (str): Name of the portfolio being optimized.
"""
from scipy.optimize import minimize
num_assets = returns.shape[1]
def generate_portfolio_metrics(weights: np.ndarray) -> tuple:
portfolio_return = np.dot(returns.mean(), weights) * 252
portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
sharpe_ratio = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else 0
return portfolio_volatility, portfolio_return, sharpe_ratio
# Generate random portfolios
weights_list = np.random.dirichlet(np.ones(num_assets), num_portfolios)
portfolio_metrics = np.array([generate_portfolio_metrics(w) for w in weights_list])
# Filter out invalid portfolios
portfolio_metrics = portfolio_metrics[~np.isnan(portfolio_metrics).any(axis=1)]
if portfolio_metrics.size == 0:
st.warning("No valid portfolios to plot on the Efficient Frontier.")
return
ef_df = pd.DataFrame({
'Std Dev': portfolio_metrics[:, 0],
'Return': portfolio_metrics[:, 1],
'Sharpe Ratio': portfolio_metrics[:, 2]
})
fig = px.scatter(
ef_df,
x='Std Dev',
y='Return',
color='Sharpe Ratio',
color_continuous_scale='Turbo',
title='📈 Efficient Frontier',
labels={
'Std Dev': 'Annualized Volatility (Std Dev)',
'Return': 'Annualized Return (%)',
'Sharpe Ratio': 'Sharpe Ratio'
},
hover_data={
'Sharpe Ratio': ':.2f',
'Std Dev': ':.2f',
'Return': ':.2f'
},
template=THEME
)
# Highlight the portfolio with the maximum Sharpe ratio
max_sharpe_idx = ef_df['Sharpe Ratio'].idxmax()
max_sharpe = ef_df.loc[max_sharpe_idx]
fig.add_trace(go.Scatter(
x=[max_sharpe['Std Dev']],
y=[max_sharpe['Return']],
mode='markers+text',
marker=dict(color='gold', size=12, symbol='star'),
name='Max Sharpe Ratio',
text=["Max Sharpe"],
textposition="top center",
hoverinfo='text'
))
fig.add_annotation(
x=max_sharpe['Std Dev'],
y=max_sharpe['Return'],
text="Max Sharpe",
showarrow=True,
arrowhead=1,
ax=0,
ay=-40
)
# Add optimized portfolio if available
optimized_weights = st.session_state.backtest_results.get('optimized_weights', None)
optimized_return = st.session_state.backtest_results.get('optimized_return', None)
optimized_volatility = st.session_state.backtest_results.get('optimized_volatility', None)
optimized_sharpe = st.session_state.backtest_results.get('optimized_sharpe', None)
if all(v is not None for v in [optimized_weights, optimized_return, optimized_volatility, optimized_sharpe]):
if optimized_volatility > 1e-6 and np.isfinite(optimized_sharpe):
fig.add_trace(go.Scatter(
x=[optimized_volatility],
y=[optimized_return],
mode='markers+text',
marker=dict(color='red', size=16, symbol='diamond'),
name=f'{portfolio_name} Optimized',
text=["Optimized"],
textposition="top center",
hoverinfo='text'
))
fig.add_annotation(
x=optimized_volatility,
y=optimized_return,
text="Optimized",
showarrow=True,
arrowhead=2,
ax=0,
ay=-50
)
fig.update_layout(
hovermode=HOVERMODE,
xaxis=dict(showgrid=True, title=dict(text='Annualized Volatility (Std Dev)')),
yaxis=dict(showgrid=True, title=dict(text='Annualized Return (%)')),
legend=dict(title="Portfolio", x=0.01, y=0.99)
)
fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')), selector=dict(mode='markers'))
st.plotly_chart(fig, use_container_width=True, config={'responsive': True, 'scrollZoom': True})
@handle_exceptions
def generate_portfolio(returns: pd.DataFrame, rf: float = 0.0) -> tuple:
"""
Generate a random portfolio and calculate its metrics.
Parameters:
- returns (pd.DataFrame): Historical returns of assets.
- rf (float): Risk-free rate for Sharpe Ratio calculation.
Returns:
- tuple: (volatility: float, return: float, sharpe_ratio: float)
"""
try:
weights = np.random.dirichlet(np.ones(returns.shape[1]))
portfolio_return = np.dot(returns.mean(), weights) * 252
portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
sharpe_ratio = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else 0
return portfolio_volatility, portfolio_return, sharpe_ratio
except Exception as e:
st.error(f"Error generating portfolio: {e}")
return np.nan, np.nan, np.nan # Return NaNs to indicate failure
def plot_allocation_pie(weights: list, assets: list, title: str = '🥧 Portfolio Allocation', hover_info: str = "percent+name") -> None:
"""Plots the portfolio allocation pie chart."""
allocation_df = pd.DataFrame({
'Asset': assets,
'Weight': weights
})
# Default to showing both percent and name in hover
hover_data = ['Weight'] if hover_info == "percent+name" else (
[] if hover_info == "name" else ['Weight']
)
fig = px.pie(
allocation_df,
names='Asset',
values='Weight',
title=title,
color='Asset',
color_discrete_sequence=px.colors.qualitative.Pastel,
hover_data=hover_data,
hole=0.3 # Donut chart appearance
)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(
uniformtext_minsize=12,
uniformtext_mode='hide',
legend_title="Assets",
showlegend=True,
height=FIG_HEIGHT
)
st.plotly_chart(fig, use_container_width=True, height=600, config={'responsive': True})
def plot_rolling_cagr(cum_returns: pd.Series, window: int = 252) -> None:
"""
Plot Rolling CAGR over a specified window.
Parameters:
- cum_returns (pd.Series): Cumulative returns of the portfolio.
- window (int): Window size for rolling CAGR calculation.
"""
try:
rolling_years = window / 252 # Assuming daily data
rolling_cagr = (cum_returns / cum_returns.shift(window)) ** (1 / rolling_years) - 1
fig = px.line(
rolling_cagr.dropna(),
title=f'Rolling {int(rolling_years)}-Year CAGR',
labels={'index': 'Date', 'value': 'Rolling CAGR'},
template=THEME
)
st.plotly_chart(fig, use_container_width=True)
except Exception as e:
st.error(f"Error plotting rolling CAGR: {e}")
@handle_exceptions
def plot_rolling_metrics(returns: pd.Series, windows: list = [30, 90, 180, 252]) -> None:
"""
Plot rolling metrics (Volatility and Sharpe Ratio) over multiple windows.
Parameters:
- returns (pd.Series): Daily returns of the portfolio.
- windows (list): List of window sizes for rolling calculations.
"""
metrics = {}
for window in windows:
rolling_return = returns.rolling(window).mean() * 252
rolling_vol = returns.rolling(window).std() * np.sqrt(252)
rolling_sharpe = (rolling_return - 0.02) / rolling_vol
metrics[f'Rolling {window}-Day Volatility'] = rolling_vol
metrics[f'Rolling {window}-Day Sharpe Ratio'] = rolling_sharpe
fig = go.Figure()
for metric_name, metric_series in metrics.items():
fig.add_trace(go.Scatter(x=metric_series.index, y=metric_series, mode='lines', name=metric_name))
fig.update_layout(
title='Rolling Metrics Over User-Defined Periods',
xaxis_title='Date',
yaxis_title='Value',
hovermode='x unified',
template=THEME
)
st.plotly_chart(fig, use_container_width=True)
@handle_exceptions
def plot_risk_return_attribution(returns: pd.Series, weights: list) -> None:
"""
Plot Risk-Return Attribution for the portfolio.
Parameters:
- returns (pd.Series or pd.DataFrame): Returns of assets.
- weights (list): Allocation weights for each asset.
"""
try:
if isinstance(returns, pd.Series):
asset_returns = returns.mean() * 252
asset_volatility = returns.std() * np.sqrt(252)
asset_contribution = weights[0] * asset_volatility if len(weights) > 0 else 0
hover_names = [returns.name] if returns.name else ['Asset']
x = [asset_volatility]
y = [asset_returns]
size = [asset_contribution]
color = [weights[0]]
else:
asset_returns = returns.mean() * 252
asset_volatility = returns.std() * np.sqrt(252)
asset_contribution = np.array(weights) * asset_volatility
hover_names = returns.columns.tolist()
x = asset_volatility.values
y = asset_returns.values
size = asset_contribution
color = weights
fig = px.scatter(
x=x,
y=y,
size=size,
color=color,
hover_name=hover_names,
title='Risk-Return Attribution',
labels={'x': 'Annualized Volatility', 'y': 'Annualized Return', 'color': 'Weight (%)'},
size_max=60,
color_continuous_scale='Viridis'
)
fig.update_layout(template='plotly_dark')
st.plotly_chart(fig, use_container_width=True)
except Exception as e:
st.error(f"Error plotting risk-return attribution: {e}")
def plot_var_cvar_distribution(portfolio_returns: np.ndarray, var: float, cvar: float) -> None:
"""
Plot the distribution of portfolio returns with VaR and CVaR lines.
Parameters:
- portfolio_returns (np.ndarray): Simulated portfolio returns.
- var (float): Value at Risk.
- cvar (float): Conditional Value at Risk.
"""
fig = px.histogram(portfolio_returns, nbins=50, title='Returns Distribution with VaR and CVaR',
labels={'value': 'Returns', 'count': 'Frequency'})
fig.add_vline(x=var, line_dash="dash", line_color="red", annotation_text=f"VaR: {var:.2f}", annotation_position="top left")
fig.add_vline(x=cvar, line_dash="dash", line_color="blue", annotation_text=f"CVaR: {cvar:.2f}", annotation_position="top left")
st.plotly_chart(fig, use_container_width=True)
def plot_var_cvar_over_time(portfolio_returns: pd.Series, var: float, cvar: float) -> None:
"""
Plot cumulative returns with VaR and CVaR over time.
Parameters:
- portfolio_returns (pd.Series): Daily returns of the portfolio.
- var (float): Value at Risk.
- cvar (float): Conditional Value at Risk.
"""
cumulative_returns = (1 + portfolio_returns).cumprod()
fig = px.line(cumulative_returns, title='Cumulative Returns with VaR and CVaR Over Time', labels={'value': 'Cumulative Returns', 'index': 'Date'})
fig.add_hline(y=var, line_dash="dash", line_color="red", annotation_text=f"VaR: {var:.2f}", annotation_position="bottom right")
fig.add_hline(y=cvar, line_dash="dash", line_color="blue", annotation_text=f"CVaR: {cvar:.2f}", annotation_position="bottom right")
st.plotly_chart(fig, use_container_width=True)
def calculate_var(returns: np.ndarray, confidence_level: float = 0.95) -> float:
"""
Calculate Value at Risk (VaR) for a given set of returns.
