Combined StrategiesExtended setexperimental liveNew

cross-family hierarchical risk-parity blend

Updated dailyData needs: highlong only

2016

Lopez de Prado, M. (2016). Building Diversified Portfolios that Outperform Out-of-Sample. Journal of Portfolio Management.

Citation only, paper link pending.

In plain terms

It spreads the bet across many of our best strategies for a stock, weighting each one so the overall mix stays balanced and steadier than any single strategy.

How it works

A meta-family that runs Hierarchical Risk Parity over the cross-family return matrix for a ticker. It pulls the single best passing champion from each alpha family (positive out-of-sample Sharpe, cross-sectionally sign-validated), then computes Lopez de Prado risk-balanced weighting weights over the correlation and variance structure of those families' return series.

Live results

54 times picked on its own · 143 times inside a blend (132 beat the stock) · updated 2026-06-06

This strategy is a frequent ingredient in blends that combine a few strategies on one stock. It has contributed to 143 such blended picks (132 of which beat simply holding the stock). Picking it on its own is only one of the ways it shows up.

How its picks scored vs. buy & hold

Each pick is graded on a recent year it was never tuned on, against simply owning the same stock

Where its edge concentrates

Share of picks in each company-size group that beat buy & hold

How often it trades

Active vs. patient. Bars on the left mean it waits for rare setups; bars on the right mean it trades often

Return vs. buy & hold

How much each pick beat or trailed simply owning the stock over the test year (extreme microcap moves trimmed)

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Data dependencies

Strategy experiments
A data feed this strategy reads, refreshed on its normal schedule.
Alpha family xs validation
A data feed this strategy reads, refreshed on its normal schedule.
Daily prices
Adjusted-close OHLCV for every US-listed ticker; primary price feed.

Expected edge

Diversifies away the de-correlation gap so the served blend is not a single dominant factor wearing many hats, raising risk-adjusted return out-of-sample.

Related families

learned stacked-ensemble family blendCombined Strategies

A machine-learning model studies how all our best strategies for a stock have behaved and learns the smartest way to combine them into one buy-or-sell call.

Explore cross-family hierarchical risk-parity blend on alphactor.ai

See which tickers this family is currently firing on, with live signals and rankings.

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