ramseshk
0446443d36
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
252 lines
9.0 KiB
Python
252 lines
9.0 KiB
Python
"""
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Spot-Perp Basis Arbitrage — delta-neutral strategy exploiting spot/perp price gaps.
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Hyperliquid has both spot and perpetual futures markets for most assets.
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The spot price and perp price should converge at settlement, but the perp
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can trade at a premium (contango) or discount (backwardation) to spot.
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Strategy:
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- When perp > spot + threshold: short perp, long spot
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- When perp < spot - threshold: long perp, short spot
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- Hold until basis converges or funding arbitrage overwhelms
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Key advantages:
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- Delta-neutral (market-neutral)
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- Low correlation to other strategies
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- Capital-efficient (spot + perp use separate collateral on HL)
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- Combinable with funding arb (earn funding while holding basis trade)
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Data needed:
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- Hyperliquid spot mid price
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- Hyperliquid perp mark price
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- Perp funding rate (for combined carry trade)
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"""
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from __future__ import annotations
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import logging
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from collections import deque
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import numpy as np
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logger = logging.getLogger(__name__)
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class SpotPerpBasisArb:
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"""Delta-neutral spot-perpetual basis arbitrage.
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Parameters
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----------
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entry_threshold_bps: float — minimum basis (in bps) to enter a trade
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exit_threshold_bps: float — basis below which to exit
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max_hold_hours: float — maximum trade duration
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size_usd: float — notional size per leg in USD
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spot_fee_rate: float — spot taker fee rate (decimal)
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perp_fee_rate: float — perp taker fee rate (decimal)
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min_expected_profit_bps: float — minimum expected profit after fees
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"""
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def __init__(
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self,
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entry_threshold_bps: float = 3.0,
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exit_threshold_bps: float = 1.0,
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max_hold_hours: float = 48.0,
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size_usd: float = 1000.0,
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spot_fee_rate: float = 0.0010,
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perp_fee_rate: float = 0.0007,
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min_expected_profit_bps: float = 1.5,
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):
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self.entry_threshold = entry_threshold_bps / 10000 # bps → decimal
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self.exit_threshold = exit_threshold_bps / 10000
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self.max_hold_hours = max_hold_hours
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self.size_usd = size_usd
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self.spot_fee_rate = spot_fee_rate
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self.perp_fee_rate = perp_fee_rate
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self.min_expected_profit = min_expected_profit_bps / 10000
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self._position: dict = {}
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self._trades: list[dict] = []
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self._basis_history: deque = deque(maxlen=500)
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def signal(
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self,
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spot_price: float,
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perp_price: float,
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funding_rate: float = 0.0,
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) -> dict:
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"""Compute basis trade signal.
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Args:
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spot_price: current spot mid price
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perp_price: current perp mark/index price
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funding_rate: current 8h funding rate (decimal)
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Returns:
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dict with action, basis_bps, expected_profit_bps, funding_apr
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"""
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if spot_price <= 0 or perp_price <= 0:
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return {"action": "HOLD", "basis_bps": 0.0}
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basis = (perp_price - spot_price) / spot_price
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basis_bps = basis * 10000
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self._basis_history.append(basis_bps)
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in_position = bool(self._position)
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if in_position:
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# Exit if basis narrows enough or max hold exceeded
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pos_basis = abs(self._position.get("entry_basis", 0))
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current_basis_abs = abs(basis)
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if current_basis_abs < self.exit_threshold:
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return {
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"action": "EXIT",
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"basis_bps": round(basis_bps, 2),
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"reason": f"basis_converged_{current_basis_abs*10000:.0f}bps",
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}
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# Compute expected profit after fees
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round_trip_fee = self.spot_fee_rate * 2 + self.perp_fee_rate * 2
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expected_profit = abs(basis) - round_trip_fee
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# Add funding carry expectation
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funding_apr = abs(funding_rate) * 365 * 3 # 8h → annual
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funding_profit = funding_apr * (self.max_hold_hours / (365 * 24))
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if not in_position and abs(basis) > self.entry_threshold and expected_profit > self.min_expected_profit:
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if basis > 0:
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return {
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"action": "SELL_PERP_BUY_SPOT",
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"basis_bps": round(basis_bps, 2),
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"expected_profit_bps": round(expected_profit * 10000, 2),
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"funding_apr_pct": round(funding_apr * 100, 2),
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"reason": f"perp_premium_{basis_bps:.0f}bps",
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}
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else:
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return {
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"action": "BUY_PERP_SELL_SPOT",
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"basis_bps": round(basis_bps, 2),
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"expected_profit_bps": round(expected_profit * 10000, 2),
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"funding_apr_pct": round(funding_apr * 100, 2),
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"reason": f"perp_discount_{abs(basis_bps):.0f}bps",
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}
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return {
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"action": "HOLD",
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"basis_bps": round(basis_bps, 2),
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"expected_profit_bps": round(expected_profit * 10000, 2) if expected_profit > 0 else 0,
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"funding_apr_pct": round(funding_apr * 100, 2),
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}
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def enter(self, action: str, spot_price: float, perp_price: float):
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"""Record a trade entry."""