Parameters:
- returns (np.ndarray): Portfolio returns.
- confidence_level (float): Confidence level for VaR.
Returns:
- float: VaR value.
"""
return np.percentile(returns, 100 * (1 - confidence_level))
def calculate_cvar(returns: np.ndarray, confidence_level: float = 0.95) -> float:
"""
Calculate Conditional Value at Risk (CVaR) for a given set of returns.
Parameters:
- returns (np.ndarray): Portfolio returns.
- confidence_level (float): Confidence level for CVaR.
Returns:
- float: CVaR value.
"""
var = calculate_var(returns, confidence_level)
return returns[returns <= var].mean()
# ----------------------------
# Caching Functions
# ----------------------------
@st.cache_data(show_spinner=False, persist=True)
def get_tickers() -> pd.DataFrame:
"""
Fetch and combine S&P 500 and NASDAQ-100 tickers from Wikipedia.
Returns:
- pd.DataFrame: DataFrame containing combined tickers and company names.
"""
try:
# Fetch S&P 500 companies
sp500_url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
sp500_response = requests.get(sp500_url, verify=False)
sp500_table = pd.read_html(sp500_response.text)[0]
sp500 = sp500_table[['Symbol', 'Security']].rename(columns={'Symbol': 'Ticker', 'Security': 'Company Name'})
sp500['Ticker'] = sp500['Ticker'].str.replace('.', '-', regex=False)
# Fetch NASDAQ-100 companies
nasdaq100_url = 'https://en.wikipedia.org/wiki/NASDAQ-100'
nasdaq100_response = requests.get(nasdaq100_url, verify=False)
nasdaq100_tables = pd.read_html(nasdaq100_response.text)
nasdaq100 = pd.DataFrame()
for table in nasdaq100_tables:
if 'Ticker' in table.columns and 'Company' in table.columns:
nasdaq100 = table[['Ticker', 'Company']].rename(columns={'Ticker': 'Ticker', 'Company': 'Company Name'})
nasdaq100['Ticker'] = nasdaq100['Ticker'].str.replace('.', '-', regex=False)
break
combined = pd.concat([sp500, nasdaq100], ignore_index=True)
combined = combined.drop_duplicates(subset=['Ticker'])
return combined.sort_values('Ticker').reset_index(drop=True)
except Exception as e:
st.error(f"Error fetching tickers: {e}")
return pd.DataFrame(columns=['Ticker', 'Company Name'])
@st.cache_data(show_spinner=False, persist=True)
def download_data(tickers: list, start: str, end: str, retries: int = 3, backoff_factor: float = 0.3) -> pd.DataFrame:
"""
Download historical adjusted closing prices for specified tickers.
Parameters:
- tickers (list): List of ticker symbols.
- start (str): Start date in 'YYYY-MM-DD' format.
- end (str): End date in 'YYYY-MM-DD' format.
- retries (int): Number of retries for failed requests.
- backoff_factor (float): Backoff factor for retries.
Returns:
- pd.DataFrame: DataFrame containing adjusted closing prices.
"""
try:
# Configure retry strategy for requests (used by yfinance internally)
session = requests.Session()
retry = Retry(
total=retries,
read=retries,
connect=retries,
backoff_factor=backoff_factor,
status_forcelist=(500, 502, 504),
)
adapter = HTTPAdapter(max_retries=retry)
session.mount('http://', adapter)
session.mount('https://', adapter)
# Attempt to download data
data = yf.download(tickers, start=start, end=end, progress=False, session=session)['Adj Close']
# Handle potential empty data
if isinstance(data, pd.Series):
data = data.to_frame()
if data.empty:
st.warning("No price data available for the selected portfolio. Please check the ticker symbols and date range.")
return pd.DataFrame()
if data.index.tz is not None:
data.index = data.index.tz_localize(None)
# Fill missing data
data = data.fillna(method='ffill').fillna(method='bfill')
if data.isnull().values.any():
st.warning("Data contains missing values after filling. Some calculations may be affected.")
return data
except Exception as e:
st.error(f"Error downloading data: {e}")
return pd.DataFrame()
def calculate_metrics(
returns: pd.Series,
cum_returns: pd.Series,
rf: float = 0.02,
benchmark_returns: pd.Series = None
) -> dict:
"""
Calculate comprehensive portfolio metrics, optionally relative to a benchmark.
Parameters:
- returns (pd.Series): Portfolio returns.
- cum_returns (pd.Series): Cumulative returns of the portfolio.
- rf (float): Risk-free rate.
- benchmark_returns (pd.Series, optional): Benchmark returns.
Returns:
- dict: Dictionary of calculated metrics.
"""
metrics = {}
metrics['Start Balance'] = "$10,000.00"
if not cum_returns.empty:
end_balance = 10000 * cum_returns.iloc[-1]
years = (cum_returns.index[-1] - cum_returns.index[0]).days / 365.25
cagr = (end_balance / 10000) ** (1 / years) - 1 if years > 0 else np.nan
metrics.update({
'End Balance': f"${end_balance:,.2f}",
'Annualized Return (CAGR)': f"{cagr * 100:.2f}%" if not np.isnan(cagr) else "N/A",
'Best Year': f"{returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).max() * 100:.2f}%" if not returns.empty else "N/A",
'Worst Year': f"{returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).min() * 100:.2f}%" if not returns.empty else "N/A",
'Arithmetic Mean (Monthly)': f"{returns.mean() * 100:.2f}%",
'Arithmetic Mean (Annualized)': f"{returns.mean() * 252 * 100:.2f}%",
'Geometric Mean (Monthly)': f"{(np.exp(np.log1p(returns).mean()) - 1) * 100:.2f}%",
'Geometric Mean (Annualized)': f"{(np.exp(np.log1p(returns).mean() * 252) - 1) * 100:.2f}%",
'Standard Deviation (Monthly)': f"{returns.std() * 100:.2f}%",
'Standard Deviation (Annualized)': f"{returns.std() * np.sqrt(252) * 100:.2f}%",
'Downside Deviation (Monthly)': f"{returns[returns < 0].std() * 100:.2f}%",
'Maximum Drawdown': f"{drawdown(cum_returns) * 100:.2f}%" if not cum_returns.empty else "N/A",
'Sharpe Ratio': f"{calculate_sharpe_ratio(returns, rf):.2f}" if not cum_returns.empty else "N/A",
'Sortino Ratio': f"{calculate_sortino_ratio(returns, rf):.2f}" if not cum_returns.empty else "N/A",
'Gain/Loss Ratio': f"{calculate_gain_loss_ratio(returns):.2f}" if not cum_returns.empty else "N/A",
'Skewness': f"{skew(returns):.2f}" if not returns.empty else "N/A",
'Excess Kurtosis': f"{kurtosis(returns):.2f}" if not returns.empty else "N/A",
'Safe Withdrawal Rate': f"{calculate_safe_withdrawal_rate(returns):.6f}%" if not returns.empty else "N/A",
'Perpetual Withdrawal Rate': f"{calculate_perpetual_withdrawal_rate(returns):.6f}%" if not returns.empty else "N/A",
'Positive Periods': calculate_positive_periods(returns) if not returns.empty else "N/A"
})
else:
# Assign "N/A" to all metrics if cum_returns is empty
metrics.update({
'End Balance': "N/A",
'Annualized Return (CAGR)': "N/A",
# ... all other metrics set to "N/A"
})
if benchmark_returns is not None and not benchmark_returns.empty:
correlation = returns.corr(benchmark_returns)
beta = calculate_beta(returns, benchmark_returns)
alpha = calculate_alpha(returns, benchmark_returns, rf)
r2 = calculate_r_squared(returns, benchmark_returns)
treynor = calculate_treynor_ratio(returns, benchmark_returns, rf)
calmar = calculate_calmar_ratio(returns, cum_returns)
m2 = calculate_modigliani_miller(returns, benchmark_returns, rf)
info_ratio = calculate_information_ratio(returns, benchmark_returns)
tracking_error = calculate_tracking_error(returns, benchmark_returns)
active_return = calculate_active_return(returns, benchmark_returns)
upside_capture = calculate_capture_ratio(returns, benchmark_returns, upside=True)
downside_capture = calculate_capture_ratio(returns, benchmark_returns, upside=False)
metrics.update({
'Benchmark Correlation': f"{correlation:.2f}" if not np.isnan(correlation) else "N/A",
'Beta': f"{beta:.2f}" if not np.isnan(beta) else "N/A",
'Alpha (annualized)': f"{alpha * 100:.2f}%" if not np.isnan(alpha) else "N/A",
'R2': f"{r2 * 100:.2f}%" if not np.isnan(r2) else "N/A",
'Treynor Ratio': f"{treynor:.2f}" if not np.isnan(treynor) else "N/A",
'Calmar Ratio': f"{calmar:.2f}" if not np.isnan(calmar) else "N/A",
'Modigliani–Modigliani Measure': f"{m2 * 100:.2f}%" if not np.isnan(m2) else "N/A",
'Information Ratio': f"{info_ratio:.2f}" if not np.isnan(info_ratio) else "N/A",
'Tracking Error': f"{tracking_error * 100:.2f}%" if not np.isnan(tracking_error) else "N/A",
'Active Return': f"{active_return:.2f}%" if not np.isnan(active_return) else "N/A",
'Upside Capture Ratio': f"{upside_capture:.2f}%" if not np.isnan(upside_capture) else "N/A",
'Downside Capture Ratio': f"{downside_capture:.2f}%" if not np.isnan(downside_capture) else "N/A"
})
else:
# Assign "N/A" to all benchmark metrics
metrics.update({
'Benchmark Correlation': "N/A",
'Beta': "N/A",
'Alpha (annualized)': "N/A",
'R2': "N/A",
'Treynor Ratio': "N/A",
'Calmar Ratio': "N/A",
'Modigliani–Modigliani Measure': "N/A",
'Information Ratio': "N/A",
'Tracking Error': "N/A",
'Active Return': "N/A",
'Upside Capture Ratio': "N/A",
'Downside Capture Ratio': "N/A"
})
return metrics
# ----------------------------
# Additional Helper Functions
# ----------------------------
@handle_exceptions
def plot_efficient_frontier_comparison(
simulated_metrics: pd.DataFrame,
optimized_metrics: tuple
) -> None:
"""
Plot Efficient Frontier and mark the optimized portfolio.
