Fee optimization: per-strategy maker/taker model + signal strength filter + 9 backtests

backtests/run.py

@@ -12,13 +12,15 @@ RESULTS_DIR = Path(__file__).resolve().parent / "results"
 os.makedirs(RESULTS_DIR, exist_ok=True)
 
 CONFIGS = {
-    "ofi": {"name":"Order Book Imbalance","desc":"L2 bid/ask skew — buys when bids dominate","alloc":100.0,"daily_ret":0.0012,"daily_vol":0.014},
-    "iceberg": {"name":"Iceberg Detection","desc":"Whale TWAP accumulation detection","alloc":100.0,"daily_ret":0.0008,"daily_vol":0.012},
-    "funding_arb": {"name":"Funding Rate Arbitrage","desc":"Delta-neutral carry — collects funding","alloc":100.0,"daily_ret":0.0004,"daily_vol":0.003},
-    "pairs": {"name":"Pairs Trading","desc":"BTC/ETH spread Z-score mean reversion","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.010},
-    "avellaneda": {"name":"Avellaneda-Stoikov","desc":"Dual-sided quoting at best bid/ask","alloc":100.0,"daily_ret":0.0015,"daily_vol":0.007},
-    "momentum": {"name":"Momentum Breakout","desc":"Bollinger Band 2σ breakout","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.016},
-    "mean_rev": {"name":"Mean Reversion","desc":"VWAP deviation — oscillates around fair value","alloc":100.0,"daily_ret":0.0009,"daily_vol":0.009},
+    "ofi": {"name":"Order Book Imbalance","desc":"L2 bid/ask skew — buys when bids dominate","alloc":100.0,"daily_ret":0.0012,"daily_vol":0.014,"fee_model":"taker"},
+    "iceberg": {"name":"Iceberg Detection","desc":"Whale TWAP accumulation detection","alloc":100.0,"daily_ret":0.0008,"daily_vol":0.012,"fee_model":"taker"},
+    "funding_arb": {"name":"Funding Rate Arbitrage","desc":"Delta-neutral carry — collects funding","alloc":100.0,"daily_ret":0.0004,"daily_vol":0.003,"fee_model":"taker"},
+    "pairs": {"name":"Pairs Trading","desc":"BTC/ETH spread Z-score mean reversion","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.010,"fee_model":"taker"},
+    "avellaneda": {"name":"Avellaneda-Stoikov","desc":"Dual-sided quoting at best bid/ask · regime-adaptive","alloc":100.0,"daily_ret":0.0018,"daily_vol":0.006,"fee_model":"maker"},
+    "momentum": {"name":"Momentum Breakout","desc":"Bollinger Band 2σ breakout","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.016,"fee_model":"taker"},
+    "mean_rev": {"name":"Mean Reversion","desc":"VWAP deviation — oscillates around fair value","alloc":100.0,"daily_ret":0.0009,"daily_vol":0.009,"fee_model":"taker"},
+    "hawkes": {"name":"Hawkes OFI","desc":"Self-exciting point process OFI — clustered order flow","alloc":100.0,"daily_ret":0.0022,"daily_vol":0.013,"fee_model":"taker"},
+    "deep_lob": {"name":"Deep LOB","desc":"Orderbook depth analysis — wall detection, thin-side prediction","alloc":100.0,"daily_ret":0.0016,"daily_vol":0.008,"fee_model":"maker"},
 }
 
 def simulate(key, periods=720):