Hedge Optmizer

Introduction to Hedge Optimizer

The Hedge Optimizer is a powerful tool designed to test and analyze various trading strategies, helping investors find the most effective approach for balancing their portfolios. By running through numerous iterations of different models, it projects the outcomes onto a spreadsheet. This allows users to compare, sort, and select the optimal strategy for investment.

How It Works

At its core, the Hedge Optimizer evaluates three main types of models:

1. Static Models
2. Moving Average Cross Models
3. Moving Average Filter Models

Each model uses a combination of long (buy) positions, referred to as the “Family,” and short (sell) positions, known as “Hedges.” By adjusting these positions within the models, the optimizer produces a variety of portfolio strategies.

Common Inputs

All models share two basic inputs:

• Family: A collection of securities you’re considering to buy and hold long-term.
• Hedges: A collection of securities you’re considering for short-selling to protect against market downturns.

Model Explained

Static Model Optimizer

This model focuses on creating portfolios with fixed proportions of selected securities. These can be rebalanced periodically (monthly or annually) to maintain the desired allocation. For example, you might set up a portfolio with 50% in SPY and 50% in TLT, rebalanced monthly.

Users can specify the mix of securities and their weightings. The optimizer then generates portfolios based on these preferences, showing various combinations and their performance.

Moving Average Cross Model

This model generates strategies based on the crossing of short-term and long-term moving averages. By specifying ranges for these averages, the optimizer creates numerous scenarios where a portfolio switches between the Family and Hedges based on these crossings, aiming to capitalize on trend changes.

Moving Average Filter Model

Similar to the Cross Model but focuses on the relative position of a security’s price to its moving average. The optimizer iterates over combinations of filters and moving averages to produce strategies that trigger trades when a security’s price moves a certain percentage above or below its moving average.

Practical Application

Once the optimizer has run through the iterations, it displays the results in a spreadsheet format. Users can then sort through these to identify the strategies that best meet their goals, whether they’re looking for risk mitigation, diversification, or specific financial outcomes.

For example, if you have a preference for a 90% long and 10% short mix, the optimizer can specifically generate and evaluate such combinations, allowing for a targeted approach to portfolio management.