◆ Regime Momentum Strategy Backtest — Hyperliquid
90-day quantitative backtest of the Regime Momentum signal for Hyperliquid (HYPE). Performance metrics, trade statistics, and risk analysis. Last updated: June 17, 2026.
Why Regime Momentum on Hyperliquid?
Hyperliquid is a native DEX-perp token whose price dynamics are tightly coupled with platform open interest and liquidity migration patterns. The Regime Momentum indicator exploits this by trade regime transitions detected by the hidden markov model. Over the 90-day test window, this combination produced a Sharpe ratio of 1.78 with 178 trades — averaging one trade every 12 hours.
The win rate of 49% combined with a 1.58× profit factor means winning trades are significantly larger than losing ones. The maximum drawdown of -28% represents the worst peak-to-trough decline, which occurred during a period of elevated volatility. This drawdown level requires careful position sizing to manage portfolio risk.
Performance Summary
Current Signal State </> API
# Current Regime Momentum signal for HYPE
$ curl command:
curl https://algotick.dev/v3/signals/regime?coin=HYPE
Endpoint: /v3/signals/regime?coin=HYPE
Trade Statistics
| Metric | Value |
|---|---|
| Best Trade | +3.8% |
| Worst Trade | -3.3% |
| Average Trade | +0.28% |
| Win Rate | 49% |
| Profit Factor | 1.58 |
| Total Trades (90d) | 178 |
| Average Holding Period | 3h |
| Max Consecutive Wins | 5 |
| Max Consecutive Losses | 5 |
Methodology
Regime Momentum: Regime momentum uses the HMM-based regime classifier to detect transitions between market states (trending, mean-reverting, volatile). When the regime shifts from one state to another, the strategy enters positions aligned with the new regime's expected behavior.
Backtest Parameters:
- Period: 90 days of 1-minute data from Hyperliquid
- Signal: regime_label from the Algo Tick API
- Position sizing: Fixed 1x leverage, no compounding
- Execution: Market orders at next bar open, 0.05% slippage + 0.02% fees
- Risk: Stop-loss at 2x ATR, take-profit at 3x ATR
Reproduce This Backtest
Don't run this backtest locally — just query our analytics endpoint:
# Fetch historical signal data for backtesting import requests BASE = "https://algotick.dev" # Get current Regime Momentum signal resp = requests.get( f"{BASE}/v3/signals/regime", params={"coin": "HYPE"} ) signal = resp.json() print(signal) # Query historical data for backtesting hist = requests.get( f"{BASE}/v3/analytics/query", params={ "metric": "regime_label", "coin": "HYPE", "hours": 2160 # 90 days } ) data = hist.json()
Our server-side Regime Momentum signal generated a 1.8 Sharpe on HYPE over the last 90 days
Don't build the infrastructure. Just query the API and focus on your alpha.
Explore API →