https://github.com/jordicorbilla/riskoptima
The RiskOptima toolkit is a comprehensive Python solution designed to assist investors in evaluating, managing, and optimizing the risk of their investment portfolios. This package implements advanced financial metrics and models to compute key risk indicators, including Value at Risk (VaR), Conditional Value at Risk (CVaR), and volatility assessme
https://github.com/jordicorbilla/riskoptima
cvar-optimization monte-carlo portfolio-optimization risk-management var-optimization
Last synced: 6 months ago
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The RiskOptima toolkit is a comprehensive Python solution designed to assist investors in evaluating, managing, and optimizing the risk of their investment portfolios. This package implements advanced financial metrics and models to compute key risk indicators, including Value at Risk (VaR), Conditional Value at Risk (CVaR), and volatility assessme
- Host: GitHub
- URL: https://github.com/jordicorbilla/riskoptima
- Owner: JordiCorbilla
- License: mit
- Created: 2024-05-26T17:37:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-13T20:39:39.000Z (6 months ago)
- Last Synced: 2025-04-15T05:48:37.824Z (6 months ago)
- Topics: cvar-optimization, monte-carlo, portfolio-optimization, risk-management, var-optimization
- Language: Jupyter Notebook
- Homepage:
- Size: 98.1 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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
### 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 |
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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](https://github.com/JordiCorbilla/RiskOptima)
## 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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