Root cause: Kalman filter needs a cointegrated pair, but the
historical runner was feeding it synthetic noise (close vs SMA).
The Kalman filter found no mean-reverting spread, producing 0 signals.
Fix: Intercept kalman_pairs in main(), fetch real ETH candles,
run the full backtest_kalman_pairs() with BTC/ETH or X/ETH data.
Results (30-day, 720h candles, BTC/ETH pair):
BTC: 35 trades, -0.36% PnL
ETH: 34 trades, -0.01% PnL (ETH/BTC pair)
HYPE: 27 trades, -0.00% PnL (HYPE/BTC pair)
VVV: 33 trades, -0.01% PnL (VVV/BTC pair)
Total: 32 historical backtests (8 strategies x 4 coins)
Live node:
- Registered in STRATEGIES dict (8th strategy)
- Signal: KalmanPairsTrader.step(eth, btc) every compute_signals()
- Adaptive hedge ratio updates with every tick
Paper trader:
- Registered in STRATEGIES dict
- Signal: KalmanPairsTrader integrated into compute_signals()
- Falls back gracefully if kalman_pairs module not importable
Historical backtests:
- Ran for BTC, ETH, HYPE, VVV (4 files)
- kalman_pairs_{TICKER}_*.json in results/historical/
- Visible on dashboard under Historical tab (8 strategies x 4 coins)
Dashboard: now shows Kalman Pairs card on all three tabs.
backtests/historical_runner.py: Fetches real 1h candles from Hyperliquid
mainnet API (candleSnapshot endpoint). Runs all 7 strategies against
actual BTC price history (721 candles, 30 days, $63,024→$63,605).
Each strategy's signal logic operates on real OHLCV data with
configurable fee tiers. Saves to backtests/results/historical/.
Results on 30d BTC data at VIP0:
Mean Reversion: +93.87% net (Sharpe 0.94)
Order Book Imbalance: +54.31% net (Sharpe 1.03)
Avellaneda-Stoikov: -1.02% net (Sharpe -0.13)
Iceberg Detection: -33.20% net
Momentum Breakout: -54.72% net
Server: Added /api/backtests/historical (list) and
/api/backtest/historical/{name} (full data) endpoints.
Dashboard: Added "Historical" tab with "Real Data" badge. Cards show
coin + mainnet source. Click opens the same detail panel with fee
tier dropdown and equity chart.
ramseshk