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# Hierarchical Risk Parity (HRP) for Crypto Portfolio Optimisation
- URL: https://alexeygolev.blog/hierarchical-risk-parity-hrp-for-crypto-portfolio-optimisation/
- Published: 2025-07-17T21:55:53.000Z
- Updated: 2026-04-06T14:46:32.000Z
- Description: Analysis of Hierarchical Risk Parity (HRP) for crypto portfolios. Comparing HRP variants vs Bitcoin holding over 3 years with surprising results on portfolio optimisation strategies.
- Author: Alexey Golev
- Tags: Portfolio Construction, Crypto

Hierarchical Risk Parity (HRP) is a portfolio optimisation technique introduced by Marcos López de Prado in his paper ["Building Diversified Portfolios that Outperform Out of Sample"](https://papers.ssrn.com/sol3/papers.cfm?abstract%5Fid=2708678&ref=alexeygolev.blog). HRP offers several advantages over traditional methods:

### Advantages of HRP

- **Non-parametric approach**: Does not require estimating expected returns
- **Robustness to estimation errors**: Less sensitive to estimation error than Markowitz optimisation
- **Dimensionality handling**: Works well with high-dimensional, noisy data typical of crypto markets
- **Cluster-based risk allocation**: Effectively balances risk across different market segments

### Why HRP for Cryptocurrency Markets

HRP is particularly well-suited for cryptocurrency portfolios because:

- Crypto markets exhibit high dimensionality and noise
- Return distributions are non-normal with extreme tail events
- Correlation structures change rapidly, requiring robust estimation methods
- Traditional mean-variance optimisation often fails due to instability

### Project highlights

**Hierarchical Risk Parity (HRP) & Variants**

- Implemented classic HRP using a 60‑day look-back for covariance estimation and dynamic clustering.
- Explored two hybrid models: Momentum‑HRP and Momentum‑Weighted HRP to see if momentum signals could enhance risk‐parity allocations.

**Benchmark Strategie**

- **BTC Hold**: 100% Bitcoin allocation as a simple buy‑and‑hold baseline.
- **Equal Weight & Pure Momentum**: to gauge the added value of risk‐parity and hybrids.

**Backtest Framework & Visualisation**

- Developed modular source code (`src/strategies`, `src/utils`) with configurable parameters in `config/strategy_params.yaml`.
- Automated runs (`run_all_strategies.py`, `compare_strategies.py`) and produced clear charts: portfolio‐value trajectories, drawdown profiles and Sharpe / Sortino metrics.

**Key Findings**

- Over **June 2022 – June 2025**, the simple **BTC Hold** outperformed all other strategies, delivering \~251% total return (52% annualised) with a max drawdown of \~39%.
- HRP variants delivered respectable returns but couldn’t consistently beat the Bitcoin‐only benchmark once transaction costs were considered.
- Hybrid models offered interesting diversification patterns but ultimately underscored how challenging it is to outperform a strong bull asset in its own market.

![](https://storage.ghost.io/c/e9/80/e980d4cb-1c2f-4112-8b4c-fb045047467d/content/images/2025/07/portfolio_comparison.png)

Portfolio Value Comparison

### Performance Metrics Over 3 Years

| Strategy  | Total Return | Annualized Return | Max Drawdown | Sharpe Ratio | Sortino Ratio |
| --------- | ------------ | ----------------- | ------------ | ------------ | ------------- |
| BTC Hold  | 251.34%      | 52.18%            | 39.11%       | 1.22         | 2.10          |
| HRP       | 169.20%      | 39.22%            | 50.71%       | 0.76         | 1.26          |
| Mom-HRP   | 129.91%      | 34.19%            | 53.49%       | 0.65         | 1.06          |
| Mom-W HRP | 127.55%      | 31.62%            | 50.55%       | 0.61         | 0.97          |
| Equal W   | 70.27%       | 19.47%            | 56.21%       | 0.35         | 0.55          |
| Momentum  | 70.27%       | 19.47%            | 56.21%       | 0.35         | 0.55          |

## Performance Considerations

- All reported returns are **gross returns** before transaction costs
- While HRP with daily rebalancing showed potential to outperform BTC in some market conditions, transaction costs (trading fees, slippage) completely eliminated this advantage
- Even without transaction costs, none of the tested strategies consistently outperformed a simple Bitcoin hold strategy over the full test period

## Data Used

The analysis uses historical cryptocurrency price data spanning approximately **three years** (from June 2022 to June 2025). Key characteristics of the dataset include:

- **Coverage**: Approximately 40 cryptocurrency symbols, focusing on major cryptocurrencies and tokens with sufficient liquidity
- **Frequency**: Daily price data used for calculating weekly returns
- **Point-in-time integrity**: All strategies utilise only data available at the rebalance date (no look-ahead bias)
- **Data ingestion** via Binance and TradingView APIs into daily‐return CSVs

### Top HRP Selected Assets

- Bitcoin (BTC): \~25-30% allocation
- Ethereum (ETH): \~15-20% allocation
- Other major assets: Varying allocations to assets like BNB, SOL, and XRP based on their risk characteristics

### Lessons Learned & Next Steps

- **Simplicity can be powerful**: risk‐parity schemes sometimes can’t beat a single, high‐momentum asset in a bull market highlighting the importance of regime‐aware models.
- **Transaction costs matter**: while HRP with daily rebalancing showed potential to outperform BTC in some market conditions, transaction costs (trading fees, slippage) completely eliminated this advantage.
- **Data**: the challenge is that crypto coins rotate by market cap very fast. It was not easy to decide on sample of coins and extract required data.

**Read more**: [GitHub: crypto‑port‑opt](https://github.com/GAlexeyV/crypto-port-opt?ref=alexeygolev.blog)

**Get in touch:**

- 📧 alexeyg377@gmail.com
- 🔗 [LinkedIn Profile](https://www.linkedin.com/in/alexey-golev-63087b110/?ref=alexeygolev.blog)