feat: funding arb strategy, queue-aware paper fills, WQI live integration
- strategies/funding_arb_strategy.py: full backtestable funding rate carry module with entry/exit thresholds, position tracking, funding payment accounting, basis stop-loss, max-hold timeout. Includes backtest_funding_arb() and run_funding_discovery() for threshold optimization - live/node_v2.py: replaced naive random fills with QueueAwareFillModel (sim/fills.py) with queue-priority simulation; integrated WQI predictor and funding arb strategies; per-coin WQI signal generation every 3 ticks; funding arb metrics in dashboard - cli.py: added 'funding' command for funding rate distribution analysis and threshold backtesting - tests/test_funding_arb.py: 20 tests covering entry/exit logic, fee accounting, signal generation, backtesting, and node integration 321 tests passing (20 new).
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ramseshk
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"""
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Funding Rate Arbitrage — backtestable strategy module.
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Delta-neutral carry trade on Hyperliquid perps. When funding rate is high:
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- Short the perpetual (collect funding payments)
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- The profit is the funding rate, not price direction
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Features:
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- Configurable entry/exit thresholds
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- Position sizing proportional to funding rate
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- Funding payment tracking with accurate Hyperliquid 8h schedule
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- Max hold time (exit after N hours regardless)
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- Stop-loss if basis widens (mark price moves against funding direction)
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- Per-trade PnL accounting with fees, funding, and mark PnL
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Usage (backtest):
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arb = FundingArb(apr_threshold=0.30, apr_exit=0.10, size=0.001)
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for hourly_funding in funding_history:
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trade = arb.tick(funding_rate, mark_price, timestamp)
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if trade:
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print(f"Trade: {trade}")
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Usage (live):
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arb = FundingArb(apr_threshold=0.30)
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signal = arb.signal(check_rates(time.time()))
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if signal["action"] != "HOLD":
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execute(signal)
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"""
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from __future__ import annotations
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import time
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from collections import deque
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from typing import Optional
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class FundingArb:
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"""Delta-neutral funding rate carry strategy.
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Logic:
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- Entry: |annualized_funding| > apr_threshold AND no position
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- Exit: |annualized_funding| < apr_exit
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OR hold_time > max_hold_hours
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OR funding direction flips (paying instead of collecting)
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OR basis stop-loss triggered
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"""
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def __init__(
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self,
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apr_threshold: float = 0.30,
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apr_exit: float = 0.10,
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size: float = 0.001,
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max_hold_hours: float = 48.0,
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basis_stop_loss_pct: float = 0.03,
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taker_fee_pct: float = 0.00045,
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maker_fee_pct: float = 0.00015,
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):
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self._apr_threshold = apr_threshold
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self._apr_exit = apr_exit
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self._size = size
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self._max_hold_seconds = max_hold_hours * 3600
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self._basis_stop_loss = basis_stop_loss_pct
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self._taker_fee = taker_fee_pct
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self._maker_fee = maker_fee_pct
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self._position: int = 0
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self._entry_price: float = 0.0
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self._entry_time: float = 0.0
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self._entry_apr: float = 0.0
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self._funding_collected: float = 0.0
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self._funding_paid: float = 0.0
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self._trades: list[dict] = []
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self._funding_history: deque[float] = deque(maxlen=200)
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self._mark_history: deque[float] = deque(maxlen=200)
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self._signals: deque[dict] = deque(maxlen=50)
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@property
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def position(self) -> int:
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return self._position
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@property
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def trades(self) -> list[dict]:
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return self._trades
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def tick(
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self,
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funding_rate_annual: float,
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mark_price: float,
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timestamp: Optional[float] = None,
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) -> dict | None:
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"""Process one funding rate observation. Returns trade dict if entry/exit occurred."""