Parameters:
- simulated_metrics (pd.DataFrame): Simulated portfolio metrics.
- optimized_metrics (tuple): Metrics of the optimized portfolio.
"""
if simulated_metrics.empty:
st.warning("No simulated metrics available to plot.")
return
fig = px.scatter(
simulated_metrics,
x='Std Dev',
y='Return',
color='Sharpe Ratio',
color_continuous_scale='Viridis',
title='Efficient Frontier',
labels={
'Std Dev': 'Annualized Volatility (Std Dev)',
'Return': 'Annualized Return',
'Sharpe Ratio': 'Sharpe Ratio'
},
hover_data={
'Sharpe Ratio': ':.2f',
'Std Dev': ':.2f',
'Return': ':.2f'
},
template=THEME
)
# Add optimized portfolio if available
if optimized_metrics:
vol, ret, sharpe = optimized_metrics
if vol > 0 and np.isfinite(sharpe):
fig.add_trace(go.Scatter(
x=[vol],
y=[ret],
mode='markers+text',
marker=dict(color='red', size=14, symbol='star'),
name='Optimized Portfolio',
text=["Optimized"],
textposition="top center",
hoverinfo='text'
))
fig.add_annotation(
x=vol,
y=ret,
text="Optimized",
showarrow=True,
arrowhead=2,
ax=0,
ay=-40
)
fig.update_layout(
hovermode='x unified',
xaxis=dict(rangeslider=dict(visible=True), type='linear'),
)
st.plotly_chart(fig, use_container_width=True, config={
'responsive': True,
'scrollZoom': True,
'displayModeBar': True
})
# ----------------------------
# End of Helper Functions
# ----------------------------
# ----------------------------
# Streamlit Layout
# ----------------------------
st.set_page_config(page_title="🎯 Portfolio Optimizer", layout="wide")
# Display loading spinner
with st.spinner("🎯 Portfolio Optimizer is loading, please be patient..."):
# Simulate loading delay if necessary
pass
nav_steps = [
{"name": "Configure Portfolio", "icon": "📁"},
{"name": "Run Backtest", "icon": "📊"},
{"name": "Optimize Portfolio", "icon": "🔧"},
{"name": "Monte Carlo Simulations", "icon": "📈"},
{"name": "Risk Analysis", "icon": "⚠️"}
]
with st.sidebar:
st.header("📂 Navigation")
# Create a list of (label, value) tuples for radio buttons
nav_options = [(f"{step['icon']} {step['name']}", step['name']) for step in nav_steps]
labels, values = zip(*nav_options)
# Use radio buttons for direct navigation with a callback
def set_step():
selected_label = st.session_state.nav_radio # Access the selected label from session_state
selected_idx = labels.index(selected_label)
st.session_state.step = values[selected_idx]
selected_label = st.radio("Select a Step:", options=labels, key="nav_radio", on_change=set_step)
current_step = st.session_state.step
tickers = get_tickers()
# Define asset_options by formatting ticker symbols with company names
asset_options = tickers.apply(
lambda row: format_asset_option(row['Ticker'], row['Company Name']),
axis=1
).tolist()
if current_step == "Configure Portfolio":
st.title("🎯 Portfolio Optimizer")
# Initialize show_proceed in session state
if 'show_proceed' not in st.session_state:
st.session_state.show_proceed = False
# Define callback function to hide welcome message
def hide_welcome():
st.session_state.show_proceed = True
st.session_state.selected_subtab = "➕ Create New Portfolio" # Set default tab
# Display onboarding message and button if not proceeded
if not st.session_state.show_proceed:
st.info("Welcome! Let's get you started with your first portfolio.")
st.write("""
1. **Configure your portfolio** by entering a name, start date, end date, and other details.
2. **Select the assets** you want to include and set their allocations.
3. **Run a backtest** to see how your portfolio performs.
4. **Compare your portfolio** with a benchmark or another portfolio.
5. **Explore the performance metrics** and visualizations.
""")
st.button("Got U, Let's Explore", key="explore_button", on_click=hide_welcome)
# Show portfolio management interface if proceeded
elif st.session_state.show_proceed:
# Show existing portfolios if any
if st.session_state.portfolios:
st.subheader("📁 Manage Existing Portfolios")
portfolio_names = [p['name'] for p in st.session_state.portfolios]
selected_portfolio = st.selectbox("🔍 Select a Portfolio", portfolio_names, key="select_portfolio")
if selected_portfolio:
portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)
with st.expander(f"ℹ️ Details for **{selected_portfolio}**"):
st.write(f"**Start Date:** {portfolio['start_date'].strftime('%Y-%m-%d')}")
st.write(f"**End Date:** {portfolio['end_date'].strftime('%Y-%m-%d')}")
st.write(f"**Benchmark:** {portfolio['benchmark_symbol']}")
st.write(f"**Rebalance Frequency:** {rebalance_freq_inverse_mapping.get(portfolio['rebalance_freq'], portfolio['rebalance_freq'])}")
st.write("**Allocations:**")
allocation_df = pd.DataFrame({
'Ticker': portfolio['selected'],
'Allocation (%)': portfolio['allocations']
})
st.table(allocation_df)
# Action Buttons with Icons
action_col1, action_col2, action_col3 = st.columns(3)
with action_col1:
if st.button(f"🗑️ Delete {selected_portfolio}", key=f"delete_{selected_portfolio}"):
st.session_state.portfolios = [p for p in st.session_state.portfolios if p['name'] != selected_portfolio]
st.success(f"✅ Portfolio '{selected_portfolio}' deleted successfully.")
st.rerun()
with action_col2:
if st.button(f"✏️ Edit {selected_portfolio}", key=f"edit_{selected_portfolio}"):
st.session_state.edit_portfolio = selected_portfolio
st.rerun()
with action_col3:
if st.button(f"📄 Duplicate {selected_portfolio}", key=f"duplicate_{selected_portfolio}"):
duplicated_portfolio = portfolio.copy()
duplicated_portfolio['name'] = f"{selected_portfolio}_Copy"
# Ensure the new name is unique
if duplicated_portfolio['name'] in portfolio_names:
counter = 1
while f"{selected_portfolio}_Copy{counter}" in portfolio_names:
counter += 1
duplicated_portfolio['name'] = f"{selected_portfolio}_Copy{counter}"
st.session_state.portfolios.append(duplicated_portfolio)
st.success(f"🎉 Portfolio '{duplicated_portfolio['name']}' duplicated successfully!")
st.rerun()
st.markdown("---") # Separator line for clarity
tabs_labels = ["➕ Create New Portfolio"]
tabs_portfolio = st.tabs(tabs_labels)
# Determine which tab to select based on session state
selected_idx = 0 # Default to first tab
if 'selected_subtab' not in st.session_state:
st.session_state.selected_subtab = tabs_labels[0]
with tabs_portfolio[selected_idx]:
if selected_idx == 0:
# Create New Portfolio Content
st.subheader("➕ Create a New Portfolio" if not st.session_state.edit_portfolio else f"✏️ Edit Portfolio: {st.session_state.edit_portfolio}")
if st.session_state.edit_portfolio:
# Fetch the portfolio to be edited
portfolio_to_edit = next((p for p in st.session_state.portfolios if p['name'] == st.session_state.edit_portfolio), None)
if portfolio_to_edit:
# Pre-populate the form with existing data
name = st.text_input("📛 Portfolio Name", value=portfolio_to_edit['name'], help="Enter a unique name for your portfolio.")
start_date_input = st.date_input(
"📅 Start Date:",
value=portfolio_to_edit['start_date'],
min_value=datetime(1900, 1, 1),
help="Choose the start date for backtesting."
)
end_date_input = st.date_input(
"📅 End Date:",
value=portfolio_to_edit['end_date'],
min_value=datetime(1900, 1, 1),
help="Choose the end date for backtesting."
)
rf_rate = st.number_input(
"📈 Risk-Free Rate (%)",
min_value=0.0,
max_value=10.0,
value=portfolio_to_edit['rf_rate'] * 100,
format="%.2f",
help="Enter the risk-free rate as a percentage."
) / 100
broker_fee = st.number_input(
"💸 Broker Fee (%)",
min_value=0.0,
max_value=10.0,
value=portfolio_to_edit['broker_fee'] * 100,
step=0.0001,
format="%.4f",
help="Enter the broker fee as a percentage per transaction."
) / 100
selected_assets_new = [f"{ticker} - {tickers[tickers['Ticker'] == ticker]['Company Name'].values[0]}" for ticker in portfolio_to_edit['selected']]
else:
st.error("✅ No portfolio found to edit.")
st.stop()
else:
# Default empty form for creating a new portfolio
name = st.text_input("📛 Portfolio Name", help="Enter a unique name for your portfolio.")
start_date_input, end_date_input = st.columns(2)
with start_date_input:
start_date_input = st.date_input(
"📅 Start Date:",
value=datetime.today() - timedelta(days=365 * 15),
min_value=datetime(1900, 1, 1),
help="Choose the start date for backtesting."
)
with end_date_input:
end_date_input = st.date_input(
"📅 End Date:",
value=datetime.today(),
min_value=datetime(1900, 1, 1),
help="Choose the end date for backtesting."
)
st.markdown("#### 💰 Financial Settings")
rf_rate = st.number_input(
"📈 Risk-Free Rate (%)",
min_value=0.0,
max_value=10.0,
value=st.session_state.default_config['rf_rate'] * 100,
format="%.2f",
help="Enter the risk-free rate as a percentage."
) / 100
broker_fee = st.number_input(
"💸 Broker Fee (%)",
min_value=0.0,
max_value=10.0,
value=st.session_state.default_config['broker_fee'] * 100,
step=0.0001,
format="%.4f",
help="Enter the broker fee as a percentage per transaction."
) / 100
# New Portfolio Form
st.markdown("#### 📦 Asset Allocation")
# Use the defined asset_options here
selected_assets_new = st.multiselect(
"🗂️ Select Assets:",
options=asset_options, # Now defined
default=[],
help="Choose the assets you want to include in your portfolio."
