An independent educational platform for evidence-based, quantitative investment strategies. Here's how we work, where our data comes from, and what our backtests actually mean.
StrategyIndex.io was built to make quantitative, rules-based investing accessible to everyday investors — particularly in Europe, where resources tend to be US-centric and ignore UCITS ETF restrictions, local tax rules, and non-USD currency risk.
We do not provide financial advice. We provide transparent, reproducible research grounded in decades of academic literature — from Fama-French factor models to Antonacci's Dual Momentum and Dalio's risk parity principles.
All backtests use total return data (dividends reinvested), not price-only data. This is the correct approach for buy-and-hold strategies where income is reinvested automatically (as with accumulating UCITS ETFs).
Tactical strategies (GEM, DM, Ivy) are evaluated on the last trading day of each calendar month. All trades are assumed to execute at the closing price on that day. We do not model intra-month signals.
All models are long-only. No leverage is used. When absolute momentum is negative, models move to cash/bonds — they never short equities. This is consistent with how a retail investor can realistically implement these strategies.
Our backtests assume zero transaction costs and zero slippage for simplicity. In practice, modern commission-free brokers (Trade Republic, Scalable Capital) make this assumption reasonable for ETF-based implementations, where bid-ask spreads are extremely tight.
Tax rules vary significantly by country. Our backtests do not model capital gains tax or dividend withholding tax. We recommend reading our Tax-Efficient Investing guide for practical EU tax guidance.
Primary source for ETF historical price and total return data. Used for all post-1993 backtests where proxy ETFs are available.
Historical T-Bill yields, Treasury bond total returns, and inflation data (CPI). Used for absolute momentum risk-free rate calculations.
Institutional-grade end-of-day price data and total return series for extended backtests going back to the 1970s using index proxies.
Antonacci (2012, 2014), Faber (2007, 2013), Fama & French (1993), Jegadeesh & Titman (1993), Browne (1999). All strategies are based on published, peer-reviewed research.
Historical data last updated: July 2026
Quantitative Researcher
Marcin leads research for systematic asset allocation at StrategyIndex.io. He holds an MS in Quantitative Finance and specializes in tactical asset allocation, momentum investing, and risk-parity portfolios adapted for European investors.
Read Full BioStrategyIndex.io is an educational and informational resource. It does not provide financial, investment, tax, or legal advice. All content, calculators, backtests, and signals are for informational purposes only and do not constitute a recommendation to buy or sell any security.
Past performance is not indicative of future results. Quantitative models that have performed well historically may not perform similarly in the future. Market conditions, interest rate environments, and factor correlations change over time. Always consult a qualified financial advisor before making investment decisions.