riskoptima 2.2.0

RiskOptima is a powerful Python toolkit for financial risk analysis, portfolio optimization, and advanced quantitative modeling. It integrates state-of-the-art methodologies, including Monte Carlo simulations, Value at Risk (VaR), Conditional VaR (CVaR), Black-Scholes, Heston, and Merton Jump Diffusion models, to aid investors in making data-driven investment decisions.

RiskOptima

RiskOptima is a comprehensive Python toolkit for evaluating, managing, and optimizing investment portfolios. This package is designed to empower investors and data scientists by combining financial risk analysis, backtesting, mean-variance optimization, and machine learning capabilities into a single, cohesive package.

Stats

https://pypistats.org/packages/riskoptima

Key Features

• Portfolio Optimization: Includes mean-variance optimization, efficient frontier calculation, and maximum Sharpe ratio portfolio construction.
• Risk Management: Compute key financial risk metrics such as Value at Risk (VaR), Conditional Value at Risk (CVaR), volatility, and drawdowns.
• Backtesting Framework: Simulate historical performance of investment strategies and analyze portfolio dynamics over time.
• Machine Learning Integration: Future-ready for implementing machine learning models for predictive analytics and advanced portfolio insights.
• Monte Carlo Simulations: Perform extensive simulations to analyze potential portfolio outcomes. See example here https://github.com/JordiCorbilla/efficient-frontier-monte-carlo-portfolio-optimization
• Comprehensive Financial Metrics: Calculate returns, Sharpe ratios, covariance matrices, and more.

Installation

See the project here: https://pypi.org/project/riskoptima/

pip install riskoptima

Usage

New modular API (backtest + factor risk + constraints)

import pandas as pd
from riskoptima import FactorRiskModel, Constraints, optimize_max_sharpe
from riskoptima import SMACrossStrategy, run_backtest, BacktestConfig, SimpleCostModel

# prices: DataFrame with Date index and asset columns
prices = pd.read_csv("prices.csv", index_col=0, parse_dates=True)
asset_returns = prices.pct_change().dropna()

# factors: Fama-French returns DataFrame (e.g. from RiskOptima.get_fff_returns)
factors = pd.read_csv("fama_french_factors.csv", index_col=0, parse_dates=True)

factor_model = FactorRiskModel(factor_returns=factors).fit(asset_returns)
factor_cov = factor_model.covariance_matrix()

constraints = Constraints(factor_bounds={"MKT": (-0.2, 0.8)})
weights = optimize_max_sharpe(
    expected_returns=asset_returns.mean() * 252,
    cov=factor_cov,
    constraints=constraints,
    factor_exposures=factor_model.exposures,
    risk_free_rate=0.02,
)

strategy = SMACrossStrategy(short_window=20, long_window=50)
config = BacktestConfig(initial_cash=1_000_000, rebalance_rule="D")
cost_model = SimpleCostModel(spread_bps=2.0, impact_coeff=0.0)
equity_curve, weights_history = run_backtest(prices, strategy, config, cost_model)

See examples/example_factor_backtest.py for a runnable end-to-end example.

Example 1: Setting up your portfolio

Create your portfolio table similar to the below:

Asset	Weight	Label	MarketCap
MO	0.04	Altria Group Inc.	110.0e9
NWN	0.14	Northwest Natural Gas	1.8e9
BKH	0.01	Black Hills Corp.	4.5e9
ED	0.01	Con Edison	30.0e9
PEP	0.09	PepsiCo Inc.	255.0e9
NFG	0.16	National Fuel Gas	5.6e9
KO	0.06	Coca-Cola Company	275.0e9
FRT	0.28	Federal Realty Inv. Trust	9.8e9
GPC	0.16	Genuine Parts Co.	25.3e9
MSEX	0.05	Middlesex Water Co.	2.4e9

import pandas as pd
from riskoptima import RiskOptima

import warnings
warnings.filterwarnings(
    "ignore", 
    category=FutureWarning, 
    message=".*DataFrame.std with axis=None is deprecated.*"
)

