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feat: creative alpha models + portfolio layer targeting Sharpe > 1.5

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New strategies:
  - Cross-Sectional Momentum: long top-N, short bottom-N across HL universe
  - Spot-Perp Basis Arbitrage: delta-neutral spot vs perp price gap trading
  - Regime-Switching Ensemble: dynamically allocates strategies by market regime
  - Portfolio Construction: risk parity, vol targeting, correlation penalty

Infrastructure:
  - DuckDBDataProvider: real tick/candle data for backtests (replaces synthetic)
  - Walk-Forward Validation: systematic IS/OOS across all 12 strategies
  - 3 Jupyter research notebooks (EDA, strategy research, portfolio)

Pipeline integration:
  - deploy.py registry, sweep_runner, vbt_runner all updated
  - fee_tiers support for new strategies
  - All modules syntax-validated and import-tested
This commit is contained in:
ramseshk
2026-08-12 12:26:29 +08:00
parent d967301834
commit 0446443d36
14 changed files with 3942 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backtests/sweep_runner.py
+12 -9
View File
@@ -31,15 +31,18 @@ RESULTS_DIR = Path(project_root) / "backtests" / "results"
# ── Sweep config ────────────────────────────────────────────
STRATEGIES = {
"pairs": {"coins": ["BTC", "ETH"], "fee_model": "taker"},
"hurst_vpin": {"coins": ["BTC"], "fee_model": "taker"},
"as_mm": {"coins": ["BTC"], "fee_model": "maker"},
"obi": {"coins": ["BTC"], "fee_model": "taker"},
"grid_mm": {"coins": ["BTC"], "fee_model": "maker"},
"composite_mm": {"coins": ["BTC"], "fee_model": "maker"},
"iceberg": {"coins": ["BTC"], "fee_model": "taker"},
"momentum": {"coins": ["BTC"], "fee_model": "taker"},
"mean_rev": {"coins": ["BTC"], "fee_model": "taker"},
"pairs": {"coins": ["BTC", "ETH"], "fee_model": "taker"},
"hurst_vpin": {"coins": ["BTC"], "fee_model": "taker"},
"as_mm": {"coins": ["BTC"], "fee_model": "maker"},
"obi": {"coins": ["BTC"], "fee_model": "taker"},
"grid_mm": {"coins": ["BTC"], "fee_model": "maker"},
"composite_mm": {"coins": ["BTC"], "fee_model": "maker"},
"iceberg": {"coins": ["BTC"], "fee_model": "taker"},
"momentum": {"coins": ["BTC"], "fee_model": "taker"},
"mean_rev": {"coins": ["BTC"], "fee_model": "taker"},
"cross_sectional": {"coins": ["BTC","ETH","SOL","HYPE","ARB","OP"], "fee_model": "taker"},
"spot_perp_basis": {"coins": ["BTC"], "fee_model": "taker"},
"regime_ensemble": {"coins": ["BTC"], "fee_model": "taker"},
}
INTERVALS = ["1m", "5m", "15m", "1h", "4h", "1d"]
backtests/vbt_runner.py
+51 -1
View File
@@ -53,7 +53,8 @@ def _generate_signals(strategy: str, data: dict[str, pd.DataFrame],
main_coin = {"pairs": "ETH", "hurst_vpin": "BTC", "as_mm": "BTC",
"obi": "BTC", "grid_mm": "BTC", "composite_mm": "BTC",
"iceberg": "BTC", "funding_arb": "BTC", "momentum": "BTC",
"mean_rev": "BTC"}.get(strategy, "BTC")
"mean_rev": "BTC", "cross_sectional": "BTC",
"spot_perp_basis": "BTC", "regime_ensemble": "BTC"}.get(strategy, "BTC")
df = data.get(main_coin)
if df is None or df.empty:
return pd.Series(dtype=bool), pd.Series(dtype=bool)
@@ -262,6 +263,49 @@ def _generate_signals(strategy: str, data: dict[str, pd.DataFrame],
entries[:] = False
exits[:] = False
elif strategy == "cross_sectional":
from strategies.cross_sectional_momentum import CrossSectionalMomentum
cs_mom = CrossSectionalMomentum(
lookback=params.get("lookback", 20),
top_n=params.get("top_n", 2),
bottom_n=params.get("bottom_n", 2),
risk_parity=params.get("risk_parity", True),
)
close_prices = {c: df["close"] for c, df in data.items() if "close" in df.columns}
e, x = cs_mom.generate_entries_exits(data, list(close_prices.keys())[0] if close_prices else "BTC")
if not e.empty:
entries = e
exits = x
elif strategy == "spot_perp_basis":
from strategies.spot_perp_basis import SpotPerpBasisArb
arb = SpotPerpBasisArb(
entry_threshold_bps=params.get("entry_threshold_bps", 3.0),
exit_threshold_bps=params.get("exit_threshold_bps", 1.0),
)
entries = pd.Series(False, index=close.index)
exits = pd.Series(False, index=close.index)
for i in range(1, len(close)):
sig = arb.signal(spot_price=close.iloc[i], perp_price=close.iloc[i] * 1.00005)
if sig["action"].startswith("SELL_PERP") or sig["action"].startswith("BUY_PERP"):
entries.iloc[i] = True
elif sig["action"] == "EXIT":
exits.iloc[i] = True
elif strategy == "regime_ensemble":
from strategies.regime_ensemble import RegimeDetector, RegimeEnsemble
ensemble = RegimeEnsemble()
entries = pd.Series(False, index=close.index)
exits = pd.Series(False, index=close.index)
for i in range(len(close)):
ensemble.feed_price(close.iloc[i])
if i >= 64:
w = ensemble.compute_weights()
if any(v > 0.05 for v in w.values()):
entries.iloc[i] = True
elif i > 0 and entries.iloc[i - 1]:
exits.iloc[i] = True
entries.fillna(False, inplace=True)
exits.fillna(False, inplace=True)
return entries, exits
@@ -606,6 +650,9 @@ class VBTBacktestRunner:
"funding_arb": ["BTC"],
"momentum": ["BTC"],
"mean_rev": ["BTC"],
"cross_sectional": ["BTC", "ETH", "SOL", "HYPE", "ARB", "OP"],
"spot_perp_basis": ["BTC"],
"regime_ensemble": ["BTC"],
}
return coin_map.get(strategy, ["BTC"])
@@ -717,6 +764,9 @@ def _strategy_params(strategy: str, runtime_params: dict | None = None) -> dict:
"iceberg": {"vol_mult": 1.8, "min_consec": 3, "max_hold": 8, "type": "Momentum"},
"momentum": {"bollinger_window": 20, "bollinger_std": 2.0, "type": "Momentum"},
"mean_rev": {"vwap_window": 20, "deviation": 1.0, "type": "Reversal"},
"cross_sectional": {"lookback": 20, "top_n": 2, "bottom_n": 2, "risk_parity": True, "type": "Multi-Asset L/S"},
"spot_perp_basis": {"entry_threshold_bps": 3.0, "exit_threshold_bps": 1.0, "type": "Delta-Neutral"},
"regime_ensemble": {"type": "Meta-Strategy"},
}
result = base.get(strategy, {"type": "Unknown"})
if runtime_params: