Combined StrategiesCore researchlive in productionNew

Risk-Balanced Blend

Updated dailyData needs: lowlong onlyshort onlylong short

2016

#101 meta_hrp — Lopez de Prado 2016 JPM Hierarchical Risk Parity allocator.

In plain terms

Instead of equal-weighting our internal sub-signals, cluster them by how correlated their returns are and give each cluster a proportionate slice of the risk budget. Stable, classic Lopez de Prado allocator that systematically beats naive 1/N out-of-sample.

How it works

Lopez de Prado 2016 introduces risk-balanced weighting as a quasi-optimal allocator that avoids the unstable matrix-inversion at the core of mean-variance. risk-balanced weighting delivers higher Sharpe out-of-sample than 1/N (DeMiguel-Garlappi-Uppal 2009) and minimum-variance, especially when asset count grows. Recipe: correlation matrix → distance matrix → single-linkage hierarchical clustering → quasi-diagonalization → recursive bisection inverse-variance allocation. Applied here over the several canonical sub-signals (same set as meta_equal_weight) for head-to-head risk-balanced weighting-vs-1/N comparison on every ticker.

Live results

166 times picked on its own · 168 times inside a blend (148 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 168 such blended picks (148 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)

Loading substrate evidence…

Data dependencies

Daily prices
Adjusted-close OHLCV for every US-listed ticker; primary price feed.

Expected edge

Reported return: +0.1 Sharpe vs 1/N; +0.2 Sharpe vs min-variance OOS
Tested over: 1991-2014

Lopez de Prado 2016: risk-balanced weighting beats 1/N by ~0.1 Sharpe and min-var by ~0.2 Sharpe OOS on equity sleeves.

Related families

Equal-Weight ConsensusCombined Strategies

Instead of trying to optimize weights across sub-signals (which overfits), just equal-weight K canonical price signals. Hard to beat out-of-sample.

Regime RouterCombined Strategies

Different alpha sleeves win in different regimes — trend in calm markets, reversion in choppy markets, defense in crises. This routes between them.

Best-Strategy OverlayCombined Strategies

Take the user's best historical strategy for this ticker, then stack additional regime filters (VIX, sector trend, short flow) on top to clean up its bad bars.

Strategy EnsembleCombined Strategies

When no single strategy clearly wins, we combine the best three TA strategies with the best three alpha-family strategies — half-and-half — and use that blend as the champion. On stocks where every individual strategy was failing, the blend rescued 2 out of 3 of them.

Explore Risk-Balanced Blend on alphactor.ai

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

Open universe with this filter →Browse all 572 families