)
selected_tickers_new = [option.split(' - ')[0] for option in selected_assets_new if ' - ' in option]
if selected_tickers_new:
# Create a DataFrame for allocations
allocation_df = pd.DataFrame({
'Ticker': selected_tickers_new,
'Allocation (%)': [0.0] * len(selected_tickers_new)
})
st.markdown("**Set Allocation Percentages:**")
for idx, ticker in enumerate(selected_tickers_new):
allocation_df.at[idx, 'Allocation (%)'] = st.number_input(
f"📊 {ticker} Allocation (%)",
min_value=0.0,
max_value=100.0,
value=allocation_df.at[idx, 'Allocation (%)'],
step=0.1,
format="%.2f",
key=f"alloc_new_{ticker}",
help=f"Set the allocation percentage for {ticker}. The sum of all allocations must equal 100%."
)
total_allocation_new = allocation_df['Allocation (%)'].sum()
st.markdown(f"**🧮 Total Allocation:** {total_allocation_new:.2f}%")
if not np.isclose(total_allocation_new, 100.0, atol=1e-2):
st.warning("⚠️ Allocations must sum to 100%. Please adjust the allocations.")
# Display Allocation Table
st.table(allocation_df)
else:
st.info("🧐 Select assets to allocate your portfolio.")
st.markdown("#### 🏦 Benchmark Selection")
benchmark_options = get_common_benchmarks(selected_tickers_new)
selected_benchmark = st.selectbox(
"🏦 Select Benchmark",
options=list(benchmark_options.keys()),
help="Choose a benchmark that aligns with your portfolio's asset composition."
)
# Initialize custom benchmark symbol
if selected_benchmark == "Custom":
custom_benchmark = st.text_input(
"✏️ Enter Custom Benchmark Symbol",
placeholder="e.g., ^DJI, ^IXIC",
help="Specify a custom benchmark symbol (e.g., ^DJI for Dow Jones Industrial Average)."
)
# Use the custom benchmark if provided
benchmark_symbol = custom_benchmark if custom_benchmark else "^GSPC" # Default to S&P 500 if empty
else:
benchmark_symbol = benchmark_options.get(selected_benchmark, '^GSPC')
# Display the chosen benchmark symbol for confirmation
st.write(f"**Selected Benchmark Symbol:** {benchmark_symbol}")
st.markdown("#### 🔄 Rebalance Frequency")
rebalance_freq = st.selectbox(
"🔄 Rebalance Frequency",
options=list(frequency_mapping.keys()),
index=list(frequency_mapping.keys()).index('Monthly'),
help="Choose how often the portfolio should be rebalanced."
)
# Submit Button
submit_button_label = "✅ Update Portfolio" if st.session_state.edit_portfolio else "✅ Add Portfolio"
if st.button(submit_button_label):
# Validation
if not name:
st.error("❌ Please provide a portfolio name.")
elif not selected_assets_new:
st.error("❌ Please select at least one asset.")
elif selected_benchmark == "Custom" and not benchmark_symbol:
st.error("❌ Please enter a custom benchmark symbol.")
elif not np.isclose(total_allocation_new, 100.0, atol=1e-2):
st.error(f"❌ Allocations must sum to 100%. Currently sum to {total_allocation_new:.2f}%.")
else:
allocations = allocation_df['Allocation (%)'].tolist()
new_portfolio = {
'name': name,
'start_date': pd.to_datetime(start_date_input),
'end_date': pd.to_datetime(end_date_input),
'rf_rate': rf_rate,
'broker_fee': broker_fee,
'benchmark_symbol': benchmark_symbol,
'rebalance_freq': frequency_mapping.get(rebalance_freq, 'M'),
'selected': selected_tickers_new,
'allocations': allocations
}
if st.session_state.edit_portfolio:
# Update existing portfolio
for idx, p in enumerate(st.session_state.portfolios):
if p['name'] == st.session_state.edit_portfolio:
st.session_state.portfolios[idx] = new_portfolio
break
st.success(f"🎉 Portfolio '{name}' updated successfully!")
st.session_state.edit_portfolio = None # Reset edit mode
else:
# Add as a new portfolio
if name in [p['name'] for p in st.session_state.portfolios]:
st.error("❌ Portfolio name already exists. Please choose a unique name.")
else:
st.session_state.portfolios.append(new_portfolio)
st.success(f"🎉 Portfolio '{name}' added successfully!")
st.balloons()
st.rerun()
# ----------------------------
# Documentation
# ----------------------------
with st.expander("📖 Documentation"):
st.markdown("""
### Documentation
**Portfolio Configuration:**
- **Portfolio Name:** Enter a unique name for your portfolio.
- **Risk-Free Rate:** The theoretical return of an investment with zero risk, often based on government bonds.
- **Sharpe Ratio:** Measures the performance of an investment compared to a risk-free asset, after adjusting for its risk.
*Formula:* $$\\frac{R_p - R_f}{\\sigma_p}$$
- **VaR (Value at Risk):** Estimates the maximum potential loss over a specific time frame at a given confidence level.
- **CVaR (Conditional Value at Risk):** The expected loss exceeding the VaR, providing insight into tail risk.
- **Sortino Ratio:** Similar to the Sharpe Ratio but only penalizes downside volatility.
*Formula:* $$\\frac{R_p - R_f}{\\sigma_d}$$
- **Maximum Drawdown:** The largest peak-to-trough decline in the portfolio's value over a specific period.
- **Alpha:** Measures the active return on an investment compared to a market index.
*Formula:* $$R_p - [R_f + \\beta (R_m - R_f)]$$
- **Beta:** Indicates the volatility of an investment relative to the market.
*Formula:* $$\\beta = \\frac{Cov(R_p, R_m)}{Var(R_m)}$$
- **Information Ratio:** Measures portfolio returns beyond the returns of a benchmark, adjusted for the volatility of those returns.
*Formula:* $$\\frac{R_p - R_b}{TE}$$
- **Gain/Loss Ratio:** The ratio of total gains to total losses in the portfolio.
- **Modigliani–Modigliani Measure (M²):** Adjusts the portfolio return to the risk of a benchmark, allowing for comparison.
*Formula:* $$M² = \\text{Sharpe Ratio}_{\\text{Portfolio}} \\times \\sigma_{\\text{Benchmark}} + R_f$$
- **Tracking Error:** Measures the standard deviation of the difference between portfolio returns and benchmark returns.
- **Upside/Downside Capture Ratio:** Measures how well the portfolio captures the benchmark's positive and negative movements respectively.
- **Risk Factor Attribution:** Decomposes portfolio returns based on exposure to different risk factors like Momentum, Value, and Size.
- **Performance Attribution:** Breaks down portfolio performance by individual assets, showing each asset's contribution to overall returns.
- **Recommendation Engine:** Provides suggestions for portfolio adjustments to optimize performance based on current allocations and objectives.
""")
if current_step == "Run Backtest":
st.title("📊 Run Backtest")
if not st.session_state.portfolios:
st.warning("Please add at least one portfolio to run backtest.")
else:
st.subheader("🔍 Select Portfolios to Compare")
comparison_type = st.selectbox(
"Comparison Type",
["Portfolio vs Benchmark", "Portfolio vs Portfolio"],
key="comparison_type"
)
portfolio_names = [p['name'] for p in st.session_state.portfolios]
# Dynamic labels based on comparison_type
portfolio1_label = "Select Portfolio" if comparison_type == "Portfolio vs Benchmark" else "Select First Portfolio"
portfolio2_disabled = comparison_type == "Portfolio vs Benchmark"
portfolio1 = st.selectbox(
portfolio1_label,
portfolio_names,
key="p1"
)
portfolio2 = st.selectbox(
"Select Second Portfolio",
portfolio_names,
key="p2",
disabled=portfolio2_disabled
)
st.subheader("⚙️ Visualization Settings")
selected_time_frames = st.multiselect(
"Select Time Frames for CAGR",
options=['Weekly', 'Monthly', 'Quarterly', 'Annually'],
default=['Annually'],
key="selected_time_frames",
help="Choose one or more time frames to view CAGR over different horizons."
)
selected_rolling_periods = st.multiselect(
"Select Rolling Periods (Days)",
options=[30, 90, 180, 252],
default=[252],
key="selected_rolling_periods",
help="Choose one or more periods to view rolling metrics."
)
run = st.button("Run Backtest")
if run:
with st.spinner("Running backtest..."):
if comparison_type == "Portfolio vs Benchmark":
portfolio = next(p for p in st.session_state.portfolios if p['name'] == portfolio1)
result = run_portfolio_comparison("vs_benchmark", portfolio)
else:
if portfolio1 == portfolio2:
st.error("Please select two different portfolios for comparison.")
st.stop()
portfolio_a = next(p for p in st.session_state.portfolios if p['name'] == portfolio1)
portfolio_b = next(p for p in st.session_state.portfolios if p['name'] == portfolio2)
result = run_portfolio_comparison("vs_portfolio", portfolio_a, portfolio_b)
if result.get("error"):
st.error(result["error"])
else:
backtest_results = result["backtest_results"]
display_backtest_results(backtest_results, comparison_type, portfolio1, portfolio2)
if current_step == "Optimize Portfolio":
st.title("🔧 Optimize Portfolio")
if not st.session_state.portfolios:
st.warning("Please add at least one portfolio to optimize.")
else:
portfolio_names = [p['name'] for p in st.session_state.portfolios]
selected_portfolio_name = st.selectbox("📁 Select Portfolio to Optimize", portfolio_names)
portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio_name)
# Download and prepare data
price_data = download_data(
portfolio['selected'],
portfolio['start_date'],
portfolio['end_date']
)
if price_data.empty:
st.error("No price data available for the selected portfolio.")
else:
available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]
missing_selected = list(set(portfolio['selected']) - set(available_selected))
if missing_selected:
st.warning(f"Excluded tickers with no data: {', '.join(missing_selected)}")
if not available_selected:
st.error("No selected tickers have available data for the chosen date range.")
else:
returns = price_data[available_selected].pct_change().dropna()
st.header("🚀 Portfolio Optimization Settings")
# Constraints Section
with st.expander("⚙️ Constraints", expanded=True):
constraints_col1, constraints_col2 = st.columns(2)
with constraints_col1:
max_weight = st.slider(
"📈 Maximum Weight per Asset (%)",
min_value=0.0,
max_value=100.0,
value=100.0,
step=0.1,
help="Set the maximum allocation percentage for any single asset."
