diff --git a/README.md b/README.md
index d8e4c36a..9b6bab07 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,127 @@
 [![Apache 2.0 License](https://img.shields.io/badge/License-APACHEv2-brightgreen.svg)](https://github.com/cvxgrp/simulator/blob/master/LICENSE)
 [![PyPI download month](https://img.shields.io/pypi/dm/cvxrisk.svg)](https://pypi.python.org/pypi/cvxrisk/)
 
+We provide an abstract `RiskModel` class. The class is designed to be used in conjunction with [cvxpy](https://github.com/cvxpy/cvxpy).
+Using this class, we can formulate a function computing a standard minimum variance portfolio as
+
+```python
+import cvxpy as cp
+
+from cvx.risk import RiskModel
+
+def minimum_variance(w: cp.Variable, risk_model: RiskModel) -> cp.Problem:
+    """Constructs a minimum variance portfolio.
+
+    Args:
+        w: cp.Variable representing the portfolio weights.
+        risk_model: A risk model.
+
+    Returns:
+        A convex optimization problem.
+    """
+    return cp.Problem(
+        cp.Minimize(risk_model.estimate_risk(w)),
+        [cp.sum(w) == 1, w >= 0]
+    )
+```
+
+The risk model is injected into the function.
+The function is not aware of the precise risk model used.
+All risk models are required to implement the `estimate_risk` method.
+
+## A first example
+
+A first example is a risk model based on the sample covariance matrix.
+We construct the risk model as follows
+
+```python
+import numpy as np
+import cvxpy as cp
+
+from cvx.risk.sample import SampleCovariance
+
+riskmodel = SampleCovariance(num=2)
+riskmodel.cov.value = np.array([[1.0, 0.5], [0.5, 2.0]])
+
+w = cp.Variable(2)
+
+minimum_variance(w, riskmodel).solve()
+print(w.value)
+```
+
+The risk model and the actual optimization problem are decoupled.
+This is good practice and keeps the code clean and maintainable.
+
+## Risk models
+
+### Sample covariance
+
+We offer two variants of the sample covariance risk model.
+The first variant is the `SampleCovariance` class.
+It relies on cxxpy's `quad_form` function to compute the variance
+```math
+w^T \Sigma w.
+```
+The second variant is the `SampleCovariance_product` class.
+It relies on cxxpy's `sum_of_squares` function to compute the variance using
+```math
+\| \Sigma^{1/2} w \|_2.
+```
+
+
+### Factor risk models
+
+Factor risk models use the projection of the weight vector into a lower
+dimensional subspace, e.g. each asset is the linear combination of $k$ factors.
+```math
+r_i = \sum_{j=1}^k \beta_{ij} f_j + \epsilon_i
+```
+XXX: Check whether $\beta_{ij}$ or $\beta_{ji}$.
+The factor time series are $f_1, \ldots, f_k$. The loadings are the coefficients $\beta_{ij}$.
+The residual returns $\epsilon_i$ are assumed to be uncorrelated with the factors.
+
+Any position $w$ in weight space corresponds to a position $f = \beta w$ in factor space.
+Factor space has only $k$ dimensions, whereas weight space has $n$ dimensions.
+The variance for a position $w$ is the sum of the variance of the
+systemic returns explained by the factors and the variance by the idiosyncratic returns.
+
+```math
+Var(r) = Var(\beta w) + Var(\epsilon w)
+```
+
+We assume the residual returns are uncorrelated and hence
+
+```math
+Var(r) = f^T \Sigma_f f + \sum_i w_i^2 Var(\epsilon_i)
+```
+
+XXX: Explore alignment of equations.
+
+where $\Sigma_f$ is the covariance matrix of the factors and $Var(\epsilon_i)$
+is the variance of the idiosyncratic returns.
+
+Again we offer two variants of the factor risk model reflecting what is widely used in practice.
+The first variant is the `FundamentalFactorRiskModel` class.
+Here the user provides the factor covariance matrix $\Sigma_f$,
+the loadings $\beta$ and the volatilities of the idiosyncratic returns $\epsilon_i$. Often such
+data is provided by the external parties, e.g. Barra or Axioma.
+
+The second variant is the `TimeseriesFactorRiskModel` class.
+Here the user provides the factor time series $f_1, \ldots, f_k$, usually obtained from
+a principal component analysis (PCA) of the asset returns. The loadings $\beta$ are computed
+via a linear regression of the asset returns on the factor time series.
+The volatilities of the idiosyncratic returns $\epsilon_i$ are computed as the standard deviation
+of the residuals of the linear regression.
+The factor covariance matrix $\Sigma_f$ may even be diagonal in this case as the factors are orthogonal.
+
+### cvar
+
+XXX: Conditional value at risk
+Relies on cxxpy's `sum_largest` function.
+
+
+
+
 ## Poetry
 
