Quantpedia’s Research Workflow: From Idea Discovery to Portfolio Construction

Quantitative strategy research is rarely about discovering a single “perfect” trading rule. In practice, robust portfolios emerge from a structured research process that filters ideas, evaluates evidence, and combines complementary strategies.

In this article, we demonstrate how such a workflow can be implemented using the tools available in Quantpedia Pro. Rather than focusing on maximizing the performance of a single strategy, we walk through the research process step by step—from thematic filtering to portfolio-level evaluation.

To make the process concrete, we use value-based equity strategies as our working example. However, the goal of the article is not to identify the ultimate value strategy, but to illustrate how a systematic research workflow can be used to build a diversified portfolio of strategies around any investment hypothesis.

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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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Combining Calendar Strategies into the Trading Portfolio

Calendar strategies are often viewed as weak when assessed individually. Their annualized returns tend to be low, market exposure is limited, and trading activity is sparse. Compared to trend following or swing strategies, which can remain invested for extended periods, calendar strategies may appear inefficient at first glance. This impression, however, largely stems from evaluating these strategies outside of their intended context. Calendar strategies are not designed to operate as standalone trading systems. Their primary role is within a portfolio, where their structural properties become relevant rather than their individual performance metrics.

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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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How to Design a Simple Multi-Timeframe Trend Strategy on Bitcoin

Bitcoin is one of the most widely discussed financial assets of the modern era. Since its inception, it has evolved from a niche digital experiment into a globally recognized investment instrument with institutional adoption and billions in daily trading volume. Despite its inherent volatility, Bitcoin has demonstrated a strong long-term growth trajectory, making it an attractive candidate for trend-based and momentum-oriented trading strategies. In this study, we apply concepts from technical analysis to construct and refine a trend-following strategy for Bitcoin, progressing step by step from a simple MACD setup toward an improved multi-timeframe model.

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