Quantitative investors usually start their research by analyzing individual trading strategies. They compare performance, risk, implementation complexity, market exposure, and the economic intuition behind each anomaly. However, once historical equity curves of individual strategies are available, a different research question becomes possible. Instead of asking only which individual strategy looks attractive, we can ask how to allocate capital across a broad universe of strategies.

The Quantpedia API makes this type of analysis practical. It provides access not only to strategy metadata, but also to historical strategy equity curves. Therefore, users can move from strategy discovery to systematic strategy portfolio construction.

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

Quantpedia Days 2026

– Celebrate with us the relentless pursuit of knowledge and ingenuity
– You can now subscribe to any of our services, be it 3-, 12- or 36-months Quantpedia Prime, Premium, or Pro subscription, and get the same 2nd subscription for your co-worker or fellow researcher for free – an offer valid between 28th May and 5th June 2026

Five new strategies have been added. Five new related research paper have been included into existing strategy reviews and five new short free blog posts have been published during last few weeks. Plus, four trading strategies have been backtested in QuantConnect in the previous two weeks.

Welcome to the Quantpedia Awards 2026 winners announcement. For the third time, we are proud to celebrate excellence in quantitative research and recognize the researchers behind innovative studies in quantitative trading. We are also pleased to see that the Quantpedia Awards have become an established and recognized brand within the quant community. This is the moment we have all been waiting for: who made it into the top five, and what will the authors of the winning papers receive?

Our study explores a weekly-rebalanced dual-momentum-based Global Tactical Asset Allocation (GTAA) strategy applied to a diversified set of ETFs. The strategy selects assets based on relative momentum and applies an absolute momentum filter to avoid declining investments. Ultimately, a single combined strategy was created by merging two sub-strategies, incorporating both shorter- and longer-term momentum signals. Backtesting over an extended period demonstrates that this approach delivers attractive risk-adjusted returns, achieving attractive Sharpe and Calmar ratios, while maintaining lower drawdowns compared to a simple equally weighted benchmark.