# Define your current porfolio with your weights and company names
asset_data = [
    {"Asset": "MO",    "Weight": 0.04, "Label": "Altria Group Inc.",       "MarketCap": 110.0e9},
    {"Asset": "NWN",   "Weight": 0.14, "Label": "Northwest Natural Gas",   "MarketCap": 1.8e9},
    {"Asset": "BKH",   "Weight": 0.01, "Label": "Black Hills Corp.",         "MarketCap": 4.5e9},
    {"Asset": "ED",    "Weight": 0.01, "Label": "Con Edison",                "MarketCap": 30.0e9},
    {"Asset": "PEP",   "Weight": 0.09, "Label": "PepsiCo Inc.",              "MarketCap": 255.0e9},
    {"Asset": "NFG",   "Weight": 0.16, "Label": "National Fuel Gas",         "MarketCap": 5.6e9},
    {"Asset": "KO",    "Weight": 0.06, "Label": "Coca-Cola Company",         "MarketCap": 275.0e9},
    {"Asset": "FRT",   "Weight": 0.28, "Label": "Federal Realty Inv. Trust", "MarketCap": 9.8e9},
    {"Asset": "GPC",   "Weight": 0.16, "Label": "Genuine Parts Co.",         "MarketCap": 25.3e9},
    {"Asset": "MSEX",  "Weight": 0.05, "Label": "Middlesex Water Co.",       "MarketCap": 2.4e9}
]
asset_table = pd.DataFrame(asset_data)

capital = 100_000

asset_table['Portfolio'] = asset_table['Weight'] * capital

ANALYSIS_START_DATE = RiskOptima.get_previous_year_date(RiskOptima.get_previous_working_day(), 1)
ANALYSIS_END_DATE   = RiskOptima.get_previous_working_day()
BENCHMARK_INDEX     = 'SPY'
RISK_FREE_RATE      = 0.05
NUMBER_OF_WEIGHTS   = 10_000
NUMBER_OF_MC_RUNS   = 1_000

Example 1: Creating a Portfolio Area Chart

If you want to know visually how's your portfolio doing right now

RiskOptima.create_portfolio_area_chart(
    asset_table,
    end_date=ANALYSIS_END_DATE,
    lookback_days=2,
    title="Portfolio Area Chart"
)

Example 2: Efficient Frontier - Monte Carlo Portfolio Optimization

RiskOptima.plot_efficient_frontier_monte_carlo(
    asset_table,
    start_date=ANALYSIS_START_DATE,
    end_date=ANALYSIS_END_DATE,
    risk_free_rate=RISK_FREE_RATE,
    num_portfolios=NUMBER_OF_WEIGHTS,
    market_benchmark=BENCHMARK_INDEX,
    set_ticks=False,
    x_pos_table=1.15,    # Position for the weight table on the plot
    y_pos_table=0.52,    # Position for the weight table on the plot
    title=f'Efficient Frontier - Monte Carlo Simulation {ANALYSIS_START_DATE} to {ANALYSIS_END_DATE}'
)

Example 3: Portfolio Optimization using Mean Variance and Machine Learning

RiskOptima.run_portfolio_optimization_mv_ml(
    asset_table=asset_table,
    training_start_date='2022-01-01',
    training_end_date='2023-11-27',
    model_type='Linear Regression',    
    risk_free_rate=RISK_FREE_RATE,
    num_portfolios=100000,
    market_benchmark=[BENCHMARK_INDEX],
    max_volatility=0.25,
    min_weight=0.03,
    max_weight=0.2
)

Example 4: Portfolio Optimization using Probability Analysis

RiskOptima.run_portfolio_probability_analysis(
    asset_table=asset_table,
    analysis_start_date=ANALYSIS_START_DATE,
    analysis_end_date=ANALYSIS_END_DATE,
    benchmark_index=BENCHMARK_INDEX,
    risk_free_rate=RISK_FREE_RATE,
    number_of_portfolio_weights=NUMBER_OF_WEIGHTS,
    trading_days_per_year=RiskOptima.get_trading_days(),
    number_of_monte_carlo_runs=NUMBER_OF_MC_RUNS
)

Example 5: Macaulay Duration

from riskoptima import RiskOptima
cf = RiskOptima.bond_cash_flows_v2(4, 1000, 0.06, 2)  # 2 years, semi-annual, hence 4 periods
md_2 = RiskOptima.macaulay_duration_v3(cf, 0.05, 2)
md_2

Example 6: Market Turns with SPY & VIX Divergence

ANALYSIS_START_DATE = RiskOptima.get_previous_year_date(RiskOptima.get_previous_working_day(), 1)
ANALYSIS_END_DATE   = RiskOptima.get_previous_working_day()

df_signals, df_exits, returns = RiskOptima.run_index_vol_divergence_signals(start_date=ANALYSIS_START_DATE, 
                                                                            end_date=ANALYSIS_END_DATE)

Documentation

For complete documentation and usage examples, visit the GitHub repository:

RiskOptima GitHub

Contributing

We welcome contributions! If you'd like to improve the package or report issues, please visit the GitHub repository.

License

RiskOptima is licensed under the MIT License.

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