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basis = (perp_price - spot_price) / spot_price
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self._position = {
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"action": action,
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"entry_spot": spot_price,
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"entry_perp": perp_price,
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"entry_basis": basis,
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"entry_time": __import__('time').time(),
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"spot_size": self.size_usd / spot_price,
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"perp_size": self.size_usd / perp_price,
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}
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def exit(self, spot_price: float, perp_price: float) -> dict:
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"""Exit the current position and compute PnL."""
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if not self._position:
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return {"pnl": 0.0, "pnl_pct": 0.0}
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entry = self._position
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action = entry["action"]
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if action == "SELL_PERP_BUY_SPOT":
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perp_pnl = (entry["entry_perp"] - perp_price) * entry["perp_size"]
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spot_pnl = (spot_price - entry["entry_spot"]) * entry["spot_size"]
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elif action == "BUY_PERP_SELL_SPOT":
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perp_pnl = (perp_price - entry["entry_perp"]) * entry["perp_size"]
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spot_pnl = (entry["entry_spot"] - spot_price) * entry["spot_size"]
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else:
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perp_pnl = 0.0
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spot_pnl = 0.0
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gross_pnl = perp_pnl + spot_pnl
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spot_fee = entry["spot_size"] * entry["entry_spot"] * self.spot_fee_rate + \
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entry["spot_size"] * spot_price * self.spot_fee_rate
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perp_fee = entry["perp_size"] * entry["entry_perp"] * self.perp_fee_rate + \
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entry["perp_size"] * perp_price * self.perp_fee_rate
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total_fee = spot_fee + perp_fee
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net_pnl = gross_pnl - total_fee
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pnl_pct = net_pnl / (self.size_usd * 2) if self.size_usd > 0 else 0
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trade = {
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"action": action,
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"entry_spot": round(entry["entry_spot"], 2),
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"entry_perp": round(entry["entry_perp"], 2),
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"exit_spot": round(spot_price, 2),
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"exit_perp": round(perp_price, 2),
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"entry_basis_bps": round(entry["entry_basis"] * 10000, 2),
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"exit_basis_bps": round((perp_price - spot_price) / spot_price * 10000, 2),
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"gross_pnl": round(gross_pnl, 4),
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"fee": round(total_fee, 4),
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"net_pnl": round(net_pnl, 4),
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"pnl_pct": round(pnl_pct * 100, 3),
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}
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self._trades.append(trade)
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self._position = {}
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return trade
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def is_in_position(self) -> bool:
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return bool(self._position)
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def summary(self) -> dict:
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"""Return strategy summary."""
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return {
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"in_position": self.is_in_position(),
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"position": self._position if self._position else None,
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"total_trades": len(self._trades),
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"total_pnl": round(sum(t["net_pnl"] for t in self._trades), 4),
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"recent_basis_bps": list(self._basis_history)[-10:],
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}
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# Multi-asset basis arb across coins
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BASIS_ARB_COINS = ["BTC", "ETH", "SOL", "HYPE", "ARB"]
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def multi_asset_basis_scan(
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spot_prices: dict[str, float],
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perp_prices: dict[str, float],
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funding_rates: dict[str, float],
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entry_threshold_bps: float = 3.0,
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) -> list[dict]:
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"""Scan all assets for basis arb opportunities.
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Returns sorted list of opportunities, best first.
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"""
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opportunities = []
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for coin in perp_prices:
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if coin not in spot_prices:
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continue
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spot = spot_prices.get(coin, 0)
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perp = perp_prices.get(coin, 0)
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fr = funding_rates.get(coin, 0)
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if spot <= 0 or perp <= 0:
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continue
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basis = (perp - spot) / spot
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if abs(basis) > entry_threshold_bps / 10000:
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opportunities.append({
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"coin": coin,
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"spot": spot,
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"perp": perp,
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"basis_bps": round(basis * 10000, 2),
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"funding_rate": round(fr, 8),
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"direction": "SELL_PERP_BUY_SPOT" if basis > 0 else "BUY_PERP_SELL_SPOT",
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})
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opportunities.sort(key=lambda x: abs(x["basis_bps"]), reverse=True)
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return opportunities
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