 We assume you share already the love for [Poetry](https://python-poetry.org). Once you have installed poetry you can perform
diff --git a/cvx/risk/cvar/__init__.py b/cvx/risk/cvar/__init__.py
index e69de29b..7c6a1b5b 100644
--- a/cvx/risk/cvar/__init__.py
+++ b/cvx/risk/cvar/__init__.py
@@ -0,0 +1,4 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+from .cvar import CVar
diff --git a/cvx/risk/factor/__init__.py b/cvx/risk/factor/__init__.py
index e69de29b..021972f2 100644
--- a/cvx/risk/factor/__init__.py
+++ b/cvx/risk/factor/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+from .fundamental import FundamentalFactorRiskModel
+from .timeseries import TimeseriesFactorRiskModel
diff --git a/cvx/risk/sample/__init__.py b/cvx/risk/sample/__init__.py
index e69de29b..de4db88f 100644
--- a/cvx/risk/sample/__init__.py
+++ b/cvx/risk/sample/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+from .sample import SampleCovariance
+from .sample import SampleCovariance_Product
diff --git a/tests/test_risk/test_cvar/test_cvar.py b/tests/test_risk/test_cvar/test_cvar.py
index 59b41e8f..8eee7bdd 100644
--- a/tests/test_risk/test_cvar/test_cvar.py
+++ b/tests/test_risk/test_cvar/test_cvar.py
@@ -6,7 +6,7 @@
 import numpy as np
 import pytest
 
-from cvx.risk.cvar.cvar import CVar
+from cvx.risk.cvar import CVar
 
 
 def test_estimate_risk():
diff --git a/tests/test_risk/test_factor/test_factor.py b/tests/test_risk/test_factor/test_factor.py
index 05e60b61..03bdbfd9 100644
--- a/tests/test_risk/test_factor/test_factor.py
+++ b/tests/test_risk/test_factor/test_factor.py
@@ -5,9 +5,9 @@
 import pandas as pd
 import pytest
 
-from cvx.risk.factor.fundamental import FundamentalFactorRiskModel
+from cvx.risk.factor import FundamentalFactorRiskModel
+from cvx.risk.factor import TimeseriesFactorRiskModel
 from cvx.risk.factor.linalg.pca import pca as principal_components
-from cvx.risk.factor.timeseries import TimeseriesFactorRiskModel
 
 
 @pytest.fixture()
diff --git a/tests/test_risk/test_sample/test_sample.py b/tests/test_risk/test_sample/test_sample.py
index 3cef4615..3745eddc 100644
--- a/tests/test_risk/test_sample/test_sample.py
+++ b/tests/test_risk/test_sample/test_sample.py
@@ -4,9 +4,9 @@
 import numpy as np
 import pandas as pd
 
-from cvx.risk.factor.fundamental import FundamentalFactorRiskModel
-from cvx.risk.sample.sample import SampleCovariance
-from cvx.risk.sample.sample import SampleCovariance_Product
+from cvx.risk.factor import FundamentalFactorRiskModel
+from cvx.risk.sample import SampleCovariance
+from cvx.risk.sample import SampleCovariance_Product
 
 
 def test_sample():