₿ Whale Flow Strategy Backtest — Bitcoin
90-day quantitative backtest of the Whale Flow signal for Bitcoin (BTC). Performance metrics, trade statistics, and risk analysis. Last updated: June 17, 2026.
Why Whale Flow on Bitcoin?
Bitcoin is the most liquid crypto asset with deep orderbooks and institutional participation, making it responsive to macro signals and cross-venue arbitrage. The Whale Flow indicator exploits this by follow institutional-sized order placement patterns. Over the 90-day test window, this combination produced a Sharpe ratio of 1.05 with 105 trades — averaging one trade every 20 hours.
The win rate of 54% combined with a 1.35× profit factor means winning trades are significantly larger than losing ones. The maximum drawdown of -5% 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 Whale Flow signal for BTC
$ curl command:
curl https://algotick.dev/v1/signals/whale-flow?coin=BTC
Endpoint: /v1/signals/whale-flow?coin=BTC
Trade Statistics
| Metric | Value |
|---|---|
| Best Trade | +8.5% |
| Worst Trade | -4.0% |
| Average Trade | +0.35% |
| Win Rate | 54% |
| Profit Factor | 1.35 |
| Total Trades (90d) | 105 |
| Average Holding Period | 2h |
| Max Consecutive Wins | 4 |
| Max Consecutive Losses | 2 |
Methodology
Whale Flow: Whale flow analysis tracks unusually large orders (>$100K notional) on the order book. When smart money accumulates on one side of the book, it often precedes directional moves. The indicator computes a whale imbalance score from -1 (all selling) to +1 (all buying).
Backtest Parameters:
- Period: 90 days of 1-minute data from Hyperliquid
- Signal: whale_imbalance 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 Whale Flow signal resp = requests.get( f"{BASE}/v1/signals/whale-flow", params={"coin": "BTC"} ) signal = resp.json() print(signal) # Query historical data for backtesting hist = requests.get( f"{BASE}/v3/analytics/query", params={ "metric": "whale_imbalance", "coin": "BTC", "hours": 2160 # 90 days } ) data = hist.json()
Our server-side Whale Flow signal generated a 1.1 Sharpe on BTC over the last 90 days
Don't build the infrastructure. Just query the API and focus on your alpha.
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