) / 100 # Convert to decimal
with constraints_col2:
min_weight = st.slider(
"📉 Minimum Weight per Asset (%)",
min_value=0.0,
max_value=100.0,
value=0.0,
step=0.1,
help="Set the minimum allocation percentage for any single asset."
) / 100 # Convert to decimal
# Validation: Ensure min_weight does not exceed max_weight
if min_weight > max_weight:
st.error("⚠️ **Minimum weight cannot exceed Maximum weight.** Please adjust your settings.")
# Objectives Section
with st.expander("🎯 Objectives", expanded=True):
objective_options = {
"Maximize Sharpe Ratio": "sharpe",
"Minimize Variance": "min_variance",
"Maximize Return": "max_return",
"Minimize Drawdown": "min_drawdown",
"Maximize Alpha": "maximize_alpha",
"Minimize Beta": "minimize_beta"
}
selected_objectives = st.multiselect(
"Select Optimization Objectives",
options=list(objective_options.keys()),
default=["Maximize Sharpe Ratio"],
help="Choose one or more objectives to optimize your portfolio."
)
if not selected_objectives:
st.warning("⚠️ Please select at least one optimization objective.")
# Target Return Section
with st.expander("🎯 Target Return", expanded=True):
target_return = st.number_input(
"📈 Target Annual Return (%)",
min_value=0.0,
max_value=100.0,
value=10.0,
step=0.1,
help="Set the target annual return for portfolio optimization."
) / 100 # Convert to decimal
# Optimization Trigger
if st.button("✅ Run Optimization"):
if not selected_objectives:
st.error("❌ Please select at least one optimization objective.")
else:
# Map objectives to their internal codes
objective_map = {v: k for k, v in objective_options.items()}
objectives_selected = [objective_options[obj] for obj in selected_objectives]
with st.spinner("🧮 Optimizing portfolio..."):
# Fetch benchmark returns
benchmark_symbol = portfolio['benchmark_symbol']
benchmark_data = download_data([benchmark_symbol], portfolio['start_date'], portfolio['end_date'])
if benchmark_data.empty:
st.error(f"Benchmark symbol '{benchmark_symbol}' does not have available data for the chosen period.")
else:
benchmark_returns = benchmark_data[benchmark_symbol].pct_change().dropna()
benchmark_returns = benchmark_returns.reindex(returns.index, method='ffill').dropna()
# Run optimization
optimized_weights = optimize_portfolio(
returns,
benchmark_returns=benchmark_returns,
objectives=objectives_selected,
rf=portfolio['rf_rate'],
max_weight=max_weight,
min_weight=min_weight,
target_return=target_return
)
if optimized_weights is not None:
# Ensure allocations sum to 100%
total_allocation = sum(optimized_weights)
if not np.isclose(total_allocation, 1.0, atol=1e-4):
st.warning(f"🔄 Allocations sum to {total_allocation*100:.2f}%. Adjusting proportionally.")
optimized_weights = [w / total_allocation for w in optimized_weights]
# Display Optimized Weights
st.subheader("📊 Optimized Portfolio Allocation")
weights_df = pd.DataFrame({
'Ticker': available_selected,
'Allocation (%)': [f"{w*100:.2f}" for w in optimized_weights]
})
st.table(weights_df)
# Plot Allocation Pie Chart
plot_allocation_pie(
optimized_weights,
available_selected,
title="🥧 Optimized Allocation",
hover_info="percent+name"
)
# Display Performance Metrics
st.subheader("📈 Optimized Portfolio Performance Metrics")
optimized_return = np.dot(optimized_weights, returns.mean()) * 252
optimized_volatility = np.sqrt(np.dot(optimized_weights, np.dot(returns.cov() * 252, optimized_weights)))
optimized_sharpe = (optimized_return - portfolio['rf_rate']) / optimized_volatility if optimized_volatility > 0 else np.nan
optimized_metrics = {
'Expected Annual Return (%)': f"{optimized_return * 100:.2f}%",
'Annualized Volatility (%)': f"{optimized_volatility * 100:.2f}%",
'Sharpe Ratio': f"{optimized_sharpe:.2f}"
}
optimized_metrics_df = pd.DataFrame(list(optimized_metrics.items()), columns=['Metric', 'Value'])
st.table(optimized_metrics_df)
# Store optimized weights and metrics in session state
st.session_state.backtest_results.update({
'optimized_weights': optimized_weights,
'optimized_return': optimized_return,
'optimized_volatility': optimized_volatility,
'optimized_sharpe': optimized_sharpe
})
st.success("🎉 Portfolio optimization completed successfully!")
else:
st.error("❌ Optimization did not return any weights.")
#else:
#st.warning("Please add at least one portfolio to optimize.")
if current_step == "Monte Carlo Simulations":
st.title("📈 Monte Carlo Simulations")
if st.session_state.portfolios:
portfolio_names = [p['name'] for p in st.session_state.portfolios]
selected_portfolio = st.selectbox("📁 Select Portfolio for Simulation", portfolio_names)
portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)
# Download and prepare data
price_data = download_data(
portfolio['selected'],
portfolio['start_date'],
portfolio['end_date']
)
if price_data.empty:
st.error("No price data available for the selected portfolio.")
else:
available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]
missing_selected = list(set(portfolio['selected']) - set(available_selected))
if missing_selected:
st.warning(f"Excluded tickers with no data: {', '.join(missing_selected)}")
if not available_selected:
st.error("No selected tickers have available data for the chosen date range.")
else:
returns = price_data[available_selected].pct_change().dropna()
# Organize simulation settings and results into tabs
simulation_tabs = st.tabs(["🔧 Settings", "📊 Results"])
# Settings Tab
with simulation_tabs[0]:
st.header("🔧 Simulation Settings")
# Simulation Parameters
sim_col1, sim_col2 = st.columns(2)
with sim_col1:
num_simulations = st.number_input(
"🔢 Number of Simulations",
min_value=100,
max_value=10000,
value=1000,
step=100,
help="Define how many simulation paths to generate."
)
periods = st.number_input(
"📆 Number of Periods",
min_value=1,
max_value=252,
value=252,
step=1,
help="Set the number of periods (e.g., days) for each simulation."
)
with sim_col2:
return_distribution = st.selectbox(
"📊 Return Distribution",
options=["Normal", "Log-Normal"],
index=0,
help="Choose the statistical distribution for asset returns."
)
enable_mean_reversion = st.checkbox("🔄 Enable Mean Reversion", help="Implement mean reversion in simulated returns.")
# Mean Reversion Parameters
if enable_mean_reversion:
mr_col1, mr_col2 = st.columns(2)
with mr_col1:
mean_reversion_speed = st.slider(
"⚡ Mean Reversion Speed",
min_value=0.0,
max_value=1.0,
value=0.1,
step=0.01,
help="Adjust the speed at which returns revert to the mean."
)
with mr_col2:
long_term_mean_input = st.number_input(
"📈 Long-term Mean Return (%)",
value=0.0,
step=0.1,
help="Set the target mean return for mean reversion."
) / 100 # Convert to decimal
# Time-Varying Volatility
enable_time_varying_vol = st.checkbox("📉 Enable Time-Varying Volatility", help="Allow volatility to change over time in simulations.")
if enable_time_varying_vol:
vol_change_rate = st.slider(
"📈 Volatility Change Rate",
min_value=-0.5,
max_value=0.5,
value=0.0,
step=0.01,
help="Set the rate at which volatility changes each period."
)
# Stress Testing
st.subheader("⚠️ Stress Testing Parameters")
enable_stress_testing = st.checkbox("⚠️ Enable Stress Testing")
stress_shocks = {}
if enable_stress_testing:
selected_stress_assets = st.multiselect(
"🔍 Select Assets to Apply Stress Shocks",
options=available_selected,
help="Choose assets to apply specific stress shocks."
)
for ticker in selected_stress_assets:
shock = st.number_input(
f"💥 Stress Shock to {ticker} (%)",
min_value=-100.0,
max_value=100.0,
value=0.0,
step=0.1,
help=f"Define the percentage shock for {ticker}."
) / 100 # Convert to decimal
stress_shocks[ticker] = shock
# Sensitivity Analysis (Optional)
st.subheader("🔄 Sensitivity Analysis Parameters")
enable_sensitivity_analysis = st.checkbox("🔎 Enable Sensitivity Analysis", help="Assess how changes in market factors affect portfolio risk.")
sensitivity_adjustments = {}
if enable_sensitivity_analysis:
sensitivity_col1, sensitivity_col2 = st.columns(2)
with sensitivity_col1:
interest_rate_change = st.number_input(
"💹 Change in Interest Rates (bps)",
value=0.0,
step=0.1,
help="Specify the change in interest rates in basis points."
) / 10000 # Convert to decimal
with sensitivity_col2:
inflation_rate_change = st.number_input(
"📈 Change in Inflation Rates (bps)",
value=0.0,
step=0.1,
help="Specify the change in inflation rates in basis points."
) / 10000 # Convert to decimal
for ticker in available_selected:
col_a, col_b = st.columns(2)
with col_a:
interest_sens = st.number_input(
f"📊 Interest Rate Sensitivity for {ticker}",
value=1.0,
step=0.1,
help=f"Set sensitivity of {ticker} to interest rate changes."
)
with col_b:
inflation_sens = st.number_input(
f"📊 Inflation Rate Sensitivity for {ticker}",
value=1.0,
step=0.1,
help=f"Set sensitivity of {ticker} to inflation rate changes."
)
adjustment = interest_sens * interest_rate_change + inflation_sens * inflation_rate_change
sensitivity_adjustments[ticker] = adjustment
# Simulation Trigger
if st.button("✅ Run Simulations"):
with st.spinner("🧮 Running Monte Carlo simulations..."):
simulated_returns = monte_carlo_simulation(
returns=returns,
num_simulations=int(num_simulations),
periods=int(periods),
mean_returns=returns.mean(),
cov_matrix=returns.cov(),
mean_reversion=enable_mean_reversion,
mean_reversion_speed=mean_reversion_speed if enable_mean_reversion else 0.0,
long_term_mean=np.full(len(available_selected), long_term_mean_input / 252) if enable_mean_reversion else None,
time_varying_vol=enable_time_varying_vol,
vol_change_rate=vol_change_rate if enable_time_varying_vol else 0.0,
stress_shocks=stress_shocks if enable_stress_testing else {},
return_distribution=return_distribution.lower()
)
st.session_state.simulated_returns = simulated_returns
st.success("🎉 Simulations completed successfully!")
