Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
Quantitative Strategic Asset Allocation, Easy for Everyone.
Riskfolio-Lib is a library for making quantitative strategic asset allocation
or portfolio optimization in Python made in Peru 🇵🇪. Its objective is to help students, academics and practitioners to build investment portfolios based on mathematically complex models with low effort. It is built on top of
CVXPY and closely integrated
with Pandas data structures.
Some of key functionalities that Riskfolio-Lib offers:
Mean Risk and Logarithmic Mean Risk (Kelly Criterion) Portfolio Optimization with 4 objective functions:
Mean Risk and Logarithmic Mean Risk (Kelly Criterion) Portfolio Optimization with 24 convex risk measures:
Dispersion Risk Measures:
**Downside Risk Measures:**
- Semi Standard Deviation.
- Square Root Semi Kurtosis.
- First Lower Partial Moment (Omega Ratio).
- Second Lower Partial Moment (Sortino Ratio).
- Conditional Value at Risk (CVaR).
- Tail Gini.
- Entropic Value at Risk (EVaR).
- Relativistic Value at Risk (RLVaR).
- Worst Case Realization (Minimax).
**Drawdown Risk Measures:**
- Average Drawdown for uncompounded cumulative returns.
- Ulcer Index for uncompounded cumulative returns.
- Conditional Drawdown at Risk (CDaR) for uncompounded cumulative returns.
- Entropic Drawdown at Risk (EDaR) for uncompounded cumulative returns.
- Relativistic Drawdown at Risk (RLDaR) for uncompounded cumulative returns.
- Maximum Drawdown (Calmar Ratio) for uncompounded cumulative returns.
Risk Parity Portfolio Optimization with 20 convex risk measures:
Dispersion Risk Measures:
**Downside Risk Measures:**
- Semi Standard Deviation.
- Square Root Semi Kurtosis.
- First Lower Partial Moment (Omega Ratio)
- Second Lower Partial Moment (Sortino Ratio)
- Conditional Value at Risk (CVaR).
- Tail Gini.
- Entropic Value at Risk (EVaR).
- Relativistic Value at Risk (RLVaR).
**Drawdown Risk Measures:**
- Ulcer Index for uncompounded cumulative returns.
- Conditional Drawdown at Risk (CDaR) for uncompounded cumulative returns.
- Entropic Drawdown at Risk (EDaR) for uncompounded cumulative returns.
- Relativistic Drawdown at Risk (RLDaR) for uncompounded cumulative returns.
Hierarchical Clustering Portfolio Optimization: Hierarchical Risk Parity (HRP) and Hierarchical Equal Risk Contribution (HERC) with 35 risk measures using naive risk parity:
Dispersion Risk Measures:
**Downside Risk Measures:**
- Semi Standard Deviation.
- Fourth Root Semi Kurtosis.
- First Lower Partial Moment (Omega Ratio).
- Second Lower Partial Moment (Sortino Ratio).
- Value at Risk (VaR).
- Conditional Value at Risk (CVaR).
- Tail Gini.
- Entropic Value at Risk (EVaR).
- Relativistic Value at Risk (RLVaR).
- Worst Case Realization (Minimax).
**Drawdown Risk Measures:**
- Average Drawdown for compounded and uncompounded cumulative returns.
- Ulcer Index for compounded and uncompounded cumulative returns.
- Drawdown at Risk (DaR) for compounded and uncompounded cumulative returns.
- Conditional Drawdown at Risk (CDaR) for compounded and uncompounded cumulative returns.
- Entropic Drawdown at Risk (EDaR) for compounded and uncompounded cumulative returns.
- Relativistic Drawdown at Risk (RLDaR) for compounded and uncompounded cumulative returns.
- Maximum Drawdown (Calmar Ratio) for compounded and uncompounded cumulative returns.
Nested Clustered Optimization (NCO) with four objective functions and the available risk measures to each objective:
Worst Case Mean Variance Portfolio Optimization.
Online documentation is available at Documentation.
The docs include a tutorial
with examples that shows the capacities of Riskfolio-Lib.
Due to Riskfolio-Lib is based on CVXPY, Riskfolio-Lib can use the same solvers available for CVXPY. The list of solvers compatible with CVXPY is available in Choosing a solver section of CVXPY’s documentation. However, to select an adequate solver for each risk measure we can use the following table that specifies which type of programming technique is used to model each risk measure.
Risk Measure | LP | QP | SOCP | SDP | EXP | POW |
---|---|---|---|---|---|---|
Variance (MV) | X | X* | ||||
Mean Absolute Deviation (MAD) | X | |||||
Gini Mean Difference (GMD) | X** | |||||
Semi Variance (MSV) | X | |||||
Kurtosis (KT) | X | |||||
Semi Kurtosis (SKT) | X | |||||
First Lower Partial Moment (FLPM) | X | |||||
Second Lower Partial Moment (SLPM) | X | |||||
Conditional Value at Risk (CVaR) | X | |||||
Tail Gini (TG) | X** | |||||
Entropic Value at Risk (EVaR) | X | |||||
Relativistic Value at Risk (RLVaR) | X** | |||||
Worst Realization (WR) | X | |||||
CVaR Range (CVRG) | X | |||||
Tail Gini Range (TGRG) | X** | |||||
EVaR Range (EVRG) | X | |||||
RLVaR Range (RVRG) | X** | |||||
Range (RG) | X | |||||
Average Drawdown (ADD) | X | |||||
Ulcer Index (UCI) | X | |||||
Conditional Drawdown at Risk (CDaR) | X | |||||
Entropic Drawdown at Risk (EDaR) | X | |||||
Relativistic Drawdown at Risk (RLDaR) | X** | |||||
Maximum Drawdown (MDD) | X |
(*) When SDP graph theory constraints are included. In the case of integer programming graph theory constraints, the model assume the SOCP formulation.
(**) For these models is highly recommended to use MOSEK as solver, due to in some cases CLARABEL cannot find a solution and SCS takes too much time to solve them.
LP - Linear Programming refers to problems with a linear objective function and linear constraints.
QP - Quadratic Programming refers to problems with a quadratic objective function and linear constraints.
SOCP - Second Order Cone Programming refers to problems with second-order cone constraints.
SDP - Semidefinite Programming refers to problems with positive semidefinite constraints.
EXP - refers to problems with exponential cone constraints.
POW - refers to problems with 3-dimensional power cone constraints.
Riskfolio-Lib supports Python 3.9 or higher.
Installation requires:
The latest stable release (and older versions) can be installed from PyPI:
pip install riskfolio-lib
If you use Riskfolio-Lib for published work, please use the following BibTeX entry:
@misc{riskfolio,
author = {Dany Cajas},
title = {Riskfolio-Lib (7.0.1)},
year = {2025},
url = {https://github.com/dcajasn/Riskfolio-Lib},
}
Riskfolio-Lib development takes place on Github: https://github.com/dcajasn/Riskfolio-Lib
Riskfolio-Lib is an open-source project, but since it’s a project that is not financed for any institution, I started charging for consultancies that are not related to errors in source code. Our fees are as follows:
All consulting must be paid in advance.
You can contact me through:
You can pay using one of the following channels:
The plan for this module is to add more functions that will be very useful
to asset managers.