skfolio 0.0.6

Portfolio optimization built on top of scikit-learn

skfolio

skfolio is a Python library for portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models. It is distributed under the 3-Clause BSD license.

Important links

• Documentation: https://skfolio.github.io/skfolio/

• Examples: https://skfolio.github.io/skfolio/auto_examples/

• User Guide: https://skfolio.github.io/skfolio/user_guide/

• GitHub Repo: https://github.com/skfolio/skfolio/

Installation

To install skfolio:

pip install -U skfolio

Dependencies

skfolio requires:

• python (>= 3.10)

• numpy (>= 1.23.4)

• scipy (>= 1.8.0)

• pandas (>= 1.4.1)

• cvxpy (>= 1.4.1)

• scikit-learn (>= 1.3.2)

• joblib (>= 1.3.2)

• plotly (>= 5.15.0)

Key Concepts

Since the development of modern portfolio theory by Markowitz (1952), mean-variance optimization (MVO) has received considerable attention. Unfortunately it faces a number of shortcomings including high sensitivity to the input parameters (expected returns and covariance), weight concentration, high turnover and poor out-of-sample performance. It is well known that naive allocation (1/N, inverse-vol, …) tend to outperform MVO out-of-sample (DeMiguel, 2007).

Numerous approaches have been developed to alleviate these shortcomings (shrinkage, bayesian approaches, additional constraints, regularization, uncertainty set, higher moments, coherent risk measures, left-tail risk optimization, distributionally robust optimization, factor model, risk-parity, hierarchical clustering, ensemble methods…)

With this large number of methods, added to the fact that they can be composed together there is the need for an unified framework to perform model selection, validation and parameter tuning while reducing the risk of data leakage and overfitting. This framework is build on scikit-learn’s API.

Available models

The current release contains:

• Optimization estimators:

  • Naive:

    • Equal-Weighted

    • Inverse-Volatility

    • Random (dirichlet)

  • Convex:

    • Mean-Risk

    • Risk Budgeting

    • Maximum Diversification

    • Distributionally Robust CVaR

  • Clustering:

    • Hierarchical Risk Parity

    • Hierarchical Equal Risk Contribution

    • Nested Clusters Optimization

  • Ensemble methods:

    • Stacking Optimization

• Moment estimators:

  • Expected Returns:

    • Empirical

    • Exponentially Weighted

    • Equilibrium

    • Shrinkage (James-Stein, Bayes-Stein, …)

  • Covariance:

    • Empirical

    • Gerber

    • Denoising

    • Denoting

    • Exponentially Weighted

    • Ledoit-Wolf

    • Oracle Approximating Shrinkage

    • Shrunk Covariance

    • Graphical lasso CV

• Distance estimator:

  • Pearson Distance

  • Kendall Distance

  • Spearman Distance

  • Covariance Distance (based on any of the above covariance estimators)

  • Distance Correlation

  • Variation of Information

• Prior estimators:

  • Empirical

  • Black & Litterman

  • Factor Model

• Uncertainty Set estimators:

  • On Expected Returns:

    • Empirical

    • Circular Bootstrap

  • On Covariance:

    • Empirical

    • Circular bootstrap

• Pre-Selection transformers:

  • Non-Dominated Selection

  • Select K Extremes (Best or Worst)

  • Drop Highly Correlated Assets

• Cross-Validation and Model Selection:

  • Compatible with all sklearn methods (KFold, …)

  • Walk Forward

  • Combinatorial Purged Cross-validation

• Hyper-Parameter Tuning:

  • Compatible with all sklearn methods (GridSearchCV, RandomizedSearchCV, …)

• Risk Measures:

  • Variance

  • Semi-Variance

  • Mean Absolute Deviation

  • First Lower Partial Moment

  • CVaR (Conditional Value at Risk)

  • EVaR (Entropic Value at Risk)

  • Worst Realization

  • CDaR (Conditional Drawdown at Risk)

  • Maximum Drawdown

  • Average Drawdown

  • EDaR (Entropic Drawdown at Risk)

  • Ulcer Index

  • Gini Mean Difference

  • Value at Risk

  • Drawdown at Risk

  • Entropic Risk Measure

  • Fourth Central Moment

  • Fourth Lower Partial Moment

  • Skew

  • Kurtosis

Quickstart

The code snippets below are designed to introduce skfolio’s functionality so you can start using it quickly.