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if timestamp is None:
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timestamp = time.time()
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if mark_price <= 0:
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return None
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self._funding_history.append(funding_rate_annual)
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self._mark_history.append(mark_price)
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abs_apr = abs(funding_rate_annual)
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action = "HOLD"
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trade = None
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if self._position == 0:
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if abs_apr > self._apr_threshold:
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action = "SELL" if funding_rate_annual > 0 else "BUY"
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self._position = -1 if funding_rate_annual > 0 else 1
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self._entry_price = mark_price
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self._entry_time = timestamp
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self._entry_apr = funding_rate_annual
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notional = self._size * mark_price
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fee = notional * self._taker_fee
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self._funding_paid += fee
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trade = {
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"action": action,
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"side": action,
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"size": self._size,
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"entry_price": mark_price,
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"apr": round(funding_rate_annual, 4),
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"apr_pct": round(funding_rate_annual * 100, 2),
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"fee": round(fee, 4),
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}
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self._signals.append({
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"timestamp": timestamp,
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"action": action,
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"apr": funding_rate_annual,
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"price": mark_price,
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})
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else:
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hold_seconds = timestamp - self._entry_time
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direction = "short" if self._position == -1 else "long"
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exit_reason = ""
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if abs_apr < self._apr_exit:
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exit_reason = f"apr_faded_to_{abs_apr*100:.1f}%"
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elif hold_seconds >= self._max_hold_seconds:
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exit_reason = f"max_hold_{hold_seconds/3600:.1f}h"
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elif (self._position == -1 and funding_rate_annual < 0) or \
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(self._position == 1 and funding_rate_annual > 0):
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exit_reason = f"funding_flipped_to_{funding_rate_annual*100:.2f}%"
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else:
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price_move = (mark_price - self._entry_price) / self._entry_price
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position_pnl_pct = price_move * self._position
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if abs(position_pnl_pct) > self._basis_stop_loss:
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exit_reason = f"basis_stop_loss_{position_pnl_pct*100:.2f}%"
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if exit_reason:
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notional = self._size * mark_price
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fee = notional * self._taker_fee
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price_pnl = self._size * (mark_price - self._entry_price) * self._position
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funding_earned = 0.0
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if isinstance(self._entry_apr, float) and self._entry_apr != 0:
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funding_rate_8h = self._entry_apr / 1095
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funding_intervals = hold_seconds / (8 * 3600)
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funding_earned = notional * abs(funding_rate_8h) * funding_intervals * 0.95
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net_pnl = price_pnl + funding_earned - fee - self._funding_paid
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action = "BUY" if self._position == -1 else "SELL"
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trade = {
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"action": f"EXIT_{exit_reason}",
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"side": action,
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"entry_price": round(self._entry_price, 2),
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"exit_price": round(mark_price, 2),
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"size": self._size,
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"direction": direction,
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"hold_hours": round(hold_seconds / 3600, 2),
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"entry_apr": round(self._entry_apr * 100, 2),
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"exit_apr": round(funding_rate_annual * 100, 2),
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"price_pnl": round(price_pnl, 4),
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"funding_earned": round(funding_earned, 4),
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"fees": round(fee + self._funding_paid, 4),
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"net_pnl": round(net_pnl, 4),
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"reason": exit_reason,
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}
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self._trades.append(trade)
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self._signals.append({
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"timestamp": timestamp,
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"action": "EXIT",
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"apr": funding_rate_annual,
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"price": mark_price,
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"reason": exit_reason,
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"pnl": net_pnl,
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})
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self._position = 0
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self._entry_price = 0.0
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self._entry_time = 0.0
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self._entry_apr = 0.0
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self._funding_paid = 0.0
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return trade
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def signal(
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self,
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funding_rate_annual: float,
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mark_price: float = 0.0,
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timestamp: Optional[float] = None,
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) -> dict:
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"""Generate trading signal without executing."""
