Trend Consensus Adaptive (Long-Only)
Long-Only
Survivorship-Bias Free
Single-Factor
Top 20 Market Cap
Tier 2
Trend Consensus Adaptive is designed to exploit the asymmetric positive expected returns of assets with positive trend consensus. It longs the top sextile (top 20%) of assets with the highest Trend Consensus, when the overall market trend is positive. It does not have any short exposure.
Unlike other portfolios, Trend Consensus Adaptive is long-only and is displayed here with 100% target gross exposure.
The asset universe consists of the most liquid and actively traded assets, identified on rolling basis - various techniques employed to keep it both stable and relevant, as well as survivorship-bias free.
To balance each asset's risk contribution, positions are scaled according to the inverse of their rolling volatility.
The factor is available (point-in-time) daily at 23:55 and 00:15 UTC.
| Benchmark (BTC) | Portfolio | |
|---|---|---|
1.01 | 1.82 | |
53.7% | 96.3% | |
0.00 | 0.55 | |
1.00 | 0.34 | |
-76.6% | -48.5% | |
61.2% | 41.9% | |
0% | 26.5% | |
100% | 39.8% | |
100% | 39.8% |
Sharpe Ratio
Rolling 12 MonthsBeta
Rolling 12 Months - Benchmark: BTCNet Exposure of Portfolio
Portfolio Variants
Trend Consensus Adaptive (Long-Only) - Top 30 Market Cap
Tier 2
Exploiting the tendency of assets with positive trend consensus, when overall market trend is positive.
Trend Consensus Adaptive (Long-Only) - Top 40 Market Cap
Tier 2
Exploiting the tendency of assets with positive trend consensus, when overall market trend is positive.
Current Weights
12/5/2025, 8:07:59 AM (UTC)
Adaptive Portfolio weights can be all zero when the risk signal scales the portfolio weights down.
The portfolio is designed to be market-neutral on a volatilty-adjusted basis, therefore it may have net directional exposure on any given day. The weighting mechanism ensures that volatility-weighted directional exposure is negligible on the portfolio level.
| Ticker | Name | Arrival Price | Weight | Date |
|---|---|---|---|---|
| BTC | Bitcoin | 92257.353 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| ETH | Ethereum | 3174.031 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| SOL | Solana | 139.771 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| XRP | Ripple | 2.094 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| DOGE | Dogecoin | 0.148 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| BNB | Binance Coin | 902.121 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| ADA | Cardano | 0.442 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| LTC | Litecoin | 83.642 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| LINK | Chainlink | 14.254 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| AVAX | Avalanche | 14.372 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| DOT | Polkadot | 2.276 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| BCH | Bitcoin Cash | 575.724 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| HBAR | Hedera | 0.139 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| TRX | TRON | 0.287 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| UNI | Uniswap | 5.955 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| TON | Toncoin | 1.601 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| XLM | Stellar | 0.252 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| SHIB | SHIB | 0.000008622168271636762 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| HYPE | HYPE | 33.35 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| XMR | Monero | 401.46 USD | 0.00% | 2025-12-05T08:07:59.074350+00:00 |
| 0.00% | ||||
Properties
Cross-Sectional | |
Derivative Exchanges | |
400+ Tickers | |
None | |
- | |
None |
2024-01-01 | |
Daily | |
23:55 & 00:15 UTC | |
Yes | |
- | |
No |
Frequently Asked Questions
What is the construction methodology of the model portfolio on display? How do I replicate it?
The portfolio is constructed by applying cross-sectional binning - quantiles. This ensures that the weights are calculated in proportion to the strength of the factor. The assets are then weighted according to the inverse of their rolling volatility, to mitigate the impact of widely different volatilities of digital assets. We provide a replication notebook in the "Resources" section that will produce a very similar portfolio than what's shown on the site.
Is the raw factor data available?
Yes, we serve the raw factor data via the "portfolio/factors" endpoint, please see the API Docs for more information.
What is smoothing? Some factors have by default setting (eg. 10 Day Moving Average) applied. Why is that?
Some of the cross-sectional factors have very high turnover, and despite being highly predictive, the alpha does not survive transaction costs. Smoothing (applying a simple moving average) is a way to reduce the turnover and the impact of transaction costs.
What is the universe of assets? How is it determined? Is it survivorship-bias free?
The universe of assets (there are multiple variants, for example, top 20, 30, 40 market cap digital assets) is the most liquid and actively traded assets, identified on rolling basis - various techniques (volume, open interest, volatility filters) are employed to keep it both stable and relevant. The universe is survivorship-bias free.
What is the rebalancing frequency?
The portfolio is rebalanced daily, at midnight UTC, weights are calculated at 23:55 and 00:15 UTC, and available point-in-time.
What are the transaction cost / slippage assumptions?
There are 0.05% transaction costs applied on each position adjustment. This is a parameter that can be adjusted in the backtest settings. Slippage, spread is not considered in the simplistic backtest that's on our site. We encourage using an independent backtesting infrastructure to validate performance.
When have been the factors developed? Is there an out-of-sample period?
Our cutoff date for our research process is 2024-01-01, we consider any data onwards as out-of-sample. Many factors have been traded live in some form from 2025-01-01.
Some of the factors look great! Should I pick an individual factor and start trading it?
We recommend using a portfolio of factors to maximize risk-adjusted returns. Alpha from individual factors is simply not consistent enough, and even running a simple arithmetic average of 3-5 relatively orthogonal factors will produce superior results.
Replication & API
Live Weights
Get live weights with the code provided below.