◆ Liquidation Cascades Strategy Backtest — Hyperliquid
90-day quantitative backtest of the Liquidation Cascades signal for Hyperliquid (HYPE). Performance metrics, trade statistics, and risk analysis. Last updated: March 12, 2026.
Performance Summary
2.53
Sharpe Ratio
-28%
Max Drawdown
60%
Win Rate
253
Total Trades
2.33
Profit Factor
0.23%
Avg Trade
Current Signal State </> API
# Current Liquidation Cascades signal for HYPE
$ curl command:
curl -H "X-API-Key: YOUR_KEY" https://algotick.dev/v1/signals/liquidations?coin=HYPE
Endpoint: /v1/signals/liquidations?coin=HYPE
# Current Liquidation Cascades signal for HYPE
$ curl command:
curl -H "X-API-Key: YOUR_KEY" https://algotick.dev/v1/signals/liquidations?coin=HYPE
Endpoint: /v1/signals/liquidations?coin=HYPE
Current Value
0.0000
Signal
quiet
Indicator
Liquidation Cascades
Trade Statistics
| Metric | Value |
|---|---|
| Best Trade | +7.3% |
| Worst Trade | -4.8% |
| Average Trade | +0.23% |
| Win Rate | 60% |
| Profit Factor | 2.33 |
| Total Trades (90d) | 253 |
| Average Holding Period | 6h |
| Max Consecutive Wins | 8 |
| Max Consecutive Losses | 5 |
Methodology
Liquidation Cascades: Liquidation cascade analysis identifies price levels where clusters of leveraged positions would be force-closed. When price approaches these levels, the resulting cascade of liquidations can create violent, predictable price moves. The strategy positions ahead of anticipated cascade zones.
Backtest Parameters:
- Period: 90 days of 1-minute data from Hyperliquid
- Signal: liquidation alerts 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 API_KEY = "YOUR_API_KEY" BASE = "https://algotick.dev" # Get current Liquidation Cascades signal resp = requests.get( f"{BASE}/v1/signals/liquidations", params={"coin": "HYPE", "api_key": API_KEY} ) signal = resp.json() print(signal) # Query historical data for backtesting hist = requests.get( f"{BASE}/v3/analytics/query", params={ "api_key": API_KEY, "metric": "liquidation alerts", "coin": "HYPE", "hours": 2160 # 90 days } ) data = hist.json()
Our server-side Liquidation Cascades signal generated a 2.5 Sharpe on HYPE over the last 90 days
Don't build the infrastructure. Just query the API and focus on your alpha.
Get API Key →