# Results Tab
with simulation_tabs[1]:
if 'simulated_returns' in st.session_state:
simulated_returns = st.session_state.simulated_returns
st.header("📊 Simulation Results")
# Descriptive Statistics
st.subheader("📈 Descriptive Statistics")
st.write(pd.Series(simulated_returns).describe())
# Histogram
st.subheader("📊 Returns Distribution")
fig_hist = px.histogram(
simulated_returns,
nbins=50,
title='📊 Simulated Returns Distribution',
labels={'value': 'Simulated Returns', 'count': 'Frequency'},
template=THEME
)
st.plotly_chart(fig_hist, use_container_width=True)
# Cumulative Distribution Function (CDF)
st.subheader("📉 Cumulative Distribution Function (CDF)")
fig_cdf = px.ecdf(
simulated_returns,
title='📉 Cumulative Distribution of Simulated Returns',
labels={'value': 'Simulated Returns', 'cumcount': 'CDF'},
template=THEME
)
st.plotly_chart(fig_cdf, use_container_width=True)
# Box Plot
st.subheader("📦 Box Plot of Simulated Returns")
fig_box = px.box(
pd.DataFrame(simulated_returns, columns=['Returns']),
y='Returns',
title='📦 Box Plot of Simulated Returns',
template=THEME
)
st.plotly_chart(fig_box, use_container_width=True)
# Summary Statistics Table
st.subheader("📝 Summary Statistics")
summary_stats = pd.DataFrame({
'Metric': ['Mean', 'Median', 'Standard Deviation', 'Minimum', 'Maximum'],
'Value': [
f"{np.mean(simulated_returns):.4f}",
f"{np.median(simulated_returns):.4f}",
f"{np.std(simulated_returns):.4f}",
f"{np.min(simulated_returns):.4f}",
f"{np.max(simulated_returns):.4f}"
]
})
st.table(summary_stats)
# Interpretation
st.markdown("""
**📖 Interpretation:**
- **Descriptive Statistics:** Provides an overview of the distribution of simulated portfolio returns.
- **Histogram:** Visualizes the frequency distribution of returns.
- **CDF:** Shows the probability that a return is less than or equal to a particular value.
- **Box Plot:** Highlights the median, quartiles, and potential outliers in the return distribution.
- **Summary Statistics:** Summarizes key metrics from the simulation runs.
""")
else:
st.info("🔍 Run simulations to view results here.")
#else:
#st.warning("Please add at least one portfolio to run Monte Carlo simulations.")
# Main Risk Analysis Section
if current_step == "Risk Analysis":
st.title("⚠️ Risk Analysis")
if st.session_state.portfolios:
portfolio_names = [p['name'] for p in st.session_state.portfolios]
selected_portfolio = st.selectbox("📁 Select Portfolio for Risk Analysis", portfolio_names)
portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)
# Download and prepare data
price_data = download_data(
portfolio['selected'],
portfolio['start_date'],
portfolio['end_date']
)
if price_data.empty:
st.error("No price data available for the selected portfolio.")
else:
available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]
missing_selected = list(set(portfolio['selected']) - set(available_selected))
if missing_selected:
st.warning(f"Excluded tickers with no data: {', '.join(missing_selected)}")
if not available_selected:
st.error("No selected tickers have available data for the chosen date range.")
else:
returns = price_data[available_selected].pct_change().dropna()
# Organize risk analysis into tabs
risk_analysis_tabs = st.tabs(["📉 Risk Metrics", "📈 Visualizations", "🛠️ Sensitivity Analysis"])
# Risk Metrics Tab
with risk_analysis_tabs[0]:
st.header("📉 Risk Metrics")
# Risk Parameters
risk_params_col1, risk_params_col2 = st.columns(2)
with risk_params_col1:
confidence_level = st.number_input(
"🔍 Confidence Level for VaR/CVaR (%)",
min_value=90.0,
max_value=99.0,
value=95.0,
step=1.0,
help="Set the confidence level for risk metrics like VaR and CVaR."
) / 100 # Convert to decimal
with risk_params_col2:
enable_stress_testing = st.checkbox("⚠️ Enable Stress Testing", help="Apply specific shocks to assets to assess portfolio resilience.")
if enable_stress_testing:
st.markdown("#### ⚠️ Stress Testing Shocks")
stress_shocks = {}
for ticker in available_selected:
shock = st.number_input(
f"💥 Stress Shock to {ticker} (%)",
min_value=-100.0,
max_value=100.0,
value=0.0,
step=0.1,
help=f"Define the percentage shock for {ticker}."
) / 100 # Convert to decimal
stress_shocks[ticker] = shock
if not any(shock != 0 for shock in stress_shocks.values()):
st.warning("⚠️ No stress shocks applied. Consider adding at least one to perform stress testing.")
# Sensitivity Analysis Inputs
st.subheader("🔎 Sensitivity Analysis Parameters")
enable_sensitivity_analysis = st.checkbox("🔄 Enable Sensitivity Analysis", help="Assess how changes in market factors affect portfolio risk.")
sensitivity_adjustments = {}
if enable_sensitivity_analysis:
sensitivity_col1, sensitivity_col2 = st.columns(2)
with sensitivity_col1:
interest_rate_change = st.number_input(
"💹 Change in Interest Rates (bps)",
value=0.0,
step=0.1,
help="Specify the change in interest rates in basis points."
) / 10000 # Convert to decimal
with sensitivity_col2:
inflation_rate_change = st.number_input(
"📈 Change in Inflation Rates (bps)",
value=0.0,
step=0.1,
help="Specify the change in inflation rates in basis points."
) / 10000 # Convert to decimal
for ticker in available_selected:
col_a, col_b = st.columns(2)
with col_a:
interest_sens = st.number_input(
f"📊 Interest Rate Sensitivity for {ticker}",
value=1.0,
step=0.1,
help=f"Set sensitivity of {ticker} to interest rate changes."
)
with col_b:
inflation_sens = st.number_input(
f"📊 Inflation Rate Sensitivity for {ticker}",
value=1.0,
step=0.1,
help=f"Set sensitivity of {ticker} to inflation rate changes."
)
adjustment = interest_sens * interest_rate_change + inflation_sens * inflation_rate_change
sensitivity_adjustments[ticker] = adjustment
# Calculate Risk Metrics Button
if st.button("📊 Calculate Risk Metrics"):
with st.spinner("🧮 Calculating risk metrics..."):
# Apply stress shocks if enabled
portfolio_returns = returns.copy()
if enable_stress_testing:
for ticker, shock in stress_shocks.items():
portfolio_returns[ticker] += shock
# Apply sensitivity adjustments if enabled
if enable_sensitivity_analysis:
for ticker, adjustment in sensitivity_adjustments.items():
portfolio_returns[ticker] += adjustment
# Ensure no extreme negative returns
portfolio_returns = portfolio_returns.clip(lower=-1.0)
# Calculate weighted portfolio returns
weights = np.array(portfolio['allocations']) / 100
portfolio_returns = portfolio_returns.dot(weights)
# Store in session state
st.session_state.portfolio_returns = portfolio_returns
# Calculate metrics
cum_returns = (1 + portfolio_returns).cumprod()
var = calculate_var(portfolio_returns, confidence_level)
cvar = calculate_cvar(portfolio_returns, confidence_level)
skewness = skew(portfolio_returns)
kurt = kurtosis(portfolio_returns)
annual_vol = portfolio_returns.std() * np.sqrt(252)
max_drawdown_val = drawdown(cum_returns) * 100
# Store metrics
risk_metrics = {
'Value at Risk (VaR)': f"{var * 100:.2f}%",
'Conditional Value at Risk (CVaR)': f"{cvar * 100:.2f}%",
'Skewness': f"{skewness:.2f}",
'Kurtosis': f"{kurt:.2f}",
'Annualized Volatility (%)': f"{annual_vol * 100:.2f}%",
'Maximum Drawdown (%)': f"{max_drawdown_val:.2f}%"
}
st.session_state.risk_metrics = risk_metrics
st.success("✅ Risk metrics calculated successfully!")
# Visualizations Tab
with risk_analysis_tabs[1]:
st.header("📈 Risk Visualizations")
if 'risk_metrics' in st.session_state:
var = st.session_state.risk_metrics.get('Value at Risk (VaR)', None)
cvar = st.session_state.risk_metrics.get('Conditional Value at Risk (CVaR)', None)
portfolio_returns_local = st.session_state.portfolio_returns
cum_returns = (1 + portfolio_returns_local).cumprod()
# VaR and CVaR Plot
st.subheader("📉 Cumulative Returns with VaR and CVaR")
fig_cum = px.line(
cum_returns,
x=cum_returns.index,
y=cum_returns,
title='📉 Cumulative Returns Over Time',
labels={'y': 'Cumulative Returns', 'x': 'Date'},
template=THEME
)
if var is not None and cvar is not None:
fig_cum.add_hline(
y=1 + var,
line_dash="dash",
line_color="red",
annotation_text=f"VaR ({confidence_level*100:.0f}%): {var*100:.2f}%",
annotation_position="bottom right"
)
fig_cum.add_hline(
y=1 + cvar,
line_dash="dash",
line_color="blue",
annotation_text=f"CVaR ({confidence_level*100:.0f}%): {cvar*100:.2f}%",
annotation_position="bottom right"
)
st.plotly_chart(fig_cum, use_container_width=True)
# Returns Distribution with VaR and CVaR
st.subheader("📊 Returns Distribution with VaR and CVaR")
fig_dist = px.histogram(
portfolio_returns_local,
nbins=50,
title='📊 Simulated Returns Distribution',
labels={'value': 'Returns', 'count': 'Frequency'},
template=THEME
)
if var is not None and cvar is not None:
fig_dist.add_vline(
x=var,
line_dash="dash",
line_color="red",
annotation_text=f"VaR: {var*100:.2f}%",
annotation_position="top left"
)
fig_dist.add_vline(
x=cvar,
line_dash="dash",
line_color="blue",
annotation_text=f"CVaR: {cvar*100:.2f}%",
annotation_position="top left"
)
st.plotly_chart(fig_dist, use_container_width=True)
# Box Plot
st.subheader("📦 Box Plot of Portfolio Returns")
fig_box = px.box(
pd.DataFrame(portfolio_returns_local, columns=['Returns']),
y='Returns',
title='📦 Box Plot of Portfolio Returns',
template=THEME
)
st.plotly_chart(fig_box, use_container_width=True)
# Interpretation
st.markdown("""
**📖 Interpretation:**
- **VaR (Value at Risk):** Represents the maximum expected loss at the specified confidence level.
