Building an AI Powered Quant Research Assistant with Quantpedia API

Artificial intelligence is gradually changing the way quantitative researchers interact with financial data. Instead of manually browsing databases, comparing strategies one by one and filtering spreadsheets, modern research workflows increasingly rely on conversational systems capable of retrieving and summarizing structured information automatically.

One practical application is combining the Quantpedia API with an LLM such as ChatGPT, Claude or Cursor AI to create a lightweight quant research assistant. In this setup, Quantpedia API provides structured access to quantitative trading strategies, performance metrics, classifications, equity curves, trading codes, and related research metadata through the official Quantpedia API, while the LLM acts as a conv

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Finding and Integrating Crisis Hedge Strategies: Improving Equity Portfolio Resilience

Most systematic trading strategies are pro cyclical by nature. They perform best when markets trend higher and volatility remains contained. During broad market expansions, equity risk premia, momentum and trend following approaches tend to generate stable positive returns.

However, during market crises or extended bear markets, many of these strategies become synchronized. Correlations increase, volatility spikes and traditional diversification weakens. In such environments, portfolios built primarily from pro cyclical strategies may experience simultaneous drawdowns. This creates a structural need for strategies that behave differently during stress periods.

Crisis hedge strategies represent such a subset. They are designed to deliver diversification benefits specifically when equity markets decline. Because of their specialized behavior, they represent only a small fraction of the overall strategy universe.

This analysis demonstrates how crisis hedge strategies can be identified, evaluated and integrated into a model portfolio using the Quantpedia Pro framework.

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Pragmatic Asset Allocation Across Market Cycles

Pragmatic Asset Allocation is a systematic, multi-asset investment strategy designed to adapt dynamically to evolving market conditions. Rather than maintaining a static equity exposure, the model actively allocates capital across a diversified set of asset classes—including equities, bonds, commodities, gold, and cash-like instruments—using momentum-based signals and disciplined periodic rebalancing. The strategy’s primary objective is to deliver attractive long-term returns while materially reducing drawdowns during adverse market environments.

It has now been two highly volatile years since we first published our paper on PAA, making this an opportune moment to review the strategy’s performance over the past year.

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Understanding Gold – Hedge, Diversifier, or Overpriced Insurance?

In Understanding Gold, Claude B. Erb and Campbell R. Harvey examine gold’s enduring reputation as a safe-haven asset and contrast popular narratives with empirical evidence. While gold has preserved purchasing power over millennia—what the authors call the “golden constant”—this does not translate into reliable short- or medium-term inflation hedging. Gold’s volatility is comparable to equities, while inflation itself is far more stable, making gold an unreliable hedge over typical investor horizons. The key insight is that gold’s real long-run return is approximately zero, which is precisely what one should expect from a hedging asset rather than a growth asset.

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Hedging Tail Risk with Robust VIXY Models

Extreme market events, once perceived as statistical outliers, have become a central concern for investors. The persistence of sharp drawdowns and volatility spikes demonstrates that the cost of ignoring tail risks is not tolerable for long-term portfolio resilience. While diversification can mitigate ordinary fluctuations, it often fails when markets move in unison under stress. This makes explicit protection against severe downside events not just desirable but necessary. Tail hedging addresses this need by providing a structured defense against the most damaging scenarios, ensuring that portfolios remain robust when traditional risk management tools fall short. Using VIXY ETF, we will present and test a range of hedging strategies designed to protect portfolios under stress. By applying robust testing frameworks, we aim to evaluate how different implementations of VIXY ETF-based tail hedges perform across a variety of market environments, highlighting both their strengths and inherent trade-offs.

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