Imports

from sklearn import set_config
from sklearn.model_selection import (
    GridSearchCV,
    KFold,
    RandomizedSearchCV,
    train_test_split,
)
from sklearn.pipeline import Pipeline
from scipy.stats import loguniform

from skfolio import RatioMeasure, RiskMeasure
from skfolio.datasets import load_factors_dataset, load_sp500_dataset
from skfolio.model_selection import (
    CombinatorialPurgedCV,
    WalkForward,
    cross_val_predict,
)
from skfolio.moments import (
    DenoiseCovariance,
    DenoteCovariance,
    EWMu,
    GerberCovariance,
    ShrunkMu,
)
from skfolio.optimization import (
    MeanRisk,
    NestedClustersOptimization,
    ObjectiveFunction,
    RiskBudgeting,
)
from skfolio.pre_selection import SelectKExtremes
from skfolio.preprocessing import prices_to_returns
from skfolio.prior import BlackLitterman, EmpiricalPrior, FactorModel
from skfolio.uncertainty_set import BootstrapMuUncertaintySet

Load Dataset

prices = load_sp500_dataset()

Train/Test split

X = prices_to_returns(prices)
X_train, X_test = train_test_split(X, test_size=0.33, shuffle=False)

Minimum Variance

model = MeanRisk()

Fit on Training Set

model.fit(X_train)

print(model.weights_)

Predict on Test Set

portfolio = model.predict(X_test)

print(portfolio.annualized_sharpe_ratio)
print(portfolio.summary())

Maximum Sortino Ratio

model = MeanRisk(
    objective_function=ObjectiveFunction.MAXIMIZE_RATIO,
    risk_measure=RiskMeasure.SEMI_VARIANCE,
)

Denoised Covariance & Shrunk Expected Returns

model = MeanRisk(
    objective_function=ObjectiveFunction.MAXIMIZE_RATIO,
    prior_estimator=EmpiricalPrior(
        mu_estimator=ShrunkMu(), covariance_estimator=DenoiseCovariance()
    ),
)

Uncertainty Set on Expected Returns

model = MeanRisk(
    objective_function=ObjectiveFunction.MAXIMIZE_RATIO,
    mu_uncertainty_set_estimator=BootstrapMuUncertaintySet(),
)

Weight Constraints & Transaction Costs

model = MeanRisk(
    min_weights={"AAPL": 0.10, "JPM": 0.05},
    max_weights=0.8,
    transaction_costs={"AAPL": 0.0001, "RRC": 0.0002},
    groups=[
        ["Equity"] * 3 + ["Fund"] * 5 + ["Bond"] * 12,
        ["US"] * 2 + ["Europe"] * 8 + ["Japan"] * 10,
    ],
    linear_constraints=[
        "Equity <= 0.5 * Bond",
        "US >= 0.1",
        "Europe >= 0.5 * Fund",
        "Japan <= 1",
    ],
)
model.fit(X_train)

Risk Parity on CVaR

model = RiskBudgeting(risk_measure=RiskMeasure.CVAR)

Risk Parity & Gerber Covariance

model = RiskBudgeting(
    prior_estimator=EmpiricalPrior(covariance_estimator=GerberCovariance())
)

Nested Cluster Optimization with Cross-Validation and Parallelization

model = NestedClustersOptimization(
    inner_estimator=MeanRisk(risk_measure=RiskMeasure.CVAR),
    outer_estimator=RiskBudgeting(risk_measure=RiskMeasure.VARIANCE),
    cv=KFold(),
    n_jobs=-1,
)