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if timestamp is None:
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timestamp = time.time()
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abs_apr = abs(funding_rate_annual)
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if self._position == 0 and abs_apr > self._apr_threshold:
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return {
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"action": "SELL" if funding_rate_annual > 0 else "BUY",
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"size": self._size,
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"apr": round(funding_rate_annual * 100, 2),
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"reason": f"apr_{abs_apr*100:.1f}%_above_{self._apr_threshold*100:.0f}%",
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}
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elif self._position != 0 and abs_apr < self._apr_exit:
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return {
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"action": "EXIT",
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"reason": f"apr_faded_to_{abs_apr*100:.1f}%",
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}
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return {"action": "HOLD"}
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def summary(self) -> dict:
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if not self._trades:
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return {
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"total_trades": 0,
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"win_rate": 0.0,
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"total_net_pnl": 0.0,
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"avg_net_pnl": 0.0,
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"avg_hold_hours": 0.0,
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"total_funding_earned": 0.0,
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"position": self._position,
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}
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wins = sum(1 for t in self._trades if t["net_pnl"] > 0)
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total_net = sum(t["net_pnl"] for t in self._trades)
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total_funding = sum(t["funding_earned"] for t in self._trades)
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hold_hours = [t["hold_hours"] for t in self._trades]
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return {
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"total_trades": len(self._trades),
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"win_rate": round(wins / len(self._trades), 3),
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"total_net_pnl": round(total_net, 4),
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"avg_net_pnl": round(total_net / len(self._trades), 4),
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"avg_hold_hours": round(sum(hold_hours) / len(hold_hours), 2),
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"total_funding_earned": round(total_funding, 4),
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"best_trade": round(max(t["net_pnl"] for t in self._trades), 4),
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"worst_trade": round(min(t["net_pnl"] for t in self._trades), 4),
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"position": self._position,
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}
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def reset(self):
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self._position = 0
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self._entry_price = 0.0
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self._entry_time = 0.0
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self._entry_apr = 0.0
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self._funding_collected = 0.0
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self._funding_paid = 0.0
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self._trades.clear()
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self._signals.clear()
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self._funding_history.clear()
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self._mark_history.clear()
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def backtest_funding_arb(
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funding_rates: list[float],
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mark_prices: list[float],
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apr_threshold: float = 0.30,
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size: float = 0.001,
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taker_fee_pct: float = 0.00045,
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) -> dict:
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"""Run funding arb backtest on a series of funding rate observations.
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Args:
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funding_rates: list of annualized funding rates (e.g., from HL API)
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mark_prices: list of corresponding mark prices
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apr_threshold: minimum annual APR to enter
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size: trade size
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taker_fee_pct: taker fee per trade
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Returns dict with trades and summary.
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"""
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arb = FundingArb(
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apr_threshold=apr_threshold,
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size=size,
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taker_fee_pct=taker_fee_pct,
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)
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min_len = min(len(funding_rates), len(mark_prices))
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for i in range(min_len):
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arb.tick(funding_rates[i], mark_prices[i], float(i))
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return arb.summary()
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def run_funding_discovery(data_dir: str = "data/raw", coin: str = "BTC",
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start_date: str = "2026-01-01", end_date: str = "2030-01-01") -> dict:
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"""Run funding rate discovery — analyze historical funding rates to find
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optimal entry/exit thresholds.
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Returns dict with rate distribution percentiles and backtest results at different thresholds.
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"""
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import numpy as np
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from data.store import read_range
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msgs = read_range(data_dir, channel="funding", coin=coin,
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start_date=start_date, end_date=end_date)
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if not msgs:
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return {"error": "No funding data available"}
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rates = []
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marks = []
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for msg in msgs:
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payload = msg.get("payload", {})
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rate = float(payload.get("funding", 0))
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mark = float(payload.get("mark_px", 0))
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if mark > 0:
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annual = rate * 1095
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rates.append(annual)
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marks.append(mark)
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if not rates:
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return {"error": "No valid funding observations"}
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a = np.array(rates)
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abs_a = np.abs(a)
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percentiles = [10, 25, 50, 75, 90, 95, 99]
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result = {
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"n_observations": len(rates),
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"rate_distribution": {
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"mean_apr_pct": round(float(np.mean(a)) * 100, 2),
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"std_apr_pct": round(float(np.std(a)) * 100, 2),
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"max_apr_pct": round(float(np.max(a)) * 100, 2),
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"min_apr_pct": round(float(np.min(a)) * 100, 2),
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"abs_percentiles": {
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f"p{p}": round(float(np.percentile(abs_a, p)) * 100, 2)
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for p in percentiles
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},
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},
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"backtests": {},
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}
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for threshold in [0.05, 0.10, 0.20, 0.30, 0.50]:
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summary = backtest_funding_arb(rates, marks, apr_threshold=threshold)
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if summary["total_trades"] > 0:
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result["backtests"][f"apr_{int(threshold*100)}pct"] = summary
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return result
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