Algo Trading

Strategy 1: Long/Short Clustered Value

THESIS

By grouping publicly traded securities using all available 10-Q data and ranking them by standard financial ratios within their groups, we score securities based on the trailing average z-scores. This methodical, data-driven approach is targeting favorable returns and low correlation to SPX. 

DEVIL'S ADVOCATE

The 10-Q data lags. Current events will negate any alpha this strategy might produce.

COUNTERPOINT

This portfolio employs two primary risk management techniques:

1. We limit positions to <5% of net liquidity

2. We rebalance the net long and net short positions when the spread is >20%. For example, will rebalance if the portfolio becomes 61% long and 39% short. 

Within this risk management framework, this strategy will take advantage of swings in security values when they overreact to current events.