Randomized Search of the L2 Norm

randomized_search = RandomizedSearchCV(
    estimator=MeanRisk(),
    cv=WalkForward(train_size=255, test_size=60),
    param_distributions={
        "l2_coef": loguniform(1e-3, 1e-1),
    },
)
randomized_search.fit(X_train)

best_model = randomized_search.best_estimator_

print(best_model.weights_)

Grid Search on Embedded Parameters

model = MeanRisk(
    objective_function=ObjectiveFunction.MAXIMIZE_RATIO,
    risk_measure=RiskMeasure.VARIANCE,
    prior_estimator=EmpiricalPrior(mu_estimator=EWMu(alpha=0.2)),
)

print(model.get_params(deep=True))

gs = GridSearchCV(
    estimator=model,
    cv=KFold(n_splits=5, shuffle=False),
    n_jobs=-1,
    param_grid={
        "risk_measure": [
            RiskMeasure.VARIANCE,
            RiskMeasure.CVAR,
            RiskMeasure.VARIANCE.CDAR,
        ],
        "prior_estimator__mu_estimator__alpha": [0.05, 0.1, 0.2, 0.5],
    },
)
gs.fit(X)

best_model = gs.best_estimator_

print(best_model.weights_)

Black & Litterman Model

views = ["AAPL - BBY == 0.03 ", "CVX - KO == 0.04", "MSFT == 0.06 "]
model = MeanRisk(
    objective_function=ObjectiveFunction.MAXIMIZE_RATIO,
    prior_estimator=BlackLitterman(views=views),
)

Factor Model

factor_prices = load_factors_dataset()

X, y = prices_to_returns(prices, factor_prices)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=False)

model = MeanRisk(prior_estimator=FactorModel())
model.fit(X_train, y_train)

print(model.weights_)

portfolio = model.predict(X_test)

print(portfolio.calmar_ratio)
print(portfolio.summary())

Factor Model & Covariance Detoning

model = MeanRisk(
    prior_estimator=FactorModel(
        factor_prior_estimator=EmpiricalPrior(covariance_estimator=DenoteCovariance())
    )
)

Black & Litterman Factor Model

factor_views = ["MTUM - QUAL == 0.03 ", "SIZE - TLT == 0.04", "VLUE == 0.06"]
model = MeanRisk(
    objective_function=ObjectiveFunction.MAXIMIZE_RATIO,
    prior_estimator=FactorModel(
        factor_prior_estimator=BlackLitterman(views=factor_views),
    ),
)

Pre-Selection Pipeline

set_config(transform_output="pandas")
model = Pipeline(
    [
        ("pre_selection", SelectKExtremes(k=10, highest=True)),
        ("optimization", MeanRisk()),
    ]
)
model.fit(X_train)

portfolio = model.predict(X_test)

K-fold Cross-Validation

model = MeanRisk()
mmp = cross_val_predict(model, X_test, cv=KFold(n_splits=5))
# mmp is the predicted MultiPeriodPortfolio object composed of 5 Portfolios (1 per testing fold)

mmp.plot_cumulative_returns()
print(mmp.summary()

Combinatorial Purged Cross-Validation

model = MeanRisk()

cv = CombinatorialPurgedCV(n_folds=10, n_test_folds=2)

print(cv.get_summary(X_train))

population = cross_val_predict(model, X_train, cv=cv)

population.plot_distribution(
    measure_list=[RatioMeasure.SHARPE_RATIO, RatioMeasure.SORTINO_RATIO]
)
population.plot_cumulative_returns()
print(population.summary())

Recognition

We would like to thanks all contributors behind our direct dependencies like scikit-learn and cvxpy but also the contributors of the following packages that were a source of inspiration:

• PyPortfolioOpt

• Riskfolio-Lib

• scikit-portfolio

• microprediction

• statsmodels

• rsome

Citation

If you use skfolio in a scientific publication, we would appreciate citations:

Bibtex entry:

@misc{riskfolio,
      author = {Hugo Delatte},
      title = {skfolio},
      year  = {2023},
      url   = {https://github.com/skfolio/skfolio}