XSMOM — Cross-Sectional Momentum

Core idea

In crypto, relative momentum persists over multi-week horizons: coins that have outperformed on a risk-adjusted basis tend to keep outperforming, and the weakest tend to keep underperforming. Ranking by vol-normalised return (return ÷ volatility) rather than raw return avoids buying high-volatility noise — a coin that rose 30% on 60% daily vol is a weaker signal than one that rose 20% on 15% vol.

Why the long filter exists

The vol-normalised metric can rank a coin highly because its volatility collapsed, not because it's trending up. The long side therefore requires the coin to be above its 50-day (now 100-day) moving average and have a positive raw 30-day return. The short side needs no filter: in a structurally risk-off altcoin regime, the weakest losers keep losing — shorting them has been the more reliable leg.

Signal — vol-normalised return (VNR)

For each symbol, over the trailing 30 days ending at the rebalance date:

raw_return   = (close[t] - close[t-30]) / close[t-30]
atr_pct      = ATR(14) / close[t]            # daily vol, as % of price
VNR          = raw_return / (atr_pct × √30)  # risk-adjusted momentum

VNR measures how far price moved per unit of volatility over the window — the standard "vol-normalised" momentum used in academic cross-sectional momentum studies.

Selection rules

• Longs (top 5 by VNR) — must pass trend filter: close > MA(100) and raw_return > 0. Only coins with genuine upward momentum qualify.
• Shorts (bottom 5 by VNR) — require VNR ≤ -0.5 (optimised floor). In the current sample no coin met this threshold, so the optimised run is effectively long-only.
• Sizing — long weights are linear in VNR (strongest signal → biggest position); shorts are equally weighted.
• Exit — positions close at the next rebalance, or earlier at a 15% stop-loss.

Headline metrics

Monthly P&L

August's figure is almost entirely the single +$2,280 week.

Rebalance frequency biggest lever

Extending the hold from 7→14 days lets winners compound and cuts rebalance whipsaw. Note: this leg of the sweep ran without the short floor, so shorts were still active here.

Stop-loss best Sharpe

15% is the single best Sharpe improvement: it caps the catastrophic single-coin blow-ups (e.g. -247% SKRUSDT week in the unfiltered run) while letting winners run.

Trend-filter MA period

MA=100 is the sweet spot — a slower regime filter that rejects more false-positive longs in chop.

Short VNR floor

Tighter floors cull weak/mean-reverting shorts and lift the profit factor from 1.23 → 1.49. Past -0.5 nothing changes: the weak cohorts simply stop qualifying.

Signal window (VNR days)

30 days has the best Sharpe / drawdown balance. 14d has higher raw P&L but 2.9× the drawdown.

Portfolio size & concentration

Concentration caps hurt: signal-proportional sizing is an alpha source, not a risk bug. 7L/7S maximises raw P&L; 5L/5S is the risk/reward compromise.

Repository layout

MOMSXperp/
├── config.py      # strategy parameters (optimised)
├── run.py         # backtest entry point
├── analysis.py    # ranking, selection, sizing, P&L engine
├── fetch.py       # Bybit V5 OHLCV fetch + local cache
├── report.py      # extended diagnostics (monthly, streaks, conc.)
├── charts.py      # matplotlib equity/side/drawdown charts
├── sweep.py       # 8-dimension parameter sweep
├── xsmom_results.json       # optimised backtest output
├── xsmom_sweep_results.json # full sweep output
└── README.md

Run it yourself

# backtest (cached data; --force to re-fetch)
python3 -m xsmom.run

# extended diagnostics
python3 -m xsmom.report

# charts (needs matplotlib)
python3 -m xsmom.charts

# full parameter sweep (2-3 min)
python3 -m xsmom.sweep

Data is cached under xsmom/data_cache/ so repeat runs are instant. No API key required for public market data.