README update

Author: ft-adm

README.md

@@ -1,10 +1,30 @@
 # CVaR optimization benchmark problems
-This repository contains Conditional Value-at-Risk (CVaR) portfolio optimization benchmark problems for fully general Monte Carlo distributions and derivatives portfolios.
+This repository contains Conditional Value-at-Risk (CVaR) portfolio optimization benchmark
+problems for fully general Monte Carlo distributions and derivatives portfolios.
 
-The starting point is the [next-generation investment framework's market representation](https://youtu.be/4ESigySdGf8?si=yWYuP9te1K1RBU7j&t=46) given by the matrix $R\in \mathbb{R}^{S\times I}$ and associated joint scenario probability vectors $p,q\in \mathbb{R}^{S}$.
+The starting point is the [next-generation investment framework's market representation](https://youtu.be/4ESigySdGf8?si=yWYuP9te1K1RBU7j&t=46)
+given by the matrix $R\in \mathbb{R}^{S\times I}$ and associated joint scenario probability
+vectors $p,q\in \mathbb{R}^{S}$.
 
-The [CVaROptBenchmarks notebook](https://github.com/fortitudo-tech/cvar-optimization-benchmarks/blob/main/CVaROptBenchmarks.ipynb) illustrates how the benchmark problems can be solved using Fortitudo Technologies' Investment Analysis module.
+The [1_CVaROptBenchmarks notebook](https://github.com/fortitudo-tech/cvar-optimization-benchmarks/blob/main/1_CVaROptBenchmarks.ipynb)
+illustrates how the benchmark problems can be solved using Fortitudo Technologies' Investment
+Analysis module.
 
-The [OptimizationExample notebook](https://github.com/fortitudo-tech/cvar-optimization-benchmarks/blob/main/OptimizationExample.ipynb) shows how you can replicate the results using the [fortitudo.tech open-source Python package](https://github.com/fortitudo-tech/fortitudo.tech) for the efficient frontier optimizations of long-only cash portfolios, which are the easiest problems to solve.
+The [2_OptimizationExample notebook](https://github.com/fortitudo-tech/cvar-optimization-benchmarks/blob/main/2_OptimizationExample.ipynb)
+shows how you can replicate the results using the [fortitudo.tech open-source Python package](https://github.com/fortitudo-tech/fortitudo.tech)
+for the efficient frontier optimizations of long-only cash portfolios, which are the easiest problems to solve.
 
-You can read much more about the next-generation investment framework in the [Portfolio Construction and Risk Management book](https://antonvorobets.substack.com/p/pcrm-book).
+## Installation Instructions
+It is recommended to install the code dependencies in a 
+[conda environment](https://conda.io/projects/conda/en/latest/user-guide/concepts/environments.html):
+
+    conda create -n cvar-optimization-benchmarks python
+    pip install cvar-optimization-benchmarks
+
+After this, you should be able to run the code in the [2_OptimizationExample notebook](https://github.com/fortitudo-tech/cvar-optimization-benchmarks/blob/main/2_OptimizationExample.ipynb).
+
+## Portfolio Construction and Risk Management book
+You can read much more about the [next-generation investment framework](https://antonvorobets.substack.com/p/anton-vorobets-next-generation-investment-framework)
+in the [Portfolio Construction and Risk Management book](https://antonvorobets.substack.com/p/pcrm-book),
+including a thorough description of CVaR optimization problems and
+[Resampled Portfolio Stacking](https://antonvorobets.substack.com/p/resampled-portfolio-stacking).