Ξ Liquidation Cascades Strategy Backtest — Ethereum
90-day quantitative backtest of the Liquidation Cascades signal for Ethereum (ETH). Performance metrics, trade statistics, and risk analysis. Last updated: June 17, 2026.
Why Liquidation Cascades on Ethereum?
Ethereum is a high-beta asset with DeFi-driven funding distortions and gas-sensitive trading costs, creating unique opportunities during network congestion. The Liquidation Cascades indicator exploits this by predict and trade cascading liquidation events. Over the 90-day test window, this combination produced a Sharpe ratio of 0.81 with 81 trades — averaging one trade every 26 hours.
The win rate of 66% combined with a 1.11× profit factor means winning trades are significantly larger than losing ones. The maximum drawdown of -6% represents the worst peak-to-trough decline, which occurred during a period of normal market conditions. This is within acceptable bounds for an algo strategy.
Performance Summary
Current Signal State </> API
# Current Liquidation Cascades signal for ETH
$ curl command:
curl https://algotick.dev/v1/signals/liquidations?coin=ETH
Endpoint: /v1/signals/liquidations?coin=ETH
Trade Statistics
| Metric | Value |
|---|---|
| Best Trade | +2.1% |
| Worst Trade | -1.6% |
| Average Trade | +0.11% |
| Win Rate | 66% |
| Profit Factor | 1.11 |
| Total Trades (90d) | 81 |
| Average Holding Period | 2h |
| Max Consecutive Wins | 4 |
| Max Consecutive Losses | 3 |
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 BASE = "https://algotick.dev" # Get current Liquidation Cascades signal resp = requests.get( f"{BASE}/v1/signals/liquidations", params={"coin": "ETH"} ) signal = resp.json() print(signal) # Query historical data for backtesting hist = requests.get( f"{BASE}/v3/analytics/query", params={ "metric": "liquidation alerts", "coin": "ETH", "hours": 2160 # 90 days } ) data = hist.json()
Our server-side Liquidation Cascades signal generated a 0.8 Sharpe on ETH over the last 90 days
Don't build the infrastructure. Just query the API and focus on your alpha.
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