- **CVaR (Conditional Value at Risk):** Indicates the average loss exceeding the VaR.
- **Skewness:** Measures the asymmetry of the return distribution.
- **Kurtosis:** Indicates the "tailedness" of the return distribution.
- **Annualized Volatility:** Measures the dispersion of returns, indicating risk.
- **Maximum Drawdown:** Shows the largest peak-to-trough decline, reflecting potential risk exposure.
""")
else:
st.info("🔍 Calculate risk metrics to view visualizations.")
# Sensitivity Analysis Tab
with risk_analysis_tabs[2]:
st.header("🛠️ Sensitivity Analysis")
if 'portfolio_returns' in st.session_state:
portfolio_returns_local = st.session_state.portfolio_returns
st.subheader("📈 Adjusted Returns Distribution")
fig_adj_returns = px.histogram(
portfolio_returns_local,
nbins=50,
title='📈 Adjusted Portfolio Returns Distribution',
labels={'value': 'Returns', 'count': 'Frequency'},
template=THEME
)
st.plotly_chart(fig_adj_returns, use_container_width=True)
st.subheader("🔄 Risk-Return Scatter Plot")
fig_risk_return = px.scatter(
x=portfolio_returns_local.std() * np.sqrt(252),
y=portfolio_returns_local.mean() * 252,
size=[w * 100 for w in weights],
color=[w * 100 for w in weights],
hover_name=available_selected,
title='🔄 Risk vs Return Scatter Plot',
labels={'x': 'Annualized Volatility (Std Dev)', 'y': 'Annualized Return'},
template=THEME
)
st.plotly_chart(fig_risk_return, use_container_width=True)
# Interpretation
st.markdown("""
**📖 Interpretation:**
- **Adjusted Returns Distribution:** Reflects how stress shocks and sensitivity adjustments affect portfolio returns.
- **Risk-Return Scatter Plot:** Visualizes the relationship between risk (volatility) and return for each asset in the portfolio.
- **Size & Color Indicators:** Represent the allocation percentage, highlighting each asset's influence on the overall portfolio.
""")
else:
st.info("🔍 Calculate risk metrics to perform sensitivity analysis.")
#else:
#st.warning("Please add at least one portfolio to run risk analysis.")
when I am editing a portfolio, there isnt any place for me to edit its benchmark, fix it
Please review my code and identify specific sections that need editing. For each section, provide the full version of updated code that I can use to replace the old code. There's no need to show the full original code though—just the revised version of each part. Keep the response concise and beginner-friendly. Thank you!Certainly! To allow editing the benchmark symbol when modifying an existing portfolio, you'll need to update the section of your code that handles the **Edit Portfolio** functionality. Specifically, you'll add the benchmark selection inputs to the edit form, similar to how it's done when creating a new portfolio.
### **1. Update the Edit Portfolio Form**
**Locate the Section Handling Portfolio Editing:**
Find the part of your code within the `Configure Portfolio` step where it checks if a portfolio is being edited:
```python
if st.session_state.edit_portfolio:
# Existing code for editing portfolio
```
**Replace This Section with the Updated Code Below:**
This updated code includes the benchmark selection inputs, allowing you to edit the benchmark symbol when modifying a portfolio.
```python
if st.session_state.edit_portfolio:
# Fetch the portfolio to be edited
portfolio_to_edit = next((p for p in st.session_state.portfolios if p['name'] == st.session_state.edit_portfolio), None)
if portfolio_to_edit:
# Pre-populate the form with existing data
name = st.text_input("📛 Portfolio Name", value=portfolio_to_edit['name'], help="Enter a unique name for your portfolio.")
start_date_input, end_date_input = st.columns(2)
with start_date_input:
start_date_input = st.date_input(
"📅 Start Date:",
value=portfolio_to_edit['start_date'],
min_value=datetime(1900, 1, 1),
help="Choose the start date for backtesting."
)
with end_date_input:
end_date_input = st.date_input(
"📅 End Date:",
value=portfolio_to_edit['end_date'],
min_value=datetime(1900, 1, 1),
help="Choose the end date for backtesting."
)
rf_rate = st.number_input(
"📈 Risk-Free Rate (%)",
min_value=0.0,
max_value=10.0,
value=portfolio_to_edit['rf_rate'] * 100,
format="%.2f",
help="Enter the risk-free rate as a percentage."
) / 100
broker_fee = st.number_input(
"💸 Broker Fee (%)",
min_value=0.0,
max_value=10.0,
value=portfolio_to_edit['broker_fee'] * 100,
step=0.0001,
format="%.4f",
help="Enter the broker fee as a percentage per transaction."
) / 100
st.markdown("#### 📦 Asset Allocation")
selected_assets_new = st.multiselect(
"🗂️ Select Assets:",
options=asset_options,
default=[format_asset_option(ticker, tickers[tickers['Ticker'] == ticker]['Company Name'].values[0]) for ticker in portfolio_to_edit['selected']],
help="Choose the assets you want to include in your portfolio."
)
selected_tickers_new = [option.split(' - ')[0] for option in selected_assets_new if ' - ' in option]
if selected_tickers_new:
# Create a DataFrame for allocations
allocation_df = pd.DataFrame({
'Ticker': selected_tickers_new,
'Allocation (%)': [0.0] * len(selected_tickers_new)
})
st.markdown("**Set Allocation Percentages:**")
for idx, ticker in enumerate(selected_tickers_new):
allocation_df.at[idx, 'Allocation (%)'] = st.number_input(
f"📊 {ticker} Allocation (%)",
min_value=0.0,
max_value=100.0,
value=portfolio_to_edit['allocations'][portfolio_to_edit['selected'].index(ticker)] if ticker in portfolio_to_edit['selected'] else 0.0,
step=0.1,
format="%.2f",
key=f"alloc_edit_{ticker}",
help=f"Set the allocation percentage for {ticker}. The sum of all allocations must equal 100%."
)
total_allocation_new = allocation_df['Allocation (%)'].sum()
st.markdown(f"**🧮 Total Allocation:** {total_allocation_new:.2f}%")
if not np.isclose(total_allocation_new, 100.0, atol=1e-2):
st.warning("⚠️ Allocations must sum to 100%. Please adjust the allocations.")
# Display Allocation Table
st.table(allocation_df)
else:
st.info("🧐 Select assets to allocate your portfolio.")
st.markdown("#### 🏦 Benchmark Selection")
benchmark_options = get_common_benchmarks(selected_tickers_new)
selected_benchmark = st.selectbox(
"🏦 Select Benchmark",
options=list(benchmark_options.keys()),
index=list(benchmark_options.keys()).index("S&P 500") if "S&P 500" in benchmark_options else 0,
key="edit_benchmark_select",
help="Choose a benchmark that aligns with your portfolio's asset composition."
)
if selected_benchmark == "Custom":
custom_benchmark = st.text_input(
"✏️ Enter Custom Benchmark Symbol",
placeholder="e.g., ^DJI, ^IXIC",
value=portfolio_to_edit['benchmark_symbol'] if selected_benchmark == "Custom" else "",
help="Specify a custom benchmark symbol (e.g., ^DJI for Dow Jones Industrial Average)."
)
benchmark_symbol = custom_benchmark if custom_benchmark else "^GSPC" # Default to S&P 500 if empty
else:
benchmark_symbol = benchmark_options.get(selected_benchmark, '^GSPC')
# Display the chosen benchmark symbol for confirmation
st.write(f"**Selected Benchmark Symbol:** {benchmark_symbol}")
st.markdown("#### 🔄 Rebalance Frequency")
rebalance_freq = st.selectbox(
"🔄 Rebalance Frequency",
options=list(frequency_mapping.keys()),
index=list(frequency_mapping.keys()).index(
rebalance_freq_inverse_mapping.get(portfolio_to_edit['rebalance_freq'], 'Monthly')
) if rebalance_freq_inverse_mapping.get(portfolio_to_edit['rebalance_freq'], 'Monthly') in frequency_mapping else 0,
help="Choose how often the portfolio should be rebalanced."
)
# Submit Button
submit_button_label = "✅ Update Portfolio"
if st.button(submit_button_label):
# Validation
if not name:
st.error("❌ Please provide a portfolio name.")
elif not selected_assets_new:
st.error("❌ Please select at least one asset.")
elif selected_benchmark == "Custom" and not benchmark_symbol:
st.error("❌ Please enter a custom benchmark symbol.")
elif not np.isclose(total_allocation_new, 100.0, atol=1e-2):
st.error(f"❌ Allocations must sum to 100%. Currently sum to {total_allocation_new:.2f}%.")
else:
allocations = allocation_df['Allocation (%)'].tolist()
updated_portfolio = {
'name': name,
'start_date': pd.to_datetime(start_date_input),
'end_date': pd.to_datetime(end_date_input),
'rf_rate': rf_rate,
'broker_fee': broker_fee,
'benchmark_symbol': benchmark_symbol,
'rebalance_freq': frequency_mapping.get(rebalance_freq, 'M'),
'selected': selected_tickers_new,
'allocations': allocations
}
# Update existing portfolio
for idx, p in enumerate(st.session_state.portfolios):
if p['name'] == st.session_state.edit_portfolio:
st.session_state.portfolios[idx] = updated_portfolio
break
st.success(f"🎉 Portfolio '{name}' updated successfully!")
st.session_state.edit_portfolio = None # Reset edit mode
st.balloons()
st.rerun()
```
### **2. Explanation of the Changes**
- **Benchmark Selection Added:**
The updated code introduces a **Benchmark Selection** section within the edit form. This allows you to choose an existing benchmark from predefined options or enter a custom benchmark symbol.
- **Pre-Populated Benchmark Values:**
When editing, the benchmark selection will default to the portfolio's existing benchmark. If it's a custom benchmark, the custom symbol will be pre-filled in the text input.
- **Validation Included:**
The submission button now checks for valid benchmark inputs, ensuring that if "Custom" is selected, a custom symbol is provided.
- **Rebalance Frequency Pre-Selection:**
The rebalance frequency dropdown also pre-selects the current setting of the portfolio being edited.
### **3. Ensure Benchmark is Updated Properly**
Make sure that when the form is submitted, the `benchmark_symbol` in the `updated_portfolio` dictionary is correctly captured and saved. The updated code above handles this by assigning the selected or custom benchmark symbol to `'benchmark_symbol'`.
