Long/Short Equity ETF
A mathematically driven long/short alpha strategy capturing mean-reversion and trend across liquid equity ETFs.
The Core Idea
The Long/Short Equity ETF strategy is a mathematically driven algorithm that combines our original momentum algorithm with an adjusted version for shorter timeframes to capture both mean-reversion and trend. The core objective is to manage risk while seeking positive results regardless of market direction.
The strategy is directionally agnostic. It can profit in rising, falling, or sideways markets by taking both long and short positions across approximately 200 liquid equity ETFs. This removes the long-only duration risk that exposes traditional equity strategies to full market drawdowns.
Portfolio Construction
The portfolio is built on a core/satellite architecture. The core allocation (50%) utilizes our original momentum algorithm focused on longer-term momentum anomalies. It is composed of multiple fixed income and equity indicators, and the algorithm determines the overall investment bias through a systematic assessment of multiple unique underlying drivers of the market.
The satellite allocation (50%) uses a modified version of the same algorithm adapted for shorter timeframes. More than 18 proprietary algorithms identify short-term anomalies and trends based on mean-reversion or trend signals. This satellite approach adds alpha by capturing shorter-term price inefficiencies with an average holding period of approximately 9.5 days.
Multi-Algorithm Methodology
The strategy screens approximately 1,700 ETFs bi-yearly, filtering for sufficient assets and liquidity. Roughly 200 equity ETFs qualify for the investable universe. Multiple proprietary algorithms then analyze each ETF across different time series, utilizing statistical filters to remove noise and volatility in shorter timeframes.
Every algorithm has been tested across extensive datasets spanning different market, economic, and political cycles to identify small inefficiencies that can be exploited. Each algorithm is subjected to Monte Carlo stress test simulations to ensure robustness, and parameters are continuously refined using AI-driven research drawing from new ideas in mathematics, finance, biology, and other sciences.
Risk Management
Risk/reward analysis is the core algorithm that governs the proprietary Risk Module, which systematically determines portfolio exposure and position size. As market characteristics change, the portfolio proactively adapts.
The core approach reduces overall volatility and drawdowns by focusing on longer trends. Market regime assessment and total algorithm scores determine which system is traded and at what level of aggressiveness. Each position is governed by more than 25 unique algorithms determining entry and exit points. Positions are dynamically monitored to analyze correlations, possible slippage, and overall flows.
The risk management process utilizes a systematic approach with no qualitative interpretation, removing the potential of human bias and error. This has allowed the strategy to maintain minimal correlation to both bonds and equities.
AI-Driven Research and Development
We utilize AI to continually analyze the latest research across quantitative finance, statistical modeling, and algorithmic trading to identify opportunities to improve our models. A particular focus is placed on strengthening the Risk Module and ensuring our risk management evolves alongside changing market dynamics.
AI also plays a critical role in real-time model monitoring. Our systems continuously evaluate algorithm performance across all active positions, flagging deviations or anomalies that require our attention. This allows us to stay ahead of shifting market conditions rather than reacting after the fact. Every enhancement is rigorously tested across multiple market regimes before being promoted to production.
Part of the QS Risk/Reward Strategy Family
Every strategy in our family shares the same core technology platform. Advanced proprietary adaptive noise filters separate true market signal from noise. Layered risk controls activate progressively as stress builds, keeping drawdowns firmly controlled through every market environment.
We utilize AI to continually research, test, and develop our strategies based on new scientific ideas in mathematics, finance, biology, and other sciences. This commitment to continuous improvement means our risk management evolves alongside the markets, incorporating the latest advances in quantitative research to identify areas of improvement.
Architectural consistency means improvements to one strategy's risk controls benefit the entire family. There are no black boxes. Every signal, overlay, gate, and allocation decision is fully transparent and explainable. Each rule has a clear economic rationale.
Past performance is not indicative of future results. All investment strategies involve risk, including possible loss of principal. The strategies described are systematic, rules-based investment programs. Information provided is for educational purposes and should not be considered investment advice. Please consult with a qualified advisor before making investment decisions.
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