portfolio_balancing

title	Portfolio Re-balancing
description	Flag holdings that exceed concentration limits, group stocks that tend to move together, and compare rebalanced portfolios by expected return and risk under normal and stressed markets.
featured	false
experience_level	intermediate
industry	Financial Services
reasoning_types	Prescriptive Rules-based Graph
tags	Multi-Reasoner Portfolio Optimization Quadratic Programming Community Detection Sensitivity Analysis Stress Testing

Full guide: Explore the Portfolio Re-balancing model, run the example, and follow the code.

What this template is for

Investment managers need to keep portfolios within concentration and compliance limits while weighing expected return against risk. This template flags concentration problems, groups correlated stocks, calculates constrained allocations, and compares them under normal and stressed markets. Adapt the investment universe, limits, objectives, and stress assumptions for your workflow.

Quickstart

Before you start, install Python 3.10 or later and get access to a Snowflake account with the RAI Native App. Graph and Prescriptive reasoning are in Public Preview; ask your RelationalAI support representative to enable Prescriptive reasoning. Preview features are for evaluation and testing, not production applications. The template pins relationalai==1.9.0 in pyproject.toml.

Use this sequence to run the bundled example:

1. Download the template

   Download the ZIP, unzip it, and enter the template directory:

   unzip portfolio_balancing.zip
   cd portfolio_balancing

2. Create a Python environment

   Create and activate a virtual environment, then update pip:

   python -m venv .venv
   source .venv/bin/activate
   python -m pip install --upgrade pip

3. Install the template

   Install the dependencies pinned in pyproject.toml:

   python -m pip install .

4. Configure your project

   Use the configuration builder in Start building with PyRel to create raiconfig.yaml in the template directory and verify your connection.

5. Run the template

   Run the bundled script from the template directory:

   python portfolio_balancing.py

   STAGE 1: COMPLIANCE ANALYSIS (rules)
   STAGE 2: GRAPH -- Covariance Clustering (Louvain)
     Louvain communities: 5 cluster(s)
   STAGE 3: BI-OBJECTIVE OPTIMIZATION
   Status: OPTIMAL
   SENSITIVITY-GUIDED FRONTIER  (reference 'base_1000', 6-solve budget per method)
     dichotomic         6          202.2972  <- tightest
   STAGE 4: CRISIS REGIME STRESS TEST
   KNEE PORTFOLIO ALLOCATIONS BY SCENARIO
     base_1000 (budget=1000, regime=base, point=p3)
     expected return=80.46, volatility=83.33
     Each knee is a candidate portfolio, not a recommendation. The amounts apply to
     the sample scenario, not to a specific account.
   

   The run flags four holdings and two sectors, groups eight stocks into five correlation clusters, and selects p3 as the candidate frontier point in each of six budget-and-market scenarios. Estimated crisis volatility is 22% to 30% above the base regime.

   These candidates are starting points for review, not recommendations or trades for a specific account. See runbook.md for the full workflow and result interpretation.