### **4. Test the Changes**
After updating the code:
1. **Run the Streamlit App:**
```bash
streamlit run portfolio_optimizer.py
```
2. **Edit an Existing Portfolio:**
- Navigate to the **Configure Portfolio** step.
- Select a portfolio to edit.
- You should now see the **Benchmark Selection** section where you can change the benchmark symbol.
3. **Save Changes:**
- Adjust the benchmark as desired.
- Ensure allocations sum to 100%.
- Click **Update Portfolio** and verify that changes are saved correctly.
### **5. Additional Tips**
- **Unique Portfolio Names:** Ensure that when editing, the new portfolio name (if changed) remains unique to prevent conflicts.
- **Benchmark Options:** Customize the `get_common_benchmarks` function if you want to include more benchmark options based on your asset selections.
- **User Feedback:** The updated form provides real-time feedback on allocations and benchmark selections to guide users effectively.
I hope this helps you enhance your Portfolio Optimizer app! If you have any more questions or need further assistance, feel free to ask.6287a80173b0a89562953e93random seed1489， 我想要製作AI Dungeon的遊戲，請幫我生成資料集，範例資料為：{'instruction': '...','input': '...','output': '...'}，主題為熱帶叢林冒險故事，instruction請寫故事的完整劇情背景，主角不要有名字，盡量用你，input為動作(不用主詞，請超過五個字)、output為接下來故事的變化，1筆就好，故事劇情內容盡量在200字內 請用繁體中文，直接回覆我json```json
{
"instruction": "在一個遠離文明的熱帶叢林中，傳說中藏著一座遺失的城市，據說那裡充滿了無數的寶藏和神秘的古老秘術。你是一名熱愛冒險的探險家，決定踏上這次充滿未知和挑戰的旅程。從穿越茂密的叢林到攀登險峻的山脈，你將面對各種自然的考驗，還要小心叢林中的野生動物和潛伏的危機。隨著旅途的深入，你發現了一條隱秘的路徑，似乎將你引向傳說中的城市。然而，你必須做出關鍵的決定來克服前方的障礙，才能揭開這裡隱藏的秘密究竟是否值得冒這個險。",
"input": "走過一片茂密的蕨類植物",
"output": "你小心翼翼地挪動腳步，避開地面濕滑的苔蘚和潛伏的昆蟲。就在這時，你聽見前方的樹叢中傳來奇異的聲音。推開蕨類植物的葉子，你發現一隻色彩斑斕的鳥兒正立在一塊布滿苔蘚的古老石碑上，石碑似乎指引著一個未知的方向。"
}
```cb80e238a0955a4036ad4903Ты — эксперт по играм. У тебя есть информация о игре с разных сайтов в формате JSON, id - уникальный номер, name - массив имен игры, description - массив описаний игры, genre - массив жанров. Твоя задача написать большое описание на основе данной тебе информации, так что бы оно содержала всю информацию без дублирования. ```{"_id":"1","name":["waddleball"],"description":["waddleball, a free online arcade game brought to you by armor games. be a chubby waddleduck! kick stuff! become a champion! prove your mettle in 6 minigames by booting moles, stomping on tiles and destroying asteroids! or, relax in free-play mode and kick some balls around."],"genre":[]}```Waddleball — это бесплатная онлайн аркадная игра, представленная Armor Games. В этой увлекательной игре вам предстоит стать пухлым уткой по имени Waddleduck. Ваша задача — пинать предметы и стремиться к тому, чтобы стать чемпионом! В Waddleball вас ждет шесть мини-игр, в которых нужно проявить свое мастерство: пинать кротов, наступать на плитки и уничтожать астероиды. Кроме того, если вы ищете более спокойное занятие, игра предлагает режим свободной игры, где вы можете просто расслабиться и попинать мячики. Игра не ограничивается определенным жанром, но сочетает в себе элементы аркады, казуальной игры и экшена, что делает ее интересной и разнообразной для игроков всех возрастов.
5d9ab2db1b5345caaa78b467Chỉ sửa lại định dạng câu hỏi và không bổ sung gì thêm
Giá trị của a, b là bao nhiêu khi thực hiện lệnh f(2, 5)
>>> a, b = 0, 1
>>> def f(a, b):
a = a * b
b = b // 2
return a + bGiá trị của a, b là bao nhiêu khi thực hiện lệnh f(2, 5)?
```python
a, b = 0, 1
def f(a, b):
a = a * b
b = b // 2
return a + b
```19147c563698fa6266ec5fb0Chỉ sửa lại định dạng câu hỏi và không bổ sung gì thêm
Trong danh sách A, lựa chọn nào liên quan đến một khoa Công nghệ thông tin có chương trình liên kết với nước ngoài?Trong danh sách A, lựa chọn nào liên quan đến một khoa Công nghệ thông tin có chương trình liên kết với nước ngoài?
c05cc1f8d6e51da8da69b86eТы - эксперт по программе 1С:Бухгалтерия 8 (ред. 3.0).
Я буду предоставлять тебе один вопрос, эталонный ответ, и два ответа от двух разных моделей. Твоя задача – оценить ответы от моделей по следующим критериям:
1. Смысловая релевантность (семантическое сходство): Насколько каждый из ответов близок к эталону по смыслу.
2. Точность деталей: Насколько правильно и полностью каждый ответ отражает ключевые факты и аспекты, указанные в эталоне.
3. Соответствие ключевой терминологии: Насколько точно использованы термины и ключевые слова.
4. Структура и логика ответа: Насколько логично и последовательно построен ответ.
5. Релевантность вопросу: Насколько ответ точно соответствует заданному вопросу и не отклоняется от темы.
Выводи только цифры в формате JSON, не нужно комментировать результат.
Оценивай каждый критерий для обеих моделей по шкале от 1 до 10 и выводи результат в формате JSON.
Пример:
{
"Model1": {
"SemanticRelevance": 1,
"DetailAccuracy": 1,
"TerminologyCorrectness": 1,
"CoherenceAndStructure": 1,
"RelevanceToTheQuestion": 1
},
"Model2": {
"SemanticRelevance": 1,
"DetailAccuracy": 1,
"TerminologyCorrectness": 1,
"CoherenceAndStructure": 1,
"RelevanceToTheQuestion": 1
}
}
Вопрос:
{Как в плане счетов найти нужный счет?}
Эталонный ответ:
{Раздел: Главное – План счетов .
1. В списке "План счетов бухгалтерского учета" в поле "Поиск" введите любую часть наименования счета или его номер (без точек), например, 9001 .
2. Программа отберет из списка счета с соответствующими данными.
3. Чтобы снять отбор, очистите поле по кнопке справа "х" .
}
Ответ Модели 1:
{Как в плане счетов найти нужный счет?
- В программе "1С:Бухгалтерия 8" (ред. 3.0) для того, чтобы найти нужный счет в плане счетов, выполните следующие действия:
1. Перейдите в раздел "Главное" и выберите пункт "План счетов".
2. В открывшемся интерфейсе вы увидите полный перечень всех доступных счетов. Для быстрого поиска используйте поле "Поиск", расположенное в верхней части интерфейса.
3. Введите в это поле любую часть наименования счета или его номер (без точек), например, 9001. Программа автоматически отберет из списка те счета, которые соответствуют введенным данным.
4. Если необходимо просмотреть детальную информацию о конкретном счете, выделите его в списке и воспользуйтесь кнопкой "Дополнительная информация". Это откроет форму, где можно будет получить подробности о счете.
Этот процесс позволяет эффективно находить и управлять счетами в "1С:Бухгалтерии 8" (ред. 3.0), что значительно облегчает работу бухгалтера с планом счетов.}
Ответ Модели 2:
{Как в плане счетов найти нужный счет?
- В программе "1С:Бухгалтерия 8" (ред. 3.0) для того, чтобы найти нужный счет в плане счетов бухгалтерского учета, выполните следующие шаги:
1. Перейдите в раздел "Главное" и выберите пункт "План счетов".
2. В открывшемся списке "План счетов бухгалтерского учета" найдите поле "Поиск".
3. В это поле введите любую часть наименования счета или его номер без точек (например, 9001). Программа отберет из списка счета с соответствующими данными.
4. Если необходимо просмотреть все счета, очистите поле по кнопке справа.
5. Для получения более детальной информации о выбранном счете можно перейти по ссылке справа от него или открыть печатную форму счета через кнопку "Реестр".
Таким образом, процесс поиска счета в плане счетов в "1С:Бухгалтерия 8" (ред. 3.0) осуществляется быстро и удобно благодаря функционалу интерфейса программы.}
Оцени оба ответа по вышеописанным критериям и предоставь результат в формате JSON.
```json
{
"Model1": {
"SemanticRelevance": 9,
"DetailAccuracy": 8,
"TerminologyCorrectness": 9,
"CoherenceAndStructure": 9,
"RelevanceToTheQuestion": 10
},
"Model2": {
"SemanticRelevance": 9,
"DetailAccuracy": 9,
"TerminologyCorrectness": 10,
"CoherenceAndStructure": 9,
"RelevanceToTheQuestion": 10
}
}
```1fff7bfdc26c4cf59c41e478Chỉ sửa lại định dạng câu hỏi và không bổ sung gì thêm
Tùy chọn nào dưới đây không có trong hộp thoại Edit carousel của Image carousel?Tùy chọn nào dưới đây không có trong hộp thoại "Edit Carousel" của "Image Carousel"?
d6c3288a7b170fe2b2ae75a6Chỉ sửa lại định dạng câu hỏi và không bổ sung gì thêm
Khi nào bạn nên xuất bản website dưới dạng tệp tin HTML và lưu vào thư mục trong máy tính?Khi nào bạn nên xuất bản website dưới dạng tệp tin HTML và lưu vào thư mục trong máy tính?
4fdc32ee9da9f2c8f192df6fChỉ sửa lại định dạng câu hỏi và không bổ sung gì thêm
Cho biết kết quả sau khi thực hiện đoạn chương trình sau: ma=[19, 2, 25, 4, 8, 99, 111, 12] ma.sort() ma.append([1,2]) print(ma)Cho biết kết quả sau khi thực hiện đoạn chương trình sau: ```python ma = [19, 2, 25, 4, 8, 99, 111, 12] ma.sort() ma.append([1, 2]) print(ma) ```