About Simfolio

Built by Investors, for Investors.

We aggregate published, peer-reviewed research to create portfolio management tools, providing investors with the infrastructure to utilize them systematically.

Simfolio is self-funded, independent, and built with one goal: to help investors by providing the tools and infrastructure to be more data-driven investors.

For more details, please see the Methodology page.

About Simfolio

Simfol.io was built by a small team of investors who wanted a more rigorous and practical way to backtest, forecast, and optimize portfolios in one place.

With backgrounds in quantitative finance, we found that many existing platform make a reasonable trade-off: prioritizing tool simplicity, while more advanced tools remain difficult to access or apply practically.

We saw room for a platform that could offer the depth required for portfolio management decisions, without sacrificing usability.

Simfol.io is built around two core workflows:

Backtesting: Understand how a portfolio would have performed historically.

Forecasting: For any given portfolio, understand the possible future paths based on a forecasting methodology optimized for distributional accuracy.

Any additional tools integrated must enhance the usefulness of the two core workflows.

Core Problem #1

The Historical Replay

Many portfolio simulation tools are scenario randomizers that simply replay history, resulting in the significant oversimplification of how financial markets behave.

Simfolio's forecasting model directly targets those weaknesses and was derived by optimizing for out-of-sample distributional accuracy.

Core Problem #2

Lookahead Bias in Traditional Portfolio Optimization

A popularized approach to portfolio optimization is to optimize asset weights once, based on in-sample data and assuming those weights would have been known at the start of any backtest.

Simfolio offers a robust solution via walk-forward portfolio optimization, in which the model can only optimize based on data it has at the time of optimization. Weights are then rolled forward until the next optimization, ensuring the backtest reflects true out-of-sample performance.

Core Problem #3

The Failure of Market Timing

Many arbitrary, tactical or signal-based strategies assume the effectiveness of market timing, indirectly asking investors to assume which asset class, signal, or regime will lead next. Across nearly all live tactical funds and large technical-rule universes, that promise usually fails to beat simple static and periodically rebalanced portfolios once data snooping, transaction costs, taxes, and out-of-sample validation are taken seriously.

Simfolio does not make that timing bet part of the core tools offered in-house; tactical or signal-based investors can still upload any daily time series data and integrate it as a custom ticker.