Momentum: 18 Backtests
We backtested 18 momentum strategies across crypto, ETFs, US stocks. 4 beat buy-and-hold before correction; best +29.2 pts/yr.
Every result below is the strategy's annualized return minus buy-and-hold on the same assets and days, charged real trading costs and scored only on data up to the discovery cutoff. See the full family index, the full method and every result, or download the raw registry (JSON).
1 of these 18 strategies was significant at p < 0.05 before any correction for multiple testing, and 4 of 18 beat buy-and-hold on the raw excess-return number across crypto, ETFs, US stocks. Across the whole registry we ran 260 tests and applied one Benjamini-Hochberg correction; only 0 of those survived it, which is why a single significant p-value inside one family isn't treated as a working edge here — the correction is explained in full on the evidence page. This family was last re-scored on 2026-09-18, using only data up to the 2025-03-15 discovery cutoff — the same freeze point every family in the registry is held to, so none of them can see data the others couldn't. Momentum is tested across 3 asset classes (crypto, ETFs, US stocks); a strategy only joins this family because of the signal it trades on, not the asset it's tested against.
- Vol-neutralized cross-sectional momentum (crypto) (crypto): +29.2 pts/yr, behind buy-and-hold, p=0.237. Source: Asness, Moskowitz, Pedersen (2013) JF 'Value and Momentum Everywhere'.
- Volume-weighted moving average (VWMA) cross (crypto): +23.2 pts/yr, behind buy-and-hold, p=0.244. Source: TradingView VWMA strategy scripts, YouTube 'volume-weighted MA' tutorials.
- 12-1 cross-sectional momentum, top 4 of 40 large caps, monthly (wide US stocks) (US stocks): +18.8 pts/yr, ahead of buy-and-hold, p=0.047. Source: Jegadeesh & Titman 1993.
- 12-1 cross-sectional momentum, top 4 of 40 large caps, monthly (US stocks): +8.8 pts/yr, behind buy-and-hold, p=0.137. Source: Jegadeesh & Titman 1993.
- Clenow dual-momentum ETF rotation (ETFs): −3.1 pts/yr, behind buy-and-hold, p=1.000. Source: Antonacci/Clenow-style dual momentum rotation.
- Vol-neutralized cross-sectional momentum (crypto) (wide US stocks) (US stocks): −3.8 pts/yr, behind buy-and-hold, p=1.000. Source: Asness, Moskowitz, Pedersen (2013) JF 'Value and Momentum Everywhere'.
- Clenow 100-day momentum + volatility parity rebalance (wide US stocks) (US stocks): −14.1 pts/yr, behind buy-and-hold, p=1.000. Source: Andreas Clenow, 'Stocks on the Move' (2015).
- Episodic pivot breakout (gap + consolidation + breakout) (wide US stocks) (US stocks): −14.2 pts/yr, behind buy-and-hold, p=1.000. Source: Kristjan Qullamaggie 'episodic pivot' setup (public trading blog/threads). Tested on price/volume only: the catalogue rule cites an earnings/news catalyst, but no fundamentals/earnings-calendar data source exists in this harness, so the catalyst is inferred purely from the gap+volume signature, not confirmed as earnings..
- On-Balance Volume (OBV) divergence (wide US stocks) (US stocks): −16.2 pts/yr, behind buy-and-hold, p=1.000. Source: YouTube 'OBV divergence secret' and r/algotrading OBV threads.
- Crypto cross-sectional momentum, top/bottom 2 of dollar-volume universe (wide US stocks) (US stocks): −17.9 pts/yr, behind buy-and-hold, p=1.000. Source: Jegadeesh-Titman style cross-sectional momentum applied to crypto.
- Clenow 100-day momentum + volatility parity rebalance (US stocks): −17.9 pts/yr, behind buy-and-hold, p=1.000. Source: Andreas Clenow, 'Stocks on the Move' (2015).
- Episodic pivot breakout (gap + consolidation + breakout) (US stocks): −17.9 pts/yr, behind buy-and-hold, p=1.000. Source: Kristjan Qullamaggie 'episodic pivot' setup (public trading blog/threads). Tested on price/volume only: the catalogue rule cites an earnings/news catalyst, but no fundamentals/earnings-calendar data source exists in this harness, so the catalyst is inferred purely from the gap+volume signature, not confirmed as earnings..
- Donchian cross-sectional momentum (top/bottom 2, 30d reb) (wide US stocks) (US stocks): −20.2 pts/yr, behind buy-and-hold, p=1.000. Source: Donchian-style trend-following adapted cross-sectionally.
- Crypto cross-sectional momentum, top/bottom 2 of dollar-volume universe (crypto): −29.3 pts/yr, behind buy-and-hold, p=1.000. Source: Jegadeesh-Titman style cross-sectional momentum applied to crypto.
- Online recursive least-squares adaptive momentum weight (crypto): −46.7 pts/yr, behind buy-and-hold, p=1.000. Source: Cont, R. (various) market microstructure and adaptive filtering literature.
- On-Balance Volume (OBV) divergence (crypto): −67.2 pts/yr, behind buy-and-hold, p=1.000. Source: YouTube 'OBV divergence secret' and r/algotrading OBV threads.
- Donchian cross-sectional momentum (top/bottom 2, 30d reb) (crypto): −90.2 pts/yr, behind buy-and-hold, p=1.000. Source: Donchian-style trend-following adapted cross-sectionally.
- Doji at extreme + volume spike reversal (crypto): −133.2 pts/yr, behind buy-and-hold, p=1.000. Source: TikTok 'doji candle secret' and volume-spike reversal reels.
Related families: Seasonality, Equity factors, Machine learning.